notebooks/autonomio_capabilities_overview.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AUTONOMIO INTRODUCTION - Capability Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='http://autonom.io/wp-content/uploads/2017/08/autonomio_logo.png' align='left'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "30th of August, 2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href='https://github.com/autonomio/core-module/blob/dev/notebooks/autonomio_data_preparation.ipynb'>DOWNLOAD THIS NOTEBOOK</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About this Demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a regular server / laptop, this demo can be excecuted in a matter of roughly 10 minutes including the hyperparameter scan. Nothing is hidden, everything actually happens with a single line command. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installing Autonomio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>pip install git+https://github.com/autonomio/core-module.git</code>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading this Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) If you're working from a desktop machine <a href=>click here</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) If you're working from a server:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>wget link</code>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running an LSTM Model to Predict Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we are going to ingest the Titanic dataset from the <a href='https://www.kaggle.com/c/titanic'>Kaggle challenge</a>. Looking at the the <a href='https://www.kaggle.com/c/titanic/kernels'>kernel examples</a> for this challenge, the ingestion involves between 20 and 100 lines of code in order to get to an acceptable result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "179 out of 891 rows dropped\n",
      "2 out of 12 columns dropped\n"
     ]
    }
   ],
   "source": [
    "# first we import the wrangler() tool\n",
    "from autonomio.commands import data, wrangler\n",
    "\n",
    "# next we load the data as it's provided in Kaggle \n",
    "titanic = data('train.csv','file')\n",
    "\n",
    "# then we prepare the data in a single command \n",
    "titanic = wrangler(titanic, y='Survived', starts_with_col='Cabin', first_fill_cols='Cabin', treshold=.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the later examples, this exact dataframe will yield us >85% result on the provided test dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a Simple Neural Network "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will continue with the Titanic dataset, as we had prepared it in the previous section. Now we just have to train a model, so we can move in to making the predictions on the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "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>batch_size</th>\n",
       "      <th>dropout</th>\n",
       "      <th>epochs</th>\n",
       "      <th>features</th>\n",
       "      <th>ind_var</th>\n",
       "      <th>layers</th>\n",
       "      <th>max_neurons</th>\n",
       "      <th>n=</th>\n",
       "      <th>network_scale</th>\n",
       "      <th>shape</th>\n",
       "      <th>y_transform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>7</td>\n",
       "      <td>Survived</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>712</td>\n",
       "      <td>2563200</td>\n",
       "      <td>funnel</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  batch_size dropout epochs features   ind_var layers max_neurons   n=  \\\n",
       "0         10       0    150        7  Survived      3           8  712   \n",
       "\n",
       "  network_scale   shape y_transform  \n",
       "0       2563200  funnel        none  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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4ju8Sh4Imc/PwaDOVb76Eq6wCe4Sdpaq3/9C3EIuJWLuFgmon/525lc9X2Bmc\nHk2neDsrC2qYvqGMEIuJx07qROeEMAzDoGzuUiK6ZPi/r4/KiiEipBOPTN9MuAXuPTazycJM3bZ8\nKhYu939cvWYTeZ9Po6HAt8eXyWYj9fxTmn0OhPinBOvv5H2Rbr1It16kWy86dLfk8C4Mw5gCTNnt\ncw/u8v9XA8Nad2j7R9eTR+rYHazNm1/+EHdlNQBlc5Yw57hLOOy1h0kYMRDY2W14vdSs3YzX5duz\nxBwawnxTFC/PzaHe5eW3zRW8dlY3wsvLsMXHYrY3/WO7bls+eZOnsnrCt5xRWITLauX5iBDGXXmU\n/7wlhseDq7IGW5zvMRvcXrLLHWwqq6e6wc3ITnHEhze/d9CCbZU8MXMrJ3aJ44ahaf4/oreVO7jj\nhw3E2i3cc2zmHn+cVje42Vxaz8bSer5bU+LfQ6ZHQig3DOtAt8Rwxs3YwqwtFXyzqpirBqUyeYXv\nCNJz+ySxJLeKpM+/YPkvU4ju15OBn7+MJbzpY+w4P0r59kJcthBcob49cvqnRnJRv+T9WuyID7Py\nyMgMvvl5JUOGdmdEx1h/69pHXiZ7/GckjhzKEROfo1e7CK4fksacaX/geOBJsNno89T/YTZ12GO7\nNeu3suCMa3FV1jDm8Yf4kERWFdYSYjFxx4iMA9pb45L+7fltU7l/gersjUsAiBt+BK4+vfh1VT6H\nzfuNLksWsP2C64l/cxyRPTs32YbX6cKRX0xYRspfPo7h9VKXnUd6RgqPntiJohon8WHW/Rrz3/3+\nNgyDVXc+Tc7H32FPb0/f8Y8R079ns/d1VVZjsloxh4X6D9va1ZbXP8FV5tsD7NTZU+l33nFsKncy\nMD2K4ZkxhFhM/LS+lA8W57OhpJ4NJTvPhWRW8MDITHokheOqrGbl7f+l8IdfscZG0efF+0k6cTgA\n/VIi+XhMb6qrKoneZY8mr9PFH2fdgCO3cI9xWaIjcVdWU/DNz00WfXbXUFSKLTFuj0XWgu9nknDs\nYCzh+14cFmJfgvV38r5It16kWy/SrRcdulu06NMWOJ3Ofd8pCOnYHYzNjrwist+ZDED/Cc+wdfwn\nlM1ZwqILbmPg5JeIH9bf3732wRfJfntyk6/f0qUnnHsJ0fGxVNU2MPm6J8maPg1LZDjJZxxHyjkn\nU7ctj7xJUymb6/uD3wa4LRasLhfd33mLV7p14P+OycJVWc0fZ99I7ebtGC88zgxrAsvza3YermMY\nfLikgPOE6Ug1AAAgAElEQVT6JHHeYUnYrb69Ygyvl/waF0/+mk29y8s3q0vIirNzStc4HB6DR2ds\nocLhpsLh5qZv1nHD0DQGpUfxy8Zyft5YxtbdLoOeFh3C1YNSyQqpJznRt/p+Tp8kZm2p4Ps1JYzI\nimV+diVWs+K8PkmMNCpY++s0ACqXrubtUbcw5ZLrSIq20zEulDjlYcMX0xm6eD7pWzZgio+l58sP\nknZcv/1+vWrWbSF30hTyP/+RvoUlRJ16DO7n78UaHcmW1z8me/xnABTPmEfpnCXED+uP1awIf/cj\nGgwDGhpYeesTlM1dRs///p9/ccpRUMyiMbfhqvAt/qW/MZ72N9xLvtvM1YNSSI0+sL2xwm1mrh6c\nylO/ZhNnMUhdMA830O3BG4k+rBsdqxto2HARObc9Qs26LcwbdRU9xt1G2kVnoJSieu1m/rzmAWrW\nbyGyVxdSR48i+YzjsET6TmLdUFRK3ufTyJs0lfrt+cQNP4K+rz1M0i57qu3K63bjbXA1e1uDw9Hs\n5z11jmYvdW6yWjDZdi5Abnz2HXI+/g6A+u35LDjjWro9MJaMa0Y3Ofxq+/tfsuahlwjPTGPotHf2\n2DOsobiMrW98AoA1Loa6DVsZvnYJF1x4epP7ndI9gWM6xTJ/WyUbS3znPiqodnLJEckMSo+mculq\nll37IPXb8lBmM67yKpZccheZ142h63+ux2S1EGYzU+N1N9luwXe/4MgtJCQ5gbgj+/vGERtF+zOP\nJywrjZl9z6Bk1kJcFVVYY5oeiuiurWP1Pc+RN3kqWTdeTLf7b/DfVrl8Hcuu+g+hqe0YMfczTCEH\ntleXEDsE4+/klpBuvUi3XqRbLzp0B82iT339gV1tpq3TsftQb95UWkfNLlfnSYywkRxpa3ZPAvDt\n4bL+qbfwOpwkn34cSScOI3HkENY8+CLb3vmcpTePY+1TT7Gu0sHltg0UvT0ZZTET1r0TeVUNhBQV\nkbVhNde99TRdH7qJuS9PIDF7C4ZSuKtryZn4HTkTv/M/ninURnaf/izoNYCjTuhPyq13krptMxs+\n+JK3LWeT+PDjhKzaAEDJA8+w4vp7wGYlIzqE4dO/IfGHKWzp0pP5/YbwU+9eXNiQS+Lvv1P2+0I2\nDzmKhpFnkpEYRXaFg+mvfYnlpy8p7tWH7SddQIe4MLolhjF9QxkvzN7e5HkIMSsy4+x0jLPTy+yg\n46IF5F/8GCsKSwn99H/E9O9Fj6RweieHs7Kglgd+2oQBHN85jmgLrL7vKcxeL5v79Kf9pnVkrlvJ\nUV9+zJq+AwlZOp+UVUvJ3PFDXSm8peWsvOh26m+9jM53XOE/h1J9TgF5k6eSO3kazuIy2o06mtTR\npxLRvSMF3/xM7mdTqFy2ZufAlaLwh1+pWr6O1AtOZeMzvsOm4kcMpHTWQtaPe40hU96iZOYCyuYu\nwRoTSec7r2bduFfJmzSF0t/+IOW8k0k+9RhW3vEUjtxCovv3wlFdg2NDNmOXz6D+xmsY0XhyX1dl\nNQXfziB30lQq/1xL/PABpI4eRdJJwzGH+haFvG43pb/+Qe5nUyiaPpsOl57NcQ/fhNWsiJ2/gNyK\nKiJ7dSGqT1cA2keGQP+upE99h9X/eZ7cT75n1R1PUTZnCbFD+7H2oRfx1jeAUlSv2sDaB19k7YMv\nNv8NoBRlsxczZ+Sl9H3tYeKP8h1WbBgG5Qv+JG/SVPK/nYGnpq7ZL7emtSNk/GPEHNEbAEdhCStu\nGUfpr380/3BmMwnHDCJ19Kk0lJSz6bl3wWTi8Dcfo/yP5WS/+RlrH3qJ3M+mkDp6FIknDGP9E29Q\n+P1MAGrWb2H9f9+gx2O3Ntnupuffw1NXT+KJw2n/r+NZfsPDbHjmbdqfdeIee8/Zrb6rYR3bqenY\nSucsYdEFt2K43EQd1p2+4x+laOos1j/+Olvf+ITyP5Zz+PhHsae3b/JzzTAMto7/FIDOd15F+kVn\n7NEdP6w/pb8vonDa76RdcKr/89VrNrHsmvup3eA7MfXW8Z+S/u8zCctIBWD9E68DkHzasbLgI1rF\nof47+WCRbr1It16kWy86dO/zRM6t6WCeSLChoYGQkNY7J0lboWN3S5qdbi+2A7jEMsCmlyZQNncJ\n/d55osnhDx6vQUW9G2/jBaRDLSYiQ5qum/60vpRnZ23bY5thVhNZcXaGdojmzF0u/zxjYxkTvlrI\n6OcfxWQycdSsiYR38h3yM39zKVvOv4HYnO0sGzyC3088k0teeYLo8lLa33o5r3YZztZyBxnOai7+\n/iPql6z0P151dCzfj76CrpmJtJ8zm7hFCzHiYgk99XgKBw7k4w01tIuw8da5Paj6ZR5LLrkTt8VK\nTlZnMjesoSYyGpfNRmxpMcaYczjqyVsofncS6x55ZZ/PX3lKGse9/Qi/PvcR0TNm+j+/cugIznv3\nITJi7UyftYYt9z9HbGEeNrOJUIsJm9mEUr4/eBsKS8Hr9X+tNS6GId+PJ7xjOvOyK3lo+mb/bW+f\n24OGNz9i0/PvYs9IYciMCVSs2sifo2/B62h6mWtz3170uPQM2o06muy3J7PxuXfBMLDGxWAOtfke\nu6AE9vFz0RIZTvKZI0kdfSohibEsu/ZBqv5c67+920M3kn7JWcwach7O4jIOf2scm174gOpVG+j2\nwFiyxl5E9drNLL/hYapXb2w6Vzp1YMi3b1C9LZdFp1+H4fYwYNKLGB6PbxFn2iy8jj3/RcIcHoY1\n2rfnjbumDndVTZPbB3z6PxKOGcyiMbdTMnM+3cfdSuZV5zfblzt5KqvvfhZP3c5fginnj6L7IzdT\nNmcxuZ9NoWzeUvD4XiNTiJXE448kdfSphHfOYPmNj1A2ZwkoRWj7RAA8jgZcZZX+7ZnsISiaLoQa\nXi/eBifKYqbrfdcT2bMTy298FGdJOcpixtTM1RU8Dc4mcwWg17N3k37xmQAUTv2NVXc8hbO0ounz\nFRFGxxsvZuMz72B4vQz66lXihhwOQN3WHH4fPgbD42XYLxOI6JbF3BMvp3rlztdvX9w1tcw+5t84\ncgpIu/B0ev73//yLLOWLVvDntQ/iyC3EEh1Jnxf/Q8yxg/0/10rnLGHhOTdii4/h6MVf+RfzdrX9\nw69ZdefTJBw3lAEfPwdAyayFLLn0Lrz1DUR0zSI0LZmSX+bR/uwT6fvaw5TOXsTCc2/GEhnOiAWf\n+w/fbG1yIudDj7z/an3SrRfp1ot06yWYuv/qPVjQLPpkZ2eTkZFxULZ9KNOxe1/NS/OqeXj6Zvok\nR/Cf4zL9hx/tS3WDm3UL11J8/nUor5eS22+m7uijqHV62FxWz9ZyBw3unX9cmhRcMSCF02I8bHhy\nPFH/PofbNhjUubx0TwzD7mqg45efszmtI0u79vV/XWK4lUuPaM/y/BpmrCnkjIlvkrVhNRUnHM8F\nHz4KQGmtiys/X03Ytm1c/PpTmLxeqnt0J3LNWgrbp/P52LtowESHmFCeOLkTCSEmNjz9FltenUji\n8UeSe/21vLKqaq+9j5zQ0X+y3OU3PUbe5KkAeMPCCB//DOkhsP6CmwDIGnsRW17+EICeT9+Ft95B\nzqc/ULNmE6XpGfzZdxAlSSmc+M0nxJQW+R/DY7WyeMgx9J83E4vbTZd7ryWiayYrbn3Cf/6i5iib\nlaQThpE6ehTrXp9I7bw/fQs637+J1+PhpYc+JGr1amJtik5xdsrmLcVwexj05avEHek7XKvox99Z\nds0D2BJiST1/FCnnn0J4VlqTxymdvYjlYx+lobBk5+saaiPp5BGkjh6FPS2ZvC9+JG/yNBx5RcQf\nPZDU0aNod/LRTfb28DY4WffYq2S/+wVZ14+h2wNjAdj23hesvvc5zOFheGrrCE1J4qg5n/m/1jAM\nKhauIPezH8j/ZgbWqAgGffUaYRkpZGdn4/p8hn/PoV3FDT+C1NGjiBtyOEU/ziZ30hSqlq9rcp+w\nTh1IHT0Kd1UNW175iNCUJI74+HnmHPtvlNXCscu+3esf/TUbtvLndQ9RtyWHHk/c3mRvkn0xPB42\nPv8em1/4AMOzc6+3kPaJpJ53Cinnn0JE5z2/h71OF4vufoqyT5qcQs5/uFhIM4eLOUvKyft6Onmf\nTaFqxXo633U1nW+/vOl2G5wUTZ9D7qSplMyYR2SvzvQd/xjhWWlseOpNNv3vfcIyUzlyxgRq1m1h\n/bjXKJu7hJTzR3HYS/cDUPzLfBZfeDvWmEiG/vjeXs9tBLDqrmfYPuErovp0ZciUtzFZmy4QO8ur\nWHHLOIp/mu1rvOAUBjx9DyablcWX3EXxT7PpfMeVdL7jyma37ywpZ2bfM0DBcSt/AGD20RfTUFhC\nyrkn0/OpO3GVVTBr2AUYThdHTn+PVXc+TeWyNXS55xo63XrZXsf/d8iiz6FH3n+1PunWi3TrRbr1\nEkzdQb/ok5+fT/v27Q/Ktg9lOnbvrbm41skNX62j0uE7P0a3xDAeP6kTkSFmfttcwYdL8okMsXDl\noBT6JPv2ilhRUMM7f+SxuqiWMyaOp/Ma39Vy1h42gCnnN/3jMTrUgrXxpLSldS7wern101dQq9fh\niI/n7evv5YhuyTx8fBar736G7RO+BqXo8tqj5Pc7golLC/wn040uLeb0Se+SlLsNpy2ED257kBcu\nH0p6TCj/nbmVmZvKGZwexSUrf2PTjj/8LRa+vPletsYl+9t2vZy1p87hPzfJ+pI6iqp9e4R4Mciv\ncvoWr8rq6dM+grG7nGTZVVHFnJGX0lBcxoCPnyd++BG+5+CRV9j6+sf+7Xd76Eayrr8Q8C1YeOoc\neEND+G51CT9vLOPCrlFEvTKe/K+mE9apAx1efIjXiywM2bIK+7hnmuxBk3jicLo/fFOzh5hYoyOw\nRPjO45OzaTPbrh9H1fK12BLjfHts7LZnB0DG1efvcYiOu7Yesz2kyWWtd+d1umgoLtv52DFRe5wA\n2vB68Tqcf3lFsB089Q1NF4NcbmYfNYa6rbkA9H7+XtJ2OyfMruPAMPzPR35+Pu0SEpl/2jVU/bmW\nsMxUUkePIuXck7Gn7zn/G4rLfNsAlNlESLsElFJ43W4WnH4dlUtXY0uIxVlSTvLpx3H4W+P22uLv\nbnDtcThTS7mqanBX+y5trpQipF28/zC6v5Kfn495+UZW3DIOV1Utnf/vCjrdeuk+vw58e9fsmDd/\neZ/aOsz2UP+c8DpdzDv5SqpXb8QaG4Wr3LdYag6zM/y3j/zPtWEYLDr/Fkp/X4QlKoI+L/yHdqOO\nbvYxSmYtZNH5t6CsFo786T0ie3Rq9n47DuNaP+41DLeH6MN70Pnuq1k85nZMITaOXvQlIYlxf9my\n8PxbKJ21kN7/u4+yuUvJmzyVmIF9GPz1a/7na82DL5L95meEpiThyCsiJCmeo+ZN2mOOtyZZ9Dn0\nyPuv1ifdepFuvUi3XoKpO+gXfWpqaoiIiDgo2z6U6dBd7/KwNK+aTaX1bC6tp9rh5NKBaf5Fmx1c\nHi93/LCBNUV19EmOoKjGSWGNkw4xoYRZTawtbnoOkaEZ0ShgbrbvkJOMnM2c88ZzeC0WTG43RkQ4\n1ZM/IMRmJSM2lI5x9iYLLN+uLmb+U+8x4sev/Z9bM/goLvtkHJ6FS1k0+lYaj1fCFGJj4KQXiR50\nGD+vKWT2298x8MtPsTrqsXdIYc0NY/nSHcOwDN/hX3dN2YjNrHjr3B60CzUz/9SrqVq+ji53X03M\ntRezJLeK4ZkxLd6LqSXc1bV46h1N9qbw1Dcw98TLqN2QTca1o+nxyC373I5hGNSs2URYVnqTxYLs\ndz5nzX+eR1nMe5xYd29qamqw1jtZcPq11G3NRVnMJJ04nOQzRmKN9Z3A1mSzEjvosBYtDPzT8r/+\nmT+ve5DwLpkMmzkBk6Vlp1Lb8b3trqmlfls+ET06tej5anZb67Yw98TL8Tb4FgGPmPgciSOHHtC2\nDrYd3c6ySlyV1XvsnXUwVK1Yx7xTrsJwe7AlxNL+nBPpcMlZ/kMtd3BVVLHi1scpmvY7AGkXn0Fk\nzy57bG/Lqx/hyC1s8R41FUtWsfTq+2nY5UpdaRedTu/n7t3r123/6BtW3fEUoantcOQWYgq1MWzG\nhCbjdpZW8Nvgc/3nUOr55B10uOzsfY7p75BFn0OPvP9qfdKtF+nWi3TrJZi6/+o9WNCcyLm0tDRo\nXqz9EczdHq/Bj+tLmbA4n7L6ple2uXfqRv5zXJb/8CSA8QtyWVNUR1KElQePz8LtMbh32kb/VaFi\n7Rb+3b895fUuJi0vYl7jYk+IxcT5fRLp/O2bVAKdb7yIwu9nUrtxG8c7Conr1/zVnY6z1GL9xXdY\nxW8nn8Ww6d/RY8HvNMz4nbUPvwxAl3uvxZFTyPYJX7H4krtof+bxmL//hSMbz23S7tRj6P38vfS2\nhvLDpNXMya70L05dcHiy7wS7+P5IX//dz3S87FyUycQJXZq/KtLfYYkMxxLZdC8Jsz2EwV+/TuWy\nNSQcO7hF21FK7XG5b4CMK88lvEsGIUnxRHbv2OJxlZaWkpGRwaBvXqds9mISjh6ELSG2xV8faMln\njsQcbieyR6cWL/jAzu9tS0R4s8/n/ojolkXnO69i/bjXCGmfSMIxg/7W9g6mHd22uOiDds6Z3UX1\n6cbgb17HVVFN/IiBexyKtYM1Jop+7z1J9luTWPfYq+R89O1fb/Ow7mTdeHGLHj+mfy8y3h9HxXMT\n/AtKmddcsM+va3fK0ay++1n/Zd273HPtHgtVtvgYsm64iI1Pv0VYVhppzZwUWoi/I5jfh+yNdOtF\nuvUi3XrRoTtoFn1iY9vOH4GtKVi7l+VV8+q8HLIbF2w6xdvpnxJJx3g7S7aVM31zFY/8vJmxQ317\nAfy8sYw1RXVYTYoHRmYR3bhHznOndeHtP/JIirBxdu9EQgwvmBSjuifw6bJCwOCCw5PxzPmDJX/8\niTUumqwbLsLrcFK7cRvF0+cSN3TPRR+v282KW8ahXC4qjj2GxcOPp0u0jZTPPmPZ1b5zgEQf3oOs\nGy5EKUVDUQlF035n+4SvAIjo0YnMq0eTOuZUlFLEA+f0TuTjZYWU1rlIiQrh/D5J/scLSYwj49yT\n93qY0sFii49ptb1CEkYM3O+v2THHQ9slkHLOSa0yjn+SUoqkE4bt99e19vd21vVjMIVYiT685yG5\nR9QOgfqZtuNqYfuilCLzmtHEDu5L3ufTMFzuPe9js5Jx5Xn7tciXkJFO1ntPkv/1dExWKxHdsvb5\nNbb4GOKG96f0t4XEDDqMzKubPzF3x7EXYbKaSRx55F8uaAlxoIL1fci+SLdepFsv0q0XHbqD5t1f\nfX09UVFRgR7GPy7Yuj1egwlL8vl0WSEG0C7CxhUDUzi6Y4z/kue9IpwkRYcxcWkBL8/N8X+t3Wri\npiPT6Za4c2+VyBALtx3l+5dvZ1klvx33b2wJsQz6/GXGHulbMHJVVDGvcc+cjjdfgjUqgsQThrH1\njU8omj6Hbg+O3WOcW1//hMqlqwlNSeLsN+5loNtMh0t6s2jdciqXrUHZrPR58X7/H319X3+UtQ+9\nhCnESur5pxDZu+seh+qcd1g7flhbSqXDzdihaXtcfSzYXuuWku7WocxmMq8e3WrbO1jayusd3bc7\n0X1b73CZHd0pZ524X1/X9d7r2JoQS9d7rv3LxTxTiI2ON13SGsMUYg9t5Xu2tUm3XqRbL9KtFx26\ng2bRx+FwBHoIAdEWumudHp79LZuIEDP/7t+epIg9T9wLUFTj5L8zt7KqsBYFXNQvmTGHt8Nm3rn4\nkfPx92x49UNOf/Yeooak8t6ifHq2C+eELnEcmRG913PcbH7xAxoKSmgoKGHJZXcz4NMXAFhy2T3U\nbdpGRNcs/7kuYgcdhiUqgtoNW6nbmkNY5s7zitSs28KGxhMr93ruHmzRkew4TWuflx9g+dhHSL/0\nrCb/Um+2h9Dr6Tv3+jyF28w8c2pnSmpdDEjb8wdPW3itDwbp1ot075/ow3vQ99WHW3cwQuwH+Z7V\ni3TrRbr1It3BK2gWfZKTkwM9hIA41LsNw+C5WduY03j+nF82lXN2r0RG921HRMjO6Tc3u4LnZm2j\nusFDfJiVe47JoG9KZJNtFfzwKyv/779gGCy/8VFOm/khZ/XuS0vU5xSQ/d4XANgSYimf/yfLxz4C\nSlE+fxkh7RM54uPnMIf6zqFjslpIOHYwBd/MoGj6HP8eEjsO6zKcLtIuPJ3EY4c0eZyILpkc+dN7\nB/ZkAZmxdjJjm7+qzqH+Wh8s0q0X6RaibdF17kq3XqRbL9KtFx26//kThBwkBQUFgR5CQBzq3V+s\nKGL21grCrCaOyorB5TH4bHkRYz5ZxdO/bmVRThWvzcvh4elbqG7wMCg9itfP6rbHgk/5gj9ZfsPD\nYBiYoyNw5Bay7pGX/bfX5xSw5oEXyPn0B9y1dexu4zNvYzhdtD/rBAZOfglLVASFP/xK4fczsUSG\nM+Dj57GnNf2G33EeluLpc/2f2/Lax1QuW0Noaju6PXxTKz5T+3aov9YHi3TrRbqFaFt0nbvSrRfp\n1ot060WH7qDZ0yc0NDTQQwiIQHTXOj0syqliQFoU4badh1PV5xRQ/Mt8Eo8bgj0tmeX5Nby9MA+A\nO4/OYFhmDGuLavjm3WmsrTH42d2ZnzeWA2AxKa4YmMLZvRP95+4B355CZXMWs+yq/+BtcJJ+6VmE\nn3EM68bcQc7E72h32rEYThcrbhmHq6IagDX3PU+7U48hdfQo4o7sR826LeROnoaymOly99WEZabR\n770nWTTmNgD6vfckkT06sbuEY4eAyUTZvKVseuF9DLeHTS9NAKD3c/dgjfpnz/Iuc1wv0q0XXbtF\n26fr3JVuvUi3XqRbLzp0B82ij93e/CExwa41uh15Rbira1t0tZjSOhcPfbEc95+r+MmmOKdPIole\nJ/lf/0zZ7MVgGFS2a8+Pdz1IiQu8Bpx/WBLDMmNw19TS8Miz9P3iJ/qaTVTcfw/T4rMwKcXdx2Q0\nOQFz/fZ8cidPI2/SFOq25gKQdMoIej5xO9W1tXS580rWP/4Gy66+H0+Nb8+e+BED8TqdlM//k7zJ\nU8mbPJXQtGQsEWHg9ZJ+6Tn+c/PED+vP8JkfYhgGEZ0zmm21xccQO6gP5fP/ZMOTb/o/n3bxGSQc\n07LLl7cmmeN6kW696Not2j5d565060W69SLdetGhO2gWfcrLy4P+rNvN2Z/umgY3lY6mlxd2bMpm\n84W34K2to/M3bxPaqQOVDg+by+rZXFpPrcvD0A7RDEmLIPvn+cx89XNOXr4Mi9sFQFHj/wDcFitO\nWwjRhflkfv0lOSf9i0HpUVw+IIXq1RtZevX91G3ahjKbMTwe4p5+jhe+eIWY/j19X19bT+EPv5I7\naYpvAalRSPtE0i44lY43X4oymykvLyfz+gspnDKLyqWrURYzXe+7nszrLkCZTNRuySFv0lRyJ03B\nkePbXc8cZqfT7Zc3aQ/v1GGfz1mvZ+4m/8ufMDxeACyRYXS44rwWPd+tTea4XqRbL7p2i7ZP17kr\n3XqRbr1It1506A6aRZ/4+PhADyEgdu+ud3mYsrYUr2HQMc5ORmwoa4rq+HlDGX9sr8Rj7LxveFUF\nY8Y/S1RlFQBT7niB7y68usn24ooKcL02n9I//yC8qpLMxs9HDOhDodlOXnUDXpOJbR27UTx4CBfG\nuOCmuxk0ZwYX3vwvOg7LInfiN6y5/wW8DU4iunfk8DfHseW1ieR++gOLL76TXk/eQfEv8yj4biae\nxvPxmEJttDvlaFJHjyL+qAFNLkUcHx+PyWLh8DcfY8vrn5ByzonEHNF7Z1dWGl3uvprOd15J2dyl\nFP7wK/EjBhCSGLffz29El0y63H3Nfn/dwSBzXC/SrRddu0Xbp+vclW69SLdepFsvOnQHzaJPdXU1\nERH/7DlWDjbDMFj/xBtsf/9LEk8Y5lsAGX5EkwWQXburHG4e/Gkzq4tqm2wnoSCXnkvnc8WKJRih\nIWwdOJTcPv0YMvFtoirLKUvPIKogny6rl9GnNIe6rl3Iigmh+ycTsX07xb+dirgEyo4ewQV3jiGu\nYyrgu+rWZ38WMjA9mnN6J2K3mlm3agxbXptI/n1PU9OrCwVf/wxA2kWn0+Ox2zCHhdLrmbtpKCqj\n5Jd5LLvmfv9jxAzsQ+roUSSfMfIvz5mzo9me3p6eT9z+l8+fMpmIH34E8cOP2M9n/tAUjHO8JaRb\nL9ItRNui69yVbr1It16kWy86dAfNoo/T6Qz0EPaLw+3l5TnbKa938e/+7emR5DufTWmdi4lLClhb\nXEu/2T+TPvFjAPK//In8L39CxUYTGhOJajzZsdvlYoPVisdrUFLnYrDH4EgThFrMuDxevE4XkRVl\nOx+4Cg774WsO++FrAMI6deC4b19n6/hP2fzSBM6fP41Bd53GxmffYdO3UzCF2Eg55yTMo0Ziy+zE\n2Vmx2Mw7L/p2ZEYMR2bENGnrfNdVFE2fTe2GbGo3ZGMOs9PrmbtIOeck/31MVguHvzWOJZfeRf22\nfNqffQKp553SokOu2tpr3VqkWy/SrRddu0Xbp+vclW69SLdepFsvOnQHzaJPcnLyvu90CNj8yke4\nLRbeSO3PykLfHjmLcqoZkRVDekwoX6wowuH20u3PRaRP9i34zDjtfOx1tfRcuoCY8hLqyyubbHPH\nNG16kXOwNf7XEh1J+38dT+r5p+CqqiH3sykUTZ2FNTaKAR8/jy0+hqyxF7F9wleUz1vKilseJ2/S\nFDCZOPzNx0g66SgAerSw0RwaQp8X72fhuTcTlpVG3/GPNnuyZEu4nUGfv9zMFvaurbzWrU269SLd\netG1W7R9us5d6daLdOtFuvWiQ3fQLPoUFBSQkdH8VZgchSUU/vAbZnsI7U495h+/1PYO5YtWsH7c\nawDUXnwdCf37M6JjDN+vKWHWlgoAzG4Xp+etoctXHwJQetklhB57Agaw6pLR1G/LY1tZXbPb7xRn\n5yeSTv8AACAASURBVNbhHYgIMTf5vD0tGVOIzf9x4rFDcNfWA76FFwBrdCQdb76UdY++4lvwAXo9\ndYd/wWd/xfTvxbHLv8UcHubfK6m17O21DmbSrRfp1ouu3aLt03XuSrdepFsv0q0XHbqDZtGnuUut\nFf00h+0ffEnxzAXg9V19afV9z+08QfBu58c5mLyGwR/PTvB/fPK3nzDo1lNJTYvn7N5JTPpuMaHf\nTiF98R8YVdUAZFw7mpMfuW63LXUmu7yenzeWszS3mkiLlx7tY+icYGdAWlSTQ6/2Zsdiz646XHEO\n2W9PwpFXRKfbryD93/864F4AS0T4vu90AHS4rF5zpFsv0q0XXbtF26fr3JVuvUi3XqRbLzp0B82i\nj81ma/Lxtve+YPW9zwGgLGYSTzwKd3UtZXOW+M+PE5qSRMp5J5M25jTCMtMO2tiW59cw4fulHPfb\nXAyTiZq0dKK3ZVP65KukvPwADV9NoesDL+B1ODGAyN5dSL/oDNIvPavZ7WXE2rlyoB0GQkVFBTEx\nMc3eb3+ZQ0MY8OkL1KzbTLvTjm2VbR4Mu7/WupBuvUi3XnTtFm2frnNXuvUi3XqRbr3o0N2y3ULa\ngMrKnee5KZzyG6vvex6AzndcybHLvqX/+08x6ItXGPHHF3S+8yrsGSk48orY/OIEfj/qQra+9RmG\nYfzV5g/Yktwq7pqygdip0zAZBuYTjuaUj5/CZA8h7/Np/HHWWFbd+TReh5OUc0/myBkfMOznD+hw\n+Tn/z96dR0l61/e9f/9qX7t6na7ZR8toB4GsiN1ghoCQMdgstjiON0TucQLHCyaOwbl4i3Fsx1sS\n7OQex8HX14TEhGAdFmMbhAlGEpIGJLSNRtLMaLbqvbuqu6u6tt/9o2pGPaNZWtJMPfM8n+/rnD7q\n7qrp+r3n+VXr0U/Pgoudf/Os774QClftovwDb7zgp2RdSBe6OSysW4t1a1HtNuGnOnetW4t1a7Fu\nLQrdkVn0GR8fB2Dh3gd58F/8CnjP7n/9z7nyw3eQGh85+bzcjs1c+Qvv43vv+Stu+dwfs/ldb8a3\n2jz+f/8R337fR2gtVi/YmKaXm/zWXYdI1Ou8fO/dALz6wz/O0JU7uOojvdO2Fu75DvFclpd+4ld4\n6X/6GEPX735er3GiW4liM1i3GuvWotptwk917lq3FuvWYt1aFLojs+iztLREc3aBvT/xi3TXmmz/\n8R/k8p/7ybM+3znH6Ctfxo2f+FVe9l8/TmKowPSXvs433/w+mgsvbuFn9eAR/vEt7+NvfvBnmLzv\nXr7/0XuINRqMvuYmhl5yNQA73/8eNr/rzYy+5iZe9bd/dsrtzJ8PhZXJ0yk2g3WrsW4tqt0m/FTn\nrnVrsW4t1q1FoTsy1/RptVosPnKA1mKNoZdezXW/9QsbPkWp/P1vYOiG3dz3479Ifd8BvvGHnyL1\nk7dTyia4YTJ/1p+z+MDDHPjjT7HlXW9h8rbXA7A2M8/9t/88qwePsgl428MPnXz+rp9+78nPXSzG\njZ/41Rfce0Kr1XrRPyNsFJvButVYtxbVbhN+qnPXurVYtxbr1qLQHZkjfcrlMp3VBgDZHVue9125\nvu3zfOZ73w7A/F9+jt/88hP8wuf38xtfOUBtrX3Kc733HPiTT3HvO/4FU1/4Gt9+30d49KO/T3Oh\nyv3/7F+xevAoU5u38/Xvfzepa3unaxVv2M3EnlddgNJTlcvlC/4zL3WKzWDdaqxbi2q3CT/VuWvd\nWqxbi3VrUeiOzKJPpVKhs1oHIJ47923Xut7TaHdptLusNDt84puH+bW/P8C+Hbupbt1GYbnK244+\nSi4Z4xsHl/i5/+8B7v2Pn+bgf+l97P1nH2bfr/0nfLvDpltfh0smeObPPsNdN/0gtQcfY2l4jC/+\n1L/kzR/9Kd5415/zvfd+hls++4kNXZj5hXSrUWwG61Zj3VpUu034qc5d69Zi3VqsW4tCd2RO78rn\n86z0j/RJ5DJnfM5Tc6v8/f557npqgfn6qUfvJGKO99+ylVuKP87DP/dxbvzGV/ihD9/Ov//CI9z0\n736bhekKC+uenxwu8pI/+jdsesvrWPz2o9z9vl+G41PUc3nu/ZkP8TvvvYWdI73Fp9zOLRelGXrd\nahSbwbrVWLcW1W4Tfqpz17q1WLcW69ai0B2ZRZ94PE6n3lv0Of1In9pam1/5u6d5uLJy8nvJuDt5\nmNPWUoYPvW4HV03k6O5+M/s//l9YfuwpEvc+wHs+/RcsTVeYH5/kwFXXccNkgWt3jrL9R99Odvtm\namtt/mg2w/3v+zAvuf8f2X7ra/mt97ySdGIwB1HFn+dpbFGg2AzWrca6tah2m/BTnbvWrcW6tVi3\nFoXuyCz6VKvVk9f0ia870met3eVX/vZpHp5aoZiO84bLR3jT7lGumcid8QLNsXSKHe97F/v/3f/D\nt9//UXyzRXrzBPnf/03+4ak1/gH4oesn2LGS5Ol/PMzdzywxu9IiN1Tg1l/553zv5SPP+ZkXU7Va\nZWRksK8ZNMVmsG411q1FtduEn+rctW4t1q3FurUodEdm0WdiYoJnTlzTJ9tb9Ol0PR//6kEenlph\nPJ/kD3/gKjYVUuf9Wdt/7Ad56o/+nG59jcRQgZs/9fsUr72C4vZ5fvcfDvG/H5k55flXT+T46Bt3\nsbmYvvBh5zExMTHw1wyaYjNYtxrr1qLabcJPde5atxbr1mLdWhS6I7PoMz8//5wjff5D/0icYjrO\nx2+9YkMLPgCpsWEu+5c/yjP/7bO8/E9/k+K1VwDwxitHGcok+OzD0wylE1w+luXKsSw3bi4Sj23s\n9vAX2vz8PLlcLpDXDopiM1i3GuvWotptwk917lq3FuvWYt1aFLojs+jjvT/l7l3PLDT40r45UnHH\nr7/5cnaNnPuOXqfb/a/ez5UfvuM5p4DdvG2Im7cNXbBxv1je+6CHMHCKzWDdaqxbi2q3CT/VuWvd\nWqxbi3VrUeiOzC3bJyYm1l3IOcMzS73PX76lyPWThRf0M890zZ9LjcLhaKdTbAbrVmPdWlS7Tfip\nzl3r1mLdWqxbi0J3ZBZ9pqamTjnSp1JdA2Dz0OCvszNIU1NTQQ9h4BSbwbrVWLcW1W4Tfqpz17q1\nWLcW69ai0B2ZRZ9CoXDKNX2O15oAbC5u7Do+YVUovLCjmMJMsRmsW411a1HtNuGnOnetW4t1a7Fu\nLQrdkVn0Adad3pXleK13pE85gDtqGWOMMcYYY4wxxgQtMos+y8vLz57elc1Q6R/pU474kT7Ly8tB\nD2HgFJvButVYtxbVbhN+qnPXurVYtxbr1qLQHZlFn8nJyZOnd5FJMyWy6DM5ORn0EAZOsRmsW411\na1HtNuGnOnetW4t1a7FuLQrdkVn0mZmZOXmkT9UlaHU9I9kE2WQ84JFdXDMzM0EPYeAUm8G61Vi3\nFtVuE36qc9e6tVi3FuvWotAdmUUf5xzt/pE+M51e1maB6/mE4bbyF5piM1i3GuvWotptwk917lq3\nFuvWYt1aFLojs+gzMlTCN1sQi3F8zQPRP7ULYHR0NOghDJxiM1i3GuvWotptwk917lq3FuvWYt1a\nFLojs+gzdfgI0Ltd+4mLOG8eiv6RPgqHo51OsRmsW411a1HtNuGnOnetW4t1a7FuLQrdkVn0ySeS\nACRyWY6fWPQRONJnaGgo6CEMnGIzWLca69ai2m3CT3XuWrcW69Zi3VoUuje06OOcu9U5t88596Rz\n7pfO8pwfds496px7xDn3qQs7zPNrLa8CJ470WQOgLHBNn06nE/QQBk6xGaxbjXVrUe024ac6d61b\ni3VrsW4tCt3nXfRxzsWBTwBvBa4D3uucu+605+wGPgK8xnt/PfBzF2Gs57QyvwhALJvhePXE6V3R\nP9JnZWUl6CEMnGIzWLca69ai2m3CT3XuWrcW69Zi3VoUujdypM8twJPe+6e9903g08A7TnvOPwc+\n4b1fAPDeT1/YYZ7fSC4PgMtmWGy0ScYcY7nkoIcxcOVyOeghDJxiM1i3GuvWotptwk917lq3FuvW\nYt1aFLoTG3jOVuDwuq+PAK847TlXATjn/hGIA7/qvf+b03/Q9PQ0d9xxB4lEgk6nwzvf+U4+8IEP\nUKlUyOfzxONxqtUqExMTzM/P471nYmKCqakpCoUCAMvLy0xOTjIzM4NzjtHRUWZmZpg/cBCA1f5r\njWUclePHKZVKzM7OUiqVaDab1Ot1yuUylUqFVCpFsVhkbm6OkZER6vU6jUbj5OOZTIZsNsvCwgJj\nY2PUajWazebJx7PZLKlUiqWlJcbHx1laWqLVap18/MU2DQ0N0el0WFlZOfkzk8nkKU3Hjh0jn89H\nqul82+nIkSOnNEehaSPbaWZmhquvvjpSTRvZTp1Oh3g8HqmmjWynJ554gq1bt0aqaSPbqV6vMz4+\nHqkmtd/l5tJzMfe/7D0bnaaNbKepqamT+yJRadrIdjqxLxKlpo1sp3379rFly5ZINW1kO9XrdSYm\nJiLVtJHtVKlUyOVykWrayHaqVConf6+FvelsnPf+nDsKzrl3A7d679/f//rHgFd47z+47jmfB1rA\nDwPbgK8DL/HeL67/WXfffbe/5pprzvl6L9Rjf/k5Dv3C75B43Sv5nbf8GP9k2xC/eesVF+W1LiXH\njh1jy5YtQQ9joBSbwbrVWLeWKHXv3bv3gT179twc9DjMsy7m/leU5u7zYd1arFuLdWuJUvfZ9sE2\ncnrXUWD7uq+39b+33hHgTu99y3t/AHgC2P1CB/tCZFwvpdG/i5fC9XwAyf+rqtgM1q3GurWodpvw\nU5271q3FurVYtxaF7o0s+twH7HbOXeacSwG3A3ee9pzPAW8AcM6N0zvd6+kLOM7zWpruHc60Gu8t\n+ijcuQs452FcUaXYDNatxrq1qHab8FOdu9atxbq1WLcWhe7zLvp479vAB4EvA48B/9N7/4hz7ted\nc2/vP+3LwJxz7lHgLuBfee/nLtagzySNA2A5dmLRx470iSrFZrBuNdatRbXbhJ/q3LVuLdatxbq1\nKHRv5ELOeO+/CHzxtO99bN3nHvhQ/yMQzeXerdaW+kmbRRZ9ms1m0EMYOMVmsG411q1FtduEn+rc\ntW4t1q3FurUodG/k9K5QaFaXAVjsJ6mc3lWv14MewsApNoN1q7FuLardJvxU5651a7FuLdatRaE7\nMos+2XjvCJ9GIkUpkyCfigc8osEol8tBD2HgFJvButVYtxbVbhN+qnPXurVYtxbr1qLQHZlFn9rc\nAgDtZFrmej4AlUol6CEMnGIzWLca69ai2m3CT3XuWrcW69Zi3VoUuiOz6OPWeufitVIpRnPJgEcz\nOKmUzgLXCYrNYN1qrFuLarcJP9W5a91arFuLdWtR6I7Mok+s1QF6iz5DaY1TuwCKxWLQQxg4xWaw\nbjXWrUW124Sf6ty1bi3WrcW6tSh0R2bRp1GtAdBKpimIXM8HYG5uLughDJxiM1i3GuvWotptwk91\n7lq3FuvWYt1aFLojs+iz/kifQnpDd6KPhJGRkaCHMHCKzWDdaqxbi2q3CT/VuWvdWqxbi3VrUeiO\nzKJPe2UVgFYyRVHo9C6FW8ydTrEZrFuNdWtR7Tbhpzp3rVuLdWuxbi0K3ZFZ9OmsNgBop7QWfRqN\nRtBDGDjFZrBuNdatRbXbhJ/q3LVuLdatxbq1KHRHZtGHZguAVipNIaVzele5XA56CAOn2AzWrca6\ntah2m/BTnbvWrcW6tVi3FoXuyCz6dFZ7h2Wpnd5VqVSCHsLAKTaDdauxbi2q3Sb8VOeudWuxbi3W\nrUWhOxKLPt57uvU1oHekj9KiTyaTCXoIA6fYDNatxrq1qHab8FOdu9atxbq1WLcWhe5ILPp0G03w\nnnYigY/FKArdvSubzQY9hIFTbAbrVmPdWlS7Tfipzl3r1mLdWqxbi0J3JBZ9nj21Kw1APqVzpM/C\nwkLQQxg4xWawbjXWrUW124Sf6ty1bi3WrcW6tSh0R2vRJ5Uil4wRj7mARzQ4Y2NjQQ9h4BSbwbrV\nWLcW1W4Tfqpz17q1WLcW69ai0B2RRZ/+7dqTKalTuwBqtVrQQxg4xWawbjXWrUW124Sf6ty1bi3W\nrcW6tSh0R2TR59kjfZQu4gzQbDaDHsLAKTaDdauxbi2q3Sb8VOeudWuxbi3WrUWhOxKLPu3+kT5q\nt2sHKJfLQQ9h4BSbwbrVWLcW1W4Tfqpz17q1WLcW69ai0B2JRZ9uvb/ok0pTEDu9q1KpBD2EgVNs\nButWY91aVLtN+KnOXevWYt1arFuLQnckFn1OXNOnlUpRELpzF2jcYu50is1g3WqsW4tqtwk/1blr\n3VqsW4t1a1HojsSiT3vdLduHxE7vSqVSQQ9h4BSbwbrVWLcW1W4Tfqpz17q1WLcW69ai0B2JRZ9T\njvQRO71raWkp6CEMnGIzWLca69ai2m3CT3XuWrcW69Zi3VoUuiOy6NM70qedSlEQO9JnfHw86CEM\nnGIzWLca69ai2m3CT3XuWrcW69Zi3VoUuiOy6KN79y6FlcnTKTaDdauxbi2q3Sb8VOeudWuxbi3W\nrUWhOyKLPv1r+qTSFFNap3e1Wq2ghzBwis1g3WqsW4tqtwk/1blr3VqsW4t1a1Hojsiij+6RPuVy\nOeghDJxiM1i3GuvWotptwk917lq3FuvWYt1aFLojsuhz4kgfvWv6VCqVoIcwcIrNYN1qrFuLarcJ\nP9W5a91arFuLdWtR6I7Gok/9xN270hTF7t6Vz+eDHsLAKTaDdauxbi2q3Sb8VOeudWuxbi3WrUWh\nOxKLPu3+6V2dVIpcMhJJGxaPax3ZBJrNYN1qrFuLarcJP9W5a91arFuLdWtR6I7ECklzeRWARC6L\ncy7g0QxWtVoNeggDp9gM1q3GurWodpvwU5271q3FurVYtxaF7kgs+rRWetf0SeWzAY9k8CYmJoIe\nwsApNoN1q7FuLardJvxU5651a7FuLdatRaE7Eos+J07vShf0Fn3m5+eDHsLAKTaDdauxbi2q3Sb8\nVOeudWuxbi3WrUWhOxKLPt3+3bsyxVzAIxk8733QQxg4xWawbjXWrUW124Sf6ty1bi3WrcW6tSh0\nR2PRp3/3rqzgoo/C4WinU2wG61Zj3VpUu034qc5d69Zi3VqsW4tCdyQWfXyjt+iTK0b/dmunm5qa\nCnoIA6fYDNatxrq1qHab8FOdu9atxbq1WLcWhe7QL/p0221cq413jnwxE/RwBq5QKAQ9hIFTbAbr\nVmPdWlS7Tfipzl3r1mLdWqxbi0J3+Bd96msAtJIpiplkwKMxxhhjjDHGGGOMuTSEftGn3b+IcyuV\nopiOBzyawVteXg56CAOn2AzWrca6tah2m/BTnbvWrcW6tVi3FoXu0C/6dPq3a28lUxRSeos+k5OT\nQQ9h4BSbwbrVWLcW1W4Tfqpz17q1WLcW69ai0B2BRZ/ekT7tVJpiOhHwaAZvZmYm6CEMnGIzWLca\n69ai2m3CT3XuWrcW69Zi3VoUuiOw6PPskT6Kp3c554IewsApNoN1q7FuLardJvxU5651a7FuLdat\nRaE7Aos+2tf0GR0dDXoIA6fYDNatxrq1qHab8FOdu9atxbq1WLcWhe4ILfqkKdjpXRIUm8G61Vi3\nFtVuE36qc9e6tVi3FuvWotAd+kWfRu3ENX1SpOPRPzTrdENDQ0EPYeAUm8G61Vi3FtVuE36qc9e6\ntVi3FuvWotAd/kWfZptmKg3ZrMT5eKfrdDpBD2HgFJvButVYtxbVbhN+qnPXurVYtxbr1qLQvaFF\nH+fcrc65fc65J51zv3SO573LOeedczdfuCGeW+q2Pfynj/0+e2//0UG95CVlZWUl6CEMnGIzWLca\n69ai2m3CT3XuWrcW69Zi3VoUus+76OOciwOfAN4KXAe81zl33RmeVwR+Frj3Qg/yXGprvZW54Vx6\nkC97ySiXy0EPYeAUm8G61Vi3FtVuE36qc9e6tVi3FuvWotC9kSN9bgGe9N4/7b1vAp8G3nGG5/0G\n8NtA4wKO77yWm20Akr49yJe9ZFQqlaCHMHCKzWDdaqxbi2q3CT/VuWvdWqxbi3VrUejeyKLPVuDw\nuq+P9L93knPuJmC79/4LF3BsG7LcP9KnIHi7doBkMhn0EAZOsRmsW411a1HtNuGnOnetW4t1a7Fu\nLQrdL/oe5865GPD7wE+e77nT09PccccdJBIJOp0O73znO/nABz5ApVIhn88Tj8epVqtMTEwwPz+P\n956JiQmmpqYoFAoALC8vMzk5yczMDM45thXyvHt3lm2lNLOzs6ysrFAul6lUKiSTSUqlErOzs5RK\nJZrNJvV6/eTjqVSKYrHI3NwcIyMj1Ot1Go3GycczmQzZbJaFhQXGxsao1Wo0m82Tj2ezWVKpFEtL\nS4yPj7O0tESr1Tr5+AttGh0dZWZmhqGhITqdzjmbOp0Ohw4dilTT+bZTu90+pTkKTRvZTqurq6yt\nrUWqaSPbqVQqcejQoUg1bWQ7ra6uMjc3F6mmjWynVCplv8tD3mQuPRdj/8ves9F5zz6f7bR+XyQq\nTRvZTif2RaLUtJHttLq6yuzsbKSaNrKdkskkc3NzkWrayHby3p/yey0KTRvZTut/r4W96Wyc9/6c\nOwrOuVcBv+q9f0v/648AeO9/q/91CXgKWO7/kTIwD7zde3//+p919913+2uuueacr/dCHTp0iJ07\nd16Un30pU+xWbAbrVmPdWqLUvXfv3gf27NkzsBs6mPOz/a8Lz7q1WLcW69YSpe6z7YNt5PSu+4Dd\nzrnLnHMp4HbgzhMPeu+XvPfj3vtd3vtdwD2cYcHnYlP9v4uK3YrNYN1qrFuLarcJP9W5a91arFuL\ndWtR6D7voo/3vg18EPgy8BjwP733jzjnft059/aLPcCNajabQQ8hEIrdis1g3WqsW4tqtwk/1blr\n3VqsW4t1a1Ho3tA1fbz3XwS+eNr3PnaW577hxQ/r+avX60G8bOAUuxWbwbrVWLcW1W4Tfqpz17q1\nWLcW69ai0L2R07tCoVwuBz2EQCh2KzaDdauxbi2q3Sb8VOeudWuxbi3WrUWhOzKLPpVKJeghBEKx\nW7EZrFuNdWtR7Tbhpzp3rVuLdWuxbi0K3ZFZ9EmlUkEPIRCK3YrNYN1qrFuLarcJP9W5a91arFuL\ndWtR6I7Mok+xWAx6CIFQ7FZsButWY91aVLtN+KnOXevWYt1arFuLQndkFn3m5uaCHkIgFLsVm8G6\n1Vi3FtVuE36qc9e6tVi3FuvWotAdmUWfkZGRoIcQCMVuxWawbjXWrUW124Sf6ty1bi3WrcW6tSh0\nR2bRR+FWa2ei2K3YDNatxrq1qHab8FOdu9atxbq1WLcWhe7ILPo0Go2ghxAIxW7FZrBuNdatRbXb\nhJ/q3LVuLdatxbq1KHRHZtGnXC4HPYRAKHYrNoN1q7FuLardJvxU5651a7FuLdatRaE7Mos+lUol\n6CEEQrFbsRmsW411a1HtNuGnOnetW4t1a7FuLQrdkVn0yWQyQQ8hEIrdis1g3WqsW4tqtwk/1blr\n3VqsW4t1a1HojsyiTzabDXoIgVDsVmwG61Zj3VpUu034qc5d69Zi3VqsW4tCd2QWfRYWFoIeQiAU\nuxWbwbrVWLcW1W4Tfqpz17q1WLcW69ai0B2ZRZ+xsbGghxAIxW7FZrBuNdatRbXbhJ/q3LVuLdat\nxbq1KHRHZtGnVqsFPYRAKHYrNoN1q7FuLardJvxU5651a7FuLdatRaE7Mos+zWYz6CEEQrFbsRms\nW411a1HtNuGnOnetW4t1a7FuLQrdkVn0KZfLQQ8hEIrdis1g3WqsW4tqtwk/1blr3VqsW4t1a1Ho\njsyiT6VSCXoIgVDsVmwG61Zj3VpUu034qc5d69Zi3VqsW4tCd2QWfRRutXYmit2KzWDdaqxbi2q3\nCT/VuWvdWqxbi3VrUeiOzKJPKpUKegiBUOxWbAbrVmPdWlS7Tfipzl3r1mLdWqxbi0J3ZBZ9lpaW\ngh5CIBS7FZvButVYtxbVbhN+qnPXurVYtxbr1qLQHZlFn/Hx8aCHEAjFbsVmsG411q1FtduEn+rc\ntW4t1q3FurUodEdm0Udhhe5MFLsVm8G61Vi3FtVuE36qc9e6tVi3FuvWotAdmUWfVqsV9BACodit\n2AzWrca6tah2m/BTnbvWrcW6tVi3FoXuyCz6lMvloIcQCMVuxWawbjXWrUW124Sf6ty1bi3WrcW6\ntSh0R2bRp1KpBD2EQCh2KzaDdauxbi2q3Sb8VOeudWuxbi3WrUWhOzKLPvl8PughBEKxW7EZrFuN\ndWtR7Tbhpzp3rVuLdWuxbi0K3ZFZ9InH40EPIRCK3YrNYN1qrFuLarcJP9W5a91arFuLdWtR6I7M\nok+1Wg16CIFQ7FZsButWY91aVLtN+KnOXevWYt1arFuLQndkFn0mJiaCHkIgFLsVm8G61Vi3FtVu\nE36qc9e6tVi3FuvWotAdmUWf+fn5oIcQCMVuxWawbjXWrUW124Sf6ty1bi3WrcW6tSh0R2bRx3sf\n9BACodit2AzWrca6tah2m/BTnbvWrcW6tVi3FoXuyCz6KByWdSaK3YrNYN1qrFuLarcJP9W5a91a\nrFuLdWtR6I7Mos/U1FTQQwiEYrdiM1i3GuvWotptwk917lq3FuvWYt1aFLojs+hTKBSCHkIgFLsV\nm8G61Vi3FtVuE36qc9e6tVi3FuvWotAdmUUfY4wxxhhjjDHGGPOsyCz6LC8vBz2EQCh2KzaDdaux\nbi2q3Sb8VOeudWuxbi3WrUWhOzKLPpOTk0EPIRCK3YrNYN1qrFuLarcJP9W5a91arFuLdWtR6I7M\nos/MzEzQQwiEYrdiM1i3GuvWotptwk917lq3FuvWYt1aFLojs+jjnAt6CIFQ7FZsButWY91aVLtN\n+KnOXevWYt1arFuLQndkFn1GR0eDHkIgFLsVm8G61Vi3FtVuE36qc9e6tVi3FuvWotAdmUUfb2EM\nygAAIABJREFUhcOyzkSxW7EZrFuNdWtR7Tbh9u2jNT557zMcXVoLeigDp/qetW4t1q3FuqMrMos+\nQ0NDQQ8hEIrdis1g3WqsW4tqtwm3Lzw+y+eeqvPY9ErQQxk41fesdWuxbi3WHV2RWfTpdDpBDyEQ\nit2KzWDdaqxbi2q3CbftwxkADi82Ah7J4Km+Z61bi3Vrse7oisyiz8qK3v9lAs1uxWawbjXWrUW1\n24Tb9lIagMOCp3epvmetW4t1a7Hu6IrMok+5XA56CIFQ7FZsButWY91aVLtNuJ080mdJ70gf1fes\ndWuxbi3WHV0bWvRxzt3qnNvnnHvSOfdLZ3j8Q865R51zDznnvuKc23nhh3pulUpl0C95SVDsVmwG\n61Zj3VpUu024besf6XNsaY1O1wc8msFSfc9atxbr1mLd0XXeRR/nXBz4BPBW4Drgvc6560572reB\nm733LwU+A/zOhR7o+SSTyUG/5CVBsVuxGaxbjXVrUe024ZZNxhnNxGh1PZVaM+jhDJTqe9a6tVi3\nFuuOro0c6XML8KT3/mnvfRP4NPCO9U/w3t/lvV/tf3kPsO3CDvP8SqXSoF/ykqDYrdgM1q3GurWo\ndpvw217SPMVL9T1r3VqsW4t1R1diA8/ZChxe9/UR4BXneP4dwJfO9MD09DR33HEHiUSCTqfDO9/5\nTj7wgQ9QqVTI5/PE43Gq1SoTExPMz8/jvWdiYoKpqSkKhQIAy8vLTE5OMjMzg3OO0dFRZmZmqNfr\njI+Ps7KyQrlcplKpkEwmKZVKzM7OUiqVaDab1Ov1k4+nUimKxSJzc3OMjIxQr9dpNBonH89kMmSz\nWRYWFhgbG6NWq9FsNk8+ns1mSaVSLC0tMT4+ztLSEq1W6+TjL7ZpaGiITqdzzqZjx46Rz+cj1XS+\n7XTkyJFTmqPQtJHtNDMzw9VXXx2ppo1sp06nQzwej1TTRrbTU089xdatWyPVZL/LNX6Xm0vPxdz/\nKsV6F3F+6MBxXj6ZsfdsiJs2sp2mpqZO7otEpWkj2+nEvkiUmjaynZ566im2bNkSqaaNbKd6vc7E\nxESkmjaynSqVCrlcLlJNG9lOlUrl5O+1sDedjfP+3OdgO+feDdzqvX9//+sfA17hvf/gGZ77z4AP\nAq/33j/nVg533323v+aaa875ei/U4uIiw8PDF+VnX8oUuxWbwbrVWLeWKHXv3bv3gT179twc9DjM\nsy7m/ten73+GP/vOHG+9eoyff92Oi/Ial6IovWefD+vWYt1arDv8zrYPtpEjfY4C29d9va3/vVM4\n594E/DJnWfC52JpNrXPJT1DsVmwG61Zj3VpUu034bcr2/nl4Uev0LtX3rHVrsW4t1h1dG7mmz33A\nbufcZc65FHA7cOf6JzjnXg78F+Dt3vvpCz/M86vX60G8bOAUuxWbwbrVWLcW1W4TfqOJDgCHlwb+\n//sCpfqetW4t1q3FuqPrvIs+3vs2vVO2vgw8BvxP7/0jzrlfd869vf+03wUKwF85577jnLvzLD/u\noimXy4N+yUuCYrdiM1i3GuvWotptwu+anZvJJmMsNdosNdpBD2dgVN+z1q3FurVYd3Rt5EgfvPdf\n9N5f5b2/wnv/m/3vfcx7f2f/8zd57ye99y/rf7z93D/xwqtUKoN+yUuCYrdiM1i3GuvWotptwm9q\naoptpTQAR4RO8VJ9z1q3FuvWYt3RtaFFnzBIpVJBDyEQit2KzWDdaqxbi2q3Cb9UKrXutu06p3ip\nvmetW4t1a7Hu6NrIhZxDoVgsBj2EQCh2KzaDdauxbi2q3Sb8Cvk820u9z5Uu5qz6nrVuLdatxbqj\nKzJH+szNzQU9hEAodis2g3WrsW4tqt0m3J74+H/mnlf+CFuOPwPA4SWdRR/V96x1a7FuLdYdXZFZ\n9BkZGQl6CIFQ7FZsButWY91aVLtNuLUWa3Tmlyh8+zsAHF7UOb1L9T1r3VqsW4t1R1dkFn0UbrV2\nJordis1g3WqsW4tqtwm3iT2vBKD1zftxwPHaGs1ON9hBDYjqe9a6tVi3FuuOrsgs+jQaOocUr6fY\nrdgM1q3GurWodptwG33t90AiTvU7j7Ej1qTr4XhV42gf1fesdWuxbi3WHV2RWfQpl8tBDyEQit2K\nzWDdaqxbi2q3CbdEPsfwLS+FbpfrD+8HdO7gpfqetW4t1q3FuqMrMos+lUol6CEEQrFbsRmsW411\na1HtNuGX+p7rANj++CMA/O0Tc3jvgxzSQKi+Z61bi3Vrse7oisyiTyaTCXoIgVDsVmwG61Zj3VpU\nu034jb7hFgBKDz1EPuG455kqf7d/PuBRXXyq71nr1mLdWqw7uiKz6JPNZoMeQiAUuxWbwbrVWLcW\n1W4TfiM3XEVm6yTtuQX+xXjv1K4/vvsI08vNgEd2cam+Z61bi3Vrse7oisyiz8LCQtBDCIRit2Iz\nWLca69ai2m3Cb3FxkfHvewUAlz/xKK/aUWK11eX3vv5MpE/zUn3PWrcW69Zi3dEVmUWfsbGxoIcQ\nCMVuxWawbjXWrUW124Tf2NgYE298FQCzd93Dz752O0PpON8+VuPjdx3k8GI074qi+p61bi3WrcW6\noysyiz61Wi3oIQRCsVuxGaxbjXVrUe024Ver1Rh73c24RJzFBx6h2Kzzoe/dQdzBPzy9yPs/8xi/\ndddBHjq+TKcbnSN/VN+z1q3FurVYd3RFZtGn2Yz2ueNno9it2AzWrca6tah2m/BrNpskinlGbrkR\nul0e+de/yw21af7bD1/HbdeMEY857npqgQ9/YT+3f+phfu/rh/jqk/Mcr62F+vQv1fesdWuxbi3W\nHV2JoAdwoZTL5aCHEAjFbsVmsG411q1FtduE34m5u+On3sn8N/dS+euvUPnrr1D6nuv5oXfdyrtf\n/yq+NOv5x0OLHKs2+fIT83z5id7dvUayCa7blOfayTzXbcpz1XiOVCIc/z9S9T1r3VqsW4t1R1c4\n/s26AZVKJeghBEKxW7EZrFuNdWtR7Tbhd2Luln/gjbz2659i5/vfQ2KowNIDj/DYR3+Ph1/7Hl7y\nb3+djz71DX5vaJY7diZ5xbYixXSchXqbfzy0xJ9+6xgf+vx+3vUXD/FvvvwUn3tkhqNLjUv6SCDV\n96x1a7FuLdYdXZE50kfhVmtnotit2AzWrca6tah2m/BbP3cLV+3i2n/78+z+yE8z9cWvMfWFrzF7\n170s3vddFu/7LgAl4PvGR3jHjdfgrr6S2S3beXJ4Ew/6PAeXmnzrcJVvHa4CUC6muHnbEC/fUuSq\n8RybCkmcc0FkPofqe9a6tVi3FuuOrsgs+qRSqaCHEAjFbsVmsG411q1FtduE35nmbiKfZet73srW\n97yV9vIKc1+/n6XvPMbSg49RffBxmrMLzH7lbvjK3QBcAVyVy5LetZXV4RFmskUOJQrM5YZ4qDTM\nN4eGWR4aJjtc5MqxLLvHc1w5nmX3WI5NhRTx2OAXglTfs9atxbq1WHd0RWbRZ2lpieHh4aCHMXCK\n3YrNYN1qrFuLarcJv/PN3UQhz+Rtr2fyttcD4L2n/swxqg/uY+nBx6g9+iS1R59ibWqW1UefBGCi\n/3G6ZirN8lCJ1XyR7xSK3J0r0MlkyAzlyY0USUxOkNg8SWbbJkYnR9lUyjKRTzKUSZBPxYldwKOE\nVN+z1q3FurVYd3RFZtFnfHw86CEEQrFbsRmsW411a1HtNuH3fOeuc47czq3kdm6l/PY3nvx+c26R\n+uHjNI5P0zg+S+P4NGvHZ3pfV2ZZOzZNqt5gdHaa0dnp875OFzicTPFkJstKYYiV4hDNUomU82Ra\nTdKtJn5kmO6ObcQv20F+ywRj40NsGh9irDzC8HCBxDmOIFJ9z1q3FuvWYt3RFZlFn6WlJfL5fNDD\nGDjFbsVmsG411q1FtduE34Wau6mxYVJjw5Redu0ZH/fe064us1aZZW12gebsAq35RVaXVlicr1Gb\nW6J9fJru8WmYnSW2vEKy1STZalKoLcHx849hBTjQ/1hLZ6gXh2jl85BK49JJXDZLd6iAHxqinUow\nlEmTiTvSqTi58WEKk2MMlccY2TxOYXKUeCHXG3urTbfZJJ7PnfWaRN12m/bSMolCjlj60j3VQPV3\nlXVrsW4tCt2RWfRptVpBDyEQit2KzWDdaqxbi2q3Cb9BzV3nHMlSkWSpSOHqy877fO89nZVVWkvL\nNKfnWK3MUjs+yxoxmqk0a4kkq8dnWHvyIN0Dh+ksLOJXG1Cvk1pZIb3WIL3WgNnzj60D1Pof69eW\nurEYzntc/y5knXSa9sQE3ckJ4t6TWF4mtryMr9Zwyyu9cScTuKuvJHPj9QxdfyWlbZsY3b6J7Gjp\njAtGsVSSRKk4sAtcq/6usm4t1q1FoTsyiz7lcjnoIQRCsVuxGaxbjXVrUe024Xepzl3nHIlCnkQh\nT3brJCVg8wb/rPee5mKV6vE5qrOLLNfq1Gt16rUV2gtV2otVWtVlWjgaXWitteguVoktLRGvVknV\namRXaqSaTQA68TjdWJzk2hrxI0fgyJFTxwp451jLZEk36vDw4zQefpwGcP4T2aCTStEaH6M7Nkas\nkCOez5FIp3CrK7hqDbeySmK4RKY8Tn7rJlKbxohtGic2PkpupEipkCGRSRFLJomlk8RSKVwyccaF\npEt1e19s1q3FurUodEdm0adSqbBz586ghzFwit2KzWDdaqxbi2q3Cb8ozl3nHOmREhMjpTNeUBrg\n0KFDZ+323lNvdVmorlLvQr3rWG12WJ6vUj98nOaRCg0XYzWTYzmbh2KB5FCBTCpBu7pM6+HHiT+6\nj8Sx4yQXFkgvLZJq1M/4WolWi/Rag/ix43Dsueew+f5Hs/9R3eDfQSeZZG2oRKM4RKdYIJXLkMln\nIeHIDZdIZDPEsylIp3GZNPFcluGJYca2jJEfKxFLpYilksSSCVwyQSyZxCUTdJtNuvU1OmtNksND\nJPLhuFVyFOf5Rli3FuuOrsgs+kT9PLyzUexWbAbrVmPdWlS7Tfipzt1zdTvnyKXi5MaLpz6wowQv\n237+H/6mq0/50ntPq+PB9Y4Kanc9K80Oy80Oy2sdavNVlg9XqB+foVFbpVlbobW6RjOXo5UvsJbO\n0FpcojM9h5udJ1ddpFBdJFddIra2hmu1iLfbJDptYp028XabeKtFbm6W3Nxzz2/r9D9OVwWeOX/d\nqT+rWKQ1MY4vFohn0r3FpG4Ht7oKq3Wcc8QKeeLFPMmhArmRIvmRIqlSgUR/sSyWStJaqtFcWKLb\naJLbsYXc5dvIbJmkObtA/UiFxvFpnHO9BahUisRQntTwEMmRIdKbxonnMuccZ1DzvDm3CLEYqZGh\nDf8Z3+3iYrFTv+c9zdkFUmPDz3nsXOz9fWE0js9QfehxRl5xI8nhjW/LQQvD9vadDpXPf43WwhLl\nd7zpeb03ziYM3S9WZBZ94vF40EMIhGK3YjNYtxrr1qLabcJPde4Osts5Ryrx7KlWyThkk3HGT/x3\nSrkA1215wT+/0/VU19qsNrs02h0a7S6dlTosLMLcAqvzS8wtrrC4sEptqUqs3cXXG/i1JvHmGrFW\nC7dax9eWiddqpOqrxNsd4p02sU6HWPfZz7vxBO1kkk48QWZ1hUStRrxWO2U8J45OOjk+oAU06F03\n6WLwpSFceRMuk4Z2G9od6HROfu6bTeLe4zptYqkUmS2byG2bJDFUoDW/xNrsAp3VOumJMTKbJ0ht\nGu0d3RRzEI/3FpzicXDQbbbo1Bt062skx4bJbi+T3bYZHLSXlmktVll68HHm/s/9LD/2FDhH6abr\n2PSmV1P6nhuIpZK4RLy3eFXIkSjkaM4tMvWFr1H5/F0s7zvAyCtupPy272P4lpcye9c9HPvMl1nZ\nf5Ds9s1sfe/b2Pojt5HdOtlr957Vpw+z8K2HWNz7CHS6pDaNkp4YY3lujmNHZlh58hCtxSo4h3OQ\nKBYoXHM5xeuuILt9M52VOu3aCr7rGf6e6xm6YTfEYiztfYRDf/YZ5r72LUo3Xc/WH7mNTf/0NbSX\nV1m47yGW9j7a/3Nd8J7sji2Mvvomhl56FbFE7z9Tu602ALFk4uR4G0cqLHzrIdZm5slfvoPCVTuJ\npVLMfPVuZv7+m6wePMrkbW9gx0+9k/TE6Lm3vfe05pdIDBVOvsaJ9/fJa4MtVGktVum22r2j1xIJ\n4rks6U1jxLPpk4tqK/sP0ajMEM+miedztBaqHPurLzHz1Xug2yU1Mcp1H/8Qk2/7vnNeh6t+dIqZ\nv/8miWKeTW95LYl87uRjreoyrYUlUqPDxAvPvTh8/UiFyufvovrdfWS3lclfsZP8lTvJX7mD5FDh\n3G+Exhpz/+d+Fvc+QiyVpHjtFRSuvYLUSIlOvUGn3iCezZAsnbqg3Wms0amvnXcBxnc6vffBadq1\nFZb3H2T58QOsPP0M+St2UH77nuccCTj7tXvZ9xt/TO2R/QA8/mv/ka3vuY2JPa+k9vjTVB/aR7u2\nwuRtr2fzD/3T54zzbGKxGN12G7qeWCq5oT8DsPTQPuqHj1O4+jLyl207Y1u33aY1v9RbcA3w35fO\ne3/+Z10gd999t7/mmmsuys8+12G2UabYrdgM1q3GurVEqXvv3r0P7Nmz5+agx2GeZftfF551n1nX\ne5bXescBOQfeQ6vjabQ7vVPe6m3m6y3mV1vQ7ZKuVknOztKqrtBYqVNfadByMZrpDM10hk7Xw8oK\nbmWVTm2Z1tIyfnmV1FqddKNOqlEn0WnTyORo5PJ04glKC7OMzE5TXFpgpThEtTRKrTQCDuKdDvF2\ni3SjQaa+QmZ1hUJtiXjnTMcuBc+nUuC7uP7Cx4vhEnF8u9/pHLFMure41ungmxf2QraJYp50eZyV\n/YeeO45sBl9vnPfPJ0dKNOcX6SyvApAcLZGeGKVVXWbt+MyGxuFSScrf/wbihRzt2gqd5VV8p4Pv\ndOm22qxNzdI4OkV3rUksm6Z047UM33wDizOzcKhC7ZH9tGsr5x2ri8doLZ59SdIlE2R3bGH1qd6x\ncJtufR3jb3gFLhHHxeJ0m0069Qbt6gqzX/8WSw88cvLPxnNZJm97PamJUea/uZfqd5+AbvdkX2ps\nmNRo7+6HraUa1QcfP+s40pvGyF2+nUQh17tuVzxOZ7VBu9ZbbFx56hnonn9tIF0ep3D1ZcRzWZb3\nHWD14FHodilcfRljr7uZ4vW7qR+psPLUM9QPHmVtdoHW/FJvYbQ8Tv7KneR2bqFxbIblJw7QODr1\nnNeIF3JseddbyG7fzMr+g1Qf2U/t4d5iT2brJPkrdjD39fvOOsZYJsXEnleTKObBe1w8TnpynOz2\nMsmRIaoPPcHCvQ+y9O1H6aybj6mxYQrXXkHxmstJjpR6v8SARKlA/rLtZHdtpfrdfRz60786ZTvF\nsmnyV+wgNTZMcqSEi8dY3neAlf2HevMrnSJ3+fbegtYPvJHN79hz3r/nF+Js+2CRWfRZXV0ll8ud\n/4kRo9it2AzWrca6tUSp2xZ9Lj22/3XhWXdwTpze1u54Wt0u7W7v9Ld219Nsd6m3u6w2O6y2utRb\nHVZaXRqtDsl4jHTCkYrH6HQ9a+0uax1Ps9mmMzePr0zjmy268Tg+nqAbj9ONJ/DxGMttT7UbY7Hl\nWVttkFuYJ7+4QKpRp54vUM8XaaVS5GpVCtVF8ss1Yv2jV2K+27uLW7f3z04iQSuZopNIkFupMbQw\nz9DiPN7BWjZHI5NjcWwTz1xxNce37yLW6bDj6X1ctu8RRuamcd0usW6XRLtFaq1Baq1BNxbnwFXX\ns//6l1HZupNd+x9l96PfoXzkEEd3XcljN/4TnrniarYefJIbHribKx99kETn2YWklUKRYzuu4PjO\ny+hkshRqS+SXa3SSCaqTm6luKrOYK9BodXEecis1xqeOMjF1jNJyFZ/N0M3lSLZbjDy5n/xsb1Gm\nnsvz3Ztfw76X3MS2A/u5fu89bKocpZ1Icnz7LmYvv5JWqUQsHiOOY/TYYSb27yM3/ewigI85wOH6\nCx0A7XyehSt2szw8QmlmikLlOInVVRovvQH/ypuJbZ6k+9dfIvet+0/ePe9cfD6HW1k982PpNLFS\nkcTwEPF0Et9q02216Syv0J1b7B0NBrh8jtQVO0hvLUOzhV+t470n9ppbqL3+dSwms4x+5St0/+ST\n+LO81knpNOlX34xbWKTxnUdOfSwRJz4xhl+q0l197uJZLJuh9H2vIv/Kl9GozNE48AzNpw/TOngY\nv9Y89+vGY2Sv3U38JddAp0v3qYOs7T9At3+ETzyTplVbpltfO+3PxXuLiuf7+WfhUkkKV+6kcPVl\n5HZtY+4b97N433ef+7x8jomf/lGu/enbaSWTfOfuR6n8+f/CPXMEf/kuMtftppBO4P/mq7Tu+87z\nG0QshnMO/zwWgBOlIsM3XcfyEwfPuHB1QnK4eMqi4BUfeh+7f/H9z298GxT5RZ8jR46wbdu2i/Kz\nL2WK3YrNYN1qrFtLlLpt0efSY/tfF551azlTt/eeru8tQnW6vUWnju993un2jnrqek/H9z8/+T1O\nPm+p0e4d/bTaO9ImnYiRivdO11nrdFlr9xanGu0ua+0uDihlEpSyCXLJ3qkiXe9pdjy1tTa1Roda\ns43vv2a766k2OlTX2lQb7d41khwkuh2S3S7xGDgcq7EEy63e65xLOhFjIp8kFY8xv9pisXHmI5CK\ni/MMz89S2XEZk2MFtpcytLpdqo0O7elZZhJZat2zn96Ury6SbDWp53rXpHJAdnWZ3HKNbizO/Pgm\n2MC1iUpzM1z+xMN04one0WOpdG9Rz8XoxhyrhSGqw6O00hmyKzU2Hz5I+chB2okk01u2M715G6vF\n0tlfwHvSjTrxdovVwlDv8LbzKCwtcOO9XyfTWCXW6eK6XTqJBO1kilYyxczmrRy46nraqTQAw3PT\nXP3QAyTaLY7s2s3RnZeffCzRajLUWCFZq5FZWcZ5z5Fdu2mnUs994W6X4tICw/MzJFot4t0OsU6H\nVjJFM5Olnc0yNzJx5j9Lb9sXUnGSeBLTM+SPHemdDrZpMwvjm/A4Nh85yOVP72NsfoaVsXFqk5Ms\nT0zSHCrRLBbxmQyFhTlK0xXyc7PUSyWWyluojU2QSiXJp2Jkk3G63hM78AwT3/gG9dUGlbFJ5ibK\nTG/ZTit97utvnVCan2Xbgf0470knYxTjjvTiPJnZOVLVJeYmyjyz8wqO7ryCdqHAaD7NSCZOaWmB\noWNHKBw9QrfeYK3jWWt70ss1SnMzlOamaeXyHHv991F9/WtJ5nO0ux5XrZGcmiK1vEJypUas3aa+\neTP17dvpZnMkGqvkKhUylQrX3HINe269OLtJZ9sHi8w1fQa5eHUpUexWbAbrVmPdWlS7Tfipzl3r\n1nKmbucccQfx2Pn/Qz8sekdNdXsLU13PkaNHKW/eQtd7UvEYxXT8lGvItDpdqmsdGq3e9aCaHU/M\n9f5ukjHHlqE06cTpizO9C5V3ur2Fqmand6RWq9ul0epSb3VZbfVOB6z3/9lod2l1Pc1Ol1TMUR5K\nUy6kKGYSrKz1Frpqa/0Lm6+1WWt7JvJJJou7GM+9mnbX0+gvnjXa3f54T3ze6X8+QuO6bTTar6bT\nXOPqkQK3ZBI0O57p5SbTy00a7S6peG9hLhWPkUo4kvFhAFabHVaavZ91YiEQYCKfYrKYYjibYLrW\n5JnFNN8a+UGK6TibCik2FVIU03HSiRjpeIyJVpfN/VMgV1tdmqXtHNi5FYcjl4xxdSpOp+uZr7eY\nW4H5ZAqKI8QcJOMxsvHe330yHiPZ/zwec3S6ntZIlub2zax0uyePjjvxT4BEDCZzvbEC1NZ6Tb2/\n097CIwCFEbhqBIBU3JFP9I6SqVx2JUd3XXnmydUEmk2IF2FzETbvfvaxaovelbvWiQ/D69/We7lU\nnF2jGTbFHJVab1vEY45rN+V5SbnA5qEUc6stZpZ7f28rrQ6r4znmdm5hbqVF6xynrMVc74y2qeUm\nU8sAGZi4svdxPtMn7ovYl5qA0QlYfxmpmRaw1H+xMdgyRm588vw/+wKLzKLPxMTZbqoZbYrdis1g\n3WqsW4tqtwk/1blr3VpUuhMxRyL27MVmL986SSZz9gvbJuMxxnIbvxvYevGYYzi78YvmDlKj0SCT\n2dgRJUE6cZTXiYWdF/Nz2l1Pp7lGNpt9zuPe9xbNamsdOl1PJhEjk4yRisdOed0Tz1ttdel0/ckj\n204c6dbxp37vxMKpw7HWefa0zHisd7H6TP/IsrFc8pTFxk7X4+nN1420LdV7R9Ql4o5M/2i63lF1\nvfEv1FZZ7cZZqLdodfzJcWYScYYycYbSCRIxd3LhsdHq/V0sN3sLjCf+/uOxE38Pz14ayePxvn+B\net/7O9o+PPi5FZlFn6mpKckL6il2KzaDdauxbi2q3Sb8VOeudWuxbi1h6Y45Rybx4o80izlHKu44\nND19xm7nHNlknGzy3Hef2ujzXqzns8AVc46RXJKR3NkXGKvzM+zcuZOtpfSFGN4l6YUtzV6CCoXz\n3IIuohS7FZvButVYtxbVbhN+qnPXurVYtxbr1qLQHZlFH2OMMcYYY4wxxhjzrMgs+iwvLwc9hEAo\ndis2g3WrsW4tqt0m/FTnrnVrsW4t1q1FoTsyiz6Tk4O/CvalQLFbsRmsW411a1HtNuGnOnetW4t1\na7FuLQrdkVn0mZmZCXoIgVDsVmwG61Zj3VpUu034qc5d69Zi3VqsW4tCd2QWfdbfxk2JYrdiM1i3\nGuvWotptwk917lq3FuvWYt1aFLojs+gzOjoa9BACodit2AzWrca6tah2m/BTnbvWrcW6tVi3FoXu\nyCz6KByWdSaK3YrNYN1qrFuLarcJP9W5a91arFuLdWtR6I7Mos/Q0FDQQwiEYrdiM1i3GuvWotpt\nwk917lq3FuvWYt1aFLojs+jT6XSCHkIgFLsVm8G61Vi3FtVuE36qc9e6tVi3FuvWotBG1x90AAAL\nS0lEQVQdmUWflZWVoIcQCMVuxWawbjXWrUW124Sf6ty1bi3WrcW6tSh0R2bRp1wuBz2EQCh2KzaD\ndauxbi2q3Sb8VOeudWuxbi3WrUWhOzKLPpVKJeghBEKxW7EZrFuNdWtR7Tbhpzp3rVuLdWuxbi0K\n3Rta9HHO3eqc2+ece9I590tneDztnPsf/cfvdc7tutADPZ/Pfe5zg37JS4Jit2IzWLca69ai2m3C\nT3XuWrcW69Zi3VoUus+76OOciwOfAN4KXAe81zl33WlPuwNY8N5fCfwB8NsXeqDn89nPfnbQL3lJ\nUOxWbAbrVmPdWlS7Tfipzl3r1mLdWqxbi0L3Ro70uQV40nv/tPe+CXwaeMdpz3kH8Of9zz8D7HHO\nuQs3zPNrt9uDfLlLhmK3YjNYtxrr1qLabcJPde5atxbr1mLdWhS6nff+3E9w7t3Ard779/e//jHg\nFd77D657zsP95xzpf/1U/zmz63/WF7/4xdrx48dPLjQNDQ3NjI6OnvKcF2p+fn78Qv2sMFHsVmwG\n6w56HINm3Voi1r1zz549E0EPwjzL9r8uPOvWYt1arFtLxLrPuA+WGOQIbrvttuIgX88YY4wxRp3t\nfxljjDG6NnJ611Fg+7qvt/W/d8bnOOcSQAmYuxADNMYYY4wxxhhjjDHP30YWfe4DdjvnLnPOpYDb\ngTtPe86dwE/0P3838FV/vvPGjDHGGGOMMcYYY8xFc97Tu7z3befcB4EvA3Hgz7z3jzjnfh2433t/\nJ/Bfgb9wzj0JzNNbGDLGGGOMMcYYY4wxAdnIkT5477/ovb/Ke3+F9/43+9/7WH/BB+99w3v/Hu/9\nld77W7z3T1/MQa/nnLvVObfPOfekc+6XBvW6g+ac2+6cu8s596hz7hHn3M/2vz/qnPs759z+/j9H\ngh7rxeCcizvnvu2c+3z/68ucc/f2t/v/6B+FFinOuWHn3Gecc4875x5zzr1KYXs7536+P8cfds79\nd+dcJorb2zn3Z8656f6F8E9874zb1/X8h37/Q865m4Ib+Ytzlu7f7c/zh5xz/9s5N7zusY/0u/c5\n594SzKhfvDN1r3vsF5xz3jk33v86MtvbRJvtg0n8O9n2v2z/K3LbW3EfzPa/tPe/NrToc6lyzsWB\nTwBvBa4D3uucuy7YUV00beAXvPfXAa8EPtBv/SXgK9773cBX+l9H0c8Cj637+reBP/DeXwksAHcE\nMqqL64+Av/HeXwPcSK8/0tvbObcV+BngZu/9DfSOLrydaG7vTwK3nva9s23ftwK7+x//F/AnAxrj\nxfBJntv9d8AN3vuXAk8AHwHo/467Hbi+/2f+uP97P4w+yXO7cc5tB94MPLPu21Ha3iaibB9MZh/M\n9r9s/yuK2/uT6O2DfRLb/zpJbf8r1Is+wC3Ak977p733TeDTwDsCHtNF4b0/7r3f2/+8Ru9fQFvp\n9f55/2l/DvxgMCO8eJxz24DvB/60/7UD3gh8pv+UyHU750rA99I7dRLvfdN7v4jA9qZ32mnW9S4K\nnwOOE8Ht7b3/Or3TYdc72/Z9B/D/+p57gGHn3ObBjPTCOlO39/5vvfft/pf30LthAPS6P+29X/Pe\nHwCepPd7P3TOsr0B/gD4RWD9dfAis71NpNk+WMT/nWz7X7b/RUS3t+I+mO1/PYfU/lfYF322AofX\nfX2k/71Ic87tAl4O3AtMeu+P9x+qAJMBDeti+kN6b8pu/+sxYHHdL6kobvfLgBngv/UPq/5T51ye\niG9v7/1R4N/TW3U/DiwBDxD97X3C2bav0u+69wFf6n8e6W7n3DuAo977B097KNLdJjIk56nYPpjt\nf9n+V9S393rq+2C2/xXh7rAv+shxzhWA/wX8nPe+uv6x/h3TInXXNOfc24Bp7/0DQY9lwBLATcCf\neO9fDqxw2qHEEd3eI/RW2S8DtgB5znBIpoIobt/zcc79Mr3TKP4y6LFcbM65HPBR4GNBj8UYszFK\n+2C2/2X7X4juf0E0t/G52P5X9IV90ecosH3d19v634sk51yS3s7GX3rvP9v/9tSJw876/5wOanwX\nyWuAtzvnDtI7dPyN9M61Hu4ffgrR3O5HgCPe+3v7X3+G3k5I1Lf3m4AD3vsZ730L+Cy9ORD17X3C\n2bZv5H/XOed+Engb8KP9nS2IdvcV9HauH+z/ftsG7HXOlYl2t4kOqXkquA9m+189tv8V7e29nuQ+\nmO1/aex/hX3R5z5gd//K8il6F5y6M+AxXRT986j/K/CY9/731z10J/AT/c9/AvjrQY/tYvLef8R7\nv817v4ve9v2q9/5HgbuAd/efFsXuCnDYOXd1/1t7gEeJ+Pamd1jxK51zuf6cP9Ed6e29ztm2753A\nj/fvKvBKYGndIcih55y7ld4pBG/33q+ue+hO4HbnXNo5dxm9C+t9K4gxXmje++967zd573f1f78d\nAW7qv/cjvb1NZNg+WIT/nWz7X7b/hdb+Fwjug9n+l9D+l/c+1B/AbfSuNv4U8MtBj+cidr6W3mGG\nDwHf6X/cRu/86q8A+4G/B0aDHutF/Dt4A/D5/ueX0/vl8yTwV0A66PFdhN6XAff3t/nngBGF7Q38\nGvA48DDwF0A6itsb+O/0zptv0fsXzh1n276Ao3eXnKeA79K7u0bgDRew+0l651Cf+N32n9c9/5f7\n3fuAtwY9/gvZfdrjB4HxqG1v+4j2h+2DRf/fyf1+2/8S2NYq+1/9Vrl9MNv/0t7/cv1AY4wxxhhj\njDHGGBMhYT+9yxhjjDHGGGOMMcacgS36GGOMMcYYY4wxxkSQLfoYY4wxxhhjjDHGRJAt+hhjjDHG\nGGOMMcZEkC36GGMuCc65Xc4575xLBD0WY4wxxhgFtv9lTPTZoo8xxhhjjDHGGGNMBNmijzHGGGOM\nMcYYY0wE2aKPMeasnHNbnHP/yzk345w74Jz7mf73f9X9/+3cz4vVVRjH8ffHcUihSCRcTI0FRSII\nBiKptAhcmGIJLmqR9h8ELsSQIKSdoC5biTt/xLRQ3LkSbGEKwhBBLmSYxqLFYMpgaOE8Le4ZuIQT\nJNeZ8ev7BV+4POcczvdsvjw8556TfJfk2yQzSW4k2dg3bn2Sy0nuJvkpyUd9bSuTHE8ymeReku+T\nrOyb9tMkvySZTvLlAi5XkiRp0Zl/SRokiz6SHivJMuAiMA68CmwHDiTZ0brsAcaA1cAZ4HyS4STD\nbdwlYA3wOXA6ybo27hiwCdjWxh4CZvumfg9Y1+b7Ksn6p7ZISZKkJcT8S9KgpaoW+x0kLUFJ3gXG\nqmptX+ww8DYwCXxQVVtafBnwK/Bx6zoGjFTVbGs/C9wEvgbuA1uqavxf870BTACjVXW7xa4BJ6rq\n3FNapiRJ0pJh/iVp0LylXdJ8XgdGktztiw0BV+glHVNzwaqaTXIbGGmhqbmEo5mkt1v1CrACuPUf\n8/7e9/tP4MUnXoEkSdKzxfxL0kB5vEvSfKaAiapa1fe8VFW7WvvoXMe20/Qa8Ft7Rltszlp6O1HT\nwAPgzQVZgSRJ0rPF/EvSQFn0kTSfa8BMki/a5X9DSTYk2dzaNyXZm2Q5cAB4CFwFfqC3Q3SonTF/\nH/gQONd2n04BJ9olhUNJtiZ5YcFXJ0mStPSYf0kaKIs+kh6rqh4Bu4F36J31ngZOAi+3LheAT4A/\ngP3A3qr6u6r+opdk7GxjvgE+q6qf27iDwI/AdeAOcBS/RZIkSeZfkgbOi5wl/W9JjgBvVdW+xX4X\nSZKk54H5l6QnYXVXkiRJkiSpgyz6SJIkSZIkdZDHuyRJkiRJkjrIf/pIkiRJkiR1kEUfSZIkSZKk\nDrLoI0mSJEmS1EEWfSRJkiRJkjrIoo8kSZIkSVIHWfSRJEmSJEnqoH8AyGZAgkU4KgQAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0a11ba1c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from autonomio.commands import train\n",
    "\n",
    "%matplotlib inline \n",
    "\n",
    "train([2,9], 'Survived', titanic, dropout=0, epoch=150, flatten='none', loss='logcosh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point we're not too interested in the accuracy of the model, but given how little work we've done, 80% is not too bad for a start. More or less, we're working \"out of the box\". Before going too deep, let's go over a few simple examples. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running an LSTM Model to Predict Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual_max</th>\n",
       "      <th>actual_min</th>\n",
       "      <th>actual_std</th>\n",
       "      <th>diff_max</th>\n",
       "      <th>diff_min</th>\n",
       "      <th>diff_std</th>\n",
       "      <th>pred_max</th>\n",
       "      <th>pred_min</th>\n",
       "      <th>pred_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.29011</td>\n",
       "      <td>-0.515472</td>\n",
       "      <td>0.169486</td>\n",
       "      <td>0.519</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.190953</td>\n",
       "      <td>-0.454584</td>\n",
       "      <td>0.144369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   actual_max  actual_min  actual_std  diff_max  diff_min  diff_std  pred_max  \\\n",
       "0     0.29011   -0.515472    0.169486     0.519     0.134     0.174  0.190953   \n",
       "\n",
       "   pred_min  pred_std  \n",
       "0 -0.454584  0.144369  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2FrVaTXFxMS6XC5PJREVFBYGBgRQVFVFfX49SqaSkpAQAo9FIZWWl57jZbEah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\ncgw5BSijwomaMr7VfcLOHkbYuOHYG40cfv3TTru2t2OuqAFAaGg8xZ4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0N3qyPk6rjZx5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\n0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qWDhBBfAg16DlQ0otfBA5OSmJZ+5u4PDTLoaQpsu7LWYu7+HllX5WO3NrPnwWcBSPnV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\n1Cb5qM2V+fRosqf58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UWxsbHuboLohuSjNslHbZKP2iQftUk+apN81Cb5qM2V+Ugh24tM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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f09c5498a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EkQwAAABAKYQMAAAAQCmEDAAAAEAphAwAAABAKYQMAAAAQCmEDAAAAEAphAwA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f09c72f53d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from autonomio.commands import data, train\n",
    "\n",
    "# loads the bitcoin price dataset from autonomio datasets\n",
    "btc = data('bitcoin_price')\n",
    "\n",
    "# make plots visible in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# run a train command using LSTM model\n",
    "train(btc['price'], model='lstm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we are getting a good starting point result, this time with zero parameter configuration. We just input the data (bitcoin daily historical price) and choose the LSTM as the model we want to use, and that's it. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with Unstructured Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the bigger headaches associated with data science is dealing with various kinds of text data. In this example we'll ingest tweets for sentiment prediction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>neg</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3131</th>\n",
       "      <td>RT @ukinfiji: Congratulations @maryroko  for b...</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.8020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32248</th>\n",
       "      <td>RT @vegasWATCH: Didn't even bother photoshoppi...</td>\n",
       "      <td>0.150</td>\n",
       "      <td>-0.1190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4813</th>\n",
       "      <td>Knicks: Derrick Rose out 3-6 weeks after \"unco...</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.1027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15969</th>\n",
       "      <td>Can you recommend anyone for this #job? Food S...</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.3612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84466</th>\n",
       "      <td>🙌 YASSS It's time for a great show Gigi💋babe:W...</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.6249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text    neg     pos\n",
       "3131   RT @ukinfiji: Congratulations @maryroko  for b...  0.340  0.8020\n",
       "32248  RT @vegasWATCH: Didn't even bother photoshoppi...  0.150 -0.1190\n",
       "4813   Knicks: Derrick Rose out 3-6 weeks after \"unco...  0.112  0.1027\n",
       "15969  Can you recommend anyone for this #job? Food S...  0.143  0.3612\n",
       "84466  🙌 YASSS It's time for a great show Gigi💋babe:W...  0.255  0.6249"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from autonomio.commands import data, train\n",
    "\n",
    "# loads the tweet sentiment dataset from autonomio datasets\n",
    "tweets = data('tweet_sentiment')\n",
    "\n",
    "# remove tweets that are not negative and sample down to ~2,000 tweets\n",
    "tweets = tweets[tweets.neg > 0].sample(frac=.1)\n",
    "\n",
    "tweets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a dataset with just negative tweets, where one column is text, and two columns are values related with sentiment. The sentiment classification is provided by NLTK, a solution widely used in academic literature and industry alike."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batch_size</th>\n",
       "      <th>dropout</th>\n",
       "      <th>epochs</th>\n",
       "      <th>features</th>\n",
       "      <th>ind_var</th>\n",
       "      <th>layers</th>\n",
       "      <th>max_neurons</th>\n",
       "      <th>n=</th>\n",
       "      <th>network_scale</th>\n",
       "      <th>shape</th>\n",
       "      <th>y_transform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0.2</td>\n",
       "      <td>150</td>\n",
       "      <td>300</td>\n",
       "      <td>neg</td>\n",
       "      <td>3</td>\n",
       "      <td>360</td>\n",
       "      <td>2165</td>\n",
       "      <td>350730000</td>\n",
       "      <td>funnel</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  batch_size dropout epochs features ind_var layers max_neurons    n=  \\\n",
       "0         10     0.2    150      300     neg      3         360  2165   \n",
       "\n",
       "  network_scale   shape y_transform  \n",
       "0     350730000  funnel         0.9  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RqQWJ+53+4wuEeHaply9K6imqaWNHi+IdVg7PS+DYIalMK0iMZGjyBbj/k63M7xiZsD/j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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f09bf942dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train('text','neg', tweets, loss='logcosh', dropout=0.2, epoch=150, flatten=.9, w_reg_values=[0.02,0.02])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally 85% accuracy is considered the upper boundary of the sentiment classification problem, as even human coders rarely agree with each other above 85%. In fact, studies have shown that even a single human coder working on the sentiment classification may have up to 20% disagreement across a larger sample without frequent calibration inverval."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performing a Grid Hyperparameter Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total tries in this scan: 72\n",
      "Scan started on: 06:22\n",
      "Estimated finish: 06:28\n"
     ]
    }
   ],
   "source": [
    "from autonomio.commands import data, hyperscan\n",
    "\n",
    "# loads the pima indians diabetes dataset from a file\n",
    "diabetes = data('diabetes.csv','file',header=None)\n",
    "\n",
    "# run a grid search covering some obvious options\n",
    "result = hyperscan([0,8],'i', diabetes, \n",
    "                   epochs=150, \n",
    "                   scan_mode='selective',\n",
    "                   batch_sizes=[12,14],\n",
    "                   layers=[2,5], \n",
    "                   losses='logcosh', \n",
    "                   optimizers=['adam','nadam'],\n",
    "                   activations=['linear','softsign','elu'],\n",
    "                   shapes=['brick','diamond'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train_acc</th>\n",
       "      <th>train_acc_mean</th>\n",
       "      <th>train_acc_min</th>\n",
       "      <th>train_acc_max</th>\n",
       "      <th>train_acc_std</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_loss_mean</th>\n",
       "      <th>train_loss_min</th>\n",
       "      <th>train_loss_max</th>\n",
       "      <th>train_loss_std</th>\n",
       "      <th>...</th>\n",
       "      <th>activation_out</th>\n",
       "      <th>loss</th>\n",
       "      <th>optimizer</th>\n",
       "      <th>epochs</th>\n",
       "      <th>layers</th>\n",
       "      <th>features</th>\n",
       "      <th>dropout</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>max_neurons</th>\n",
       "      <th>network_scale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.645914</td>\n",
       "      <td>0.645914</td>\n",
       "      <td>0.429961</td>\n",
       "      <td>0.661479</td>\n",
       "      <td>2.958391e-09</td>\n",
       "      <td>0.153596</td>\n",
       "      <td>0.153596</td>\n",
       "      <td>0.144191</td>\n",
       "      <td>0.244953</td>\n",
       "      <td>8.409436e-10</td>\n",
       "      <td>...</td>\n",
       "      <td>sigmoid</td>\n",
       "      <td>logcosh</td>\n",
       "      <td>adam</td>\n",
       "      <td>150</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>2073600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.657588</td>\n",
       "      <td>0.657588</td>\n",
       "      <td>0.552529</td>\n",
       "      <td>0.671206</td>\n",
       "      <td>9.123927e-10</td>\n",
       "      <td>0.148532</td>\n",
       "      <td>0.148532</td>\n",
       "      <td>0.141534</td>\n",
       "      <td>0.192596</td>\n",
       "      <td>2.583403e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>sigmoid</td>\n",
       "      <td>logcosh</td>\n",
       "      <td>adam</td>\n",
       "      <td>150</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>2073600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.663424</td>\n",
       "      <td>0.663424</td>\n",
       "      <td>0.657588</td>\n",
       "      <td>0.663424</td>\n",
       "      <td>2.665621e-09</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.148287</td>\n",
       "      <td>1.098756e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>sigmoid</td>\n",
       "      <td>logcosh</td>\n",
       "      <td>adam</td>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>3110400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.659533</td>\n",
       "      <td>0.659533</td>\n",
       "      <td>0.624514</td>\n",
       "      <td>0.663424</td>\n",
       "      <td>9.942123e-10</td>\n",
       "      <td>0.147688</td>\n",
       "      <td>0.147688</td>\n",
       "      <td>0.145920</td>\n",
       "      <td>0.160361</td>\n",
       "      <td>8.342318e-10</td>\n",
       "      <td>...</td>\n",
       "      <td>sigmoid</td>\n",
       "      <td>logcosh</td>\n",
       "      <td>adam</td>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>3110400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.653696</td>\n",
       "      <td>0.653696</td>\n",
       "      <td>0.599222</td>\n",
       "      <td>0.653697</td>\n",
       "      <td>1.932551e-09</td>\n",
       "      <td>0.150220</td>\n",
       "      <td>0.150220</td>\n",
       "      <td>0.149833</td>\n",
       "      <td>0.171242</td>\n",
       "      <td>1.089007e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>sigmoid</td>\n",
       "      <td>logcosh</td>\n",
       "      <td>adam</td>\n",
       "      <td>150</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>4147200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   train_acc  train_acc_mean  train_acc_min  train_acc_max  train_acc_std  \\\n",
       "0   0.645914        0.645914       0.429961       0.661479   2.958391e-09   \n",
       "1   0.657588        0.657588       0.552529       0.671206   9.123927e-10   \n",
       "2   0.663424        0.663424       0.657588       0.663424   2.665621e-09   \n",
       "3   0.659533        0.659533       0.624514       0.663424   9.942123e-10   \n",
       "4   0.653696        0.653696       0.599222       0.653697   1.932551e-09   \n",
       "\n",
       "   train_loss  train_loss_mean  train_loss_min  train_loss_max  \\\n",
       "0    0.153596         0.153596        0.144191        0.244953   \n",
       "1    0.148532         0.148532        0.141534        0.192596   \n",
       "2    0.146000         0.146000        0.146000        0.148287   \n",
       "3    0.147688         0.147688        0.145920        0.160361   \n",
       "4    0.150220         0.150220        0.149833        0.171242   \n",
       "\n",
       "   train_loss_std      ...        activation_out     loss  optimizer  epochs  \\\n",
       "0    8.409436e-10      ...               sigmoid  logcosh       adam     150   \n",
       "1    2.583403e-09      ...               sigmoid  logcosh       adam     150   \n",
       "2    1.098756e-09      ...               sigmoid  logcosh       adam     150   \n",
       "3    8.342318e-10      ...               sigmoid  logcosh       adam     150   \n",
       "4    1.089007e-09      ...               sigmoid  logcosh       adam     150   \n",
       "\n",
       "   layers  features  dropout  batch_size  max_neurons  network_scale  \n",
       "0       2         8        0          12            9        2073600  \n",
       "1       2         8        0          13            9        2073600  \n",
       "2       3         8        0          12            9        3110400  \n",
       "3       3         8        0          13            9        3110400  \n",
       "4       4         8        0          12            9        4147200  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['train_acc' 'train_acc_mean' 'train_acc_min' 'train_acc_max'\n",
      " 'train_acc_std' 'train_loss' 'train_loss_mean' 'train_loss_min'\n",
      " 'train_loss_max' 'train_loss_std' 'test_acc' 'test_acc_mean'\n",
      " 'test_acc_min' 'test_acc_max' 'test_acc_std' 'test_loss' 'test_loss_mean'\n",
      " 'test_loss_min' 'test_loss_max' 'test_loss_std' 'shape' 'activation'\n",
      " 'activation_out' 'loss' 'optimizer' 'epochs' 'layers' 'features' 'dropout'\n",
      " 'batch_size' 'max_neurons' 'network_scale']\n"
     ]
    }
   ],
   "source": [
    "print result.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, in return we get a dataframe with the results of the hyperparameter scan. Let's take a loot at the findings from this small scan. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing Hyperparameter Grid Search Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One part of hyperparameter scanning (grid search) is the convinience at which the initial configuration can be made, and another is how results can be analyzed. Below are some examples of the in-built capabilities that streamline the analysis part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>test_acc_max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>activation</th>\n",
       "      <th>batch_size</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">elu</th>\n",
       "      <th>13</th>\n",
       "      <td>0.803150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.799213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">softsign</th>\n",
       "      <th>13</th>\n",
       "      <td>0.775591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.736220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">linear</th>\n",
       "      <th>12</th>\n",
       "      <td>0.708661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.677165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       test_acc_max\n",
       "activation batch_size              \n",
       "elu        13              0.803150\n",
       "           12              0.799213\n",
       "softsign   13              0.775591\n",
       "           12              0.736220\n",
       "linear     12              0.708661\n",
       "           13              0.677165"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from autonomio.hyperstats import hyper_descriptive\n",
    "\n",
    "# provides a groupby table based on selections\n",
    "hyper_descriptive(result,['activation','batch_size'],'test_acc_max','max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Gi0Nm2iMszEMW5qEHSTkYv0zm/o+dzzm/uJmsPXvePJiVxeE3XTvhdyV5BME8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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f09af0e08d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comparing any number of parameters in a grid\n",
    "from autonomio.plots.paramgrid import paramgrid\n",
    "\n",
    "paramgrid(result,'layers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MVnwMy62xsTHvIRQS3exo5kO3tGV1WfNbdnYd9wrfkRpqi/WfDMxrH7rZ0cyH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f09be933250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comparing two values of a parameter side-by-side\n",
    "from autonomio.plots.duaparam import duaparam\n",
    "\n",
    "duaparam(result,'activation','elu','softsign')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HP/4xCgsLUVlZic7OTojFYnC5XGZiu9mzZ6tNRbBv3z7U1dWBz+ejv78fAoEA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\ncCYiIiKSQP4BX8tT2aASgG4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f09c6c8b5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comparing four dimensions of data in one plot\n",
    "from autonomio.plots.quadparam import quadparam\n",
    "%matplotlib inline\n",
    "\n",
    "quadparam(result,'test_acc_max','train_acc_max','test_loss_max','train_loss_max')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to understand parameters is to also see what is not working, as opposed to just what is working. Also it's generally more valuable to understand how combinations work rathern than single parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>test_acc_max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>activation</th>\n",
       "      <th>layers</th>\n",
       "      <th>batch_size</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">elu</th>\n",
       "      <th>4</th>\n",
       "      <th>13</th>\n",
       "      <td>0.803150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>13</th>\n",
       "      <td>0.799213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>12</th>\n",
       "      <td>0.799213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>12</th>\n",
       "      <td>0.791339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>12</th>\n",
       "      <td>0.779528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">softsign</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>13</th>\n",
       "      <td>0.775591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.736220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>13</th>\n",
       "      <td>0.724409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.724409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>13</th>\n",
       "      <td>0.720472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elu</th>\n",
       "      <th>2</th>\n",
       "      <th>13</th>\n",
       "      <td>0.716535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear</th>\n",
       "      <th>2</th>\n",
       "      <th>12</th>\n",
       "      <td>0.708661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>softsign</th>\n",
       "      <th>4</th>\n",
       "      <th>12</th>\n",
       "      <td>0.696850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">linear</th>\n",
       "      <th>4</th>\n",
       "      <th>13</th>\n",
       "      <td>0.677165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>13</th>\n",
       "      <td>0.657480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>12</th>\n",
       "      <td>0.657480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>12</th>\n",
       "      <td>0.657480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.653543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              test_acc_max\n",
       "activation layers batch_size              \n",
       "elu        4      13              0.803150\n",
       "           3      13              0.799213\n",
       "           4      12              0.799213\n",
       "           2      12              0.791339\n",
       "           3      12              0.779528\n",
       "softsign   2      13              0.775591\n",
       "                  12              0.736220\n",
       "           3      13              0.724409\n",
       "                  12              0.724409\n",
       "           4      13              0.720472\n",
       "elu        2      13              0.716535\n",
       "linear     2      12              0.708661\n",
       "softsign   4      12              0.696850\n",
       "linear     4      13              0.677165\n",
       "           2      13              0.657480\n",
       "           4      12              0.657480\n",
       "           3      12              0.657480\n",
       "                  13              0.653543"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# provides a groupby table based on selections\n",
    "hyper_descriptive(result,['activation','layers','batch_size'],'test_acc_max','max')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Saving a Model for Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving a Trained Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Probably the most common usecase for deep learning is training a model, saving it, and then using it as part of some solution outside of the training environment / data. Let's go back to the Titanic example to do that. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model titanic_raw have been saved.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batch_size</th>\n",
       "      <th>dropout</th>\n",
       "      <th>epochs</th>\n",
       "      <th>features</th>\n",
       "      <th>ind_var</th>\n",
       "      <th>layers</th>\n",
       "      <th>max_neurons</th>\n",
       "      <th>n=</th>\n",
       "      <th>network_scale</th>\n",
       "      <th>shape</th>\n",
       "      <th>y_transform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>0.1</td>\n",
       "      <td>500</td>\n",
       "      <td>7</td>\n",
       "      <td>Survived</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>712</td>\n",
       "      <td>17088000</td>\n",
       "      <td>brick</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  batch_size dropout epochs features   ind_var layers max_neurons   n=  \\\n",
       "0         12     0.1    500        7  Survived      6           8  712   \n",
       "\n",
       "  network_scale  shape y_transform  \n",
       "0      17088000  brick        none  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from autonomio.commands import train\n",
    "\n",
    "train([2,9],\n",
    "      'Survived', \n",
    "      titanic, \n",
    "      dropout=0.1, \n",
    "      epoch=500,\n",
    "      layers=6,\n",
    "      batch_size=12,\n",
    "      flatten='none', \n",
    "      loss='logcosh',\n",
    "      activation='elu',\n",
    "      shape='brick',\n",
    "      validation_split=.1,\n",
    "      save_model='titanic_raw')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, this time we have envoked a few additional parameters in hopes to improve the model slightly, and also are saving the model to a file. Even though the score is ~80%, getting a similar score in Kaggle typically requires 50 to 100 lines of code.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using a Saved Model for Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "87 out of 418 rows dropped\n",
      "2 out of 11 columns dropped\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predictions</th>\n",
       "      <th>median_prediction</th>\n",
       "      <th>mean_prediction</th>\n",
       "      <th>std_prediction</th>\n",
       "      <th>min_prediction</th>\n",
       "      <th>max_prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>331.0</td>\n",
       "      <td>0.192387</td>\n",
       "      <td>0.424853</td>\n",
       "      <td>0.374319</td>\n",
       "      <td>0.072659</td>\n",
       "      <td>0.999907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   predictions  median_prediction  mean_prediction  std_prediction  \\\n",
       "0        331.0           0.192387         0.424853        0.374319   \n",
       "\n",
       "   min_prediction  max_prediction  \n",
       "0        0.072659        0.999907  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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3p62tLQsXLswXvvCFasc6KKW2tra2aoeAw9nLL7+c+vr67Ny5Mz169MjixYsz\nbNiwzJw5M5dddlkuu+yy3HXXXfnSl76UoUOHtr9u2rRp+Yu/+IsqJoeOU844+E2NjY0ZPnx4du3a\nVaXE0DnKHQv//M//nLvvvjtdunTJ6aefngceeCD9+vWrcnroGOWMg7fffjs333xz1qxZk65du6ZP\nnz5ZuHBh+2bScKSbPXt2nnzyyWzfvj29evXKSSedlC1btnzo8+DBBx/M/PnzkyTjx4/PwoUL07Vr\n12pGPyiKAwAAAKCQpQoAAABAIcUBAAAAUEhxAAAAABRSHAAAAACFFAcAAABAIcUBAAAAUEhxAAAA\nABRSHAAAAACFFAcAAABAof8LLFDfLZSTmDsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc07171910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from autonomio.commands import predictor, data, wrangler\n",
    "\n",
    "# loading the Titanic Kaggle test data from disc\n",
    "titanic_test = data('test.csv','file')\n",
    "\n",
    "# transforming the data in the same way as the data for the trained model\n",
    "titanic_test = wrangler(titanic_test, starts_with_col='Cabin', first_fill_cols='Cabin', treshold=.7)\n",
    "\n",
    "# making predictions with the saved model\n",
    "predictions = predictor(titanic_test,'titanic_raw', labels='PassengerId')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Plotting for Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it's useful for prediction evaluation to be able to see how specific items are ranked, for this Autonomio provides an interactive plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predictions</th>\n",
       "      <th>median_prediction</th>\n",
       "      <th>mean_prediction</th>\n",
       "      <th>std_prediction</th>\n",
       "      <th>min_prediction</th>\n",
       "      <th>max_prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>331.0</td>\n",
       "      <td>0.192387</td>\n",
       "      <td>0.424853</td>\n",
       "      <td>0.374319</td>\n",
       "      <td>0.072659</td>\n",
       "      <td>0.999907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   predictions  median_prediction  mean_prediction  std_prediction  \\\n",
       "0        331.0           0.192387         0.424853        0.374319   \n",
       "\n",
       "   min_prediction  max_prediction  \n",
       "0        0.072659        0.999907  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc06dfa7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from autonomio.plots.scatterz import scatterz\n",
    "\n",
    "# making predictions with interactive plot data output\n",
    "predictions = predictor(titanic_test,\n",
    "                        'titanic_raw',\n",
    "                        labels='PassengerId',\n",
    "                        interactive=True,\n",
    "                        interactive_x='Fare')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then using the output from the predictor, we can separately invoke the interactive plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el128401396209127359527293558922\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el128401396209127359527293558922\", {\"axes\": [{\"xlim\": [-14.649744000000002, 530.14974400000006], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"hanging\", \"h_anchor\": \"middle\", \"color\": \"#808080\", \"text\": \"Fare\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 18.0, \"position\": [0.5, -0.065535320088300208], \"rotation\": -0.0, \"id\": \"el12840139620906926160\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#808080\", \"text\": \"Prediction\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 18.0, \"position\": [-0.077004928315412197, 0.49999999999999994], \"rotation\": -90.0, \"id\": \"el12840139620910841680\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-14.649744000000002, 530.14974400000006], \"ylim\": [0.051838047504425049, 1.0319668281078338], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"none\", \"gridOn\": true}, \"visible\": true, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"none\", \"gridOn\": true}, \"visible\": true, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el12840139620912651536\", \"ydomain\": [0.051838047504425049, 1.0319668281078338], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 0.44731684579412084], [0.13260155, 0.5], [0.0, 0.5], [-0.13260155, 0.5], [-0.25978993539242673, 0.44731684579412084], [-0.3535533905932738, 0.3535533905932738], [-0.44731684579412084, 0.25978993539242673], [-0.5, 0.13260155], [-0.5, 0.0], [-0.5, -0.13260155], [-0.44731684579412084, -0.25978993539242673], [-0.3535533905932738, -0.3535533905932738], [-0.25978993539242673, -0.44731684579412084], [-0.13260155, -0.5], [0.0, -0.5]], [\"M\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"Z\"]]], \"edgecolors\": [\"#348ABD\"], \"edgewidths\": [0.5], \"offsets\": \"data01\", \"yindex\": 1, \"id\": \"el12840139620916480080\", \"pathtransforms\": [[5.0, 0.0, 0.0, 5.0, 0.0, 0.0]], \"pathcoordinates\": \"display\", \"offsetcoordinates\": \"data\", \"zorder\": 1, \"xindex\": 0, \"alphas\": [0.5], \"facecolors\": [\"#348ABD\"]}], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.755]}], \"height\": 576.0, \"width\": 576.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}, {\"voffset\": 10, \"labels\": [\" 1093 \", \" 1199 \", \" 1235 \", \" 1006 \", \" 1048 \", \" 966 \", \" 1110 \", \" 1292 \", \" 951 \", \" 1173 \", \" 945 \", \" 1131 \", \" 1206 \", \" 1263 \", \" 1076 \", \" 1306 \", \" 916 \", \" 1009 \", \" 961 \", \" 1303 \", \" 1164 \", \" 1248 \", \" 936 \", \" 992 \", \" 1014 \", \" 988 \", \" 1267 \", \" 906 \", \" 1042 \", \" 1266 \", \" 940 \", \" 969 \", \" 1216 \", \" 1289 \", \" 1004 \", \" 1033 \", \" 1100 \", \" 1242 \", \" 1074 \", \" 1287 \", \" 918 \", \" 1256 \", \" 904 \", \" 1283 \", \" 1155 \", \" 1246 \", \" 1294 \", \" 1260 \", \" 984 \", \" 1197 \", \" 1116 \", \" 1132 \", \" 1071 \", \" 1123 \", \" 913 \", \" 1218 \", \" 1114 \", \" 1070 \", \" 1301 \", \" 1254 \", \" 1241 \", \" 1068 \", \" 1105 \", \" 1138 \", \" 935 \", \" 1277 \", \" 1054 \", \" 1222 \", \" 1176 \", \" 1067 \", \" 1133 \", \" 1078 \", \" 1142 \", \" 1150 \", \" 1140 \", \" 1011 \", \" 1012 \", \" 907 \", \" 1253 \", \" 1154 \", \" 1167 \", \" 1259 \", \" 1188 \", \" 1112 \", \" 972 \", \" 1095 \", \" 981 \", \" 1130 \", \" 1144 \", \" 1017 \", \" 942 \", \" 926 \", \" 944 \", \" 1088 \", \" 1094 \", \" 1061 \", \" 1237 \", \" 929 \", \" 979 \", \" 1207 \", \" 958 \", \" 1089 \", \" 967 \", \" 1172 \", \" 1126 \", \" 1198 \", \" 990 \", \" 900 \", \" 1005 \", \" 1030 \", \" 955 \", \" 1049 \", \" 1137 \", \" 1051 \", \" 962 \", \" 971 \", \" 1057 \", \" 1073 \", \" 1299 \", \" 978 \", \" 1251 \", \" 960 \", \" 965 \", \" 941 \", \" 1010 \", \" 1227 \", \" 1304 \", \" 964 \", \" 1275 \", \" 1069 \", \" 924 \", \" 982 \", \" 1053 \", \" 898 \", \" 1223 \", \" 1225 \", \" 1185 \", \" 973 \", \" 920 \", \" 1162 \", \" 1296 \", \" 1183 \", \" 1175 \", \" 1134 \", \" 896 \", \" 938 \", \" 1128 \", \" 1098 \", \" 1045 \", \" 1270 \", \" 1205 \", \" 996 \", \" 1282 \", \" 1201 \", \" 1179 \", \" 1050 \", \" 1239 \", \" 1247 \", \" 1107 \", \" 1058 \", \" 910 \", \" 1297 \", \" 1023 \", \" 1200 \", \" 915 \", \" 911 \", \" 1295 \", \" 1001 \", \" 1086 \", \" 1190 \", \" 986 \", \" 956 \", \" 1284 \", \" 1208 \", \" 1122 \", \" 1215 \", \" 1104 \", \" 1244 \", \" 959 \", \" 1036 \", \" 1219 \", \" 1268 \", \" 1034 \", \" 1230 \", \" 903 \", \" 912 \", \" 1214 \", \" 974 \", \" 1245 \", \" 1035 \", \" 1084 \", \" 893 \", \" 1109 \", \" 993 \", \" 1220 \", \" 1082 \", \" 899 \", \" 1169 \", \" 1139 \", \" 997 \", \" 1041 \", \" 1170 \", \" 1032 \", \" 897 \", \" 1102 \", \" 923 \", \" 922 \", \" 1211 \", \" 1221 \", \" 943 \", \" 1194 \", \" 1293 \", \" 1240 \", \" 1281 \", \" 1232 \", \" 1269 \", \" 1209 \", \" 1238 \", \" 1029 \", \" 1261 \", \" 949 \", \" 1213 \", \" 1171 \", \" 1072 \", \" 970 \", \" 1090 \", \" 953 \", \" 1156 \", \" 1007 \", \" 1077 \", \" 1099 \", \" 905 \", \" 1059 \", \" 901 \", \" 1228 \", \" 1243 \", \" 1096 \", \" 1161 \", \" 1262 \", \" 952 \", \" 1002 \", \" 1168 \", \" 1121 \", \" 1202 \", \" 908 \", \" 1271 \", \" 954 \", \" 1298 \", \" 1064 \", \" 1106 \"], \"hoffset\": 0, \"location\": \"mouse\", \"type\": \"tooltip\", \"id\": \"el12840139620916480080\"}], \"data\": {\"data01\": [[14.4, 0.9999065399169922], [9.35, 0.999840259552002], [512.3292, 0.9995019435882568], [221.7792, 0.9992967844009399], [221.7792, 0.99924635887146], [211.5, 0.9991925358772278], [211.5, 0.9990593791007996], [164.8667, 0.9988088607788086], [262.375, 0.9986905455589294], [13.775, 0.9986695051193237], [263.0, 0.998318076133728], [106.425, 0.9983162879943848], [135.6333, 0.9980965256690979], [134.5, 0.998059093952179], [247.5208, 0.9980323910713196], [108.9, 0.9979971051216125], [262.375, 0.9979962706565857], [16.7, 0.9979740977287292], [263.0, 0.9979643821716309], [90.0, 0.9978772401809692], [136.7792, 0.9977719187736511], [51.4792, 0.9976286292076111], [52.5542, 0.9974671602249146], [55.4417, 0.9972922205924988], [57.75, 0.9970224499702454], [78.85, 0.9969485402107239], 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[14.4542, 0.08798076212406158], [16.0, 0.08794044703245163], [10.5, 0.08778513222932816], [26.0, 0.08778371661901474], [34.375, 0.0875939354300499], [24.15, 0.08741852641105652], [13.0, 0.08737943321466446], [10.5, 0.08715048432350159], [10.5, 0.08715048432350159], [8.6625, 0.08696429431438446], [11.5, 0.08672772347927094], [7.8958, 0.08619587868452072], [15.0458, 0.08568152040243149], [10.5, 0.0854414850473404], [13.0, 0.0851234421133995], [8.6625, 0.08508065342903137], [12.35, 0.08465655893087387], [31.3875, 0.08446834236383438], [7.75, 0.0841374546289444], [10.5, 0.08409726619720459], [13.9, 0.08405221253633499], [7.775, 0.08389833569526672]]}, \"id\": \"el12840139620912735952\"});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el128401396209127359527293558922\", {\"axes\": [{\"xlim\": [-14.649744000000002, 530.14974400000006], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"hanging\", \"h_anchor\": \"middle\", \"color\": \"#808080\", \"text\": \"Fare\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 18.0, \"position\": [0.5, -0.065535320088300208], \"rotation\": -0.0, \"id\": \"el12840139620906926160\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#808080\", \"text\": \"Prediction\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 18.0, \"position\": [-0.077004928315412197, 0.49999999999999994], \"rotation\": -90.0, \"id\": \"el12840139620910841680\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-14.649744000000002, 530.14974400000006], \"ylim\": [0.051838047504425049, 1.0319668281078338], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"none\", \"gridOn\": true}, \"visible\": true, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"none\", \"gridOn\": true}, \"visible\": true, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el12840139620912651536\", \"ydomain\": [0.051838047504425049, 1.0319668281078338], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 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{\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}, {\"voffset\": 10, \"labels\": [\" 1093 \", \" 1199 \", \" 1235 \", \" 1006 \", \" 1048 \", \" 966 \", \" 1110 \", \" 1292 \", \" 951 \", \" 1173 \", \" 945 \", \" 1131 \", \" 1206 \", \" 1263 \", \" 1076 \", \" 1306 \", \" 916 \", \" 1009 \", \" 961 \", \" 1303 \", \" 1164 \", \" 1248 \", \" 936 \", \" 992 \", \" 1014 \", \" 988 \", \" 1267 \", \" 906 \", \" 1042 \", \" 1266 \", \" 940 \", \" 969 \", \" 1216 \", \" 1289 \", \" 1004 \", \" 1033 \", \" 1100 \", \" 1242 \", \" 1074 \", \" 1287 \", \" 918 \", \" 1256 \", \" 904 \", \" 1283 \", \" 1155 \", \" 1246 \", \" 1294 \", \" 1260 \", \" 984 \", \" 1197 \", \" 1116 \", \" 1132 \", \" 1071 \", \" 1123 \", \" 913 \", \" 1218 \", \" 1114 \", \" 1070 \", \" 1301 \", \" 1254 \", \" 1241 \", \" 1068 \", \" 1105 \", \" 1138 \", \" 935 \", \" 1277 \", \" 1054 \", \" 1222 \", \" 1176 \", \" 1067 \", \" 1133 \", \" 1078 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1247 \", \" 1107 \", \" 1058 \", \" 910 \", \" 1297 \", \" 1023 \", \" 1200 \", \" 915 \", \" 911 \", \" 1295 \", \" 1001 \", \" 1086 \", \" 1190 \", \" 986 \", \" 956 \", \" 1284 \", \" 1208 \", \" 1122 \", \" 1215 \", \" 1104 \", \" 1244 \", \" 959 \", \" 1036 \", \" 1219 \", \" 1268 \", \" 1034 \", \" 1230 \", \" 903 \", \" 912 \", \" 1214 \", \" 974 \", \" 1245 \", \" 1035 \", \" 1084 \", \" 893 \", \" 1109 \", \" 993 \", \" 1220 \", \" 1082 \", \" 899 \", \" 1169 \", \" 1139 \", \" 997 \", \" 1041 \", \" 1170 \", \" 1032 \", \" 897 \", \" 1102 \", \" 923 \", \" 922 \", \" 1211 \", \" 1221 \", \" 943 \", \" 1194 \", \" 1293 \", \" 1240 \", \" 1281 \", \" 1232 \", \" 1269 \", \" 1209 \", \" 1238 \", \" 1029 \", \" 1261 \", \" 949 \", \" 1213 \", \" 1171 \", \" 1072 \", \" 970 \", \" 1090 \", \" 953 \", \" 1156 \", \" 1007 \", \" 1077 \", \" 1099 \", \" 905 \", \" 1059 \", \" 901 \", \" 1228 \", \" 1243 \", \" 1096 \", \" 1161 \", \" 1262 \", \" 952 \", \" 1002 \", \" 1168 \", \" 1121 \", \" 1202 \", \" 908 \", \" 1271 \", \" 954 \", \" 1298 \", \" 1064 \", \" 1106 \"], \"hoffset\": 0, \"location\": \"mouse\", \"type\": \"tooltip\", \"id\": \"el12840139620916480080\"}], \"data\": {\"data01\": [[14.4, 0.9999065399169922], [9.35, 0.999840259552002], [512.3292, 0.9995019435882568], [221.7792, 0.9992967844009399], [221.7792, 0.99924635887146], [211.5, 0.9991925358772278], [211.5, 0.9990593791007996], [164.8667, 0.9988088607788086], [262.375, 0.9986905455589294], [13.775, 0.9986695051193237], [263.0, 0.998318076133728], [106.425, 0.9983162879943848], [135.6333, 0.9980965256690979], [134.5, 0.998059093952179], [247.5208, 0.9980323910713196], [108.9, 0.9979971051216125], [262.375, 0.9979962706565857], [16.7, 0.9979740977287292], [263.0, 0.9979643821716309], [90.0, 0.9978772401809692], [136.7792, 0.9977719187736511], [51.4792, 0.9976286292076111], [52.5542, 0.9974671602249146], [55.4417, 0.9972922205924988], [57.75, 0.9970224499702454], [78.85, 0.9969485402107239], 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[26.0, 0.10449554026126862], [26.0, 0.1034800186753273], [29.0, 0.10211773961782455], [26.0, 0.10191258043050766], [32.5, 0.1018180102109909], [22.525, 0.09965702891349792], [26.0, 0.09935081005096436], [21.0, 0.09909418225288391], [46.9, 0.0988871306180954], [9.225, 0.09858602285385132], [22.525, 0.09835503995418549], [31.5, 0.09761135280132294], [26.0, 0.09756144881248474], [31.5, 0.09753812104463577], [13.0, 0.09671033173799515], [15.0333, 0.0965946614742279], [21.0, 0.09516244381666183], [21.0, 0.0950075164437294], [13.5, 0.09492755681276321], [21.075, 0.0939791351556778], [10.5, 0.09298072755336761], [11.5, 0.09232478588819504], [10.5, 0.0918397456407547], [13.0, 0.09167928248643875], [13.0, 0.09167928248643875], [13.8583, 0.09130626916885376], [7.65, 0.09088298678398132], [7.2292, 0.0899672880768776], [10.5, 0.08918935060501099], [13.0, 0.08866921067237854], [13.0, 0.08866921067237854], [10.5, 0.08846176415681839], [13.5, 0.0883261039853096], [12.7375, 0.08815155923366547], [14.4542, 0.08798076212406158], [16.0, 0.08794044703245163], [10.5, 0.08778513222932816], [26.0, 0.08778371661901474], [34.375, 0.0875939354300499], [24.15, 0.08741852641105652], [13.0, 0.08737943321466446], [10.5, 0.08715048432350159], [10.5, 0.08715048432350159], [8.6625, 0.08696429431438446], [11.5, 0.08672772347927094], [7.8958, 0.08619587868452072], [15.0458, 0.08568152040243149], [10.5, 0.0854414850473404], [13.0, 0.0851234421133995], [8.6625, 0.08508065342903137], [12.35, 0.08465655893087387], [31.3875, 0.08446834236383438], [7.75, 0.0841374546289444], [10.5, 0.08409726619720459], [13.9, 0.08405221253633499], [7.775, 0.08389833569526672]]}, \"id\": \"el12840139620912735952\"});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el128401396209127359527293558922\", {\"axes\": [{\"xlim\": [-14.649744000000002, 530.14974400000006], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"hanging\", \"h_anchor\": \"middle\", \"color\": \"#808080\", \"text\": \"Fare\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 18.0, \"position\": [0.5, -0.065535320088300208], \"rotation\": -0.0, \"id\": \"el12840139620906926160\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#808080\", \"text\": \"Prediction\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 18.0, \"position\": [-0.077004928315412197, 0.49999999999999994], \"rotation\": -90.0, \"id\": \"el12840139620910841680\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-14.649744000000002, 530.14974400000006], \"ylim\": [0.051838047504425049, 1.0319668281078338], \"paths\": [], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"none\", \"gridOn\": true}, \"visible\": true, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"none\", \"gridOn\": true}, \"visible\": true, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 7, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el12840139620912651536\", \"ydomain\": [0.051838047504425049, 1.0319668281078338], \"collections\": [{\"paths\": [[[[0.0, -0.5], [0.13260155, -0.5], [0.25978993539242673, -0.44731684579412084], [0.3535533905932738, -0.3535533905932738], [0.44731684579412084, -0.25978993539242673], [0.5, -0.13260155], [0.5, 0.0], [0.5, 0.13260155], [0.44731684579412084, 0.25978993539242673], [0.3535533905932738, 0.3535533905932738], [0.25978993539242673, 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# invoking the interactive plot \n",
    "scatterz('Fare','Prediction', predictions, labels='PassengerId')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More or less everything that has to do with the deep learning workflow is as easy as that with Autonomio. Go ahead and try now with your own dataset next! :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install Autonomio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code> pip install git+https://github.com/autonomio/core-module.git </code>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='https://img09.deviantart.net/0248/i/2013/295/d/8/that_s_all_folks__by_surrimugge-d6rfav1.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href='http://autonom.io'>Autonomio Website</a>\n",
    "\n",
    "<a href='https://github.com/autonomio/core-module'>On Github</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='http://autonom.io/wp-content/uploads/2017/08/autonomio_logo.png' align='left'>"
   ]
  }
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