19_6 Results Analysis 6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as dates\n",
    "import numpy as np\n",
    "import os\n",
    "import time\n",
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(139678, 535)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_file_name = \"lotOfFeatures.csv\"\n",
    "input_file_path = os.path.join(os.path.join(os.getcwd(), \"data\"), input_file_name)\n",
    "\n",
    "original_data = pd.read_csv(input_file_path, index_col=0, parse_dates=True)\n",
    "originalSize = original_data.shape[1]\n",
    "original_data.head()\n",
    "original_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excluding Testing Set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spliting the dataset into three parts: training, testing and evaluation (last 10 percent)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "start_time = '2012-02-01 00:00:00'\n",
    "#start_time = '2013-01-01 00:00:00'\n",
    "#start_time = '2014-02-01 00:00:00'\n",
    "splitting_point = int(0.8 * original_data.shape[0])\n",
    "splitting_time = original_data.iloc[[splitting_point]].index.to_pydatetime()[0]\n",
    "#data = original_data[:splitting_time]\n",
    "data = original_data[start_time:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2014, 4, 17, 0, 0)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splitting_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1f233128>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABH4AAAFGCAYAAAAcpFh+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmcFOWdP/DPIGiMxiPGI2hWkxhbk2hMNmaTrDGuJl6I\nuIlJNJvEqL/NtRoT8UKJiAcegIAHKoeg4iCIyHAfw30MMAwwF0wNw9zMfR89Mz19/P7o6Z4+qrqr\nuu7qz/v18iVTXV311FPPVd96qjojEAiAiIiIiIiIiIicZ4TZCSAiIiIiIiIiIn0w8ENERERERERE\n5FAM/BARERERERERORQDP0REREREREREDsXADxERERERERGRQzHwQ0RERERERETkUCOTreByueYD\nuB1AoyAIV0UsfwjA3wB4AawRBOFJ3VJJRERERERERESKyZnxswDAzZELXC7X9QDGArhSEIQrAUzT\nPmlERERERERERKRG0sCPIAi7ALTHLP4rgJcFQfAOrdOiQ9qIiIiIiIiIiEiFpI96SbgMwHUul2sK\ngD4AjwmCcCDRF1wu1ykArgFQD8CX4n6JiIiIiIiIiGjYSQC+DCBXEISB2A9TDfyMBHC2IAg/dLlc\n1wBYCuBrSb5zDYCdKe6PiIiIiIiIiIik/QTArtiFqQZ+agAsBwBBEHJdLpff5XKdIwhCa4Lv1APA\nRx99hAsuuCDF3RJRurv2vWsBALvuj2vPiIiIiMhM1wbHadjFcRqRkRoaGvA///M/wFDcJZbcwE/G\n0H8hKwDcAGC7y+W6DMCoJEEfYOjxrgsuuAAXXXSRzN0SEcU4I/g/tiNEREREFsVxGpFZRF+rI+fn\n3DMBXA/gHJfLVQ1gEoD3ACxwuVyFAAYA/EG7dBIRERERERERkRaSBn4EQfitxEe/1zgtRERERERE\nRESkoaQ/505ERERERERERPbEwA8RERERERERkUMx8ENERERERERE5FAM/BARERERERERORQDP0RE\nREREREREDsXADxERERERERHZyj333CO6fMKECdi4cSMA4KOPPsJNN92EK664Ah0dHeF1ysvLcffd\nd+PKK6/EggULor6/Y8cO3HLLLbj55psxZ86c8PL169fj9ttvxxVXXIHi4uKo77z77ru46aabcOut\nt2LXrl3h5cXFxRg7dixuvvlmvPjii+HlCxcuxJgxYzBu3Djcd999qK+vj9peT08PfvrTn+KFF15Q\nmCviGPghIiIiIiIiIltZvHhx0nX+/d//HQsXLsTo0aOjlp911lmYOHEiHnjggajlfr8fzz//PObP\nn4/Vq1djzZo1OH78OADgsssuw5tvvolrrrkm6jvHjx/HunXrsHbtWsydOxeTJ09GIBAAADz77LN4\n8cUXsWHDBlRWVmLnzp0AgG9+85tYvnw5srKycNNNN+HVV1+N2uasWbPi9qMGAz9EREREREREZCvf\n/e53w/9+7rnncOutt+L+++9Ha2trePnll1+O0aNHhwMxIV/84hfx7W9/GyNHjoxaXlBQgIsvvhgX\nXnghRo0ahTFjxmDz5s0AgK997Wu45JJL4ra1efNm3HbbbRg5ciQuuugiXHzxxSgoKEBzczN6e3tx\n1VVXAQDuvPNOZGdnAwB+8IMf4JRTTgEAXH311WhsbAxvr6ioCG1tbbj22mvVZlHYyOSrEBERERER\nERHFe29VMXbnn9B0m//5nQtx/9hvJVwnIyMDALBx40ZUVVVh3bp1aGpqwpgxY3DXXXeltN/GxkZ8\n+ctfDv99/vnno7CwMOl3rr766qjvNDY24qSTTsIFF1wQtzzWsmXLcN111wEAAoEAXnnlFUybNg17\n9uxJ6RjEMPBDRERERERERLZ04MABjBkzBgBw3nnn4Yc//KHJKZIvKysLxcXF+PDDDwEAmZmZuP76\n63H++ecDQNzsolQx8ENEREREREREKbl/7LeSzs4xW2h2UDLnn38+6urqwn83NjbivPPOS/qdyJcz\nNzQ04Pzzz49b3tjYGA7oAMCePXswZ84cLFq0CKNGjQIAHDp0CAcPHkRmZiZ6e3vh9Xpx2mmn4ZFH\nHpGVfimmvuOnvasffQNeyc+9Pj+a2twGpoiIiIiIiIiIrC40G+aaa67B2rVr4ff70dTUhH379omu\nKzV7JnL5lVdeierqapw4cQIejwdr1qzBjTfemPA7N9xwA9auXQuPx4OamhpUV1fjqquuwrnnnosv\nfOELKCgoQCAQwIoVK8LbOnLkCCZNmoS3334bZ599dnhb06ZNw5YtW7B582Y88cQTuPPOO1UHfQCT\nZ/z8YfIGnDxyBD59Zazo58/N24tDpc1458kbceG5pxucOiIiIiIiIiKyotAsnp///OfYu3cvxowZ\ng9GjR0e99PnDDz/EvHnz0NrainHjxuGnP/0pnn/+ebS0tOCXv/wlent7MWLECHzwwQdYs2YNTjvt\nNPzrX//C/fffj0AggLvuugtf//rXAQDZ2dl4/vnn0d7ejr/85S+4/PLLMW/ePFx66aW49dZbMWbM\nGIwcORKTJk0Kp+2ZZ57BhAkTMDAwgOuuuy78Lp+pU6eir68PDz/8MAKBAEaPHo3Zs2frl1daPTOW\njMvlugRAxebNm3HRRRcBAMaOzwIArJo+TvQ7oc+f+uM1+NGVo0XXIaL0csnMSwAAlf+oNDUdRERE\nRBTjkkuC/6+sNDMVRGmntrY2NJvoq4IgVMZ+zp9zJyIiIiIiIiJyKAZ+iIiIiIiIiIgcioEfIiIi\nIiIiIiKHYuCHiIiIiIiIiMihbBH4Mej900REREREREREjmKLwA8RERERERERESnHwA8RERERERER\nkUMx8ENERERERERE5FAM/BARERERERERORQDP0REREREREREDsXADxERERERERGRQzHwQ0RERERE\nRETkUCOTreByueYDuB1AoyAIV8V8Nh7AVABfEgShTZ8kEhERERERERFRKuTM+FkA4ObYhS6X6yIA\nPwdQpXWiiIiIiIiIiIhIvaSBH0EQdgFoF/loBoDHNE8RERERERERERFpIqV3/LhcrjsA1AiCUKj0\nu//vxU3YuC96ktBz8/fihff2Yd2eCgCA1+fHyx/kppI0HBSaMHZ8FsaOz8LS7NKUtkFERERERERE\n5ASKAz8ul+tUAE8BmBSxOEPJNt5Yehju/sHw37lHGrGvuAGzPy0AABwubcbu/Lrw5wEF2540Jyf8\n7w/XHVWSLCIiIiIiIiIiR0llxs/XAVwCIN/lclUAuAhAnsvlOk/JRgIJojl+v5JQDxERERERERER\niUn6q15DMob+gyAIRQAuCH0wFPz5niAIYu8BIiIiIiIiIiIikySd8eNyuTIB7AFwmcvlqna5XPfF\nrBKAwke9ACAj0TcUb42IiIiIiIiIiGIlnfEjCMJvk3z+Ne2SI45xICIiIiIiIiIi5VL6VS+j8Y0/\nRERERERERETKWTLwwxk+RERERERERETqWTLwQ0RERERERERE6jHwQ0RERERERETkUJYM/GQk/Mkv\nIiIiIiIiIiKSw5KBHyIiIiIiIiIiUs+0wI+iWT38WS8iIiIiIiIiIsU444eIiIiIiIiIyKEY+CEi\nIiIiIiIicigGfoiIiIiIiIiIHMoegR/+yBcRERERERERkWLmvdxZ4fqFx1vg7h9UvJ9Br2/o/358\nuO4oevuUb0Mv7V39OFbTbnYyiOKUVLahq9djdjKIiIiIiIhIJVvM+Ckqa8FTs3fj2bl7FX/3vZXF\nAICHpm3F0uxS3D1xrdbJS9kfJm/AIzN3oG/Aa3ZSiMIaWnvx2Bs78dC0rWYnhYiIiIiIiFQyLfCT\n6BfaY3/pvbapBwBwtLJN8X4OH2sGAJxo7lH8XaN4Bn1mJ4EorK2rP+r/REREREREZF+2mPETSBgm\nSvLd1L9KRERERERERGRrtgj8EBERERERERGRcpYM/GRo+jNenPJDRERERERERIn1O/T9u5YM/KQb\nPo5GVqJt4JWIiIiIiMj6cgrr8Kun1mDTviqzk6I5817urCDaoSYwwqAKERERERERESWyaX81AGD1\n7gqTU6I9x8/4YdyHiIiIiIiIiBIJP/ngwCCC4wM/RERERERERESJZDj4jRfOD/zYIFqn5ufqibTm\n5AaPiIiIiIhIjJNfE2PNwA8vPImIiIiIiIhIJy+/n4uP1peYnQxDWDPwoyHOpiEiIiIiIiKiSLsL\n6vDxJiH8t5OffHB84IeIiIiIiIiIKF1ZMvCjZaDNyc/pEenCwZFuIiIiIiIiMU6OHYxMtoLL5ZoP\n4HYAjYIgXDW07FUAYwEMADgO4D5BELr0TKijObiAEREREREREZF55Mz4WQDg5phlGwF8SxCEqwEc\nAzBB6Y6NiqYxpkJEREREREREiaT1O34EQdgFoD1mWbYgCP6hP/cCuEjLRMVmuJOnXBERERERERGR\nuZwcd9DiHT/3A1in9EtHK9tEl2ftOI5/vZsTtSwyEHRIaIr67HBpEx54cRMaWntFt9fU5o5b1ji0\nbMrC/Xj703wlySaVGlp78cCLm5Bf2mx2UkiCgwPdREREREREaUdV4Mflcj0NYFAQhEyl331rmXjA\nZV5WUcLvPTMnOij08gcH0NTmxsqd5bL3PXto3zmF9Vi7p1L290i9z7aVoanNjVcXHTA7KURERERE\nREQAnP2oV9KXO0txuVx/BHAbgBtS2oCT51EpxJwgIiIiIiIiIj3IDfxkIOIJEJfLdQuAxwBcJwjC\ngB4Ji8QYkfPwnFpXhpND3URERERERCIOOfh1JHJ+zj0TwPUAznG5XNUAJgF4CsDJADa5XC4A2CsI\nwt90TGdSAUYSbIFBBSIiIiIiIrISd/8gPIM+s5Ohm6SBH0EQfiuyeIEOaUkJwwh2xUAdERERERER\nmW8gIuhTXtcJn8+Pk07S4rewrME5R2Jj6TRbiYE6IiIiIiIisrJ9xQ1mJ0FTtgj8BOTMDkmf2AkR\nERERERER6WTAYY992SLwk0gqr4xJpxk2VsVTQERERERERKQ/0wI/vO5PU3zWi4iIiIiIiCzMaRMV\nbD/jh+zJYfWIiIiIiIiIyJIcE/hhIIFIG6k8PklEREREROQczoowOCDww6tUO+JZIyIiIiIiIivY\nllcb9Tcf9TKB0zKdnBY/JSIiIiIiIrt6b1Wx2UnQlS0CP3Io+aUuBh3Mk8HniIiIiIiIiMjCnDb5\nxPaBH8YRbMppNclBMvggHhERERERpTVnXa/aPvDjBOkUA2FIgYiIiIiIiMg4pgV+ut2Dstf1DPok\nP+vq9QAA1u6pVPS4V6yG1l7sOFQr+fm2vBo0tbujllXWd2H/kYaU9+lknT0D2LSvCj6fP2q5f+gc\n9fZ7U952/4AXG/dVoX/Ai0AggK15NWhu7wt/fqymHYeEJmw7WIumtuFzVtfcg935ddh+sBaNQ8sH\nvX5s2leFHrcHuUcaUFHXieb2PmzLq8G6PRXI3FCCfk/qaVWjtqkbewrqRD8rKGuGUNVmcIq009jm\njqtvJVVtKChrBgDklzZj9rJ8/ObpNWjv7o9azzPogzemXBHZXbjN9KfRnQAiIiIiMsRIs3acKJgT\n61hNh+jy2KBL1o5yWdsTm3Xyp5eyEQgAXx19Jr5y/heiPiupasP0zIM47dRR+PiF28LLH5q2FQCw\nctodfHdNjJfez0VxeSt8/gBu+dEl4eVSgQwlFq45gjW7K1BxohPXXn0hXss8iDNPPxmLJt8KAHhk\n5o7wup87+SR88tLtAIA/v7w5vPzkkSPw6StjsXpXOd5bVYyconrkHmkEAHzh8yej2+0Jr9vv8eH+\nsd9SnW6l/vrKFgDAosm34MzTT4n67Om39wAAVk0fZ3i6tPDnl7Lh8wdw8QVn4OIvnwEAeOz1nQCC\nxzTx3T3hdf/w7Iao4+zoGTA2sUQGmLJwP45UtMEfAG7+4cVmJ4eIiIiIHMTWj3o1tPZG/V1V35Xy\ntkKThSIv+EM6uoMXmr198mcppTuhqh0AwjNrQtq61F+0Vw6d56qG7vBskM6e+PMGBIM2Yjze4IyR\nE809AICyiOBibBmobki9XGlhQOIYdGNADDM0q6FLpL4RpaPhNrM3yZpEREREpDenvY7F1oGfVDns\nHFqamsfvjJQolfY4AiIiIiIiItKC064B0zLwo5TeEyBsEhtRxC5PvoUf0WPkxxzMWyIiIiIiIl3Z\nOvCjx89Oi20z2ft7nBi4UcuouI/avJeTTrvMWiIiIiIiIiL1nHYJaOvAT6wApw9Yjl0qDMuOOZjv\nREF2mSVJRERElB6cdZ3iqMCPbjggV84uVzEykumsKk9EVmSXIDkRERER2Q8DP6QrPa9lMjK020HC\niy6zL8hsEkMjIiIiIiJyAqfdlHNU4Ef2O38UnsRkW3VYmdCEgyb88HEkIiIiIiIiitPj9uDlD3JR\nUddpdlISclTgR4sLdLsELOxCzxcja7npRNtyWrQ3GVYBIuOx7yEiIiKyn+XbyrA7vw6T5uSYnZSE\nbB34MWqgnOxXvSieXXKM55aIiIiIiIgiyb33P+j1AwD6PV79EqMBewd+zE4AmU67x7Ckt5NuM36M\nxLwlCmJdICIiIiK92DrwYxkqR+xOfIeMXSbShJKZ8FEvB54fIiIiIiIikuCwu3IM/JCu9KwvWgaX\nEv6ol7PqfFJ8/I3IeKx2RERERKSXkclWcLlc8wHcDqBREISrhpadDWAJgIsBVAL4tSAI1n6NdYSE\nMzg4+NZIMCMtP1tGzpQf0g+znYiIiIiILMZplylyZvwsAHBzzLInAWQLguACsAXABK0TZiXJ7sQ6\nrVBowai712rjNZzdQkRWwhg0ERERkfmcNiZLGvgRBGEXgPaYxeMAvD/07/cB3KlxuhLy+YNnwR9z\nMvyxCyRofRLl7tdJkh1z+GfcI1ZTm0+BQCBuG/6Ik+n1+RX/fHwgXJYSvdw5+JnP51e0bSlalxex\nfFHDjFiYVulPx7pITsEgNBGRU4mNTzhmITKX0utGu0v1HT/nCYLQCACCIDQAOE+7JCV352MrsTm3\nGnNWFEYt35pXK+v7BWUtUX8v2SQkXN8zOHzBn1NYF/f5L59cjY37qmTtW5TNytySTQLGPbYSze19\nop/3uD3oG/BFLVu4uhjjHlupar/PzMnBryasRnF5K4DgeXwt82D48/9+fBXueFT+Pv78UjZW764A\ngLj0RgoEgOz91bjz8VV4ZOb2FFMf9PFQ3rV0iOddKia+swe/fnqNZtszUkV9Jz7dckx22Xh3eYHk\nZy+8tw/jHlvJgRTZFMstEZETrdpZjnGPrUR1Q1d4WWtnH8Y9thKLN5SYmDKi9Pbc/H1mJ8FQWr3c\n2fAR63urihWtf/U3zpX8bNH6xI1ufUtv+N9Lsks1SY+dhfIr/1iT6OfHa4df9xQqGJ9uLVO938Ol\nzfB4tZl1AwB1Eec1mVlLDgEAjtV0qNrnR+G8a1a1nUgFZS0Y8EgHrqxs+8FaLFxzRPb6oUCdmH3F\nDQASz94isjo+fUpE5CyhG9V7CuvDy/KPBW9CZ25MfPOZiPRz4Ghj4hUcdk2RauCn0eVynQ8ALpfr\nAgDiEQALOeO0k2Wtl/KY22EFQ28jT7LPD8ql2zRAIzFniaKxuSEiIiIircm9+s5AdExkJYA/Dv37\nXgBZGqbJciLvwEoNyjlWF+eEoInZR5DBd38QpQHWcyIiIiKrkHsNaJfLXTk/554J4HoA57hcrmoA\nkwC8DOATl8t1P4AqAL/WM5FmkzP1Xs0Jt0lZSV88QURERESkET7WS+RE1q7YSQM/giD8VuKjn2mc\nFkX0iqyJ/7y3tU+i1QQiIyWS58k+0ZSAjdJqN3rUY9ZWIiIiIiJSwy4zeeSyz4tW4uhzJsQeTYqK\nBTkgkGEk5goRERER0TDepCKyPqfd/Ldx4Mc4chpnp0UE9WefLi/dzq34rDciIiIiIiKyI9sGfgx9\n1EvOO360T4oj8OXOZDgGroiIiMhiHDAkJkovDquz9g38GLgvvX9VyQnBEUfj+dGPHlnL80VERERE\nRBRm38CPgRd3ev+ql5M5IVvMPgYnT2Bx2rOzRGrxRgARURpw8uCOiCzJxoEfk/YrdaHKwbpjpdup\n5VCEyHi8BiAiIiKyDqddAto28GPkqZD1cmfdU2FTTsgYk48h3QJPROmI9ZyIyNkiA/yM9ROR0Wwc\n+DEQb8Uq4rQLGD6OpB+nlRUitfirekRERETmc9p1im0DP0aeiMhxuNR+nVYwtOKEbOG5JSKj8B0/\nRERERFbgrDGZbQM/Sqk5bfLuvzqrYFAa44QDIsNxog8RERGR/djl6RDbBn70yl7RwbeMETlv0pJe\neEFIREREZG+8ViCyF6V11urXbLYN/Ax4fLLXbe/ux87DJ6KWHatpF123qr4bY8dnhf/7/bPrMTg4\nvK/K+i6MHZ+FnMK6qO/5/AHMzSpMmI4V249DqGqLW/7wa9tQ3dAFABj0+sL7zt5fFbX9xRtKUNvU\nLbrtmsZuLN4owOc3rlcZGPSLLo9MwfqcSmw5UBO3jtfnR9+AV5+ExVi7pwJtXf0pf1+PRy9mfnwI\nALD/SAO25sXnj5icwnr87dXNqGkcLgMVdZ2apam1sw+L1h+VrFtFx1uwZncFPlx3FJPm5qCzZ0By\nW16fH5kbSlDX0pNwn26RMnC8tiNu2dNv70b5iehjXbT+KDyDPjS3B9M9vLwEgHSdyT/WjA17KxOm\nyyq8Pj8+Wl+C+pZes5MiqaqhC0s2CXxESSNrdleYnQQAwX5z0bqj6O0b1HS7nkEfFq0/iqY2t6bb\nJSJz+f0BLNkkoGpoPCslEAhgaXYpKuvF16tv6UXmhhJ4feJjTC14fX789+Mr48byUgrKmrEupzL8\n9+6COjw0bWvSY4014PFi0fqjaOnok7xAzD3SEDVuzt5fjbySRkX7sYujFW1YufO4afuva+nBU7N3\nY9XOctPSQKnx+wP4eJMQvn4meWwb+FHi2Tl745Y9MnOH6LqzlhyK+rujewC78uM7hikLc+OWrdxR\nLnkx3Nzeh/kri/Do6zvjPnP3e/HQ9G0AgBmLh/c/a8nh8L/3FtYjc6OAR2ZuF93+P2ZsR+aGEuwr\nqhf9XA9SFyi+mM56xuKDous98+4ezdMk5u1PC/Dy+/HnSy69LmcbWnvx/Px9eC1TPH9iTVm4HzWN\nPXhw2tbwsr8PlRstvPR+LpZsKsXyrWWin0+YvRvvLC/A0uxSHCxpwuxP8yW3tS2vFos3CnjyzV0J\n9yl28fePGfFlvKCsBQ+/ti1q2ZJNpVizuwJTFu7Dkk2l4eXLthwDAOwpqEPmRgHjZ0XX9Ynv7MGb\nn0in3Uo259bg400CJsxOnI9menDqVixaX4LDpc1mJ8UR+j0+uPu1DbakYtbHh7AkuzQqqKqF1bsq\nsGRTKSbPj++Xici+Dh9rxqL1JXhw6taE6xWXt+LDdUfx0DTx9Z58aycWbxSwTeZNsVTMXpYPry8g\nOpYX8/TbezB7WT78QzdXX34/F5X1XUmPNdZn249jyaZSTFm4X3Kd5+bvixo3z1pyCM/OdWZ7+fib\nOzF3RRE6uqVvJOrpiTd3ofB4C+asKNQ10EjaOyg04aP1JVHXRJRcWgR+apsTzzpIZmBQ/uwiqRk3\ng97E2wh1Jg2t4nf2e/o8AIC+AfHteIbSaOQFQ3evR3S53EBJnYGzGBrbrDdjYtCbWifj12lWV2Nr\nMAjTkWAmT6SWjj7Jz7rdwbLRrnNn3tXrQX2r+MyBnqGZCu5+Y2aW6SGUj62dqc9YM4rY7C1KjV51\nXImm9mD9bu/Stg539Qa312DhWWxEpJzc8Wey2d5tQ21Ot1u/8WxjijMO1T7GEWrbG9vcyOALFcN8\nfnOCLmYFnEi90GxkTjZXJi0CP0bS73EH53UQxlZW5+Uf8ReQiIiIiIjIfFa/LEmPwI/as6DBSZS7\nCam7CXLvMliiwFkhDaQ7y9+tskRlIFIuHUpuOhwjEcXLsPrbT4nI8qw2hrD8NdGQ9Aj82IhUwbFk\ncZJIlPxZGFarttZk1Bgp9FOEsoOMCc4fx3Xa4IwmIiIiIiJSi4EfGRJd4MrehsoLOPkX4/aR7te0\nVr2ot0vUmshJMiT+TURE2rLEzSkrpMEirDAc5umwF56v1KRF4Edte6KkQdKv8bJgEZc4VivO97FE\nJ+8wVg8QWWAcQSRbQOLfZtPixofodq10kERkGDuPx9huEZGdpUXgx1Zs3CESUfpi0+Usel2c8f0e\nRM5k9ZtBkcxOayBg7wAYkdmMisE6Ldg70uwEGMEKJ01uGqT6AVu93FkuWyXWPMwmsgMWU2dhu0NE\nSsidHWh20IWIqKndjRff2292MgzHGT8WI3U31Il3BuxyXaFX1vPONxGFWLU14EUaEWnKCk2KFdJA\n1sIxeVpZml2K8rpOzban12PxWmPgx2KkX/jrvAYp3e8oW/XlznbjvJpBlA7Y/hEREa8HSDmrjv2t\nHj9Mi0e9jBxg6tV4yS9IbD3FqKmHZueoZRsRi6RL6vxwIEGkntZ3sSzbnhGRKnJnB7IJICK1DLyy\nN2xPajS396GxrRdnnZJ4vTQJ/Khj5MwMyUe9DEuBevKzyx6VKV2Ezps2F2bGlFgGd4j0wQANESnB\nILEyfIyWyDxOe93G/S9sBAC89tfvJFyPj3ppTLdn/GxVQOXlgZEX7YwPEOnLTi0UmY9BW6L0ZIUf\nK0m1v2KzRZRu7DW69Xh9CT9XNePH5XL9E8ADAPwACgHcJwiCR802nUrurCGpDlFuseNg2j54qnTG\nykBERGQIO81gscTdfgskgYjSS8ozflwu12gADwH4niAIVyEYRLpbq4TZVjpN+FGZJiMvy62YfaSN\nlO/c2SAwZIMkkg543onIqewUINIPG/lIdvlFJCIAWLnzOP45czt8Pr/ZSVFM7aNeJwE4zeVyjQTw\neQB16pPkHP0DXrR09AGIbuJbOvow6I0vLCeae+KWNbT2IhAIwGtA4erq9WhyMdzvSTzNLMQuFzdy\n0zno9cPdPyj5eU9f9Gc+3/CGO7oHJPP+eG0n/H71meXuH0RbV3/cfga9PrR396OrNzhZLzLIOOj1\nobWzD4Oz9KLRAAAgAElEQVQiUwf7BryS+4rcxrGadnUJTyI2X0UTYTHu/kF4BqPzNLL+BQIBdPYM\nmJE0itHZI103NRVTXn3+AHrc4hNok7U1qehxexQNYiLT5/MH0C2S1kAgEG5XQqTajdqmbtQ190hu\nK1WdPQNxaRBzorlH8jzL+T6RU/X0Dcoag4q1ST5/AHWxY9uIpq61s0+yTVDahfv9gXA/Gqqzkf9u\n6ehD/9C+Um0//f7E+RDaV7fbEx63SbVn7V39cctqGruTbjtS5H7sRq9+tVdmeY0lNs5VKlTeevoG\nbRkU0JO7f1D02lcLkXU7pKvXE64zPn8Aze194WtyQMlN4/hyOndFEcpqOlDX0ptiioclqsNaXZdH\nSvlRL0EQ6lwu13QA1QDcADYKgpCtWco05PWZ0yj+6qk1AICHf/PdqEbovuc34otnxL92+y8vb45b\n9r9TsvGza/4N2bnVsvaZ6pGW1XTgnzO34/b//Cr+/IurUtxK0GuZB2WtF3vhq6eWzvgOVq5EHXGk\n+57fgM4eD1ZNHxf32cCgD/dMXBu17OHXtoX//ftn1+OG738F/7zne3Hfff69fcoSLOE3Twf3f9N/\nXIyHfn11ePm9kzeg2z08CMo/1hL+9y+eWA0A+MLnR8Vtr7pBOl/c/cMN8CMzd+D5P/8IV192HgBA\nqGpL8QjiHalolfxsa16NZvvR2m+eXotTTzkJS6fcDiAYHHtk5g6M/cnX8Kc7r8SH647ik83HcNWl\nXzI5pfLZc+iZWEllGx57Yyfu/OnX8cAd39Z3ZzGd+4S3duFoZRuWvHgbPv+56Pr355ez0dzeJ9rW\npKJ/wIt7/rUOl37lLMz4x08BDNfvPQX1ot95+u3dKC5vxeIXbsMr7+fi8LFmLJp8C848fbhve2tZ\nPjbsrcLr46/HV0efCQBYvasieLgR21q+9RgWrD4Stf25T/0MF5xzmqrjWrnjOOZmFQEAZj1yPb52\n4Zmi6738fi52F9ThvC9+HvOf/nnUZ1vzavBa5kH87ZdX4dYff1VVeojsxufz456Ja3HReafj7Sdu\nTLju60sPxy2787GVAIDHf/d9/OS7FwJA1MXSH58LvpA01JbtKxpub5Re70xddAC78utw9WXn4nBp\nM9549L+w6/AJLMkuxbP/+0M8O3dveF8lVandkHpk5g688eh/iX62YvtxzF9ZhL/84iq8s7wA/375\neXj47u/iD89uiFvX6/Pj3c8Ko5b96509OHysWXLf//PMOjx93w/ww29/GUDwYve+5zfiP751ASbe\n/x8pHY+ZMjcImm/T7w/g7olr8eVzTsOcp36m6Lv3Tt6AJS+OUbX/1xYfxLa8WgDAxRd8AW8+doOq\n7TnJb55eiy+e8Tm8P+lmTba3/WBt+N/3PR/djqzLqcTsZfkAgIn3/QB5JU1Yl1MJAFg57Q5rPOqJ\nxHW46HgLJszejbtu+AbuHfNNzfap5lGvswCMA3AxgNEATne5XL/VKmGRTh51kh6blU1tAdmaV4OD\nQlPUsrYu+Xfz5QZ91CgoC3Y2q3dXyP6ONaqNdXT2SN8VlrpzH2nLAWMCFRv3VUX9HRn0kSJnnUin\njIpuWtZElKtEAxulEg3ehBQHdkbpGxgOfIaCbat2lgMAPtt2HABQUNYS/0UyzKGhdnvF9uOG7/to\nZTBA2iZyV7i5vS9umRqhu2JlNR2yv1NcHgy6tnb2het0Y5s7ap0Ne4NtzZFy6QAtAKzZUxm3rKKu\nU3ZapKzfO9zWFSdIw+6C4GTlppj0A8C2ocGlEf0wkdUMDN2gq22Kn5GuxOrd5eF/H6uW7pv3FIoH\nmuXYlR+sx4dLg+3RkYpWrNgRbLt3HDqR8nYjVdZ3SX62Pic4zlmaHQxo5JU0oa5ZfEZAZP8fImds\nlBORP6Hg/L7ihqTfs6KVO4f7Va0e//MPRQvrW5XPxIi8YZmqUNAHAKoS3BxNV2LjmVQdONoo+Vn2\n/uG+P6eoPhz0iWKBi9hQexJZh3v7BrE7vw65R4LHFzv+LK1uR/mJ1MdHal7u/DMA5YIgtAGAy+Va\nDuDHADJVbFOU2YE5JdOsTL/rbZfnp8jhElRaFtE4dnjfUDIW6EOJiEhDWt0ZN6OLc0C3SkRpZnpm\nHnKPNOKM004W/Xz8rB0AkPKMbzWBn2oAP3S5XJ8DMADgRgC5KrZnWenQd7CDJLIOs4PdZIJ0O+k2\n63RsllwiTdi9VTIy/cNthH57dcJNIjF8uTNZldFVruh4cKZ/qu8WPJrkaYeUH/USBGE/gGUADgHI\nR7Clm5Pq9ojISaRbSqt07w4dPxE5gjb1U/1G7H7hS6SKRhVAbsBC08BGxLYCEv/WQ7rF8IkIKQ03\n9Ggr5q0oTPi5mhk/EARhMoDJarYhh9ltqJI+QqxDCQSM6wgM/Yl0s08MEZGe0qWRU3OYcjodB+Qj\n48SUjjR71EuTrSjngKaHiBRK9M4ova/JU910t9uDh6ZtFX3/l5bU/pw7pTHOmCBp0U1f5OCR5YbI\nYvSuk2I3RHTepVas8usfRGbQrPSb8Y4fc3evC6cch144viS55LZt+gaJhje+v7gBrQp+gTrVF2Xb\nIvBj93rMZ1eJSAleaqa36McSTEyIidL0sIksRauLHjPGwcG207jeNHSEkXt06jt5tMCsISNo9Ytx\nYoyp3+L7uHfyBuw/ovwX/WwR+DGf+hNrVANnZEPKG6EkjT16umF7YGMOPXea9oe8SiFKmd/s6hOx\nf92rMjtD5cwuH0QSItuLirpOFJa1aLdxlU3FgSPSP2kvRdU7foiI7MrS4wwbDhx5XawduY8XBQIB\n0x9FknXabVieiUjDdj1iQ0a1WQEEwk2PY/onpxwHkZ4SNDFKq1C3e/jXtf4+fVtKybESW8z4MX/I\nqC4FnIVD6YcFUQ3W4/Rm18cDlJTbpIdosSywWHKIbMXs+sNXLlgPxzlkhjeWHkJXr0d2ALrfo+/L\nlo1mixk/5jfX8lNg9njd7P0TJcVCGodZQhwEa0+LesXzQqReKnVRdf0NmHMLim0GkXVtzq3BKaNO\nUjSzWi9mNBW2mPHjhIsiq3cEDshiIiJNGdlum3JhRERkBJltlW5NWkD0n/Zm8esKIqty93tlr6u0\nvfAHgD+9lI15WUWKvld0vFXhnlJji8CPnZg+nZRXAmRxLKFEzqHF3TCx6xfT+9IY7FqJUhdZn40K\nqAeM3Fl4hzG/6mXc3m0n4MRgHFmOWBMg1Z+LjmcUFs4Bjxf1Lb3I2nE8anlzex98MW+5j0xbdm61\nRJqS71PJOMwWgR+rz5ZJJhAIWH7QaPMsJlLOQpUy9iLXjvXR7u00kRg9fwqWKF1YqLvVHztDIkvb\nfqhWds8uu+lKsMHK+i7c/8JGvPphrtytKfLJ5mOy17VH4Mfk/SvqsCTWtXo/kFqfbPGDIktKqwEg\nkUyp9BFaVSXnBjc0bGzYbhGlzIwX1gdMesePnjh+IrIOry+A7P1V6HF7hmf8RQzmWjr6AABlNe0A\ngD0F9VHf12rstTWvRva6tgj8sJ2Tj3lFRESkDavftCGyA3PGpgGRf0G36Elo5i6bDCJzaVkHEwWt\n1+yuwKwlhzE98+DwviN2/sjM7RqmBPDHPCqWSktmj8CPjaIZHq8fAKKe46tq6NZ0H59sLsVH60vC\nf6+MeI7QM+jHKx/k4sGpW1BW2yH6/c251fh4k6A6Hb39g/jtv9Zi7PgsjB2fhYdf26Z4G69l5qG6\noStu+Qdrj2BX/gnVadRDW1d/+N/b8mqQuaFEct33VhUjp7BedhkeOz4rrmLLNT0zD62dwehySVUb\nZn18CN1uD2YsPojK+vg8Xri6GMu2yJ8eKEbuXbx9RcEo9+INJZqUPaXe/jRf0R3HoxVteH3JIfh8\nftHPF607ip2HTiT9Tv+AFzMWH0RFXWd42e6CurjtlZ8Y/rzf4xX9+chAIIBZHx/C7yetFz2fkZra\n3JiemYe2rn68/Wk+DgpN4c92HKrF2PFZaGxzAwDyS5sxW2H+FJe34o2lh6PauSkLh6ew9vYN4rXM\nPGzaV4VZHx/CoFc8HwGgvasf0zPz8PEmAWPHZyGnMFhWBr0+zPz4IEqr22WnS0tbDlRHtbOJ1DR2\n47XMvOBdnxQszS7FgMyfDN2wt0p0eWtnH6Zn5qGp3Z1SGhKJLBtN7W7c/8JG0fUembkDd09ci4q6\nTjS394WXv7O8QPRFh0++tQvzVxaFy2Kkpra+uGWJlFQF6583ov519Q6G/71wzREAwD0Tg33Ws3Nz\nMHZ8Fqpi6lJk+9TRPYB9xQ0AgPKhOjx53t5wn/frp9agobU36vuDXh9mfXwoabn9eJOAcY+txJGK\n4AsdWzr6cN9zGzB2fBYWri4GAExddABjx2fF5X9kW59MIBDA3KxC5B5pkLW+lhoj2qFYNY3dGDs+\nC+8PnZeCsma8tSxfdv/X2tmH3/5rLV5csC+l2SQfrjuKnYeTjzHc/YOSfaiTBAIBTP3wAP7nmXVY\nvascc1YUJszX8hOduGfiWry1LF/W9qsjxsH9A+Jt3eNv7MS2vFpZaR07PguvZeYlXE+oag9fhIXq\nsRamx+z3/6ZuiWp3ItuzRH1fMgdLmuKWbT9UG/eekEhdvR5Mz8zDieaeqOUVdZ2Ysfgg+gekX2rr\n9wfw1rJ85B9rTpq2UH8T2c5bg/y2oKdvMPlKGjpY0oSx47OwaZ94H260bQdrZY9xjNQ2NCZsihkX\n9AyNK2saE19T3zNxLUqqko8bmzvUl91QPSuribzeHo78tHcPANAujqHFZmwR+DHbaZ8bJXvdbUPT\nrfJLhxvO3r5BTafSf7D2aNTgdG7EgHprXg125dehqqEbj7+xU/T7Mz8+pEllH/D40O0ebjgjL17l\n2ppXi8nz9kYt8/n8+GTzMbzywQHVadTKV0efEf73/Ij8np55EIs3igcyuno9+GxbGaYs3K9o0Hig\npDGlNG7Lq8W7nxUCAB57fSeyc6sx8e092HKgBk/N3h21bv+AF59uLQsPulOVrAEOCY1TMiXySmue\nweiB5do9lYrOweNv7sSm/dXYL3GxtCS7FK8uOpD0O+tyKrHlQA2efGtXeNnL78c/4xs5/fPNpeID\n6RPNPcjOrUZHzwAmvrNbdJ2QWUsOYVteLSa+swdr91Ri0pyc8GdTFwUHrX9+KRsAMPHdPVi3pxLH\nasQDxWKefGsXNu6rwuHS+IEpAHy2vQxb82rx+tLDyM6tRk5hfLAr5L1VxdiWNzwAmbJwPwBgx6ET\n2Jxbg/GzdshOl5ZmLD4ke93n5+/D1rzalAOpH647KnvdNz85PPxHxGhi7ooibMurxZtLD4t8S53j\ntcNt+xtLDycc7Pf2DWLC7N2Y/Wl0Oc7acRz9nuiLjuLyVqzYHv0CxJAFQ8EPuR57PVj/ciLqUndE\nIC7UJoQG+3lDF1UPTtsatZ3IvvGDtfHt44Gjw+1z34AX0z+KvgjcebgO2bnVScvtR+tL4PcH8MSb\nwbbhneUFaOkMBkg+3VqGQa8PO4aCy5Ft1+xl+VFtfTLNHX1YuaMcz83fJ2t9Lb0+1A7NFwn6vfBe\nMD2hOvP023uwPqcSRyvbZG17XlYRut2D2FvUIBo4TCQQCGBpdile/TD5GGPN7grRPtRp6lt6sePw\nCXT1evDuZ4VYtbMcjW1uyQuNJ97ciZ6+QazPqRQN7Inp7AleBG0/JB7ckXvua5uCF1tbkwSJduUP\n9zuxYwI1YoNT1Q3d2FtUL7ruLhnBRSmT5uaI3nyS2hcQDChvy6vFS0P9aMiEt3Zhy4EarN1TKfld\noaod63MqMfGdPUnT9s7yAmzLq8Vby7Tvb4zy2bYyQ/c3aW5wHPa6Dn10KqZ/lGfKjdhk5mUFxzKz\nlkSPwZZvPYatebV4NuaaMZbcgJ5YMFj03c5yoi0Rl/iKZgkrDQ1oEEEaqXoLBjB7qvWIEfIT4B6K\npg96tetklIi8a6zmToORYiupFSd4nXPmqaioCw6++zzyfgYw8m6Z1MwRMV4V583dH52X3X3BC5/e\nvuiZCH6Nws+J7jyZSSxVPp/yKZJer/Lji/zOwNBgU8lPR/ZJ3JGLzOsed+KOrXeoHMSed6ntAfFT\nSOWIzdOQ2NkridoiqeP12KT9AoCeoXwWm6lllFDddye4o5sqn3/4XMS2MWJ6+wbF1zOguRhU0NYm\nI1U2I/XG1O1U+/5EbURkPQut1yezTUl1BqkWQu2QWJ8pNTiPLGuJROaXnscYassStaVOINaXJ8rX\nyLbO6Pf3KBt3GHMB4fX6RW/wqh2Hix1pom2GZvT0xtSvUDvlSdA+eWXWPSCiHdKhvzFK7I0IsoZQ\nmYody/SH22L9ZmrNWHwwblkgEMCi9UdFb1IVl8f/BLsZIQslcRLO+NFJRsxZsNpP05IykQMbvcc4\nemw+3Uqf2EA0pTpoQgsuJ516BMPNeqTWTo/ySuNbHZwoth8nIpIi1ndbrX9LmB6LpVV36Xa8TqFj\npdp2UHwW4ZJN8h/HFxM7lKhv6YXX55c1lg9I/DtVtgj8mN1wanE3w7BjsOE41ezzawRFh6giP+Tm\nJS9onC0d6pQVGX3n24zTLPexZdE2xoBmR8umzcxWMllR4s2k1LF91JaV81OsPdAluRxTEWkrpmEx\nq4bJad86ht7lI2cbZTUd+NNL2bIeNZZLSRtsi8CPpXuVWAYmVfQiw0ZZZSdGZqseA3o7VSEtOD+w\nJff4bJwPNiq0oeKmVYojD92IYJKa6mKj02SIVPMj0Tmwe5BH9zJi42YunTi9rdDyXZ4hYnmmV3G3\neztDpAel9SLZ2seGfngpp7BeVpsRWmPLgRrUt/SKrtMu8z1rgE3e8WNLMedSr0cztNqu0Xeq00E6\nZKlVj9Eq5TmV6mnaI1cc9NmORYq5bLYLyJqY3GSn1g7nPlH22aUo2CCbTadfWVS/YSPLmdF9aEqH\nZtGGwypjNrK+dCwp5Sc6Rd8/FBL7rsFE7BH4MXmEkEohMyLFoumyyWDKzlLpoJR8hf2fddjl4iTO\nUBnSPf0S2497x1mCMs2Ak/VFnSG71okU6HEHXy2lddpuwTYj+r+UdmGzfKQgo+qwE0pHKnXPbu0L\n2ViKRU1tGVVaL8Revq62W+vuFf9xgfU5lRj9pdMUbcsej3rZkCGNISMExrFJVrNISIvNGzvnldzm\nxc5DMjudHq2beycEw8y6HtByt3KOgdc95rNigC6d6FcH9NmwXn2/YUEmNY/mapcMImM5sfBq0GS8\nt6pY0foM/NiYRX9JOwUxP7NtweMydBqqmpc7y/yyVsMTTs+NZ6WLdiUp0fJUpu1lmOGn3viyFntu\n2QZYm6nnx6ComJXaXLuSOlVyyo/cIqb8PGlwXm3/qJfC94uwKsjGrEoPavvALQdqVKchQ+LfZmDg\nRw4NWgd9GmO+3NkokTO4mMUkWwotvBMvpJ0+KyJ0p9d5Z05btisGFkywUx6tMHWWjgPbWDKX5aql\nRIKslkwi2SxeeBP1KlKfyTkkrXsrBn5kSCWKzye9lLB4bbYx55QR+zDigiYNftMrvbHeWgLrj3VF\nBsj1bHOdGIhPJ0bWYbFyqMcsIDXlXY9fnrQSZe/TtOhBUEI8beow8GNjzin71j+SqA5Ct2nN6r5H\n2rPsuyMsd3sxmsWTZysJ7yJp1FQoOV9qZpzYrVwYObsmdldOuihxwrHYrOgayn5jFn3Sa798iGHz\n5FNq7NI+W3Y8rpDSYYXfH8CA16fZ/hn40ZjkdC5dfs49fm+273gcS/55UdMGx31X5wbd3qXN3qlP\nJNwO6HzhKnfrNhlXpG4oI+wygDKCaYM0u0WXdGbmo2E8E6QHJeWKzUE0M24WWJVTHpulxKx8nuWm\nbdnmY5rt0x6BHxsOpg0Z9NovW2wrMqv1vrgz5LRatx3Uho3rhpyky/5VLxucZxs273G0zmajsyTp\nOUjwuQNOn6aYH8ZT2s7xHJlEYcZLtUtWPX96jPtF80Cnfj3dbhzzRk2QVbNBOlkWTXAKPttWJmu9\nxrZezfY5Us2XXS7XmQDmAfg2AD+A+wVB2KdFwmwvbtq29rsQ7w9scKVnR85pZ8jqTHqZPAdB9mPl\nM2aHoGMyph5DkpNr++pqs/Jh9+xOxsp3xdUx7rjEAicckyfi9FpFqZBqipzYRO0rbjB8n2pn/MwC\nsFYQhCsAfAfAUfVJsh6rDrB4oWYfRp0qw8uEVYugSAdhVN6ovmumYedm537STs2b3QckRqXfTucU\nMPaiLdG+1D3+a36mm58CsiO92iU9qoRU/bXaLJpE6VH2YmQNEqPD9qyV2/ZgtTyTnOlntYTaVMqB\nH5fLdQaAnwiCsAAABEHwCoLQpVnKIni8fj02K9ugyfuX0jfgRdaO4wnXaWxzo72rH+7+QewuqENP\n32D4s8gBYWObO+67ze19yCmsC//d0NqL1s6+hPtz9w8m/FxM34AP7y4vQEllm+LvmqG6sTtu2a78\nE+F/93u8WL2rHHsL68PLFLVXKlq30uoOWQP9ju6BlPeRqmRlR0vdvfHlcNDrxyGhKfz3qp3l8PkD\nqKjrRNHxFrR39aOhNXo6ZXldJwZjXqrW3tUf9XdjmxsrtkfXw263B0cqWtHYGl2vZNUPGad/wOND\nW0w6xLR0yM/zz7YdV32Ocgrr4PXFt5eR+VrV0CUrH+qaeyQ/G/T6cLy2I+H3a5u60eP2JN2PXInq\nVVuXdH06WtGG1bvKw9/vcXtwIsGxxe5rXlYRmtrdaO+OL58hRcdbcKi0GQBQWR/sht39gzhY0oTi\n8lYcLGkK92M1jd04XNoEn394H6XV7eF/C1VtUZ8BwLyVReE2I1natZS9vzppWensGUB1w/DQY/pH\neagRaaMBRPV/ifR7vACAovKWpOuGLveO1bTjw3VHUVQ2/J3GNjfauvpRVtMBX0S96BYpl36F7X51\nYxf2FtWH+26/P4Di8taE9cInUjdDjla04VhNu+TnIYFAAGW1HZLjooFBHyrqOgEkvnD3DKp7WWVs\nGW3v6sfOwyck1g6Wa6EqOMaIzOqSyraEdbu6IViWIg/F3T+IqoYuHK/tiOsf5Ogf8CKvpBH5pc0o\nKGtWvI2+AS+q6uOH22U1HSgub0XTULkDgv1uU3t0P+T1+VFW04ETzT3o6k3cRtY2Dtf3UDpj0ytU\nJi83ANDn8aJMonyK9Weh09LQ2ov5K4vwwdojceu5+wej6n8ssTLYG9Om9PQNSrYZPn8A63MqJbcf\nUlbbIR78kVmtE9XNWKFjij2vgUAA2bnVsrbhGfSh/ESwnvr9AZTVdES1QVUxedrQ2itr3Fjf0ovO\nHuPHlyGx47NIsWOCPQX1kutGGhj0YfWucsmyq0YgEED2/irRfq6yvivcF+kpEAie/1CbKlWn5I7d\nkolsP1o6+qLGqaG0AEC3ezA83nD3D2LtngoAwWtGK9zQUMJKyVXzqNdXAbS4XK4FCM72OQDgYUEQ\njLu6M0ilSAebjBH3Cf/43Mak6/y/FzcBAL501qlxF4GBwHAHsmFvVdx3738huP23n7gBF533Bfzv\nlOyk+/vHjO1J1xGzencFVu+uwKrp42C9+HP0XZLWzn509gzgzNNPCS975YMD4X//asKauO/vSjAg\njd9X6rw+P8bP2hH+u6VTvBOUcy61Jqe8aiVUdiPNWHwQTe3DdWDB6iNYtuUYut3RHVmwDAYtzS7F\niaYePHnvNeFlf5i8IWr9UB0L6e334t7JG0QvjH7z9NqkaZfq0LL3Rw/o7p28ISqtYmKujVDbJD64\nBYD9Rxqw/7mGpNtMZMrCXNzyo0tw2ueiu5Yl2aX43a1XoLWzDw9O3YrRXzoN7074GYDgRYyYlTvL\nw/8+UtGKb371nPDfUxflIaewHi/8+cf4zmXnxn233+PFX1/ZglNPGYmlU8akfDyRZn58CP+853tx\nyyODibGnrqnNjcff3AkA6OgZwO9uuQL3Tt4Aj9eP5a+MxaiR4vdeIi9CDpc244EXNomu19jai3+7\n4AxMmL07vGzA40NDay8mzN4d1ebf8P2v4MFffQd/e3ULAOB3t1yO3/zcBSB43kIefX0n7h5aHiJU\nteP3z67H4hdui79QlGiwCsqSB02SmbXkEGYtOZSwTP5u0vq4ZX97dYvod/4ks9175t0cvPrQT9DQ\nGn9DRExTmxuPzNwRtzyybfjVjd/AH277JgDg9yJpVppfnT0evLhgP4Bgm7V2TwXe/awQAPDmo/+F\ni798BoDoR3jeX3sU94/9luj2QuU0Wf3fW9SAKQv348ZrvoJ/3B1fH154bx8OlzZj+sPXJdyOu1/d\nBU1kfvn9gXC7fMbnTxZtEx59Xfz4HntjJ5764zX40ZWj477T1O4WnYr/yMwd4QuSH1/1ZUy49weK\n0v7EW7vCF90A8JOrL8Tjv/++7O8//sZOVNZ3Ye5TP8MF55wGIDhO/efM6PHXqunjwv1u5HHPWVGI\ndXsqAQAjRmQga+odomPW8rrOqLHNa5kH8cQfrsEvnlgdtd6riw7gJ9+9MGpZYVkLrrz0S1HL/vzS\nZsljujemX40UOV75ZPMxvPHof4X//r+pW9HS0YdFk2+R/H6sv76yJervP03ZhG73IJa8eBs+/7lR\nUZ/NXVGINbsrkm5zucx3dUi58/FVstcN9TNvfpIftfyNpYdF169vib9h8NL7uThwtBEv/9+1KKvt\nwLysIlxxyRfDnz84dSvmP/1znPfFzwMYPgfJ2oc/vTS0nrxD0ZzU2DZ2TFBR1ynr5hkA3PXkcHl/\n+f+uxbe+dk6CtZWZm1WEVTvLMWvJ4ai8rarvwkPTtuLyi8/G1L8nbkvVWrmjHAtWF+O3N7lwz82X\n46Hp29DU5sYHz96Ms7/wOQDBAM2DU7fiwnNPxztP3qhqf794YnX4WO97Prp92ltUj46hwGFTmxt/\neXkzlr8yFo/M3BE1pt5xSPyaql9kPFks4waO1WkZOFLzqNdIAN8D8JYgCN8D4AbwpCapsjHTo5AS\nEYa9KjMAACAASURBVCexO/9yUyp38AuIdzBO1KHwjsaRCvmzmdQWoWM12t+VcILIoE9IbNBHzO6C\nuqTrRBp10gjVswTPiggqhigJHkrRun6KvRdid/4JyfdFhNqhuoh0nDLqpKT7iQ2+5wzNpjt+Qrys\nD3iCd5Skgkqp2HKgRnT50QQzFZsj2t1Q4C40gzXRXf46medJrEwHl7vj2vwdh07AMzhcLvNKmmK/\nFpZ7VPy5cy1nUJlBbKaNmETnVEzsnXcx+yMCCLGzVUQpfM/B4aEZXwBQKzEra2+RvDvciYRmzeyU\nGHgfjpl5ZoiIPDmSwsxhoUp8xorYTGggetab3FkDkSKDPgASzlQSE8rbyPQlCurH2p0/3Kf5E5TF\nmobobe7Kl98XHo85Ri1FVoFQOyc1JhOrLr0xM/9C4wCx/mJdTmUKKUySAJ1s2i8+20dspuiBo40A\ngMq6znCdjW33mhXMGE7NcObo/bhx7JigqkF+fYlUWadtuZYKKoba8BKJtklLoZtXB0qCZaJpqF2J\nnOEVqmd6z/gtEZk9OOj1xe33SEWr6Pf7RGZInWg25rr0wJFGyc+iy7e57wZQE/ipBVAjCELodsAy\nBANBaS10wWP3dz5EMj2YRZZltefXrUCLun/KycmDIU5x0kkOaiwTMPP9CWRvitsUq5UfJelRm3ZZ\nlcdqGWRN6nJJvzzm2SPSjpxxvFFjEjtfO9dLPIoPWGtMl3LgRxCERgA1LpfrsqFFNwI4okmqbMxW\nQRKZabXREenGTqfVSMwX7UlmqZ17RAdL9ayw7jiHLqdSYqNW+ZUgOcdsRlrTqlqleLBp1fao7DdV\nl2A9XiStJlGavURZiw0Nb8MuZdImyVTESnnv3F8XtA5VP+cO4O8APnK5XKMAlAO4T32SnCF2wGPF\ngJD1UuRcytoy+58Ztt0qSF3waZCnRnWq0j/Hmdr+rVycMkYk+jWmyJNpXL0Wu+COzXqt+iTO+rMe\nyXOSBqcqlWJtweGZLFaqe7HtiV3zNJbZYxkjslFNX2B2/jiWBeqPGXXYieXJ7w9YKgagKvAjCEI+\ngGuSrkiW7ATlpsmB9dDSrFhWpEg10nY6BrtwQj00qvOz4l2j2ENPmBMmVyC9cs+JzUJGRoYuB6bV\nJq1XE/SlaRsTsSkLNikqmgl5X7TgIStmxfNmJie2wXJwTKqOUUFmsbGbFcdzStz73Iao9yWZfThq\n3vFDiZh1Yi08ACV57NRB2SmtRjG7UQesOcMw3elxRniezSf3HJg1eLVCCTGynMp6w48VMsVqNC6e\nzGP9WOVxTyD9zrPmx5tuGZiEdUq2diKDPqnSspQw8JPWZBYltkuqsW1PH3qdaycUIbvfuRGT6Iii\njjf2BGpQUIwuE+nUjikJVljpkZtEDE+nCdVd0yCTjZorJ9ZNqUNSclrUn0IbFYIUpVJ0rBR8kiN2\n6GGv1KcPuWNEJ44ljcLATxqTO1DgXWVSim2y9tjR2ZvURbdY+8oW15rEzlVGRoYuF91S/a6cZsCI\nLjvZPhzRWlm8IuqdPKv+qpeR1Ha7VssFq77bzS7Bc9KX6mEui1FSDPzoxA7XaKwf8sX1lQozzw7l\nQUvpHCtUP1BMr8yzfVlJcL51D5qr2Lzds53MITcAbWj5Urgzld25dVgo4Xo0dVIlTdGuTB58qb6O\nFctYhRvVs5jYZWyr6B17aUZO3TXu59xtUqBUMPsIGfjRSfyvepmUEA3YOOk2ZZ8c52ww7TFLRSgc\nDBg5dpA75d3QgadIkhRlSRoMvlJhVt1U/FiF1C8DSmwntXZcyaNwxtHvHLFOKKHFeTC6uuly0WlS\nsVFyKCzZ6S10s9EKj++proLmH4IutHhPUAgDPwax4l18XrQbh1lNSoh1fnbqz9Lhro1SUm2A2HJd\nHh3SfpOOpugc6D6xK/kOpKpc5GKjxyGGtQKRr9JiZ5tUOv2iq+nXkTYojjZIomNZKe+tcJ3KoaP+\nGPgxiBUiqXFkv+NH32TYgZENohPym423OmrLgAOKUBSlxcm0H1VMeOKiP9PkjrjUe2DUb5pESJ0y\nKwyYjZe4lBlaBiOyX95jC/rOcDKKluVO6zGq9XLLOdi+m8eJbb2VrjnktkOsA6lj4Edj4QoUUyqt\n2FiEUpR8EGS9tNtNugVCrNSRGE+fk23FMmTFNBkt1TwwemZCXDJT2L1YP6bkMDgbQ77IrIocDOtR\n51I7LYm/ZNaZTimkY9NyGZVsJfVQ85RYd5So/tERkzs5i2as9j9rrvH2Yph9GpUy45rRChMU5J4n\ni1YLW2DgR2PhQmujUmmlMY9VB2BGJsuaOUBGkSprWnTKRg1+zB8+mCPRI26KAiQmtwLpev4iiZ4B\ni/ZPsSLLT1RsQIPkp9SGGNl/2uQc2YGanLTjeUjbdk9qzCEjQ6wQLJDDhsXRcLHjDjPqsNgYyh4l\nzD4Y+EljoUqddL6PoYM24/ZlJGV3xfVLh9akkmq3uytWo/qXwWxUhtKF9GNC2m9TLRYfZYysb1a5\n0JLzc+6pvOvLkKBnSruwRr7rxU59tpILUiX1xS7tnppzxbFBkI2Ke1qRe154/lLHwI9eYh/1snJj\nmyRxhv4ih4H7IrIVO/V0ZqfVpKuYhBcklu4ESA49gndK9yVFKsgipyawZJIYs5txK7BTQMxu2CVa\nQ2jckihIatTsn3T4YRCzD3Gkubt3LrPOa31rr+x1s3OrMXdFUdzyseOzov5++f1c3PzDi1WnTY5H\nZmxHVUNXXFqWv3K7IfuX68FpW/Ef37pA9vptXf2y133zk8P42Q/+LZVkWYadO3Svzx+3bPnWMtz2\n40vw/6ZskrEF8YOvjijXkfoHvHHLGlrdccuUtCldvfE//Tjo9ePZuXuTfvdoRRu63R48/96+8LIH\nf3U1bv7hxejq9eBvr24OL+/s8cR9v9s9KCuNY8dn4d0JN4p+5hn0Rf391rJ83PQfF2PEiOhcONHc\ni+LyVjz51i4AwKUXnYm7brwM3/nGueF1ymo6cOlXzpKVpmQ+Wl+CjzcJ4b+nP3xd1Oeb9lfj7ptc\nOO/sz+Ovr2xGbVNP+LPefi+WbTkW/vt3k9bjsd/9O3581ej4HcmsPy8u2I9V08fFLX/lwwNxyzxe\nP9z9w2VNqG6X3G5VvXhZFUvXzI8PJU+oQ/T2xZftyvou9Moo85VDebo+pzLus0FvfJvzzqcF4X//\nc+Z2AMBzf/oRSmvEz1tz+3CbMXVRHq777kXI3l+FWUsOh5c3trnR1ObGAy8G27GvjT4Tv7zhUvzn\ndy6UTHeoD86aegdGjMiA3x8sBD5/IPzZqunjsHhDCVoj+rnXlx7GN4bq3eFjzZi/sggP3PFt9Hu8\neHDq1qh9+PzDBetf7+YAABY+cxPOOfNUfLjuKJZml2L+0z/HeV/8PABgwapiyfRG9j2DXj/+9FI2\nWjr6otb51VNrov7u9/iwYvtxzF9ZhHPPPhXznvp5XFvj9fnR2tmHc848VXLfalTUdeLv07eF/145\n7Q4AwB2Prgwvu+TLZ2DmI9eH/548L9ie/+W/r4TXn7jRCJ2r/7xqdFw5fuDFTbL7lzW7K0SXP/nm\nzqi/dxw6gV/deJnMrYp7f80RVNR1xi0Xqy9zPisU3UZvv7z+KFJLRx8ee2NnXLlJReHxFlXfFzur\nUxbmxi2LHbc3t/dhy4Ea3PD9r2BG5sHw8mVbjmHcdV8P//2ORL4BwBNv7sKq6eOitj12fBbeefJG\nFJe3Agge39jxWbj2O6Px6O++H16vsd2NUSeNwNmBADIyMjBnRSFW7SwPf37XDd9Avye6n489jku/\nchZm/OOnmLJwP3IK66PWm/b3n+DR16PL3K0/viRuW+9OuBH7ihrwXkSb8fD0bfjv678OMaF2AABG\nZABvPX5D1OdzVxThwnNPx1WXnotfPLEqvPwr538Bb4y/XnSbUmYsPhjVXvn9AZTVdmD8rB2yvr9m\nVzne+awQv7rxG/jDbd+M+mzmxwdxwTmnoeBYC+668Rs496xT8bdXt+APt10RVy+PVLQBiB8T/GPG\ndlx60ZmY8c/r8dz84fHg2PFZWDntDsxbWYSVO4bP6eO/+z6+8W9n4X+nZIeX/fwH/4a//+a7ceWz\npaMPSzeXRm3z8ovPFh8PiTh+Ir5dAICtB2plfT+dpcWMn5NHGneY33WdBwA48/RTopZf+x15hdlI\nYkEfKRv2VumYkmHldZ1Rg8CQfcUNhuxfyg++GR/k0TNNx2s7dNu2ls6KKedOEBrQRFqwuhg7D58Q\nDXTEau+KD7oAwP/FXOyE7MqvU5ZAGcTSWVDWLOu7E9/ZHRX0AYLBSABYvas8attZ24+rSGVwYClG\nrL050dwTt2zjvqpw0AcAymo78fL70YPiFxfsi/1ayiKDPgDwwnvx237gheBFdWTQJ+T9NUei/p66\nKA/1LfKD9XJ1dIuXwQWrpS+YI4ldWEmpaeyWva6tiESvIweqkdbmiF8Mi3lrWX7csvxj8XVz+6H4\nAewzc3KigneRymqjB8J+fyAq6BPy/trhMlhe14mpi/KSphkYvqkkdXMpc6MQV2+P1Qz3YyuG2oo9\nBXVobIsObIsFGpdml0b9f8Ls4Xq+fFuZZDojHxUrLGuRdfG+r6g+fLHX3N4n2tYAwAsL9ifdVqoi\ngz4A0NrZj/aYelxZ3yWaV+98VohPJMpmrN0F8f1NU5s77pwA4kGHd5YXiCwFSqqiLxorpYLHCu04\ndCJumVgfXVAmHmDp6k3eZ8f6aH2JJkEfAHHnUCmx8bBcMxYHAz4dPdFpCPXnqXpoWvxYZld+XVxf\nMOjzh8cLkUEfAFE3QaSUDbUfsUEfAHFBHwBYt6cybtmfX9ocFfQBgu1evcjNNQDhdgAA/AFg9rL4\n8v7s3L3YeTi6XNY0dqO0Wtm4fcuBmqi/TzT3xI29EgkF7T7ZHJ+Xm3Nr8NH6EhQeb8GkOTl4cOoW\nAMAHa48qSmOoX+mLuUHZ1euJCvoAwKuLDkSNx4DgzTAx0zPz4s5XSVW77LHH0co20eVyxzjpLD0C\nP6NOkr3uKJVBolOG9hU7levUUzi5Sg217zU48/STVX3/c6fIL0PpRPLxAhvP1pRKu36TmGJeqKfT\nnuTOwtJi72rrq1geKClTkavaePKZ5lRP17ZxvdaCzyeef1ac4ShZX8TSKuMA9Dz1mvYXKZwLuRfX\nRhZ/yZf8S/VPFiyDdjP82yzMzFSI9S92Hgsmpv2xZmSIblYTKuKHitjxpe7GM7dSpEXgxwoNjxXS\nYGtqG9R0v2Ih1eT2Z3rVdSu0IbFJyFDYg8QdQ0BioGCTsYMWyRQ7fssevmUTZm96DJalNpnynjRq\nf8RuFmh5+HoW0REGNsIBBBRluRX6B6Pw2pLMl0YVTgdKA9uknbQI/BiJ0U5r4h0cY9m5GqgPEtq7\n50p07jJi3nshdaRy70obUS/1Lot2KuumJ9X0BJDZUm4dZV4RRI3BZO4sdtPDv3gaU2ANnfKjbPX0\nurmVRg2JDh2M6pkpEsvt1BeqJZqHGvwaK69VSG9pEfhx6k9pp5N0GtLYSXoFOkVmZhh4p964WiB9\nTFqnQPNZCUazTUJJKSWn1u53KeUcq1aBBd1/tUWDOim1CcPjPoqm/OiUEAvSe9jh9CCa2joo+euG\naTQedHYJSV0aFQHbSo/Aj4H7SoefoiNKxtbVQHXaHdzzxT3rpSyzjCoX0fvR93yoPaYUX7liDjvX\nax2pf6eV9vQqQmqPVay+yLlgTLTXgMS/UxYI7TNmhqORj3opbATSqWrq1jymUybqwFZ9mcyESVZ5\nWw9ybYZZram0CPwYKZ0i3kZirpLZ5Fdtpb2UtXq1hI96xV4I6ZQGTd/1ysZDO2mel1L9u+rp+TbI\nV62uc/QOnkSeo1T3FAj/P/rEGH2tpyTIlk7Xoek0zrbikUoWNRWJteJxAgneRWNsMmwjndohu0qL\nwA/LIanFMkRitBl/qrzzpLFEqYlNg+rZLjZ/1IvP4zuXWNl06oxeI6+j5WahkrzW/hFU8QwxdsaP\nsvWdWjZJe1YsKnYL5umVhzbLhjh2T386SIvAjzIstZZk8mlhqSCKJ3VHWv6YSP/Qie7bV7sD0e+z\nxbET1Y966TFalpqdZPLIXG5eKUmn5o96DTHzXS8BBBRdXFrwWp7STDrdCBFrG1gH5bNCcN0sZh9i\nWgR+jGyK0qfZM1Y6dShkLslfrJC5TGmjHvdLV5IdorLtpizBBZdRnbLaa1Ojrm1td3fLbulNE2l1\nWvRuQlLITOmZhyaembQqFNZgxZc6W7GPkX65c/wysy9ypajOVh2OKxBQFuwlSkVaBH6s0XKyNqth\nlwtBIqXkF03z25C4FKh88aF0vWSFdSKeVfOlGrwNf0+nn4LWSiplLP7n3MXXG2Hko16G7cl+dP9V\nL/O7Wl0ZGeCSe66sOkaXKgtiy9NhtopW+O4k86RH4McSLNqqkSxsjNKHZOct1lOJLNOv7zemDUn8\njh9jXu6cTjXO0F9CSZ9s1UD8SdDrMSldnvSS3JfcuYv6EGtfxWdOxv6ilrztR73cOcXybvqsywRp\nkJIxIn0qt271MF3G6RYP3tqVFuXSqgGwWHZJJ8VLj8APo7D2p7KRYRHQhxMbf6PfgRH/qJfUmuY/\nZhX/cmeFP+cecwyG5LSOO9GiqBha3hxYX53B+idGq9Yn9WCMtutFktsuGRv4EW9fpdpcDnGcyex3\ncomSKIOWTKsEGyXVkdL5mszsYx+pdgMul2sEgAMAagVBuEN9kszFxoCIxLBpUPKLPOLL4+6oSja4\n2uV22tzFJbIBvR+XSuXiM75dEl/Piu+ACTP7asJAbNHV0aukqDkvUvXWqsEkJUFZuax5pPqQfNQr\nfZox02gx4+dhAEc02I5+LNFwsDSrofbiTfU7gtR9nRzKyHLhxA7R/vXKPo/+kDJKzoHaumngj3ql\nvq5mU37EEiDjaynsP9VAjeR4w+BHvfirXuL0ah8tHdizEum3oWvOb9G+ULfgmUWP1zisg3pTFfhx\nuVwXAbgNwDxtkqMPJfVI9cVV2ldaImfSr0OWt2ErdIexd7RGSLxXQvKdz2Kf6Nxm6j2Q4oyi9KHX\nyzvtVILU5oAV2rFYcY962fDlzk68MSCJV8eqGPkSYtX9o0Xf55RW9U0Exz32pXbGzwwAj8Fe4xay\nIbP7ea/Pb24CLKqstgN9A17sLqhD5oYSvPT+fmzOrbbs9Fw5pAZFBWXNccvED1PZiCB2G0cr2xR9\n30ibc6s132ZTuztumVTxqWroglCVPH8iv97V60kxZcl1uwf/f3t3Hr1HVed5/PPbkpiERUDWQFhs\nLjaKC7YtIC7Yit0t0m13jwd16Dk9Ou2MjozNzNgT9QhHoDkCh3ZppxmQVQ20eGyCzb7ThAghhD0X\nJesv+SUh+/bjtz7zx/Mk/vJUVX5Vz63lVtX7dQ6H/Oqp5VY9937r1ve5VZXZuvMwPt7Q/OfX5La9\ntLe1Y1d2322YZ+36XLcX1/pNwTb00tINofNOFpnPufAOXT13kRqNhta8vmPP9H2NhlgxsC1WOZ98\nYUDPLAkew9/0bwlM2zk4oqGRsT1/r924SwtfWTfpNu5/aqX++uJ7tX7zro4vTi783mN64bXg8VuU\n5/ffCD+TjEcMgchitMrc+2zq60zDlh1Dmax3eGRMTzy/RiMe9ffeGB6bfKZ9WD2hDe/mWlOGR8OP\nT9h5e95jS7U9RpzO/qbvtvXGXPG4Y1/27ieXa3BoVP/j6kdilSnOsQpzz5PLNTY2rrUbd+qcC+/Y\n57wXX7cg8rORUbf6hjiKzRp2/IwfY8yfSlpnrV1sjPmwit6TfUhSsBJfr1baynXbnZYfHBp1Wv7a\nf33Rafmq+u4tC/Xh98zSI4v690yb//yAjj/ygAJL5SYqaTX/+YFYy+8/oy/N4uxxxCEztGLt3u1g\neGRMU/p6Yi0f9mv1koRJpmVr4l3gbYvZeWk0pP71wY5p1LxfueLhWPPmad3G4EV3EmHV7bCDpzut\nM8qRb5mx19+PLFqlq+c+m8m2wvzjrelu6zvX/zrys1WO54wwazbsTH2dkpyvbi698anAtG9d82To\nvE88F0y+tW/+oYWrZGa/Wf/3F88H5h0LSTx85cp47fKykHJK0o9ufy4w7ZFF/Xq5LT5dfN0C3XnV\nuYF522P2pm1v6D9fcr8+/J5ZscoVliCa86MndOl/PT0wfXgknwujqCpx6Q0Rdd7bHnj67pq/PJP1\n3vhvL+vxxaszWXenrvllsA0m8aXLHwxMy+oyZ/GrwcTo7Q/9RotCkr0BDWnNhmBf4O6MvuuJfdZ9\nefG1jRGfxGtwP7r9udD4Fub+p1bEmi/MP93+nIZHx2Jdqyx8ZZ02bBkM/SzsvBGVbHYR9QNn6O+u\nlbsuL3aHXEb8nCHpU8aYpZLmSvqIMebmdIpVnLCvoyfBazLLPNIhqd4etwFjcS9YJWnztjectjUS\n8QtFXmYfvl+h28/SS8uCJ8b+kF+ZyiLZMOhge585fUronMdE1IG4EWP24fsHpiUZidbXF2yvYaNt\nkog6VINvRCRaA/PHj5e+Dq3eusNt1EnYfh196EyndUY56i17r3fVuvK2U0l6eVl04nLzdrdzhq9n\n8mOPCMaBpau3Rt522anfrgqOwslb2EimJMJGO4RJEvNzHf0bUq71m8Mv2DwNj6USZzRp3vYV43wz\nsDE8Mb50zdbJF+6Stm4PnktfXbnZtVihXGNLFpbH/GEtyoqB+D927BgMH6380tJgfz7JudDX8yZ+\np+MRP9baOZLmSJIx5kOSLrTWnp9WwdLkWhF7e7s15jjcEvWW5z3VeatRrjMbMQ9gFofZ9btLfHuB\nwyuZfa1nYaMFkvwAELqvLgVKuK1Ey6dTjFxU9UGtPT35vM477URSWrL4cc3HPU26n2VqmyiWj/Ud\n+fZx3J/Vlk0tqvCl0gTF7mQab/XyXqK3c2RXjMqpRwMF0lHF5pLd24zKcxnjnEzxNaNVdiVqcEme\nRZPFbsWpg7vbOvU1PyWqwpXgZc3Oor1lVLG8PH45cu0Pufenir/YJWZN7uq5iwrdfscjfiay1j4q\n6dE01pUFH1+1WgUcl/gq3VkO2be6JAXDvtaoXXetAWU/pK6jYMrEufw57X/SdlqmOlimsiaRV9Oo\n8ijVdpXY1ZLHTIQr1dfqUtioN8SX6gCUR6KQx5dQKbUY8ZME1Ts/lehsxVTlelW1fXNOFGc1f9ib\n0As++ImPVdv8mb3JI8cD45rUzfcrLFNZ3bgmLpJ8r1U8l7Xv0u59LDohlM1toOH7VLr4Cid1Odw+\n3gZbpnNLFRQdx6P5Wq7qIPHTLqMzvbdtzAEjqRClzNU9SacoSRWOHAmU1+iOfDbT3FZIcEiyn5Ue\nIdcux2f8wE+FV/ew7Zc5iCdUxf4Zkiu6GQJA1mqR+HF+gGmCTkHkL9gVPKO4386Q5BfVcvfMqvj9\n71blfetEZjXVMUEQNq/77WcRD5qNeRCqUHeyOK5ZqcLxjqtUpwwPvxdfD1+eb5gpsg7Vqa0iWiaP\n+PG1cSM3aVzXZoG6mb1aJH4SVVtqXX441ii5sJEpiU+SZe7hu3YeyrzvLY1x19un3N4KlqcyRWzn\n2xly7e3muK06idmOon5Y8rQZAm6yerhzjj+yFy/k2ZaOB7Zc+4+yqkniJ//WVIcOQ55BytcLIVRP\n3idfl2f8JBo1F3vO+LI6VOHP8ShRDChTWRMo016VqhOdwoF12d/QO73aVujzqNvI2OBxmVNXo13N\nip9h28tChUrydsLAshEH32WdiNadIDYW3S6oAemqSeInAdcHddao/1F0MCiXeh2sKtb3uKJ23fnC\nKYsqlGcjTjA6qkydPZ8viGutRF9L0a9zL3I7ieUYs8rUtEtUVCRAP9tXJW9xJS8+Okfip03o8xoI\nvBE4MHFVuw5VbOeSnBBT2PUi64b7MzBc35yUYF6nLWXH9Xa/XHNv+W0KCaTxQPnU61HXPv8sh9i3\nesVfPK/2mnSEI20bcZWyLXsmNGY4Hlgf37bWlGOyPbct1VdNEj+OlbbaV+0OaKKoHm9rddjr3PMv\nxV6iLphC3+qVZMWuIdtt8WTbymBjvAHNnWs7zrUOFd6Sg3wdyZbNQ+orwL8qVD4extIsSuRr266T\nos/b3iaZPC1WldQi8ZOkfVHnkAUP+xOpqfK+TabGu56SqPv6yyP84cwFFCSG9nJV+QIgz31z7USn\nUV+cnvETUoDA6qpbVRIdvLyatqchpNoqHA9912go4jmGeZcknkx+8MlzZE2SF3OEvlHW0y8Gk6pF\n4odYnhW3hl+v76W6QbK6e5YPtwcipliQDiS5uC66rL4KvVWMg1UrXn7bZTo/u75NKJ1SAPXm0A7r\nds6r2e7CI7VI/CRBW0yi+F85gSKl0lmpYDuIGxmiDp/rK9LzVOY4VuULXudnqdd8qHD7KKbdfxf9\ng00W7S3RPpW5waN0qG4ok7BYWreknu9I/KSOCh4bwaAaQr/G8l4Jlen2F9cTqmsTTHaoGvETQrEn\n+ip+YbN4ZkmyrVWTt88wyEjap9P2tr37bx9P29FvB4zHy7ri4XFGNWTVxal7lc35vSDBdSb5rcLD\n21slT2NxxfQWXYA8DA6NxZ43rOEMj46nWJoqyS8c9PWWO0fpY2c5LVt2DIVMLe8OX3Ttgty2dc6F\nd+i0dxwRa96e7vA2MPc+q18+8lvdfNHZmjYlWUi/eu6iRPO36+0JL1PcC7GxiJE9Uev1kWts6nbs\nhX/qf86LPe8Pf/6c07Zu+reXnZbv1I2/ekn3Llihmy/6hPp6u7V240598bIH9rnMPQuWO23zM9+4\nK/a8i1993WlbDz69Mva8r/VvDZ2edb7an3NY/IJEHat2vb1uF0GPL16tM991VOx1ZCHi9FCo/3jR\nPUUXIZENWwaLLkLA8Ej865e4Hnx6Vex5z7nwjtjzPr54dSfF+Z2QxrVu0y63dWakp9vtBRa3EPRx\n4QAAFSRJREFU3W9jz/vZb92t3p4u/eHbj9A9Ty6PnG/BiwOx1+l6ujj/ontDp2/aFnY9EN+SFZsC\n0waHRp3Wib15eKpAebi+uzD+8n98+nFu20KukiRbfbN917DT8klHDD35QryT9bFH7B86/Wf3LtHg\n0KhWDGxLtN00nHrSoaHTw59bE3+95pg3B5ePv3gmDjlgWuj0d50YPAZJ9vWAmVODE4ve2Qhx62ra\nfvHwb7VjcESvb25eBPzro69Nusy9C1ZkXazUuCaOmrLN/JRoIGRiHzn1aKflv3fbsymV5HeSPvvt\n7SccknoZXG3Z7nYRiPqY8aa+0Ol25eZMtheWuEni4JD+QJI1zr0vfuJn+65hbd4+tM+kjyTtGByJ\nX4CIwn7o3bMC0/IcWf7Ys8Hk4W/7t4TOe9LsYD8RkyPxkzJ/fhUrgQQHq8qdTlRLne5njuo8Od8u\n5/x8Frflw3RHjELq7SE45a2MR3zGNH8HWMdpL7vn8fFc7Bpz+3p7UipJccJGHtbpXIRyi3yrV1bb\n83Cdud7mFFHYaVPLEwuPP+qAootQSiR+clLN869jFjilUpRBnfa1TqrZrrORc7+uUK7VglelRuhq\n+z9qp+iYm9f2E2+HkIGyq3sd9vW8VvfvpWJI/KAcyh54yl5+eKnoi6DMVHW/fONrR3MSZXoAe36y\nbTRVTkZGVqeYu+xrbazs+QFwlUXjqEB7K9NhoR/QGRI/KatAu0+ARpcVhmiXQ77fUrnrhPt94uX5\nqT9JWV2fh1RHnHnSFau6eVInnUfTlT0ORcj37YBAufnYNvI8ryXZfx+PFTpH4gcO8nvgV9kDT5V/\nLcXe8vymfa1Xrh0YH/cr8k1l/hW18vilL0x9j0lmbbC+hxSoNNeYEZ5o5fEX8B+JHxSIMAfE5dpR\nKfxiOUmi18PQEH03iIeFjSnXh0mmYHd5i67KdVTeWu6g7DvtYyAFQjTUoLrmKNeRi3yxXiHxk7Za\n1e/8et++Dq+Oq+TFR5Siv9hEyZT6VMLC97U+hxoxZV8lHLaQ4HbDopOFhbftnCTdz7ocF8AHWURB\nX3/PKDq2RB0XX4+X70j8wAEdjbg4UjXClw1HRXe0gHY+18lkz9RKsOKQKwuPD0NAiYoKlF4Wt49h\nH7fZ51qK6iDxkxuqaLsydaCAMImuIRx/nnBtLnne6hX1oNG4ZSh7aEj04MSy72wBCr9tsQPZlzjd\nLXh7C2O+D1ErTBqbJragTLyNOSgW1SJVJH5SRuDKBh0YIChZgiGLRpTsYjN2GcrU3kv0+tOy253v\nKV/aJ3suubCy17eiH6hfwjwk4J2ibyMtmg97n0U/0XmNPhyYCiHxg8KUvbOZCJkrVFJ4vQ47T7u/\nRtlp8cyEjm7ytbAVwYV2UNZVripVujS7kbCg4bOXZm9Rd41y/XCeRUlzTWCX/GUb6ByJH5RC2S+k\nyl16RAmrlpnVVcdt5Xp7DBfmsYV2dnMKGCRQ8sM5IB2hx7Emt38B8FfZr1PKhu5LZ3o7XdAYM0vS\nzZIOkzQu6Vpr7ffTKlhZ0e4B1EeyU2/8Z/yEvWWI4IqmMj7jp+yq3Px8rE1pHO8qf2cAOpdkdJVr\nGCEO+cVlxM+opL+z1p4s6TRJXzbGnJROsVALSd7EkWEx8kDgqyaSEdmo+2Gt+/5PhrxPulzfdJWn\nsJib6BaRkjSuMt32AtRN6O3suZcCSK7jxI+1dq21dnHr3zskvSLpqLQKVjUl6WvkikOCKsqqXpfp\nQiD8GT/VTJQ571KBh4T8CSI12v9shE6vm/hPNctZcXeMApBocCiFjm/1msgYc6ykd0n6dRrrK7Pv\n3rJQ371lYWD6P9z0dAGlydboWH4Pa129fofTtoq2Yctgovnvf2plRiVBUbZsH3Jaft5jS/f5eRG3\nv1w378XQ6a79n5eXbQxMm/f4vvd/ogUvDiTe5oIXBzR9Wq9OeetbQj9fvzm8Dd/x2GuBac8sWR97\nu2HLX3bTU7GXd/Gz+2wu20nLFy97QNOn9WrXG6NFFyWxXW+M6if3vFJ0MWILtOGGNDg0qieeX1NE\ncfa4+LoFgWkLX4nf3lx96fIHA9MGh0Z12/1W818Y0MVfPE0H7jd1z2ePPduvKX09Wrtxpw47aIYu\nuzFe2770hqd0+ilHxi7X3U8uD0x74NcrYi8PFOn1LYO6a/6yoosR29VzFwWmPb54tdM6F7/6utPy\niUR00ha8uDYw7Y5Hg32UVDYW03d+XPvUQqqcEz/GmJmSbpd0QWvkD5C6K3/6TNFFyNW9C8rdYdtv\n+hRt3zVcdDG8stkx8fPK8k2BaROTp0WMptk5OBI6PWoYdNzk1M8f/E1g2kMLV8Uu1w9//lzseXe7\n9IbmBdmdV52baLmwpG5Y8j/KXfOXB6a5JgmrrIxJn91uu//VzNb9zt87JFHCcaKw2BHWUud6kCh8\neVkwDv7s3iW5bX9wKLz+/eSeZhkuueHXuvKrH9wz/YqfdNZ32bZzWPc8ubyjZXf751++4LQ8kJdv\nXTM/9Mfgnu4ujY37N5RmxdrtgWlJfpzyVVi//db7i4/7SI/TW72MMb1qJn1usdbekU6RAJTd/jOm\nFF2EWjv1pENTX6e3bxADoLcdd1Cm629IWrdpZ6bbyEPWl5DL1mzLeAtA9UR1Lw49aHq+BakJ/1Jp\nyIvr69yvl/SytfZ7aRQGQDXU5bo//HXuxW5f8vSkXsHn+wBVFdZa65bQ7ei5asQ5IDX1ijjVREj0\ni8vr3M+Q9DlJLxhjnlWznzDHWntPWoUDUE41uz6oBU7eQH20j/BrNBpchMXg4V0pQGnRnLJRxRdt\nIJ6OEz/W2ick9aRYFgAolaLftDVx+xN/jS/6Ai38lcsASiOkwXZXIKOf5Hqnq6NISqQDUkNzQpTy\nn44K4XqrFwCEICIXKYu+UpJ10lcDKqiqYT1ivzpJ7DPiBwDgKxI/ANCp0E4+Pf8wjCwG/BT6rLKQ\neaow4idzEw4mt1MA8FGuz6LMb1OIgcQPAJRVnmfUJG/1iprOdSOQic5uS4oWmrSoWfvt5Jgy4gcA\n4CsSPwCAVIVd+zQaDUb9ACURGPGjRkVG/MQPQkU/ww2oO9ogkC4SPwDQofAER7Hbr8K2AOQn9OIq\nmPlBQiS6ATe0ofLjlle/kPgBAADAPlVjxA+AsiBlAKSLxA8AdKjoXzLy3D4/2gD1ETbgpwp5n6qO\nyASAuIruu6I4JH4AAJmjnwFkxykpE3anV0iD7apC5gdAaRBxKoC+n1dI/ABAiqp7jqvungHYt0aj\nUYkRPwDKg15HNjiu+TjtHUcUXYQAEj8AgMw11GB4MVBSjUY1RvyERaBOXtseb2PEO8AJbaj0svoG\ny3A+6un2r4wkfgAAk0rU/6KvBpRaWHsvQT8bADCZPJ91RvLOK71FFwBA9Qxs2Fl0EXKxJmQ/+9dv\nz237S1Zs3vPvZ+16jTca6unu0pLlm0LKtcNpWyvXxt+vleuC8w5s2KnN24acytCptRt3anBoVN1d\nXZo6pUdDw2Pqbv0SMzo2rql9PXvmXfP6Do2MjhdSTqBTq0LaXFzLB7ZNur4Va7dpaf/Wjrfhi+Vr\ngvuw5vXw2LhsTfC4xLF09VZ1dUljY1zwAC7Wbx4sugiVlKQ/52rJ8s2Tz9SB1Y592jzsHBwpuggB\nXXll4owxx0padtxZf6++6Qflsk0A1fPgzC9Kkj6649qCSwIAAICJrruu2U/7whfopwF5Gtm1Scse\nulySjrPWLm//nFu9AAAAAAAAKsrbxM8V//3MwLQpE4bjAwAAAAAAYN+8TfyE3oHGA6IAAAAAAABi\n8zbxAwAAAAAAADfeJn4avA8YAAAAAADAibeJHwAAAAAAALjxNvHD43wAAAAAAADceJv4AQAAAAAA\nSNsxh+8Xe97Pf+KkDEuSD28TP42QIT8MAgIAAAAAAC7+6X+dpT8+/di9pn37C+/XnVedG5j3Mx8z\nOuu9R+817bZL/0S/uPyTe0378KmzEpXhx9/8WKz57rzqXO03vS/Rutv5m/gpugAAAAAAAKCSulyW\n7erSlL6evaZt3znsViApkGCasMXAlJu+fXbs9Xqb+CHzAwAAAAAAstDVtXcyZZFdL0nq7QmmSdpm\nVXd3MBHzzJL1zmX6y7N+L3R6+/Yl6aD9p8Ver7eJH17nDgAAAAAAstCeTNk5OCJJuuKrZwbm7W6b\nOSTvk8jXz39v6PSjDwt/9lBY4icJfxM/5H0AAAAAAEAG2kf8jI6NS5J6QrI6n2t7wHP7skkNj4wn\nmr8r4sY0c8ybYy3vlPgxxnzCGLPEGPOqMebrLusKIPEDAAAAAAAycHDbrVK7Ez9ht3EdfMCb9vrb\nNfHT1aVEOY+ot5B99ux4bxzrOPFjjOmW9ENJZ0s6WdJ5xpjU3nMWdqsXo4AAAAAAAEBShx40XZJ0\n3seNJOmcM4/f6/Px8WbCYeJtXd/+wvtD1xU2KkiSPnnGcbHKMj7e0HhEguOqCz4YmPbNv/nD8PXE\nTJK4jPh5n6TfWGtXWGtHJN0qKfjusw6R5AEAAAAAAGn48Tc+pjuvOnfPKJkpfT17PWvnvW87TJJ0\nwMypkqT9pk/ZMy3KH/3BMXv+vd/0Pv3tp0+JVZbZh++vmdOnhH52YsjtW2+a2rvX37tv8Tqslcya\nTO/ks0Q6StKqCX/3q5kMitIjSSODW2KtfP26AY3s2rTXtPGebo2NJbsXDkC19LTy1e3xAQAAAMUa\n6KGfBn/19/cHps0+qKHPfOhQrRjYrt8/qmfPPHM+e6IOOmDaXstc8jcn6675y3Ty8Qfvmf7nZxyi\nxvAWrVq7XRec9w719/drzudO1JU/eUYHHzBNX/r0KZra16Pr572kE44+UMvWbNWRh8zUtK4d2rJx\nh+Z87kQ9/fI63fXEMn3/wo/sWe///sxbdekNT+lTHzxhz7Sr/9s79dN7rV7r36ILPn2c+vv71SXp\n/D86Uguf26llzWLu/Y75lq5Gh0NrjDF/Ielsa+1/af39eUnvs9Z+NWL+D0h6vKONAQAAAAAAYF/O\ntNb+e/tElxE/qyUdM+HvWa1pUZ6WdKakAUljDtsFAAAAAABAU4+kI9TMuwS4jPjpkWQlfVTNZM5T\nks6z1r7SWTkBAAAAAACQpo4f7mytHZP0FUn3SXpJ0q0kfQAAAAAAAPzR8YgfAAAAAAAA+M3lde6o\nIGPM9kk+f9gY8568ygOgWogxALJCfAGQFeILyo7ED9oxBAxAlogxALJCfAGQFeILSo3ED9p1GWM+\nZIy5c/cEY8wPjDHnF1koAJVBjAGQFeILgKwQX1BqJH4QpiGy2ohhsmGvQARiDCZFfEGHiC+YFPEF\nHSK+YFK+xhcSPwBccPIDkBXiC4CsEF8AZMXL+NJbdAHgpVFJPRP+nlZUQeA/Y8x0SfMkHSipT9K3\nrLXzjDGzJd0t6d8lnS6pX9K51tqhwgoLXxBjEAvxBR0gviAW4gs6QHxBLD7GF0b8oF1D0gpJv2+M\n6TPGHCjpowWXCX57Q9KfWWvfK+ksSVdN+Oytkn5grX27pK2S/qKA8sEvxBgkQXxBEsQXJEF8QRLE\nFyThXXwh8YM9jDE9koastasl/YukFyXdKmnRhNm8HLqGQnVJutwY85ykByQdaYw5tPXZMmvtC61/\nPyPp2ALKB08QY9AB4gtiIb6gA8QXxEJ8QQe8iy/c6oWJ3i7pNUmy1n5d0tfbZ7DWnpV3oeC1Lkmf\nl3SwpHdba8eNMcv0u6GvE4ctjokhsXVHjEESxBckQXxBEsQXJEF8QRJexhdG/ECSZIz5W0k/lfSN\nosuC0tlf0vpWUPuIpNkTPusqqEzwDDEGHSK+YFLEF3SI+IJJEV/QIe/iCyN+IEmy1l4j6Zqiy4Hy\naA17fUPNk+GvWkMZF0p6ZcJsDHuFJGIMkiG+IAniC5IgviAJ4guS8Dm+dDUaxDUAyRlj3inpGmvt\n+4suC4BqIb4AyArxBUBWfI4v3OoFIDGGvQLICvEFQFaILwCy4nt8YcQPAAAAAABARTHiBwAAAAAA\noKJ4uDOASRljZkm6WdJhksYlXWut/b4x5s2SblPzSfXLJf0Ha+1WY8xBkm6X9AeSbrDWfrW1njdJ\n+rmkEySNSrrTWjsn7/0B4I+04ktrXXdLOlzN/s3jkr5srWVoM1BTacaXCeucJ+lYa+0pOe0GAA+l\n3H95WNIRkgbVfPjzx621G9IsLyN+AMQxKunvrLUnSzpN0peNMSdJ+ntJD1hrjaSHJP2f1vxvSPqm\npAtD1nWFtfZtkt4t6QPGmLMzLz0An6UZX/7KWvtua+07JB0q6a8yLz0An6UZX2SM+XNJ2zIvNYAy\nSDW+SDqv1Yd5T9pJH4nED4AYrLVrrbWLW//eoeYrCWdJOlfSTa3ZbpL0Z615dllr50saalvPoLX2\n0da/RyUtaq0HQE2lFV8mLC9jTJ+kKeKVzECtpRlfjDEzJH1N0iU5FB2A59KMLy2Z5mZI/ABIxBhz\nrKR3SVog6TBr7TqpGfzU/IU97noOlHSOpAczKCaAEkojvhhj7pG0Vs1f5W/PpqQAyiaF+PIdSVeq\neSsGAOyR0vXRjcaYRcaYb2ZRRhI/AGIzxsxU80LqglZmu/3X9Fi/rhtjeiT9TNI/WmuXp1pIAKWU\nVnyx1n5Czfvkp0o6K9VCAigl1/hijHmnpBOstfMkdbX+A4C0+i+fbd2mfqakM40xn0+5mCR+AMRj\njOlVM6jdYq29ozV5nTHmsNbnh0taH3N1/0+Stdb+IP2SAiiblOOLrLXDkuapOdwaQI2lFF9Ok3Sq\nMWapmg+OP9EY81BWZQZQDmn1X6y1A63/71Tzx/H3pV1WEj8A4rpe0svW2u9NmDZP0n9q/fuvJd3R\nvpDafhUzxlwiaX9r7deyKCSAUnKOL8aYGa0O1u6O2J9KWpJJaQGUiXN8sdb+s7V2lrX2eEkfaE6y\njCgEkEb/pccYc3Dr332SPinpxbQL2tVo8NxDAPtmjDlD0mOSXlBzuGJD0hxJT0n6F0lHS1qh5usK\nt7SWWSZpPzUfsLpF0sclbZe0Ss2Hnw231vNDa+31ee4PAH+kGF82SfpVa1q3pIclfc1aO57n/gDw\nR1rxxVq7ZMI6Z0u6k9e5A/WWYv9lZWs9vZJ6JD2g5tvCUk3UkPgBAAAAAACoKG71AgAAAAAAqCgS\nPwAAAAAAABVF4gcAAAAAAKCiSPwAAAAAAABUFIkfAAAAAACAiiLxAwAAAAAAUFEkfgAAAAAAACrq\n/wNB3N+l3IMy1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x32501b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = original_data[[\"id11010024\"]].plot(figsize=(20,5)) \n",
    "original_data.iloc(splitting_point)\n",
    "f.axvline(start_time, color='g')\n",
    "f.axvline(splitting_time, color='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define Machine Learning Models and Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn import cross_validation\n",
    "from sklearn import svm\n",
    "from sklearn import neighbors\n",
    "from sklearn import metrics\n",
    "import regression_metrics as additional_metrics\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.learning_curve import learning_curve\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.metrics import explained_variance_score\n",
    "#from neural_network import PyBrainNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Feature Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "autoregressive_ftrs = ['measurement', 'shifted_measurement_1d', 'shifted_measurement_2d', 'shifted_measurement_1w', \n",
    "                       'measurement_roll_mean_1h', 'measurement_roll_mean_6h', 'measurement_roll_mean_d', \n",
    "                       'measurement_roll_mean_w', 'measurement_roll_mean_m', 'measurement_roll_min2_d',\n",
    "                       'measurement_roll_min2_w', 'measurement_roll_max2_d', 'measurement_roll_max2_w',\n",
    "                       'measurement_roll_var_6h', 'measurement_roll_var_d', 'measurement_roll_var_w']\n",
    "\n",
    "wetaher_current_ftrs =  ['p', 't2m_termin', 'rh_termin', 'padavine', 'veter_hitrost', 'veter_vek_smer', 'energija_gl',\n",
    " 'energija_di', 'p_roll_mean_1h', 'p_roll_mean_6h', 'p_roll_mean_d', 'p_roll_mean_w', 'p_roll_mean_m', 'p_roll_var_1h',\n",
    " 'p_roll_var_d', 't2m_termin_roll_mean_1h', 't2m_termin_roll_mean_6h', 't2m_termin_roll_mean_d', 't2m_termin_roll_mean_w',\n",
    " 't2m_termin_roll_mean_m', 't2m_termin_roll_min2_d', 't2m_termin_roll_min2_w', 't2m_termin_roll_max2_d',\n",
    " 't2m_termin_roll_max2_w', 't2m_termin_roll_var_1h', 't2m_termin_roll_var_d', 'rh_termin_roll_mean_1h',\n",
    " 'rh_termin_roll_mean_6h', 'rh_termin_roll_mean_d', 'rh_termin_roll_mean_w', 'rh_termin_roll_mean_m',\n",
    " 'rh_termin_roll_min2_d', 'rh_termin_roll_min2_w', 'rh_termin_roll_max2_d', 'rh_termin_roll_max2_w',\n",
    " 'rh_termin_roll_var_1h', 'rh_termin_roll_var_d', 'energija_gl_roll_mean_1h', 'energija_gl_roll_mean_6h',\n",
    " 'energija_gl_roll_mean_d', 'energija_gl_roll_mean_w', 'energija_gl_roll_mean_m', 'energija_gl_roll_var_1h',\n",
    " 'energija_gl_roll_var_d']\n",
    "\n",
    "weather_forecasted_ftrs =  ['shifted_p', 'shifted_t2m_termin', 'shifted_rh_termin', 'shifted_padavine', \n",
    "                            'shifted_veter_hitrost', 'shifted_veter_vek_smer', 'shifted_energija_gl']\n",
    "\n",
    "date_time_ftrs =  ['shifted_HourOfDay', 'shifted_DayOfWeek', 'shifted_Month', 'shifted_Weekday', 'shifted_Weekend', \n",
    "                   'shifted_Holiday', 'shifted_DayBeforeHoliday', 'shifted_DayAfterHodliay', 'shifted_BusinessDay', \n",
    "                   'shifted_BusinessDay_sum', 'shifted_Holiday_sum']\n",
    "\n",
    "top20_ftrs = ['shifted_measurement_1w', 'shifted_measurement_1d', 'measurement_roll_mean_d', 'measurement', \n",
    "              'measurement_roll_mean_1h', 'measurement_roll_mean_6h', 'measurement_roll_max2_w', 'shifted_BusinessDay', \n",
    "              'measurement_roll_mean_w', 'shifted_HourOfDay', 't2m_termin_roll_mean_m', 'shifted_Holiday', \n",
    "              'shifted_energija_gl', 'shifted_DayOfWeek', 'shifted_t2m_termin', 'p_roll_var_d', 'shifted_measurement_2d', \n",
    "              'measurement_roll_mean_m', 'p_roll_mean_m', 'rh_termin_roll_mean_w']\n",
    "\n",
    "top10_ftrs = ['shifted_measurement_1w', 'shifted_measurement_1d', 'measurement_roll_mean_d', 'measurement', \n",
    "              'measurement_roll_mean_1h', 'measurement_roll_mean_6h', 'measurement_roll_max2_w', 'shifted_BusinessDay', \n",
    "              'measurement_roll_mean_w', 'shifted_HourOfDay']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_ftrs = autoregressive_ftrs + wetaher_current_ftrs + weather_forecasted_ftrs + date_time_ftrs \n",
    "ar_wc_wf_ftrs = autoregressive_ftrs + wetaher_current_ftrs + weather_forecasted_ftrs\n",
    "ar_wc_dt_ftrs = autoregressive_ftrs + wetaher_current_ftrs + date_time_ftrs \n",
    "ar_wf_dt_ftrs = autoregressive_ftrs + weather_forecasted_ftrs + date_time_ftrs \n",
    "ar_dt_ftrs = autoregressive_ftrs + date_time_ftrs \n",
    "ar_ftrs = autoregressive_ftrs\n",
    "top20_ftrs = top20_ftrs\n",
    "top10_ftrs = top10_ftrs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class NaiveModel:\n",
    "    \n",
    "    def __init__(self, target):\n",
    "        self.target = target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "horizons = range(1,25)\n",
    "\n",
    "baselines = [{'name': \"Last value\", 'init': \"NaiveModel('measurement')\"},\n",
    "             {'name': \"Previous day\", 'init': \"NaiveModel('shifted_measurement_1d')\"},\n",
    "             {'name': \"Previous week\", 'init': \"NaiveModel('shifted_measurement_1w')\"},\n",
    "             #{'name': \"Measurement average\", 'init': \"NaiveModel('measurement_avr')\"},\n",
    "            ]\n",
    "\n",
    "models = [#{'name': \"Ridge Regression\", 'init': \"linear_model.Ridge()\"},\n",
    "          #{'name': \"K Nearest Neighbors\", 'init': \"neighbors.KNeighborsRegressor(n_neighbors = 5)\"},\n",
    "          {'name': \"Random Forest\", 'init': \"RandomForestRegressor(n_estimators = 100, min_samples_leaf = 10, n_jobs=16)\"},\n",
    "          #{'name': \"SVR\", 'init': \"svm.SVR(kernel ='rbf', C = 10, gamma = .001)\"},\n",
    "          #{'name': \"Neural Network\", 'init': \"PyBrainNN(hidden_size = 100, learningrate = 0.001, momentum = 0.1, maxEpochs=10)\"},\n",
    "          ]\n",
    "\n",
    "data_sets = [{'name': \"Baselines\", 'ftrs': ar_ftrs},\n",
    "             #{'name': \"AR\", 'ftrs': ar_ftrs},\n",
    "             #{'name': \"AR_WC_WF\", 'ftrs': ar_wc_wf_ftrs},\n",
    "             #{'name': \"AR_DT\", 'ftrs': ar_dt_ftrs},             \n",
    "             #{'name': \"AR_WC_DT\", 'ftrs': ar_wc_dt_ftrs},\n",
    "             #{'name': \"AR_WF_DT\", 'ftrs': ar_wf_dt_ftrs},    \n",
    "             #{'name': \"ALL\", 'ftrs': all_ftrs},\n",
    "             {'name': \"TOP_20\", 'ftrs': top20_ftrs},\n",
    "             #{'name': \"TOP_10\", 'ftrs': top10_ftrs},\n",
    "             ]\n",
    "\n",
    "error_metrics = [{'name': \"R2 Score\", 'short': \"R2\", 'function': metrics.r2_score},\n",
    "                 #{'name': \"Explained Variance Score\", 'short': \"EV\", 'function': metrics.explained_variance_score},\n",
    "                 {'name': \"Mean Absolute Error\", 'short': \"MAE\", 'function': metrics.mean_absolute_error},\n",
    "                 #{'name': \"Mean Squared Error\", 'short': \"MSE\", 'function': metrics.mean_squared_error},\n",
    "                 {'name': \"Mean Absolute Percentage Error\", 'short': \"MAPE\", 'function': additional_metrics.mean_absolute_percentage_error},\n",
    "                 {'name': \"Mean Absolute Scaled Error\", 'short': \"MASE\", 'function': additional_metrics.mean_absolute_scaled_error}\n",
    "                ]\n",
    "\n",
    "#validation_df = pd.DataFrame(index=pd.MultiIndex.from_arrays([[],[],[]], names=['sensor', 'horizon', 'error metric']),\n",
    "#                             columns=pd.MultiIndex.from_arrays([[],[]], names=['method', 'data set']))\n",
    "\n",
    "validation_df = pd.DataFrame(index=pd.MultiIndex.from_arrays([[],[],[]], names=['horizon', 'dataset', 'model']),\n",
    "                             columns=pd.MultiIndex.from_arrays([[],[]], names=['sensor', 'error metric'])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results_df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with sensor: id11010024\n",
      "\n",
      "\t-->Prediction horizont: 20h. Shifting runtime: 0.74s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python27\\lib\\site-packages\\ipykernel\\__main__.py:86: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Python27\\lib\\site-packages\\ipykernel\\__main__.py:87: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Python27\\lib\\site-packages\\ipykernel\\__main__.py:88: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Python27\\lib\\site-packages\\ipykernel\\__main__.py:90: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\t\t-->Evaluation (2014-04-18 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (77376L, 16L), y_train: (77376L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -7.639\n",
      "\t\t\t\t\t-->MAE: 1.423\n",
      "\t\t\t\t\t-->MAPE: 127.857\n",
      "\t\t\t\t\t-->MASE: 1.097\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -6.775\n",
      "\t\t\t\t\t-->MAE: 1.237\n",
      "\t\t\t\t\t-->MAPE: 121.429\n",
      "\t\t\t\t\t-->MASE: 0.954\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.824\n",
      "\t\t\t\t\t-->MAE: 0.619\n",
      "\t\t\t\t\t-->MAPE: 84.286\n",
      "\t\t\t\t\t-->MASE: 0.477\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (77376L, 20L), y_train: (77376L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.39s\n",
      "\t\t\t\t\t-->R2: 0.032\n",
      "\t\t\t\t\t-->MAE: 0.502\n",
      "\t\t\t\t\t-->MAPE: 60.864\n",
      "\t\t\t\t\t-->MASE: 0.387\n",
      "\n",
      "\t\t-->Evaluation (2014-04-19 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (77472L, 16L), y_train: (77472L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.525\n",
      "\t\t\t\t\t-->MAE: 0.691\n",
      "\t\t\t\t\t-->MAPE: 81.313\n",
      "\t\t\t\t\t-->MASE: 0.532\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.343\n",
      "\t\t\t\t\t-->MAE: 0.588\n",
      "\t\t\t\t\t-->MAPE: 67.929\n",
      "\t\t\t\t\t-->MASE: 0.453\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.026\n",
      "\t\t\t\t\t-->MAE: 0.598\n",
      "\t\t\t\t\t-->MAPE: 82.323\n",
      "\t\t\t\t\t-->MASE: 0.461\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (77472L, 20L), y_train: (77472L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.75s\n",
      "\t\t\t\t\t-->R2: 0.349\n",
      "\t\t\t\t\t-->MAE: 0.520\n",
      "\t\t\t\t\t-->MAPE: 62.298\n",
      "\t\t\t\t\t-->MASE: 0.401\n",
      "\n",
      "\t\t-->Evaluation (2014-04-20 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (77568L, 16L), y_train: (77568L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -2.880\n",
      "\t\t\t\t\t-->MAE: 0.371\n",
      "\t\t\t\t\t-->MAPE: 40.000\n",
      "\t\t\t\t\t-->MASE: 0.286\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -4.443\n",
      "\t\t\t\t\t-->MAE: 0.670\n",
      "\t\t\t\t\t-->MAPE: 96.000\n",
      "\t\t\t\t\t-->MASE: 0.517\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.748\n",
      "\t\t\t\t\t-->MAE: 0.526\n",
      "\t\t\t\t\t-->MAPE: 100.000\n",
      "\t\t\t\t\t-->MASE: 0.405\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (77568L, 20L), y_train: (77568L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.55s\n",
      "\t\t\t\t\t-->R2: -0.062\n",
      "\t\t\t\t\t-->MAE: 0.403\n",
      "\t\t\t\t\t-->MAPE: 67.029\n",
      "\t\t\t\t\t-->MASE: 0.311\n",
      "\n",
      "\t\t-->Evaluation (2014-04-21 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (77664L, 16L), y_train: (77664L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.051\n",
      "\t\t\t\t\t-->MAE: 0.206\n",
      "\t\t\t\t\t-->MAPE: 38.462\n",
      "\t\t\t\t\t-->MASE: 0.159\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.365\n",
      "\t\t\t\t\t-->MAE: 0.464\n",
      "\t\t\t\t\t-->MAPE: 88.462\n",
      "\t\t\t\t\t-->MASE: 0.358\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -26.114\n",
      "\t\t\t\t\t-->MAE: 1.608\n",
      "\t\t\t\t\t-->MAPE: 153.846\n",
      "\t\t\t\t\t-->MASE: 1.241\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (77664L, 20L), y_train: (77664L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.90s\n",
      "\t\t\t\t\t-->R2: -2.233\n",
      "\t\t\t\t\t-->MAE: 0.573\n",
      "\t\t\t\t\t-->MAPE: 66.263\n",
      "\t\t\t\t\t-->MASE: 0.442\n",
      "\n",
      "\t\t-->Evaluation (2014-04-22 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (77760L, 16L), y_train: (77760L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.556\n",
      "\t\t\t\t\t-->MAE: 1.320\n",
      "\t\t\t\t\t-->MAPE: 74.846\n",
      "\t\t\t\t\t-->MASE: 1.020\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.601\n",
      "\t\t\t\t\t-->MAE: 1.423\n",
      "\t\t\t\t\t-->MAPE: 83.951\n",
      "\t\t\t\t\t-->MASE: 1.099\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.741\n",
      "\t\t\t\t\t-->MAE: 0.588\n",
      "\t\t\t\t\t-->MAPE: 38.488\n",
      "\t\t\t\t\t-->MASE: 0.454\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (77760L, 20L), y_train: (77760L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.78s\n",
      "\t\t\t\t\t-->R2: 0.759\n",
      "\t\t\t\t\t-->MAE: 0.594\n",
      "\t\t\t\t\t-->MAPE: 45.164\n",
      "\t\t\t\t\t-->MASE: 0.459\n",
      "\n",
      "\t\t-->Evaluation (2014-04-23 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (77856L, 16L), y_train: (77856L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.387\n",
      "\t\t\t\t\t-->MAE: 1.247\n",
      "\t\t\t\t\t-->MAPE: 64.152\n",
      "\t\t\t\t\t-->MASE: 0.964\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.577\n",
      "\t\t\t\t\t-->MAE: 0.660\n",
      "\t\t\t\t\t-->MAPE: 54.766\n",
      "\t\t\t\t\t-->MASE: 0.510\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.593\n",
      "\t\t\t\t\t-->MAE: 0.722\n",
      "\t\t\t\t\t-->MAPE: 53.918\n",
      "\t\t\t\t\t-->MASE: 0.558\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (77856L, 20L), y_train: (77856L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.90s\n",
      "\t\t\t\t\t-->R2: 0.766\n",
      "\t\t\t\t\t-->MAE: 0.594\n",
      "\t\t\t\t\t-->MAPE: 48.106\n",
      "\t\t\t\t\t-->MASE: 0.459\n",
      "\n",
      "\t\t-->Evaluation (2014-04-24 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (77952L, 16L), y_train: (77952L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.506\n",
      "\t\t\t\t\t-->MAE: 1.402\n",
      "\t\t\t\t\t-->MAPE: 70.000\n",
      "\t\t\t\t\t-->MASE: 1.083\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.686\n",
      "\t\t\t\t\t-->MAE: 0.629\n",
      "\t\t\t\t\t-->MAPE: 43.830\n",
      "\t\t\t\t\t-->MASE: 0.486\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.659\n",
      "\t\t\t\t\t-->MAE: 0.660\n",
      "\t\t\t\t\t-->MAPE: 45.205\n",
      "\t\t\t\t\t-->MASE: 0.510\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (77952L, 20L), y_train: (77952L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.87s\n",
      "\t\t\t\t\t-->R2: 0.819\n",
      "\t\t\t\t\t-->MAE: 0.550\n",
      "\t\t\t\t\t-->MAPE: 36.469\n",
      "\t\t\t\t\t-->MASE: 0.425\n",
      "\n",
      "\t\t-->Evaluation (2014-04-25 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (78048L, 16L), y_train: (78048L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -4.525\n",
      "\t\t\t\t\t-->MAE: 1.660\n",
      "\t\t\t\t\t-->MAPE: 114.028\n",
      "\t\t\t\t\t-->MASE: 1.282\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -3.288\n",
      "\t\t\t\t\t-->MAE: 1.351\n",
      "\t\t\t\t\t-->MAPE: 134.722\n",
      "\t\t\t\t\t-->MASE: 1.043\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.422\n",
      "\t\t\t\t\t-->MAE: 0.753\n",
      "\t\t\t\t\t-->MAPE: 87.083\n",
      "\t\t\t\t\t-->MASE: 0.581\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (78048L, 20L), y_train: (78048L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.21s\n",
      "\t\t\t\t\t-->R2: 0.037\n",
      "\t\t\t\t\t-->MAE: 0.619\n",
      "\t\t\t\t\t-->MAPE: 59.277\n",
      "\t\t\t\t\t-->MASE: 0.479\n",
      "\n",
      "\t\t-->Evaluation (2014-04-26 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (78144L, 16L), y_train: (78144L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -5.313\n",
      "\t\t\t\t\t-->MAE: 0.649\n",
      "\t\t\t\t\t-->MAPE: 85.185\n",
      "\t\t\t\t\t-->MASE: 0.502\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -4.184\n",
      "\t\t\t\t\t-->MAE: 0.423\n",
      "\t\t\t\t\t-->MAPE: 44.444\n",
      "\t\t\t\t\t-->MASE: 0.326\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -3.157\n",
      "\t\t\t\t\t-->MAE: 0.546\n",
      "\t\t\t\t\t-->MAPE: 85.185\n",
      "\t\t\t\t\t-->MASE: 0.422\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (78144L, 20L), y_train: (78144L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.58s\n",
      "\t\t\t\t\t-->R2: 0.068\n",
      "\t\t\t\t\t-->MAE: 0.394\n",
      "\t\t\t\t\t-->MAPE: 51.081\n",
      "\t\t\t\t\t-->MASE: 0.304\n",
      "\n",
      "\t\t-->Evaluation (2014-04-27 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (78240L, 16L), y_train: (78240L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.455\n",
      "\t\t\t\t\t-->MAE: 0.278\n",
      "\t\t\t\t\t-->MAPE: 48.000\n",
      "\t\t\t\t\t-->MASE: 0.215\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.401\n",
      "\t\t\t\t\t-->MAE: 0.268\n",
      "\t\t\t\t\t-->MAPE: 48.000\n",
      "\t\t\t\t\t-->MASE: 0.207\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.138\n",
      "\t\t\t\t\t-->MAE: 0.165\n",
      "\t\t\t\t\t-->MAPE: 32.000\n",
      "\t\t\t\t\t-->MASE: 0.127\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (78240L, 20L), y_train: (78240L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.99s\n",
      "\t\t\t\t\t-->R2: 0.004\n",
      "\t\t\t\t\t-->MAE: 0.386\n",
      "\t\t\t\t\t-->MAPE: 72.611\n",
      "\t\t\t\t\t-->MASE: 0.298\n",
      "\n",
      "\t\t-->Evaluation (2014-04-28 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (78336L, 16L), y_train: (78336L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.570\n",
      "\t\t\t\t\t-->MAE: 1.526\n",
      "\t\t\t\t\t-->MAPE: 92.827\n",
      "\t\t\t\t\t-->MASE: 1.180\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.549\n",
      "\t\t\t\t\t-->MAE: 1.381\n",
      "\t\t\t\t\t-->MAPE: 79.643\n",
      "\t\t\t\t\t-->MASE: 1.069\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.588\n",
      "\t\t\t\t\t-->MAE: 1.474\n",
      "\t\t\t\t\t-->MAPE: 84.940\n",
      "\t\t\t\t\t-->MASE: 1.140\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (78336L, 20L), y_train: (78336L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.03s\n",
      "\t\t\t\t\t-->R2: 0.386\n",
      "\t\t\t\t\t-->MAE: 0.943\n",
      "\t\t\t\t\t-->MAPE: 48.639\n",
      "\t\t\t\t\t-->MASE: 0.729\n",
      "\n",
      "\t\t-->Evaluation (2014-04-29 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (78432L, 16L), y_train: (78432L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.572\n",
      "\t\t\t\t\t-->MAE: 1.814\n",
      "\t\t\t\t\t-->MAPE: 78.998\n",
      "\t\t\t\t\t-->MASE: 1.403\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.561\n",
      "\t\t\t\t\t-->MAE: 0.928\n",
      "\t\t\t\t\t-->MAPE: 54.395\n",
      "\t\t\t\t\t-->MASE: 0.718\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.477\n",
      "\t\t\t\t\t-->MAE: 1.000\n",
      "\t\t\t\t\t-->MAPE: 57.813\n",
      "\t\t\t\t\t-->MASE: 0.773\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (78432L, 20L), y_train: (78432L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.02s\n",
      "\t\t\t\t\t-->R2: 0.604\n",
      "\t\t\t\t\t-->MAE: 0.899\n",
      "\t\t\t\t\t-->MAPE: 40.720\n",
      "\t\t\t\t\t-->MASE: 0.695\n",
      "\n",
      "\t\t-->Evaluation (2014-04-30 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (78528L, 16L), y_train: (78528L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.527\n",
      "\t\t\t\t\t-->MAE: 1.742\n",
      "\t\t\t\t\t-->MAPE: 78.287\n",
      "\t\t\t\t\t-->MASE: 1.347\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.520\n",
      "\t\t\t\t\t-->MAE: 0.835\n",
      "\t\t\t\t\t-->MAPE: 49.935\n",
      "\t\t\t\t\t-->MASE: 0.645\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.506\n",
      "\t\t\t\t\t-->MAE: 0.887\n",
      "\t\t\t\t\t-->MAPE: 49.139\n",
      "\t\t\t\t\t-->MASE: 0.685\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (78528L, 20L), y_train: (78528L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.57s\n",
      "\t\t\t\t\t-->R2: 0.742\n",
      "\t\t\t\t\t-->MAE: 0.700\n",
      "\t\t\t\t\t-->MAPE: 36.100\n",
      "\t\t\t\t\t-->MASE: 0.541\n",
      "\n",
      "\t\t-->Evaluation (2014-05-01 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (78624L, 16L), y_train: (78624L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -30.902\n",
      "\t\t\t\t\t-->MAE: 1.753\n",
      "\t\t\t\t\t-->MAPE: 164.000\n",
      "\t\t\t\t\t-->MASE: 1.354\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -30.848\n",
      "\t\t\t\t\t-->MAE: 1.784\n",
      "\t\t\t\t\t-->MAPE: 176.000\n",
      "\t\t\t\t\t-->MASE: 1.378\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -22.172\n",
      "\t\t\t\t\t-->MAE: 1.402\n",
      "\t\t\t\t\t-->MAPE: 120.000\n",
      "\t\t\t\t\t-->MASE: 1.083\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (78624L, 20L), y_train: (78624L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.67s\n",
      "\t\t\t\t\t-->R2: -3.916\n",
      "\t\t\t\t\t-->MAE: 0.659\n",
      "\t\t\t\t\t-->MAPE: 71.194\n",
      "\t\t\t\t\t-->MASE: 0.510\n",
      "\n",
      "\t\t-->Evaluation (2014-05-02 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (78720L, 16L), y_train: (78720L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.848\n",
      "\t\t\t\t\t-->MAE: 0.371\n",
      "\t\t\t\t\t-->MAPE: 70.370\n",
      "\t\t\t\t\t-->MASE: 0.287\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.540\n",
      "\t\t\t\t\t-->MAE: 0.309\n",
      "\t\t\t\t\t-->MAPE: 59.259\n",
      "\t\t\t\t\t-->MASE: 0.239\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -4.799\n",
      "\t\t\t\t\t-->MAE: 0.546\n",
      "\t\t\t\t\t-->MAPE: 66.667\n",
      "\t\t\t\t\t-->MASE: 0.422\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (78720L, 20L), y_train: (78720L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.29s\n",
      "\t\t\t\t\t-->R2: -0.187\n",
      "\t\t\t\t\t-->MAE: 0.475\n",
      "\t\t\t\t\t-->MAPE: 55.752\n",
      "\t\t\t\t\t-->MASE: 0.367\n",
      "\n",
      "\t\t-->Evaluation (2014-05-03 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (78816L, 16L), y_train: (78816L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.970\n",
      "\t\t\t\t\t-->MAE: 0.433\n",
      "\t\t\t\t\t-->MAPE: 75.000\n",
      "\t\t\t\t\t-->MASE: 0.335\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.702\n",
      "\t\t\t\t\t-->MAE: 0.371\n",
      "\t\t\t\t\t-->MAPE: 64.286\n",
      "\t\t\t\t\t-->MASE: 0.287\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.702\n",
      "\t\t\t\t\t-->MAE: 0.371\n",
      "\t\t\t\t\t-->MAPE: 64.286\n",
      "\t\t\t\t\t-->MASE: 0.287\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (78816L, 20L), y_train: (78816L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.32s\n",
      "\t\t\t\t\t-->R2: -0.034\n",
      "\t\t\t\t\t-->MAE: 0.444\n",
      "\t\t\t\t\t-->MAPE: 61.287\n",
      "\t\t\t\t\t-->MASE: 0.343\n",
      "\n",
      "\t\t-->Evaluation (2014-05-04 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (78912L, 16L), y_train: (78912L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.371\n",
      "\t\t\t\t\t-->MAE: 0.433\n",
      "\t\t\t\t\t-->MAPE: 76.000\n",
      "\t\t\t\t\t-->MASE: 0.335\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.263\n",
      "\t\t\t\t\t-->MAE: 0.412\n",
      "\t\t\t\t\t-->MAPE: 72.000\n",
      "\t\t\t\t\t-->MASE: 0.319\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.509\n",
      "\t\t\t\t\t-->MAE: 0.289\n",
      "\t\t\t\t\t-->MAPE: 56.000\n",
      "\t\t\t\t\t-->MASE: 0.223\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (78912L, 20L), y_train: (78912L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.43s\n",
      "\t\t\t\t\t-->R2: -0.170\n",
      "\t\t\t\t\t-->MAE: 0.450\n",
      "\t\t\t\t\t-->MAPE: 65.620\n",
      "\t\t\t\t\t-->MASE: 0.348\n",
      "\n",
      "\t\t-->Evaluation (2014-05-05 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (79008L, 16L), y_train: (79008L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.503\n",
      "\t\t\t\t\t-->MAE: 1.237\n",
      "\t\t\t\t\t-->MAPE: 70.000\n",
      "\t\t\t\t\t-->MASE: 0.958\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.627\n",
      "\t\t\t\t\t-->MAE: 1.474\n",
      "\t\t\t\t\t-->MAPE: 89.561\n",
      "\t\t\t\t\t-->MASE: 1.141\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.756\n",
      "\t\t\t\t\t-->MAE: 0.505\n",
      "\t\t\t\t\t-->MAPE: 37.339\n",
      "\t\t\t\t\t-->MASE: 0.391\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (79008L, 20L), y_train: (79008L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.67s\n",
      "\t\t\t\t\t-->R2: 0.746\n",
      "\t\t\t\t\t-->MAE: 0.634\n",
      "\t\t\t\t\t-->MAPE: 47.120\n",
      "\t\t\t\t\t-->MASE: 0.491\n",
      "\n",
      "\t\t-->Evaluation (2014-05-06 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (79104L, 16L), y_train: (79104L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.464\n",
      "\t\t\t\t\t-->MAE: 1.340\n",
      "\t\t\t\t\t-->MAPE: 71.132\n",
      "\t\t\t\t\t-->MASE: 1.038\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.613\n",
      "\t\t\t\t\t-->MAE: 0.680\n",
      "\t\t\t\t\t-->MAPE: 48.994\n",
      "\t\t\t\t\t-->MASE: 0.527\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.012\n",
      "\t\t\t\t\t-->MAE: 0.835\n",
      "\t\t\t\t\t-->MAPE: 57.327\n",
      "\t\t\t\t\t-->MASE: 0.647\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (79104L, 20L), y_train: (79104L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.41s\n",
      "\t\t\t\t\t-->R2: 0.764\n",
      "\t\t\t\t\t-->MAE: 0.595\n",
      "\t\t\t\t\t-->MAPE: 43.166\n",
      "\t\t\t\t\t-->MASE: 0.460\n",
      "\n",
      "\t\t-->Evaluation (2014-05-07 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (79200L, 16L), y_train: (79200L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.622\n",
      "\t\t\t\t\t-->MAE: 1.526\n",
      "\t\t\t\t\t-->MAPE: 85.648\n",
      "\t\t\t\t\t-->MASE: 1.181\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.620\n",
      "\t\t\t\t\t-->MAE: 0.701\n",
      "\t\t\t\t\t-->MAPE: 51.327\n",
      "\t\t\t\t\t-->MASE: 0.543\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.356\n",
      "\t\t\t\t\t-->MAE: 0.701\n",
      "\t\t\t\t\t-->MAPE: 44.290\n",
      "\t\t\t\t\t-->MASE: 0.543\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (79200L, 20L), y_train: (79200L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.48s\n",
      "\t\t\t\t\t-->R2: 0.750\n",
      "\t\t\t\t\t-->MAE: 0.617\n",
      "\t\t\t\t\t-->MAPE: 43.575\n",
      "\t\t\t\t\t-->MASE: 0.477\n",
      "\n",
      "\t\t-->Evaluation (2014-05-08 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (79296L, 16L), y_train: (79296L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.574\n",
      "\t\t\t\t\t-->MAE: 1.309\n",
      "\t\t\t\t\t-->MAPE: 62.845\n",
      "\t\t\t\t\t-->MASE: 1.013\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.648\n",
      "\t\t\t\t\t-->MAE: 0.577\n",
      "\t\t\t\t\t-->MAPE: 43.448\n",
      "\t\t\t\t\t-->MASE: 0.447\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.710\n",
      "\t\t\t\t\t-->MAE: 1.433\n",
      "\t\t\t\t\t-->MAPE: 90.374\n",
      "\t\t\t\t\t-->MASE: 1.109\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (79296L, 20L), y_train: (79296L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.34s\n",
      "\t\t\t\t\t-->R2: 0.777\n",
      "\t\t\t\t\t-->MAE: 0.517\n",
      "\t\t\t\t\t-->MAPE: 37.253\n",
      "\t\t\t\t\t-->MASE: 0.400\n",
      "\n",
      "\t\t-->Evaluation (2014-05-09 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (79392L, 16L), y_train: (79392L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -15.975\n",
      "\t\t\t\t\t-->MAE: 1.361\n",
      "\t\t\t\t\t-->MAPE: 115.625\n",
      "\t\t\t\t\t-->MASE: 1.053\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -15.229\n",
      "\t\t\t\t\t-->MAE: 1.402\n",
      "\t\t\t\t\t-->MAPE: 153.125\n",
      "\t\t\t\t\t-->MASE: 1.085\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.472\n",
      "\t\t\t\t\t-->MAE: 0.546\n",
      "\t\t\t\t\t-->MAPE: 90.625\n",
      "\t\t\t\t\t-->MASE: 0.423\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (79392L, 20L), y_train: (79392L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.65s\n",
      "\t\t\t\t\t-->R2: -0.077\n",
      "\t\t\t\t\t-->MAE: 0.456\n",
      "\t\t\t\t\t-->MAPE: 53.771\n",
      "\t\t\t\t\t-->MASE: 0.353\n",
      "\n",
      "\t\t-->Evaluation (2014-05-10 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (79488L, 16L), y_train: (79488L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.796\n",
      "\t\t\t\t\t-->MAE: 0.361\n",
      "\t\t\t\t\t-->MAPE: 55.556\n",
      "\t\t\t\t\t-->MASE: 0.279\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.207\n",
      "\t\t\t\t\t-->MAE: 0.443\n",
      "\t\t\t\t\t-->MAPE: 70.370\n",
      "\t\t\t\t\t-->MASE: 0.343\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.669\n",
      "\t\t\t\t\t-->MAE: 0.515\n",
      "\t\t\t\t\t-->MAPE: 88.889\n",
      "\t\t\t\t\t-->MASE: 0.399\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (79488L, 20L), y_train: (79488L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.62s\n",
      "\t\t\t\t\t-->R2: 0.042\n",
      "\t\t\t\t\t-->MAE: 0.388\n",
      "\t\t\t\t\t-->MAPE: 69.504\n",
      "\t\t\t\t\t-->MASE: 0.300\n",
      "\n",
      "\t\t-->Evaluation (2014-05-11 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (79584L, 16L), y_train: (79584L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.787\n",
      "\t\t\t\t\t-->MAE: 0.351\n",
      "\t\t\t\t\t-->MAPE: 65.385\n",
      "\t\t\t\t\t-->MASE: 0.272\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.103\n",
      "\t\t\t\t\t-->MAE: 0.216\n",
      "\t\t\t\t\t-->MAPE: 38.462\n",
      "\t\t\t\t\t-->MASE: 0.168\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.680\n",
      "\t\t\t\t\t-->MAE: 0.526\n",
      "\t\t\t\t\t-->MAPE: 100.000\n",
      "\t\t\t\t\t-->MASE: 0.407\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (79584L, 20L), y_train: (79584L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.33s\n",
      "\t\t\t\t\t-->R2: 0.080\n",
      "\t\t\t\t\t-->MAE: 0.347\n",
      "\t\t\t\t\t-->MAPE: 74.068\n",
      "\t\t\t\t\t-->MASE: 0.269\n",
      "\n",
      "\t\t-->Evaluation (2014-05-12 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (79680L, 16L), y_train: (79680L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.615\n",
      "\t\t\t\t\t-->MAE: 1.340\n",
      "\t\t\t\t\t-->MAPE: 79.454\n",
      "\t\t\t\t\t-->MASE: 1.039\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.670\n",
      "\t\t\t\t\t-->MAE: 1.474\n",
      "\t\t\t\t\t-->MAPE: 89.598\n",
      "\t\t\t\t\t-->MASE: 1.143\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.654\n",
      "\t\t\t\t\t-->MAE: 0.670\n",
      "\t\t\t\t\t-->MAPE: 45.431\n",
      "\t\t\t\t\t-->MASE: 0.520\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (79680L, 20L), y_train: (79680L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.37s\n",
      "\t\t\t\t\t-->R2: 0.765\n",
      "\t\t\t\t\t-->MAE: 0.598\n",
      "\t\t\t\t\t-->MAPE: 44.042\n",
      "\t\t\t\t\t-->MASE: 0.463\n",
      "\n",
      "\t\t-->Evaluation (2014-05-13 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (79776L, 16L), y_train: (79776L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.169\n",
      "\t\t\t\t\t-->MAE: 1.289\n",
      "\t\t\t\t\t-->MAPE: 59.967\n",
      "\t\t\t\t\t-->MASE: 0.999\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.699\n",
      "\t\t\t\t\t-->MAE: 0.639\n",
      "\t\t\t\t\t-->MAPE: 36.144\n",
      "\t\t\t\t\t-->MASE: 0.496\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.715\n",
      "\t\t\t\t\t-->MAE: 0.608\n",
      "\t\t\t\t\t-->MAPE: 43.366\n",
      "\t\t\t\t\t-->MASE: 0.472\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (79776L, 20L), y_train: (79776L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.50s\n",
      "\t\t\t\t\t-->R2: 0.796\n",
      "\t\t\t\t\t-->MAE: 0.646\n",
      "\t\t\t\t\t-->MAPE: 38.394\n",
      "\t\t\t\t\t-->MASE: 0.501\n",
      "\n",
      "\t\t-->Evaluation (2014-05-14 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (79872L, 16L), y_train: (79872L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.764\n",
      "\t\t\t\t\t-->MAE: 1.371\n",
      "\t\t\t\t\t-->MAPE: 82.661\n",
      "\t\t\t\t\t-->MASE: 1.063\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.558\n",
      "\t\t\t\t\t-->MAE: 0.722\n",
      "\t\t\t\t\t-->MAPE: 57.953\n",
      "\t\t\t\t\t-->MASE: 0.559\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.638\n",
      "\t\t\t\t\t-->MAE: 0.526\n",
      "\t\t\t\t\t-->MAPE: 43.392\n",
      "\t\t\t\t\t-->MASE: 0.408\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (79872L, 20L), y_train: (79872L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.63s\n",
      "\t\t\t\t\t-->R2: 0.805\n",
      "\t\t\t\t\t-->MAE: 0.536\n",
      "\t\t\t\t\t-->MAPE: 41.684\n",
      "\t\t\t\t\t-->MASE: 0.415\n",
      "\n",
      "\t\t-->Evaluation (2014-05-15 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (79968L, 16L), y_train: (79968L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.435\n",
      "\t\t\t\t\t-->MAE: 1.340\n",
      "\t\t\t\t\t-->MAPE: 77.673\n",
      "\t\t\t\t\t-->MASE: 1.039\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.570\n",
      "\t\t\t\t\t-->MAE: 0.794\n",
      "\t\t\t\t\t-->MAPE: 53.805\n",
      "\t\t\t\t\t-->MASE: 0.615\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.683\n",
      "\t\t\t\t\t-->MAE: 0.619\n",
      "\t\t\t\t\t-->MAPE: 41.761\n",
      "\t\t\t\t\t-->MASE: 0.479\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (79968L, 20L), y_train: (79968L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.64s\n",
      "\t\t\t\t\t-->R2: 0.753\n",
      "\t\t\t\t\t-->MAE: 0.609\n",
      "\t\t\t\t\t-->MAPE: 42.952\n",
      "\t\t\t\t\t-->MASE: 0.472\n",
      "\n",
      "\t\t-->Evaluation (2014-05-16 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (80064L, 16L), y_train: (80064L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -3.405\n",
      "\t\t\t\t\t-->MAE: 1.268\n",
      "\t\t\t\t\t-->MAPE: 96.905\n",
      "\t\t\t\t\t-->MASE: 0.983\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -2.573\n",
      "\t\t\t\t\t-->MAE: 1.093\n",
      "\t\t\t\t\t-->MAPE: 115.357\n",
      "\t\t\t\t\t-->MASE: 0.847\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.018\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 57.262\n",
      "\t\t\t\t\t-->MASE: 0.368\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (80064L, 20L), y_train: (80064L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.59s\n",
      "\t\t\t\t\t-->R2: -0.021\n",
      "\t\t\t\t\t-->MAE: 0.610\n",
      "\t\t\t\t\t-->MAPE: 66.108\n",
      "\t\t\t\t\t-->MASE: 0.473\n",
      "\n",
      "\t\t-->Evaluation (2014-05-17 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (80160L, 16L), y_train: (80160L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -3.747\n",
      "\t\t\t\t\t-->MAE: 0.773\n",
      "\t\t\t\t\t-->MAPE: 88.889\n",
      "\t\t\t\t\t-->MASE: 0.599\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -2.988\n",
      "\t\t\t\t\t-->MAE: 0.691\n",
      "\t\t\t\t\t-->MAPE: 91.358\n",
      "\t\t\t\t\t-->MASE: 0.535\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.127\n",
      "\t\t\t\t\t-->MAE: 0.515\n",
      "\t\t\t\t\t-->MAPE: 88.889\n",
      "\t\t\t\t\t-->MASE: 0.400\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (80160L, 20L), y_train: (80160L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.48s\n",
      "\t\t\t\t\t-->R2: -0.240\n",
      "\t\t\t\t\t-->MAE: 0.482\n",
      "\t\t\t\t\t-->MAPE: 66.719\n",
      "\t\t\t\t\t-->MASE: 0.374\n",
      "\n",
      "\t\t-->Evaluation (2014-05-18 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (80256L, 16L), y_train: (80256L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.802\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 84.000\n",
      "\t\t\t\t\t-->MASE: 0.368\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.802\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 84.000\n",
      "\t\t\t\t\t-->MASE: 0.368\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.748\n",
      "\t\t\t\t\t-->MAE: 0.526\n",
      "\t\t\t\t\t-->MAPE: 100.000\n",
      "\t\t\t\t\t-->MASE: 0.408\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (80256L, 20L), y_train: (80256L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.32s\n",
      "\t\t\t\t\t-->R2: -0.073\n",
      "\t\t\t\t\t-->MAE: 0.415\n",
      "\t\t\t\t\t-->MAPE: 71.284\n",
      "\t\t\t\t\t-->MASE: 0.322\n",
      "\n",
      "\t\t-->Evaluation (2014-05-19 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (80352L, 16L), y_train: (80352L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.606\n",
      "\t\t\t\t\t-->MAE: 1.474\n",
      "\t\t\t\t\t-->MAPE: 84.971\n",
      "\t\t\t\t\t-->MASE: 1.144\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.613\n",
      "\t\t\t\t\t-->MAE: 1.515\n",
      "\t\t\t\t\t-->MAPE: 87.836\n",
      "\t\t\t\t\t-->MASE: 1.176\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.599\n",
      "\t\t\t\t\t-->MAE: 0.711\n",
      "\t\t\t\t\t-->MAPE: 43.918\n",
      "\t\t\t\t\t-->MASE: 0.552\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (80352L, 20L), y_train: (80352L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.34s\n",
      "\t\t\t\t\t-->R2: 0.810\n",
      "\t\t\t\t\t-->MAE: 0.529\n",
      "\t\t\t\t\t-->MAPE: 35.820\n",
      "\t\t\t\t\t-->MASE: 0.411\n",
      "\n",
      "\t\t-->Evaluation (2014-05-20 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (80448L, 16L), y_train: (80448L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.602\n",
      "\t\t\t\t\t-->MAE: 1.495\n",
      "\t\t\t\t\t-->MAPE: 83.459\n",
      "\t\t\t\t\t-->MASE: 1.160\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.636\n",
      "\t\t\t\t\t-->MAE: 0.619\n",
      "\t\t\t\t\t-->MAPE: 40.094\n",
      "\t\t\t\t\t-->MASE: 0.480\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.696\n",
      "\t\t\t\t\t-->MAE: 0.546\n",
      "\t\t\t\t\t-->MAPE: 43.931\n",
      "\t\t\t\t\t-->MASE: 0.424\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (80448L, 20L), y_train: (80448L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.14s\n",
      "\t\t\t\t\t-->R2: 0.808\n",
      "\t\t\t\t\t-->MAE: 0.582\n",
      "\t\t\t\t\t-->MAPE: 40.248\n",
      "\t\t\t\t\t-->MASE: 0.452\n",
      "\n",
      "\t\t-->Evaluation (2014-05-21 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (80544L, 16L), y_train: (80544L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.332\n",
      "\t\t\t\t\t-->MAE: 1.351\n",
      "\t\t\t\t\t-->MAPE: 71.698\n",
      "\t\t\t\t\t-->MASE: 1.048\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.632\n",
      "\t\t\t\t\t-->MAE: 0.711\n",
      "\t\t\t\t\t-->MAPE: 53.239\n",
      "\t\t\t\t\t-->MASE: 0.552\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.664\n",
      "\t\t\t\t\t-->MAE: 0.680\n",
      "\t\t\t\t\t-->MAPE: 46.572\n",
      "\t\t\t\t\t-->MASE: 0.528\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (80544L, 20L), y_train: (80544L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.47s\n",
      "\t\t\t\t\t-->R2: 0.784\n",
      "\t\t\t\t\t-->MAE: 0.616\n",
      "\t\t\t\t\t-->MAPE: 44.728\n",
      "\t\t\t\t\t-->MASE: 0.478\n",
      "\n",
      "\t\t-->Evaluation (2014-05-22 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (80640L, 16L), y_train: (80640L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.680\n",
      "\t\t\t\t\t-->MAE: 1.392\n",
      "\t\t\t\t\t-->MAPE: 78.879\n",
      "\t\t\t\t\t-->MASE: 1.080\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.515\n",
      "\t\t\t\t\t-->MAE: 0.784\n",
      "\t\t\t\t\t-->MAPE: 63.247\n",
      "\t\t\t\t\t-->MASE: 0.608\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.561\n",
      "\t\t\t\t\t-->MAE: 0.732\n",
      "\t\t\t\t\t-->MAPE: 56.264\n",
      "\t\t\t\t\t-->MASE: 0.568\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (80640L, 20L), y_train: (80640L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.50s\n",
      "\t\t\t\t\t-->R2: 0.755\n",
      "\t\t\t\t\t-->MAE: 0.615\n",
      "\t\t\t\t\t-->MAPE: 48.586\n",
      "\t\t\t\t\t-->MASE: 0.477\n",
      "\n",
      "\t\t-->Evaluation (2014-05-23 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (80736L, 16L), y_train: (80736L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.309\n",
      "\t\t\t\t\t-->MAE: 1.289\n",
      "\t\t\t\t\t-->MAPE: 66.374\n",
      "\t\t\t\t\t-->MASE: 1.000\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.583\n",
      "\t\t\t\t\t-->MAE: 0.691\n",
      "\t\t\t\t\t-->MAPE: 44.152\n",
      "\t\t\t\t\t-->MASE: 0.536\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.214\n",
      "\t\t\t\t\t-->MAE: 1.237\n",
      "\t\t\t\t\t-->MAPE: 73.801\n",
      "\t\t\t\t\t-->MASE: 0.960\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (80736L, 20L), y_train: (80736L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.85s\n",
      "\t\t\t\t\t-->R2: -0.319\n",
      "\t\t\t\t\t-->MAE: 1.297\n",
      "\t\t\t\t\t-->MAPE: 74.768\n",
      "\t\t\t\t\t-->MASE: 1.006\n",
      "\n",
      "\t\t-->Evaluation (2014-05-24 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (80832L, 16L), y_train: (80832L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -19.478\n",
      "\t\t\t\t\t-->MAE: 1.392\n",
      "\t\t\t\t\t-->MAPE: 122.222\n",
      "\t\t\t\t\t-->MASE: 1.080\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -18.862\n",
      "\t\t\t\t\t-->MAE: 1.309\n",
      "\t\t\t\t\t-->MAPE: 114.815\n",
      "\t\t\t\t\t-->MASE: 1.016\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.361\n",
      "\t\t\t\t\t-->MAE: 0.412\n",
      "\t\t\t\t\t-->MAPE: 70.370\n",
      "\t\t\t\t\t-->MASE: 0.320\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (80832L, 20L), y_train: (80832L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.36s\n",
      "\t\t\t\t\t-->R2: -0.293\n",
      "\t\t\t\t\t-->MAE: 0.457\n",
      "\t\t\t\t\t-->MAPE: 53.971\n",
      "\t\t\t\t\t-->MASE: 0.355\n",
      "\n",
      "\t\t-->Evaluation (2014-05-25 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (80928L, 16L), y_train: (80928L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.401\n",
      "\t\t\t\t\t-->MAE: 0.268\n",
      "\t\t\t\t\t-->MAPE: 48.000\n",
      "\t\t\t\t\t-->MASE: 0.208\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.724\n",
      "\t\t\t\t\t-->MAE: 0.330\n",
      "\t\t\t\t\t-->MAPE: 60.000\n",
      "\t\t\t\t\t-->MASE: 0.256\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.353\n",
      "\t\t\t\t\t-->MAE: 0.124\n",
      "\t\t\t\t\t-->MAPE: 24.000\n",
      "\t\t\t\t\t-->MASE: 0.096\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (80928L, 20L), y_train: (80928L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.73s\n",
      "\t\t\t\t\t-->R2: -0.207\n",
      "\t\t\t\t\t-->MAE: 0.415\n",
      "\t\t\t\t\t-->MAPE: 81.785\n",
      "\t\t\t\t\t-->MASE: 0.322\n",
      "\n",
      "\t\t-->Evaluation (2014-05-26 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (81024L, 16L), y_train: (81024L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.472\n",
      "\t\t\t\t\t-->MAE: 1.361\n",
      "\t\t\t\t\t-->MAPE: 80.409\n",
      "\t\t\t\t\t-->MASE: 1.056\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.493\n",
      "\t\t\t\t\t-->MAE: 1.361\n",
      "\t\t\t\t\t-->MAPE: 79.843\n",
      "\t\t\t\t\t-->MASE: 1.056\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.620\n",
      "\t\t\t\t\t-->MAE: 0.649\n",
      "\t\t\t\t\t-->MAPE: 42.013\n",
      "\t\t\t\t\t-->MASE: 0.504\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (81024L, 20L), y_train: (81024L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.84s\n",
      "\t\t\t\t\t-->R2: 0.681\n",
      "\t\t\t\t\t-->MAE: 0.758\n",
      "\t\t\t\t\t-->MAPE: 52.347\n",
      "\t\t\t\t\t-->MASE: 0.588\n",
      "\n",
      "\t\t-->Evaluation (2014-05-27 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (81120L, 16L), y_train: (81120L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.683\n",
      "\t\t\t\t\t-->MAE: 1.742\n",
      "\t\t\t\t\t-->MAPE: 86.096\n",
      "\t\t\t\t\t-->MASE: 1.352\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.624\n",
      "\t\t\t\t\t-->MAE: 0.701\n",
      "\t\t\t\t\t-->MAPE: 42.763\n",
      "\t\t\t\t\t-->MASE: 0.544\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.621\n",
      "\t\t\t\t\t-->MAE: 0.691\n",
      "\t\t\t\t\t-->MAPE: 42.339\n",
      "\t\t\t\t\t-->MASE: 0.536\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (81120L, 20L), y_train: (81120L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.37s\n",
      "\t\t\t\t\t-->R2: 0.723\n",
      "\t\t\t\t\t-->MAE: 0.706\n",
      "\t\t\t\t\t-->MAPE: 45.368\n",
      "\t\t\t\t\t-->MASE: 0.548\n",
      "\n",
      "\t\t-->Evaluation (2014-05-28 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (81216L, 16L), y_train: (81216L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.486\n",
      "\t\t\t\t\t-->MAE: 1.258\n",
      "\t\t\t\t\t-->MAPE: 74.697\n",
      "\t\t\t\t\t-->MASE: 0.976\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.361\n",
      "\t\t\t\t\t-->MAE: 0.784\n",
      "\t\t\t\t\t-->MAPE: 58.485\n",
      "\t\t\t\t\t-->MASE: 0.608\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.703\n",
      "\t\t\t\t\t-->MAE: 0.536\n",
      "\t\t\t\t\t-->MAPE: 39.455\n",
      "\t\t\t\t\t-->MASE: 0.416\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (81216L, 20L), y_train: (81216L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.71s\n",
      "\t\t\t\t\t-->R2: 0.775\n",
      "\t\t\t\t\t-->MAE: 0.543\n",
      "\t\t\t\t\t-->MAPE: 40.136\n",
      "\t\t\t\t\t-->MASE: 0.421\n",
      "\n",
      "\t\t-->Evaluation (2014-05-29 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (81312L, 16L), y_train: (81312L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.511\n",
      "\t\t\t\t\t-->MAE: 1.433\n",
      "\t\t\t\t\t-->MAPE: 82.458\n",
      "\t\t\t\t\t-->MASE: 1.112\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.654\n",
      "\t\t\t\t\t-->MAE: 0.608\n",
      "\t\t\t\t\t-->MAPE: 43.136\n",
      "\t\t\t\t\t-->MASE: 0.472\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.480\n",
      "\t\t\t\t\t-->MAE: 0.773\n",
      "\t\t\t\t\t-->MAPE: 50.452\n",
      "\t\t\t\t\t-->MASE: 0.600\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (81312L, 20L), y_train: (81312L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.55s\n",
      "\t\t\t\t\t-->R2: 0.717\n",
      "\t\t\t\t\t-->MAE: 0.662\n",
      "\t\t\t\t\t-->MAPE: 47.051\n",
      "\t\t\t\t\t-->MASE: 0.513\n",
      "\n",
      "\t\t-->Evaluation (2014-05-30 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (81408L, 16L), y_train: (81408L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -9.140\n",
      "\t\t\t\t\t-->MAE: 1.505\n",
      "\t\t\t\t\t-->MAPE: 126.754\n",
      "\t\t\t\t\t-->MASE: 1.168\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -8.640\n",
      "\t\t\t\t\t-->MAE: 1.443\n",
      "\t\t\t\t\t-->MAPE: 135.088\n",
      "\t\t\t\t\t-->MASE: 1.120\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -8.790\n",
      "\t\t\t\t\t-->MAE: 1.485\n",
      "\t\t\t\t\t-->MAPE: 143.860\n",
      "\t\t\t\t\t-->MASE: 1.152\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (81408L, 20L), y_train: (81408L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.40s\n",
      "\t\t\t\t\t-->R2: -1.277\n",
      "\t\t\t\t\t-->MAE: 0.771\n",
      "\t\t\t\t\t-->MAPE: 61.374\n",
      "\t\t\t\t\t-->MASE: 0.598\n",
      "\n",
      "\t\t-->Evaluation (2014-05-31 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (81504L, 16L), y_train: (81504L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.056\n",
      "\t\t\t\t\t-->MAE: 0.588\n",
      "\t\t\t\t\t-->MAPE: 71.905\n",
      "\t\t\t\t\t-->MASE: 0.456\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.253\n",
      "\t\t\t\t\t-->MAE: 0.639\n",
      "\t\t\t\t\t-->MAPE: 80.000\n",
      "\t\t\t\t\t-->MASE: 0.496\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.436\n",
      "\t\t\t\t\t-->MAE: 0.443\n",
      "\t\t\t\t\t-->MAPE: 68.571\n",
      "\t\t\t\t\t-->MASE: 0.344\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (81504L, 20L), y_train: (81504L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.48s\n",
      "\t\t\t\t\t-->R2: -0.010\n",
      "\t\t\t\t\t-->MAE: 0.506\n",
      "\t\t\t\t\t-->MAPE: 68.309\n",
      "\t\t\t\t\t-->MASE: 0.392\n",
      "\n",
      "\t\t-->Evaluation (2014-06-01 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (81600L, 16L), y_train: (81600L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.606\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 66.667\n",
      "\t\t\t\t\t-->MASE: 0.368\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -2.089\n",
      "\t\t\t\t\t-->MAE: 0.557\n",
      "\t\t\t\t\t-->MAPE: 76.667\n",
      "\t\t\t\t\t-->MASE: 0.432\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.172\n",
      "\t\t\t\t\t-->MAE: 0.464\n",
      "\t\t\t\t\t-->MAPE: 83.333\n",
      "\t\t\t\t\t-->MASE: 0.360\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (81600L, 20L), y_train: (81600L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.92s\n",
      "\t\t\t\t\t-->R2: -0.059\n",
      "\t\t\t\t\t-->MAE: 0.435\n",
      "\t\t\t\t\t-->MAPE: 69.844\n",
      "\t\t\t\t\t-->MASE: 0.337\n",
      "\n",
      "\t\t-->Evaluation (2014-06-02 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (81696L, 16L), y_train: (81696L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.635\n",
      "\t\t\t\t\t-->MAE: 1.392\n",
      "\t\t\t\t\t-->MAPE: 74.425\n",
      "\t\t\t\t\t-->MASE: 1.081\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.627\n",
      "\t\t\t\t\t-->MAE: 1.351\n",
      "\t\t\t\t\t-->MAPE: 70.833\n",
      "\t\t\t\t\t-->MASE: 1.049\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.669\n",
      "\t\t\t\t\t-->MAE: 0.639\n",
      "\t\t\t\t\t-->MAPE: 45.546\n",
      "\t\t\t\t\t-->MASE: 0.496\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (81696L, 20L), y_train: (81696L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.08s\n",
      "\t\t\t\t\t-->R2: 0.801\n",
      "\t\t\t\t\t-->MAE: 0.591\n",
      "\t\t\t\t\t-->MAPE: 37.619\n",
      "\t\t\t\t\t-->MASE: 0.459\n",
      "\n",
      "\t\t-->Evaluation (2014-06-03 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (81792L, 16L), y_train: (81792L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.526\n",
      "\t\t\t\t\t-->MAE: 1.330\n",
      "\t\t\t\t\t-->MAPE: 72.193\n",
      "\t\t\t\t\t-->MASE: 1.033\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.613\n",
      "\t\t\t\t\t-->MAE: 0.680\n",
      "\t\t\t\t\t-->MAPE: 49.415\n",
      "\t\t\t\t\t-->MASE: 0.528\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.381\n",
      "\t\t\t\t\t-->MAE: 0.887\n",
      "\t\t\t\t\t-->MAPE: 73.421\n",
      "\t\t\t\t\t-->MASE: 0.689\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (81792L, 20L), y_train: (81792L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.08s\n",
      "\t\t\t\t\t-->R2: 0.756\n",
      "\t\t\t\t\t-->MAE: 0.616\n",
      "\t\t\t\t\t-->MAPE: 46.351\n",
      "\t\t\t\t\t-->MASE: 0.479\n",
      "\n",
      "\t\t-->Evaluation (2014-06-04 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (81888L, 16L), y_train: (81888L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.308\n",
      "\t\t\t\t\t-->MAE: 1.227\n",
      "\t\t\t\t\t-->MAPE: 60.679\n",
      "\t\t\t\t\t-->MASE: 0.953\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.807\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 33.457\n",
      "\t\t\t\t\t-->MASE: 0.368\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.768\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 34.599\n",
      "\t\t\t\t\t-->MASE: 0.368\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (81888L, 20L), y_train: (81888L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.38s\n",
      "\t\t\t\t\t-->R2: 0.859\n",
      "\t\t\t\t\t-->MAE: 0.492\n",
      "\t\t\t\t\t-->MAPE: 34.244\n",
      "\t\t\t\t\t-->MASE: 0.382\n",
      "\n",
      "\t\t-->Evaluation (2014-06-05 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (81984L, 16L), y_train: (81984L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.554\n",
      "\t\t\t\t\t-->MAE: 1.361\n",
      "\t\t\t\t\t-->MAPE: 77.547\n",
      "\t\t\t\t\t-->MASE: 1.057\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.742\n",
      "\t\t\t\t\t-->MAE: 0.515\n",
      "\t\t\t\t\t-->MAPE: 37.673\n",
      "\t\t\t\t\t-->MASE: 0.400\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.647\n",
      "\t\t\t\t\t-->MAE: 0.649\n",
      "\t\t\t\t\t-->MAPE: 44.151\n",
      "\t\t\t\t\t-->MASE: 0.504\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (81984L, 20L), y_train: (81984L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.54s\n",
      "\t\t\t\t\t-->R2: 0.855\n",
      "\t\t\t\t\t-->MAE: 0.487\n",
      "\t\t\t\t\t-->MAPE: 36.583\n",
      "\t\t\t\t\t-->MASE: 0.378\n",
      "\n",
      "\t\t-->Evaluation (2014-06-06 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (82080L, 16L), y_train: (82080L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -16.395\n",
      "\t\t\t\t\t-->MAE: 1.351\n",
      "\t\t\t\t\t-->MAPE: 121.875\n",
      "\t\t\t\t\t-->MASE: 1.049\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -14.902\n",
      "\t\t\t\t\t-->MAE: 1.186\n",
      "\t\t\t\t\t-->MAPE: 121.875\n",
      "\t\t\t\t\t-->MASE: 0.921\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -2.171\n",
      "\t\t\t\t\t-->MAE: 0.577\n",
      "\t\t\t\t\t-->MAPE: 75.000\n",
      "\t\t\t\t\t-->MASE: 0.448\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (82080L, 20L), y_train: (82080L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.38s\n",
      "\t\t\t\t\t-->R2: -0.199\n",
      "\t\t\t\t\t-->MAE: 0.455\n",
      "\t\t\t\t\t-->MAPE: 44.410\n",
      "\t\t\t\t\t-->MASE: 0.353\n",
      "\n",
      "\t\t-->Evaluation (2014-06-07 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (82176L, 16L), y_train: (82176L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.887\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 71.667\n",
      "\t\t\t\t\t-->MASE: 0.368\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.258\n",
      "\t\t\t\t\t-->MAE: 0.330\n",
      "\t\t\t\t\t-->MAPE: 50.000\n",
      "\t\t\t\t\t-->MASE: 0.256\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.358\n",
      "\t\t\t\t\t-->MAE: 0.557\n",
      "\t\t\t\t\t-->MAPE: 83.333\n",
      "\t\t\t\t\t-->MASE: 0.432\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (82176L, 20L), y_train: (82176L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.43s\n",
      "\t\t\t\t\t-->R2: 0.170\n",
      "\t\t\t\t\t-->MAE: 0.399\n",
      "\t\t\t\t\t-->MAPE: 64.666\n",
      "\t\t\t\t\t-->MASE: 0.310\n",
      "\n",
      "\t\t-->Evaluation (2014-06-08 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (82272L, 16L), y_train: (82272L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.417\n",
      "\t\t\t\t\t-->MAE: 0.433\n",
      "\t\t\t\t\t-->MAPE: 69.231\n",
      "\t\t\t\t\t-->MASE: 0.336\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.312\n",
      "\t\t\t\t\t-->MAE: 0.412\n",
      "\t\t\t\t\t-->MAPE: 65.385\n",
      "\t\t\t\t\t-->MASE: 0.320\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.892\n",
      "\t\t\t\t\t-->MAE: 0.371\n",
      "\t\t\t\t\t-->MAPE: 61.538\n",
      "\t\t\t\t\t-->MASE: 0.288\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (82272L, 20L), y_train: (82272L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.26s\n",
      "\t\t\t\t\t-->R2: -0.122\n",
      "\t\t\t\t\t-->MAE: 0.420\n",
      "\t\t\t\t\t-->MAPE: 72.759\n",
      "\t\t\t\t\t-->MASE: 0.326\n",
      "\n",
      "\t\t-->Evaluation (2014-06-09 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (82368L, 16L), y_train: (82368L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.785\n",
      "\t\t\t\t\t-->MAE: 1.876\n",
      "\t\t\t\t\t-->MAPE: 92.139\n",
      "\t\t\t\t\t-->MASE: 1.459\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.759\n",
      "\t\t\t\t\t-->MAE: 1.742\n",
      "\t\t\t\t\t-->MAPE: 80.056\n",
      "\t\t\t\t\t-->MASE: 1.355\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.550\n",
      "\t\t\t\t\t-->MAE: 0.866\n",
      "\t\t\t\t\t-->MAPE: 46.333\n",
      "\t\t\t\t\t-->MASE: 0.673\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (82368L, 20L), y_train: (82368L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.06s\n",
      "\t\t\t\t\t-->R2: 0.692\n",
      "\t\t\t\t\t-->MAE: 0.759\n",
      "\t\t\t\t\t-->MAPE: 43.021\n",
      "\t\t\t\t\t-->MASE: 0.590\n",
      "\n",
      "\t\t-->Evaluation (2014-06-10 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (82464L, 16L), y_train: (82464L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.531\n",
      "\t\t\t\t\t-->MAE: 1.856\n",
      "\t\t\t\t\t-->MAPE: 87.237\n",
      "\t\t\t\t\t-->MASE: 1.442\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.632\n",
      "\t\t\t\t\t-->MAE: 0.866\n",
      "\t\t\t\t\t-->MAPE: 54.852\n",
      "\t\t\t\t\t-->MASE: 0.673\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.547\n",
      "\t\t\t\t\t-->MAE: 0.907\n",
      "\t\t\t\t\t-->MAPE: 49.228\n",
      "\t\t\t\t\t-->MASE: 0.705\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (82464L, 20L), y_train: (82464L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.37s\n",
      "\t\t\t\t\t-->R2: 0.782\n",
      "\t\t\t\t\t-->MAE: 0.733\n",
      "\t\t\t\t\t-->MAPE: 46.245\n",
      "\t\t\t\t\t-->MASE: 0.570\n",
      "\n",
      "\t\t-->Evaluation (2014-06-11 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (82560L, 16L), y_train: (82560L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.183\n",
      "\t\t\t\t\t-->MAE: 1.969\n",
      "\t\t\t\t\t-->MAPE: 80.313\n",
      "\t\t\t\t\t-->MASE: 1.530\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.768\n",
      "\t\t\t\t\t-->MAE: 0.814\n",
      "\t\t\t\t\t-->MAPE: 43.226\n",
      "\t\t\t\t\t-->MASE: 0.633\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.501\n",
      "\t\t\t\t\t-->MAE: 1.124\n",
      "\t\t\t\t\t-->MAPE: 50.319\n",
      "\t\t\t\t\t-->MASE: 0.873\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (82560L, 20L), y_train: (82560L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.11s\n",
      "\t\t\t\t\t-->R2: 0.813\n",
      "\t\t\t\t\t-->MAE: 0.788\n",
      "\t\t\t\t\t-->MAPE: 35.461\n",
      "\t\t\t\t\t-->MASE: 0.612\n",
      "\n",
      "\t\t-->Evaluation (2014-06-12 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (82656L, 16L), y_train: (82656L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.349\n",
      "\t\t\t\t\t-->MAE: 2.309\n",
      "\t\t\t\t\t-->MAPE: 103.242\n",
      "\t\t\t\t\t-->MASE: 1.793\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.852\n",
      "\t\t\t\t\t-->MAE: 0.660\n",
      "\t\t\t\t\t-->MAPE: 42.046\n",
      "\t\t\t\t\t-->MASE: 0.512\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.459\n",
      "\t\t\t\t\t-->MAE: 1.289\n",
      "\t\t\t\t\t-->MAPE: 60.998\n",
      "\t\t\t\t\t-->MASE: 1.000\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (82656L, 20L), y_train: (82656L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.53s\n",
      "\t\t\t\t\t-->R2: 0.838\n",
      "\t\t\t\t\t-->MAE: 0.803\n",
      "\t\t\t\t\t-->MAPE: 41.872\n",
      "\t\t\t\t\t-->MASE: 0.623\n",
      "\n",
      "\t\t-->Evaluation (2014-06-13 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (82752L, 16L), y_train: (82752L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.456\n",
      "\t\t\t\t\t-->MAE: 2.062\n",
      "\t\t\t\t\t-->MAPE: 101.359\n",
      "\t\t\t\t\t-->MASE: 1.599\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.727\n",
      "\t\t\t\t\t-->MAE: 0.845\n",
      "\t\t\t\t\t-->MAPE: 50.896\n",
      "\t\t\t\t\t-->MASE: 0.656\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.420\n",
      "\t\t\t\t\t-->MAE: 1.876\n",
      "\t\t\t\t\t-->MAPE: 87.745\n",
      "\t\t\t\t\t-->MASE: 1.455\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (82752L, 20L), y_train: (82752L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.95s\n",
      "\t\t\t\t\t-->R2: -0.347\n",
      "\t\t\t\t\t-->MAE: 1.843\n",
      "\t\t\t\t\t-->MAPE: 80.948\n",
      "\t\t\t\t\t-->MASE: 1.430\n",
      "\n",
      "\t\t-->Evaluation (2014-06-14 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (82848L, 16L), y_train: (82848L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -37.245\n",
      "\t\t\t\t\t-->MAE: 1.938\n",
      "\t\t\t\t\t-->MAPE: 173.214\n",
      "\t\t\t\t\t-->MASE: 1.502\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -35.050\n",
      "\t\t\t\t\t-->MAE: 1.784\n",
      "\t\t\t\t\t-->MAPE: 180.357\n",
      "\t\t\t\t\t-->MASE: 1.382\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.553\n",
      "\t\t\t\t\t-->MAE: 0.546\n",
      "\t\t\t\t\t-->MAPE: 87.500\n",
      "\t\t\t\t\t-->MASE: 0.424\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (82848L, 20L), y_train: (82848L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.27s\n",
      "\t\t\t\t\t-->R2: -0.183\n",
      "\t\t\t\t\t-->MAE: 0.482\n",
      "\t\t\t\t\t-->MAPE: 51.230\n",
      "\t\t\t\t\t-->MASE: 0.373\n",
      "\n",
      "\t\t-->Evaluation (2014-06-15 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (82944L, 16L), y_train: (82944L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.694\n",
      "\t\t\t\t\t-->MAE: 0.495\n",
      "\t\t\t\t\t-->MAPE: 88.000\n",
      "\t\t\t\t\t-->MASE: 0.383\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.156\n",
      "\t\t\t\t\t-->MAE: 0.392\n",
      "\t\t\t\t\t-->MAPE: 68.000\n",
      "\t\t\t\t\t-->MASE: 0.303\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.748\n",
      "\t\t\t\t\t-->MAE: 0.526\n",
      "\t\t\t\t\t-->MAPE: 100.000\n",
      "\t\t\t\t\t-->MASE: 0.407\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (82944L, 20L), y_train: (82944L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.21s\n",
      "\t\t\t\t\t-->R2: -0.022\n",
      "\t\t\t\t\t-->MAE: 0.414\n",
      "\t\t\t\t\t-->MAPE: 65.915\n",
      "\t\t\t\t\t-->MASE: 0.320\n",
      "\n",
      "\t\t-->Evaluation (2014-06-16 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (83040L, 16L), y_train: (83040L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.563\n",
      "\t\t\t\t\t-->MAE: 1.351\n",
      "\t\t\t\t\t-->MAPE: 91.993\n",
      "\t\t\t\t\t-->MASE: 1.047\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.572\n",
      "\t\t\t\t\t-->MAE: 1.330\n",
      "\t\t\t\t\t-->MAPE: 89.314\n",
      "\t\t\t\t\t-->MASE: 1.031\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.113\n",
      "\t\t\t\t\t-->MAE: 1.041\n",
      "\t\t\t\t\t-->MAPE: 72.974\n",
      "\t\t\t\t\t-->MASE: 0.807\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (83040L, 20L), y_train: (83040L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 7.96s\n",
      "\t\t\t\t\t-->R2: 0.497\n",
      "\t\t\t\t\t-->MAE: 0.810\n",
      "\t\t\t\t\t-->MAPE: 53.186\n",
      "\t\t\t\t\t-->MASE: 0.628\n",
      "\n",
      "\t\t-->Evaluation (2014-06-17 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (83136L, 16L), y_train: (83136L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.685\n",
      "\t\t\t\t\t-->MAE: 1.351\n",
      "\t\t\t\t\t-->MAPE: 79.340\n",
      "\t\t\t\t\t-->MASE: 1.047\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.674\n",
      "\t\t\t\t\t-->MAE: 0.505\n",
      "\t\t\t\t\t-->MAPE: 39.214\n",
      "\t\t\t\t\t-->MASE: 0.392\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.075\n",
      "\t\t\t\t\t-->MAE: 1.031\n",
      "\t\t\t\t\t-->MAPE: 77.673\n",
      "\t\t\t\t\t-->MASE: 0.799\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (83136L, 20L), y_train: (83136L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.79s\n",
      "\t\t\t\t\t-->R2: 0.733\n",
      "\t\t\t\t\t-->MAE: 0.614\n",
      "\t\t\t\t\t-->MAPE: 43.213\n",
      "\t\t\t\t\t-->MASE: 0.476\n",
      "\n",
      "\t\t-->Evaluation (2014-06-18 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (83232L, 16L), y_train: (83232L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.515\n",
      "\t\t\t\t\t-->MAE: 1.330\n",
      "\t\t\t\t\t-->MAPE: 70.957\n",
      "\t\t\t\t\t-->MASE: 1.031\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.598\n",
      "\t\t\t\t\t-->MAE: 0.670\n",
      "\t\t\t\t\t-->MAPE: 52.994\n",
      "\t\t\t\t\t-->MASE: 0.519\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.316\n",
      "\t\t\t\t\t-->MAE: 1.216\n",
      "\t\t\t\t\t-->MAPE: 86.543\n",
      "\t\t\t\t\t-->MASE: 0.943\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (83232L, 20L), y_train: (83232L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.58s\n",
      "\t\t\t\t\t-->R2: 0.771\n",
      "\t\t\t\t\t-->MAE: 0.591\n",
      "\t\t\t\t\t-->MAPE: 40.988\n",
      "\t\t\t\t\t-->MASE: 0.458\n",
      "\n",
      "\t\t-->Evaluation (2014-06-19 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (83328L, 16L), y_train: (83328L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.647\n",
      "\t\t\t\t\t-->MAE: 1.351\n",
      "\t\t\t\t\t-->MAPE: 80.655\n",
      "\t\t\t\t\t-->MASE: 1.047\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.575\n",
      "\t\t\t\t\t-->MAE: 0.711\n",
      "\t\t\t\t\t-->MAPE: 57.054\n",
      "\t\t\t\t\t-->MASE: 0.551\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.559\n",
      "\t\t\t\t\t-->MAE: 1.000\n",
      "\t\t\t\t\t-->MAPE: 61.875\n",
      "\t\t\t\t\t-->MASE: 0.775\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (83328L, 20L), y_train: (83328L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.11s\n",
      "\t\t\t\t\t-->R2: 0.717\n",
      "\t\t\t\t\t-->MAE: 0.617\n",
      "\t\t\t\t\t-->MAPE: 44.578\n",
      "\t\t\t\t\t-->MASE: 0.478\n",
      "\n",
      "\t\t-->Evaluation (2014-06-20 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (83424L, 16L), y_train: (83424L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -7.939\n",
      "\t\t\t\t\t-->MAE: 1.433\n",
      "\t\t\t\t\t-->MAPE: 121.429\n",
      "\t\t\t\t\t-->MASE: 1.111\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -6.662\n",
      "\t\t\t\t\t-->MAE: 1.237\n",
      "\t\t\t\t\t-->MAPE: 117.143\n",
      "\t\t\t\t\t-->MASE: 0.959\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -19.306\n",
      "\t\t\t\t\t-->MAE: 1.928\n",
      "\t\t\t\t\t-->MAPE: 205.714\n",
      "\t\t\t\t\t-->MASE: 1.494\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (83424L, 20L), y_train: (83424L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.45s\n",
      "\t\t\t\t\t-->R2: -0.046\n",
      "\t\t\t\t\t-->MAE: 0.561\n",
      "\t\t\t\t\t-->MAPE: 50.998\n",
      "\t\t\t\t\t-->MASE: 0.435\n",
      "\n",
      "\t\t-->Evaluation (2014-06-21 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (83520L, 16L), y_train: (83520L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.926\n",
      "\t\t\t\t\t-->MAE: 0.443\n",
      "\t\t\t\t\t-->MAPE: 65.000\n",
      "\t\t\t\t\t-->MASE: 0.344\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.712\n",
      "\t\t\t\t\t-->MAE: 0.546\n",
      "\t\t\t\t\t-->MAPE: 70.000\n",
      "\t\t\t\t\t-->MASE: 0.423\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.926\n",
      "\t\t\t\t\t-->MAE: 0.485\n",
      "\t\t\t\t\t-->MAPE: 81.667\n",
      "\t\t\t\t\t-->MASE: 0.375\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (83520L, 20L), y_train: (83520L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.39s\n",
      "\t\t\t\t\t-->R2: 0.010\n",
      "\t\t\t\t\t-->MAE: 0.453\n",
      "\t\t\t\t\t-->MAPE: 67.136\n",
      "\t\t\t\t\t-->MASE: 0.351\n",
      "\n",
      "\t\t-->Evaluation (2014-06-22 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (83616L, 16L), y_train: (83616L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.641\n",
      "\t\t\t\t\t-->MAE: 0.464\n",
      "\t\t\t\t\t-->MAPE: 76.000\n",
      "\t\t\t\t\t-->MASE: 0.360\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.778\n",
      "\t\t\t\t\t-->MAE: 0.299\n",
      "\t\t\t\t\t-->MAPE: 44.000\n",
      "\t\t\t\t\t-->MASE: 0.232\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.694\n",
      "\t\t\t\t\t-->MAE: 0.515\n",
      "\t\t\t\t\t-->MAPE: 100.000\n",
      "\t\t\t\t\t-->MASE: 0.400\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (83616L, 20L), y_train: (83616L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.28s\n",
      "\t\t\t\t\t-->R2: -0.024\n",
      "\t\t\t\t\t-->MAE: 0.420\n",
      "\t\t\t\t\t-->MAPE: 62.034\n",
      "\t\t\t\t\t-->MASE: 0.325\n",
      "\n",
      "\t\t-->Evaluation (2014-06-23 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (83712L, 16L), y_train: (83712L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.668\n",
      "\t\t\t\t\t-->MAE: 1.619\n",
      "\t\t\t\t\t-->MAPE: 90.316\n",
      "\t\t\t\t\t-->MASE: 1.256\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.662\n",
      "\t\t\t\t\t-->MAE: 1.577\n",
      "\t\t\t\t\t-->MAPE: 86.724\n",
      "\t\t\t\t\t-->MASE: 1.224\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.639\n",
      "\t\t\t\t\t-->MAE: 0.784\n",
      "\t\t\t\t\t-->MAPE: 53.420\n",
      "\t\t\t\t\t-->MASE: 0.608\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (83712L, 20L), y_train: (83712L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.23s\n",
      "\t\t\t\t\t-->R2: 0.777\n",
      "\t\t\t\t\t-->MAE: 0.644\n",
      "\t\t\t\t\t-->MAPE: 40.326\n",
      "\t\t\t\t\t-->MASE: 0.500\n",
      "\n",
      "\t\t-->Evaluation (2014-06-24 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (83808L, 16L), y_train: (83808L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.106\n",
      "\t\t\t\t\t-->MAE: 1.711\n",
      "\t\t\t\t\t-->MAPE: 74.946\n",
      "\t\t\t\t\t-->MASE: 1.328\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.371\n",
      "\t\t\t\t\t-->MAE: 0.918\n",
      "\t\t\t\t\t-->MAPE: 57.634\n",
      "\t\t\t\t\t-->MASE: 0.712\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.279\n",
      "\t\t\t\t\t-->MAE: 0.866\n",
      "\t\t\t\t\t-->MAPE: 52.742\n",
      "\t\t\t\t\t-->MASE: 0.672\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (83808L, 20L), y_train: (83808L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.31s\n",
      "\t\t\t\t\t-->R2: 0.526\n",
      "\t\t\t\t\t-->MAE: 0.832\n",
      "\t\t\t\t\t-->MAPE: 50.782\n",
      "\t\t\t\t\t-->MASE: 0.646\n",
      "\n",
      "\t\t-->Evaluation (2014-06-25 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (83904L, 16L), y_train: (83904L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -7.217\n",
      "\t\t\t\t\t-->MAE: 1.619\n",
      "\t\t\t\t\t-->MAPE: 127.000\n",
      "\t\t\t\t\t-->MASE: 1.255\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -6.856\n",
      "\t\t\t\t\t-->MAE: 1.454\n",
      "\t\t\t\t\t-->MAPE: 111.000\n",
      "\t\t\t\t\t-->MASE: 1.127\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -5.791\n",
      "\t\t\t\t\t-->MAE: 1.402\n",
      "\t\t\t\t\t-->MAPE: 112.000\n",
      "\t\t\t\t\t-->MASE: 1.087\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (83904L, 20L), y_train: (83904L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.09s\n",
      "\t\t\t\t\t-->R2: -0.715\n",
      "\t\t\t\t\t-->MAE: 0.731\n",
      "\t\t\t\t\t-->MAPE: 65.663\n",
      "\t\t\t\t\t-->MASE: 0.567\n",
      "\n",
      "\t\t-->Evaluation (2014-06-26 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (84000L, 16L), y_train: (84000L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.604\n",
      "\t\t\t\t\t-->MAE: 1.371\n",
      "\t\t\t\t\t-->MAPE: 71.448\n",
      "\t\t\t\t\t-->MASE: 1.063\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.584\n",
      "\t\t\t\t\t-->MAE: 1.330\n",
      "\t\t\t\t\t-->MAPE: 71.038\n",
      "\t\t\t\t\t-->MASE: 1.031\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.520\n",
      "\t\t\t\t\t-->MAE: 0.639\n",
      "\t\t\t\t\t-->MAPE: 41.694\n",
      "\t\t\t\t\t-->MASE: 0.496\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (84000L, 20L), y_train: (84000L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.56s\n",
      "\t\t\t\t\t-->R2: 0.505\n",
      "\t\t\t\t\t-->MAE: 0.798\n",
      "\t\t\t\t\t-->MAPE: 39.941\n",
      "\t\t\t\t\t-->MASE: 0.619\n",
      "\n",
      "\t\t-->Evaluation (2014-06-27 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (84096L, 16L), y_train: (84096L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -6.050\n",
      "\t\t\t\t\t-->MAE: 1.371\n",
      "\t\t\t\t\t-->MAPE: 97.727\n",
      "\t\t\t\t\t-->MASE: 1.063\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -4.979\n",
      "\t\t\t\t\t-->MAE: 1.340\n",
      "\t\t\t\t\t-->MAPE: 110.227\n",
      "\t\t\t\t\t-->MASE: 1.039\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.131\n",
      "\t\t\t\t\t-->MAE: 0.515\n",
      "\t\t\t\t\t-->MAPE: 67.045\n",
      "\t\t\t\t\t-->MASE: 0.400\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (84096L, 20L), y_train: (84096L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.89s\n",
      "\t\t\t\t\t-->R2: -0.071\n",
      "\t\t\t\t\t-->MAE: 0.634\n",
      "\t\t\t\t\t-->MAPE: 58.499\n",
      "\t\t\t\t\t-->MASE: 0.492\n",
      "\n",
      "\t\t-->Evaluation (2014-06-28 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (84192L, 16L), y_train: (84192L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.685\n",
      "\t\t\t\t\t-->MAE: 0.505\n",
      "\t\t\t\t\t-->MAPE: 58.621\n",
      "\t\t\t\t\t-->MASE: 0.392\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -2.565\n",
      "\t\t\t\t\t-->MAE: 0.608\n",
      "\t\t\t\t\t-->MAPE: 62.069\n",
      "\t\t\t\t\t-->MASE: 0.472\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.760\n",
      "\t\t\t\t\t-->MAE: 0.412\n",
      "\t\t\t\t\t-->MAPE: 70.690\n",
      "\t\t\t\t\t-->MASE: 0.320\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (84192L, 20L), y_train: (84192L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.13s\n",
      "\t\t\t\t\t-->R2: 0.024\n",
      "\t\t\t\t\t-->MAE: 0.439\n",
      "\t\t\t\t\t-->MAPE: 59.475\n",
      "\t\t\t\t\t-->MASE: 0.340\n",
      "\n",
      "\t\t-->Evaluation (2014-06-29 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (84288L, 16L), y_train: (84288L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.533\n",
      "\t\t\t\t\t-->MAE: 0.464\n",
      "\t\t\t\t\t-->MAPE: 80.000\n",
      "\t\t\t\t\t-->MASE: 0.360\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.641\n",
      "\t\t\t\t\t-->MAE: 0.485\n",
      "\t\t\t\t\t-->MAPE: 84.000\n",
      "\t\t\t\t\t-->MASE: 0.376\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.694\n",
      "\t\t\t\t\t-->MAE: 0.515\n",
      "\t\t\t\t\t-->MAPE: 100.000\n",
      "\t\t\t\t\t-->MASE: 0.400\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (84288L, 20L), y_train: (84288L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.67s\n",
      "\t\t\t\t\t-->R2: -0.028\n",
      "\t\t\t\t\t-->MAE: 0.415\n",
      "\t\t\t\t\t-->MAPE: 65.726\n",
      "\t\t\t\t\t-->MASE: 0.322\n",
      "\n",
      "\t\t-->Evaluation (2014-06-30 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (84384L, 16L), y_train: (84384L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.635\n",
      "\t\t\t\t\t-->MAE: 1.454\n",
      "\t\t\t\t\t-->MAPE: 93.056\n",
      "\t\t\t\t\t-->MASE: 1.128\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.611\n",
      "\t\t\t\t\t-->MAE: 1.433\n",
      "\t\t\t\t\t-->MAPE: 91.451\n",
      "\t\t\t\t\t-->MASE: 1.112\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.566\n",
      "\t\t\t\t\t-->MAE: 0.742\n",
      "\t\t\t\t\t-->MAPE: 51.019\n",
      "\t\t\t\t\t-->MASE: 0.576\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (84384L, 20L), y_train: (84384L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.44s\n",
      "\t\t\t\t\t-->R2: 0.819\n",
      "\t\t\t\t\t-->MAE: 0.552\n",
      "\t\t\t\t\t-->MAPE: 38.763\n",
      "\t\t\t\t\t-->MASE: 0.428\n",
      "\n",
      "\t\t-->Evaluation (2014-07-01 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (84480L, 16L), y_train: (84480L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.827\n",
      "\t\t\t\t\t-->MAE: 1.423\n",
      "\t\t\t\t\t-->MAPE: 78.713\n",
      "\t\t\t\t\t-->MASE: 1.104\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.613\n",
      "\t\t\t\t\t-->MAE: 0.629\n",
      "\t\t\t\t\t-->MAPE: 46.228\n",
      "\t\t\t\t\t-->MASE: 0.488\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.397\n",
      "\t\t\t\t\t-->MAE: 0.784\n",
      "\t\t\t\t\t-->MAPE: 55.760\n",
      "\t\t\t\t\t-->MASE: 0.608\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (84480L, 20L), y_train: (84480L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.57s\n",
      "\t\t\t\t\t-->R2: 0.707\n",
      "\t\t\t\t\t-->MAE: 0.652\n",
      "\t\t\t\t\t-->MAPE: 43.496\n",
      "\t\t\t\t\t-->MASE: 0.506\n",
      "\n",
      "\t\t-->Evaluation (2014-07-02 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (84576L, 16L), y_train: (84576L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.336\n",
      "\t\t\t\t\t-->MAE: 1.227\n",
      "\t\t\t\t\t-->MAPE: 64.532\n",
      "\t\t\t\t\t-->MASE: 0.952\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.605\n",
      "\t\t\t\t\t-->MAE: 0.608\n",
      "\t\t\t\t\t-->MAPE: 42.456\n",
      "\t\t\t\t\t-->MASE: 0.472\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.385\n",
      "\t\t\t\t\t-->MAE: 1.299\n",
      "\t\t\t\t\t-->MAPE: 74.211\n",
      "\t\t\t\t\t-->MASE: 1.008\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (84576L, 20L), y_train: (84576L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.57s\n",
      "\t\t\t\t\t-->R2: 0.720\n",
      "\t\t\t\t\t-->MAE: 0.589\n",
      "\t\t\t\t\t-->MAPE: 30.856\n",
      "\t\t\t\t\t-->MASE: 0.457\n",
      "\n",
      "\t\t-->Evaluation (2014-07-03 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (84672L, 16L), y_train: (84672L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.413\n",
      "\t\t\t\t\t-->MAE: 1.330\n",
      "\t\t\t\t\t-->MAPE: 75.234\n",
      "\t\t\t\t\t-->MASE: 1.032\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.546\n",
      "\t\t\t\t\t-->MAE: 0.742\n",
      "\t\t\t\t\t-->MAPE: 53.772\n",
      "\t\t\t\t\t-->MASE: 0.576\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.459\n",
      "\t\t\t\t\t-->MAE: 0.794\n",
      "\t\t\t\t\t-->MAPE: 45.000\n",
      "\t\t\t\t\t-->MASE: 0.616\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (84672L, 20L), y_train: (84672L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.33s\n",
      "\t\t\t\t\t-->R2: 0.738\n",
      "\t\t\t\t\t-->MAE: 0.626\n",
      "\t\t\t\t\t-->MAPE: 39.037\n",
      "\t\t\t\t\t-->MASE: 0.486\n",
      "\n",
      "\t\t-->Evaluation (2014-07-04 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (84768L, 16L), y_train: (84768L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -11.535\n",
      "\t\t\t\t\t-->MAE: 1.546\n",
      "\t\t\t\t\t-->MAPE: 133.824\n",
      "\t\t\t\t\t-->MASE: 1.200\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -9.542\n",
      "\t\t\t\t\t-->MAE: 1.258\n",
      "\t\t\t\t\t-->MAPE: 119.118\n",
      "\t\t\t\t\t-->MASE: 0.976\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.832\n",
      "\t\t\t\t\t-->MAE: 0.485\n",
      "\t\t\t\t\t-->MAPE: 52.941\n",
      "\t\t\t\t\t-->MASE: 0.376\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (84768L, 20L), y_train: (84768L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.49s\n",
      "\t\t\t\t\t-->R2: -0.115\n",
      "\t\t\t\t\t-->MAE: 0.527\n",
      "\t\t\t\t\t-->MAPE: 47.023\n",
      "\t\t\t\t\t-->MASE: 0.409\n",
      "\n",
      "\t\t-->Evaluation (2014-07-05 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (84864L, 16L), y_train: (84864L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.812\n",
      "\t\t\t\t\t-->MAE: 0.536\n",
      "\t\t\t\t\t-->MAPE: 82.143\n",
      "\t\t\t\t\t-->MASE: 0.416\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -2.213\n",
      "\t\t\t\t\t-->MAE: 0.598\n",
      "\t\t\t\t\t-->MAPE: 89.286\n",
      "\t\t\t\t\t-->MASE: 0.464\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.908\n",
      "\t\t\t\t\t-->MAE: 0.371\n",
      "\t\t\t\t\t-->MAPE: 60.714\n",
      "\t\t\t\t\t-->MASE: 0.288\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (84864L, 20L), y_train: (84864L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.38s\n",
      "\t\t\t\t\t-->R2: -0.049\n",
      "\t\t\t\t\t-->MAE: 0.429\n",
      "\t\t\t\t\t-->MAPE: 62.696\n",
      "\t\t\t\t\t-->MASE: 0.333\n",
      "\n",
      "\t\t-->Evaluation (2014-07-06 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (84960L, 16L), y_train: (84960L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.491\n",
      "\t\t\t\t\t-->MAE: 0.464\n",
      "\t\t\t\t\t-->MAPE: 87.500\n",
      "\t\t\t\t\t-->MASE: 0.360\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.658\n",
      "\t\t\t\t\t-->MAE: 0.495\n",
      "\t\t\t\t\t-->MAPE: 91.667\n",
      "\t\t\t\t\t-->MASE: 0.384\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.827\n",
      "\t\t\t\t\t-->MAE: 0.340\n",
      "\t\t\t\t\t-->MAPE: 66.667\n",
      "\t\t\t\t\t-->MASE: 0.264\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (84960L, 20L), y_train: (84960L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.80s\n",
      "\t\t\t\t\t-->R2: 0.022\n",
      "\t\t\t\t\t-->MAE: 0.398\n",
      "\t\t\t\t\t-->MAPE: 63.664\n",
      "\t\t\t\t\t-->MASE: 0.309\n",
      "\n",
      "\t\t-->Evaluation (2014-07-07 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (85056L, 16L), y_train: (85056L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.676\n",
      "\t\t\t\t\t-->MAE: 1.732\n",
      "\t\t\t\t\t-->MAPE: 89.095\n",
      "\t\t\t\t\t-->MASE: 1.346\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.685\n",
      "\t\t\t\t\t-->MAE: 1.763\n",
      "\t\t\t\t\t-->MAPE: 92.787\n",
      "\t\t\t\t\t-->MASE: 1.370\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.530\n",
      "\t\t\t\t\t-->MAE: 0.918\n",
      "\t\t\t\t\t-->MAPE: 55.008\n",
      "\t\t\t\t\t-->MASE: 0.713\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (85056L, 20L), y_train: (85056L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.38s\n",
      "\t\t\t\t\t-->R2: 0.711\n",
      "\t\t\t\t\t-->MAE: 0.747\n",
      "\t\t\t\t\t-->MAPE: 43.150\n",
      "\t\t\t\t\t-->MASE: 0.580\n",
      "\n",
      "\t\t-->Evaluation (2014-07-08 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (85152L, 16L), y_train: (85152L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.522\n",
      "\t\t\t\t\t-->MAE: 1.309\n",
      "\t\t\t\t\t-->MAPE: 70.897\n",
      "\t\t\t\t\t-->MASE: 1.017\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.411\n",
      "\t\t\t\t\t-->MAE: 0.814\n",
      "\t\t\t\t\t-->MAPE: 55.000\n",
      "\t\t\t\t\t-->MASE: 0.633\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.569\n",
      "\t\t\t\t\t-->MAE: 0.753\n",
      "\t\t\t\t\t-->MAPE: 53.942\n",
      "\t\t\t\t\t-->MASE: 0.584\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (85152L, 20L), y_train: (85152L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.65s\n",
      "\t\t\t\t\t-->R2: 0.760\n",
      "\t\t\t\t\t-->MAE: 0.625\n",
      "\t\t\t\t\t-->MAPE: 41.365\n",
      "\t\t\t\t\t-->MASE: 0.485\n",
      "\n",
      "\t\t-->Evaluation (2014-07-09 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (85248L, 16L), y_train: (85248L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.520\n",
      "\t\t\t\t\t-->MAE: 1.443\n",
      "\t\t\t\t\t-->MAPE: 73.960\n",
      "\t\t\t\t\t-->MASE: 1.121\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.688\n",
      "\t\t\t\t\t-->MAE: 0.598\n",
      "\t\t\t\t\t-->MAPE: 43.851\n",
      "\t\t\t\t\t-->MASE: 0.464\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.591\n",
      "\t\t\t\t\t-->MAE: 0.722\n",
      "\t\t\t\t\t-->MAPE: 47.596\n",
      "\t\t\t\t\t-->MASE: 0.560\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (85248L, 20L), y_train: (85248L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.23s\n",
      "\t\t\t\t\t-->R2: 0.773\n",
      "\t\t\t\t\t-->MAE: 0.581\n",
      "\t\t\t\t\t-->MAPE: 36.300\n",
      "\t\t\t\t\t-->MASE: 0.452\n",
      "\n",
      "\t\t-->Evaluation (2014-07-10 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (85344L, 16L), y_train: (85344L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.515\n",
      "\t\t\t\t\t-->MAE: 1.289\n",
      "\t\t\t\t\t-->MAPE: 78.302\n",
      "\t\t\t\t\t-->MASE: 1.001\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.456\n",
      "\t\t\t\t\t-->MAE: 0.629\n",
      "\t\t\t\t\t-->MAPE: 47.013\n",
      "\t\t\t\t\t-->MASE: 0.488\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.613\n",
      "\t\t\t\t\t-->MAE: 0.546\n",
      "\t\t\t\t\t-->MAPE: 39.151\n",
      "\t\t\t\t\t-->MASE: 0.424\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (85344L, 20L), y_train: (85344L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.26s\n",
      "\t\t\t\t\t-->R2: 0.772\n",
      "\t\t\t\t\t-->MAE: 0.553\n",
      "\t\t\t\t\t-->MAPE: 39.547\n",
      "\t\t\t\t\t-->MASE: 0.429\n",
      "\n",
      "\t\t-->Evaluation (2014-07-11 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (85440L, 16L), y_train: (85440L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -6.707\n",
      "\t\t\t\t\t-->MAE: 1.443\n",
      "\t\t\t\t\t-->MAPE: 133.824\n",
      "\t\t\t\t\t-->MASE: 1.121\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -6.313\n",
      "\t\t\t\t\t-->MAE: 1.340\n",
      "\t\t\t\t\t-->MAPE: 125.735\n",
      "\t\t\t\t\t-->MASE: 1.041\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.204\n",
      "\t\t\t\t\t-->MAE: 0.464\n",
      "\t\t\t\t\t-->MAPE: 60.784\n",
      "\t\t\t\t\t-->MASE: 0.360\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (85440L, 20L), y_train: (85440L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.63s\n",
      "\t\t\t\t\t-->R2: 0.010\n",
      "\t\t\t\t\t-->MAE: 0.518\n",
      "\t\t\t\t\t-->MAPE: 62.414\n",
      "\t\t\t\t\t-->MASE: 0.402\n",
      "\n",
      "\t\t-->Evaluation (2014-07-12 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (85536L, 16L), y_train: (85536L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -2.258\n",
      "\t\t\t\t\t-->MAE: 0.515\n",
      "\t\t\t\t\t-->MAPE: 73.077\n",
      "\t\t\t\t\t-->MASE: 0.400\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -2.351\n",
      "\t\t\t\t\t-->MAE: 0.515\n",
      "\t\t\t\t\t-->MAPE: 67.308\n",
      "\t\t\t\t\t-->MASE: 0.400\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.908\n",
      "\t\t\t\t\t-->MAE: 0.423\n",
      "\t\t\t\t\t-->MAPE: 75.000\n",
      "\t\t\t\t\t-->MASE: 0.328\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (85536L, 20L), y_train: (85536L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.39s\n",
      "\t\t\t\t\t-->R2: -0.009\n",
      "\t\t\t\t\t-->MAE: 0.429\n",
      "\t\t\t\t\t-->MAPE: 64.956\n",
      "\t\t\t\t\t-->MASE: 0.333\n",
      "\n",
      "\t\t-->Evaluation (2014-07-13 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (85632L, 16L), y_train: (85632L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.563\n",
      "\t\t\t\t\t-->MAE: 0.299\n",
      "\t\t\t\t\t-->MAPE: 56.000\n",
      "\t\t\t\t\t-->MASE: 0.232\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.293\n",
      "\t\t\t\t\t-->MAE: 0.227\n",
      "\t\t\t\t\t-->MAPE: 40.000\n",
      "\t\t\t\t\t-->MASE: 0.176\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.778\n",
      "\t\t\t\t\t-->MAE: 0.340\n",
      "\t\t\t\t\t-->MAPE: 68.000\n",
      "\t\t\t\t\t-->MASE: 0.264\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (85632L, 20L), y_train: (85632L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.42s\n",
      "\t\t\t\t\t-->R2: -0.075\n",
      "\t\t\t\t\t-->MAE: 0.409\n",
      "\t\t\t\t\t-->MAPE: 72.620\n",
      "\t\t\t\t\t-->MASE: 0.318\n",
      "\n",
      "\t\t-->Evaluation (2014-07-14 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (85728L, 16L), y_train: (85728L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.565\n",
      "\t\t\t\t\t-->MAE: 1.351\n",
      "\t\t\t\t\t-->MAPE: 87.264\n",
      "\t\t\t\t\t-->MASE: 1.050\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.531\n",
      "\t\t\t\t\t-->MAE: 1.227\n",
      "\t\t\t\t\t-->MAPE: 75.629\n",
      "\t\t\t\t\t-->MASE: 0.954\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.341\n",
      "\t\t\t\t\t-->MAE: 0.794\n",
      "\t\t\t\t\t-->MAPE: 51.572\n",
      "\t\t\t\t\t-->MASE: 0.617\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (85728L, 20L), y_train: (85728L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.03s\n",
      "\t\t\t\t\t-->R2: 0.743\n",
      "\t\t\t\t\t-->MAE: 0.544\n",
      "\t\t\t\t\t-->MAPE: 35.254\n",
      "\t\t\t\t\t-->MASE: 0.423\n",
      "\n",
      "\t\t-->Evaluation (2014-07-15 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (85824L, 16L), y_train: (85824L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.611\n",
      "\t\t\t\t\t-->MAE: 1.361\n",
      "\t\t\t\t\t-->MAPE: 77.244\n",
      "\t\t\t\t\t-->MASE: 1.058\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.766\n",
      "\t\t\t\t\t-->MAE: 0.515\n",
      "\t\t\t\t\t-->MAPE: 42.147\n",
      "\t\t\t\t\t-->MASE: 0.401\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.710\n",
      "\t\t\t\t\t-->MAE: 0.495\n",
      "\t\t\t\t\t-->MAPE: 46.474\n",
      "\t\t\t\t\t-->MASE: 0.385\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (85824L, 20L), y_train: (85824L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.42s\n",
      "\t\t\t\t\t-->R2: 0.855\n",
      "\t\t\t\t\t-->MAE: 0.491\n",
      "\t\t\t\t\t-->MAPE: 37.526\n",
      "\t\t\t\t\t-->MASE: 0.382\n",
      "\n",
      "\t\t-->Evaluation (2014-07-16 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (85920L, 16L), y_train: (85920L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.654\n",
      "\t\t\t\t\t-->MAE: 1.495\n",
      "\t\t\t\t\t-->MAPE: 74.463\n",
      "\t\t\t\t\t-->MASE: 1.162\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.647\n",
      "\t\t\t\t\t-->MAE: 0.660\n",
      "\t\t\t\t\t-->MAPE: 45.178\n",
      "\t\t\t\t\t-->MASE: 0.513\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.725\n",
      "\t\t\t\t\t-->MAE: 0.557\n",
      "\t\t\t\t\t-->MAPE: 36.582\n",
      "\t\t\t\t\t-->MASE: 0.433\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (85920L, 20L), y_train: (85920L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.25s\n",
      "\t\t\t\t\t-->R2: 0.804\n",
      "\t\t\t\t\t-->MAE: 0.587\n",
      "\t\t\t\t\t-->MAPE: 36.930\n",
      "\t\t\t\t\t-->MASE: 0.456\n",
      "\n",
      "\t\t-->Evaluation (2014-07-17 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (86016L, 16L), y_train: (86016L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.302\n",
      "\t\t\t\t\t-->MAE: 1.567\n",
      "\t\t\t\t\t-->MAPE: 69.466\n",
      "\t\t\t\t\t-->MASE: 1.218\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.813\n",
      "\t\t\t\t\t-->MAE: 0.598\n",
      "\t\t\t\t\t-->MAPE: 39.446\n",
      "\t\t\t\t\t-->MASE: 0.465\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.706\n",
      "\t\t\t\t\t-->MAE: 0.649\n",
      "\t\t\t\t\t-->MAPE: 36.831\n",
      "\t\t\t\t\t-->MASE: 0.505\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (86016L, 20L), y_train: (86016L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.33s\n",
      "\t\t\t\t\t-->R2: 0.902\n",
      "\t\t\t\t\t-->MAE: 0.491\n",
      "\t\t\t\t\t-->MAPE: 28.595\n",
      "\t\t\t\t\t-->MASE: 0.381\n",
      "\n",
      "\t\t-->Evaluation (2014-07-18 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (86112L, 16L), y_train: (86112L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -10.195\n",
      "\t\t\t\t\t-->MAE: 1.825\n",
      "\t\t\t\t\t-->MAPE: 141.288\n",
      "\t\t\t\t\t-->MASE: 1.418\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -6.719\n",
      "\t\t\t\t\t-->MAE: 1.474\n",
      "\t\t\t\t\t-->MAPE: 153.409\n",
      "\t\t\t\t\t-->MASE: 1.146\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.495\n",
      "\t\t\t\t\t-->MAE: 0.557\n",
      "\t\t\t\t\t-->MAPE: 65.152\n",
      "\t\t\t\t\t-->MASE: 0.433\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (86112L, 20L), y_train: (86112L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.43s\n",
      "\t\t\t\t\t-->R2: 0.207\n",
      "\t\t\t\t\t-->MAE: 0.524\n",
      "\t\t\t\t\t-->MAPE: 51.594\n",
      "\t\t\t\t\t-->MASE: 0.407\n",
      "\n",
      "\t\t-->Evaluation (2014-07-19 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (86208L, 16L), y_train: (86208L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.189\n",
      "\t\t\t\t\t-->MAE: 0.557\n",
      "\t\t\t\t\t-->MAPE: 56.250\n",
      "\t\t\t\t\t-->MASE: 0.432\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.919\n",
      "\t\t\t\t\t-->MAE: 0.722\n",
      "\t\t\t\t\t-->MAPE: 79.688\n",
      "\t\t\t\t\t-->MASE: 0.561\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.703\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 75.000\n",
      "\t\t\t\t\t-->MASE: 0.368\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (86208L, 20L), y_train: (86208L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.56s\n",
      "\t\t\t\t\t-->R2: 0.021\n",
      "\t\t\t\t\t-->MAE: 0.476\n",
      "\t\t\t\t\t-->MAPE: 62.942\n",
      "\t\t\t\t\t-->MASE: 0.370\n",
      "\n",
      "\t\t-->Evaluation (2014-07-20 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (86304L, 16L), y_train: (86304L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -2.018\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 68.000\n",
      "\t\t\t\t\t-->MASE: 0.369\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -2.341\n",
      "\t\t\t\t\t-->MAE: 0.557\n",
      "\t\t\t\t\t-->MAPE: 88.000\n",
      "\t\t\t\t\t-->MASE: 0.433\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.138\n",
      "\t\t\t\t\t-->MAE: 0.165\n",
      "\t\t\t\t\t-->MAPE: 32.000\n",
      "\t\t\t\t\t-->MASE: 0.128\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (86304L, 20L), y_train: (86304L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.18s\n",
      "\t\t\t\t\t-->R2: 0.061\n",
      "\t\t\t\t\t-->MAE: 0.381\n",
      "\t\t\t\t\t-->MAPE: 67.483\n",
      "\t\t\t\t\t-->MASE: 0.296\n",
      "\n",
      "\t\t-->Evaluation (2014-07-21 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (86400L, 16L), y_train: (86400L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.748\n",
      "\t\t\t\t\t-->MAE: 1.536\n",
      "\t\t\t\t\t-->MAPE: 86.250\n",
      "\t\t\t\t\t-->MASE: 1.195\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.748\n",
      "\t\t\t\t\t-->MAE: 1.557\n",
      "\t\t\t\t\t-->MAPE: 88.000\n",
      "\t\t\t\t\t-->MASE: 1.211\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.526\n",
      "\t\t\t\t\t-->MAE: 0.701\n",
      "\t\t\t\t\t-->MAPE: 43.556\n",
      "\t\t\t\t\t-->MASE: 0.545\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (86400L, 20L), y_train: (86400L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.44s\n",
      "\t\t\t\t\t-->R2: 0.697\n",
      "\t\t\t\t\t-->MAE: 0.698\n",
      "\t\t\t\t\t-->MAPE: 48.962\n",
      "\t\t\t\t\t-->MASE: 0.543\n",
      "\n",
      "\t\t-->Evaluation (2014-07-22 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (86496L, 16L), y_train: (86496L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.260\n",
      "\t\t\t\t\t-->MAE: 1.206\n",
      "\t\t\t\t\t-->MAPE: 73.426\n",
      "\t\t\t\t\t-->MASE: 0.938\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.429\n",
      "\t\t\t\t\t-->MAE: 0.804\n",
      "\t\t\t\t\t-->MAPE: 62.778\n",
      "\t\t\t\t\t-->MASE: 0.625\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.693\n",
      "\t\t\t\t\t-->MAE: 0.536\n",
      "\t\t\t\t\t-->MAPE: 40.802\n",
      "\t\t\t\t\t-->MASE: 0.417\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (86496L, 20L), y_train: (86496L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.60s\n",
      "\t\t\t\t\t-->R2: 0.745\n",
      "\t\t\t\t\t-->MAE: 0.584\n",
      "\t\t\t\t\t-->MAPE: 41.288\n",
      "\t\t\t\t\t-->MASE: 0.454\n",
      "\n",
      "\t\t-->Evaluation (2014-07-23 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (86592L, 16L), y_train: (86592L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.512\n",
      "\t\t\t\t\t-->MAE: 1.536\n",
      "\t\t\t\t\t-->MAPE: 69.000\n",
      "\t\t\t\t\t-->MASE: 1.195\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.563\n",
      "\t\t\t\t\t-->MAE: 0.691\n",
      "\t\t\t\t\t-->MAPE: 41.306\n",
      "\t\t\t\t\t-->MASE: 0.537\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.557\n",
      "\t\t\t\t\t-->MAE: 0.753\n",
      "\t\t\t\t\t-->MAPE: 51.222\n",
      "\t\t\t\t\t-->MASE: 0.585\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (86592L, 20L), y_train: (86592L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.45s\n",
      "\t\t\t\t\t-->R2: 0.611\n",
      "\t\t\t\t\t-->MAE: 0.748\n",
      "\t\t\t\t\t-->MAPE: 44.125\n",
      "\t\t\t\t\t-->MASE: 0.582\n",
      "\n",
      "\t\t-->Evaluation (2014-07-24 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (86688L, 16L), y_train: (86688L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.636\n",
      "\t\t\t\t\t-->MAE: 1.371\n",
      "\t\t\t\t\t-->MAPE: 82.436\n",
      "\t\t\t\t\t-->MASE: 1.066\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.190\n",
      "\t\t\t\t\t-->MAE: 0.763\n",
      "\t\t\t\t\t-->MAPE: 51.667\n",
      "\t\t\t\t\t-->MASE: 0.593\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.267\n",
      "\t\t\t\t\t-->MAE: 0.814\n",
      "\t\t\t\t\t-->MAPE: 55.833\n",
      "\t\t\t\t\t-->MASE: 0.633\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (86688L, 20L), y_train: (86688L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.09s\n",
      "\t\t\t\t\t-->R2: 0.736\n",
      "\t\t\t\t\t-->MAE: 0.608\n",
      "\t\t\t\t\t-->MAPE: 39.021\n",
      "\t\t\t\t\t-->MASE: 0.472\n",
      "\n",
      "\t\t-->Evaluation (2014-07-25 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (86784L, 16L), y_train: (86784L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -7.650\n",
      "\t\t\t\t\t-->MAE: 1.268\n",
      "\t\t\t\t\t-->MAPE: 101.250\n",
      "\t\t\t\t\t-->MASE: 0.986\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -6.379\n",
      "\t\t\t\t\t-->MAE: 1.144\n",
      "\t\t\t\t\t-->MAPE: 126.250\n",
      "\t\t\t\t\t-->MASE: 0.890\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.907\n",
      "\t\t\t\t\t-->MAE: 0.608\n",
      "\t\t\t\t\t-->MAPE: 73.750\n",
      "\t\t\t\t\t-->MASE: 0.473\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (86784L, 20L), y_train: (86784L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.43s\n",
      "\t\t\t\t\t-->R2: 0.064\n",
      "\t\t\t\t\t-->MAE: 0.506\n",
      "\t\t\t\t\t-->MAPE: 55.577\n",
      "\t\t\t\t\t-->MASE: 0.393\n",
      "\n",
      "\t\t-->Evaluation (2014-07-26 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (86880L, 16L), y_train: (86880L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.268\n",
      "\t\t\t\t\t-->MAE: 0.443\n",
      "\t\t\t\t\t-->MAPE: 56.667\n",
      "\t\t\t\t\t-->MASE: 0.345\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -2.378\n",
      "\t\t\t\t\t-->MAE: 0.619\n",
      "\t\t\t\t\t-->MAPE: 76.667\n",
      "\t\t\t\t\t-->MASE: 0.481\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.654\n",
      "\t\t\t\t\t-->MAE: 0.526\n",
      "\t\t\t\t\t-->MAPE: 83.333\n",
      "\t\t\t\t\t-->MASE: 0.409\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (86880L, 20L), y_train: (86880L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.08s\n",
      "\t\t\t\t\t-->R2: -0.172\n",
      "\t\t\t\t\t-->MAE: 0.451\n",
      "\t\t\t\t\t-->MAPE: 61.137\n",
      "\t\t\t\t\t-->MASE: 0.351\n",
      "\n",
      "\t\t-->Evaluation (2014-07-27 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (86976L, 16L), y_train: (86976L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.940\n",
      "\t\t\t\t\t-->MAE: 0.371\n",
      "\t\t\t\t\t-->MAPE: 60.000\n",
      "\t\t\t\t\t-->MASE: 0.289\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.886\n",
      "\t\t\t\t\t-->MAE: 0.361\n",
      "\t\t\t\t\t-->MAPE: 60.000\n",
      "\t\t\t\t\t-->MASE: 0.281\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.587\n",
      "\t\t\t\t\t-->MAE: 0.495\n",
      "\t\t\t\t\t-->MAPE: 96.000\n",
      "\t\t\t\t\t-->MASE: 0.385\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (86976L, 20L), y_train: (86976L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.26s\n",
      "\t\t\t\t\t-->R2: 0.015\n",
      "\t\t\t\t\t-->MAE: 0.390\n",
      "\t\t\t\t\t-->MAPE: 69.252\n",
      "\t\t\t\t\t-->MASE: 0.303\n",
      "\n",
      "\t\t-->Evaluation (2014-07-28 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (87072L, 16L), y_train: (87072L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.551\n",
      "\t\t\t\t\t-->MAE: 1.289\n",
      "\t\t\t\t\t-->MAPE: 88.727\n",
      "\t\t\t\t\t-->MASE: 1.003\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.523\n",
      "\t\t\t\t\t-->MAE: 1.165\n",
      "\t\t\t\t\t-->MAPE: 77.212\n",
      "\t\t\t\t\t-->MASE: 0.907\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.294\n",
      "\t\t\t\t\t-->MAE: 0.866\n",
      "\t\t\t\t\t-->MAPE: 68.576\n",
      "\t\t\t\t\t-->MASE: 0.674\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (87072L, 20L), y_train: (87072L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.88s\n",
      "\t\t\t\t\t-->R2: 0.742\n",
      "\t\t\t\t\t-->MAE: 0.576\n",
      "\t\t\t\t\t-->MAPE: 40.036\n",
      "\t\t\t\t\t-->MASE: 0.448\n",
      "\n",
      "\t\t-->Evaluation (2014-07-29 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (87168L, 16L), y_train: (87168L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.453\n",
      "\t\t\t\t\t-->MAE: 1.433\n",
      "\t\t\t\t\t-->MAPE: 92.500\n",
      "\t\t\t\t\t-->MASE: 1.116\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.689\n",
      "\t\t\t\t\t-->MAE: 0.567\n",
      "\t\t\t\t\t-->MAPE: 45.536\n",
      "\t\t\t\t\t-->MASE: 0.441\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.648\n",
      "\t\t\t\t\t-->MAE: 0.608\n",
      "\t\t\t\t\t-->MAPE: 49.315\n",
      "\t\t\t\t\t-->MASE: 0.474\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (87168L, 20L), y_train: (87168L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.04s\n",
      "\t\t\t\t\t-->R2: 0.775\n",
      "\t\t\t\t\t-->MAE: 0.602\n",
      "\t\t\t\t\t-->MAPE: 42.192\n",
      "\t\t\t\t\t-->MASE: 0.469\n",
      "\n",
      "\t\t-->Evaluation (2014-07-30 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (87264L, 16L), y_train: (87264L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.238\n",
      "\t\t\t\t\t-->MAE: 1.381\n",
      "\t\t\t\t\t-->MAPE: 81.069\n",
      "\t\t\t\t\t-->MASE: 1.075\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.640\n",
      "\t\t\t\t\t-->MAE: 0.660\n",
      "\t\t\t\t\t-->MAPE: 38.270\n",
      "\t\t\t\t\t-->MASE: 0.514\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.483\n",
      "\t\t\t\t\t-->MAE: 0.763\n",
      "\t\t\t\t\t-->MAPE: 50.314\n",
      "\t\t\t\t\t-->MASE: 0.594\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (87264L, 20L), y_train: (87264L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.21s\n",
      "\t\t\t\t\t-->R2: 0.788\n",
      "\t\t\t\t\t-->MAE: 0.631\n",
      "\t\t\t\t\t-->MAPE: 33.943\n",
      "\t\t\t\t\t-->MASE: 0.491\n",
      "\n",
      "\t\t-->Evaluation (2014-07-31 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (87360L, 16L), y_train: (87360L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.858\n",
      "\t\t\t\t\t-->MAE: 1.443\n",
      "\t\t\t\t\t-->MAPE: 74.000\n",
      "\t\t\t\t\t-->MASE: 1.123\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.558\n",
      "\t\t\t\t\t-->MAE: 0.784\n",
      "\t\t\t\t\t-->MAPE: 59.545\n",
      "\t\t\t\t\t-->MASE: 0.610\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.700\n",
      "\t\t\t\t\t-->MAE: 0.536\n",
      "\t\t\t\t\t-->MAPE: 40.515\n",
      "\t\t\t\t\t-->MASE: 0.417\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (87360L, 20L), y_train: (87360L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.72s\n",
      "\t\t\t\t\t-->R2: 0.801\n",
      "\t\t\t\t\t-->MAE: 0.555\n",
      "\t\t\t\t\t-->MAPE: 36.705\n",
      "\t\t\t\t\t-->MASE: 0.432\n",
      "\n",
      "\t\t-->Evaluation (2014-08-01 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (87456L, 16L), y_train: (87456L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -15.259\n",
      "\t\t\t\t\t-->MAE: 1.237\n",
      "\t\t\t\t\t-->MAPE: 112.121\n",
      "\t\t\t\t\t-->MASE: 0.963\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -15.259\n",
      "\t\t\t\t\t-->MAE: 1.278\n",
      "\t\t\t\t\t-->MAPE: 124.242\n",
      "\t\t\t\t\t-->MASE: 0.995\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.710\n",
      "\t\t\t\t\t-->MAE: 0.546\n",
      "\t\t\t\t\t-->MAPE: 69.697\n",
      "\t\t\t\t\t-->MASE: 0.425\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (87456L, 20L), y_train: (87456L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.73s\n",
      "\t\t\t\t\t-->R2: -0.847\n",
      "\t\t\t\t\t-->MAE: 0.542\n",
      "\t\t\t\t\t-->MAPE: 35.413\n",
      "\t\t\t\t\t-->MASE: 0.422\n",
      "\n",
      "\t\t-->Evaluation (2014-08-02 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (87552L, 16L), y_train: (87552L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.292\n",
      "\t\t\t\t\t-->MAE: 0.546\n",
      "\t\t\t\t\t-->MAPE: 68.750\n",
      "\t\t\t\t\t-->MASE: 0.425\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.223\n",
      "\t\t\t\t\t-->MAE: 0.505\n",
      "\t\t\t\t\t-->MAPE: 62.500\n",
      "\t\t\t\t\t-->MASE: 0.393\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.070\n",
      "\t\t\t\t\t-->MAE: 0.371\n",
      "\t\t\t\t\t-->MAPE: 50.000\n",
      "\t\t\t\t\t-->MASE: 0.289\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (87552L, 20L), y_train: (87552L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.77s\n",
      "\t\t\t\t\t-->R2: 0.032\n",
      "\t\t\t\t\t-->MAE: 0.554\n",
      "\t\t\t\t\t-->MAPE: 68.126\n",
      "\t\t\t\t\t-->MASE: 0.431\n",
      "\n",
      "\t\t-->Evaluation (2014-08-03 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (87648L, 16L), y_train: (87648L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -3.257\n",
      "\t\t\t\t\t-->MAE: 0.505\n",
      "\t\t\t\t\t-->MAPE: 68.000\n",
      "\t\t\t\t\t-->MASE: 0.393\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -3.149\n",
      "\t\t\t\t\t-->MAE: 0.505\n",
      "\t\t\t\t\t-->MAPE: 72.000\n",
      "\t\t\t\t\t-->MASE: 0.393\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.246\n",
      "\t\t\t\t\t-->MAE: 0.144\n",
      "\t\t\t\t\t-->MAPE: 28.000\n",
      "\t\t\t\t\t-->MASE: 0.112\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (87648L, 20L), y_train: (87648L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.19s\n",
      "\t\t\t\t\t-->R2: 0.128\n",
      "\t\t\t\t\t-->MAE: 0.377\n",
      "\t\t\t\t\t-->MAPE: 58.310\n",
      "\t\t\t\t\t-->MASE: 0.294\n",
      "\n",
      "\t\t-->Evaluation (2014-08-04 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (87744L, 16L), y_train: (87744L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.569\n",
      "\t\t\t\t\t-->MAE: 0.495\n",
      "\t\t\t\t\t-->MAPE: 74.242\n",
      "\t\t\t\t\t-->MASE: 0.386\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.088\n",
      "\t\t\t\t\t-->MAE: 0.320\n",
      "\t\t\t\t\t-->MAPE: 51.515\n",
      "\t\t\t\t\t-->MASE: 0.249\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -7.303\n",
      "\t\t\t\t\t-->MAE: 1.258\n",
      "\t\t\t\t\t-->MAPE: 108.081\n",
      "\t\t\t\t\t-->MASE: 0.980\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (87744L, 20L), y_train: (87744L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.75s\n",
      "\t\t\t\t\t-->R2: -6.581\n",
      "\t\t\t\t\t-->MAE: 1.291\n",
      "\t\t\t\t\t-->MAPE: 88.925\n",
      "\t\t\t\t\t-->MASE: 1.006\n",
      "\n",
      "\t\t-->Evaluation (2014-08-05 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (87840L, 16L), y_train: (87840L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -2.213\n",
      "\t\t\t\t\t-->MAE: 0.557\n",
      "\t\t\t\t\t-->MAPE: 85.714\n",
      "\t\t\t\t\t-->MASE: 0.434\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -2.213\n",
      "\t\t\t\t\t-->MAE: 0.557\n",
      "\t\t\t\t\t-->MAPE: 85.714\n",
      "\t\t\t\t\t-->MASE: 0.434\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -18.028\n",
      "\t\t\t\t\t-->MAE: 1.330\n",
      "\t\t\t\t\t-->MAPE: 121.429\n",
      "\t\t\t\t\t-->MASE: 1.037\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (87840L, 20L), y_train: (87840L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.96s\n",
      "\t\t\t\t\t-->R2: -2.195\n",
      "\t\t\t\t\t-->MAE: 0.683\n",
      "\t\t\t\t\t-->MAPE: 51.358\n",
      "\t\t\t\t\t-->MASE: 0.532\n",
      "\n",
      "\t\t-->Evaluation (2014-08-06 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (87936L, 16L), y_train: (87936L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.711\n",
      "\t\t\t\t\t-->MAE: 0.505\n",
      "\t\t\t\t\t-->MAPE: 75.000\n",
      "\t\t\t\t\t-->MASE: 0.394\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.651\n",
      "\t\t\t\t\t-->MAE: 0.505\n",
      "\t\t\t\t\t-->MAPE: 76.471\n",
      "\t\t\t\t\t-->MASE: 0.394\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -12.325\n",
      "\t\t\t\t\t-->MAE: 1.381\n",
      "\t\t\t\t\t-->MAPE: 105.882\n",
      "\t\t\t\t\t-->MASE: 1.078\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (87936L, 20L), y_train: (87936L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.61s\n",
      "\t\t\t\t\t-->R2: -0.165\n",
      "\t\t\t\t\t-->MAE: 0.544\n",
      "\t\t\t\t\t-->MAPE: 52.910\n",
      "\t\t\t\t\t-->MASE: 0.424\n",
      "\n",
      "\t\t-->Evaluation (2014-08-07 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (88032L, 16L), y_train: (88032L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.534\n",
      "\t\t\t\t\t-->MAE: 0.526\n",
      "\t\t\t\t\t-->MAPE: 58.081\n",
      "\t\t\t\t\t-->MASE: 0.411\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.039\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 64.141\n",
      "\t\t\t\t\t-->MASE: 0.370\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -6.317\n",
      "\t\t\t\t\t-->MAE: 1.485\n",
      "\t\t\t\t\t-->MAPE: 137.374\n",
      "\t\t\t\t\t-->MASE: 1.159\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (88032L, 20L), y_train: (88032L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.87s\n",
      "\t\t\t\t\t-->R2: 0.069\n",
      "\t\t\t\t\t-->MAE: 0.590\n",
      "\t\t\t\t\t-->MAPE: 64.322\n",
      "\t\t\t\t\t-->MASE: 0.461\n",
      "\n",
      "\t\t-->Evaluation (2014-08-08 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (88128L, 16L), y_train: (88128L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.233\n",
      "\t\t\t\t\t-->MAE: 0.732\n",
      "\t\t\t\t\t-->MAPE: 82.353\n",
      "\t\t\t\t\t-->MASE: 0.572\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.133\n",
      "\t\t\t\t\t-->MAE: 0.464\n",
      "\t\t\t\t\t-->MAPE: 66.667\n",
      "\t\t\t\t\t-->MASE: 0.362\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.421\n",
      "\t\t\t\t\t-->MAE: 0.567\n",
      "\t\t\t\t\t-->MAPE: 70.588\n",
      "\t\t\t\t\t-->MASE: 0.443\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (88128L, 20L), y_train: (88128L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.76s\n",
      "\t\t\t\t\t-->R2: 0.363\n",
      "\t\t\t\t\t-->MAE: 0.503\n",
      "\t\t\t\t\t-->MAPE: 53.296\n",
      "\t\t\t\t\t-->MASE: 0.393\n",
      "\n",
      "\t\t-->Evaluation (2014-08-09 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (88224L, 16L), y_train: (88224L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.946\n",
      "\t\t\t\t\t-->MAE: 0.505\n",
      "\t\t\t\t\t-->MAPE: 72.043\n",
      "\t\t\t\t\t-->MASE: 0.395\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.996\n",
      "\t\t\t\t\t-->MAE: 0.691\n",
      "\t\t\t\t\t-->MAPE: 89.247\n",
      "\t\t\t\t\t-->MASE: 0.540\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.823\n",
      "\t\t\t\t\t-->MAE: 0.567\n",
      "\t\t\t\t\t-->MAPE: 96.774\n",
      "\t\t\t\t\t-->MASE: 0.443\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (88224L, 20L), y_train: (88224L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.56s\n",
      "\t\t\t\t\t-->R2: -0.632\n",
      "\t\t\t\t\t-->MAE: 0.564\n",
      "\t\t\t\t\t-->MAPE: 68.920\n",
      "\t\t\t\t\t-->MASE: 0.441\n",
      "\n",
      "\t\t-->Evaluation (2014-08-10 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (88320L, 16L), y_train: (88320L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -2.449\n",
      "\t\t\t\t\t-->MAE: 0.577\n",
      "\t\t\t\t\t-->MAPE: 96.000\n",
      "\t\t\t\t\t-->MASE: 0.452\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -2.341\n",
      "\t\t\t\t\t-->MAE: 0.577\n",
      "\t\t\t\t\t-->MAPE: 100.000\n",
      "\t\t\t\t\t-->MASE: 0.452\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.724\n",
      "\t\t\t\t\t-->MAE: 0.330\n",
      "\t\t\t\t\t-->MAPE: 64.000\n",
      "\t\t\t\t\t-->MASE: 0.258\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (88320L, 20L), y_train: (88320L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.57s\n",
      "\t\t\t\t\t-->R2: -0.006\n",
      "\t\t\t\t\t-->MAE: 0.400\n",
      "\t\t\t\t\t-->MAPE: 66.755\n",
      "\t\t\t\t\t-->MASE: 0.313\n",
      "\n",
      "\t\t-->Evaluation (2014-08-11 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (88416L, 16L), y_train: (88416L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.294\n",
      "\t\t\t\t\t-->MAE: 0.763\n",
      "\t\t\t\t\t-->MAPE: 82.807\n",
      "\t\t\t\t\t-->MASE: 0.597\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.333\n",
      "\t\t\t\t\t-->MAE: 0.835\n",
      "\t\t\t\t\t-->MAPE: 91.140\n",
      "\t\t\t\t\t-->MASE: 0.654\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.018\n",
      "\t\t\t\t\t-->MAE: 0.680\n",
      "\t\t\t\t\t-->MAPE: 71.228\n",
      "\t\t\t\t\t-->MASE: 0.533\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (88416L, 20L), y_train: (88416L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.66s\n",
      "\t\t\t\t\t-->R2: -1.022\n",
      "\t\t\t\t\t-->MAE: 1.246\n",
      "\t\t\t\t\t-->MAPE: 90.723\n",
      "\t\t\t\t\t-->MASE: 0.976\n",
      "\n",
      "\t\t-->Evaluation (2014-08-12 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (88512L, 16L), y_train: (88512L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.184\n",
      "\t\t\t\t\t-->MAE: 1.031\n",
      "\t\t\t\t\t-->MAPE: 84.649\n",
      "\t\t\t\t\t-->MASE: 0.807\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.132\n",
      "\t\t\t\t\t-->MAE: 0.577\n",
      "\t\t\t\t\t-->MAPE: 59.167\n",
      "\t\t\t\t\t-->MASE: 0.452\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.317\n",
      "\t\t\t\t\t-->MAE: 0.784\n",
      "\t\t\t\t\t-->MAPE: 81.579\n",
      "\t\t\t\t\t-->MASE: 0.614\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (88512L, 20L), y_train: (88512L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.54s\n",
      "\t\t\t\t\t-->R2: 0.141\n",
      "\t\t\t\t\t-->MAE: 0.732\n",
      "\t\t\t\t\t-->MAPE: 62.839\n",
      "\t\t\t\t\t-->MASE: 0.573\n",
      "\n",
      "\t\t-->Evaluation (2014-08-13 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (88608L, 16L), y_train: (88608L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.337\n",
      "\t\t\t\t\t-->MAE: 0.938\n",
      "\t\t\t\t\t-->MAPE: 88.415\n",
      "\t\t\t\t\t-->MASE: 0.735\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.711\n",
      "\t\t\t\t\t-->MAE: 0.753\n",
      "\t\t\t\t\t-->MAPE: 88.211\n",
      "\t\t\t\t\t-->MASE: 0.589\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.118\n",
      "\t\t\t\t\t-->MAE: 0.619\n",
      "\t\t\t\t\t-->MAPE: 64.634\n",
      "\t\t\t\t\t-->MASE: 0.484\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (88608L, 20L), y_train: (88608L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.40s\n",
      "\t\t\t\t\t-->R2: 0.167\n",
      "\t\t\t\t\t-->MAE: 0.659\n",
      "\t\t\t\t\t-->MAPE: 61.753\n",
      "\t\t\t\t\t-->MASE: 0.516\n",
      "\n",
      "\t\t-->Evaluation (2014-08-14 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (88704L, 16L), y_train: (88704L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.711\n",
      "\t\t\t\t\t-->MAE: 0.588\n",
      "\t\t\t\t\t-->MAPE: 61.520\n",
      "\t\t\t\t\t-->MASE: 0.460\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.010\n",
      "\t\t\t\t\t-->MAE: 0.691\n",
      "\t\t\t\t\t-->MAPE: 72.549\n",
      "\t\t\t\t\t-->MASE: 0.541\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.878\n",
      "\t\t\t\t\t-->MAE: 0.649\n",
      "\t\t\t\t\t-->MAPE: 75.735\n",
      "\t\t\t\t\t-->MASE: 0.509\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (88704L, 20L), y_train: (88704L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.90s\n",
      "\t\t\t\t\t-->R2: -0.158\n",
      "\t\t\t\t\t-->MAE: 0.638\n",
      "\t\t\t\t\t-->MAPE: 59.958\n",
      "\t\t\t\t\t-->MASE: 0.500\n",
      "\n",
      "\t\t-->Evaluation (2014-08-15 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (88800L, 16L), y_train: (88800L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -4.119\n",
      "\t\t\t\t\t-->MAE: 0.670\n",
      "\t\t\t\t\t-->MAPE: 100.000\n",
      "\t\t\t\t\t-->MASE: 0.525\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -3.904\n",
      "\t\t\t\t\t-->MAE: 0.629\n",
      "\t\t\t\t\t-->MAPE: 92.000\n",
      "\t\t\t\t\t-->MASE: 0.493\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -3.473\n",
      "\t\t\t\t\t-->MAE: 0.588\n",
      "\t\t\t\t\t-->MAPE: 80.000\n",
      "\t\t\t\t\t-->MASE: 0.461\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (88800L, 20L), y_train: (88800L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.03s\n",
      "\t\t\t\t\t-->R2: -0.260\n",
      "\t\t\t\t\t-->MAE: 0.417\n",
      "\t\t\t\t\t-->MAPE: 67.358\n",
      "\t\t\t\t\t-->MASE: 0.327\n",
      "\n",
      "\t\t-->Evaluation (2014-08-16 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (88896L, 16L), y_train: (88896L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.591\n",
      "\t\t\t\t\t-->MAE: 0.320\n",
      "\t\t\t\t\t-->MAPE: 62.963\n",
      "\t\t\t\t\t-->MASE: 0.251\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.745\n",
      "\t\t\t\t\t-->MAE: 0.351\n",
      "\t\t\t\t\t-->MAPE: 66.667\n",
      "\t\t\t\t\t-->MASE: 0.275\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.874\n",
      "\t\t\t\t\t-->MAE: 0.515\n",
      "\t\t\t\t\t-->MAPE: 85.185\n",
      "\t\t\t\t\t-->MASE: 0.404\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (88896L, 20L), y_train: (88896L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.24s\n",
      "\t\t\t\t\t-->R2: -0.023\n",
      "\t\t\t\t\t-->MAE: 0.424\n",
      "\t\t\t\t\t-->MAPE: 63.743\n",
      "\t\t\t\t\t-->MASE: 0.333\n",
      "\n",
      "\t\t-->Evaluation (2014-08-17 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (88992L, 16L), y_train: (88992L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.209\n",
      "\t\t\t\t\t-->MAE: 0.423\n",
      "\t\t\t\t\t-->MAPE: 76.000\n",
      "\t\t\t\t\t-->MASE: 0.332\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.048\n",
      "\t\t\t\t\t-->MAE: 0.392\n",
      "\t\t\t\t\t-->MAPE: 72.000\n",
      "\t\t\t\t\t-->MASE: 0.307\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.030\n",
      "\t\t\t\t\t-->MAE: 0.186\n",
      "\t\t\t\t\t-->MAPE: 36.000\n",
      "\t\t\t\t\t-->MASE: 0.146\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (88992L, 20L), y_train: (88992L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.04s\n",
      "\t\t\t\t\t-->R2: 0.128\n",
      "\t\t\t\t\t-->MAE: 0.377\n",
      "\t\t\t\t\t-->MAPE: 59.799\n",
      "\t\t\t\t\t-->MASE: 0.296\n",
      "\n",
      "\t\t-->Evaluation (2014-08-18 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (89088L, 16L), y_train: (89088L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.608\n",
      "\t\t\t\t\t-->MAE: 1.258\n",
      "\t\t\t\t\t-->MAPE: 88.522\n",
      "\t\t\t\t\t-->MASE: 0.988\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.613\n",
      "\t\t\t\t\t-->MAE: 1.268\n",
      "\t\t\t\t\t-->MAPE: 88.522\n",
      "\t\t\t\t\t-->MASE: 0.996\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.608\n",
      "\t\t\t\t\t-->MAE: 1.216\n",
      "\t\t\t\t\t-->MAPE: 84.277\n",
      "\t\t\t\t\t-->MASE: 0.956\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (89088L, 20L), y_train: (89088L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.00s\n",
      "\t\t\t\t\t-->R2: 0.505\n",
      "\t\t\t\t\t-->MAE: 0.721\n",
      "\t\t\t\t\t-->MAPE: 44.940\n",
      "\t\t\t\t\t-->MASE: 0.566\n",
      "\n",
      "\t\t-->Evaluation (2014-08-19 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (89184L, 16L), y_train: (89184L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.531\n",
      "\t\t\t\t\t-->MAE: 1.278\n",
      "\t\t\t\t\t-->MAPE: 68.981\n",
      "\t\t\t\t\t-->MASE: 1.004\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.805\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 37.191\n",
      "\t\t\t\t\t-->MASE: 0.372\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.836\n",
      "\t\t\t\t\t-->MAE: 1.546\n",
      "\t\t\t\t\t-->MAPE: 108.179\n",
      "\t\t\t\t\t-->MASE: 1.215\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (89184L, 20L), y_train: (89184L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.25s\n",
      "\t\t\t\t\t-->R2: 0.751\n",
      "\t\t\t\t\t-->MAE: 0.603\n",
      "\t\t\t\t\t-->MAPE: 37.069\n",
      "\t\t\t\t\t-->MASE: 0.474\n",
      "\n",
      "\t\t-->Evaluation (2014-08-20 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (89280L, 16L), y_train: (89280L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.378\n",
      "\t\t\t\t\t-->MAE: 1.268\n",
      "\t\t\t\t\t-->MAPE: 73.360\n",
      "\t\t\t\t\t-->MASE: 0.996\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.736\n",
      "\t\t\t\t\t-->MAE: 0.546\n",
      "\t\t\t\t\t-->MAPE: 41.613\n",
      "\t\t\t\t\t-->MASE: 0.429\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.392\n",
      "\t\t\t\t\t-->MAE: 1.216\n",
      "\t\t\t\t\t-->MAPE: 78.978\n",
      "\t\t\t\t\t-->MASE: 0.955\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (89280L, 20L), y_train: (89280L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.63s\n",
      "\t\t\t\t\t-->R2: 0.848\n",
      "\t\t\t\t\t-->MAE: 0.503\n",
      "\t\t\t\t\t-->MAPE: 35.437\n",
      "\t\t\t\t\t-->MASE: 0.395\n",
      "\n",
      "\t\t-->Evaluation (2014-08-21 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (89376L, 16L), y_train: (89376L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.649\n",
      "\t\t\t\t\t-->MAE: 1.206\n",
      "\t\t\t\t\t-->MAPE: 88.285\n",
      "\t\t\t\t\t-->MASE: 0.947\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.632\n",
      "\t\t\t\t\t-->MAE: 0.577\n",
      "\t\t\t\t\t-->MAPE: 41.546\n",
      "\t\t\t\t\t-->MASE: 0.453\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.637\n",
      "\t\t\t\t\t-->MAE: 1.186\n",
      "\t\t\t\t\t-->MAPE: 74.275\n",
      "\t\t\t\t\t-->MASE: 0.931\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (89376L, 20L), y_train: (89376L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.45s\n",
      "\t\t\t\t\t-->R2: 0.789\n",
      "\t\t\t\t\t-->MAE: 0.526\n",
      "\t\t\t\t\t-->MAPE: 32.440\n",
      "\t\t\t\t\t-->MASE: 0.413\n",
      "\n",
      "\t\t-->Evaluation (2014-08-22 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (89472L, 16L), y_train: (89472L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -5.638\n",
      "\t\t\t\t\t-->MAE: 1.320\n",
      "\t\t\t\t\t-->MAPE: 109.375\n",
      "\t\t\t\t\t-->MASE: 1.037\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -5.210\n",
      "\t\t\t\t\t-->MAE: 1.268\n",
      "\t\t\t\t\t-->MAPE: 111.458\n",
      "\t\t\t\t\t-->MASE: 0.996\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.405\n",
      "\t\t\t\t\t-->MAE: 0.443\n",
      "\t\t\t\t\t-->MAPE: 68.750\n",
      "\t\t\t\t\t-->MASE: 0.348\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (89472L, 20L), y_train: (89472L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.69s\n",
      "\t\t\t\t\t-->R2: -0.118\n",
      "\t\t\t\t\t-->MAE: 0.568\n",
      "\t\t\t\t\t-->MAPE: 40.572\n",
      "\t\t\t\t\t-->MASE: 0.446\n",
      "\n",
      "\t\t-->Evaluation (2014-08-23 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (89568L, 16L), y_train: (89568L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.568\n",
      "\t\t\t\t\t-->MAE: 0.639\n",
      "\t\t\t\t\t-->MAPE: 86.979\n",
      "\t\t\t\t\t-->MASE: 0.502\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.835\n",
      "\t\t\t\t\t-->MAE: 0.649\n",
      "\t\t\t\t\t-->MAPE: 83.854\n",
      "\t\t\t\t\t-->MASE: 0.510\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.283\n",
      "\t\t\t\t\t-->MAE: 0.515\n",
      "\t\t\t\t\t-->MAPE: 71.354\n",
      "\t\t\t\t\t-->MASE: 0.405\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (89568L, 20L), y_train: (89568L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.03s\n",
      "\t\t\t\t\t-->R2: 0.026\n",
      "\t\t\t\t\t-->MAE: 0.621\n",
      "\t\t\t\t\t-->MAPE: 61.015\n",
      "\t\t\t\t\t-->MASE: 0.488\n",
      "\n",
      "\t\t-->Evaluation (2014-08-24 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (89664L, 16L), y_train: (89664L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -3.559\n",
      "\t\t\t\t\t-->MAE: 0.577\n",
      "\t\t\t\t\t-->MAPE: 91.304\n",
      "\t\t\t\t\t-->MASE: 0.454\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -3.673\n",
      "\t\t\t\t\t-->MAE: 0.577\n",
      "\t\t\t\t\t-->MAPE: 86.957\n",
      "\t\t\t\t\t-->MASE: 0.454\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.824\n",
      "\t\t\t\t\t-->MAE: 0.330\n",
      "\t\t\t\t\t-->MAPE: 65.217\n",
      "\t\t\t\t\t-->MASE: 0.259\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (89664L, 20L), y_train: (89664L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.42s\n",
      "\t\t\t\t\t-->R2: -0.138\n",
      "\t\t\t\t\t-->MAE: 0.407\n",
      "\t\t\t\t\t-->MAPE: 64.782\n",
      "\t\t\t\t\t-->MASE: 0.320\n",
      "\n",
      "\t\t-->Evaluation (2014-08-25 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (89760L, 16L), y_train: (89760L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.584\n",
      "\t\t\t\t\t-->MAE: 1.155\n",
      "\t\t\t\t\t-->MAPE: 75.309\n",
      "\t\t\t\t\t-->MASE: 0.908\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.614\n",
      "\t\t\t\t\t-->MAE: 1.155\n",
      "\t\t\t\t\t-->MAPE: 73.302\n",
      "\t\t\t\t\t-->MASE: 0.908\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.766\n",
      "\t\t\t\t\t-->MAE: 0.464\n",
      "\t\t\t\t\t-->MAPE: 37.037\n",
      "\t\t\t\t\t-->MASE: 0.365\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (89760L, 20L), y_train: (89760L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.73s\n",
      "\t\t\t\t\t-->R2: 0.826\n",
      "\t\t\t\t\t-->MAE: 0.509\n",
      "\t\t\t\t\t-->MAPE: 36.721\n",
      "\t\t\t\t\t-->MASE: 0.400\n",
      "\n",
      "\t\t-->Evaluation (2014-08-26 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (89856L, 16L), y_train: (89856L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.486\n",
      "\t\t\t\t\t-->MAE: 1.526\n",
      "\t\t\t\t\t-->MAPE: 70.167\n",
      "\t\t\t\t\t-->MASE: 1.200\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.734\n",
      "\t\t\t\t\t-->MAE: 0.577\n",
      "\t\t\t\t\t-->MAPE: 36.694\n",
      "\t\t\t\t\t-->MASE: 0.454\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.795\n",
      "\t\t\t\t\t-->MAE: 0.526\n",
      "\t\t\t\t\t-->MAPE: 33.389\n",
      "\t\t\t\t\t-->MASE: 0.413\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (89856L, 20L), y_train: (89856L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.87s\n",
      "\t\t\t\t\t-->R2: 0.805\n",
      "\t\t\t\t\t-->MAE: 0.643\n",
      "\t\t\t\t\t-->MAPE: 35.675\n",
      "\t\t\t\t\t-->MASE: 0.506\n",
      "\n",
      "\t\t-->Evaluation (2014-08-27 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (89952L, 16L), y_train: (89952L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.530\n",
      "\t\t\t\t\t-->MAE: 1.351\n",
      "\t\t\t\t\t-->MAPE: 78.869\n",
      "\t\t\t\t\t-->MASE: 1.062\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.432\n",
      "\t\t\t\t\t-->MAE: 0.825\n",
      "\t\t\t\t\t-->MAPE: 57.738\n",
      "\t\t\t\t\t-->MASE: 0.648\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.791\n",
      "\t\t\t\t\t-->MAE: 0.371\n",
      "\t\t\t\t\t-->MAPE: 20.387\n",
      "\t\t\t\t\t-->MASE: 0.292\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (89952L, 20L), y_train: (89952L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.63s\n",
      "\t\t\t\t\t-->R2: 0.778\n",
      "\t\t\t\t\t-->MAE: 0.548\n",
      "\t\t\t\t\t-->MAPE: 34.770\n",
      "\t\t\t\t\t-->MASE: 0.431\n",
      "\n",
      "\t\t-->Evaluation (2014-08-28 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (90048L, 16L), y_train: (90048L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.459\n",
      "\t\t\t\t\t-->MAE: 1.144\n",
      "\t\t\t\t\t-->MAPE: 62.333\n",
      "\t\t\t\t\t-->MASE: 0.900\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.690\n",
      "\t\t\t\t\t-->MAE: 0.567\n",
      "\t\t\t\t\t-->MAPE: 42.667\n",
      "\t\t\t\t\t-->MASE: 0.446\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.759\n",
      "\t\t\t\t\t-->MAE: 0.505\n",
      "\t\t\t\t\t-->MAPE: 24.833\n",
      "\t\t\t\t\t-->MASE: 0.397\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (90048L, 20L), y_train: (90048L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.45s\n",
      "\t\t\t\t\t-->R2: 0.853\n",
      "\t\t\t\t\t-->MAE: 0.506\n",
      "\t\t\t\t\t-->MAPE: 27.097\n",
      "\t\t\t\t\t-->MASE: 0.398\n",
      "\n",
      "\t\t-->Evaluation (2014-08-29 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (90144L, 16L), y_train: (90144L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -13.650\n",
      "\t\t\t\t\t-->MAE: 1.268\n",
      "\t\t\t\t\t-->MAPE: 129.032\n",
      "\t\t\t\t\t-->MASE: 0.997\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -12.796\n",
      "\t\t\t\t\t-->MAE: 1.186\n",
      "\t\t\t\t\t-->MAPE: 132.258\n",
      "\t\t\t\t\t-->MASE: 0.932\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -2.082\n",
      "\t\t\t\t\t-->MAE: 0.402\n",
      "\t\t\t\t\t-->MAPE: 51.613\n",
      "\t\t\t\t\t-->MASE: 0.316\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (90144L, 20L), y_train: (90144L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.12s\n",
      "\t\t\t\t\t-->R2: -0.023\n",
      "\t\t\t\t\t-->MAE: 0.413\n",
      "\t\t\t\t\t-->MAPE: 53.513\n",
      "\t\t\t\t\t-->MASE: 0.325\n",
      "\n",
      "\t\t-->Evaluation (2014-08-30 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (90240L, 16L), y_train: (90240L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.479\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 80.000\n",
      "\t\t\t\t\t-->MASE: 0.373\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.048\n",
      "\t\t\t\t\t-->MAE: 0.392\n",
      "\t\t\t\t\t-->MAPE: 64.000\n",
      "\t\t\t\t\t-->MASE: 0.308\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -2.664\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 72.000\n",
      "\t\t\t\t\t-->MASE: 0.373\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (90240L, 20L), y_train: (90240L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.96s\n",
      "\t\t\t\t\t-->R2: -0.216\n",
      "\t\t\t\t\t-->MAE: 0.416\n",
      "\t\t\t\t\t-->MAPE: 58.997\n",
      "\t\t\t\t\t-->MASE: 0.327\n",
      "\n",
      "\t\t-->Evaluation (2014-08-31 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (90336L, 16L), y_train: (90336L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.052\n",
      "\t\t\t\t\t-->MAE: 0.371\n",
      "\t\t\t\t\t-->MAPE: 73.913\n",
      "\t\t\t\t\t-->MASE: 0.292\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.710\n",
      "\t\t\t\t\t-->MAE: 0.309\n",
      "\t\t\t\t\t-->MAPE: 60.870\n",
      "\t\t\t\t\t-->MASE: 0.243\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.166\n",
      "\t\t\t\t\t-->MAE: 0.392\n",
      "\t\t\t\t\t-->MAPE: 82.609\n",
      "\t\t\t\t\t-->MASE: 0.308\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (90336L, 20L), y_train: (90336L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.01s\n",
      "\t\t\t\t\t-->R2: -0.261\n",
      "\t\t\t\t\t-->MAE: 0.407\n",
      "\t\t\t\t\t-->MAPE: 72.404\n",
      "\t\t\t\t\t-->MASE: 0.320\n",
      "\n",
      "\t\t-->Evaluation (2014-09-01 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (90432L, 16L), y_train: (90432L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.584\n",
      "\t\t\t\t\t-->MAE: 1.433\n",
      "\t\t\t\t\t-->MAPE: 86.795\n",
      "\t\t\t\t\t-->MASE: 1.128\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.521\n",
      "\t\t\t\t\t-->MAE: 1.392\n",
      "\t\t\t\t\t-->MAPE: 84.341\n",
      "\t\t\t\t\t-->MASE: 1.096\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.703\n",
      "\t\t\t\t\t-->MAE: 0.629\n",
      "\t\t\t\t\t-->MAPE: 45.266\n",
      "\t\t\t\t\t-->MASE: 0.495\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (90432L, 20L), y_train: (90432L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.81s\n",
      "\t\t\t\t\t-->R2: 0.845\n",
      "\t\t\t\t\t-->MAE: 0.523\n",
      "\t\t\t\t\t-->MAPE: 32.625\n",
      "\t\t\t\t\t-->MASE: 0.412\n",
      "\n",
      "\t\t-->Evaluation (2014-09-02 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (90528L, 16L), y_train: (90528L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.459\n",
      "\t\t\t\t\t-->MAE: 1.412\n",
      "\t\t\t\t\t-->MAPE: 81.579\n",
      "\t\t\t\t\t-->MASE: 1.112\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.779\n",
      "\t\t\t\t\t-->MAE: 0.505\n",
      "\t\t\t\t\t-->MAPE: 40.205\n",
      "\t\t\t\t\t-->MASE: 0.398\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.732\n",
      "\t\t\t\t\t-->MAE: 0.619\n",
      "\t\t\t\t\t-->MAPE: 40.731\n",
      "\t\t\t\t\t-->MASE: 0.487\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (90528L, 20L), y_train: (90528L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.74s\n",
      "\t\t\t\t\t-->R2: 0.872\n",
      "\t\t\t\t\t-->MAE: 0.479\n",
      "\t\t\t\t\t-->MAPE: 32.954\n",
      "\t\t\t\t\t-->MASE: 0.377\n",
      "\n",
      "\t\t-->Evaluation (2014-09-03 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (90624L, 16L), y_train: (90624L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.520\n",
      "\t\t\t\t\t-->MAE: 1.402\n",
      "\t\t\t\t\t-->MAPE: 81.827\n",
      "\t\t\t\t\t-->MASE: 1.104\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.794\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 30.577\n",
      "\t\t\t\t\t-->MASE: 0.373\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.763\n",
      "\t\t\t\t\t-->MAE: 0.515\n",
      "\t\t\t\t\t-->MAPE: 34.968\n",
      "\t\t\t\t\t-->MASE: 0.406\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (90624L, 20L), y_train: (90624L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.90s\n",
      "\t\t\t\t\t-->R2: 0.878\n",
      "\t\t\t\t\t-->MAE: 0.458\n",
      "\t\t\t\t\t-->MAPE: 29.995\n",
      "\t\t\t\t\t-->MASE: 0.360\n",
      "\n",
      "\t\t-->Evaluation (2014-09-04 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (90720L, 16L), y_train: (90720L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.830\n",
      "\t\t\t\t\t-->MAE: 1.732\n",
      "\t\t\t\t\t-->MAPE: 84.528\n",
      "\t\t\t\t\t-->MASE: 1.363\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.392\n",
      "\t\t\t\t\t-->MAE: 0.804\n",
      "\t\t\t\t\t-->MAPE: 46.111\n",
      "\t\t\t\t\t-->MASE: 0.633\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.409\n",
      "\t\t\t\t\t-->MAE: 0.856\n",
      "\t\t\t\t\t-->MAPE: 52.000\n",
      "\t\t\t\t\t-->MASE: 0.673\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (90720L, 20L), y_train: (90720L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.48s\n",
      "\t\t\t\t\t-->R2: 0.560\n",
      "\t\t\t\t\t-->MAE: 0.757\n",
      "\t\t\t\t\t-->MAPE: 43.680\n",
      "\t\t\t\t\t-->MASE: 0.596\n",
      "\n",
      "\t\t-->Evaluation (2014-09-05 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (90816L, 16L), y_train: (90816L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -22.148\n",
      "\t\t\t\t\t-->MAE: 1.649\n",
      "\t\t\t\t\t-->MAPE: 148.485\n",
      "\t\t\t\t\t-->MASE: 1.298\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -20.127\n",
      "\t\t\t\t\t-->MAE: 1.505\n",
      "\t\t\t\t\t-->MAPE: 172.727\n",
      "\t\t\t\t\t-->MASE: 1.184\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.929\n",
      "\t\t\t\t\t-->MAE: 0.433\n",
      "\t\t\t\t\t-->MAPE: 66.667\n",
      "\t\t\t\t\t-->MASE: 0.341\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (90816L, 20L), y_train: (90816L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.18s\n",
      "\t\t\t\t\t-->R2: -0.204\n",
      "\t\t\t\t\t-->MAE: 0.470\n",
      "\t\t\t\t\t-->MAPE: 43.264\n",
      "\t\t\t\t\t-->MASE: 0.370\n",
      "\n",
      "\t\t-->Evaluation (2014-09-06 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (90912L, 16L), y_train: (90912L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.730\n",
      "\t\t\t\t\t-->MAE: 0.536\n",
      "\t\t\t\t\t-->MAPE: 78.333\n",
      "\t\t\t\t\t-->MASE: 0.422\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.359\n",
      "\t\t\t\t\t-->MAE: 0.454\n",
      "\t\t\t\t\t-->MAPE: 68.333\n",
      "\t\t\t\t\t-->MASE: 0.357\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.792\n",
      "\t\t\t\t\t-->MAE: 0.557\n",
      "\t\t\t\t\t-->MAPE: 95.000\n",
      "\t\t\t\t\t-->MASE: 0.438\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (90912L, 20L), y_train: (90912L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.26s\n",
      "\t\t\t\t\t-->R2: 0.101\n",
      "\t\t\t\t\t-->MAE: 0.482\n",
      "\t\t\t\t\t-->MAPE: 62.412\n",
      "\t\t\t\t\t-->MASE: 0.379\n",
      "\n",
      "\t\t-->Evaluation (2014-09-07 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (91008L, 16L), y_train: (91008L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.602\n",
      "\t\t\t\t\t-->MAE: 0.381\n",
      "\t\t\t\t\t-->MAPE: 54.167\n",
      "\t\t\t\t\t-->MASE: 0.300\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.495\n",
      "\t\t\t\t\t-->MAE: 0.216\n",
      "\t\t\t\t\t-->MAPE: 29.167\n",
      "\t\t\t\t\t-->MASE: 0.170\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.938\n",
      "\t\t\t\t\t-->MAE: 0.361\n",
      "\t\t\t\t\t-->MAPE: 75.000\n",
      "\t\t\t\t\t-->MASE: 0.284\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (91008L, 20L), y_train: (91008L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.86s\n",
      "\t\t\t\t\t-->R2: -0.102\n",
      "\t\t\t\t\t-->MAE: 0.397\n",
      "\t\t\t\t\t-->MAPE: 75.411\n",
      "\t\t\t\t\t-->MASE: 0.312\n",
      "\n",
      "\t\t-->Evaluation (2014-09-08 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (91104L, 16L), y_train: (91104L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.590\n",
      "\t\t\t\t\t-->MAE: 1.351\n",
      "\t\t\t\t\t-->MAPE: 91.503\n",
      "\t\t\t\t\t-->MASE: 1.064\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.617\n",
      "\t\t\t\t\t-->MAE: 1.330\n",
      "\t\t\t\t\t-->MAPE: 88.889\n",
      "\t\t\t\t\t-->MASE: 1.047\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.724\n",
      "\t\t\t\t\t-->MAE: 0.464\n",
      "\t\t\t\t\t-->MAPE: 33.007\n",
      "\t\t\t\t\t-->MASE: 0.365\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (91104L, 20L), y_train: (91104L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.19s\n",
      "\t\t\t\t\t-->R2: 0.885\n",
      "\t\t\t\t\t-->MAE: 0.454\n",
      "\t\t\t\t\t-->MAPE: 32.141\n",
      "\t\t\t\t\t-->MASE: 0.358\n",
      "\n",
      "\t\t-->Evaluation (2014-09-09 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (91200L, 16L), y_train: (91200L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.479\n",
      "\t\t\t\t\t-->MAE: 1.268\n",
      "\t\t\t\t\t-->MAPE: 76.981\n",
      "\t\t\t\t\t-->MASE: 0.999\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.678\n",
      "\t\t\t\t\t-->MAE: 0.567\n",
      "\t\t\t\t\t-->MAPE: 46.195\n",
      "\t\t\t\t\t-->MASE: 0.446\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.603\n",
      "\t\t\t\t\t-->MAE: 0.649\n",
      "\t\t\t\t\t-->MAPE: 45.031\n",
      "\t\t\t\t\t-->MASE: 0.511\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (91200L, 20L), y_train: (91200L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.72s\n",
      "\t\t\t\t\t-->R2: 0.800\n",
      "\t\t\t\t\t-->MAE: 0.538\n",
      "\t\t\t\t\t-->MAPE: 39.201\n",
      "\t\t\t\t\t-->MASE: 0.423\n",
      "\n",
      "\t\t-->Evaluation (2014-09-10 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (91296L, 16L), y_train: (91296L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.361\n",
      "\t\t\t\t\t-->MAE: 1.196\n",
      "\t\t\t\t\t-->MAPE: 72.013\n",
      "\t\t\t\t\t-->MASE: 0.942\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.586\n",
      "\t\t\t\t\t-->MAE: 0.639\n",
      "\t\t\t\t\t-->MAPE: 53.679\n",
      "\t\t\t\t\t-->MASE: 0.503\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.646\n",
      "\t\t\t\t\t-->MAE: 0.608\n",
      "\t\t\t\t\t-->MAPE: 49.528\n",
      "\t\t\t\t\t-->MASE: 0.479\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (91296L, 20L), y_train: (91296L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.23s\n",
      "\t\t\t\t\t-->R2: 0.763\n",
      "\t\t\t\t\t-->MAE: 0.573\n",
      "\t\t\t\t\t-->MAPE: 42.697\n",
      "\t\t\t\t\t-->MASE: 0.451\n",
      "\n",
      "\t\t-->Evaluation (2014-09-11 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (91392L, 16L), y_train: (91392L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.693\n",
      "\t\t\t\t\t-->MAE: 1.557\n",
      "\t\t\t\t\t-->MAPE: 67.514\n",
      "\t\t\t\t\t-->MASE: 1.226\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.578\n",
      "\t\t\t\t\t-->MAE: 0.670\n",
      "\t\t\t\t\t-->MAPE: 39.407\n",
      "\t\t\t\t\t-->MASE: 0.528\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.780\n",
      "\t\t\t\t\t-->MAE: 0.433\n",
      "\t\t\t\t\t-->MAPE: 27.119\n",
      "\t\t\t\t\t-->MASE: 0.341\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (91392L, 20L), y_train: (91392L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.94s\n",
      "\t\t\t\t\t-->R2: 0.806\n",
      "\t\t\t\t\t-->MAE: 0.579\n",
      "\t\t\t\t\t-->MAPE: 34.921\n",
      "\t\t\t\t\t-->MASE: 0.456\n",
      "\n",
      "\t\t-->Evaluation (2014-09-12 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (91488L, 16L), y_train: (91488L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -20.678\n",
      "\t\t\t\t\t-->MAE: 1.649\n",
      "\t\t\t\t\t-->MAPE: 157.576\n",
      "\t\t\t\t\t-->MASE: 1.299\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -20.219\n",
      "\t\t\t\t\t-->MAE: 1.608\n",
      "\t\t\t\t\t-->MAPE: 160.606\n",
      "\t\t\t\t\t-->MASE: 1.266\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.388\n",
      "\t\t\t\t\t-->MAE: 0.536\n",
      "\t\t\t\t\t-->MAPE: 78.788\n",
      "\t\t\t\t\t-->MASE: 0.422\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (91488L, 20L), y_train: (91488L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.86s\n",
      "\t\t\t\t\t-->R2: -0.175\n",
      "\t\t\t\t\t-->MAE: 0.477\n",
      "\t\t\t\t\t-->MAPE: 57.156\n",
      "\t\t\t\t\t-->MASE: 0.375\n",
      "\n",
      "\t\t-->Evaluation (2014-09-13 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (91584L, 16L), y_train: (91584L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.798\n",
      "\t\t\t\t\t-->MAE: 0.423\n",
      "\t\t\t\t\t-->MAPE: 63.462\n",
      "\t\t\t\t\t-->MASE: 0.333\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.798\n",
      "\t\t\t\t\t-->MAE: 0.423\n",
      "\t\t\t\t\t-->MAPE: 63.462\n",
      "\t\t\t\t\t-->MASE: 0.333\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.798\n",
      "\t\t\t\t\t-->MAE: 0.402\n",
      "\t\t\t\t\t-->MAPE: 67.308\n",
      "\t\t\t\t\t-->MASE: 0.316\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (91584L, 20L), y_train: (91584L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.96s\n",
      "\t\t\t\t\t-->R2: 0.048\n",
      "\t\t\t\t\t-->MAE: 0.425\n",
      "\t\t\t\t\t-->MAPE: 57.499\n",
      "\t\t\t\t\t-->MASE: 0.334\n",
      "\n",
      "\t\t-->Evaluation (2014-09-14 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (91680L, 16L), y_train: (91680L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.995\n",
      "\t\t\t\t\t-->MAE: 0.340\n",
      "\t\t\t\t\t-->MAPE: 65.217\n",
      "\t\t\t\t\t-->MASE: 0.268\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.881\n",
      "\t\t\t\t\t-->MAE: 0.299\n",
      "\t\t\t\t\t-->MAPE: 52.174\n",
      "\t\t\t\t\t-->MASE: 0.235\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.487\n",
      "\t\t\t\t\t-->MAE: 0.093\n",
      "\t\t\t\t\t-->MAPE: 17.391\n",
      "\t\t\t\t\t-->MASE: 0.073\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (91680L, 20L), y_train: (91680L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.18s\n",
      "\t\t\t\t\t-->R2: 0.375\n",
      "\t\t\t\t\t-->MAE: 0.299\n",
      "\t\t\t\t\t-->MAPE: 49.979\n",
      "\t\t\t\t\t-->MASE: 0.236\n",
      "\n",
      "\t\t-->Evaluation (2014-09-15 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (91776L, 16L), y_train: (91776L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.617\n",
      "\t\t\t\t\t-->MAE: 1.351\n",
      "\t\t\t\t\t-->MAPE: 88.056\n",
      "\t\t\t\t\t-->MASE: 1.064\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.585\n",
      "\t\t\t\t\t-->MAE: 1.278\n",
      "\t\t\t\t\t-->MAPE: 82.562\n",
      "\t\t\t\t\t-->MASE: 1.008\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.748\n",
      "\t\t\t\t\t-->MAE: 0.495\n",
      "\t\t\t\t\t-->MAPE: 40.340\n",
      "\t\t\t\t\t-->MASE: 0.390\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (91776L, 20L), y_train: (91776L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.82s\n",
      "\t\t\t\t\t-->R2: 0.410\n",
      "\t\t\t\t\t-->MAE: 0.853\n",
      "\t\t\t\t\t-->MAPE: 47.418\n",
      "\t\t\t\t\t-->MASE: 0.672\n",
      "\n",
      "\t\t-->Evaluation (2014-09-16 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (91872L, 16L), y_train: (91872L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.422\n",
      "\t\t\t\t\t-->MAE: 1.423\n",
      "\t\t\t\t\t-->MAPE: 75.957\n",
      "\t\t\t\t\t-->MASE: 1.121\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.699\n",
      "\t\t\t\t\t-->MAE: 0.619\n",
      "\t\t\t\t\t-->MAPE: 44.969\n",
      "\t\t\t\t\t-->MASE: 0.488\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.582\n",
      "\t\t\t\t\t-->MAE: 0.649\n",
      "\t\t\t\t\t-->MAPE: 45.062\n",
      "\t\t\t\t\t-->MASE: 0.512\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (91872L, 20L), y_train: (91872L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.51s\n",
      "\t\t\t\t\t-->R2: 0.786\n",
      "\t\t\t\t\t-->MAE: 0.579\n",
      "\t\t\t\t\t-->MAPE: 33.599\n",
      "\t\t\t\t\t-->MASE: 0.457\n",
      "\n",
      "\t\t-->Evaluation (2014-09-17 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (91968L, 16L), y_train: (91968L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.398\n",
      "\t\t\t\t\t-->MAE: 1.402\n",
      "\t\t\t\t\t-->MAPE: 81.667\n",
      "\t\t\t\t\t-->MASE: 1.105\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.639\n",
      "\t\t\t\t\t-->MAE: 0.701\n",
      "\t\t\t\t\t-->MAPE: 49.423\n",
      "\t\t\t\t\t-->MASE: 0.552\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.760\n",
      "\t\t\t\t\t-->MAE: 0.485\n",
      "\t\t\t\t\t-->MAPE: 32.724\n",
      "\t\t\t\t\t-->MASE: 0.382\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (91968L, 20L), y_train: (91968L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.81s\n",
      "\t\t\t\t\t-->R2: 0.828\n",
      "\t\t\t\t\t-->MAE: 0.533\n",
      "\t\t\t\t\t-->MAPE: 30.644\n",
      "\t\t\t\t\t-->MASE: 0.420\n",
      "\n",
      "\t\t-->Evaluation (2014-09-18 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (92064L, 16L), y_train: (92064L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.504\n",
      "\t\t\t\t\t-->MAE: 1.381\n",
      "\t\t\t\t\t-->MAPE: 79.560\n",
      "\t\t\t\t\t-->MASE: 1.089\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.728\n",
      "\t\t\t\t\t-->MAE: 0.619\n",
      "\t\t\t\t\t-->MAPE: 51.918\n",
      "\t\t\t\t\t-->MASE: 0.487\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.431\n",
      "\t\t\t\t\t-->MAE: 0.866\n",
      "\t\t\t\t\t-->MAPE: 67.296\n",
      "\t\t\t\t\t-->MASE: 0.682\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (92064L, 20L), y_train: (92064L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.94s\n",
      "\t\t\t\t\t-->R2: 0.819\n",
      "\t\t\t\t\t-->MAE: 0.549\n",
      "\t\t\t\t\t-->MAPE: 39.546\n",
      "\t\t\t\t\t-->MASE: 0.432\n",
      "\n",
      "\t\t-->Evaluation (2014-09-19 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (92160L, 16L), y_train: (92160L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -6.286\n",
      "\t\t\t\t\t-->MAE: 1.515\n",
      "\t\t\t\t\t-->MAPE: 125.203\n",
      "\t\t\t\t\t-->MASE: 1.194\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -5.248\n",
      "\t\t\t\t\t-->MAE: 1.371\n",
      "\t\t\t\t\t-->MAPE: 151.626\n",
      "\t\t\t\t\t-->MASE: 1.080\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.242\n",
      "\t\t\t\t\t-->MAE: 0.526\n",
      "\t\t\t\t\t-->MAPE: 63.821\n",
      "\t\t\t\t\t-->MASE: 0.414\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (92160L, 20L), y_train: (92160L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.50s\n",
      "\t\t\t\t\t-->R2: 0.072\n",
      "\t\t\t\t\t-->MAE: 0.585\n",
      "\t\t\t\t\t-->MAPE: 55.694\n",
      "\t\t\t\t\t-->MASE: 0.461\n",
      "\n",
      "\t\t-->Evaluation (2014-09-20 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (92256L, 16L), y_train: (92256L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.345\n",
      "\t\t\t\t\t-->MAE: 0.577\n",
      "\t\t\t\t\t-->MAPE: 65.104\n",
      "\t\t\t\t\t-->MASE: 0.455\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.344\n",
      "\t\t\t\t\t-->MAE: 0.845\n",
      "\t\t\t\t\t-->MAPE: 91.667\n",
      "\t\t\t\t\t-->MASE: 0.666\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.039\n",
      "\t\t\t\t\t-->MAE: 0.351\n",
      "\t\t\t\t\t-->MAPE: 52.083\n",
      "\t\t\t\t\t-->MASE: 0.276\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (92256L, 20L), y_train: (92256L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.39s\n",
      "\t\t\t\t\t-->R2: 0.284\n",
      "\t\t\t\t\t-->MAE: 0.462\n",
      "\t\t\t\t\t-->MAPE: 50.637\n",
      "\t\t\t\t\t-->MASE: 0.364\n",
      "\n",
      "\t\t-->Evaluation (2014-09-21 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (92352L, 16L), y_train: (92352L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -3.445\n",
      "\t\t\t\t\t-->MAE: 0.598\n",
      "\t\t\t\t\t-->MAPE: 95.652\n",
      "\t\t\t\t\t-->MASE: 0.471\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -3.274\n",
      "\t\t\t\t\t-->MAE: 0.546\n",
      "\t\t\t\t\t-->MAPE: 82.609\n",
      "\t\t\t\t\t-->MASE: 0.431\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.622\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 100.000\n",
      "\t\t\t\t\t-->MASE: 0.374\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (92352L, 20L), y_train: (92352L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.99s\n",
      "\t\t\t\t\t-->R2: -0.338\n",
      "\t\t\t\t\t-->MAE: 0.408\n",
      "\t\t\t\t\t-->MAPE: 80.991\n",
      "\t\t\t\t\t-->MASE: 0.322\n",
      "\n",
      "\t\t-->Evaluation (2014-09-22 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (92448L, 16L), y_train: (92448L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.552\n",
      "\t\t\t\t\t-->MAE: 1.340\n",
      "\t\t\t\t\t-->MAPE: 88.814\n",
      "\t\t\t\t\t-->MASE: 1.057\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.503\n",
      "\t\t\t\t\t-->MAE: 1.278\n",
      "\t\t\t\t\t-->MAPE: 83.045\n",
      "\t\t\t\t\t-->MASE: 1.008\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.636\n",
      "\t\t\t\t\t-->MAE: 0.680\n",
      "\t\t\t\t\t-->MAPE: 51.090\n",
      "\t\t\t\t\t-->MASE: 0.537\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (92448L, 20L), y_train: (92448L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.95s\n",
      "\t\t\t\t\t-->R2: 0.772\n",
      "\t\t\t\t\t-->MAE: 0.586\n",
      "\t\t\t\t\t-->MAPE: 42.011\n",
      "\t\t\t\t\t-->MASE: 0.462\n",
      "\n",
      "\t\t-->Evaluation (2014-09-23 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (92544L, 16L), y_train: (92544L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.230\n",
      "\t\t\t\t\t-->MAE: 1.289\n",
      "\t\t\t\t\t-->MAPE: 79.000\n",
      "\t\t\t\t\t-->MASE: 1.016\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.686\n",
      "\t\t\t\t\t-->MAE: 0.588\n",
      "\t\t\t\t\t-->MAPE: 42.200\n",
      "\t\t\t\t\t-->MASE: 0.463\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.662\n",
      "\t\t\t\t\t-->MAE: 0.546\n",
      "\t\t\t\t\t-->MAPE: 38.267\n",
      "\t\t\t\t\t-->MASE: 0.431\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (92544L, 20L), y_train: (92544L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.19s\n",
      "\t\t\t\t\t-->R2: 0.840\n",
      "\t\t\t\t\t-->MAE: 0.524\n",
      "\t\t\t\t\t-->MAPE: 36.316\n",
      "\t\t\t\t\t-->MASE: 0.413\n",
      "\n",
      "\t\t-->Evaluation (2014-09-24 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (92640L, 16L), y_train: (92640L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.706\n",
      "\t\t\t\t\t-->MAE: 1.474\n",
      "\t\t\t\t\t-->MAPE: 84.808\n",
      "\t\t\t\t\t-->MASE: 1.162\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.731\n",
      "\t\t\t\t\t-->MAE: 0.588\n",
      "\t\t\t\t\t-->MAPE: 48.269\n",
      "\t\t\t\t\t-->MASE: 0.463\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.769\n",
      "\t\t\t\t\t-->MAE: 0.495\n",
      "\t\t\t\t\t-->MAPE: 37.372\n",
      "\t\t\t\t\t-->MASE: 0.390\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (92640L, 20L), y_train: (92640L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.95s\n",
      "\t\t\t\t\t-->R2: 0.876\n",
      "\t\t\t\t\t-->MAE: 0.457\n",
      "\t\t\t\t\t-->MAPE: 34.778\n",
      "\t\t\t\t\t-->MASE: 0.360\n",
      "\n",
      "\t\t-->Evaluation (2014-09-25 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (92736L, 16L), y_train: (92736L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.487\n",
      "\t\t\t\t\t-->MAE: 1.340\n",
      "\t\t\t\t\t-->MAPE: 87.579\n",
      "\t\t\t\t\t-->MASE: 1.057\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.725\n",
      "\t\t\t\t\t-->MAE: 0.557\n",
      "\t\t\t\t\t-->MAPE: 45.975\n",
      "\t\t\t\t\t-->MASE: 0.439\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.706\n",
      "\t\t\t\t\t-->MAE: 0.557\n",
      "\t\t\t\t\t-->MAPE: 44.151\n",
      "\t\t\t\t\t-->MASE: 0.439\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (92736L, 20L), y_train: (92736L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.80s\n",
      "\t\t\t\t\t-->R2: 0.819\n",
      "\t\t\t\t\t-->MAE: 0.531\n",
      "\t\t\t\t\t-->MAPE: 40.671\n",
      "\t\t\t\t\t-->MASE: 0.418\n",
      "\n",
      "\t\t-->Evaluation (2014-09-26 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (92832L, 16L), y_train: (92832L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -14.198\n",
      "\t\t\t\t\t-->MAE: 1.340\n",
      "\t\t\t\t\t-->MAPE: 120.000\n",
      "\t\t\t\t\t-->MASE: 1.056\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -12.678\n",
      "\t\t\t\t\t-->MAE: 1.237\n",
      "\t\t\t\t\t-->MAPE: 140.000\n",
      "\t\t\t\t\t-->MASE: 0.975\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -2.531\n",
      "\t\t\t\t\t-->MAE: 0.649\n",
      "\t\t\t\t\t-->MAPE: 82.857\n",
      "\t\t\t\t\t-->MASE: 0.512\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (92832L, 20L), y_train: (92832L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.20s\n",
      "\t\t\t\t\t-->R2: -0.008\n",
      "\t\t\t\t\t-->MAE: 0.439\n",
      "\t\t\t\t\t-->MAPE: 52.124\n",
      "\t\t\t\t\t-->MASE: 0.346\n",
      "\n",
      "\t\t-->Evaluation (2014-09-27 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (92928L, 16L), y_train: (92928L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.714\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 68.000\n",
      "\t\t\t\t\t-->MASE: 0.374\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.780\n",
      "\t\t\t\t\t-->MAE: 0.495\n",
      "\t\t\t\t\t-->MAPE: 72.000\n",
      "\t\t\t\t\t-->MASE: 0.390\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.341\n",
      "\t\t\t\t\t-->MAE: 0.588\n",
      "\t\t\t\t\t-->MAPE: 91.333\n",
      "\t\t\t\t\t-->MASE: 0.463\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (92928L, 20L), y_train: (92928L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.62s\n",
      "\t\t\t\t\t-->R2: -0.045\n",
      "\t\t\t\t\t-->MAE: 0.476\n",
      "\t\t\t\t\t-->MAPE: 66.976\n",
      "\t\t\t\t\t-->MASE: 0.375\n",
      "\n",
      "\t\t-->Evaluation (2014-09-28 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (93024L, 16L), y_train: (93024L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -2.078\n",
      "\t\t\t\t\t-->MAE: 0.495\n",
      "\t\t\t\t\t-->MAPE: 100.000\n",
      "\t\t\t\t\t-->MASE: 0.390\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.622\n",
      "\t\t\t\t\t-->MAE: 0.412\n",
      "\t\t\t\t\t-->MAPE: 82.609\n",
      "\t\t\t\t\t-->MASE: 0.325\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.622\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 100.000\n",
      "\t\t\t\t\t-->MASE: 0.374\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (93024L, 20L), y_train: (93024L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.82s\n",
      "\t\t\t\t\t-->R2: -0.110\n",
      "\t\t\t\t\t-->MAE: 0.375\n",
      "\t\t\t\t\t-->MAPE: 78.357\n",
      "\t\t\t\t\t-->MASE: 0.296\n",
      "\n",
      "\t\t-->Evaluation (2014-09-29 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (93120L, 16L), y_train: (93120L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.546\n",
      "\t\t\t\t\t-->MAE: 1.268\n",
      "\t\t\t\t\t-->MAPE: 75.417\n",
      "\t\t\t\t\t-->MASE: 1.001\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.500\n",
      "\t\t\t\t\t-->MAE: 1.247\n",
      "\t\t\t\t\t-->MAPE: 74.551\n",
      "\t\t\t\t\t-->MASE: 0.985\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.647\n",
      "\t\t\t\t\t-->MAE: 0.649\n",
      "\t\t\t\t\t-->MAPE: 46.795\n",
      "\t\t\t\t\t-->MASE: 0.513\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (93120L, 20L), y_train: (93120L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.38s\n",
      "\t\t\t\t\t-->R2: 0.819\n",
      "\t\t\t\t\t-->MAE: 0.557\n",
      "\t\t\t\t\t-->MAPE: 37.360\n",
      "\t\t\t\t\t-->MASE: 0.440\n",
      "\n",
      "\t\t-->Evaluation (2014-09-30 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (93216L, 16L), y_train: (93216L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.448\n",
      "\t\t\t\t\t-->MAE: 1.247\n",
      "\t\t\t\t\t-->MAPE: 73.648\n",
      "\t\t\t\t\t-->MASE: 0.985\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.678\n",
      "\t\t\t\t\t-->MAE: 0.485\n",
      "\t\t\t\t\t-->MAPE: 43.302\n",
      "\t\t\t\t\t-->MASE: 0.382\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.721\n",
      "\t\t\t\t\t-->MAE: 0.546\n",
      "\t\t\t\t\t-->MAPE: 46.226\n",
      "\t\t\t\t\t-->MASE: 0.431\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (93216L, 20L), y_train: (93216L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.55s\n",
      "\t\t\t\t\t-->R2: 0.781\n",
      "\t\t\t\t\t-->MAE: 0.565\n",
      "\t\t\t\t\t-->MAPE: 40.960\n",
      "\t\t\t\t\t-->MASE: 0.446\n",
      "\n",
      "\t\t-->Evaluation (2014-10-01 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (93312L, 16L), y_train: (93312L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.412\n",
      "\t\t\t\t\t-->MAE: 1.351\n",
      "\t\t\t\t\t-->MAPE: 68.494\n",
      "\t\t\t\t\t-->MASE: 1.066\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.731\n",
      "\t\t\t\t\t-->MAE: 0.526\n",
      "\t\t\t\t\t-->MAPE: 34.647\n",
      "\t\t\t\t\t-->MASE: 0.415\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.797\n",
      "\t\t\t\t\t-->MAE: 0.485\n",
      "\t\t\t\t\t-->MAPE: 35.609\n",
      "\t\t\t\t\t-->MASE: 0.382\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (93312L, 20L), y_train: (93312L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.22s\n",
      "\t\t\t\t\t-->R2: 0.855\n",
      "\t\t\t\t\t-->MAE: 0.527\n",
      "\t\t\t\t\t-->MAPE: 32.372\n",
      "\t\t\t\t\t-->MASE: 0.416\n",
      "\n",
      "\t\t-->Evaluation (2014-10-02 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (93408L, 16L), y_train: (93408L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.638\n",
      "\t\t\t\t\t-->MAE: 1.464\n",
      "\t\t\t\t\t-->MAPE: 85.673\n",
      "\t\t\t\t\t-->MASE: 1.155\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.727\n",
      "\t\t\t\t\t-->MAE: 0.505\n",
      "\t\t\t\t\t-->MAPE: 36.795\n",
      "\t\t\t\t\t-->MASE: 0.399\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.748\n",
      "\t\t\t\t\t-->MAE: 0.536\n",
      "\t\t\t\t\t-->MAPE: 41.410\n",
      "\t\t\t\t\t-->MASE: 0.423\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (93408L, 20L), y_train: (93408L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.28s\n",
      "\t\t\t\t\t-->R2: 0.848\n",
      "\t\t\t\t\t-->MAE: 0.516\n",
      "\t\t\t\t\t-->MAPE: 36.336\n",
      "\t\t\t\t\t-->MASE: 0.407\n",
      "\n",
      "\t\t-->Evaluation (2014-10-03 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (93504L, 16L), y_train: (93504L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -16.301\n",
      "\t\t\t\t\t-->MAE: 1.330\n",
      "\t\t\t\t\t-->MAPE: 112.500\n",
      "\t\t\t\t\t-->MASE: 1.049\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -15.369\n",
      "\t\t\t\t\t-->MAE: 1.392\n",
      "\t\t\t\t\t-->MAPE: 162.500\n",
      "\t\t\t\t\t-->MASE: 1.098\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.819\n",
      "\t\t\t\t\t-->MAE: 0.402\n",
      "\t\t\t\t\t-->MAPE: 56.250\n",
      "\t\t\t\t\t-->MASE: 0.317\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (93504L, 20L), y_train: (93504L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.50s\n",
      "\t\t\t\t\t-->R2: -0.235\n",
      "\t\t\t\t\t-->MAE: 0.490\n",
      "\t\t\t\t\t-->MAPE: 53.012\n",
      "\t\t\t\t\t-->MASE: 0.387\n",
      "\n",
      "\t\t-->Evaluation (2014-10-04 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (93600L, 16L), y_train: (93600L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.598\n",
      "\t\t\t\t\t-->MAE: 0.649\n",
      "\t\t\t\t\t-->MAPE: 73.469\n",
      "\t\t\t\t\t-->MASE: 0.512\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.268\n",
      "\t\t\t\t\t-->MAE: 0.567\n",
      "\t\t\t\t\t-->MAPE: 73.469\n",
      "\t\t\t\t\t-->MASE: 0.447\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -2.134\n",
      "\t\t\t\t\t-->MAE: 0.701\n",
      "\t\t\t\t\t-->MAPE: 91.837\n",
      "\t\t\t\t\t-->MASE: 0.553\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (93600L, 20L), y_train: (93600L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.18s\n",
      "\t\t\t\t\t-->R2: -0.068\n",
      "\t\t\t\t\t-->MAE: 0.489\n",
      "\t\t\t\t\t-->MAPE: 58.051\n",
      "\t\t\t\t\t-->MASE: 0.386\n",
      "\n",
      "\t\t-->Evaluation (2014-10-05 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (93696L, 16L), y_train: (93696L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.146\n",
      "\t\t\t\t\t-->MAE: 0.536\n",
      "\t\t\t\t\t-->MAPE: 53.191\n",
      "\t\t\t\t\t-->MASE: 0.423\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.651\n",
      "\t\t\t\t\t-->MAE: 0.412\n",
      "\t\t\t\t\t-->MAPE: 40.426\n",
      "\t\t\t\t\t-->MASE: 0.326\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.477\n",
      "\t\t\t\t\t-->MAE: 0.619\n",
      "\t\t\t\t\t-->MAPE: 89.362\n",
      "\t\t\t\t\t-->MASE: 0.488\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (93696L, 20L), y_train: (93696L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.31s\n",
      "\t\t\t\t\t-->R2: 0.181\n",
      "\t\t\t\t\t-->MAE: 0.385\n",
      "\t\t\t\t\t-->MAPE: 32.324\n",
      "\t\t\t\t\t-->MASE: 0.304\n",
      "\n",
      "\t\t-->Evaluation (2014-10-06 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (93792L, 16L), y_train: (93792L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.966\n",
      "\t\t\t\t\t-->MAE: 1.423\n",
      "\t\t\t\t\t-->MAPE: 65.983\n",
      "\t\t\t\t\t-->MASE: 1.124\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.956\n",
      "\t\t\t\t\t-->MAE: 1.361\n",
      "\t\t\t\t\t-->MAPE: 61.774\n",
      "\t\t\t\t\t-->MASE: 1.075\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.610\n",
      "\t\t\t\t\t-->MAE: 0.649\n",
      "\t\t\t\t\t-->MAPE: 53.056\n",
      "\t\t\t\t\t-->MASE: 0.513\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (93792L, 20L), y_train: (93792L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.61s\n",
      "\t\t\t\t\t-->R2: 0.745\n",
      "\t\t\t\t\t-->MAE: 0.547\n",
      "\t\t\t\t\t-->MAPE: 29.012\n",
      "\t\t\t\t\t-->MASE: 0.432\n",
      "\n",
      "\t\t-->Evaluation (2014-10-07 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (93888L, 16L), y_train: (93888L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.727\n",
      "\t\t\t\t\t-->MAE: 1.464\n",
      "\t\t\t\t\t-->MAPE: 76.001\n",
      "\t\t\t\t\t-->MASE: 1.156\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.277\n",
      "\t\t\t\t\t-->MAE: 0.773\n",
      "\t\t\t\t\t-->MAPE: 31.610\n",
      "\t\t\t\t\t-->MASE: 0.611\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.240\n",
      "\t\t\t\t\t-->MAE: 1.000\n",
      "\t\t\t\t\t-->MAPE: 58.680\n",
      "\t\t\t\t\t-->MASE: 0.790\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (93888L, 20L), y_train: (93888L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.10s\n",
      "\t\t\t\t\t-->R2: 0.436\n",
      "\t\t\t\t\t-->MAE: 0.746\n",
      "\t\t\t\t\t-->MAPE: 32.913\n",
      "\t\t\t\t\t-->MASE: 0.589\n",
      "\n",
      "\t\t-->Evaluation (2014-10-08 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (93984L, 16L), y_train: (93984L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.869\n",
      "\t\t\t\t\t-->MAE: 1.423\n",
      "\t\t\t\t\t-->MAPE: 71.862\n",
      "\t\t\t\t\t-->MASE: 1.123\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.284\n",
      "\t\t\t\t\t-->MAE: 0.660\n",
      "\t\t\t\t\t-->MAPE: 26.578\n",
      "\t\t\t\t\t-->MASE: 0.521\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.380\n",
      "\t\t\t\t\t-->MAE: 0.907\n",
      "\t\t\t\t\t-->MAPE: 56.879\n",
      "\t\t\t\t\t-->MASE: 0.716\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (93984L, 20L), y_train: (93984L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.66s\n",
      "\t\t\t\t\t-->R2: 0.722\n",
      "\t\t\t\t\t-->MAE: 0.500\n",
      "\t\t\t\t\t-->MAPE: 20.807\n",
      "\t\t\t\t\t-->MASE: 0.395\n",
      "\n",
      "\t\t-->Evaluation (2014-10-09 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (94080L, 16L), y_train: (94080L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.862\n",
      "\t\t\t\t\t-->MAE: 1.155\n",
      "\t\t\t\t\t-->MAPE: 69.907\n",
      "\t\t\t\t\t-->MASE: 0.912\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.513\n",
      "\t\t\t\t\t-->MAE: 0.515\n",
      "\t\t\t\t\t-->MAPE: 21.796\n",
      "\t\t\t\t\t-->MASE: 0.407\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.556\n",
      "\t\t\t\t\t-->MAE: 0.670\n",
      "\t\t\t\t\t-->MAPE: 52.889\n",
      "\t\t\t\t\t-->MASE: 0.529\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (94080L, 20L), y_train: (94080L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.90s\n",
      "\t\t\t\t\t-->R2: 0.716\n",
      "\t\t\t\t\t-->MAE: 0.430\n",
      "\t\t\t\t\t-->MAPE: 18.654\n",
      "\t\t\t\t\t-->MASE: 0.340\n",
      "\n",
      "\t\t-->Evaluation (2014-10-10 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (94176L, 16L), y_train: (94176L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -9.794\n",
      "\t\t\t\t\t-->MAE: 1.113\n",
      "\t\t\t\t\t-->MAPE: 78.000\n",
      "\t\t\t\t\t-->MASE: 0.879\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -10.469\n",
      "\t\t\t\t\t-->MAE: 1.134\n",
      "\t\t\t\t\t-->MAPE: 70.667\n",
      "\t\t\t\t\t-->MASE: 0.895\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.624\n",
      "\t\t\t\t\t-->MAE: 0.619\n",
      "\t\t\t\t\t-->MAPE: 65.333\n",
      "\t\t\t\t\t-->MASE: 0.488\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (94176L, 20L), y_train: (94176L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.03s\n",
      "\t\t\t\t\t-->R2: -0.145\n",
      "\t\t\t\t\t-->MAE: 0.371\n",
      "\t\t\t\t\t-->MAPE: 18.043\n",
      "\t\t\t\t\t-->MASE: 0.293\n",
      "\n",
      "\t\t-->Evaluation (2014-10-11 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (94272L, 16L), y_train: (94272L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.920\n",
      "\t\t\t\t\t-->MAE: 0.485\n",
      "\t\t\t\t\t-->MAPE: 25.000\n",
      "\t\t\t\t\t-->MASE: 0.383\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.781\n",
      "\t\t\t\t\t-->MAE: 0.443\n",
      "\t\t\t\t\t-->MAPE: 22.414\n",
      "\t\t\t\t\t-->MASE: 0.350\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.467\n",
      "\t\t\t\t\t-->MAE: 0.412\n",
      "\t\t\t\t\t-->MAPE: 41.379\n",
      "\t\t\t\t\t-->MASE: 0.326\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (94272L, 20L), y_train: (94272L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.32s\n",
      "\t\t\t\t\t-->R2: 0.199\n",
      "\t\t\t\t\t-->MAE: 0.384\n",
      "\t\t\t\t\t-->MAPE: 22.105\n",
      "\t\t\t\t\t-->MASE: 0.303\n",
      "\n",
      "\t\t-->Evaluation (2014-10-12 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (94368L, 16L), y_train: (94368L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.089\n",
      "\t\t\t\t\t-->MAE: 0.495\n",
      "\t\t\t\t\t-->MAPE: 44.068\n",
      "\t\t\t\t\t-->MASE: 0.391\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.156\n",
      "\t\t\t\t\t-->MAE: 0.216\n",
      "\t\t\t\t\t-->MAPE: 21.186\n",
      "\t\t\t\t\t-->MASE: 0.171\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.005\n",
      "\t\t\t\t\t-->MAE: 0.258\n",
      "\t\t\t\t\t-->MAPE: 31.356\n",
      "\t\t\t\t\t-->MASE: 0.204\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (94368L, 20L), y_train: (94368L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.18s\n",
      "\t\t\t\t\t-->R2: 0.475\n",
      "\t\t\t\t\t-->MAE: 0.302\n",
      "\t\t\t\t\t-->MAPE: 23.663\n",
      "\t\t\t\t\t-->MASE: 0.238\n",
      "\n",
      "\t\t-->Evaluation (2014-10-13 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (94464L, 16L), y_train: (94464L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.051\n",
      "\t\t\t\t\t-->MAE: 1.258\n",
      "\t\t\t\t\t-->MAPE: 47.510\n",
      "\t\t\t\t\t-->MASE: 0.994\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.208\n",
      "\t\t\t\t\t-->MAE: 1.268\n",
      "\t\t\t\t\t-->MAPE: 47.950\n",
      "\t\t\t\t\t-->MASE: 1.003\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.490\n",
      "\t\t\t\t\t-->MAE: 0.577\n",
      "\t\t\t\t\t-->MAPE: 29.617\n",
      "\t\t\t\t\t-->MASE: 0.456\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (94464L, 20L), y_train: (94464L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.08s\n",
      "\t\t\t\t\t-->R2: 0.591\n",
      "\t\t\t\t\t-->MAE: 0.543\n",
      "\t\t\t\t\t-->MAPE: 19.549\n",
      "\t\t\t\t\t-->MASE: 0.430\n",
      "\n",
      "\t\t-->Evaluation (2014-10-14 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (94560L, 16L), y_train: (94560L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.864\n",
      "\t\t\t\t\t-->MAE: 1.155\n",
      "\t\t\t\t\t-->MAPE: 70.974\n",
      "\t\t\t\t\t-->MASE: 0.913\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.527\n",
      "\t\t\t\t\t-->MAE: 0.495\n",
      "\t\t\t\t\t-->MAPE: 26.030\n",
      "\t\t\t\t\t-->MASE: 0.391\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.106\n",
      "\t\t\t\t\t-->MAE: 0.629\n",
      "\t\t\t\t\t-->MAPE: 28.090\n",
      "\t\t\t\t\t-->MASE: 0.497\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (94560L, 20L), y_train: (94560L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.23s\n",
      "\t\t\t\t\t-->R2: 0.743\n",
      "\t\t\t\t\t-->MAE: 0.366\n",
      "\t\t\t\t\t-->MAPE: 16.944\n",
      "\t\t\t\t\t-->MASE: 0.289\n",
      "\n",
      "\t\t-->Evaluation (2014-10-15 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (94656L, 16L), y_train: (94656L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.577\n",
      "\t\t\t\t\t-->MAE: 1.010\n",
      "\t\t\t\t\t-->MAPE: 56.799\n",
      "\t\t\t\t\t-->MASE: 0.799\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.665\n",
      "\t\t\t\t\t-->MAE: 0.351\n",
      "\t\t\t\t\t-->MAPE: 14.318\n",
      "\t\t\t\t\t-->MASE: 0.277\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.336\n",
      "\t\t\t\t\t-->MAE: 0.567\n",
      "\t\t\t\t\t-->MAPE: 25.322\n",
      "\t\t\t\t\t-->MASE: 0.448\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (94656L, 20L), y_train: (94656L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.92s\n",
      "\t\t\t\t\t-->R2: 0.725\n",
      "\t\t\t\t\t-->MAE: 0.418\n",
      "\t\t\t\t\t-->MAPE: 17.989\n",
      "\t\t\t\t\t-->MASE: 0.331\n",
      "\n",
      "\t\t-->Evaluation (2014-10-16 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (94752L, 16L), y_train: (94752L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.565\n",
      "\t\t\t\t\t-->MAE: 1.062\n",
      "\t\t\t\t\t-->MAPE: 58.877\n",
      "\t\t\t\t\t-->MASE: 0.840\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.715\n",
      "\t\t\t\t\t-->MAE: 0.361\n",
      "\t\t\t\t\t-->MAPE: 15.525\n",
      "\t\t\t\t\t-->MASE: 0.285\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.658\n",
      "\t\t\t\t\t-->MAE: 0.412\n",
      "\t\t\t\t\t-->MAPE: 16.830\n",
      "\t\t\t\t\t-->MASE: 0.326\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (94752L, 20L), y_train: (94752L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.99s\n",
      "\t\t\t\t\t-->R2: 0.807\n",
      "\t\t\t\t\t-->MAE: 0.345\n",
      "\t\t\t\t\t-->MAPE: 13.110\n",
      "\t\t\t\t\t-->MASE: 0.273\n",
      "\n",
      "\t\t-->Evaluation (2014-10-17 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (94848L, 16L), y_train: (94848L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -11.105\n",
      "\t\t\t\t\t-->MAE: 1.134\n",
      "\t\t\t\t\t-->MAPE: 84.247\n",
      "\t\t\t\t\t-->MASE: 0.897\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -11.841\n",
      "\t\t\t\t\t-->MAE: 1.052\n",
      "\t\t\t\t\t-->MAPE: 50.685\n",
      "\t\t\t\t\t-->MASE: 0.832\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.554\n",
      "\t\t\t\t\t-->MAE: 0.247\n",
      "\t\t\t\t\t-->MAPE: 13.014\n",
      "\t\t\t\t\t-->MASE: 0.196\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (94848L, 20L), y_train: (94848L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.97s\n",
      "\t\t\t\t\t-->R2: 0.078\n",
      "\t\t\t\t\t-->MAE: 0.313\n",
      "\t\t\t\t\t-->MAPE: 15.978\n",
      "\t\t\t\t\t-->MASE: 0.248\n",
      "\n",
      "\t\t-->Evaluation (2014-10-18 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (94944L, 16L), y_train: (94944L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.877\n",
      "\t\t\t\t\t-->MAE: 0.371\n",
      "\t\t\t\t\t-->MAPE: 19.355\n",
      "\t\t\t\t\t-->MASE: 0.294\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.430\n",
      "\t\t\t\t\t-->MAE: 0.247\n",
      "\t\t\t\t\t-->MAPE: 8.065\n",
      "\t\t\t\t\t-->MASE: 0.196\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.240\n",
      "\t\t\t\t\t-->MAE: 0.175\n",
      "\t\t\t\t\t-->MAPE: 19.355\n",
      "\t\t\t\t\t-->MASE: 0.139\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (94944L, 20L), y_train: (94944L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.06s\n",
      "\t\t\t\t\t-->R2: 0.516\n",
      "\t\t\t\t\t-->MAE: 0.239\n",
      "\t\t\t\t\t-->MAPE: 14.814\n",
      "\t\t\t\t\t-->MASE: 0.189\n",
      "\n",
      "\t\t-->Evaluation (2014-10-19 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (95040L, 16L), y_train: (95040L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.723\n",
      "\t\t\t\t\t-->MAE: 0.402\n",
      "\t\t\t\t\t-->MAPE: 31.148\n",
      "\t\t\t\t\t-->MASE: 0.318\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.337\n",
      "\t\t\t\t\t-->MAE: 0.155\n",
      "\t\t\t\t\t-->MAPE: 11.475\n",
      "\t\t\t\t\t-->MASE: 0.122\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.337\n",
      "\t\t\t\t\t-->MAE: 0.155\n",
      "\t\t\t\t\t-->MAPE: 14.754\n",
      "\t\t\t\t\t-->MASE: 0.122\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (95040L, 20L), y_train: (95040L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.87s\n",
      "\t\t\t\t\t-->R2: 0.365\n",
      "\t\t\t\t\t-->MAE: 0.282\n",
      "\t\t\t\t\t-->MAPE: 13.926\n",
      "\t\t\t\t\t-->MASE: 0.223\n",
      "\n",
      "\t\t-->Evaluation (2014-10-20 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (95136L, 16L), y_train: (95136L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.333\n",
      "\t\t\t\t\t-->MAE: 1.423\n",
      "\t\t\t\t\t-->MAPE: 47.326\n",
      "\t\t\t\t\t-->MASE: 1.127\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.361\n",
      "\t\t\t\t\t-->MAE: 1.402\n",
      "\t\t\t\t\t-->MAPE: 43.385\n",
      "\t\t\t\t\t-->MASE: 1.111\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.545\n",
      "\t\t\t\t\t-->MAE: 0.598\n",
      "\t\t\t\t\t-->MAPE: 28.056\n",
      "\t\t\t\t\t-->MASE: 0.474\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (95136L, 20L), y_train: (95136L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.93s\n",
      "\t\t\t\t\t-->R2: 0.627\n",
      "\t\t\t\t\t-->MAE: 0.546\n",
      "\t\t\t\t\t-->MAPE: 21.734\n",
      "\t\t\t\t\t-->MASE: 0.433\n",
      "\n",
      "\t\t-->Evaluation (2014-10-21 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (95232L, 16L), y_train: (95232L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.408\n",
      "\t\t\t\t\t-->MAE: 1.237\n",
      "\t\t\t\t\t-->MAPE: 68.815\n",
      "\t\t\t\t\t-->MASE: 0.980\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.538\n",
      "\t\t\t\t\t-->MAE: 0.639\n",
      "\t\t\t\t\t-->MAPE: 21.132\n",
      "\t\t\t\t\t-->MASE: 0.506\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.576\n",
      "\t\t\t\t\t-->MAE: 0.619\n",
      "\t\t\t\t\t-->MAPE: 21.198\n",
      "\t\t\t\t\t-->MASE: 0.490\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (95232L, 20L), y_train: (95232L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.02s\n",
      "\t\t\t\t\t-->R2: 0.653\n",
      "\t\t\t\t\t-->MAE: 0.559\n",
      "\t\t\t\t\t-->MAPE: 18.629\n",
      "\t\t\t\t\t-->MASE: 0.443\n",
      "\n",
      "\t\t-->Evaluation (2014-10-22 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (95328L, 16L), y_train: (95328L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.738\n",
      "\t\t\t\t\t-->MAE: 1.206\n",
      "\t\t\t\t\t-->MAPE: 73.258\n",
      "\t\t\t\t\t-->MASE: 0.955\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.349\n",
      "\t\t\t\t\t-->MAE: 0.629\n",
      "\t\t\t\t\t-->MAPE: 20.243\n",
      "\t\t\t\t\t-->MASE: 0.498\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.618\n",
      "\t\t\t\t\t-->MAE: 0.402\n",
      "\t\t\t\t\t-->MAPE: 15.337\n",
      "\t\t\t\t\t-->MASE: 0.318\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (95328L, 20L), y_train: (95328L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.02s\n",
      "\t\t\t\t\t-->R2: 0.763\n",
      "\t\t\t\t\t-->MAE: 0.397\n",
      "\t\t\t\t\t-->MAPE: 12.151\n",
      "\t\t\t\t\t-->MASE: 0.314\n",
      "\n",
      "\t\t-->Evaluation (2014-10-23 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (95424L, 16L), y_train: (95424L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.738\n",
      "\t\t\t\t\t-->MAE: 1.381\n",
      "\t\t\t\t\t-->MAPE: 66.316\n",
      "\t\t\t\t\t-->MASE: 1.094\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.571\n",
      "\t\t\t\t\t-->MAE: 0.619\n",
      "\t\t\t\t\t-->MAPE: 20.579\n",
      "\t\t\t\t\t-->MASE: 0.490\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.609\n",
      "\t\t\t\t\t-->MAE: 0.557\n",
      "\t\t\t\t\t-->MAPE: 17.561\n",
      "\t\t\t\t\t-->MASE: 0.441\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (95424L, 20L), y_train: (95424L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.98s\n",
      "\t\t\t\t\t-->R2: 0.760\n",
      "\t\t\t\t\t-->MAE: 0.502\n",
      "\t\t\t\t\t-->MAPE: 17.519\n",
      "\t\t\t\t\t-->MASE: 0.398\n",
      "\n",
      "\t\t-->Evaluation (2014-10-24 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (95520L, 16L), y_train: (95520L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -23.012\n",
      "\t\t\t\t\t-->MAE: 1.515\n",
      "\t\t\t\t\t-->MAPE: 120.714\n",
      "\t\t\t\t\t-->MASE: 1.200\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -25.485\n",
      "\t\t\t\t\t-->MAE: 1.495\n",
      "\t\t\t\t\t-->MAPE: 80.000\n",
      "\t\t\t\t\t-->MASE: 1.184\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.759\n",
      "\t\t\t\t\t-->MAE: 0.299\n",
      "\t\t\t\t\t-->MAPE: 17.143\n",
      "\t\t\t\t\t-->MASE: 0.237\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (95520L, 20L), y_train: (95520L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.95s\n",
      "\t\t\t\t\t-->R2: -1.496\n",
      "\t\t\t\t\t-->MAE: 0.466\n",
      "\t\t\t\t\t-->MAPE: 15.424\n",
      "\t\t\t\t\t-->MASE: 0.369\n",
      "\n",
      "\t\t-->Evaluation (2014-10-25 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (95616L, 16L), y_train: (95616L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.913\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 32.812\n",
      "\t\t\t\t\t-->MASE: 0.376\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.395\n",
      "\t\t\t\t\t-->MAE: 0.340\n",
      "\t\t\t\t\t-->MAPE: 20.312\n",
      "\t\t\t\t\t-->MASE: 0.269\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.044\n",
      "\t\t\t\t\t-->MAE: 0.247\n",
      "\t\t\t\t\t-->MAPE: 20.312\n",
      "\t\t\t\t\t-->MASE: 0.196\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (95616L, 20L), y_train: (95616L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.82s\n",
      "\t\t\t\t\t-->R2: 0.367\n",
      "\t\t\t\t\t-->MAE: 0.283\n",
      "\t\t\t\t\t-->MAPE: 19.542\n",
      "\t\t\t\t\t-->MASE: 0.224\n",
      "\n",
      "\t\t-->Evaluation (2014-10-26 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (95712L, 16L), y_train: (95712L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.857\n",
      "\t\t\t\t\t-->MAE: 0.423\n",
      "\t\t\t\t\t-->MAPE: 31.746\n",
      "\t\t\t\t\t-->MASE: 0.335\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.223\n",
      "\t\t\t\t\t-->MAE: 0.278\n",
      "\t\t\t\t\t-->MAPE: 22.222\n",
      "\t\t\t\t\t-->MASE: 0.221\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.366\n",
      "\t\t\t\t\t-->MAE: 0.144\n",
      "\t\t\t\t\t-->MAPE: 12.698\n",
      "\t\t\t\t\t-->MASE: 0.114\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (95712L, 20L), y_train: (95712L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.80s\n",
      "\t\t\t\t\t-->R2: 0.634\n",
      "\t\t\t\t\t-->MAE: 0.196\n",
      "\t\t\t\t\t-->MAPE: 13.913\n",
      "\t\t\t\t\t-->MASE: 0.155\n",
      "\n",
      "\t\t-->Evaluation (2014-10-27 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (95808L, 16L), y_train: (95808L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.122\n",
      "\t\t\t\t\t-->MAE: 1.526\n",
      "\t\t\t\t\t-->MAPE: 56.629\n",
      "\t\t\t\t\t-->MASE: 1.210\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.291\n",
      "\t\t\t\t\t-->MAE: 1.485\n",
      "\t\t\t\t\t-->MAPE: 47.903\n",
      "\t\t\t\t\t-->MASE: 1.177\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.005\n",
      "\t\t\t\t\t-->MAE: 0.969\n",
      "\t\t\t\t\t-->MAPE: 42.772\n",
      "\t\t\t\t\t-->MASE: 0.768\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (95808L, 20L), y_train: (95808L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.11s\n",
      "\t\t\t\t\t-->R2: 0.402\n",
      "\t\t\t\t\t-->MAE: 0.738\n",
      "\t\t\t\t\t-->MAPE: 33.513\n",
      "\t\t\t\t\t-->MASE: 0.586\n",
      "\n",
      "\t\t-->Evaluation (2014-10-28 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (95904L, 16L), y_train: (95904L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.309\n",
      "\t\t\t\t\t-->MAE: 1.258\n",
      "\t\t\t\t\t-->MAPE: 70.290\n",
      "\t\t\t\t\t-->MASE: 0.997\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.571\n",
      "\t\t\t\t\t-->MAE: 0.691\n",
      "\t\t\t\t\t-->MAPE: 29.946\n",
      "\t\t\t\t\t-->MASE: 0.548\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.235\n",
      "\t\t\t\t\t-->MAE: 0.918\n",
      "\t\t\t\t\t-->MAPE: 36.920\n",
      "\t\t\t\t\t-->MASE: 0.727\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (95904L, 20L), y_train: (95904L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.94s\n",
      "\t\t\t\t\t-->R2: 0.683\n",
      "\t\t\t\t\t-->MAE: 0.624\n",
      "\t\t\t\t\t-->MAPE: 24.866\n",
      "\t\t\t\t\t-->MASE: 0.495\n",
      "\n",
      "\t\t-->Evaluation (2014-10-29 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (96000L, 16L), y_train: (96000L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.353\n",
      "\t\t\t\t\t-->MAE: 1.165\n",
      "\t\t\t\t\t-->MAPE: 62.985\n",
      "\t\t\t\t\t-->MASE: 0.923\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.533\n",
      "\t\t\t\t\t-->MAE: 0.557\n",
      "\t\t\t\t\t-->MAPE: 21.566\n",
      "\t\t\t\t\t-->MASE: 0.441\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.037\n",
      "\t\t\t\t\t-->MAE: 0.907\n",
      "\t\t\t\t\t-->MAPE: 33.208\n",
      "\t\t\t\t\t-->MASE: 0.719\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (96000L, 20L), y_train: (96000L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.84s\n",
      "\t\t\t\t\t-->R2: 0.671\n",
      "\t\t\t\t\t-->MAE: 0.511\n",
      "\t\t\t\t\t-->MAPE: 20.448\n",
      "\t\t\t\t\t-->MASE: 0.405\n",
      "\n",
      "\t\t-->Evaluation (2014-10-30 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (96096L, 16L), y_train: (96096L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.749\n",
      "\t\t\t\t\t-->MAE: 1.351\n",
      "\t\t\t\t\t-->MAPE: 79.364\n",
      "\t\t\t\t\t-->MASE: 1.071\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.702\n",
      "\t\t\t\t\t-->MAE: 0.485\n",
      "\t\t\t\t\t-->MAPE: 17.148\n",
      "\t\t\t\t\t-->MASE: 0.384\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.101\n",
      "\t\t\t\t\t-->MAE: 0.979\n",
      "\t\t\t\t\t-->MAPE: 38.574\n",
      "\t\t\t\t\t-->MASE: 0.776\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (96096L, 20L), y_train: (96096L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.83s\n",
      "\t\t\t\t\t-->R2: 0.689\n",
      "\t\t\t\t\t-->MAE: 0.542\n",
      "\t\t\t\t\t-->MAPE: 19.883\n",
      "\t\t\t\t\t-->MASE: 0.430\n",
      "\n",
      "\t\t-->Evaluation (2014-10-31 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (96192L, 16L), y_train: (96192L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -16.751\n",
      "\t\t\t\t\t-->MAE: 1.670\n",
      "\t\t\t\t\t-->MAPE: 105.085\n",
      "\t\t\t\t\t-->MASE: 1.324\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -18.879\n",
      "\t\t\t\t\t-->MAE: 1.670\n",
      "\t\t\t\t\t-->MAPE: 57.627\n",
      "\t\t\t\t\t-->MASE: 1.324\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.197\n",
      "\t\t\t\t\t-->MAE: 0.351\n",
      "\t\t\t\t\t-->MAPE: 18.644\n",
      "\t\t\t\t\t-->MASE: 0.278\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (96192L, 20L), y_train: (96192L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.94s\n",
      "\t\t\t\t\t-->R2: 0.342\n",
      "\t\t\t\t\t-->MAE: 0.324\n",
      "\t\t\t\t\t-->MAPE: 17.799\n",
      "\t\t\t\t\t-->MASE: 0.257\n",
      "\n",
      "\t\t-->Evaluation (2014-11-01 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (96288L, 16L), y_train: (96288L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.883\n",
      "\t\t\t\t\t-->MAE: 0.423\n",
      "\t\t\t\t\t-->MAPE: 37.500\n",
      "\t\t\t\t\t-->MASE: 0.335\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.332\n",
      "\t\t\t\t\t-->MAE: 0.299\n",
      "\t\t\t\t\t-->MAPE: 29.688\n",
      "\t\t\t\t\t-->MASE: 0.237\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.173\n",
      "\t\t\t\t\t-->MAE: 0.186\n",
      "\t\t\t\t\t-->MAPE: 15.625\n",
      "\t\t\t\t\t-->MASE: 0.147\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (96288L, 20L), y_train: (96288L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.12s\n",
      "\t\t\t\t\t-->R2: 0.592\n",
      "\t\t\t\t\t-->MAE: 0.228\n",
      "\t\t\t\t\t-->MAPE: 17.501\n",
      "\t\t\t\t\t-->MASE: 0.180\n",
      "\n",
      "\t\t-->Evaluation (2014-11-02 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (96384L, 16L), y_train: (96384L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.721\n",
      "\t\t\t\t\t-->MAE: 0.392\n",
      "\t\t\t\t\t-->MAPE: 30.159\n",
      "\t\t\t\t\t-->MASE: 0.311\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.230\n",
      "\t\t\t\t\t-->MAE: 0.175\n",
      "\t\t\t\t\t-->MAPE: 12.698\n",
      "\t\t\t\t\t-->MASE: 0.139\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.004\n",
      "\t\t\t\t\t-->MAE: 0.227\n",
      "\t\t\t\t\t-->MAPE: 17.460\n",
      "\t\t\t\t\t-->MASE: 0.180\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (96384L, 20L), y_train: (96384L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.98s\n",
      "\t\t\t\t\t-->R2: 0.520\n",
      "\t\t\t\t\t-->MAE: 0.212\n",
      "\t\t\t\t\t-->MAPE: 16.369\n",
      "\t\t\t\t\t-->MASE: 0.168\n",
      "\n",
      "\t\t-->Evaluation (2014-11-03 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (96480L, 16L), y_train: (96480L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.117\n",
      "\t\t\t\t\t-->MAE: 2.113\n",
      "\t\t\t\t\t-->MAPE: 53.425\n",
      "\t\t\t\t\t-->MASE: 1.677\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.279\n",
      "\t\t\t\t\t-->MAE: 2.124\n",
      "\t\t\t\t\t-->MAPE: 45.453\n",
      "\t\t\t\t\t-->MASE: 1.685\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.326\n",
      "\t\t\t\t\t-->MAE: 1.113\n",
      "\t\t\t\t\t-->MAPE: 32.907\n",
      "\t\t\t\t\t-->MASE: 0.883\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (96480L, 20L), y_train: (96480L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.87s\n",
      "\t\t\t\t\t-->R2: 0.498\n",
      "\t\t\t\t\t-->MAE: 0.808\n",
      "\t\t\t\t\t-->MAPE: 18.430\n",
      "\t\t\t\t\t-->MASE: 0.641\n",
      "\n",
      "\t\t-->Evaluation (2014-11-04 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (96576L, 16L), y_train: (96576L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.747\n",
      "\t\t\t\t\t-->MAE: 1.454\n",
      "\t\t\t\t\t-->MAPE: 85.167\n",
      "\t\t\t\t\t-->MASE: 1.153\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.189\n",
      "\t\t\t\t\t-->MAE: 0.711\n",
      "\t\t\t\t\t-->MAPE: 26.068\n",
      "\t\t\t\t\t-->MASE: 0.564\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.467\n",
      "\t\t\t\t\t-->MAE: 0.701\n",
      "\t\t\t\t\t-->MAPE: 27.256\n",
      "\t\t\t\t\t-->MASE: 0.556\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (96576L, 20L), y_train: (96576L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.04s\n",
      "\t\t\t\t\t-->R2: 0.704\n",
      "\t\t\t\t\t-->MAE: 0.528\n",
      "\t\t\t\t\t-->MAPE: 22.344\n",
      "\t\t\t\t\t-->MASE: 0.419\n",
      "\n",
      "\t\t-->Evaluation (2014-11-05 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (96672L, 16L), y_train: (96672L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.398\n",
      "\t\t\t\t\t-->MAE: 1.608\n",
      "\t\t\t\t\t-->MAPE: 85.924\n",
      "\t\t\t\t\t-->MASE: 1.275\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.671\n",
      "\t\t\t\t\t-->MAE: 0.619\n",
      "\t\t\t\t\t-->MAPE: 15.558\n",
      "\t\t\t\t\t-->MASE: 0.490\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.555\n",
      "\t\t\t\t\t-->MAE: 0.763\n",
      "\t\t\t\t\t-->MAPE: 23.080\n",
      "\t\t\t\t\t-->MASE: 0.605\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (96672L, 20L), y_train: (96672L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.07s\n",
      "\t\t\t\t\t-->R2: 0.810\n",
      "\t\t\t\t\t-->MAE: 0.528\n",
      "\t\t\t\t\t-->MAPE: 17.046\n",
      "\t\t\t\t\t-->MASE: 0.418\n",
      "\n",
      "\t\t-->Evaluation (2014-11-06 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (96768L, 16L), y_train: (96768L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.242\n",
      "\t\t\t\t\t-->MAE: 1.649\n",
      "\t\t\t\t\t-->MAPE: 73.115\n",
      "\t\t\t\t\t-->MASE: 1.307\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.716\n",
      "\t\t\t\t\t-->MAE: 0.660\n",
      "\t\t\t\t\t-->MAPE: 20.105\n",
      "\t\t\t\t\t-->MASE: 0.523\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.499\n",
      "\t\t\t\t\t-->MAE: 0.732\n",
      "\t\t\t\t\t-->MAPE: 14.983\n",
      "\t\t\t\t\t-->MASE: 0.580\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (96768L, 20L), y_train: (96768L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.90s\n",
      "\t\t\t\t\t-->R2: 0.764\n",
      "\t\t\t\t\t-->MAE: 0.566\n",
      "\t\t\t\t\t-->MAPE: 15.355\n",
      "\t\t\t\t\t-->MASE: 0.449\n",
      "\n",
      "\t\t-->Evaluation (2014-11-07 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (96864L, 16L), y_train: (96864L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -9.019\n",
      "\t\t\t\t\t-->MAE: 2.052\n",
      "\t\t\t\t\t-->MAPE: 145.795\n",
      "\t\t\t\t\t-->MASE: 1.625\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -6.995\n",
      "\t\t\t\t\t-->MAE: 1.649\n",
      "\t\t\t\t\t-->MAPE: 117.229\n",
      "\t\t\t\t\t-->MASE: 1.307\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.754\n",
      "\t\t\t\t\t-->MAE: 0.753\n",
      "\t\t\t\t\t-->MAPE: 47.926\n",
      "\t\t\t\t\t-->MASE: 0.596\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (96864L, 20L), y_train: (96864L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.09s\n",
      "\t\t\t\t\t-->R2: 0.425\n",
      "\t\t\t\t\t-->MAE: 0.453\n",
      "\t\t\t\t\t-->MAPE: 25.865\n",
      "\t\t\t\t\t-->MASE: 0.359\n",
      "\n",
      "\t\t-->Evaluation (2014-11-08 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (96960L, 16L), y_train: (96960L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.067\n",
      "\t\t\t\t\t-->MAE: 0.825\n",
      "\t\t\t\t\t-->MAPE: 41.063\n",
      "\t\t\t\t\t-->MASE: 0.653\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.458\n",
      "\t\t\t\t\t-->MAE: 0.835\n",
      "\t\t\t\t\t-->MAPE: 39.372\n",
      "\t\t\t\t\t-->MASE: 0.661\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.323\n",
      "\t\t\t\t\t-->MAE: 0.505\n",
      "\t\t\t\t\t-->MAPE: 28.261\n",
      "\t\t\t\t\t-->MASE: 0.400\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (96960L, 20L), y_train: (96960L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.06s\n",
      "\t\t\t\t\t-->R2: 0.029\n",
      "\t\t\t\t\t-->MAE: 0.494\n",
      "\t\t\t\t\t-->MAPE: 19.941\n",
      "\t\t\t\t\t-->MASE: 0.391\n",
      "\n",
      "\t\t-->Evaluation (2014-11-09 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (97056L, 16L), y_train: (97056L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -4.363\n",
      "\t\t\t\t\t-->MAE: 0.588\n",
      "\t\t\t\t\t-->MAPE: 33.846\n",
      "\t\t\t\t\t-->MASE: 0.465\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -3.757\n",
      "\t\t\t\t\t-->MAE: 0.474\n",
      "\t\t\t\t\t-->MAPE: 29.231\n",
      "\t\t\t\t\t-->MASE: 0.376\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.067\n",
      "\t\t\t\t\t-->MAE: 0.206\n",
      "\t\t\t\t\t-->MAPE: 16.923\n",
      "\t\t\t\t\t-->MASE: 0.163\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (97056L, 20L), y_train: (97056L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.09s\n",
      "\t\t\t\t\t-->R2: 0.603\n",
      "\t\t\t\t\t-->MAE: 0.204\n",
      "\t\t\t\t\t-->MAPE: 13.214\n",
      "\t\t\t\t\t-->MASE: 0.161\n",
      "\n",
      "\t\t-->Evaluation (2014-11-10 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (97152L, 16L), y_train: (97152L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.598\n",
      "\t\t\t\t\t-->MAE: 2.103\n",
      "\t\t\t\t\t-->MAPE: 78.075\n",
      "\t\t\t\t\t-->MASE: 1.667\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.716\n",
      "\t\t\t\t\t-->MAE: 2.227\n",
      "\t\t\t\t\t-->MAPE: 70.831\n",
      "\t\t\t\t\t-->MASE: 1.765\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.541\n",
      "\t\t\t\t\t-->MAE: 1.155\n",
      "\t\t\t\t\t-->MAPE: 41.840\n",
      "\t\t\t\t\t-->MASE: 0.915\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (97152L, 20L), y_train: (97152L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.86s\n",
      "\t\t\t\t\t-->R2: 0.708\n",
      "\t\t\t\t\t-->MAE: 0.924\n",
      "\t\t\t\t\t-->MAPE: 33.734\n",
      "\t\t\t\t\t-->MASE: 0.732\n",
      "\n",
      "\t\t-->Evaluation (2014-11-11 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (97248L, 16L), y_train: (97248L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.384\n",
      "\t\t\t\t\t-->MAE: 1.969\n",
      "\t\t\t\t\t-->MAPE: 80.673\n",
      "\t\t\t\t\t-->MASE: 1.559\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.766\n",
      "\t\t\t\t\t-->MAE: 0.691\n",
      "\t\t\t\t\t-->MAPE: 36.786\n",
      "\t\t\t\t\t-->MASE: 0.547\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.673\n",
      "\t\t\t\t\t-->MAE: 1.031\n",
      "\t\t\t\t\t-->MAPE: 26.219\n",
      "\t\t\t\t\t-->MASE: 0.816\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (97248L, 20L), y_train: (97248L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.19s\n",
      "\t\t\t\t\t-->R2: 0.840\n",
      "\t\t\t\t\t-->MAE: 0.747\n",
      "\t\t\t\t\t-->MAPE: 22.634\n",
      "\t\t\t\t\t-->MASE: 0.592\n",
      "\n",
      "\t\t-->Evaluation (2014-11-12 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (97344L, 16L), y_train: (97344L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.339\n",
      "\t\t\t\t\t-->MAE: 1.928\n",
      "\t\t\t\t\t-->MAPE: 94.505\n",
      "\t\t\t\t\t-->MASE: 1.526\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.720\n",
      "\t\t\t\t\t-->MAE: 0.732\n",
      "\t\t\t\t\t-->MAPE: 47.553\n",
      "\t\t\t\t\t-->MASE: 0.579\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.615\n",
      "\t\t\t\t\t-->MAE: 1.041\n",
      "\t\t\t\t\t-->MAPE: 40.109\n",
      "\t\t\t\t\t-->MASE: 0.824\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (97344L, 20L), y_train: (97344L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.18s\n",
      "\t\t\t\t\t-->R2: 0.741\n",
      "\t\t\t\t\t-->MAE: 0.855\n",
      "\t\t\t\t\t-->MAPE: 46.530\n",
      "\t\t\t\t\t-->MASE: 0.676\n",
      "\n",
      "\t\t-->Evaluation (2014-11-13 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (97440L, 16L), y_train: (97440L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.620\n",
      "\t\t\t\t\t-->MAE: 2.258\n",
      "\t\t\t\t\t-->MAPE: 71.423\n",
      "\t\t\t\t\t-->MASE: 1.786\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.286\n",
      "\t\t\t\t\t-->MAE: 1.206\n",
      "\t\t\t\t\t-->MAPE: 45.889\n",
      "\t\t\t\t\t-->MASE: 0.954\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.402\n",
      "\t\t\t\t\t-->MAE: 1.278\n",
      "\t\t\t\t\t-->MAPE: 30.457\n",
      "\t\t\t\t\t-->MASE: 1.011\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (97440L, 20L), y_train: (97440L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.03s\n",
      "\t\t\t\t\t-->R2: 0.481\n",
      "\t\t\t\t\t-->MAE: 0.962\n",
      "\t\t\t\t\t-->MAPE: 32.350\n",
      "\t\t\t\t\t-->MASE: 0.761\n",
      "\n",
      "\t\t-->Evaluation (2014-11-14 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (97536L, 16L), y_train: (97536L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -18.768\n",
      "\t\t\t\t\t-->MAE: 2.649\n",
      "\t\t\t\t\t-->MAPE: 194.595\n",
      "\t\t\t\t\t-->MASE: 2.094\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -15.041\n",
      "\t\t\t\t\t-->MAE: 2.268\n",
      "\t\t\t\t\t-->MAPE: 226.577\n",
      "\t\t\t\t\t-->MASE: 1.793\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.683\n",
      "\t\t\t\t\t-->MAE: 0.784\n",
      "\t\t\t\t\t-->MAPE: 36.036\n",
      "\t\t\t\t\t-->MASE: 0.619\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (97536L, 20L), y_train: (97536L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.14s\n",
      "\t\t\t\t\t-->R2: -0.410\n",
      "\t\t\t\t\t-->MAE: 0.736\n",
      "\t\t\t\t\t-->MAPE: 57.708\n",
      "\t\t\t\t\t-->MASE: 0.582\n",
      "\n",
      "\t\t-->Evaluation (2014-11-15 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (97632L, 16L), y_train: (97632L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.236\n",
      "\t\t\t\t\t-->MAE: 0.856\n",
      "\t\t\t\t\t-->MAPE: 68.306\n",
      "\t\t\t\t\t-->MASE: 0.676\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.690\n",
      "\t\t\t\t\t-->MAE: 0.979\n",
      "\t\t\t\t\t-->MAPE: 83.194\n",
      "\t\t\t\t\t-->MASE: 0.773\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.597\n",
      "\t\t\t\t\t-->MAE: 0.392\n",
      "\t\t\t\t\t-->MAPE: 22.278\n",
      "\t\t\t\t\t-->MASE: 0.309\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (97632L, 20L), y_train: (97632L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.19s\n",
      "\t\t\t\t\t-->R2: 0.413\n",
      "\t\t\t\t\t-->MAE: 0.652\n",
      "\t\t\t\t\t-->MAPE: 56.282\n",
      "\t\t\t\t\t-->MASE: 0.515\n",
      "\n",
      "\t\t-->Evaluation (2014-11-16 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (97728L, 16L), y_train: (97728L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -6.306\n",
      "\t\t\t\t\t-->MAE: 0.835\n",
      "\t\t\t\t\t-->MAPE: 58.000\n",
      "\t\t\t\t\t-->MASE: 0.660\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -5.646\n",
      "\t\t\t\t\t-->MAE: 0.649\n",
      "\t\t\t\t\t-->MAPE: 38.000\n",
      "\t\t\t\t\t-->MASE: 0.513\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.445\n",
      "\t\t\t\t\t-->MAE: 0.361\n",
      "\t\t\t\t\t-->MAPE: 20.000\n",
      "\t\t\t\t\t-->MASE: 0.285\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (97728L, 20L), y_train: (97728L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 8.98s\n",
      "\t\t\t\t\t-->R2: 0.039\n",
      "\t\t\t\t\t-->MAE: 0.387\n",
      "\t\t\t\t\t-->MAPE: 25.033\n",
      "\t\t\t\t\t-->MASE: 0.306\n",
      "\n",
      "\t\t-->Evaluation (2014-11-17 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (97824L, 16L), y_train: (97824L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.978\n",
      "\t\t\t\t\t-->MAE: 2.227\n",
      "\t\t\t\t\t-->MAPE: 70.892\n",
      "\t\t\t\t\t-->MASE: 1.759\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.064\n",
      "\t\t\t\t\t-->MAE: 2.021\n",
      "\t\t\t\t\t-->MAPE: 51.936\n",
      "\t\t\t\t\t-->MASE: 1.597\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.530\n",
      "\t\t\t\t\t-->MAE: 1.021\n",
      "\t\t\t\t\t-->MAPE: 50.340\n",
      "\t\t\t\t\t-->MASE: 0.806\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (97824L, 20L), y_train: (97824L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.04s\n",
      "\t\t\t\t\t-->R2: 0.824\n",
      "\t\t\t\t\t-->MAE: 0.614\n",
      "\t\t\t\t\t-->MAPE: 14.984\n",
      "\t\t\t\t\t-->MASE: 0.485\n",
      "\n",
      "\t\t-->Evaluation (2014-11-18 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (97920L, 16L), y_train: (97920L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.466\n",
      "\t\t\t\t\t-->MAE: 2.082\n",
      "\t\t\t\t\t-->MAPE: 93.792\n",
      "\t\t\t\t\t-->MASE: 1.644\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.729\n",
      "\t\t\t\t\t-->MAE: 0.722\n",
      "\t\t\t\t\t-->MAPE: 18.659\n",
      "\t\t\t\t\t-->MASE: 0.570\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.739\n",
      "\t\t\t\t\t-->MAE: 0.887\n",
      "\t\t\t\t\t-->MAPE: 48.147\n",
      "\t\t\t\t\t-->MASE: 0.700\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (97920L, 20L), y_train: (97920L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.20s\n",
      "\t\t\t\t\t-->R2: 0.810\n",
      "\t\t\t\t\t-->MAE: 0.705\n",
      "\t\t\t\t\t-->MAPE: 20.832\n",
      "\t\t\t\t\t-->MASE: 0.557\n",
      "\n",
      "\t\t-->Evaluation (2014-11-19 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (98016L, 16L), y_train: (98016L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -4.672\n",
      "\t\t\t\t\t-->MAE: 2.661\n",
      "\t\t\t\t\t-->MAPE: 45.865\n",
      "\t\t\t\t\t-->MASE: 2.099\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -2.523\n",
      "\t\t\t\t\t-->MAE: 1.869\n",
      "\t\t\t\t\t-->MAPE: 69.317\n",
      "\t\t\t\t\t-->MASE: 1.475\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.340\n",
      "\t\t\t\t\t-->MAE: 1.556\n",
      "\t\t\t\t\t-->MAPE: 81.598\n",
      "\t\t\t\t\t-->MASE: 1.227\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (98016L, 20L), y_train: (98016L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.64s\n",
      "\t\t\t\t\t-->R2: -1.050\n",
      "\t\t\t\t\t-->MAE: 1.490\n",
      "\t\t\t\t\t-->MAPE: 45.438\n",
      "\t\t\t\t\t-->MASE: 1.176\n",
      "\n",
      "\t\t-->Evaluation (2014-11-20 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (98112L, 16L), y_train: (98112L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: 0.050\n",
      "\t\t\t\t\t-->MAE: 1.435\n",
      "\t\t\t\t\t-->MAPE: 69.717\n",
      "\t\t\t\t\t-->MASE: 1.131\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.853\n",
      "\t\t\t\t\t-->MAE: 1.781\n",
      "\t\t\t\t\t-->MAPE: 68.275\n",
      "\t\t\t\t\t-->MASE: 1.404\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.022\n",
      "\t\t\t\t\t-->MAE: 1.347\n",
      "\t\t\t\t\t-->MAPE: 103.509\n",
      "\t\t\t\t\t-->MASE: 1.062\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (98112L, 20L), y_train: (98112L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.78s\n",
      "\t\t\t\t\t-->R2: 0.580\n",
      "\t\t\t\t\t-->MAE: 0.863\n",
      "\t\t\t\t\t-->MAPE: 33.464\n",
      "\t\t\t\t\t-->MASE: 0.680\n",
      "\n",
      "\t\t-->Evaluation (2014-11-21 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (98208L, 16L), y_train: (98208L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -4.191\n",
      "\t\t\t\t\t-->MAE: 2.134\n",
      "\t\t\t\t\t-->MAPE: 274.118\n",
      "\t\t\t\t\t-->MASE: 1.682\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -3.100\n",
      "\t\t\t\t\t-->MAE: 1.671\n",
      "\t\t\t\t\t-->MAPE: 261.210\n",
      "\t\t\t\t\t-->MASE: 1.317\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.068\n",
      "\t\t\t\t\t-->MAE: 0.922\n",
      "\t\t\t\t\t-->MAPE: 77.148\n",
      "\t\t\t\t\t-->MASE: 0.726\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (98208L, 20L), y_train: (98208L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.11s\n",
      "\t\t\t\t\t-->R2: 0.418\n",
      "\t\t\t\t\t-->MAE: 0.545\n",
      "\t\t\t\t\t-->MAPE: 35.901\n",
      "\t\t\t\t\t-->MASE: 0.429\n",
      "\n",
      "\t\t-->Evaluation (2014-11-22 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (98304L, 16L), y_train: (98304L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -9.485\n",
      "\t\t\t\t\t-->MAE: 0.771\n",
      "\t\t\t\t\t-->MAPE: 231.779\n",
      "\t\t\t\t\t-->MASE: 0.607\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -10.422\n",
      "\t\t\t\t\t-->MAE: 0.794\n",
      "\t\t\t\t\t-->MAPE: 212.350\n",
      "\t\t\t\t\t-->MASE: 0.625\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -2.177\n",
      "\t\t\t\t\t-->MAE: 0.546\n",
      "\t\t\t\t\t-->MAPE: 92.862\n",
      "\t\t\t\t\t-->MASE: 0.430\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (98304L, 20L), y_train: (98304L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.31s\n",
      "\t\t\t\t\t-->R2: 0.094\n",
      "\t\t\t\t\t-->MAE: 0.277\n",
      "\t\t\t\t\t-->MAPE: 61.658\n",
      "\t\t\t\t\t-->MASE: 0.218\n",
      "\n",
      "\t\t-->Evaluation (2014-11-23 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (98400L, 16L), y_train: (98400L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -3.727\n",
      "\t\t\t\t\t-->MAE: 0.332\n",
      "\t\t\t\t\t-->MAPE: 99.554\n",
      "\t\t\t\t\t-->MASE: 0.262\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -3.151\n",
      "\t\t\t\t\t-->MAE: 0.218\n",
      "\t\t\t\t\t-->MAPE: 82.375\n",
      "\t\t\t\t\t-->MASE: 0.171\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -2.119\n",
      "\t\t\t\t\t-->MAE: 0.385\n",
      "\t\t\t\t\t-->MAPE: 93.062\n",
      "\t\t\t\t\t-->MASE: 0.303\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (98400L, 20L), y_train: (98400L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.49s\n",
      "\t\t\t\t\t-->R2: 0.811\n",
      "\t\t\t\t\t-->MAE: 0.090\n",
      "\t\t\t\t\t-->MAPE: 20.672\n",
      "\t\t\t\t\t-->MASE: 0.071\n",
      "\n",
      "\t\t-->Evaluation (2014-11-24 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (98496L, 16L), y_train: (98496L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.899\n",
      "\t\t\t\t\t-->MAE: 2.301\n",
      "\t\t\t\t\t-->MAPE: 54.033\n",
      "\t\t\t\t\t-->MASE: 1.814\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.950\n",
      "\t\t\t\t\t-->MAE: 2.308\n",
      "\t\t\t\t\t-->MAPE: 48.061\n",
      "\t\t\t\t\t-->MASE: 1.820\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.736\n",
      "\t\t\t\t\t-->MAE: 0.854\n",
      "\t\t\t\t\t-->MAPE: 39.238\n",
      "\t\t\t\t\t-->MASE: 0.673\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (98496L, 20L), y_train: (98496L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.27s\n",
      "\t\t\t\t\t-->R2: 0.762\n",
      "\t\t\t\t\t-->MAE: 0.712\n",
      "\t\t\t\t\t-->MAPE: 19.564\n",
      "\t\t\t\t\t-->MASE: 0.561\n",
      "\n",
      "\t\t-->Evaluation (2014-11-25 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (98592L, 16L), y_train: (98592L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.099\n",
      "\t\t\t\t\t-->MAE: 1.902\n",
      "\t\t\t\t\t-->MAPE: 163.089\n",
      "\t\t\t\t\t-->MASE: 1.498\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.708\n",
      "\t\t\t\t\t-->MAE: 0.598\n",
      "\t\t\t\t\t-->MAPE: 19.073\n",
      "\t\t\t\t\t-->MASE: 0.471\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.497\n",
      "\t\t\t\t\t-->MAE: 1.041\n",
      "\t\t\t\t\t-->MAPE: 50.251\n",
      "\t\t\t\t\t-->MASE: 0.820\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (98592L, 20L), y_train: (98592L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.74s\n",
      "\t\t\t\t\t-->R2: 0.842\n",
      "\t\t\t\t\t-->MAE: 0.430\n",
      "\t\t\t\t\t-->MAPE: 16.461\n",
      "\t\t\t\t\t-->MASE: 0.338\n",
      "\n",
      "\t\t-->Evaluation (2014-11-26 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (98688L, 16L), y_train: (98688L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.312\n",
      "\t\t\t\t\t-->MAE: 2.879\n",
      "\t\t\t\t\t-->MAPE: 150.572\n",
      "\t\t\t\t\t-->MASE: 2.267\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.588\n",
      "\t\t\t\t\t-->MAE: 1.223\n",
      "\t\t\t\t\t-->MAPE: 22.034\n",
      "\t\t\t\t\t-->MASE: 0.963\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.436\n",
      "\t\t\t\t\t-->MAE: 2.578\n",
      "\t\t\t\t\t-->MAPE: 68.633\n",
      "\t\t\t\t\t-->MASE: 2.030\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (98688L, 20L), y_train: (98688L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.08s\n",
      "\t\t\t\t\t-->R2: 0.578\n",
      "\t\t\t\t\t-->MAE: 1.348\n",
      "\t\t\t\t\t-->MAPE: 24.942\n",
      "\t\t\t\t\t-->MASE: 1.062\n",
      "\n",
      "\t\t-->Evaluation (2014-11-27 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (98784L, 16L), y_train: (98784L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.431\n",
      "\t\t\t\t\t-->MAE: 3.505\n",
      "\t\t\t\t\t-->MAPE: 183.010\n",
      "\t\t\t\t\t-->MASE: 2.756\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.744\n",
      "\t\t\t\t\t-->MAE: 1.126\n",
      "\t\t\t\t\t-->MAPE: 18.167\n",
      "\t\t\t\t\t-->MASE: 0.885\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.280\n",
      "\t\t\t\t\t-->MAE: 2.162\n",
      "\t\t\t\t\t-->MAPE: 29.910\n",
      "\t\t\t\t\t-->MASE: 1.700\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (98784L, 20L), y_train: (98784L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.05s\n",
      "\t\t\t\t\t-->R2: 0.503\n",
      "\t\t\t\t\t-->MAE: 1.861\n",
      "\t\t\t\t\t-->MAPE: 29.433\n",
      "\t\t\t\t\t-->MASE: 1.464\n",
      "\n",
      "\t\t-->Evaluation (2014-11-28 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (98880L, 16L), y_train: (98880L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -9.183\n",
      "\t\t\t\t\t-->MAE: 3.901\n",
      "\t\t\t\t\t-->MAPE: 447.900\n",
      "\t\t\t\t\t-->MASE: 3.062\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -5.166\n",
      "\t\t\t\t\t-->MAE: 2.699\n",
      "\t\t\t\t\t-->MAPE: 246.628\n",
      "\t\t\t\t\t-->MASE: 2.119\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.222\n",
      "\t\t\t\t\t-->MAE: 0.849\n",
      "\t\t\t\t\t-->MAPE: 37.847\n",
      "\t\t\t\t\t-->MASE: 0.667\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (98880L, 20L), y_train: (98880L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.89s\n",
      "\t\t\t\t\t-->R2: 0.084\n",
      "\t\t\t\t\t-->MAE: 0.955\n",
      "\t\t\t\t\t-->MAPE: 49.760\n",
      "\t\t\t\t\t-->MASE: 0.750\n",
      "\n",
      "\t\t-->Evaluation (2014-11-29 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (98976L, 16L), y_train: (98976L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -9.265\n",
      "\t\t\t\t\t-->MAE: 1.116\n",
      "\t\t\t\t\t-->MAPE: 195.278\n",
      "\t\t\t\t\t-->MASE: 0.875\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -11.921\n",
      "\t\t\t\t\t-->MAE: 1.154\n",
      "\t\t\t\t\t-->MAPE: 332.946\n",
      "\t\t\t\t\t-->MASE: 0.904\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.274\n",
      "\t\t\t\t\t-->MAE: 0.267\n",
      "\t\t\t\t\t-->MAPE: 30.849\n",
      "\t\t\t\t\t-->MASE: 0.209\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (98976L, 20L), y_train: (98976L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.25s\n",
      "\t\t\t\t\t-->R2: 0.195\n",
      "\t\t\t\t\t-->MAE: 0.274\n",
      "\t\t\t\t\t-->MAPE: 46.357\n",
      "\t\t\t\t\t-->MASE: 0.215\n",
      "\n",
      "\t\t-->Evaluation (2014-11-30 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (99072L, 16L), y_train: (99072L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -6.161\n",
      "\t\t\t\t\t-->MAE: 0.338\n",
      "\t\t\t\t\t-->MAPE: 155.018\n",
      "\t\t\t\t\t-->MASE: 0.265\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -7.002\n",
      "\t\t\t\t\t-->MAE: 0.334\n",
      "\t\t\t\t\t-->MAPE: 187.142\n",
      "\t\t\t\t\t-->MASE: 0.262\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.753\n",
      "\t\t\t\t\t-->MAE: 0.085\n",
      "\t\t\t\t\t-->MAPE: 29.453\n",
      "\t\t\t\t\t-->MASE: 0.066\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (99072L, 20L), y_train: (99072L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.33s\n",
      "\t\t\t\t\t-->R2: 0.069\n",
      "\t\t\t\t\t-->MAE: 0.176\n",
      "\t\t\t\t\t-->MAPE: 85.653\n",
      "\t\t\t\t\t-->MASE: 0.138\n",
      "\n",
      "\t\t-->Evaluation (2014-12-01 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (99168L, 16L), y_train: (99168L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.846\n",
      "\t\t\t\t\t-->MAE: 3.343\n",
      "\t\t\t\t\t-->MAPE: 57.734\n",
      "\t\t\t\t\t-->MASE: 2.621\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.921\n",
      "\t\t\t\t\t-->MAE: 3.355\n",
      "\t\t\t\t\t-->MAPE: 50.686\n",
      "\t\t\t\t\t-->MASE: 2.630\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.600\n",
      "\t\t\t\t\t-->MAE: 1.312\n",
      "\t\t\t\t\t-->MAPE: 23.313\n",
      "\t\t\t\t\t-->MASE: 1.029\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (99168L, 20L), y_train: (99168L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.32s\n",
      "\t\t\t\t\t-->R2: 0.636\n",
      "\t\t\t\t\t-->MAE: 1.384\n",
      "\t\t\t\t\t-->MAPE: 24.534\n",
      "\t\t\t\t\t-->MASE: 1.085\n",
      "\n",
      "\t\t-->Evaluation (2014-12-02 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (99264L, 16L), y_train: (99264L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.374\n",
      "\t\t\t\t\t-->MAE: 2.772\n",
      "\t\t\t\t\t-->MAPE: 181.299\n",
      "\t\t\t\t\t-->MASE: 2.170\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.871\n",
      "\t\t\t\t\t-->MAE: 0.642\n",
      "\t\t\t\t\t-->MAPE: 16.323\n",
      "\t\t\t\t\t-->MASE: 0.503\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.558\n",
      "\t\t\t\t\t-->MAE: 1.159\n",
      "\t\t\t\t\t-->MAPE: 21.902\n",
      "\t\t\t\t\t-->MASE: 0.907\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (99264L, 20L), y_train: (99264L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.93s\n",
      "\t\t\t\t\t-->R2: 0.812\n",
      "\t\t\t\t\t-->MAE: 0.817\n",
      "\t\t\t\t\t-->MAPE: 24.344\n",
      "\t\t\t\t\t-->MASE: 0.640\n",
      "\n",
      "\t\t-->Evaluation (2014-12-03 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (99360L, 16L), y_train: (99360L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.258\n",
      "\t\t\t\t\t-->MAE: 3.044\n",
      "\t\t\t\t\t-->MAPE: 164.143\n",
      "\t\t\t\t\t-->MASE: 2.380\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.890\n",
      "\t\t\t\t\t-->MAE: 0.765\n",
      "\t\t\t\t\t-->MAPE: 16.506\n",
      "\t\t\t\t\t-->MASE: 0.598\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.854\n",
      "\t\t\t\t\t-->MAE: 0.843\n",
      "\t\t\t\t\t-->MAPE: 17.593\n",
      "\t\t\t\t\t-->MASE: 0.659\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (99360L, 20L), y_train: (99360L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.16s\n",
      "\t\t\t\t\t-->R2: 0.856\n",
      "\t\t\t\t\t-->MAE: 0.798\n",
      "\t\t\t\t\t-->MAPE: 15.231\n",
      "\t\t\t\t\t-->MASE: 0.624\n",
      "\n",
      "\t\t-->Evaluation (2014-12-04 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (99456L, 16L), y_train: (99456L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.378\n",
      "\t\t\t\t\t-->MAE: 3.219\n",
      "\t\t\t\t\t-->MAPE: 182.856\n",
      "\t\t\t\t\t-->MASE: 2.513\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.947\n",
      "\t\t\t\t\t-->MAE: 0.457\n",
      "\t\t\t\t\t-->MAPE: 13.816\n",
      "\t\t\t\t\t-->MASE: 0.357\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.818\n",
      "\t\t\t\t\t-->MAE: 0.890\n",
      "\t\t\t\t\t-->MAPE: 21.417\n",
      "\t\t\t\t\t-->MASE: 0.695\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (99456L, 20L), y_train: (99456L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.34s\n",
      "\t\t\t\t\t-->R2: 0.827\n",
      "\t\t\t\t\t-->MAE: 0.925\n",
      "\t\t\t\t\t-->MAPE: 19.628\n",
      "\t\t\t\t\t-->MASE: 0.722\n",
      "\n",
      "\t\t-->Evaluation (2014-12-05 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (99552L, 16L), y_train: (99552L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -9.005\n",
      "\t\t\t\t\t-->MAE: 3.520\n",
      "\t\t\t\t\t-->MAPE: 318.154\n",
      "\t\t\t\t\t-->MASE: 2.744\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -6.084\n",
      "\t\t\t\t\t-->MAE: 2.301\n",
      "\t\t\t\t\t-->MAPE: 243.555\n",
      "\t\t\t\t\t-->MASE: 1.794\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.290\n",
      "\t\t\t\t\t-->MAE: 0.733\n",
      "\t\t\t\t\t-->MAPE: 83.817\n",
      "\t\t\t\t\t-->MASE: 0.571\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (99552L, 20L), y_train: (99552L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.05s\n",
      "\t\t\t\t\t-->R2: -4.307\n",
      "\t\t\t\t\t-->MAE: 1.660\n",
      "\t\t\t\t\t-->MAPE: 199.877\n",
      "\t\t\t\t\t-->MASE: 1.294\n",
      "\n",
      "\t\t-->Evaluation (2014-12-06 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (99648L, 16L), y_train: (99648L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: 0.015\n",
      "\t\t\t\t\t-->MAE: 0.793\n",
      "\t\t\t\t\t-->MAPE: 255.409\n",
      "\t\t\t\t\t-->MASE: 0.617\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.710\n",
      "\t\t\t\t\t-->MAE: 1.380\n",
      "\t\t\t\t\t-->MAPE: 416.724\n",
      "\t\t\t\t\t-->MASE: 1.074\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.379\n",
      "\t\t\t\t\t-->MAE: 0.467\n",
      "\t\t\t\t\t-->MAPE: 81.671\n",
      "\t\t\t\t\t-->MASE: 0.363\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (99648L, 20L), y_train: (99648L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.36s\n",
      "\t\t\t\t\t-->R2: 0.293\n",
      "\t\t\t\t\t-->MAE: 0.528\n",
      "\t\t\t\t\t-->MAPE: 117.195\n",
      "\t\t\t\t\t-->MASE: 0.411\n",
      "\n",
      "\t\t-->Evaluation (2014-12-07 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (99744L, 16L), y_train: (99744L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -35.191\n",
      "\t\t\t\t\t-->MAE: 0.615\n",
      "\t\t\t\t\t-->MAPE: 194.291\n",
      "\t\t\t\t\t-->MASE: 0.479\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -36.675\n",
      "\t\t\t\t\t-->MAE: 0.505\n",
      "\t\t\t\t\t-->MAPE: 222.259\n",
      "\t\t\t\t\t-->MASE: 0.393\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.849\n",
      "\t\t\t\t\t-->MAE: 0.069\n",
      "\t\t\t\t\t-->MAPE: 17.720\n",
      "\t\t\t\t\t-->MASE: 0.054\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (99744L, 20L), y_train: (99744L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.88s\n",
      "\t\t\t\t\t-->R2: 0.839\n",
      "\t\t\t\t\t-->MAE: 0.079\n",
      "\t\t\t\t\t-->MAPE: 22.856\n",
      "\t\t\t\t\t-->MASE: 0.062\n",
      "\n",
      "\t\t-->Evaluation (2014-12-08 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (99840L, 16L), y_train: (99840L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.117\n",
      "\t\t\t\t\t-->MAE: 1.765\n",
      "\t\t\t\t\t-->MAPE: 53.511\n",
      "\t\t\t\t\t-->MASE: 1.375\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.214\n",
      "\t\t\t\t\t-->MAE: 1.775\n",
      "\t\t\t\t\t-->MAPE: 48.279\n",
      "\t\t\t\t\t-->MASE: 1.383\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.498\n",
      "\t\t\t\t\t-->MAE: 1.669\n",
      "\t\t\t\t\t-->MAPE: 48.483\n",
      "\t\t\t\t\t-->MASE: 1.300\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (99840L, 20L), y_train: (99840L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.01s\n",
      "\t\t\t\t\t-->R2: -0.304\n",
      "\t\t\t\t\t-->MAE: 1.244\n",
      "\t\t\t\t\t-->MAPE: 36.959\n",
      "\t\t\t\t\t-->MASE: 0.969\n",
      "\n",
      "\t\t-->Evaluation (2014-12-09 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (99936L, 16L), y_train: (99936L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.367\n",
      "\t\t\t\t\t-->MAE: 1.415\n",
      "\t\t\t\t\t-->MAPE: 108.222\n",
      "\t\t\t\t\t-->MASE: 1.102\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.839\n",
      "\t\t\t\t\t-->MAE: 0.427\n",
      "\t\t\t\t\t-->MAPE: 16.657\n",
      "\t\t\t\t\t-->MASE: 0.332\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.198\n",
      "\t\t\t\t\t-->MAE: 1.161\n",
      "\t\t\t\t\t-->MAPE: 33.173\n",
      "\t\t\t\t\t-->MASE: 0.904\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (99936L, 20L), y_train: (99936L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.15s\n",
      "\t\t\t\t\t-->R2: 0.876\n",
      "\t\t\t\t\t-->MAE: 0.418\n",
      "\t\t\t\t\t-->MAPE: 17.288\n",
      "\t\t\t\t\t-->MASE: 0.326\n",
      "\n",
      "\t\t-->Evaluation (2014-12-10 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (100032L, 16L), y_train: (100032L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.479\n",
      "\t\t\t\t\t-->MAE: 1.528\n",
      "\t\t\t\t\t-->MAPE: 103.419\n",
      "\t\t\t\t\t-->MASE: 1.190\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.782\n",
      "\t\t\t\t\t-->MAE: 0.429\n",
      "\t\t\t\t\t-->MAPE: 16.518\n",
      "\t\t\t\t\t-->MASE: 0.334\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.765\n",
      "\t\t\t\t\t-->MAE: 1.924\n",
      "\t\t\t\t\t-->MAPE: 61.560\n",
      "\t\t\t\t\t-->MASE: 1.498\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (100032L, 20L), y_train: (100032L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.40s\n",
      "\t\t\t\t\t-->R2: 0.759\n",
      "\t\t\t\t\t-->MAE: 0.496\n",
      "\t\t\t\t\t-->MAPE: 18.780\n",
      "\t\t\t\t\t-->MASE: 0.386\n",
      "\n",
      "\t\t-->Evaluation (2014-12-11 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (100128L, 16L), y_train: (100128L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.193\n",
      "\t\t\t\t\t-->MAE: 1.328\n",
      "\t\t\t\t\t-->MAPE: 95.621\n",
      "\t\t\t\t\t-->MASE: 1.034\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.759\n",
      "\t\t\t\t\t-->MAE: 0.476\n",
      "\t\t\t\t\t-->MAPE: 19.757\n",
      "\t\t\t\t\t-->MASE: 0.371\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.066\n",
      "\t\t\t\t\t-->MAE: 1.609\n",
      "\t\t\t\t\t-->MAPE: 46.301\n",
      "\t\t\t\t\t-->MASE: 1.253\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (100128L, 20L), y_train: (100128L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.29s\n",
      "\t\t\t\t\t-->R2: 0.895\n",
      "\t\t\t\t\t-->MAE: 0.334\n",
      "\t\t\t\t\t-->MAPE: 13.581\n",
      "\t\t\t\t\t-->MASE: 0.260\n",
      "\n",
      "\t\t-->Evaluation (2014-12-12 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (100224L, 16L), y_train: (100224L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.650\n",
      "\t\t\t\t\t-->MAE: 1.493\n",
      "\t\t\t\t\t-->MAPE: 112.229\n",
      "\t\t\t\t\t-->MASE: 1.162\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.025\n",
      "\t\t\t\t\t-->MAE: 1.044\n",
      "\t\t\t\t\t-->MAPE: 108.697\n",
      "\t\t\t\t\t-->MASE: 0.813\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.297\n",
      "\t\t\t\t\t-->MAE: 1.115\n",
      "\t\t\t\t\t-->MAPE: 111.283\n",
      "\t\t\t\t\t-->MASE: 0.868\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (100224L, 20L), y_train: (100224L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.74s\n",
      "\t\t\t\t\t-->R2: 0.013\n",
      "\t\t\t\t\t-->MAE: 1.025\n",
      "\t\t\t\t\t-->MAPE: 68.647\n",
      "\t\t\t\t\t-->MASE: 0.798\n",
      "\n",
      "\t\t-->Evaluation (2014-12-13 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (100320L, 16L), y_train: (100320L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.059\n",
      "\t\t\t\t\t-->MAE: 1.515\n",
      "\t\t\t\t\t-->MAPE: 416.321\n",
      "\t\t\t\t\t-->MASE: 1.180\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.533\n",
      "\t\t\t\t\t-->MAE: 1.227\n",
      "\t\t\t\t\t-->MAPE: 436.183\n",
      "\t\t\t\t\t-->MASE: 0.955\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.889\n",
      "\t\t\t\t\t-->MAE: 0.220\n",
      "\t\t\t\t\t-->MAPE: 27.890\n",
      "\t\t\t\t\t-->MASE: 0.171\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (100320L, 20L), y_train: (100320L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.73s\n",
      "\t\t\t\t\t-->R2: 0.592\n",
      "\t\t\t\t\t-->MAE: 0.469\n",
      "\t\t\t\t\t-->MAPE: 80.696\n",
      "\t\t\t\t\t-->MASE: 0.365\n",
      "\n",
      "\t\t-->Evaluation (2014-12-14 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (100416L, 16L), y_train: (100416L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.989\n",
      "\t\t\t\t\t-->MAE: 0.936\n",
      "\t\t\t\t\t-->MAPE: 85.982\n",
      "\t\t\t\t\t-->MASE: 0.728\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -2.332\n",
      "\t\t\t\t\t-->MAE: 1.168\n",
      "\t\t\t\t\t-->MAPE: 221.592\n",
      "\t\t\t\t\t-->MASE: 0.909\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.441\n",
      "\t\t\t\t\t-->MAE: 0.773\n",
      "\t\t\t\t\t-->MAPE: 37.388\n",
      "\t\t\t\t\t-->MASE: 0.602\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (100416L, 20L), y_train: (100416L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.69s\n",
      "\t\t\t\t\t-->R2: -0.426\n",
      "\t\t\t\t\t-->MAE: 0.774\n",
      "\t\t\t\t\t-->MAPE: 41.098\n",
      "\t\t\t\t\t-->MASE: 0.602\n",
      "\n",
      "\t\t-->Evaluation (2014-12-15 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (100512L, 16L), y_train: (100512L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.151\n",
      "\t\t\t\t\t-->MAE: 2.487\n",
      "\t\t\t\t\t-->MAPE: 126.431\n",
      "\t\t\t\t\t-->MASE: 1.936\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.592\n",
      "\t\t\t\t\t-->MAE: 1.807\n",
      "\t\t\t\t\t-->MAPE: 69.351\n",
      "\t\t\t\t\t-->MASE: 1.407\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.776\n",
      "\t\t\t\t\t-->MAE: 0.578\n",
      "\t\t\t\t\t-->MAPE: 16.766\n",
      "\t\t\t\t\t-->MASE: 0.450\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (100512L, 20L), y_train: (100512L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.16s\n",
      "\t\t\t\t\t-->R2: 0.777\n",
      "\t\t\t\t\t-->MAE: 0.625\n",
      "\t\t\t\t\t-->MAPE: 41.103\n",
      "\t\t\t\t\t-->MASE: 0.487\n",
      "\n",
      "\t\t-->Evaluation (2014-12-16 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (100608L, 16L), y_train: (100608L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.146\n",
      "\t\t\t\t\t-->MAE: 1.698\n",
      "\t\t\t\t\t-->MAPE: 106.148\n",
      "\t\t\t\t\t-->MASE: 1.320\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.945\n",
      "\t\t\t\t\t-->MAE: 0.289\n",
      "\t\t\t\t\t-->MAPE: 11.595\n",
      "\t\t\t\t\t-->MASE: 0.224\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.890\n",
      "\t\t\t\t\t-->MAE: 0.411\n",
      "\t\t\t\t\t-->MAPE: 13.380\n",
      "\t\t\t\t\t-->MASE: 0.320\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (100608L, 20L), y_train: (100608L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.94s\n",
      "\t\t\t\t\t-->R2: 0.949\n",
      "\t\t\t\t\t-->MAE: 0.308\n",
      "\t\t\t\t\t-->MAPE: 14.493\n",
      "\t\t\t\t\t-->MASE: 0.239\n",
      "\n",
      "\t\t-->Evaluation (2014-12-17 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (100704L, 16L), y_train: (100704L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.649\n",
      "\t\t\t\t\t-->MAE: 1.445\n",
      "\t\t\t\t\t-->MAPE: 109.252\n",
      "\t\t\t\t\t-->MASE: 1.124\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.525\n",
      "\t\t\t\t\t-->MAE: 0.611\n",
      "\t\t\t\t\t-->MAPE: 21.018\n",
      "\t\t\t\t\t-->MASE: 0.475\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.667\n",
      "\t\t\t\t\t-->MAE: 0.468\n",
      "\t\t\t\t\t-->MAPE: 24.145\n",
      "\t\t\t\t\t-->MASE: 0.364\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (100704L, 20L), y_train: (100704L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 9.99s\n",
      "\t\t\t\t\t-->R2: 0.708\n",
      "\t\t\t\t\t-->MAE: 0.525\n",
      "\t\t\t\t\t-->MAPE: 23.670\n",
      "\t\t\t\t\t-->MASE: 0.408\n",
      "\n",
      "\t\t-->Evaluation (2014-12-18 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (100800L, 16L), y_train: (100800L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.401\n",
      "\t\t\t\t\t-->MAE: 1.255\n",
      "\t\t\t\t\t-->MAPE: 100.369\n",
      "\t\t\t\t\t-->MASE: 0.975\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.914\n",
      "\t\t\t\t\t-->MAE: 0.269\n",
      "\t\t\t\t\t-->MAPE: 11.917\n",
      "\t\t\t\t\t-->MASE: 0.209\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.780\n",
      "\t\t\t\t\t-->MAE: 0.387\n",
      "\t\t\t\t\t-->MAPE: 21.307\n",
      "\t\t\t\t\t-->MASE: 0.301\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (100800L, 20L), y_train: (100800L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.05s\n",
      "\t\t\t\t\t-->R2: 0.884\n",
      "\t\t\t\t\t-->MAE: 0.305\n",
      "\t\t\t\t\t-->MAPE: 15.529\n",
      "\t\t\t\t\t-->MASE: 0.237\n",
      "\n",
      "\t\t-->Evaluation (2014-12-19 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (100896L, 16L), y_train: (100896L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -2.416\n",
      "\t\t\t\t\t-->MAE: 1.698\n",
      "\t\t\t\t\t-->MAPE: 143.673\n",
      "\t\t\t\t\t-->MASE: 1.320\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.803\n",
      "\t\t\t\t\t-->MAE: 1.099\n",
      "\t\t\t\t\t-->MAPE: 78.913\n",
      "\t\t\t\t\t-->MASE: 0.854\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.333\n",
      "\t\t\t\t\t-->MAE: 0.847\n",
      "\t\t\t\t\t-->MAPE: 68.726\n",
      "\t\t\t\t\t-->MASE: 0.659\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (100896L, 20L), y_train: (100896L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.33s\n",
      "\t\t\t\t\t-->R2: -0.380\n",
      "\t\t\t\t\t-->MAE: 0.882\n",
      "\t\t\t\t\t-->MAPE: 66.356\n",
      "\t\t\t\t\t-->MASE: 0.686\n",
      "\n",
      "\t\t-->Evaluation (2014-12-20 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (100992L, 16L), y_train: (100992L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: 0.079\n",
      "\t\t\t\t\t-->MAE: 0.933\n",
      "\t\t\t\t\t-->MAPE: 257.407\n",
      "\t\t\t\t\t-->MASE: 0.725\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.725\n",
      "\t\t\t\t\t-->MAE: 1.306\n",
      "\t\t\t\t\t-->MAPE: 351.440\n",
      "\t\t\t\t\t-->MASE: 1.015\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.783\n",
      "\t\t\t\t\t-->MAE: 0.286\n",
      "\t\t\t\t\t-->MAPE: 30.103\n",
      "\t\t\t\t\t-->MASE: 0.222\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (100992L, 20L), y_train: (100992L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.38s\n",
      "\t\t\t\t\t-->R2: 0.641\n",
      "\t\t\t\t\t-->MAE: 0.440\n",
      "\t\t\t\t\t-->MAPE: 84.492\n",
      "\t\t\t\t\t-->MASE: 0.342\n",
      "\n",
      "\t\t-->Evaluation (2014-12-21 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (101088L, 16L), y_train: (101088L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -54.609\n",
      "\t\t\t\t\t-->MAE: 0.704\n",
      "\t\t\t\t\t-->MAPE: 232.478\n",
      "\t\t\t\t\t-->MASE: 0.547\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -56.415\n",
      "\t\t\t\t\t-->MAE: 0.597\n",
      "\t\t\t\t\t-->MAPE: 288.549\n",
      "\t\t\t\t\t-->MASE: 0.464\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -30.836\n",
      "\t\t\t\t\t-->MAE: 0.780\n",
      "\t\t\t\t\t-->MAPE: 259.775\n",
      "\t\t\t\t\t-->MASE: 0.607\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (101088L, 20L), y_train: (101088L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.23s\n",
      "\t\t\t\t\t-->R2: -63.999\n",
      "\t\t\t\t\t-->MAE: 1.101\n",
      "\t\t\t\t\t-->MAPE: 390.898\n",
      "\t\t\t\t\t-->MASE: 0.856\n",
      "\n",
      "\t\t-->Evaluation (2014-12-22 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (101184L, 16L), y_train: (101184L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.049\n",
      "\t\t\t\t\t-->MAE: 2.047\n",
      "\t\t\t\t\t-->MAPE: 55.227\n",
      "\t\t\t\t\t-->MASE: 1.592\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.184\n",
      "\t\t\t\t\t-->MAE: 2.061\n",
      "\t\t\t\t\t-->MAPE: 49.380\n",
      "\t\t\t\t\t-->MASE: 1.603\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.913\n",
      "\t\t\t\t\t-->MAE: 0.312\n",
      "\t\t\t\t\t-->MAPE: 11.326\n",
      "\t\t\t\t\t-->MASE: 0.243\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (101184L, 20L), y_train: (101184L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.74s\n",
      "\t\t\t\t\t-->R2: 0.914\n",
      "\t\t\t\t\t-->MAE: 0.381\n",
      "\t\t\t\t\t-->MAPE: 17.158\n",
      "\t\t\t\t\t-->MASE: 0.296\n",
      "\n",
      "\t\t-->Evaluation (2014-12-23 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (101280L, 16L), y_train: (101280L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.272\n",
      "\t\t\t\t\t-->MAE: 1.775\n",
      "\t\t\t\t\t-->MAPE: 109.371\n",
      "\t\t\t\t\t-->MASE: 1.380\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.938\n",
      "\t\t\t\t\t-->MAE: 0.307\n",
      "\t\t\t\t\t-->MAPE: 12.612\n",
      "\t\t\t\t\t-->MASE: 0.239\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.943\n",
      "\t\t\t\t\t-->MAE: 0.310\n",
      "\t\t\t\t\t-->MAPE: 13.049\n",
      "\t\t\t\t\t-->MASE: 0.241\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (101280L, 20L), y_train: (101280L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.58s\n",
      "\t\t\t\t\t-->R2: 0.947\n",
      "\t\t\t\t\t-->MAE: 0.291\n",
      "\t\t\t\t\t-->MAPE: 12.947\n",
      "\t\t\t\t\t-->MASE: 0.226\n",
      "\n",
      "\t\t-->Evaluation (2014-12-24 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (101376L, 16L), y_train: (101376L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: 0.331\n",
      "\t\t\t\t\t-->MAE: 1.401\n",
      "\t\t\t\t\t-->MAPE: 102.262\n",
      "\t\t\t\t\t-->MASE: 1.088\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.609\n",
      "\t\t\t\t\t-->MAE: 1.057\n",
      "\t\t\t\t\t-->MAPE: 126.407\n",
      "\t\t\t\t\t-->MASE: 0.821\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.577\n",
      "\t\t\t\t\t-->MAE: 1.127\n",
      "\t\t\t\t\t-->MAPE: 107.380\n",
      "\t\t\t\t\t-->MASE: 0.875\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (101376L, 20L), y_train: (101376L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.15s\n",
      "\t\t\t\t\t-->R2: 0.551\n",
      "\t\t\t\t\t-->MAE: 1.058\n",
      "\t\t\t\t\t-->MAPE: 138.624\n",
      "\t\t\t\t\t-->MASE: 0.822\n",
      "\n",
      "\t\t-->Evaluation (2014-12-25 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (101472L, 16L), y_train: (101472L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -135.567\n",
      "\t\t\t\t\t-->MAE: 2.009\n",
      "\t\t\t\t\t-->MAPE: 538.658\n",
      "\t\t\t\t\t-->MASE: 1.561\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -149.272\n",
      "\t\t\t\t\t-->MAE: 2.133\n",
      "\t\t\t\t\t-->MAPE: 810.684\n",
      "\t\t\t\t\t-->MASE: 1.657\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -85.843\n",
      "\t\t\t\t\t-->MAE: 1.620\n",
      "\t\t\t\t\t-->MAPE: 629.297\n",
      "\t\t\t\t\t-->MASE: 1.258\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (101472L, 20L), y_train: (101472L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.65s\n",
      "\t\t\t\t\t-->R2: -2.905\n",
      "\t\t\t\t\t-->MAE: 0.233\n",
      "\t\t\t\t\t-->MAPE: 69.013\n",
      "\t\t\t\t\t-->MASE: 0.181\n",
      "\n",
      "\t\t-->Evaluation (2014-12-26 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (101568L, 16L), y_train: (101568L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.475\n",
      "\t\t\t\t\t-->MAE: 0.200\n",
      "\t\t\t\t\t-->MAPE: 74.995\n",
      "\t\t\t\t\t-->MASE: 0.155\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.791\n",
      "\t\t\t\t\t-->MAE: 0.071\n",
      "\t\t\t\t\t-->MAPE: 20.620\n",
      "\t\t\t\t\t-->MASE: 0.055\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -44.090\n",
      "\t\t\t\t\t-->MAE: 0.946\n",
      "\t\t\t\t\t-->MAPE: 448.806\n",
      "\t\t\t\t\t-->MASE: 0.735\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (101568L, 20L), y_train: (101568L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.43s\n",
      "\t\t\t\t\t-->R2: 0.073\n",
      "\t\t\t\t\t-->MAE: 0.172\n",
      "\t\t\t\t\t-->MAPE: 56.736\n",
      "\t\t\t\t\t-->MASE: 0.133\n",
      "\n",
      "\t\t-->Evaluation (2014-12-27 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (101664L, 16L), y_train: (101664L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.325\n",
      "\t\t\t\t\t-->MAE: 0.573\n",
      "\t\t\t\t\t-->MAPE: 77.735\n",
      "\t\t\t\t\t-->MASE: 0.445\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.250\n",
      "\t\t\t\t\t-->MAE: 0.427\n",
      "\t\t\t\t\t-->MAPE: 24.009\n",
      "\t\t\t\t\t-->MASE: 0.332\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.438\n",
      "\t\t\t\t\t-->MAE: 0.265\n",
      "\t\t\t\t\t-->MAPE: 23.716\n",
      "\t\t\t\t\t-->MASE: 0.206\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (101664L, 20L), y_train: (101664L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 11.04s\n",
      "\t\t\t\t\t-->R2: 0.854\n",
      "\t\t\t\t\t-->MAE: 0.276\n",
      "\t\t\t\t\t-->MAPE: 51.832\n",
      "\t\t\t\t\t-->MASE: 0.215\n",
      "\n",
      "\t\t-->Evaluation (2014-12-28 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (101760L, 16L), y_train: (101760L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -21.887\n",
      "\t\t\t\t\t-->MAE: 0.531\n",
      "\t\t\t\t\t-->MAPE: 182.096\n",
      "\t\t\t\t\t-->MASE: 0.413\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -23.332\n",
      "\t\t\t\t\t-->MAE: 0.448\n",
      "\t\t\t\t\t-->MAPE: 237.171\n",
      "\t\t\t\t\t-->MASE: 0.349\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.821\n",
      "\t\t\t\t\t-->MAE: 0.065\n",
      "\t\t\t\t\t-->MAPE: 15.178\n",
      "\t\t\t\t\t-->MASE: 0.050\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (101760L, 20L), y_train: (101760L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.27s\n",
      "\t\t\t\t\t-->R2: 0.551\n",
      "\t\t\t\t\t-->MAE: 0.140\n",
      "\t\t\t\t\t-->MAPE: 47.079\n",
      "\t\t\t\t\t-->MASE: 0.109\n",
      "\n",
      "\t\t-->Evaluation (2014-12-29 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (101856L, 16L), y_train: (101856L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.357\n",
      "\t\t\t\t\t-->MAE: 1.012\n",
      "\t\t\t\t\t-->MAPE: 50.400\n",
      "\t\t\t\t\t-->MASE: 0.787\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.491\n",
      "\t\t\t\t\t-->MAE: 1.011\n",
      "\t\t\t\t\t-->MAPE: 44.246\n",
      "\t\t\t\t\t-->MASE: 0.787\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.285\n",
      "\t\t\t\t\t-->MAE: 1.502\n",
      "\t\t\t\t\t-->MAPE: 130.988\n",
      "\t\t\t\t\t-->MASE: 1.168\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (101856L, 20L), y_train: (101856L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.24s\n",
      "\t\t\t\t\t-->R2: -0.132\n",
      "\t\t\t\t\t-->MAE: 1.051\n",
      "\t\t\t\t\t-->MAPE: 93.293\n",
      "\t\t\t\t\t-->MASE: 0.817\n",
      "\n",
      "\t\t-->Evaluation (2014-12-30 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (101952L, 16L), y_train: (101952L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.609\n",
      "\t\t\t\t\t-->MAE: 0.964\n",
      "\t\t\t\t\t-->MAPE: 79.121\n",
      "\t\t\t\t\t-->MASE: 0.750\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.201\n",
      "\t\t\t\t\t-->MAE: 0.520\n",
      "\t\t\t\t\t-->MAPE: 31.115\n",
      "\t\t\t\t\t-->MASE: 0.404\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -4.486\n",
      "\t\t\t\t\t-->MAE: 1.376\n",
      "\t\t\t\t\t-->MAPE: 149.933\n",
      "\t\t\t\t\t-->MASE: 1.071\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (101952L, 20L), y_train: (101952L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.31s\n",
      "\t\t\t\t\t-->R2: -0.158\n",
      "\t\t\t\t\t-->MAE: 0.634\n",
      "\t\t\t\t\t-->MAPE: 77.857\n",
      "\t\t\t\t\t-->MASE: 0.493\n",
      "\n",
      "\t\t-->Evaluation (2014-12-31 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (102048L, 16L), y_train: (102048L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -2.870\n",
      "\t\t\t\t\t-->MAE: 0.668\n",
      "\t\t\t\t\t-->MAPE: 217.991\n",
      "\t\t\t\t\t-->MASE: 0.520\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -4.124\n",
      "\t\t\t\t\t-->MAE: 0.755\n",
      "\t\t\t\t\t-->MAPE: 266.388\n",
      "\t\t\t\t\t-->MASE: 0.587\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -24.779\n",
      "\t\t\t\t\t-->MAE: 1.967\n",
      "\t\t\t\t\t-->MAPE: 615.513\n",
      "\t\t\t\t\t-->MASE: 1.531\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (102048L, 20L), y_train: (102048L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.10s\n",
      "\t\t\t\t\t-->R2: -3.892\n",
      "\t\t\t\t\t-->MAE: 0.861\n",
      "\t\t\t\t\t-->MAPE: 314.095\n",
      "\t\t\t\t\t-->MASE: 0.670\n",
      "\n",
      "\t\t-->Evaluation (2015-01-01 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (102144L, 16L), y_train: (102144L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -4.562\n",
      "\t\t\t\t\t-->MAE: 0.386\n",
      "\t\t\t\t\t-->MAPE: 122.309\n",
      "\t\t\t\t\t-->MASE: 0.300\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -4.418\n",
      "\t\t\t\t\t-->MAE: 0.258\n",
      "\t\t\t\t\t-->MAPE: 110.354\n",
      "\t\t\t\t\t-->MASE: 0.201\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.681\n",
      "\t\t\t\t\t-->MAE: 0.104\n",
      "\t\t\t\t\t-->MAPE: 27.977\n",
      "\t\t\t\t\t-->MASE: 0.081\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (102144L, 20L), y_train: (102144L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.23s\n",
      "\t\t\t\t\t-->R2: -0.321\n",
      "\t\t\t\t\t-->MAE: 0.235\n",
      "\t\t\t\t\t-->MAPE: 87.266\n",
      "\t\t\t\t\t-->MASE: 0.183\n",
      "\n",
      "\t\t-->Evaluation (2015-01-02 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (102240L, 16L), y_train: (102240L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.155\n",
      "\t\t\t\t\t-->MAE: 0.824\n",
      "\t\t\t\t\t-->MAPE: 45.172\n",
      "\t\t\t\t\t-->MASE: 0.642\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.344\n",
      "\t\t\t\t\t-->MAE: 0.823\n",
      "\t\t\t\t\t-->MAPE: 39.859\n",
      "\t\t\t\t\t-->MASE: 0.641\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.342\n",
      "\t\t\t\t\t-->MAE: 0.806\n",
      "\t\t\t\t\t-->MAPE: 36.058\n",
      "\t\t\t\t\t-->MASE: 0.628\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (102240L, 20L), y_train: (102240L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.95s\n",
      "\t\t\t\t\t-->R2: -0.188\n",
      "\t\t\t\t\t-->MAE: 0.761\n",
      "\t\t\t\t\t-->MAPE: 42.632\n",
      "\t\t\t\t\t-->MASE: 0.593\n",
      "\n",
      "\t\t-->Evaluation (2015-01-03 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (102336L, 16L), y_train: (102336L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: 0.261\n",
      "\t\t\t\t\t-->MAE: 0.531\n",
      "\t\t\t\t\t-->MAPE: 166.481\n",
      "\t\t\t\t\t-->MASE: 0.414\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -2.213\n",
      "\t\t\t\t\t-->MAE: 1.102\n",
      "\t\t\t\t\t-->MAPE: 351.423\n",
      "\t\t\t\t\t-->MASE: 0.859\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.841\n",
      "\t\t\t\t\t-->MAE: 0.215\n",
      "\t\t\t\t\t-->MAPE: 27.666\n",
      "\t\t\t\t\t-->MASE: 0.168\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (102336L, 20L), y_train: (102336L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 11.02s\n",
      "\t\t\t\t\t-->R2: 0.100\n",
      "\t\t\t\t\t-->MAE: 0.816\n",
      "\t\t\t\t\t-->MAPE: 220.594\n",
      "\t\t\t\t\t-->MASE: 0.636\n",
      "\n",
      "\t\t-->Evaluation (2015-01-04 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (102432L, 16L), y_train: (102432L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -23.493\n",
      "\t\t\t\t\t-->MAE: 0.590\n",
      "\t\t\t\t\t-->MAPE: 204.419\n",
      "\t\t\t\t\t-->MASE: 0.460\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -24.335\n",
      "\t\t\t\t\t-->MAE: 0.496\n",
      "\t\t\t\t\t-->MAPE: 222.950\n",
      "\t\t\t\t\t-->MASE: 0.387\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.723\n",
      "\t\t\t\t\t-->MAE: 0.092\n",
      "\t\t\t\t\t-->MAPE: 27.140\n",
      "\t\t\t\t\t-->MASE: 0.072\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (102432L, 20L), y_train: (102432L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.89s\n",
      "\t\t\t\t\t-->R2: -0.402\n",
      "\t\t\t\t\t-->MAE: 0.219\n",
      "\t\t\t\t\t-->MAPE: 100.104\n",
      "\t\t\t\t\t-->MASE: 0.171\n",
      "\n",
      "\t\t-->Evaluation (2015-01-05 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (102528L, 16L), y_train: (102528L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.992\n",
      "\t\t\t\t\t-->MAE: 1.707\n",
      "\t\t\t\t\t-->MAPE: 54.170\n",
      "\t\t\t\t\t-->MASE: 1.332\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.072\n",
      "\t\t\t\t\t-->MAE: 1.706\n",
      "\t\t\t\t\t-->MAPE: 49.579\n",
      "\t\t\t\t\t-->MASE: 1.331\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.355\n",
      "\t\t\t\t\t-->MAE: 1.403\n",
      "\t\t\t\t\t-->MAPE: 70.955\n",
      "\t\t\t\t\t-->MASE: 1.095\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (102528L, 20L), y_train: (102528L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.13s\n",
      "\t\t\t\t\t-->R2: 0.568\n",
      "\t\t\t\t\t-->MAE: 0.722\n",
      "\t\t\t\t\t-->MAPE: 51.419\n",
      "\t\t\t\t\t-->MASE: 0.563\n",
      "\n",
      "\t\t-->Evaluation (2015-01-06 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (102624L, 16L), y_train: (102624L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.455\n",
      "\t\t\t\t\t-->MAE: 1.576\n",
      "\t\t\t\t\t-->MAPE: 101.461\n",
      "\t\t\t\t\t-->MASE: 1.229\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.415\n",
      "\t\t\t\t\t-->MAE: 0.678\n",
      "\t\t\t\t\t-->MAPE: 18.690\n",
      "\t\t\t\t\t-->MASE: 0.529\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.021\n",
      "\t\t\t\t\t-->MAE: 1.284\n",
      "\t\t\t\t\t-->MAPE: 33.187\n",
      "\t\t\t\t\t-->MASE: 1.001\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (102624L, 20L), y_train: (102624L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.63s\n",
      "\t\t\t\t\t-->R2: 0.636\n",
      "\t\t\t\t\t-->MAE: 0.545\n",
      "\t\t\t\t\t-->MAPE: 17.314\n",
      "\t\t\t\t\t-->MASE: 0.425\n",
      "\n",
      "\t\t-->Evaluation (2015-01-07 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (102720L, 16L), y_train: (102720L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.326\n",
      "\t\t\t\t\t-->MAE: 1.854\n",
      "\t\t\t\t\t-->MAPE: 113.257\n",
      "\t\t\t\t\t-->MASE: 1.445\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.811\n",
      "\t\t\t\t\t-->MAE: 0.582\n",
      "\t\t\t\t\t-->MAPE: 15.014\n",
      "\t\t\t\t\t-->MASE: 0.454\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.877\n",
      "\t\t\t\t\t-->MAE: 2.210\n",
      "\t\t\t\t\t-->MAPE: 46.822\n",
      "\t\t\t\t\t-->MASE: 1.723\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (102720L, 20L), y_train: (102720L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.73s\n",
      "\t\t\t\t\t-->R2: 0.681\n",
      "\t\t\t\t\t-->MAE: 0.757\n",
      "\t\t\t\t\t-->MAPE: 19.580\n",
      "\t\t\t\t\t-->MASE: 0.590\n",
      "\n",
      "\t\t-->Evaluation (2015-01-08 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (102816L, 16L), y_train: (102816L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.720\n",
      "\t\t\t\t\t-->MAE: 2.012\n",
      "\t\t\t\t\t-->MAPE: 122.086\n",
      "\t\t\t\t\t-->MASE: 1.568\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.709\n",
      "\t\t\t\t\t-->MAE: 0.599\n",
      "\t\t\t\t\t-->MAPE: 21.859\n",
      "\t\t\t\t\t-->MASE: 0.467\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -1.138\n",
      "\t\t\t\t\t-->MAE: 2.096\n",
      "\t\t\t\t\t-->MAPE: 52.624\n",
      "\t\t\t\t\t-->MASE: 1.633\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (102816L, 20L), y_train: (102816L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.41s\n",
      "\t\t\t\t\t-->R2: 0.691\n",
      "\t\t\t\t\t-->MAE: 0.715\n",
      "\t\t\t\t\t-->MAPE: 25.269\n",
      "\t\t\t\t\t-->MASE: 0.557\n",
      "\n",
      "\t\t-->Evaluation (2015-01-09 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (102912L, 16L), y_train: (102912L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.954\n",
      "\t\t\t\t\t-->MAE: 2.102\n",
      "\t\t\t\t\t-->MAPE: 270.314\n",
      "\t\t\t\t\t-->MASE: 1.637\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.115\n",
      "\t\t\t\t\t-->MAE: 1.118\n",
      "\t\t\t\t\t-->MAPE: 180.362\n",
      "\t\t\t\t\t-->MASE: 0.870\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.484\n",
      "\t\t\t\t\t-->MAE: 0.677\n",
      "\t\t\t\t\t-->MAPE: 33.512\n",
      "\t\t\t\t\t-->MASE: 0.528\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (102912L, 20L), y_train: (102912L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.42s\n",
      "\t\t\t\t\t-->R2: 0.604\n",
      "\t\t\t\t\t-->MAE: 0.656\n",
      "\t\t\t\t\t-->MAPE: 71.860\n",
      "\t\t\t\t\t-->MASE: 0.511\n",
      "\n",
      "\t\t-->Evaluation (2015-01-10 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (103008L, 16L), y_train: (103008L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.601\n",
      "\t\t\t\t\t-->MAE: 0.966\n",
      "\t\t\t\t\t-->MAPE: 341.229\n",
      "\t\t\t\t\t-->MASE: 0.752\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.546\n",
      "\t\t\t\t\t-->MAE: 1.061\n",
      "\t\t\t\t\t-->MAPE: 417.807\n",
      "\t\t\t\t\t-->MASE: 0.826\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.868\n",
      "\t\t\t\t\t-->MAE: 0.197\n",
      "\t\t\t\t\t-->MAPE: 18.318\n",
      "\t\t\t\t\t-->MASE: 0.153\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (103008L, 20L), y_train: (103008L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.38s\n",
      "\t\t\t\t\t-->R2: 0.703\n",
      "\t\t\t\t\t-->MAE: 0.345\n",
      "\t\t\t\t\t-->MAPE: 83.372\n",
      "\t\t\t\t\t-->MASE: 0.269\n",
      "\n",
      "\t\t-->Evaluation (2015-01-11 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (103104L, 16L), y_train: (103104L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -28.988\n",
      "\t\t\t\t\t-->MAE: 0.561\n",
      "\t\t\t\t\t-->MAPE: 185.131\n",
      "\t\t\t\t\t-->MASE: 0.437\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -31.167\n",
      "\t\t\t\t\t-->MAE: 0.488\n",
      "\t\t\t\t\t-->MAPE: 227.887\n",
      "\t\t\t\t\t-->MASE: 0.380\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.702\n",
      "\t\t\t\t\t-->MAE: 0.086\n",
      "\t\t\t\t\t-->MAPE: 23.396\n",
      "\t\t\t\t\t-->MASE: 0.067\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (103104L, 20L), y_train: (103104L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.51s\n",
      "\t\t\t\t\t-->R2: 0.576\n",
      "\t\t\t\t\t-->MAE: 0.128\n",
      "\t\t\t\t\t-->MAPE: 46.381\n",
      "\t\t\t\t\t-->MASE: 0.099\n",
      "\n",
      "\t\t-->Evaluation (2015-01-12 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (103200L, 16L), y_train: (103200L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.042\n",
      "\t\t\t\t\t-->MAE: 1.741\n",
      "\t\t\t\t\t-->MAPE: 54.921\n",
      "\t\t\t\t\t-->MASE: 1.356\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.130\n",
      "\t\t\t\t\t-->MAE: 1.740\n",
      "\t\t\t\t\t-->MAPE: 50.061\n",
      "\t\t\t\t\t-->MASE: 1.356\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.835\n",
      "\t\t\t\t\t-->MAE: 0.359\n",
      "\t\t\t\t\t-->MAPE: 13.955\n",
      "\t\t\t\t\t-->MASE: 0.279\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (103200L, 20L), y_train: (103200L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.52s\n",
      "\t\t\t\t\t-->R2: 0.577\n",
      "\t\t\t\t\t-->MAE: 0.764\n",
      "\t\t\t\t\t-->MAPE: 42.328\n",
      "\t\t\t\t\t-->MASE: 0.595\n",
      "\n",
      "\t\t-->Evaluation (2015-01-13 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (103296L, 16L), y_train: (103296L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.350\n",
      "\t\t\t\t\t-->MAE: 1.396\n",
      "\t\t\t\t\t-->MAPE: 100.747\n",
      "\t\t\t\t\t-->MASE: 1.087\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.866\n",
      "\t\t\t\t\t-->MAE: 0.340\n",
      "\t\t\t\t\t-->MAPE: 16.603\n",
      "\t\t\t\t\t-->MASE: 0.265\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.404\n",
      "\t\t\t\t\t-->MAE: 0.631\n",
      "\t\t\t\t\t-->MAPE: 55.860\n",
      "\t\t\t\t\t-->MASE: 0.491\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (103296L, 20L), y_train: (103296L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.38s\n",
      "\t\t\t\t\t-->R2: 0.764\n",
      "\t\t\t\t\t-->MAE: 0.485\n",
      "\t\t\t\t\t-->MAPE: 30.280\n",
      "\t\t\t\t\t-->MASE: 0.378\n",
      "\n",
      "\t\t-->Evaluation (2015-01-14 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (103392L, 16L), y_train: (103392L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.088\n",
      "\t\t\t\t\t-->MAE: 1.533\n",
      "\t\t\t\t\t-->MAPE: 103.562\n",
      "\t\t\t\t\t-->MASE: 1.194\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.633\n",
      "\t\t\t\t\t-->MAE: 0.764\n",
      "\t\t\t\t\t-->MAPE: 23.462\n",
      "\t\t\t\t\t-->MASE: 0.595\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.693\n",
      "\t\t\t\t\t-->MAE: 0.594\n",
      "\t\t\t\t\t-->MAPE: 21.113\n",
      "\t\t\t\t\t-->MASE: 0.462\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (103392L, 20L), y_train: (103392L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.59s\n",
      "\t\t\t\t\t-->R2: 0.913\n",
      "\t\t\t\t\t-->MAE: 0.355\n",
      "\t\t\t\t\t-->MAPE: 15.844\n",
      "\t\t\t\t\t-->MASE: 0.277\n",
      "\n",
      "\t\t-->Evaluation (2015-01-15 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (103488L, 16L), y_train: (103488L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.480\n",
      "\t\t\t\t\t-->MAE: 1.827\n",
      "\t\t\t\t\t-->MAPE: 121.903\n",
      "\t\t\t\t\t-->MASE: 1.422\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.928\n",
      "\t\t\t\t\t-->MAE: 0.329\n",
      "\t\t\t\t\t-->MAPE: 12.525\n",
      "\t\t\t\t\t-->MASE: 0.256\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.736\n",
      "\t\t\t\t\t-->MAE: 0.553\n",
      "\t\t\t\t\t-->MAPE: 22.503\n",
      "\t\t\t\t\t-->MASE: 0.430\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (103488L, 20L), y_train: (103488L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.20s\n",
      "\t\t\t\t\t-->R2: 0.898\n",
      "\t\t\t\t\t-->MAE: 0.396\n",
      "\t\t\t\t\t-->MAPE: 15.436\n",
      "\t\t\t\t\t-->MASE: 0.308\n",
      "\n",
      "\t\t-->Evaluation (2015-01-16 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (103584L, 16L), y_train: (103584L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -3.521\n",
      "\t\t\t\t\t-->MAE: 2.078\n",
      "\t\t\t\t\t-->MAPE: 240.289\n",
      "\t\t\t\t\t-->MASE: 1.617\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.250\n",
      "\t\t\t\t\t-->MAE: 1.296\n",
      "\t\t\t\t\t-->MAPE: 133.916\n",
      "\t\t\t\t\t-->MASE: 1.008\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.625\n",
      "\t\t\t\t\t-->MAE: 0.401\n",
      "\t\t\t\t\t-->MAPE: 25.240\n",
      "\t\t\t\t\t-->MASE: 0.312\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (103584L, 20L), y_train: (103584L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.46s\n",
      "\t\t\t\t\t-->R2: 0.275\n",
      "\t\t\t\t\t-->MAE: 0.705\n",
      "\t\t\t\t\t-->MAPE: 87.301\n",
      "\t\t\t\t\t-->MASE: 0.548\n",
      "\n",
      "\t\t-->Evaluation (2015-01-17 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (103680L, 16L), y_train: (103680L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -2.122\n",
      "\t\t\t\t\t-->MAE: 0.857\n",
      "\t\t\t\t\t-->MAPE: 390.826\n",
      "\t\t\t\t\t-->MASE: 0.666\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -3.433\n",
      "\t\t\t\t\t-->MAE: 0.978\n",
      "\t\t\t\t\t-->MAPE: 454.664\n",
      "\t\t\t\t\t-->MASE: 0.761\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.364\n",
      "\t\t\t\t\t-->MAE: 0.246\n",
      "\t\t\t\t\t-->MAPE: 32.350\n",
      "\t\t\t\t\t-->MASE: 0.192\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (103680L, 20L), y_train: (103680L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.36s\n",
      "\t\t\t\t\t-->R2: 0.684\n",
      "\t\t\t\t\t-->MAE: 0.245\n",
      "\t\t\t\t\t-->MAPE: 88.495\n",
      "\t\t\t\t\t-->MASE: 0.191\n",
      "\n",
      "\t\t-->Evaluation (2015-01-18 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (103776L, 16L), y_train: (103776L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -13.272\n",
      "\t\t\t\t\t-->MAE: 0.461\n",
      "\t\t\t\t\t-->MAPE: 206.222\n",
      "\t\t\t\t\t-->MASE: 0.359\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -13.666\n",
      "\t\t\t\t\t-->MAE: 0.377\n",
      "\t\t\t\t\t-->MAPE: 201.430\n",
      "\t\t\t\t\t-->MASE: 0.294\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.770\n",
      "\t\t\t\t\t-->MAE: 0.080\n",
      "\t\t\t\t\t-->MAPE: 27.858\n",
      "\t\t\t\t\t-->MASE: 0.063\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (103776L, 20L), y_train: (103776L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.45s\n",
      "\t\t\t\t\t-->R2: 0.674\n",
      "\t\t\t\t\t-->MAE: 0.111\n",
      "\t\t\t\t\t-->MAPE: 44.823\n",
      "\t\t\t\t\t-->MASE: 0.087\n",
      "\n",
      "\t\t-->Evaluation (2015-01-19 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (103872L, 16L), y_train: (103872L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.135\n",
      "\t\t\t\t\t-->MAE: 1.869\n",
      "\t\t\t\t\t-->MAPE: 56.325\n",
      "\t\t\t\t\t-->MASE: 1.455\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.257\n",
      "\t\t\t\t\t-->MAE: 1.880\n",
      "\t\t\t\t\t-->MAPE: 50.957\n",
      "\t\t\t\t\t-->MASE: 1.464\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.692\n",
      "\t\t\t\t\t-->MAE: 0.480\n",
      "\t\t\t\t\t-->MAPE: 20.538\n",
      "\t\t\t\t\t-->MASE: 0.374\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (103872L, 20L), y_train: (103872L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.38s\n",
      "\t\t\t\t\t-->R2: 0.847\n",
      "\t\t\t\t\t-->MAE: 0.387\n",
      "\t\t\t\t\t-->MAPE: 25.007\n",
      "\t\t\t\t\t-->MASE: 0.301\n",
      "\n",
      "\t\t-->Evaluation (2015-01-20 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (103968L, 16L), y_train: (103968L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.274\n",
      "\t\t\t\t\t-->MAE: 1.620\n",
      "\t\t\t\t\t-->MAPE: 116.364\n",
      "\t\t\t\t\t-->MASE: 1.260\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.853\n",
      "\t\t\t\t\t-->MAE: 0.491\n",
      "\t\t\t\t\t-->MAPE: 15.651\n",
      "\t\t\t\t\t-->MASE: 0.382\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.677\n",
      "\t\t\t\t\t-->MAE: 0.676\n",
      "\t\t\t\t\t-->MAPE: 21.422\n",
      "\t\t\t\t\t-->MASE: 0.526\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (103968L, 20L), y_train: (103968L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.39s\n",
      "\t\t\t\t\t-->R2: 0.882\n",
      "\t\t\t\t\t-->MAE: 0.481\n",
      "\t\t\t\t\t-->MAPE: 16.469\n",
      "\t\t\t\t\t-->MASE: 0.375\n",
      "\n",
      "\t\t-->Evaluation (2015-01-21 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (104064L, 16L), y_train: (104064L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.273\n",
      "\t\t\t\t\t-->MAE: 1.542\n",
      "\t\t\t\t\t-->MAPE: 108.046\n",
      "\t\t\t\t\t-->MASE: 1.200\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.863\n",
      "\t\t\t\t\t-->MAE: 0.387\n",
      "\t\t\t\t\t-->MAPE: 15.966\n",
      "\t\t\t\t\t-->MASE: 0.301\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.836\n",
      "\t\t\t\t\t-->MAE: 0.386\n",
      "\t\t\t\t\t-->MAPE: 14.979\n",
      "\t\t\t\t\t-->MASE: 0.300\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (104064L, 20L), y_train: (104064L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.50s\n",
      "\t\t\t\t\t-->R2: 0.879\n",
      "\t\t\t\t\t-->MAE: 0.365\n",
      "\t\t\t\t\t-->MAPE: 14.861\n",
      "\t\t\t\t\t-->MASE: 0.284\n",
      "\n",
      "\t\t-->Evaluation (2015-01-22 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (104160L, 16L), y_train: (104160L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.167\n",
      "\t\t\t\t\t-->MAE: 1.668\n",
      "\t\t\t\t\t-->MAPE: 115.884\n",
      "\t\t\t\t\t-->MASE: 1.297\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.830\n",
      "\t\t\t\t\t-->MAE: 0.473\n",
      "\t\t\t\t\t-->MAPE: 13.307\n",
      "\t\t\t\t\t-->MASE: 0.368\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.911\n",
      "\t\t\t\t\t-->MAE: 0.344\n",
      "\t\t\t\t\t-->MAPE: 12.781\n",
      "\t\t\t\t\t-->MASE: 0.268\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (104160L, 20L), y_train: (104160L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.79s\n",
      "\t\t\t\t\t-->R2: 0.932\n",
      "\t\t\t\t\t-->MAE: 0.338\n",
      "\t\t\t\t\t-->MAPE: 12.671\n",
      "\t\t\t\t\t-->MASE: 0.263\n",
      "\n",
      "\t\t-->Evaluation (2015-01-23 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (104256L, 16L), y_train: (104256L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -11.789\n",
      "\t\t\t\t\t-->MAE: 2.115\n",
      "\t\t\t\t\t-->MAPE: 340.444\n",
      "\t\t\t\t\t-->MASE: 1.645\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -9.476\n",
      "\t\t\t\t\t-->MAE: 1.577\n",
      "\t\t\t\t\t-->MAPE: 285.302\n",
      "\t\t\t\t\t-->MASE: 1.226\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.791\n",
      "\t\t\t\t\t-->MAE: 0.558\n",
      "\t\t\t\t\t-->MAPE: 79.335\n",
      "\t\t\t\t\t-->MASE: 0.434\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (104256L, 20L), y_train: (104256L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 11.55s\n",
      "\t\t\t\t\t-->R2: -0.213\n",
      "\t\t\t\t\t-->MAE: 0.462\n",
      "\t\t\t\t\t-->MAPE: 88.138\n",
      "\t\t\t\t\t-->MASE: 0.359\n",
      "\n",
      "\t\t-->Evaluation (2015-01-24 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (104352L, 16L), y_train: (104352L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: 0.411\n",
      "\t\t\t\t\t-->MAE: 0.686\n",
      "\t\t\t\t\t-->MAPE: 162.978\n",
      "\t\t\t\t\t-->MASE: 0.533\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.298\n",
      "\t\t\t\t\t-->MAE: 0.956\n",
      "\t\t\t\t\t-->MAPE: 255.428\n",
      "\t\t\t\t\t-->MASE: 0.743\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.694\n",
      "\t\t\t\t\t-->MAE: 0.335\n",
      "\t\t\t\t\t-->MAPE: 27.753\n",
      "\t\t\t\t\t-->MASE: 0.260\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (104352L, 20L), y_train: (104352L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 11.30s\n",
      "\t\t\t\t\t-->R2: 0.603\n",
      "\t\t\t\t\t-->MAE: 0.373\n",
      "\t\t\t\t\t-->MAPE: 40.389\n",
      "\t\t\t\t\t-->MASE: 0.290\n",
      "\n",
      "\t\t-->Evaluation (2015-01-25 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (104448L, 16L), y_train: (104448L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -48.272\n",
      "\t\t\t\t\t-->MAE: 0.693\n",
      "\t\t\t\t\t-->MAPE: 277.798\n",
      "\t\t\t\t\t-->MASE: 0.539\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -49.490\n",
      "\t\t\t\t\t-->MAE: 0.597\n",
      "\t\t\t\t\t-->MAPE: 316.163\n",
      "\t\t\t\t\t-->MASE: 0.464\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.850\n",
      "\t\t\t\t\t-->MAE: 0.068\n",
      "\t\t\t\t\t-->MAPE: 22.134\n",
      "\t\t\t\t\t-->MASE: 0.053\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (104448L, 20L), y_train: (104448L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.56s\n",
      "\t\t\t\t\t-->R2: 0.696\n",
      "\t\t\t\t\t-->MAE: 0.099\n",
      "\t\t\t\t\t-->MAPE: 41.173\n",
      "\t\t\t\t\t-->MASE: 0.077\n",
      "\n",
      "\t\t-->Evaluation (2015-01-26 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (104544L, 16L), y_train: (104544L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.232\n",
      "\t\t\t\t\t-->MAE: 1.822\n",
      "\t\t\t\t\t-->MAPE: 60.983\n",
      "\t\t\t\t\t-->MASE: 1.417\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.324\n",
      "\t\t\t\t\t-->MAE: 1.819\n",
      "\t\t\t\t\t-->MAPE: 53.091\n",
      "\t\t\t\t\t-->MASE: 1.414\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.669\n",
      "\t\t\t\t\t-->MAE: 0.524\n",
      "\t\t\t\t\t-->MAPE: 44.137\n",
      "\t\t\t\t\t-->MASE: 0.407\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (104544L, 20L), y_train: (104544L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.61s\n",
      "\t\t\t\t\t-->R2: 0.751\n",
      "\t\t\t\t\t-->MAE: 0.524\n",
      "\t\t\t\t\t-->MAPE: 40.846\n",
      "\t\t\t\t\t-->MASE: 0.408\n",
      "\n",
      "\t\t-->Evaluation (2015-01-27 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (104640L, 16L), y_train: (104640L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: 0.120\n",
      "\t\t\t\t\t-->MAE: 1.236\n",
      "\t\t\t\t\t-->MAPE: 67.254\n",
      "\t\t\t\t\t-->MASE: 0.961\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.609\n",
      "\t\t\t\t\t-->MAE: 0.870\n",
      "\t\t\t\t\t-->MAPE: 55.010\n",
      "\t\t\t\t\t-->MASE: 0.676\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.417\n",
      "\t\t\t\t\t-->MAE: 1.016\n",
      "\t\t\t\t\t-->MAPE: 61.547\n",
      "\t\t\t\t\t-->MASE: 0.790\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (104640L, 20L), y_train: (104640L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.48s\n",
      "\t\t\t\t\t-->R2: 0.666\n",
      "\t\t\t\t\t-->MAE: 0.783\n",
      "\t\t\t\t\t-->MAPE: 50.829\n",
      "\t\t\t\t\t-->MASE: 0.609\n",
      "\n",
      "\t\t-->Evaluation (2015-01-28 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (104736L, 16L), y_train: (104736L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.111\n",
      "\t\t\t\t\t-->MAE: 1.677\n",
      "\t\t\t\t\t-->MAPE: 159.382\n",
      "\t\t\t\t\t-->MASE: 1.304\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.626\n",
      "\t\t\t\t\t-->MAE: 0.755\n",
      "\t\t\t\t\t-->MAPE: 67.457\n",
      "\t\t\t\t\t-->MASE: 0.587\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.748\n",
      "\t\t\t\t\t-->MAE: 0.481\n",
      "\t\t\t\t\t-->MAPE: 20.141\n",
      "\t\t\t\t\t-->MASE: 0.374\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (104736L, 20L), y_train: (104736L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.56s\n",
      "\t\t\t\t\t-->R2: 0.876\n",
      "\t\t\t\t\t-->MAE: 0.351\n",
      "\t\t\t\t\t-->MAPE: 25.192\n",
      "\t\t\t\t\t-->MASE: 0.273\n",
      "\n",
      "\t\t-->Evaluation (2015-01-29 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (104832L, 16L), y_train: (104832L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.472\n",
      "\t\t\t\t\t-->MAE: 1.966\n",
      "\t\t\t\t\t-->MAPE: 96.856\n",
      "\t\t\t\t\t-->MASE: 1.528\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: 0.544\n",
      "\t\t\t\t\t-->MAE: 0.953\n",
      "\t\t\t\t\t-->MAPE: 24.321\n",
      "\t\t\t\t\t-->MASE: 0.740\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.746\n",
      "\t\t\t\t\t-->MAE: 0.649\n",
      "\t\t\t\t\t-->MAPE: 19.562\n",
      "\t\t\t\t\t-->MASE: 0.505\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (104832L, 20L), y_train: (104832L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.40s\n",
      "\t\t\t\t\t-->R2: 0.787\n",
      "\t\t\t\t\t-->MAE: 0.637\n",
      "\t\t\t\t\t-->MAPE: 22.501\n",
      "\t\t\t\t\t-->MASE: 0.495\n",
      "\n",
      "\t\t-->Evaluation (2015-01-30 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (104928L, 16L), y_train: (104928L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -2.532\n",
      "\t\t\t\t\t-->MAE: 2.613\n",
      "\t\t\t\t\t-->MAPE: 300.093\n",
      "\t\t\t\t\t-->MASE: 2.030\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -0.618\n",
      "\t\t\t\t\t-->MAE: 1.569\n",
      "\t\t\t\t\t-->MAPE: 186.249\n",
      "\t\t\t\t\t-->MASE: 1.219\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.398\n",
      "\t\t\t\t\t-->MAE: 0.767\n",
      "\t\t\t\t\t-->MAPE: 43.927\n",
      "\t\t\t\t\t-->MASE: 0.596\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (104928L, 20L), y_train: (104928L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.49s\n",
      "\t\t\t\t\t-->R2: 0.265\n",
      "\t\t\t\t\t-->MAE: 0.825\n",
      "\t\t\t\t\t-->MAPE: 52.953\n",
      "\t\t\t\t\t-->MASE: 0.641\n",
      "\n",
      "\t\t-->Evaluation (2015-01-31 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (105024L, 16L), y_train: (105024L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -0.443\n",
      "\t\t\t\t\t-->MAE: 0.909\n",
      "\t\t\t\t\t-->MAPE: 390.828\n",
      "\t\t\t\t\t-->MASE: 0.706\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -2.446\n",
      "\t\t\t\t\t-->MAE: 1.399\n",
      "\t\t\t\t\t-->MAPE: 591.063\n",
      "\t\t\t\t\t-->MASE: 1.086\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.666\n",
      "\t\t\t\t\t-->MAE: 0.254\n",
      "\t\t\t\t\t-->MAPE: 45.898\n",
      "\t\t\t\t\t-->MASE: 0.197\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (105024L, 20L), y_train: (105024L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.72s\n",
      "\t\t\t\t\t-->R2: 0.479\n",
      "\t\t\t\t\t-->MAE: 0.432\n",
      "\t\t\t\t\t-->MAPE: 95.487\n",
      "\t\t\t\t\t-->MASE: 0.336\n",
      "\n",
      "\t\t-->Evaluation (2015-02-01 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (105120L, 16L), y_train: (105120L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -35.231\n",
      "\t\t\t\t\t-->MAE: 0.574\n",
      "\t\t\t\t\t-->MAPE: 225.395\n",
      "\t\t\t\t\t-->MASE: 0.446\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -34.807\n",
      "\t\t\t\t\t-->MAE: 0.451\n",
      "\t\t\t\t\t-->MAPE: 235.223\n",
      "\t\t\t\t\t-->MASE: 0.350\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: 0.822\n",
      "\t\t\t\t\t-->MAE: 0.074\n",
      "\t\t\t\t\t-->MAPE: 21.635\n",
      "\t\t\t\t\t-->MASE: 0.058\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (105120L, 20L), y_train: (105120L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 11.72s\n",
      "\t\t\t\t\t-->R2: 0.800\n",
      "\t\t\t\t\t-->MAE: 0.083\n",
      "\t\t\t\t\t-->MAPE: 31.479\n",
      "\t\t\t\t\t-->MASE: 0.064\n",
      "\n",
      "\t\t-->Evaluation (2015-02-02 00:00:00)\n",
      "\t\t\t-->Dataset: Baselines. X_train: (105216L, 16L), y_train: (105216L,)\n",
      "\t\t\t\t-->Computing Last value\n",
      "\t\t\t\t\t-->R2: -1.900\n",
      "\t\t\t\t\t-->MAE: 0.094\n",
      "\t\t\t\t\t-->MAPE: 13.616\n",
      "\t\t\t\t\t-->MASE: 0.073\n",
      "\t\t\t\t-->Computing Previous day\n",
      "\t\t\t\t\t-->R2: -1.400\n",
      "\t\t\t\t\t-->MAE: 0.088\n",
      "\t\t\t\t\t-->MAPE: 12.165\n",
      "\t\t\t\t\t-->MASE: 0.068\n",
      "\t\t\t\t-->Computing Previous week\n",
      "\t\t\t\t\t-->R2: -0.600\n",
      "\t\t\t\t\t-->MAE: 0.075\n",
      "\t\t\t\t\t-->MAPE: 10.776\n",
      "\t\t\t\t\t-->MASE: 0.058\n",
      "\t\t\t-->Dataset: TOP_20. X_train: (105216L, 20L), y_train: (105216L,)\n",
      "\t\t\t\t-->Training model: Random Forest, runtime: 10.58s\n",
      "\t\t\t\t\t-->R2: 0.061\n",
      "\t\t\t\t\t-->MAE: 0.053\n",
      "\t\t\t\t\t-->MAPE: 7.418\n",
      "\t\t\t\t\t-->MASE: 0.041\n",
      "                              true  last value  previous day  previous week  \\\n",
      "horizons values                                                               \n",
      "20       2014-04-17 00:00:00   0.0         1.0           1.0            0.0   \n",
      "         2014-04-17 00:15:00   0.0         0.0           0.0            1.0   \n",
      "         2014-04-17 00:30:00   0.0         0.0           0.0            0.0   \n",
      "         2014-04-17 00:45:00   1.0         0.0           0.0            0.0   \n",
      "         2014-04-17 01:00:00   0.0         0.0           1.0            1.0   \n",
      "         2014-04-17 01:15:00   0.0         1.0           0.0            0.0   \n",
      "         2014-04-17 01:30:00   0.0         0.0           0.0            0.0   \n",
      "         2014-04-17 01:45:00   1.0         0.0           0.0            0.0   \n",
      "         2014-04-17 02:00:00   0.0         1.0           0.0            1.0   \n",
      "         2014-04-17 02:15:00   0.0         0.0           1.0            0.0   \n",
      "         2014-04-17 02:30:00   0.0         0.0           0.0            0.0   \n",
      "         2014-04-17 02:45:00   1.0         0.0           0.0            0.0   \n",
      "         2014-04-17 03:00:00   0.0         0.0           1.0            1.0   \n",
      "         2014-04-17 03:15:00   0.0         1.0           0.0            0.0   \n",
      "         2014-04-17 03:30:00   1.0         0.0           0.0            0.0   \n",
      "         2014-04-17 03:45:00   0.0         0.0           0.0            1.0   \n",
      "         2014-04-17 04:00:00   0.0         0.0           1.0            0.0   \n",
      "         2014-04-17 04:15:00   0.0         1.0           0.0            0.0   \n",
      "         2014-04-17 04:30:00   1.0         0.0           0.0            0.0   \n",
      "         2014-04-17 04:45:00   0.0         0.0           0.0            1.0   \n",
      "         2014-04-17 05:00:00   0.0         0.0           0.0            0.0   \n",
      "         2014-04-17 05:15:00   0.0         1.0           1.0            0.0   \n",
      "         2014-04-17 05:30:00   1.0         0.0           0.0            0.0   \n",
      "         2014-04-17 05:45:00   0.0         2.0           0.0            1.0   \n",
      "         2014-04-17 06:00:00   0.0         4.0           1.0            0.0   \n",
      "         2014-04-17 06:15:00   1.0         4.0           0.0            0.0   \n",
      "         2014-04-17 06:30:00   0.0         3.0           0.0            0.0   \n",
      "         2014-04-17 06:45:00   0.0         4.0           0.0            1.0   \n",
      "         2014-04-17 07:00:00   0.0         2.0           0.0            0.0   \n",
      "         2014-04-17 07:15:00   1.0         4.0           1.0            0.0   \n",
      "...                            ...         ...           ...            ...   \n",
      "         2015-01-31 20:30:00   0.2         0.6           0.1            0.2   \n",
      "         2015-01-31 20:45:00   0.2         0.6           0.2            0.3   \n",
      "         2015-01-31 21:00:00   0.2         0.7           0.1            0.2   \n",
      "         2015-01-31 21:15:00   0.3         0.7           0.2            0.1   \n",
      "         2015-01-31 21:30:00   0.4         0.8           0.3            0.4   \n",
      "         2015-01-31 21:45:00   0.6         0.6           0.7            0.7   \n",
      "         2015-01-31 22:00:00   0.6         0.6           0.6            0.8   \n",
      "         2015-01-31 22:15:00   0.7         0.7           0.6            0.6   \n",
      "         2015-01-31 22:30:00   0.7         0.7           0.7            0.7   \n",
      "         2015-01-31 22:45:00   0.9         0.7           0.8            0.7   \n",
      "         2015-01-31 23:00:00   0.6         0.8           0.6            0.7   \n",
      "         2015-01-31 23:15:00   0.7         0.7           0.6            0.6   \n",
      "         2015-01-31 23:30:00   0.6         0.7           0.7            0.8   \n",
      "         2015-01-31 23:45:00   0.7         0.6           0.6            0.7   \n",
      "         2015-02-01 00:00:00   0.7         0.7           0.7            0.7   \n",
      "         2015-02-01 00:15:00   0.6         0.8           0.8            0.6   \n",
      "         2015-02-01 00:30:00   0.9         0.6           0.6            0.8   \n",
      "         2015-02-01 00:45:00   0.7         0.7           0.6            0.6   \n",
      "         2015-02-01 01:00:00   0.6         0.7           0.7            0.7   \n",
      "         2015-02-01 01:15:00   0.7         0.6           0.7            0.6   \n",
      "         2015-02-01 01:30:00   0.7         0.7           0.8            0.7   \n",
      "         2015-02-01 01:45:00   0.6         0.9           0.6            0.8   \n",
      "         2015-02-01 02:00:00   0.8         0.7           0.6            0.7   \n",
      "         2015-02-01 02:15:00   0.7         0.7           0.7            0.7   \n",
      "         2015-02-01 02:30:00   0.6         0.7           0.7            0.7   \n",
      "         2015-02-01 02:45:00   0.7         0.6           0.7            0.8   \n",
      "         2015-02-01 03:00:00   0.7         0.7           0.8            0.6   \n",
      "         2015-02-01 03:15:00   0.7         0.8           0.7            0.7   \n",
      "         2015-02-01 03:30:00   0.8         0.7           0.7            0.6   \n",
      "         2015-02-01 03:45:00   0.7         0.7           0.6            0.7   \n",
      "\n",
      "                               offline     batch  \n",
      "horizons values                                   \n",
      "20       2014-04-17 00:00:00  0.280215  0.357897  \n",
      "         2014-04-17 00:15:00  0.201563  0.180256  \n",
      "         2014-04-17 00:30:00  0.404310  0.312246  \n",
      "         2014-04-17 00:45:00  0.350508  0.307373  \n",
      "         2014-04-17 01:00:00  0.197699  0.254435  \n",
      "         2014-04-17 01:15:00  0.321310  0.308740  \n",
      "         2014-04-17 01:30:00  0.356196  0.291254  \n",
      "         2014-04-17 01:45:00  0.285574  0.269530  \n",
      "         2014-04-17 02:00:00  0.159335  0.144108  \n",
      "         2014-04-17 02:15:00  0.253990  0.285330  \n",
      "         2014-04-17 02:30:00  0.351742  0.268566  \n",
      "         2014-04-17 02:45:00  0.342077  0.254325  \n",
      "         2014-04-17 03:00:00  0.271073  0.227186  \n",
      "         2014-04-17 03:15:00  0.371491  0.304599  \n",
      "         2014-04-17 03:30:00  0.353859  0.289870  \n",
      "         2014-04-17 03:45:00  0.344252  0.205162  \n",
      "         2014-04-17 04:00:00  0.277180  0.316347  \n",
      "         2014-04-17 04:15:00  0.239722  0.329048  \n",
      "         2014-04-17 04:30:00  0.294803  0.320636  \n",
      "         2014-04-17 04:45:00  0.263893  0.222654  \n",
      "         2014-04-17 05:00:00  0.289153  0.338046  \n",
      "         2014-04-17 05:15:00  0.258992  0.376282  \n",
      "         2014-04-17 05:30:00  0.258206  0.373622  \n",
      "         2014-04-17 05:45:00  0.281765  0.188309  \n",
      "         2014-04-17 06:00:00  0.280823  0.316515  \n",
      "         2014-04-17 06:15:00  0.304068  0.255847  \n",
      "         2014-04-17 06:30:00  0.310158  0.264881  \n",
      "         2014-04-17 06:45:00  0.299605  0.308858  \n",
      "         2014-04-17 07:00:00  0.396525  0.315505  \n",
      "         2014-04-17 07:15:00  0.215133  0.344605  \n",
      "...                                ...       ...  \n",
      "         2015-01-31 20:30:00  0.414942  0.285642  \n",
      "         2015-01-31 20:45:00  0.421994  0.299950  \n",
      "         2015-01-31 21:00:00  0.415211  0.283901  \n",
      "         2015-01-31 21:15:00  0.444918  0.300383  \n",
      "         2015-01-31 21:30:00  0.503053  0.495879  \n",
      "         2015-01-31 21:45:00  0.485403  0.643100  \n",
      "         2015-01-31 22:00:00  0.494346  0.762871  \n",
      "         2015-01-31 22:15:00  0.549143  0.755287  \n",
      "         2015-01-31 22:30:00  0.520117  0.804496  \n",
      "         2015-01-31 22:45:00  0.679034  0.726952  \n",
      "         2015-01-31 23:00:00  0.688352  0.711071  \n",
      "         2015-01-31 23:15:00  0.715448  0.732582  \n",
      "         2015-01-31 23:30:00  0.738167  0.738876  \n",
      "         2015-01-31 23:45:00  0.724446  0.715928  \n",
      "         2015-02-01 00:00:00  0.737425  0.683694  \n",
      "         2015-02-01 00:15:00  0.730818  0.679707  \n",
      "         2015-02-01 00:30:00  0.741589  0.704215  \n",
      "         2015-02-01 00:45:00  0.733725  0.696235  \n",
      "         2015-02-01 01:00:00  0.755112  0.686427  \n",
      "         2015-02-01 01:15:00  0.705003  0.689346  \n",
      "         2015-02-01 01:30:00  0.710562  0.694651  \n",
      "         2015-02-01 01:45:00  0.688100  0.700684  \n",
      "         2015-02-01 02:00:00  0.694696  0.691812  \n",
      "         2015-02-01 02:15:00  0.695053  0.698565  \n",
      "         2015-02-01 02:30:00  0.705312  0.688886  \n",
      "         2015-02-01 02:45:00  0.702865  0.704483  \n",
      "         2015-02-01 03:00:00  0.639371  0.692192  \n",
      "         2015-02-01 03:15:00  0.625313  0.682858  \n",
      "         2015-02-01 03:30:00  0.627194  0.694418  \n",
      "         2015-02-01 03:45:00  0.627194  0.690414  \n",
      "\n",
      "[27856 rows x 6 columns]\n",
      "Finished.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python27\\lib\\site-packages\\ipykernel\\__main__.py:91: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "validation_df = pd.DataFrame(index=pd.MultiIndex.from_arrays([[],[],[],[]], names=['horizon', 'testing_day','dataset', 'model']),\n",
    "                             columns=pd.MultiIndex.from_arrays([[],[]], names=['sensor', 'error metric'])) \n",
    "\n",
    "#results_df = pd.DataFrame(index=pd.MultiIndex.from_arrays([[],[]], names=['horizon', 'ts']))\n",
    "results_df = pd.DataFrame()\n",
    "\n",
    "output_file_name = \"results_id11010024.csv\"\n",
    "output_file_path = os.path.join(os.path.join(os.getcwd(), \"data\"), output_file_name)\n",
    "\n",
    "#horizons = [2, 10]\n",
    "horizons = [20]\n",
    "#horizons = range(1,25)\n",
    "\n",
    "\n",
    "def start_prediction_valuation(verbose=False):\n",
    "    \n",
    "    arr = []\n",
    "\n",
    "    #sensor_list = list(data)[:15] # Sensor ids (30)\n",
    "    #sensor_list = ['id14061721', 'id14061674', 'id14061709']\n",
    "    #sensor_list = ['id14061721']\n",
    "    sensor_list = ['id11010024']\n",
    "\n",
    "    for sensor_name in sensor_list:\n",
    "        print \"Working with sensor: {}\".format(sensor_name)\n",
    "\n",
    "        # Create dataframe for specific sensor\n",
    "        sensor_ftrs_to_drop = [col for col in list(data) if col.startswith('id') and not col.startswith(sensor_name)]\n",
    "        sensor_data_ftrs = [col for col in list(data) if col not in sensor_ftrs_to_drop]\n",
    "        sensor_data = data[sensor_data_ftrs]\n",
    "\n",
    "        # Replace 'sensor id' column names with 'measurement'\n",
    "        sensor_data.rename(columns=lambda col: col.replace(sensor_name, 'measurement') if sensor_name in col else col, inplace=True)\n",
    "\n",
    "        # Compute localized average\n",
    "        average = sensor_data.groupby([sensor_data.index.dayofweek.values, sensor_data.index.time])['measurement'].mean()\n",
    "        sensor_data = sensor_data.join(pd.DataFrame(average).rename(columns={\"measurement\":\"measurement_avr\"}), \n",
    "                         on=[sensor_data.index.dayofweek.values, sensor_data.index.time], how = 'left')\n",
    "        \n",
    "        #for horizon in range (1,25):\n",
    "        for horizon in horizons:\n",
    "\n",
    "            # Prepare columns to shift\n",
    "            columnsToShift = list(sensor_data)\n",
    "            shiftedColumns = [\"shifted_\" + col for col in columnsToShift]\n",
    "\n",
    "            # Shift columns\n",
    "            start = time.time() # Start measuring time\n",
    "            sensor_data[shiftedColumns] = sensor_data[columnsToShift].shift(-1 * horizon, freq='H')\n",
    "            sensor_data.dropna(inplace=True)\n",
    "            runtime = time.time() - start # Calculate runtime\n",
    "            \n",
    "            if verbose: print \"\\n\\t-->Prediction horizont: {}h. Shifting runtime: {:.2f}s\".format(horizon, runtime)\n",
    "\n",
    "            #splitting_point = int(0.8 * data.shape[0]) # this is the reason for differences in baseline results (we use original_data in first which is wrong)\n",
    "            #original_splitting_time = data.iloc[[splitting_point]].index.to_pydatetime()[0] # here as well\n",
    "            splitting_point = int(0.8 * original_data.shape[0])\n",
    "            original_splitting_time = original_data.iloc[[splitting_point]].index.to_pydatetime()[0]\n",
    "            splitting_time = original_splitting_time.replace(hour=0, minute=0, second=0, microsecond=0) # setting to middnight\n",
    "            latest_time = data.iloc[[-1]].index.to_pydatetime()[0]\n",
    "            \n",
    "            \n",
    "            # Split Training and Testing set\n",
    "            training_data = sensor_data[:splitting_time]\n",
    "            testing_data = sensor_data[splitting_time:]\n",
    "            \n",
    "            # Create training vectors\n",
    "            X_train = training_data[ar_wf_dt_ftrs].values\n",
    "            y_train = training_data[[\"shifted_measurement\"]].values.ravel()\n",
    "            #print \"Using dummy features:\", X_train.shape\n",
    "\n",
    "            # Create testing vectors\n",
    "            X_test = testing_data[ar_wf_dt_ftrs].values\n",
    "            y_test = testing_data[[\"shifted_measurement\"]].values.ravel()\n",
    "            \n",
    "            # Initializing model\n",
    "            mdl = RandomForestRegressor(n_estimators = 100, min_samples_leaf = 10, n_jobs=16)\n",
    "            mdl.fit(X_train,y_train)\n",
    "            pred = mdl.predict(X_test)\n",
    "            \n",
    "\n",
    "            # Prepare results df\n",
    "            results_df = testing_data[[\"shifted_measurement\"]]\n",
    "            results_df.rename(columns={\"shifted_measurement\":\"true\"}, inplace=True)\n",
    "\n",
    "            results_df['last value'] = testing_data[[\"measurement\"]]\n",
    "            results_df['previous day'] = testing_data[[\"shifted_measurement_1d\"]]\n",
    "            results_df['previous week'] = testing_data[[\"shifted_measurement_1w\"]]           \n",
    "            \n",
    "            results_df['offline'] = mdl.predict(X_test)\n",
    "            results_df['batch'] = np.nan\n",
    "            \n",
    "            arr.append(results_df)\n",
    "            \n",
    "            while (splitting_time < latest_time):\n",
    "                                \n",
    "                eval_start_time = splitting_time\n",
    "                splitting_time = splitting_time + timedelta(days=1)\n",
    "                eval_end_time = splitting_time\n",
    "                    \n",
    "                # Split Training and Testing set\n",
    "                training_data = sensor_data[:eval_start_time - timedelta(minutes=15)]\n",
    "                testing_data = sensor_data[eval_start_time:eval_end_time]\n",
    "                \n",
    "                # init temoporary batch datafreame\n",
    "                batch_df_temp = pd.DataFrame(index = testing_data.index)\n",
    "\n",
    "                # Create dummy feature vector for linear regression\n",
    "                sensor_data_dummy = sensor_data.join(pd.get_dummies(sensor_data['shifted_HourOfDay'], \n",
    "                        prefix='dummy_shifted_HourOfDay').join(pd.get_dummies(sensor_data['shifted_DayOfWeek'], \n",
    "                        prefix='dummy_shifted_DayOfWeek').join(pd.get_dummies(sensor_data['shifted_Month'], \n",
    "                        prefix='dummy_shifted_Month'))))\n",
    "                #sensor_data_dummy.dropna(inplace=True)\n",
    "                dummy_ftrs = [col for col in list(sensor_data_dummy.columns) if col.startswith('dummy')]\n",
    "                training_data_dummy = sensor_data_dummy[:eval_start_time - timedelta(minutes=15)]\n",
    "                testing_data_dummy = sensor_data_dummy[eval_start_time:eval_start_time]\n",
    "                \n",
    "                if verbose: print \"\\n\\t\\t-->{} ({})\".format('Evaluation', splitting_time)\n",
    "                \n",
    "                for data_set in data_sets:              \n",
    "                \n",
    "                    # Create training vectors\n",
    "                    X_train = training_data[data_set['ftrs']].values\n",
    "                    y_train = training_data[[\"shifted_measurement\"]].values.ravel()\n",
    "                    #print \"Using dummy features:\", X_train.shape\n",
    "\n",
    "                    # Create testing vectors\n",
    "                    X_test = testing_data[data_set['ftrs']].values\n",
    "                    y_test = testing_data[[\"shifted_measurement\"]].values.ravel()\n",
    "                    #print \"Using dummy features:\", X_test.shape\n",
    "\n",
    "                    if verbose: print \"\\t\\t\\t-->Dataset: {}. X_train: {}, y_train: {}\".format(data_set['name'], \n",
    "                                                                                              X_train.shape, y_train.shape)\n",
    "\n",
    "                    if data_set['name'] == \"Baselines\":\n",
    "                        for baseline in baselines:\n",
    "\n",
    "                            if verbose: print \"\\t\\t\\t\\t-->Computing {}\".format(baseline['name'])\n",
    "                            bl = eval(baseline['init'])\n",
    "\n",
    "                            for metric in error_metrics:\n",
    "                                if metric['short'] == 'MASE':\n",
    "                                    error = metric['function'](y_train, training_data[[\"measurement\"]].values.ravel(),\n",
    "                                                               y_test, testing_data[bl.target])\n",
    "                                else:\n",
    "                                    error = metric['function'](y_test, testing_data[bl.target])\n",
    "\n",
    "                                validation_df.loc[(horizon, splitting_time, baseline['name'], data_set['name']), (sensor_name, metric['short'])] = error\n",
    "\n",
    "                                if verbose: print \"\\t\\t\\t\\t\\t-->{}: {:.3f}\".format(metric['short'], error)\n",
    "\n",
    "                    else:    \n",
    "                        for model in models: \n",
    "                            # Initializing model\n",
    "                            mdl = eval(model['init'])\n",
    "\n",
    "                            # Model leraning\n",
    "                            start = time.time() # Start measuring time\n",
    "                            if model['name'] == \"Ridge Regression\":\n",
    "                                mdl.fit(X_train_dummy, y_train_dummy)\n",
    "                            else:\n",
    "                                mdl.fit(X_train,y_train)\n",
    "                                \n",
    "                            # predictions\n",
    "                            pred = mdl.predict(X_test)\n",
    "                            batch_df_temp['batch'] = pred\n",
    "                                \n",
    "                            runtime = time.time() - start # Calculate runtime\n",
    "                            \n",
    "                            results_df.update(batch_df_temp)\n",
    "\n",
    "                            if verbose: print \"\\t\\t\\t\\t-->Training model: {}, runtime: {:.2f}s\".format(model['name'], runtime)\n",
    "\n",
    "                            for metric in error_metrics:\n",
    "                                #print \"\\t-->Error metric: {}\".format(metric['name'])\n",
    "\n",
    "                                if model['name'] == \"Ridge Regression\":\n",
    "                                    if metric['short'] == 'MASE':\n",
    "                                        error = metric['function'](y_train_dummy, training_data[[\"measurement\"]].values.ravel(),\n",
    "                                                                   y_test_dummy, mdl.predict(X_test_dummy))\n",
    "                                    else:\n",
    "                                        error = metric['function'](y_test_dummy, mdl.predict(X_test_dummy))\n",
    "\n",
    "                                else: \n",
    "                                    if metric['short'] == 'MASE':\n",
    "                                        error = metric['function'](y_train, training_data[[\"measurement\"]].values.ravel(), \n",
    "                                                                   y_test, mdl.predict(X_test))\n",
    "                                    else:\n",
    "                                        error = metric['function'](y_test, mdl.predict(X_test))\n",
    "\n",
    "                                #validation_df.loc[(sensor_name, horizon, metric['short']),(data_set['name'], model['name'])] = error\n",
    "                                validation_df.loc[(horizon, splitting_time, data_set['name'], model['name']), (sensor_name, metric['short'])] = error\n",
    "\n",
    "                                if verbose: print \"\\t\\t\\t\\t\\t-->{}: {:.3f}\".format(metric['short'], error)\n",
    "\n",
    "            \n",
    "                #print results_df\n",
    "        \n",
    "        all_results = pd.concat(arr, keys=horizons)\n",
    "        all_results.index.set_names(['horizons', 'values'], inplace=True)\n",
    "\n",
    "        print all_results\n",
    "        \n",
    "        all_results.to_csv(output_file_path)\n",
    "        \n",
    "start_prediction_valuation(verbose=True)\n",
    "#start_prediction_valuation(verbose=False)\n",
    "#validation_df\n",
    "\n",
    "print \"Finished.\"\n",
    "\n",
    "#output_file_name = \"testni_eval_top20_id12041992.csv\"\n",
    "#output_file_path = os.path.join(os.path.join(os.getcwd(), \"data\"), output_file_name)\n",
    "\n",
    "#validation_df.to_csv(output_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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>true</th>\n",
       "      <th>last value</th>\n",
       "      <th>previous day</th>\n",
       "      <th>previous week</th>\n",
       "      <th>offline</th>\n",
       "      <th>batch</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horizons</th>\n",
       "      <th>values</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"61\" valign=\"top\">20</th>\n",
       "      <th>2014-04-17 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.261299</td>\n",
       "      <td>0.426695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 00:15:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.171855</td>\n",
       "      <td>0.199961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 00:30:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.357864</td>\n",
       "      <td>0.337836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 00:45:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.341056</td>\n",
       "      <td>0.309874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 01:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.214796</td>\n",
       "      <td>0.251093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 01:15:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.299780</td>\n",
       "      <td>0.337469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 01:30:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.356398</td>\n",
       "      <td>0.304319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 01:45:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.305233</td>\n",
       "      <td>0.289283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 02:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.173487</td>\n",
       "      <td>0.182545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 02:15:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.263771</td>\n",
       "      <td>0.405222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 02:30:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.322937</td>\n",
       "      <td>0.314287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 02:45:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.307693</td>\n",
       "      <td>0.320839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 03:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.204197</td>\n",
       "      <td>0.239636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 03:15:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.313680</td>\n",
       "      <td>0.338691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 03:30:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.409911</td>\n",
       "      <td>0.352838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 03:45:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.395950</td>\n",
       "      <td>0.182027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 04:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.278781</td>\n",
       "      <td>0.361459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 04:15:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.232276</td>\n",
       "      <td>0.343491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 04:30:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.215190</td>\n",
       "      <td>0.325883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 04:45:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.257617</td>\n",
       "      <td>0.218918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 05:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.287206</td>\n",
       "      <td>0.344268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 05:15:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.309328</td>\n",
       "      <td>0.458021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 05:30:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.269004</td>\n",
       "      <td>0.396748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 05:45:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.271081</td>\n",
       "      <td>0.181132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 06:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.298976</td>\n",
       "      <td>0.283710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 06:15:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.304839</td>\n",
       "      <td>0.307063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 06:30:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.291901</td>\n",
       "      <td>0.286413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 06:45:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.275291</td>\n",
       "      <td>0.247733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 07:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.378465</td>\n",
       "      <td>0.361172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-17 07:15:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.255152</td>\n",
       "      <td>0.369180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31 20:30:00</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.440341</td>\n",
       "      <td>0.278283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31 20:45:00</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.501625</td>\n",
       "      <td>0.261489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31 21:00:00</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.491656</td>\n",
       "      <td>0.239120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31 21:15:00</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.498405</td>\n",
       "      <td>0.264454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31 21:30:00</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.614397</td>\n",
       "      <td>0.415354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31 21:45:00</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.511284</td>\n",
       "      <td>0.636935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31 22:00:00</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.507907</td>\n",
       "      <td>0.796163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31 22:15:00</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.500895</td>\n",
       "      <td>0.781900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31 22:30:00</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.530755</td>\n",
       "      <td>0.958974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31 22:45:00</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.729484</td>\n",
       "      <td>0.744920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31 23:00:00</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.699075</td>\n",
       "      <td>0.734326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31 23:15:00</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.740982</td>\n",
       "      <td>0.735246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31 23:30:00</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.779830</td>\n",
       "      <td>0.731716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31 23:45:00</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.727319</td>\n",
       "      <td>0.738611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01 00:00:00</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.724544</td>\n",
       "      <td>0.685366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01 00:15:00</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.735238</td>\n",
       "      <td>0.674612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01 00:30:00</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.749984</td>\n",
       "      <td>0.699658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01 00:45:00</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.748121</td>\n",
       "      <td>0.692393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01 01:00:00</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.754601</td>\n",
       "      <td>0.684444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01 01:15:00</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.747488</td>\n",
       "      <td>0.681218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01 01:30:00</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.741493</td>\n",
       "      <td>0.693480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01 01:45:00</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.737610</td>\n",
       "      <td>0.696552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01 02:00:00</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.727951</td>\n",
       "      <td>0.683484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01 02:15:00</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.724144</td>\n",
       "      <td>0.694432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01 02:30:00</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.731040</td>\n",
       "      <td>0.688251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01 02:45:00</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.724503</td>\n",
       "      <td>0.698981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01 03:00:00</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.659234</td>\n",
       "      <td>0.691054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01 03:15:00</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.641959</td>\n",
       "      <td>0.683764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01 03:30:00</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.644261</td>\n",
       "      <td>0.691510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01 03:45:00</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.644261</td>\n",
       "      <td>0.683903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27856 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              true  last value  previous day  previous week  \\\n",
       "horizons values                                                               \n",
       "20       2014-04-17 00:00:00   0.0         1.0           1.0            0.0   \n",
       "         2014-04-17 00:15:00   0.0         0.0           0.0            1.0   \n",
       "         2014-04-17 00:30:00   0.0         0.0           0.0            0.0   \n",
       "         2014-04-17 00:45:00   1.0         0.0           0.0            0.0   \n",
       "         2014-04-17 01:00:00   0.0         0.0           1.0            1.0   \n",
       "         2014-04-17 01:15:00   0.0         1.0           0.0            0.0   \n",
       "         2014-04-17 01:30:00   0.0         0.0           0.0            0.0   \n",
       "         2014-04-17 01:45:00   1.0         0.0           0.0            0.0   \n",
       "         2014-04-17 02:00:00   0.0         1.0           0.0            1.0   \n",
       "         2014-04-17 02:15:00   0.0         0.0           1.0            0.0   \n",
       "         2014-04-17 02:30:00   0.0         0.0           0.0            0.0   \n",
       "         2014-04-17 02:45:00   1.0         0.0           0.0            0.0   \n",
       "         2014-04-17 03:00:00   0.0         0.0           1.0            1.0   \n",
       "         2014-04-17 03:15:00   0.0         1.0           0.0            0.0   \n",
       "         2014-04-17 03:30:00   1.0         0.0           0.0            0.0   \n",
       "         2014-04-17 03:45:00   0.0         0.0           0.0            1.0   \n",
       "         2014-04-17 04:00:00   0.0         0.0           1.0            0.0   \n",
       "         2014-04-17 04:15:00   0.0         1.0           0.0            0.0   \n",
       "         2014-04-17 04:30:00   1.0         0.0           0.0            0.0   \n",
       "         2014-04-17 04:45:00   0.0         0.0           0.0            1.0   \n",
       "         2014-04-17 05:00:00   0.0         0.0           0.0            0.0   \n",
       "         2014-04-17 05:15:00   0.0         1.0           1.0            0.0   \n",
       "         2014-04-17 05:30:00   1.0         0.0           0.0            0.0   \n",
       "         2014-04-17 05:45:00   0.0         2.0           0.0            1.0   \n",
       "         2014-04-17 06:00:00   0.0         4.0           1.0            0.0   \n",
       "         2014-04-17 06:15:00   1.0         4.0           0.0            0.0   \n",
       "         2014-04-17 06:30:00   0.0         3.0           0.0            0.0   \n",
       "         2014-04-17 06:45:00   0.0         4.0           0.0            1.0   \n",
       "         2014-04-17 07:00:00   0.0         2.0           0.0            0.0   \n",
       "         2014-04-17 07:15:00   1.0         4.0           1.0            0.0   \n",
       "...                            ...         ...           ...            ...   \n",
       "         2015-01-31 20:30:00   0.2         0.6           0.1            0.2   \n",
       "         2015-01-31 20:45:00   0.2         0.6           0.2            0.3   \n",
       "         2015-01-31 21:00:00   0.2         0.7           0.1            0.2   \n",
       "         2015-01-31 21:15:00   0.3         0.7           0.2            0.1   \n",
       "         2015-01-31 21:30:00   0.4         0.8           0.3            0.4   \n",
       "         2015-01-31 21:45:00   0.6         0.6           0.7            0.7   \n",
       "         2015-01-31 22:00:00   0.6         0.6           0.6            0.8   \n",
       "         2015-01-31 22:15:00   0.7         0.7           0.6            0.6   \n",
       "         2015-01-31 22:30:00   0.7         0.7           0.7            0.7   \n",
       "         2015-01-31 22:45:00   0.9         0.7           0.8            0.7   \n",
       "         2015-01-31 23:00:00   0.6         0.8           0.6            0.7   \n",
       "         2015-01-31 23:15:00   0.7         0.7           0.6            0.6   \n",
       "         2015-01-31 23:30:00   0.6         0.7           0.7            0.8   \n",
       "         2015-01-31 23:45:00   0.7         0.6           0.6            0.7   \n",
       "         2015-02-01 00:00:00   0.7         0.7           0.7            0.7   \n",
       "         2015-02-01 00:15:00   0.6         0.8           0.8            0.6   \n",
       "         2015-02-01 00:30:00   0.9         0.6           0.6            0.8   \n",
       "         2015-02-01 00:45:00   0.7         0.7           0.6            0.6   \n",
       "         2015-02-01 01:00:00   0.6         0.7           0.7            0.7   \n",
       "         2015-02-01 01:15:00   0.7         0.6           0.7            0.6   \n",
       "         2015-02-01 01:30:00   0.7         0.7           0.8            0.7   \n",
       "         2015-02-01 01:45:00   0.6         0.9           0.6            0.8   \n",
       "         2015-02-01 02:00:00   0.8         0.7           0.6            0.7   \n",
       "         2015-02-01 02:15:00   0.7         0.7           0.7            0.7   \n",
       "         2015-02-01 02:30:00   0.6         0.7           0.7            0.7   \n",
       "         2015-02-01 02:45:00   0.7         0.6           0.7            0.8   \n",
       "         2015-02-01 03:00:00   0.7         0.7           0.8            0.6   \n",
       "         2015-02-01 03:15:00   0.7         0.8           0.7            0.7   \n",
       "         2015-02-01 03:30:00   0.8         0.7           0.7            0.6   \n",
       "         2015-02-01 03:45:00   0.7         0.7           0.6            0.7   \n",
       "\n",
       "                               offline     batch  \n",
       "horizons values                                   \n",
       "20       2014-04-17 00:00:00  0.261299  0.426695  \n",
       "         2014-04-17 00:15:00  0.171855  0.199961  \n",
       "         2014-04-17 00:30:00  0.357864  0.337836  \n",
       "         2014-04-17 00:45:00  0.341056  0.309874  \n",
       "         2014-04-17 01:00:00  0.214796  0.251093  \n",
       "         2014-04-17 01:15:00  0.299780  0.337469  \n",
       "         2014-04-17 01:30:00  0.356398  0.304319  \n",
       "         2014-04-17 01:45:00  0.305233  0.289283  \n",
       "         2014-04-17 02:00:00  0.173487  0.182545  \n",
       "         2014-04-17 02:15:00  0.263771  0.405222  \n",
       "         2014-04-17 02:30:00  0.322937  0.314287  \n",
       "         2014-04-17 02:45:00  0.307693  0.320839  \n",
       "         2014-04-17 03:00:00  0.204197  0.239636  \n",
       "         2014-04-17 03:15:00  0.313680  0.338691  \n",
       "         2014-04-17 03:30:00  0.409911  0.352838  \n",
       "         2014-04-17 03:45:00  0.395950  0.182027  \n",
       "         2014-04-17 04:00:00  0.278781  0.361459  \n",
       "         2014-04-17 04:15:00  0.232276  0.343491  \n",
       "         2014-04-17 04:30:00  0.215190  0.325883  \n",
       "         2014-04-17 04:45:00  0.257617  0.218918  \n",
       "         2014-04-17 05:00:00  0.287206  0.344268  \n",
       "         2014-04-17 05:15:00  0.309328  0.458021  \n",
       "         2014-04-17 05:30:00  0.269004  0.396748  \n",
       "         2014-04-17 05:45:00  0.271081  0.181132  \n",
       "         2014-04-17 06:00:00  0.298976  0.283710  \n",
       "         2014-04-17 06:15:00  0.304839  0.307063  \n",
       "         2014-04-17 06:30:00  0.291901  0.286413  \n",
       "         2014-04-17 06:45:00  0.275291  0.247733  \n",
       "         2014-04-17 07:00:00  0.378465  0.361172  \n",
       "         2014-04-17 07:15:00  0.255152  0.369180  \n",
       "...                                ...       ...  \n",
       "         2015-01-31 20:30:00  0.440341  0.278283  \n",
       "         2015-01-31 20:45:00  0.501625  0.261489  \n",
       "         2015-01-31 21:00:00  0.491656  0.239120  \n",
       "         2015-01-31 21:15:00  0.498405  0.264454  \n",
       "         2015-01-31 21:30:00  0.614397  0.415354  \n",
       "         2015-01-31 21:45:00  0.511284  0.636935  \n",
       "         2015-01-31 22:00:00  0.507907  0.796163  \n",
       "         2015-01-31 22:15:00  0.500895  0.781900  \n",
       "         2015-01-31 22:30:00  0.530755  0.958974  \n",
       "         2015-01-31 22:45:00  0.729484  0.744920  \n",
       "         2015-01-31 23:00:00  0.699075  0.734326  \n",
       "         2015-01-31 23:15:00  0.740982  0.735246  \n",
       "         2015-01-31 23:30:00  0.779830  0.731716  \n",
       "         2015-01-31 23:45:00  0.727319  0.738611  \n",
       "         2015-02-01 00:00:00  0.724544  0.685366  \n",
       "         2015-02-01 00:15:00  0.735238  0.674612  \n",
       "         2015-02-01 00:30:00  0.749984  0.699658  \n",
       "         2015-02-01 00:45:00  0.748121  0.692393  \n",
       "         2015-02-01 01:00:00  0.754601  0.684444  \n",
       "         2015-02-01 01:15:00  0.747488  0.681218  \n",
       "         2015-02-01 01:30:00  0.741493  0.693480  \n",
       "         2015-02-01 01:45:00  0.737610  0.696552  \n",
       "         2015-02-01 02:00:00  0.727951  0.683484  \n",
       "         2015-02-01 02:15:00  0.724144  0.694432  \n",
       "         2015-02-01 02:30:00  0.731040  0.688251  \n",
       "         2015-02-01 02:45:00  0.724503  0.698981  \n",
       "         2015-02-01 03:00:00  0.659234  0.691054  \n",
       "         2015-02-01 03:15:00  0.641959  0.683764  \n",
       "         2015-02-01 03:30:00  0.644261  0.691510  \n",
       "         2015-02-01 03:45:00  0.644261  0.683903  \n",
       "\n",
       "[27856 rows x 6 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read from csv\n",
    "input_file_path = os.path.join(os.path.join(os.getcwd(), \"data\"), output_file_name)\n",
    "test =  pd.read_csv(input_file_path, index_col=[0,1])\n",
    "\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b44c748>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4gAAAEPCAYAAAAAmuqHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhQKA/weEokBi12ublgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe360668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4gAAAEPCAYAAAAAmuqHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12188d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.loc[20]['2014-04-17 00:00:00':'2014-04-20 00:00:00'][['true','offline','batch']].plot(figsize=(15,4))\n",
    "test.loc[20]['2015-01-27 00:00:00':'2015-02-01 23:59:00'][['true','offline','batch']].plot(figsize=(15,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python27\\lib\\site-packages\\ipykernel\\__main__.py:1: FutureWarning: to_datetime is deprecated. Use pd.to_datetime(...)\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "<type 'tuple'> is not convertible to datetime",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-3e6c165fb994>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdayofyear\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Python27\\lib\\site-packages\\pandas\\core\\indexes\\base.pyc\u001b[0m in \u001b[0;36mto_datetime\u001b[1;34m(self, dayfirst)\u001b[0m\n\u001b[0;32m   1024\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mDatetimeIndex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparsed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1025\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1026\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mDatetimeIndex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1027\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1028\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_assert_can_do_setop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Python27\\lib\\site-packages\\pandas\\util\\_decorators.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     90\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 91\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     92\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     93\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_deprecate_kwarg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Python27\\lib\\site-packages\\pandas\\core\\indexes\\datetimes.pyc\u001b[0m in \u001b[0;36m__new__\u001b[1;34m(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, closed, ambiguous, dtype, **kwargs)\u001b[0m\n\u001b[0;32m    333\u001b[0m                 is_integer_dtype(data)):\n\u001b[0;32m    334\u001b[0m             data = tools.to_datetime(data, dayfirst=dayfirst,\n\u001b[1;32m--> 335\u001b[1;33m                                      yearfirst=yearfirst)\n\u001b[0m\u001b[0;32m    336\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    337\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0missubclass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime64\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mis_datetimetz\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Python27\\lib\\site-packages\\pandas\\core\\tools\\datetimes.pyc\u001b[0m in \u001b[0;36mto_datetime\u001b[1;34m(arg, errors, dayfirst, yearfirst, utc, box, format, exact, unit, infer_datetime_format, origin)\u001b[0m\n\u001b[0;32m    514\u001b[0m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_convert_listlike\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    515\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 516\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_convert_listlike\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    517\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    518\u001b[0m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_convert_listlike\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Python27\\lib\\site-packages\\pandas\\core\\tools\\datetimes.pyc\u001b[0m in \u001b[0;36m_convert_listlike\u001b[1;34m(arg, box, format, name, tz)\u001b[0m\n\u001b[0;32m    433\u001b[0m                     \u001b[0mdayfirst\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdayfirst\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m                     \u001b[0myearfirst\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myearfirst\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 435\u001b[1;33m                     \u001b[0mrequire_iso8601\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequire_iso8601\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    436\u001b[0m                 )\n\u001b[0;32m    437\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime (pandas\\_libs\\tslib.c:46617)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime (pandas\\_libs\\tslib.c:46321)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime (pandas\\_libs\\tslib.c:45268)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: <type 'tuple'> is not convertible to datetime"
     ]
    }
   ],
   "source": [
    "a=test.loc[20].groupby(test.index.to_datetime().dayofyear, sort=False).mean()\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python27\\lib\\site-packages\\ipykernel\\__main__.py:14: FutureWarning: to_datetime is deprecated. Use pd.to_datetime(...)\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>r2_batch</th>\n",
       "      <th>r2_lv</th>\n",
       "      <th>r2_offline</th>\n",
       "      <th>r2_pd</th>\n",
       "      <th>r2_pw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.650629</td>\n",
       "      <td>-0.466792</td>\n",
       "      <td>0.631788</td>\n",
       "      <td>0.410490</td>\n",
       "      <td>-0.029031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.687785</td>\n",
       "      <td>-0.337743</td>\n",
       "      <td>0.684113</td>\n",
       "      <td>0.809421</td>\n",
       "      <td>-0.893095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.701322</td>\n",
       "      <td>-0.736158</td>\n",
       "      <td>0.687807</td>\n",
       "      <td>0.706376</td>\n",
       "      <td>-1.157494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.624968</td>\n",
       "      <td>-1.965073</td>\n",
       "      <td>0.515317</td>\n",
       "      <td>-0.118795</td>\n",
       "      <td>0.481865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.678785</td>\n",
       "      <td>-0.601358</td>\n",
       "      <td>0.578641</td>\n",
       "      <td>-1.546461</td>\n",
       "      <td>0.868344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.468132</td>\n",
       "      <td>-29.816674</td>\n",
       "      <td>0.582218</td>\n",
       "      <td>-32.056645</td>\n",
       "      <td>0.693515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.521862</td>\n",
       "      <td>-1.057234</td>\n",
       "      <td>0.746526</td>\n",
       "      <td>-1.146427</td>\n",
       "      <td>0.833297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.758160</td>\n",
       "      <td>-0.359141</td>\n",
       "      <td>0.692031</td>\n",
       "      <td>0.864793</td>\n",
       "      <td>0.399927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.909564</td>\n",
       "      <td>-0.096185</td>\n",
       "      <td>0.953991</td>\n",
       "      <td>0.629611</td>\n",
       "      <td>0.690272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.873872</td>\n",
       "      <td>-0.491005</td>\n",
       "      <td>0.875105</td>\n",
       "      <td>0.927543</td>\n",
       "      <td>0.734044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.354559</td>\n",
       "      <td>-3.530549</td>\n",
       "      <td>0.662975</td>\n",
       "      <td>-1.254719</td>\n",
       "      <td>0.624117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.671737</td>\n",
       "      <td>-2.123816</td>\n",
       "      <td>0.766234</td>\n",
       "      <td>-3.433615</td>\n",
       "      <td>0.364245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.700352</td>\n",
       "      <td>-13.395065</td>\n",
       "      <td>0.606281</td>\n",
       "      <td>-13.789877</td>\n",
       "      <td>0.774888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.837492</td>\n",
       "      <td>-1.152574</td>\n",
       "      <td>0.827718</td>\n",
       "      <td>-1.275944</td>\n",
       "      <td>0.689295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.880729</td>\n",
       "      <td>-0.284406</td>\n",
       "      <td>0.849612</td>\n",
       "      <td>0.851784</td>\n",
       "      <td>0.674527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.871255</td>\n",
       "      <td>-0.281267</td>\n",
       "      <td>0.856674</td>\n",
       "      <td>0.862423</td>\n",
       "      <td>0.834486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.933512</td>\n",
       "      <td>-0.175062</td>\n",
       "      <td>0.907404</td>\n",
       "      <td>0.829141</td>\n",
       "      <td>0.910675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.317719</td>\n",
       "      <td>-11.829821</td>\n",
       "      <td>0.561348</td>\n",
       "      <td>-9.509816</td>\n",
       "      <td>-0.794865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.594524</td>\n",
       "      <td>0.410395</td>\n",
       "      <td>0.269791</td>\n",
       "      <td>-0.298593</td>\n",
       "      <td>0.694110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.660273</td>\n",
       "      <td>-48.667396</td>\n",
       "      <td>0.665574</td>\n",
       "      <td>-49.895473</td>\n",
       "      <td>0.848800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.736809</td>\n",
       "      <td>-1.235391</td>\n",
       "      <td>0.774954</td>\n",
       "      <td>-1.326738</td>\n",
       "      <td>0.670142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.672874</td>\n",
       "      <td>0.111891</td>\n",
       "      <td>0.659381</td>\n",
       "      <td>0.606048</td>\n",
       "      <td>0.410655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.875290</td>\n",
       "      <td>-1.125175</td>\n",
       "      <td>0.827008</td>\n",
       "      <td>0.623866</td>\n",
       "      <td>0.746783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.775426</td>\n",
       "      <td>-0.486030</td>\n",
       "      <td>0.744487</td>\n",
       "      <td>0.540018</td>\n",
       "      <td>0.743473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.269248</td>\n",
       "      <td>-2.541302</td>\n",
       "      <td>0.444240</td>\n",
       "      <td>-0.622494</td>\n",
       "      <td>0.396854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.423520</td>\n",
       "      <td>-0.442879</td>\n",
       "      <td>0.243073</td>\n",
       "      <td>-2.446676</td>\n",
       "      <td>0.665970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.744658</td>\n",
       "      <td>-35.502552</td>\n",
       "      <td>0.690066</td>\n",
       "      <td>-35.075245</td>\n",
       "      <td>0.820260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.034068</td>\n",
       "      <td>-1.900000</td>\n",
       "      <td>-0.464647</td>\n",
       "      <td>-1.400000</td>\n",
       "      <td>-0.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    r2_batch      r2_lv  r2_offline      r2_pd     r2_pw\n",
       "5   0.650629  -0.466792    0.631788   0.410490 -0.029031\n",
       "6   0.687785  -0.337743    0.684113   0.809421 -0.893095\n",
       "7   0.701322  -0.736158    0.687807   0.706376 -1.157494\n",
       "8   0.624968  -1.965073    0.515317  -0.118795  0.481865\n",
       "9   0.678785  -0.601358    0.578641  -1.546461  0.868344\n",
       "10  0.468132 -29.816674    0.582218 -32.056645  0.693515\n",
       "11  0.521862  -1.057234    0.746526  -1.146427  0.833297\n",
       "12  0.758160  -0.359141    0.692031   0.864793  0.399927\n",
       "13  0.909564  -0.096185    0.953991   0.629611  0.690272\n",
       "14  0.873872  -0.491005    0.875105   0.927543  0.734044\n",
       "15  0.354559  -3.530549    0.662975  -1.254719  0.624117\n",
       "16  0.671737  -2.123816    0.766234  -3.433615  0.364245\n",
       "17  0.700352 -13.395065    0.606281 -13.789877  0.774888\n",
       "18  0.837492  -1.152574    0.827718  -1.275944  0.689295\n",
       "19  0.880729  -0.284406    0.849612   0.851784  0.674527\n",
       "20  0.871255  -0.281267    0.856674   0.862423  0.834486\n",
       "21  0.933512  -0.175062    0.907404   0.829141  0.910675\n",
       "22 -0.317719 -11.829821    0.561348  -9.509816 -0.794865\n",
       "23  0.594524   0.410395    0.269791  -0.298593  0.694110\n",
       "24  0.660273 -48.667396    0.665574 -49.895473  0.848800\n",
       "25  0.736809  -1.235391    0.774954  -1.326738  0.670142\n",
       "26  0.672874   0.111891    0.659381   0.606048  0.410655\n",
       "27  0.875290  -1.125175    0.827008   0.623866  0.746783\n",
       "28  0.775426  -0.486030    0.744487   0.540018  0.743473\n",
       "29  0.269248  -2.541302    0.444240  -0.622494  0.396854\n",
       "30  0.423520  -0.442879    0.243073  -2.446676  0.665970\n",
       "31  0.744658 -35.502552    0.690066 -35.075245  0.820260\n",
       "32  0.034068  -1.900000   -0.464647  -1.400000 -0.600000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "def r2( g ):\n",
    "    r2_lv = r2_score( g['true'], g['last value'] )\n",
    "    r2_pd = r2_score( g['true'], g['previous day'] )\n",
    "    r2_pw = r2_score( g['true'], g['previous week'] )\n",
    "    r2_offline = r2_score( g['true'], g['offline'] )\n",
    "    r2_batch = r2_score( g['true'], g['batch'] )\n",
    "    return pd.Series( dict(  r2_lv=r2_lv, r2_pd=r2_pd, r2_pw=r2_pw, r2_offline=r2_offline, r2_batch=r2_batch ) )\n",
    "\n",
    "#subset = test['2014-04-17 00:00:00':'2014-04-27 00:00:00']\n",
    "subset = test.loc[20]['2015-01-05 00:00:00':'2015-02-01 23:59:00']\n",
    "#subset = test['2014-04-17 00:00:00':'2015-02-01 23:59:00']\n",
    "a=subset.groupby(subset.index.to_datetime().dayofyear, sort=False).apply(r2)\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bar plot for last month. Major improvement for batch is not visible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1531ce48>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA28AAAD+CAYAAACtHT2HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqQT+P/6fsnEwY7wxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ed6ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a[['r2_offline','r2_batch']].plot( kind='bar', figsize=(15,4), ylim=(-3, 1.1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diff is not very informative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python27\\lib\\site-packages\\ipykernel\\__main__.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15bdc470>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21739940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#subset = test.loc[20]['2014-04-17 00:00:00':'2015-02-01 23:59:00']\n",
    "#a=subset.groupby(subset.index.to_datetime().dayofyear, sort=False).apply(r2)\n",
    "\n",
    "b = a[['r2_offline','r2_batch']]\n",
    "b['diff'] = b['r2_batch'] - b['r2_offline']\n",
    "b.reset_index()['diff'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatter plot for entire testing dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python27\\lib\\site-packages\\ipykernel\\__main__.py:2: FutureWarning: to_datetime is deprecated. Use pd.to_datetime(...)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b1022b0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEACAYAAACznAEdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPGg7q3BBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x190542b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset = test.loc[20]['2014-04-17 00:00:00':'2015-02-01 23:59:00']\n",
    "a=subset.groupby(subset.index.to_datetime().dayofyear, sort=False).apply(r2)\n",
    "\n",
    "b = a[['r2_offline','r2_batch']]\n",
    "#a[['r2_offline','r2_batch']].plot( kind='hist', figsize=(15,4))\n",
    "#a[['r2_offline','r2_batch']].hist(figsize=(15,4), bins=20)\n",
    "#a[['r2_offline','r2_batch']].plot.hist(bins=30)\n",
    "b[b > 0].plot.hist(bins=30, alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYFb73wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15f6e780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-deep')\n",
    "\n",
    "b = a[['r2_offline','r2_batch']]\n",
    "\n",
    "c = b[b > 0].reset_index()[['r2_offline','r2_batch']]\n",
    "\n",
    "c = c.fillna(0)\n",
    "\n",
    "x = c[\"r2_offline\"].values\n",
    "y = c[\"r2_batch\"].values\n",
    "data = np.vstack([x, y]).T\n",
    "bins = np.linspace(0, 1, 30)\n",
    "\n",
    "plt.hist(data, bins, alpha=0.7, label=['Offline', 'Batch'])\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python27\\lib\\site-packages\\ipykernel\\__main__.py:2: FutureWarning: to_datetime is deprecated. Use pd.to_datetime(...)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f4639b0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAEACAYAAAA3JQ6uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAQRnAAAAAOAAgjMAAAAAcMD/BxQU+0r28mIzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x33f076d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset = test.loc[20]['2014-04-17 00:00:00':'2015-02-01 23:59:00']\n",
    "a=subset.groupby(subset.index.to_datetime().dayofyear, sort=False).apply(r2)\n",
    "\n",
    "b = a[['r2_offline','r2_batch']]\n",
    "\n",
    "#a[['r2_offline','r2_batch']].plot( figsize=(15,4), marker='*')\n",
    "b[b > 0].reset_index()[['r2_offline','r2_batch']].plot( figsize=(15,4), marker='.', linestyle='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatter plot for entire testing dataset with regression plot trend, showing that Batch is better. Unfortunatelly the trend is downword. Maybe try with different sensor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python27\\lib\\site-packages\\ipykernel\\__main__.py:6: FutureWarning: to_datetime is deprecated. Use pd.to_datetime(...)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxMAAAESCAYAAACGmYjcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15932c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "subset = test.loc[20]['2014-04-17 00:00:00':'2015-02-01 23:59:00']\n",
    "a=subset.groupby(subset.index.to_datetime().dayofyear, sort=False).apply(r2)\n",
    "\n",
    "b = a[['r2_offline','r2_batch']]\n",
    "\n",
    "#c = b.reset_index()[['r2_offline','r2_batch']]\n",
    "c = b[b > 0].reset_index()[['r2_offline','r2_batch']]\n",
    "c = c.fillna(0)\n",
    "c['index'] = c.index\n",
    "\n",
    "plt.figure(figsize=(13,4))\n",
    "#sns.pairplot(c, size=5, aspect=.8, kind=\"reg\");\n",
    "g = sns.regplot(y=\"r2_offline\", x=\"index\", data=c, label='Offline');\n",
    "g.set(xlim=(0,300), ylim=(0,1))\n",
    "sns.regplot(y=\"r2_batch\", x=\"index\", data=c, label='Batch');\n",
    "\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Offline slope: -0.000525964532375\n",
      "Batch slope: -0.00329930970361\n"
     ]
    }
   ],
   "source": [
    "import scipy\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x=g.get_lines()[0].get_xdata(),y=g.get_lines()[0].get_ydata())\n",
    "print \"Offline slope:\", slope\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x=g.get_lines()[1].get_xdata(),y=g.get_lines()[1].get_ydata())\n",
    "print \"Batch slope:\", slope"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## MAPE analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import regression_metrics as additional_metrics\n",
    "\n",
    "def mape(g):\n",
    "    mape_lv = additional_metrics.mean_absolute_percentage_error( g['true'], g['last value'] )\n",
    "    mape_pd = additional_metrics.mean_absolute_percentage_error( g['true'], g['previous day'] )\n",
    "    mape_pw = additional_metrics.mean_absolute_percentage_error( g['true'], g['previous week'] )\n",
    "    mape_offline = additional_metrics.mean_absolute_percentage_error( g['true'], g['offline'] )\n",
    "    mape_batch = additional_metrics.mean_absolute_percentage_error( g['true'], g['batch'] )\n",
    "    return pd.Series( dict(  mape_lv=mape_lv, mape_pd=mape_pd, mape_pw=mape_pw, mape_offline=mape_offline, mape_batch=mape_batch ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python27\\lib\\site-packages\\ipykernel\\__main__.py:2: FutureWarning: to_datetime is deprecated. Use pd.to_datetime(...)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxAAAAESCAYAAABtrjPfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXuYHOV95/t5q7p7pmdGc5/RCA2yhITu4mpsC7BsI5aY\nE2MsnJh1CH7W2cByEs5ZINjniWPHm2RtJ5vHfvDGTrCctbFN4viC8CUOCwvYYIMAO4BlIYRAFkYS\nSHOf0cx0T3ddzh916aru6p6+VE9f5v3wiOmu7q5+q7ouv+/7uwnTNE0kEolEIpFIJBKJpAiUWg9A\nIpFIJBKJRCKRNA5SQEgkEolEIpFIJJKikQJCIpFIJBKJRCKRFI0UEBKJRCKRSCQSiaRopICQSCQS\niUQikUgkRSMFhEQikUgkEolEIimaSDVXfuzYMW6//XaEEJimyfHjx/mv//W/cu2113L77bdz8uRJ\nhoeHueuuu1ixYkU1hyKRSCQSiUQikUhCQCxVHwjDMNi1axff+c53uPfee+nu7uamm25i7969zMzM\ncOeddy7FMCQSiUQikUgkEkkFLFkI05NPPsmaNWtYtWoVjzzyCHv27AFgz549PPzww0s1DIlEIpFI\nJBKJRFIBSyYg/u3f/o33vOc9AIyPj9Pf3w/AwMAAExMTSzUMiUQikUgkEolEUgFVzYFwSKfTPPro\no26YkhDC93r282ySySQHDx5kYGAAVVWrNk6JRCKRSCQSiaRZ0XWd0dFRtm/fTmtra9nrWRIB8fjj\nj7Nt2zZ6e3sB6OvrY2xsjP7+fkZHR93l+Th48CA33HDDUgxVIpFIJBKJRCJpav7pn/6JN7/5zWV/\nfkkExI9+9CM3fAngiiuuYN++fdx8883cf//97N69u+DnBwYGAGtjh4aGqjrWYhidSjAxk+Tb/+cl\n3/Jb3n8+A93xgp99+dTrfHH/P/mW/fHOGzh36KzQx7kU/OiJYzx7+DQAF21eyW9ftq7GIyqfetuW\nehlPpeMYn5/kH//9m75lf3jxB+lr6ynq86NTCYBFzy2JRCKRSCSFOXXqFDfccINrW5dL1QVEIpHg\nySef5C//8i/dZTfddBO33XYb9913H6tXr+auu+4quA4nbGloaIjh4WEARmbHABjs6K/SyPNjD4Hx\n+RZ+eugIAG/fupELt5+76Gcj8R66n9tEovUEAPHkMFvWbWOor71q460m/+X6YU6NzwE07DYAnBqf\n4/Drr9DWaR1Ph1/XuTbeU7NtqqfxVPobDzPMu7QTPHPieQDeMnwB52/cUfznh0v+SolEIpFIJAWo\nNCWg6gIiHo/z1FNP+ZZ1d3dzzz33lL3OfYce4IlXnwXgsrUXcd3WqysZYtlEzjpKLPVL+7EKbFz0\nM0N97Vy17kqf8GhkwxsaWzhUk1qK3LCp9De+buvVXL7mEqD0/dFM+1EikUgkkmZgSUKYwmRkdowH\nDz3FbCINwIPzT3H5mkuWxLjwzsKOzI7xzInniUasQlbPnHi+6HF84MqN7LpwtbsuSe0Z6mtn546z\n2P+r1wHYueOsvL9NMbPx+w494JtxL1XkljKeRqGcc7TS/RgmzeBpq1fkvpVIJJLGouEExOjUvCse\nAGYTaUan5hnsqO73fvvhIz5j7p1vK5z4vRjyRll/FCPsso+DD1yZ63VyxGVaM4DSxKXDqfE5dl24\nelkLTWc/OhS7HxczRsvxaBTzu0vKQ+5biUQiaTwaTkD0xfuIJ4d9OQR98b6qfuep8Tn3Bgew/1ev\ns+vC1bxl+ALf7OhSh1i8cOI1ALYNr1nS721mChnq+Y6DoM9MziRdodsRj5Y0hmY1qJZilnmxfVeO\nR6OU311SGnLfSiQSSWPScAIiO4fgok0rUVoSwNLfcCqJ666Uv/jBvRyZPgTAxq6tfPK9v7+k3++l\n3mLUax0OYSzE0SdXgi1y9cmVGAtxKMJLVq8GVaW/cTmiaLCjvySRvti+K9ej0Yw454hDrY+vMKj1\neS9pbuTxJZH4aTgBAZlQk4eOPcKhiYd56cnqxkcXikevhfHxwonXXPEAcGT6EC+ceK0mnoh6ilGH\n6s7el5KXsGJ2K/GE9XtE9CrH11WZSn/jSkRRNUV6WjOKCn9stnwU5xyZmEkCgt7Olpp5usLYtyOz\nY/zoiWMcODTnrqNZvHaS+qCRvcLeyR8pgsJB7keLhhQQAEpLgkMTL7jPqz2bWE+Jz5PJKUxhIEyl\npuOotxldx1DV1FmgOrP3xRwHlRhF9Was1sNvXOx3LbbvvB6NyZkk+uRK/vGlX7NzR3JRg6Cezv9K\ncM6RtGYwl9AAWNEWramnq5J961TkOzU+R7xjmBWzW+vGaydpDurVK1wM3smfePJspo5aNbFLFUH1\nFmVQS4oVk6dPn+Yv/uIvOHr0KKZp8s53vpOPfvSjRCIR7rjjDo4ePcp1113H29/+du644w4UReHz\nn/88733ve3nuuecYGRnhU5/6FJ///OeXcvNKomEFRC2ohwuGc0GIxDRSmoGqt7Kxa6vMg7A503HI\nlx8Dl5T0+WIulFbIHBQKm6vEKKoXY/XU+BzjifmK17OUomixfXfd1qvZ1LmNu/cdcD1DxRoE9XD+\nNyvl7NtscZtoPUE8sabhPX4SSRh4z4+0ZnB8+hC9ajcRvaMkEVRvUQa1pBQxeeutt3LDDTfwvve9\nD9M0+fjHP87nPvc5PvzhD3Pw4EEeeughAPbu3cu73/1ubrnlFgCEEAAMDg7WtXiABhYQpcZHNwPe\nC8JZ3X0kUil+d/MeLt9wXk3GU2+/gdKSQO05DbZ9r/acLik/ppgLZSkX00oMzjCM1UrcrN5Zlu71\nZ5NoPQ6U/xsvpShabP198b5la2R6xVx7PAIIohGl5p6uSom3REgsWB6VRt8WSX1Rb17hpaQePND5\nCDOMaGRinkRKY3hwBaoiKl7f/v37aW1t5X3vex9giYI//dM/Zffu3Tz66KOMjIywZ88edu/ezb/8\ny7+gKAr79+/na1/7GqZpAnDy5EluueUWfvjDH3L//ffz6KOPkkgkOH78OFdeeSUf+chHAHjiiSf4\nu7/7O1KpFGvWrOEzn/kM8Xi84m0ohoYVEFB5fHSjx7HFYzE2Dp1V0zGEFaMelou0p7OVjrYYgNuj\no9jvX+xCuZQX01okLTtkz7JMHR3mD393FwPdbRVta61DscDan8vZIAC/mHNoxO0f7Ognnjyb43Y+\n2Nq2jfw//9e7GnJbJPVNvXiFS8E7wReNKGzs2srUmDVx0ujXvDBzUh5+5jf86IljAJx7dg8379lB\nRA22HYq9d7zyyits27bNt6yjo4PVq1fz13/91/zJn/wJ999/v/tae3s7H/7wh4GMByKbw4cP873v\nfY9oNMq73/1uPvShDxGLxfiHf/gH7rnnHlpbW/nyl7/MV77yFf74j/+49B1RBg0tIKA2BlatqLcZ\nf4dKxxCWi7Re90+p1DJpOR998T4GO2p/wylH9Aftz1IMgmaM/21k48Hh1PgcU0eH6VW7AZgfW55e\nJcnS0IjnTPYEX6nXz6B7qrEQ59TCXM32R5j3t7Rm8G9Pvuo+f/n4JC8em2DHhvzX+krEpGmaroeh\nVHbu3El7u/V9GzZs4OTJk8zMzPDKK6/wwQ9+ENM00TSNCy64oKz1l0PDC4hyaOSkqFqWjq0GYc/q\nl7t/ihEfSyFQ6sFlXK8z9OWI/kL7s9HjfxvdgxoWyzUcTSIpBu+9o5wKZ5evucS9p/7kqQn+5sGf\nA7WZeB2ZHQslL68QeRwAPhbbjxs2bODBBx/0LZudneWNN94gErHM7uxS2osRi8Xcx4qioOs6pmly\n2WWX8dnPfrakdYXFshQQjU4zCIdqUk3x0QgCLgwBUG8u+1JEf1geg3oQc/loRA9q2NSr0JVImoHs\nyZNLV+6q6cSrdzzd6/0VpcodQzSi8L53rOd7j1mVkrad08/WdZU3Jt65cyef/exn+f73v8+1116L\nruv8zd/8Dddddx2tra1MzS7wN1+3hFjitXHeel5m/KV4KM4//3z+6q/+itdee401a9aQSCQ4ffo0\na9eurXgbimFZCgh546kf6i3syPnuQkZoNccX1v4IQwA04jkR5DGop+MrDBrZgxo29SZ0JZJmIGjy\nZFPntgKfWNrxJFqP84e/u4u+eF/F5/2uC4c5b8MAC2mdge44SghJ1ABf/OIX+eQnP8nf//3fY5om\nu3bt4vbbb+eFl14lkdTc950YmWHLXMp9ni8HIoje3l4+85nPcMcdd5BKpRBCcNttt0kBUW3kjad+\ncEprAnVRjrbWYStheTma6bguRvTn8xgstj/zhQLVm7iVBNNMx7kkXJoxf6lWDHS31dXEq1XUI5zv\n717REsp6vKxcuZK77747Z/nQqrN4+wf+3H1+7puv4foPZsrNP/vsswCsXr2aH/7whwDs2bOHPXv2\nuO/xrvetb30r3/3ud0MffzEsWwEB8sZTL/jDMRZv6lVN6iVspdzva+YbZiWi37s/vIJhsVAgr/gA\na//Wet+G1b0ZmvM4kUig9hNBjUy+yZMPXNlfk4nXZprMaaYImGUtICS1R4ZjFKYUQ2853DALHRfF\n3GS8gmH7+n4OHh1zX8t37A129Fdl31ZixFfavbnZjxPJ8qZeJoIamXye21rdmxsh/7BYmiUCRgoI\nicRDPc10lGLo1esNc6lnugvdZLLF6nMvjQD+fiHjiXGU2UTV+n843o8nTz9esREfRvfmejlOJJJ6\nZTlXO6u360K9jacSmuF4kgKiBOrF7d9MF7R6dOfVQ4PCMAy9sflJoHbHa61muovd3mhEYfv6fp5/\n9VUA+tdO8Y1D+4HFx1vOvnW8H5o6S+qsn9PT2QpII14iCZuwJoJktTOJJD9SQBSJ1xjaOngu797w\nzprc8MO+oNWDGKlHd16pv62zHx9/7mRNbjjZN8z2WBtff95KrKpFmEo9znQHidXIWUeJRX6JZmi8\nbiTooStnvGHs22zvx2wiTWtLhIiqlNQxvVLqycMmkVSTMCaCZHitpBD1YD/VEikgisBrDE0mpnno\nlZ/yy1MvctmaNy+pYRb2Ba2eZlca+QR09mNaMzgzn6a306roUMnvU46h59wwx+YnXQMXamu8pzUD\nYEmN5EJ4xarSkuBzT95njU1XmEsm6Ii1E1VyL4th7VtNnQXAnB5i1DyFELCxa+uS/jbNFEsskRRC\nHt+SalFP9lOtqI+7epGMz0+6YUS1IK1rzKUT7vNnTjxf0/FUQpAYKbUzoiR3P84l0q7RXCnXbb2a\nOy69iTsuvalooTrY0U9/W08o3z8yO1b28T3Y0U88eTanxuc4NT5HPHl23dzMh/rac0RdVI3QHo27\nz/N1Ii933w71tdO9/gQTPU8y3v0kAH1Tl9I7eSlTR4eX/NxzPCsSiSQYx2PpUA/htZL6YDH7acuW\nLezZs4drr72W6667jueffz5oNS5nzpzhn//5nxf93htvvJEXXnih/IGHTEN5ID775F7OOuusJQ/J\ncGaDn/jNLwBoj8UDZymrTT3mC0gyRCMK7fHMcRHG71OOkRdGmEql+QunxueYOjpMr9oNwNRYB6fG\n5+rqeM3eT7917jsWnZkvd9+OzI6RaD3OUKQdTTcYE6dg8hwiekdIWyORSMKmHsNrJfVPPB7n/vvv\nB+BnP/sZn/3sZ/nGN76R9/3T09N885vf5Pd+7/eWaoih0FACIpFOkja0moRkOG7/B195jBdGjgC1\niR8O64ImxUg4ZO/H377snLq44VQSphJm/kK9G8jZ+8mdRSow7Er2bTRi5Tx0xKNg5WHLc08iqWPk\nudn8jM6Nk9QWWL1iCEVZPDBnMfvJNE338ZkzZ+jqsnLr5ufn+aM/+iNmZmbQNI3bbruNK664gs99\n7nMcP36cPXv2cOmll/KRj3yEvXv38sMf/hBVVdm1axd33HEHAA888AD/7b/9N86cOcOnPvUpLr74\n4jB3RUlUXUCcOXOGP/uzP+Pll19GURQ+/elPs3btWm6//XZOnjzJ8PAwd911FytWrKj2UCpmsKOf\nGy94f82rMYV1QZOzK+FQr/ux3hKW62nfeHH2UykxraXu2xxvx9a3cem7dgH1dcxIJBLJcuLHv36S\nB195DID1vW/iwxddT0RRF/1cofv+wsICe/bsIZlMMjY2xte+9jUAWltb+eIXv0h7ezuTk5Ncf/31\nXHHFFfzJn/wJL7/8suu1ePzxx/nxj3/MfffdRywWY2Zmxl23rut85zvf4bHHHuMLX/gCX/3qV0PZ\nD+VQdQHxqU99ine84x38z//5P9E0jUQiwd13383OnTu56aab2Lt3L1/60pe48847F11Xq9oChsJb\n1mRm/muRBd9MscPSeAmHZtqPYVXqqVdhFcRSVFyRycv1y3KvpiKRLEc0XeOho4+7z49O/IaXxo6y\nbbC4hOh814vW1lZXDDz//PN89KMf5V//9V8xDIPPfe5z/PznP0dRFEZGRhgfH8/5/P79+7nuuuuI\nxWIAdHZ2uq9dddVVAGzfvp3XX38957NBGIaBruuktTSarjMzO7P4h4qgqgJidnaWX/ziF/z1X/+1\n9WWRCCtWrOCRRx7h3nvvBWDPnj3ceOONRQmIltffQmq2Fy22HrbKLHhJfVJrD1UYhGXshm2QBe3b\nRtrfYYyxkba3EZD3EYlE4iAQoa7vggsuYHJykomJCR577DEmJyf53ve+h6IoXHHFFSwsLJS0PkVR\nmE/MM5eYJ5VKMT41gWEamJgYhoFhGvZz0E0d09oohBAoqoKqqiRFKpRtq6qAOHHiBD09Pfzpn/4p\nhw8fZvv27XzsYx9jfHyc/n7r5jcwMMDExERR61P1NiJ6B/t/9Tpb1vXKGs01RhoyudSqeVo1qLff\nNWjfhrW/GyXkqpmOr3pA1vqXNBrSWxYeETXCezbt5l9fegTTNNkycC6bB9ZXvF5vDsTRo0cxDIOe\nnh7OnDlDb28vpmnyxBNP8Prrr3NmbpbW1hbOnDnD2NQ4hmmwdcdW7vnq17j40jcTiUWZnpmmY8UK\nkukFRmbH6Un2M2vMY2CwoKazvl1BsQusqlUOMqrq2jVN49ChQ/z5n/85O3bs4NOf/jR79+5FCL/C\ny34uqX+kIZNLPTZPaxaC9u3m/g2h7u96D7lqluOrHg0gpz9HvSf9S5Y30lsWPpetuYTtg5tY0NP0\nt/WgiOK6G2SHBemGjmmaGKbBwsIC77nmPVg6wuTOj32UE2Ovc8HbLmLf9+/nt695Dxu3bGTN2jXM\nmQlWdHex7YLt3HjD7/OWS9/Kf/l//2+OHH2ZW/7wFmLRKG+97G385z+6CVVViMYiRCIRhBA1t52r\nKiCGhoYYGhpix44dgBW79eUvf5m+vj7Gxsbo7+9ndHSU3t7ekta7c8dZ7FjfX9KMoZwtD49mMWQk\nkmyca4i8XlSHejOAnP4cR6YPAVZTv6G+d9Z0TBJJENJbVj1WxDpo03WSyaQlBnTNCgEydDc8yHps\nWqFC9jKhKG5YkBACJ/rp4ad/HPg9/asG+Pt77g587c/+6hO+5x/8Tzfwwf90g2/Z5+7+vPu4q7uL\nf/7+t8rf6BCoqoDo7+9n1apVHDt2jHXr1vHUU0+xYcMGNmzYwL59+7j55pu5//772b17d1Hru+X9\n57NqVUYoFDtjKGfLJUtBWMnHklyC9u3WwXOrsr/r9XpRb8dXqZ6EejSAvP05ABKR44zMjsnztoZI\n8S4pF69XQLe9ArqdF2Daxr8rCgSu1wCBXwwothLwOCMEKovXZlpeVL0K08c//nHuvPNONE3j7LPP\n5jOf+Qy6rnPbbbdx3333sXr1au66666i1jXQHc+52Sx285Gz5eFTb4ZMPVHrSjv1GB4SFkH71rsM\nqNj4q/frxXVbr2ZT5zYAtg2vqdk46s2TUCnRSHFhC5LqErZ4b6brYaPkaYVBkBAw7PAgJ0zI/Yfz\n3MxJFhZCkGv1L12OQLNT9b23efNm7rvvvpzl99xzT7W/WlJFam0o1zO12h/NZtQFEbRvBzv669Zr\nEDb+3zhZk9+4XE9CkAHkrK9WhpCcDKkfwhbvzXg9rPc8rWzyCQFvvoATErSoEMgJ9xeuV0B6BmpD\nQ8mvHz1xjP9y/XBJn5E3iOoh92OGWrvd6zE8ZKkoZHiUOgNZz9eLZviNvQbQ48+d5G++/nOgtgae\nnAxpPprhXMnHUm6Drus+EWAYHkPfyHgDTEHGO+CEDAnyCwGfw08KgUaloQTEs4dPlzVbJW8Qkmqy\nXGa/G41yZyDrJUyoXnE8CT89dASAt2/dWNI1eaivve4MPHlfqD31LN4bEceY13UdTdfc5GDAnfE3\nvWFBAc+9AkBRrH9AgAgAgWIvljJgudBQAqIS5IVIUg3qJWa+lvGxQbP8Sxl7HGR4GAvxsg3UeggT\nCqKeYqAjZx0llvql/VgF6mMfSRqbMBtY1su5Ui6GYc3ya5rmigDdNKzXXCPfCvkxPUa/8xpgG/oC\nRclKDs5B2P9JASApnoYSEBdtXtlwFwGJZKmoRXxs0Cx/LWKPsw0PR8A4pDWD0anEovul3mbGs6mH\nGGhHNDuJx6WK5pHZMZQWGt7Ak1SHsCZfanmuuKE8huHG/zuhPbqnIpATEuQ89oYEYZcJFR4BkL9J\nsjX7L8OAJEtJQwmI375sXa2HIJH4qDe3+1LeKIOM7Vp2iPfud+8M5MRMEhB85QcHyxI0o1MJd531\nQL2Moxyyw/3+vwt3AY29TZL6pdzjyjRN1+jXNI20rmGamdl+70y/WxUoKwTImfl3BICiKHkEgLCz\nAGQlMElj0VACQiKpBpUmQMscm/rkA1duZMu6Xr6074A7W76YoMkOfehoi/GVHxwEmqeSS6WUK5rr\nJdxP0vw4sf/OP03XChj9zsy/FRLkeAeEEKAIVFW1hEBgJSDwJgFLJMuJhhIQJ09PYKqtKEKgKgJF\nESjC+quqCtFIhGg04p7wEslihJUAvRyNoKA441I7xFebge54yTX+ndCH0amEKx7ALz6aqb58OUjR\nLKkW2cm/zl830dee6feKACc8CFg8+TcHGf4jkZRDQwmISLQVJdIKgA7oJmACBphpE11fwNDn7QQi\n0xYXuEJDVRUUgf1XEItGiMWimfJikmWFnBGtnKA44w9cuZFtm63ztNZVjMpNpiz0nmasL18sXm9d\nqedJvYX7SaqDE/uv6zqapqE5nX+d8B/PTL+z3JsXYJomQhFMJKYQisJgZ/+iyb+qTP2VSJachhIQ\nhRBCEIlEIBK8SSageQWHaaLPO4LDQGBaQiNLbCiKIKKqRCIqLbbYkN4NiSRDtrHt8+rM1L6sbbnJ\nlPkan9VzknU1CcNbJz0X9U+h2v85M/8By7KTfwuH/1iz/5Es8/+hVx7nwKkXAThvaAtXbdhV7c2W\nSCQl0jQColS8gmN0ch6AgZ64+7rr4dBhQQc9kcbQkxiGjrBnSLwhVNZjS3CoiiAajRKzw6lUVc6N\n1CNyRjR86tWrU66Bny0+sqs7LRfC/F1rfSwsB9zYfzsB2C376fnrFQBuY7Cs5l+Lhf84tf8hvPCf\n8blJVzwAHDj1Ihev2kFfe09I3yCRSMKgoQTExJlxou0xBAJFKNZfrPJmkUjUn+xUJA88eYznXhoB\n4MJNg1x9aXClp0JCwNYZruBAB2NBQ9OSmIaBN5xqcmESRQgG2/tc4aEqAlVRiEYjRKPRkrdBUj6l\nzIgu97j35Yr3926G+vKSxsMRBOl0mrSetsKATMP9p3seG6adA1BEArBs/iWRVI+xSauCX79ncrqZ\naCgBYURAj3jikLATrnQDI6Vj2qFJCiCEArbQUBXhEx1CWMJjYibJMwePoygqilB47qUR3rxlJQM9\nbRWPVVEUYrGYb9n/efURDoxaSZnnDWznP6zd7WyG7TJOYBizYAbkb/jCqzKCIxKJyByOCilmRnQ5\nx72XwnLw6tRDL4alZjn8rktBUHlQw9BdQaCbeo44MMEVBJFIJLAcqEBpmjyAvvYezhva4gthkt6H\nYJrdQG1kHnjy1azJ6bW1HVAVaCgBEYQQYtEwoYzkMHzLEyKJGUugmQamYYIGp6feQKPNTs2ykq0V\nIRBCQUEQUSJEItGSDffxxIQrHgAOjB7kopUX0hfvBfC4iqOB49fAFRvo9o1oNmm1pjcMK6RKkMnf\ncD0bws3fcMa8nKi0RCvUf3OxemM5eHUabbxhIPMXLBwB4DQIcyoEmWSSgQ1PUrA3QRhwuwO73oGc\nBOFmkgPlcdWGXVy8ageAFA95WA4GaqMyNplwfxuA514a4ZItK5tO6DW8gCiViWlLsfd2xenrbmP7\nhkEOHRsHYOu6PgZXdvreb0UlObFJsGCm0VOzmAkdw8AWGEompMr2bihCQRUqaiRCNBLFMPzipVKc\nHI5IQNK4G0ll4Fao0s7MY+g6AhNVFbmeDVdsRIjZno1GTxYPq0SrpHSkV6c5aXTh4FQIMgwDTdfQ\ndN2ahMEq/6kb1nU+X3MwwzR8DcJUVc1TIchKJghKEJYUhxQO+VkuBqqkvllWAuLxZ0/4xMKui4bZ\nddEw29f3AZaoWIzFqj1ZNrv1f9NMo6d1zKSOYcKaltUcnjiCQLC5bzP6gs54agwhFFRbcESjsdA9\nBUJYSd1E/d4Nn2fDCaVKZnI3nMpUQuBLGBci4+1wnkcial0JjzCTPpdb3HsYXpvFWCqvzlJsi2Rp\ncPoDpNNpX3UgtzxoQH+AwApBdoOwyZkUiqIw0OsJWfVduoT9n8wRkEgkxdPfE+fCTYM+D1G2uBuf\nmwQaWygvGwExMZ1wxQPAoWPjbF/fR29XvCjhUA7ZYuMdG97B+cnzAehu7QYy3g0NHcNYQE+ewdQN\nMO0Cd0IhoqiAQHVCqZxwKjWCoqih5kEE5W44uH4YJyYMZ4G3SpW/JK4AX5WqTHncTOM/RVHqPrRq\nucS9N5PXppm2BRo33Csf3sRgb6dgtzeAofuWGaaJECBUxV9OO293YJG3QpAM/5A0MsUYqJLacvWl\na7lky0ogN0elWcoULxsB4WBErBAmRavNyWYstFgPWnNfc/MgctMgAG8olWW1G0bS8nDMW/G1wrDX\nIZwQqsyVtpiOAAAgAElEQVRjBYVoNEY0Gq2KsV5slaq0Zi8zrcZ/ppGww7ss48DxbAhheT4iqmUk\nOJ6QiKq4wiwaUfN+bzWSPpvFcMuH47VJa5Y7qpolWKvt1anXcrLl8u2Hj/DTQ0cAePvWjXUV7uV4\nBrx5AU5nYG+XYCdB2PkrFOH2CnDPYZ8YqE6TMBn+IWkGChmokvog6HdppjLFy0pAtJ51kpH0awCs\njq+lt+v8Jf3+oBCqSihGcOi24DBNE21hFuYtl34mZ8MRGXaVKpGZt3NK5KpqpCphVUH5G9lozgM7\nxGpBdwyWFLquu2VyhcDK5/D05bh8cCdbOjehCMHKjj5mzswSUVWidn+Oegi1qjcmZ5LMJtIAdMTz\nHFghsVy8OpVyanyOh449TKLnBAAPHXuNXePVSeJ3Gog5TcQ0XcNwkoGdjsKY6EYmMdgwDYQt7L3N\nw1wx4J5mCgoyHEgiCYtmEg7NUFHKNE3fX9znmdc1Xfcst/4upBZIKkkM5312eKYb6YGZWYf9OdOw\nwjFNz3rct9tPTPu73fHYz0cnZkLZ3oYSEN9+4DU6e+aJRhWiEYWY/TcaUYhGhfs4e/nBV8b49chp\n5ntOE4u10N6mkoydZio55YYSVZtCIVRLQSYPIvc1J1Fctz0bU8kpALpauqwSuWkDdEt4WGVwvaLD\nThpXVCusKhIlalepqlZp2cUqb3k9Hp0tgwDMpfJ7PdTs/A5PRSthvxaJqERUxd2uZuzVYSzE0SdX\nQqtlqOqTKzEW4tBRve+slnCot7KjlYQfjSfGSdi/CUCi9QTjiXGGCF6XN1HYEQSaoVt5Ap4Sobon\nXChTMtTqIqzYYUK+BGEVnORgldqK70rih2X4h0RSGT5DOcBIdiYXHEPXBMYTk5iGSU9rt/WaYd1/\nTdPk4Wde41dHrXy17ev72X3JGtcadkxfwzR9DkqfsWwvME0T7Puydzz+sTvvC15Rzvs9n7Oe+wdm\nel8E9/vdL3D/OA+irF1xDkemXgFgU8+5kG5jUkt7ViHAjsTwrSRgmxdFBHxcCWdysKEExMnT84yc\nmS3z03F442LmgSl7yed/epyW6ElbaChEIyIjTtxl9nLP85h3eaCYyXxGCazQUb88cWI/L01aoRKb\nejZy2fBOy1DPc7z5k8ZN9PS8mzQuTMv4Rjh9OOy4ZGGdTONTSVSh0N/T7uZ0OA0Bw/J2ZLqMty3q\n9XDL/QbkeBhJzfV4WBdNwzonFafMrz/0SggnnAx3mWoLFFXNiBFfp9c6YMXsVuKJNQBE9CoqhyWg\nXsqOllJtygkH8nYRjooUMQxmk9YNpr01gmnOcXLsdesGLXA7CQNuJ2E83gDh3IwC+gfUMkm4HCEQ\nRvywDP+QLDXFzE57/xqm3evKcJ7b77PPc2eG2rRzg7zrAvwz04ZlWOfMVGfNUluGf+YN7hhMYRnO\npsdg9hq3XiPZXu7c6wGeeuOZjF3Ru4nLhnfifGhiOsGvjs2AYuVeHjw2w9b1qfImV0XA48AGisWt\nolq8fc3l7BjcDrBkk9jVoKEERGdHFKUlQlozSKeNosRXIXTdZF7P5BRUg4iaER+arpLSdISAFW1R\n/vfjpzOekywBEouILEGStdyzTA1JpEwlp9yTHOClySNs69/iO8C9ZXCzWaxClYMJPBYQzmUYSfSE\njqk7YVYCYQKeUrlgXZgUxREjTkdyax+oiopQFCJqhIee/g3PHxlFCIWLNq/M22W8GEox9AOFiPOa\naWIYVgUZJ/zKuQG4QsQVJcInQBTbAHReUxWBoqpWvw9Px1nXWCyRZqw2VYlwcIx558aq63YIj2Gi\nGZYb2pnBR4jMjJs9u2YKk5HJeX787y/adyWTHz87xca1Kn3drfbN2PMZ61stwe35TTs6V/CO7Rfz\n7MkXALho9TZ6u63+MfmShBuBcoRAmPHDUjhUH+fcsZ+4f7zGstdQNmxr1jTNQAPZMLwzzJkQj+ww\nDkH+GWrrliIwnBAQM78lUWi2OtsYt8bm/z7fRzxGt/W99l9Mz+x0xvj2PHWv50JkvU8ETS/n25gC\nr3lW411bGNeVqeQUL0297I45yK5YjjTD9jeUgPjPv7OewaEhwIklM0mnDUtQaNbjlGa4AsNZ/uKx\nCd4Ym8M0oWuFSn93G8KMkk4bjMyOcyaZwNAVYiJOlFb7c9Y6jEInXRFouhXzllhwRIqtulMaE1Ph\nxKGpivB5Q7xiJBYkUOz3xrKWp8wkyek2FNVAKAZCNUguGGhRg4iqhJbDUSicq1BOh4MTcoWnI7mD\nYVghV+Ojc/z8lVdAWLGD+1+YYHiVTm9nHKFYogPTtMOxbKPbvqBbAsUKyVKEgqKqdsUrpeJqV8U0\nPnS20d26ACECmVwQwzBcYxbHpSwynh7HI2JtmzOOjIcEW6iYpskVFw5y3jkrEMBgbzszZ2at7VYU\nu0GhUjCEq54qBTnGv1vzX9PsOH7TH86D9dwyZkwM25sGYDqTa0pGmBWa0Xexf14zAmrcLzyVmIra\nUtqlt9kaa1UqBDTdOu+dIgv1TnbIhxlkiAbMPLuzw1mzzvYnPQauyBjrrmGaG/vsHK/OrDa2IUvQ\ne8GddQ7aFut1e3yFwjqE3zoNMpa9hrLXSC7JQC4G76oUe5yLrD7fy9nLG1HE1wO9XXG2ruvz2Ral\neB+ckOt6NMjreWxh0FACwosQwg0TWoyLt/UGzpxPJafY9/JPfJHE1517rftjW6UETVecOKIiI1JM\nn1hJuY+zlnuXZYmbdNpAr1Cl6IaJnjJJpsJoVrfJ9+zvnzwBnLAMTDtnQAgYOT3Orw7PEW+JBIiR\noFCwjJBJLqRJp2yjVrH+arphzzRVdrNwPAXRaAw14lci0dYY0Xj+ErWm75HjujUxNANjQWcqMYVp\nmnS3dLtCwxEgzk3bMdLd1/HfBK1H9gwaJgoZg3x8OoEiVAb7Ogp6ErJnzBxRU+q+c9t/eAWKCStW\nrMA0TWYXTAxDc6vqaJqGYdhhXFgx88K0588EPPLMbzhwdBQEnLd+gP/w1jfZU4H2rJ9zqmbFi9pm\nkp1jg+/O7MwYmqbheoBM28duba8tltxfz3QNJQGoioIpTLfcsfB4kVw7xbtvvY+9+yXrHA3SDkGC\nYkWbwvZ13Tz/shVvf8G5g6xoU1hYWPB9ThRhJHVGOzBMI/NZM/dYcHIWXG+d57go59QqMDnrfneh\n4847c+x6cQyTufkEuu73/E6dmUWkCpthAhVlto+RxKsADMfXQkplIpV/Mqboq6vHVg4OK8mEcbgf\nyTKmfbPQPlvfmX32H3QlzTxnz0IXOmbybbSzvIJQj6UI8ZA0D92t3Wzq2egLjc42qEvtx+UQFHJd\nL9Tz2MKi6gLiiiuuoKOjA0WxYr6/+93vMj09ze23387JkycZHh7mrrvuYsWKFVUdRznxdEIIIqog\nokK8pXrzC7ruEReuSMkVLZqWuzztETOpgGXOY02vTKQY9lSN16A4PbYALOT5xGL4b0NfuPcoQpCV\nCC+yPCdWUnwsZ5n13uw8lOGBLo69Po0iYNPaHla0t5QsUhyPgS+Gs8DFIFeIFMYxQB77+Wv2DIzJ\n5jf1cvn5q/HM67ljCbLoLNNGZAxwijMY/avyG0KZsFaBEKbjwrAM0kjGkHH25eRMkhdHp4l2WgLt\nxZFpdiSS9nlXnsmRLfoXk8fZ7zdNkzRYB68BaJlcHS+VCNdC4Q/W63DxliHWre6yxtYZZ2wq+Jwx\nFzlevCLDO1xn7BljN7M+gSPCMlPDxW5r0HgWEzr5tsENN7Q9XzGli3N7NvnOqY5YH+lFTpmJ6QRn\njq9mRcQK4TozFmdkUzr8YhR5jOoc0Zj1XM5CSyS5XDa8k239W4D8s/GlnsPFhFzXinoeW5hUXUAI\nIfjGN75BV1eXu2zv3r3s3LmTm266ib179/KlL32JO++8s9pDyaEYZbwUqKplpLZWUaQYhpnxethC\n4xcnf8mrUycwDIVV8dWs7zyXQ78e5/VRK9yrtyvOqr529zNvjM4xM5fCNKElGqElFsl4ZNKVixTT\nhFTa8tjMJULacPsWf/r0FI8/bbkTs5PiXWESmIuioLPAoalJFKUPoRo8O3qatsQb9LStCEygj0Ts\n5PFiRicEE9MJDr82hWKHNR05McMFm4eWrEJXPVJquFzQ+7PjhR1qMYM62FfdCZJGpBijIh+16uMj\nkUjKo9mMZ8kSCAgnBtnLI488wr333gvAnj17uPHGG4sSEKOJUcy5rL4FZPcxUDKlRe3E20IzbpXc\nxBoJRRG0xFRa7AieqeQUp8RhWu2Q42lm6Ol/EzOH5uiwbZ20meBtF5ztM2QLJVEbponm9ZBoTgiX\n6fOiZMK6/F6SVDorvCvLo+KEglWKtW4dkqUkz/sN2PsPjgKjed8dzUmCz1/hSzcMzsz4Q7pefvUM\nPV3pguWK66nCV6VxrF5KLXlc6xLJy51C14TFKPWaG+Zx1uzxyRJJI1Hq+VgvE8BB1PPYwmRJPBB/\n8Ad/gKIo/Mf/+B/53d/9XcbHx+nvt6qjDAwMMDExUdS6vnt4H7HT5d0snG7MwlPRJ7uZmu81N77d\nL0i87/E3YvO85vseYT93knUznaH9z73jyAiiwDEGjMX5fPZns7fZ+fxioRf5KHSzVoQgZocYVQtv\n8rwb1mULi4mpBGndpDUWcZel0hnRkhEpQSFj/pyWSmWKK1KKrvDlFwM/euzUop9wK3xlCYtMTkpA\nJa+AUK+wKnyVG8cqaVzCbo5ZDGEcZ8shPlkiaRTKPR/reQK4nscWFlUXEN/85jcZHBxkYmKCP/iD\nP2DdunU5HoGlaMhlVVsxSsioW16oQmXfq/+CttrKyQBBTI3wrV8/7Rck+YRTtjjzCTBbLAV6iwLE\nWj7PkiuSsgRSTHDwtXF+88YZBIJ1q7q5aPOQZ9xR3+eddUyfSaGg0NfV5q7TifF28lK8CfATczNo\nmkmLEnfFSSm5KKm0X7SEX+ErfHIqfBXMOxEeMTKXJUZErvfFXofTH8Oh1FnmMGelJcVTLc9PMR6N\nSr5jucQnSyTVIGzPXaXnYz2ft/U8tjCouoAYHLQ6Aff29nLllVdy4MAB+vr6GBsbo7+/n9HRUXp7\ne4ta12WrL6NzoMvumurpnmqXYnQfO/XZsbqtOrWmve8zvF1Y8XzGeS1rfaYtQIyAdbnrb2B1opt6\npiqKnYqxwAIL5eZI14IB688BDQ4cLH81foFkixuCw+SEUFBiCkqLV/hYjyNCIZrPg4UCCDAFpun8\nBdMQGAaYhoJpYj3WBbphPTZ0Yf8F3f0nrL+aJXw0zVqmpU2sVhNO9SLFqkvq/CPz2AxYhinQTYGe\nEiFV+ApGcZLns4SFItqIqII3Tunse+h4Ts+UmKfi10DXCt66tZVoRKGvK87Y5IJP+ETU8npjLEYl\n4TuSXGrh0ZBIgpAhbrlIz53ES1UFRCKRwDAM2tvbmZ+f52c/+xm33norV1xxBfv27ePmm2/m/vvv\nZ/fu3UWt77zB7W4fiHpjYjqBaZr0dLWim3qmprwrNrJED1kCqNBrPtHjFTOe585jssSR/d7jp2cY\nn50FTHo62hjsi/vfY7/P9AkzM8/3ZGrn63nElHdbGg0T0xJUZvVm9stGtf8t0itDLP6W0nBKMzki\nxBE/hlMByHkNV7Bk3uuIF3I+74gXA8GCaf3LFjjmgoBkrrhx3mMGiiLFLnNqCTWBQFUUIopqVVZT\nVCKq808hqipEIypR1f4XVYmpKtFohJj9WktUJRaJEIupxCIqB14e49cnpxECtq7r5Z1vHl4Sb2q9\n4Hh+Dh4/AcD2s4crElJLlcuyXOKTJeUjDeVcquW5k+dj41JVATE2Nsatt96KEAJd17nmmmu4/PLL\n2b59O7fddhv33Xcfq1ev5q677qrmMEqinBnFep81m5hOcODwA+jxWQCmJ9t536Z3Lcmsqdtl1ydU\n/KInI16KFFnkCp/Dx8Y5MTEOAlb1dHHOcJdHTHm+D5P5ZIoXXx3HanNt/Vs33EksquR8d36BlfFy\nWY91z7JsL1cTeKsEWDVA9dzF1KayUTmYQNr+tygGhasUR8BcA5iC/SmF/U9YAkbYIsZuSZgpYeqE\n6nnylVRvOJ6ioAoFVVGtxn32v4jzV7WWK4rierQWzaNaJHcrfx5Voc9nvGxG/69RecXatgEd01zd\nECJqOcQnS8pDhrgtPfJ8bEyqKiDOPvtsvv/97+cs7+7u5p577qnmV5dFOUKgESrAnEnNkI6PuM/T\n8RHOpGbopfpjdJtkCVCrWCV9Ormfycg0AMMDQ1yy6uKC728/k/Vbb15a0ecVEl6PVT5v1dRMAgOD\nFe1R19OkG4t5q/xeJN93eprB+QVW/jC903MjzNgNuzqi7XS3dgeM2dPpOU9YYU64oGeMjYbVG8wk\nKFm+uE4gnjc23ub7+NnJUX528gkgU7TCJ3ACcqGy86C04TSJpA4I2ltjPPT6EZQ3FilckaeoRHZe\nVb6CFCfOnFykQEZueKLI9z5PwYzsdTWCsJJI8lFtT4EUDo1Hw3aiDptGEALFYpqm1f3X/tfRptIa\nU0ksWPOu8ZYobS2C1ELS0yHVnkX2dEj1dur13fyEVXHJfep57OvMauLr3JrTwCvoPc4HvevPepDd\nzGp6YYbD4y/hNBk+PP4SmzrX09XSmdsJ1h7D27b1s+nsDgB6OuOk3GQPX5tZZwN94zCDXhPC7YDs\njlEI/350Gq/Zf1VhCapI1mno20+myWPPneDwsQkQomYeLqtr+/d9F/mr1l4Z+kXfG3ZXSoieX5Tk\niqZgr5ZpheB5xI+mG/Y/Hc3wPDd0NMPAMAx0XUc3DaZmkyRTGiYm0ahCi8eDle2xsvxwjufJtESH\n8w/Tbtbn/LOVhDCshn6iwbxVVFi0osX6M23C9HSow6opTlf6QpX+ihI9AdX1AotW5PNMBVTsK7Yc\neqDnK0BIZeeNZX+3sz5nn9QTMqQmP9JTIPHSUAIigk5EaO7zfJedyQXrrtPTYjWvM7PeK7IMQgTE\nIgamkXIjNcBa1qLqOTdBb+faoZ4Y29Z28sLRMQC2re9nqCcGWLPKrqHtNSLdcVTWBde34qztEwgU\nVUEAfV0refv683jujRcBuPisbWxdsyazH+rsAu7sk7FJK5ysv6ewiGudg9hxv3djaKCLvvaewHU7\nrF6ZJRYC9sdi+ybT/dd0RYXbCdjMhG6ZholhKybLK2AGG1deAQeMTyc4/OsRsN9/6OgbXLSxh54V\nlpXliDDDMDEM0/q86RmX/bozFnujEEKxu00LTLBv+n6BUwscYVVNb1WYlBryaJomuu6t1GX6KnO5\nPVDs0sJW5S6dtKbbjzVSmm59XtesEsW6TkrT0Q3d7jqvW8dXkDjJK16MwPeIAp8r+HrB73aEkWH3\nPTERiul6c4TnPe5nMDDtdWSEWGPhDeWUZCgkkoKq8OWUK89TqrySEL2IorKh+xwEgni0ledHfhno\nXVqs2mAhkdWo3iopHCQODSUgero76OvpLPieh155nAOnLEP5vKEtXLVhV1Hr7u5awVt3vInnXrJC\nfS7cNMj6sweK+uzv/IdtvPPNxRm7teLdm97FJcMXAAQa1sUyPjdZ0jqKFQFehBA88OSrvt/i6kvX\n5n1/X3sP5w1t8f3u+cZXijgodqxhrSuIRFoQjbX6lnWt6KC3zOPMFRKm6RMWXpFjGnaeimEAAsM0\nWdm2gm195/Li+BFMYHPPufS1dqDrKYQQGIazHtvzhbNf7JukIhBKfd8Yy6FUD6UQgkhEEIko0Lr4\n+8vFX4Y4T7+TbNHie09meU65Ylv46JXWIa6YfOLFMtAdYRKNQCQqiESw972JGrGfq6CqAjUCqmqi\nRkBRQFVBVUBRTRTVWmb9s9apKPhEj+NtCqzWl88rFvC+QA9ZUN6Yu67ccMdGwxFUdVm0okZ4hcTi\nHqfSQ/wCPUbleqvy5FTlDwMswsPmvtf/vma7fzQ6DSUgFmN8btI1IgEOnHqRi1ftKNrYvfrStVyy\nZSVQuhCoV+HgpRLhAKWLs1JEgJexyYT7OYDnXhrhki0rC+7jqzbs4uJVO4DKt7NWBImz/p44F24a\n9O3HSo4118tQBu/tvpLL5i7OGWM+DMMSJbqho2kGuqGDaaAbJqZhmTqGK2IsLwqms9wvRHBvcgJF\nURBK9ZoVNgOqKlBVldaW6nlyHJGS+WdmdZP3C45sMZPdbT4VIGY0vZBB7FTeItCb56SWLFA4F75S\nBOT0O8nX1LElX1PHWOYzsYAGkJGI8IWN5sMpWpGdA+UP5bPC9az3FQ7xCyxakacQhjenKjeHyi9+\ncoRPVv5WdgXAfCJrsRLrzr9Gw8mNa0A9WFVKLrEe4BVa1AsVkLtVdOGJIsRU/u/xjqUIkeUZd61o\nKgERBo0gBJYKr/cgSJyd07GB7paewH1WjgjIR4pZjo2P0N/zpoLvK1c4lOMlCZuHXnmcX75xCNMw\n2T64iSvPudz1FFxxwRDnr+sGIRjsbkNLpN1QOCEETmAHdj6J+9i0Qpr8eSC4IUp2tJMdTiJ8/5Q8\nBnop+1hRFFCs5PlYrPx94wgRN/9AtxPITStsywB3ezOCxDKOHA+KFaFmV0kK0SOynGvFL4VIMQwT\nb1hXjlcl3/IsgZLKauqY7X2pBBNIpa3vJxHOdgcRze4UH1FsAZLV6DGoyaNneTSiEotGAxs9KiV0\nnm8kComhjCCx/k6dSfC/9x9zQ+bA5F2XDNPRFs0tdR4kZjwiKzsvy/e6m9NVubfK+W4npyu76mGz\neKvqusR6DQkM+SsQUrcwMRfK9zaVgCgllCVsSg3tqXeyvQdvOb/L9/rM7ALffOglVL29JO9CMXhn\n3cejh9HbRvnWwed5+jcbuHX3e933OYZi9j/XeLYNSMfQVlAwMW1Fb/FvT77Ks4et7bxo0yDvufwc\n98Lqu7zaM+Oqrfi93beFEFbkRKnGqP3+0TNj/PK1FxCAoggOj7zM1ee+k6HOQWu2XQjWrSxjR2bh\nzYPI3meGYXjCmDI3JBMrpMnZI5Z3wLBzJzxL7Z3lzPaZzquefA8nBtz07mE378ISHM4/L14hEq2g\nu4UjRDRdQ9dN1yOi6VaolitIPF6RbFGCYpdfVRT2v/GMrBVfZRRF0BJTaalAgC6GYZpoAbkoqbRH\ngKSt4yQV4Dlx3+8RM0E5LZWaa9b36pCsnvEUUUWW2PB6UQI8JwGd6GMBYsb7XK2BSCklt0qkEqja\nmPvciCToiHTwpq4QLsJ1xOLeqlzPUt4QvSDPVYBgy64KmNtHyi9yCgm2wqXhC3nYnEqBwaKyEYVV\nqUUrUolwZjqaSkBAbUJZys27qFfyeQ8ccabpBvpMP616u+917wy+VwSYpsn55/bR2aayMJ+01LCi\nIEzLOMxULMI1TN9zyToGugXfOPATRBo04OXkYV49/la2rD4bRSggBIpiK277r2NwO4ZoIaP+1Pgc\nh46maI1as8eHfp3imstXsKqvvSr7NR9pRaMl3uJbFo1GUdXiZ3ZPjVszCkMFxl5J+FI1cISL89cK\nddIsMePeUEz7RqO74sfpLg92Sq1huD3khBDWsRDw+7tCJFLejLlpWnkhum4wMjfOK5Mv4UidI5Mv\ncd7ARnpautB0R3hZeQLWSC2Ph6ooKCX8rpLqowhBLGoZxdXCNE00PVt4mB7PSFa4l0+AZDwnqaD3\npg00+zWzQttH0000XSexUD2RoirCFRaqauWltMaiGW+KLTQM3SASVehsj7nLc4WLIGnMEYkI+ju6\nXaGiquVf55wGiYeOjZPoOIraNc4jp15i00JzTRK4xVaEDAf1ki2sig3Rc+5L2e8LDqvL3xA48Lvz\nhfgZhUWWX7Rl1jWXnA1lXzWdgICl9QJUmndRL3hno9PpNLqWdmqsYpomqeQC7xp+Gxf0bmHyTJJv\nHzqBjhWjKVBQNEFUU93KPgJ439vO5bKNw6hCYfXKLmLRGKqqukbdyKw1yzPY0R84pvWrk8SO+A3r\nrs5ueroaa98uxmBHP28ZvoBnTjwPwFuGL8i7T4L49sNH2P+r1wHYueMsPnDlxqqME4oTKsUihChJ\nJC2G4S2zquukdc268NoCw0kOh0xIg1sRy7PMMI2MGFEtIeIct0JVUVSV1nRLjhDo6eygrz24yINh\nh16lNWtsVghWxtOhG3bIlYl9obfyRFAUFKFaQjmEfVVOo0xJ5QghXCO4WsnzToUvbx6JNwE+ZeeU\n5CTFewVKwLLsJHyjwpQC3TDRF3SSvsSUolo7LsIb7iNFISvMq5CXJMvTElHo6ehg6zk6v5w0UbVu\nUrMmB+d/w5r4ufS3d7sipZ4mZJYrYV/TloOwGjl1iif414rX05QCoh5i2pcax3hywjQwTLdqweT8\nNEIIBjv63GVWCI5AVSyjRFFUVNtYGljdy9u3pnj64CmEEOw8fzVv3rgJgDdxNgCjpzp9Ruv2c4Lz\nE3TVEglt8Tbf8n2HHvAZzNdtvTrns9uG17CxaytHpg8BsLFrK9uG11S6q1yG+trZueMs33aEYRiX\nw3Vbr+byNZcA+QVVEKfG59zxA+z/1evsunB1VbZjKYVKOTheh0ik8suaZYxZxr6maWi65psBGmzp\nZUfPJp4/9QIAO4Y206m2k0osYGIiFAVFzQgPxRYe0VjxIViOx0PT7SR0XXeFhWGHW1mVkIR13gOG\nbmAKJdDbUU6jzGykAKlfvBW+qvnrOGWItZyqXQGiRTPc8DDv8vmFFK+fGcHUBYahYOoKLaIdXYd0\n2qi4wpdhwELKYCFVaQL1ub5nX376dcC6BiqCHG9I1OMhiQUIlGjEWe5/r7s8K4E+IkVKQcK4pknK\np+kERLmVf8qlUN6FG1tuWLFppuGEXvjj8hHYZdmEG87j4OtfgR0r7om9dzP/VQU1phKNRH3hG8UY\n6kH83ru3ccUla4Hg2eYPXLmRXReuzvs65BcJI7Nj7nKAZ048z+VrLgk0nD/53t/nhROvAYQqHhyK\n2RAbg7kAACAASURBVI6lohThsNQspVCpByxjLEIkEqGlpSXwPR9+2/WBXjRHfKS1NKl0GsPQXXe0\nZmgYbrKjdX0QikCJqD7vnDMGoarE1NKS0A3DQLd7RzjejrHJeQ6+8ga6ksAEfvXyApvXdNDT0YLp\nJNfZgiOfwSJv1hKwkufjqgoVJM9bjSmf9C277txr6W7tZmI6wbf+zxGcNDbThKve+ibaWmNuLko6\nbTCVmOXnr/8SU1cwDYGhq5yzYj2KGS1Ylri4Cl+LY5iwkDZYSFevylOmwlduZS5LpIicZYXyVGLR\n3ET8Yit81RvN1Py3UWkqAVFJ5R83DlvXwS4zqYhcQ915DLjJuNecs5tLV14ICAY6+qyEWqw4bDWi\nuiEQ3vj8IBYL6SmVUgz1IJQWJ9Em2EgsZDxW+t1eqiEcILO/h/rq13BfjFp6UUanEu4YlitBx7NX\nfMRbC197HLGhaRopLY1u6LbgsPI+HLHhJLkL1bqmFAr9UhQFJab4vB2aEUHrPclC7BQALakhVg28\nlf6euLVu3UDTNNK6jqHrbnWr8amEJXYMk1+9nBGQB195g81rOunvaZc5HZKSKdTtubcrzrZz+nxi\ndePaoCpn3dB7PKuQwTlFj8FX4atASNd0YhZNM4mIlkwSvee9qayclOpU+AKoXl5KsEARHs9IgJcl\nJyclf55KM1f4Ws40lYCwBICGsMUApiCVTKEno7bhb2VaZmr3ZgSBqqhEY9ZN32vsF0tfV29FY993\n6AGeePVZAC5be1HRnoJqUa7nohgqjfkPg2pu31KzFF6UbKHS0RbjKz84CIQbzhRmjkUj4BUbrYsE\nxztiI5VKkdJSrrCwyhpmHiNAiai+UC4RS6J2jkHSeq52jiFiSSCeCf2KRnwj8HpzN67pobW1zTMW\nnf7uNrrao66Xwwk7MQysHI+sJHJ3AqWAl0OyfLhseCfDLZbB/6YBf4WjXRcNs319H1A4XO6y4Z1s\n698ClF5KufgKX+Xn3C1a4SugWldQLkpuUn3mfZpWed0gt8JXFUVKRBU5osIfvuUXKNl5KrEsz0k0\nqrB2VTdHT0whBGw9p7em3oflGN7ZUAJCX9Axkppr8FuJLhkBsKJ1ngvWD3DoyAJCCC69YJg3b1z6\nOO1SPQkjs2M8eOgpZhNWItmD80+VPVvvpVxDPch7sKlzG33xvqINu8W+u9yYf4dKDM0wvSNLxWLH\n1FIY3I5QGZ1KuOIBwgtnqvcci1rjFRtttOV9n67rLKQWrGIIpsHpM6NMJaZoj6q0CKvKWURVSCVT\npKIpVDXXo5HtzT3y2iQb1/Rw5DWrXPVFm1exajA4YTwIx6vheDmchoK64Ukctz2/BvZjE1Csa7sU\nHc2HPyQunRMSV6whVs89WOq2wlfWe/NV+PKGgYVW4St0kWJdF069Mclj+6dyShD7+qXk66OS896M\nmMkWLkEVvrzH8jlrW3jLtqGyjstGEyENJSBW9a5keGB14GvujHIrXPzObVy1bndNZjHLmdkenZp3\nxQPAbCLN6NQ8gx2Vj6dSQx1gcibJ3fsOENE7SjLsFvvucsfTyIZmOWFq2cfUpSutMsG1OL6DvlNT\nZxlPjDOUJ9StGJZbjkU1UVXVKloQ9x87/d19zKXmAes4uuDsbVauRjpt9cbw5GqQNtGSGk4jLVOY\n7Nw6wKXbB1AUlZX9pV2crCRycrwchTBN0wqt0jW0tOXlMAwT3a5W5VSuMu3eHU7jQCeBPIxGgZLq\nIOPXw2OpK3w51bwCGzS6PVE8IiQw3MsMWGZQYe58ngpf4ZJd4UtRYHp2ASHAjM4z8sI4z778Bn3t\nnazpWZUbGhaQnxKLKvz7i6d55cSk5U1Z18s7Lj67ehsREg0lIPKRPaN8aOIF3r35UvLF7i/VOIqd\n2e6L9xFPDpNoPQFAPDlMX7wvZ91QntFd6me83oO0ZqBPriSiWwZDqYZd2LP6YRiatQqhKkdcZh9T\nDx56ih8/rJcs5sLEG850puMQas9pvnHoV7xlprFDwZqN7GNnLjXPhy74HfrbetzjXVVVYgEZ2oO9\nA1xxUZqfHrLiy9++dSNvPvdcN18j7eRreHI1sps2GabV+VwoItDLsRhCCCvfI6KSJ489ByefI61p\naB5Ph7dkrtMY0PJyWILE6hGouonkksah0WZtGxFfha8qiRTAU4bYyArnMnnu8AivnTqDacLKvnbe\ntKrL5zGZXUiipU2EGQnso+Isq06FL3uiItUOtKMB88DxY2O5KyiItZ43Xp/gp89M53hDcnNPPLkr\n+brQB+SnVFpAwKFoATE/P8/09LTbwAngrLPOCmUQy52hvnauWnel72btNYgrjdcvJ9zH8R6MTs3z\njy/9uqTvWwo01WqE4gibUgnDM1MKYYRNpTWD2UQaJ9umlrP0H7hyI9s2t/KVA08QjVh3lEpCweqp\npG41CLtAQrl4xcNiRM46Siz1S/uxihAbi87XcHAKU6TTaVd0mJiWN8H052+YwkSoVhnefIUmFsOb\nz1EKpmmiazqptIbudCo3TX8+hxQaoeFt1gZWonQ5AkBWBmsuVNWabGjNqvA1MZ1gdOYMTjX4M8k5\ntq5f7R4zT5zYz2lfMn3+hn+6YdpVuIzApHhvg0ZvueLshHtvnsr0bIpkSsNAB0MFs9KQNWEnz1en\nwld6fiKU9RR1lf3CF77A//pf/4uenkwykRCCRx55JJRBVEo9JOVWOo58ibCVGp6VhPsMdvQz2AE7\ndyRrYtgFGV1Dfe10rz/h6w0x1PfORdcVJKJqbcwVQ/YxFU8Oly2awmagu81ym4dEPZXUDZNaJexX\ncj1yrjvO71vsdSf7nHUM+mh08f4XhmFVglpILaBlVaPSHa+GU40qT+nbchFCEIlGFhUejofDKzQs\nz4s1e2qaZqYpIMLtryPKFETNTLGJ0vmQYVASsEoCO5W4AF6aPMK2/i15cxBURaC2qEC4EwAT0wme\nHfsFr83/GtOEDZ0buXjwksAKXzkeEs3g6PFpRicTmCZ0trfQ09nqKUMcnHRfa4oSEPv27ePRRx/1\nCYh6Y6lnlIsZB1g31GLHE2Q0jc1PkjY0okrp0WZhxZXXwrAr1EMi0XqcoYg1jkTk+KL7+NsPH/F5\nd2oR9uMYc95KW8UeF95j6idPTeT1VC011RDuzSQcoPYJ+0t5XaxUKCmKQiwWCwyp8uItfZvW0qR1\nLSd0yrTL3+qGXlTp21LGWKyHw9B1qwlgWkc3dEtYGP7O465nw+46vtwSxqWxLymGsDxW1aS3K86V\nXW9nKrkDKD25/x2XrCwpHK/cCl9pzWBi1ODYo6VvYzZFWaWDg4OsWLGi8m+rMvUyozzY0R/KrKOz\njtmFORDQ09pVM+/KUhp2xRhdxc58nxqf46FjD5PosfJLHjr2GrvGaxP2o72+ntQrVgUKLbYetmbG\nCIX3sbPtkbN+7gsrgdomkNeLcJfkp9y8qVLEYTHnbFglekstfatpmlX6Vk+j65rVsds03M7iVm6E\n6S6zenlazTkdwVAOSglNAL0J4+m0bod+GRjgig43Z8MujVtM879mphGMymowlZwC6rv6VDXI57Eq\n1FNkKQnjdynl+K2kwtfIKYUf3F3yx3IoKCC+8IUvANDZ2cn111/Prl27fLM4t956a+UjaEKcm2la\ns+LXypl19N6Qe+JdpHWND13wO2wdPLfodYQZV14vMdylGjbjiXE3OR0g0Xqi4opB5eB4gyL4k9Ef\nf+5k0SFm5YaVVJtaf389Uy/hleUQpjisVeU0IQTRaJRoNFr0Ge/kbTjdxHVDzwiM7AZ/dliVUBUU\nVSk7pKrUhHEnZ8Mpi2vafTgMMyM2DPu5YZiQ1W28Wag0DKrReOLEfp+hXG4PjEYl329cST+QMMj+\nXQrlYDQTRXkgzjvvvGqPo6kYnZpnbCpBYkEDoCO+ePzvYkTVCP1tpYeQhRF+tNQx3GH2kBjobqMj\nHnXL5MZb6qfw2OhUoqFLl4YhKutFmFaTWntpKpn5L3a8hc7ZUkMpa31MePM2ikkWdxv8pVOk02m7\noZ+RIzJ000ogt0raKhWFVHlzNopJZzcMA13TSWuWMDJME103LG+MJ1HcKYcrhIIQSkPkbywH4QC5\nsf7/fvpZXhx/EUVRl5XRmo9KhEMl3oNSczCaiYLWlONh0DSNxx57jN27dzMxMcGjjz7K+9///iUZ\nYKPx7YeP8PhzJ5iK9KL2nCaiCvTJlRgLcSgh9zXMmctKDNJaxXCH1UNisKOf39r6Np549VmmZxdg\neoh//M6v2bkjuaS5EEHeoIHu0m58YR4TlYaThBmiV8k6GoVaGcNLOfMfhlBqxGPCG1LFIqe0491I\npVKk9bS/DG6W8BCKQCjWuisNUVIUBSWmEI0VN5nl5G+kU1Z+SU7+RlYJXEtsqE3l3ahndENnQVug\nLWIdcMvJaA2b5eo9CIOipmM/8YlPYBgGu3fvBuDpp5/mwIED/OVf/mVVB9do+GbaTm9AnzyL3u44\nraK8/JFaz1zWmrC2+bqtV7Opc5vbDA9qM9sf5A0qNcQsjGOiUqOyUlF5anyO8cR4w3UDbzRq0Zwv\n6PcrNpSyGpMVtfZmZFNKVSpv+VvN0H2eDJ93wzQRAitZXFXLztnwjdPN31g8gcMXTqX5vRu6btqV\ns2zRYVemarZQqqUgO9a/JdKCosh9WAlheA/qJQejFhQlIA4ePMgPf/hDAHp7e/nbv/1brrnmmqK/\nxDAM3v/+97Ny5Uruvvtupqenuf322zl58iTDw8PcddddDZGkXSzRiEJ7PMJcog1Vb2PnhaXlHnhn\nh40Fa4bh1EJxM8ZhJSo6NHIMt5e+eF9dlD/N/l3KCTGrZP/XuuOzUxFLV+bRh5L0dFaxK5Gkbqin\nSm6NgtN8r5gwKqcqVSqdchv8+StTefM3Kmvwl00p4VT+MriZ7uJez4ZhJ4q7YVSR5ZkkHoQ31v+F\nsReXpdFaj9Q6B6NWFCUgDMNgZGSEwcFBAMbHx0ua5fj617/O+vXrmZ21mn/t3buXnTt3ctNNN7F3\n716+9KUvceedd5Yx/PCpZMbKO9PW29nKrgv7+e3L1pV0w/TODne0xZidTzExkwQEvZ0tBWeMqxWu\n0AyekEoSyl848RoA24bX5LwWxgxnLXMe0prB6FRiSTp5Z1fEUuZidLQZRCNKwwrTeqbemvMt9t3F\nHFfFnm+1Lp+71DhCoKXIlt1ukng6jeYrg2sJD83QfCLEEhyVJYpD6WVw05pml8HNJInnNPczTISi\nLpsQKsdArbXR2gzVoML0HjTyfiiXogTELbfcwp49e7j44osxTZMDBw7wsY99rKgvOHXqFI899hi3\n3HILX/3qVwF45JFHuPfeewHYs2cPN954Y10IiDBmrCqZafPODqc1gxePTdDX1cpcwkrGXtEWzTtj\nnD2z/PhzJ9iyrpcd68O5YTbDjdfpngzBYiCIv/jBvb6mdZ987++7r+079ICvr0MjzHB6jUpHmH7l\nBwdLFpzliMrsiliGkmLPxuvYOHRWUxxf9Ug516Nahv0UOq4a3aNQTziCo5gQJfB0FNfSpDUnLyIT\nVuXrwWGXxxWKcDuLlyM4FFWlRV28KpVTAtcK9wr2amTK3wqEohBR1bpPDi+GWhmtzZQ3UGsh1sgU\nJSCuueYa3vKWt/D8888TiUT4xCc+QXd3cTv605/+NB/96Ec5c+aMu2x8fJz+fuvmMDAwwMREOG21\nKwnfCXPGqtZVdCZmkszpM/z99/fzrvM21aRxWtihVGHgM0BmFjdAXjjxmiseAI5MH+KFE6+xbXgN\nI7NjPHjoKbe604PzTzXMDOcHrtzIlnW9fGnfAbckbDmhTKVua3ZFrI54VIqHJaCU37QejPSg46HU\n63OzhF7WC6XkbkBusz/d0Dk1PotuGvT3tGa8Gx7xoaiKm8dRiuDwlsAtJoRK13TSac1q7leok7gQ\ny66xXzE0Y9WhRh57LSlKQFx//fV861vf4rd+67cA6yS89tpr3byIfPzkJz+hv7+fLVu28PTTT+d9\nXxgnZ9jhO1ZoxzyDSxg2750djkYUtqzrZXY+RTIVAQTRiJI3DMH57OPPnWCu8zBqz2lmVMFDx44v\neeO0WtV8L0TYIQ2jU/OuIQwwm0gv+fFSCQPd8aKb8ZVCodlrb0UsKK0bt6T6hHmOBE0gLLVnoxlC\nLxuV7GZ/i90TvIJjIZ2yxYXur1LlyeFwksZL9W6UUo3KCaFKpbWcPhtBYkMRakn5Gs0QAiRZ3hQU\nEB/60Id45plnANi8ebN7YqiqyhVXXLHoyp999lkeffRRHnvsMRYWFpibm+MjH/kI/f39jI2N0d/f\nz+joKL29vRVtRBiJod4Zq8mZJPrkSv7xpaUv95kdcuDciB0KbdMHrtzI0FmCu3/xsPtbLXXjtFon\n6YbJtuE1bOza6gthckKf+uJ9xJPDbkhOPDlMX7yvrO8p17CqxMtTjfj4YmavpVHX/AQZi5V6Nsr1\nKDhFKEopoS0Jl2LuCT7B0Vpc0nhOhSpvsrieyeEwMcsKp8qEUC2eV+LN19B03U4Gxx5Lbgfxp0/9\nnCNTL4MQDRcCtJyrDkn8FBQQX//61wH47//9v/Pxj3+85JXfcccd3HHHHQA888wzfOUrX+Fv//Zv\n+R//43+wb98+br75Zu6//363PGytqZdyn97vKvV7N6/pYcUhf5jIQHdbqONrRMo1QD753t8PTKIe\n6mvnqnVX8tND1kX07Vs3lnWMlGtYheHlCbMyTimz11I41CdhhP0EGYvbNreG4tkoVXzWoydUEg6l\nVKgKCqdy+23YXg7TNFwhgiL+//buPjqq8s4D+HfekkwyIS8zeQEiiIEIASLaSNUoayEVgeUlqKxW\ntFu7ZN2tpVKtWtrd2rr2tLXtcrbudqHVwwFRThVotbQLkp42KL6LjRJAQGoISN4TmGRCMnPv/jGZ\nYTLMy7137p17Z/L9nNNTM8zLM3Pvnfn9nuf3PA/MipON+PftcHfi5PkTsJhNgCjiWM9RzCm5EnkZ\n44I7iIuCCAGA4BMgjuwibqRJ4pw3QIDEEqZvfetbePXVV9Hf7+/x9Pl8aG1txTe+8Q1FL1pfX48H\nH3wQO3bswMSJE7FhwwZFzxOgZm+qUZb7VErvMhGjrfwSSmnvd7QJ14kG4EpLRtQc5THKsYnEaGv4\njwWJjhB19How7BU0KY8DpLcpnUZCU53evwnh5VTxhC6J6xV8o0qpQncWFyDAZPYvMytnKVyzyTJS\n8nQxOclz5MCZMy7i/QNL3w57vfCO7LMhhKxI5U86hKSvSCVcGMmWuBL3mCUpgfj6178Oj8eDlpYW\nVFdX45133sGcOXNkvdDcuXMxd+5cAEB+fj42b94su7GxqNWbqveXnRr0LhPRY813qdT+PIzy/ro8\nXTC7PboF22pPWjXCZF45Yi31m2oS3aDw/MAQQpednlk2CXPPJXdCc5enC16LO6U7g9KJkX8Twkld\nEje4MlVIKZUI8eISuCH7cYgmMZhsOHMKUFU6A01nDwMAqkpnwJlTEPV15Cx9G9jUL5BsBJa/FQMl\nVSOb+omiaWQuycheGxazrFWpGt9vRfPJLgBA5RQn5l1TJvmxlD4kJRAnT57E3r178eSTT+K2227D\nI488onj0QUtqfTGp/WWnR0+q3r22Wv1IGHF1JykinQNKg+7wJDe/vBVbm98IPoeUYFuLc1KtxDXV\n1vCPtdTvWBHa4184LgvDXgH3LZsVXEY6mZ0ageRzaMIgPD0lyHVXpmRHULpJt89fzspU4cnGsikL\ncF3xHAiigHx7HoQLvuCKVDBhVBmVHKGb+kkh+PxzNrxeAT6f9+Kyt0BwdEMURVzcSRzodQ/h0Il2\nmExmwGRC88kuzCp3ojDPLqutRtLd5wGAlH4PepB0ljmdTphMJkyZMgVHjx7FihUrMDQ0pHXbVKE0\n4FTry07JXgEs3YgsVWuaY/WmKw2sAklul6crmDwA0oJtLXv3x9o5G2up31SlRpJus5pRlD/6xzgZ\n50Zo8lkwLgvD2T24r+qKlD4elPoiJRuF+ZEXjxFFMThfY3h42D9XI2SS+KgyKlGAyaJ8gz+zxYIM\niwUStwOBIAiwWd3wT98YBkQAImA1+WCBFz7f6A3+jDh/IxxHU5STlEBMmzYNTzzxBO666y48/PDD\naG9vx/DwcPwH6kzvgFPJXgGpVrqRLKla0yylN11pYFXqzIHZ7UmoPa9/+i6mu6aisniaojZogWv4\n60fpd2aySj/lJjf+RGZsLyKRqqO2Y5XJZILNZvMnG3E6xAMTxIeGh/wb/I1MBPeFJxsjCYfJbIJ5\nZM6Gos39zGaUFo3DtbPKcPBoOwDg6iuLMe3y4oj3D8zf8CdEkedvBEY4fIIIjGz0ZzGbk5JwdPd5\ngskDgLQYTUkmSQnE448/joMHD2Lq1KlYu3YtDhw4gJ/97Gdaty0hiQacaowCyN0rINVKN0hfgXNU\nabDd4+lD/7AHz7y/HTWTqg2VrOo9j0eqWEv9pppEvzO1rnOXktxIST7H0giv3p1opK3QCeLxRNpr\nI7DcbfheG/7lZqPvtbHohstx7YwSAICrIHqwHTp/Q8pc6+BGf14ffL6QXcXhL6nyCcLF+Ryh+2/I\nnMNB6pCUQFgsFjgcDmzduhVWqxVf+tKXcMUVV2jdNt2oNQqg5l4B6URJj1iqTm7Xqjc9/Bz95g1r\ngq8npT2vf/ou+oc9yMmww2a24u3WD3DluJlw2p2G+VxTJcCLttTvWOz5VfO9hgb6cpKbWMnnWBrh\nTdVRWzWMpSRRKjl7bYQvfTvs80KEGCyb8ok+FNhtEEQBFzwX/PtsmE3+UqUEAnk5G/0Bo/ffEEQB\nXq8AAfCXUQUTj5EkRARMFgvMJnNwd/HCPDsqpzhHlTBx9EE6SQnEli1bsG3bNnzhC1+AKIrYvHkz\n7r//ftTV1WndPsUCAafcdfrVHAWQu1fAWCjdSKRHLJVW8gildm+60nM08KO6snIRprum4pn3t8Nm\n9n8F9JwbDO5/wp5K+cJHHeSc50ZJNIyUpIcH+jeUzJP1+EjXgpTrxijHYixL9BiMpSRRK0qWvo24\nsZ8gXFJSBROCczYSSTYAeftviKLoL6fy+XcXF3w++AQR8z9XitlX5EIQgcJcO4YvDEL0fwiwmC3B\nZEOqsTQhW1IC8eKLL2LHjh1wOPy1N//6r/+K1atXGzqBAADrhBPIGPrryH9bACQ/KJIb9CYabBq5\n50WNHrFU/WHX+3hE+lGtmVSNt1s/wLBXgK+nRNfNEwOMfP5KJec8D000qipzsKRmiq7v3QhJerRA\nX05yoyQITbdyHyMlhFIlegxYBqwPORv7he6zEbqpXzDZCLkNIyMbVqs14WTDZDL59+ywXrpEr7Ng\n9B4c4cmGb2R3cRGAz+efsyEKon/SuMkEk9kCq8WC/R+cGVMTsiUlEHa7fdTqAXa7HRlSp+3rJPBF\nEtjQSOoXiRajAHK/tJW+Hntexga552i0H9VAstrRO4BfH/1E83bHM9bO39BE47yjGfvaW/FRY47k\n1dq0Eun7ygg981KTm2hBaKzrJl3LfYyQEEqVrseARpO6zwbgLz/yer3+0Q3f8MUN/QTfSHmSf2K2\nV/COjGxYgqMbiYiVbERqo8/rw2ed59B84jNA9C9O9dHxM5g5JRcFjix/OZXo3+TPbDbDInN5XqOK\n+S6efvppAP6N3+666y4sXrwYVqsV//d//4fLL788Ge3TRapM4AxltJ6XSD3JSsvK6FJqnaPFDheK\nHcD1swd17ak81NqC1//2vuyE34jk9vxesLVjwN4Ck6j8vWsZ4P9m38ejrlmte+ZjBfrx3l+8IDQV\nv9sTNVa+Y/3XgD2hDkAjJMp0kdlsRkZGhqQO68BeG0NDQ/D6vBdLqSAEN/QLjHIo3UU8WhvNGWZk\n27NhywhburowH64COzp7PBAhojA3c6TUa/Qmf8H/F/1zNUSYYDb7Exglq2Uli6Q0qKqqCgAwODgI\nALjxxhu1a5FKEh1JGCs/LlqI1pPc7u7EBdeHyJh6BIB+ZWV60OKHSeo5Gpw4HbIfSfhj9eypDASo\n3QX9cNhtKBgnZb0O42p3d+Lm6wrjfp6lzhzkl7fiaN+H8Fn6kWnJgs2aK/v1tCyDOtvVj70n98FT\n4F8IYu/JFszr0r5XWMtAP9LzpWK5T7pJ5BiMHnUqxzdvkH/upFsJ21gjdWO/s139EEURrrzMiHtt\nhC99G2uvja7+HgCAM6cArgI7rr6yeNTytq4CO/544G+jblt0w+VxC70CG/wND3nh9fmCq06FrkQV\nSDb03GcjZgLxwAMPxH2Cf/7nf8bGjRtVa5CaxlJvk1EmYEcbCXmt5R28/um7aOvvRE6GHQVZeYp6\nWlOxRt4IP0zeM+UYOu7z/3dGOVB56X30CJiCPcYWIPNCMdxohyM7I2KSkwrkbBzZ7u6EJ+sUxlvH\n4dwFwOMbxLDgRc2kasnvXesyqC5PV3AVOQDwZLWiy9OFUmh/rihduEJpEJpK5T7pSskxUKP0ieVT\nY0PE32KFe228crQBB898CACoKp2BBeU3Yv6cUlx1RT7MFgtKXA509niCyQMAHDzajmtnlMRc+hYI\n3eAv9siLKIrweX3BeRrhoxrBPTdG1uUNTApXS8KFWG1tbWq0QzOpGIQoZdSEqXOgZ1RS0T/kgSMj\nJ7gCkFSpWCMf7YcpIBk/UIE2WKH/JOlIzjuag0Fq5oVi3Fe1MiX3UlCycSTg3+zMac3HsM+Lr15z\np6JN/bwW96hAX60SsKL8bDjstuB7cthtkjZmUzPRl/tciSQCRrkmxjIeA9KC0iQx0l4b7e5OHOk7\nAXuO/7vw2PlPsTT7FhTlOHGZ8+JeG26fD4JHHHmUCNEkwjfkw/DgEARRjLnXhhQmkwlWmxVWW/xY\nKrDk7dCwF+esYtz7S5FwAmHk+qyxSO/EIdJIiCu7AABgs1iRY7Ojf9gT/Dep7TXaHI9E7H79fYNZ\nLQAAIABJREFUJD464Q+KxvpQuTnTA0tBGzCyobYtvydldw6Wu3Fk+LVSM7ladvIQPq/IYbcF55Go\nodjhwsLK62KWv4VTM9FX+lwMQscONcrPjFLCxjkYqS18r4283HG4+XP9o86rq6eWB+8vZflbn+CD\nABEwIaEVqS4ueZuJfoc6v7HpMRWcDCXSSEggUCqw56FmcjVunXpzSgb/coX/MM0qdwWTByA5owFG\n+XGMpmBcFhzZ/qFaNYPfZAvfODLzQnHcx0QbNZTT6x7ocd97UkBz9yEA6pYwyhnZVDPRT6eJ9aQt\nNcrP9C5hM0KpazpT83dQTsl4rPNK6fK3Xp834lwNLZa/jYUJBGki/GJKtLzKKHM8lAj9AgEwKoHQ\now1GSh5S+biGC904sj/7OGz5PdjavA1zz8XuOY+0W7LUeRShr32vcxna3TdEfM5EJfuYhE6st2da\nkefITOnkUk+pOG9MCTW+1/T6buQcjORQ83dQTkyjxnFUY/lbf8IhQhwSEm4PoEICIYrq1FJR+kv0\nB8yoczykCP0C0Ws0wKg/Rql8XMOtqq3AzOlZeLbpr7BZ/b1KUnvOz3b1o8vTpWgeRYDen58aCWHo\nnB2xrxR9OWcwMOjFlfmVur+/VJOK88YoMXqWQaVCCZYeKyFKpVayH2/5W9/AcMTb5ZKVQPT19SEv\nL2/UbStWrFClIURSpEMAYdTRAD2lw3ENKMrPlt1bHihfGBTPo7dwCFaLf25ZvHkURqRWQjjsFTDU\ndgWQUYq8fDt6u3Jxtquf14xE6TRvLN2pVV6jZxkUS7ASk4rJvqRfucOHD+PWW2/F8uXL0dbWhi9+\n8Ys4dMhfa/uP//iPWraPKC2VOnMYCKWpQC98QLxe+NDyBYsvB76eEgQGdu2DZXDanZq2N6Dd3Rns\nAUuUf4PCi7s8B3ompQgEUwE5ljxkmeTvj0HJF+tYxzq/1Dz3UtWq2go8eu+1ePTeaxUF35HKoORc\nd4nQ87XTQaRkPxWuB0kjEP/xH/+B//7v/8ZDDz2EkpISPP744/je976Hl156Sev2URpJheFNIjUo\n7YW3Wc3IOTcd2WI5rFaT5N3aEx361qr3S2mvZGCULnzFMn53SJfs+UWxjnWs8ysVe161wvObUomk\nBMLj8aC8/OLSUzU1Nfjxj3+sWaMo/XB4k8YaqcFaePnCkporZJW4JRqAaVXqkujE0FJnDr66bFZS\nOx4SScSM2EGSrPlFsY51rPNLzXMv9NgZ8VhoTc/V9oy+0p/RpepiIpISiPz8fBw5ciS458PLL798\nyVwIomi4wgRRbErnxYyFOne1vyeiBZfREjEpSYWRO0jS6VyIJvTY2QcvQ++JMgDGOxZa03N+Hef2\nJSYVFxORlEA8/vjjePTRR3Hs2DFUV1dj8uTJeOqpp7RuGxHRmKHGj+6wV5A96Vqr3q/wTe6klmNp\nKVqgHy0Re63lnbijO+EdJI0HWzFjSiFml6dGEKCGWD3Qsc4vNc690GM37BVwqq8ZhZZ8WH0OyZ1V\nyRqxSMbr6HmN6X19p7pUSRwCJCUQkyZNwgsvvIC2tjYIgoDx48dr3S5KIxzeJNJGaADWc24Qvp4S\n/ProJ7h+9qCsnteVlYtw5biZAICZZZNUa591wglkDP115L8tAPTpDW53d6Kjd0DWSGjnQI/s0Z3u\nc4Po93ixcWcT5l1dxt7vEbF6V7U696RK1uiRkUepiJSQlEAcOXIEjzzyCNra2iCKIq644gr8+Mc/\nxuTJk2M+bmhoCHfffTeGh4fh8/mwcOFCPPDAA+jr68O6detw+vRplJWVYcOGDcjN5Sob6YzDm0Ta\nCARg/7uzCVaff+hBbpng6OBGXvIRTaBnWO+dpAPlLcNeAecdBch1V15yn0g94a7sAknPH+ggaTzY\nin6PFzl2G2xW85gs1Yz1XqMd90TPvdBjZ7OaUZFXid5Ox8jzxe6sSlZ5Lct4k2sszoHRg6QEYv36\n9Vi3bh2+8IUvAABeffVVfPvb38bzzz8f83EZGRnYsmUL7HY7fD4f7rrrLsybNw979uzB9ddfjzVr\n1mDTpk3YuHEjHn744cTfDRkaL+aLxsrusJQcTrszmDzIlc7BTWh5i81qhqWgDV7PJFh9jkuCy0i9\n5FLLa1bVVmDGlEJs3NnEHbNlUOvcCz92DCDHLo70JI+kbzpRFIPJAwB88YtfxMDAgKQXsNvtAPyj\nEV6vFwDQ0NCAuro6AEBdXR327dsnq9FEqWxn8x/x8wO/ws8P/Ao7m/+od3MoDYTvnWCEMkG5+2Ek\nQ8G4LNy/sirqWvuh+1cA/sD0nsq7cU/l3XFXt5pd7sK8q8uCfxvhGIwlocdO6j47ybpujHh9piPu\nR5FckkYgqqur8T//8z9YtWoVLBYL/vCHP6C8vBxnzvgP1IQJE6I+VhAErFy5Ei0tLbj77rtRVVWF\nrq4uuFz+C72oqAjd3d0qvBUi4xsLq+aQPsLLBKWOcmk5R0nvlUUilSbJqbOXW17DUk15jDA/LvSY\nmTM9aHd3anKupvO5YbQRH6/FDQCKR2VJGkkJRENDAwBcsnHc6tWrYTKZgv8eidlsxm9/+1u43W58\n7Wtfw7Fjx4LLwQaE/01ERPIFfsDl7g2hZXCjd3KsJIlRMuk6wChBVKowQmBd6syRdc0oDZjT8dww\nUslQqTMH+eWt+LivGQBQkVeJUufNurUn3UlKIP70pz8l/EIOhwNz587F/v374XQ60dnZCZfLhY6O\nDhQWFib8/ESpIFU3jKHUERjlGvYKAKSPcqVjcBMg5xqTMuma1KX3uSdnZNhIAXMsyRgVSOYkdCD+\ne2l3d8KTdQqlVv/9PNZTmo0okcQE4pNPPsHzzz+PgYEBiKIIQRDQ2tqKbdu2xXxcd3c3bDYbcnNz\nMTg4iAMHDqC+vh7z58/Hzp07UV9fj127dmHBggWqvBmiVKB3WQelv55zg3B7hgEADrtN59akDjmT\nroHYgY3RyjoocVICZiMc91RJcqRQ8l64kEFySEog1q1bhwULFuC9995DXV0dGhsbMW3atLiP6+jo\nwGOPPQZBECAIAhYvXoy/+7u/w1VXXYUHH3wQO3bswMSJE7Fhw4aE3whRKjFi4sCVodKDcMEOX08J\nkNUKAPD1lEC4YAdYDixbwbgs3HNdFZx25yUBYazAJp0CuLFAjZHhs1392P36SXx0wv89Gu+4a5Vo\nJHNVNa3nsMh9LxzhTy5JCYQgCFi7di28Xi8qKytx55134s4774z7uCuvvBK7du265Pb8/Hxs3rxZ\ndmOJSBtya+bJ2HLdlbB7/JOFOZFQOqmTrmMFNum8LG46kzIyHC1g/s2+j9F4sBVnuwaQY7ehcFxm\nzOMemmDOKndhSc2UlD0/jDCHJRRH+JNHUgJht9sxNDSEyy+/HIcOHUJ1dTUuXLigdduIKAm4MlR6\nMcLKNkYjp7dX752RST9SvvPCA+bwhLHfM4zcbFvUMprQ+3efG8QfD/wNB4+2q7ZzuR7Xv1bPr/S9\n8LcrOSQlEMuWLcP999+Pn/70p/iHf/gH7N+/H6WlpVq3jYiIFDBar6Ce5JYTSVm6NVZgwwQu/UU6\nnjarGTl2K/o9/v2u4h33Ya8QvC+g7khVOl3/6fRe0o2kBGLx4sUQBAHPP/885s6diw8//BA33nij\n1m0joiRg3Wh64o+t/BpqOfePFdhE+zcjTLAldYUmjIXjsjDv6tglSYH7Nx70z1HKsUcfrZAq0nmV\nTudYOr2XdCIpgVizZg2uvPJKTJgwAePHj8f48eO1bhcRJRHrRo2JE9uNLVZgI2fSdbIwgdGG3F7y\nwP3DJ10rOS5GOK9obJKUQADAD3/4Qy3bQUQ6Y5BqLJzYnji55URalR8ZYWI1A01tKdlU7qvLZiWU\n1BnhvKKxS1ICUVtbixdffBHXXXcdLBZL8PYJEyZo1jAiorGKE9vVo7R3WOr9taTWiAEDTeOOvhit\nPURSSUogzp8/j02bNqGgoCB4m8lkQkNDg2YNIyIiUoOS3mG1X1/uyAZHDNSj9mdplNJCTtgnPUlK\nIPbu3Ys33ngDWVlZWreHiGjM48T29CNnZEPtEYNkBZpGCaxDhX+WjQdbMWNKIWaXK2uj0UoLjTRi\npiejjjABxrwu1CApgbjsssvQ19fHBIKIKEk4sT39JBLcdPR6EnoOrQNNLQNrtYLD7nOD6Pd4sXFn\n06h9F6QGeEYtLTRi0KymeMfHyKN1Rks41SQpgTCZTFiyZAmmTZsGm80WvH3Lli2aNYyIaKzTOzAh\n+dTobQwfMXBkZ+DZlz8CkFiApFWgKTewlpMQJBochi6b2u/xBpdNDYzqHGhrVBzgDXsFdPQOoJib\nvWsmXgCu5mid2iMFRk041SIpgbj//vu1bgcREVFKU7O3MTBi0NHrCSYPQOpPgJaTEASCQ6/FDUD5\ne19VW4EZUwqxcWfTqD0XujxdsgK80NLCnnOD8PWU4NdHP4m64SAlJpkBeDqPFGhF0u4lc+fOjfg/\nIiLSztmu/mBvLRlbpGAn0KOpVKkzB0X59kSbprlAYB0Qbc5OpN7ieOf3eUczugsOoLvgAM47mhW3\ncXa5C/OuLgv+ff3sCSjKz5b9PCsrF+GeyruRceZa5LorAUh7H6SNwAhTgJL5PVpcu4D06yJVSd4H\ngoiIksfIdb2UPGpOgNZyMqcWc3bMmR5YCtoA//QPWAraYM70AEh8HggACBegaLECp90Jqy+96paM\nOAlZ6mISRp5Ins5z2ZhAEBEZDNftTz1arpylRoAUWqJRWTgTt0xZgFJnjqqBY7z3qyQZKhiXBUd2\nBgCMKj9SqtSZE5acl+ObN8gL8NJt+VQjd1ZIDcAT+fy1XvUu3RKHACYQREREKtCytzGRACm0RKPn\n3CD2tL2Jdw6YkZ9ZCPfAEAB1A8dYScmq2grMnO5f0XFm2aSYz6NFYKdWcm7kXm85UqGzIhkBeLJG\nCtJpSVcmEEREBpNuPZxjiZEDg2GvALdnGADg9Yo4fKYbpc7sUasSJXqexevNHjVZ9Vz8yapGLgHh\nNZletD6/0m2iduLjgUREpLpVtRV49N5r8ei91xqqpIAia3d3qjLxUgvhkzntg2Ww+NQPfuNNklY6\nWbXY4VItuFNj0q2RyT0P0/3zMAqtJmrriSMQREQGxR/y1JAKPYuBnvzdr59EU3M/YAVmTBldwjRW\nzje1yo+0KkdROi9F6XmYLuVYlFxMIIiIiOKIFtRJWaveKCvcFDtc+MpCF85WX2yPmm2LV3qn9WRV\nuW1NhFZJo9IJzYnumaD3uZkKErlWjHTuq4UJBBERUQyJrFKTyGO16uEODYCkBEOhgVO8NsXrzTby\nnAaptNrgLBUmNI9VaqxUlQ7nfigmEERERFHEC+pi9SwqDQjPdvVj78kGNHcfCj6nXmVRoYFTfnkr\nPFmn4rYp3vtLh+DJaNKxh9so1Ezs0umYMIEgIiKKw2txAwCsPgc6ev07mwUCCDV7Fn+z72Psb/4Y\n3QVvwmG3oWBcluQebrVLpUIDJ6/FjY/7mlFqzYHNalat110JJSMzao7maBWsJ7r6Wrr1cJOxMYEg\nIiKKotSZg/zyVnzc1wwAyPNNxrMv+/8ttJQhUsAmNyAMBuwW/99uzzAc2RmSNlAz0mZgWq51r2Tu\ngRbzFZQE61I+l0QnNDNxUB+X1Y5M0wTi7NmzeOSRR9DV1QWz2Yw77rgD9957L/r6+rBu3TqcPn0a\nZWVl2LBhA3Jzc7VsChERkWzt7k54sk6h1JoDr09AZ++nKLRMhNXnkFTKoCQgtPocsA+WwZPVCiB6\nD3foEqlvfHgmOEoSr11SA/zQwMnqc6AirxIe68USpkiP13JFKiVzD7SarwBE/vyifbZyPhcGp+pL\ndHSOK1VdStMEwmKx4Nvf/jZmzJiB/v5+rFy5EjU1Ndi5cyeuv/56rFmzBps2bcLGjRvx8MMPa9kU\nIiIixUJHAXzmAQD+QF8KqQFHaMCe665EzaRrsaRmSsRANXTEYVa5C+cdzcGEwz5YBuDaiK8RHsjG\n60UfHTjdHDP50DJYV8uwz4vOgZ6kjo6kwueSztQanVMjcTDKimxq0DSBKCoqQlFREQAgJycH5eXl\naGtrQ0NDA5577jkAQF1dHe655x4mEEREZDih9e42qxkF2bnogT8YrMirRKnzZsnPpVYJS/ikzg/+\n9jeg9Cxwwf+3paAN5kwPgNhLzu459he83vIubGar5EnRega9SuYehD6mx9MHmIAtH7xkiNERiu5Q\nawsAYGbZpISex0grWxmpzFANSZsD0draiiNHjuCqq65CV1cXXC7/RVVUVITu7u5kNYOIiEiWQL17\n50APtnzwEjLt/uDDYz2FdnenpFIWrUtY8hyZyHNkAoCkORPDPi/6hz1wZPpfS42J2slYCWhl5SJc\nOW4mAOnB5crKRZjumopn3t8Om9kf9iQzwOcKSfJ8/+XngnOOKvIq8b1lq3VuUeKMlMioJSkJRH9/\nP9auXYv169cjJycHJpNp1L+H/01ERGQkoQFfvAA9UpmQmr3T4ZM6b6qsgHWCJW6AGh7I5mTYgwG1\nFFJ6ULVeCWh0GwYl9+K6sgtkvVe54iUJXCFJmkOtLcHkAQA+7mvGodYWxSMRnACtHc0TCK/Xi7Vr\n12L58uWora0FADidTnR2dsLlcqGjowOFhYVaN4OIiCghUnqSI5WyTHdNVb0tl5Y6VUgKUEMD2dda\n3rnkvUQrswrvQW082IoZUwoxuzxyoqKFRHpxkzU6EusYMHHQRyIToNWas5COiYzmCcT69esxdepU\nfPnLXw7eNn/+fOzcuRP19fXYtWsXFixYoHUziIiIEqakJ9mVXZBw8BopsA8PQKQ+Z+B+4e8l1iTg\nLs9A8PHd5wbR7/Fi484mzLu6LGVquZMxCsAkITEzyyahIq9yVAlTovMgAGUJgNpzFtJtJSdNE4j3\n3nsPr7zyCioqKrBixQqYTCasW7cOa9aswYMPPogdO3Zg4sSJ2LBhg5bNICIiUk2sIDFaT3e84DVW\nT2doYF9ZOBO3TFmgWgASaEu0ScChoxT55Zeh4+gE9Hu8yLHbYLOak1rLrUYvLgP80bTcs0Op7y1b\nrdokaqW0mrOQDolDgKYJxOc+9zkcPnw44r9t3rxZy5cmIiLSRbRkIVrisPv1k/johD+QC+/pDA3s\ne84NYk/bm3jngBk3VVbg5usKoz6vXJ0DPRgWvKPmCXQO9IxKKjxZp7Bqyefxm92nJE3U1kK69eKG\nSnYwv7P5j3j9b+8DAGouv0bVVamUCnwGaiQO6bRkqhFxJ2oiIiKVSQkCf7PvYzQebMXZrgHk2G0o\nHJcZtadz2CvA7RkO/r335D68NdQDm9Wc8JKkgREO94V+wAQUZOVhbtkcuLILLrnv9EkFmHe1qGst\ndzoGhFpuwBdJu7sTe5rfDJ5Tewbe1H3ZWTU/g0TLj9JxzoLamEAQERElWXiJRL9nGLnZtkt69gMl\nUYGeYv8mcYAnqxV5kLcEayShIxwF9jwM+7y4d87tqCyeBgARy7FW1brSdhRAD3rsIdHROzAqIXV7\nhtHRO4BiaXsjqk7Nz0Bp+VH4iEU6j3apgQkEERGRTmxWM3LsVvR7vAAi93QGSqJ2v34STc398Frc\ncNgvTTZUIZohDmVe8tpA7AnclFqcdifsg2Wjdi932p06t0o7XZ4umN2eqAlJtBELnufRMYEgIiJK\nstASicJxWZh3tQtLaqZEDViKHS58ZaELZ6v9vaQH2uLv+yDFqJ2azw3C11OCXx/9ZNQeC7Gem3Xm\niQnU/Cd7o7lSZw5umVKL/c0fA/DvJaLnMVRzmd3w8qP88lZsbX4j+LzhpVHpuMlbMjCBICIiUkDq\npNdo91NSIhG430qnekuSBnZ3/t+dTbD6/DUsb3x4BjOnZ6EoP1t2r20oJhjRhdf8f/OGNQCSN4na\naCU6SnYZjybw3ro8XcHkAZBeGtXR6wFgjM/FqJhAEBERySR1wme0+wUCa3OmZ+Se8gMVNQNNp90Z\nTB4A4LyjGc82vR51kvah1hbsb/4YVlxMOMJ7bdVeRz+dkhGt5z1ITW6N9Fkq3WU8GnOmBybfhbj3\nCx+xcGRn4NmXPxppR+LnbbpiAkFERCSD1OAv2v3+/GY33vjwDM47mmEpaEPBuKykrLwTz6xyFz46\n0QmvxQ1LQRts1qxR7Q68v8Dyn90F/bAPliHXXXnJc6ldFqJ2MmLE/Q/UkuwVndSg9vkS+hnkZGSj\nf8i/EWK00qjAiEVHryeYPKjRjnTGBIKIiChJOnoH8MaHZ+C1uP0TWD3+Hs9EV50BRvcmy+mtDw3O\nZ5W7cN01V2Br84cR7xtIimxWMxx2G87jU2RccOHm6XM0C7K0DC71CrDVrPkPpceKTkYT/hn0Dw3g\n3jm3w5VdEPNzCD2fvBY3AIwalaPRmEAQERHJIDX4i3Q//0o3n6jWlkg983J668OD849OdGJJzRRp\nwa3tAkyiB7aCw7BOyAVw8XW0WEdfjaDOSAF2vN3JxxKt912IlzyEtiO/vBUf9zUDACryKlHqvFm1\ndqQTJhBEREQySQ3+It0vECjZB8tGSoXMinqgI/XMz5hSqEpvfazdtOeWzcHrn76L/mEPHJnZsGdE\nHkFRa5JuKgR1Skui1E4ctBrZSAa1zpdEPoN2dyc8WadQavW//jnTSTS3Hwvui0IXMYEgIiJSQGpQ\nEn31pWtDJlH7gxe1gj2pvfWxen6FC3b/ncKeYmXlIkx3TcUz72+HzRw7jFCjFzk8qPNYTyn+rLQI\nsI1QEhUqlUc21Bp1SPQzsFnN6PH0oX/Yg2fe346aSdW6H1ejYQJBRESUZBcDpRzFAWik4H92uUt2\nb32knt94ZVCVxdNQM6k6qT3dam2cp2aAbaSSqFB6v74RJJJgBkbYcjLssJmthjmuRsIEgoiISCeJ\nBqDhwb/S3vrwCdhSyqCkBOJqrHakxaiBkQNBuZ9ZOq8opQc5I2xjGT8ZIiKiJAsEfWqIVPahVm99\nPLGCVjVLe4xalqN2ciP3MzNa+VS60GOELdUwgSAiIkqi8KAvPAAFlM+HUCOgVWNFHC1Ke4wawKmV\n3Mj9zNT8jDmKcSmjJq1GwQSCiIgoSSIFfd+8YU0wUHmt5R38/MCvACjvUVYj8FFrRZyxIpUDTI5i\nRJfKx1VryRnjJCIioqgCgUp4cqG01KnY4Uo4+Cl15ihOHgIjIQEsAYlP7memxmccKaFVs7yO0hdH\nIIiIiJJEiwnBRi0/YQmIfHI/M37GyWPU60wvTCCIiIiSKN4mbbGSi/AgxujlJwy25EvmZnSpvPFc\nMiXzOjvb1Q/A+KWDTCCIiIiSLFqQFqtHOTyIuXHStYbcg4BSC0cxYkvmXh/x9l8xEs6BICIiMpBI\n8xciBTGdAz3JbhqlKTXmzFBiIu2/EhiNMCImEERERCnIlV3AicpEGuOCAJGxhImIiMjgotWqs/yE\nSHvJuM7U2H8lmTRNINavX48///nPcDqdeOWVVwAAfX19WLduHU6fPo2ysjJs2LABubm5WjaDiIgo\n5cWafE1E2krGdZZK+69oWsK0cuVKPPPMM6Nu27RpE66//nrs2bMHn//857Fx40Ytm0BERJQ2WKtO\nlN4S2X8lmTRNIKqrqzFu3LhRtzU0NKCurg4AUFdXh3379mnZBCIiIiIiUlHSJ1F3d3fD5fL3nhQV\nFaG7uzvZTSAiIiIiIoV0X4XJZDLp3QQiIiIiIpIo6QmE0+lEZ6d/J82Ojg4UFhYmuwlERERERKSQ\n5gmEKIqj/p4/fz527twJANi1axcWLFigdROIiIiIiEglmiYQDz30EO68806cPHkSN998M3bs2IH6\n+nocOHAACxcuxJtvvon6+notm0BERERERCrSdB+In/3sZxFv37x5s5YvS0REREREGtF9EjURERER\nEQHt7k60uzv1bkZcmo5AEBERERFRfDub/4i3Wz8AAMwtm4OVlYt0blF0HIEgIiIiItJRu7szmDwA\nwNutHxh6JIIJBBERERERScYEgoiIiIhIR8UOF+aWzQn+PbdsDoodLh1bFBvnQBARERER6Wxl5SLc\nOOlaADB08gAwgSAiIiIiMgSjJw4BLGEiIiIiIiLJmEAQEREREZFkTCCIiIiIiEgyJhBERERERCQZ\nEwgiIiIiIpKMCQQREREREUnGBIKIiIiIiCRjAkFERERERJIxgSAiIiIiIsmYQBARERERkWRMIIiI\niIiISDImEEREREREJBkTCCIiIiIikowJBBERERERScYEgoiIiIiIJNMtgWhsbMStt96KhQsXYtOm\nTXo1g4iIiIiIZNAlgRAEAU888QSeeeYZ/P73v8fu3btx4sQJPZpCREREREQy6JJANDU1YfLkyZg4\ncSJsNhuWLFmChoYGPZpCREREREQy6JJAtLW1Yfz48cG/S0pK0N7erkdTiIiIiIhIBqveDZDC5/MB\nAM6ePatzS4iIiIiIUlMglg7E1krpkkCUlJTgzJkzwb/b2tpQXFwc9f4dHR0AgLvvvlvzthERERER\npbOOjg5MnjxZ8eN1SSBmz56NlpYWnD59GkVFRdi9ezd+/vOfR73/rFmzsG3bNhQVFcFisSSxpURE\nRERE6cHn86GjowOzZs1K6HlMoiiKKrVJlsbGRjz55JMQRRG333476uvr9WgGERERERHJoFsCQURE\nREREqYc7URMRERERkWRMIIiIiIiISDImEEREREREJJnhE4jGxkbceuutWLhwITZt2qR3c0gl8+fP\nx7Jly7BixQrcfvvtAIC+vj7cd999WLhwIb761a/i/PnzOreS5Fi/fj1uuOEGLF26NHhbrGO6ceNG\n3HLLLVi0aBFee+01PZpMMkU6xk8//TTmzZuHuro61NXVobGxMfhvPMap5+zZs7j33nuxZMkSLF26\nFFu2bAHAazmdhB/jrVu3AuC1nE6GhoZwxx13YMWKFVi6dCmefvppACpfx6KB+Xw+sba4QPgaAAAG\n/UlEQVS2VmxtbRWHhobEZcuWicePH9e7WaSC+fPni729vaNu+8lPfiJu2rRJFEVR3Lhxo/jUU0/p\n0TRS6J133hGbm5vFv//7vw/eFu2YHjt2TFy+fLk4PDwsnjp1SqytrRUFQdCl3SRdpGP8i1/8Qnz2\n2Wcvue/x48d5jFNQe3u72NzcLIqiKLrdbvGWW24Rjx8/zms5jUQ7xryW08vAwIAoiqLo9XrFO+64\nQ/zrX/+q6nVs6BGIpqYmTJ48GRMnToTNZsOSJUvQ0NCgd7NIBaIoQhCEUbc1NDSgrq4OAFBXV4d9\n+/bp0TRSqLq6GuPGjRt1W7Rj+qc//QmLFy+G1WpFWVkZJk+ejKampqS3meSJdIwB//UcrqGhgcc4\nBRUVFWHGjBkAgJycHJSXl6OtrY3XchqJdIzb29sB8FpOJ3a7HYB/NMLr9QJQ9zfZ0AlEW1sbxo8f\nH/y7pKQkeJJTajOZTLjvvvtw22234cUXXwQAdHV1weVyAfB/wXV3d+vZRFJBd3d3xGMa6dpua2vT\npY2UuOeeew7Lly/Hd77zneCQOI9x6mttbcWRI0dw1VVXRf1+5nFObYFjXFVVBYDXcjoRBAErVqxA\nTU0NampqUFVVpep1bOgEgtLXCy+8gF27duFXv/oVtm3bhnfffRcmk2nUfcL/ptTHY5p+vvSlL6Gh\noQG/+93v4HK58KMf/UjvJpEK+vv7sXbtWqxfvx45OTn8fk5D4ceY13J6MZvN+O1vf4vGxkY0NTXh\n2LFjql7Hhk4gSkpKcObMmeDfbW1tKC4u1rFFpJbAcSwsLERtbS2amprgdDrR2dkJAOjo6EBhYaGe\nTSQVRDumJSUl+Oyzz4L3O3v2LEpKSnRpIyWmsLAw+CO0atWq4LA3j3Hq8nq9WLt2LZYvX47a2loA\nvJbTTaRjzGs5PTkcDsydOxf79+9X9To2dAIxe/ZstLS04PTp0xgaGsLu3buxYMECvZtFCfJ4POjv\n7wcADAwM4LXXXkNFRQXmz5+PnTt3AgB27drFY52Cwutnox3T+fPn4w9/+AOGhoZw6tQptLS0BIfQ\nydjCj3FHR0fwv1999VVUVFQA4DFOZevXr8fUqVPx5S9/OXgbr+X0EukY81pOH93d3cEStMHBQRw4\ncADl5eWqXscmMdKMGQNpbGzEk08+CVEUcfvtt6O+vl7vJlGCTp06hQceeAAmkwk+nw9Lly5FfX09\nent78eCDD+Kzzz7DxIkTsWHDhogTNsmYHnroIbz11lvo7e2Fy+XC17/+ddTW1uIb3/hGxGO6ceNG\nvPTSS7BarfjOd76DG2+8Ued3QPFEOsZvvfUWDh8+DLPZjIkTJ+IHP/hBsMaWxzj1vPfee1i9ejUq\nKipgMplgMpmwbt06VFVVRf1+5nFOLdGO8e9//3tey2ni6NGjeOyxxyAIAgRBwOLFi/Ev//IvMeMs\nucfY8AkEEREREREZh6FLmIiIiIiIyFiYQBARERERkWRMIIiIiIiISDImEEREREREJBkTCCIiIiIi\nkowJBBERERERScYEgoiIIvroo4/wb//2b5LvP336dA1bQ0RERsF9IIiISBUzZszA4cOH9W4GERFp\nzKp3A4iIyJjefvtt/OIXvwAAVFVV4b333kNPTw+++93v4qabbsLp06fxrW99Cx6PB1VVVcHHDQwM\n4Ac/+AGOHTsGQRCwZs0aLF68GD/60Y/Q3d2Nn/zkJ3jllVewbds2vPDCCzCZTHq9RSIiUoAlTERE\nFJPJZILX68X27dvx2GOPYcOGDQCAJ554Arfddht27dqFa665Jnj/X/7yl5g1axZ27NiBrVu34pe/\n/CVaW1uxbt06HDp0CLt378Z//ud/4qc//SmTByKiFMQEgoiI4rrpppsAANOmTUNfXx8A4K233sKi\nRYsAAMuWLYPV6h/UPnDgALZv344VK1Zg9erV8Hg8OH78ODIzM/HDH/4QDz/8MP7pn/4JZWVl+rwZ\nIiJKCEuYiIgorszMTAD+0YjA1DmTyQRBEIL/bTb7+6QEQcBTTz2FGTNmAAC6urqQn58PAPjkk0/g\ndDpx6NChZL8FIiJSCUcgiIhIkZqaGvzud78DAOzZswdDQ0MAgOuuuw7PP/88AKC9vR3Lli3DmTNn\n0NbWhv/6r//C9u3bcfjwYfzlL3/Rre1ERKQcEwgiIooq1hyF7373u9i7dy+WL1+O/fv3w+FwAAC+\n9rWvYXBwEEuXLsVXvvIVPPLII7jsssvw7//+77jvvvtQVlaGxx9/HN///vfhdruT9VaIiEglXMaV\niIiIiIgk4wgEERERERFJxgSCiIiIiIgkYwJBRERERESSMYEgIiIiIiLJmEAQEREREZFkTCCIiIiI\niEgyJhBERERERCQZEwgiIiIiIpLs/wE7DI1Q+TkA7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x380aa710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset = test.loc[20]['2014-04-17 00:00:00':'2015-02-01 23:59:00']\n",
    "a=subset.groupby(subset.index.to_datetime().dayofyear, sort=False).apply(mape)\n",
    "a\n",
    "\n",
    "b = a[['mape_offline','mape_batch']]\n",
    "\n",
    "c = b[b > 0].reset_index()[['mape_offline','mape_batch']]\n",
    "c = c.fillna(0)\n",
    "#c = b.reset_index()[['mape_offline','mape_batch']]\n",
    "c['index'] = c.index\n",
    "\n",
    "plt.figure(figsize=(13,4))\n",
    "#sns.pairplot(c, size=5, aspect=.8, kind=\"reg\");\n",
    "g = sns.regplot(y=\"mape_offline\", x=\"index\", data=c, label='Offline');\n",
    "g.set(xlim=(0,300), ylim=(0,70))\n",
    "sns.regplot(y=\"mape_batch\", x=\"index\", data=c, label='Batch');\n",
    "\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Offline slope: -0.0301269784077\n",
      "Batch slope: -0.0108998422488\n"
     ]
    }
   ],
   "source": [
    "import scipy\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x=g.get_lines()[0].get_xdata(),y=g.get_lines()[0].get_ydata())\n",
    "print \"Offline slope:\", slope\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x=g.get_lines()[1].get_xdata(),y=g.get_lines()[1].get_ydata())\n",
    "print \"Batch slope:\", slope"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Future work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- more horizons\n",
    "- more sensors\n",
    "- online approach"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}