notebooks/Chapter_07.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99.98</td>\n",
       "      <td>93417.0</td>\n",
       "      <td>2020-01-02 14:30:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99.78</td>\n",
       "      <td>16685.0</td>\n",
       "      <td>2020-01-02 14:31:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.14</td>\n",
       "      <td>21998.0</td>\n",
       "      <td>2020-01-02 14:32:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.35</td>\n",
       "      <td>18348.0</td>\n",
       "      <td>2020-01-02 14:33:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.55</td>\n",
       "      <td>22181.0</td>\n",
       "      <td>2020-01-02 14:34:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    close   volume                       date\n",
       "0   99.98  93417.0  2020-01-02 14:30:00+00:00\n",
       "1   99.78  16685.0  2020-01-02 14:31:00+00:00\n",
       "2  100.14  21998.0  2020-01-02 14:32:00+00:00\n",
       "3  100.35  18348.0  2020-01-02 14:33:00+00:00\n",
       "4  100.55  22181.0  2020-01-02 14:34:00+00:00"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from random import seed\n",
    "\n",
    "seed(42)\n",
    "\n",
    "price_df = pd.read_csv(f\"../data/twlo_prices.csv\")\n",
    "\n",
    "price_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_df.index = pd.to_datetime(price_df['date'])  #  A\n",
    "price_df.index = price_df.index.tz_convert('US/Pacific')  # A\n",
    "\n",
    "price_df.sort_index(inplace=True)  # B\n",
    "\n",
    "del price_df['date']  # C\n",
    "\n",
    "# A Set our index to be the date column\n",
    "# B Sort our dataframe by the index (time)\n",
    "# C Delete the date column because it is now our index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02 06:30:00-08:00</th>\n",
       "      <td>99.98</td>\n",
       "      <td>93417.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 06:31:00-08:00</th>\n",
       "      <td>99.78</td>\n",
       "      <td>16685.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 06:32:00-08:00</th>\n",
       "      <td>100.14</td>\n",
       "      <td>21998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 06:33:00-08:00</th>\n",
       "      <td>100.35</td>\n",
       "      <td>18348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 06:34:00-08:00</th>\n",
       "      <td>100.55</td>\n",
       "      <td>22181.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            close   volume\n",
       "date                                      \n",
       "2020-01-02 06:30:00-08:00   99.98  93417.0\n",
       "2020-01-02 06:31:00-08:00   99.78  16685.0\n",
       "2020-01-02 06:32:00-08:00  100.14  21998.0\n",
       "2020-01-02 06:33:00-08:00  100.35  18348.0\n",
       "2020-01-02 06:34:00-08:00  100.55  22181.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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evv8Y3z+rc9jHP/2E4/wRKdHSt0Mztu47mlSXSCPXoB8qLKFyh1RslKmSIcLkWwb5KzElg7B97iKSKSJf4hTCfldV57qbbhWRxSLyvIj4khW3BzZ5dt/sthmGkQC+M7Bjldt3HDgWsN6tTWQRHJGmBQjFok17OVJUyo0vfOFv++aBiyr0+9E5XWjZqB7dq9E47adnRazBZ9xnVFIEPB4ozgzcQSccx7CeibqFVE/Yxl1VS1W1H9ABGCgiJwFPAV2AfsA24GG3e6hvQoXbtYjcIiLzRWR+QUFBiF0MwwhGVSu4N1o2rNrHuyuoCEajCF0SXVrFHs631g1B/NCtxfq9wfkBqYZ9Oc0v7pPH6Z1bhEzR6005fFL7phFr8LliQpUUjJWvtx3gT2+vwHkwSU6EjJeIo2VUdR/wATBCVXe4Rr8MeAbH9QLOSN07lOgAVEjHpqpPq+oAVR3QqlWrSKUYRp3k319s4vxHPgxoCxVu99uLyyfxlJQGWspIjXvHBBSXliADmOUmfc/OzAAJXbXp/BNj8/tH+ntHwlV/+4wnZq/hSFFJVVmIa4xwo2VaiUgzd7kBcD6wQkS8b2MuB3wZeaYCY0Sknoh0BroB8+Km2jCSyNZ9R9m+/1j1HROEb+RbHd8/qzMvfd8Zb5WUlQVM6qmfXf3M1UQTbAB9Pv3jGuU46QpCjNx9RS2iNZ7hpjcIZsX2A9X+zw+6M2qPFJWmwLg9/GiZPOAlEcnEuSFMUdVpIvJPEemH829YD/wQQFWXicgUYDlQAoy1SBkjXThz0vuAk842Gby1tOIMzcrwGZk9h4vpfvdb/vbWcYxhj5ZgA/ibkT25aUhnWjaqR0YlI3cfuVHenKIZue89XMSIxz4mK0MYcVJb9h0p5uWbTq9yn1QYuYcbLbMYqBBQqqrXV7HPRGBi9NIMI7VZW3CIkjLlo28KuGnICTV+/rFDu3DjmZ39CbFC4QshvPkf8wPavQUjquP2MCNxIiXYlZSVmeGPTRdC11v1NQUnIovmnEeKSsjNqd4E9r//XcCpEjXNfRG7ftdh8lsG1j296azOPDvHyQRZWn2Rq4RjM1QNI0p2HCjk4r/M4YHpXyfl/Jf0bUerxvXo17FZpX3iMfP95CheXIZDcRUWMEOEjXuO8OzHawHo1roRI09uS5b7C51yfPNK962KMs8dI9obBMDvZ1T8n/sMO8DBCCpiJQoz7oYRJYr6y7clsjbn+l2HWb0zvKyKwUy4tDeX928fVcGIxvWdUW1VN49YCH7J68W3xXfjLFNFEHJzspj207N48tpTojqn19OzyVPBqfL+5Tt4J1od9WTILC1T8sdND9hvaxVPUzWFGXfDiABv2tvH3y+f+p/Ii/nchz7g/Ec+qtD+lVvXtCpyc7J49Dv96NA8+gLMiUp8VVUx7P8uCszXfriwlIb1HFfSSe2bRj271OvHLwrDd+K9Gbz18yH+5Y9X7SpvX1oxZv4n53aNSl88MeNuGBEw2H2ZCvDpmvI8LbE84kfLBb0qT4wVTG4Yed2DGZjfAiCgXF48CY69r4r9R4tpEqKiVKSUeqx1OMf7avM+/3Jl/+K1IVIIV/UepKYw424YEeCdROMlOO48UfieHE7Ma0LzavKxePHFkEfCX6/pz8xfDIlb2GTwjNOlW0JXYwqmqKSMo8WlNK0iyVm4+FxNEN7I/fInP/UvZ4pw3aBOAdtLSsv4fG35Tf6ME5zcPp0SMC8gUsy4G0YEnNsjuZPtet4zE4i8uo+3aMcJQVEelZGbk0XPtk0iOk9VPH39gID1gZ1bhLWfz3hWlTY4XFo3ru+P/vHG/YeiNMhtlJkh3HvpSQFtT36wxv8El5uTyfBeTrqB449LvnG3GqqGEQHBsdffH9yZl+du4LrTj4/7uRZv3hcQlbFuV/njf6ROoKYNsll674XkZmcmr3hEkF+jdePKY+0Hdz2OT1Y7RvO7zzvzH5dvPVBp/0jo3c65YVVl3JdvPVAh4iUjQ8jMEK45vZO/GtQjnjQGTRtk873B+Yzu147jUiDlr43cDSMCgp/kfzOyJ/WzMqqccBMNf521iksf/4TP1+7xt33/xfKEW9GE2jWql5U0ww5OdJGXHlU8FZzcvlmFtslfbKrYMQp8+Wx+/driSjNUjvzLx3zn6c/96+d0b0UjNyY+O0NCvgweP/JERCQlDDvYyN0wImLGksDIiKzMDDIyJO7GPVRiK+/I/ZsdFfOipzrBf6K+HSqPn2/XrH6Ftsm3DIqLjpxMx7iv2H6QvUeKK+SSLwsy3Pde2psbzsz3r4sI+44UV3iSuLSS9zHJwoy7YURAcIgeOC/agv2zieZACkySiRTfX+j443KZdfs5ZGVW7jgI9fK0TZOKBj9WQj3IjH11YcD6zoOBOWUKDjpRPlc+9SmpjLllDCNGdh8u4pW5G9l3pKjSPnsPF4VdpCJ45BiKRJaJSxS+31+gSsMO0LtdxVF9IhxKodwrpwbNfg1+SpruPr15JzKlImbcDSNMguOyg+O/t+0/xty1u3l9YWB5tc/W7Kb//e8GFKmoinBcPDX9pBAPfIoljDkBoaKB4jWXwJtDPtSN1HtjuePCHvzpW32CdMRFRsIx424YYTLggfcAOK9na9b+fiQrg6oI5eZk8p2nP+f2KV8FtL8y16lbGm6q3lB2+9Wbq85CWBvo0LwB3Vo34r7RvaPaP17zxLxJ0wpLypg4fTn7j5S7uXw311En5zF2aFea5Qb65Fs3DnQP/eHKk1lw9/nxERdHzLgbRhj4CksD/O36U0NGnVz5VOgSwd6p6pVRWFJKiRuKM/mLjQB869QO/u3JmAEbb+plZfLu7ecwpFt4cwU+HTeMSVec7F+P1zvretnlZu+VuRt55uN1XPL4HAD+M38T1z7rVBD9/ln5Iff/6zWBeXpOPb5FykTIeAm3WEd9EZknIl+JyDIRuddtbyEi74rIKvdnc88+40VktYisFJELE/ULGEai2b7/GHe/sdS/nh3kL/YVp65sOv3+o9W//Oxx90wuffwT7vvfcn775jIARvXJI6+pM0o8UlTConuG+/s//O2+kf0StZB2zRowZmD5jNDSOFl3rzvtxU+dTI4b3SRiz3kyO2ZWMqv3tPwWrJ80ihNahTcZLFmEO3IvBIapal+ceqkjRGQQMA6YpardgFnuOiLSCxgD9AZGAE+6hT4Mo9ZRUlYe3B7K3xqq8MWMJdsirta0fNsBnv+k3LgM7dGaQe509r2Hi2neMIdVEy/ixe+dxpWeUX268+rNp3Pf6N50DnNmbXV43TLefO4zlmxjxfby7JuZ1TwtTbqiD6ce3zwlUg2EIizjrg6+V8bZ7keB0cBLbvtLwGXu8mhgsqoWquo6YDXl9VUNo1bhfQEYyqXw1AdrKrT95JWF/OjlBWEdf8nmijlWbh3qZBX0RW60aOT4fbMzMzi3R2x1RGsbZ3ZpyXfPyI/b8bz5ZfYcLo9wWrhhb0C/zGrenA7s3ILXfnxmwAvaVCLsOHd35L0A6Ao8oapzRaSNqm4DUNVtIuL71rUHPvfsvtltM4xahzeion+nZhW2r98dOi94oTu9vWfbxgEjwmB+N3VphbaWrjG/9vROnNS+acJyqtdFKkuE5i22AYktpl0ThH3LUdVSVe0HdAAGishJVXQPdcur4DATkVtEZL6IzC8oCC+SwDBqmnMf+sC//J3TOoa9ny+HelWGHWDhxn0V2nxx4CJihj0BZGcKPxtWdc71TimQ/CsWIn6eUNV9wAc4vvQdIpIH4P7c6XbbDHivgg7A1hDHelpVB6jqgFatkpttzzCCGfHYR1z/3Fx/THmjelnkNa1Y9GL5faHjBbyP/D7CnciUU80kHyM2Vk0cye0X9Ei2jIQSbrRMKxFp5i43AM4HVgBTgRvcbjcAb7rLU4ExIlJPRDoD3YB5cdRtGAlnxfaDAWGMSyZcELKf96VcX88oOyuEz7awikyEo/rk+ZebxSG9rVG3CXd4kAfMFpHFwBfAu6o6DZgEDBeRVcBwdx1VXQZMAZYDM4Gxqprac3UNoxrCmVk5pGtL//LcdXsqbF8SVKDiLU8isjsvLB9JnpPkvPFG7SfcaJnFqtpfVfuo6kmqep/bvltVz1PVbu7PPZ59JqpqF1XtoapvJeoXMNKT/ve9U2PVjeLJ9WcE5nXff7Q4IG/5Pz/bwCerd5E/bjqrdx7ix6+UJ6nKzcniylOcEEdvuJ5hRIM59oyUZO+RYlbvPJQStSjDoWfbxlxxSnuOC0ofq6ocLS5l0AlO1aGpX231z4C81J0V6SND4OGr+rJ+0qiaEW1UIJ1eXtfuWB8j7Xn8/dU86JmCHi5fbdpH6yb1Qr4ATQQzf3F2yPZ3lu3g4LESerdrSm5OFu+v2OnfFpzZMRy3j5FY3hg7mAdnfE23No2TLSVmbORupDQzl26rvlMIRj/xCUP+MDvOaiLnztcWA8609uBUssGYaa957hp5YoW28SNPDMjrU1uxkbuRckzxlFPbeyT6ohShcnVHSk5mBvdcfGJcZoUOqM64m3WvcX5wVmd2HS7k7x+uTbaUuGMjdyOl+GbHQe5+Yykt45RlL5zCF1VRVFrG9Wfk0zGC/CG+2aVe2jdrEBAmGYqGtXxGZG0kI0MYdXJe9R1rIWbcjZTi4XdWUlRaxpQfltfL3LQn9PT+UCzfeoDCknJf9tpd0dcabZ6bzfWDjq++YxD/++lZFdruubhXpdPeARbdM7xCtkmjZmhS35lTcIabpC1dsG+TkVK8vWwHAA1yyg3hL4OKX1TGpj1HGPmXj7nt31/623Iyow8pPFpcGqAjXPKaNuCK/oGplJZvO1BJb4fmDSuO9o2aIb9lQ/54ZR8eD8rTXtsx424kjZXbD3LRnz9m9c6KuVe86VbX7z4c1vF80/1nLNnubwunZF0oysqUY8VlVY62q+L3V5zMNM8Ivk2ItMBG6nDVaR1TsuBGLJhxN5LG1c98ztfbDnD+Ix9V2OYNCzxcWBLW8f76/qoKbdEad1+agAZRGvf62Zmc1L68Fuc1nqITPr787XDuuLAH790eOozSMGLB3uAYSSO4yPMxTzX5xvWzOC2/OV+s38vhouozV6zeeZD3vt5ZoX3P4SJOiGImv6+yfYPs2MY/C+8ZTnFpWcgY9ma5OYwdWnVmQsOIFhu5G0nhszW7K5Sf63/fu/7l+tmZXNi7bdjHWxyi4AXA47NXR6XPb9yj8Ll7adEwhzZN6ldoP76Wp5M1Uh8z7kZSuPqZzyu0HS0OHKEXVFKT1Mu+I0UUlZTxxfq9Ibev2hFdtMxR92khWp97ZfRs68x8vPb0im4aw4gnZtyNlKOFGzlyYtsmVfZTVfrd9y5jX10YUNv0yWtP8S9HW9/ymN8tE1/j7huxp2rdTSN9MONupBy3D+8OwPm92gCVz9y86aX5ALy7fEfACHvkyXlMvsWJky8urTx/elXMc9P1Bud/iRXfe4bMDLv0jMQSbrGOjiIyW0S+FpFlIvJzt32CiGwRkS/dz0jPPuNFZLWIrBSR0KVqDMPlppe+8C9f504cauj6uysLeJnlJuHq36kZB485/vu8po5/e2C+k4Vx/obQ7prquG/acgDGv74kqv0rw5cSIVQhD8OIJ+EOH0qAX6rqicAgYKyI9HK3Paqq/dzPDAB32xigN045vifdAtuGEZJQkS7eCJP1uw5z4Fj5C9jr3LS5AIs27mPK/M1Ou3tjyIjRePpuDveO7h3TcYL5zgCn+uSJeVW7nAwjVsIKhVTVbcA2d/mgiHwNtK9il9HAZFUtBNaJyGpgIPBZjHqNOoqvSPXSey+kUb0s5qzeFbLf1SHiySPlxU/WMW+945aJ95T0i07Os3ztRo0QseNPRPKB/oBv6HSriCwWkedFxJf2rj2wybPbZqq+GRhGWHgzRvrwVjpqHofaoxP+t9y/bPlejNpKRN9cEWkEvAb8QlUPAE8BXYB+OCP7h31dQ+xewXMqIreIyHwRmV9QUBCJFKOW8NKn67nsiU/C7j/3N+dVuf2+acv53guBtdYfuOwk/3KoyUI7DhwL69xrCw4x9tWFAW3ZmeYbN2onYRt3EcnGMeyvqOrrAKq6Q1VLVbUMeAbH9QLOSL2jZ/cOwNbgY6rq06o6QFUHtGplBYHTjZLSMn43dRlfbtrHp2t2sXF3xeyOvheLf7yyD4snXBBywk8ws1eWDwSeuvYU3l2+o8r+f/twDeBEqlSWAnjz3iMMe/hDpi8OLA6SbvlGjLpDuNEyAjwHfK2qj3javYmQLweWustTgTEiUk9EOgPdgMDhlpHWqCq/fq080uSaZ+Zy9p9mU3AwcGLSKZ0cT17HFrn+1Kvh8rNhXbno5Dze+7pq4+7zm3f5zQx+9Z/QGSb//F7FvDT3x/llqmHUJOGO3AcD1wPDgsIe/ygiS0RkMTAUuA1AVZcBU4DlwExgrKrGN2DYSGmmfrWV1xZurtC+puAQy7eWp7/NdEfulSX4GtWn8kIK553oxMGf0z30U9/LPzgdCEyn+/qiLRX6/e3DNfxnQaBWEbj+jPxKz20YqU640TJzCO1Hn1HFPhOBiVHqMmo5W/eF9nOv2HYg4IWluq9iKvNsP3HNKTxxDazbdZgLH/2IIndS0qJ7hvuN9m8v6c37K3Yy6co+Aftud33tbyzawmluaGMoJr21ImD903HD/LNkDaO2Ylkhjbjy+drdjHm6Yt4YH194JhW9c9vZ3Oca+uri0ju3bMg3Ey/i8fdXsftwUcBovEXDHBZPqDhPbuV25wnh9YVb+OHZXQK2bd57hLUFhzk7xKi/XbMGVWoxjNqAGXcjroQy7BMu6eUfrRcWOyPvtk3q071NYx76dl+em7O2ypG1l1uHdQtbS3Gp81RwtLiUs/8029++puAQ5z38IYDFnBtpiwXxGnElM8QI/IYz8/3LvqpLd47oAUDbpvW5a1SvkPvFyqFKinzMXlFxNqyPqwZ0iLsOw0gGNnI34oovMdZn44fRPDeHY8WliAjNcrPZd6SYLq0asX73EXKyEj+u6FXJFP8Hpn8dsN6heQM27z0KwLcHdAy1i2HUOmzkbiSEhvWyqJ+dSbNc96XnxU4qomEntgZqJnFWcKWnUPS77x3KypTL+7fnme8O4NROzavdxzBqA2bcjYQQHLPuy4t+13+dqRDLt1Usih1vSsIw7vuOFLN1/zFyMjMY3qtNzAnHDCNVMONuxJ3r3cyMXhZt2hewvrYgugpJkeCz0ye0bFht30xLM2CkGeZzN6JGVSktU7re9VZAe269itmdf3VBD57+aK1/3ZsPJlHccGY+ew4X8ZOhXZn01greWrqNfUeKQ/ZtXN8uBSO9sG+0ETE7Dxxj/9FifvjPBazddbjC9tkrdjL+ohMD2oJfoPp88YmkfnYm40c6Oh684mTuvLAHT324hluHdWXql1u5+w3HRfTxnUM5rpFNWjLSCzPuRkTM+noHP3DL21XGmoKKBj8VaN4wh9+4xv7MLuV52jtaPVMjDTHjbkREdYYdqk/b+/n4qrfXBJ1a5DKkW0t+dl74k6IMozZhxt2IOy0rSZP72Hf60aF5A9o2rT6tb6LJyszgn25iMcNIR8y411E+/KaAw4UlHCosYcDxzTmhVaNq9zkcNOOzfbMGbNl3NOxzXtbfinEZRk1hoZB1lBuen8dPXlnInf+3mMdC5DIPRfDMzhk/G+Jf/umwrnHVZxhGbIRbrKOjiMwWka9FZJmI/NxtbyEi74rIKvdnc88+40VktYisFJGKKfuMlKBjiwYUu2l0Q3GsuJRVO5wJR6d3dpJ7ndujFesnjaJpbjZLJlzAry7ozjWnO4Wpq8q/bhhGzRHuyL0E+KWqnggMAsaKSC9gHDBLVbsBs9x13G1jgN7ACOBJEakY/GwklYv75NEgOxNfnYxjxaVMX7yNG1+Yx3o3xPHXry1m+KMfsePAMX7x7y+BwERgjetnc+uwbuQ1bcBHdwzlz9/pV7O/hGEYIQnLuKvqNlVd6C4fBL4G2gOjgZfcbi8Bl7nLo4HJqlqoquuA1ZTXVzWSzLb9jp982uJtZIj4qyD1vGcmY19dyAcrCzj3oQ8AWODmX//W3z7177+2klDHTsflkpVpnj7DSAUifqEqIvlAf2Au0EZVt4FzAxCR1m639oA3sfdmt81IAb7ZUT71f8X2g6zYfpBFG/fSMCeTw0Xl1RCXbtnvz5a4aU/5i9NDx0Kn0jUMI3WIaJglIo2A14BfqOqBqrqGaKuQxUlEbhGR+SIyv6CgIMQuRqxs2XeUV+ZuCGj752cbKvS7/MlPAww7wMV/nRPymFmWh8UwUp6wjbuIZOMY9ldU9XW3eYeI5Lnb8wBfFYTNgDcxdgdga/AxVfVpVR2gqgNatQpd5NiIjRGPfcRd/13KeQ9/wAcrd3KosIT3vt4BwA/PPiGqY35vcH4cFRqGkQjCjZYR4Dnga1V9xLNpKnCDu3wD8KanfYyI1BORzkA3YF58JBuRcNB1oawpOMyNL3zBSb97279t7LCu1M8O/ArM+uU5VR6vSf0scnNseoRhpDrhjtwHA9cDw0TkS/czEpgEDBeRVcBwdx1VXQZMAZYDM4Gxqloa+tBGsmhSP5szTijPsfLyD06nSxWTmbq1bhSyELVhGKlHWEMwVZ1DaD86QMhEIao6EZgYpS4jDuw6VFhtnwY55RGqZ3VrCUCLhjnsOVzEpX3b0Tw3m5dcH/2dI3omRqhhGHHH4tbSGK2+EBHi3rN9k5AAWjd2csPcPOQE7h1dnnf9/BNbYxhG7cCcp2lMcMW4gfktmLd+DwBv/dxJHdDZrVLkLVbRtIFTIu/AMaewxboHR+K8djEMo7ZgI/c05XdvLuXbf/ssoG3Cpb39yz3aNAYg2510lOOZfHS5m+Arz83eaIbdMGofNnJPU14KimXv3LIhvdo18Wdy9BWC9s1O9RrwMQM7MbJPXoUi14Zh1B7MuKc5p+U355azu3CGW3nowzvOpczji1fXuAe7cMywG0btxox7GrLeU9f0Pz86M2BbcO6X6wYdz4erdgW8UDUMo/Zjxj0N+f2Mr6vv5NK6SX3eHDs4gWoMw0gG9kI1Ddl3tDjZEgzDSDJm3A3DMNIQM+5pSP1sq4tiGHUdM+5pyJxVlj7ZMOo6ZtzTkElX9AHg79efmmQlhmEkCzPuaUiTBk4QVKcWuUlWYhhGsjDjnoYUlzoTk7KtYpJh1FnCLdbxvIjsFJGlnrYJIrIlKL+7b9t4EVktIitFxBKA1zAlZWUAZGXYvdsw6irhXv0vAiNCtD+qqv3czwwAEekFjAF6u/s8KSIWvlGDFJe4I/csM+6GUVcJ6+pX1Y+APWEeczQwWVULVXUdsBoYGKU+IwqK3ZF7dnDCGMMw6gyxDu1uFZHFrtumudvWHtjk6bPZbTNqiBLX5x6cR8YwjLpDLFf/U0AXoB+wDXjYbQ81XAxZE0hEbhGR+SIyv6DAYrPjRXGpO3K3F6qGUWeJ2rir6g5VLVXVMuAZyl0vm4GOnq4dgK2VHONpVR2gqgNatWoVrRQjiPJoGRu5G0ZdJeqrX0TyPKuXA75ImqnAGBGpJyKdgW7AvOglGpHiG7lnmc/dMOosYaX8FZF/AecCLUVkM/A74FwR6YfjclkP/BBAVZeJyBRgOVACjFXV0rgrNyqlpLQMEcg0424YdZawjLuqXh2i+bkq+k8EJkYryoiN4jIlOyPDap8aRh3GnLJpSHFJGVn2MtUw6jRm3D0UlpRSVhYysKdWUVKm9jLVMOo4ZgFcFm/eR4+7Z/LHt1f621SVopKyJKqKjqLSMguDNIw6jhl3l0sf/wSAFz9d52/7/Yyv6X73W3y6ehdfbdpH/rjpvL9iR7Ikhk1JaZnllTGMOk6dL5D9ytwNzF+/179+rNgZqT/1wRqe+dgx9Nc8O9e//fsvzmf9pFE1KzJCSkqV7CwbuRtGXabOGvedB48xcOKskNv2Hi7iDzNXVLrv4s376NOhWYKUxU5RaRnZNnI3jDpNWluAf3+xkfxx09m+/5i/7cNvCuh33zs89/G6Svfrf/+7AevrHhzJJ+OG+dd/9Z+v4i82jkxbvI21uw4nW4ZhGEkkbY377kOF/Pq1JQAMenAWqspPXlnADc/PY9+RYv7+0dqA/t8f3Dlg3fdC8jcjeyIitG/WgBe+dxoA3+w4xLpdh9lzuKgGfhPDMIzISVu3zNXPfB6w3nn8jJD9/nfrWZzcoSkAz39SPppfdu8IcoLyoQ/t0ZqebRuzYvtBhj70Af06NuONsYPjrDw2Nu05kmwJhmGkAGk3ci8rU/LHTeebHYfC6u8z7ADtmtb3Lwcbdh//unmQf/nLTfuiExlnikvLuH3Kl6wtOMSlj89JthzDMFKAtBu5vzJ3g3+5b4emPHvDabRqXA+A7fuP0aZJPYpKy+gz4R3GnNYxYN9Xbx7Etc/O5e3bzq70+M0b5gSs97znLZ797mlkZMCZXVrG8TcJnyVb9vP6wi18vGoXe48UJ0WDYRiphaimxozMAQMG6Pz582M+Tv646QC8OXYwfTs2q7RfSWkZmRkSVf6VKfM3cef/La7Q/vGdQ+nYIjfi40XC3sNF7D5cRNfWjSgrU4Y/+iFrCiq+PF1674U0qpd2927DMIIQkQWqOiC4Pa2u/ic/WO1frsqwQ2xViq4a0DGkcR/yx9kJj4E/50+zOXCshC6tGoY06gAtG9Uzw24YdZy08bkfKSrhjzOd1AGV+cvjyboHR4Zsf+SdlSHbY2XJ5v3kj5vOgWMlAJUadoCRJ7dNiAbDMGoPaWHcjxWX8uCM8klHX/52eMLPWZk75y/vrw7ZHiuXVPKi9MYz8/3Lvptaq0b1EqLBMIzaQ1jG3S2AvVNElnraWojIuyKyyv3Z3LNtvIisFpGVInJhIoT72Lb/KD3vmck/P3depL5729nk5tSMS+Lxa/qT17Q+y+8L/BV3Hyqsdl9VpeBg9f2CueGM4wPWB3ctf4n7zQMXMf/u8/nxuV0iPq5hGOlFuCP3F4ERQW3jgFmq2g2Y5a4jIr2AMUBvd58nRSQzLmpDcMaD7/uXRaBpbnaiTlWBi/u047Px51W4mdz4whfV7tt5/AxOm/geizburbav19UzdlhXwKmytH7SKIb3asP9o3uz8gHn39OyUb2Y3icYhpEehGUFVPUjYE9Q82jgJXf5JeAyT/tkVS1U1XXAasqLZyeMiZefxGs/PpPWjetX3zkBeH3wS7bsD0h5EMyBY+Xhipc/+Sl/+3ANR4sqr0TodfX4biTf9Yzgrz8jn3pZCbt/GoZRC4lliNdGVbcBuD9bu+3tgU2efpvdtgqIyC0iMl9E5hcUFEQl4v1fnsMLN57Gtacfzymdmle/Q4IQEZ6+/lT/+qAHZ1FaSeGPPhPeCVif9NYK7vrvkoC2X075ivxx0wNcPJNvGUSjell8+dvh3D2qVxzVG4aRbiTi+T3Um8aQVk5Vn1bVAao6oFWrVlGd7IRWjRjas3X1HWuAC3q35ZGr+vrX3/xyS9j7vr4osO9rCzcDcOoD7wEw4PjmDDrhOACa5eZY8WvDMKokFuO+Q0TyANyfO932zYB36mcHYGsM56lVXNC7PAzxH59tqKInPH/jgLBz0/zp232r72QYhuESi3GfCtzgLt8AvOlpHyMi9USkM9ANmBfDeWoV3slDwblnysqUktLysn3DerahX4jJVvf+b5l/ecbPhnDzkM7kH5fYma+GYaQX4YZC/gv4DOghIptF5AfAJGC4iKwChrvrqOoyYAqwHJgJjFXVyt8WpiHe3O9ernjqU7re9VaF9usGdQKciVgAL3yyHoCsDKFXuybcNapXVGkSDMOou4QVEK6qV1ey6bxK+k8EJkYrqrbTvlkD/3JZmZLh+scryyIp7muKXr99myeuOcXfPvXWsxIn0jCMtMYCohPM5r1HQ7aP6pPnX77BM8v01Xnlfvpe7ZokTJdhGOmNGfcE8btLnFDFs/80G3Dqrnr5bM1u/3LX1o38y5+sdtq9aQUMwzAixYx7gvAmL3v58w1c+vgnAdt/cX63gHWvK+fuUScy4dLeiRVoGEZaY8Y9QXiTd939hj8lDw2yM1k/aRTfPSM/oL/vJezVAztx05ATakSjYRjpiyX9ThDtmzcI2f7nMf0q3SfRueANw6g72Mg9QfTKC/0ydOX2gzWsxDCMuogZ9wQhInw2fhgDO7cIaK+fbQm+DMNIPGbcE0he0wZMuuLkgLbvDc5PjhjDMOoUZtwTTOeWDRGBm4d0Zv2kUZZr3TCMGsFeqCYYEWHt70da+gDDMGoUG0bWAGbYDcOoacy4G4ZhpCFm3A3DMNIQM+6GYRhpSMwvVEVkPXAQKAVKVHWAiLQA/g3kA+uBq1R1b6znMgzDMMIjXiP3oaraT1UHuOvjgFmq2g2Y5a4bhmEYNUSi3DKjgZfc5ZeAyxJ0HsMwDCME8YhzV+AdEVHg76r6NNBGVbcBqOo2EWkdakcRuQW4xV09JCIrozh/S2BXFPvFk1TQAKYjmFTQkQoaIPk6kn1+H6mgI94ajg/VKKoa01FFpJ2qbnUN+LvAT4GpqtrM02evqjaP6USVn3++xx2UFFJBg+lITR2poCEVdCT7/Kmko6Y0xOyWUdWt7s+dwH+BgcAOEckDcH/ujPU8hmEYRvjEZNxFpKGINPYtAxcAS4GpwA1utxuAN2M5j2EYhhEZsfrc2wD/dafXZwGvqupMEfkCmCIiPwA2At+O8TxV8XQCjx0uqaABTEcwqaAjFTRA8nUk+/w+UkFHjWiI2eduGIZhpB42Q9UwDCMNMeNuGIaRhphxN2otYrmUDaNSaoVxT4WL2M2X41tOih4ROVdEWiXj3EE6fikiF7jLyfzfNPYtJFNHinw/U0GDXSPlOpJ+jaS0cReR0SLyEtA3iRpGiMhHwGMi8jCA1vBbaI+Ga4HCmjx3kI4LRORt4NfAd6Hm/xaujuEiMgd4SETuTKKOVPh+poIGu0bKdaTENYJ74pT6UB7BMxRYDCwAfgw0r0kNQCZOaoTPcXLldAI+AC6qQQ0ZwNXAAeDbyfp/ADnAA8DHwEicXEETgWzf/6sG9XQAPgEuwRm5Twf+4P3u1JCOpH4/U0GDXSMBOlLmGvF9UmrkLiKi7l8LWAdcCNwBnA70qUkNqloKzAHOUtU3gWM4M22XiUiGr2+CNZQBW4F/AKvdbd8WkQ4ikp1IDUE6ioA3VXWIqs4A9gJjVLXY8/9KGEG/Y09giar+T1UPAk8At4lIN1XVGnwEXoczaS8p30+PBrtG7BoJScoYdxG5FXhdRG4Tkbaqul5Vt6nq+8AO4BwRaV+DGvJUdbmqlojIKcAbOPnpfw084tslgRpuF5GWOBfPYuApEVkBXAX8FXgyURqCdPj+Fl+47dmq+iGwVkQuSsS5q9DRBPgGOEtEznC7tAaWAXcnWMdPRORKd1mATaq6PYnfT7tG7BqpmmQ8LoR4rLkc+ALnMfMF4HGgn2d7H+Bl4Iqg/eL2uFOVBqAz0MldbgjsAwbUwN/hCaAH0A54EOjv9msBFACn1uD/o6/vb+6e/1ngghr+XjyFMyv6B8CLOO6ZV93/z1dAfgI0NAb+BmwHDgFZbnuG7/uX7O9nKmiwayQ510hVn1QZuZ8OPKWqs4EJOI+bP/NtVNXFOH/Ik0RkmIj82m2P5+NOKA0/d8+zTlU3usuHgSlAkzieuzIN64E71EnOdq+qLnI17MEZJTVKgIZQOrx/C3XP3wDni43vEbyGdNyrqs8BNwO3qeo1OCku5uH4XeOKOq6fD1W1LTANx5iAYzTV7ZOs76ddI3aNVEqNnjDY9+VZXwtcA6CqG3BekjUUkUs93f8F3IRTvq9lqOMlQENukAZE5G6gN7A80nNHoeF/QGMRuVRVj3n63+NqWBGthgh1hPp/vAwMFJH66vg9a0LHVKC5iFyuji9zntvvfpzR4sEE6Zjq/vwFcLXr3y8VkSxPn7h8P6vQVGPXSIQaEnKNRKAhoddIBDoSeo1EQ03fTbK9K55Rxf8BR0RktLu+Deetey9xaAT8GVgC9FHVO4L2T6gGABG5SJzwu+7At1R1exTnjlXDEBGZ7Wq4UlV3xKAhKh2eL3cDYDJO7dxYiVRHDwAR6SYibwIn4YziixOhQ1UPi0iG+z9/EudxG1UtUVUVJyPqX4jP9xMRyQzWQM1eIxFrcPeJ5zUSrYZ4XyNR/T/ctnheI5FTE74f4AzgPzhfvl5Aptvu818K8D1gJuV+zDuACb5+QOskabjXXc4HTkqyho5AryT+P37nOUZmCnwvGgMdEqgjE8hwlzM8/Te6+7QBTnPb4vH9vC9Eu+/3rqlrJBoN8b5GYtEQz2skGh2/8/SN+RqJ5ZPwkbs4FZoeB2YAu3H8Ut8HZ+TjdmsAvI1z93taRNoB/YFiXz91ioEkQ0OR22+9qi5NsoZNqhrTo26MOnzbUScMLlk6fN+Lg6q6OYE6SlW1zB0VN/Xs9gecl7kfA7lu31i+nzfg1Bq+W0Suctuy3OP6RoqJvkZi0RCvayQeGuJxjcSiI27XSMwk+u4BDAf+5S43xInLnQb0dNsewPkj9cd5w/wAzuPNk8TpzmcaTEcMOu7HGZ0NcdcvwvHjPgRkx0nD+TiTsy4ANnrafU8QE2rg+2kaUkxHzL9H3A/ozMz6DTDKXW8FrAK6uOstgN/hjH5ycULZugQdI9c0xK7BdMRfB47bpmOcNFzsrmfi3ihwYrbv9/RtneDvZ53WkEo64v2J34Gci+QN4CPgRzgz1b7lbpsEPOYuZwBnAc8ALTz7Z5iG+GgwHQnREY93DKE0XO5uy3F/9gb2A21C7J+o72ed05BKOhL1id+BYBBOvKlv/XrgU3e5L86j7fnu+ok44WUN4/zPMg2mI2V1VKXBXfc99j8LvOAuxzVPi2lIPR2J+sT6x/kucC7O42s27t0N57HmVOAZdz0DuBGneHZX4Fac+NRmcfgHmQbTkbI6wtDwd3ddCIzIKcPJTzKOGG8upiH1dNTEJ+IC2W4MZ1scv1MZsAZntuDPVXWHiGSqM8HjRNwIA3UC+F90IxPG4SR/ullV90V6ftNgOlJdR4QamrsaFFAROR54FCcSZ6xGGX1iGlJPR40T4V3P95jSHXjZXc7CSdLzelCffwBXucttPcfIieVuZBpMRyrriEFDK/dnM2CgaYhdQyrpSMYnrJG7G+N5H5ApIjNwckaUghNfKyI/A7aKyDnqZEMDJ8nSOhG5D7hCREao6mZ1UmNGjGkwHamsI04aRqqTn2VeiFOYhlqoI6mEcec7Byfj3lM4jzIfASNwZukN9PT7MTDbdyfEye28AXgM9y4Yw93XNJiOlNVhGlJHQyrpSPYnnD/UEOB6z/qT7h/lRmCB25aB49OaAhwPdHH/QKfERaRpMB0prMM0pI6GVNKR7E84f6hcoB7lfqlrgQfd5S+Bn7rLA4DJCRFpGkxHCuswDamjIZV0JPtTbW4ZVT2iqoVanidhOE4SfHAS55woItNw0o0ugPiXtTINpiOVdZiG1NGQSjqSTgR3w0ycR5m3gK5uW1ect8lnAe0TfScyDaYjlXWYhtTRkEo6kvWpduTuoQwn6H8X0Me9890DlKnqHFXdEsGxosU0mI5U1mEaUkdDKulIDhHeCQfh/MHmAD9Ixt3INJiOVNZhGlJHQyrpSMbHl2Q+LESkA07+hUdUtTDsHeOIaTAdqazDNKSOhlTSkQwiMu6GYRhG7SASn7thGIZRSzDjbhiGkYaYcTcMw0hDzLgbhmGkIWbcDcMw0hAz7obhIiITRORXVWy/TER61aQmw4gWM+6GET6XAWbcjVqBxbkbdRoRuQunruYmnORSC3Cq3d8C5ACrcSbB9AOmudv2A1e6h3gCaAUcwSnNt6IG5RtGpZhxN+osInIq8CJwOk7ptYXA33Aq3e92+zwA7FDVv4rIi8A0Vf0/d9ss4EequkpETsdJKzus5n8Tw6hIxAWyDSONGAL8V1WPAIjIVLf9JNeoNwMaAW8H7ygijYAzgf94ssXWS7RgwwgXM+5GXSfUo+uLwGWq+pWI3AicG6JPBrBPVfslTJlhxIC9UDXqMh8Bl4tIAxFpDFzitjcGtolINk4VHx8H3W2o6gGcYsrfBqfYg4j0rTnphlE15nM36jSeF6obgM3AcuAwcKfbtgRorKo3ishg4BmgEPgWTirZp4A8nLzhk1X1vhr/JQwjBGbcDcMw0hBzyxiGYaQhZtwNwzDSEDPuhmEYaYgZd8MwjDTEjLthGEYaYsbdMAwjDTHjbhiGkYb8P37KskGCMrhuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_df['close'].plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>day</th>\n",
       "      <th>day_close_price</th>\n",
       "      <th>pct_change_eod</th>\n",
       "      <th>stock_price_rose</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02 06:30:00-08:00</th>\n",
       "      <td>99.98</td>\n",
       "      <td>93417.0</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>103.15</td>\n",
       "      <td>0.031706</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 06:31:00-08:00</th>\n",
       "      <td>99.78</td>\n",
       "      <td>16685.0</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>103.15</td>\n",
       "      <td>0.033774</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 06:32:00-08:00</th>\n",
       "      <td>100.14</td>\n",
       "      <td>21998.0</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>103.15</td>\n",
       "      <td>0.030058</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 06:33:00-08:00</th>\n",
       "      <td>100.35</td>\n",
       "      <td>18348.0</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>103.15</td>\n",
       "      <td>0.027902</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 06:34:00-08:00</th>\n",
       "      <td>100.55</td>\n",
       "      <td>22181.0</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>103.15</td>\n",
       "      <td>0.025858</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            close   volume         day  day_close_price  \\\n",
       "date                                                                      \n",
       "2020-01-02 06:30:00-08:00   99.98  93417.0  2020-01-02           103.15   \n",
       "2020-01-02 06:31:00-08:00   99.78  16685.0  2020-01-02           103.15   \n",
       "2020-01-02 06:32:00-08:00  100.14  21998.0  2020-01-02           103.15   \n",
       "2020-01-02 06:33:00-08:00  100.35  18348.0  2020-01-02           103.15   \n",
       "2020-01-02 06:34:00-08:00  100.55  22181.0  2020-01-02           103.15   \n",
       "\n",
       "                           pct_change_eod  stock_price_rose  \n",
       "date                                                         \n",
       "2020-01-02 06:30:00-08:00        0.031706              True  \n",
       "2020-01-02 06:31:00-08:00        0.033774              True  \n",
       "2020-01-02 06:32:00-08:00        0.030058              True  \n",
       "2020-01-02 06:33:00-08:00        0.027902              True  \n",
       "2020-01-02 06:34:00-08:00        0.025858              True  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_price_of_the_day = price_df.groupby(price_df.index.date).tail(1)['close'].rename('day_close_price')  # A\n",
    "last_price_of_the_day.index = last_price_of_the_day.index.date  # A\n",
    "\n",
    "price_df['day'] = price_df.index.date  # B\n",
    "\n",
    "price_df = price_df.merge(last_price_of_the_day, left_on='day', right_index=True)  # C\n",
    "\n",
    "price_df['pct_change_eod'] = (price_df['day_close_price'] - price_df['close']) / price_df['close']   # D\n",
    "\n",
    "price_df['stock_price_rose'] = price_df['pct_change_eod'] > 0  # E\n",
    "\n",
    "price_df.head()\n",
    "\n",
    "\n",
    "# A calculate the ending price of TWLO for each day\n",
    "# B Add a column to our price dataframe to represent the date\n",
    "# C Merge the ending prices into our granular dataframe\n",
    "# D the percent change from now until the end of the day\n",
    "# E Create our response column: a binary response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Date/time related features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Day of the Week')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Datetime features\n",
    "\n",
    "price_df['feature__dayofweek'] = price_df.index.dayofweek  # A\n",
    "price_df['feature__morning'] = price_df.index.hour < 12  # B\n",
    "\n",
    "# A An ordinal feature representing the day of the week\n",
    "# B A binary feature for whether it is before noon or not\n",
    "\n",
    "price_df['feature__dayofweek'].value_counts().sort_index().plot(kind='bar', title='Day of the Week Count')\n",
    "plt.xlabel('Day of the Week')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Is Morning')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_df['feature__morning'].value_counts().plot(kind='bar', title='Is Morning Counts')\n",
    "plt.xlabel('Is Morning')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lag / Rolling Window features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_df['feature__lag_30_min_ago_price'] = price_df['close'].shift(30, freq='T')\n",
    "price_df['feature__lag_7_day_ago_price'] = price_df['close'].shift(7, freq='D')\n",
    "\n",
    "price_df['feature__lag_7_day_ago_price'].plot(figsize=(20,10), title='Close vs Lag Close')\n",
    "price_df['close'].plot()\n",
    "plt.legend()\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ooOM7HRERERERkUYCIsQkaWtpEwD/FUlERAMJgyQiIiIiIiKVhIQIsSJpf3UrACDRGP7LK6vdiaU7KiGlDPu5iYh6g0ESERERERGRSsqQZ7a5mAzhr0i6550duO3VLdhe3hz2cxMR9QaDJCIiIiIiIijVP3an7HUgFIkeSe9sOR72cxIR9QWDJCIiIiIiIgBVzWYAwJDMxF49XqfrbS2Tb4dr21y3HU5ObSOi2MB1KImIiIiIiAAcb+oEAAzLTArq+I2/WozdFc24/vmNQT+HlBJCCHz/pU04YWQWBIBbTh/j89j3t3ZVIznZI4mIYgSDJCIiIiIiInQFSUODDJLy0kwYnZsa1LGF6YmoajHD4ZQw6AW+2FONL/ZUAwAunT0M+Wndq6B2HO/qi8SKJCKKFZzaRkREREREBKBCDZKGZAQ/tU0X5BXVDQuKAQBWh7PbPmf3TQCAVftr3Y5hkEREsYFBEhEREREREZQgKTfVhERj8M22DUEmSUkJyjk7rA5Ir2lqwUxbszNIIqIYwSCJiIiIiIgGvcrmTry+sQzDQmy0rQ+ywbYWTnVaHd2mqQUTETnYI4mIYgSDJCIiIiIiGvRO+t0KAEBBemhBkiHIIClZrUgy2xzdQiHvCiV3o3JTAHBqGxHFDjbbJiIiIiIiUmUmG0M6XhdkkJSkViT9Z20JGtutHvt85Uj/WHUYAHC0rh0Am20TUexgkERERERERKTKSTWFdHywFUlakPTahtKgjl+6s8LjfjB9lIiI+gOnthEREREREamykxNCOj7oHknq1Dajvvvxvqqadh1v8bjPZttEFCsYJBEREREREakmFKaFdHyoPZJsjq5ASAuhvHskmW2Obo+32p0hjYuIKFIYJBERERER0aDmHuScNj4vpMcGW5GkTW1zpz3S6ZURbTja4LqtnZ5BEhHFCgZJREREREQ0aB2qacNHOyoBAA9cMDnkxwvR+yBJC6G8+x+tOVTnuj1rRBYAwMIgiYhiBJttExERERHRoLX4ydWu22PyUiL2PFqPJHdakOTwCpK+OliH+aOz8fOzJmBkTjLmPbacFUkx6Lk1R1Fa346HL54a7aEQ9auIVyQJIfRCiK1CiI/U+9lCiC+EEAfVv7Pcjr1XCHFICLFfCHFOpMdGRERERESkGZOX2uvH9jTDLVBFkvvUutpWC/ZWtuDUcXmYOyobmclGAIDVwSAp1jzy0R68uO5YtIdBYVLfZkFThzXaw4gL/TG17Q4Ae93u3wNguZRyHIDl6n0IISYDuArAFADnAvi7EKL7uy0REREREQ14dW0W3PnGdp+Np8Olsb3rojHRqMOwzKReneef156A5XcuDHiMUa/rtmKbqyLJLSP6+rAyre2UsbkAgAS9csnGqW2xpaGdgcNAc8Jvl2Hmb76I9jDiQkSDJCFEEYAlAP7ttvliAC+qt18EcInb9tellBYp5VEAhwDMjeT4iIiIiIgoNv3+0314e0s53tt6POznllLinS3lWOQ2rW10bip0QTbO9nbu1EKMyu15WlyiwfNzcr3o3iPpq4N1yEgyYuqwDABKD6YEvQ4We+QCNQrd8r3V0R4CxYitpY148P1d3VZfHMgi3SPpKQB3A3BfQ7NASlkJAFLKSiFEvrp9GID1bseVq9uIiIiIiIj67JGP9iAlQY/NpY1Ye6ges0ZkuipLRkWwP5LGZNSj1WJ33de5KpKUC1ApJdYcrMOCsTkeq8GZDDr2SIohDqfEXW/tiPYwKEIO1bRibH5azweqLv371wCAXy2ZjARD78LoeBOxiiQhxAUAaqSUm4N9iI9t3SI9IcQPhBCbhBCbamtr+zRGIiIiIiIaHJo7bXhuzVH8ZcUh7ChrxiMXT8Hbt56Ma+ePAADkpiREfAxJCZ6XXwavVdsO17ahqsWMU8bmeRzXarGjoqkz4uOj4Byta4/2ECjMWs021+2rnlmPFrf7gRx3+3fpvfriQBbJqW0LAFwkhCgB8DqAM4UQLwOoFkIMAQD17xr1+HIAw90eXwSgwvukUspnpJRzpJRz8vLyvHcTEREREdEAUN6oXKAZ9X2/ZJFS4vsvbXLdX3bn6bjupGLodAIvry8FALwbgSl03rSpbQXpJgCAzjW1Tdn/1UGlP9Kp43K7Pfaz3ZxKFSuEVwmEnY3Q455WFQgAdW1WlNZ39PiYA9WtWPD4Cp/nGOgiFiRJKe+VUhZJKYuhNNFeIaW8FsAHAL6nHvY9AO+rtz8AcJUQwiSEGAVgHIANkRofERERERHFpsZ2K74+XA8AGJmT3Ofzjbr3Y2w42uC6X5Ce6Lr9m4unAABe+8H8Pj9PT0xG5fJr7qgcAMA5UwoBdF2ArjlYh5E5yRie7fk16wQwtzg74uOj4HRaPftVrT7AmTLxTsuAMpKUVRKDaW5/zCtssjNIiqjHAZwlhDgI4Cz1PqSUuwG8AWAPgE8B3CalZEc5IiIiIqJB5qllB1y3w31p9rPF4z3uf/ekYpQ8vgRThmaE+Zm60yqSJhamoeTxJThjojLDwiklbA4n1h+pd63W5m7K0AwkJXBB61jR6bWS4CDKDwak/VWtrtUSF4xVQl5bEFVm3s21B1NFUqSbbQMApJSrAKxSb9cDWOTnuEcBPNofYyIiIiIiothzoLoVL39TitF5KThS2w5nGC7ORuemoLbVgp0PnxOGEfaedxjkWrXNKbG1tAntVofPaW2j81Kw0a2iiqLLYvMMGQz6wdFgeaA656kvXbe1sDeY5vbe70x25+CZ4hiNiiQiIiIiIqJupJSuldV+cfYEAOGp9khLNOCE4qy+n6iPTOpFqtYbSah/O6TEmoO10AngpDHdg6TJQ9JR0WxGU4e1/wZLflkdnhVJFU2duOmFjfjRK5ux63hzlEZF4ZCohr3f/c8GV5VSsAZTRRKDJCIiIiIiignL99bgq4N1+Oni8chRV1Hznj7SG1aHDEvT7r7SeiRpzZr16qptUgJfHarD9KJMV48Wd5OGpAMA9lS29M9AKSDvapVfvbsLy/fV4OOdVVi5r8bPoygepJm6Jm1d/ew3MNuC77bT0mmPxJBiUvTfTYmIiIiIaMB4f9txPPbx3pAfZ7U78ejHezEmLwXXnTQSOp3nimZ9Ud7YgVhYmVubNqNNhFK/RLy5qQxbS5sweWi6z8e5gqQKBkmxQGvE/MrN8zA8O8ljXwy8zKgPvMPaDUcb8P423ys66ryW7/vHqkMRG1esYZBERERERERhc8fr2/DMl0d6rCR6b+txHKxudd1/8esSHK1rx/0XTIZRr3OFLM4+JEA1rWb8dcVBtJrtWLa3utfnCZdEtSJJuwDVwrL3tlUAALaXNfl8XF6aCXqdwNMrBs+FaizTKpJGZCfj3vMmeeyLhcCSPEkpsXRHJexBNNA26AQ23LcIwzKVgPC7/9mAO17f5vNY7/cmaxDnHygYJBERERERUdhVt1j87nM4JX72xjZc8re1WLm/BvVtFvxl+UEsnJCHMybkq0dpFUm9vzL/xZs78IfPD/R8YD9JNCoVSVKtW9F7VTTMGJ7p97EOp0Rzpw1tlsEzfSbcwjFNEgBsDuU8Rr0Op4/P89jXl9crRcZnu6tx26tb8I9VhwMed9r4PNx/wWTkpyfi5lNH9Xhek8EzTpk9Ivp92PoLgyQiIiIiIgq7b47W+9331LIDkBJotzrwf5/swzlPfYVWix0/WTTOdYxWkdSXy3L3iqdYoFUkaat+eU+NuemUni9eGVT0TlOHFfN/txyPf7Kvz+fSVucy6AVS3HrqCBG+sIrCp75dCbWPN3X63J9k1OP7p47CSzfOxZi8VABKZVJPtH+/d52jLAyQk5oQjuHGBQZJREREREQUdnVt/lcYc5+ita+qFXVtyoXe2PxU13btIq0vF+aVzWbX7fmjs3t9nnDReiSZ7UoD31aLzWN/po9G25pbTx8DILgLXPL0xGf7MPM3X6C6xYJ/rg5clRIMV0WSrutyOsGggwB7JMUirfLP36pqDimh13lGI973G9ut3d6LHOr90bkpAAZXDzNDz4cQERERERGFZmJhWsiPSU/sClK0IMnZy7Yj7qstbX3gLCSb9L07URglqUuLd1qVL6rD4rkilD5ASKStYjeIVhjvs5pWM3aWN+NvK/seHrnTXlsGvfLz+ua+RTAZdJjz22WsGItBWi8yh4+fjdMp4XBKeC/q6B3YznrkCxh0AjmpCchNNSEn1eTqaZaXZgIACDF4Ql4GSUREREREFDbDs5NQ1tCJsoYOdFodSDTqerzA+uJnpyHdqxpHe4ivi79gbCppdN3OSomNKScmo2dFkvdXpgsQJIkwNB8fTF5YexQPfbgHgFItpDXIBpQqt95e9G8+1oAnPtsPQOmRBAAF6YkAtKltfRk1RYJWkeSrYuj3n+2HwykxvsAz+PYOdeeOysackVmoa7Ogrs2KujYLmjuVikKHU2J4dhKq3CogBzoGSUREREREFDbaRds97+zEPe/shEEnkJ5kRFqiAWmJBtdqSO5G5qQgwatxbV+ntq05VNerx0VSovo1ahUt3l+bd88kd8L1/YjQ4Nz0JWiJFVqIBAA/WjgGTy076LrfYXV49DYKRqvZht9/uh8vf3MMAPCduSO6vWYFBCvGYlCH+u9tX5Vnz7RXvynFP1cfxjXzRuCiGUM99mnVZprpwzJw97kTPbY5nRK7K1owrSgDQ9KTUNXCIImIiIiIiChkw7KSUFLfgYcvmoIOqwMtZhtazTa0mu04WteOz3ZXd3uM9wU50FUR0NsVtdccqu3dAyNIW7VNa7Y91CtU817FzZ22J1LNnD/aUYHXNpTi6rkjcdurW/D1PWd2G1+88g532i32kIKkL/ZU44H3dqG61YwbTh6FO88e7/PxQnStyEexwe5w4vm1R133O6x2JCcYsGp/DR54fxcWTsjDwxdN6Raceoe6d507odu5dTqBaUUZAICCjETsKG/C9rKmgKsvDhQMkoiIiIiIKCy2ljZi7aF63LFoHL53cnG3/cv3VuOmFzcBAM6eXIDzphVi2Z4an+fSepb0Zmrbs18ewa7jsdf4NkkNkjrVCokJXn2kdAGWQtK5prZFZGi4/dWtAID8NGWa1pcHanHV3BGRebJ+kJFkdE09stg9e1FtKW3CuVMLgzrPX1ccxB8+P4CJhWn453UnYGaAkIBT22LPoidX41h9h+v+0h2VmDI0A7e/uhXjC9Lw16tnw+DdIAnA14e7KhpLHl/S4/MUppvwYX0HLv7bWty/ZBJuPnV0eL6AGMVV24iIiIiIKCz+79N9yE014fun+b6Icv+U/8dnjsOls4rwt2tmBzzW2Yvk5C/LD/Z8UBRoFUna1DbvqodAFUla/6RILy+vrZx3oLotos8TSa1mmytEAgCr3QmdAL6+50zkpCTgT18cCPp19YfPDwAAPvzxKQFDJED5eUb650Oh0UKkVb9YCAC4660duO65b5BqMuD5609Eqp/KtK8P14f0PFqfLACoabX0brBxhEESERERERH1md3hxPojDfj2iUV+L84e/Xiv6/awrMDTprqmtoV+Yd5utQMAfnDaaOx75NyQHx8pJqNy+WVRGz9799YO2CNJ/TvSPXi0n93BmtYejoxdr20o9bhvsTth0OswNDMJD140BfurW/HhjoqA53jmy8O45G9rXfeNPqpWvAlwVb1Y4h7qFeemuG7Xt1vxn+tPRGFGoq+HAQCumz8SgNJfKxju55pQEPqKlfGGQRIREREREfWZFo5keK2+5m7BmBzX7axk/8cBXaFKb6a2nTmxAABw/rQhriqgWOAdE3n3ZQnU31r7LkS64sWmNqU6UB2/QZL3t+jVb0pdq7ZdMG0IJham4U9fHIDN4USbxY5nvjzcrULpsY/3YZu6vHuwlIqkvoycwqmpw+Zx/xdnjwcAXH9yMSYPTQ/42JtOGYUVd57ercG2P4VuFUmzRmSGNtA4xCCJiIiIiIj6TAuSTAb/wc2Csbmu2z2tCqZVJHVY7CGPRbuQmzQktioDesoYAn1PXlhbAgD4YHvgSpq+0irAqlssvaoGiwX2AOPW6QR+cfYElNR34K3N5Xjs47147ON9WLa3exP4kAnAySQpZnj/JEJp3C+EwOi81KCPd5/aZoqh8DpSGCQREREREVGfaQ2NTT5WYNOkB6hW8qYFSe7LuAdLqz4xBupeHUWBIzTfKpo7AQC1Eei/4l7l5B7ClNS3h/25+oPd4RkhpCR4XtgvmpSPmcMz8ZflB1Gnfj/N9l4uD+gm0NRE6n92p/Iz/fac4QCATccaAABvbykP+3O5B0kJQUyDjHcD/yskIiIiIqKIe3n9MQBAQ4fV7zFFal8kfz2U3LlflJc3dgQ4srtDNUqjaJ13E6IYtOLO04M6TmvEHYkqIW0VOaBrahsA7K+Kz+ltNq/Sk/GFaa4m4oBSbXL3ORNQ2WzG53uUSqRAUwa/uW9RUM8rWJEUU7RAcfbITABd027vOS+46WqhSHAL0AdDkNTzOzgREREREZEfB6pbcct/N7vCnrIG/6FPUVYyVt+1EMMyAzfaBroqkgDgO8+ux5d3ndHjdDjN0p2VQR3X33xlDKPzUnH57KIeqyS0rz0Ss80a3XrJuAdVgX6WsSzJqwLJ4ZTdqsBOHpuLk8fkuFbn8pf/JCfokZ9mCup52SMpdhTfs9R126BWJqYlKvFHoD5u4RCjhZBhNQi+RCIiIiIiipS/rzyEo3XtsKmf/vcU9ozMSYEhiE/s9W7nKWvoDNj3xtvJbk29Y5H3t+iJK6bj4KPnBXyMlqtFouKlya2KzOY2Law3jc5jQXGOskKXtvKW2ebw2cj8F+dMcN2WfjpYjcpNCTrAVFZti8/v2UCmrZZ4z3mT8KOFY3DOlMKIPl8sNfiPFAZJRERERETUa3qvj997Wo0tWMLrSsW7700gWpVJrClIVypb5o7K9tiu04kel5d/+OIpAIATi7MDHtcb7qtb2d2mhcVrJqL1xtGqvA5Ut0H46Ew1e0QWDGpC5+9rdV82vidCiB4bqlP/ykgy4tSxea7bd587scd/a731ys3z8MQV0yN2/lgy8L9CIiIiIiKKmOZOpZpl2rAMAMB3TyqOyPNo4UA8G5mTgtV3LcTPz5rQ88FexuUrK9AZ9eHv+9ToVpHkXvkVqG9QLOuwKj2f3L8Wf0VFD144GYAy7dKXkdm+t/uiE4AzTle6G6huP2MsMsIUbvdkwdhcXKk29h7oGCQREREREVGvLdtbAwAYnq30PQpXo9n0RCMeuXiKa0rXQbWBdk+0Hj8XzxwalnGE28icFI/+T8HSuXokhR5UHK5tw5NfHPAbDLn3SHIP7OI0R8LB6jYkGfVBhUBaxZG/H8nInOCDJKNeF9IUTIq8604aGe0hDEgMkoiIiIiIqFcO1bS63Q7/SmnXnVSMq+eNABB8dUyp2iB6wdjcsI0jFmjhk6MXhVmL/rgaf1l+EOv8TPlrau+qSHJvth2vkci+qhaML0jFt0/sqg7x1+dI68W163gz/vDZ/m6vsxHZwU9tczglSuvjs0H5QDUY+hVFA4MkIiIiIiLqlbe3HHfdPlCtBEm9qbYJZOH4fAAIuu/I/qoWAMDEwrSwjiPatC+/Nw2wh2YkAgCaOm0+97tvd2+2Ha+No/dXtWJCYRrOmlzg2jY6z3cgpAVMD324B39deQhWh9MjTNKWjg9GVYsZG0oacN+7Oxko0YDGIImIiIiIiHrlQFUr0tUltTX6IFe4CpZB7QlkC7LZ9v6qNgjR1VNooHBNbQth6tQ3R+rxxqYynKg2926z2H0e59EjKc6bbde2WlDfbsXEwnSYDF3VKBdO9z3V0Tv4lBI43tQJALjltNEe5wjWq9+U4taXN4f8OAqfNJMB35k7ItrDGLAYJBERERERxbBOqwOl9R34xZvb8eamsmgPx0OrxY5xBUpgk6gusa0L8xWGVolkD3JO1/7qFozMTkZSwsCa0tI1tS34dOfbz6zH3W/tQGaS0my4ttXi8zj3Vdua3aqT4rHZ9sr9Ss+uiYVpMBm6XowLJ+T5PN47SKppseCJz/YDAM6Z2vtl4i12h8f9NzeV4enlB3t9PgqRgMfPn8LL0PMhREREREQULYufXO2qkOi0OWJqVaANRxuQkWSEUS9gtilBT9grktQL/WCbGK85WIf5o3PCOoZYoFUk9WZqW6taiVTdYva5370iaeX+Wtft+IuRgLvf2gEAmFCYBpMabibodX575XgHSTe9uNHV2L0v0yPdA7+aFjPuUsf140Xjen1O8vT3VYdw1qQCV5jtTsrwT7OlLozoiIiIiIhimBYiAYjJK/vmThuS1Iv0zGRj2C/ejGpVgS2IiiSzzYEWs921/PtAok3xC6UiSfOO2svKX5DkXpGk0etE3PZIAoCcVBOSEwx44ILJ+Oxnp/k9zjv4PFLXDgBITtAjOSG0ugv3aZ5tFuU1uGp/Dc7781chnYd6ZrU78ftP9+Oyv3/tc79TSr8r8VHfMUgiIiIiIooTsXRh7z7tqcWsVLz85MxxflfH6i2jTpva1vPXfqBaWUUuPWngTbzQAo/eBEmaGr9T26we9/U6AZ2Izx5J3m46ZRRG5fpfec17Kqb2/fXXnDuQTfefhfvOnwgAqGuz4LGP9+L65zciN9WECQVpvTon+ab9nNqsvvt+KUESk6RIYZBERERERBQnPtlV1a/P99XBWnzrn+t8hhda8+tLZw1zbbvupJFhH4NWiWN39lyRpPVT8tdYOZ7p1PKKvoSJW0ubsODxFa77NocTeytb0OhVkaQXAgICfcisIsbplPjmSL3ffQBw5QlFQZ/P4Kep16jc1JDHlmDQ4YYFo1z3n/nyCK6ZNwLv374A4wvTYrKiMF7Z1PcDf/8cnBJhD7Wpy8CL6omIiIiI4sxrG0pxythcDM9Odm2zOZz4v0/2dTv2qWUHsGBsLk4szo74uH76+jbUt1vR2GFFbqrJY58W7EwoTMPnPzsNqSaDK8gJJ2MIq7aZbcp0In/9cOJZqBVJnX6m9x1v6sTdb23HnsoWHKhug9Wu/BxH5abgqDqtS6dTLtBlDCYfL64rwcMf7sFvLp6Co3XtuPe8SUhQpz9+sbcaAPDm5nI8ceWMoM7n7yU7LDOpV+Nz/zfwj2tm47xpQwAAB6tbXdPmqO9W7qsJuF9yaltEsSKJiIiIiCiKGtqtuPednbjlv57LhX+6qwr/XnO02/FPLTuIK/+5zue5vthTjVe+ORa2sWkf6PuqgtGaXxt0AuML0jC0lxfePdEqRoKpSNIafg/E1Zq0iqTGdivKGzt6PP5ogNDiiz3VyEpOwPUnFyPNpNQW3HhKVyWNXghY7E78x8frL9q0Rti/fn83nl9bgvH3f4KbX9yIF78uwe7jzSGfz9/0p7mjsvo0TgCuEAkA9lW19vl81MVfULrmYB1+98leOKX/ny31HSuSiIiIiIiiqKReueBvtXhOLwomOHFX1WzG91/aBAC4Zl74p5h503oWGSL8sb8hhIokbcl100CsSFK/z39ZcQh/WXEIJY8vCXj8kbo2v/u2PHCWa9rPM18eAQB8fagO508rxMc7q1yhVTDf8/726jelHvenDE3HwZo2LNsbuELFH+/m8HNGZmFbWRPOnFjQ6zFS5PlaqQ0Arn3uG9ft/HSTz2Oo7xgkERERERFFUYlaOVLW0Omx/enlh4J6vNMp8cqGUvzexzS4vnL1GPHKE6qazdhe3gQAMERgOps7baqQFlzVtJrx95WH8aslk7pNpdMqkhKNA68iyXt1sZ4crvFfkeSrd8zCCXnYdbwFQOTDwXBJMxmw9CenAgCO1bdj5b4aPPThHrz2/flBn8N7qmBDuxUF6Yl9GtdnPz3N72tQSsnePWHgXiXp73u6eBLDwEgZeO+wRERERERxpMTPFCTvfiqppu6fAR+sbsW3/rUOD7y3C9OHZ2DqsHSMCePKUNqlmXdbnjP/uMo1FU/rYRQpBld1jBIS/ebDPXjh6xJ8vru627FaRdJA7JHkpye0X94VSb++YHLA45MTDMhJTQCAbs23Y8nUYemu262WrhW7Ruak4PoFo1Dy+BKcNCYn6PN5V10dqWvvVqUUqgmFaRiZ4/vfoT0WO5jHIfcA8Fv/WofNxxo89hdlJUVsui0xSCIiIiIiiqqj9YH73WgXtavuWoiXbpzr2v6nLw7g/L98hUO1bXjiiul4+aZ5KM5JCeuS7V0FSZ4n7XDrT+Jv1atw0Sqe7K5l2ZXVtLSKKHdas+2B2CMp1O/zLq9+QeP9TAXSCAHkpMb+VKBwTznLT1O+5omFgb8/4aI1N6e+0YKkU8floqS+A5f/Yx1+oE7tBYDTxudFa2iDAqe2ERERERFFyXNrjuLD7RWu+18drMWCMbmuHjUA8O/vzUGyUY/cVBNOG5+HacMysPN4M/68/CAumjEUv75wsmtFNSFEWNfZ0hraBrr4NUS4Ikn7Vkg1IRuaoUw72nC0odux//5KaQ6dnDDwLnNCKZKRUuJwrWdFW4GffjF/+vYM/Ox/2zGhIA2Havz3VYoF5Y0drtdBuGSlJODIY+dDpxP4y/KDePKLAyht6LmZeW9plXXUN1qQdMeicZg8NB3Pry3BP1cddu2/4eTiKI1scBh477BERERERHHi6RUHPe5f99wGDMtMwhUnFLm2pScacMLIbNf9+aOz0dhhxSOXTMUZE/I9Hq8TCOuFdotZmTr06jeluPf8Sd3256WZMEatEIoUbeUlrSeKVpm0rawJTqd0hW5vbS53reg1ECuSQumro/WKcpeRbPR57KWzinD25EKkmAxBT2lrbLfijv9tQ25KAs6dWoizpxQGPbbeeuKzffjbSiUoMOhEWKeIaa+h7586Gk9+cSBs5/WFFUnh4VDfD/Q6geQEA247Yyy+M3cEZj/yBQD/zbgpPBgkERERERFFSZN64X7r6WPw08Xj8MWearyxqQx/8QqY3P1qyWT8aonvfjcC3fsZhYO/nkMbf7U4/E/mpStIUu7b3So63tt2HJfNLsInOytx91vbAShTWpITBl6PJABYMn0Ilu6o7PG4FrPyuipMT0RVixkAkJHkO0gCgBS1/5bWI6knz605ii8P1AIAyhs7Ix4kLd1R6QqRAO01Ef4XelI/vG4sDJL67Pm1R/Hwh3sAeDbdzk4J7vVLfccgiYiIiIgoyoZnJyHRqMeFM4biwhlDcbypE39dcQivbSjFzOFZQZ9HmdoW/gvsoqzoNa3VCnG8K5IA4OdvbEdOqgk/eX0rZo3Iwks3znWFIgPR6NzgGqk//OFuAHCFSABgMujxy3MnYv7obH8PQ25KcD2Slu+rcd3eUNJ9imG43fbqFo/7+ekmlDd2+jk6th2pa8fw7ORoDyOuaSESAJTUdXhUbFL/GLjvskREREREMU4IQEpgbrHnhdCwzCT87rJp+N1l03p1vnBw7+Xy1LKDSEs0oigrCcOzkpGcoMfVc0eE54l6oFUkaV+Xe5CUlWzELf/dhHH5afjP9ScO6BAJAEYHuSLfvqpWj/tahdYPF44J+Lj0pK7v37DMJBxv8h3W7K1sCWockTIqNwXfmTsCpT00qu+ND28/Ba3m8K9al5KgR7vVgaeWHcDpbAQdNiNyGMpFw8B+pyUiIiIiimHnTS3E5mONYevnISAgJfDmpjJkJSdg8eTer3BV1dxVzXK8qRO3vrzZY/+qA7W4v9dnD57WZNqpBkjuy343dtgwJi8F/71pbsCpWwPFqNwg+1F5hYnBNh/X+jAl6HU4bXwelu2tRkVTJzKTja5zNHeGP2QJ1Zi8VNx2xtiInHtaUUZEzrv8zoWY/7vluHx2Uc8HU1A++vEpmDosMj8vCoxBEhERERFRlJhtTuSlhW/JdaE2277rrR0AgJLHl/T6XGWNSrXHKzfPw+Qh6Tje1Inyxg6UN3bit0v39lvTYO8eSd6rXr1887y4WLY+HEYFObXtqrnD8djH+3Dt/BF4eX0pUkzB9/758PZTkJ2agL+vPAQpJU5+fAUA5bUkpcR97+7s1djDKdjKrFiSlaIEnbEQxMUz98UEGCJFD4MkIiIiIqIoMdscSDSEr8FvVbMZFW6VRH2h9aAZnpWMrJQEZKUkuC7cxuSlYsrQ9LA8T0+0HknaKk0Op4ROdAVLQzKi17+pvwVbdZWeqBw3LFOZ9pPkp1m6L1pFjk6Ibo3b/7exzKPZ9/SiDOwob4aUMqRV5TSf7qrErS9vwYo7T8doP6v/eQeHADA62MqsGGIy6JFqMqC+zRrtocS1nqbuvvOjk13VixQ5A29dTCIiIiKiOGG2OfyuiNYbaw7Vhe1c5Y2d0AlgSGZit31nTMxHfnr37ZEghECCQQeL3QEAsDkkDHpexgSihW5piUrdQIIh9O+XEtZ1XZAfqmnFQx/uxoKxOa5t500dAgCoa7N6TDkM1q0vK020395S7vcYX+eNx4okQKlKauxgkNQX2qvhjkXjfO6fPSILc4rZfDvS+A5MRERERNQPalrN3S6KzTYnEo2x+St5eWMHhmQkwRgDoU2ayYA2sx0A4HA6YdCFXv0y0MgApRnay0wLknrzMxRCeLxeb391K5ITDPjTt2a6tiWpr90TH12GRz7a432KoLVbHD63d1jtroqkuaO6woHCfgoxwy07OQEN7QyS+kILN/keEF3R/1+BiIiIiGiAq2uzYO6jy/GjVzbjic/2uUKA5k4bkoJshBwMUy8qT/wpb+jEsKzYmDaWmmhAm0UJkmwOyYtIALVtFr/7tNeXXv0+9eb7pRMCdkdXkLSvqhV/vHKGRyVaWmLXVLvl+6pDfg7NdB8Nrt/ZUo5Zv/kCr20oBQCcO6Wwa2xx+vPPSklgRVIfaflpvL4GBgoGSUREREREEdbUoTTY/Wx3Nf628jDq2qw4VNOK402dOLE4K2zPYw9jb5C6dktYG4H3RapHRRKntgHotjKbO62SKEH9PhVmhF7Bk2jUodPWVSl00ymjcMbEfADA8zeciDvPGo9zpxbi8cumAQC+PWd4yM8xQV2tMCs5wbXN7nDikY/24OdvbIfF7kSl2vPLoFeCg160YooZ2SkJ7JHUR86emiRRv+A7MBERERFRhCV4BR8SEp/srAIAnONWadFX3lPnvjpYiyO1bb06l8XmDGsj8L5INRnQqlYk2Tm1DQC6NcL2tW/eqBw8eOFkPHLJ1JDPf+38kR737z53guv2GRPy8eNF45BiMuDyE3q/nL1WZWZRVwBsbLfie89vwHNrjuJ7JynPr01tM+h0WH7n6dhw3+JeP1+0ZSezIilcdPGcKA4AEQuShBCJQogNQojtQojdQoiH1e0PCSGOCyG2qX/Od3vMvUKIQ0KI/UKIcyI1NiIiIiKi/uR9zeN0Au9sPY4TRmahIIL9Xq57bgPO/OPqXj223WpHckJsBElpiUZXRZKdU9sAKGGk331q1YZOB9ywYJRrFbdQDM1Mwl+vngVAqRwy+QkVtQv63hTDaUGS1eHEnooWXPjXNdhY0ognrpiOhy6aouyza0GSwJi81JipkuuNrJQEdFgdMNt894SinmkVScyRoit8E7K7swA4U0rZJoQwAlgjhPhE3fcnKeUf3A8WQkwGcBWAKQCGAlgmhBgvpeS/MiIiIiKKa96zMTYfa8TRunbM8NEbJlZ0WB0xFCR19UiyOyX0eoFfnD0e04syozuwKAoU3GiVaX2t2shOUaacZaX4D6K0Z+jNlCPtZ/rUsgOobDIjI8mIN245CTOHZ7qO0YIk/QAID7XvZ2OHFUMyYqP/WLxx9UiK/5dDXItYRZJUaHW0RvVPoHeXiwG8LqW0SCmPAjgEYG6kxkdERERE1F+8L7K16S0XzRwa1ud56ca5+O5JI3HViZ79al7fUOq6aA+GwylhtTuRHMZG4H2RajKgusWMp5YdgMXugFGnw+1njsNp4/OiPbR+d895EwEEt2pbX4MkGcR5tF29aV2jBV5HatsxeWg6PvjxAo8QSSeUaiWgq0dSPNN6QbFPUu+5KpIQ/6+HeBbRHklCCL0QYhuAGgBfSCm/UXfdLoTYIYT4jxBC6y44DECZ28PL1W3e5/yBEGKTEGJTbW1tJIdPRERERBQWB2s8+xQ99MFuAIDVHt7GsaeNz8NvLp7arQ/LPe/sxJqDwf/u3G5VQieTMTZaqqYmGmCxO/HUsoP4eGfVgKhO6a0ctaolUHBjd4SniieYaURCCAgRONjyRet9pHn1+/OQn+Y5zVOvE65/IwZdbLwW+yIntasiiXpHe5Vxalt0RfRfo5TSIaWcCaAIwFwhxFQA/wAwBsBMAJUA/qge7uul0O3dSEr5jJRyjpRyTl7e4PsEgoiIiIjiz5++OOBxX+uLNLEwLSLPt6+qtdu2UFZ0W7mvBgDwyjfHwjamvkg1eVZGDeZV27TqoM92V2HtoTqfx3TYHDDoBBIMffs+BVvZJBB6j6TG9q4wxWTQ+ezBJIRAQ7sFwMCY2qZVJDW0W2G2OeAM4yqLg4VU80fBJCmq+uUdWErZBGAVgHOllNVqwOQE8Cy6pq+VA3CvwS0CUNEf4yMiIiIiiqQ9lS0e94tzkwEAw7Ii0ydFu/D/zcVTcMtpowGENvWoSB3XTQtGhX1svZGW6BUkDYBQobe0wpzfLt2La/79jc9jOq0OJIWhv5UzyF5LOiECNv/2pcGtKkdrrO3NandiS2kT9DqBYZnx31NI65FU12bFxAc+xUMf7o7yiOKP9jobxG8BMSGSq7blCSEy1dtJABYD2CeEGOJ22KUAdqm3PwBwlRDCJIQYBWAcgA2RGh8RERERUX/YV9UVIqWrgcjaQ/UAAGOEKmv+9O2ZWDQxH9+ZOwIXzlD6ML3wdUnQj9cKJcbkp0ZgdKHrXpE0eK8ig+kN02G1IyUM/a2mDlOawd+woDjwmEToFUkNakXSa9+fj+/MHRHw2FW/WIhpMdyYPlgZSUYIARytU6a6vrf1eJRHFH+019ngfQeIDZHsnjcEwItCCD2UwOoNKeVHQoj/CiFmQpm2VgLgFgCQUu4WQrwBYA8AO4DbuGIbEREREcUzKSV+8+Ee1/1zpxbijU3lEX/emcMz8dz1JwIA6tqUqUGbjzUG/XitCXKsTCfqFiTFyLiioacZPQ3t1rCtuJeXZkLJ40uCGJMIudm2FiRpVTqBDM9ODu3kMUqvE8hMMmJHeTMAoChrYHxd/Unr26UbxO8BsSBiQZKUcgeAWT62XxfgMY8CeDRSYyIiIiIi6k+f76nG14fr8ZuLp+C7JxXj9Q2l/RIkuUs0hh4ouIKkGOlDktptaht7JAHA6NwUj33byppwyd/WAgCmDE3vxzGF3mxb65GUlWKMxJBiVlZKgitIitTU1oFiX1ULLnx6DVb+YqErdJOsSIoJg/cdmIiIiIgogsw2Bx5duhfjC1JxtTp1p6nT1u/jmDcqGwBw/cnFQT9Ga8wdK1PI0kyeYcO6I/VRGkn0uWd7Q736Bu13m0YZjoqkYAkIV6VIsF7boCzYrTWgHiwS3KazDoS+T5H0+oYy2BwSn++udm2TrpUEY+O9abBikEREREREFAH/WXsUpQ0dePDCKa5VxrQw5ztzR+DdH53cL+MQQiDJqA9pBS+na2pbbFwueDfbHszsjq7AxrvBtXu1UlIYeiQFS68TcDiDP97hlK4G9JHqExar3KeLmoyD62sPlVENsjttXR1vtFc8c6To4jsyEREREVGYVbeY8dcVh3D25AIsGJvr2p5o1AfVcybcTEYdLLbg24/aY3xq2yOXTI3SSKKvornTddsZILxJ7sWUxt5SgqTgk6Tyxo4Ijia2uQdJofaVGmwKM5SKrbe3lONHC8dAiK7Kt55WEqTIYgRKRERERBRGS3dUYt5jy2F3SPxqyaRoDwcAYDLoYLEHf6GvhQKx2mz7InUlusHIPXzwrkhyn+7Tn1PbjHrhCh+DO37wXoa6ByDu1WXUnUmtojxS245PdlUBYI+kWDF4/wUTEREREUXAba9uAQBcOacII3NSeji6f5gM+pCCJO3YUKbDRZLJaxwZSYOrQbM796bWgbKbpH4MkpSKpOBDES2gnDyk/xqCxwr379J/1h6N2jiCtf5IPd7YWBaV59beh1IS9PhoRwUAsCIpRsTG/wxERERE5Ndtr2zBO1v6d6Uv6rufnzU+2kNwUSqSgp/aVtNiAQDkp5siNaSQuFfaFA3yla488poA2U1/ViQZdDrYQqiu0cKA7540MlJDilnVzeZoDyEkVz2zHne/vSMqz629ZxVmJMJqV14zrhyVOVJUMUgiIiIiinFLd1bi529sxx8/3x9SGEDRlRlDq1EdrGnDxzur8LuP9+LTXZWo8nExu7O8GU9+vh+A0uMpyahHmin2Wqr+3+XToz2EqHJ6VCT5D2/6s9m2QR9ajyStemkwVpVUtcRHkORwSry5KXAlktMpYQ6h91qoLDblNZWcYIBdfX1pL/nB+NqJJbH3PwMRERERudjdlkJ6esUhnDExH7NHZEVxRBSsWOkv5O75tSX415fKa2pIRiJmjchEcoIB35k7Aje9uBFNHTb84PQxqG61oCDdFJNLbLs3Lx+M3LOjTccaIaX0+XPqz4okvU7AFsLUNlcYEMS/kYQB2k9pbH4qDDH4HqF54rP9+Ofqw677Vruz21TXR5buwfNrS3Dw0fMi0vfKoj6nUd81dVLrCxa737nBgUESERERUQzzbmArucwP9cLM4Zmw2J1477aTsaeiBVtLm7CtrAlbyxpR1tCJtzaXo0CdxlbVbEZ1ixn5aYlRHjX54v0ecNLvVmBaUQZazTasP9Lg2p7Sn822dTo4Qpja1lWRFPi4bb8+K6iwKR4VpJtwuKY92sPwKzfVs6LynKe+xMIJeRibn4qxeakYV5CG1zaUAgBsDmeEgiQHEvQ6deqkExVNnWg12wEAuoGZL8YNBklEREREMcy7gS1X+aHeeO+2Ba7bs0ZkYZZbVVvxPUsBANkpJlS3WPDcmiPYcLQBF0wf0u/jpJ55F/5UtZiR2WBEWqLnpV1/Tm3T60JbtU2bktdT1V4sTQ8Nt4wkIzojOC2sr7TX00s3zsWNL2xEqsmA/20sQ4e1+5gj9fnGq9+UwmJ3YkOJEpCe/PgKGPXKa0awJimqGCQRERERxTDv4MjRw2/sh2vbMCI7eVAvrx0t7209jp/+b1u0h9FrOSnKRftrG5S+KAXprEiKRdKrw/a6e8/EkAylAfntr27BRzsqAfRzs229cPWwCYYWJMXi1Mn+YjLoQ1rprr9pK6ZNGZqOQ4+dD0DpiVTR3IlDNW04VNOG3y7dCwDYcLQBDe1WXH5CUVie2+mUePTjvT5XmtSaug/il05MYJBEREREFMO0i7MpQ9Oxu6Il4IVHZXMnFv1xNQCg5PEl/TI+6hLPIRIA7Chv8rhfECMrtpGnwgzPVevcq3rcGxAn9euqbSKkUMTpapgcoQHFAYNOoM1ij/Yw/NIaXbv3RdLpBIqyklGUlYyFE/JR0WTGf9YexQ0vbASAsARJZpsDP319Gz7dXRXwuIwkY5+fi3qPH1URERERxTBtusikIekAuk91c6f1jiAKRVFWElJNBrR4vX5YkRSbrpk7As9+d47rvsGtWYx7MJNs7M8gSRfStFvtfUw/iMtKlu+rifYQfCpv7EDxPUvx6MdKtZHJ4P91dLSuLazPXd9mwXeeXY/P9lThgQsmY8bwTADAoUfPwz+vPcF13Lj8VJw6Li+sz02hYUUSERERUQyzqau2mdRPhQMt9z1QVzeiyMpOSUB5Y2e37bHWbNsQYh+egUqnEzhrcoHrvntF0oTCdAAVAJQl0/uL0iOJU9tCcfnsYXj2q6M+V0OLpp+8ttXjvtaTyJesMPawKqlrx/ee34CqZjP+cc1snDt1CK4/uRhSShj0Opw7tdB17M/PGh+Tq2IOJrHziiUiIiKibjYfawQAjMpNARC42bYhwC/8RP7sKG/2uT3Wpratu3cRVtx5erSHEXPcl5C/YUGx63a/Tm3Td4V8m481oPiepdhT0eL3eC1zGsxhgFbx1+mjeXU0eU+3CxT2effsq2k19/p5n/ziABrarHjtB/Nx7lSl0b9eJ2Dw8QHJOVMKu22j/sUgiYiIiCiGvbmpHEVZSThpTA6AwBVJg/nT/ViSk5KA//1gfrSH0Wf5MTa1LS/NhNF5qdEeRsxxD2PcQ6X+bLa9qaQRW0ubsOFoA55bcxQAsOZQrd/jW8w2AECqafBOkNEqxjps8Tsl2XuqdWO7rdfnqm21YEJhGma7rSjpj24QB5CxYvD+yyUiIiKKcf9cfRhrDtXhp4vHuVZhc/iZPXK8qRM/eGlTP46O/PnoJ6e4VtGKZ4P5Ij+euIdH+igFSdoy9t/617qgjq9oUqZSDsuM/38nvaX9fDpirCIpFNqHF0MyElHZ3PtqJECZxh1LU/woMP6kiIiIiGLU45/sAwBcNqsIVnUZ5N0VyjSkTqsDx5s64XRK/HddCc5+cjWO1rUDAIZnD96Ls1gwmBsIU/9zD49ElFZtO29qIbJTPPvlCPj/d1DRpIQOBRmxNX2yPzz17Zm4bv5I188n1qa2nT9tSNDH3r9kEi6bPQw/P2s8AKC0oaPXz2tzSp/T2Cg28SdFREREFKOunjcCADAiJ9nVbLuxwwoAmPTrT7Hg8RW44Ok1eOD93Zg1Iguf/fQ0XDJzaMALOIq8eJt2MTa/a7qYNvRTx+VGaTQUKn9TWvuz+X5GktGjMgoAAuWpFU2dyEszBVwRbKC6ZNYwPHLJVFdFklbNFSsSQ1jtryA9EU9+ayaO1SsB0vf7UBVrszuR0EOfvwcvnIyJhWm9fg4KH9arEhEREcUonYDrU/5MdXWcyUPSId36JO2pbMHvL5+OK+cUQQiB3FQTKps7cay+HSNzUqIy7sEu3iqS3v7hyWhotyI90YAV+2pw11s7kJFkjPawqI/6s2daolHfLRAJ0M4NFc2dGJoRWz24+lusTW3rtDrwwfbj3foeBUNbDAJQpmTfevqYkM9hdzph0AUOP29YMAo3LBgV8rkp/FiRRERERBSjbHbpqirQPu13OKXr018A+L/Lp+FbJw53XTR+/7TRsDkkTn9iFcwx9kl3PHnx6xIU37PU1RQ4FPFWkZSRZMSo3BTkpJpcTd0vnTUsyqOieJKWaECr2bNptD1AIFHR1Imhg7g/EgAkGZWajk5rbDTb/r9P9+GXb+/E57urQn7sZbOH4VfnTwLQNSU7VCV1HeghR6IYwh8VERERUYyyOpwwGpRQQgsn7E6Jrw52rYY0KtdzFauC9ETXJ/1vbi7vp5EOPE98th8AsL+qNeTHxvOS5kVZySh5fAkWTSqI9lAojuSlde91VN7ou1+OlBKVzeYB0ZC+L2KtIun1jaUAgO3lSh++K08owojs5KAeK4TAzaf2vlKovs0Cq8OJj3eGHmJRdDBIIiIiIopRTR1WONVV2rSKpFazHQ+8v9t1jK+VtX5/xQwAwLh8LpXeW20WpUrgfxvLQn5svE1tI+qr3NTuQdKM4Zk+j23utKHD6sDQzME9tS0phoIki90Bs61rSVC9TuCJK2fgy7vPCPocQghMLEzDGRPyQn7+WPgeUGjYI4mIiIgoRq3c31V5pFW5/Hn5Qde286cVYnxB97BIW0LZ7gi91wX1Hadn0GDjqyLJ6Kdx8vGmTgDg1LYYWrXtjCdWedy/rJdTW00GHYJtsWRzOCEAGPS6gI3ZKTbxvzkiIiKiOOBrutTfrznB53LJBvUCzuZ0dttHoRmeFXhqx9QHP8PDH+722MaKJBps8nxUJNn8BNmVTWYADJKSjbFTkVTRbPa4f/+Syb06j04n4AzUZd3NuF99gsVPrsb6I/W4680dAIBbTh/dq+el/scgiYiIiCgOhBJOGHWsSArFU8sO4GC1Zy+kHHW1vJ6m37RZ7Hh+bYnHtnjukUTx75wpBfjeSSP79Tl9VST5e/8pqW8HgEG/aptBr0OCXocOW2w02z59fB5eunEuPvrxKchI7t2qjXohQlr1raS+Ayv31WDdkXoAwOwRWb16Xup/nNpGREREFKPG5KVgYmE6gNBWAtMqkhysSArI5nDikY/24KV1x/Du1uNYfZfSD8TplKhvtwIAzPbQv4f9uew6kbd/XTen358zxa1X22WzhuGdrcdh93r/Kb5nKeaPzsb2MqWZs6++SoNNUoI+6lPbtOBneHYSThsfen8jdzpdaEESALSY7chKNuLxy6fjzIn5fXp+6j+sSCIiIiKKUVLCb++IUbkpfh+nNeb2N7WEgJpWM6759zd4ad0xAMA5UwrxnzVH0W6xu3q4AMBbm8pQfM9SNHVYA57P2ovAiWggOqFYqSrx9f6z/kgDOm1KcBJKOD5QJSfooz61rbbVAgDYWtrU53PphPL/ViDPfHkY8x9b7rr/2oZSNHbYcM6UQhh9TNWm2MSfFBEREVGMckjpMU3qW3OKXLd/d9k0v4/T+iZ5VwSQYvOxRlz49BrsKG/Cn6+aCb1O4Jkvj+A3H+3BB9srsL+qa5qbthT20br2bueRbldM3/rXusgPnCiGpSUqVUkJ6vuPzcH3n57EQkWS9l/MVXNH9PlcRr2ux958j328D1Ut5oDHUOxjkEREREQUo47Vd+BYfYfr/nXzi123UxL8dyjQKpLYI6m71zaU4qpn1sFk0OPdHy3AxTOHwb0uIj3RiP1qv6SfLh7n2n6wuq3bubTpbwCwrawpUkMm8umBCybjrMkF0R6GizZVzaQ2kba5Vem9+k2p6/ZJo3NwYjF74QBKRZJWobXreLNHOB0JLWYbjtV7huIO9TkNYagQS9DrGCAOEgySiIiIiGKQNr3KPaAoSO/qKRKo2kjrkWQPsVfFQLfucD3ufWcnpgzNwIe3n4JJQ5T+U+7fJ4eUOFDdimGZSfjJmV1B0gtfl3SbvrbzeHP/DJzIh5tOGYVnv9v//ZD80RrUa4GEUyr9dx5dugf3vbsTADB/dDbsTicMOl6GAkCy0YAOqx3rDtfjgqfXdGvcH25X/mMdTn9ilcc2radROFabNOp1vZrmm62+dih+8F8wERERUQz6cHtFt205bs1pD9d2n2ql0S7SGCR5uuP1rQCAiYVpflcl+mDbcRyobsP4glSPHi57Klvwu0/2ehybnaxc/CwYmxOhERPFD60iSVv+vc1iw60vb8azXx3F904aiUlD0pGcYIDNIV1h92CnTW07WKNUQe6raonYc727tdxVbele+aTdDEfPqgSDLujefO7Tti+ZOazPz039i0ESERERUQx6f1v3IMn9F+9Tx+X6faxRq0jiFAMPLWYbACA9yf/S1sv21qDVbEOW1yfkNy4YhefXlngEfFpQN1mtbCIazHJSlX8zDeqUz2e/Oorle6vx0IWT8fDFU5GgV1b0sjudbKqs0ppt//r93QCAlftrI/I8x5s68bP/bXfd1/rBvbf1ON7cXA4gPKt8GvU6VDR1YtfxZtT00AfpdLcV4vhyiD/+J9cTERERUVQcqmnF3krlk+lLZg71eUygfhZa4BTqMswD3fnThuCdLcexcELgJa7tDtnt+3vv+ROxo7wJv3x7ByYNScPY/DTX99dk0EdszETxQqtIqmvr6h323PdOxBnqku4tZju2lzdjQkFaWPrxDARJXqu2Dc1IjMjzbPfq4fb82pJu0+jWHKrHt0/sW8PtDqsdFrsTFzy9BmkmA3Y+fI7fY02GrvSIK/jFH2Z/RERERDHmg20V0Algw68W4amrZvk8JlCPEe3T/hazPSLji1czijIBABMK0gIeZ3dK18p3y35+Ov529WwY9Tr89erZSE7Q45b/bkabxe7qU+V+QUQ0WJ1YnA0AGJ2b4tqmhUhA18qH+6tbWZGkSjIqzba1CtPLZhf18Ije8e5B9PnPTsOyn5+OFXeejscuVVYA1Vbd64tit599q6X7/z/uTdcz3aYXv7GxrM/PTf2L/4KJiIiIYoiUEu9vr8BJY3KQn+b/0+lAvWq1T/v/svwgDtV0X21ssHI1le3h0++6NgtaOpVpcGPzU7Fk+hAAQGFGIv5y1SwcrWvH9/6zwbUqnsnIX6mJThmXi1W/WIiL/VRRumOPJIUytc2OBDVY6+m9qbe0leE04wvSMDY/FaPzUvGducPxyMVTcP+SSX1+npwATbOP1La5mq4DwOJJBXjl5nkAgMYOW5+fm/oX/9cjIiIiiiE7yptxrL4DF8/w3Xx02rAMAIEvONz3LX5yNZo7++eX9B3lTShv7OiX5+oNrQlwMNMoPtpR6XP7yWNzcf60Idh8rBHff2kTAE5tI9IU56ZABLH6F1dtUyQlGGC2OdGkvkdHqq+dtpLaLaePxs8Wj/fYJ4TAdScVIzmh7xVJJmPXe2GW14IGH+/0fE8tzk3BgrG5+MmZY/Hh7af0+bmpf7FHEhEREVEMeX9bBRL0OpwztdDn/v/eNBdbShsD/tIvhEBuaoKrV0mn1YGMAA2mw+Wiv65FklGPvY+cG/Hn6g0tSApmmeur5/nvFTJzeCY+2lEJi51T24h6w8iKJABKRRIA1LQqjakjtdKmVj152awiTCgMPLW3LxLcfq5DM5M89k0oVBYl+PNVMzGnOBvD1P0/P3tCxMZDkcMgiYiIiChGOJwSH+2owOkT8vwGP5nJCThzYkGP53KvCugp6DDbHEg09q2qpk3th+E9hSKWaB/2BzN9ZMEY/6vipSd6/mzY74XI05PfmoHJQ/2vZsipbQptCm1ZQyeAyC2QoPVzi/T33X2KmnsTcaDraxubn+oKkSh+8X89IiIiohjxzdF61LRaguox0pPaVovrtkP6vzhZuqMS0x76DKX1fZuSdqxeaaSbEMOhilaR5KsgacHYHPzt6tmu+4HCpvFen+gH+v4SDUaXzS7CxMIAQRKntgEADtd69rCLdEVSpFfLu+KEIvxw4RhcPHMoOqyezbYtdiVY6uuHFhQbWJFEREREFCM+2FaBlAQ9FgVRcdSTxy6dhv9tKsP2siY4/Vyc1LdZ8MD7u2BzSNS0mjEiJ7nXz1dSpwRRfTlHpLmabXslSSWPLwHguUR2oAuumcMzPe5zKXMi6o0Uk+fluBb4hFuwCw30VW6qCb88dyIe+3gvqlssuPGFjZg0JA2ThqRjT2ULAE4FHigYJBERERHFgLKGDnyyqwpnTS5AUkLfP7G9et4ICKGEI/4qZh78YDca2pU+Sn39JLxErUganhW7UxZ6upgqcht7sFNAblwwCiNjODwjikVVzeZoDyEmpHj1utOmoIWb9n9ApIMkzbfmFKG6xYy9lS1YfaDWY8peLFetUvAYJBERERFF2Uc7KnD7q1sBABeFYVqbRqu88dV349NdVfhoRyVOHZeLrw7W+a1aClZJnRIkxfIKZlJKCAG/q0pluy1d3dPUm5dvmoe0RANmDM+E1e7Eoon5+MU5EzBpiP/pPESkON7UGe0hxATviqR2i+8ecyv31aC8qRPXzR/Zq+fxV40ZKWPz0/Dnq2YBUHrwHappwwVPr1HGwArOAYFxIBEREVGUPfvlEdftU8flhe282jL33h9yN3VY8cD7uzB5SDpuO2MsgPBVJJU29K3XUqTsr2rF14frA15IuQdMPVUknTIuFzPUKW4JBh2eu/5EhkhEAVx14nDXbYYJivQkzyDpP2uP+jzuhhc24oH3dvX6eaRakaSLwvc90ajH1GEZrgUkdP0UZlFkMUgiIiIiioKaFrOrGemls4a5todzBTDtVN5T2x75aC8a26144srprn4VfV0tqERt1r2nsqXPjbvDaVtZE077/Uqc89SX2FPZ4grO/NGWJWffI6Lwevzy6Sh5fAkevXQq/nr1rGgPJyZcMnNYt22Vzf6rtUKpHG0x21zv6/1dkeTLKWOVlTDZbHtgYJBEREREFAVzH1uOyb/+DACQrn5S++lPTw3rc+jV6Vl2dd37qmYzzv7Tary9pRw/XDgGU4ZmuKZw9SVIarPYPVaJ++pQbR9GHbxDNW2uT9r9ueRva1Ha0IGfLR6Ptb88Ez87a3zA4wvSEwGwYoIoUq6ZNxJFWewrBniGKgsnKNWor28o83t8vdrTridWuxPTH/ocD36gVDFpPbyjWQ30x2/NwPI7Tw9LD0CKPgZJRERERP3M+1NlbaWeVFN421cmqNU1NvX8t7+6BQeqleWmbz9TqczRApO+TG1bf7je4/6hmjY/R4bP2kN1WPzkary5uTzgcfNGZWPOyCzcsXgcstx6IPlTqAZJnTbfvUqIiMLFPbD+13Un4PTxeXh9YylsDt9Nt6tbgmtSrr1/vby+FEDX/zk9tH6LqESjHmPyUqM3AAorBklERERE/czm1rTo5//bhhazDUB4p7UBXQ2jtZWAOqzKxcXjl01zNcXuCpJ6v1rQzS9t8rj//NoSvNVDwNNXB6tbAQA7y5s9tjuc0qORr90pQ5pKoTU7z0kxhWGURET+uU+hNRn0uHb+SFS3WLB8b43HcSlqFU9VsxlNHVY8texAwGlu3vuc/bxqGw18DJKIiIiI+plWgQQA72w9jpfXHwMQ/l/yDV4VSRfMGAIAuHime08m0W1M4fCLN7eH9XzeXI3E3aa2dVjtGHPfx1jw+ApXVZTV7nR9jcG4bv5IrL93ESYUpoV3wEREXrzf88+YkIchGYl45ZtjHtvz0pRgu7LFjN98uAdPLTuIFfs8wyZ33hWmWp88NrqmcIlYkCSESBRCbBBCbBdC7BZCPKxuzxZCfCGEOKj+neX2mHuFEIeEEPuFEOdEamxERERE0eT9S36VOl0h3A2etQonrUfSVwfqAMDVYBtQVhwDlMDF3TX/Xh90GDQmLwUA8OVdZ+CHC8f0bdBBStB7jrumxYxv/2u9a7+2Ep7N4Qyp0ksIgcKMxDCOlIjIN+8gyaDX4TtzR+Crg3UoqWt3bc9MVqblVjebUaE24zYa/L+vefe80yqUWJFE4RLJiiQLgDOllDMAzARwrhBiPoB7ACyXUo4DsFy9DyHEZABXAZgC4FwAfxdCsBMXERERDTh2r/4XZptyP+wVSer56tutKG/swLojSi8j9yWgtSDJ4jWmtYfqg56edv60IdAJYEROMmYUZYZh5D3TGrZ22BzYV9WCS/62Fodru3oz/W9TGZo7bLDana6vkYgolvgKua86cTj0OoFXN5S6tmmxUGWzGYdqlIApJ0DPt+X7qj3ua2/vrEiicAlvR0c3UllCQ/vf3Kj+kQAuBrBQ3f4igFUAfqluf11KaQFwVAhxCMBcAOsiNUYiIiKiaPC1QlqqyYCkMC+LrD3Nj17Z4reRt9Yr6bBbg2yLPbRG01a3qp/JQ9J7MdLQ3PTCRmSoK90t3VGJ1ftrkWLS441bTsIFT69xHff+9uOwOpyu6iUioljz1LdnQqLr/4T89EScPbkAb24qw8/PGo9Eox4OtYfd21u6wn1ngBUrvznS4HG/a2pbOEdOg1lE/1cVQuiFENsA1AD4Qkr5DYACKWUlAKh/56uHDwPgvtZhubqNiIiIaECx+QiSrjihCIYwBx5aE28AaLPYcd7UQozL91w1R5vm9sLXJa5txxs7EQqbXbqCpIKMyDapllJi+b4avLP1uGvb8OxkvHfbAkwdluFxbFOHDa1mO1ITI/bZKRFRn1wyaxgunVXkse3a+SPR2GHDJ7sqAfjuYefrAwkA2HC0Aa1u7/0Op8TGow0YmpEIwYokCpOIBklSSoeUciaAIgBzhRBTAxzu61Xd7V+HEOIHQohNQohNtbW1YRopERERUf9pM9u7bbt2/siwP4/7NIbcVBPsTtlt+px7tU67RRlXmRokBXvNYXd2NbTWR/hCxfvaKTc1AW/eehKGZCR1O9Zsc6C504bsAFNAiIhizcljcjA6NwUvr1emt/kKjfxVJH3rX+uwcn/XdfL3X9qEdUfqccOCUZEZLA1K/VLnK6VsgjKF7VwA1UKIIQCg/q21my8HMNztYUUAKnyc6xkp5Rwp5Zy8vLxIDpuIiIgoIsoaOjzu6wQw1qtSKBwWTczH3edOwKWzhsEpJZw+giT3fkkr9yu/lpWq4xvqI5zx5aV1x9DYoXwCrlVVjcpN6fP4fbF59XJa+pNTfU7bM+iEa0zaNDgiongghMDV80Zg87FG7Dre7DNIcjiBz3ZXYW9lS8DpyNrqblfNHe73GKJQRazOVwiRB8AmpWwSQiQBWAzg/wB8AOB7AB5X/35ffcgHAF4VQjwJYCiAcQA2RGp8RERERNGiBTUb7lsEvU4EXH2nL3Q6gR8tHIuHPtgNh1PCIbsHSe5uf3Urlkwb4gq6clN7V8kzb1R297LyMHFf8W5CQRoK0n2vsKbXCXRalQorrQ8UEVG8uOKEIvx26V5c8PQaFGV1D/X/seqQR+VRbmqCR2Xm2PxUHFJ73+WlmZCWyECdwieSFUlDAKwUQuwAsBFKj6SPoARIZwkhDgI4S70PKeVuAG8A2APgUwC3SSlD6/RIREREFAdKGzqQkqBHXpoJOakmpEf4F3ydEEqQ5JQ+V+0peXyJ6/ZjH+91BUm9DYOMel23lenCxf2T+bMmF3Tbv+FXi/DNfYtg0Al02hzqeNgXhIjiS2ZyV5Df0G7ttn/Vga4Q6ednjcdZkwuQ5TaNd87ILNftOxaNi9AoabCK5KptOwDM8rG9HsAiP495FMCjkRoTERERUbTd+MJGrNhXg4mFaf3W+FQIpdm2xeYMWJEEKMdpFVP+mrn2xKAXHpVD4SRl4CApPy1RHYMOnTYlzPK1xDYRUbzosHavrzh5TA7WHqrHn6+aiYtnKmtUOZwSY+77GABwwsgsvL5RWcvKFKGqVxq8+IoiIiIi6kdav4rh2cn99pyf7FRW/tlQ0oDNxxoDHjs8O9lVkdTrIEmng83HKkPh4D6kaV6rtHmOQcDsqkjir7xEFN+mF3m+32mVrLNHZPk6HLPctpuMnN5L4cX/VYmIiIjCrNVs6zGEGdGPQVJqYvBF6E0dNrSoq8rtq2rt8XhfU9iMehGxqW1aRdK35wz3aBTuLcGgw8FqZfzJCbyIIqL45h0YaUG5e7WRe8XpyJyu/2NYkUThxlcUERERURg5nRLTHvoc97+3M+Bx/RkkWe3BhzpNHd17cQTSZlFCp19fMNm1zaDXRWxqm3baqUX+q5EAIClBj8YOG4ZnJ+HksTkRGQsRUX/x7vVmVcN6f9OV3SsxExgkUZjxFUVEREQURtov929uKg943PDs7qvwREq7W3+NnqpzQp2S1tKpBEnpSV0Nw406AVuEK5J66i51pLYdAHDveZO4ahsRxT29zvPS3WaX6nb/74baLhOn91KY8RVFREREFEYWtfqnp4qc/qxI0kKdtfecic33n+XzmPvOnwgASPOaBtfoY7Ugdy1mGwAg3e1xep3odX+lnmhn9bX6nC/nTS2MyDiIiPqT91uezelUt/t/L9QCfiMrkijMIrZqGxEREdFgVNtqcd3+0xcHcLypE8cbO1HR3IlvzRnu2penri7WH7QpDikJeiT5qUi6fHYRHvt4X7cArMVs81hS2ltLpxokuVUk2RxOVDab+zpsn5xqRVIPi89hdG4KjtS199vKeEREkeT9TmZ39PxemJ5oRFOHjQsOUNjxFUVEREQURu5BzV9WHMRXB2thtjvQ0G7FppIG1770EBpg99WrN8/DTxaNQ4Zb2ONN+8TarE6Du3SWspx0Y4ct4Lm7KpK6zv3etgqPfeGk5Vw95UNf/Px0HHz0vLA/PxFRf1l910KkmpT/K7pVJKmVpoGqM70rTInChUESERERURg53Sp69j1yLr65bzHe/dECjM5NgQSQm5qAa+aN6NdKmXEFafj5WeMDPmeC+ol1p7oS0NxR2TDqBX79/i7UtPivLurqkdR1wfKr8ycBAD7dWdXnsQPAxpIGfPc/G+Bwyq4eST18//Q6wU/hiSiujcxJwbRhysICAgLFOcm46kSlslVbVdM7SPrr1bPws8XjAXQFSa0RCPVpcOP/rkRERERh5N4byL3J8/byZqzaXwunDL6/T39K0OsgBNBqVoKhwoxEXDh9KHaUN+Okx1f4fZyrIsmt2unmU0dhVG4K3tkauOF4sK785zp8eaAW5Y0dUHOkmPweEhGFW366CYBSkbTqrjNwx+JxHvu93wovmD7UdczPz5qARKMOU4cGXuWSKFQMkoiIiGJY8T1LsfCJlSEvyU7Ro/Xw8cfmcPbY3ycadDqBcfmpWHOoDgBg0AnceMooAAjYOLul0wYhgNSErookIQQunTUM64804HhTZ8DnffyTfVi5vyaoMb68/ljQPZKIiAaCvFQ1SFLv60MI0eeOysa+R84L2OeOqDcYJBEREcUoq7r6V0l9B874wyqPKVMUu7Sg4/4lk3zut9idMdsA+ooTily39TqBqeqUihOLs/w+psVsR5rJAJ1XsnPJTKXH0vvbjgd8zn+uPowbnt8Y1Pie/eoofvTKFgA990giIhoItIqkdrV/nfd7baLR9wIKRJHEIImIiChGVbv1pWnssPVY2UGxQcv7hmQk+dxvtTtjNgS5bHZXkGTQKb8m5qYmYHxBmt/HtHTaPKa1aUbkJGPOyCy8u+U4pJSwO5zYfKzBxxlCs7uiBQCnthHR4JCXpgRJ2oqg7hVJdywa5/MxRJHGIImIiChG1bVZPO7f9+7OKI2EQqFNAws09SpWQ5DcVJOr6bbWp1oIgUDFcC1mG9ISfa8Gd+nsYThY04bdFS3407IDuPwf67C9rMnnsfe+swMVIYSlsVrVRUQUTrnq1DbtdwL3iqRRuSlRGRMRgyQiIqIYZVGntmm+OlgXpZFQKFxBkleSdNc5E1y3YzkCyU1Vemno1YoknYBrpTRfWjrtSPezxPQF04YiQa/Du1uPY1+lssJQTavF57GvbSjDA+/tCnqcsfw9JCIKF++KJIPb/y3FDJIoShgkERERxSjvIInig5a5eDdE/dHCMbhZbV6trYwWi1rUsWnj1wkRuNm22ffUNgDISDbijIl5eH9bhat3VKBm5HY/zzOhIK3bJ++xWtVFRBROeV4VSXr3ICknOSpjIvL98RERERFFndZs+/qTi/HC1yXRHUwYfbi9AgvG5iJ7gK4i49BWFfP6uE4IgV8tmYSx+ak4cVR2FEYWnDaLEiSlqlVGOh9T2xxOiRX7alCck4x9Va0BQ89LZxXhs93VWH+kwfVYjXel09j8VI/7X+ypxs7yJiSb9Mg3mXC0rt21j6u2EdFgkJ2SgCtPKMJVc4cD8AzRM5MH5v+jFPsYJBEREcWoz3dXQa8T+OHCMXjh6xKMyYv/Evb6Ngt+/NpWzB6RiXd+tCDaw4mIrh5J3ZMOIQSumjuiv4cUEr1OqUDKV6dT6HTdA59/f3UEv/tkH04Yqazm5h7weDtjYh4ykoxo7rQB6ApIga7vlRaWjsj2/HT9+y9tct3W+oRoWJBERIOBEAJPXDnDdV/PFJ1iAKe2ERERxaDVB2rx5uZy3HLaaBSkJ+LimUNhcwToeBwntGqdkvqOKI8kcrTQJV6nXmnXKEa127bF5kSL2eZxTHmj0hT7eGPPzbFNBj2WTB/iuq8FSQ6nRJMaLpltDtc2f7ybz7PZNhENRsyRKBYwSCIiIooxrWYb7n17B8bmp+In6tK+eakm1LSaAzY9jgvq8O2Ogdv/SQtD4vVT41duno8rTiiCUa+Mv6bVgmV7azyO0TIcq/pz1I7157JZw1y3LepjHvpgN+b8dhkA4FBNG4DA/ZN+cfZ4vP3Dk0L4SoiIBh6G6BQLOLWNiIgoxjz+yT5Utpjx9g9PRqJRDwDITzfBbHNi7K8+wWc/Pa1bL5l4oTVT9tdUeSDQvrR4rUiaOyobc330cPrVuzsxMicZQzOTsL2sCYDS6LWh3Yq3bj054Dm1KXAAYLYq1Uf/XX8MgLIi0d+vmY25jy139Wfylmoy4NbTx8Cg12HXw+fgxa9LcNKYnN58eUREce/syQW44oSiaA+DBjFWJBEREcWQrw/X4ZVvSnHTglGYPaLr4ltb/tfhlLj++Q3RGl6fadU6HVYHXl5/DM4BGCg5XVPbojyQMPtgWwUe+3gfbn91K7aXNwMAKpvNANBj43T3T9C1aWznTS0EAKz95ZmuVd+eWnbQ5+PbLHYY1Kl2qSYDbjtjLNITfa8UR0Q00D3z3Tk4e0phtIdBgxgrkoiIiGJEh9WOe97eieKcZNx59gSPfXmpia7b/qo24oHNbUrb/e/twqQhaThhZOyuYNYb8T61zZ+dD5+DFrMNlU1mnPPUlwCA6hYlSDIZg/9sUut1lJWSgNxUExIMuh6nOi6ZNiTgfiIiIuo/DJKIiIhixBOf7UdpQwf+94P5SErQe+zLT+9asaqpwwYpZVz2SahoMnvcNxn0fo6MX3anEooMpCApM1mp/klPNCK90IjcVBPq2iyuaXwmffA/x6Nqo3Wz1YGkBCWAMuh1yElJ8DmlDgB+uHBMH0ZPRERE4cSpbURERDFgb2ULXvi6BN87aSTmje7e+yXPa+nzimZzt2PiweHaNo/7gZorx6tWs1IxlpY4MD6v2/Obc7D+3kUe2x64YBIApUcSABgNwYdmx+rbAQBmuwOJbkFifbsVn+yqwop91d0eo60gR0RERNHH/5WJiIhiwIMf7IaUwLXzR/rcr1WEaJbv7X6xHQ+OdAuSojSQCOoKkgZGD5/kBIOr6btG699VolYXhRL0lDZ0wO5wotPq6FZ5BwA3vrAJ976zE+1uUzjjsPiOiIhowGKQREREFAM2HG0A4P+C3Hsa26/f3x3xMUXC4dp2j/uSFUlxyft1aghhGp+UwPGmTqzcX4vyxs5u+285bTRe31iK8//ylWsbcyQiIqLYMXB/wyEiIooT7o2GB1JfHV+0qW0jc5JxrL5jgFYk2aDXCSQZB17/J02CwTNICrVf1wtflwAAGtqtrm2/v2I6khP0uGD6UJw5MR93vrndtW9oZlLvB0tERERhxSCJiIgoynZXtLhuG/Q9X5CPL0hFrlfPpHjQZrGjstmMu86ZgOlFGbjuuQ0DtiIpLdEQl83Qg+VebfXHK2eE/Pjn15ZgelEGclISXNu+NWe46/a80Tn45I5T8ftP9yMjyYgUE39lJSIiihWc2kZERBRl647Uu24Hqkh65JKpyE01ITMpAfYYLuXZfKwR4+//xLXMu+ZQjVKNNCYvBTo1ZInhL6PXWs22AT2tDVCmts0ekYknvzUDl59QFNRjvKuY7A4Jvc7/r6JpiUY8cslU/OKcCX0aKxEREYUXgyQiIqIo+/pwV5CkC1DFct38kdh0/2IY9ALOGE5gnltzBFa7E+vdAjIAuORvawEAY/JSXT1vBuqqbWmmgdFoO5B3frQAl80OLkQCgI9/cgoeuWSq635jhxUOpzPAI4iIiCgWMUgiIiIKk093VaH4nqVodOv70pN3tpTjywO1rvvZyQkBjlbodQKOGA5gtEbMNofvkGBETrJr2lcMfxm9pk1tI09j89NwnduqhJXNZqzcXxvgEURERBSLGCQRERH10bH6dky4/xP8+v1dAIB9Va1BP/ZAtTLd65KZQ1Hy+BLogmi2rROxXZGUrC7p3m5xeGy/YPoQJCfoYTLooX2ZA7FHUovZhrTEgV+RRERERIMTgyQiIqI+en9bBSx2J2palZ5A9hCm64wvSAUA/HTx+KAfE+sVSSkJSjVOm8Xu2ialRFWzGflpSpNwLTCL4Tys11rNdqSzIiko/71pbrSHQERERCEK6rccIcR4AP8AUCClnCqEmA7gIinlbyM6OiIiojjgnekIBL9al9mmhE6JISwVrxMCfmaNRc13/7MBh6pbUdFsxvnTCgEAR2vb8cbGMqw7Uo91h+tR1WJ2Ha9VJB1v6ojGcCNqMDTbDofc1AScOi4v2sMgIiKiEAVbkfQsgHsB2ABASrkDwFWRGhQREVE8kfBMkkJZ9f2+d3cCABKNwRcJ63WIualtXx6oRUWzEhR9vLMKAPC/TWW4++0d+OpgLeYUZ3kcb7Ur4//l2zv7d6ARJqVEm8XOqW1BOLE4O9pDICIiol4I9uOyZCnlBuH5m7Hd38FERESDRXOnDUfr2j22hZAjuYRSkRTrU9s0d587AYsnFWBcfiqEEPhox1LXvtKG9gCPjF/tVgecEqxICsLEwvRoD4GIiIh6IdjfcuqEEGMA5SNXIcQVACojNioiIqIY12l14Hef7MVL644hO8VrpbUgk6SvD9e5bifoQ6lI0sEea3PbfLhxwSi/AdnY/LR+Hk3/aDXbAIAVSUH48Zljoz0EIiIi6oVgg6TbADwDYKIQ4jiAowCujdioiIiIYtw1/16PLaVNAICGdmvIj7c5nLjphU2u+8Gs1qZJNupRUh/bvYUWTyrwGSKNzktRb8V+RVVvtJqVgm1WJPUslNc8ERERxY6gfsuRUh4BsFgIkQJAJ6UMfl1jIiKiAUgEaoQUREZyuLYNnTZHr577f5vKAAB7KloweWj0pwdJdZrdt+cMd43t39+b0+24jb9ajOQELVwamCFCV0USgyQiIiIamIKqoxdCPCaEyJRStkspW4UQWUIIrthGRESD1sicZI/7w7OTcMvpowEA9iAaYde3KVVMv7tsGvb+5txejaGqpbNXjws37esdnp0EAJg3yncT5bw0E1JMSsAyc3ima7vF3rtALRa1uCqSOLWNiIiIBqZgGzKcJ6Vs0u5IKRsBnB+REREREcWB/VWexbkr71yI86YOAQDYnT33L9LCk0lD0pGUEHyjbQB46ca5AHqoigqTraWNKL5nKSqb/YdWDjVI0ut0+PKuM/D8DSf2eF69TuD/Lp8GAKhpsYRnsDFAm9qWzookvxZPyo/2EIiIiKgPgv0tRy+EMEkpLQAghEgCYIrcsIiIiGKbd4Zj0OtgUHu+2B09VyRpxxh60SemOEfpM6RVNUXS/e/tAgBsKmnEhTOSfB6jVSQZ9QIjvCq1AilITwQAVLeYMTw7+MfFMjbb7tk/rz0B1jhoFk9ERES+BRskvQxguRDieSidH24E8GLERkVERBTjEvQ6GPUCNrfQyKBXg6QgprY51b5Cul5UFeWkKqvE1bdFtpLneFMndle0AACGZvoOkQDA4dAqkkL7WrqCpIFXkcQeSf4Z9DoYQlilkIiIiGJLsM22fy+E2AlgEZTumI9IKT+L6MiIiIhimK+syKBTLo5tQVRbaIeEGr4AQHKCHolGHep7sVpcKL79r3Wu2/YAX5NNncoXanVVoRokVbWYezG62NRqtkGvE25NxYmIiIgGlqA/LpNSfgLgkwiOhYiIKG443JKkMeqS9lqQ4giiIskhtSqe0J9bCIGcFBPqIlyRZLZ1hUef7KrCvNE5Po9z75EUisxkIxIMOtQMoCDpbysPA+if/lVERERE0RDwNz4hxBr171YhRIvbn1YhREv/DJGIiCj22BxOVx+cE0ZmAeia2qZNbwrE6ez91DYAyE1NiHiPpKQEHYZmKFVDgZpHa1P5Qq1IEkKgIN00oCqSiIiIiAa6gEGSlPIU9e80KWW62580KWV6/wyRiIgo9jicEvNGZePJb83AQxdNAQBY7EoFz2+X7vH5mDaLHU6nhN3hdKvi6V2QlJNqQn17ZCuS6tusmK2GZIUZPfdI0oK0UBSmJ6J6gAVJualcj4SIiIgGrh6ntgkhdAB2SCmn9sN4iIiI4oLDKWHQ63DZ7CLXtlHqamqXu23TdFodmPrgZ1gybQiW7qxEsbq6WW8rknJSErBiXw12HW/G1GEZvTpHIHaHEx1WB4aoFUntFs8qq0M1rchKTkBOqgnNncpKZUnG0PsCtXTasb+6FU6nhK6XoVqsifSUQyIiIqJo6rGZgZTSCWC7EGJEP4yHiIgoLticzm5TuXQ6gWGZSa7KJHfasvBLd1YCAErqOwAAxl6uXpWtrtx2wdNrevX4nmjT1dLV6XvtVqWa6os91fj2v9Zh8ZNf4rdL9wIAtpU3AUCvAq391a0AgLLGjjCMOrqk2veqKMt/9RYRERFRvAu22fYQALuFEBsAtGsbpZQXRWRUREREMc7p9F1NNCwzCcebOoM+T4j9qV1yU8I/feq5NUexYl81rp47EhMKUwEAJqMywH+sOoz3th5HSX0HhmUmISPJ6ArHtpU2ITfV1KsAZc7ILGw61ogEQ/wvB6+Fb9+eMzzKIyEiIiKKnGCDpIdDPbEQYjiAlwAUAnACeEZK+WchxEMAvg+gVj30Pinlx+pj7gVwEwAHgJ9IKT8L9XmJiIj6g5QSvmZiDc1MxMaSxm7btVXavGkVP6HKUSuSfGmz2JFs1Ic8VeyRj5TeTmsP1bu26XU6DM9OQllDJzKSE/DXcybg3CmFuPTvX0NbnG5rWSNmDs/s1UplV84pwqZjjQhiobuYJaWEEAI2h1KJZhwAoRgRERGRPwGDJCFEIoBbAYwFsBPAc1LKnpeiUdgB3Cml3CKESAOwWQjxhbrvT1LKP3g912QAVwGYAmAogGVCiPFSSkfwXw4REVH/cEo/FUlZSfhwRyUcTunRSNvu6J6UbLp/MRJ70VcIUJpt+9JmsWPqg5/h+6eOwrxROVg8uSCo89W0+m54bdQLPHPdHJhtDo+wSCeUPlFNHVYcqW332RcqGAa1JMvu6D4dMB68v+047nh9G9bdeyaSjcqvVb2drkhEREQUD3r6TedFAHOghEjnAfhjsCeWUlZKKbeot1sB7AUwLMBDLgbwupTSIqU8CuAQgLnBPh8REVF/ckrpc1ra0MwkOJzSI5ipajbje//Z4Lr/xBXT8dz35vRpda+cFN8VSS1q4+tnvzqKm1/ahF3Hm4M63+r9ta7bvzh7vOu2QafDpCHpmDUiy6PiSKcTcErpqr6aNSIz1C9BPY/yd21rfDaofn1DGQDgUE0bbE61IqkXq9cRERERxYuegqTJUsprpZT/AnAFgFN78yRCiGIAswB8o266XQixQwjxHyFElrptGIAyt4eVw0fwJIT4gRBikxBiU21trfduIiKifuGU8DmVa2im0ieooqkTFrsDf191CGf+cRWO1LlaDOLKOcOxaFJwlUL++AuhbF6VPY0d1qDOt3J/jev27WeOQ5pJqa7xbiiu0QslSPrv+mMAgOlFmUE9j7d9lUqz7Tte39arx0eb1vT8uuc2YP5jywEoK/QRERERDVQ99UiyaTeklPbe9D4QQqQCeBvAT6WULUKIfwB4BIBU//4jgBsB+Dp5t3kAUspnADwDAHPmzInjjgpERBTP/PVIGqYGSZf/Yx1GZCejtKEDZ08uwP1LJmNDSQNOG5cblufP9lORtHJfjcd9X1PqvNkcTnx1oA4njc7BgxdNBgAsnJiPD7dXoM3ie0a7Tgg4nBIF6co4Uk3Btl30pI0u3iqSHE4Jh1NiTG6Ka5vWbFtbiY6IiIhoIOrpt74ZQogW9bYAkKTeFwCklDI90IOFEEYoIdIrUsp3oDyo2m3/swA+Uu+WA3Bf5qQIQEWwXwgREVF/ckrps0eSVpEEAKUNHXjpxrk4bXweAGBETnLYnt97lbMWsw2PLd2L1zeWeWy3qhVKqw/Uot1ix/nThnQ719G6drRa7Pj2icMxsVD5r/3y2cPw4fYKv9O0dDpl5bp3thzv09exqaTBY5zxYNX+Gjz4wW7kp5m6VWIlJ+jxu8umRWdgRERERP0gYJAkpexdB1AAQilfeg7AXinlk27bh0gpK9W7lwLYpd7+AMCrQognoTTbHgdgA4iIiPrIbHPg6RUHcfMpo5Hlp5InVP6abbtX5vzlO7NcIVIkLdtTjV+9t9NnVc/W0iZY7E785LWtAIDHLp2Gi2cORYrbOLeXNQGAx/dm4YR8fHD7Akwa4vszI71OuBpkpyf2rhoJAO47fxKu+Oc6AMCx+naMzEnp4RHRU9nciUc+2oOPd1YBAI7Vd3QL9L68+wyYDL3+9YmIiIgo5kVyWZEFAK4DcKYQYpv653wAvxdC7BRC7ABwBoCfAYCUcjeANwDsAfApgNu4YhsREYXD0ysO4m8rD+PPyw+G7ZxOKdHTjO+Eflq96+aXNiErOQHv3bYAiyfle+z75+rDrhAJAO57dyee+Gy/xzF3vbUDQPfpadOLMv2uQKYTAg4pMTw7CYv70O9pTnE2hmYkAgBOf2JVr88TSWsP1eHqZ9dj8R9XY/neGo9m5GsP1btuD8tM6lMDdSIiIqJ40PuPEHsgpVwD332PPg7wmEcBPBqpMRER0eD0xqZyAMALX5fgplNGYXh236eYOZ2+p7a5m93LlcxC9dPF4/CjhWO7Vcf4M9lPlVFyQvCVNF8drAMAFKYn9nm5+//ePA+L/ri6T+eIpGv+rawVcubEfDx80RQMz07GX5Yf8piOt/3Bs5FkZCUSERERDXz981EpERFRlJz9p9UeU75O/f1K1LSY+3xep1SmdwWSn57Y5+cJxk8Xj3cLkXpeGMMpfTfgTk8yhvzcVS1mGPq43P2YvNQ+PT5SrHYnmjtsGJqRCINO4LnvzXGFkHNHZXscm5FkDDrIIyIiIopn/I2HiIgGtAPVbd22fbij0uO+lBLST7jiTzBT2yLtJ2eO7bbtoYsm46IZQ/HFz07DB7cv8NiXpk5d01YX02iVSMPcGoWHoq8VSQBw+ewiDMnon+AtWNMe+gwzfvM5MpMTsHBCHtxXr7XalWqkF2+ci/2/PTdaQyQiIiLqdwySiIhoQMtL696zpqa1qyLJbHNg1L0fY8HjK0I6r/TTbBsAlkwb0q3fUCT8/OwJKHl8ice2oqxk/OU7szCuIM1jRbGclAQs/cmpAOBqki2lxN9WHkKHNfSWhMVuK9DtqWgJcGRw+qmdVEgsalhktjmQ6DVtbf5opSJpVE4Km2sTERHRoBL533KJiIiiKCclAVOHpmPl/lrXth1lzfjtR3uw6Vgjdlc0AwAqmkOb7uaUEv5mtv3tmtm9Hm+4/eOa2fjhK1tw59kTkJ+uhGotZjvsDiceeH83XttQijMn5uM3F08J6byr7joD7RY7pjz4GTps9j6PU0D4nXLXX9Yfqcfxxk5cfkKRx/ZOm6Nb/6M7Fo/HFScMx4icvvfbIiIiIoonMfj5HxERUe89++URFN+zFA51+pbZ5kBqomfvn3VH6vHS+mMw6gVuOmV0yM+xpbQRdqfEUq8pcrHojIn5+Mc1s/HtE4e7qmqe/OIAvv/SJry2oRQ/WjgG//7uHBRlhR6IpJgM+OjHp+CVm+f3eZw6nVLlFYzffbIXxfcs7fNzervqmfW4883t3ba3W+zdGpHrdYIhEhEREQ1KrEgiIqIB5dGP9wIADta0YmJhOsw2J5KMOvz+8umYOSITZ//pSwDArofOcTVH/ufqw0Gf//UNpfj1+7sBAN86cXiYRx9+iUY9zps2pNv21Qdq8dtLpuLa+SP7dP6pwzL69PguAs4gg6R/rT4CQJmaJ8LUqKqh3ep3X4vZjsQQVrQjIiIiGsgYJBER0YBSnJOMkvoOV0WSxe6AyaB3hT4nj8nBmRPzfa6wFSiYsNqdePjD3Xjlm1KcOi4XT39nFjKTEyL3hUTI0IxEVDSb8ex352DRpIJoD8dFmSYY2tQ2q8MZtv5EXx2s7batMD0RVeoKf29vLse9500Ky3MRERERxTMGSURENKBoDbDvenMHDHoBq90Jk1to9Or3u0/D0gnAKf0HE1XNZtz+6hZsOtaIW04fjbvPmQi9vwZJMe7920+B2eZwLWMfK4T6MwiF2Rq+IGnlvhrX7apmMwozEjEiO9kVJNW1+a9YIiIiIhpMGCQREdGAtKdSWUnMoBM+q4/c3X3uRDz+yT44nd33We1OzP/dcgDA09+ZhQtnDA37WPuTr1XsYsHB6jY0tFuxrawJM4dnBvWYqhYzMpKNPR8YhPe2Vbhun/r7FbA5JKYNy3BVJY3NTw3L8xARERHFOzbbJiKiAaHNYse/vzqCI3XtHtvtTokUU+DPTbTiIoePbs+dVgcA4KoTh8d9iBTLvjnaAAC4+tn1AY+rcltdb+fx5rA8956KFo/7NofyOmjqtCIrRZm+OHtEZliei4iIiCjesSKJiIjiTk2rGVXNZkwvykR9mwUvfF2CF78uQYvZ9zL05/toNu1Omw7na/l5m1qmNHloeh9HTcHosDoC9qpaub9rCtqo3JQwPafv102HxYHJQ9Lxf5dPw4TCtLA8FxEREVG8Y0USERHFnUV/XI2L/roWD76/Cwv+bwX+uvIQTh6Ti/duWwDv1kXzR2f3GDi4giQfTXp2litVL1tLm8IydurZ2kP1PrebbQ7c+85O1/0Xvi7Bp7sq+/x8iUalz9Kjl05Fmlv1Wn27FZ/trsb0osyw9WIiIiIiincMkoiIKO60qpVHr24oxUUzhuKLn52Of153AmYOz8Q39y3Gyl8sxKnjcgEA18zreXl7rXG2r2bPWgWMeyUMRdYLXx/1uf3TXVUe9z/cXoFbX94S8FwOp8RrG0p9hoQai12pOhuWmYSdD5/jsS83Nf5W5iMiIiKKJE5tIyKiuJOWaECr2Y6v7j4ThRmJHvvy0kzISzPh79fMxpqDdTh3amGP59OpQZLDR9igbTP10LCbwiM7JQHL99XgWH07RuYolWRSSvx3/TGkJHT/tSWzh2bbl/xtLXYeb8a+yhY8fPFUn8dY7EofLK3q6O/XzMaPXlECqkUTC3r9tRARERENRPytmIiI4k5+mglLpg3pFiK5S0s04rxpQ/z22nGnTYeTPnokLRirVDb97rJpvRssheStW0+CXgi8+PUx17ble2vw6/d34843twMAfnX+JNe+wnT/rwGgqyF3SX2H/+fcXA4AqG5RGnkXZSW59hn0Pb9+iIiIiAYTBklERBR3alosrr424aBXwyZfq7Zpm4ZlJoft+ag7LcwbmpmE86cNwZubytBmUaYwdtocHsdmJHVVIe2rag143qvnjQAALJ6U7/eY9ETlfPlpJgDA9KJMzC3OBgD4nxBHRERENDgxSCIiorjS1GFFq8WOt7eUh+2cXau2dd9ndSghRgKntvULIYAbFhSj1WLHO+rP2Pt7X5ybgjduOSmo82lBYKDKtOlFGQCUEEtz+5ljAQAHegiqiIiIiAYb/lZMRERx5b2tx8N+Tq1Hkq+GzDa7ss3IKU79Qi8EZo3IwozhmXhhbQmcTtktSNIJYO6obHxrTpHPqW27K5rxxqYyFN+zFBa1msnpo9pMo/XB0rst+XfquFzcdsYY3Os2jY6IiIiI2GybiIjiiJQSr28sAwCsuPP0sJ1Xr+YUvppt251akMTPXiJpzshsbChpcFUO3XByMX76v2348mAtTF7f+xHZyjRDg14Hu9Ppse/TXVW49eXNrvva1Le6Nqvf59YyJp1bkCSEwF3nTOz9F0REREQ0QPG3YiIiihs7jzdjX1UrHr10KkbnpYbtvF1T23yt2qYEFe7VKhR+z10/Bx/9+BTX9/n8aUOQl2bCC1+XeExLW33XQuSrVUhGnYDN0fUzk1J6hEgAsKeyBQBQ1dzp97m13lj8ERMRERH1jEESERHFjU93VQEAFk7w3zi5N9ISlQJdX1UrWkWSgSlDRKUlGjF1WIbrfoJBh2vnjcSq/bU4XNvm2q5VIwFqRZJDCfocTolfv7/b7/nLGwMESdrUtiBW+CMiIiIa7BgkERFR3Pj7qsMAgNzUhLCed0ZRJgBg87HGbvt89c+h/nH1vBFI0Ovw/Nqjrm3u1UkGvYDdKdFpdeDWlzfjv+uP4VtzinyeSwuSPtlZiX1VLR77pFaRxJ8xERERUY8YJBERUdyYPCQdAGAy6MN63pxUE8bkpWBTSQMAoLS+w9V4+xO1Csqg43+Z/S0vzYQLZgzB4dp2n/uNOh0sdie+8+x6LNtbjd9cPAWPXjqt23FzR2WjoqkTDqfED1/ZgnOf+spjPyuSiIiIiILH34qJiChujMpLwZi8lIice87IbCzfV4NfvrUDpz2xEh/vqoTDKV1VSqxIio4bTh7luv3q9+d57Fu+rwYAsK2sCf+89gR896Rin03RL545FHanRHWLueuxe6tdlUhamyUdgyQiIiKiHjFIIiKiuGG2OpBoDG81kmZOcRYA4H+blFXh3t1yHF/sqQYA3HvexG5L0FP/mFaUgTkjlZ9NmsnosW+v2kj7D1fOwDlTCj32nVichT9eOQNHf3c+irKUvkruU9puenETrntuA/ZVtbhNbYvYl0FEREQ0YBiiPQAiIqJgWR3OiAU6JxZne9xfvq/GVfFy86mjI/KcFJwfnDYaW17ejGyv3lhLpg/B0h2VOGtSgcf2TfcvRqrJ4Aodi7KSAAD/21jmOubBCyfjqWUHcf6fv0JxjlLlxqozIiIiop4xSCIiophlsTtgtTuRlqhUotgdEsYIlY2MzEn2u48BQ3SdPaUQ2x48G+mJnhVJT1wxHT88fQwykj2356aaPO4Py1SCpM92V7u23bBgFC6ZOQx/Xn4Q/11/DDrBnzMRERFRMBgkERFRTLHYHVhzsA5Ld1Tiiz3VSEzQY+OvFgNQmiJH6mJf+OmPM2tEZkSej0LjHSIBQHKCAVOHZfT4WPfpkHNHZeMPV8wAAGSlJOChi6bg2vkjUNbYGfYm7kREREQDEYMkIiKKCaX1HXhq2QF8sacarRY70hMNyElNQEl9Bw7VtGLxk18i1WTo92Bna2lTvz4fRdYJI7Mwwqv6bGx+Gsbmp0VpRERERETxhW0liYioXzmcEv/bWAqbw+mx/bWNpXh323GcO7UQz99wIjbdfxYunjkMAPDBtgoAQJvFjoPVbf063vREfuYykEwPooKJiIiIiPzjb8dERNRv6tss+PJgLX759k7Ut1vxo4VjXfuO1rYjzWTAE1fOcG3TprENz+6qIKlyW8K9Pzz73Tn9+nwUWYsnF/R8EBERERH5xSCJiIjC7mhdO1JMeuSnJbq2Ha5tw6I/rsbwbKXxcVlDB+rbLMhJNaGm1YxPd1d1O4/WDqnNYgcAPH/9iRibnxrRsQsBXH9yMU4fn4eFE/Ij+lzUf04dl4uvDtbBqGcxNhEREVFfMEgiIqKwklLijD+sAgCUPL7EtX1vZQsAoKyhEwDw2oYyvLahDCWPL8Gbm8p9nkunJkltZiVImlOc5VrBLRLcx0sDy7PfnYPmTlu0h0FEREQU9xgkERFRWL237bjP7TkpJp/bS+s78MRn+33u06krqbVZlSApOYH/bVHvJBr1Hqu3EREREVHvsL6biIj6zOGUqGuzoN1ix6NL97m23/P2DljsDgDo1lxbc9oTK/2eV5vaVt2s9EXSeiYREREREVF0MEgiIqI++8Pn+zHnt8uw7nA96tosru2vbyzDOX/6Esv2VMNqV4KkV2+eF/R5tYqk99RV24iIiIiIKLoYJBERUZ99sacaAPCP1YcBAHcsGoe5o7Lx3PfmQK8TuPmlTXjwg90AgOzUBJQ8vgS5qcpUt/TErulqiyd5NrfWgqT5o7Mj/jUQEREREVHP2GyCiIj6TK8GPpuPNQIALpg+BD87azwA4LTxeXh5/TH86YsDAIAUtc+RVrnUYrbjhJFZePuHJ0NK6XledSpbqsmAoRmJICIiIiKi6GKQREREfVbfbvW4X+AW+hj1OtywYBQunjkMu443Y3h2crfHawGUEJ49kLRgqbyxE3o9+yMREREREUUbp7YREVGftVvsrtsF6SakJxq7HZOdkoDTxue57t973sQez7uxRAmY9lW1uqa5ERERERFR9DBIIiKKcQ3tVnx1sBa1rZaeD46CNosdCYau/06qW4Ib5y2nj0HJ40sCHtNitrluH6vv6N0AiYiIiIgobDi1jYgoRjR32mCxOVDW2IltZU3YXtaEbWVNKG1QApTvzB2O3102Pcqj7G7qg58BAN669ST89H/bMDKn+9S1QL66+wwkGvU+9yUndG0flpnU+0ESEREREVFYMEgiIooRs37zOZxuvaaHZCRiRlEmvjN3BJ5bcxTtFkf0BueHe3PsOcXZWH3XGd0aZvfEV88kzU8WjcP+qla8f9spyEjuPl2OiIiIiIj6F4MkIqIYoYVI/7ruBMwcnomC9K6G1W9uKoMjxICmP9jVQc8ZmQVAW2UtfL2MpgzNwKq7zgjb+YiIiIiIqG8YJBERxZhzphR226bTCTidMRgkOZQxLZpUEOWREBERERFRf2CzbSKiGGBzOAEAN50yyud+vRBwxmRFkjJuo54rqhERERERDQYMkoiIYkCnTel/NCQj0ed+nU5AzZpiikOtktIJBklERERERIMBgyQiohhgsSkpkcng+21Zr0OMViQpYzKwIomIiIiIaFBgkEREFAOqW8wAgNo2q8/9eiFc1T/9oaKpE80dth6P0/o2KU22iYiIiIhooItYkCSEGC6EWCmE2CuE2C2EuEPdni2E+EIIcVD9O8vtMfcKIQ4JIfYLIc6J1NiIiGLNf9cdAwC8+k2pz/06XfA9kraWNuLON7a7gqevDtaizWIPaTwnP74C8363rMfj7nlnJwCgsd13AEZERERERANLJFdtswO4U0q5RQiRBmCzEOILANcDWC6lfFwIcQ+AewD8UggxGcBVAKYAGApgmRBivJTSEcExEhHFhKP17QCAUbnJPveHUpF0zb+/QYfVAb0OOFLbjk3HGgEARx47H7oQKofMNiecTun3McX3LHXdbmjvuXqJiIiIiIjiX8QqkqSUlVLKLertVgB7AQwDcDGAF9XDXgRwiXr7YgCvSyktUsqjAA4BmBup8RERxZJjapBUmJHkc78uhFXbOqxK/v7GpnJXiAQAo+/72DWFLlj/WXvU53bvUOvl9cdCOi8REREREcWnfumRJIQoBjALwDcACqSUlYASNgHIVw8bBqDM7WHl6jbvc/1ACLFJCLGptrY2ouMmIuov1S0WAIDBT/WPXheeHkk7yptDOv63S/eirs3SbXt9u+e2y0/o9nZNREREREQDUMSDJCFEKoC3AfxUStkS6FAf27pdNUkpn5FSzpFSzsnLywvXMImIompCQRoA4L7zJ/ncv+5IPTaWNPrcF4yLZw4FAKSY9Fi1vwbtfnomrdxXg90VnmGTryomu0N5e75u/kh8cPsCPHrJtF6PjYiIiIiI4kdEgyQhhBFKiPSKlPIddXO1EGKIun8IgBp1ezmA4W4PLwJQEcnxERHFiupWM66dPwJ5aaaAxzV3Kr2IWsw214pp3sbkpWDB2BzX/YmFaTh5jHK/pK4D1z+/EXe/vaPbed/cVIYbXtiIJX9Z47Ev0ajv9hxaddSM4ZmYXpQZUu8lIiIiIiKKXxFrti2EEACeA7BXSvmk264PAHwPwOPq3++7bX9VCPEklGbb4wBsiNT4iIhiRavZhqYOG4Zn+W607a6mxQydAKY/9DluOW00frxoHA7VtGHLsUZsKW3ElmONqGg2Y3pRJgBgSEYiPv3padhW1gQAaOpUVldbuqMSP1vchrH5qQCARX9chbo23yuv6UT3kEgLkvT9MkGaiIiIiIhiRSRXbVsA4DoAO4UQ29Rt90EJkN4QQtwEoBTAlQAgpdwthHgDwB4oK77dxhXbiGgwWHuoHgAwPNt/kPT2D0/C5f9Yh/KmTleF0L++PIJ/fXnEdczQjETMGpmFm0Zk4cLpQ3DOlALMHpEFoKv3UllDh+v4C57+CvcvmYxr5o3wGyIBgM3h7LbN7gqSmCQREREREQ0mEQuSpJRr4LvvEQAs8vOYRwE8GqkxERGF28HqVpz1py/x9g9Pwgkjs3t1jltf3gwAASuStH3ljZ0Yr/ZTcrfu3jMxxGvFt3OnDul23GsblDUNnv7OLLyxqQz3v7cLq/bXdDsOAG5cMAr/WXsUVrsTUkoIt8qkP3y2HwCwtbQRF80YGujLIyIiIiKiAYQfJRMR9cGaQ3UAgA+29a6lm5RdfY6KspL8HpebakKCQYfyxg6fvZG8QyRvaYmenxuMyUvFizfMxQMXTMaXB+p8PmZkjhJeff+lTRh178cAAKdT4rGP9+LT3VUAgClDMwI+LxERERERDSyRnNpGRDTgJRiUPP7Fdcew+kAtVt11RkiPd7iFQpnJRr/H6XQCRZlJKG/o9HgMANyxaFyPzzMyJwUAYNQL2BwSqSYDdDqBm04ZhXmjsnHB00qD7ZLHl0BKCavDiQ1HGwAAlc3Kqm1WuxN3v7Ud722rwHdPGomfLBqH3NTAzcGJiIiIiGhgYUUSEVEfJLh1my6p7+o/NP7+T/DHz/d7HGu2OfDlgVqPbTZHVygkfDS1djcs6//bu+/wOMprj+O/oy7Zki25ypZxw72AC8VU0w2mhJZAuEACCQTIJYVQLiWhhOAAISShhRBqAoQaiqkG00yxMeCCC25y71WSrbb73j92tV5JK1t1Z0f6fp5Hj6fuHPnsSjNHb8nUqq07I+MTSdIp+/XQpUf2q1esPTtmRq6Xkbo77mE9cqodZ2ZKT0lWao2RtM975HP995s1uvqEQbrl1GEUkQAAAIA2iEISADRBenjg6yrBoFN5ZVDllUH97f3F1fb99pW5uuDR6Vq8oUiS9PrsNRry27fqfa2C3CzNWrVdP3osNKHlwf3y9LdzRykrrX6NSzu1T4ssJyXtLlpVFbCOGtSl2vGpydULWzMKt+rOM0fqiqP23WvRCwAAAEDrRNc2AGiC9JTq9fiLn5ihqQs3xjx24bpQAam4LDQh5fy1Oxp0raoxlFZt3SVJOrhfpwafP3vVdklSUo1C0PQbjlGHzOpd6+atLaq2/v2xBfr+Ab0adE0AAAAArQuFJABogspA9fGKahaRXpy5Sr3ystQrL1NVPdKqBti2Oie2jK3mYNz79erYoPN7dtx9fnKNQlLX7Ixaxx+2b+dq64fWWAcAAADQ9lBIAoAmqAwGq62bSVETsemq52fVOqdq931TQ13f3vrl4crNSqt1XE3RM7MVTprY4FijC0lJ9ejY3D5996+Iv507SiePzG/wNQEAAAC0LoyRBACNMH/tDvW5brI+X7q52vYTh3evtv7+VUfqiYsO1O+/NzyyrbyyevFpcPccdcup3SKopugxjhqjIDcrspyctPfWUFWFpJNH5uuU/XowLhIAAAAAWiQBQGNUFZCemb6y2vaSsoCG5ufojNE9NX5QV/Xr0l79urSXFOrSdtMr32rbznKt217a4Gv2zsva+0F70DOqa1zNMZJiyUxL1sfXHKX8DnsvcgEAAABoGygkAUAjZNSYra1KSVmlMtOS9ZPD+9XaN2qfXEnSz/71VaOuWZ9WRHsSXUiq72v1amLxCgAAAEDrQtc2AGiEmrO1nTm6QJJUVFqpzDqKTLGKT2//8oh6X7OpXctyMnbPylZzsG0AAAAAqA9aJAFAI6SnVC8Kfbe+SJK0cH2RFoaXa8pIrV58ap+eokHds1smwL1IamLrJgAAAABtEy2SAKARUpOrF2LmrN4eWb5mwqCY59RskXTZ+P7NHxgAAAAAtCBaJAFAIwSdkyRNHJGvyXPWav6tE/TCzJU676Dedbb26ZCZqg6Zqdq+q0KSdMG43nGLt0p2RoqKSivjfl0AAAAArQMtkgCgEQLB0L9XHjNAhZMmKjMtWeeP67PHLmOpyUn6w+kjIuvZUWMWxcs7vzpCj/5obNyvCwAAAKB1oJAEAHsRCDqVVwarbwu3SEpu4E/RiSPzmyusRsnvkKmjB3fzNAYAAAAA/kXXNgDYi0ufmqkp89ercNLEyLZgMFRISmrE7GdP//QglZQFmi0+AAAAAIgXCkkAsBdT5q+PLJdVBrRkQ4m+WblNkpTciNnPDunfudGxpCSZThzhbasmAAAAAG0XhSQAqKdj7/lQyzaVKBBujdQxK1W57dLiGsPiP5wU1+sBAAAAQDQKSQBQT91zMjRhWHcN6p6tQd2z1bdzO6U2dJAkAAAAAPAxCkkAUE+PXDhWGanJXocBAAAAAJ6hkAQAe3FI/04qrwxSRAIAAADQ5tEnAwD2oqwyqLQUflwCAAAAAE9GALAXgaBTCmMhAQAAAACFJACoy6biMo39/bsqLqtUknkdDQAAAAB4j0ISANTh6udnaVNxuRZvKFaSUUkCAAAAAApJAFCHQ/p3jizTIgkAAAAAKCQBQJ2yM3ZPbEmLJAAAAACgkAQAdaoIOq9DAAAAAICEQiEJAOpQURmMLBduLvEwEgAAAABIDBSSAKAOFYHdhaTv1hd7GAkAAAAAJAYKSQBQh+hCEgAAAACAQhIA1Gn1tlKvQwAAAACAhEIhCQDq8Mz0FZHlAV3bexgJAAAAACSGlL0fAgBt00kjuuuNOes095YTlJJkXocDAAAAAJ6jkAQAdchOT1W3nHS1T+dHJQAAAABIdG0DgDoFnFOy0RIJAAAAAKpQSAKAOgSDTkl0aQMAAACACApJAFCHgHNKppAEAAAAABEUkgCgDkEnurYBAAAAQBQKSQBQh2DQiToSAAAAAOxGIQkA6hAI0rUNAAAAAKJRSAKAOgScUxJNkgAAAAAggkISANShIhBUego/JgEAAACgCk9IAFCHsoqg0lOSvQ4DAAAAABIGhSQAqENZZUDpqfyYBAAAAIAqPCEBQB1KK+jaBgAAAADReEICgDqEWiTRtQ0AAAAAqlBIAoA6lFXSIgkAAAAAorXYE5KZPWpmG8xsbtS2m81stZl9E/46KWrf/5nZYjNbaGYntFRcAFBfoUISLZIAAAAAoEpL/qn9cUkTYmz/s3Nu//DXG5JkZkMlnSNpWPicB8yMpzcAniqtCNAiCQAAAACitNgTknPuI0lb6nn4aZKedc6VOeeWSVos6cCWig0A6qOsMsisbQAAAAAQxYsnpJ+b2exw17fc8LaeklZGHbMqvA0APOGcU3llUBl0bQMAAACAiHgXkh6U1F/S/pLWSvpTeLvFONbFegEzu8TMvjSzLzdu3NgiQQJAWWVQkmiRBAAAAABR4vqE5Jxb75wLOOeCkv6h3d3XVknqFXVogaQ1dbzGw865sc65sV26dGnZgAG0WZFCEi2SAAAAACAiroUkM8uPWj1dUtWMbq9KOsfM0s2sr6QBkqbHMzYAiFZWEZAkBtsGAAAAgCgpLfXCZvaMpPGSOpvZKkm/kzTezPZXqNtaoaRLJck5962ZPSdpnqRKSVc45wItFRsA7E1Vi6SMVFokAQAAAECVFiskOefOjbH5n3s4/nZJt7dUPADQEGWVtEgCAAAAgJp4QgKAGEorqsZI4sckAAAAAFThCQkAYoi0SKJrGwAAAABEUEgCgBjKwi2SMmiRBAAAAAARPCEBQAxVg23TIgkAAAAAdqOQBAAxMNg2AAAAANTGExIAxBBpkUQhCQAAAAAieEICgBhKK0ItkjLo2gYAAAAAERSSACAGWiQBAAAAQG08IQFADFWztjHYNgAAAADsRiEJAGJgsG0AAAAAqI0nJACIobQiqOQkU2oyPyYBAAAAoApPSAAQQ1llgNZIAAAAAFADT0kAEENZZZBCEgAAAADUwFMSAMRQVhFUegoDbQMAAABANApJ8MyXhVt0zQuzIoMaA4mkrDKgjFR+RAIAAABAtBSvA0DbdPvkefrnJ8sUdNLFh/XToO7ZXocEVFNKiyQAAAAAqIVCEuJu8uy1+sfHyyLrpRW0SELiKasMKJ0WSQAAAABQDU9JiLv/frO62vquRhSSNhSV6v0F65srJKAWBtsGAAAAgNp4SkLcfbxoY7X1krLKBr/G6fd/qose/1KzV23Tis076zxua0m5rn1htvpcN1nllcEGXwdtV6iQRNc2AAAAAIhGIQlxtXRjsUorQgWdu84aKUmatWp7g19n9bZdkqRT75umI+6aqmDQ1TrmX58v16jb3tV/vlwpSbro8RkqbkTRCm1TaQWDbQMAAABATYyRhLh6f8EGSdIn1x6ljNRQa4+/vrdIvz5uYINe55jBXfVe+LUkaeCNb6pnbqZ6dgx/5WbqkahxmCTpk8WbNPx3b6tw0sQmfhdoC2iRBAAAAAC18ed2tIh7p3ynPtdNlnPVWwotWFekrtnpKsjNUnZG/eqYC9bt0D8/WaY+103Wuu2lKimrVOf26eqWk643rjxcVx03UBcf3lcjCzqqtCKgjxZt1F/eWxRpfTR+UBct/cNJkddbvKG4+b5RtFpllQHGSAIAAACAGmiRhGZXEQjq3imLJIWmUM9M292q44WZq5TfIUOS6t3aY8K9H0eWD77jPUlS5/bpykpL1tAeORraI6fWOWWVAS3ZUKLL/j1TN04coqQki+y78NHpeunyQ9QtJ6Ph3xzajLKKILO2AQAAAEANFJLQZDOXb5FzUkl5QCbp2RkrIvuOvGuqLh/fX+mpyUpLDj2Ur91eGtl/zYRBuvOthSouq1T79Npvx0CMsY8kaVNxmQZ2a19nTOkpoSLTh1cfVWvf6m279PyXK/XzowfU91tEG0TXNgAAAACojUISmuzMBz+rc9+GojLd/Nq8attSk3e3DurTqZ0kafnmEg3r0aHW+Tt2VUiSfnfKUAWCTr+fPF+SdNn4/jq4X6dGxZuabCopDzTqXLQdpRUBWiQBAAAAQA08JbUBHyzcoKuem9Wk16g51lF9Pf+zcfrqpuP0+f8dow9+M16d2qXp7rP3i+zfXUjaGfP8qnGO2qWn6CeH99OffxA69/Lx/XXkwC4NiuXVnx+qO88aqYqA04MfLGnMt4M2wjmnssqgTLb3gwEAAACgDaFFUhvwo8dmSJImnTlCqckNrx3+9pW5evKz5Tp9VE/9+Qf7V9u3o7Qi5jknDu+uB84bLbPqD+Izbzqu2nrvTlmSpGWbSrSzvFI/+PvnOnJgFxVuLtGSjSVaujE0MHbHzFRJ0umjCnT6qIIGfw+SNLKgo0YWdNQ1L8yWJK3cslO98rIa9Vpo3T78bqMk6aEPl+i6Ewd7HA0AAAAAJA4KSW3I9l0V6tw+vUHnzFm1XU9+tlyS9PLXq2sVkp4K75OkadcdLZP08EdLdcPEIbWKSLG0C4+LdNfbC/X4p4XaWFSmOau3q3enLPXv0l6H7dtJA7tl68hBDWt9tCe98jK1cssu/fW9RborqnUUUKXqvfvr4wZ6HAkAAAAAJBYKSa1YWWVAj08rjKyP/f0UDcnP0Zu/OLzOc5xzck6aMn+9RvfO1Sn3fVJt/5ptu9SjY6YkadriTbrr7YXKzUrVh9ccpZyMUKuhm08d1qh4e3bM1MaiMs2++fjIa7WEj685Wre+Nk9PfFaoy4/aV307t4vsCwadzFSvIhhar0AwKEkN7j4JAAAAAK0dhaRWqLisUsN/93bMffPX7lCf6ybr0+uOjhSEol3/8lw9M31FjDNDLvvXTP3n0nHKSE3WeY98IUm674ejm6XwUzUeUksWkar8bHw/PT19uf4y5Tvde86oyPYzHvxUWWnJevqnB7d4DEhcu8pDhaTMNGZtAwAAAIBoFJJaoedmrKy17bLx/asNMH3IpPeV3yFDHbPSlNcuVR2z0pSblRqziHTTyUN18WF99c6363TJUzN143/n6q6zRkb2H7pv52aJe/GG4mZ5nfromp2hC8f10cMfL9XPj95X+3bN1qdLNumbldviFgMS17vz1kmSMlIoJAEAAABANApJrVBVr6w7zhihkrJKXTCuj9JSkvTgB0s0JD9H89fukBQqAG3bWa6tOys0f80Obd1ZHnmNL64/RgvWFWlQt2x1ywmNq3T8sO668pgB+ut7izSiZwftk5el9JTmnfjv0iP7Nevr7fla/fWvz5frz1MW6b5zR+n3r8+XJHXNbtg4Umhd+l//hgLB0CyF9HAEAAAAgOooJLUiJWWVemvuOt3y2jxJ0tljCpQSNUvbgtsmKDnJ5JyUnGRKTqr9lLxsU4nap6eoS3a6uuVk1Nr/y2MGaN6a7brt9Xlql56isb1zmxz3OQf00rMzVuqB80brpBH5TX69+sprl6YfH9pX901drLNGF2heuMCWEuP/BW1DIOgiRSRJMT8DAAAAANCWUUjyuWDQadqSTXrpq9V6a+467aoISJIO7pdXrYgkSRmpe++mEz3wdCxJSaZ7frC/vnffNC3dVKK0ZmiRdPvpI3TjyUPVPj3+b8efHN5XT3xaqB8/PiOyrSKqkIC2ZdXWnZHl00f1bJb3NwAAAAC0JhSSfK7f9W9IknIyUvS9UT11xuieGts7t0VnHcvJSNXDF4zR9+7/tFkGxk5OMk+KSJLUMStNFx/eV/dOWRTZVhkIakNRqXaVB9S7054La/CPz5Zs1u1vzNMLPzukzqLq+f+cLkm6fHx/XTNhcDzDAwAAAABfoJDkY9t3VUSWp99wbL1aHDWXfbtm681fHO5ZAag5XXRY9ULS1p0VOugP78m50FhR5ZVB9crL8jBCNIdbXvtWC9YVafGGYg3v2aHWfuecVmwJtUgaWVB7PwAAAABAot+Gjz0wdbEk6dEfjY1rEalKr7ws5bZLi/t1m1tORqouOrRvtW0u3LvtoD+8p8PvnFrrnPLKoO58a4HueWdhPEJEM2gXLnqWhrt/1vTiV6slSeceuI8mDI/fWF0AAAAA4CcUknxq5Zademxaoc4aU6CjB3fzOhzf++0pQ1U4aWK9jl2+uURnPfSpHvhgiV6fs7aFI0NzWbe9VJJ0x5sLNHf19lr7f/P8LEnSLacOi2tcAAAAAOAnFJJ86vA7p6o8ENQvjhngdSitytUnDIq5PRgegPu1WWs08a+fqHBTibLTU7R0Y0lkHxJDRSCoaYs3qaSsMrJtc3GZVm/bJUmauXyrTv7bJ9XOcW53DhlgGwAAAADq5v8Bbtqg6NYUBbmZHkbS+lx2ZH/17pSlMb1zNe6O9yPbr395jibPWaui0kqN6Z2rv5yzvw77Y6jLW7/r39CxQ7rqkQsP8CrsVm37rgqVVgTULSdjj8ftLK/Us9NX6oEPlmhTcZkkRVqZXRVubVSXssqgJOnM0QXNEDEAAAAAtF4UknzmPzNW6NoX50TWW3J2trYoKcl08sgekqR98rIigy8/O2OlpNBsXr86bqBSk6u3Wpkyf0N8A21Djr77A20uKa+z62HhphK99PVqPflZobbtrNDY3rmRQtKm4jJ1bp+uLwu36sJxvTV+cFf9+LEZkkJjJVWNLVbVemm/XgyyDQAAAAB7Qh8OH/l08aZIEeny8f0179YTPI6odRvWI6fWtmsmDK5VRJKkofm1j0Xz2FxSXue+Ndt2afzdH+iv7y3S2N55evGycXrhskMi+8f+foo+XbxJxWWVKikP6ND+nSP7tu3cPevhzvLQANwZKfEftB4AAAAA/IRCkk889VmhfvjIF5H1ayYMVlYaDcpaUs1C0rkH7lNt/eZThionI0XjB3VRchItw1ratMWbVF4Z1Al//ki3vjZPfa6brCc/Wy5JunHiED1y4ViN6Z0nqXquZi7fKkkakp+jtJQkdW6fLkn6YtnmyPhWFYFQ1zbGRwIAAACAPeOpyQdWbtmpm175NrI+ap+O3gXThgzrUb2b0zPTV1Rb/9GhfTX75hPULi1FO8t3D+z81Yqt2rKHVjRonPMe+UIXPPqFFq4v0qPTlkmSHvpwiSTp8AFdqh17xxkjNOXXR0iS1oRnaxuSny1JevnyQ5SZmqxfPPuNDvvj+7rt9Xn6YtkWSRSSAAAAAGBvaNLiAyf+5ePI8tM/PUg9OjDAdjzUbJF0wrBuMY/LSkvWzvKAvl6xVf26tNcZD3yqvp3baepvxschytYjEHTqf/0b6piVqq7Z6ercPl2d2qcrOz1FOZmpWr1tlz5fuiXmuV2y02ttSw93U9u+K1TUq2rB1ysvS1/ccIzem79ek2ev1VOfLVd5VYukGN0WAQAAAAC7UUhKcIWbSlRcVqmJI/J1/3mjvQ6nTemak6H9CjronAP30RdLN+uaCYNjHpeRmqy120t1+gOfRrYt21QSrzBbheKySt38aqjV3badFTqob542F5dr7urtMpMuOqyvbnt9Xq3zrj9psKbM26AOmam19qWHWxe9MWedpFDBr0pORqpOH1Wg00cVaEdphabMW6+vV2zTAX3zWuLbAwAAAIBWg0JSgluzbZck6ayxTEvuhVd+fpik2uMjRXtz7tqY2ws3lahP53YtEldrEgg6Df/d25H17jkZ+vv5Y2sdV1VIevaSg/XbV+bqu/XFmjAsX5cc0T/m6+ZEFZfy2qWpe4eM2MdlpOqM0QU6YzSfMQAAAADYG/pxJJDCTSV66atV1baVhbvcxGpxgcSQkhT7Y3TPu9/FORJ/KqsMVFt//mfj9nj8QX3z9Owl43TzKUPVK6/ubp4Zqcka0ztXE0fka+pV45WTwWcIAAAAAJqKQlICOfaeD/Xr52bpjAem6anPl2trSbnKKxm7JdGdNCJfknTracOqbX911hqt3LLTi5B8pawiGFn+8sZj1Ssva4/Hm5ny2qXpR4f2ldmeZ8t78bJDdP95o9UhiyISAAAAADQHurYlAOecZi7fqn5d2um79cUqKq3UTf+dq1te/TbSNSqd2aQS1g0Th+hn4/upa3aGzj+4t/7x8VLNWrVdk2ev1aS3Fuj+HzK21Z58tnRzZLlz+9qDZgMAAAAAEkeLVSfM7FEz22Bmc6O25ZnZu2a2KPxvbtS+/zOzxWa20MxOaKm4EtFVz83SWQ99pu/WF+uIgV30zq+O0OQrD9NFh/VVUWmFUpNDLTCQmJKTTF2zQ+PvmJkuOaK/ksMtZZZuTJxBt4vLKjV39Xavw6hl5vKtkqQX9tKlDQAAAADgvZZskfS4pPskPRm17TpJ7znnJpnZdeH1a81sqKRzJA2T1EPSFDMb6JwLqA2YuWJrZLl9erLMTMN6dNCwHh107YTB2rGrQrkUknzlmgmD9OqsNTp5ZL7XoURc+tSXmrZ4sxbdfqJSE6Sr5Pi7pqpw806de2Avje2z5xnTRu/TUaP3yd3jMQAAAACAltVihSTn3Edm1qfG5tMkjQ8vPyHpA0nXhrc/65wrk7TMzBZLOlDSZy0VXyJZvjk0js4F43rXephOTjKKSD7Uo0NoEOi73l6oK47a19NY3pizVpf/+6vI+sJ1RRres4OHEUl3vrVA5ZVBFYbf+785ftBez3np8kNbOiwAAAAAwF7Eu1lCN+fcWkkK/9s1vL2npJVRx60Kb6vFzC4xsy/N7MuNGze2aLDxMrZ3rvLapenW04br1P16eB0OmkFS0u5BoL3uThZdRJKkK5/52qNIdnvggyV65JNlkfVOjI0EAAAAAL6QGP1bpFhTL7lYBzrnHnbOjXXOje3SpUsLhxUfL1x2iL666Tivw0Azqxpk+44358u5mG/nFldcVllr29JNJVq8oTiyvqWkXP/4aKn6X/9GZJZAAAAAAABiifesbevNLN85t9bM8iVtCG9fJalX1HEFktbEOTagWU0cma/1O4bq1tfn6cPvNmr8oK57P6kZVQSCGv67tyPr3XMytG5HqSTp2Hs+1LkH7qMZhVuqFZWenbFCF4zr06TrOucUCDql1DEO0/LNoQHIzx5TIDPp+KHdm3Q9AAAAAED8xLtF0quSLgwvXyjplajt55hZupn1lTRA0vQ4xwY0u/85uLd6d8rSHW8sUCDodM+73+mZ6Svicu3py7ZUW//g6vEqnDQxsv7iV6tUkJupq0/YPT7Rvz5f3uTr/t9Lc3T8vR/Vuf/xTwuVmmz6zQmDdOdZ++nYod2afE0AAAAAQHy0WIskM3tGoYG1O5vZKkm/kzRJ0nNmdrGkFZLOliTn3Ldm9pykeZIqJV3RVmZsQ+uWlpKka04YrCue/kovzlylv763SJL03vwNuuKo/hrVgrOQpUSN0xRdQOrZMVOrt+3S/FsnKDl8zMH9OunMBz/Vd+uLa71OQz07Y2Wd+4pKK/TcjJU6eWQPdcvJaPK1AAAAAADx1ZKztp1bx65j6jj+dkm3t1Q8gFdOGtFd+/fqqD+9uzCybcr89SrIzWzRQlJJeWh8pBcvO6Ta9sd+fICWbiyJFJEkaUzvXHXJTtfGojKt3rZLPTtmNvn6E+79SIO6Z2tgt2wN6NpeA7tla+rCDSopD+isMQVNfn0AAAAAQPzFe4wkoM0xM90wcYjOfuizatu7ZLfsTGWrtu6SJPXoWL3lz8BuoeJOTTefMkxXPP2VDp30vmbffLxyMlKbdP3uHTL0ZeFWvfJN7eHOenfKatJrAwAAAAC8QSEJiIMD+uTp+KHd9M689ZFtZS08Q9rXK7apc/t0da9nF7Jx/TtFluet2aGD+3Xaw9GxBYOh2emOGtRFj/34QEmh7myLNhRr0foiXfviHElSfoemt3gCAAAAAMQfhSQgTq49cXCkkGQmlVa07DBgMwq36IA+uTKzvR8sKa9dWmR5fXh2t4Z6+evVkqQjB3aJbMvOSNXofXI1ep9c9eiYqY8XbarWrQ4AAAAA4B/xnrUNaLP6d2mvO88aqT+dvZ+ckx7+aKlOve8TLd9c0uzXWr+jVKu27tKY3g0bg+mQcKukyoBr8DX/9M5CXfX8LB3YJ09n1DEG0uEDuuj6k4Y0+LUBAAAAAImBQhIQR98f20tnRhVZZq/arklvLmj263xZuFWSNLZPXoPOu/ec/SVJOxvRWupv7y+WJD158YFNHl8JAAAAAJCYKCQBHmuJbl5fLt+ijNQkDeuR06Dz2qWlyEza0MiubZKUkZrc6HMBAAAAAImNMZIADzxw3mhtLinXTf+dq0ExZlBrqoXrijQkP0epyQ2rFbdLT9EBvfP09rfrdNXxg+p1zol/+VhFpRUa1iNH3eo5sDcAAAAAwJ9okQR44KQR+Tr/4N5KS0lScXllo19n4boivT57Ta3tJWWVym5k97KTRnTXd+uLtXhD0V6PLa0IaP7aHVq1dZfmrd2hjll0aQMAAACA1oxCEuCh7PQUFZU2vpB0wr0f6edPf11r+87ygLIa2cXsxBH5kqRj7/lor8d++N3GyLJz0oXj+jTqmgAAAAAAf6BrG+Chzu3TtWFHWZNfpyIQrNaNbWd5QFlpjSskRXdPc87JLPYYTs45/e8zu4tY0647Wj07ZjbqmgAAAAAAf6BFEuChgtxMrd62q8mv8/WKbdXWV2/bpYxGFpKi3f3OQn2yaFPMfa/OWqPyymBknSISAAAAALR+FJIAD/XMzdSqrTub/DpPfb5cb3+7TkWlFVq5JfR6T3+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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_df['feature__rolling_close_mean_60'] = price_df['close'].rolling('60min').mean()\n",
    "\n",
    "price_df['feature__rolling_close_mean_60'].plot(figsize=(20, 10), title='Rolling 60min Close')\n",
    "\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exercise 7.1 Calculate the rolling 2 and a half hour average closing price and plot that value over the \n",
    "# entirety of the training set.\n",
    "\n",
    "\n",
    "price_df['feature__rolling_close_mean_150'] = price_df['close'].rolling('150min').mean()\n",
    "\n",
    "price_df['feature__rolling_close_mean_150'].plot(figsize=(20, 10), title='Rolling 150min Close')\n",
    "\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n",
    "\n",
    "del price_df['feature__rolling_close_mean_150']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_df['feature__rolling_close_mean_60'] = price_df['close'].rolling('60min').mean() # A\n",
    "price_df['feature__rolling_close_std_60'] = price_df['close'].rolling('60min').std() # B\n",
    "price_df['feature__rolling_volume_mean_60'] = price_df['volume'].rolling('60min').mean() # C\n",
    "price_df['feature__rolling_volume_std_60'] = price_df['volume'].rolling('60min').std() # D\n",
    "\n",
    "price_df.dropna(inplace=True)\n",
    "\n",
    "price_df['feature__rolling_close_mean_60'].plot(figsize=(20, 10), title='Rolling 60min Close')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n",
    "\n",
    "# A Rolling 60-minute average price\n",
    "# B Rolling 60-minute standard deviation of price\n",
    "# C Rolling 60-minute average volume\n",
    "# D Rolling 60-minute standard deviation of volume\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Expanding Window features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_df['feature__expanding_close_mean'] = price_df['close'].expanding(200).mean()\n",
    "price_df['feature__expanding_volume_mean'] = price_df['volume'].expanding(200).mean()\n",
    "\n",
    "price_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_df['feature__expanding_close_mean'].plot(figsize=(20, 10), title='Expanding Window Close')\n",
    "\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Shares')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qAQAAgM6LDiYAaIPwEjkC4IQb1C1PL3xvikb3LVSWx93k/jG3vqainAwHKgMAAADA7+ABoA0Y8u2swwZ2jRguSVJFrU8vLdyS4IoAAAAASARMANAmPoZ8J737Zq0MB4EAAAAAEoOAKc3sr67T4s37nC4DSFv+UAcTS+SSztAeeZKke99coW8+9onD1QAAAACdCwFTmrn6X5/ojL98IGv57T0QDyyRS14vf/9off2owZKkd5bvdLgaAAAAoHMhYEoBbcmK5q/f0+bHAIheaOUVDUzJJzfTo4sOH+B0GQAAAECnRMCUxDpy/eonYQLiIvS1xRK55DSsZ374bT9zmAAAAICEIWBKU1xWAbE3d81uPfT+GkkskUtW9f9dhtw0U9/7zwJ9tHq3gxUBAAAAnQMBU5qigQmIvX9+uFZzVu3WxEFdNaRHfusPgONmLtqmSx76WHsqap0uBQAAAEhrBExpytLDBMRcdZ1fB/Ut1HPfnaLivEyny0EzThrdq8mxLfuqHKgEAAAA6Dw8TheA+KCDCYi9Gq9PWR5y+WT30JUTJUkbSyu1Yvt+feOx+dpbWedwVQAAAEB6I2BKUwRMQOzVeP3Kz+LbZqoYUJyrylqfJOmdZTtUkO2R22Xkdhl5XEZul0vZGS71KcpxuFIAAAAg9XGllKZYIgfE3mcb9qp3YbbTZaANuuRmSJIe/mCtHv5gbcRz/nzxOJ09rl8iywIAAADSDgFTCmhPWEQHExAf28qqnS4BbdCrMFt/umicXC6jvEy3vH4rX/BPnc+vHz+zUOt3V+qVL7bo5NG9ldmGJZB7K2tVkJ0hv7XKcLN0EgAAAJ0bAVMSM23cBf3JuRvCb/tJmICYG9QtV4cN6OJ0GWijcw5rvjvpx88s1L1vrpAkXX1UiW47c0xUz1lR49W4X72pKUO7ac7q3frfd4/UhEHFMakXAAAASEX8yjWN3PHykvDbxEtA7Hl9Vm4X3zbTVWWNL6rzrLVatq1MkjRn9W5J0vsrd8WtLgAAACAV0MGURuo3LVm/c3UA6crnt/K42thaiJSQn+VRbpY7qnMH3zizybGC7IxYlwQAAACkFH4Vn2ayMwL/pAz5BmLP67dyuwmY0snPTx2pn586UuU1Xv3rw3W67slPVeONrpPpb5eO14JfnihJuvOVpSq5YUbUjwUAAADSDQFTmjEKXPwyggmIPZ/fTwdTmvne1GH63tRh4duvfLFVK7eXt/iYLrkZumLyIE07pI+65Wc1uO9vb69SaUVtXGoFAAAAkhkBU5oJDQYnXwJiz+u3chMwpaV106fphe9NkSRtb2GnQGutKmq8Ksg+sML8zR8dq0eumihJuu/tVRp/55vxLbYFr3yxRZPufkslN8zQEx+tc6wOAAAAdD7MYEoBbelGcplQBxMRExBrzGBKb72LsiVJ2+oFTG8u3a6qOp/qvH7V+fyqrvOpzmeVXy9gGt6rQCXd8zRpcLHmrS2VJNV4fcryRDfTKZaue/Kz8NuvLdmmK44sSXgNAAAA6JwImJKYacd1bOghfvIlIOYCHUw0fqar7sHlbje/sFivLNyqG08fpW89Pj/iuUN75De4neF26ZlvH6n/zF2vm19YrL2VdepVGL+AqaLGq+/8e4F2ldfK6/PL67eq9R7Y3SEnw62inAzHgi4AAAB0PgRM6Sa8RI6ECYg1OpjSW4bbpTF9C7VkS5k+WrNbm/ZUSZLuOmesjhneXZkelzLcLmVnuJWfFfm/z665mZKk+ev2aGy/QmW4XfK4jbIz3CqM4U5z63ZX6P2Vu3TogC4q6ZYrj9ulDJfR859tVq/CLG0vq9HMRds0c9FreuX7R2tsv6KYvTYAAAAQCQFTmnExhAmIC2utfMxgSnszrj9GT3y0Tre8uETf+8+nkqRxA7poULe8qB4f+vS49slPm9z3l0sO05mH9o1JnbvKA4PEvzZlkM49rH/4+L0XjZMkXfnPeZq9Yqckae2uCgImAAAAxB0BUxqxsuGLG5bIAbHlDX5R0cGU/noWZrf7sSeN7q3rjhumgd1y5TZGXr9ftT6rW19crJXb98esxtKKGklqtivq8a9P0tZ9VTrynre1v9obs9cFAAAAmkPAlKK8Pr+G3fyq7jhrjK6aUhI+bkJDvmlhAmLKFwqY3MxgSndHD+ve4HZbun/cLqOfnjKyyfF731iu0sraDtd24HUCn4eDuuU2e05oud5NLyxSYY5HZxwSm+4pAAAAIBICphRVUeOTJP3+jeUNA6bg32wiB8QWHUydR16WR+umT4vpc3bNy9SeirqYPV9VbaArKTez+f/GszMODPe+7snPNHVkz2ZnRwEAAAAdxa/iU0CkrCjUoeRqtNXcgQ4mALHk8wW+qpjBhPbolpep0orYdTCFfsmQm9nyDnHv/HSqbjtztCRpR1l1zF4fAAAAaIyAKYkZNX8hG5qx1ChfCt/2p+AQpjmrd2lXeY3TZQARef2BLeA9bgImtN3+aq8+WrNbby3dHl5u2RGVUXQwSdLg7nkaWBxYRrdie3mHXxcAAABoDgFTirLBNXCNL3VT5dK3qtanylpv+EJrf3WdLn1oribe9ZbDlQGRhT5X6WBCexzav4sk6ZuPz9ec1bs6/HwVtT5luI0yPa3/N94tP0uSVFYVuyV6AAAAQGMMY0hRod9/N14iF7qdzDOY1u6q0En3vtdgpo03BTuu0Lkwgwkd8ZsLDtHlkwfpzL9+oO1lHe/UrKr1tdq9FNK3S2BXvJ//7wt5/VaXHjGww68PAAAANEbAlKL8oQ6m5pbIJXHCtKOsWl6/1cWHD1DfLjmq8fq0obRKLy/c4nRpgCSpzufX/xZs0u6KWlXV+lRV5wsv3wzt3gW01aDugaVqezu4m9xFD3ykpVvLVBDlwO7i3EwNKM7RxtIq3fvmcgImAAAAxAUBU6oKz2BqNOS74d1J7axD+2pKcDvwnftrCJiQNBZu3Ksbnl8kSXKZwJyb7Ay3hvbI0+g+hQ5Xh1RVkOWRx2W0pwMB0/ef+kxz15ZKki6dFF1Q5HG79P7Pj9fvXl+mf7y3Rn6/lYtOPAAAAMQYAVOKCg/5rnfM2nq7yCVxB1Okygpz+FRE8qj1BgZ6/+ebR2jK0G5NglygPYwx6pKbodKK9s1Cen/lzgZB/I2nH9SmxxfnZcnnt9pf7VVRbka7agAAAACaE9e1HsaYHxljlhhjFhtjnjLGZBtjbjfGbDbGfB78c3q98280xqwyxiw3xpxS7/gEY8yi4H33Ga72ZIMxTZMZTMF/0ZQYaVSv9CxPYKvtbnmZDhUDHBD6+snyuAiXEFO7ymv11LwNKrlhhl5dtFVz1+yO6nEbdlfqikfmhW+vuOu0Nr92cV4gVNpdwW6dAAAAiL24tY0YY/pJul7SaGttlTHmGUkXB+/+o7X2943OHx28f4ykvpLeMsaMsNb6JN0v6RpJH0uaKelUSa/Gq/ZkE6kbKdzB1HgGU0otkmtoYHGuJgzq6nQZQL0ZZ4RLiK0RvfK1Ynu5JOm7//lUkrTwtpNVlNNyR9H89aXht9dNn9au1+6aGwjwP15Tqjrfgf8jVu0o19SRPZQX5UwnAAAAIJJ4/zTpkZRjjKmTlCtpi6SSZs49W9J/rbU1ktYaY1ZJmmSMWSep0Fr7kSQZYx6XdI46Q8DUwrVtKHRq3MEUupnEK+SarY3d5JAsfOGvL4cLQdp540dfUa3XrxG/PPBf2I6y6lYDpq7B7s4nvjGp3a8dCpBuemFRk/uOG9lD/7q6/c8NAAAAxC1gstZuNsb8XtIGSVWS3rDWvmGMmSLpOmPMlZLmS/qJtXaPpH4KdCiFbAoeqwu+3fh4E8aYaxTodNLAgem9S05zIU0ocErmmCYUjrkbhWNul5HP73eiJKCB5gJcIBYyPS797JSRqqnz6b63V2nT3ir16ZKj/AgdRH6/1ZCbZoZv9y7MbvfrThzUVXefO1b5WR5luA+skP/efz5VQTYzmQAAANAxcZvBZIzpqkBX0mAFlrzlGWMuV2C521BJ4yRtlfSH0EMiPI1t4XjTg9Y+aK2daK2d2KNHj469A0nONrtELsCfxC1M89YFlnrsqWw46NbtMtpRVqMrHpmr0oqObeMNdEQo5yRgQrxce9wwnXNY4HclV//rE4297fUm5zz8/hq9sXR7+Pb1xw/TkB757X5NY4wuO2KQzh7XT6cf3Cf856A+haqs9bb7eQEAAAApvkvkTpS01lq7U5KMMc9LmmKt/XfoBGPMQ5JeCd7cJGlAvcf3V2BJ3abg242Pd2rNDflWCiyRu//d1ZKkzzbu0alje4ePu4zR/PV7JEn/+Xi9vn/CcEfqAw7MYHK4EKS17gVZDW5P/vUsuYxU57eqqvWpvOZA6NM1N0M/PnlkfOrIz9TOckJ9AAAAdEw8d5HbIGmyMSY3uOvbCZK+NMb0qXfOuZIWB99+SdLFxpgsY8xgScMlzbPWbpW03xgzOfg8V0p6MY51p4TmhnyHl8glccBUE9wCvqKm4W/Ml24tC79dnM9ucnBO6OuLDibEU2F2htbec7ouPjzwu5XsDJemDOuuEw/qpfPGH1gJ3qcoW899d0rc6li0eZ8Wbtyr4TfP1Ocb98btdQAAAJDe4jmDaa4x5jlJn0rySvpM0oOSHjbGjFNgmds6Sd8Onr8kuNPc0uD51wZ3kJOk70p6VFKOAsO903/AdysizYipv54wmZfIGRMIwPKzmp/50bdLTgIrAhoKzwljyjfizBijk8f00n8/2ai7zz1YRw3rHr7v7HF9tWVvtc48tG9ca3jqW5M1/dVlem/FTs1fV6pxA7rE9fUAAACQnuK6i5y19jZJtzU6fEUL598t6e4Ix+dLGhvb6lJHpKgo1GGxdldFg+Op0HHxrWOG6MHZa3TGIX2aPcfnS96ADOnvQAeTs3WgczhuZE998Ivj1L9rboPjEwYVa8Kg+L/+QX0K9a+vHa6Rt7yq3cy/AwAAQDvFc4kcOqila9vmOpRMCsxgGtA10J3Uq4XdkLzsJgcH+cIzmEiYEH/GmCbhUqK5XEbFeZnatb/G0ToAAACQugiYUlTzAVNwBlPkjfaSgi/YHuJpoT3E60/e+pH+DixBdbgQIIG2l9Xo2QWb9PVHP2FXOQAAALQZAVOK8jUTwByYwZS4WtoqFB65Wrh6b+79AxLBH2HGGZDu7jx7jDLdLr29bIdW7Sh3uhwAAACkGAKmFNXcErgDS+SSN6BproOpd70lcwRMcFJohSYBEzqTK44s0VPXTJYkZjEBAACgzQiYUlRzS+Rc4SVyycvXzA5d7/18qt768bGBcwiY4KBwBxPfIdHJFOdlSpL2EDABAACgjbh8SlHNLpFLhQ4mX+SAKcvjVm5mYGNDAiY4yYZ3kaODCZ1LKGAqJWACAABAGxEwpajm8pfQ5XBpRZ02llYmrJ62CHcwRbh4Dy2bY8g3nORjBhM6qcLsQMj/u9eXa+6a3Q5XAwAAgFRCwJQCIjUjRVoiZ60NtzB96/H5Oua378S7tHbx+a2MiTzkO9TV1NwSQCAR/Owih04qtBNpjdevSx+eq4oadpMDAABAdAiYkphpoXvC30yHz/6quniVEzNev20y4DskFDB5fQRMcE7oy6ulr0EgXX104/G64bRR8vmtdpXXOF0OAAAAUgQBU4ryNdPhU1XnS3Albef32ybzl0LoYEIysHQwoRPrU5Sjkb0LJEm7ypnFBAAAgOgQMKWo0DbqjaXC9bDXbyPOX5LqdTAxgwkOCnUIMoMJnVX3vCxJ0ssLtzDwGwAAAFEhYEpRzXUwpcKSHl8UHUzsIgcnhT79Is0JAzqDwpzAsO9H56zTRQ985HA1AAAASAUepwtA+zS3hCwF8qWWAyZDwATnMeQbnV2/Ljka0iNPVbU+bdxTKWttSvwCAwAAAM6hgylFrd1ZEfF4Kvz87/VbuV2RP/XoYEIyOBAwpcAXFBAHHrdLb/9kqq6aUqLqOr8qa5N/vh8AAACcRQdTSghc7Pr9Vm8v26GH3l+juWtLI56ZChfE/hZ2kTPGyGUImOCs8BK5FPh6AuKpOC9TkrS7vFZ5WfzIAAAAgObx02ISC13allV79eDs1frvJxu1ZmeF+hZl65JJA/TUvI3NPiaZeVtYIidJHper2RlTQCKEOpjIl9DZdQsGTHe8vESHDewiKRDA/u/TTfr9Vw/V4SXFDlYHAACAZELAlAKu/tcnkqTRfQr154vH6fSD+2j97oomAZNVqgz59rcYMLlcdDDBWZYOJkCSlJsZ+DFh1rIdmrVsR4P7rn/qM3104wlOlAUAAIAkRMCUQu44e0z4t8XN5S+pcD3ss2q9g4mACQ7y+xnyDUjSkUO76fnvTdGYvoUNAtfjfv+uDhvY1cHKAAAAkGwImFJUcyvIUqHjotUOJmYwwWGhT7+WPk+BzmJ8hCCpR0GW9lTUOlANAAAAkhW7yKUofzMJUypcDvtaGPItBXYvImCCkw7MYEqFrygg8Xx+qw9W7dLJf3xPm/ZUOl0OAAAAkgABU4pqroMpma+HrbV64L3VWry5rMVOK5cx8hIwwUF+a1keB7RgR1mNJGnF9nL97Z3VDlcDAACAZEDAlKLqdzCV3DBDq3eWS0ruJXI7y2t0z6vLVFHr1dSRPZo9z+My4Rk4gBMCAVPyfi0BTptx/dF68IoJkqRB3XIdrgYAAADJgBlMSawt17cvfr4lfoXESCgT+/kpo3TpEQObPc/tooMJzvLb5A5rAad1y8/SyWN6KyfDrV37a5wuBwAAAEmADqYUNbRHfoPb6XQp7HYZ+fx+p8tAJ+a3NqmXmwLJoignQx+u3q05q3c5XQoAAAAcRsCUonIy3crLdDtdRlxsKK3U/32+RSU3zFB1nc/pclLSul0VuuD+OQxLbydr2UEOiMa2smp9ubVMlz40V3PX7Ha6HAAAADiIgCmF1d/hypjARfHeyjoHK2pZc4PJW7Jud0XsC0lTXp9f2/ZV67MNezT19+9q/vo9uuKRuaqs9TpdWsrx+5nBBETj/svGa1TvAknSr19dpv/MXe9wRQAAAHAKAVOaKc7LdLqEmMrJSM8urXi44pF5mnzPLJ379znhY3NW79Z1T37mYFWpyW+Te0dGIFmcdnAfvfL9oyVJCzfu1c0vLNbmvVUOVwUAAAAnEDClsEjXvx538l8Vt3bhvvyuU/WHrx4qSQz7boPmLup27K9OcCWpj13kgOh53C7desZo9S3KliRtL+N7DgAAQGdEwJQmQjlMOlwSZ3ncyg52LjFDKDqlFbXaUFrZ5PipY3qrqpY5Vm0VCJicrgJIHV8/erAeuGKiJOmnzyzUH95Y7nBFAAAASDQCplRW7wL4odlrJEkZ7uT9J7WKPiwKDViu87GbXDRWbt/f4HaWx6Xbzhyt/GyPquv4GLYVHUxA2/UoyJIkrdlVob+8vUpevn8DAAB0KsmbRkCmUT9S48vd+rergrutHTm0m64/flh8C0uAjOBSPzqYopOb6ZEknTe+nySpf9ccXX3UYGVnuMKfG4heYAYTARPQFr2LsnXrGaN11LBukqQ9SbzpBAAAAGLP43QBiC1jjH580gjd9/Yqp0tpVjSX7aEOJmYwRaciuFPc+eP7qzg3UxcdPkBSYEh6NQFTm1lrlcTNgEDS+vrRg9WrMFsfrtqtXeU14a4mAAAApD8CphSWzh0WHlfg6t7rI2BqzX2zVurB4BLJgmyPfnnG6PB9ORluVdX5ZK1N68+XWPP7xRI5oJ1C88tO+/P7cruMPC6jDLdL2Rku/e6rh+q4kT2dLRAAAABxQcCUwvZVHVh+0CU3Q3sjLEdI1WDhQAcTMzxa88m6UuVkuvXtY4fooD6FDe7LznTLWqnG6w8PTkfrfMxgAtot9H3o0AFddPSwbvL6rLx+q0fnrNMna0sJmAAAANIUAVOayM1wa68iBUxSslwn2zY0IzGDKXpen1VJt1x9/4ThTe7LCYZKpRW16pqbKWMCnTnz1pZq0uBiZXpYBxaJ39qk+boBUk1J9zytmz6tyfGXF27R7vJaByoCAABAIhAwpYnmYhi/tXJFNfUocaK5cHcxgylqPr8NLylsbP3uSknSlOlvN7nv218ZohtPOyiutaUqa1kiB8Ra9/ws7SqvcboMAAAAxAkBU5oIdQc1viRO1XjGEwyY/ARMYdvLqrV5b5VGN1oGV1XnU5fcjIiP2VsZ6BYY1btAZ4/rJyur3eW1euSDtSrMjvwYBINZ8iUgpnIz3Zq1bIfW767QwOLclFy+DQAAgOYRMCWxtvzs/bWjSjT91WVNjrdlWVoyCXWP0MF0wBG/ntXmxxw3qqf+7/Mt+sOFh2pM3yJJUo3Xp0c+WKt/vLda1x43LNZlpgU/HUxAzM1fv0eS9JXfvas/XzxOZ4/r53BFAAAAiCUCpjQxqDg34nF/EiVMbanETQdTA3PX7A6/fcGE/hrSI0+SVFPn159nrWz2cWeP66epI3qqqF6HU5YnMJdpf7VXkuT1+bWtrFr9u0b+HOqM/NaGl2kCiI3Xf3isXvhssx5+f42Wb9vvdDkAAACIMQImJIwNhl0miplQoSVyviQKyJyydV+VLnrw4/DtI4d00/kT+ksKfExbCpgkNQiXGrvj5SX614frJEnzf3miuudndbzgNGBZIgfE3MjeBbrhtFH6x3ur9fd3A39OP7i3Zi7apjk3HK/ivEx2uwQAAEhhBExpoqUh38kiXEobhnx3tl3k1uwsV7+uOeEuI0mqqvU1OGdoz/zw28YYffCL41Scl9mu1wuFS5JUVlVHwBTk81uWyAFxMqR7ntbsqpAkzVy0TdKBjQgW33GK8rP40QQAACAV8VNcmmguR0qifCksmst2t+l8AdP2smod/4f3lJvp1tJfnRo+3vhD4GnUWhOrpW1lwSVzCHzMGUAMxMesn3xFkjT4xplN7ht72+vq1yVHbpcJ//G4jLIz3Jp+/sEa1buwyWMAAACQHAiYUkhL17u2mR6mZFpiFt7pLooLd3eadzBZa7Vo8z7tqaxTTZ1PNV6/Pli5S5JUWevTife+p7vPGavt+2s0uk+BJOnOc8aqutanMX07foHlMk2Dq3T9WLcHS+SA+An9H3DHWWO0eme5Hv9ofYP7jxzaTT6/lddv5fP7Vev1660vd+jDVbsJmAAAAJIYAVOaaLaDyZ/YOloSCsGi6mAKXt2/s3yH7n93tV774bHK9LjiWF1izVtb2mCuUmOrdpSH73/2O0dKkrrkZOjMyYNi8vor7jpNO8trdOQ9b4ePVdbSwSRJm/ZUqrSiliVyQJxdNaVEkvStY4aoZ2GW5q4p1VHDuoe//4dYazXylte0vazagSoBAAAQLQKmJNaWy9vmek+Ss4Op9XNDFxih+Rxb9lappHtevEpLuPKaQJjzm/MP1pi+RcrOcCnL49Yxv32nybmhi6pYBh4et6vJnJOKGgKmfVV1mvq7d+X1Wx0zvLvT5QCdwoDgLqjHjugR8X5jjGq9fj04e43OHtdXY/oWJbI8AAAARCl9WkI6ufAObY0yiGRa9hSe8R3NkO9GJ9X5kqgVKwZCYdtBfQo1tl+RhvUs0IDi3PDskfpC73usG2pyMxsGTN/596e67OHmu6o6g4oar7x+q+9OHaq/Xzbe6XIANDLtvg+0eme502UAAAAgAgKmNJdcu8iFlsi1npQ0HmRd402zgCn4d+OPxfs/P05L7jilwbE/vLEieG5sNQ6yJOnDVbvD/06dUeg9L+mWq4LsDEdrAXDAzOuP0bePHSJJWr+7wuFqAAAAEAkBU5pLqoAp+HdUHUyNwo9kej9iobmOM1dwtyTpQAC0aU+VpKZDuWNh3fRpWnX3aepdmB0eHp5uYV5btCUEBZA4o/sW6mtHlUiStu5jFhMAAEAyImBKE83lL0m0Qk77quokSSu272/13MbdNcn0fsRCa+/OB784Ti9dd1SDY7O+3B6XWjxulz6+6QR9dUJ/SYFd7Dore6C1DECS6ZGfJUl6+P21WrB+j8PVAAAAoDECpjRhm4ks/EmUzOQFZ/4M6tb6sG63abqLUDpproMppH/XXPUoyGpwbEiP+A45D81k6szDvsOD6J0tA0AEHnfgR5a1uyp0/v1ztINd5QAAAJIKAVOaS6Yh3yF5ma1vXti0gyn53o+OCP2zRJqDFNIjP0s/OGG43vzRsfrrpYfpu1OHxbWmnMzA0ry/v7tKy7e13mWWjkJBrYn1RHUAMfHPr03UlKHdJElbWCoHAACQVFq/0kdKaC5/8SZRwBS6eG8hUwlrHLx4fcnzfsTC715fLqlpp1Z9xhj96KQRkqThvQriXtPeylpJ0lPzNmrVjnI9+50pcX/NZEMHE5Dcjh/VS4XZGZqz+qPwsmsAAAAkBzqYklkbrnKbC5iSqYPJH5wdHdWQ70bnJFNQFgtrdwV2QUqm9yrDfeDbwd7Kznnh1pZB9ACc0S04i2ne2t2qruu8M+MAAACSDQFTmvA3s/tVMgVMB+ZEtX713niJUroFTF1yMyRJvQqzHa7kAE/9gKmTdga0NhsLgPN6FQYCpr+9s1rn/n2Ow9UAAAAghIApzSVVwBRaftSOi3dfqP0pTZx3WH/lZ3lUlJPhdClhU0f2CA8W37m/xuFqnBH6anGRMAFJKzfToye+MUnHDO+uldv3J9VmFgAAAJ0ZAVNKaf6it7kfr71JGMxEe/Hetyhb543vJyn9ZjD5/P6oZlElUvf8LH1y84k6YVRPSdKTczfohc826ZUvtuj1Jdv0p7dWqKo2vZejpNkseSBtHTO8h048qJe8fqvS4Pw4AAAAOIsh3+kiFWYwhZfxRWfOjSdo8eZ9ev7TzbrmiQX6wQnDw0OvU53P2gZL0pKJK5h83fTCoib3/f3d1Vpx12mJLimB2EUOSBUed+DrdOX2cnUPzmUCAACAcwiY0oRtJmFKptlF7VkiV1Z9YBbQG0u3p0/A5E/eZViZnkDwdcsZo3X8qJ6q8/m1Yvt+XffkZ6r1Jl9HXCz52UUOSBk9gqHSJQ99rKKcDHlcRm6XCfztNsrL9Oivlx6mYT3jvwsnAAAACJjSXjJ1MLVnvo273rleX/qEG36/VZI2MIUv2noVZmlw9zxJ0qBuuU6WlDAdmRMGILGmjuypiyYOkN9a5WV55PX75fNbeX1WFbVezVy0TQvW7yFgAgAASBACpiTWeEe4ljQ3OyaZOpj8tu3tIa56g4qSKSzrKJ+1DcKzZPKzU0aqZ2GWThvbJ3wsy+MOv11ywwy99eNj0/KiLdQJ2JavPQDOyPS49JsLDol4X3WdTzMXvaYdZZ1zwwIAAAAnJGkPBSJrPmBp7p5k2l2nHfmSRvUuUEGWRz0KslSXhAPL28vvtw3Cs2SSl+XR96YOk7uF+k68d3YCK0ocOpiA9JCd4VZRToZ2lhMwAQAAJAoBU5pIhQ6m9gxQLsjO0KI7TtFXRvSQL412kvNZ22KAk+wmDS52uoS4CH0dpfA/DYCg/CyP5q4pVXmN1+lSAAAAOgUCpjQRXtrT6MLYl0RdPx25eM9wmyQLyzqmzudXRrIOYYrCvLWlOvMvH6TVXCyp/rB8EiYg1e3YX63l2/dr/K/e1KY9lU6XAwAAkPZS9wq3E2quS6ml+5IplDmwQ1fbL97drvQImKy1emreBi3bul+ZKRYwvfvTqXr7J18J3160eZ+e/3SzgxXFHkvkgPQx++fH6Zpjh6jW59fKHeVOlwMAAJD2orrCNcYMNcZkBd+eaoy53hjTJa6VoVXv/HSqXvn+0ZKan8GUTIOxrY3cZRUNj8uVFt0yG0ordePzi7RpT5UOL+nqdDltUtI9T0N65Os35x+swuzA/gAPvr/G4arig3wJSH19inJ01ZQSSdK2fdXOFgMAANAJRNtC8T9JPmPMMEmPSBos6cm4VYWIGkdFg7vnqUdBVouP8SbR3KKOLD7ypEkHU10wJPv9hYfqjrPHOlxN+1x0+EBdd/wwSdKqHeUa96s3HK4odvy27XPCACSvnsH/I99aup1ZTAAAAHEWbcDkt9Z6JZ0r6U/W2h9J6tPKY5BIzayRS64OpsDf7bl4d6fJDCZ/mgyRvvSIQeG391bWJdXnWUe0Z6dDAMkrNOtu1rIdmnLPLFXX+RyuCAAAIH1FGzDVGWMukXSVpFeCxzLiUxJCGucwkS56Q8eau7xPplCmI0vkMlyutAgxQh0yrhTvkMnLdOuyIwaGb6/ZmR7zTcJddqn9zwOgnl9OO0iThxSrrNrLUjkAAIA4ijZgulrSkZLuttauNcYMlvTv+JWFSCLGK8EL4eaGfCfVLnLBv9tz7f72sh3y+a1KK2pjWVLChf45Ur2DyRiju889OHz7gdlrtHjzPi3bVqZVO8q1fneFNu2pTLl/L5smASCAA755zBB9//jhkqRtZQRMAAAA8eKJ5iRr7VJjzC8kDQzeXitpemuPM8b8SNI3FcgWFikQVOVKelpSiaR1ki601u4Jnn+jpG9I8km63lr7evD4BEmPSsqRNFPSD6xtaU+1zqO1HdmSq4Mp8LerHenK0q1lgb+3lOno4d1jWVZCWaXXjJ+CbI/2V3v13IJNem7BpojnPPnNIzRlWGr8m4W/WtLjnwdAUK/CbEnSvz5cqz5F2RrULc/higAAANJPtLvInSnpc0mvBW+PM8a81Mpj+km6XtJEa+1YSW5JF0u6QdIsa+1wSbOCt2WMGR28f4ykUyX93RjjDj7d/ZKukTQ8+OfU6N/FzqG5vC2ZlpWFByh34DlSfX7GjC+2Ol1CTF00cYAkqWtuhh64YoL+ftl4/fnicbr3wkN10+mjJEmb9lQ5WWKbMIMJSE/9u+ZIkl5fsl0XPfCxw9UAAACkp2iXyN0uaZKkvZJkrf1cgZ3kWuORlGOM8SjQubRF0tmSHgve/5ikc4Jvny3pv9bammCH1CpJk4wxfSQVWms/CnYtPV7vMZ1KpAwp1Ajjb+bCOJkCpljMt6nxJs+Sv/b4+7urJUnvLNvhcCWxcfXRg1Wcl6nnv3eUThnTW6cf3Ednj+un88b31/nj+0uSqlIqFEyvDjMAAdkZbr3706m67IiB2lZWrdoU/78EAAAgGUW1RE6S11q7r9FFV4vJhbV2szHm95I2SKqS9Ia19g1jTC9r7dbgOVuNMT2DD+knqf6vFTcFj9UF3258HGp9yPc9ry7TEx+vV6bHpSyPO/i3S93yMvW7rx6q/KxoPwU67kCXVfsv3mt9qRRWNPWtYwbroffX6vazxjhdSkz065KjT285KeJ9OZmBBsRUCpjoYALSV0n3PI3pWyRJ2l1Roz5FOQ5XBAAAkF6i7WBabIy5VJLbGDPcGPMXSXNaeoAxpqsCXUmDJfWVlGeMubylh0Q4Zls4Huk1rzHGzDfGzN+5c2dL5aWNaDotJpUU66DeherXJUeF2R6VVdXp1cXbtGL7/gRUeEDoH60jA66r61L7t85ZHrdcJvDb9HSX7Qm8j5W1qRMwhTsBSZiAtNSzIEuStKOsJnzM77fy+lL7/xYAAIBkEG37yvcl3SypRtKTkl6XdFcrjzlR0lpr7U5JMsY8L2mKpO3GmD7B7qU+kkJrhTZJGlDv8f0VWFK3Kfh24+NNWGsflPSgJE2cODF51oa1U+Nr3Jbmmjd33+Qhxbr3onENjs1esVNX/nNeszvPxUuoxo4sP6pJoW6YSOp8fmW4o811U5vLZZSd4UqpuVnhz1F6mIC0FOqsPPtvHyrDbeT12/D/hdcfP0w/Pnmkg9UBAACktlYDpuCg7ZestScqEDJFa4OkycaYXAWWyJ0gab6kCklXKbAL3VWSXgye/5KkJ40x9yrQ8TRc0jxrrc8Ys98YM1nSXElXSvpLG+pIa61dBkeawRTagj3RG/F1ZPnRBRP667kFm3T7y0v1wapdchkjlzFyu4xcLqOCbI9uPv0g5SVwyV9b3fXKUr26eJsyO0nAJEm5mR5VpVAHUyy67AAkrwmDuur4UT1VmO1R3y458riM3C6X/vjWCn22ca/T5QEAAKS0Vq/GgwFPpTGmyFq7L9onttbONcY8J+lTSV5JnynQXZQv6RljzDcUCKG+Gjx/iTHmGUlLg+dfa60NXZl+V9KjknIkvRr8Ax1YytNcVuSNGDAF/k70/G/bgeVH9ef4bNlbLb+18lsrn9+qstanrfuqddahfTV5SLcYVRtbdT6/Hv5grfoWZeuiwwe0/oA0kZPhTskZTDQwAekpO8Otf37t8CbH3162neH+AAAAHRRtu0e1pEXGmDcV6ECSJFlrr2/pQdba2yTd1uhwjQLdTJHOv1vS3RGOz5c0Nspa01akPKi5pTwF2R7tr/bqiMFNA5fQD9H+RHcwBf92teOH+K8fNVgzvtiqZ79zpA4vKW5w37y1pbrwgY/k9SXvqsjQjkVXTSnRt78y1OFqEicn051iHUwskQM6o4WbAr8/+/NbK3X10SUqzM5wuCIAAIDUE23ANCP4B0nKNoqfJg/ppjeXbtdhA7s0OTfUwZTo2TgdCbQmDOqqddOnRbwvwx14h+r8yTukNRQwZXo6z/I4KfU6mMSQb6BTGtojT6t3VuiPb61Qv645umBC/9YfBAAAgAaiCpistY/FuxC0UzNL5Fzh401DnZU7yiVJd7y8VFNH9oxndQ10ZIlcS0JDs+u8SRww+TpnwLSnslaLNu/Tg7NX61vHDEn6JSiskAM6p1k/marqOp9G3fKatu6tcrocAACAlBTV1a4xZrgx5jljzFJjzJrQn3gXh4a2l1U3OWYazVMK3Q4tQ4u08/K+qjpJ0tpdFU3vjKtAke1ZItcST7CDKdK8qWQR7mDqRAO+JWnTnsCF2q9nLtNvX1/ucDWtOxCCEjEBnU12hlvFeZn6cluZtu1r+v8tAAAAWhbt1e6/JN2vwPDt4yQ9LumJeBWFgMYXuVsj/MDb3GWwy9X8nCW3Q1tkNQ7BYsXjCnYwRUrTkkRNJ10iN+P6o8Nvv7l0u4OVRCf09UK+BHROpRW1mrlomybfM0tLtkS9rwkAAAAUfcCUY62dJclYa9dba2+XdHz8ykIkLY0wajyDydXCIG+ntmAPd4fEeAFSaAZTKgz5zupkAdOYvkX64YnDJUmrgkszk9mBQfSOlgHAIXecNUbFeZmSpI2l8V0q5/X59fD7a1RZ643r6wAAACRKtFe71cYYl6SVxpjrjDHnSkrc8B5IihwWhbqcGt/lDi+dixQwOXP1HN6hK14zmJK4g6mzzmCSpEmDi1s/KUkcmFlGwgR0RldNKdFbP/6KJGnrvvgFTPe/u1rDbn5Vd834UqNvfV1/fHNFxJmJAAAAqSTaq90fSsqVdL2kCZKukHRVnGpCGzS7RC7UwRQhc3FqidyBDqbY8oR3kUveH84PzGByO1xJ4o3pWyRJ+uW0gxyupHXheIl8Cei0uuZmSApshPHEx+vj8hpzVu8Kd0pJ0p9nrQzPrAMAAEhVUQVM1tpPrLXl1tpN1tqrrbXnWWs/jndxaL9QZ5MvqWYwhTqYYrxELjiDadf+mpg+byxVBJdAdMYOppyMQKhWXedzuJIoxCkEBZA6jDEa2atAknTL/y3W+t2x3xDj/ZW7VFpRq4W3nqx/XX24JGlzC7vXVdZ6VXLDDP07ToEXAABALES7i9wIY8xDxpg3jDFvh/7Euzg0FKl9PpTVNL4vtFlZpMc4tUQuJOZDvoMdTH+etTK2TxxDM77YKkkqrUjeECxeMj0uZbiNKmqTP2A6sIyTiAnozF7/0bF6+brAJgVf+d27evHzzTF53nvfWN7guYpyM1TSLU+SNGf17nC3a33XPD5fo299XZL0y/9brJIbZuixOes0Z/WumNQEAAAQK54oz3tW0j8kPSQp+a8S04S30fq2SOMZQgOzG98XCpEijSVyOmCKtdAMpmT1/Keb9NyCTZKkwd3zHa7GGXU+q/vfXa0dZTX63QWHhHc5TDbxWsYJIPUM73Xg+/UP/vu5zjq0b9Thc53Pr2sen6+Ne6rkMlJ1nV81Xp+2lx34JcNJo3tJkvp2yZYk3TdrpV76fLPe/dlxDZ7rjQg7cN720hJJ0j+/NlHHj+rVtncMAAAgTqK9Mvdaa++31s6z1i4I/YlrZdDeyroGtyNNGAp3MDU6npcVyA49ES7kncpj4nXxnuwB0x/fWhF+u1dhloOVOO9/n25SaWWt02U0K/w5SsIEdHrZGW6tmz5NN50+SpJUXhP9bm/rd1foneU7tWpHuYZ0z9f4gV00dcSBvVFG9S7QL04dKUnK8rj1y2kHaerIHlq3u1L7quoiPueyO0/VsjtP1U9OGqGD+wVm2/3s2S/0VoQACgAAwAktdjAZY0LbP71sjPmepBckhX/9Zq0tjWNtnV7jVvmWNpg5cGEcuDL+8UkjlJfl0bnj+zU51/ld5GL7+k7NlIqWz2d1wYT+uuWM0SrKyXC6HEfccsZofbp+j2Ys2qrKGp+UpI1coTlh6dblB6D9ehUGOoy2l1WrIDu67+Gh/+f+fPE4nT3uwP/Dt545WvurvepdlN3g/G8eM0R9u+To3eU7tbG0Ulk981Xj9avO55fbZfSdrwxRdnCe3fdPGK7vHTdMQ2+aqd0Vtfr+U59p6a9OYWkvAABwXGtL5BYo0BwT+qnlp43uHxLzitAutlEPU16WRz8+aUTEcx0LmDrp8iOv38rjMp02XJKkbxw9WH2LsjVj0dbwwPNklLz7EAJwSo/8QOfptf/5TFNH9lCG26VMj0tz1+7WLWeM1qjehU0eE9rUIMvTcOfQvCxPuMO4scJgeHXGXz5ocl/jx7hdRuumT9MTH6/XLf+3WNvLapqEVgAAAInWWsB0kaSN1tqtkmSMuUrS+ZLWSbo9rpWhicYhklR/yHf0z+NUx09nXX7ktzbpu6wSIXSBVJnMAVMn/RwF0Lw+XXIkScu379e63RWq9fnD3ytO/dP7Wjd9WpPHVNcFOpCzM6Jfwj2xpKtOGNVTPQqyNLBbrjKDQVa2x62Tx0SeszSwOFeSNGPRVk0eUiyPyyW3yyjL41L/rjl0NQEAgIRqLWD6h6QTJckYc6ykeyR9X9I4SQ9KuiCexaGhloZ8b9rT/PbGjY3qE9h+edyALrEoK2qh8k0n62Hy+QmYJCkvK/Cb/IqaZN4nILiMs5N9jgJo3uDueVr6q1OUm3ngRyaf32roTTObfUxNsIMptKwtGtkZbj3ytcPbVFu/4IDwO19Z2uS+X5w6St+dOrRNzwcAANARrQVM7npzli6S9KC19n+S/meM+TyulaFNPt+4J+pz+xQFfht75qF941VORNaGZjDF7zXKquvCywyShddvmekjhS/O6GACkGrqh0tS653AFbWBgCkvM9rNettnWM8CPXTlRPn8VsZIXp+V1+/XXTO+1PJtZXF9bQAAgMZa6912G2NCPx2dIOntevfF96cmNBFpFVxo2dzqnRVRP8+BZXWJnTiTiFc75PY3tHVf9N1cieAPzmDq7EIXWuVJ3MEU7rLjnwtAlK785zxtLK1scKy8JrATXEF2/H9UOml0L506trdOGdNb0w7po7PH9dOQ7nlas6tCO/fXqNbr129eW6bSirbt4LlsW5le+WKLFm/eF6fKAQBAumntJ5+nJL1njNklqUrS+5JkjBkmiZ84Ei1CIORxBTLCK48cpMc/Wh/V04SunROcL4Wv3uN98V5Vm1wBhpclcpKk3OASuZToYGKJHIBWXD55oP798QbNXrFT76/cpUuPGBi+LxSkh77vJdrKHeUqrajV4Xe/FT62aU+VrjtumEb2LmjxsTvKqvXq4m267aUlkiSXkebedKJ6FGTFtWYAAJD6WuxgstbeLeknkh6VdLQ90PLiUmAWE+IomiAmdEpbfksaGvoZaWh4IsR76Gitzx/X52+rGq9fW/dVO12G4/KDQ75vfXGJSm6YIZ8/+fZsC31N0MEEoDV3nXOwVv/6dLldRlv2Nuyc9Qb/H8pyOxMwPf71SfrqhP4Njr28cItO+dNsLVjf8pL6W15cHA6XJMlvpVU7yuNSJwAASC+tbm9irf3YWvuCtbai3rEV1tpP41saohG6EG7LtbpTHUzxDLT+/Y0j9P3jh0mSar3JEzCFLjJeWrjF4Uqcl+VxaXD3vPDtdbujX9aZKKGvCRrOAETD7TLqVZClLfsaB0yBbyZutzPfTMb2K9Lvvnqo5v/yRH16y0n6wQnDw/c9/tE6vbl0e7OPfX1J4L5vHTNYd587VpJ0yUMf6943VyR8aT0AAEgt0e+fC8dF+rEu1A3kb8MPfeEZTDGoqS0OLD+KvaOHd9eEQV0lSXW+5PkBuCq4k9B3vsJOPsYYvfPTqfralBJJBy7AkknyVQQg2W3ZV63nP92sCx/4SHuCc468wd/6OD1/r3t+lorzMvWjk0Zo4W0nS5Je/HyLvvX4fO2rqmtwbo3Xp/KaA0uYb542WpccfmDZ332zVmrn/prEFA4AAFISg7rTgDFq05VxaL5M4juYgq8fp5+3M9yBvDSZll5V1wU6mPp1zXG4kuTxlRE99OicdapIwllMB347TwsTgOj84/IJunvmUs1bW6pFm/fp2BE95PMHvvc7HTDVV5STofsuOUyvL9mmGV9s1fRXl2l0nwLVeP2q9fn129eWh8+dft7BkiSXy2jd9Gl6e9l2ff3R+brs4bk6YkixPC6XMtxGHrdL5x7WTyN6tTzXCQAAdA4ETCmkuUCojflSvQ6mBO8iF+cByqEf5L0JmsFUXuPVvLW7dfyoXs2eUx3sYMrJcGYORzLKyQx8LJJtGHt9zGACEK1Tx/bWmL6FOua374R3MQ110ibbBg9nHdpXI3rla8YXW/XUvA0Rz7l88kCdcFDD/9f6d82VFBgevruiVnU+v7w+q6o6n7bsrdKfLz4s7rUDAIDkR8CUQpoLhIwxbZqLEA6YHJrBFK+Ld0+wg8mboA6mG59fpJcXbtGsn3xFQ3vkRzwntEQuO4PVqCGhz7vLHp6r/CyP3C4jj8uE/87JdOsvl4zX6L6FjtWWXJeEAJJdr8JsSdK2fYElZL7g7qHx3tSiPUb1LtR/r5ms3oXZys1yK8vjVpbHpcpan3aV10TsRhrRq0Dv//w49e+a0+B9GvnLV/Xi51sImAAAgCQCprRg1Law6MASOYd2kYvT84Y7mPzx72Cq8/n1cnBw946ymmYDpl3lgYuNZJw35JTQVteF2R59deIA+fxWXr9fPr9VRY1PLy3covnrS50JmMIhaPJdFAJIXpmewC8R/vjWCi3Zsk+DuuUm1fK4xiYP6dbkWHaGW8V5mc0+ZkBxbpNjQ3rk68utZRpz62u675LDmnQ+AQCAzoWAKYU0u0TOtK1rx7EOpji/nju8RC7+79g5f/sw/Pb+6rpmz6sJ7mjncWgnoWQ0rGe+FvzyRBXnZTYJcqrrAgHTrS8u0ZVHliS8NjqYALTXr889WDe9sEhvLN2uU8b0SuqAKVb++bWJeuGzzbpv1kp9uGo3ARMAAJ0c63ZSSHOxiZHRo3PWSZLeWbaj1edxBS/qHeupidsSucATJ2LI94bSyvDb3fKzIp7zxpJtuun5RZKkrrnN/1a4M+qWnxWxSyjb4VlV4YAp/a8LAcTYpUcM1ONfnyRJ2lhalXTzl+KhT1GOvjd1mEq65emDVTt12cMfa/XOcqfLAgAADiFgSiHNdgDV+xl2Z3nrWwiHTvcnuIUp3q/mcSVwBpOVRvUOzKmo9UZekvfnWSu1dV+1JAKmtuhTlK3zxvdztIZ4DaIHkN76dgnMYlq6tSy8s2lnMLpvoVZsL9eHq3brqn/O08Pvr0mqHV0BAEBisEQuhTQ75Lv+OVH8POfUErnw68d7F7kEzGCyOtBtU+vz69MNe7S3sla13sA8oe1lNVqypUyStG76tLjXk0627qvW859uVp+ibF1/wnBleRLX1cTlEICO6F2UE367f4SZRenqnvMO1vXHD9fVj36itbsqdNeMLzW0R76OG9XT6dIAAEACETClAVe99TzNhVD1GaeWyMU50UrUDKbFm/epvMarNcFlAFf9c15cX6+z+ts7q3Xs8B46IsIw2ngJDb5niRyA9sjP8ug35x+srrmZmjqy84QrWR63Srrn6f+uPUpLtuzTpQ/N1bx1pSrKzVCGy6Van19en19ev9WSLft00ujeGtw9z+myAQBAjBEwpYH6F8PRZjjGOLiLXIrPYPr2EwskSWXV3gbHf3HqKB07orsy3C5luF067vfvxrWOdHXbmaO1dEuZnl2wSdv3t77kM5boYALQURcdPtDpEhxTlJOhyYO7qSgnQ/e/u1r3v7s64nm/nrlMn996kvKzPPJ0oqWEAACkOwKmVNLcLnL13t60pyqqpzJyYBe5OD9/omYwbd4b+WNckO3RmL5FcX3tzuDqowZr5/4aPbtgk/ZV1ib2xRnyDQAd4nIZvfqDYzRn9W799NmF+slJI3Rw/6LwL18ufOAjSdK4X72paQf30d8uG+9wxQAAIFYImFJIszO+23E17DImquV08RCva/fwDCZf/GcwRbKlmeAJbZefFfjWVF7jS+jrhr4m2vM1BQAI6NslRxdM6K8LJvRvct/IXgVavn2/JOnD1bsSXRoAAIgj+pKTWLQXueU13tZPavLcUqI3eIl3x5TbHRryHb8Xen3Jtga3102fppJugUGufbrkNLjv75eN1/9de1Tcakln2RkuuV1G5TV1CX3d0Oco8RIAxMdL3z9Kb/34WLmMtLeyTif84V2t2lHudFkAACAGCJiSWDxnJBkZ53aRi1N3SKiDKZ4zmP769qrw2+/+dGrg2KXjlel26aSDejU49/SD+2jcgC5xqyWdGWPk81v97Z3VuvXFxQmbFxZ6FRqYACA+sjxuDetZoKe/faQmDS7W6p0Venf5DqfLAgAAMUDAlEJieZFtTHQ7zsVSvEOC0C5yFTVezYlT232dz6+TR/cKdC4Fd8AZ269IK+4+Tb2LsuPymp3d4x+t1+6KxMxiOtDBRMIEAPF0eEmxnr5msiTprhlfameCN3UAAACxR8CUQmKZzwR2kYvd87XpteP0vBnBId/3vb1Klz40Ny4t93U+vzI8fNkkwkNXTtSVRw6SJFXVJnYWEwAg/up3NE++Z5Y27al0sBoAANBRXCmnkFjmQYElcum1KbvL1TC62huHHch27q9Ju49bsjppdC8dXlIsSaquS0zAdGDId0JeDgA6vVk/+Yq+ftRg+fxWq3dWOF0OAADoAAKmFJKXFbtN/1wOdDAlOpaJx7DvsmqvZi7a1vqJiImcDLckqSpRARNDvgEgoYb2yNc3jxksid1YAQBIdQRMKeT0g3vH7LmMMQnfRe7Aa8fvud/68bHht+M17PuwgV3i8rxoKiczGDAlaIlc+DOGhAkAEqZnQZYk6cbnF+mh2WscrgYAALQXAVMSa7zbmiuGyYyRE0O+4/8aw3oWhN+Ox7Kq3Ey3JgzsGvPnRWThgClBHUyhT1KGfANA4njcLnXPz5Qk3T3zS+0qZ+A3AACpiIAphaTPkO/4Xrx/+9ghkg6EE7Hk81u53YQPiRJeIpfgDiZmMAFAYs3/5Ul6+MqJkqTXl2zT9rJqbdlbpY2llZq3tlT7quocrhAAALQmdkN9kFKMSfyQ70S92omje+mB2Wvk98f+uX1+K4+L9CFRmMEEAJ1HSfc8SdLNLyzWzS8sbnBfj4IsfXLziU6UBQAAokTA1EkZk/ih2wdePL5Pn+EONObV+mIbSlhr5fVbuV00/iVKopfIhULXxstTAQDxN7RHnn580ghV1vo0oDhHbmPkchn9/LkvtHN/jT5YuUsTS7oqOyP2HcoAAKDjCJhSSCxnJrmMSfwucgl6wYzgErZab8uvV1nr1ZItZarz+jVlWPdWnzc0M5wOpsTJTvASOQCAc4wxuv6E4U2Ov7poq95ZvlOXPzJXp47prX9cMcGB6gAAQGtoxeikjCS/Q0OY4t0ckhnsYKqq86rG23ww8e0nFuir//hIlz48N6qtkb3BNXduAqaEyQ12MN0140vN+GJr3F8vPIMp7q8EAIjWny46TL+cdpAkqTJRmz4AAIA2I2DqpBxdIhdnoeVNP3p6oUb+8rVmz9tTWRt+e+mWslaf1xdsYSJgSpzQckdJuvbJT+P+euEZTPwTA0DSKMrN0DePCWzgMXvFTk26+y0tWL9HFz7wkTaWVjpcHQAACCFgSiHFuZkxey7jwBK58GvH+fkra71RneerNwR81rIdUZwf+ICxRC6x3v3pVJ0/vr+k+C+zPNDBxL8xACSbF743RSeM6qkd+2v0m9eWad7aUh3z23d01ytL5fXFYWcPAADQJgRMSSx0iXva2N6ac8Px6lmYHdPnTvgucgl6uZG9C3Ro/6JWz6ur98Pozv01rZ4fCphctLckVEn3PA3tGdhZqLouvhcQlm3kACBpHTawqx752uGSpHlrS8PHH/5grf798XotWL9Hc9fs1pxVu3T/u6u1bFvr3ckAACB2GPKdAlwuo75dcmL6nMYkLvBp+trxvXrP8rj14nVHq+SGGS2eV11vjsOu8tYDptpgIBVthxRiJy8z8K2qstYb3lkunsgQASB5/eyUkXp54RYN7ZGvSyYN1OWPzNXtLy9tct5vXlumddOnOVAhAACdEwFTJ+UyJqa70kUj0a/Xku1l1dq058Bg72gCph1lgXMWbtoXt7oQWWjYd2WtT93i+Do0MAFA8rv2uGG69rhhkgKdpz84YbgKsj0a1jNfGW6X3C6j65/6TDv21+jd5Ts0ZWh3ZXpo2gcAIN4ImDqpwC5yzr12Ivn9Vq5Gc5O+3NqwbT6agCk7IxBynHFIn9gVh6jkhjuY4rt7UCgEjXeXHQAgNowx+tFJI5oc712UrR37a/S1f30iSXQyAQCQAPw6p5NyYsi3U0vy9lc3XdJWfwc5KbrZPgeGfPNlk2i5WaEOpvguT3TqcxQAEFtPfWuy7jhrTPj2C59tUo03vr+kAACgs+NKOU1kBrdz/+35h0R1vjHOLVlLdHPIvqq6Jsdq2jgseuHGveGtkN3sIpdwuRkHlsglAv/CAJDa8rI8umpKiSYPKZYk/ejphXp10bYWH/PR6t36aPXuRJQHAEBaYolcKmglB/rgF8cpN9Oj4rzMqJ/SiSHfTjWHRAqYKhoFFecd1k+3vbhYt581JuLyqLP/9mH4bQKmxMvLCnyrqqiJcwdT8G9WyAFAenj860dob2WtJv16ln749OeasWir3ly6XVLgl3O9i7JV5/OrzufXrvJAd/PiO05RfhY/IgMA0FZ0MCWxaC9y+3fNbVO4JElGRos379P7K3e2o7KOMQnuD4kUMFU2Ciqe/2yzHvtovXaW1+jzjXsb3OdrNKzKQ8DkmGueWKA/vLE8bs9/YMg3/8YAkA4yPS71LMwO3w51I0vSkB55mjCoq44Z3l2njOkdPj72ttf15NwNCa0TAIB0wK9nOqkNwR+wrnhknr64/WQVZmfE/TWdmm8TTQdTyKS7Z0mSFt56sopyAx+TqrqG5zYeGI74G9GrQMeO6KEPVu7Um0u36ycnj4zL6xwY8h2XpwcAOCSaId/fPnaofvv6Mr3yxVa9vWyHLj1iYAIqAwAgfRAwJbFEBTJ13rbNI+qoZJjBFBoWnZ/lUXmEZVen/Gm2BnbLld9vVdPo40MHU+Jlelx6/OuT9OOnP9fctaVxex2GfANA5zWwW67+eul4Ldz0tt76crueW7BJZxzSJ7yLLAAAaBlL5FJBnPMMf4Iuqp0aKh6xg6km0JVUlBO5c2tbWbWMpKwMl7rkNjwnw82XjVMKsiMHgrFGBxMAdF4nHtRLkvTTZxdqxhdbHa4GAIDUQQcT5E/Ttg2XCYRnv3ltWZP38X+fbpIk5WYGfiuZk+FusBRu2iF99LdLx4dvl9wwQ5J042mjNG5AlzhXjubkBwMma23EYewdZYOfJ8xgAoDO67Yzx+inJ4/UmNte1/8+3aQx/QqV7XGrW36mChIwUgAAgFRFwISEBUyJzrHqd2b97vXIg6GvOHKQzh7XTx+t3qXv/PvT8PH+XXMinv/trwyNaY1om4LsDPn8VlV1PuVmxv7bV5pmrQCANgrtXjpn9W6d+qf3JUkFWR7Nu/lE5WSyZA4AgEgImFJBHC565910gl78fIvunvllwpbIhSR6+VF+lkef3nKSpKbL9LI8gR8SGy97O3xQcWKKQ5us3lEuSdpYWqWRvQvi9joskQMA3HfJYfpkbamOHNpNn2/cqwdnr9Gpf54tv7WqqfOrxuvXvqo6jexVoNd/dKzT5QIA4DgCpiQWz4vcnoXZ4dlC/kQnTAlyyaSBemreBl0yaYAyPS3PTQrdf/Sw7rr1zNEa0atheDHv5hNUU5fYYehoavv+GknBIezFucpwG2W4XSrI9mj6+YdoaI/8Dj1/6CuBfAkAcNahfXXWoX0lSUcP764dZdWq81lleVzKynAp0+3SYx+t1/Lt+x2uFACA5EDA1Im5gglWus5guue8g3XZEQM1tl9Rq+cOCQYTp4zp1SRckqSeBdkxrw9t9/3jh2n2ip06pH+RhvbIV63Pr/3VXs1esVML1u3peMAU/FKIx3wnAEDqKszO0J8uPqzJ8c837dPCjXtVcsMMXXnkIP3q7LHh+6pqfbprxlL96uyxcrMDLQCgEyBg6sRcwaaehC+RS2B/SDThkiT165Kjpb86RTlsRZzUDi8p1rrp0xoc21tZq3G/ejMmu8uFllByGQAAiMbtZ47Wd/69QNvLavT4R+uVl+XRt44ZovF3vhk+Z/bKnbp26jBluF3yuI0y3S6NH9RVvQr55RUAIL0QMEFeX2KWftkk75SKx9BoxF9oEGtFLAKmcAdTh58KANAJHDawq+bedGJ4t9n7312t+99d3eCcjaVVuuH5RU0e+4/LJ+jIod1UlMPOdACA9MAVdSf21NyNkqQnPl7foKU73rh4RyxluF3K9LhUUevr8HOFZzDxSQoAaIMPfnGclm/br288Nl+SlJPh1svfP1o1Xp8KszPkcRvVea3q/H6d8If3JEnf+fcCXXrEQP363IOdLB0AgJghYOrE9lTWSpJ2Bgcnx1uSNzAhheVlumPSwcQnKQCgPfp3zVX/rrlacddpMqbp7rT1Lbr9ZL315Xbd+cqXenLuBs1esVPPfPtI9e2Sk8CKAQCIvZa31kJaCw2c9CV4CBO9IYi1vCxPbJbIiQ47AED7ZXpcLYZLklSQnaFzD+uv648fJknatKdKU6a/nYjyAACIKwKmFGAVnwAo0QETvSGIl/wsT2yGfPNJCgBIkK8dNVhr7zk9fHtfZZ2D1QAA0HFxC5iMMSONMZ/X+1NmjPmhMeZ2Y8zmesdPr/eYG40xq4wxy40xp9Q7PsEYsyh4332mkwxIifdua55gwORNdAdT5/jnQwLlZXlUURuDJXKiww4AkDj1fyYaf9ebWrVjv4PVAADQMXELmKy1y62146y14yRNkFQp6YXg3X8M3WetnSlJxpjRki6WNEbSqZL+bowJ7Rl/v6RrJA0P/jk1XnV3Jq5gwORPUNsG3SGIlwXr9+jDVbtVcsMMbd1X1e7nsbIEoACAhPrH5RN07mH95PNb/fvjDSqrppMJAJCaErVE7gRJq62161s452xJ/7XW1lhr10paJWmSMaaPpEJr7Uc2sM/945LOiXvFnYCHGUxIE3++eFz47R89/Xm7n8daPj8BAIl16tjeuvfCQyVJj85Zpyn3vB2bjSsAAEiwRAVMF0t6qt7t64wxXxhj/mmM6Ro81k/SxnrnbAoe6xd8u/HxJowx1xhj5htj5u/cuTN21acpd4KXyMVrlhRw9rh++uyWkyRJRw3t3u7nYcg3AMAJxhj96+rDdfLoXiqv8Wr97kqnSwIAoM3iHjAZYzIlnSXp2eCh+yUNlTRO0lZJfwidGuHhtoXjTQ9a+6C1dqK1dmKPHj06Unan4HEF/vkT3sHEBTziID/bI0nqyKdzoIOJT1AAQOIdN7KnrgvuLPfA7NWavWKn5qzepU/WlerzjXtVGaNZgwAAxIsnAa9xmqRPrbXbJSn0tyQZYx6S9Erw5iZJA+o9rr+kLcHj/SMcRwddMmmgPli1S2ce0ichr8cMJsRTaFvoN5Zu0/kT+ql/19w2P4cVa+QAAM4ZWBz4v+vFz7foxc8b/rh77mH99MeLxjlQFQAA0UlEwHSJ6i2PM8b0sdZuDd48V9Li4NsvSXrSGHOvpL4KDPOeZ631GWP2G2MmS5or6UpJf0lA3UkjXsFMSffADzF9uuTE5wWawRBlxNOSLWWa+rt3teCWk1SUk9G2B5MvAQAc1CU3U7eeMVoDi3PVNS9DtV4rr9+vP721Ul9uLXO6PAAAWhTXgMkYkyvpJEnfrnf4t8aYcQosc1sXus9au8QY84ykpZK8kq611vqCj/mupEcl5Uh6Nfgn7cU7hwktBbKJ2kUuIa+Czqww26Oyaq+8fqsdZdVtDpj4HAUAOO3rRw9ucuwvb6/Ssm37ddnDH+uHJ47Q4SXFDlQGAEDL4howWWsrJXVrdOyKFs6/W9LdEY7PlzQ25gV2csERTB2aWQMkky9uP0UfrNylyx+Zq/nr92jxln0697D+rT+wHhrsAADJ5vLJgzRvbak+XlOqZz7ZSMAEAEhKidpFDknoQAdTgl6QIUxIgOK8TEnSjc8v0o+eXqgar6+VRxxgrWXINwAg6Zx1aF+tmz5NPr/Vsws26cp/ztMGdpoDACQZAqYkFu88xhW8jrYJXBhEdwjiLRQwheyrrIv6sdbyOQoASF4FWYHFB7NX7NTby7a3cjYAAIlFwJQC4nXBGxq2naglcvQvIRG65Dacu7Svqg0BkxjyDQBIXovuOEVr7zldeZluraODCQCQZAiYOrFQcJWoId8SF++Iv+wMt9780bH6x+UTJEmXPTxXD7+/JqrHBjqY+CwFACQvY4wqan16dM463f7SElXXRb8UHACAeIrrkG8kN5dJ7AwmRjAhUYb3KtCizfskSTv21+iuGV/qm8cMafVxVpYQFACQ9O4+d6zumblMj85Zp/wsj44d0UNul5HHZQJ/uwNvDyzOU6aH3ycDABKDgKkTC11I+xPZwUR3CBLkhIN6aWBxrjaUBpYQlFbUyue38vr98vqsdpXX6ND+XeRyHfictKyRAwCkgMuOGKQTRvXS5Htm6a/vrNJf31kV8bxzD+unP140LrHFAQA6LQKmTizhHUxMYUICFeVkaPbPj9MvnvtCT8/fqPF3vtnknG8ePVi/PGN0+HZbdpwDAMBJvYuy9fQ1k1Xt9cttjHzWyhf8JYrPb/XnWSv1wmebVVpRq7PH9dV54/s7XTIAIM0RMHVioWaihHYwJeyVgIDQ5/dXRvTQiaN7yeMyqqr16VevLNXDH6wNB0x3vrJUT83bqEklxU6WCwBA1I4Y0q3Z+95Yul3Ltu3Xeyt26r0VO3X8qJ7qkpvZ7PkAAHQUi7KTWLw7fsJDvuP6KgcwgwlOuPLIEknSXeeM1RWTB+mSSQP19aMHNzjHWqtH56zTSaN76bGvT3KgSgAAYuue8w7W/757ZPj2uF+9qQ9W7nKwIgBAuiNgSgEmTn0/JrxEjuQH6evg/kVaN32aBhTnNntOrc8vn99q3IAuysl0J7A6AADiIzvDrQmDivXpLSfp9jNHyxhp/vpSp8sCAKQxAqZOLDTbOHEzmA50TQHJYvKvZ2nNzgpJUk4G4RIAIL0U52Xqa0cNlrXSn95aqVcXbeWXiwCAuCBgSmLx/r8/1Bnl52cMdGLbyqq1aNM+SVIu3UsAgDR12MAukqTv/udTfbphr6O1AADSE0O+O7FwB1MCd3eL13I/oK3W3nO6Vu4o18l/nK2t+6olSblZfEsEAKSn/31nilbtDPy/d/79c9SnKFsuY+RxG7ldRh6Xkdvl0tVHlejCiQOcLhcAkIK4mkpicV9OFt5FLs6vE0Q3NpKJMUZdcjIkSX98a4UkKY8OJgBAmnK5jIb3zNfPThmpTXuq5PP75fVb+fw28LfPasGGPXr6k40ETACAdiFg6sRcJsFDmCTRwIRkUhgMmEIWbtqnEw7q5VA1AADElzFG1x43rNn7z79/jhas36ML//GRfn7qSE0sKU5gdQCAVMcMpiQW79wnFDAlrIMpgUvxgGhkNxrqffSw7g5VAgCA8248bZQy3S4t2LBHz3+22elyAAAphg6mVBCnrp/Q0/oT2MFEAxOSzU9OGqEJg7pqcI889SnKcbocAAAcM7GkWCvuPk2n/HG2npy7QeeM66fxA7vI4+Z30gCA1vG/RScW6mBKWL5EAxOS0PdPGK4pw7oTLgEAELR8+35J0oUPfEQnEwAgagRMSSzueUx4yHcCO5hoYQIAAEhq8395ov588ThJ0tItZc4WAwBIGQRMnZgrwWEPDUwAAADJr3t+ls4e10+S9OicdTrjL+9r894qh6sCACQ7AqYkFu/8x4SHfCdyBhMtTAAAAKngn1+bqMNLumrx5jK9tnibqut88vr8soncgRgAkDIY8p3E4v1fd6iDKVE/I/DDCAAAQOo4flQvTR3RU0Numqk7X1mqO19ZGr7P7TJyu4w8LqNrjxuma48b5mClAIBkQMCUAuLV8xPqJvK3Iffx+a12ldeoV2F2+16TBiYAAICU4XIZXTJpgLbtq9akwd3k9fnl9Vv5/FZev9WMRVv09rIdBEwAAAKmziwU9sxZvUurd5br9189tNXH/Pb1ZXrgvTX65OYT1aMgq02vRwMTAABA6rnnvEOavW93eY2eXbBJCzfu1Zi+hfK4mcABAJ0V/wN0YqGA6f2Vu/Tcgk1RLWF7+pONkqQd+6vb95rtehQAAACSUXFepiTp7L99qAdmr3G4GgCAkwiYkli8Zxa5Gq1Xa22p3KMfrtXeyrqIj40GDUwAAADp5WenjNS/rj5c/brkaMmWfU6XAwBwEAFTCjBxGlzUNGBqOQL6b7B7SZJqvf52vWa83hcAAAAknsft0nEje0qSZi7apnvfXKHSilqHqwIAOIGAKQXEq5OpcdTTWsBU/+5MT9s/dZjBBAAAkJ5+eOJwSdJ9s1bqhc82O1wNAMAJBEydWONmotYCoNU7y8NvtxZGNfua7XoUAAAAktlXJw7QuunT1DU3Q6t2lLf+AABA2iFgSgHxWlbW+HlbCo2q63zy1hvS5G/HCrkar6/tDwIAAEDK2FNZp6fmbdDRv3lbG0srnS4HAJBABEwIa2nId3Vdw3DI18YOpspar2Yu2qpJg4vbUxoAAABSwLPfOVInHtRTm/ZUae7aUqfLAQAkEAETwlrqYGocPrV1idzTn2zUnso6fXfq0PaUBgAAgBRweEmx/nH5BEnST59dqO/9Z4Eqa70OVwUASAQCphQQz7lF7//8OP3slJGSJNvCsrfGgZK/pXanCB6ds06Hl3TVxBI6mAAAANKZx+1SVnBDmJmLtunT9XudLQgAkBAepwuAswYU5yov0y2plQ6mRoGSr40B0/ayap06pnfbCwQAAEDKWX7XadpRVq1Jv56lyx+ZqxG98uV2ueRxGbmDfzLcRtcfP1xThnV3ulwAQAwQMKWA9u3XFj2XK9Aj1bYlcm17DWvjN6wcAAAAyadHQZYuPnyAvtxapr5dcuT1W/nq/fl0wx49M38jARMApAkCJoSDn5ZCoyZL5No4gykQMLW5NAAAAKQoY4ymn39Is/ePvvU1/d/nW+Rxu/STk0eoT1FOAqsDAMQaM5hSQLxzmWADk2wLoVHjJXFtDZj81oZfBwAAALjjrDGSpOcWbNKri7Y5XA0AoKMImCBXFB1MjfOkts5gspJM3KMyAAAApIqvThygtfecrqKcDK3aWe50OQCADiJgSmJtbBJqt1BnUUtdSb5G97W1NjqYAAAA0JgxRvuq6vTk3A26+MGPtLu8xumSAADtRMCUAuI9u+jADKaWhnx3bBc5a8UQJgAAADTx6NWHa3SfQn28plTz1pY6XQ4AoJ0ImBAOi47+zTsquWFGxHP8jQKlxh1NLQnNdqKDCQAAAI1NHdlT//vuFEnSZxv3tjgXFACQvAiYoNKK2lbP8fobL5FrS8AU+NtFBxMAAAAiyMl0S5IenL1GR97ztiprvQ5XBABoKwKmJHbawb118eED9Mtpo+P6OtEEP42XxPn80T9/aHkd8RIAAACa8+K1R2naIX20raxaK7Yz9BsAUo3H6QLQvCyPW9PPPyTurxNNY1Fdo0SppXlNjYWyKRdr5AAAANCMQwd00U9OGqEZX2zVbS8u1kWHD5THbeRxGbldRh6XSxluo/GDuqpbXmZ4jigAIDkQMCGq2UiNl8i1JWCyYh09AAAAWjewOFeStHDTPi3ctKjZ8zwuoz9dPE5nHNI3UaUBAFpBwASZRovXar1+ZXoarp5s3MHUll3kmMEEAACAaHjcLn16y0mq9QZ+9vT6/fL5rep8Vj6/1Sl/mh08bnXdk5/puic/0+e3nqQuuZlOlg0AEAET1HSJXGWtV5mehv9JNw6U2pAvhQMm8iUAAAC0pjiv+bDoljNGa8W2/Xpx4WZV1wVCqHG/elNPXzNZRwzplqgSAQARMOQbTdave9xNPy28vkYBUxsSptByOkYwAQAAoCO+cfRg/eaCQ7TsztO08NaTw8cvevBjbSytdLAyAAAdTGgS/ERa/taRId+hMxsvxQMAAADaqyg3Q+/9bKo+37hXP/jv5/rpswt1/vj+yvCEBoK7NLGkq7rnZzldKgB0CgRMaDIbKVJ3UuPQydemXeQC57JEDgAAALE0qFue+nbJ0Q/++7nmri3V3LWlDe6fdkgf/e3S8Q5VBwCdCwETmgQ/kcKjuo7MYAo2PzHkGwAAALGW4Xbps1tOUlWdT1ZSndcvr9+vO15eqmVby5wuDwA6DWYwITyDKZT/RFoiV1Pna3C7LTOYrOhgAgAAQPx0zctU3y456tclRyXd8zSsZ4F6FWZr9c4K/fa1ZVq/u8LpEgEg7dHBhPBkpFDj0oUPfKQ+RdkNzvl4TcN240ghVHNCp9LBBAAAgESZdkgfPbdgk/7+7mrt2F+j33/1UKdLAoC0RgcTmgQ/peW18ls1+NNYm4Z8M4MJAAAACXbcyJ5aN32aBnXL1XMLNumKR+ZqR1m102UBQNqigwlNgp9bzhytCycOaHDssTnrdNtLS8K32xIwhQIqQ8IEAACABJs6ooce+2i93l+5S5N+PUvrpk9zuiQASEt0MEGuRrlPpPlKQ3vkS5IOG9glcE5bhnyHZjC1qzoAAACg/W4/a4zW/Pr08O03lmxr0zxRAEB0CJgg0yj6ifT/bX52oNmtpFuepLbNYLLMYAIAAIBDjDFy1fuN6jVPLNDHa3Y7WBEApCcCJjRZIhdp+du4AV10/2Xj9auzx0iSqut8qvH6mpwXiZ8ZTAAAAHDYsjtP1Vs/PlaS9OmGPQ5XAwDph4AJTTqLbDPzlU47uI9yMwOdTH95e5XOuO+DqJ7/QAdT+2sEAAAAOiI7w61hPQskSb9/Y4WO+e3bKq/xOlwVAKQPAiY06Syq8fqbPbd+SLR1X3S7cBzoYCJhAgAAgLOe+faROm1sb20srdIXm/Y6XQ4ApA12kUOTDqacTHez59YPiaKdwxTqYCJeAgAAgNMmDS7WkB55enXxNl360FydeFAvZXqMMtwuZbhdOvewfjpqWHenywSAlEPAhHAH0xmH9NGlRwzUlKHR/Yfqa2YpXWMM+QYAAEAy6Z6fpQsn9tfsFbu0eW+VvD6/6nx+bd1Xrc17qgiYAKAdCJjQoCspmnDprnPGavaKnZq1bEdUz8+QbwAAACSb315waJNjP39uoWYu2iZrLeMdAKCNCJgQXroWZUOSLp88SDvKqvXG0u1RnR96WjqYAAAAkMwKsjNUXuPV4Btn6rcXHKILJw7o8HNW1/m0p7JWXp+Vz2/ls4G/vT6rV77YosKcDA3unqfjR/VUhpsRuQBSFwETwsGPVZQJkyS3K/Cfn99v5Wplezg6mAAAAJAKfnTSCGVnuPT0J5v029eW6YH3Vuu3FxyqCYO6tuv5rLUadctrUZ+/bvq0dr0OACQDAiaEd4bzN795XBOhX654/VaZrQRM4SHfJEwAAABIYvlZHv3slFH6z9wN2ltZp13ltTr//jm69IiBuvG0USrIzmjT8727Ymf47d9dcIjcLiO3y8jjcsntMnr6kw2av26P9td4JUklN8wIn//Gj47ViF4FUb/W9rJq5Wa6leF2yRN8ndDP3zv2V6swO0PXPfmZLpzYXyeP6d2m9wMAokHAhHBnkT/aNXJSuGspmsfYUAdT20sDAAAAEu7Fa4/SK19s1e9eXy5JenLuBk0Y2FXnT+jfpuepCAZHM64/WmP6FjW5/9SxgaDnnle/1APvrWlw38l/nK2D+xUpw23kcbuUGdzhLlINa3dV6LjfvxtVTW99uV0XTuyvG047SMV5mW16fwCgJQRMCP9mI/p4SfIEAyafP4qAKfg3M5gAAACQCgZ1y9O1xw3T96YOlbXSkJtm6ifPLtQTH6/XU9+arJxMd6vPcdqf39f63RWSpC65LQc5N552kG487SB9uGqX+hRl6+YXFmv97gp1z89Unc+qzufXnNW79MGqXdq6r0pnj+unAcW5kqSZi7bqybkbJElDeuTpggn95fVZeX1+3ff2qoiv98z8TTpsYFddMmlgWz4sANAiAiZoaI98SdJXRvSI+jGhsMgbRcAU6nJqZSUdAAAAkFSMMTJG+sflE/SP91br8417tXDTXk0e0q3Fx+0qr9GXW8skSZceMVB9CrOjer2jhgV2dH7qmslN7rtv1krd++YK/f6NFVq+vVx3nDVG2Rkufe8/n0qSjh3RQw9eMUHZGQfCr+tPGK5l2/ZrVO8CVdT6VJSTIWutBt84Uzc+v0gLN+7VneeMZbg4gJiIW8BkjBkp6el6h4ZIulXS48HjJZLWSbrQWrsn+JgbJX1Dkk/S9dba14PHJ0h6VFKOpJmSfmBtG9ZzoUXDeubr81tPUlFO9GvK3aElctEETMHZTjQwAQAAIBWdOra3Di/pqgl3vaWLH/xYo/sUBnaC8/vlt5LX79fG0ipJ0tp7TtfeylpJ0p1nj9EVR5bEpIbvHz9Ml0waqON//65eXrhFLy/cEr7vzEP76i+XHNbkMR63S2P7BZbmFeUEQiRjjH5+6kj96c2V+u8nG3XBhP6aWFIckxoBdG5xC5istcsljZMkY4xb0mZJL0i6QdIsa+10Y8wNwdu/MMaMlnSxpDGS+kp6yxgzwlrrk3S/pGskfaxAwHSqpFfjVXtn1FrbbmPhJXLRzGBSaBc5EiYAAACkpm75WfrG0YP1+ca96pqbKbdL4WHdHpfRxtLNkqTBN84MP6Z/cBlbLBhj1KMgS09+a7Luf2+VZi7aJkn65bSDdNWUkjY91/emDtOFEwdo4l1v6YJ/fKThPfOV4XYpw22U4Xbp8smDdM5h/WJWO4DOIVFL5E6QtNpau94Yc7akqcHjj0l6V9IvJJ0t6b/W2hpJa40xqyRNMsask1Rorf1Ikowxj0s6RwRMjnK1ZQZTaBe5eBYEAAAAxNktZ4xu9r7LjxykX8/4UvPX7wkf65GfFfMaDu5fpL9fNqHDz9M9P0vXHTdM89aWqlt+pup8ftX5rN5bsVPz1+9RaUWtzjikjwpzMrR1X7U8LhOe+wQAkSQqYLpY0lPBt3tZa7dKkrV2qzGmZ/B4PwU6lEI2BY/VBd9ufBwOcpt2BEx0MAEAACBNjR/YVc99d4r++vZKvfLFVv36vIM1pm+h02W16KenjGxy7BfPfaGn52/Ur15Zql+9srTJ/Wcd2le//+qhyvQwtwlAQ3EPmIwxmZLOknRja6dGOGZbOB7pta5RYCmdBg5kR4R4crehgyk05JvZgQAAAEh31x0/XNcdP9zpMtrtNxcconvOO1hjbntdVXW+Jve/tHCL3l2+Q+ce1k8et0sZbpcy3UanHdxHB/VJ7kANQHwlooPpNEmfWmu3B29vN8b0CXYv9ZG0I3h8k6QB9R7XX9KW4PH+EY43Ya19UNKDkjRx4kSGgMdRWwKm0H9MNXX+uNYEAAAAoONcLqN3fjpVq3eWKz/Lo75dctSjIEv7qup06B1vqKzaqxcXblGd1686v1Wt16+/vrNKS+44VTmZ7tZfAEBaSkTAdIkOLI+TpJckXSVpevDvF+sdf9IYc68CQ76HS5pnrfUZY/YbYyZLmivpSkl/SUDdaIG7DUO+P9uwV5L0+ca9Ou3gPvEsCwAAAEAM9C7KVu+i7AbHinIy9OWvTlV2hqvB+IuT7n1PK3eU66BbX9PvLjhEX504oPHTAegE4rpoyRiTK+kkSc/XOzxd0knGmJXB+6ZLkrV2iaRnJC2V9Jqka4M7yEnSdyU9LGmVpNViwLfjQgGTP4oOpkP7B7ZGPX5Uz1bOBAAAAJDMcjLdTWar/u97U3T9CYFlgXfN+FJ7Kmq1c3+Nar2sYAA6k7h2MFlrKyV1a3RstwK7ykU6/25Jd0c4Pl/S2HjUiPYJDfn2RhEwhc4JhVIAAAAA0kdhdoZ+fNII3TdrpfZV1emwO98M33fF5EH61jFDNLAbO9AB6S5Ru8ghzbjaMIMptIzORcAEAAAApK3ZPztO/5m3Xg+8tyZ87ImP1+uJj9crN9Otfl1y5HYZedxG28tqNKxHvp66ZrKDFQOIJQImtIsntEQuihlMoWV0bkPABAAAAKSrgd1ydeNpB+nG0w6SJK3Yvl/vLd+pu2d+qcpan4b3ypfXZ+XzWy3eXKad+2tUcsMM3Xz6QfrWsUMcrh5ARxEwoV1C3UjRLJHzsUQOAAAA6HRG9CrQiF4FWre7Qj88cYR6FGSF79tYWqk7X1mqN5Zu190zv9ST8zbodxccooklxQ5WDKAj4jrkG+kr1I0UzZBvAiYAAACg87r73IMbhEuSNKA4Vw9eOVG3nTlaWR6X1u6q0AX/+EjT7ntfW/ZWOVQpgI6ggwntEloiN3/9Hi3dWqYrjyxp9tzQDCYCJgAAAAD1XX3UYF191GDd+uJiPf3JRi3ZUqYLH/hIX5tSIo/LyO12yW1M4G2X0ZFDu6lvlxynywYQAQET2iW0RG76q8skKWLAZK3Vki1l4Q4mFzOYAAAAAETwq7PH6o6zxmjwjTO1aU+V7prxZcTzTjyopx6+6vAEVwcgGgRMaJdoupFe+WKrvv/UZzphVE9JB7qeAAAAAKAxY4wW3X6yfH4rY4z8fiuvPzAU3Ov366YXFuutL3fo/Pvn6GenjNTkId2cLhlAPQRMaJfWAqYlW/bpnpmB3zp8sXlfVI8BAAAA0LkVZGc0e9/PTxkpa63eX7lLFz/4sV774TEa2atAhpUSQFJgyDfaxd3KN/G/v7taW/ZVSzrQueQiYAIAAADQTmP7FemJbxwRvn3qn97X399d7WBFAOojYEK7tNaN5PNZ5WS4JUl9irIDj+E3CwAAAAA6aPEdp+j+y8ZLkn73+nKd87cPtb2s2uGqALBEDu3SeGC332/DHUp+v9VrS7aF71u5vVwSS+QAAAAAdFx+lkenHdxH9182Xg+9v0afbtirbz42X18/ukQuY+RxueR2BXeecxsdNqCLuuRmOl02kPYImNAu+6vrGtyu8/uV5Qp0LG3aU9Xw3BqvuuVlqiCbTzcAAAAAsXHawX102sF9VHLDDC3avE8/enphxPOmHdJHf7t0fIKrAzofrvjRLrU+f4PbPr8Nv52d0XTl5Yc3HK/s4JI5AAAAAIiVRbefrB37a+Q2psGuc36/dPP/LdKML7ZqWI8V+urE/urfNdfpcoG0RcCEdjFquNytzmebOTOAcAn4//buPEyussz7+PfuJWQhC5ANkkBAlhASCBAicWQRh91XIDKKOOAyKirKjLiOgi+CCiqOog6ioIgbDvLq4AYoEhFQxAACEgGBhEBIwr4ZSNJd9/tHncRO6EA6le7Tp/L9XFdfVJ06ldzNryr99F3PeR5JkiT1hqED29e6+9wHDtqJN3/rRs75zd+48o7FnH/8dMZvNsid56Re4CLfWi9Txw1nQFsLB+8yBlh9BlPtxXtNkiRJktQn9ttxFPPPOpxtthjMnYufYZ/PzeZHNz1YdllSU7LBpPUyfHA7d3/qUPbZYRQAHV0umetMO0ySJEmS+o9L3/UKTj9iFwA+fOltvP68P/CwO89JG5SXyKkh7a31qaULHl/K0uWdrOisvWCRb0mSJEkq06ihm3D8zIlsOXwQX7/mXm6c/zgzPvMbZu0xjjNnTWWTNpf0kBplg0kNufZvjwJw9Hl/KLkSSZIkSXpxB04ew4GTx/Cpn8/lguvm8eObF5IJB0waTXtrCwPagraWFiaNHcroYQPLLleqFBtMasjgAfVO/27jh/PmV0xkQFsL7a0tnPDdmwC46G0z2GLIgDJLlCRJkqTVnPKayXzgoJ3Y+RNX8JNbFvKTWxa+4JwvvmE3Dpg0huGDul9AXNLqbDCpIQPa6st4Hb3neGbtMX7V8XOOmcbwQe3st+OoskqTJEmSpLUaNKCVGz/+ap5+roPMZEVnsqKzxiVzHuD7f1zA+//nVmbtMY6zj96NlhZ3nZNeig0mNaS12N6zY42t446YNq6MciRJkiRpnY0eOpDRQ1c/tuv44bz3gO2ZeebV/Pjmhfz81kV88y3TV21wJKl77iKnhqzs5HfW3DlOkiRJUvVFBFsOH8TsD+7P8TO3YXlnjbdfNIfr73mUdMdsaa1sMKkhK2cw1fyHVpIkSVIT2XbkEE4/Ygp7TdyMZR013nTBH5l918NllyX1WzaY1JB37/8yDt5lDG/Ya+uyS5EkSZKkDe7id+zNVSfvB8Dbvj2HY8+/gaXLO0quSup/olmn+E2fPj3nzJlTdhmSJEmSpCbw01sf4oc3LuD39z7GtAkjOGzqWNpaWmhvDdpbW/in7UcyYfPBZZcp9bqIuCkzp7/guA0mSZIkSZJe2vKOGjuecnm3j43cdACXvfeVbDV8IBHuOqfmZYNJkiRJkqQG1WrJilqNjs5kRWeNFZ3JiT+4mRvnPQ7Ahw7eiRNftX2f1nTV3CVcccdi2ltb+L//ZzID21v79O/XxsUGkyRJkiRJvWDp8g6+d8P9fOaXdwJwyC5j+eihk5g4csgG/XvmzH+ch556nqGbtDF+s0HcdP8T3PvIs5x/7bzVzpsxcXPaWoO21hbaW4KdtxzGCfttx6abtDm7Sg2zwSRJkiRJUi+67m+P8qFLb2XRU88D8KU3TOPQqWPZpG3dZxSdd829XHj9PJY8vQyA3SaMYEVHjY5ajbuXPLvW571y+5Fcd8+jAMzcbgs6ajWWdya3PvDkaufNP+vwHn5X0upsMEmSJEmS1AcmfvQXq25P2HwQHz9sModMGdvj5wLsu+MoBhQLiS94fCkPP7OMQ6eM5W9LnuXQqWOZNmEECx5fygGTRpMJA9tbaW35xyyl+x/7OyddfAu3PvjUqmPH7b0NZxw5pcHvUhsrG0ySJEmSJPWBzOTp5zvY44xf01mr/879nv1fxj47jGJAWxS7z7Ww3aghq62X9Nizy9jzU1ex746jOO9f92BgWystLRvmkrazr7yLr86+Z7VjO4zelLf800SOnbG1l85pndlgkiRJkiSpD3V01lj45HPs9/nfdvv4oVPG8rHDdubPDzxJLZOtNx/MUef+ns8cNZVjX751r9R0+e2LuOC6edx0/xOrjr1m1y3Zb8dRDGirN77aWoJdx49g7PCBvVKDqs0GkyRJkiRJJbh5wRM8uXQ5g9rbip3nanz+yru4c/Ez3Z7/o3fNZK+Jm/dqTcuLdZ0mf+LKbh+fMm4YP3vvK53ZpBdYW4OprYxiJEmSJEnaWOyx9WYvOLbViEF87Ce3s/ngAWw5YiDfu2EBAB88aEemTRjR6zUNaGthAC3cdMo/89yKTjIpml/J56+8k6v++jDb/ucv+eRrd+HNr5jY6/Wo+pzBJEmSJElSyWq1JIJ+MWPo6edX8KVf/41vXT8PgJMO2J5jX76Nl8wJ8BI5SZIkSZLUAz+++UFOvuTWVfe3Gj6QC986g53GDi2xKpXNBpMkSZIkSeqxK/6ymB/f/CC/mrsEgKN2H8eZs6autgOeNh6uwSRJkiRJknrskCljOWTKWL7wq7v4ytX38JNbFrLoqed4za5b0d4atLbUd55rbQnaW4OZ241k+OD2sstWH3MGkyRJkiRJWicdnTW2//jlL3rOm16+NZ8+amofVaS+5gwmSZIkSZLUkLbWFuaefjBLl3dSy6SzlnR0Jh21pLNW45hv/JHv/3EBi596ng8fMsn1mjYiNpgkSZIkSdI6GzygjcEDum8nnPumPfjEZX/hN3c+zO0Ln+K84/Zk9wkj+sXueOpdXiInSZIkSZI2qMPOuZa5i55edX/W7uP49FFTGTTAhcGrzl3kJEmSJElSn3hueSd3PPQUR5/3h9WOHzplLG2t/1gUvK0laGsNXj99AruOH1FOseoRG0ySJEmSJKlPddaSzOSgL/2Oh558jgmbDa6v21Qr1m+q1Vjy9DIALjh+OvvsOJJN2pzl1J/ZYJIkSZIkSf3Ohy+9lUvmPAjAgZPHcOrhk9l6i8ElV6W1WVuDqaWMYiRJkiRJkgDOmrUrsz+4PwC/nruEfT8/mz/c+1i5RanHbDBJkiRJkqTStLQE244cwlUn78sph+8MwBvPv4ETf3Azz6/oLLk6ravu9xWUJEmSJEnqQ9uPHsr2o4fS3trC+dfexy9uW8QvblvEa3fbirbWYEBrC22twQGTRnPApDFll6s1uAaTJEmSJEnqV5Z1dLLTKVcAsO3IISzvqK22IPgOozflxFdtz5G7jwMgM4mI0urdmLjItyRJkiRJqrT7HnmWk354C39Z+DQA++wwkgMnj+ETl92x6pxj9prAAZNG097aQnsx6+llozZl1NBNyiq7qdhgkiRJkiRJTeHKOxZz0sW3sKyjtk7nbzV8IFd/cH8Gtrf2cmXNzwaTJEmSJElqKpf9eSF/mv84r9tjPNMmjOCi389n5NBNmLjFEFZ01ljRmZx/7X38eu4SAA6cPIbPHDXV2UwNsMEkSZIkSZI2OkuXd3Deb+/ly1ffs+rYGUdO4bi9tymxquqywSRJkiRJkjZai596nu/dcD9fnV1vNO04ZlO+ftx0th05pOTKqmVtDaaWMoqRJEmSJEnqS2OHD+SDB+/Exe/Ym312GMndS57l0HN+x8U3LqBZJ9/0JWcwSZIkSZKkjc6+n5vNgseXAtDaEhwwaTQDil3n/r6sg2kTRvDeA3Youcr+x0vkJEmSJEmSCp215LFnlzHjM78BYNLYoXTUkhWdNe5/bOmqY+/cdztm7TG+zFL7lbU1mNrKKEaSJEmSJKlMrS3B6GEDuerk/Rg9bBOGDWxf9djsux7mrRf+iTsXP8PJl9zKQ08+xxtnbM0Wm7r73No4g0mSJEmSJKkbX7/mXs68/E6gPpvphP2244jdxtHSEiVXVh4vkZMkSZIkSVoPEz/6i9Xu3/ixVzN62MCSqimXu8hJkiRJkiSthxv+89Wcc8y0VfdnnnU18x/9e3kF9UOuwSRJkiRJkvQixg4fyBHTxrHNFkO49u5H+MKv72b/s3/L+M0G0dYStLW20NYStBa3T9h3Ow6bumXZZfcpG0ySJEmSJEnrYNqEEUybMIIRg9u55u5HGTawjY5a0llLOmo1OmvJVX99mPd8/2ZOfc1kZu0+js2GDCi77D7hGkySJEmSJEkbyHf/MJ9TL7sDgL2325wvvmEaWw4fVHJVG45rMEmSJEmSJPWy42ZOZN6ZhzF8UDs33Pc4M8+8mmv/9kjZZfU6G0ySJEmSJEkbUETw2w/uz1mzpgJw3DdvLLmi3ucaTJIkSZIkSRvYZkMGcMyMrYmATTdpL7ucXmeDSZIkSZIkqZe8Ya+tyy6hT3iJnCRJkiRJkhpig0mSJEmSJEkN6dUGU0SMiIhLI+LOiPhrRMyMiNMiYmFE/Ln4OqzL+f8ZEfdExF0RcXCX43tGxO3FY1+OiOjNuiVJkiRJkrTuensG0znAFZk5CdgN+Gtx/IuZOa34+iVAREwGjgF2AQ4Bzo2I1uL8rwHvBHYovg7p5bolSZIkSZK0jnqtwRQRw4B9gW8CZObyzHzyRZ5yBPDDzFyWmfOAe4AZEbElMCwz/5CZCXwHOLK36pYkSZIkSVLP9OYMpu2AR4ALI+KWiLggIoYUj703Im6LiG9FxGbFsXHAA12e/2BxbFxxe83jLxAR74yIOREx55FHHtmg34wkSZIkSZK615sNpjZgD+Brmbk78Hfgo9Qvd3sZMA1YBHyhOL+7dZXyRY6/8GDmNzJzemZOHzVqVGPVS5IkSZIkaZ30ZoPpQeDBzPxjcf9SYI/MXJKZnZlZA84HZnQ5f0KX548HHiqOj+/muCRJkiRJkvqBXmswZeZi4IGI2Kk49GpgbrGm0kpHAX8pbv8UOCYiNomIbakv5n1jZi4CnomIvYvd444HLuutuiVJkiRJktQzbb38578P+H5EDADuA94KfDkiplG/zG0+cAJAZt4REZcAc4EO4MTM7Cz+nHcD3wYGAZcXX5IkSZIkSeoHor4xW/OZPn16zpkzp+wyJEmSJEmSmkZE3JSZ09c83ptrMEmSJEmSJGkjYINJkiRJkiRJDbHBJEmSJEmSpIbYYJIkSZIkSVJDbDBJkiRJkiSpITaYJEmSJEmS1BAbTJIkSZIkSWqIDSZJkiRJkiQ1xAaTJEmSJEmSGhKZWXYNvSIiHgHuL7uOwkjg0bKL0Hoxu+ozw+Zgjs3DLKvPDJuDOVaPmTUHc6w+M4RtMnPUmgebtsHUn0TEnMycXnYd6jmzqz4zbA7m2DzMsvrMsDmYY/WYWXMwx+ozw7XzEjlJkiRJkiQ1xAaTJEmSJEmSGmKDqW98o+wCtN7MrvrMsDmYY/Mwy+ozw+ZgjtVjZs3BHKvPDNfCNZgkSZIkSZLUEGcwSZIkSZIkqSE2mCRJkiRJktQQG0ySJGmDiogouwZJkiT1LRtMG4AD6WqLiM273DbLCoqI/SNiVNl1aP1FxAci4qDitu/D6hu68oZ5VpO5VZ8ZVpPj0upzXFp9jkvXnw2mBkTEERFxEbBb2bWo5yLikIj4HfCliPgCQLrqfaV0yfBNwLKy61HPRcRBEXEl8BHgePB9WGURcWBEXAecHREfBvOsGsc21WeG1eS4tPocl1af49LGtZVdQNVERGRmRsSrgDOAFcDMiLg/M58ouTy9hKID3QL8G/A24EzgFuA7EXFoZl5eZn16aUWGAbwB+Drwb5n5o3KrUk8UGbYDnwD2o/4+HADsFRHtQIc/zKsnIsYDpwFnAb8FfhgRW2TmR1b+7CyzPr00xzbV5fi0mhyXVp/j0upzXLphOYOpB9YYIM8DDgY+BLwc2LW0wrROVuaXmZ3AdcArM/My4HngYeCOiGhZeW6JpWotumRYAx4CvgPcUzz2LxExvvhBYIb9VJcMlwOXZeY+mflL4AngmMxc4Q/x6ljjfTYJuD0zf5aZzwD/Dbw/InYofvH1Pdn/zQMOwrFNpTg+rSbHpdXnuLT6HJdueDaY1lFEvBf4cUS8PyLGZub8zFyUmVcDS4D9ImJcyWVqLdbIb8vMnJuZHRGxB/C/wETqUyH/a+VTyqlUa9Mlw5MjYiT1wdhtwNci4k7g9cBXgHNXPqWcSrU23bwP/1Qcb8/Ma4D7IuLQcqvUulojz2HA3cArI2Jmccpo4A7glLJq1IuLiPdExOuK2wE8kJmLHdtUh+PTanJcWn2OS6vPcWnvsMG0DiLiKODNwJepfxJ0SkRM63LK94EdqX9S1PV5/kPSD3ST38e75LeyOz0D+DDwloiYXnwSoX5ijQynAp8Etgd+DswG3piZ/0J9ivmREbGnGfYva3kfrlwfpCPqi5reD3SWVKJ6oJs8P0t9vYkvAidExPXUZ8LMAqZFxEQ/Aew/ImJoRJxH/XKAiyKircin60wzxzb9nOPTanJcWn2OS6vPcWnvscG0bl4OfC0zZ1NfX2IecNLKBzPzNuBPwJSIOCAiPlIcdzDdP3SX378DZOa8zFxQ3P47cAkwrKQ6tXZrZjgf+FBmPgR8MjNvAcjMx6l/8rdpOWXqRbzY+zCL7AYBrwJYeVmA+q3u8vxkZn4TeAfw/sw8FlgA3Ag8XVaheqHiEsZrMnMs9V+I/rt4aNWlVo5tKsHxaTU5Lq0+x6XV57i0l/g/qos1P9Hpcv8+4FiAzLwf+AUwJCJe2+X0i4G3A/8DjOzuz1Pv6mF+g9fIj4g4BdgFmNv71ao7PcjwZ8DQiHhtZj7f5fxTqWd4Z99UrDU1+O/o94AZETHQT/r6hx7k+VNgs4g4qliv4MbivDOAIcAzfVSy1vAiGf60+O9/AG+M+lpZnRHR1uUcxzb9kOPTanJcWn2OS6vPcWnvs8G0uvaud7p8wnMpsDQijijuL6K+Q87kqNsUOAe4Hdg1Mz+0xvPVN3qcH0BEHBr1bbV3BI7OzMV9U666sb4Z7hMRs6ln+LrMXNI35aob6/XvaHFsEPBDnI7cn/Q0z50AImKHiLgMmEJ9NtOKvilX3eg2w8z8e0S0FD/zzgUuKI53ZGZGxBDqlw44tukHIqJ15W3Hp9XR09yK5zgu7UfWM0PHpf3I+vz7WRxzXLoebDABETEzIn4EfD4iJq98EUZEW3HKE8BPgHdHRGTmU9SnOg4sXqTPA/+emYdn5qIyvoeNWQP5DSoe/yvwrsw83vzKsQEynA+cmJnHmWE5Gshwky4/7C/LzPNtRpSvkZ+LxeOLqb8nX+vAuhwvkmFrrDHVPzM/CmxbPGdMROxVXJ5zkmOb8hR5nA6Q9Z3GVh5f+cuP49N+qIHcHJf2Exsgw/k4Li1VAxk6Lm3QRt9giojRwFeBXwKPUb/28m1Q/xSvOG0QcCX1zuY3ImIrYHdgxcrzMvPhPi5dNJzf8uK8+Zn5lz4uXYUNlOEDmekU8pI0mOHKx1cbAKg8G+jn4jOZ+WAfl67CS2TYmZm1YnbL8C5P+yxwPXAtMLg417FNSSLizcBF1Bfufn1xrA1W+wTe8Wk/02Bujkv7gQ2UoePSEjWYoePSBm30DSZgN+DuzLwQ+ALwY+CIiJgEEBGfot7dHAN8gPqWrz8AngTOKqNgrcb8qs8Mq88Mm4t5Vt9LZXgG9csDphT3DwXeR31L9F2yvj2zyrUQOAA4BDgb6g2jLjPRTsP3YX9kbtVnhtVnhiWKje0y7Ig4kvr1sbdm5i8iYhTwe+CQzLw36lsSvo/6p3efpL4mwamZeW+XP2NwZi7t++plftVnhtVnhs3FPKuv0QwjYjLwTGY+UMo3oK4Z3paZPy9+EWrJzBVRX49ndmaeWpw7GvgSvg9LZ27VZ4bVZ4b9y0YzgykiRkXE/wInA48DF0bE0Zn5CPD/qA+8oN65/A2wOfVr2I8tBmer/l/54ut75ld9Zlh9ZthczLP6NkCGrQCZOdfmUjm6yfBbUd+NsRNYuVbICcBJETEG6pcu+j4sl7lVnxlWnxn2TxtNgwl4GXB9Zu6bmedRnw53cvHYxcCkiPjnrG9B+Bj1KXPLAKK+y4pbE5bL/KrPDKvPDJuLeVZfoxm6vkT5ustw5W5vyyOiNTPvAH5EcelGcUkjxTm+D8thbtVnhtVnhv1QUzeYIuL4iNg/IgYDNwHfKY63AnOBO4pTb6e+BeGXImJ74NXUu57t4IuvLOZXfWZYfWbYXMyz+syw+tYhw9uL+wEkQGa+HXhzRDwB7BZr7ASo3mdu1WeG1WeG/V/bS59SLcWLaSz1hbpqwL3AO6hv07qk6GR2RsTOFLunFIOsb0f9msyPApOAd2Tmk2V8Dxsz86s+M6w+M2wu5ll9Zlh9PcxwM1i121FGxDbAF6nv8HdiusNYnzG36jPD6jPDammqBlOXF9dQYGFm/mvUtyT8IvANYFaX0w+ivoMKETE2Mxdn5uciYkBmLu/76mV+1WeG1WeGzcU8q88Mq6+BDEdlfT2tp4CzMvPGvq59Y2Zu1WeG1WeG1dMUDabiRXY60BoRvwSGAZ2wakvCk4CHImK//MfWu88C8yLidGBWRBySmQ86AOt75ld9Zlh9ZthczLP6zLD6NlCGh2XmAsBfjvqIuVWfGVafGVZX5a8/jIj9qF9/uRlwD3AGsAJ4VUTMgFVT5E4HTiue0wq8jXqHcxjwqsx8sM+Ll/k1ATOsPjNsLuZZfWZYfRswwwV9XvxGzNyqzwyrzwyrrRlmMNWAszPzuwARsTuwLfAJ4GvAnlFfyOsn1F+U21D/vs8DvpOZN5dTtgrmV31mWH1m2FzMs/rMsPrMsJrMrfrMsPrMsMIqP4OJenfzkqJrCXA9sHVmfpv6lLr3ZX2hy/FALTPvz8x7M/M/fPH1C+ZXfWZYfWbYXMyz+syw+sywmsyt+syw+sywwirfYMrMpZm5LDM7i0MHAo8Ut98K7BwRPwcupv5iXbkSvfoB86s+M6w+M2wu5ll9Zlh9ZlhN5lZ9Zlh9ZlhtzXCJHLDqussExgA/LQ4/A3wMmALMy8yFsOqaTfUj5ld9Zlh9ZthczLP6zLD6zLCazK36zLD6zLCaKj+DqYsa0A48CuxadDVPpT5t7rqVLz71W+ZXfWZYfWbYXMyz+syw+sywmsyt+syw+sywgqKZmn0RsTfw++Lrwsz8ZsklqQfMr/rMsPrMsLmYZ/WZYfWZYTWZW/WZYfWZYfU0W4NpPHAc8F+ZuazsetQz5ld9Zlh9ZthczLP6zLD6zLCazK36zLD6zLB6mqrBJEmSJEmSpL7XTGswSZIkSZIkqQQ2mCRJkiRJktQQG0ySJEmSJElqiA0mSZIkSZIkNcQGkyRJ0gYUEVtExJ+Lr8URsbC4/WxEnFt2fZIkSb3BXeQkSZJ6SUScBjybmWeXXYskSVJvcgaTJElSH4iI/SPi58Xt0yLiooj4VUTMj4hZEfG5iLg9Iq6IiPbivD0j4pqIuCkiroyILcv9LiRJkrpng0mSJKkcLwMOB44AvgfMzsypwHPA4UWT6SvA0Zm5J/At4NNlFStJkvRi2souQJIkaSN1eWauiIjbgVbgiuL47cBEYCdgCvDriKA4Z1EJdUqSJL0kG0ySJEnlWAaQmbWIWJH/WBizRn2MFsAdmTmzrAIlSZLWlZfISZIk9U93AaMiYiZARLRHxC4l1yRJktQtG0ySJEn9UGYuB44GPhsRtwJ/Bl5RalGSJElrEf+YjS1JkiRJkiT1nDOYJEmSJEmS1BAbTJIkSZIkSWqIDSZJkiRJkiQ1xAaTJEmSJEmSGmKDSZIkSZIkSQ2xwSRJkiRJkqSG2GCSJEmSJElSQ2wwSZIkSZIkqSH/H9bQ0GiRuULMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_df['feature__expanding_volume_mean'].plot(figsize=(20, 10), title='Expanding Window Volume')  # A\n",
    "\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Shares')\n",
    "\n",
    "# A plot our expanding window features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run ./Base.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline  # A\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "clf = RandomForestClassifier(random_state=0)\n",
    "\n",
    "ml_pipeline = Pipeline([  # B\n",
    "    ('scale', StandardScaler()),\n",
    "    ('classifier', clf)\n",
    "])\n",
    "\n",
    "params = {  # C\n",
    "    'classifier__criterion': ['gini', 'entropy'],\n",
    "    'classifier__min_samples_split': [2, 3, 5],\n",
    "    \n",
    "    'classifier__max_depth': [10, None],\n",
    "    'classifier__max_features': [None, 'auto']\n",
    "}\n",
    "\n",
    "# A Import the scikit-learn Pipeline object\n",
    "# B Create a pipeline with feature scaling and our classifier\n",
    "# C Create the base gridsearch parameters\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import TimeSeriesSplit  # A\n",
    "\n",
    "tscv = TimeSeriesSplit(n_splits=2)  # A\n",
    "\n",
    "# A This splitter will give us train/test splits optimized for time data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0\n",
      "-------------\n",
      "Training between 2020-01-09 and 2020-07-01. Testing between 2020-07-01 and 2020-12-22\n",
      "\n",
      "Iteration 1\n",
      "-------------\n",
      "Training between 2020-01-09 and 2020-12-22. Testing between 2020-12-22 and 2021-07-08\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i, (train_index, test_index) in enumerate(tscv.split(price_df)):\n",
    "    train_times, test_times = price_df.iloc[train_index].index, price_df.iloc[test_index].index\n",
    "    print(f'Iteration {i}\\n-------------')\n",
    "    print(f'''Training between {train_times.min().date()} and {train_times.max().date()}. Testing between {test_times.min().date()} and {test_times.max().date()}\\n'''\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "feature__rolling_volume_mean_60   -0.003444\n",
       "feature__rolling_volume_std_60    -0.002263\n",
       "feature__expanding_volume_mean    -0.002219\n",
       "feature__dayofweek                -0.001477\n",
       "feature__morning                   0.025632\n",
       "feature__expanding_close_mean      0.033297\n",
       "feature__rolling_close_mean_60     0.040732\n",
       "feature__lag_30_min_ago_price      0.040766\n",
       "feature__lag_7_day_ago_price       0.041196\n",
       "feature__rolling_close_std_60      0.055433\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_df.filter(regex='feature__').corrwith(price_df['stock_price_rose']).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_data(price_df):\n",
    "    ''' This function takes in our price dataframe and splits it into a training and validation set\n",
    "    as well as filtering our rows to only use rows that are on the hour\n",
    "    '''\n",
    "    downsized_price_df = price_df[(price_df.index.minute == 0)]  # A\n",
    "    train_df, test_df = downsized_price_df[:'2021-05-31'], downsized_price_df['2021-06-01':]  # B\n",
    "    train_X, test_X = train_df.filter(regex='feature'), test_df.filter(regex='feature')  # C\n",
    "    train_y, test_y = train_df['stock_price_rose'], test_df['stock_price_rose'] # D\n",
    "    return train_df, test_df, train_X, train_y, test_X, test_y\n",
    "\n",
    "# A limit our data to only make trades at the 0 minute mark. Usually 6-7 times a day\n",
    "# B Split our dataframe into training and validation (before and after June 2021)\n",
    "# C Use the pandas filter method to select the features based on the prefix feature__ we have been adding\n",
    "# D Split our target variable based on our June 2021 split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date-time/Lag/Window/Rolling features + Random Forest\n",
      "==========================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.50      0.82      0.62        72\n",
      "        True       0.41      0.13      0.20        69\n",
      "\n",
      "    accuracy                           0.48       141\n",
      "   macro avg       0.45      0.47      0.41       141\n",
      "weighted avg       0.45      0.48      0.41       141\n",
      "\n",
      "Best params: {'classifier__criterion': 'entropy', 'classifier__max_depth': None, 'classifier__max_features': 'auto', 'classifier__min_samples_split': 3}\n",
      "Overall took 16.38 seconds\n"
     ]
    }
   ],
   "source": [
    "train_df, test_df, train_X, train_y, test_X, test_y = split_data(price_df)\n",
    "\n",
    "print(\"Date-time/Lag/Window/Rolling features + Random Forest\\n==========================\")\n",
    "best_model, test_preds, test_probas = advanced_grid_search(\n",
    "    train_X, train_y,\n",
    "    test_X, test_y,\n",
    "    ml_pipeline, params, \n",
    "    cv=tscv, include_probas=True  # C\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_gains(df, response, predictions):\n",
    "    ''' A simulation of acting on the First prediction of the day '''\n",
    "    df['predictions'] =  predictions\n",
    "    df['movement_correct_multiplier'] = (predictions == response).map({True: 1, False: -1})\n",
    "    df['gain'] = df['movement_correct_multiplier'] * df['pct_change_eod'].abs()\n",
    "    \n",
    "    \n",
    "    bullish = df[predictions == True]\n",
    "    bullish_gains = bullish.sort_index().groupby(bullish.index.date).head(1)['gain']\n",
    "    bullish_gains.cumsum().plot(label='Bullish Only', legend=True)\n",
    "    print(f'% of time with profit: {(bullish_gains.cumsum() > 0).mean():.3f}')\n",
    "    print(f'Total Gains for bullish is {bullish_gains.sum():.3f}')\n",
    "    \n",
    "    bearish = df[predictions == False]\n",
    "    bearish_gains = bearish.sort_index().groupby(bearish.index.date).head(1)['gain']\n",
    "    bearish_gains.cumsum().plot(label='Bearish Only', legend=True)\n",
    "    print(f'% of time with profit: {(bearish_gains.cumsum() > 0).mean():.3f}')\n",
    "    print(f'Total Gains for bearish is {bearish_gains.sum():.3f}')\n",
    "    \n",
    "    gains = df.sort_index().groupby(df.index.date).head(1)['gain']\n",
    "    gains.cumsum().plot(label='All Predictions', legend=True)\n",
    "    print(f'% of time with profit: {(gains.cumsum() > 0).mean():.3f}')\n",
    "    print(f'Total Gains for all predictions is {gains.sum():.3f}')\n",
    "    \n",
    "    plt.title('Gains')\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel('Cumulative Gains')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of time with profit: 0.077\n",
      "Total Gains for bullish is -0.043\n",
      "% of time with profit: 0.500\n",
      "Total Gains for bearish is -0.004\n",
      "% of time with profit: 0.308\n",
      "Total Gains for all predictions is -0.021\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gains(test_df.copy(), test_y, test_preds)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.510638\n",
       "True     0.489362\n",
       "Name: stock_price_rose, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_y.value_counts(normalize=True)  # A\n",
    "\n",
    "# A Guessing bearish has a null accuracy of just over 51%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Domain Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_features = pd.DataFrame()  # A\n",
    "\n",
    "daily_features['first_5_min_avg_close'] = price_df.groupby(\n",
    "    price_df.index.date\n",
    ")['close'].apply(lambda x: x.head().mean())  #  B\n",
    "daily_features['last_5_min_avg_close'] = price_df.groupby(\n",
    "    price_df.index.date\n",
    ")['close'].apply(lambda x: x.tail().mean())  # B\n",
    "\n",
    "\n",
    "daily_features['feature__overnight_change_close'] = \\\n",
    "    (daily_features['first_5_min_avg_close'] - daily_features['last_5_min_avg_close'].shift(1)) / daily_features['last_5_min_avg_close'].shift(1)  # C\n",
    "daily_features['feature__monthly_pct_change_close'] = daily_features['first_5_min_avg_close'].pct_change(periods=31)  # D\n",
    "daily_features['feature__expanding_average_close'] = daily_features['first_5_min_avg_close'].expanding(31).mean()  # E\n",
    "\n",
    "# A Make a dataframe to hold stats about the day itself\n",
    "# B Average the first and last 5 minutes of the day to get an opening and closing price\n",
    "# C The overnight change (pct change from the  previous closing price to the current opening price)\n",
    "# D A rolling percent change of opening price (window  of 31 datapoints)\n",
    "# E An expanding window function of average opening price (omitting the first 31 datapoints for stability)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MACD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def macd(ticker):  # A\n",
    "    exp1 = ticker.ewm(span=12, adjust=False).mean()\n",
    "    exp2 = ticker.ewm(span=26, adjust=False).mean()\n",
    "    macd = exp1 - exp2\n",
    "    return macd.ewm(span=9, adjust=False).mean()\n",
    "\n",
    "daily_features['feature__macd'] = macd(daily_features['first_5_min_avg_close'])  # B\n",
    "\n",
    "price_df = price_df.merge(daily_features, left_on=price_df.index.date, right_index=True)  # C\n",
    "price_df.dropna(inplace=True)\n",
    "\n",
    "# A function to calculate macd\n",
    "# B calculate macd using the opening prices\n",
    "# C Merge the daily features into the main price Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>day_close_price</th>\n",
       "      <th>pct_change_eod</th>\n",
       "      <th>feature__dayofweek</th>\n",
       "      <th>feature__lag_30_min_ago_price</th>\n",
       "      <th>feature__lag_7_day_ago_price</th>\n",
       "      <th>feature__rolling_close_mean_60</th>\n",
       "      <th>feature__rolling_close_std_60</th>\n",
       "      <th>feature__rolling_volume_mean_60</th>\n",
       "      <th>feature__rolling_volume_std_60</th>\n",
       "      <th>feature__expanding_close_mean</th>\n",
       "      <th>feature__expanding_volume_mean</th>\n",
       "      <th>first_5_min_avg_close</th>\n",
       "      <th>last_5_min_avg_close</th>\n",
       "      <th>feature__overnight_change_close</th>\n",
       "      <th>feature__monthly_pct_change_close</th>\n",
       "      <th>feature__expanding_average_close</th>\n",
       "      <th>feature__macd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "      <td>113262.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>270.992093</td>\n",
       "      <td>6444.631606</td>\n",
       "      <td>270.922408</td>\n",
       "      <td>-0.000185</td>\n",
       "      <td>1.995179</td>\n",
       "      <td>270.976685</td>\n",
       "      <td>267.298720</td>\n",
       "      <td>270.979216</td>\n",
       "      <td>1.099435</td>\n",
       "      <td>6661.142241</td>\n",
       "      <td>5587.306914</td>\n",
       "      <td>181.558979</td>\n",
       "      <td>7741.485050</td>\n",
       "      <td>270.970102</td>\n",
       "      <td>270.972966</td>\n",
       "      <td>0.003689</td>\n",
       "      <td>0.150072</td>\n",
       "      <td>182.494635</td>\n",
       "      <td>4.934485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>95.228036</td>\n",
       "      <td>10041.718219</td>\n",
       "      <td>95.259111</td>\n",
       "      <td>0.016404</td>\n",
       "      <td>1.371313</td>\n",
       "      <td>95.265620</td>\n",
       "      <td>96.272019</td>\n",
       "      <td>95.254057</td>\n",
       "      <td>0.816496</td>\n",
       "      <td>7376.052809</td>\n",
       "      <td>7034.929168</td>\n",
       "      <td>49.924718</td>\n",
       "      <td>753.499926</td>\n",
       "      <td>95.516392</td>\n",
       "      <td>95.264332</td>\n",
       "      <td>0.033377</td>\n",
       "      <td>0.333523</td>\n",
       "      <td>51.334508</td>\n",
       "      <td>9.326529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>69.440000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.700000</td>\n",
       "      <td>-0.087467</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.245000</td>\n",
       "      <td>69.440000</td>\n",
       "      <td>69.987349</td>\n",
       "      <td>0.085950</td>\n",
       "      <td>729.133333</td>\n",
       "      <td>466.680555</td>\n",
       "      <td>106.952994</td>\n",
       "      <td>6547.614079</td>\n",
       "      <td>69.653000</td>\n",
       "      <td>71.838000</td>\n",
       "      <td>-0.120443</td>\n",
       "      <td>-0.430004</td>\n",
       "      <td>106.561747</td>\n",
       "      <td>-16.825520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>214.276250</td>\n",
       "      <td>2093.000000</td>\n",
       "      <td>215.250000</td>\n",
       "      <td>-0.007639</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>214.162500</td>\n",
       "      <td>207.768200</td>\n",
       "      <td>214.244976</td>\n",
       "      <td>0.556499</td>\n",
       "      <td>3211.189831</td>\n",
       "      <td>2422.956264</td>\n",
       "      <td>132.880295</td>\n",
       "      <td>7037.573174</td>\n",
       "      <td>214.278720</td>\n",
       "      <td>215.362000</td>\n",
       "      <td>-0.013399</td>\n",
       "      <td>-0.073450</td>\n",
       "      <td>132.829049</td>\n",
       "      <td>-2.763562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>285.350003</td>\n",
       "      <td>3887.000000</td>\n",
       "      <td>286.290000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>285.240000</td>\n",
       "      <td>281.295600</td>\n",
       "      <td>285.126472</td>\n",
       "      <td>0.871947</td>\n",
       "      <td>4830.758333</td>\n",
       "      <td>3804.346371</td>\n",
       "      <td>183.339998</td>\n",
       "      <td>7754.690987</td>\n",
       "      <td>287.052000</td>\n",
       "      <td>286.612000</td>\n",
       "      <td>0.003853</td>\n",
       "      <td>0.121232</td>\n",
       "      <td>183.079541</td>\n",
       "      <td>4.979540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>349.167501</td>\n",
       "      <td>7301.000000</td>\n",
       "      <td>350.090000</td>\n",
       "      <td>0.007760</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>349.010007</td>\n",
       "      <td>344.980011</td>\n",
       "      <td>349.181354</td>\n",
       "      <td>1.389385</td>\n",
       "      <td>7674.200000</td>\n",
       "      <td>6318.315287</td>\n",
       "      <td>230.147001</td>\n",
       "      <td>8272.942389</td>\n",
       "      <td>346.935999</td>\n",
       "      <td>349.810000</td>\n",
       "      <td>0.019907</td>\n",
       "      <td>0.255093</td>\n",
       "      <td>231.866110</td>\n",
       "      <td>12.669828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>450.668200</td>\n",
       "      <td>836241.000000</td>\n",
       "      <td>443.490000</td>\n",
       "      <td>0.097804</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>454.960000</td>\n",
       "      <td>450.668200</td>\n",
       "      <td>448.370166</td>\n",
       "      <td>9.065367</td>\n",
       "      <td>191924.580645</td>\n",
       "      <td>205686.517125</td>\n",
       "      <td>257.759615</td>\n",
       "      <td>9380.736161</td>\n",
       "      <td>446.977660</td>\n",
       "      <td>443.699500</td>\n",
       "      <td>0.345628</td>\n",
       "      <td>1.315708</td>\n",
       "      <td>262.179296</td>\n",
       "      <td>22.514321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               close         volume  day_close_price  pct_change_eod  \\\n",
       "count  113262.000000  113262.000000    113262.000000   113262.000000   \n",
       "mean      270.992093    6444.631606       270.922408       -0.000185   \n",
       "std        95.228036   10041.718219        95.259111        0.016404   \n",
       "min        69.440000       0.000000        71.700000       -0.087467   \n",
       "25%       214.276250    2093.000000       215.250000       -0.007639   \n",
       "50%       285.350003    3887.000000       286.290000        0.000000   \n",
       "75%       349.167501    7301.000000       350.090000        0.007760   \n",
       "max       450.668200  836241.000000       443.490000        0.097804   \n",
       "\n",
       "       feature__dayofweek  feature__lag_30_min_ago_price  \\\n",
       "count       113262.000000                  113262.000000   \n",
       "mean             1.995179                     270.976685   \n",
       "std              1.371313                      95.265620   \n",
       "min              0.000000                      68.245000   \n",
       "25%              1.000000                     214.162500   \n",
       "50%              2.000000                     285.240000   \n",
       "75%              3.000000                     349.010007   \n",
       "max              4.000000                     454.960000   \n",
       "\n",
       "       feature__lag_7_day_ago_price  feature__rolling_close_mean_60  \\\n",
       "count                 113262.000000                   113262.000000   \n",
       "mean                     267.298720                      270.979216   \n",
       "std                       96.272019                       95.254057   \n",
       "min                       69.440000                       69.987349   \n",
       "25%                      207.768200                      214.244976   \n",
       "50%                      281.295600                      285.126472   \n",
       "75%                      344.980011                      349.181354   \n",
       "max                      450.668200                      448.370166   \n",
       "\n",
       "       feature__rolling_close_std_60  feature__rolling_volume_mean_60  \\\n",
       "count                  113262.000000                    113262.000000   \n",
       "mean                        1.099435                      6661.142241   \n",
       "std                         0.816496                      7376.052809   \n",
       "min                         0.085950                       729.133333   \n",
       "25%                         0.556499                      3211.189831   \n",
       "50%                         0.871947                      4830.758333   \n",
       "75%                         1.389385                      7674.200000   \n",
       "max                         9.065367                    191924.580645   \n",
       "\n",
       "       feature__rolling_volume_std_60  feature__expanding_close_mean  \\\n",
       "count                   113262.000000                  113262.000000   \n",
       "mean                      5587.306914                     181.558979   \n",
       "std                       7034.929168                      49.924718   \n",
       "min                        466.680555                     106.952994   \n",
       "25%                       2422.956264                     132.880295   \n",
       "50%                       3804.346371                     183.339998   \n",
       "75%                       6318.315287                     230.147001   \n",
       "max                     205686.517125                     257.759615   \n",
       "\n",
       "       feature__expanding_volume_mean  first_5_min_avg_close  \\\n",
       "count                   113262.000000          113262.000000   \n",
       "mean                      7741.485050             270.970102   \n",
       "std                        753.499926              95.516392   \n",
       "min                       6547.614079              69.653000   \n",
       "25%                       7037.573174             214.278720   \n",
       "50%                       7754.690987             287.052000   \n",
       "75%                       8272.942389             346.935999   \n",
       "max                       9380.736161             446.977660   \n",
       "\n",
       "       last_5_min_avg_close  feature__overnight_change_close  \\\n",
       "count         113262.000000                    113262.000000   \n",
       "mean             270.972966                         0.003689   \n",
       "std               95.264332                         0.033377   \n",
       "min               71.838000                        -0.120443   \n",
       "25%              215.362000                        -0.013399   \n",
       "50%              286.612000                         0.003853   \n",
       "75%              349.810000                         0.019907   \n",
       "max              443.699500                         0.345628   \n",
       "\n",
       "       feature__monthly_pct_change_close  feature__expanding_average_close  \\\n",
       "count                      113262.000000                     113262.000000   \n",
       "mean                            0.150072                        182.494635   \n",
       "std                             0.333523                         51.334508   \n",
       "min                            -0.430004                        106.561747   \n",
       "25%                            -0.073450                        132.829049   \n",
       "50%                             0.121232                        183.079541   \n",
       "75%                             0.255093                        231.866110   \n",
       "max                             1.315708                        262.179296   \n",
       "\n",
       "       feature__macd  \n",
       "count  113262.000000  \n",
       "mean        4.934485  \n",
       "std         9.326529  \n",
       "min       -16.825520  \n",
       "25%        -2.763562  \n",
       "50%         4.979540  \n",
       "75%        12.669828  \n",
       "max        22.514321  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2019-12-01 00:31:34-08:00</th>\n",
       "      <td>RT @BrianFeroldi: Tech stocks I follow ranked ...</td>\n",
       "      <td>1.201056e+18</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2019-12-01 01:03:56-08:00</th>\n",
       "      <td>Benjamin Graham and the Power of Growth Stocks...</td>\n",
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       "      <th>2019-12-01 01:25:16-08:00</th>\n",
       "      <td>RT @BrianFeroldi: Tech stocks I follow ranked ...</td>\n",
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       "    <tr>\n",
       "      <th>2019-12-01 01:34:11-08:00</th>\n",
       "      <td>RT @BrianFeroldi: Tech stocks I follow ranked ...</td>\n",
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       "      <th>2019-12-01 01:55:24-08:00</th>\n",
       "      <td>RT @BrianFeroldi: Tech stocks I follow ranked ...</td>\n",
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      "text/plain": [
       "                                                                        text  \\\n",
       "date_tweeted                                                                   \n",
       "2019-12-01 00:31:34-08:00  RT @BrianFeroldi: Tech stocks I follow ranked ...   \n",
       "2019-12-01 01:03:56-08:00  Benjamin Graham and the Power of Growth Stocks...   \n",
       "2019-12-01 01:25:16-08:00  RT @BrianFeroldi: Tech stocks I follow ranked ...   \n",
       "2019-12-01 01:34:11-08:00  RT @BrianFeroldi: Tech stocks I follow ranked ...   \n",
       "2019-12-01 01:55:24-08:00  RT @BrianFeroldi: Tech stocks I follow ranked ...   \n",
       "\n",
       "                           tweet_unique_id author_verified  num_followers  \n",
       "date_tweeted                                                               \n",
       "2019-12-01 00:31:34-08:00     1.201056e+18           False         1557.0  \n",
       "2019-12-01 01:03:56-08:00     1.201064e+18           False         1150.0  \n",
       "2019-12-01 01:25:16-08:00     1.201070e+18           False         3887.0  \n",
       "2019-12-01 01:34:11-08:00     1.201072e+18           False          881.0  \n",
       "2019-12-01 01:55:24-08:00     1.201077e+18           False            6.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_df = pd.read_csv(f\"../data/twlo_tweets.csv\", encoding='ISO-8859-1')\n",
    "\n",
    "tweet_df.index = pd.to_datetime(tweet_df['date_tweeted'])\n",
    "tweet_df.index = tweet_df.index.tz_convert('US/Pacific')\n",
    "del tweet_df['date_tweeted']\n",
    "\n",
    "tweet_df.sort_index(inplace=True)\n",
    "tweet_df.dropna(inplace=True)\n",
    "\n",
    "tweet_df = tweet_df['2019-12-01':'2021-07-08']\n",
    "\n",
    "\n",
    "tweet_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Verified Tweets')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rolling_1_day_verified_count = tweet_df.resample('1T')['author_verified'].sum().rolling('1D').sum()\n",
    "\n",
    "rolling_1_day_verified_count.plot(title='Daily Rolling Count of Verified Tweets')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Number of Verified Tweets')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Tweets')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rolling_7_day_total_tweets = tweet_df.resample('1T')['tweet_unique_id'].count().rolling('7D').sum()\n",
    "\n",
    "rolling_7_day_total_tweets.plot(title='Weekly Rolling Count of Tweets')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Number of Tweets')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_stats = pd.DataFrame({\n",
    "    'feature__rolling_7_day_total_tweets': rolling_7_day_total_tweets,\n",
    "    'feature__rolling_1_day_verified_count': rolling_1_day_verified_count\n",
    "})  # A\n",
    "\n",
    "twitter_stats.index = pd.to_datetime(twitter_stats.index)  # B\n",
    "twitter_stats.index = twitter_stats.index.tz_convert('US/Pacific')  # B\n",
    "\n",
    "price_df = price_df.merge(twitter_stats, left_index=True, right_index=True)  # C\n",
    "\n",
    "# A Create a dataframe with the twitter stats\n",
    "# B Standardize the index to make the following merge easier\n",
    "# C Merge twitter stats into our price dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "feature__rolling_7_day_total_tweets     -0.030404\n",
       "feature__dayofweek                      -0.002365\n",
       "feature__expanding_volume_mean          -0.000644\n",
       "feature__monthly_pct_change_close        0.001672\n",
       "feature__rolling_1_day_verified_count    0.005921\n",
       "feature__rolling_volume_mean_60          0.007773\n",
       "feature__rolling_volume_std_60           0.010038\n",
       "feature__expanding_close_mean            0.024770\n",
       "feature__expanding_average_close         0.024801\n",
       "feature__morning                         0.025106\n",
       "feature__rolling_close_mean_60           0.030839\n",
       "feature__lag_30_min_ago_price            0.030878\n",
       "feature__lag_7_day_ago_price             0.031859\n",
       "feature__macd                            0.037216\n",
       "feature__overnight_change_close          0.045098\n",
       "feature__rolling_close_std_60            0.051801\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Exercise 7.2\n",
    "\n",
    "price_df.filter(\n",
    "    regex='feature__'\n",
    ").corrwith(\n",
    "    price_df['stock_price_rose']\n",
    ").sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'split_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [25]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_df, test_df, train_X, train_y, test_X, test_y \u001b[38;5;241m=\u001b[39m \u001b[43msplit_data\u001b[49m(price_df)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAdd Domain Features\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m==========================\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      4\u001b[0m best_model, test_preds, test_probas \u001b[38;5;241m=\u001b[39m advanced_grid_search(\n\u001b[1;32m      5\u001b[0m     train_X, train_y,\n\u001b[1;32m      6\u001b[0m     test_X, test_y,\n\u001b[1;32m      7\u001b[0m     ml_pipeline, params, \n\u001b[1;32m      8\u001b[0m     cv\u001b[38;5;241m=\u001b[39mtscv, include_probas\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m  \u001b[38;5;66;03m# C\u001b[39;00m\n\u001b[1;32m      9\u001b[0m )\n",
      "\u001b[0;31mNameError\u001b[0m: name 'split_data' is not defined"
     ]
    }
   ],
   "source": [
    "train_df, test_df, train_X, train_y, test_X, test_y = split_data(price_df)\n",
    "\n",
    "print(\"Add Domain Features\\n==========================\")\n",
    "best_model, test_preds, test_probas = advanced_grid_search(\n",
    "    train_X, train_y,\n",
    "    test_X, test_y,\n",
    "    ml_pipeline, params, \n",
    "    cv=tscv, include_probas=True  # C\n",
    ")\n",
    "\n",
    "plot_gains(test_df.copy(), test_y, test_preds)\n",
    "\n",
    "# A new features in price_df require a new split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_df.to_csv('fifteen_percent_gains_features.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Selection (SFM) \n",
      "==========================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.49      0.71      0.58        72\n",
      "        True       0.43      0.23      0.30        69\n",
      "\n",
      "    accuracy                           0.48       141\n",
      "   macro avg       0.46      0.47      0.44       141\n",
      "weighted avg       0.46      0.48      0.44       141\n",
      "\n",
      "Best params: {'classifier__criterion': 'entropy', 'classifier__max_depth': 10, 'classifier__max_features': None, 'classifier__min_samples_split': 3, 'select_from_model__estimator': LogisticRegression(random_state=0), 'select_from_model__threshold': 'median'}\n",
      "Overall took 263.71 seconds\n",
      "% of time with profit: 0.000\n",
      "Total Gains for bullish is -0.038\n",
      "% of time with profit: 1.000\n",
      "Total Gains for bearish is 0.017\n",
      "% of time with profit: 0.923\n",
      "Total Gains for all predictions is 0.004\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=20, max_depth=None, random_state=0)  # A\n",
    "lr = LogisticRegression(random_state=0)\n",
    "\n",
    "ml_pipeline = Pipeline([  \n",
    "    ('scale', StandardScaler()),\n",
    "    ('select_from_model', SelectFromModel(estimator=rf)),\n",
    "    ('classifier', clf)\n",
    "])\n",
    "\n",
    "params.update({\n",
    "    'select_from_model__threshold': [\n",
    "        '0.5 * mean', 'mean', '0.5 * median', 'median'\n",
    "    ],\n",
    "    'select_from_model__estimator':  [rf, lr]\n",
    "})\n",
    "\n",
    "print(\"Feature Selection (SFM) \\n==========================\")\n",
    "best_model, test_preds, test_probas = advanced_grid_search(\n",
    "    train_X, train_y,\n",
    "    test_X, test_y,\n",
    "    ml_pipeline, params, \n",
    "    cv=tscv, include_probas=True  # C\n",
    ")\n",
    "\n",
    "del params['select_from_model__threshold']\n",
    "del params['select_from_model__estimator']\n",
    "\n",
    "plot_gains(test_df.copy(), test_y, test_preds)\n",
    "\n",
    "# The feature importances in this random forest will dictate which features to select"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1958, 16)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Selection (RFE) \n",
      "==========================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.45      0.58      0.51        72\n",
      "        True       0.38      0.26      0.31        69\n",
      "\n",
      "    accuracy                           0.43       141\n",
      "   macro avg       0.41      0.42      0.41       141\n",
      "weighted avg       0.41      0.43      0.41       141\n",
      "\n",
      "Best params: {'classifier__criterion': 'entropy', 'classifier__max_depth': 10, 'classifier__max_features': None, 'classifier__min_samples_split': 5, 'rfe__estimator': LogisticRegression(random_state=0), 'rfe__n_features_to_select': 0.8}\n",
      "Overall took 675.54 seconds\n",
      "% of time with profit: 0.000\n",
      "Total Gains for bullish is -0.063\n",
      "% of time with profit: 0.381\n",
      "Total Gains for bearish is -0.027\n",
      "% of time with profit: 0.077\n",
      "Total Gains for all predictions is -0.100\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "\n",
    "ml_pipeline = Pipeline([  \n",
    "    ('scale', StandardScaler()),\n",
    "    ('rfe', RFE(estimator=rf)),  # A\n",
    "    ('classifier', clf)\n",
    "])\n",
    "\n",
    "params.update({\n",
    "    'rfe__n_features_to_select': [0.6, 0.7, 0.8, 0.9],\n",
    "    'rfe__estimator':  [rf, lr]\n",
    "})\n",
    "\n",
    "print(\"Feature Selection (RFE) \\n==========================\")\n",
    "best_model, test_preds, test_probas = advanced_grid_search(\n",
    "    train_X, train_y,\n",
    "    test_X, test_y,\n",
    "    ml_pipeline, params, \n",
    "    cv=tscv, include_probas=True  # C\n",
    ")\n",
    "del params['rfe__n_features_to_select']\n",
    "del params['rfe__estimator']\n",
    "\n",
    "plot_gains(test_df.copy(), test_y, test_preds)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('feature__dayofweek', 4),\n",
       " ('feature__morning', 1),\n",
       " ('feature__lag_30_min_ago_price', 1),\n",
       " ('feature__lag_7_day_ago_price', 1),\n",
       " ('feature__rolling_close_mean_60', 1),\n",
       " ('feature__rolling_close_std_60', 2),\n",
       " ('feature__rolling_volume_mean_60', 5),\n",
       " ('feature__rolling_volume_std_60', 1),\n",
       " ('feature__expanding_close_mean', 1),\n",
       " ('feature__expanding_volume_mean', 1),\n",
       " ('feature__overnight_change_close', 1),\n",
       " ('feature__monthly_pct_change_close', 1),\n",
       " ('feature__expanding_average_close', 1),\n",
       " ('feature__macd', 1),\n",
       " ('feature__rolling_7_day_total_tweets', 1),\n",
       " ('feature__rolling_1_day_verified_count', 3)]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(train_df.filter(regex='feature__').columns, best_model.named_steps['rfe'].ranking_))  # A\n",
    "\n",
    "# A get the ranking of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
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       "      <td>11751.794554</td>\n",
       "      <td>23090.412883</td>\n",
       "      <td>108.405694</td>\n",
       "      <td>45369.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.273961e+06</td>\n",
       "      <td>2.503132e+06</td>\n",
       "      <td>11751.794554</td>\n",
       "      <td>4.918258e+06</td>\n",
       "      <td>23090.412883</td>\n",
       "      <td>108.405694</td>\n",
       "      <td>9663597.0</td>\n",
       "      <td>45369.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113257</th>\n",
       "      <td>1.0</td>\n",
       "      <td>384.732251</td>\n",
       "      <td>361.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>148018.905101</td>\n",
       "      <td>138888.342677</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>130321.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.694765e+07</td>\n",
       "      <td>5.343482e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.013869e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47045881.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113258</th>\n",
       "      <td>1.0</td>\n",
       "      <td>384.714454</td>\n",
       "      <td>361.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>148005.211319</td>\n",
       "      <td>138881.917989</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>130321.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.693974e+07</td>\n",
       "      <td>5.342988e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.013637e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47045881.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113259</th>\n",
       "      <td>1.0</td>\n",
       "      <td>384.690810</td>\n",
       "      <td>361.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147987.019194</td>\n",
       "      <td>138873.382361</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>130321.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.692925e+07</td>\n",
       "      <td>5.342331e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.013329e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47045881.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113260</th>\n",
       "      <td>1.0</td>\n",
       "      <td>384.676488</td>\n",
       "      <td>361.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147976.000273</td>\n",
       "      <td>138868.212099</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>130321.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.692289e+07</td>\n",
       "      <td>5.341934e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.013142e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47045881.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113261</th>\n",
       "      <td>1.0</td>\n",
       "      <td>384.656996</td>\n",
       "      <td>362.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147961.004870</td>\n",
       "      <td>139245.832692</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>131044.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.691424e+07</td>\n",
       "      <td>5.356188e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.040699e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47437928.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113262 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          1          x0     x1   x2           x0^2          x0 x1       x0 x2  \\\n",
       "0       1.0  108.297719  213.0  1.0   11728.396017   23067.414223  108.297719   \n",
       "1       1.0  108.330916  213.0  1.0   11735.587280   23074.485028  108.330916   \n",
       "2       1.0  108.358464  213.0  1.0   11741.556642   23080.352755  108.358464   \n",
       "3       1.0  108.384685  213.0  1.0   11747.240006   23085.937968  108.384685   \n",
       "4       1.0  108.405694  213.0  1.0   11751.794554   23090.412883  108.405694   \n",
       "...     ...         ...    ...  ...            ...            ...         ...   \n",
       "113257  1.0  384.732251  361.0  0.0  148018.905101  138888.342677    0.000000   \n",
       "113258  1.0  384.714454  361.0  0.0  148005.211319  138881.917989    0.000000   \n",
       "113259  1.0  384.690810  361.0  0.0  147987.019194  138873.382361    0.000000   \n",
       "113260  1.0  384.676488  361.0  0.0  147976.000273  138868.212099    0.000000   \n",
       "113261  1.0  384.656996  362.0  0.0  147961.004870  139245.832692    0.000000   \n",
       "\n",
       "            x1^2  x1 x2  x2^2          x0^3       x0^2 x1       x0^2 x2  \\\n",
       "0        45369.0  213.0   1.0  1.270159e+06  2.498148e+06  11728.396017   \n",
       "1        45369.0  213.0   1.0  1.271327e+06  2.499680e+06  11735.587280   \n",
       "2        45369.0  213.0   1.0  1.272297e+06  2.500952e+06  11741.556642   \n",
       "3        45369.0  213.0   1.0  1.273221e+06  2.502162e+06  11747.240006   \n",
       "4        45369.0  213.0   1.0  1.273961e+06  2.503132e+06  11751.794554   \n",
       "...          ...    ...   ...           ...           ...           ...   \n",
       "113257  130321.0    0.0   0.0  5.694765e+07  5.343482e+07      0.000000   \n",
       "113258  130321.0    0.0   0.0  5.693974e+07  5.342988e+07      0.000000   \n",
       "113259  130321.0    0.0   0.0  5.692925e+07  5.342331e+07      0.000000   \n",
       "113260  130321.0    0.0   0.0  5.692289e+07  5.341934e+07      0.000000   \n",
       "113261  131044.0    0.0   0.0  5.691424e+07  5.356188e+07      0.000000   \n",
       "\n",
       "             x0 x1^2      x0 x1 x2     x0 x2^2        x1^3  x1^2 x2  x1 x2^2  \\\n",
       "0       4.913359e+06  23067.414223  108.297719   9663597.0  45369.0    213.0   \n",
       "1       4.914865e+06  23074.485028  108.330916   9663597.0  45369.0    213.0   \n",
       "2       4.916115e+06  23080.352755  108.358464   9663597.0  45369.0    213.0   \n",
       "3       4.917305e+06  23085.937968  108.384685   9663597.0  45369.0    213.0   \n",
       "4       4.918258e+06  23090.412883  108.405694   9663597.0  45369.0    213.0   \n",
       "...              ...           ...         ...         ...      ...      ...   \n",
       "113257  5.013869e+07      0.000000    0.000000  47045881.0      0.0      0.0   \n",
       "113258  5.013637e+07      0.000000    0.000000  47045881.0      0.0      0.0   \n",
       "113259  5.013329e+07      0.000000    0.000000  47045881.0      0.0      0.0   \n",
       "113260  5.013142e+07      0.000000    0.000000  47045881.0      0.0      0.0   \n",
       "113261  5.040699e+07      0.000000    0.000000  47437928.0      0.0      0.0   \n",
       "\n",
       "        x2^3  \n",
       "0        1.0  \n",
       "1        1.0  \n",
       "2        1.0  \n",
       "3        1.0  \n",
       "4        1.0  \n",
       "...      ...  \n",
       "113257   0.0  \n",
       "113258   0.0  \n",
       "113259   0.0  \n",
       "113260   0.0  \n",
       "113261   0.0  \n",
       "\n",
       "[113262 rows x 20 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "p = PolynomialFeatures(3)\n",
    "small_poly_features = p.fit_transform(\n",
    "    price_df[['feature__rolling_close_mean_60',\n",
    "    'feature__rolling_7_day_total_tweets', \n",
    "    'feature__morning']])\n",
    "\n",
    "pd.DataFrame(small_poly_features, columns=p.get_feature_names())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Polynomial Features \n",
      "==========================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.53      0.68      0.59        72\n",
      "        True       0.52      0.36      0.43        69\n",
      "\n",
      "    accuracy                           0.52       141\n",
      "   macro avg       0.52      0.52      0.51       141\n",
      "weighted avg       0.52      0.52      0.51       141\n",
      "\n",
      "Best params: {'classifier__criterion': 'entropy', 'classifier__max_depth': None, 'classifier__max_features': None, 'classifier__min_samples_split': 2, 'poly__degree': 2, 'select_from_model__estimator': RandomForestClassifier(n_estimators=20, random_state=0), 'select_from_model__threshold': 'mean'}\n",
      "Overall took 947.51 seconds\n",
      "% of time with profit: 0.737\n",
      "Total Gains for bullish is 0.028\n",
      "% of time with profit: 1.000\n",
      "Total Gains for bearish is 0.095\n",
      "% of time with profit: 1.000\n",
      "Total Gains for all predictions is 0.158\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "ml_pipeline = Pipeline([  \n",
    "    ('poly', PolynomialFeatures(1, include_bias=False)),\n",
    "    ('scale', StandardScaler()),\n",
    "    ('select_from_model', SelectFromModel(estimator=rf)),  # A\n",
    "    ('classifier', clf)\n",
    "])\n",
    "\n",
    "params.update({\n",
    "    'select_from_model__threshold': ['0.5 * mean', 'mean', '0.5 * median', 'median'],\n",
    "    'select_from_model__estimator':  [rf, lr],\n",
    "    'poly__degree': [2],\n",
    "})\n",
    "\n",
    "print(\"Polynomial Features \\n==========================\")\n",
    "best_model, test_preds, test_probas = advanced_grid_search(\n",
    "    train_X, train_y,\n",
    "    test_X, test_y,\n",
    "    ml_pipeline, params, \n",
    "    cv=tscv, include_probas=True  # C\n",
    ")\n",
    "\n",
    "del params['poly__degree']\n",
    "del params['select_from_model__threshold']\n",
    "del params['select_from_model__estimator']\n",
    "\n",
    "plot_gains(test_df.copy(), test_y, test_preds)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('poly', PolynomialFeatures(include_bias=False)),\n",
      "                ('scale', StandardScaler()),\n",
      "                ('select_from_model',\n",
      "                 SelectFromModel(estimator=RandomForestClassifier(n_estimators=20,\n",
      "                                                                  random_state=0),\n",
      "                                 threshold='mean')),\n",
      "                ('classifier',\n",
      "                 RandomForestClassifier(criterion='entropy', max_features=None,\n",
      "                                        random_state=0))])\n"
     ]
    }
   ],
   "source": [
    "print(best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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