docs/source/examples/regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training and Evaluating Simple Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy.random as random\n",
    "\n",
    "random.seed(150)\n",
    "\n",
    "dates = pd.DataFrame({'score_date': pd.date_range('2016-01-01', '2016-12-31')})\n",
    "dates['key'] = 1\n",
    "\n",
    "ids = pd.DataFrame({'id': np.arange(0, 100)})\n",
    "ids['key'] = 1\n",
    "\n",
    "data = pd.merge(ids, dates).drop('key', axis=1)\n",
    "\n",
    "data['x1'] = 23 * random.randn(data.shape[0]) + 500\n",
    "data['x2'] = 59 * random.randn(data.shape[0]) + 235\n",
    "data['x3'] = 73 * random.randn(data.shape[0]) + 793  # Noise feature.\n",
    "\n",
    "data['y'] = 0.37*data['x1'] + 0.97*data['x2'] + 0.32*data['x2']**2 - 5.0*data['id']*0.2 + \\\n",
    "            np.cos(pd.to_datetime(data['score_date']).astype(int)*200)*20.0\n",
    "\n",
    "nan_idx = np.random.randint(0, data.shape[0], size=100)  # Inject nan in x1.\n",
    "data.loc[nan_idx, 'x1'] = np.nan\n",
    "\n",
    "nan_idx = np.random.randint(0, data.shape[0], size=100)  # Inject nan in x2.\n",
    "data.loc[nan_idx, 'x2'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>score_date</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>494.678185</td>\n",
       "      <td>212.949976</td>\n",
       "      <td>829.392324</td>\n",
       "      <td>14880.912784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>508.984104</td>\n",
       "      <td>158.174242</td>\n",
       "      <td>744.755316</td>\n",
       "      <td>8365.032313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>508.240842</td>\n",
       "      <td>291.856424</td>\n",
       "      <td>680.169336</td>\n",
       "      <td>27720.701968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>513.023623</td>\n",
       "      <td>137.007911</td>\n",
       "      <td>767.888326</td>\n",
       "      <td>6325.554470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>503.823382</td>\n",
       "      <td>279.123602</td>\n",
       "      <td>812.656548</td>\n",
       "      <td>25402.890737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id score_date          x1          x2          x3             y\n",
       "0   0 2016-01-01  494.678185  212.949976  829.392324  14880.912784\n",
       "1   0 2016-01-02  508.984104  158.174242  744.755316   8365.032313\n",
       "2   0 2016-01-03  508.240842  291.856424  680.169336  27720.701968\n",
       "3   0 2016-01-04  513.023623  137.007911  767.888326   6325.554470\n",
       "4   0 2016-01-05  503.823382  279.123602  812.656548  25402.890737"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Up52rE3vL4USBmecw95zDZPvo1fjS7pYuwiEpiRqaS5TlORQpebydthbe73E8FzgW87BOGOFAHaWK5TBXk3hmORz7nfVEg+lWmptr8K436cjanS9XEyk/tbCC5XAU3F8UreR6HlXcSrYWviCEg5wbbwUA7B9v4aUf/gG2HuiNzziBhAP9ry4c+j2J04s/lrQDjxTzEcrKJ9opR+n4+eoWraha/H+cyKMSfkuC2nVpXSPNJqQdnAOdpzPLCfcr9zyJWx/eM6tzdAMpr3MxN2w9MImNe8fx+L7eQsWPe+EgpcQ9TxxklkP3Y2gSn22nshFbvlCPYwfSsBzmw62UFw6zmRjueeJg5VLgbbIcuhDSiTw6fbXMxZIUWA6uxX50YMksLYdP3/EErl23rfuOs4BWHOeg72Vutt6OO+6FwwPbDuP1/3YX7n0yjR+uYrpHcxTK6jmHYxf8jcxHtJKLc5jNdV//b3fhVz9xZ6V921F1t9LRKD1BBrvr2kWWgyvajO5ztsKhEydz/hyKiPZ+ILEsqao47oXDeCsdhIdVLZaj6VaaS7+ix+wwLxnS7MSuFQlnOjHQcU8enKy0P02aXaOVjiIhDbjfQ2ESnCPPgVx3sx1vUSzn3IKaqwhJgD9PLxwM0EslP2s1t9LcWA6x5xyOWcx3hrTLrTTTCajXpW/bFTmHJJn7SREANu0dwzn/+yY8eSAVbtqtVEJI278Zi/3QmFfPpT1Ly6EdJ3M+ZjOvwszaapactwUn1Ll7O+dxLxxoQqYBUc2tNEeEtI9WOmZhFN6bqwxpdl6XcJgp+UvCoWoJIZosq1gO80FIbzs0hXacYMfhKQCckC4RDtZMZy7202/LYe6FQxGXUhX8OLutccnzLENPVVkXIuyHXYXwmTtC2lsONr58z5O46/EDR7sZ80NI82glFyE9w+tOK8WnXrFMBPngy/aXKkt5PkJZOxEFapgROy7BlBXQNLcbGdLqMyUazpZzmA+30mz5SH6P9ilmyjkc98KhLJOy2zFzZzl44UB4Vx9XrpoNZMHnfoIPTuLCjN9naTk0KgqHVgXOoWyC7jfscVFWB80ux00w3EpqopxaQG6l2VZP6ETFyk2ZJVaG496tZE/wVR5QZ644hzmMZfaYHUxCeo6ildh7PzLdyf0el/iNy0DCod4l+ohQhXPQwRPz0Fft8WZXVTXapYWDtd1Rsnu6X26leSgjMluvAheAdtfJBH1v5zzuhYP9sKsMurmqgeQth2MX85Ehzfvi2HTecuC/9yKfyH1S1a2UCYfi/TMScx4sB10i31SeXGO1iLh1Fd4j4TAbt1KiBMP8WQ4zayu/xzxZX0zwl+G4Fw45zqHCs+8kZmftF5KCju1x9MF7yXxwDi7hwLtFL23ILIeKwoEI6RJLY6Yk5kxgF8qjIeuazJIiy4ETslIiihOWBDfze+jMU+LqbDkHEviAq+5U+t+HslrIcw5VLAcisubIcvB5Dn3FwYk2Lv372/DQztEZn8MMBexHq/Lgk/9YF7dSL3OE5hxqvVoO3TmH+bAcOroWkkVIOy6tJ9GyUNZYapI+Pf/MlTHt6pqvaKUZzg38HqV1u2XlSMpw3AuHsgJdRbA7a7/go5V6x+7RabzrGw8ampGNnYensGt0elbLjM4L56DOO1APurqVqCLq2z6/DvduLV+xLXMr9cY5lO2dzGNfzaogm9aKa0IuWvfAXiaU537MRjhoPmSOrf1ZWw5xseVQxuGU4bgXDvl46ArHeM7hmMG7v7kBX75nG37w2L7CfWgA95oMxsET3+aMc1CDdOlgw2k52IN6fDrCdx7eg3VbypeOpPvuViuJQBNJ2X32sub6bJGLUioJvcyKV5rbuSCJE2mECs/GUu/MU+LqbOuucS+H/c56WSKZ47gXDvkCXd0f0JxlSJdoB+0owcV/dyu+vWF3X6+50EEaW1m+lp0FPxNwxXCuo5WWDtUrWQ5V/d3TavnZeo9upbKJX0/Q80CPtfV4M9vluu1iyyH7HEupl+Tl558JtKtrrmsrFcwNByfaOnO8DJ0Sy2GmHovjXjjMhHPIuIH5y3M4ONHGniMt/M31D/X1mgsd9P6CEulAA2M2a3ObhPSMT1MKupclg1WEQ/a9m1uE3EqNqm4lshxKbnQuC8HZsJNOy8rrZ7yEud0u2c016dm4hCh/IJ5jnrCoesKL/uF7eNE/fq/r8R2mGNlT3JxxDkKIASHEPUKInwohHhJC/K3afqYQ4sdCiE1CiP8UQjTU9qb6vkn9vpad611q+8+EEC9n2y9X2zYJId7Z2y2UY2aWg+qsfR4YWZ5DvrOSR+BoLK5yLIMeR1hSG4Imglm5ldhzH53qGJpnv0DdaclgHeOtqFRxSaSsHMAwU0K6bCzM55K2dggrtcu92A/9Lw5ljeIs9DQQZoJYr5ivaKUi7d6VLOlCOefg3t4NVXpTC8CLpZTnA7gAwOVCiEsAfAjAR6SUZwE4BOAtav+3ADiktn9E7QchxLkA3gDgWQAuB/BxIUQohAgB/AuAVwA4F8Ab1b59QW4AVgplNc3cfqHKOrFHY3GVYxn0/sqyeek99epW2nl4Ch+7dSOklIZW9bHbNuK3Pn1P743tAhqcS4fqAFJOgcPIc0gybbXTpdNSgbmwKucQubVvo60lZbP7DVLCOla0krvwntvNY3IOiVYqmrWwL4T0fJXs7gvnkAvfL+ZwytC1N8kUFAZSV38SwIsBfE1tvwbAa9TnK9R3qN8vE0IItf0rUsqWlPIJAJsAXKT+NkkpN0sp2wC+ovbtC2ZiOcyV1qT9pQ5NkAajJ6tN0KApKyrXKyH95IFJPOdvbsZn7ngCH7n1Mewfb8MumnHPlvIIoZmA+tXSoQaAfJa0XRm26roiFLZZVbGoZDnMq1vJypAu4TuKXE70iBq1AJ0kW960WQ+6Ctfyts2PBdWtYnM3HswIZZ0vtxIAKA3/AQB7AdwC4HEAh6WUpPpsB7BGfV4DYFvaSBkBGAWwgm+3jina3hfkzc/ux2QZm/3tEGWcgybh+tgJp9rxglg/twxSFk8UBNKaqloOWw9O4Mh0hC370zV1E8tymCvQIF0ymFoONu/A+1si+Ypm3TiHVChWncjbFbTh+ayt1LEm4LIEvKISNNRPBmoB2lFWC2mgFs7OrUQur/niHAqu061vVyGk56S2kpQyllJeAOA0pJr+M3q6Sp8ghHibEGKdEGLdvn3FoY0cM6rKqv2M85fnoDUHq32HJ9t4dPeRGV3vDZ+6Gx++5bEZHXusoEpZAU1IV+QJSHNuM1/3fFA9eeFgWg5mEpysXB1YC4eKN5G5lUrcm/NYWymy3Lhlk1mRgqWFQT00Vm5r1oNZupXm5zl0y0VwVfHlMDOk7XOr/3MZrSSlPAzgewAuBbBUCEFVXU8DsEN93gHgdABQvy8BcIBvt44p2u66/iellBdKKS9ctWpVpTbbEr8nQrrflkNJRyvyOX7q9s34zU//eEbX2z06hd2j0zM69mjBNp/pFZRNZJEmpKtNAjRZdLhwmLNarBlojiLOwbYc7PIZ2epg1aKVKruV1PnKhkKZ37/fIM0+soSCcw3pAgubvjbrqeVAvzdrsxMOdngtbfvsHU+UJmb2fJ0uStBkF5dpGeeQ5Y301qYq0UqrhBBL1edBAC8F8AhSIfE6tduVAK5Tn69X36F+/65MR/z1AN6gopnOBHA2gHsA/ATA2Sr6qYGUtL6+t9soRtlC5N2O6TchXTbYi8zlw5MdHJmqFrHgut5C4zByWo92rRTfR7tHy4FMdO57nxe3EgtlBfKRKGZV1nzNoSLMheVQ5la67oEdePbf3Ny3ybFjcSuu8hmtKMavfuJO/EQlBBZxiQOKgNacQy2cZW2lvKC6f9thvPeGh7Guj7xUt1yEKcea4xwm5+D2lszFeg6nALhGRRUFAK6VUt4ghHgYwFeEEO8HcD+Az6j9PwPgC0KITQAOIp3sIaV8SAhxLYCHAUQA3iGljAFACPGHAG4GEAL4rJSyb8H+RVK0DJog6/OMUTbYi4i2VpSgHSeQUkJUXepLoRPLvgu4uUacSCMyqUrqf6/RSnZBttStNA+Wg7rGYD0EkE/O4n0zFQ7Uzi6WwwwJ6dIkuJIM6fd8cwPGpiOMtyIsrzUqXbMMmUWdGJFj/J1s2juOe7dmmeK5cE3mVmpFibbCBuqBHnczGkNMkNLxHcst2Q/opU2jBLc8vAcveeZJRltdKwca7SxZ7Gem/FGVaKUHpZTPlVI+R0p5npTyvWr7ZinlRVLKs6SUvyalbKnt0+r7Wer3zexcH5BSPk1K+XQp5U1s+41SynPUbx/o6Q66YEZ5DhXjy3tFKedQ8OJowpuJoDqalsNUO3aWiOiGREr88Vfux92b09XhqnEO5FbqkXNglsN8cA50L81aKhzsSd/OkK4aGDHd7tFyiLPJsltbpcxPKhNdJqpeQe+2E5sWHH8edihz0SJeA4pjsC2Hr67bhnPefRMe31dcf2v7oUn80Zfvx+hU1m95v6Nr2O6vfoDu59HdY3jr59fh+z8zOdWunEMJIa2DOvrtVlroKEuzL8LcryHdg3CYRU36KJn7FayK8NfXbcAffPG+no9rdRJc98BOLRyqWA7klqhuOZicQyIxL5wDDdJmPR12Hau9NiFdtcY/lc+o2kV6IaTtdvHj+tW3MgsusQRktk8gyoUDFwbtiBHSKjHw7s0H0Ykl/ug/7i9sx43rd+G/f7oT/3jzo3pb25FprfnBEqHdiRP8ysd/hNs3Vg2cMV/e4am28b0r58AisvJ5Dur/iVJbKZESX7h7a1d3QK9VWSUblL34Kp/YP9G1LlLZeg5FL872j/eCKJZ9CWX9yj1P4pf/6faejtl9ZBp7x3onw1uxuXqX1tZK3gX9VrV8Bj3LluEy6LmpPYOHVwIOqzbnVso06jJkoay93X/Z3M67TZEQ6Jdw4HkOdpY4wc6BdIWyBiKtTNuJpeFmAgBa6mLT3vHCOWD14gEAwH/dl8XDcLesFooVivHtHWvhvicP44++XCyMCK/+5zvw1XXbjW1URJGy3rtbDtnvdHudOMEv/OP3cNOGXQDmKJT1WMTYdIT3fHMDHt83UbqfbRJ369B8wPbS+f/wP+7D73/x3tLooBlZDlF+HdxHdx8xTF8XpCq/0I8B/M5vrMeGHb2F03bYYitlsAe9HTqYlDwzfi0g06C7oW1ZDnEyP4Q0PY4BZTnYPusZWw6d7pYARy95DmXn7Vckk+ZWrP5atjqfy10cCJEmwbE1n8lKo8iwdpzotaVz7VAvaKIdY++RaWMbkE+OLeuTRCAfniwfp1Gc4MHtozmrl9baaIbVhAMfa/Tcpjoxth6YxEZVyr7X17VghQNJ/24Dp2q00qGJNi58/624/8nDelsvWjeFJ379vu2F+xSZ4zc8uBMPbj/sOkR3Gnr5Ukpc/tHb8eZ/Ly/vkEVG9W/W64W0bUdJpednuwsyLdI04cu04s4MLQdeGmE+CGkdXqm0WTs5y3apVF3JrIolwNGZpVtJb+9T38rcXIlxLd4+ux+7VjsLAoF6qEJZ1c/E7/Cw4UMFEza/9vbDU2nbHJZDFVfnEStM2TUW7t16EA9sc4/7mhIKJNy6uZUih3Cw1+Q4Yaqy0m12m/yqVmXdMzaN/eMtPLZnTG/rhcw9bekQgNRvWYQijeN9NzyMz925xXmMnsjUfwpr5UKs/Fr940166VvtuKJwsEyHbHEVs0OXTZC9JsFlnEN2DfvsZSXCZwrqe/VQpAXh7GglI5S1ep5DLwvFrNtyEGMqhLbcrcQmm/myHJg7CDA1Xfve7HFPbqVGLUA7znMOPDji8KTpz3ddY9dhshyyZ2+P37LnzYXRdx7ajbP/90342e4xY59f/cRdeN2/3uU8vqYq7Da05VAeyhobxLl7njlh3Ep0n91uuMyva+zniHjpJQyUSNEyM9L2WfJrF2m9LSts7sBECwCweKA8Cnku6kP1Ykm1o6SS1WJXW21HNFGY4ZmVQlkdz/Ab923Hl+95Mtc2/t9lOVRdOKcX0GQaiFTDzUcrZZ8TWT3PoShHxoUP3/IYTlrUxIvOWVWpZHfZefu1Fjq3FouilWwlx1UiIhQCjdAsn2G7lYCPktWmAAAgAElEQVTiMWoIh9Epdd28kKzSJ3lRxf+6P+UweChuN5BbKeMcmJCKE7znmxuw8/AUpjsx7ti437nEbJ6XqXz5tA297X7sofvAqeZWovNw314vhDSPUy5C0WCPEolEurVezTmo8x6cSLWeJcqNVXitOagP1YtZ2lb5Gd1ghyhqy8EqJ1L2nimixPXs//TanwIAnn/GMpyzepFxjax8Rn7glFWBnSloUgloEivNc6geGNFLaOXPdo/h5eedjMOTbewsy3Mo4BxcmvRMEcUJ/uLrD+IRpVHbHFlSIqBc7mIudHlSHJAWOVw2VMehyU4l4bBTWQ7tEsuh7P65pbJH8Re9lJSnU9MVPnLrY9h9ZBp//yvPxrZDU/jC3VvxnNOWYN2WQ/jPddvwc2uXZccWtO/EcSuR5dADwQwUDyDSgjhZtXn/OL7/s72V2kOTWbvEtZH5/vKTQlE0EmnDNKGlFUSBxQNdhEMP2mRV9DIZVLUc7JykvFtJXdshaP76ug247oEd+rd2nA/dPXPlMADgkz/U6TZ5IthBSNdmIBzWbx8t18bVZcNAoF4Lcs/H1v4yAd/FrdTDUpZj0xEWD9QhhKhOSLP9eFb3bPvWo7vH8I37dui+n49Wyvbt5gFIpEQQpIQ0D2UdbqbC4fBkBycvGUw/T5W7lVaONDPLwUFIl0UdErilsm88tfarBkzwa/DrkwXMLcVHlRucB6hoy2EGpYM4Fq5wQCYdywdkuTlKyBKpsv2nOwne/O8/qdQeWxt1oUjjiBJZeJxNSJPlsHigju2HJnHOu29yFubTWmcfOYde3GxVOQfb2a+XjEzMiBqXYPr8XVvxx195AAcmssFuC9lFyv3GtcV2lB80NusQVlxVjbBxzxj+5z/fgQ+xGHkbiXYrUchllyS4iq7BqlU3pzsx2nGCRQM1hEKUjpsirZ1PQrMVDvXQnH7svJzeLAcKZQ3QiaUWxKRERYnEyYubALq7lU5fPoidKuqQv6Nu0Up/+98P4T3f3AAAmtfhmO4hebBq8UGXgCrKbj9hhAPhb65/CE/9qxsLf+8W5UCgl1EU5tYNdHyRBZAkWSx9PklFFroO8m4lxTkM1rDt4BTaUYInHOG8rpows0WvbqUo6R4FZHdg2x1WFFnDv3/30cy6s0lpnQNRsqawK8+hbOU5F+j93fDT4oCEREoIAQghUAvybiV+T1Jmik03IVvVSqT1IxYP1hEGonKeA59/eJ2vfidYlrmVurlIeChrO84K7y0ezCzsJYN1DNbDYkJaXe+0ZUPYpaKVzOVGzUnXbsO//2gLvnD3VgCmW4n6Ui+LUbncqWTMckuR3hPvv4XrbPeoJy5Y4UAP41HyVxYMoKqcg71gDMWiA+Wuq+lOjPuePKQn49QdkG9LXNLRiyKKpJTMcjDdSoEQmOqkA9VVziCeA86hJ7eSFRFUBPv92NnLLfV/oh1hpxqwADBZEL1hV2Z1CW1XZrItxHrlHMjy2MHaaINIU0AtSmO7lYosh5JnmDCXWHn0TAc7DqVtWzxQgxDl+5v9NXtefIGi2QoHW+jZbiUz16JYkKbf08g3WkeblAHufh1shJp3cIHOeerSAewbbyFJzNpkWUBJd2E8Ph3pSCOqkdQT52C5sADgqatGAJiWg2tuok0nbrSS9b3ILVOdc1CWQ5uEQ6h/K5P433pwF173iTuxf6xVun9RUpG9RCX/vRNnGi3dH7mVOrHUEQyuiTJb+7afbqXeLAegu9ZbJBxiZXXQef79R1vwio/drifxiVY5ga/b7CitYfcVKfOhrLVA4OGdRyqHx1aZKGPlFweUWylK0IpiHcVihrLyflD8DF1RKi684IPfxWs/ficAZG6lioQ036+fbiX7PfDkNcDUhsv4mXTfLJQVyJSExYNZzE2zFmLJUKOrW2m4UYOU6Rjizz4rn0Hfy4RxhGXDqWCiOaVqSXl+f/way1QQCl+L3rWfdsV2eWbdsHCFg3WjRe6cnOVQ8ELp4RJpxN0KZRJ/bLqDRJqDxtUW/vK6ZWG74vY7OeGQZXq6Jsp+178BqgsaHpLYNQ/Ffo963eS8q210qqOfXdHC67ZgdkWRuUJI7X5xZDrCK6++He/8+vrS9hP4Oz0w3nLukzDLgaJq/u5bj+BXP3EnHt83nrMcsiS4EuHQpS8ROEG6aIDcSiWWg3HebHs/CWl7nKSBAUWWQ/k4TmT6bInHoDFrWw5LB+uFbiV6hyRg4kRqj0B6TWpLd8thrNXB8uGU45hQylsvhLRNfvNtfL0Z+t1Y7Kdg7J8wGdI2irT7qhnS9MBJyvPBVPZStVDpFPu0ATNygJvILu2D7sXQdtVnIl+jJBMOLsthLhYs6rU8g/3ZhpR5X39HR664Q2HpOUwUCAdbkEd68GTbXQLEvjNSPm59ZE9h+zn4IKZyBbl9ZOY3rofpWsfkFt1zZNpRPsMd+sxBgqMWCP1+pjsxrrlzS6EitGigBiEEyoy6ojwH/nxnmwSXcysxHzpQzjnkOYjUfUPCgcbGCMsHGqiFWDpUx+GC0jNJkpaLp0g1u+R9xu2428AxPh1hubIc6Jn1FsqaWQRv/R9n4hfOWZVT+OIkcz3yPq3djCcqIZ1zKxVaDtWilejF0wvkE1NZWQYXke22HFgnK1m1CeCWQ17b3a+00k4sdfSDq9Z7v0JZuYVWVdDw+4+SlBy8/KM/xK9/6u6uPmue5+B6jq0uwmEmloOrZDcNLHu1tiLwiaIsA5fcSo0wQCdKdCmNVicxJnPJzllmfdH9NWqBbvNHb92Iq65/CN8qyNZPLYfycihFeQ5GkmifLQfbrVQWrWQLPiklgiDvVqoHAYYb6TMeqAcYadYK+06UmNZHZNUHs0PRu4WyLh1qqPtKj+u2JgMHr6QaBgFqgTBqUFF7M04t1opHUXmPEybPwZYOlS2HIrcSWQ4O6V7FcuDH9cI5uLQvXTXUsEbSjkAuiyjuYjlU8FdXQdnyg0UwyV+JqU6MR3eP4c7HD+BrrPqk6941IZ0kTuGgLQc10BY1U82QJoAiy8FlhfH7spWGXiJL6ByEopX7EpktZFRToawDtcwNwl+VkQRXMgnR/TVrgd6fkq6K3v2igRoCIUo1f8NykKkGffVtG42w4V4Xj7HhCuWtmufgWhueMqSBbMyKABhWfWSwEWK4WSt0SdL7odIVUWJWNbZDWXnz7bFxROWT8HyZ2zfux8s/8kMVuVj+7PgEHwZpgIRLONF+7TjRQk3XncuVGCm9ZA4LVjhUtxyquZUykzx/njIiiV6AlFlCl4vELOIZXMXL7JLStO3AeMuIRCDhsG+snSsX3ktiVBm4YPzuo3txw4M7ux7Dra5Okhj3seVAFnbralubtdttORDPkg7wVYtSvy65D2wrzxmtVFLTiNDrQOLv90jBIkc8Wok4Bwp8mGzHOXK5w/pW0XvklgOQTtgkIKnoHIcQwEhDCYeSvmFbDut3jOLDtzyG2x7JwoZnaznYArgTl2VIl7+z1GUnmOWQcYfUNwZqobYcXJNzFJtupSLh4EqCs5XK8VYHiwZquVyOn+0Zw0Q76lqnjARIKhwCQzjwteh58AoJRm11WBfZVODuLMLCFQ45IrPAcqiY52BbAG+86HT9W5mvkHeQIVr+sYvlUEROE4rcSntZRFQarZS269ZH9uD3v3ivUdiLl+qYTcVRfu+fvn0zPvH9x7seY0/EXFhuOTCpP5e5lTqxNGrU2+cet4WD0g7tCSdbQrTY7eeyHHoF7wdHinzaMgtrrIcB2rHUIdNj0x0zQigxE5yKrIAcicrCn6noHMdIo4YgEAgDUSoA7UmaxsUhh+Xwl197EO+74eHikxXAdlNGVlXWpGScOJPggswlREpCGAjdNwYaIUYGakik20OgLYcgcyvx+aOsfAYvuyOlxHQnwWA9RN2RTDnRirsGd8QsijEU6fuy8ysiJkzjRKJOCoLmK8xr/GyPWfivGxascLBR3XIoEg6Kc1Av+dcvOgNf/4MXpNtKhAOPZhhSnbBMOHDi0NU+gBHS7LqdKNHuglOXDKRuJcuH2YpiLQiqRrF0A9fEJztxpXMZ9WisooJbmeXgGh+ckHa5dmzO4SS1QMvIgEn+ETjnQM/GlV9QNlFWcc3xScQu10xIlIsAABo1YVgOY9NRvnxGFyUCyCxP0hqve2AnDinOo8GEA/1OGeNB1zwH897oHR5ifAq1af2OUTy8s7f1PgCHW8mqysqb181/niR2KGvaDwIhMNwgyyHQLiaXaylKEsOt1InNaCU9OTvGGB+LXGDblgNdu1sUX8KsglooDMuBJ7jyPkMWjy7ZPUuFZ8EKh6pupTzn4D6fzTmEgdCaV5lbiXcQ8nu7rBjuGzbIaUeD2l0shzXLBg23EuHPrv0pznzXjcb98GvPBEZ0SlK8JvU+ZtXwd9FmUUdPXTmMHYemslyGEs4hKnQrZcJBCGDFcEr6jagaOjnLIclcMy7+AaDyGcU4MO4mmDkMzqHIrSRNt1LECNgjluUgYbpYipI8iY9oKBfSn3/1p85y7lSdlDKGg6Ccc7DzHKiv8ddPkw+V5egVOULaKp9h3n8X4aAypElTJ3doIEzOgfqJO/xb1b0iQjpJ0IkSbd3pFeCsBakAd0BKPRRO4TDRiiqVRKF7DJTlkLmKaYyYzyvjHNLvs02AXbjCwbpvlwsCqB6tZJNNjZrQWl1ZIhR/AUMNt9+bn79ZD02T3TGmXJxDK06w90g6AZ+8ZNDIcyDwEMoqWmcVFGUcc9y0fhd+7gO36nWf7Wxkeh7nrF6EKJE609klGLNIDDchTe9ivBVjuFHTLoPMrZS3HEibtLPNs33Ktawqy53qfhMGxYR0wpPg0gxpuscjU5HxbLkwS9tc3m8bDhdSbByfXiezHESpu9EOZeVKAk3AXKGayTK2rgxpO5zXdS/8+92bD+DTt29GnGTVboFMkw8Doe95oB5qK8IVsRQniXbhUHuiJNHcjV0+IykQDnRf9TBAveZyK0XOcWS0RWbXqSkexE5uixPT0qL30q8cp4UrHCxdj0/IcSLxu9esww8e24cokUblT/t5/e41P8EX7tqSiwhp1kKtMZSFsvIJjipAui2HzA9sTtyOCdKVBBdJ7B2bxvLhBobqITpxUujuktJK+5+FBmELIJeLhYQCuRbMUFap7+Oc1Wn6P/EOdOuNWhZumLmV3MUI6dyT7QjDzRCD6rgs+Sk7hpZKHWqYXJA9kblCWTlIKJeBBuLSoXqh5cCjleqhMKyqI9Od3MToirG3QROFi1+4d+sh/Ml/PmBU/T1jRVqlNuxCSNsaPH+upATxMO6ZRMW5rDx+m1VqK73hk3fj/d96xBnKSnWsaFwO1EJNTrtClDPLISOk27HEoFISf/DYXuwdm84pkoAZMZhZDgHqjnVBxltRV84hSaQet8QR5TgPS5jSGCgrVtkLFqxwKKrmCQAPbDuEWx/Zg3d/cz3iRBoDx9YQb31kL95z3UO5CbRZD7TlUCWUFQAGG905h2YtsBLi8i8wC2U13Up7jrRw0qImamFqYhatKxtZ7p/ZVGYt8uEb20jDUYPKToKjSYBKJhNhS8e999XPwmfe/HNpWxkhnWlgmXSnc423Igw3alqoENHLBSo1lbRF+s2d51A8kGiyf2L/BD5/1xbnPvS8lw01CgnpWGbLolK0krYcbELashyKXAS8X9m4feM+/Nf9OzDWSqNj/vCXzsLfvfbZANIJp2zusKOVuJJAVhq9v6l2NctBSlPTdQkUvs3kHCxrz5HgZWRIR7F24WVupUC3vdByyBHSmVvpy/dswxfvftKpmfNxQvkMjTDjHJ5x8iJ8789/Mb1227QcXDUeYykNyyEMsiq6Ebs+fyw1y60055yDEOJ0IcT3hBAPCyEeEkL8sdq+XAhxixBio/q/TG0XQoirhRCbhBAPCiGex851pdp/oxDiSrb9+UKI9eqYq4VwPS4T9m1zLeRWFW535soRRIk06iQVluy2RkpqObhj5zn4RD/cKI5W4iRVN5eP7VYSaknJfWPTOGnxgJ5YiirI2slEszEvi/IGOGg808RnWA5xZjksGTRJY+rsPHywrf25WQISf388Q3q4WWNabIJGGBj9gLQzsi4o0cylhZaNIxrsv/LxH+Gvr3vIOanR5LVsuF6YOEekKaCEQ5QYbiW7Po7LLWSD7rHh8G23mJUFpC4l0qx1wlQR0W3lOfB+QJYYFX5rRe7gARtXXf8Q3nJNWgL/Hf9xH752b369dX6eogg/IJ0ADQEmUwVBuxA7ie6PlAvTrIVaUEwU5AbVLEI6is3548hUp8ByyJ4Pjct6LTtXLcwsmPFWrIX9i85Zhbe88MxcW1J+L30WQSAQisxyoMJ/HYuQpqKDRbWVekUVyyEC8GdSynMBXALgHUKIcwG8E8BtUsqzAdymvgPAKwCcrf7eBuATQCpMAFwF4GIAFwG4igSK2uet7LjLe70RPiF9VwmH6XYaXTNQ48LBfbytmTRrAUtQKu74XCvXnINz8iANL+w6cbctt9JIo4Z2lGD/eBsrhxs6W7JQOERmfHZV8zKKE7zq6ttxy8NZyYjpgoxjc1u6TxjkLYdOnHEOS4dM4cCjMUgf4BnS9HnQUQRxohVjuBliiFx5UWoh2gQ6YAYKOEubJOWENFloVM3TNejotMuHG+V5DjbnwNxK9hrSlaKVSjgHOrfWZNk+pFUXkdJ2WC1/rjTBkmAAqkV0bdo7jvU7RiGlxHce2o3th/IVbLnlx625MouV9g0DlgTXiUEeneXDTQQiFY4jJdFKFA5LlkOcpH2wyfof5wuMPId23nKoM8uhFphWC73P1z73VDz/jGwVN90WZhWklkOQE0pxASFdVFK8V3QVDlLKXVLK+9TnMQCPAFgD4AoA16jdrgHwGvX5CgCflynuBrBUCHEKgJcDuEVKeVBKeQjALQAuV78tllLeLdPe8Hl2rpJ2md/1esCJxOP7UmJ2j/IPNiuU37YHX7MWoBamySfleQ7McqCIGcf+3PznAsUZytoxLYeRgRracYKDE20sH26gFqariBUtHtKKzZDTqov0TLRjPLTzCDbsGNXbcpYDO9d0J8avf+puPLg93T90WA4d5lYiy4GEGj3zgJGAWbRSFmNOmj8/93grwkizprXYdBDblkN6PA8UcAmHRJbnOdhlD1xuOpoolg41MN6KnP0sVhE1QKrl8SxwrpECipC2hCwAbDs4iS/cvTUXlutyK2l+ppVPiiNivDBAw/L3TzvcSlxBqRKtNN6KsH+8jbFWlCPYL33qCqPNaduy390Wq+l246GsU53MrfQrz1uDr/7+pVg61MiEg8O6i2LLckgSdJIsix1QyYoOtxJX1KaYcCBh1QgDDNZDCEECJr3PWhC4gwlkZjmEIm2TLRxsJSXjHIqfWS/oiXMQQqwF8FwAPwawWkpJxVt2A1itPq8BsI0dtl1tK9u+3bG9FLauR53z0GQbUSJRDwV2qXVgl6pJqVkLctoZgT/oZi3QmuxALSjPkGYvgCahIu0USLkMKYFR0kIdL5A4DiJnhxohRqc6mOrEWDHS1BPLZKFbqZrWaYMGJp8IbEHHB8T2Q5O48/EDungcTTimcHC5lZQgl5lbyV7IhBPS3PJrReng3HF4CitHmmiETDjUQtNyiEk4ZFFn9loO6T1lysZ7fvlcPHXVsPH7ZMecSFyWAz3j5UMNSAn8+ImD+PubHjH6mGTCoR6m/YAmlbHpCLzbJNIdyvnBbz+K93xzA+7YtF+1vdhysN1KfB9qRxEdZRfAMwnpUB3LhEMFtxJp6xv3ZFF1qxY1sfEDr8Arn32y0WZ+b/Zn17Z2nOg1pAGyHNQYrod4/hnLddtpgrahw2E155B3K423snyUrQcm8aFvP5o+B8NyyJ43dysJlXMxziyHWiCc745bDun4EDmhZD8TulaW6zRzrhHoQTgIIUYAfB3A/yulNDJelMY/OzFVrQ1vE0KsE0Ksa7fM2HPqnHtUZMl5a5boyeUlz1yNL/3uxThz5TDzxyWFHZFrYQP1sEsoa37QlHEOy1QxrldefXvuugSa4IgHGWyEOgFuhbIcpCwuPteJEjPSxTGZ/WTLQXz4lseMbXSfLv+pfR8AcoumEFFkJsFlz3mwkWaMTllupVAIPVnRvrx0wYBlOTy0cxSjUx1c+rQVmqwutRxYcmKR5UB39ZafPxOvPv9U43fbQnNzDur9qryLN37qbvzbDzbn+lhWWyntYzRhplp49j75Yj/p76Rppsf/6w8eN7a7SmVoy6HjcCupj4VuJcul4yKk73vyEL6i1jWuJBymSThkmbpE2tLz4M+rSHnT7WK/t6PEyHNIZCYAObIJ2l3iphZmlkOs1nPgC39NtrOJ/dHdY/jE9x/H/dsOmZYDPW/uVlL/h5th6laKM8XI9e4My0FxcnrtBkeeBcAshwLh0SsqCQchRB2pYPiSlPIbavMe5RKC+k9FV3YAOJ0dfpraVrb9NMf2HKSUn5RSXiilvLDeaBi/aeGgYtKfvWaJ/q1ZD/DCs1YaZYov/rvb8HPvv1Xvwwc89zEO1MPKSXCpj1EURCul2978grV44VkrsONwusQnP35QE+CJPqam/Ki71Zq2qVspI8zOPWVx7lqdOCkMl40TiZ/tHsOv/etduPq2jcZx1O5thybx4v/zfTy+bzxnfvP22oum0G85t1KHJrBAPU9LOASZcODvgbSxJiNbW1GC2zemWvMLnrZSlwxoxym3ZIc0Axnn0IoSdCLHJJOk0Uo0l9hLhObcSiVJjrQgi70dUNFKLJQVMAW8HYZraMaq3bSex482HcChiXYXyyFt9xSLniFoy6FL3g99Nghp5T69acNu/NN3N+n77FaIjwThY8xyoOcQOqxOM9fCMaaYwJhsR6jXAksAuuNaaILOnS9JLQdesrtjWQ4TrTgX2bhvrNWVc6gHWeRUWj5DWQ6h23LguTdhINKkxYTqLdH8YAsH4pHS73PuVlKRQ58B8IiU8sPsp+sBXKk+XwngOrb9TSpq6RIAo8r9dDOAlwkhliki+mUAbla/HRFCXKKu9SZ2rkLYfZoGwj6yHE7NhEOozERepvjARNtYBLzIcmjWg8qhrPUwncjbUYLdo9N6MLSjBDdvSEnegXqIV5x3CoDUBcav26ilpXlpIFIhsHoY6JIMy0caRuz0qy84FRv+9uU4//SlelvbEg78Gt9avwuv+NgPnb+R1rZhxxFs3jeBn+0ey5WC4LWaDlmlqckXbxLSmVupWQsxWCAcdOIRmwRoPz5Xt6MEP37iIJ5x8iKsWtTUWuySwbqyHHgZAytaKYqdyZIUrUSXCdikIgRy7js3IS11O4xnwn3oiQRF5dKEwDN1eax8ItPr0L3Tvew5Mo2VI6li9OMnDuRqK3HYa180nW6l3oUDEdK565XwDnEi9aS5cS+zHFSbdGYzu05XzkFy4RCjEQqnALRRVJk1TohzYBnScWK4NSfaUc7a2j/eNiyHTDhklgwJiZFmDRv3jmG94vXCIOMl1iwdxKuec4q698xy5NF8cZKV9LCfN6/KesODO3H/tnymfC+oYjm8EMBvAXixEOIB9fdKAB8E8FIhxEYAL1HfAeBGAJsBbALwKQBvVw0+COB9AH6i/t6rtkHt82l1zOMAbureLItziLLBAwDPWpNp1PRgg5KlETlBxjWFVBtNX/aN63fhkV1mDRk+UYTKf9iKElzy97fhin++AwDw4PbD+M912/A/zl6Jp60a0SUfDoy3jcmwFgjDUunEMiW12KBeOdzUlgOQWhsjzZouC0DHmWGQ2eet+yeMQWeUulCfD0ykAnaiFeHIVEe3l0DHj1qWAz0LFyEtRDpY+P3RIEuTfNT+TLOnAccHeSuKcWiijVOWpDWVzlm9CO9/zXn48OvPR7MWFFgOWVG+tsNyIEKarsM1zkXNWi6fxJXARPe+2BYORty+yTkAZkhlOsFlsepRkk1MdP7do9N46bknY7Ae4u7NBzPLwRHKSu/JGa2kCWngnicO4sx3fQv//N2N7Fhu8VicQ7134cDvc6NhOQTG/5YlTHUbunAOE60I9TDlCkM93t1tWVQiHILcYj/ScCtNtPL1xfaNtUxXrLpXI1pJjdnhRg2P7RnHe765Id2HEeCDjRD/8uvPw3AjxCd/uBmXfzR1PXPlKWYWpR0swpPg3n/DI/jWg+71PKrC/ZYZpJR3IFOqbFzm2F8CeEfBuT4L4LOO7esAnNetLcYx1nfqmHvHWlg6VMfaFRmpSA9WiOLEn/FWNtGZnENGSL/9S/cBALZ88FX6d2NyVxM5TY6P70uLzNEk90cvPhuDjRArRtJKogcmWkZHCwKRXi+K9blrlja0fKShNRsgc0URGQ4ot1JBot0+awnL6U7MNOu03TQvTHViHJnuYNlww6jjnxYoC3F4yrIc1DuwhUM7SjTJP1gP9WSr8xxEFsrKJ5hJVgKB0I7S/A4ewfSbl5wBIBXqVDX0mju34Jo7t6TPhtVdKgtldbmVFg3Uc+tluEpZxEkqAEcsrZpfjyYfwKyDQ31mohWhWQvQjhOtOQ7UA52BPNWOcWQ6wmnLBnHh2mX48RMH8dynpBajy3IguIQDPdI4kdi0dxxSAv//dx7Db126FksG60afsUNZB+pmCWn9XEp4B+6e3H0kK0eSWQ5mwTygPEPa3jbZjlnYqDD4HRtPWzWCG9bvwgPbDuMCZnFTqZVssR+ZRis1uFspX/pi1+iUkfjIn7ct/LigAcyJn8aDbfHwkh5xInOrVhI439LLkqRFWLAZ0nm3UmY5rF40gOFmTT8s0gRCUexj5fVwDLeSioApCgflHaWmyCV7AqLJks67XGniByfaRgcPRXo8EaCUlEMdi8pM1Fmnp4mPT0qdKDGL+7H22KUgyhbBmWzHODIVYfmQaTnQPduENN1LO0q00ErdSll9Gprs+HlClV6VuIMAACAASURBVOST7m+6lWzPQCtKJ8lBh/baZJFlV13/EDbvT4XzUJ1xDk5CmtbjUBontxwG8pZDEedQC4QOSsj2ZZOsUXgvuwa9u8l2rMOuKQmOrNgokXpSPXnxAE5ZMmBwDq5QVgJpsobLhYWy8vuhAoqJlEatnilDOITOibfMcihaYIf3bcAu9Jj+78Tu9zbFhHaUyJygKXIrvfuXz8VIs4ZP374Zo5MdrH3nt/DVddvUqmuC8R8xpDSj5VylQnYensbO0Sxnw+QczDmI+iShFmZ9X4d2W882DE23EnEONhem8xwSWVrypyoWrnCwvmeEdAsnLU4186etSmv5ZGamUGF5eak6OsUtB05Ip26ionVn+eCnsDSb7KIJmDov+Yz3j7cN0zm0LYc4QS0INOk63AghhDAsB5pY+KTUyhHSxZYD99HbUVmTrQhHpjtYOlQ3JunH907gwHjL4VbKOIdmPeVPaD0Hmrx6IaRJGzSJWbIc8l23KLLMiFaKsggQApXPoC28BP/igTqmOrFV9sHt4giEMCw4/kxon9CyHIAsPya1HNLPUnEOA1rIJjoo4eQlA1oJiSoIB1o1j+f7cEKaKwW00mCcSFad1GE5OCbesoglyhpfs3TQ2E5naZRYDq+6+nZc90C2yBRp+3bEke3CKbIclg83cNqyQYy3ImzYmfr+v7puu34/NKFPUSCFpe3bgm7n4SlsPzSFpywfMu6BV2WlMXxwwrS2wyArI37WSeZ8pfdh0Xycc7AjCXm0UlmEZVUsWOGQq62kOuYhlSgGAE9TD5s6WSAEksRddItntXLTjyYzvi4w1yQNy0G5gEYtQWJbDrR84IHxlmk5BAKDjcwnH8VpaN1LnnmSOr9J3gHIVSUFHKGszIqwK4x2txw6WDxoLnf4P//5Dlz6we/mCWk1aXbiRIfxRUqLoQFmCAcjGsM8B5A+57pVWjqzHPLhf82aGcpK4LWVSLvlx+fcSuxeRwZqmGzHxkCkZ3vj+l246/G06CBZDsNNs11cm06MaCUmHBqZ5aBXdFOhjNpyiKV+d6sXDyh+JfN/V3IrsWuGbLLhbST3YZzAaAvnHAbqodEf9L1GXLBnY+zIdAf3PJHSizQBEmxCnT9nCnzYqgo1nn3SCL7357+I3/n5MwHkAyIaWks3S4S4MKA8AlQh+NSlAzlC2mVxAfnFnDbvn8DhyQ7WrhxW9549bxJUZO1/7rcvMp5dLRBYtaiJL/3uxfjYGy5Q7TYbzhPzoiQryGe7O3lY9ywDldJ2zP4URwdFSXDjrUiX6CXL4cmDaecKgnRCcpm4RwosB5pwDk5kv28/lK1mxid3yna0hQNJcV3XJhBYNtzAwQkzWikMhO60dO5aIHDFBWvwjbe/AP+uitPVAq51kuXAOQcrRl4vNylzbiUj4c0WDp1Yr4VrazPtKMmFsmacg1Sx62lYL3crDXJCmvlYnaGsnRj1WmBo7dOdWFkOLrdS6LQKycpodTLLYcASDkmSkcUut9KE5cIAUg7qjZ+6W5+D3h8f27ZbKautlO00zDKOSYFICWmJwXoWOUNKzeLBms7pcEUr3feelxr3P+VKggsoYcqc1PePc7cSuSnS5059oFkLc64PIBuDG3aM4ty/vhk3rt+FDTtG8eHvPIYPfftRAOkEz0GC1lVVN1YWXYtZe2euHMa5pywCAPx480HjXHVLeXK1kdBUXOIuZY2dsnQwF8pKgsoWhEWLOa1dkVoO3K3U0NZM+v/5ZyzD/3PZ2foYmvRfeNZKLFILVtmxBVSVFYBRG8wOsddCraSiQy9YsMLBBvnYxqcj/ZAve0aqcZ+zOu1MVMN+zFH7hr9wHg3UrKWuCq6lbOPCgU1m9TBdIMjWLDLLIZuQVgw3sH/c5BwCYWrWKSGdvqLnPWUZzlO5GzWH5XDZM0/CL5yzCkBx4b2xVpQTAGWWw9h0hPFWhMWDNWfpYVsI0r104gSNWjowosR0Kw02QrZwTD6U1c5zqAVmoUKy8FyWA7kAbZ8wDdI2811z65CS4DK3UvZ8Fw/UMdmOjZDToiS4WhggCITmOOx94yTjHBphXsADmXVpcw7E3aT7hGiEIfiCSJQpDuRzLSa6ENJpdnFqOe0fb+vt1EZyxdJ5B5TL0Aa1haypP732AfzyP92Bz6nAACAbi/oYayU7nkCWSLN/Ur9/2qoRrBxp4Ps/22uci+5Pu5VK6ndSSPUOVd+podyXtdAhHEosB+7OoyAYSmas1/LRSvYxrudot7sWmLxEUeYzWSc2FzFTLFjhkKutpHzb7TjRE+b5py/Fune/RGe8BipaqahqJoFryc16GknC1859smAd5DAIMNgIcdAyd23OIT1viFsf2YOPf3+T3lYLAiM6qqNqvdjgEwvd63lrluADrz1PPwuusZKPkqyGp67MIrk4cWX7KSksePFAHaFjLdy8W0m5w1TyXi0U6ERESGfRGhnnQPcttLZtuJU6aew6CZFGGGhrxSZ+gcxysFduo1X9uOWQcytJFFsOndiItnFxDhHjE4bYZP+XX38QL//ID/V1tFupxt8ht1TdnEMUZ2txN2uBdtNNOnIYeDgnkLkfmkyA8NBICiBYPtTIOAdGSEdxSkhTdv9AvcByoDpRSoC7wmvPWu22HBpqUZyWnpDTEtW8f1K+khACF525XJdtIWQJZ5TXVCwcUn4q0YoeKVS8ztdUmwSveR9cWeHCbu3K1HIgRaLOsq35Ofi7Ch1Kl/1sueXAOQfXcWEgvOXgIqRp0ie3EgCsHGnqKBQh0ofbi3CgMtA8MoeTuoZbKRQYbtZy5p5rwfdnnZrmYWzel0UvBIFAsx5qQpo0GRs1h0uC2gpQR+caK4X5ppP9H112Fn5LhX+ahLTZ7r0kHAbrhiuLYGsokeYcUi26HgYYb0eYaEVaMA7UQxatlF4vECxaibWB3Eo6X6EZ6sCAIsshkWloIQe5+1pRFmnCQ2F14b2CUFYgy/1I79N8Th++5THc/+QhLciH2bk375vQC7tLmZ17OcsdGWYuskbOcshI4cxKCHRfclkF9j24Q1nJrSS1pbdipJG5lRghPR3FSGRadWDlSBNnrBhyKi2dWGLjnjGthCy38mOAjHOoWZYiWT663HWY1kHjIZn8mheqWkkctuVQVvl/oB5gqh1rPiNK0vLXqaKSktLTTFAVgT/T1Wo9c01IB2ZVVgKvwOC0HKxtNUs4FC3elRLX5UsM9IKFKxws02H9jlG8/4aHAeRjzQlhUOxWMvZjnUpbDpNtVf4hMNdnZhNFLRC6djwHH9SEv3rlM3GhVao3DFKibPO+Cax957ew/dCkc1Lm23jnpI6YEtIyp40fUrzJM09ZrHMDpg3LwZz0dmvLoebsxPY9EfGdEtJppMa3HtyF+548zEJZw5zlwKOVODk63Y51zDqQ8ioUITVYYDkAwI7DpnAgy4FHK9mcA8DcSiyJigjm/cwa6SRmqYirb9uIR3ePZZaDgw8BVFVW9bhOVhMJYAl4LRxgENLkVmoo1xXdq6uoHgDwbjNVIhziJO2f9TDAypGmtrqIQwlEZp2ce+pirHv3S3DKkkGnVj7eivDSj/wQ/7lum24zAPzVK5+h91k8UMfigRpOWtQ09qHlNKk/1kOhuQ4Cv6ZdHBHI+iKNA4fhojFQDzHZjrQiQblBpLXXgsAQVEVo1gJ8/89/EZ9+04X6+pOq3waBYJyDqXASXILHditxa8ZeyMs4ThX3O+HdSi58U4W7FQmHqm4lHi1Avt394y0sH05LVxiF5SxC2lVaoB0nqIfCMBdHmjWsWWaG9YXKrUR4bM+4QVwSXNuAzFVBhDTFZ9PkR0lry4YaWvMssxxooC4ezBPShJOXZJMcDfRIWQ5cg9ecQz1UmacJi1bKTGkjlLVjhrIONUJntJG+hnp2Ow7ZlkOWuU4+blM4ZLX8ARgTBF1n/7hpObiiompaOOTbNq3CYalv8X4y3DQDIIA04IIvVBWpGlX0u11+ww5l5QrEZDs2eB2AFd5TFklqOTR1tBJlc4eB0CGjXCC7+oMdpknupd9+4ZlG+1773DW4XJWQySwHM5S1FgQGGZ1uy655xoq8cNA5BSQcunAOR6YjHdXTUYmHNf3uBSu9LfAfb70Y1/7epbnzNGoB1q4cxkvOXa2vy6POdLQS5xzYGHc9R9utlPIg3KJ0cw5kOfRLOHTNkD5WURapRa4AG4FIH25RQo7eL8i/yD1HprF0qJEjPO1QVpdgSge1O/SSIxTmpAW4O49NkBGoA7aV/7SpEs5IgJG/fslgXXM2X7t3O6Y6MX7j4jMK49QXD9QLBdLJSwZ0NBhZUZ04UTWisvPRUyLhNx0lbCW4IFuZjL1YynNox+n74pOTawImYWgvIkOVL1tRzDgHi5CWeUKaQosBYPO+rORDFMtcGCHtD5icA2F0qqOTrGy4Cem0sBx976jYdeqPtH2qE6nSJJblwC4z2Y5yfY1cLolMCelGLUiDJMayPAcSKKNKqeB923UfNgc13ooQiHSivf0vf0lbJX97xXnYPTqNz/7oCd03SbHhrhx7BTp+TTtfgp+jbgl5F5rWOIuUQpVVzRW6plYtCPCCp63MBWAAbnJ5qh3lMqP5++FzgcszYAu1NEPabKcLlEx6wruVysrRcs6Bg2qid3UrsadCL3/36DSWDaWTJK//07HdSo5rt+PYGYduC4wwEDmN2GXSFrl4iIhrR2bBMJq0D0+2MaDWxqb7uvPxA/jCXVsB5AlpwtKhYsvhVG45WNFKHFtUZmhWeTYTWrw8AEfq7hB4pqo8y2s8DbjcSmri3LR33NheDwPtHnStLpdGK/HaSul2nvF87brtWKnKnnQS9xKtNNCHHW0bVYv58IFPRfpGDOFAhLRU65KkkUGRKkNCGnaTWQ415pbTbWH9hmuyhJALB3XeRQM1TLSjtPKnpHIuIXbSuigsU56Of/X5p+oQa9tykDK9HyEETlo0oN8jkAn6TmRbDlloayJNtyfv967xZEcGFWVIA/kyFp04dRXykupUqaDOrF4bDTaG6fpk8fL7qhnCoTfLIRXSmeVQtPwnuWeL1pbvFQtWOHC8+QVr8Xsveqr+XuZWklWilUS+E+4ba2HJYD1d3rFgJbd6aLqVSNvm7gCOnOUQiFyndQmCIh8oFQ2jyAs6V8wsB4o44aYtmaEuy6EWCKxePODUcIA0PpxAA53yMz7+G8/Du1/1TADQq/ORZTTVzrKOg6B4INfDAP/4uufg2t+7FE8/mU0ujoFKfvx7nzxkbKflI1uKcwgsTTtOJBKZJcFpIREKPYFf+tQV+PofXKrv06WdlXEOR6Y6RokOIBN2nJDmAm46ijHUCFEL02VhW1G2ZCX9n2hFOZcRvwfALOhntzWR0JbDQD3U36mC7PKhBp5Qgp2HyNL5n7pqWE/6tuXA78cGCVCK+af2UVgtWfmm5VA+XdF40pxDmXCwFDMqc6/Lm7CoH7JEqGoyh5FYGBDJzxLyrCQ43k6giJC2vzPLIZE5xZjGeaii/k74aCWOkxY3cenTVujvI0WWQ5B2uK6cg/Ei0050ZDrCUKOm1/4l2BnOLtO77dCkgfzACZWmZm5zWA4l0RNp+xKVUJWRmQBweKqjJztutZBwcPl3T1k6kJYMLrjmKcxy4LWV6mGAVz77FLxFZbO+9rnpkh10f60oNjOkS4TDUKOGi85crklMwO1WOluFFbajxHA71AKhk8bIqsmXzwDIsaRX7woELjh9GT5z5YX43O/8nF7IJ0qk069L57SzpAFmObDXSdE8PKyVJo7P37UViwfq+PWLn4J6EOTKkNCkNKFyQewJJTeJ5dxK6X/NOYQZvzLdTrRbaflwQ082y5jlQOHFvGqwnRTJ78dGLQyw5YOvwluVUseroQ7UQ1XNwOQcSuZ63RZ+72WyxA5oiBIlECkMli1Kxd+PrZTwMcxdr3VLUBmWA49WqkBIc8shTrJFgAgjzbreLwi85WAgFEKb/ECx5SBE6sfcb9UXskPuXJYDkGbapm4lNyFdD4UhmLibxzVIbO0lECLnC3US0iW9vlELFCGdMMshbe/oZFonCYBZQ6adrTtBoOd50qJ08ndpOLXAfO5ZnkMWBimEwMPvfTn+4XXPAZANrql2lqhX5Fay759qZvHzcCwfbui6Vc84OYs/J86BsrXroSkcaBEV2sRLd4eBwGXPXI1mLdTPvRNL5wAssxxGpzqYaEdGu1eotvJaXLy/vfLZJ+OkRQNpvkicGPkiOs+hHTmFq/08c24lbTlkoaw0YU51Yr3eNR8bS5nlQIK9wYSD7VYC3CvUFYH6zKAq7GdbDnYI8ed+++fwYpXoyo+nibhXt1LEhHc9yNxKvO/bQsWMPOKCgoSDg5A2LIf8WHa5lXjhPZtzIFd2qELCveXAEAZCD7RGGOS0b72fcivZwmGplVFqJMGxF5lZDmkntVfrCoPAEEz0SyvqwXKwo05cnIP2qeZ+UgRuGpZHA5M606HJNpYONox9gdRHmkaGxFizdBCnLhnA81WYLU0OrnYsHWoYgyVb/zkxNKKhRk0/U3o3m/eP49O3b9b3XcQdcvcPCSrAHcoKAGeflAqFc5hwoJpXlOfQrNnCgaqyQrcHyA9cXd8mLuIclOXgaNvOw1MYm44MN9yfvezpOGf1CH7x6av0NrN0S6ium4ZPt1k/on5JYZO5Ym0l7g/AXOyHLD0tuFVkFR9XNcsqJpcgZcID0PXH7vjLX8LpyweNdlYB3dugKjBph7LayYe/+PST8JH/dYH+bk/EpUlw7DmPNGupK1ZmlkONWw7s2dn9jo9hLkTKCenss6uJLsuB3leUmOX4UxdpRsALLxxM1IJMwylyKQGZH3OfWvOBsMwqSR0UCIeBeqg4BzUJWhLcHkB8fQNXtqitVdFiP/Y2GzRJubSyhrJsokSiXksn3Yi5lZYNZ/ed1VxKib92lGDFSAN3vusyXKLcdFSu2zXQlg7VDU04y3OQKOJFqM7RH3/lAWxRCUih6tSZzz/b3xQO5ZYDAJyjMnCfvtqyHOpZnkM9DAytMkdIC9O9RNCJW4m7sm9ZtBJl83I33DmrF+E7f/ILhvXF+5suQR2IXOlznefQilALs8nDVTzQPi/A8hxUuChxDkDKB1FkFY2rZcMNgy+JmVuJJieyHEaatYw4L+AcXKB3PdQIEQap8jVdUt4lPSZv5WeF98osh6z/LB2qI1ILZGXBCFnZFkM42JY9UyD4fg0tqExXF28n4E7Uc63nQH0xSfI8J+1fU0pWwaoEPeO4EA5hGKBZC7FooFboUgLShz6tisnxJCQSDqvU5HMOS/FvGJZDiEYYaLeSTQzZoazUuVJfcfdQVtdKdWW1V1wDr17LOIcwCFBT/uqfbjuMA+MtLGGWg7mwTqSTrADociFcc7SxbKhuRGfxqqxFoa+u56DzC0Re6BW5lYrCeS9+6gosHqjh3FP5SoCBLqDo4hw0IW21x55gKXO2EydOzoEG8NNXL8Jq1laAC4d8CGatQBnhcft2jSqeIZ1yDsI4l/26itxKUsLpVqKcjOXD6X3Y9ZrI7d2opYs4NcJsKduhRk2/Q5dSVASaUAcV5xBLqctpAO71IswJOdP6+T26YAsHCv/mlgPBWHnRshy4IulSajQhbfBK5a4229NUCwLTcmCcQ6MWGPdbJhB7xXEhHGhArBxplguHQGCviuM+lZn31PFfff6puO4dL8RrLlijf+MvcrAeav8v4LIcTLcSVfts9+BWmrBq1LsmQfJpv+nStbnfyK3U6qQTyWnLBnHrI3twxb/8CInMu9AIk+00B4DaRIQu1c53tWPJYAPnnrIYH/1fF+B/nL1Sd9o0WqnIcsgPjNDS2Dmhy69blHnM8YrzTsZ973mpYQ2GAU+CS3R4KEHnOQhzUnHzLIHhVvrsmy/ERWuXq+PStv782Svx4796ifHOKbyWWw4EmlwBk6zkE0wUm6XP7XDImiVg7eefy6DmhHScoBkG2qXZ6sSY7qScFUVULS1Y8IkEGJ2fOIisndU5B9utZBfecxU8dLpyqpTsZmNv8UA9W+dD5N89tw7sQAi+HykPvC1rlg6iHgqcxuabbtZUzkXIigHytaWBtB9Qm3kmdT+wYJPgOOjhnLp0oDTcjZtcfJCS6RwnEuezZQMBcxAONlK3EhGIRJCl7qp0oNgTWCcxiUQOm5DmPl6CSwMfbITY+IFXFIa5tqM00W9Rs4bnPmUZvn7fdv37kkG3cJjqxGhFibYEfuV5a3D26hE85zQlHArcSkIIvOa5a3Dj+l3YR5aDct24QGZ5LRDGAupA5hJZOtTQ5SrKShe4IJQJzidDvUJflK4hTSUoCOl6DjIfyloQKcYJ6QvXLsdpy3fhni2OCKEwMFwhQpgZ5Rz1UKAdm5p25lZKrb92nHcr0f0FlkCzSU17kqbfqfBePTQth6lOumaGditZSgW5lYyM7VYm2G3ivAo4Id1WSZIm55AXDpk1J3PaehW30nAjRKMWaJdYjVlrul01Zjl0SVJNLfVYWwpnrBjGI++9vDDPwYWyZUKj2MxzaISm1Vh0y3y8VcVxYTnQw/mH152PD/7Kswv34w/dsBzUAHBFoBjCgTiH2OQcqKORhsonpk7cm+XwsnNX45rfuSh3bzZoMXUbDWXZjLcijAzUNLFMKEoenGzHhvtLCKEFQ1E7eH0bTtR3kmK3El/20j43/W+EmQXWKDhPN9iadVMV3mvHCeo1cyUzqsqaJ6TdwjdKEt1XBuuhfmb2M7KfwcqRZqGwqzsmU10rqCb0okkuXz5lxpa1PZfnwArvUf/khDSt050JB1NpSSx/PN0r5fk0aqaQqAIaI0ONmnPVxqIMfrKS7GVCq7iVhps11IJskSiaI/i741ZYtwoGWRVW7payrLguCo8ruMAovMfGTpp7oSylErdSr0oWcJxYDvRCXCn1HHwy5ZYDaUUulp9rXEONEI1a5lailzRYD40Fzhc1azgQpZoI1eFxcw5WRxMpMfsL56xi1khvL7WutNWx6XTRIxIOrz7/VDxl+RCuuOBU53GT7ahQiKXnNTvd53/nIryA5ZaQRg1QVVZ3J3URydwsBtLBsHighvFWlBtY7/ilp+mM3TIYIYZKaHfi1IfdtCwHWibUJqSdpUsCoUtY0/reNAHaEzK9uwtOX4oHth3W6zO7QAOc9wlOsOo8h3rmviFQ2CfAwji7hLLywnudWOYI6Wm1TjdZsrZbSZdRV+elY0moZ9xI76GsVBI8sWoruUqlp/csgA7jHAqsJ45BJhwaNVaBVSsqeSFNbTOuXfDOy8ZtWbVYwB2tpENZlVuJ1idp1ALNURAh7QI9o15wXAiHqn42/r64eb9Y1WJykYwNy3KggQpkZu7iwToOT3V0O0YGarqAWVsJB3f5DDdJSNed7iSFpTKK0KgFODKdJlyNNOs4+6QRvPkFa/G655+mFwtyYbIVF7q/0raZ25cM1o2Jm3zxFN5bNDhc588IabqWwOLBOnaOTufO8/+9/Bn24U7wiYHyHIC03s/SIXPZ00TCIqTpntyWQ0cJB3LDcMvR3hcAfvOSM7BvrKWXe3WBNE2TkM608ig218UIAtOdQv3rhWetdLa9qGorJcFxt1K22l6AFcNNnLJkAM88xVykJ7Ysh6csH8LWA5PaJ28T51WQWQ5hqhxZoawuQhowLSygtzyH4WY6puk6er0N9vyMiqoFQlbvS8fPQFPX57TeXSCYG1AlwTVrAePPmOVQ4mnoFceFcKg6gdKL5FFNQmSmsCs80cU5dGKJ7Ycm8fMf+h4A4DcufgrOWb1Iv4ALz1iO6U6MPUdaKj49dnMOJSZqPZyZcKiHgS7NPTJQQxAI/M2rn9X1uNse3Ytdo9PFlkPBxEdo1AQ6icxKMBd0xkC7ePIDnUcJLVbcSJF7qhcIkXEQ460IJy1q5pPgkCekXUoHRStNd7J1rOndFrmVhhohbv+LXyrVZMmt5CrBnoZVJiqZ0syD6MRpEtyigTpu/dNfwGmq0q+tfRZNarzwHt3PkelILVGa+uPvetdlufaSZ4PO+8xTFuP2jfu1gNH5GD1wDjTJDzbSJLgoTjDdSTDcCDHRjp2cA8AigiwXV1nXyTiHGmphViRSV2UtiFayXUJF4c5la3p3g/3uhDCT4OJYolkPMabWSdHjRhS7lXqdR4AKnIMQ4rNCiL1CiA1s23IhxC1CiI3q/zK1XQghrhZCbBJCPCiEeB475kq1/0YhxJVs+/OFEOvVMVeLbjaXA91qrhDowZ2yJKsVVA8yjclVaZN37kHlVmrHCW5cv0tvX7WoiRedkyUy/Z/Xn4+/UBpuRiR2txxc+RVFIZtFqIcZueZaW6IIX77nSQBmVVSOxYN1g8xu1OxBkVoONIDLOiMNzEueuhyP/90r9XYetURtn40GxkHPc3w6cuc5yIyQ1vV1nAmI6UQ92c4sh6ZVw4pAxzdrQalg4PsaoZm6Dwi9XrSxmqDlzjrrpJFCK6aothJfXY6OpTDmomRSfq90XsorIdeZizjvBppoeSjrdCfWLq1eOYcy0LOjnAxaVEgT+5yQDvLPnGBPxpQDVRY12Q3OYnw6lJVKuWfuRV5mvKibzWQcVTnicwAut7a9E8BtUsqzAdymvgPAKwCcrf7eBuATQCpMAFwF4GIAFwG4igSK2uet7Dj7WoXIMiGr7U8P+OQlg3py45VQnW4lHkpZTzOkozgxfOfOBXlU2zqlbqU855DdWzYx9IJGLcuQ7NZBz1uzOLftdc8/zbnvn7zkHHzxLRfn2kegcMuoi+UAZP5enjkNmJo7Tbz9Fg6kbWXJTmmlXm6lFeU50P4UrZRZDun/YuHQfYLM3BFcS82E1LhjOdAii8W1rWhSI42Zol4atUAvc1uUhQ6w8hnqvE9XGenbDk4Z1+vFrcTX7QiEwP1PHsZ3Ht6jEzdfdu5q53E8jBbIxkwB4S2q8gAAF7JJREFURQEg7WsD9f/b3pkHyVHdd/z7655r79VqV6vV6trVSoCQkJCEELIQNxIiREoMQXbMFRUkXEWoIjFHOUGhnICrCOW4SAhJFESIwQ5OylAJZUOgCufgisMhTABFQIEidCCjBSR07csf/X7dr7tf93TPzs70aN+namtne3pmfvum+/3e73i/n1MoM2eTm8WoTrSAV68o+FlMcBHEyvGCeZPj/9kYdAsJ93+SAWk1S0xtb8uPg/d+wvWz/zXlThBCPA9gb+DwWgCb5ePNANYpxx8WDi8A6CSiPgCrADwthNgrhPglgKcBrJbPtQshXhBOa7eHlfcqi5clktRycH73KVVGczZhoLsFFgE3yQqRKjklVaxUsFy3kpqPrrs53Ybph0Zk6WKNctBkK7mvjwhylkOdTON2iwPA47+zHC/d4bkM7r1kQSi7ielozmOG7JELhC0arlbLFWvj3EE86YTTAvm3Vyq7Gm4lwBtP3q1uKdbB0RGBt3YMu/2AdbnuDC8ODhw+6q6s+bsNpgrqMpDKyae1HCxy06d1O6h1C4jyAWnnt9vSUr5HKWe5lkPULnQgnK3E7T+DMYc07hU3waNg+zZ6NedzeOn2c/CdixdoXxf08/NKP7ihNEhr0bGG1cWd993r778oCyzI0oFwG9Ok6C55NVvJyQb0lK9PkUn528vc+0mo9B16hRDsV/kYAKv0fgAfKud9JI/FHf9IczwR7g7ShBMo37x9nSVf5c2WYg7b/uTCyNcVbAsHRo6iWfonDx0d8Zm4ugmMJ09e8aUNSKv+5jS0K42OylkOal8HAKHOdEH8G47CvtYjR4XnVopZ8fNnhmIuygooyYa3NASzgNSV+s7hL/HL/Ydx1VdmOnLEWQ6yfPbeLw65PYP5/wlaDrogcxQ6t5J6bD93fFPGLG5xFLwnoq41tjI59bSpYLv90uOUQ9ByKOVtfHf9Qrc3eiXZSnx/NuVtvPyeV3Z9254vMKldvz8EcK41i5Q0XjnuwVbCQf78Nxehf0ITNv/H++4xW7kugLAyCN7HwWv4+1efitZirmxGUhw8wd949hCuP2vIkYs8T4QQ/mwxgmrlOO/RVsoD+7ysPkJ6eUZ9BwohBBFVqZpHPER0DRx3FQqTh9zjSbOVdsuCe30dJW0Tjii4o1pTXpbPODrii09EBS4BL46hu0nUC4soYDkEgmtJUfdvtEd0xFNRL+JyqcA6+RjHFy/cRkixbiW2HAr+c0hZsauZM9XAX1rZswYLOcvdcHfydGdfhxWYZFQ4VXjXZwcxX2Z/8YSt2zEPJJsgdZOR6lr8nHtFayyLZs0kHvSFRwWkufKouzs5b7t9GXQNlRied9Xrc61aWSBgVSVBtRzUzKRgocwgheCmRzlGEd00XXh1ryrSoIIJXgP8Ob9+cj+mdTWH3EfLZ3XHf2gC+CpS40B8LfGidMQdf8v9LtSAtFpN9+zjJ6GlmHM7NialUofuTukSgvy9Sx7fDmCact5UeSzu+FTNcS1CiAeFEEuEEEsA72JKajnsGnY0aV9Hk3sBBbNwdBTlxjanzLUVahikW93nE1gO6o3eWsj5mr6wSyKpy4xRV//l3EpBemNWZ4D//wxO/vnASjROqfHO8KjdphaRO+FVqx9uMTCpsgJQc+NPnOJM9t4+B9336gSH93x+0C0E6FkO/tnIdSulsRyUQL+6z4EnAFXJ/WLHMADgHE2KbDl3CO+E55Rr162Ut92EhjjLIep9g8fTZCsdUWIOD3xjMTbKLLvVJ8b773O25be4lD0BSVAXiHwN9nc6LtRga1A1C+vm8+akThhhLls2IzK+F9yzAngxA87yG1ESAlRLlxdYXBUXADZdeQpm9YR7bpejUsvhCQBXALhb/v6xcvwGInoMTvB5nxBiBxH9BMAfK0Ho8wHcJoTYS0TDRLQMwIsALgfwvaRCsHIolwnC7Bz2LAc3mJ1gZa6m+fFFqF40UStMwGsAr7uJVLkfvWYZpk3wfPpFZdWYhv5Ob4LXNZ2Jo5x/WB3mcEBaxlg0ZY6DRMUcvH0OUDLIxshy4JiD/J9720vuKi1un0POsrBz+AsIAfQE3ErB9o2uWynBBKnNVtJYj6oVwtf/BfP7Qu8X7ufgH+v2Uh4WefcE/w9NBdtd+CRSDhHXTCW1lVi5lvI2zj7eWYF//dTpZRd/OYt89xdfi+XcSoy6m5nHbcVQN+5BOHuPP0eXip2Gu9bNi3xOt+jlhRnfX6pbSXBdKItcS1CdS4DKKrWWVQ5E9CiAMwF0E9FHcLKO7gbwQyLaAOADAL8hT/8XAGsAbAWwH8BVjmBiLxHdBeBled4fCSE4yH0dnIyoJgBPyZ9EHElpOfDATu4ohYJXcRRzNpry/kJjateruLLaw7Jfta5zmUpwgxqvINO6lXjFw3JXE863dpr5+OUKutHiU1mlnzowJh3NBeCT/chZlhtzqFZXq2DMwfMtO7KoBQnjdkjnbcIO6cvtdS2H0WcrBQu2sZyAf4GgWiH3XboA+/Yf1saWyjX7sSynBequz770Pa8qhLhsJU/uCOVQQbYSK1c13pQkWy1YsobHMmkpIZ3lcOKUcCYffxYQnVZbDYTiMgrKxYkJp8/uwfuffIA18/vw5Gv/557DrvOpXX4XcVJFqVJWOQghvhbxVGhnjMw4uj7ifTYB2KQ5/gqAaDUaQ7BwWzk2X7UUz7y1E22lvFs0L8nKvGBbSvZM2HKIeg3gKRFdjf8kr0/rVuppK5Y/KcA9X52PpoQBYK9IXlA5OHJ+mcBy4BV6cGXa31nCax86Exc3wFm/dFro9ZWgTh4F26tVU9AoB3cPjC7RQPk+OEjK7x3KVrKTT5B8rjbmEJFnz61XdZRTDoBTL2mndLUW7PB3ksRyiFoEBJsSJYGVa7BLWxIZ1OuNxyuqjpju9YyaFHH85DZs//SA79ypckU+q6cVYwVbBep3yI/ZTb1wWqdrffyz3HNlW+S6BNlFzK7PlDX3ADT4DunlsybiZ+/uSZzRM3dKu1vn3/HPJZt8gy4JwK8c2DpQYaXD5+m6g8XhWTbpLIdKSvZeesr0xOfmLP0uTP5/DxziVNYYt1LeX3aCmdLBu3udwPr7d0dnkCXhzON6sGX7PgDhFNBgVoq6wY8viahsJYZvPJ7Q4zbBlUPnQvLSW8PHylFuExzgFJzctucL32eo1lzcJrhrVg7iwee3RWbluJvgEigYxnOnpFQOtuUbI7cxTlK3kjKmqpv5yRtXhM5dPGMCfnTtaVgwtTP0XLXQBfv562TLQbXq3FRW5bvobS/hkQ2nuinGScdCpWGVw/z+DiyeMQE/e3dPRRMiESFvWYncNq3FHIo5f/6+qhx6WsOBXE+JOJq8Ja3lUOEO6bHGtgi6253dc65bKS4gHWE5cKaVGuwfDQ9d5VW39TcQ8rJA2IWhNkGKahMK+CdZ7uDmllMOprLmHAslyXeYsymUsaY2+2GSuHqAcAkGnYJSy3DzJN6c0K10+5oTcPuaEyKfr8StxKTd27NuYT8+HvbSNnncks6Hun0OzvvoZV88o/I9DElwA9KKXCTLdnMMU7WuLI0btLe95LNuxp3lELdZKQk5pYlGHH940dxQLZlP9x/CiqFu3LVuHga6w5kAPFmyW6klxm2jM9+DFSbT8NRNp486YBZFzra0GdNq7SKgzCa4KMtBBtPVG71aqKvDwZ5WnDY4EQ98YzH+betu/Oe2T3xuJd3NxrDSm9hSCMUEjgRq/3AHuiTkZcaNuhL3rAnvPdoSpCcD4UQLnRxqpVUeezXbLdjPPA1zetsw2N2CQc29EcVfXrYYj7zwAaZ3NZc/WeHCk/wBeZ5Uk66W1Wu1ms1yKoXnmuACy7bIvb/Ue0fdBMcE41BjEnPIMnFlDpKgVjSMY2iS0qyeJ/0Dh9FStLWKAfACymxhNEdkDv38W+dpV9lxu1/LcUKfPphWDdTeASpc2pkn9viYg5cZo8KWA2fQVBN1clw22IVCzsLqeZPxzFs7AQCdilsprp/Dx1K2+VM7QucH3UpLB7rcm7kcpbwdmozVHtJMW8L05HKF9wDPcmgp2OiRVtDMid71PBqrdVpXM5695cxUr5nd24aNaysKP/pQS00kQb1Wo5ph1RKeyEMZgcpOeVU5xG3adN+zAjkaWjnoNGYa8jalnnxVkzVuF28uoeWgblbxf044GJkFcpZ+zDgQzr0W4uQu51Yqt+mpEvw1ibzP/UzGi5JmK82Z1Irn39mNu5RJjPuRbzh9wHfuRQum4KIF+v4ZQa5cPhOnz/ZvoPI2wXmyt6ZMHGB0yoEth8kdJddimTEx3ao9i/D3l9Ry0MWR6onnVgpYDuQphyaNcrCIsPFXT9TePyMV+JUaWjnErfCSkLet9FVPlZssLj1VjU1YlD4DYzSWw1jCGwGD8Mpzh8zuiN0EF6EcuF9xlMIcDTnbwrVnzsKqwIYqjm/4A9JOVVhdv+3fX308rjtryCdjSzE36uD5tK5mTAu4U3TF+JLu6VFbrwoRna0E+N1LUZZwI8FjlHQ+VK/nsbj20jKiSWUFHFehzq2kzoNXLJ8Z+55pOCaUQ6WWQ9KYg4oakIwLMqs7pNsqqLWSH0XMYSzJWaTdGzKhuQDb8vYAxLmVeHNe0K1ERNj8W0tT+anT8M3V4UZBnnLwTwpP3rhCu2O8kLPQlavNBEKBmFqS1FKG74lizukLostWYheV6lILdnxrRPiWqSTmkIUEEJY7OGXYRBg+GK575VoOsW6lcWY5nH/iZHxx8EjF2j5vWaknX/X8OMtBXfFHxRviSFObvpbYFvlKPDCWRehuLbh54XEWz7kn9OLOi+Zqt/SfofTFqAU6txIAzMzQCponrKSZSoCqHGxHOWgsB05ayMJquZq4jYwS73PI1j3GOi2YMu60g3Ue+5RDgsSccedW6u9swg1nh8tsJ2XlnJ6QKV+OxG4lK5mFEQVv6c9C9oRKzrIiFVZPW9ENJscptbZSHld+ZSDy+VrCloPOhZQVeGWbynIgz3IA9G6lC+ZNxr9v3YPfW32c7/i9lyxwlWYjYlfoVoqqE1VreJUfVA6qK0nde9XZnEcpH31fAuPQrTRakrTPDOJr/hMTHLRkvZdDR0di01gjP0ezASoLRMUcAC/uAGTP4omCV89ZyFKJwq3umiJu5fYg4eqodlixtBRzuO/ShaHjX40oCNcoWCkD0u5GyIwsELh+Y3Bd2N7ktTZWEywuWTINy4e6YzdIVuJWaow7OEPk7WSWAwD0dhQTnRf3OVkzeXM2RSqsSW0l33mNwPevPhW/fcZgqlV5rclVYjlYfsshjWJpdMiNOSQ7n8eqKyPxFi/m4L+HeAFTytm+50p5u2w5j8tPm5m6AdC4thwqQZ0Yy/ml+zub8OHeAxX1k2XlkDW30srZPZExFLWuU1ZM9HKcNLUTJ41hKYRqwNdCRcohny2XSS3g+216V3x/EoaLO2bFtejFHPzHWTmkiT0xc3rb8Pqdq0Abk7/GKIeUqJZDVOVGxqmQujd10T3A2ah14Ul9ZXss1JpbVh0X+ZyqLLOWZdXIuNlKFQSkSznbqYc1jr6PGRNb8NeXL8Gpg8nKXHCG1soaJ0NEwZZD8DvjjLrR7FxPw/hZTlQJVTmUK8XMpQgqiRsMTWrD/V9flKoHb71Ru2JlzeJpZNg9ElcILwgHpGd2t2D6MbCxLS3nzu1NXGpk7pR2/PTmlbjuzFljLFUyuElRlOWQ5joYDY0z82QEnvS6W8v7J6dWuZBc1mkp5nCu7Eo2mh66Bj9cBr0St9KGFQN45uYzxkSuY4k5vW2ZuWZ5MRAVc6igHXRFGLdSSrpbC7jhrKHIFn8qXA7iU9mTdzzw4GVLsL9KfZ8NDtykKs0ue3XX7HhyKR0L3HvJAnzv2XfdHuUMK4eDh8eu0ZCKUQ4pIaJYv7sKF6MbPjA+LAfA8ZNWEoA3RMPB5Mkp4k+jrR5gqB9Dk1rx3fUnh467ymEMu9CpmLt4DJk9qRVrF07B1acP1lsUQ4MxUdm1vO7kfnx+8EiqrnhxPSkMjYmnHGpjmRvlMIbkbEu7AjAY4nhz46pQi8iogmpRTJvQjJaCjc6WbKRnGkYPK4ex7F+tYpSDwZAxKim3EuQrQxPxxp2rTLzhGMK4lQwGw6gholBVT0NjU+sSL8YhaTAYDA0Al1ivVZKBsRwMBoOhAbAswrd+ZS6WJdz5PVqMcjAYDIYGYcOK2pW6z4xbiYhWE9HbRLSViG6ttzwGg8EwnsmEciAiG8D9AC4AMBfA14hobn2lMhgMhvFLJpQDgKUAtgohtgkhDgF4DMDaOstkMBgM45asKId+AB8qf38kj/kgomuI6BUiemX37t01E85gMBjGG1lRDokQQjwohFgihFjS05ON2usGg8FwLJIV5bAdgFo4Zqo8ZjAYDIY6kBXl8DKA2UQ0QEQFAOsBPFFnmQwGg2Hckol9DkKII0R0A4CfALABbBJCvFlnsQwGg2HcQoK7WTcYRPQZgLfrLUdKugHsqbcQFWDkri1G7toynuSeIYRIFLDNhOVQIW8LIZbUW4g0ENErjSYzYOSuNUbu2mLk1pOVmIPBYDAYMoRRDgaDwWAI0cjK4cF6C1ABjSgzYOSuNUbu2mLk1tCwAWmDwWAwjB2NbDkYDAaDYYxoOOXQSKW9ieh9InqDiF4lolfksS4iepqI3pW/J2RAzk1EtIuItijHtHKSw5/J8X+diBZlTO47iWi7HPNXiWiN8txtUu63iWhVnWSeRkTPEdEviOhNIrpJHs/0eMfInfXxLhHRS0T0mpR7ozw+QEQvSvl+IDffgoiK8u+t8vmZGZP7ISJ6TxnvhfJ49a8TIUTD/MDZIPe/AAYBFAC8BmBuveWKkfd9AN2BY98BcKt8fCuAezIg50oAiwBsKScngDUAngJAAJYBeDFjct8J4BbNuXPl9VIEMCCvI7sOMvcBWCQftwF4R8qW6fGOkTvr400AWuXjPIAX5Tj+EMB6efwBANfKx9cBeEA+Xg/gB3Ua7yi5HwJwseb8ql8njWY5HAulvdcC2Cwfbwawro6yAACEEM8D2Bs4HCXnWgAPC4cXAHQSUV9tJPUTIXcUawE8JoQ4KIR4D8BWONdTTRFC7BBC/Fw+/gzAW3AqEGd6vGPkjiIr4y2EEJ/LP/PyRwA4G8Dj8nhwvPl7eBzAOURUm6bNCjFyR1H166TRlEOi0t4ZQgD4KRH9FxFdI4/1CiF2yMcfA+itj2hliZKzEb6DG6RpvUlx22VObumyOBnOqrBhxjsgN5Dx8SYim4heBbALwNNwrJhPhRBHNLK5csvn9wGYWFuJHYJyCyF4vL8tx/s+IirKY1Uf70ZTDo3GCiHEIjgd7q4nopXqk8KxBzOfLtYockr+AsAsAAsB7ABwb33F0UNErQB+BOB3hRDD6nNZHm+N3JkfbyHEUSHEQjjVnpcCOL7OIiUiKDcRzQNwGxz5TwHQBeCbY/X5jaYcGqq0txBiu/y9C8A/wbkwd7K5J3/vqp+EsUTJmenvQAixU95UIwD+Cp4rIzNyE1EezgT790KIf5SHMz/eOrkbYbwZIcSnAJ4DcBoctwuXD1Jlc+WWz3cA+KTGovpQ5F4t3XtCCHEQwN9iDMe70ZRDw5T2JqIWImrjxwDOB7AFjrxXyNOuAPDj+khYlig5nwBwucyOWAZgn+IOqTsBP+uvwRlzwJF7vcxGGQAwG8BLdZCPAPwNgLeEEH+qPJXp8Y6SuwHGu4eIOuXjJgDnwYmXPAfgYnlacLz5e7gYwLPSkqspEXL/j7KAIDhxEnW8q3udjHXUvdo/cKLy78DxG95Rb3li5ByEk63xGoA3WVY4/st/BfAugGcAdGVA1kfhuAQOw/FVboiSE042xP1y/N8AsCRjcv+dlOt1ecP0KeffIeV+G8AFdZJ5BRyX0esAXpU/a7I+3jFyZ328TwLw31K+LQD+QB4fhKOstgL4BwBFebwk/94qnx/MmNzPyvHeAuAReBlNVb9OzA5pg8FgMIRoNLeSwWAwGGqAUQ4Gg8FgCGGUg8FgMBhCGOVgMBgMhhBGORgMBoMhhFEOBoPBYAhhlIPBYDAYQhjlYDAYDIYQ/w8lzZ6lJR11WQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['y'][data['id'] == 0].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fklearn.preprocessing.splitting import space_time_split_dataset\n",
    "\n",
    "train_start = '2016-01-01'\n",
    "train_end = '2016-06-30'\n",
    "holdout_end = '2016-12-31'\n",
    "\n",
    "split_fn = space_time_split_dataset(\n",
    "    train_start_date=train_start,\n",
    "    train_end_date=train_end,\n",
    "    holdout_end_date=holdout_end,\n",
    "    split_seed=50,\n",
    "    space_holdout_percentage=.05,\n",
    "    space_column='id',\n",
    "    time_column='score_date',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17195, 6), (905, 6), (18400, 6), (920, 6))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set, intime_outspace_hdout, outime_inspace_hdout, outime_outspace_hdout = split_fn(data)\n",
    "\n",
    "train_set.shape, intime_outspace_hdout.shape, outime_inspace_hdout.shape, outime_outspace_hdout.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define learner function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "FEATURES = ['x1', 'x2', 'x3']\n",
    "TARGET = ['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fklearn.training.imputation import imputer\n",
    "\n",
    "my_imputer = imputer(columns_to_impute=FEATURES, impute_strategy='median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fklearn.training.transformation import standard_scaler\n",
    "\n",
    "my_scaler = standard_scaler(columns_to_scale=FEATURES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fklearn.training.regression import xgb_regression_learner\n",
    "\n",
    "my_model = xgb_regression_learner(\n",
    "    features=['x1', 'x2', 'x3'],\n",
    "    target='y',\n",
    "    prediction_column='prediction',\n",
    "    extra_params={'seed': 139, 'nthread': 8},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fklearn.training.transformation import ecdfer\n",
    "\n",
    "my_ecdefer = ecdfer(prediction_column='prediction', ecdf_column='prediction_ecdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fklearn.training.pipeline import build_pipeline\n",
    "\n",
    "my_learner = build_pipeline(my_imputer, my_scaler, my_model, my_ecdefer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "(prediction_function, _, logs) = my_learner(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'imputer': {'impute_strategy': 'median',\n",
       "  'columns_to_impute': ['x1', 'x2', 'x3'],\n",
       "  'training_proportion_of_nulls': {'x1': 0.002675196277987787,\n",
       "   'x2': 0.0023844140738586797,\n",
       "   'x3': 0.0},\n",
       "  'statistics': array([499.92366097, 234.4110308 , 792.50378949]),\n",
       "  'running_time': '0.031 s'},\n",
       " 'standard_scaler': {'standard_scaler': {'copy': True,\n",
       "   'with_mean': True,\n",
       "   'with_std': True},\n",
       "  'transformed_column': ['x1', 'x2', 'x3'],\n",
       "  'running_time': '0.018 s'},\n",
       " 'xgb_regression_learner': {'features': ['x1', 'x2', 'x3'],\n",
       "  'target': 'y',\n",
       "  'prediction_column': 'prediction',\n",
       "  'package': 'xgboost',\n",
       "  'package_version': '0.82',\n",
       "  'parameters': {'seed': 139,\n",
       "   'nthread': 8,\n",
       "   'eta': 0.1,\n",
       "   'objective': 'reg:linear',\n",
       "   'num_estimators': 100},\n",
       "  'feature_importance': {'x2': 2415, 'x3': 514, 'x1': 919},\n",
       "  'training_samples': 17195,\n",
       "  'running_time': '1.191 s'},\n",
       " 'ecdfer': {'nobs': 17195,\n",
       "  'prediction_column': 'prediction',\n",
       "  'ascending': True,\n",
       "  'transformed_column': ['prediction_ecdf'],\n",
       "  'running_time': '0.006 s'},\n",
       " '__fkml__': {'pipeline': ['imputer',\n",
       "   'standard_scaler',\n",
       "   'xgb_regression_learner',\n",
       "   'ecdfer'],\n",
       "  'output_columns': ['id',\n",
       "   'score_date',\n",
       "   'x1',\n",
       "   'x2',\n",
       "   'x3',\n",
       "   'y',\n",
       "   'prediction',\n",
       "   'prediction_ecdf'],\n",
       "  'features': ['id', 'score_date', 'x1', 'x2', 'x3', 'y'],\n",
       "  'learners': {'imputer': {'fn': <function fklearn.training.imputation.imputer.<locals>.p(new_data_set: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "    'log': {'imputer': {'impute_strategy': 'median',\n",
       "      'columns_to_impute': ['x1', 'x2', 'x3'],\n",
       "      'training_proportion_of_nulls': {'x1': 0.002675196277987787,\n",
       "       'x2': 0.0023844140738586797,\n",
       "       'x3': 0.0},\n",
       "      'statistics': array([499.92366097, 234.4110308 , 792.50378949]),\n",
       "      'running_time': '0.031 s'}}},\n",
       "   'standard_scaler': {'fn': <function fklearn.training.transformation.standard_scaler.<locals>.p(new_data_set: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "    'log': {'standard_scaler': {'standard_scaler': {'copy': True,\n",
       "       'with_mean': True,\n",
       "       'with_std': True},\n",
       "      'transformed_column': ['x1', 'x2', 'x3'],\n",
       "      'running_time': '0.018 s'}}},\n",
       "   'xgb_regression_learner': {'fn': <function fklearn.training.regression.xgb_regression_learner.<locals>.p(new_df: pandas.core.frame.DataFrame, apply_shap: bool = False) -> pandas.core.frame.DataFrame>,\n",
       "    'log': {'xgb_regression_learner': {'features': ['x1', 'x2', 'x3'],\n",
       "      'target': 'y',\n",
       "      'prediction_column': 'prediction',\n",
       "      'package': 'xgboost',\n",
       "      'package_version': '0.82',\n",
       "      'parameters': {'seed': 139,\n",
       "       'nthread': 8,\n",
       "       'eta': 0.1,\n",
       "       'objective': 'reg:linear',\n",
       "       'num_estimators': 100},\n",
       "      'feature_importance': {'x2': 2415, 'x3': 514, 'x1': 919},\n",
       "      'training_samples': 17195,\n",
       "      'running_time': '1.191 s'}}},\n",
       "   'ecdfer': {'fn': <function fklearn.training.transformation.ecdfer.<locals>.p(new_df: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "    'log': {'ecdfer': {'nobs': 17195,\n",
       "      'prediction_column': 'prediction',\n",
       "      'ascending': True,\n",
       "      'transformed_column': ['prediction_ecdf'],\n",
       "      'running_time': '0.006 s'}}}}}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fklearn.validation.evaluators import combined_evaluators, mean_prediction_evaluator, r2_evaluator, mse_evaluator\n",
    "\n",
    "my_evaluator = combined_evaluators(\n",
    "    evaluators=[\n",
    "        mean_prediction_evaluator(prediction_column='prediction'),\n",
    "        r2_evaluator(prediction_column='prediction', target_column='y'),\n",
    "        mse_evaluator(prediction_column='prediction', target_column='y'),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_evaluator__prediction': 18914.416,\n",
       " 'r2_evaluator__y': 0.9906295827453058,\n",
       " 'mse_evaluator__y': 673677.8680663708}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions = prediction_function(outime_outspace_hdout)\n",
    "\n",
    "my_evaluator(test_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run K-Fold Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fklearn.validation.validator import validator\n",
    "from fklearn.metrics.pd_extractors import extract, evaluator_extractor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fklearn.validation.splitters import k_fold_splitter\n",
    "\n",
    "my_split_fn = k_fold_splitter(n_splits=2, random_state=42)\n",
    "\n",
    "cv_results = validator(train_set, split_fn=my_split_fn, train_fn=my_learner, eval_fn=my_evaluator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>r2_evaluator__y</th>\n",
       "      <th>fold_num</th>\n",
       "      <th>train_size</th>\n",
       "      <th>test_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.995754</td>\n",
       "      <td>0</td>\n",
       "      <td>8597</td>\n",
       "      <td>8598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.996796</td>\n",
       "      <td>1</td>\n",
       "      <td>8598</td>\n",
       "      <td>8597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   r2_evaluator__y  fold_num  train_size  test_size\n",
       "0         0.995754         0        8597       8598\n",
       "0         0.996796         1        8598       8597"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract(cv_results['validator_log'], evaluator_extractor(evaluator_name='r2_evaluator__y'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Inverse Learning Curve Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fklearn.validation.splitters import reverse_time_learning_curve_splitter\n",
    "\n",
    "sc_split_fn = reverse_time_learning_curve_splitter(\n",
    "    training_time_limit=train_end,\n",
    "    time_column='score_date', \n",
    "    freq='W',\n",
    "    min_samples=500,\n",
    ")\n",
    "\n",
    "lc_results = validator(train_set, split_fn=sc_split_fn, train_fn=my_learner, eval_fn=my_evaluator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r2_evaluator__y</th>\n",
       "      <th>fold_num</th>\n",
       "      <th>train_start</th>\n",
       "      <th>train_end</th>\n",
       "      <th>train_size</th>\n",
       "      <th>test_start</th>\n",
       "      <th>test_end</th>\n",
       "      <th>test_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.992036</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-06-19</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>760</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999510</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>1425</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999422</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-05</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>2090</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999832</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-05-29</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>2755</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999643</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-05-22</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>3420</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999692</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-05-15</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>4085</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999651</td>\n",
       "      <td>6</td>\n",
       "      <td>2016-05-08</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>4750</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999730</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>5415</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999647</td>\n",
       "      <td>8</td>\n",
       "      <td>2016-04-24</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>6080</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999898</td>\n",
       "      <td>9</td>\n",
       "      <td>2016-04-17</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>6745</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999858</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-04-10</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>7410</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999951</td>\n",
       "      <td>11</td>\n",
       "      <td>2016-04-03</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>8075</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999948</td>\n",
       "      <td>12</td>\n",
       "      <td>2016-03-27</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>8740</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999962</td>\n",
       "      <td>13</td>\n",
       "      <td>2016-03-20</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>9405</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999964</td>\n",
       "      <td>14</td>\n",
       "      <td>2016-03-13</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>10070</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999967</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-03-06</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>10735</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999965</td>\n",
       "      <td>16</td>\n",
       "      <td>2016-02-28</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>11400</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999874</td>\n",
       "      <td>17</td>\n",
       "      <td>2016-02-21</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>12065</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999955</td>\n",
       "      <td>18</td>\n",
       "      <td>2016-02-14</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>12730</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999945</td>\n",
       "      <td>19</td>\n",
       "      <td>2016-02-07</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>13395</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999947</td>\n",
       "      <td>20</td>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>14060</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999925</td>\n",
       "      <td>21</td>\n",
       "      <td>2016-01-24</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>14725</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.998547</td>\n",
       "      <td>22</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>15390</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.997911</td>\n",
       "      <td>23</td>\n",
       "      <td>2016-01-10</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>16055</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.998664</td>\n",
       "      <td>24</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>16720</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   r2_evaluator__y  fold_num train_start  train_end  train_size test_start  \\\n",
       "0         0.992036         0  2016-06-19 2016-06-26         760 2016-06-27   \n",
       "0         0.999510         1  2016-06-12 2016-06-26        1425 2016-06-27   \n",
       "0         0.999422         2  2016-06-05 2016-06-26        2090 2016-06-27   \n",
       "0         0.999832         3  2016-05-29 2016-06-26        2755 2016-06-27   \n",
       "0         0.999643         4  2016-05-22 2016-06-26        3420 2016-06-27   \n",
       "0         0.999692         5  2016-05-15 2016-06-26        4085 2016-06-27   \n",
       "0         0.999651         6  2016-05-08 2016-06-26        4750 2016-06-27   \n",
       "0         0.999730         7  2016-05-01 2016-06-26        5415 2016-06-27   \n",
       "0         0.999647         8  2016-04-24 2016-06-26        6080 2016-06-27   \n",
       "0         0.999898         9  2016-04-17 2016-06-26        6745 2016-06-27   \n",
       "0         0.999858        10  2016-04-10 2016-06-26        7410 2016-06-27   \n",
       "0         0.999951        11  2016-04-03 2016-06-26        8075 2016-06-27   \n",
       "0         0.999948        12  2016-03-27 2016-06-26        8740 2016-06-27   \n",
       "0         0.999962        13  2016-03-20 2016-06-26        9405 2016-06-27   \n",
       "0         0.999964        14  2016-03-13 2016-06-26       10070 2016-06-27   \n",
       "0         0.999967        15  2016-03-06 2016-06-26       10735 2016-06-27   \n",
       "0         0.999965        16  2016-02-28 2016-06-26       11400 2016-06-27   \n",
       "0         0.999874        17  2016-02-21 2016-06-26       12065 2016-06-27   \n",
       "0         0.999955        18  2016-02-14 2016-06-26       12730 2016-06-27   \n",
       "0         0.999945        19  2016-02-07 2016-06-26       13395 2016-06-27   \n",
       "0         0.999947        20  2016-01-31 2016-06-26       14060 2016-06-27   \n",
       "0         0.999925        21  2016-01-24 2016-06-26       14725 2016-06-27   \n",
       "0         0.998547        22  2016-01-17 2016-06-26       15390 2016-06-27   \n",
       "0         0.997911        23  2016-01-10 2016-06-26       16055 2016-06-27   \n",
       "0         0.998664        24  2016-01-03 2016-06-26       16720 2016-06-27   \n",
       "\n",
       "    test_end  test_size  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  \n",
       "0 2016-06-29        285  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract(lc_results['validator_log'], evaluator_extractor(evaluator_name='r2_evaluator__y'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Stability Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fklearn.validation.splitters import stability_curve_time_splitter\n",
    "\n",
    "sc_split_fn = stability_curve_time_splitter(\n",
    "    training_time_limit='2016-06-01',\n",
    "    time_column='score_date',\n",
    "    freq='M',\n",
    "    min_samples=1000,\n",
    ")\n",
    "\n",
    "sc_results = validator(data, split_fn=sc_split_fn, train_fn=my_learner, eval_fn=my_evaluator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r2_evaluator__y</th>\n",
       "      <th>fold_num</th>\n",
       "      <th>train_start</th>\n",
       "      <th>train_end</th>\n",
       "      <th>train_size</th>\n",
       "      <th>test_start</th>\n",
       "      <th>test_end</th>\n",
       "      <th>test_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.998124</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>15300</td>\n",
       "      <td>2016-06-02</td>\n",
       "      <td>2016-06-30</td>\n",
       "      <td>2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.993554</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>15300</td>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>2016-07-31</td>\n",
       "      <td>3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.998540</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>15300</td>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>2016-08-31</td>\n",
       "      <td>3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.997695</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>15300</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.995735</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>15300</td>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>2016-10-31</td>\n",
       "      <td>3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.997235</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>15300</td>\n",
       "      <td>2016-11-01</td>\n",
       "      <td>2016-11-30</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.998376</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>15300</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>3100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   r2_evaluator__y  fold_num train_start  train_end  train_size test_start  \\\n",
       "0         0.998124         0  2016-01-01 2016-06-01       15300 2016-06-02   \n",
       "0         0.993554         0  2016-01-01 2016-06-01       15300 2016-07-01   \n",
       "0         0.998540         0  2016-01-01 2016-06-01       15300 2016-08-01   \n",
       "0         0.997695         0  2016-01-01 2016-06-01       15300 2016-09-01   \n",
       "0         0.995735         0  2016-01-01 2016-06-01       15300 2016-10-01   \n",
       "0         0.997235         0  2016-01-01 2016-06-01       15300 2016-11-01   \n",
       "0         0.998376         0  2016-01-01 2016-06-01       15300 2016-12-01   \n",
       "\n",
       "    test_end  test_size  \n",
       "0 2016-06-30       2900  \n",
       "0 2016-07-31       3100  \n",
       "0 2016-08-31       3100  \n",
       "0 2016-09-30       3000  \n",
       "0 2016-10-31       3100  \n",
       "0 2016-11-30       3000  \n",
       "0 2016-12-31       3100  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract(sc_results['validator_log'], evaluator_extractor(evaluator_name='r2_evaluator__y'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Forward Stability Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timedelta\n",
    "from fklearn.validation.splitters import forward_stability_curve_time_splitter\n",
    "\n",
    "fw_split_fn = forward_stability_curve_time_splitter(\n",
    "    training_time_start='2016-01-01',\n",
    "    training_time_end='2016-03-01',\n",
    "    holdout_gap=timedelta(days=30),\n",
    "    holdout_size=timedelta(days=30),\n",
    "    step=timedelta(days=30),\n",
    "    time_column='score_date',\n",
    ")\n",
    "\n",
    "fw_sc_results = validator(data, split_fn=fw_split_fn, train_fn=my_learner, eval_fn=my_evaluator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r2_evaluator__y</th>\n",
       "      <th>fold_num</th>\n",
       "      <th>train_start</th>\n",
       "      <th>train_end</th>\n",
       "      <th>train_size</th>\n",
       "      <th>test_start</th>\n",
       "      <th>test_end</th>\n",
       "      <th>test_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.995640</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-02-29</td>\n",
       "      <td>6000</td>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.992730</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>2016-03-30</td>\n",
       "      <td>6000</td>\n",
       "      <td>2016-04-30</td>\n",
       "      <td>2016-05-29</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.998711</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>6000</td>\n",
       "      <td>2016-05-30</td>\n",
       "      <td>2016-06-28</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.992840</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>2016-05-29</td>\n",
       "      <td>6000</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>2016-07-28</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.996390</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-04-30</td>\n",
       "      <td>2016-06-28</td>\n",
       "      <td>6000</td>\n",
       "      <td>2016-07-29</td>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.996138</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-05-30</td>\n",
       "      <td>2016-07-28</td>\n",
       "      <td>6000</td>\n",
       "      <td>2016-08-28</td>\n",
       "      <td>2016-09-26</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.994155</td>\n",
       "      <td>6</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>6000</td>\n",
       "      <td>2016-09-27</td>\n",
       "      <td>2016-10-26</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.997055</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-07-29</td>\n",
       "      <td>2016-09-26</td>\n",
       "      <td>6000</td>\n",
       "      <td>2016-10-27</td>\n",
       "      <td>2016-11-25</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.998399</td>\n",
       "      <td>8</td>\n",
       "      <td>2016-08-28</td>\n",
       "      <td>2016-10-26</td>\n",
       "      <td>6000</td>\n",
       "      <td>2016-11-26</td>\n",
       "      <td>2016-12-25</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   r2_evaluator__y  fold_num train_start  train_end  train_size test_start  \\\n",
       "0         0.995640         0  2016-01-01 2016-02-29        6000 2016-03-31   \n",
       "0         0.992730         1  2016-01-31 2016-03-30        6000 2016-04-30   \n",
       "0         0.998711         2  2016-03-01 2016-04-29        6000 2016-05-30   \n",
       "0         0.992840         3  2016-03-31 2016-05-29        6000 2016-06-29   \n",
       "0         0.996390         4  2016-04-30 2016-06-28        6000 2016-07-29   \n",
       "0         0.996138         5  2016-05-30 2016-07-28        6000 2016-08-28   \n",
       "0         0.994155         6  2016-06-29 2016-08-27        6000 2016-09-27   \n",
       "0         0.997055         7  2016-07-29 2016-09-26        6000 2016-10-27   \n",
       "0         0.998399         8  2016-08-28 2016-10-26        6000 2016-11-26   \n",
       "\n",
       "    test_end  test_size  \n",
       "0 2016-04-29       3000  \n",
       "0 2016-05-29       3000  \n",
       "0 2016-06-28       3000  \n",
       "0 2016-07-28       3000  \n",
       "0 2016-08-27       3000  \n",
       "0 2016-09-26       3000  \n",
       "0 2016-10-26       3000  \n",
       "0 2016-11-25       3000  \n",
       "0 2016-12-25       3000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract(fw_sc_results['validator_log'], evaluator_extractor(evaluator_name='r2_evaluator__y'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Spatial Learning Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fklearn.validation.splitters import spatial_learning_curve_splitter\n",
    "\n",
    "spatial_split_fn = spatial_learning_curve_splitter(\n",
    "    train_percentages=np.linspace(0.1, 1, 10),\n",
    "    space_column='id',\n",
    "    time_column='score_date',\n",
    "    training_limit='2016-06-01',\n",
    "    holdout_gap=timedelta(days=180),\n",
    "    random_state=0,    \n",
    ")\n",
    "\n",
    "spatial_lc_results = validator(data, split_fn=spatial_split_fn, train_fn=my_learner, eval_fn=my_evaluator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>r2_evaluator__y</th>\n",
       "      <th>fold_num</th>\n",
       "      <th>train_start</th>\n",
       "      <th>train_end</th>\n",
       "      <th>train_size</th>\n",
       "      <th>test_start</th>\n",
       "      <th>test_end</th>\n",
       "      <th>test_size</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.994729</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>1530</td>\n",
       "      <td>2016-11-29</td>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>3300</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.990840</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>3060</td>\n",
       "      <td>2016-11-29</td>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>3300</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.997053</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>4590</td>\n",
       "      <td>2016-11-29</td>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>3300</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.994885</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>6120</td>\n",
       "      <td>2016-11-29</td>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>3300</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.996923</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>7650</td>\n",
       "      <td>2016-11-29</td>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>3300</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.997560</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>9180</td>\n",
       "      <td>2016-11-29</td>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>3300</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.998197</td>\n",
       "      <td>6</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>10710</td>\n",
       "      <td>2016-11-29</td>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>3300</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.998720</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>12240</td>\n",
       "      <td>2016-11-29</td>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>3300</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.997620</td>\n",
       "      <td>8</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>13770</td>\n",
       "      <td>2016-11-29</td>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>3300</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.998228</td>\n",
       "      <td>9</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>15300</td>\n",
       "      <td>2016-11-29</td>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>3300</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   r2_evaluator__y  fold_num train_start  train_end  train_size test_start  \\\n",
       "0         0.994729         0  2016-01-01 2016-06-01        1530 2016-11-29   \n",
       "0         0.990840         1  2016-01-01 2016-06-01        3060 2016-11-29   \n",
       "0         0.997053         2  2016-01-01 2016-06-01        4590 2016-11-29   \n",
       "0         0.994885         3  2016-01-01 2016-06-01        6120 2016-11-29   \n",
       "0         0.996923         4  2016-01-01 2016-06-01        7650 2016-11-29   \n",
       "0         0.997560         5  2016-01-01 2016-06-01        9180 2016-11-29   \n",
       "0         0.998197         6  2016-01-01 2016-06-01       10710 2016-11-29   \n",
       "0         0.998720         7  2016-01-01 2016-06-01       12240 2016-11-29   \n",
       "0         0.997620         8  2016-01-01 2016-06-01       13770 2016-11-29   \n",
       "0         0.998228         9  2016-01-01 2016-06-01       15300 2016-11-29   \n",
       "\n",
       "    test_end  test_size  percentage  \n",
       "0 2016-12-31       3300         0.1  \n",
       "0 2016-12-31       3300         0.2  \n",
       "0 2016-12-31       3300         0.3  \n",
       "0 2016-12-31       3300         0.4  \n",
       "0 2016-12-31       3300         0.5  \n",
       "0 2016-12-31       3300         0.6  \n",
       "0 2016-12-31       3300         0.7  \n",
       "0 2016-12-31       3300         0.8  \n",
       "0 2016-12-31       3300         0.9  \n",
       "0 2016-12-31       3300         1.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract(spatial_lc_results['validator_log'], evaluator_extractor(evaluator_name='r2_evaluator__y'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'train_log': [{'imputer': {'impute_strategy': 'median',\n",
       "     'columns_to_impute': ['x2'],\n",
       "     'training_proportion_of_nulls': {'x2': 0.0018611143422123998},\n",
       "     'statistics': array([234.52197656]),\n",
       "     'running_time': '0.012 s'},\n",
       "    'standard_scaler': {'standard_scaler': {'copy': True,\n",
       "      'with_mean': True,\n",
       "      'with_std': True},\n",
       "     'transformed_column': ['x2'],\n",
       "     'running_time': '0.009 s'},\n",
       "    'xgb_regression_learner': {'features': ['x2'],\n",
       "     'target': 'y',\n",
       "     'prediction_column': 'prediction',\n",
       "     'package': 'xgboost',\n",
       "     'package_version': '0.82',\n",
       "     'parameters': {'seed': 139,\n",
       "      'nthread': 8,\n",
       "      'eta': 0.1,\n",
       "      'objective': 'reg:linear',\n",
       "      'num_estimators': 100},\n",
       "     'feature_importance': {'x2': 2833},\n",
       "     'training_samples': 8597,\n",
       "     'running_time': '0.447 s'},\n",
       "    'ecdfer': {'nobs': 8597,\n",
       "     'prediction_column': 'prediction',\n",
       "     'ascending': True,\n",
       "     'transformed_column': ['prediction_ecdf'],\n",
       "     'running_time': '0.003 s'},\n",
       "    '__fkml__': {'pipeline': ['imputer',\n",
       "      'standard_scaler',\n",
       "      'xgb_regression_learner',\n",
       "      'ecdfer'],\n",
       "     'output_columns': ['id',\n",
       "      'score_date',\n",
       "      'x1',\n",
       "      'x2',\n",
       "      'x3',\n",
       "      'y',\n",
       "      'prediction',\n",
       "      'prediction_ecdf'],\n",
       "     'features': ['id', 'score_date', 'x1', 'x2', 'x3', 'y'],\n",
       "     'learners': {'imputer': {'fn': <function fklearn.training.imputation.imputer.<locals>.p(new_data_set: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'imputer': {'impute_strategy': 'median',\n",
       "         'columns_to_impute': ['x2'],\n",
       "         'training_proportion_of_nulls': {'x2': 0.0018611143422123998},\n",
       "         'statistics': array([234.52197656]),\n",
       "         'running_time': '0.012 s'}}},\n",
       "      'standard_scaler': {'fn': <function fklearn.training.transformation.standard_scaler.<locals>.p(new_data_set: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'standard_scaler': {'standard_scaler': {'copy': True,\n",
       "          'with_mean': True,\n",
       "          'with_std': True},\n",
       "         'transformed_column': ['x2'],\n",
       "         'running_time': '0.009 s'}}},\n",
       "      'xgb_regression_learner': {'fn': <function fklearn.training.regression.xgb_regression_learner.<locals>.p(new_df: pandas.core.frame.DataFrame, apply_shap: bool = False) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'xgb_regression_learner': {'features': ['x2'],\n",
       "         'target': 'y',\n",
       "         'prediction_column': 'prediction',\n",
       "         'package': 'xgboost',\n",
       "         'package_version': '0.82',\n",
       "         'parameters': {'seed': 139,\n",
       "          'nthread': 8,\n",
       "          'eta': 0.1,\n",
       "          'objective': 'reg:linear',\n",
       "          'num_estimators': 100},\n",
       "         'feature_importance': {'x2': 2833},\n",
       "         'training_samples': 8597,\n",
       "         'running_time': '0.447 s'}}},\n",
       "      'ecdfer': {'fn': <function fklearn.training.transformation.ecdfer.<locals>.p(new_df: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'ecdfer': {'nobs': 8597,\n",
       "         'prediction_column': 'prediction',\n",
       "         'ascending': True,\n",
       "         'transformed_column': ['prediction_ecdf'],\n",
       "         'running_time': '0.003 s'}}}}}},\n",
       "   {'imputer': {'impute_strategy': 'median',\n",
       "     'columns_to_impute': ['x2'],\n",
       "     'training_proportion_of_nulls': {'x2': 0.002907652942544778},\n",
       "     'statistics': array([234.0620775]),\n",
       "     'running_time': '0.009 s'},\n",
       "    'standard_scaler': {'standard_scaler': {'copy': True,\n",
       "      'with_mean': True,\n",
       "      'with_std': True},\n",
       "     'transformed_column': ['x2'],\n",
       "     'running_time': '0.005 s'},\n",
       "    'xgb_regression_learner': {'features': ['x2'],\n",
       "     'target': 'y',\n",
       "     'prediction_column': 'prediction',\n",
       "     'package': 'xgboost',\n",
       "     'package_version': '0.82',\n",
       "     'parameters': {'seed': 139,\n",
       "      'nthread': 8,\n",
       "      'eta': 0.1,\n",
       "      'objective': 'reg:linear',\n",
       "      'num_estimators': 100},\n",
       "     'feature_importance': {'x2': 2682},\n",
       "     'training_samples': 8598,\n",
       "     'running_time': '0.464 s'},\n",
       "    'ecdfer': {'nobs': 8598,\n",
       "     'prediction_column': 'prediction',\n",
       "     'ascending': True,\n",
       "     'transformed_column': ['prediction_ecdf'],\n",
       "     'running_time': '0.004 s'},\n",
       "    '__fkml__': {'pipeline': ['imputer',\n",
       "      'standard_scaler',\n",
       "      'xgb_regression_learner',\n",
       "      'ecdfer'],\n",
       "     'output_columns': ['id',\n",
       "      'score_date',\n",
       "      'x1',\n",
       "      'x2',\n",
       "      'x3',\n",
       "      'y',\n",
       "      'prediction',\n",
       "      'prediction_ecdf'],\n",
       "     'features': ['id', 'score_date', 'x1', 'x2', 'x3', 'y'],\n",
       "     'learners': {'imputer': {'fn': <function fklearn.training.imputation.imputer.<locals>.p(new_data_set: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'imputer': {'impute_strategy': 'median',\n",
       "         'columns_to_impute': ['x2'],\n",
       "         'training_proportion_of_nulls': {'x2': 0.002907652942544778},\n",
       "         'statistics': array([234.0620775]),\n",
       "         'running_time': '0.009 s'}}},\n",
       "      'standard_scaler': {'fn': <function fklearn.training.transformation.standard_scaler.<locals>.p(new_data_set: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'standard_scaler': {'standard_scaler': {'copy': True,\n",
       "          'with_mean': True,\n",
       "          'with_std': True},\n",
       "         'transformed_column': ['x2'],\n",
       "         'running_time': '0.005 s'}}},\n",
       "      'xgb_regression_learner': {'fn': <function fklearn.training.regression.xgb_regression_learner.<locals>.p(new_df: pandas.core.frame.DataFrame, apply_shap: bool = False) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'xgb_regression_learner': {'features': ['x2'],\n",
       "         'target': 'y',\n",
       "         'prediction_column': 'prediction',\n",
       "         'package': 'xgboost',\n",
       "         'package_version': '0.82',\n",
       "         'parameters': {'seed': 139,\n",
       "          'nthread': 8,\n",
       "          'eta': 0.1,\n",
       "          'objective': 'reg:linear',\n",
       "          'num_estimators': 100},\n",
       "         'feature_importance': {'x2': 2682},\n",
       "         'training_samples': 8598,\n",
       "         'running_time': '0.464 s'}}},\n",
       "      'ecdfer': {'fn': <function fklearn.training.transformation.ecdfer.<locals>.p(new_df: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'ecdfer': {'nobs': 8598,\n",
       "         'prediction_column': 'prediction',\n",
       "         'ascending': True,\n",
       "         'transformed_column': ['prediction_ecdf'],\n",
       "         'running_time': '0.004 s'}}}}}}],\n",
       "  'validator_log': [{'fold_num': 0,\n",
       "    'eval_results': [{'mean_evaluator__prediction': 19001.398,\n",
       "      'r2_evaluator__y': 0.9967869656965197,\n",
       "      'mse_evaluator__y': 264683.44314774574}],\n",
       "    'split_log': {'train_size': 8597, 'test_size': 8598}},\n",
       "   {'fold_num': 1,\n",
       "    'eval_results': [{'mean_evaluator__prediction': 19067.227,\n",
       "      'r2_evaluator__y': 0.9972940430229065,\n",
       "      'mse_evaluator__y': 222424.24657619736}],\n",
       "    'split_log': {'train_size': 8598, 'test_size': 8597}}]},\n",
       " {'train_log': [{'imputer': {'impute_strategy': 'median',\n",
       "     'columns_to_impute': ['x2', 'x1'],\n",
       "     'training_proportion_of_nulls': {'x2': 0.0018611143422123998,\n",
       "      'x1': 0.0032569500988716992},\n",
       "     'statistics': array([234.52197656, 499.86013654]),\n",
       "     'running_time': '0.016 s'},\n",
       "    'standard_scaler': {'standard_scaler': {'copy': True,\n",
       "      'with_mean': True,\n",
       "      'with_std': True},\n",
       "     'transformed_column': ['x2', 'x1'],\n",
       "     'running_time': '0.008 s'},\n",
       "    'xgb_regression_learner': {'features': ['x2', 'x1'],\n",
       "     'target': 'y',\n",
       "     'prediction_column': 'prediction',\n",
       "     'package': 'xgboost',\n",
       "     'package_version': '0.82',\n",
       "     'parameters': {'seed': 139,\n",
       "      'nthread': 8,\n",
       "      'eta': 0.1,\n",
       "      'objective': 'reg:linear',\n",
       "      'num_estimators': 100},\n",
       "     'feature_importance': {'x2': 2116, 'x1': 1034},\n",
       "     'training_samples': 8597,\n",
       "     'running_time': '0.490 s'},\n",
       "    'ecdfer': {'nobs': 8597,\n",
       "     'prediction_column': 'prediction',\n",
       "     'ascending': True,\n",
       "     'transformed_column': ['prediction_ecdf'],\n",
       "     'running_time': '0.003 s'},\n",
       "    '__fkml__': {'pipeline': ['imputer',\n",
       "      'standard_scaler',\n",
       "      'xgb_regression_learner',\n",
       "      'ecdfer'],\n",
       "     'output_columns': ['id',\n",
       "      'score_date',\n",
       "      'x1',\n",
       "      'x2',\n",
       "      'x3',\n",
       "      'y',\n",
       "      'prediction',\n",
       "      'prediction_ecdf'],\n",
       "     'features': ['id', 'score_date', 'x1', 'x2', 'x3', 'y'],\n",
       "     'learners': {'imputer': {'fn': <function fklearn.training.imputation.imputer.<locals>.p(new_data_set: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'imputer': {'impute_strategy': 'median',\n",
       "         'columns_to_impute': ['x2', 'x1'],\n",
       "         'training_proportion_of_nulls': {'x2': 0.0018611143422123998,\n",
       "          'x1': 0.0032569500988716992},\n",
       "         'statistics': array([234.52197656, 499.86013654]),\n",
       "         'running_time': '0.016 s'}}},\n",
       "      'standard_scaler': {'fn': <function fklearn.training.transformation.standard_scaler.<locals>.p(new_data_set: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'standard_scaler': {'standard_scaler': {'copy': True,\n",
       "          'with_mean': True,\n",
       "          'with_std': True},\n",
       "         'transformed_column': ['x2', 'x1'],\n",
       "         'running_time': '0.008 s'}}},\n",
       "      'xgb_regression_learner': {'fn': <function fklearn.training.regression.xgb_regression_learner.<locals>.p(new_df: pandas.core.frame.DataFrame, apply_shap: bool = False) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'xgb_regression_learner': {'features': ['x2', 'x1'],\n",
       "         'target': 'y',\n",
       "         'prediction_column': 'prediction',\n",
       "         'package': 'xgboost',\n",
       "         'package_version': '0.82',\n",
       "         'parameters': {'seed': 139,\n",
       "          'nthread': 8,\n",
       "          'eta': 0.1,\n",
       "          'objective': 'reg:linear',\n",
       "          'num_estimators': 100},\n",
       "         'feature_importance': {'x2': 2116, 'x1': 1034},\n",
       "         'training_samples': 8597,\n",
       "         'running_time': '0.490 s'}}},\n",
       "      'ecdfer': {'fn': <function fklearn.training.transformation.ecdfer.<locals>.p(new_df: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'ecdfer': {'nobs': 8597,\n",
       "         'prediction_column': 'prediction',\n",
       "         'ascending': True,\n",
       "         'transformed_column': ['prediction_ecdf'],\n",
       "         'running_time': '0.003 s'}}}}}},\n",
       "   {'imputer': {'impute_strategy': 'median',\n",
       "     'columns_to_impute': ['x2', 'x1'],\n",
       "     'training_proportion_of_nulls': {'x2': 0.002907652942544778,\n",
       "      'x1': 0.00209351011863224},\n",
       "     'statistics': array([234.0620775 , 499.96967609]),\n",
       "     'running_time': '0.011 s'},\n",
       "    'standard_scaler': {'standard_scaler': {'copy': True,\n",
       "      'with_mean': True,\n",
       "      'with_std': True},\n",
       "     'transformed_column': ['x2', 'x1'],\n",
       "     'running_time': '0.006 s'},\n",
       "    'xgb_regression_learner': {'features': ['x2', 'x1'],\n",
       "     'target': 'y',\n",
       "     'prediction_column': 'prediction',\n",
       "     'package': 'xgboost',\n",
       "     'package_version': '0.82',\n",
       "     'parameters': {'seed': 139,\n",
       "      'nthread': 8,\n",
       "      'eta': 0.1,\n",
       "      'objective': 'reg:linear',\n",
       "      'num_estimators': 100},\n",
       "     'feature_importance': {'x2': 1998, 'x1': 767},\n",
       "     'training_samples': 8598,\n",
       "     'running_time': '0.545 s'},\n",
       "    'ecdfer': {'nobs': 8598,\n",
       "     'prediction_column': 'prediction',\n",
       "     'ascending': True,\n",
       "     'transformed_column': ['prediction_ecdf'],\n",
       "     'running_time': '0.003 s'},\n",
       "    '__fkml__': {'pipeline': ['imputer',\n",
       "      'standard_scaler',\n",
       "      'xgb_regression_learner',\n",
       "      'ecdfer'],\n",
       "     'output_columns': ['id',\n",
       "      'score_date',\n",
       "      'x1',\n",
       "      'x2',\n",
       "      'x3',\n",
       "      'y',\n",
       "      'prediction',\n",
       "      'prediction_ecdf'],\n",
       "     'features': ['id', 'score_date', 'x1', 'x2', 'x3', 'y'],\n",
       "     'learners': {'imputer': {'fn': <function fklearn.training.imputation.imputer.<locals>.p(new_data_set: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'imputer': {'impute_strategy': 'median',\n",
       "         'columns_to_impute': ['x2', 'x1'],\n",
       "         'training_proportion_of_nulls': {'x2': 0.002907652942544778,\n",
       "          'x1': 0.00209351011863224},\n",
       "         'statistics': array([234.0620775 , 499.96967609]),\n",
       "         'running_time': '0.011 s'}}},\n",
       "      'standard_scaler': {'fn': <function fklearn.training.transformation.standard_scaler.<locals>.p(new_data_set: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'standard_scaler': {'standard_scaler': {'copy': True,\n",
       "          'with_mean': True,\n",
       "          'with_std': True},\n",
       "         'transformed_column': ['x2', 'x1'],\n",
       "         'running_time': '0.006 s'}}},\n",
       "      'xgb_regression_learner': {'fn': <function fklearn.training.regression.xgb_regression_learner.<locals>.p(new_df: pandas.core.frame.DataFrame, apply_shap: bool = False) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'xgb_regression_learner': {'features': ['x2', 'x1'],\n",
       "         'target': 'y',\n",
       "         'prediction_column': 'prediction',\n",
       "         'package': 'xgboost',\n",
       "         'package_version': '0.82',\n",
       "         'parameters': {'seed': 139,\n",
       "          'nthread': 8,\n",
       "          'eta': 0.1,\n",
       "          'objective': 'reg:linear',\n",
       "          'num_estimators': 100},\n",
       "         'feature_importance': {'x2': 1998, 'x1': 767},\n",
       "         'training_samples': 8598,\n",
       "         'running_time': '0.545 s'}}},\n",
       "      'ecdfer': {'fn': <function fklearn.training.transformation.ecdfer.<locals>.p(new_df: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'ecdfer': {'nobs': 8598,\n",
       "         'prediction_column': 'prediction',\n",
       "         'ascending': True,\n",
       "         'transformed_column': ['prediction_ecdf'],\n",
       "         'running_time': '0.003 s'}}}}}}],\n",
       "  'validator_log': [{'fold_num': 0,\n",
       "    'eval_results': [{'mean_evaluator__prediction': 18998.422,\n",
       "      'r2_evaluator__y': 0.996420620394994,\n",
       "      'mse_evaluator__y': 294862.2481744438}],\n",
       "    'split_log': {'train_size': 8597, 'test_size': 8598}},\n",
       "   {'fold_num': 1,\n",
       "    'eval_results': [{'mean_evaluator__prediction': 19067.477,\n",
       "      'r2_evaluator__y': 0.9971315905847774,\n",
       "      'mse_evaluator__y': 235777.51178373926}],\n",
       "    'split_log': {'train_size': 8598, 'test_size': 8597}}]},\n",
       " {'train_log': [{'imputer': {'impute_strategy': 'median',\n",
       "     'columns_to_impute': ['x1', 'x2', 'x3'],\n",
       "     'training_proportion_of_nulls': {'x1': 0.0032569500988716992,\n",
       "      'x2': 0.0018611143422123998,\n",
       "      'x3': 0.0},\n",
       "     'statistics': array([499.86013654, 234.52197656, 792.01836178]),\n",
       "     'running_time': '0.017 s'},\n",
       "    'standard_scaler': {'standard_scaler': {'copy': True,\n",
       "      'with_mean': True,\n",
       "      'with_std': True},\n",
       "     'transformed_column': ['x1', 'x2', 'x3'],\n",
       "     'running_time': '0.008 s'},\n",
       "    'xgb_regression_learner': {'features': ['x1', 'x2', 'x3'],\n",
       "     'target': 'y',\n",
       "     'prediction_column': 'prediction',\n",
       "     'package': 'xgboost',\n",
       "     'package_version': '0.82',\n",
       "     'parameters': {'seed': 139,\n",
       "      'nthread': 8,\n",
       "      'eta': 0.1,\n",
       "      'objective': 'reg:linear',\n",
       "      'num_estimators': 100},\n",
       "     'feature_importance': {'x2': 1990, 'x3': 401, 'x1': 830},\n",
       "     'training_samples': 8597,\n",
       "     'running_time': '0.697 s'},\n",
       "    'ecdfer': {'nobs': 8597,\n",
       "     'prediction_column': 'prediction',\n",
       "     'ascending': True,\n",
       "     'transformed_column': ['prediction_ecdf'],\n",
       "     'running_time': '0.005 s'},\n",
       "    '__fkml__': {'pipeline': ['imputer',\n",
       "      'standard_scaler',\n",
       "      'xgb_regression_learner',\n",
       "      'ecdfer'],\n",
       "     'output_columns': ['id',\n",
       "      'score_date',\n",
       "      'x1',\n",
       "      'x2',\n",
       "      'x3',\n",
       "      'y',\n",
       "      'prediction',\n",
       "      'prediction_ecdf'],\n",
       "     'features': ['id', 'score_date', 'x1', 'x2', 'x3', 'y'],\n",
       "     'learners': {'imputer': {'fn': <function fklearn.training.imputation.imputer.<locals>.p(new_data_set: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'imputer': {'impute_strategy': 'median',\n",
       "         'columns_to_impute': ['x1', 'x2', 'x3'],\n",
       "         'training_proportion_of_nulls': {'x1': 0.0032569500988716992,\n",
       "          'x2': 0.0018611143422123998,\n",
       "          'x3': 0.0},\n",
       "         'statistics': array([499.86013654, 234.52197656, 792.01836178]),\n",
       "         'running_time': '0.017 s'}}},\n",
       "      'standard_scaler': {'fn': <function fklearn.training.transformation.standard_scaler.<locals>.p(new_data_set: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'standard_scaler': {'standard_scaler': {'copy': True,\n",
       "          'with_mean': True,\n",
       "          'with_std': True},\n",
       "         'transformed_column': ['x1', 'x2', 'x3'],\n",
       "         'running_time': '0.008 s'}}},\n",
       "      'xgb_regression_learner': {'fn': <function fklearn.training.regression.xgb_regression_learner.<locals>.p(new_df: pandas.core.frame.DataFrame, apply_shap: bool = False) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'xgb_regression_learner': {'features': ['x1', 'x2', 'x3'],\n",
       "         'target': 'y',\n",
       "         'prediction_column': 'prediction',\n",
       "         'package': 'xgboost',\n",
       "         'package_version': '0.82',\n",
       "         'parameters': {'seed': 139,\n",
       "          'nthread': 8,\n",
       "          'eta': 0.1,\n",
       "          'objective': 'reg:linear',\n",
       "          'num_estimators': 100},\n",
       "         'feature_importance': {'x2': 1990, 'x3': 401, 'x1': 830},\n",
       "         'training_samples': 8597,\n",
       "         'running_time': '0.697 s'}}},\n",
       "      'ecdfer': {'fn': <function fklearn.training.transformation.ecdfer.<locals>.p(new_df: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'ecdfer': {'nobs': 8597,\n",
       "         'prediction_column': 'prediction',\n",
       "         'ascending': True,\n",
       "         'transformed_column': ['prediction_ecdf'],\n",
       "         'running_time': '0.005 s'}}}}}},\n",
       "   {'imputer': {'impute_strategy': 'median',\n",
       "     'columns_to_impute': ['x1', 'x2', 'x3'],\n",
       "     'training_proportion_of_nulls': {'x1': 0.00209351011863224,\n",
       "      'x2': 0.002907652942544778,\n",
       "      'x3': 0.0},\n",
       "     'statistics': array([499.96967609, 234.0620775 , 793.02418833]),\n",
       "     'running_time': '0.020 s'},\n",
       "    'standard_scaler': {'standard_scaler': {'copy': True,\n",
       "      'with_mean': True,\n",
       "      'with_std': True},\n",
       "     'transformed_column': ['x1', 'x2', 'x3'],\n",
       "     'running_time': '0.012 s'},\n",
       "    'xgb_regression_learner': {'features': ['x1', 'x2', 'x3'],\n",
       "     'target': 'y',\n",
       "     'prediction_column': 'prediction',\n",
       "     'package': 'xgboost',\n",
       "     'package_version': '0.82',\n",
       "     'parameters': {'seed': 139,\n",
       "      'nthread': 8,\n",
       "      'eta': 0.1,\n",
       "      'objective': 'reg:linear',\n",
       "      'num_estimators': 100},\n",
       "     'feature_importance': {'x2': 1799, 'x1': 689, 'x3': 312},\n",
       "     'training_samples': 8598,\n",
       "     'running_time': '0.632 s'},\n",
       "    'ecdfer': {'nobs': 8598,\n",
       "     'prediction_column': 'prediction',\n",
       "     'ascending': True,\n",
       "     'transformed_column': ['prediction_ecdf'],\n",
       "     'running_time': '0.004 s'},\n",
       "    '__fkml__': {'pipeline': ['imputer',\n",
       "      'standard_scaler',\n",
       "      'xgb_regression_learner',\n",
       "      'ecdfer'],\n",
       "     'output_columns': ['id',\n",
       "      'score_date',\n",
       "      'x1',\n",
       "      'x2',\n",
       "      'x3',\n",
       "      'y',\n",
       "      'prediction',\n",
       "      'prediction_ecdf'],\n",
       "     'features': ['id', 'score_date', 'x1', 'x2', 'x3', 'y'],\n",
       "     'learners': {'imputer': {'fn': <function fklearn.training.imputation.imputer.<locals>.p(new_data_set: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'imputer': {'impute_strategy': 'median',\n",
       "         'columns_to_impute': ['x1', 'x2', 'x3'],\n",
       "         'training_proportion_of_nulls': {'x1': 0.00209351011863224,\n",
       "          'x2': 0.002907652942544778,\n",
       "          'x3': 0.0},\n",
       "         'statistics': array([499.96967609, 234.0620775 , 793.02418833]),\n",
       "         'running_time': '0.020 s'}}},\n",
       "      'standard_scaler': {'fn': <function fklearn.training.transformation.standard_scaler.<locals>.p(new_data_set: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'standard_scaler': {'standard_scaler': {'copy': True,\n",
       "          'with_mean': True,\n",
       "          'with_std': True},\n",
       "         'transformed_column': ['x1', 'x2', 'x3'],\n",
       "         'running_time': '0.012 s'}}},\n",
       "      'xgb_regression_learner': {'fn': <function fklearn.training.regression.xgb_regression_learner.<locals>.p(new_df: pandas.core.frame.DataFrame, apply_shap: bool = False) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'xgb_regression_learner': {'features': ['x1', 'x2', 'x3'],\n",
       "         'target': 'y',\n",
       "         'prediction_column': 'prediction',\n",
       "         'package': 'xgboost',\n",
       "         'package_version': '0.82',\n",
       "         'parameters': {'seed': 139,\n",
       "          'nthread': 8,\n",
       "          'eta': 0.1,\n",
       "          'objective': 'reg:linear',\n",
       "          'num_estimators': 100},\n",
       "         'feature_importance': {'x2': 1799, 'x1': 689, 'x3': 312},\n",
       "         'training_samples': 8598,\n",
       "         'running_time': '0.632 s'}}},\n",
       "      'ecdfer': {'fn': <function fklearn.training.transformation.ecdfer.<locals>.p(new_df: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame>,\n",
       "       'log': {'ecdfer': {'nobs': 8598,\n",
       "         'prediction_column': 'prediction',\n",
       "         'ascending': True,\n",
       "         'transformed_column': ['prediction_ecdf'],\n",
       "         'running_time': '0.004 s'}}}}}}],\n",
       "  'validator_log': [{'fold_num': 0,\n",
       "    'eval_results': [{'mean_evaluator__prediction': 19002.934,\n",
       "      'r2_evaluator__y': 0.9957544083219314,\n",
       "      'mse_evaluator__y': 349743.4877472073}],\n",
       "    'split_log': {'train_size': 8597, 'test_size': 8598}},\n",
       "   {'fold_num': 1,\n",
       "    'eval_results': [{'mean_evaluator__prediction': 19066.275,\n",
       "      'r2_evaluator__y': 0.9967958244067132,\n",
       "      'mse_evaluator__y': 263376.819464493}],\n",
       "    'split_log': {'train_size': 8598, 'test_size': 8597}}]}]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from toolz import curry\n",
    "from fklearn.tuning.selectors import feature_importance_backward_selection\n",
    "\n",
    "# There might be columns that are features but are also auxiliary for evaluation, add them on the auxiliary list.\n",
    "AUXILIARY_COLUMNS = list(set(train_set.columns) - set(FEATURES))\n",
    "\n",
    "base_common_extractor = evaluator_extractor(evaluator_name='r2_evaluator__y')\n",
    "\n",
    "@curry\n",
    "def selector_pipeline(train_set, features):\n",
    "    pipeline = build_pipeline(\n",
    "        imputer(columns_to_impute=features, impute_strategy='median'),\n",
    "        standard_scaler(columns_to_scale=features),\n",
    "        xgb_regression_learner(\n",
    "            features=features, \n",
    "            target='y', \n",
    "            prediction_column='prediction', \n",
    "            extra_params={'seed': 139, 'nthread': 8}\n",
    "        ),\n",
    "        ecdfer(prediction_column='prediction', ecdf_column='prediction_ecdf'),\n",
    "    )\n",
    "    return pipeline(train_set)\n",
    "\n",
    "logs_fibs = feature_importance_backward_selection(\n",
    "    train_set,\n",
    "    selector_pipeline,\n",
    "    FEATURES,\n",
    "    my_split_fn,\n",
    "    my_evaluator,\n",
    "    base_common_extractor,\n",
    "    metric_name='y',\n",
    "    num_removed_by_step=1,\n",
    "    threshold=0.0001,\n",
    "    early_stop=100,\n",
    "    iter_limit=3,\n",
    "    min_remaining_features=1,\n",
    ")\n",
    "\n",
    "logs_fibs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick showing on how to get best features from selection logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['x2'], 0.997040504359713)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fklearn.tuning.samplers import get_used_features, get_avg_metric_from_extractor, order_feature_importance_avg_from_logs\n",
    "\n",
    "features = list(map(lambda x: get_used_features(x), logs_fibs))\n",
    "auc = list(map(lambda x: get_avg_metric_from_extractor(x, base_common_extractor, 'r2_evaluator__y'), logs_fibs))\n",
    "\n",
    "sorted(zip(features, auc), reverse=True, key=lambda x: x[1])[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpretability: SHAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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RegExp(\"^(data|aria)-[\"+r.ATTRIBUTE_NAME_CHAR+\"]*$\")),Properties:{accept:0,acceptCharset:0,accessKey:0,action:0,allowFullScreen:o,allowTransparency:0,alt:0,as:0,async:o,autoComplete:0,autoPlay:o,capture:o,cellPadding:0,cellSpacing:0,charSet:0,challenge:0,checked:i|o,cite:0,classID:0,className:0,cols:u,colSpan:0,content:0,contentEditable:0,contextMenu:0,controls:o,controlsList:0,coords:0,crossOrigin:0,data:0,dateTime:0,default:o,defer:o,dir:0,disabled:o,download:c,draggable:0,encType:0,form:0,formAction:0,formEncType:0,formMethod:0,formNoValidate:o,formTarget:0,frameBorder:0,headers:0,height:0,hidden:o,high:0,href:0,hrefLang:0,htmlFor:0,httpEquiv:0,icon:0,id:0,inputMode:0,integrity:0,is:0,keyParams:0,keyType:0,kind:0,label:0,lang:0,list:0,loop:o,low:0,manifest:0,marginHeight:0,marginWidth:0,max:0,maxLength:0,media:0,mediaGroup:0,method:0,min:0,minLength:0,multiple:i|o,muted:i|o,name:0,nonce:0,noValidate:o,open:o,optimum:0,pattern:0,placeholder:0,playsInline:o,poster:0,preload:0,profile:0,radioGroup:0,readOnly:o,referrerPolicy:0,rel:0,required:o,reversed:o,role:0,rows:u,rowSpan:a,sandbox:0,scope:0,scoped:o,scrolling:0,seamless:o,selected:i|o,shape:0,size:u,sizes:0,span:u,spellCheck:0,src:0,srcDoc:0,srcLang:0,srcSet:0,start:a,step:0,style:0,summary:0,tabIndex:0,target:0,title:0,type:0,useMap:0,value:0,width:0,wmode:0,wrap:0,about:0,datatype:0,inlist:0,prefix:0,property:0,resource:0,typeof:0,vocab:0,autoCapitalize:0,autoCorrect:0,autoSave:0,color:0,itemProp:0,itemScope:o,itemType:0,itemID:0,itemRef:0,results:0,security:0,unselectable:0},DOMAttributeNames:{acceptCharset:\"accept-charset\",className:\"class\",htmlFor:\"for\",httpEquiv:\"http-equiv\"},DOMPropertyNames:{},DOMMutationMethods:{value:function(t,e){if(null==e)return t.removeAttribute(\"value\");\"number\"!==t.type||!1===t.hasAttribute(\"value\")?t.setAttribute(\"value\",\"\"+e):t.validity&&!t.validity.badInput&&t.ownerDocument.activeElement!==t&&t.setAttribute(\"value\",\"\"+e)}}};t.exports=s},function(t,e,n){\"use strict\";(function(e){function r(t,e,n,r){var i=void 0===t[n];null!=e&&i&&(t[n]=o(e,!0))}var i=n(24),o=n(174),a=(n(85),n(96)),u=n(177);n(2);void 0!==e&&e.env;var c={instantiateChildren:function(t,e,n,i){if(null==t)return null;var o={};return u(t,r,o),o},updateChildren:function(t,e,n,r,u,c,s,l,f){if(e||t){var p,h;for(p in e)if(e.hasOwnProperty(p)){h=t&&t[p];var d=h&&h._currentElement,v=e[p];if(null!=h&&a(d,v))i.receiveComponent(h,v,u,l),e[p]=h;else{h&&(r[p]=i.getHostNode(h),i.unmountComponent(h,!1));var g=o(v,!0);e[p]=g;var m=i.mountComponent(g,u,c,s,l,f);n.push(m)}}for(p in t)!t.hasOwnProperty(p)||e&&e.hasOwnProperty(p)||(h=t[p],r[p]=i.getHostNode(h),i.unmountComponent(h,!1))}},unmountChildren:function(t,e){for(var n in t)if(t.hasOwnProperty(n)){var r=t[n];i.unmountComponent(r,e)}}};t.exports=c}).call(e,n(156))},function(t,e,n){\"use strict\";var r=n(82),i=n(364),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\"use strict\";function r(t){}function i(t){return!(!t.prototype||!t.prototype.isReactComponent)}function o(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var a=n(1),u=n(3),c=n(26),s=n(87),l=n(15),f=n(88),p=n(39),h=(n(9),n(168)),d=n(24),v=n(51),g=(n(0),n(81)),m=n(96),y=(n(2),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=p.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return e};var _=1,b={construct:function(t){this._currentElement=t,this._rootNodeID=0,this._compositeType=null,this._instance=null,this._hostParent=null,this._hostContainerInfo=null,this._updateBatchNumber=null,this._pendingElement=null,this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._renderedNodeType=null,this._renderedComponent=null,this._context=null,this._mountOrder=0,this._topLevelWrapper=null,this._pendingCallbacks=null,this._calledComponentWillUnmount=!1},mountComponent:function(t,e,n,u){this._context=u,this._mountOrder=_++,this._hostParent=e,this._hostContainerInfo=n;var s,l=this._currentElement.props,f=this._processContext(u),h=this._currentElement.type,d=t.getUpdateQueue(),g=i(h),m=this._constructComponent(g,l,f,d);g||null!=m&&null!=m.render?o(h)?this._compositeType=y.PureClass:this._compositeType=y.ImpureClass:(s=m,null===m||!1===m||c.isValidElement(m)||a(\"105\",h.displayName||h.name||\"Component\"),m=new r(h),this._compositeType=y.StatelessFunctional);m.props=l,m.context=f,m.refs=v,m.updater=d,this._instance=m,p.set(m,this);var b=m.state;void 0===b&&(m.state=b=null),(\"object\"!=typeof b||Array.isArray(b))&&a(\"106\",this.getName()||\"ReactCompositeComponent\"),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1;var x;return x=m.unstable_handleError?this.performInitialMountWithErrorHandling(s,e,n,t,u):this.performInitialMount(s,e,n,t,u),m.componentDidMount&&t.getReactMountReady().enqueue(m.componentDidMount,m),x},_constructComponent:function(t,e,n,r){return this._constructComponentWithoutOwner(t,e,n,r)},_constructComponentWithoutOwner:function(t,e,n,r){var i=this._currentElement.type;return t?new i(e,n,r):i(e,n,r)},performInitialMountWithErrorHandling:function(t,e,n,r,i){var o,a=r.checkpoint();try{o=this.performInitialMount(t,e,n,r,i)}catch(u){r.rollback(a),this._instance.unstable_handleError(u),this._pendingStateQueue&&(this._instance.state=this._processPendingState(this._instance.props,this._instance.context)),a=r.checkpoint(),this._renderedComponent.unmountComponent(!0),r.rollback(a),o=this.performInitialMount(t,e,n,r,i)}return o},performInitialMount:function(t,e,n,r,i){var o=this._instance,a=0;o.componentWillMount&&(o.componentWillMount(),this._pendingStateQueue&&(o.state=this._processPendingState(o.props,o.context))),void 0===t&&(t=this._renderValidatedComponent());var u=h.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return d.getHostNode(this._renderedComponent)},unmountComponent:function(t){if(this._renderedComponent){var e=this._instance;if(e.componentWillUnmount&&!e._calledComponentWillUnmount)if(e._calledComponentWillUnmount=!0,t){var n=this.getName()+\".componentWillUnmount()\";f.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(d.unmountComponent(this._renderedComponent,t),this._renderedNodeType=null,this._renderedComponent=null,this._instance=null),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._pendingCallbacks=null,this._pendingElement=null,this._context=null,this._rootNodeID=0,this._topLevelWrapper=null,p.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return v;var r={};for(var i in n)r[i]=t[i];return r},_processContext:function(t){var e=this._maskContext(t);return e},_processChildContext:function(t){var e,n=this._currentElement.type,r=this._instance;if(r.getChildContext&&(e=r.getChildContext()),e){\"object\"!=typeof n.childContextTypes&&a(\"107\",this.getName()||\"ReactCompositeComponent\");for(var i in e)i in n.childContextTypes||a(\"108\",this.getName()||\"ReactCompositeComponent\",i);return u({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?d.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o&&a(\"136\",this.getName()||\"ReactCompositeComponent\");var u,c=!1;this._context===i?u=o.context:(u=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,u);var f=this._processPendingState(l,u),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,u):this._compositeType===y.PureClass&&(p=!g(s,l)||!g(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,u,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=u)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=u({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var c=r[a];u(o,\"function\"==typeof c?c.call(n,o,t,e):c)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(m(r,i))d.receiveComponent(n,i,t,this._processChildContext(e));else{var a=d.getHostNode(n);d.unmountComponent(n,!1);var u=h.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){s.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t=this._instance;return t.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==y.StatelessFunctional){l.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{l.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||!1===t||c.isValidElement(t)||a(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n&&a(\"110\");var r=e.getPublicInstance();(n.refs===v?n.refs={}:n.refs)[t]=r},detachRef:function(t){delete this.getPublicInstance().refs[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===y.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=b},function(t,e,n){\"use strict\";var r=n(4),i=n(372),o=n(167),a=n(24),u=n(12),c=n(385),s=n(401),l=n(171),f=n(408);n(2);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&($[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML)&&g(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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Y)Y.hasOwnProperty(n)&&t._wrapperState.listeners.push(M.trapBubbledEvent(n,Y[n],e));break;case\"source\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e),M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topReset\",\"reset\",e),M.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function p(){P.postUpdateWrapper(this)}function h(t){Z.call(Q,t)||(X.test(t)||g(\"65\",t),Q[t]=!0)}function d(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function v(t){var e=t.type;h(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var g=n(1),m=n(3),y=n(346),_=n(348),b=n(20),x=n(83),w=n(21),C=n(160),k=n(22),E=n(84),M=n(53),T=n(161),S=n(4),N=n(365),A=n(366),P=n(162),O=n(369),I=(n(9),n(378)),D=n(383),R=(n(11),n(56)),L=(n(0),n(95),n(81),n(173)),U=(n(97),n(2),T),F=k.deleteListener,j=S.getNodeFromInstance,B=M.listenTo,V=E.registrationNameModules,W={string:!0,number:!0},z=\"__html\",H={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},q=11,Y={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},K={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},G={listing:!0,pre:!0,textarea:!0},$=m({menuitem:!0},K),X=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,Q={},Z={}.hasOwnProperty,J=1;v.displayName=\"ReactDOMComponent\",v.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=J++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(f,this);break;case\"input\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this);break;case\"option\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o);break;case\"select\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(f,this);break;case\"textarea\":O.mountWrapper(this,o,e),o=O.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this)}i(this,o);var a,p;null!=e?(a=e._namespaceURI,p=e._tag):n._tag&&(a=n._namespaceURI,p=n._tag),(null==a||a===x.svg&&\"foreignobject\"===p)&&(a=x.html),a===x.html&&(\"svg\"===this._tag?a=x.svg:\"math\"===this._tag&&(a=x.mathml)),this._namespaceURI=a;var h;if(t.useCreateElement){var d,v=n._ownerDocument;if(a===x.html)if(\"script\"===this._tag){var g=v.createElement(\"div\"),m=this._currentElement.type;g.innerHTML=\"<\"+m+\"></\"+m+\">\",d=g.removeChild(g.firstChild)}else d=o.is?v.createElement(this._currentElement.type,o.is):v.createElement(this._currentElement.type);else d=v.createElementNS(a,this._currentElement.type);S.precacheNode(this,d),this._flags|=U.hasCachedChildNodes,this._hostParent||C.setAttributeForRoot(d),this._updateDOMProperties(null,o,t);var _=b(d);this._createInitialChildren(t,o,r,_),h=_}else{var w=this._createOpenTagMarkupAndPutListeners(t,o),k=this._createContentMarkup(t,o,r);h=!k&&K[this._tag]?w+\"/>\":w+\">\"+k+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"select\":case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return h},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(V.hasOwnProperty(r))i&&o(this,r,i,t);else{\"style\"===r&&(i&&(i=this._previousStyleCopy=m({},e.style)),i=_.createMarkupForStyles(i,this));var a=null;null!=this._tag&&d(this._tag,e)?H.hasOwnProperty(r)||(a=C.createMarkupForCustomAttribute(r,i)):a=C.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+C.createMarkupForRoot()),n+=\" \"+C.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=R(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return G[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&b.queueHTML(r,i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&b.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)b.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"option\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"select\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"textarea\":o=O.getHostProps(this,o),a=O.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":N.updateWrapper(this),L.updateValueIfChanged(this);break;case\"textarea\":O.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(p,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(\"style\"===r){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else V.hasOwnProperty(r)?t[r]&&F(this,r):d(this._tag,t)?H.hasOwnProperty(r)||C.deleteValueForAttribute(j(this),r):(w.properties[r]||w.isCustomAttribute(r))&&C.deleteValueForProperty(j(this),r);for(r in e){var c=e[r],s=\"style\"===r?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(\"style\"===r)if(c?c=this._previousStyleCopy=m({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(V.hasOwnProperty(r))c?o(this,r,c,n):s&&F(this,r);else if(d(this._tag,e))H.hasOwnProperty(r)||C.setValueForAttribute(j(this),r,c);else if(w.properties[r]||w.isCustomAttribute(r)){var l=j(this);null!=c?C.setValueForProperty(l,r,c):C.deleteValueForProperty(l,r)}}a&&_.setValueForStyles(j(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=W[typeof t.children]?t.children:null,o=W[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return j(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"input\":case\"textarea\":L.stopTracking(this);break;case\"html\":case\"head\":case\"body\":g(\"66\",this._tag)}this.unmountChildren(t),S.uncacheNode(this),k.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return j(this)}},m(v.prototype,v.Mixin,I.Mixin),t.exports=v},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(97),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" 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c=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<c.length;p++){var h=c[p];if(h!==o&&h.form===o.form){var d=l.getInstanceFromNode(h);d||a(\"90\"),f.asap(r,d)}}}return n}var a=n(1),u=n(3),c=n(160),s=n(86),l=n(4),f=n(12),p=(n(0),n(2),{getHostProps:function(t,e){var n=s.getValue(e),r=s.getChecked(e);return u({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:o.bind(t),controlled:i(e)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&c.setValueForProperty(l.getNodeFromInstance(t),\"checked\",n||!1);var r=l.getNodeFromInstance(t),i=s.getValue(e);if(null!=i)if(0===i&&\"\"===r.value)r.value=\"0\";else if(\"number\"===e.type){var o=parseFloat(r.value,10)||0;(i!=o||i==o&&r.value!=i)&&(r.value=\"\"+i)}else r.value!==\"\"+i&&(r.value=\"\"+i);else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=l.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=p},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(162),c=(n(2),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){a.getNodeFromInstance(t).setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length;return{start:o,end:o+r}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(405),l=n(172),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(1),i=n(3),o=n(82),a=n(20),u=n(4),c=n(56),s=(n(0),n(97),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var s=n._ownerDocument,l=s.createComment(o),f=s.createComment(\" /react-text \"),p=a(s.createDocumentFragment());return a.queueChild(p,a(l)),this._stringText&&a.queueChild(p,a(s.createTextNode(this._stringText))),a.queueChild(p,a(f)),u.precacheNode(this,l),this._closingComment=f,p}var h=c(this._stringText);return t.renderToStaticMarkup?h:\"\\x3c!--\"+o+\"--\\x3e\"+h+\"\\x3c!-- /react-text --\\x3e\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n&&r(\"67\",this._domID),8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(1),a=n(3),u=n(86),c=n(4),s=n(12),l=(n(0),n(2),{getHostProps:function(t,e){return null!=e.dangerouslySetInnerHTML&&o(\"91\"),a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a&&o(\"92\"),Array.isArray(c)&&(c.length<=1||o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t||c(\"33\"),\"_hostNode\"in e||c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in 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i=n(345),o=n(347),a=n(349),u=n(351),c=n(352),s=n(355),l=n(357),f=n(360),p=n(4),h=n(362),d=n(370),v=n(368),g=n(371),m=n(375),y=n(376),_=n(381),b=n(386),x=n(387),w=n(388),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof Symbol&&Symbol.for&&Symbol.for(\"react.element\")||60103;t.exports=r},function(t,e,n){\"use strict\";function r(t){i.enqueueEvents(t),i.processEventQueue(!1)}var i=n(22),o={handleTopLevel:function(t,e,n,o){r(i.extractEvents(t,e,n,o))}};t.exports=o},function(t,e,n){\"use strict\";function r(t){for(;t._hostParent;)t=t._hostParent;var e=f.getNodeFromInstance(t),n=e.parentNode;return f.getClosestInstanceFromNode(n)}function i(t,e){this.topLevelType=t,this.nativeEvent=e,this.ancestors=[]}function o(t){var e=h(t.nativeEvent),n=f.getClosestInstanceFromNode(e),i=n;do{t.ancestors.push(i),i=i&&r(i)}while(i);for(var o=0;o<t.ancestors.length;o++)n=t.ancestors[o],v._handleTopLevel(t.topLevelType,n,t.nativeEvent,h(t.nativeEvent))}function 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap\n",
    "\n",
    "shap.initjs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "holdout = pd.concat((intime_outspace_hdout, outime_inspace_hdout, outime_outspace_hdout))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = prediction_function(holdout, apply_shap=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap_values = np.vstack(preds['shap_values'])\n",
    "\n",
    "shap_expected_value = preds.shap_expected_value.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RWw4WzIJXHgR3PQa3POj/Xq76CPzsFvjhn7zHKJ+DT5wKr3ghnPZF78I/7gC47Ho47kC48C1w0kVw7Z3wmiL86Gz4x5Pwrq/BvBnJxzKwdDX88zFw1rHw8o/D+k1w86fh4z+F2x/xHoezvwcPPQ3nvxF6+uFzV8I+C+DzZ8IDK2CfhTBnmi//pl746a2w70K4ayncvRQ+/kaYM92n33y/P3d7Htryvh7rN8M/7dW4D52HVsCVf/XX5wWLGnOO7aAV+wmLyBuArwC/AE4D3oPXxM8ALxtxPltzRxeLxfnAXcCHoii6PNl2KbA78LIoiuK6/T8D7BNF0WtGXJttZ5us7dn/U2Zt/9b3ayiatP2qVqOfU9FNa5W4d2ss4XR7si10jjlOmaZKW5LvnFJMVxz7UbGSQwLnaI9dpV2YxOJtL/sI5yARv3ypTN4lXZQyZc33DxBmhViVoFT22+KYfH+55v0QDJQzbmhHrpTpFqXefZ4f8G3Dad1zqWs6drT3VttYBZJgLCptvJkLWXFHS0ZAU5d0kBFYqbiEU8FN61gNxsq2GVeFPU7cyFXB8yN4VV8NtVHWWhPwJTVCnrqdsyhBxXUd171ulLQNubaNIq7bxxg1hZyfwCRlaht0N+jFsMM0uPVCmDsdFr4LNvbUpocBrP0u/NeP4cu/GjyPNx4GP/7Q2JfttgfhsP/07xoBrvkovOqgbc2tIWq5TD416EO+UD/aNHUWkfuBN6nq3SKyTlVnikgeeEpVR9yfcas2fhRFTwNvBr5SLBaXFIvFM4DjgVMHEeAAeClw9yjqMu40XYDBf9Gm3kaR6heuSNVFi1S7KkGmXbj6Mo6DgJIIm0Qq7+L+wMtFDhJh8fnmsiIq4idaSJadBDgRQvHdhmqe7LRslcCxqrUapKNs1X2hS/bjToS8ZuoZCBL7DJz4tuEg0zacK6Xl9PKb2pnpttT1XtueWrU5XWL5p7mkg3pUncBptHR6ldMwLE0czZo52st+9QJQyanqOK53W2vdFq2UMD2+5loxnPtFBllutLvmOUJWgKFxAgyweiP8+Gb40U1bCjD4v+nLboBvXTd0Hj+5BVatH/uyfe6qzBgEwBeuGvtzbCc6xK/JzAVSt3P2K3tURRuRoz2KouuA/wauAv4XOC2KopWD7HoxMBPfTtxwuru7t3m5ZQiptL8mJmptdyQRCEP/pUzios4IniTR0UI6cIffXhrk+7Dmiym1fjMEqpQDvzWWgDiVB/VuZJd+J/gMfOR0sqHmjyNxR28VETQQXC5A1NdZykqwxbGpdJKeOXFLZ7sTZduPBVcjeFlRDZCKLZztVZz+P6jZXrlcGQHUymdA9Qpu6UJOz5x19qet1fU5p2dMXd1Zx3b1k2NwV/aWZze2jUa/1Ht3ngH77jL0DofsTbz3TkOn7zANpndt13tv0OUDd685TX+mDK3yXnXJX3r9r8ncAbylbtubgL+OJpPR1OISYFfgziiKbqhPLBaLFwPHAcdEUbRhNIXYVqZOnbpNy38+dRwKtzUqjbEZSze1gtP3duVdrtVjMq+KQJUZTsmrMi0ZC7oz6boUB+EWL5UAKIuPuc07VxkikmQUrTAJGAuSvPyAGYLGrvIxEOdCXDKoiOLbkFVBg0yLbCljYaifxrCmLKq1lrL6qGhxfl8X1A9Smba4+rJL0iVJExt1SwlKe/K6jHxVPxPScmaDPWojp7OO5ayApzJdu0+t5ZsGbqV7+ytf/eQZyh6uuUA1dvZg9avff8Ly+hcNn54L4PC9R5bXjE7vYs7SnoMX7lpdTy9dLoQ7v5DxOoH8/EPQmRlQJft23GUOfOUd1fWj900+jIFFme5i+y2CmVOqZX/7UfC6F8HFb6PjbS+Hw5bAx98E86bD4p1h9lRoL8AHXwMv3ovw6v+Ed78CTjkUXnswnHo4vPkIOPkQuPY8aMtv83tvyOWPngxnHgULZsPJh9D21bO2Oc9Gkb4F6n9N5t+BT4rIH4EuEbkW+ATwgdFkMqIuSomb+bdAN3AIcG4URZdl0r6ebH/ZEBZyoxjTt8/KzY6P3+LYOACPrYfbV9ZZj2NJZWhK2bLNtxR7Ayg7TCXAQJy4pF1NPnuW4sr0hLNK5cq7o7Nc9qNqqdIRZ74bVekqlRH1My6FzpFPu0EBhYFSzfsnLJUoZM4pcUxhINNP2Dly/eXqIB+lsu9jHDtySZ/hdCalIClPvlS1pIOSI5cGWjslLCXzHjsvpwWysuOHrExlNFdp9611B3s3fFxJrw2OSmXYVboWBXXdkCTpCpXa4GFyjqDGMoWAcs21Cmq6JaXnSoXcEVKGuteHVNp80+3pOlTbhKlZry5n/91GFsyCFWu3zCYNDkxJ++C9bL9qP+Wb7odNfXDeyT6Qa/oUuP58uOV+uOp23wb64ApYuR7229UHOAUBPPSUz2fGFOjr98FIhy2BjjboH/Dn7cjMJ/3wU/5chy2G/hLsvbMPWgIf6NSeh1wiwL39/nwL53ixNcaThijjY/LZQR/y3fU/mqrEItIJvBpvoC4DrlHVYfrQDJLHCEX4PLyZfTBQBK4GDgUeAL4PLAZeHkXRmtGcfAwYVxNgfZ9y1SMx77h2y5Ekt4myqxVbqIpzGpSVfaYHymzRZQk/9vPuTpkaO2bEriJ6XWU/FaCo0hZnRMgpnckQlPlk+sFcMjRlAH5awcz+UirRloqwQjgwUCPKqBKWY8KyQ8oxYblqCYeluDJSVqFczTMsJQLtkmkOky5bEkOuL64MbynOUciOPEVMIXPb04E6koolgps6ExyFSlDVln2EQ2pHxsplHN61+VaFMu3jK3X5pAwuwmnZYoIt+v4qqbNNMuuDCzJJmqtdH8xDnZIPfdoHjoeLzvTPT6kMtzwAy9bA6Uda/2FjLGmIKD4inxv0Cd9Dz2m6Oby9jCQ6+mjgSuCQKIr+kWw7Fx+sdRZwI75LUjbK4aYoio5rRIHraBk/XOwcty53HPtz6B1J3EwaFe3wj21l+Mq0UddtWbtS7PtkZtzIM2Pn+5mpstdAiUKmyThQZXrSJzhMBukQhfZyXJlfuJBYwHnnh5cUVT/LUhz7eYKdo1DOWHvOkSuX/bjRqXCqt1xzZefnFC5XR8kKy4nbO4nGTqOhJXbky0pYrs6cBCBlR6En8wGQiHDVsZsdmEPJ14hrVki9cKZR02GdJRxWRLUa7pWKriSCGdaJX5AIZFgngrUDdWQt42x/4zQ/V/dBkG6r7X8MIOe8Crnzcbgu0+WwLYAjlsDv/4st+PsTMK3Df6Tlc94SNIzxpSGi+LB8ftB3/Z56dtNEWETSWQC2QFVfMuJ8bMSsxvA/fytzzo3DDOCRim62BtmRs5yrNbdTC8bVHjPFOaYmo1/NK8dMr5tmsGugRMEpbWlktCodAyXCdMxnVQpl39WIZDjLwDnEqZ+y0PluTNlyhM4R9g8QZs6VijCq5PoHvLWYjJCVIk79qFqJO1rwsypVJnVQJeyPCcp4V7T6aQcLNRZpOiMSQG0a+CEt/X5aY9mmQ00GyXG5iuhR2b92qEo3hAhXXdJZNzOkIV2puJM5d2KJByQBay7ZT+CYvclfdzaGMUloiCg+KBcP+q7fWz/YTBF+a92mHYF/Bi5X1QtGmo/NotQg3ntgjvce6Jef2Rxz9E+UR9dB/0iihiFxESZtf6pQHrx1Oo2GdoALAgacI21JC5yjTdmia1IpF/o+vEmQVkolOAtq/pSyA2SkAVUaBtWobnxgFs4Rln27tSYBZ1mHejYYqxJrHHpfalhWgpIjUIEQnHpx1uS/bB/dGBJrWIgr4VW+36/fWhtfXBs5rXVu5sFIndm1AVkuEehqnaphXzIlT/4jx5B/7T70v+gL0FMiG5Ud/PcbaHv/Ubh7n8LNm0o4qwsJzQ1sGCOhFa0tVf1u/TYRuQL4NmAi3ErM6wr5x9v9cn/ZcegPYu5eDV156Onzw2Ru8ZSpViM3a7or+WAZiR0FVdrVd5iJRcg572YOE6HPJ8FYmh0iU3ULeXLgXdB1XpF0wA4XBpVZkSTTNlwvqi4IkFATC9YLcRwIoVNUIF+pp1AW9R8Q6ShZgaD5AO1PB9yQZCCp9GMkO5JVOhJVbXxzMqZnIn2uIpap/Vo7VnTtdUg7JmnN9mpv4KC4M9N/+Abye85BYzesgOY2f2HItGDfnZrfscIwJhgt0B1ppKwA9hvNASbC40xbLuCOt9Y+UOt6Hdc97vj0TTF3rUzagmMFTQK3KlMdknRbEi9ywFqBQGG2KoVEkMGLaqhKDIQilJNtPoK5Ggmdc94iDusEOHCZxz7pz5vGAKVu63orMlBXMwBIOtRlOkRlOkylABoKOuAInFbc0RoILhTCuJp31dbMWq2+VbW+E1btJ4GXX9/kngY/pSM/azJRgv8ECSsCDLOvPY3ut/wSXbUZBGRqgfbT9mPqV0+oDLlpFqxhjC8tOmzl2+s2dQKvB4YZ0H1LTIRbgJkdAW/YJ+AN+1Rvxy1PlDj8mwOJeoH37WqlTThOxnhuU+hSZXrq5hahN4CpZT/ZwkAixrnkp6q0OSrRW07ED8SBF7FUXgKtdSUDaBD4qQrxI3WlUyCSjhWd9H3Our5dIORLzo8bTW0asqU7OM2Pyr+1o09V3cB+jqLsPMOpCzp1YbvM/tNOW8zMt+9L294zKT/ZTTivI2mDF4KuHPEzPbQV5wPQ9cw5I7lthmGME60owmw5UMdm4Fb8wFYjxkS4RTls1zz6yXzNtqc3Om5eWubJZ0tMKQhHPi/Hu7+xkTWrlFAdZaCgW4pdDLQnrt9Atcax44eqFMpBQD4RY5xDg4Cy+vmFBR9VnQ7eUXE1A6GSEWCtfCtk21JVaic3AEhnaFJJJo1KuipJ0l3JBX7Ch5BUgNOZlKr1kyQkKqyILxXLVwkoLJlOYYcO5vy/g5l63G6VU+cXTNvieucWTh/N7TEMYxxpgYE5tkBVjxqLfEyEJxDzpwW8Yb8CUB3V54/nzeHZ7pgp7cLqtWXe/uln6e9zxEoy4rFSSKxXxQc81Vi4yeAfuTp3tEo6LrTWjKyVtgN7l3KYTGMnUK4KNQGo832Hg3L13JrpzyppP2QRXJi4vwe0ZgjqXT6wBCkrnXtNo/+RDTz1xfsyoqvMeuNuLP7xyyv7u94yQUfOD5tp0+oZxqShVSxhEdl963uBqj424jyti9LkZPnTA5z9iWfo6YlpUx8EJSi5Upn2dFAOTSKik/7CAqgqQTkm72I/gEfiAg/SvsPZgUKcI+gbIJe4vCU5FvyNCXsHyLvq9AeS9hN2LhkxK9PG7CDojwljCDoCjn7gRNrndY7nJTMMY/tpiFr+Tb466Lv+QP3XcVVnEclO3T4UqqojHqrNLOFJyoL5BX781YWDpv3X+x5jzdMDNV2MyiK+369zBIEQhAE6EKMOJBBcMnBH6vgVhSAQCpW+RooGAXHBtxsX8sqJPzyau7/xIOvvW4vb7Oic08bASzd7C/vbysCz/X7CBgUJ4PA/HMvsF80dj8tjGMYEolUsYVUd86hMs4Sfo8Sx4pwShkIQDP2Ab1wzwPL7u9ntgKkU2nL0bSqTKwQUOkIkELpX9vCDU26htDlGyjGiSmFqyJuvOorOOe1b5Hf11X6i+BNOOKFhdTMMo2k0RC1vl0sGfdcfrGe1hjpvB2YJP0cJQyEMt/78TptTYJ8jZlfWu2YWatKn7tjJWX96ef1hhmEYY0bcIpZwFhHJAf8KHAnMoSbUZuTDVlqHR8MwDKOlSUdcr/81mf8G3g38CTgIuAKYC2wx1e9wmAgbhmEYLU068nv9r8m8HjhOVb8ElJN/XweMquuSuaMNwzCMlqZFg3868XMIA/SKSKeqPiAiB4wmExNhwzAMo6VpAat3MO4HDgb+CkTA+SKyET9+9IgxETYMwzBamhZo/x2M91GZ6o4PAl8DpgLvGk0mJsLGuHHrt/7C8957K3PK63iAm9ir70KCcMR92g3DeI7SiiKsqrdnlh8GXrYt+bSkjW9MTjo+eA0L3bPMLW9ifm4DD7bbRAmGYWyddNT4+l8zEZG7ReQcERl8VKQRYiJsjBthe46pAz0ATO/bjHTZsJSGYWydOJBBf03mfHyb8P0i8kcRebeIzBptJibCxrhmKp+eAAAf5ElEQVSxKZjC2vx0Hu7ahU1hJ5tlSrOLZBjGBMCFMuivmajqL1X1FGA+cBlwIrBMRP5vNPlYm7Axbty20/N5pH0XQFBRJO97uBuGYQyHyzfd6h0SVe0WkR8C6/FT3L1qNMebJWyMG/M2riUd2U1UmLN5TXMLZBjGhMAFMuivmYjnGBG5FHgG757+DbDbsAfWYZawMW7M6FvNs7mdASgHwqL1y7ZyhGEYBrhcS1rCTwGbgB8Dh6nq/duSiYmwMW58/thXszmfY/dn1nHXoh059t65fKnZhTIMo+VpdvvvELxWVf+6vZmYCBvjxtTefm5csiu37+Gt4Vfd/XCTS2QYxkQgbkERHgsBBmsTNsaRI+97kN2Xr2HKpj4Ov/dxDn34wWYXyTCMCYDK4L/JgFnCxrhx5b778liuAzbF/HnWHOYVD+LkZhfKMIyWpwX6BDcME2Fj3OjOVR+3OAhwbvL+YRmGMXa0aJvwmLDdIlwsFj8JHA88H/hTFEXbNH6mMfk54skV3DdzOqUwZMHGDbx4+XJg/2YXyzCMFqdVLWEReTnwJmCuqp4gIkVgmqreMNI8xsISfhQ4DzgWWDwG+RktzDfvdnz9HseBc4VvHOsnX7j6UccNd21mn2vuYL+b/kHf7Gk8s1oo5UMISzy44yL2XPMku02Zxdd/9QeCEGas38i6WQEfPPYWZq/v4YEpXWxqb6Mwq52uQ3birH2FWS+Yzgf+ELNpAL64/Hb2vuZ2ZOEM8hccT7Db7CZfCcMwxguV1hNhEXkvfialb0GlZa0X+DJw6Ejz2aoIF4vF+cBdwIeiKLo82XYpsDvwsiiKvp1ss8GPJjm/X+p41+8dAHc8o/THMafvI7zmlw7oQJYcypV/eghWlFm1w464IKC3q4NODbh/4RKe9/gy1u7gh1ZdN30601ev4po99uDZfI61ef8ovvCpVYT/6OZdj7bz2B1lumP/x/etP23ms398BID4ugfpeOxjSEdh/C+CYRjjTotawu8HjlHVpSLyH8m2B4C9R5PJVqOjoyh6Gngz8JVisbikWCyegXc/nxpFUTz80Y2lu7vblsdx+XdLlSy3rYi5fWV1XYOAuxbsxNr2KWjgHy0N/b+lXEgb1cclDkPaFdaHARty1ekM75s7m+cvX8VTUzsqAgxwx8Jdqida2Y0+vbFp18GWbdmWB19uFK04YhZ+7uB0xKH05ZgHBkaTiajq1vcCisXi+cBpwI7A66IoumGQ9MPHuU14ZIU3xoR7VjsO+K7DJesfO0R47R4Bh/6wTF8sdA4McO2Xv0GfdvDA/IVoGNLb2Y7LhWxub2PPZY+zZtocVIT2Uj+u91m+cfgxrC3keKqQB+CYh59g6Z47snNfiaWLd+DJXi/in776Kt73pz8CIPvtRPsd5yA5m4vYMFqMhijjN/f65aDv+nc+dGLTlFhEfg7cqaqfEpG1qjpLRD4M7K+qp400n9G0CV8CfAS4rV6AjecG++0Q8JfT4ZK7lJcsFM54vhfIv52R49ZH+tnrxvvhoJ2ZvddM5t7wLG5TmY1tOZa378Qeyx7k0sMPZPHT69l71bNcu3hXXnvXCk75+13ssG4zD0+dzrqpU2g/fCde98J2XrffVKbtnOdLf3Ns7IczikVy14TIwhnk/vkQE2DDeA6hzbd6B+O9wNUi8k5gqog8CHQDrx5NJiOyhIvFYgD8NjnBIcC5URRdVrfP+ZglbAzD4vcv44x7/sBLH/0Hv99rP54K5vP1a49udrEMwxg7GqKWlzz//wZ915/1j9c00xIO8Bp0MLAr3jX9V1V1wx5Yx0gt4XOBBcnJisDVxWLxr1EU3VssFvNAmOQVFIvFdkCjKOofTUGMyc+r776d/7zxSgAOffJhLj7gxCaXyDCMiUAaW9IqiEiIn7xhRjJ85TYPYTmS6OijgbOBQ6Io2gz8sVgsXgT8rFgsFoGvAG/NHNILPAEs2tZCGZOT2T0b69Y3NakkhmFMJFzQWs1PqhqLyEPAbPxsStvMVkU4af+dVrftk8Ank9Uzk59hDMu9M/bmsek7sPuG1Tw4c0eezi9odpEMw5gAtGib8A+Aa0TkS8ByMs2j4z1Yh2GMiFc8uIJP7Xc6Pe3KQUufZcnKNc0ukmEYE4AWFeF/Sf49v2674sfRGBEmwsa4MV1LfPim2yvrD+68QxNLYxjGRMG1WJswgKruNhb5tF7NjEnLypnT6CNHDwV6wxzrO9ubXSTDMCYAKjLobzJglrAxbix58iE2MQUAdbDzxuVNLpFhGBMBF7SevSgiyxiim6yq7jLY9sEwETbGjdzm6ljPokBfW/MKYxjGhKEVRRg4vW59Pn5Chx+PJhMTYWPcmFXqZi2zAEFw7Lz52WYXyTCMCUCLtgn/sX6biNyIH9jqSyPNx0TYGDemsyEZ3TxPB/0UdHOzi2QYxgRgArX/9gOjCtgyETbGjWUdM1nY0007BXL0cH9uB3ZudqEMw2h5WtEdLSIX1G3qBF4F/GY0+ZgIG+PGizd/mGunX8LO/at5srCIV218d7OLZBjGBKAVRRhYWLe+GbgY+P5oMjERNsaVgct35nF25oQTTmh2UQzDmCC0wNzBg/ERVV1Zv1FEdgS22D4ULfl5YRiGYRgpLdpP+KEhtt83mkzMEjYMwzBamrg13dFbfAWIyDSgIVMZGoZhGEZTcNI6IpwZpKNDRJ6sS54N/Gg0+ZkIG4ZhGC1NiwVmnY63gn8NvCWzXYFnVPXB0WRmImyMK6WeEo9f1gsWl2UYxgjRpjf/VkkH6RCROaras735mQgb48bH9/4JhXAa8zYp5x7we2Yc2M/Zl7662cUyDKPFacU2YVXtEZH9gSOAOWTaiFX1vJHmYyJsjBvtYReHPbaMtoGY3Z5dz/U2VIdhGCOgFUVYRN4F/DfwO+A4/CAdrwCuGk0+JsLGuLHn6nXcvGQn5m7qZtWU6ey1el2zi2QYxgTANb870mB8GHilqt4kIutU9UQROQ5402gyab3PC2PSsnJ2O/suX8Pa9mkc/vBS1s4obP0gwzCe8ziRQX9NZq6q3pQsOxEJVPU3jDLixSxhY9yIJcdJH3wTA/kcc9dv4jM/+G2zi2QYxgSgxaKjU5aLyCJVXYofuOO1IrIGGBhNJibCxrhxw767M5D3j9yqGVO4d5d5TS6RYRgTgbg1h628CFgCLAUuAH4OFIB/H00mJsLGuNFZ6q0sh7GjM+5rYmkMw5goxC00WEeKqn4ns/wbEZkJFFR102jyMRE2xg3J5Tn0yTWs7cizYEMvGzvam10kwzAmAK4lDWEQkdn46Qvnq+pFIjJHRGao6vKR5mEibIwbPV1TWbKmu7K+euaMJpbGMIyJQot2UToSuAKIgMPw7uk9gbMZRXBW69XMmLRsaG+rjGzemwtY29HZ1PIYhjExiEUG/TWZLwJvVNVXAuVk21+AF40mE7OEjXHjBU+t58+7z6McBkzrG+CwB1Y1u0iGYUwAyi1oCQOLVPX6ZFmTfwcYpa5utwgXi8Vrgf2ALmAD8DPgI1EU9W9v3sbkYtb6bl6yFESVchAwfcOo4hcMw3iO0qJtwveJyLGqem1m28uAv48mk7H4vPgPYFEURdOAInAQ8LExyNeYbITQn8/T19aGC0OIRzXtpmEYz1FiCQb9NZkPAT8Qke/ipzX8OvAd4JzRZLJVS7hYLM4H7gI+FEXR5cm2S4HdgZdFUXRX3SEO2Hs0hTAmDjf8ZCV3Xr+WadOE9X9bg4tdxRETS8C8g2ax7u61BJsH6OloYzYDHH5TxGxdQ//LT4GkHccFAeWOdu7Pnc/swpOsCXZmWbgHv9p/dx7Yu5Pr99gXF4QALJ4F17w+ZOcp8I5rHbc9rZy4h/C5l4ZNugqGYYwn5RbsJ6yqt4nIfvipDS8DlgEvGk1kNIxAhKMoerpYLL4ZuKJYLN4BHAwcD+wfRVEMUCwWvwq8FegE1gE2Nc4k5In7NnHd5U8D0PN4HwUH4qpTh4g6Vt++hs4+3xIxbVMPPfmQPePHubtzMW29fXRP7fI7qyKlATbE81nYu5R9+AcB7Zx+Uy8nvfikigADPLAW3nOd46iFwg/u94r/+Ug5YoHjNXs0/WvYMIwGU2p+EFYFEdlRVVcCqOpT+KjobWZEb7Aoiq7DzxZxFfC/wGlRFK3MpP8rMAV4AXAJMKovgW2lu7vblsdxua8nrmyrhCHUIVqbEJbLBCiBwpTeXmatXU/X5h7mrXqWDufzi5NvwZAygSrt/eUt8l3bE7NxoDbvDf3Nvya2bMu2XF1uFC02dvRD2RUR+cX2ZCaqQ7xN6ygWizsCTwC3RVF05DD7nQJ8IIqiQ7anYCNkZIU3xoQ4Vn544ePc/5cNdLYpuTWbEVXE+dsQBwHBjDbyq7rJlWP6CgVcOeaNf70J2gb49ZLD6Z7SRSmfo6O3n85Nmzjq3r+zmDtYG+zA424/rn/B8/jqq/fjyVlzK+fNB3DtyQGLZwlH/TTmwbVw5AL47ckh7bnW+UI2DIOG/EG+8p0rBn3X//abO4/7C0BEulV1amZ9rarO2tb8RhQdXSwWA+B7wDXAIcVi8e1RFF02TJ57bmuBjNYlDIW3nLs7vZvKtHWElPpiNjzdS5AP2LCql2nzOtlhl07isuOxv3cz/3ldtOehf+MhbPjdUvq+tpL1M6cDsLmrk903beCOD7yUp198KtNciafiTt53/GzeqSHltoDH1jvmT4H5U0IKof9bu+9tIev7YFaHia9hPFcotVab8JgafyPtonQusADfHlwEri4Wi3/Fd1BeDFwH9AAvBM7DT25sTFI6pvjHpq0rx9w9/AfhnF27KulhLmDPA6ZX1nMdObresg9931lTzUQE1yacfvFhW+SfDmY5t2vL1pJAhFkdY1AJwzAmDAMt1CYM5ETkKKpWf/06qnrDiDPb2g7FYvFo/DBch0RRtBn4Y7FYvAjfH/hk/MTG3wFC4BngF8DHR1oA47nDAIrin9RSEFCy1gTDMEZAC8wdnGUVPho65dm6dcX3HhoRI4mOvgGYVrftk8Ank9VDR3oy47lNoa/MPxbMoC0u05PLM/spm8DBMIyt099CIqyqi8YyPxu20hg3Xnb/nUzpLbF8ziz2f/wJCm5ds4tkGMYEoL+12oTHFBNhY9zo6FNeeed9FZf02hnWx9cwjK3T20KW8FhjImyMHzFMoZuQmBJ5eja3NbtEhmFMAFppsI6xxkTYGDfiDiE34AfoKFDCtReaXCLDMCYCG0yEDWP76W7vhA0bK+vLZ83joCaWxzCMiYGJsGGMAYW4r6ZTkmKzKBmGMQImcWCWRcYY40ZXvB4FlAAhZofNq5tdJMMwjKZiImyMG0tnzGU2DzOTpUwJnuDhOfObXSTDMCYCgQz+mwSYO9oYNx5u25H7jjiNXdc9wyOzF9D9zMatH2QYhjGJ24TNEjbGjQ/efzJruoXunmk80xPz2ftPaXaRDMOYCMgQv0mAWcLGuPKSCxTo4J0nHN/sohiGMVGYxJawibBhGIbR2pgIG4ZhGEaTMBE2DMMwjCYxeTXYRNgwDMNoccwSNgzDMIwmYSJsGIZhGE1iEnemNRE2DMMwWhyzhA3DMAyjOUxeDTYRNgzDMFqcSTJO9GCYCBuGYRitzeTVYBNhwzAMo9WZvCpsImwYhmG0NpNXg02EDcMwjBbHRNgwDMMwmsQkDsyaxF2gDcMwDKO1MUvYMAzDaG0m8bCVZgkbhmEYrY0M8RtsV5GlIrLvuJVtOzFL2DAMw2htJq8hbJawYRiG0eKMwhIe9HCRg0XkzyJyT/Lvwcn2C0XknGT5FBFxIjI3Wf+1iLxijGuyBSbChmEYRmsjMvhvRIdKAbgCOFdV9wP+C7gi2X49cEyy6zHAbcDRIpIHXgzcPNZVqWdCu6NF5Fpgztb2y+Vyc8rl8ppxKFJDsXq0HpOlLlaP1mOC1uW3qvrKsc5Uz8ltj0N6b2BAVa8HUNXrRGQg2X4L8JNEkA8DzgZOBlYA96pqz/aVfOtMaBEe6c0uFotRFEXFRpen0Vg9Wo/JUherR+sxmerSqqhqr4jcA5wKPA38AfgCsBxvJTccc0cbhmEYk5kHgYKIHAUgIkcD+WQ7eLH9OHC9qvbjBfhMxkmEJ7QlbBiGYRiDcJ2IlDPrJwJfFpEuYDNwsqoOJGnXA5+gKrrXA4cCfx2Pgj5XRPgbzS7AGGH1aD0mS12sHq3HZKrLuKGqi4ZIOmSI/f9MJtZaVS8CLhr7kg2OqOp4ncswDMMwjAzWJmwYhmEYTWLCuaOLxeJX8P25+oFNwPuiKIqStHnA94FFQC/wriiK/tKotO2sx+nAh4F9gPdHUfS/mbRO4NvAQUAZODuKomsalTaeFIvFvYDvArOBZ4Ezoih6eLzLkZTl88BJ+Hv7giiK7t1aGRuRNgb1mI1/Rp8HDAAPA++Oomh1sVj8J+DrQAewFDg9iqJVyXFjnjZG9bkS2A1w+L/x90ZRdNdEuy+Z+nwMOJ/kGZuI98RoHBPREv4N/mF+IXAh8JNM2oXAn6Io2gt4D3B5sViUBqZtD3cBbwJ+OEja2cDGKIr2AE4AvlUsFqc0MG08uQT4SnI9v4J/cTSLK4GXAE/UbR+ujI1I214UuCiKor2jKHoB8CjwmWKxGACXA+9Jzvsn4DMAjUgbQ94aRdELoyg6APg8cFmyfaLdF4rF4oHAP5E8YxP4nhgNYsKJcBRF10RRVEpW/wwsSB5CgFPwf1REUXQz3louNjBte+pxbxRF9+G/9ut5I8nLIPkqj4DjGpg2LhSLxbnAgcCPkk0/Ag4sFos7jGc5UqIoujmKomXZbcOVsRFpY1SPtVEU3ZjZdBuwK97r0Zc8t+Cf41OS5UakjQlRFG3IrE4H3ES8L8VisQ0v7P+S2Twh74nROCacCNfxb8CvoihyiUtOoijKjjDzJLCwEWkNqU2VXai1zrLnbETaeLEQWBFFUQyQ/PtUE8oxHMOVsRFpY0ryQfovwP9Rd8+T5zgoFouzGpQ2lvX4VrFYfBL4FPBWJuZ9uQC4PIqipZltE/aeGI2h5dqEi8Xi3/AP1WDMS/9gisXim4DT8O7ElmOk9TCMMeZ/8O2o/4vvGzkhiaLoHQDFYvEtwOfw4/1OGIrF4iF4j9n/a3ZZjNam5SzhKIoOjKJozhC/VIBPxH8hHxtF0TPJcc8madmxpHcBljUibSzqMQxP4t2Jg52zEWnjxTJg52KxGAIk/+7UhHIMx3BlbETamJEEmu0JvDGKIkfdPU+eYxdF0doGpY05URR9HzgKP4rRRLovRwJLgMeLxeJSYAFwLbAHE/yeGGNLy4nw1igWi68GLsYL8NK65J8BZyX7HY6PFLyjgWmN4mfAu5Nz7gkcDPy2gWnjQhKteRd+nFaSf++Momj1eJZjOIYrYyPSxqrcxWLx0/i2wddFUdSfbL4D6EieW/DP8c8amDYW9ZhSLBYXZtZPANYCE+q+RFH0mSiKdoqiaFEURYvwHxHH4q36CXVPjMYy4QbrKBaLq/HdMLJ/KMdEUfRssVjcER8luCu+O9FZURTdmhw35mnbWY9T8X+QM5P6bAZeEUXRfcVisQv4DnAAEAMfjqLoquS4MU8bT4rF4mJ8l5CZwDp8l5AHhz+qYWX5MvB6YEdgDfBsFEXPH66MjUgbg3o8H7gXeAj/jAI8HkXRicVi8VB8QF471a4rzyTHjXnaGNRlHnAV0IV/Ttfiu9P9baLdl7p6LQVeHfkuShPqnhiNZcKJsGEYhmFMFiacO9owDMMwJgsmwoZhGIbRJEyEDcMwDKNJmAgbhmEYRpMwETYMwzCMJmEibIw7IrJIRFREFjT4PGeJyPcz678RkQ838pzG4IjIIyJy5gj3HZfnYzwQkbak7oubXRajNTERbmFEZHcR+ZmIrBSRTSKyTER+KSKFJP1MEXlkkOOG2v7m5OX2sUHSbhSR/uQ8G0TkThE5qTE1azwi0oUfu/f8dJuqHqeqFzWtUFshuTeHb31PY3tpxLUWkZeKSDm7TVX78eMBfG4sz2VMHkyEW5tfA08DewNTgUPwQ99t61SK78YPfvDPIhIOkv4JVZ2Cn1v1R8BPRGSvbTxXszkd+LuqPtrsghjPeX4EHC0iezS7IEbrYSLcoojIbLz4XqKqG9SzXFUvSb6uR5vfEuAI/Iw08xlmGkNVLQNfBULgBYPk9R4Ruatu224iEovIomT924nl3i0i94nIacOU7XwRua5u240icm5mfV8RuVZEVovIkyJyoYjkh6ny64DfD5VnxuX51qR8m0Xk1yIyU0Q+IyKrEg/EezLHn5m4Fv9DRJ5O9vlCthxbq7eI7Cciv03qsTatt4jcnezyu8Qb8a0hrlWniHwpOccaEblSRHbJpN+YlOmKpAyPishrh7pImTp9QESWJ8d8XkRmJ3lsFJEHslajiORE5DwReUxE1onI9SKybyY9LyIXZ67hfwxy3iNE5ObkGjwqIh8SkRF/XIrISSJyd+K1uVtETsykbeEJEpHvpNd0qGstIkuTet2cbI9E5ODB8shsWyoip4vITvi5zsPk2E0i8lYAVd0I3A68ZqT1M547mAi3KKr6LPAP4FsicoaI7DOal9QgvAu4R1WvwVvY7x5qR/Hu7vcAJeDuQXb5IbBYRPbPbDsTuFFVlybrNwP7AzPwbuHviMg+21JwEZkL/BH4BbAz3iPwcuAjwxx2IHDfCLI/CTgcP6HFIuAvwKP4gfzfBnwxK3L44Ut3AXZPynECcE4mfch6i8j8pB5/TM61I8nk66r6wuT4V6jqFFV9xxDl/W/8JPH/lJRlDXC11Ho23gp8AT8X7/8C3xWRzmGuwa5JeXdPrsV78YKSDqv6C+Dbmf3PAc4AXpXU4Sbg9yIyLUn/f8CrgUOB3ZK6ViYYSK7Hr5P8dwCOx09L+pZhylhBRA4FfpCcZzbwn8CPROTFIzl+K9f6LOB9wCzg58CvM/UaLs+n8B+2cZLnFFX9bmaXv+OfScOowUS4tXkpcCPwfvxA88+IyH/VifFuIrI++8NbsRVEpB3/0kxfpJcCx8mWgS8fTY5fDrwWOElVt2hbVtV1+PF935bkL/gX/2WZfS5V1WdVNVbVHwP3JPXZFs4A7lbVr6vqgKquAC5Mtg/FTGDjCPL+hKquTT56rgFKqvpNVS2r6m/w4wkfkNnfAeeoam/i6r4I/wECbLXebwEeUdULVXVzUpcaD8BwiEiAv87nquoKVd2MfzaWAC/K7PoTVb1VVR3wDbwY7zlM1r3Ax5Py3I3/8LpdVW9T1Rg/dvoeIjI92f9twGdV9YHEK3MBfpzn45P0M5L0R1S1FzgbyI6P+6/Az1T1quQ6PYD/WBjufmY5E7hCVX+T3KdfAb8E3j7C44fjUlW9Q1UHgM/ir82rxyDfjXhhN4waTIRbGFVdo6r/qaoH4i2VDwPnkYhfwuOqOiP7w7/ksrwBmIJ/mYK3QlYD9dbWp5I85qrqoap69TDF+zZwWuKKPTop3y/Ai4WIXCAiDybuwvXAC/FWz7awG3BY3YfGZXgrbCjWAVu1YPBt7ik9devptqmZ9VWq2pNZX4qfpm4k9V6En2RhW9kBaAMeTzeo6ib8DEPZieifzqRvThazdahnVSLYKfXXIa1vmsfCujI4/HVIy7AgWc+WYVUmv92AU+vu58fwzSQjoeb8CY9Sew22laXpgvqB9Z8kub/byTR8PIZh1GAiPEFQ1R5V/Q7estp/K7vX8y58++69IrISb+nOZOgArZHwe6Af7449E/hxYvWAnxLuHXhX78zkw+Buhg4o68bPmpNlp8zyE8B1dR8b05MgsqG4E9gm9/dWmFvn2l2Ev56w9XovZXiLdGuzqazGX/NF6QYRmQLMZXznZF5WV4YgWU/LsKIuvYvaD7AngMvq7uc0VX3+tpw/YffM+bf2PMHQ1zpbbsE3PaT3tyZfEcnhr31K9kOmnn3xz6Rh1GAi3KKIDxC6UHxAUj4JhjkJ/8d80yjy2QffznciXrzT34vwluSrtqV8iZvye8C/46cDvCyTPA0o40UjEJG34y3CobgDOFBEDkrq+W94aynle0BRRN4uIu2Jxbm7iLxymDyvBF42+pptlQD4rIh0iMjueFdr2va3tXpfDuwtPrCrU0QKIpIt40qGEenE4vwe8AkR2Sn5GPgC8ADw1zGq30j4DvBhEdkriR/4KJADfpWkfx84R0SeJyIdeJd99l3zVeBNInJC5tneR0SOHOH5vwucJCLHikgoIsfhn8G0ueUu/MfSq5Nn5UTgJXV5DHWt3y4iByYennOAzky97gCOER+E2AZ8CsgGB67EB2Zln11EZCr+7+3/Rlg/4zmEiXDrMoD/yv4F3o21GjgX+HdVHc2E3e8G/qaqV6vqyszvHvzE30MGaI2AbwNH4l3iWRH4Lj7A6RG8VbQPw3w4qOqNwMXAb/Fu0HnALZn0lcBR+IjnpXhX8y/x1s9QfB94YSKUY8kTeMvocXwdf4sXGdhKvZPgnZfig8qW41/a2aCujwIXiI84/voQ5/8AEOGjbZ/Eu3Bfk3wUjRefw3e7+R3wDL454hVJFDD49vprgdvw1+lJ/HUDQFXvxbezvh9/v1fhhX1EzRWqegu+bfzz+GfhIuB0Vb0tSX8UH1z1DfzfziuBK+qyGepafwP4cpLvG4HjVXVDkvYDvJD+De/+fhJ/n9NyPQR8Dfhr4mZPA81OBf6gqg+PpH7GcwubT9iYtIjIWcBhqjqiqNsR5HcmPijK+ntOQkRkKf7+Xr61fUeRZxtwL/5D6f6xyteYPOSaXQDDaBSqeglwSbPLYTx3SaLHh4sDMJ7jmDvaMAzDMJqEuaMNwzAMo0mYJWwYhmEYTcJE2DAMwzCahImwYRiGYTQJE2HDMAzDaBImwoZhGIbRJEyEDcMwDKNJ/H+MtHzUoUy9gAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values, preds[FEATURES])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.dependence_plot('x2', shap_values, preds[FEATURES])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='iZ6EY03VGVTRU1KZ3XJAV'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"output value\"], \"baseValue\": 19032.693359375, \"outValue\": 18143.857421875, \"link\": \"identity\", \"featureNames\": [\"x1\", \"x2\", \"x3\"], \"features\": {\"0\": {\"effect\": 22.808385848999023, \"value\": -0.5909508890519074}, \"1\": {\"effect\": -902.2159423828125, \"value\": 0.019562954783350232}, \"2\": {\"effect\": -9.428001403808594, \"value\": -0.5524935702430895}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('iZ6EY03VGVTRU1KZ3XJAV')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap.force_plot(shap_expected_value, shap_values[0, :], preds[FEATURES].iloc[0, :])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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