gbm-vs-xgboost-vs-lightgbm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f2e65080-5c69-cd2e-d515-8c569cbf8fca",
    "_uuid": "77fa257e680d18739ce82d0059ed6fda2e8805db"
   },
   "source": [
    "# Motivation\n",
    "\n",
    "I have been \"Kaggling\" (that can be a verb, right?) since before **GBM** was cool. An over trained set of **GBM** models got me a 2nd place finish in the [Dunnhumby's Shopper Challenge](https://www.kaggle.com/c/dunnhumbychallenge/leaderboard).  Since then I have spent my time working on projects that didn't require boosted algorithms. Now that I have some free time, I wanted to explore **xgboost** (the GBM killer) and its new challenger **lightGBM**. This notebook will explore the speed and accuracy of each model and discuss any observations I have along the way.\n",
    "\n",
    "# Boring Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "d3c61522-9fc4-8337-a68a-ec10015da972",
    "_uuid": "249b3008eb0369e9798770deef1b718cf383f01a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Type 'citation(\"pROC\")' for a citation.\n",
      "\n",
      "Attaching package: ‘pROC’\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    cov, smooth, var\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Libraries\n",
    "library(pROC, quietly=TRUE)\n",
    "library(microbenchmark, quietly=TRUE)\n",
    "\n",
    "# Set seed so the train/test split is reproducible\n",
    "set.seed(42)\n",
    "\n",
    "# Read in the data and split it into train/test subsets\n",
    "credit.card.data = read.csv(\"../input/creditcard.csv\")\n",
    "\n",
    "train.test.split <- sample(2\n",
    "\t, nrow(credit.card.data)\n",
    "\t, replace = TRUE\n",
    "\t, prob = c(0.7, 0.3))\n",
    "train = credit.card.data[train.test.split == 1,]\n",
    "test = credit.card.data[train.test.split == 2,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "287aaa2e-5014-1a66-3d17-5c6b557ae1f1",
    "_uuid": "66894ad6e819cdbf8fcc6f405aa21a8e253bb9fb"
   },
   "source": [
    "# Feature Creation\n",
    "\n",
    "This section is empty. Converting the time values to hour or day would probably improve the accuracy, but that is not the purpose of this kernel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "61aff1b1-0c3e-f917-387f-29b05fde367a",
    "_uuid": "917d1460eacd67d667c65776419cfad619cd05ee"
   },
   "source": [
    "# Modeling\n",
    "\n",
    "I have attempted to select a common set of parameters for each model, but that is not entirely possible. (*max_depth* vs *num_leaves* in **xgboost** and **lightGBM**) The following are some of the assumptions and choices made during this modeling process.\n",
    "\n",
    "* The data will be placed into the their preferred data formats before calling the models.\n",
    "* Models will not be trained with cross-validation.\n",
    "* If possible, different number of cores will be used during the speed analysis. (future mod)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6ce35725-33be-6d6e-2720-6e2cd0ffea21",
    "_uuid": "b61a0bd60e061b13b17ce9c74e91e9b24f696625"
   },
   "source": [
    "## GBM\n",
    "\n",
    "Training the GBM is slow enough, I am not going to bother microbenchmarking it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b9c54cbb-a876-558c-fff1-ccdb0b4f60c3",
    "_uuid": "dcaf3b8b22c9c04a4b17d96c5d2ed854fab07665"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loaded gbm 2.1.3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "   user  system elapsed \n",
       "242.608   0.304 242.907 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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06pHgIAAJugx04Jgp06oaGSkiKpqarn\nAABAe/TYKUGwUyc0VETk9GnVcwAAoD1Lj926dbJ6NT129sM3rY4l2HE1FgCgR/TYKUGwUycs\nTIRgBwDQJ3rslCDYqWNZsUtOVj0HAADao8dOCYKdOlyKBQAAmiLYqVOqlHh6EuwAAIBWCHZK\nUWUHANApeuyUINgpFRbGPXYAAF2ix04Jgp1SoaGSmiqXLqmeAwAAjdFjpwTftFJ0FAMAdIoe\nOyUIdkrReAIA0Cl67JQg2ClF4wkAQKfosVOCYKcUwQ4AAGiHYKcUwQ4AAGiHYKeUh4f4+hLs\nAAD6Q4+dEgQ71UJDCXYAAP2hx04Jgp1qoaFy+jQbwQEAOkOPnRJ806qFhsqtW3L+vOo5AADQ\nEj12ShDsVAsLExE5eVL1HAAAaIkeOyUIdqpVqiQicuiQ6jkAANASPXZKEOxUq1JFROTgQdVz\nAAAAh0ewU61SJXFxkQMHVM8BAAAcHsFONRcXKV+eYAcA0Bl67JQg2BUCVavKsWOSkaF6DgAA\nNEOPnRIEu0KgalXJypJjx1TPAQCAZuixU4JvuhBg/wQAQHfosVOCYFcIVK0qItxmBwDQE3rs\nlCDYFQJVqojBQJUdAEBP6LFTgmBXCHh6SmAgl2IBAEABEewKhypV5NAhMZtVzwEAABwYwa5w\nqFJFrl0Tk0n1HAAAaIMeOyUIdoVD5coibIwFAOgHPXZKEOwKBxpPAAD6Qo+dEnzThQPBDgCg\nL/TYKUGwKxxCQ6VECYIdAEA36LFTgmBXOBgMUrmy7N+veg4AALRBj50SBLtCo1o1+esvuXpV\n9RwAAMBREewKDcuDxbgaCwAA8otgV2hYgh1XYwEAukCPnRL6CXbPPPPMax/uUT1FAVSrJiJy\n4IDqOQAA0AA9dkroJ9jNnDlzweozqqcogPLlpVgxgh0AQB/osVPCWfUAeTN8+PBc3r2eNGP4\n8M3WfLIwcnaWSpUIdgAAfbD02K1fL02bSkIC2c5OHCzY/feD99Oyzfd791rSjBEj/v614wU7\nEalSRX7+WdLTxc1N9SgAABTIHT12ERGqByoaHCzYHd/4XYf2vbddTHvk1TF9YsP//VbHjh19\nawz7emQdRaNpoUoVyc6Wo0elRg3VowAAUCD02CnhYMEuoOGziSei33zi0S8mvuVd6rsZ73d3\nNfz/u25+Dz3+eGt10xVY5coiIgcPEuwAAEA+ON4Vb5cSlcctPzTvg6cWfRRfvtnLf17OUD2R\ndnhiLAAAKADHC3YiIgbnTu98f2ztFL8930eH15u4Okn1QBqpXFkMBjl0SPUcAAAUFD12Sjhm\nsBMRkeCHX/r9xLYX6l3v26pS16EzVY+jBU9PCQpixQ4AoAP02CnhwMFORFy9IietOTJzaKef\nP4xXPYtGqlSRQ4fEfN+dvwAAOAR67JRwsM0T92BwfWrE3Jjm7Rb8mVIipIrqaQqsWjVZs0ZO\nn5bQUNWjAACQf/TYKeH4wU5ERMKaxQ9opnoITVSvLiKybx/BDgDg0OixU4L8XMjcDnYAADgy\neuyUINgVMpYGO4IdAADIOwe7FHv69GkrPxkSEmLTSWzF21uCggh2AAAgHxws2IVafeeZ2XE3\nllavLps3i9ksBsODPwwAQKFEj50SDhbsdq6aM3nU21NWnxSRVo/Guegy+dSoIatWSVKSlCun\nehQAAPLJ0mNn2RVLj53dOFiwqx3bbXLLToYw38mnr8/9ZbG3sx6TnWX/xJ49BDsAgOOy9Ngl\nJkpMDF0n9uOA37TBZcB7NVUPYUs1a4qI7Nmjeg4AAPLP0mPXrJnExkpOjuppigwHDHYiAS0a\n+/r66vYOtOrVxWgk2AEAHNodPXawD4cMdl7lP7l48aKXk06TnYeHVKhAsAMAODR67JRwyGCn\nf5GRcviwpKerngMAADgSB9s88S85puQkk8lkMpmuZjoHBQUHh4SEhwXqZJ9sZKQsWCAHDkid\nOqpHAQAADsPxgl12WtIPEyZNnTp105HLd7zlGR7Vq0/ffv26h7k5WXu07Oxly5al57o2lpSU\nJCI59rzzMzJSRGTPHoIdAMBB0WOnhIMFu6yb++OqRq9MvmF0KdWkXdcqoQF+fr5ukp6ScumC\n6WjCivWjB/WYMnHOtv2LKrtb9Udbu3Zthw4drPnkiRMnCjZ7XvBgMQCAg6PHTgkHC3Yrn2u/\nMvlGo96TF4550f+uZbmcrKuLPu7Zcdj8di+uOTKztTUHbN68+aJFi3JfsZs4ceK6devKFbhV\nbsyYMV988cWhQ4dcHvhfLhUriru77N1bwDMCAKAKPXZKOFiwe2/Fac/gAZsm9brnu0Znr8eH\nzpv2a1CvxcNErAp2Tk5O7du3z/0zy5YtExFjgf9VJicnnzhx4uzZsw9+MJqTk1StSrADADgu\nS4+dZcUuIYFsZycO9jUfTssqHvJI7p+p0bh0VtpB+8yTJ6VLlxaRCxcuWPXpGjXk1Cm5csW2\nMwEAYBv02CnhYMGuhXexK4fHmm7ddx+DOefmzHnJxbxb2HMqK1mC3cWLF636dPXqYjbL/v22\nnQkAANugx04JBwt2wwdGpV9eFRkVP2/NzvQ70p05a9/GJf1bVRl77ErUG8OVjJc7Pz8/ydOK\nnfBgMQAAkAcOdo9dzUErRv4ZPXTurC6xs1y9AsoF+/v6+blJxqVLKefPnDhzKUNEqncZsfzt\nwvgw2bxdir3deAIAAGAdBwt2BqP7u3N2P/PG0kmTJi1Yu/P00X2H9meLiNHFo0xgSJt2Hfv0\n6dM+uqDbV20kb8EuNFT8/WX7dtvOBACAbdBjp4SDBTuL8AZxoxrEjRIRybmWcj7V7F7Gz6vw\nX1TOW7ATkbp1Zd06yczkfxAAAIdDj50ShT8O5c5Y0jcg0BFSnYiUKlXK2dk5D8GuXj1JT5cD\nB2w5FAAANmHpsVu3TlavpuvEfvim7cdgMPj6+lq7K1ZE6tUTEa7GAgAckaXHrlkziY0Vez6V\ns4jTW7C7dW1jQEBAQECA6kHurXTp0nlYsatfX4RgBwBwSPTYKaG3YGfOuXXu3Llz586pHuTe\n8hbsLPsn/vjDlhMBAGAT9Ngp4ZCbJ3Lh6ll/8+bNqqe4Lz8/v8uXL2dlZTk7W/fN16sna9ey\nfwIAAFhDbyt2BqeSDRs2bNiwoepB7q106dJmszklJcXaH6hfX9LTZd8+Ww4FAAB0wnFX7HJM\nyUkmk8lkMl3NdA4KCg4OCQkPC3QxqJ4rV7cbT8qUKWPVD9zeP1G7ti3nAgBAY/TYKeF4wS47\nLemHCZOmTp266cjlO97yDI/q1advv37dw9yclMz2QHmusru9f6JnT5sNBQCA9uixU8LBgl3W\nzf1xVaNXJt8wupRq0q5rldAAPz9fN0lPSbl0wXQ0YcX60YN6TJk4Z9v+RZXdC+MfzRLs8tB4\nEhwsgYHsnwAAOBxLj11iosTE0GNnP4Ux/eRi5XPtVybfaNR78sIxL/rftSyXk3V10cc9Ow6b\n3+7FNUdmtlYyYe78/PwkTyt2IlK3rqxezf4JAIBjsfTYWVbsEhLIdnbiYF/zeytOewYP2DSp\n192pTkSMzl6PD503LSYwefEw+89mjTxfihWRevUkI4P9EwAAx0KPnRIOFuwOp2UVD3kk98/U\naFw6K+2gfebJq/wEu7p1RagpBgA4GHrslHCwYNfCu9iVw2NNt+77aBJzzs2Z85KLebew51TW\n8/PzMxgMebjHTv7ZGLtjh41GAgAAuuFgwW74wKj0y6sio+LnrdmZfke6M2ft27ikf6sqY49d\niXpjuJLxHsjZ2dnb2ztvK3YhIVKmDMEOAAA8kINtnqg5aMXIP6OHzp3VJXaWq1dAuWB/Xz8/\nN8m4dCnl/JkTZy5liEj1LiOWv11T9aT35efnl7dgJyJ16sj69ZKVJVY+rwIAANXosVPCwVbs\nDEb3d+fsPrF1yaDn4sK8jaeP7tu0ISFhQ+LuQ8nmEiFtnn1z0Zbje38c5mEsvD3FeXtcrEXd\nupKWJvv322YiAAC0Z+mxE6HHzq4ccgUovEHcqAZxo0REcq6lnE81u5fx83KUiFq6dOnff//d\nbDYbDFanT0tN8bZtUrPwrkQCAPBv9Ngp4ejftLGkb0Cg46Q6ESldunRWVtaVK1fy8DMNGoiI\nbNtmo5EAANCcpceuWTOJjZWc+256hMYcKBHpRJ4fPiEiwcESFCS//26rmQAA0Bo9dkoQ7Owt\nPw+fEJGoKNm7V9LSbDITAABao8dOCYKdveWno1hEoqIkM1P+/NMmMwEAAF0g2NlbPoOd5Ta7\nP/6wwUQAAEAnCHb25u/vL/kIdvXqicHAg8UAAI6CHjslCHb2FhAQICJnz57N24/5+Eh4OMEO\nAOAo6LFTgmBnb/7+/k5OTiaTKc8/Wa+eHDggqak2GAoAAI1ZeuzWrZPVq+mxsx++aXtzdnb2\n8/PL84qdiNSrJ9nZsmuXDYYCAEBj9NgpQbBTICAgIJ8rdiJcjQUAOAR67JQg2CkQGBiYz2Bn\nMPD8CQCAQ6DHTgmCnQIBAQGpqak3btzI24/5+EhEhGzZYpuhAACAwyPYKRAYGCgi+Vm0a9hQ\njh6VPD2ODAAAFBkEOwXy2XgiItHRYjbL1q3azwQAgKbosVOCYKeAJdjlc8VOhGAHACj86LFT\nwln1AEWR5VJsflbsIiOleHHZvFn7mQAA0JSlxy4xUWJi6LGzH75pBfJ/KdbZWaKiZOtWyc7W\nfiwAALRDj50SBDsF8r95QkQaNpTr12X/fo1nAgBAU/TYKUGwU6BEiRJeXl6nTp3Kzw83aiQi\nXI0FABRy9NgpQbBTIywsLDk5OT8/+dBDYjAQ7AAAwN0IdmqEhYWdOnXKbDbn+Sf9/KR8eWqK\nAQDA3Qh2aoSGhqanp1+4cCE/PxwTI4cOUVMMACjM6LFTgmCnRmhoqIjk8za7hx4Ss1kSEzWe\nCQAA7dBjpwTBTo2wsDARyedtdjExIkKwAwAUZpYeu3XrZPVqeuzsh29ajQKt2FWvLj4+BDsA\nQGFGj50SBDs1LCt2+Qx2BoM0aiTbt0t6usZjAQCgEXrslCDYqREcHGw0GvN5KVZEmjSRjAz2\nxgIACi167JQg2Knh6uoaEBCQ/2BnuR913TrtJgIAAA6PYKdMWFjYyZMn8/nD9euLp+ffa9wA\nAAAiQrBTKDw8/OzZs+n5u0/O2VliYmTzZklL03ouAAA0QI+dEgQ7ZcLDw81mc/4X7Zo35zY7\nAEChRY+dEgQ7ZcLDw0UkKSkpnz/frJmIyNq1Go0DAICW6LFTgm9aGUuwO3HiRD5/vl498fYm\n2AEACid67JQg2CljCXb5vxTr5CRNmsjWrXLjhoZTAQCgCXrslCDYKVO2bFmj0Zj/FTsRad5c\nMjNl40bthgIAQBv02ClBsFPGzc0tICAg//fYiUiLFiLcZgcAAP5GsFMpPDy8QMGuZk3x8yPY\nAQAAC4KdSuXKlTt//vzNmzfz+fMGgzRrJjt2yOXLms4FAEBB0WOnBMFOpfLly5vN5oLeZped\nLRs2aDcUAAAaoMdOCYKdShUqVBCRY8eO5f8QLVuKiCQkaDQRAADaoMdOCb5plcqXLy8FDHaV\nK0tIiKxZo9lMAABogR47JQh2KllW7I4fP16go7RsKfv2yenT2swEAIAW6LFTgmCnUmBgoIeH\nR4FW7ETkkUdEuBoLAChc6LFTgmCnksFgKFeuXEFX7GJjxWCQ1as1GgoAADgqgp1i5cuXT0pK\nys7Ozv8hypSRyEhZtUrMZu3mAgAAjodgp1iFChUyMjL++uuvAh2ldWs5e1Z27dJoKAAACooe\nOyUIdopVrFhRRA4dOlSgo7RtKyKyfLkWEwEAoAF67JQg2ClWo0YNEdm7d2+BjtK4sXh5EewA\nAIUHPXZK8E0rFhkZKQUPdi4u0ry5bN4sV65oMxYAAAVDj50SBDvFfHx8AgMD9+zZU9ADtW4t\nWVmUngAACgl67JQg2KkXGRm5b9++nAL+50ybNiIiv/6qyUgAABQQPXZKEOzUi4yMvHnz5okT\nJwp0lPBwqViRYAcAQFFGsFPPsn9Cg6uxbdrIyZNy8KAGMwEAAAeUt2Bnzrryx9pFk78c/+kn\nH4pI+rnjt+jELTBt9k/IP6UnixcXeCIAAAqKHjsl8hDsTOsmPVQ2JKrFY7pLQiEAACAASURB\nVK/07T9oyLsikrQw3ie09pcbzthsvCKhatWqRqNx3759BT1Qy5ZSsqQsXKjFUAAAFAg9dkpY\nG+xSTT/Vbd3v9/MuPQaN/HxIpOVF3zqdy6cdeq1l1VlnUm02of55eHiUL19egxW7YsWkTRvZ\nskVMJi3mAgAg/+ixU8Lab/rnHm+cyzR8tvXId6PefaJpgOXF0g3e+P3gyjLG1LeenmuzCYuE\nGjVqHDp06NatWwU90GOPSU4OV2MBAMrRY6eEtcHu/Y3nfKqNH1DX747X3Uo3GVfT78K2/2o9\nWNFSo0aNzMzMw4cPF/RAcXHi6srVWACAcvTYKWFtsDuXmV2ibMQ93/IL9cjO4Da7Aqlevbpo\nsn/Cy0uaN5eEBLl2TYOxAADIL3rslLA22LUu5XZh21fpdy+lmrN+2Hy+mHdTbccqarR5YqzF\nY49JRgbPjQUAoAiyNti981rNmxd+bDZwyrXs/y84MeekzXn/0WlnUyv2+I9txru3NNOh5T99\nN27ydwk7z9797o4Fc2fMmGHPeQouIiLC1dV19+7dGhzrscfEaJSff9bgUAAAwKE4W/m5moOX\n9l5cdfLY3gGzR9crd1VEnun86O4dG/ckXfeq0HnJR1G2HPJ/bPp60ON9Pr+QmS0iBoPhoe6j\nVk1/091ouP2B2f1fGn36+jPPPGO3kQrO1dW1WrVqf/75pwbHCgqShg1l6VJJSxN3dw0OCABA\n3tFjp4S1K3ZGZ59JW5KXThpSyf365q0XRWTmguXJaX4vf/D98QM/hhVzsuWQ/+/CtmGNXx59\n2RjcZ/AHk8Z90ik6KHHGoLrPzbHP2W2qVq1ap06dunjxogbHeuIJuXGDx4sBABSix06JvBTL\nGIo92vujXSfOpt+8fGTfwXNX06+cPT7lP/E+LvZrp5n+7JcGo/uMA3u//Pg/vV97e17i0Y/b\nhR2a0f2T3Sl2m8FGatWqJSLaXI3t0kUMBpk3T4NDAQCQL/TYKZGXb9qctX7RjA+HT3d286pY\nrbJ/yWKDnn9l9NSFGXZ8qtiEpGu+Nb7sVs7z798b3d78cVUFN6dPOo5w9Geb1a5dW0R27dql\nwbFCQiQqSpYuZRsSAEAVeuyUsDbYZWecevWRSs0ei/940qrbL276edpbL3cs3+SFM7fs9DeW\nkpnjERL671ec3SMWfBRz9fgXbybeYyOFA6lTp47BYNAm2IlI+/Zy5YokJmpzNAAA8ogeOyWs\nDXa/vd564pqkuk8PX7Hm09svrj6yd+qgdmcSp7UetMk2492psZfrhW3f3NG6UqPvgta+7pM6\n9Dif6cD/ReDt7R0aGqplsBORJUu0ORoAAHlEj50S1ga74bOPe/g/tXXGe41rBN5+0a10xRdH\nLR4Y4nn0+5G2Ge9Og54Iv3l+dsxLHxy4cPP2iwZnnx9WvJN2aWX9x4abbmXbZxJbiIyMPHDg\nQFZWlgbHqlVLypYl2AEAUKRYG+y2Xc/0rfmcs+Eebz1az+/WNTtd8msy9pd2Vbx3fDu0mn/x\noGpdb79euv67v37Y+dTykeUCKs29kGafYTQXGRmZkZFx5MgRbQ4XFyeHDsm+fdocDQAAFHrW\nBrsaHs43ku99lXDX4avO7vd+2pjmnN0jFu09NfOjfm2bRXukX/73W63embf1+09igm+cytBi\nxUuFyMhIEdmzZ482h+vWTURk9mxtjgYAQF7QY6eEtcFuWOuQy4cH9fp++x2v753X/40DlwIa\n2+/JEwanEk8PGb9s7Zajx1fd8VaD+LfX7Dl34fSxLescssJN42DXpImULSuzZ4vZ0XcMAwAc\nDz12Slj75IlHvp3dYPXDX/Wov+LL9p2b1w4J8ks9f3pv4qIf1x1y8aj8zcw4m06ZFwa/4PJ+\nweVVj5EfVapUcXV11SzYGQzSpYuMHi1//CFR9ns0CAAA8k+PXWKixMTQY2c/1gY7V88G649s\nHTV40EfTloz5ffHt1+t37D9mzAeNfdxsM17R4uLiEhERoVmwE5FOnWT0aFm0iGAHALAzS4/d\n+vXStKkkJJDt7CQPX7ObX633vv716oXTf25ZN2/W7BXrtpwwXdm2YGzjsiVsN19RU7NmzRMn\nTly7dk2bw0VHS5ky7I0FANgfPXZKWLtid5ubT1Ct6KBa0bYY5sFOnz5t5SdDQkJsOomN1K5d\ne9asWX/++efDDz+sweGMRmnbVr77Tk6elLJlNTggAADWocdOCWuDnTn7+ldvvzDup7XHz924\n5wdMN9JK3bMNRVOhoaEP/pCIiJgdc8dAnTp1RGTnzp3aBDsRaddOvvtOliyRV1/V5oAAAKCw\nsjbYbfsgtvdnv3sEVIx6qFYx4z0CnKeTzVOdiOxcNWfyqLenrD4pIq0ejXOxxznt6naw0+yI\nbdtKiRIyZw7BDgAA3bM22PX/fFdQ8w/3rxriZZcAdz+1Y7tNbtnJEOY7+fT1ub8s9rb9GqGd\n+fr6hoWFaRnsPDykQweZPVuSkiQ8XLPDAgCQK3rslLB288Te1MzOX/VRm+r+ZnAZ8F5N1UPY\nUJ06dfbv35+enq7ZEZ9+WsxmmooBAPZEj50S1ga7lt7FcjJzbDqK9QJaNPb19TUUgpBpC3Xq\n1MnKytqn4aPAWrUSPz+CHQDAniw9duvWyerVdJ3Yj7Xf9EcjHvmxxwdpOYViR4JX+U8uXrxY\nKJYPbaBu3boismPHDs2O6OIiXbrInj2yd69mxwQAIFeWHrtmzSQ2VnIKy9KQ/lkb7Kq9uvDz\n2KSIhp0m/7hk2+6DSXex5ZBFiyXYaXmbnYg89ZQIz40FANgPPXZKWLt5wmD4OwK+0m3hPT9g\n93qRHFNykslkMplMVzOdg4KCg0NCwsMCdbBPNjg4uEyZMhoHu5gYCQuTmTNl5EgWxAEAdkCP\nnRLWBrtXC01ZRnZa0g8TJk2dOnXTkct3vOUZHtWrT99+/bqHuTkpmU0rderUWb9+fVZWlrNz\nnhuk781olGeflQ8+kNWrpVUrbY4JAAAKGWtzw4QJE2w6h5Wybu6Pqxq9MvmG0aVUk3Zdq4QG\n+Pn5ukl6SsqlC6ajCSvWjx7UY8rEOdv2L6rsbtUfLTs7e9myZblvQbVcaM6x4w0CdevWXbFi\nxcGDB2vUqKHZQV94QT76SL75hmAHAIBeabAgtKJNRM+/+v21p1/BD/VAK59rvzL5RqPekxeO\nedH/rmW5nKyriz7u2XHY/HYvrjkys7U1B1y7dm2HDh2s+eSJEyfyPG5+3a4p1jLYlSsnzZvL\nL79ISor4+mp2WAAA7oUeOyXyEOxOrv5uwvyEExdu/vvFnKwra9YcMwckaz3Yvb234rRn8IBN\nk3rd812js9fjQ+dN+zWo1+JhIlYFu+bNmy9atCj3FbuJEyeuW7euXLly+Zk4Xyz7J7Zv3x4f\nH6/lcV94QdaskdmzpW9fLQ8LAMBdLD1269fTY2dX1ga7M2sHR7T+76171Z24l6n/+ew3NZ3q\nvg6nZRWv9kjun6nRuHTW1oNWHtDJyal9+/a5f2bZsmUiYrTjnoNy5cr5+Pho2Xhi0amTeHvL\ntGkEOwCArVl67BITJSaGbXv2Y+03/VXPyVlGr683Hrxx/eKoxoE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fi4sMHCjnz8vXX9vpjAAAR0aPnRIEO0fVvHnzYsWK2S/YiciLL0pQ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\nDx8+pKamJiYm3rt3r2byKywsfPv2bUFBQX5+fk5Ozv+1d+fxT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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VFPAQC8J5Zh957C+qcemlk/dWr9tOnP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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      "roc.default(response = test$Class, predictor = gbm.test, plot = TRUE,     col = \"red\")\n",
      "\n",
      "Data: gbm.test in 85504 controls (test$Class 0) < 164 cases (test$Class 1).\n",
      "Area under the curve: 0.9498\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZGwsOtakF/Qseumu53z8wq79Kl3zJA+HavWL87sXABJpeogTRIWdmcO6br8rSv+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\nIR0KIlwLoB5VBxD1zhN5HY4454dHnPPDyopVC/61uFvx0L7dOkS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CyC/s3G9A8eHjjp44ceL4kfb+yjmqDgBC4sKu1tD9xt6839ibQwih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\nAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(gbm, quietly=TRUE)\n",
    "\n",
    "# Get the time to train the GBM model\n",
    "system.time(\n",
    "\tgbm.model <- gbm(Class ~ .\n",
    "\t\t, distribution = \"bernoulli\"\n",
    "\t\t, data = rbind(train, test)\n",
    "\t\t, n.trees = 500\n",
    "\t\t, interaction.depth = 3\n",
    "\t\t, n.minobsinnode = 100\n",
    "\t\t, shrinkage = 0.01\n",
    "\t\t, bag.fraction = 0.5\n",
    "\t\t, train.fraction = nrow(train) / (nrow(train) + nrow(test))\n",
    "\t\t)\n",
    ")\n",
    "# Determine best iteration based on test data\n",
    "best.iter = gbm.perf(gbm.model, method = \"test\")\n",
    "\n",
    "# Get feature importance\n",
    "gbm.feature.imp = summary(gbm.model, n.trees = best.iter)\n",
    "\n",
    "# Plot and calculate AUC on test data\n",
    "gbm.test = predict(gbm.model, newdata = test, n.trees = best.iter)\n",
    "auc.gbm = roc(test$Class, gbm.test, plot = TRUE, col = \"red\")\n",
    "print(auc.gbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "7f694f3f-43e3-0c58-8f6f-f07de5c2967d",
    "_uuid": "70730eb828cd134045fa852cd82337289b49cc1a"
   },
   "source": [
    "## xgboost\n",
    "\n",
    "Per [Andrea's comment][1] about tree depth between lightGBM and xgboost, I am adding a second xgboost model to address this. The original xgboost model has `max.depth = 3` which allows for up to 7 decision splits in the tree. \n",
    "\n",
    "I have now added in `tree_growth = 'hist'` into the comparisons. Thanks [CPMP][2]!\n",
    "\n",
    "\n",
    "  [1]: https://www.kaggle.com/nschneider/d/dalpozz/creditcardfraud/gbm-vs-xgboost-vs-lightgbm#178605 \"Andrea's Comment\"\n",
    "  [2]: https://www.kaggle.com/nschneider/gbm-vs-xgboost-vs-lightgbm#209155 \"CPMP's Comment\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "f8d702bc-f7c9-d00a-b82d-ff74b5167974",
    "_uuid": "2c2d6c8eaa480ec5c3e890d6ba9649d9e7d0b6eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\ttest-auc:0.917094 \n",
      "Will train until test_auc hasn't improved in 40 rounds.\n",
      "\n",
      "[21]\ttest-auc:0.943909 \n",
      "[41]\ttest-auc:0.943850 \n",
      "[61]\ttest-auc:0.958839 \n",
      "[81]\ttest-auc:0.960277 \n",
      "[101]\ttest-auc:0.960277 \n",
      "Stopping. Best iteration:\n",
      "[75]\ttest-auc:0.962100\n",
      "\n",
      "[1]\ttest-auc:0.917094 \n",
      "Will train until test_auc hasn't improved in 40 rounds.\n",
      "\n",
      "[21]\ttest-auc:0.943909 \n",
      "[41]\ttest-auc:0.943850 \n",
      "[61]\ttest-auc:0.958839 \n",
      "[81]\ttest-auc:0.960277 \n",
      "[101]\ttest-auc:0.960277 \n",
      "Stopping. Best iteration:\n",
      "[75]\ttest-auc:0.962100\n",
      "\n",
      "[1]\ttest-auc:0.917094 \n",
      "Will train until test_auc hasn't improved in 40 rounds.\n",
      "\n",
      "[21]\ttest-auc:0.943909 \n",
      "[41]\ttest-auc:0.943850 \n",
      "[61]\ttest-auc:0.958839 \n",
      "[81]\ttest-auc:0.960277 \n",
      "[101]\ttest-auc:0.960277 \n",
      "Stopping. Best iteration:\n",
      "[75]\ttest-auc:0.962100\n",
      "\n",
      "[1]\ttest-auc:0.917094 \n",
      "Will train until test_auc hasn't improved in 40 rounds.\n",
      "\n",
      "[21]\ttest-auc:0.943909 \n",
      "[41]\ttest-auc:0.943850 \n",
      "[61]\ttest-auc:0.958839 \n",
      "[81]\ttest-auc:0.960277 \n",
      "[101]\ttest-auc:0.960277 \n",
      "Stopping. Best iteration:\n",
      "[75]\ttest-auc:0.962100\n",
      "\n",
      "[1]\ttest-auc:0.917094 \n",
      "Will train until test_auc hasn't improved in 40 rounds.\n",
      "\n",
      "[21]\ttest-auc:0.943909 \n",
      "[41]\ttest-auc:0.943850 \n",
      "[61]\ttest-auc:0.958839 \n",
      "[81]\ttest-auc:0.960277 \n",
      "[101]\ttest-auc:0.960277 \n",
      "Stopping. Best iteration:\n",
      "[75]\ttest-auc:0.962100\n",
      "\n",
      "Unit: seconds\n",
      "                                                                                                                                                                                                                                                                                                                                                  expr\n",
      " xgb.model.speed <- xgb.train(data = xgb.data.train, params = list(objective = \"binary:logistic\",      eta = 0.1, max.depth = 3, min_child_weight = 100, subsample = 1,      colsample_bytree = 1, nthread = 3, eval_metric = \"auc\"),      watchlist = list(test = xgb.data.test), nrounds = 500, early_stopping_rounds = 40,      print_every_n = 20)\n",
      "      min       lq    mean   median       uq      max neval\n",
      " 11.81437 11.85168 11.8613 11.85613 11.86979 11.91451     5\n",
      "NULL\n",
      "\n",
      "Call:\n",
      "roc.default(response = test$Class, predictor = xgb.test.speed,     plot = TRUE, col = \"blue\")\n",
      "\n",
      "Data: xgb.test.speed in 85504 controls (test$Class 0) < 164 cases (test$Class 1).\n",
      "Area under the curve: 0.9621\n"
     ]
    },
    {
     "data": {
      "image/png": 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bSjAsSIqoNkiNlLsfd/umyHPUavq7oQwm9KVu7Y497/Vl0IIbTucd4veuy4+IOf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\nAAAARiDYAQAAADDi/wBpHMUQBDc5cgAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(xgboost, quietly=TRUE)\n",
    "xgb.data.train <- xgb.DMatrix(as.matrix(train[, colnames(train) != \"Class\"]), label = train$Class)\n",
    "xgb.data.test <- xgb.DMatrix(as.matrix(test[, colnames(test) != \"Class\"]), label = test$Class)\n",
    "\n",
    "# Get the time to train the xgboost model\n",
    "xgb.bench.speed = microbenchmark(\n",
    "\txgb.model.speed <- xgb.train(data = xgb.data.train\n",
    "\t\t, params = list(objective = \"binary:logistic\"\n",
    "\t\t\t, eta = 0.1\n",
    "\t\t\t, max.depth = 3\n",
    "\t\t\t, min_child_weight = 100\n",
    "\t\t\t, subsample = 1\n",
    "\t\t\t, colsample_bytree = 1\n",
    "\t\t\t, nthread = 3\n",
    "\t\t\t, eval_metric = \"auc\"\n",
    "\t\t\t)\n",
    "\t\t, watchlist = list(test = xgb.data.test)\n",
    "\t\t, nrounds = 500\n",
    "\t\t, early_stopping_rounds = 40\n",
    "\t\t, print_every_n = 20\n",
    "\t\t)\n",
    "    , times = 5L\n",
    ")\n",
    "print(xgb.bench.speed)\n",
    "print(xgb.model.speed$bestScore)\n",
    "\n",
    "# Make predictions on test set for ROC curve\n",
    "xgb.test.speed = predict(xgb.model.speed\n",
    "                   , newdata = as.matrix(test[, colnames(test) != \"Class\"])\n",
    "                   , ntreelimit = xgb.model.speed$bestInd)\n",
    "auc.xgb.speed = roc(test$Class, xgb.test.speed, plot = TRUE, col = \"blue\")\n",
    "print(auc.xgb.speed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "703c52e7-5c3b-c2b3-c9b3-7f72dbd13b5a",
    "_uuid": "8391f54c08512bddf0e8d61202ae28cecb5491e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\ttest-auc:0.917094 \n",
      "Will train until test_auc hasn't improved in 40 rounds.\n",
      "\n",
      "[21]\ttest-auc:0.944424 \n",
      "[41]\ttest-auc:0.944060 \n",
      "[61]\ttest-auc:0.961498 \n",
      "[81]\ttest-auc:0.961751 \n",
      "[101]\ttest-auc:0.961751 \n",
      "Stopping. Best iteration:\n",
      "[69]\ttest-auc:0.962970\n",
      "\n",
      "[1]\ttest-auc:0.917094 \n",
      "Will train until test_auc hasn't improved in 40 rounds.\n",
      "\n",
      "[21]\ttest-auc:0.944424 \n",
      "[41]\ttest-auc:0.944060 \n",
      "[61]\ttest-auc:0.961498 \n",
      "[81]\ttest-auc:0.961751 \n",
      "[101]\ttest-auc:0.961751 \n",
      "Stopping. Best iteration:\n",
      "[69]\ttest-auc:0.962970\n",
      "\n",
      "[1]\ttest-auc:0.917094 \n",
      "Will train until test_auc hasn't improved in 40 rounds.\n",
      "\n",
      "[21]\ttest-auc:0.944424 \n",
      "[41]\ttest-auc:0.944060 \n",
      "[61]\ttest-auc:0.961498 \n",
      "[81]\ttest-auc:0.961751 \n",
      "[101]\ttest-auc:0.961751 \n",
      "Stopping. Best iteration:\n",
      "[69]\ttest-auc:0.962970\n",
      "\n",
      "[1]\ttest-auc:0.917094 \n",
      "Will train until test_auc hasn't improved in 40 rounds.\n",
      "\n",
      "[21]\ttest-auc:0.944424 \n",
      "[41]\ttest-auc:0.944060 \n",
      "[61]\ttest-auc:0.961498 \n",
      "[81]\ttest-auc:0.961751 \n",
      "[101]\ttest-auc:0.961751 \n",
      "Stopping. Best iteration:\n",
      "[69]\ttest-auc:0.962970\n",
      "\n",
      "[1]\ttest-auc:0.917094 \n",
      "Will train until test_auc hasn't improved in 40 rounds.\n",
      "\n",
      "[21]\ttest-auc:0.944424 \n",
      "[41]\ttest-auc:0.944060 \n",
      "[61]\ttest-auc:0.961498 \n",
      "[81]\ttest-auc:0.961751 \n",
      "[101]\ttest-auc:0.961751 \n",
      "Stopping. Best iteration:\n",
      "[69]\ttest-auc:0.962970\n",
      "\n",
      "Unit: seconds\n",
      "                                                                                                                                                                                                                                                                                                                                                expr\n",
      " xgb.model.acc <- xgb.train(data = xgb.data.train, params = list(objective = \"binary:logistic\",      eta = 0.1, max.depth = 7, min_child_weight = 100, subsample = 1,      colsample_bytree = 1, nthread = 3, eval_metric = \"auc\"),      watchlist = list(test = xgb.data.test), nrounds = 500, early_stopping_rounds = 40,      print_every_n = 20)\n",
      "      min       lq     mean   median      uq      max neval\n",
      " 12.24183 12.28351 12.35888 12.34905 12.4526 12.46739     5\n",
      "NULL\n",
      "\n",
      "Call:\n",
      "roc.default(response = test$Class, predictor = xgb.test.acc,     plot = TRUE, col = \"blue\")\n",
      "\n",
      "Data: xgb.test.acc in 85504 controls (test$Class 0) < 164 cases (test$Class 1).\n",
      "Area under the curve: 0.963\n"
     ]
    },
    {
     "data": {
      "image/png": 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oRUKrz8cpgzp+rLfv3Co4/u+E916BBa\ntWrQuQCIAVUXI8IuJ9x4Y7jrrk1ftmgRevWKbhoA4kPVxYuXYnPCn//8lS9dzhWAmlB1sSPs\nckJZ2abtTp3CtddGNwoAMaHq4kjY5YT166s2fvSjUFwcvv71SKcBIOupupgSdsn35Zdh2bKq\n7YKCSEcBIA5UXXwJu+T785/DF19UbbdtG+koAGQ9VRdrwi75Jk+u2ujePZx6aqSjAJDdVF3c\n+biTxJo5M0yaFMrLwx//WHXLiBHOhwVgu1RdAgi7ZPr44/CNb4R1675y43HHRTQNAFlP1SWD\nl2KTac6cLauuTZtwxBERTQNAdlN1ieGIXTJVVm7aPuCA0KJFuO660LRpdAMBkK1UXZIIu3h7\n//0wZkxYu3bL2xct2rQ9bVpo0yaTQwEQG6ouYYRdvJ1wQpgzZwdr/CMFYJtUXfJ4j128ffDB\nDha0aRNatMjIKADEiqpLJEfs4m3je+n22y907Ljlvc2bhyuvDI39kAH4KlWXVH7nx8CaNeGm\nm8I//7mNu1Kpqo1Ro8Ipp2RyKADiStUlmLCLgfvuC/fcs4M1eV5UB6AGVF2yyYEY+PjjHSxo\n1CjstltGRgEgzlRd4jliFwPl5VUbXbqEYcO2sWDIkLD//pmcCID4UXW5QNhli9LScPnl4a23\ntnHXwoVVG3vvHR54IJNDAZAQqi5HCLts8dBD4be/3cEa57cCUAeqLnd4j122+PTTHa/p06fh\n5wAgWVRdTnEIKFtUVFRttGsXLrhgGwt22WXbtwPA9qi6XCPsojd1arjiivDvf1d92alT+MlP\nIh0IgERQdTlI2EXvhhu+cr1Xb6QDoP5UXW7yHrvorVr1lS8HDoxoDgCSQtXlLEeHssiIEeHU\nU8OJJ0Y9BwBxpupymbCL0vr1YcSIMH9+1Zd9+4bTT490IABiTtXlOGEXpb/+NUyatOnL/Pzo\nRgEg/lQd3mMXpTVrNm0XFITBg6MbBYCYU3UER+yyx6xZYZ99oh4CgHhSdWzgiF1k3nvvKx84\n3KtXdKMAEGeqjo2EXWTGjw/LllVtN2rkDXYA1IWqY3PCLjJr127aPuGE0KRJdKMAEE+qji0I\nu2iUlIQlS6q2998/TJgQ6TQAxJCqY2tOnojA66+Hb3xj0ymxTZuGPIENQG2oOrZJUETg6ae/\n8kEnXoQFoFZUHdsj7CKw+bvrCgrCRRdFNwoAcaPqqIaXYjOtsjIUF1dtH3xweP75sNNOkQ4E\nQHyoOqrniF2mnX9+ePTRqu3mzVUdADWl6tghYZdpkydv2m7RIro5AIgVVUdNCLtMKy+v2thp\np3D11ZGOAkBMqDpqSNhF5kc/CkOGRD0EAFlP1VFzwi6j/vKXsGJF1bZ/mADskKqjVoRdRr3y\nSkilqrZbtYp0FACynqqjtoRdRm2sus6dwwknRDoKANlN1VEHwi6jNoZdu3ahefNIRwEgi6k6\n6kbYAUB2UXXUmbDLqI1H7ABgm1Qd9ZGQS4p9uejV8U+9vL51r0FfP/qAXq2jHmfH/DsFYGuq\njnqK3xG71f9+8Zzjvr5L28I2HXe75K4pIYRPXvhx154DL77y+su+e8rBvXe56jevRD0jANSa\nqqP+YnbEbs2SiXv3PunjteXN2nZpvHz+Azd864sOz826/Jbyjl+/7funty3/5H9/evevLjm8\nff9Pb9q/XdTDboOXYgHYJlVHWsTsiN0zZ478eG35DY+8UVryyeerP7n52G6PnP+tuRU7z3h/\n6s3XXHzZ9bdPnzOpZV6jX533RNSTbtuaNVUbzZpFOgcA2UTVkS4xC7ufvLqs9a43//iMg0II\n+U073fDQPSGE9n3u7dOyYMOCZu2P/FHPos/n3hvllDWQnx/1BABkB1VHGsUs7D4sqyjc+ZCN\nXzYt+noIofWe7Tdfs1vnwoq1CzM9GQDUnqojvWIWdoNaN/liwR/K//NOtVXzfxdCKHn9n5uv\nmbZgdUHLgzI/W014jx0AG6k60i5mYXfTGb3WfDph8Mh7XvvnvNen/d8ZQ36Y37Roxdyr/ueN\nTzcs+PiFH/7041Wdj7g22jm3Z2PY5cXsGw9Amqk6GkLMzood9ItJJ08/eML9V/e//+oQQl7j\nNr9+690Xj+598YA9HjvuW23LF/9pyl/zm3T89f8MiXpSANguVUcDiVnY5Tft8cSbHz14zz3T\n/v6PLwq6nHbVnafts/M5b/xx+REnvjDx0RBCmx7fePjZJ4/rUBj1pACwbaqOhhOzsAshNMov\nOu/aW8/b7JbmnY78y3tL5vzz7fKiXvv06BDVYACwQ6qOBhW/sNu2vGa99+8f9RAAUB1VR0Pz\nHn4AyARVRwYk5Yjdf6xbNb37HieHEJYsWVKT9RUVFZMnTy4rK6tmzYIFC0IIlZWV6RgQgFyk\n6siMpIVdqnLd0qVLa75+2rRpxx9/fE1Wzp8/v65DAZDTVB0Zk7Swa9LqkJkzZ9Z8/eDBgydO\nnFj9EbsxY8a89NJLPXv2rPd0AOQcVUcmJS3sGuW3HjBgQM3X5+fnDx8+vPo1kydPDiHk+Uxh\nAGpJ1ZFh8Q27yuKFC4qLi4uLi1eub9ylS9eu3br16N65wD8ZALKDqiPz4hd2FaULHrpv7Lhx\n42bMW7HFXa169L145GWXX35m92b5kcy2QxUVVRv+dQMkm6ojEjELu/I1s4fu1X/qwtV5BTsd\nPuzUPXfp1L59u2ahrKRk+afFH7w45eWfXXfuA2Mee332xN6F2fhX2xh2BQWRzgFAQ1J1RCUb\n66caU88bPnXh6kMvuf+Zey7YeavDcpXlKyf++PwRNz857IIX5o0/JpIJAchxqo4IxeyEgFum\nLGrV9aoZYy/euupCCHmNi04YNeH3gzovfPbmzM8GAKqOaMUs7OaWlrfoNqT6Nfse1qG89P3M\nzFNbGz/k2L90gORRdUQuZmF3ZJumn88dXbxuuxeBSFWuGT9hYdM2R2ZyqprbGHb5WXp2BwB1\npOrIBjELu1uv6Vu24vn9+p494YVZZVvUXar83el/uvLoPUd/+Hnfq2+NZDwAcpOqI0vE7OSJ\nPtdNueOt/qMef+SUox5pUtSpZ9ed27Vv3yysXb68ZNni+YuXrw0h7HPKbc9d3yfqSQHIFaqO\n7BGzsGuUV3jTY++cdfWksWPHPjVt1qIP3p0zuyKEkFfQvGPnbscOGzFy5Mjh/bP32l+pVNWG\nf/UAyaDqyCoxC7sNevQbele/oXeFEELlqpJlX6YKO7YvisWLysIOIElUHdkmlmG3mbzW7Tq1\njnoIAHKQqiMLxeI4FwBkF1VHdhJ2AFA7qo6sJewAoBZUHdlM2AFATak6spywA4AaUXVkP2EH\nADum6ogFYQcAO6DqiAthBwDVUXXEiLADgO1SdcSLsAOAbVN1xI6wA4BtUHXEkbADgC2pOmJK\n2AHAV6g64kvYAcAmqo5YE3YAUEXVEXfCDgBCUHUkgrADAFVHQgg7AHKdqiMxhB0AOU3VkSTC\nDoDcpepIGGEHQI5SdSSPsAMgF6k6EknYAZBzVB1JJewAyC2qjgQTdgDkEFVHsgk7AHKFqiPx\nhB0AOUHVkQuEHQDJp+rIEcIOgIRTdeQOYQdAkqk6coqwAyCxVB25RtgBkEyqjhwk7ABIIFVH\nbhJ2ACSNqiNnCTsAEkXVkcuEHQDJoerIccIOgIRQdSDsAEgCVQdB2AGQAKoONhB2AMSbqoON\nhB0AMabqYHPCDoC4UnWwBWEHQCypOtiasAMgflQdbJOwy6jKyqoNT0EAdabqYHuEXTQ8CwHU\njaqDagg7AGJD1UH1hB0A8aDqYIeEHQAxoOqgJoQdANlO1UENCTsAspqqg5oTdgBkL1UHtSLs\nAMhSqg5qS9gBkI1UHdSBsAMg66g6qBthB0B2UXVQZ8IOgCyi6qA+hB0A2ULVQT0JOwCygqqD\n+hN2AERP1UFaCDsAIqbqIF2EHQBRUnWQRsIOgMioOkgvYQdANFQdpJ2wAyACqg4agrADINNU\nHTQQYQdARqk6aDjCDoDMUXXQoIQdABmi6qChCTsAMkHVQQYIOwAanKqDzBB2ADQsVQcZI+wA\naECqDjJJ2AHQUFQdZJiwA6BBqDrIPGEHQPqpOoiEsAMgzVQdREXYAZBOqg4iJOwASBtVB9ES\ndgCkh6qDyAk7ANJA1UE2EHYA1Jeqgywh7ACoF1UH2UPYAVB3qg6yirADoI5UHWQbYQdAXag6\nyELCDoBaU3WQnYQdALWj6iBrCTsAakHVQTYTdgDUlKqDLCfsAKgRVQfZT9gBsGOqDmJB2AGw\nA6oO4kLYAVAdVQcxIuwA2C5VB/Ei7ADYNlUHsSPsANgGVQdxJOwA2JKqg5gSdgB8haqD+Goc\n9QB19Olnazu0b7phu7R49ovT/zF7/sqv9dm//xGDuhbmRzsbQHypOoi1+B2xW/jir/vv3m7I\nz/+14cu/3HtVjx59hp167nXXX3HSt47YbZe+9z73YbQTAsSUqoO4i9kRu8/f/+XuQ75fkd/2\n/+3TJoTwyV8uH3LFfQXNe1z6XxceuHuHRf+a+etf/uGq4X2azS2+sFfrqIcFiBNVBwkQs7C7\n54TbyvNaPf7PD0/uXRRC+NX5f8hr3ObJD98d3ql5CCGEC688f1CnfS4cdfrjF752YbSjAsSI\nqoNkiNlLsfd9tHKn3UdvqLoQwu+Kv2y75z3/qboQQmiz5/m/2rPt8nd/HtGAAPGj6iAxYhZ2\nrfLz8pu12vhl87xGBS07bLGme4dmFeuWZHYugLhSdZAkMQu7C7u3Wv7+Da+UlG348tq9dlox\nZ/SXlamNC1Lly+9+u6RZmyERDQgQJ6oOEiZmYXfB/15RXvrhMfsNH/f09NLK1HnPjG7z5bRv\nXP7rFeWVIYTyNQvuOHfQi5+X9bniB1FPCpDtVB0kT8zCruOht74y9ntNP3vxohMPb9O24yFD\n7+7Yodk/xlzeoUXbPXr3aN2q1y2PvL/rkGum/OCAqCcFyGqqDhIpZmEXQhh4yZgln8373Y+/\nf3CP9quKP3z7ky9DCJXrv/zs84o+x573hymzPpr6s6J8z1AA26XqIKli9nEnGxS07vXdG37x\n3Rt+EUIoL121dHlp2447Fzb2xASwY6oOEiyWYbe5xoWtu3b1WcQANaLqINni91IsAHWj6iDx\nYn/EbgvrVk3vvsfJIYQlS2r0UXYVFRWTJ08uKyurZs2CBQtCCJWVlekYECAaqg5yQdLCLlW5\nbunSpTVfP23atOOPP74mK+fPn1/XoQAipuogRyQt7Jq0OmTmzJk1Xz948OCJEydWf8RuzJgx\nL730Us+ePes9HUAEVB3kjqSFXaP81gMGDKj5+vz8/OHDh1e/ZvLkySGEvDzvRwTiR9VBTolv\n2FUWL1xQXFxcXFy8cn3jLl26du3WrUf3zgWesgD+Q9VBrolf2FWULnjovrHjxo2bMW/FFne1\n6tH34pGXXX75md2b5UcyG0D2UHWQg2IWduVrZg/dq//UhavzCnY6fNipe+7SqX37ds1CWUnJ\n8k+LP3hxyss/u+7cB8Y89vrsib0LY/ZXA0gjVQe5KWb1M/W84VMXrj70kvufueeCnbc6LFdZ\nvnLij88fcfOTwy54Yd74YyKZECByqg5yVsxOCLhlyqJWXa+aMfbirasuhJDXuOiEURN+P6jz\nwmdvzvxsANlA1UEui1nYzS0tb9FtSPVr9j2sQ3np+5mZByCrqDrIcTELuyPbNP187ujiddu9\nCESqcs34CQubtjkyk1MBZANVB8Qs7G69pm/Ziuf363v2hBdmlW1Rd6nyd6f/6cqj9xz94ed9\nr741kvEAoqLqgBC7kyf6XDfljrf6j3r8kVOOeqRJUaeeXXdu1759s7B2+fKSZYvnL16+NoSw\nzym3PXd9n6gnBcgcVQdsELOwa5RXeNNj75x19aSxY8c+NW3Wog/enTO7IoSQV9C8Y+duxw4b\nMXLkyOH9XfsLyCGqDtgoZmG3QY9+Q+/qN/SuEEKoXFWy7MtUYcf2RTF7URkgHVQdsLlYht1m\n8lq369Q66iEAIqHqgC04zgUQS6oO2JqwA4gfVQdsk7ADiBlVB2xPOsPupItufOrlf5Wn0viQ\nAHyFqgOqkc6we2rcT076xn5tdulzwXV3vfj2ojQ+MgBB1QE7ks6we/dvf+YkKcEAACAASURB\nVBx16Rld133w27tv+OYBu3Ta5+vX/uiBN/+9Mo27AMhZqg7YoXSG3d6HHX/7fY/MWfr5G88/\n8V/nn9jsk1d//t+XHNKz7V6Djr9zzOMfrliXxn0B5BRVB9REA5w80ajJQUed8tP/eXJ+ScmM\nP/3h8jOP/XzWc6MuPX33Dm0OHXrOfY/8+dO1FenfKUByqTqghhr2rNi8/MbNClu0atE4hJCq\nKH31uYcvP/PYru12u+Ke5xt0vwCJoeqAmkt/2FWUffrChHGXfue4Xdq0HfCt7/x03P990eGg\nS2/6+YuzFnzxyTvjfnzN1/IX33v10RdMcXYFwA6oOqBW0nlJsWd+f8/TTz/97JRXVqyvDCHs\nesDgq0866aSTThq4V6eNay644WdnXTissP3gybe9HY7tlsa9AySMqgNqK51hN+L/Xd2oUd5u\nfY+++KSTTjrpxEO+1nabywpa9urYsWOPg9qlcdcACaPqgDpIZ9jdMvoPJ510wn7dWlW/LL9p\n9yVLlqRxvwAJo+qAuknne+yGDdqracum27xr9fx/vfnW3DTuCyCpVB1QZ+kMu759+37v5cXb\nvGvOb84aMHBYGvcFkEiqDqiPNLwUO3r06I3bH0/83ej5W761LlX+xYuPflRZUVj/fQEkmKoD\n6ikNYff9739/4/a8393x/e0s6zH0F/XfF0BSqTqg/tIQds8/X/Vpw0OGDDng1ofuHtRp6zWN\nm7cbMODA+u8LIJFUHZAWaQi7o446asPGMcccc8CQIUcN7Fj/xwTIHaoOSJd0ftzJlClT0vho\nALlA1QFplIawe/jhh0MIg0/9zvrFC6tf2aNHj/rvDiAxVB2QXmkIu7PPPjuEMOm4k4f27Fn9\nylQqVf/dASSDqgPSLg1h179//xBCm8Z5l156af0fDSAXqDqgIaQh7P7+979v2Bh43331fzSA\nxFN1QANJ55Un9jp8xE9/+8fi0vI0PiZAwqg6oOGkM+zen/7M9RecsEubzsed/f0nXni73Bvq\nAL5K1QENKp1hVzLvtTF3XnNY72bPPTz6tKMO2GnXg0aO+uVrH65I4y4A4kvVAQ0tnWHXdre+\n3/vvn730zsdLZr/yy1uu6NNi8dg7rxqwe7u9vUQL5DxVB2RAOsNuo457Dbzi1l++8t6Sj99+\n6e4bL2m55K8bXqJtiH0BZD9VB2RGg4Tdf1SuKS1dt379hk+vq1j3WUPuCyBLqTogY9J5SbEq\nqXVvvzzpqaeeevKpp9/95MsQQstufb57zTWnn3Za+vcFkN1UHZBJ6Qy7vz/36FNPPfn005M+\nKCkLIRTu3PvMy0477fTThw7aq0EPDAJkJ1UHZFg6w+7Q474TQmjapufJF5562umnHz/4gCae\nxIBcpeqAzEtn2A0/56rTTj99xDH9mud5/gJymqoDIpHOsJv44D1pfDSAmFJ1QFTSEHYPP/xw\nCGHwqd9Zv3hh9St79OhR/90BZDNVB0QoDWF39tlnhxAmHXfy0J49q1+ZSrnKGJBkqg6IVhrC\nrn///iGENo3zLr300vo/GkBMqTogcmkIu7///e8bNgbed1/9Hw0gjlQdkA3S+QFz//jHP+Z+\nvm6bd62e/68335qbxn0BZA9VB2SJdIZd3759v/fy4m3eNec3Zw0YOCyN+wLIEqoOyB5peCl2\n9OjRG7c/nvi70fPbbrEgVf7Fi49+VFlRWP99AWQVVQdklTSE3fe///2N2/N+d8f3t7Osx9Bf\n1H9fANlD1QHZJg1h9/zzz2/YGDJkyAG3PnT3oE7b2E3zdgMGHFj/fQFkCVUHZKE0hN1RRx21\nYeOYY445YMiQowZ2rP9jAmQzVQdkp3ReUmzKlClpfDSA7KTqgKzlkmIAtaDqgGzmkmIANaXq\ngCznkmIANaLqgOznkmIAO6bqgFhI55Untra25N2JT4z/y9/fXes1WCC2VB0QF+kNu9Rffn3V\ngP2+9tNFX4QQSpdN3Kf7gd8+7awhh+67xzHXLFtfmdZ9AWSCqgNiJJ1h9/64bw+57Jevv/9Z\ny/y8EMLTp37vo9KKky67/QcXDFz4/C+G/vCtNO4LIANUHRAv6Qy7n4x6saDFPq8uXTayc4tU\n5ZfX/31Z273unnDvqB+Oe+X8Ti3eG+uSYkCcqDogdtIZdk+XlHY44K5D2jYNIXxZ/JtFa8v3\nunbYhrvOOKh96fLJadwXQINSdUAcpTPsmjZqtPGD6j6e+H8hhDOO7brhy8r1lSFVnsZ9ATQc\nVQfEVDrD7uyOzUveuenD0vJUZelP7vxnk5YHXNSpRQihsrzk5//4tGnR19O4L4AGouqA+Epn\n2F0++oR1q9/ar8e+A/bb5Q+LV/c+f3TjRqH4xbuOOmD3P68o6/WdG9K4L4CGoOqAWEtn2PU4\n6Q8v3vu97gVLX39/zX7HXTH5p4eFED59fcK0d1fsPeKGP/98YBr3BZB2qg6IuzRceWJzgy8b\n8/5lY9ZVpJrkVz0h9jxlzOzTe+y1a4f07gggvVQdkABpDrsNNlZdCKFVr757NcQ+ANJH1QHJ\nkM6XYlMVXzxw7Sl779q+2XasKHdlMSDrqDogMdJ5xO71O4+65OevNe+0W9+B+zfN28YzY6t8\nT5dAdlF1QJKkM+yu/MXbXQb/cPbzNxYJOCAOVB2QMOl8KfZfX64/6TcjVR0QC6oOSJ50ht03\n2zStXF+ZxgcEaCCqDkikdIbdj24b8sS5d5ZWOkMCyGqqDkiqdL7Hbu9Ln/nFJyftMeDE/772\n/IP33K1D62ZbLOjRo0cadwdQB6oOSLB0hl2jRlXH/7532jPbXJBKOZgHREnVAcmWzrC79NJL\n0/hoAOml6oDES2fY3XfffWl8NIA0UnVALkj/JcVS5Z+/8be//mP2gi+++OK/bvjvsqUf5e3c\nq4mnUCA6qg7IEek8KzaEUPzS2IG7dut75Le/d9mV1914UwhhwTNnt93lgF//dXF6dwRQQ6oO\nyB3pDLsvi//voGMuf21ZwbnX3fGLG/fbcGO7A0/qVTrnim/u9cjiL9O4r5jaePaI3yyQGaoO\nyCnpDLunz7166fpGP3913v/eddPJR3TacGOHfle/9v7Ujnlf/td3Hk/jvgB2SNUBuSadYXf7\n9KVt9/7VVQe13+L2Zh0O/2Wf9p++/tM07gugeqoOyEHpDLul6yta7rrHNu9qv0vzirXeZgdk\niKoDclM6w+6YnZp9+vpvyra+Wmyq/KGZy5q2OSKN+wLYHlUH5Kx0ht0Pruiz5tMnvnHNA6sq\nNl1hIlVZ+tjtx/1+yZe7nfvfadwXwDapOiCXpfNz7PrcMOmSZ/e6f/QlnR792cE9V4YQzjrp\nuHfenP7PBV8Ufe2kP/2obxr3BbA1VQfkuHQesctr3Hbs3xdOGnvj7oVfzHz1sxDC+KeeW1ja\n/qI7//DRe090b5qfxn0BbEHVAaT7yhONmh53yY+Ou+RH5WUrF3y0pHW3Hju3bprmXQBsRdUB\nhIa4pFjV4zYr2m3vogZ6cIDNqTqADdIWdqnKsk8WVnTr0WLjLfNefHDsk682att90NFnnHj4\nrunaEcDmVB3ARml4j135l/OuG9GvTWHLvme8vPHG6XcN3+Ob590zZuwv7rzx5CN6nvDDF+q/\nI4AtqDqAzdU37CrK5h/Z64C7n3m92W4HDz2m64Yby5ZPGnzjpK5Dvj/5pVeefujeA4uaTBx1\nzNgFq+o9LcAmqg5gC/V9Kfbtu07627I1h974yF9/eEbj/zypvnPnDeWp1BMTfjKwdZMQBh45\nKK+o16U/ufTl700aXt95AUIIqg5gW+p7xG7cA/OatDxw0u2nN97sSXXcowvyGhcNbN1kw5et\ne448vUPzT18dV899AWyg6gC2qb5h98eS0qJe1++0WdaVr/nX75d+2Wynb22+7Ni2zdat+ns9\n9wUQVB3A9tU37FZVpJq0+crHmnw2646KVKrrsd/b/MbyVEhVltVzXwCqDqAa9Q27A1oUrJr/\n4ua3zLhtZgjhG9fsvfmNE0tKC1oeUM99ATlO1QFUr75hd8XB7b/4+O6fvvnZhi/L1/zzyr8W\n5+W3GrXnThvXfD7nVxNLSnfa66J67gvIZaoOYIfqe1bssb8d1XS37/3g0P0+ufGavj1a/une\nUYvWlnf5+r27/OfKsB9Pf/DUE64PIVxw/7eqfSSA7VJ1ADVR37Br3fPi18YvGXrenb+67b82\n3NK0aP/Hnj4thLBm2UP79Ll0wdIvQgh9L3nojv3b1XNfQG5SdQA1lIZLivU5/ZY5R3z7T1Ne\neetf/y7adc/TLjinR/PGIYTKdUtX5HU44tghR51+2Q/OHVz/HQE5SNUB1Fx6rhXbvPMBp373\ngFO/emPLbld/vvjatDx+YqRSUU8AsaLqAGolDdeKjejB482vJ9ghVQdQW9oLyEaqDqAOhB2Q\ndVQdQN0kJ+zOOuusK374z6inAOpL1QHUWXLCbvz48U/9ZXHUUwD1ouoA6iM9Z8VmzK233lrN\nvV8sePjWW2fWZCWQhVQdQD3FLOx+euftpRXb/ciQVQsevu22qm1hB/Gi6gDqL2Zh99H0/z1+\n+CWvf1Y65NJ7Rh7VY/O7RowY0W7fm//njgMjGg2oO1UHkBYxC7tOA855ZX7/a08+7t4x/9Vm\np/99+PYzm2z2/N+s/cATTjgmuumAulB1AOkSv5MnClr2/uVzcybcecbEH53d6xsXvbVibdQT\nAXWn6gDSKH5hF0IIjRqf+IM/fDjtgfb//EP/HgeP+cuCqAcC6kLVAaRXPMMuhBBC169f+Nr8\n1//fwV9cdvTup44aH/U4QO2oOoC0i3HYhRCaFO039oV540ed+PQPz456FqAWVB1AQ4jZyRPb\n0KjJGbc9PmjwsKfeKmnZbc+opwF2TNUBNJD4h10IIYTu3zj7qm9EPQRQA6oOoOHE+6VYIF5U\nHUCDSsgRu43WrZrefY+TQwhLliypyfqKiorJkyeXlZVVs2bBggUhhMrKynQMCLlL1QE0tKSF\nXapy3dKlS2u+ftq0accff3xNVs6fP7+uQwGqDiATkhZ2TVodMnPmzJqvHzx48MSJE6s/Yjdm\nzJiXXnqpZ8+e9Z4OcpSqA8iMpIVdo/zWAwYMqPn6/Pz84cOHV79m8uTJIYS8PO9HhLpQdQAZ\nE9+wqyxeuKC4uLi4uHjl+sZdunTt2q1bj+6dC/zKgGyi6gAyKX5hV1G64KH7xo4bN27GvBVb\n3NWqR9+LR152+eVndm+WH8lswOZUHUCGxSzsytfMHrpX/6kLV+cV7HT4sFP33KVT+/btmoWy\nkpLlnxZ/8OKUl3923bkPjHns9dkTexdm418tlYp6AsgUVQeQedlYP9WYet7wqQtXH3rJ/c/c\nc8HOWx2WqyxfOfHH54+4+clhF7wwb/wxkUxYQ37HkWyqDiASMTsh4JYpi1p1vWrG2Iu3rroQ\nQl7johNGTfj9oM4Ln70587MBG6g6gKjELOzmlpa36Dak+jX7HtahvPT9zMwDbEHVAUQoZmF3\nZJumn88dXbxuuxeBSFWuGT9hYdM2R2ZyKmADVQcQrZiF3a3X9C1b8fx+fc+e8MKssi3qLlX+\n7vQ/XXn0nqM//Lzv1bdGMh7kMlUHELmYnTzR57opd7zVf9Tjj5xy1CNNijr17Lpzu/btm4W1\ny5eXLFs8f/HytSGEfU657bnr+0Q9KeQWVQeQDWIWdo3yCm967J2zrp40duzYp6bNWvTBu3Nm\nV4QQ8gqad+zc7dhhI0aOHDm8v2t/QUapOoAsEbOw26BHv6F39Rt6VwghVK4qWfZlqrBj+6KY\nvagMSaHqALJHLMNuM3mt23VqHfUQkLNUHUBWcZwLqCNVB5BthB1QF6oOIAsJO6DWVB1AdhJ2\nQO2oOoCsJeyAWlB1ANlM2AE1peoAspywA2pE1QFkP2EH7JiqA4gFYQfsgKoDiAthB1RH1QHE\niLADtkvVAcSLsAO2TdUBxI6wy6hUKuoJoGZUHUAcCbto+C1JNlN1ADEl7ICvUHUA8SXsgE1U\nHUCsCTugiqoDiDthB4Sg6gASQdgBqg4gIYQd5DpVB5AYwg5ymqoDSBJhB7lL1QEkjLCDHKXq\nAJJH2EEuUnUAiSTsIOeoOoCkEnaQW1QdQIIJO8ghqg4g2YQd5ApVB5B4wg5ygqoDyAXCDpJP\n1QHkCGEHCafqAHKHsIMkU3UAOUXYQWKpOoBcI+wgmVQdQA4SdpBAqg4gNwm7jEqlop6AHKDq\nAHKWsIuGX7U0EFUHkMuEHSSHqgPIccIOEkLVASDsIAlUHQBB2EECqDoANhB2EG+qDoCNhB3E\nmKoDYHPCDuJK1QGwBWEHsaTqANiasIP4UXUAbJOwg5hRdQBsj7CDOFF1AFRD2EFsqDoAqifs\nIB5UHQA7JOwgBlQdADUh7CDbqToAakjYQVZTdQDUnLCD7KXqAKgVYQdZStUBUFvCDrKRqgOg\nDoQdZB1VB0DdCDvILqoOgDoTdhmVSkU9AdlN1QFQH8IuGn5fszVVB0A9CTvICqoOgPoTdhA9\nVQdAWgg7iJiqAyBdhB1ESdUBkEbCDiKj6gBIL2EH0VB1AKSdsIMIqDoAGoKwg0xTdQA0EGEH\nGaXqAGg4wg4yR9UB0KCEHWSIqgOgoQk7yARVB0AGCDtocKoOgMwQdtCwVB0AGSPsoAGpOgAy\nSdhBQ1F1AGSYsIMGoeoAyDxhB+mn6gCIhLCDNFN1AERF2EE6qToAIiTsIG1UHQDREnYZlUpF\nPQENRtUBEDlhFw2/9BNG1QGQDYQd1JeqAyBLCDuoF1UHQPYQdlB3qg6ArCLsoI5UHQDZRthB\nXag6ALKQsINaU3UAZCdhB7Wj6gDIWsIOakHVAZDNhB3UlKoDIMsJO6gRVQdA9hN2sGOqDoBY\nEHawA6oOgLgQdlAdVQdAjAg72C5VB0C8CDvYNlUHQOwIO9gGVQdAHDWOeoC6KC2e89L0mXNL\nwn79jz3ywE5b3PvmU4/PXrP+rLPOimQ2EkDVARBT8Qu7Gf9z3Qkjf/Hp+ooQQqNGjQaeedfz\nD15bmLfpV++jV174s0VfCDvqRtUBEF8xC7tPX7/5sIt+lt9kl5E3XLRf58Z/efTeJx++7qBG\n3d77wxlRj0YSqDoAYi1mYffgOb9ulFf48Hv/Oq1nqxDCJZdd+ZNv9/7Bw2f+5Nqjb+jTLurp\niDdVB0DcxezkifsWrGq37683VF0IIeQ1u/aJ57/WLP8nI25LRToYcafqAEiAmIVdyfrK5t12\n2fyWxoV7PPWjQSs/uvfaV5ZENVXNpeRnVlJ1ACRDzMLusKImn77+27LKr9y472VPHdOucOzx\n5y5bX7mdP5d1lEP2UHUAJEbMwu66k3usWfbooAvvfO/TNRtvbNS47UNTflC6fOoh3761eF1F\nhOMRO6oOgCSJWdgdPvqPw/Zs8+bvRu29c4sue5+68fYOh9z05x+e9PFzd/TstPvjn5ZGOCEx\nouoASJiYhV3jwj0m/uvj8T+6/Fvf6N+8bMXmdx39gwmv/uEng7qu/nhteVTjESOqDoDkidnH\nnYQQGuW3/M6Nv/rOjdu4q9/Z179w9nWffTL/ww8+yPhcxImqAyCR4hd2O9Kofdde7bv2inoM\nspeqAyCpYvZSLNSTqgMgwZJ2xG7dqund9zg5hLBkSY0+1q6iomLy5MllZWXVrFmwYEEIobIy\nNp+lwvaoOgCSLWlhl6pct3Tp0pqvnzZt2vHHH1+TlfPnz6/rUGQFVQdA4iUt7Jq0OmTmzJk1\nXz948OCJEydWf8RuzJgxL730Us+ePes9HZFRdQDkgqSFXaP81gMGDKj5+vz8/OHDh1e/ZvLk\nySGEvDzvR4wrVQdAjohv2FUWL1xQXFxcXFy8cn3jLl26du3WrUf3zgV+ZfNVqg6A3BG/sKso\nXfDQfWPHjRs3Y96KLe5q1aPvxSMvu/zyM7s3y49kNrKNqgMgp8Qs7MrXzB66V/+pC1fnFex0\n+LBT99ylU/v27ZqFspKS5Z8Wf/DilJd/dt25D4x57PXZE3sXxuyvRtqpOgByTczqZ+p5w6cu\nXH3oJfc/c88FO291WK6yfOXEH58/4uYnh13wwrzxx0QyIVlC1QGQg2J2QsAtUxa16nrVjLEX\nb111IYS8xkUnjJrw+0GdFz57c+ZnI3uoOgByU8zCbm5peYtuQ6pfs+9hHcpL38/MPGQhVQdA\nzopZ2B3Zpunnc0cXr9vuRSBSlWvGT1jYtM2RmZyK7KHqAMhlMQu7W6/pW7bi+f36nj3hhVll\nW9Rdqvzd6X+68ug9R3/4ed+rb41kPKKl6gDIcTE7eaLPdVPueKv/qMcfOeWoR5oUderZded2\n7ds3C2uXLy9Ztnj+4uVrQwj7nHLbc9f3iXpSMk3VAUDMwq5RXuFNj71z1tWTxo4d+9S0WYs+\neHfO7IoQQl5B846dux07bMTIkSOH93ftr5yj6gAgxC7sNujRb+hd/YbeFUIIlatKln2ZKuzY\nvigWLyqnUlFPkESqDgA2iGXYbSavdbtOraMeog60R7qoOgDYKBbHuWDbVB0AbE7YEVeqDgC2\nIOyIJVUHAFsTdsSPqgOAbRJ2xIyqA4DtEXbEiaoDgGoIO2JD1QFA9YQd8aDqAGCHhB0xoOoA\noCaEHdlO1QFADQk7spqqA4CaE3ZkL1UHALUi7MhSqg4AakvYkY1UHQDUgbAj66g6AKgbYUd2\nUXUAUGfCjiyi6gCgPoQd2ULVAUA9CTuygqoDgPoTdkRP1QFAWgg7IqbqACBdhF1GpVJRT5Bl\nVB0ApJGwi4aACaoOANJN2BENVQcAaSfsiICqA4CGIOzINFUHAA1E2JFRqg4AGo6wI3NUHQA0\nKGFHhqg6AGhowo5MUHUAkAHCjgan6gAgM4QdDUvVAUDGCDsakKoDgEwSdjQUVQcAGSbsaBCq\nDgAyT9iRfqoOACIh7EgzVQcAURF2pJOqA4AICTvSRtUBQLSEHemh6gAgcsKONFB1AJANhB31\npeoAIEsIO+pF1QFA9hB21J2qA4CsIuwyKpWKeoL0UXUAkG2EXTTiXkGqDgCykLCj1lQdAGQn\nYUftqDoAyFrCjlpQdQCQzYQdNaXqACDLCTtqRNUBQPYTduyYqgOAWBB27ICqA4C4EHZUR9UB\nQIwIO7ZL1QFAvAg7tk3VAUDsCDu2QdUBQBwJO7ak6gAgpoQdX6HqACC+hB2bqDoAiDVhRxVV\nBwBxJ+wIQdUBQCIIO1QdACSEsMt1qg4AEkPY5TRVBwBJIuxyl6oDgIQRdjlK1QFA8gi7jEql\nop4ghKDqACChhF00IkwpVQcASSXscouqA4AEE3Y5RNUBQLIJu1yh6gAg8YRdTlB1AJALhF3y\nqToAyBHCLuFUHQDkDmGXZKoOAHKKsEssVQcAuUbYJZOqA4AcJOwSSNUBQG4Sdkmj6gAgZwm7\nRFF1AJDLhF1yqDoAyHHCLiFUHQAg7JJA1QEAQdglgKoDADYQdvGm6gCAjYRdjKk6AGBzwi6u\nVB0AsAVhF0uqDgDYmrCLH1UHAGyTsMuoVKr+j6DqAIBtE3bRqFuPqToAoBrCLjZUHQBQPWEX\nD6oOANghYRcDqg4AqAlhl+1UHQBQQ8Iuq6k6AKDmhF32UnUAQK0Iuyyl6gCA2moc9QB1Vlm8\ncEFxcXFxcfHK9Y27dOnatVu3Ht07FySif1QdAFAH8Qu7itIFD903dty4cTPmrdjirlY9+l48\n8rLLLz+ze7P8SGZLC1UHANRNzMKufM3soXv1n7pwdV7BTocPO3XPXTq1b9+uWSgrKVn+afEH\nL055+WfXnfvAmMdenz2xd2HM/mobqDoAoM5iVj9Tzxs+deHqQy+5/5l7Lth5q8NyleUrJ/74\n/BE3PznsghfmjT8mkgnrQ9UBAPURs5MnbpmyqFXXq2aMvXjrqgsh5DUuOmHUhN8P6rzw2Zsz\nP1s9qToAoJ5iFnZzS8tbdBtS/Zp9D+tQXvp+ZuZJF1UHANRfzMLuyDZNP587unhd5fYWpCrX\njJ+wsGmbIzM5VT2pOgAgLWIWdrde07dsxfP79T17wguzyraou1T5u9P/dOXRe47+8PO+V98a\nyXh1oOoAgHSJ2ckTfa6bcsdb/Uc9/sgpRz3SpKhTz647t2vfvllYu3x5ybLF8xcvXxtC2OeU\n2567vk/Uk9aIqgMA0ihmYdcor/Cmx9456+pJY8eOfWrarEUfvDtndkUIIa+gecfO3Y4dNmLk\nyJHD+/eMeswaUXUAQHrFLOw26NFv6F39ht4VQgiVq0qWfZkq7Ni+KGYvKoeg6gCA9Ipl2G0m\nr3W7Tq2jHqIOSkpKVB0AkF6xO86VEGvWrFF1AEB6xf2I3ZbWrZrefY+TQwhLliypyfqKiorJ\nkyeXlZVVs2bBggUhhMrK7X7GSh00b95c1QEA6ZW0sEtVrlu6dGnN10+bNu3444+vycpFixbV\ndagqqVSqoGBJCJ1DCAce2E7UAQDplbSwa9LqkJkzZ9Z8/eDBgydOnFj9EbtJkyY9+OCDZ555\nZn0G23AO7NFHF6dS/bt06XJz/K55BgBku6SFXaP81gMGDKj5+vz8/OHDh1e/ZvHixQ8++GDj\nxnX/Xm38ZJPdd+92zjmdHasDABpCfMOusnjhguLi4uLi4pXrG3fp0rVrt249uncuyL5m8nl1\nAEBmxC/sKkoXPHTf2HHjxs2Yt2KLu1r16HvxyMsuv/zM7s3yI5lta6oOAMiYmIVd+ZrZQ/fq\nP3Xh6ryCnQ4fduqeu3Rq375ds1BWUrL80+IPXpzy8s+uO/eBMY+9Pnti78Lo/2qqDgDIpOjr\np1amnjd86sLVh15y/zP3XLDzVoflKstXTvzx+SNufnLYBS/MG39MJBNupOoAgAyL2QcU3zJl\nUauuV80Ye/HWVRdCyGtcdMKoCb8f1HnhsxGfdKrqAIDMi1nYzS0tb9FtSPVr9j2sQ3np+5mZ\nZ5tUHQAQiZiF3ZFtmn4+d3Txuu1eBCJVuWb8hIVN2xyZyam+MoCqf81XiAAAFChJREFUAwAi\nErOwu/WavmUrnt+v79kTXphVtkXdpcrfnf6nK4/ec/SHn/e9+tZIxlN1AECEYnbyRJ/rptzx\nVv9Rjz9yylGPNCnq1LPrzu3at28W1i5fXrJs8fzFy9eGEPY55bbnru+T+dlUHQAQrZiFXaO8\nwpsee+esqyeNHTv2qWmzFn3w7pzZFSGEvILmHTt3O3bYiJEjRw7v3zPzg6k6ACByMQu7DXr0\nG3pXv6F3hRBC5aqSZV+mCju2L4rwRWVVBwBkg1iG3WbyWrfr1DrSCVQdAJAlYnbyRLZRdQBA\n9hB2dafqAICsIuzqSNUBANlG2NWFqgMAspCwqzVVBwBkJ2FXO6oOAMhawq4WVB0AkM2EXS2o\nOgAgm8X9A4ozJC8vb/ny5aWlpaoOAMhawq5GTjnllNLS0pYtWzZt2vTNN9+sz0OVl5f//ve/\n33XXXfPyHC7NtMrKyg8//PBrX/uab35U/Agi5JsfOT+CaFVWVv773//+7ne/27hxfeNnzpw5\naRmpIQi7HSsoKGjduvXMmTPvvffeioqKqMcBAOrogQceSNdDFRQUpOuh0qhRKpWKeoZst3Ll\nyvHjx69evTot36u333770UcfPfzww7t3717/R6NWFi5c+Le//c03P0J+BBHyzY+cH0G0Nnz/\nzzjjjP3337/+j1ZYWHjuuecWFRXV/6HSLEVmPfHEEyGEJ554IupBcpFvfuT8CCLkmx85P4Jo\n5cj338v8AAAJIewAABJC2AEAJISwAwBICGEHAJAQwg4AICGEHQBAQgg7AICEEHYAAAkh7DKt\nsLBw43+TYb75kfMjiJBvfuT8CKKVI99/14rNtIqKihdeeOGb3/xmfn5+1LPkHN/8yPkRRMg3\nP3J+BNHKke+/sAMASAgvxQIAJISwAwBICGEHAJAQwg4AICGEHQBAQgg7AICEEHYAAAkh7ADg\n/7d353FRVe8fwJ87M8wMqyyyCiKbikgBagouqIgKLrjhQiYJLlm5RKkImZDmkqZGJdYXtMX6\nln0tcycRF8wgFDUX1FRUUHFhUZAZGJj5/QHyAzR1LgzjHD/vv+TcM8cPz2s89/Ey9wLACDR2\nAAAAAIxAYwcAAADACDR2AAAAAIxAYwcAAADACDR2AAAAAIxAYwcAAADACDR2AAAAAIxAYwcA\nAADACDR2AAAAAIxAY6cpxediOI47cr/y6VOV8p+WzXrFzd5QLLawdR49bdH5UoXmAzJK/WIq\nFXc2xEX6enU01dd3cHtp2KSYozfLWyYsg3i9mS+nfjU+sIuVqaGBhX33/uN/+jO/BZKyqWmb\nSdGpREOR0Cf+uOYCMo7P/lO44YMIP59OlsYSK8eO/Ye9tiXrZsuEZRCP9z+T518VaIKyMraH\nNRH9ca/iqXM/DXUlIn1rtyFjQ1/paEdEhnbBl2VVLRCTPeoWU1l1b5KbKRFZdPId82rYoD6e\nRCSSOOwuKG/J2Mzg8WY+8Z/JAo4T6Vv3HDQmdFiAiUjACcTL/yhoscwsacpmonhw1s9UQkTe\ncdmazskqdetfJb86rJ0xEZl36jkufMqoIH8DoYDjhOPWZLZkbGbweP8zef5FY9fMslK3f7lm\nyZBu9jV981Mbu9tHo4nIzP21/IrqmpGvp3sTkWdUuubDsoZHMf9e6UdE7pHfKpS1Izlb3iIi\ni85LWiAwY3jUX160z0QkMLQNSL9d20mXnP+ptZ5Q0sq/UvlvL4LHa9pmUrXI37Zm10Jjxw+P\n+mfHdSEi5/GJstpXqIrO/NZWKhIIjQ49w0UBqI9H/Vk9/6Kxa2buBnr1L4g+tbHb5GdLRKuv\n3K8bUSoKXfVFYuMuuv1fBm3gUcyNHcyJ6JtbD+oPttfXE+iZazIpm3jU/8+ZHkQUe/Ju/cGM\ntR/FxsaeL1doMiyDmrKZHFkayHGiqKT+aOx441H/6bZGHKeXXVpZfzB7kTcRBe68qsmwDOJR\nf1bPv2jsmplcLpfJZDKZbL2b2bM0dl2NxSJ950bvoY2erYloW6FMczmZxKOY23rZEVH02aK6\nkerKAhORQGzcRZNJ2cSj/hOtDEVSJ1ycaxa8N5M7R1dLBJxfTGphzgQ0drzxqH93Z0fnTmGN\nBv/5rg8R9f72gmZiMotH/Vk9/+LmiWYmkUikUqlUKhVz3FMnKxUFR0srpWbBwobjHXtZEdHO\nIrlmMrKJXzF7Jy80FQk+D5y4JeNcaYX8xsWsmNAe96uUgxb+R/ORmcKj/ipl+S+FMv3WI8rO\n7pg6fqhHu9ZGlo49B4QkpV1ukchM4b2ZKMpOBvWLNvGenbYkQMMZWcav/hmXrlw6832DIVVF\nQvzfHMeFD7DTUFQm8ag/w+dfkbYDvNCq5deISCRt12jcwMGAiK6XPsMdtfAQv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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train a deeper xgboost model to compare accuarcy.\n",
    "xgb.bench.acc = microbenchmark(\n",
    "\txgb.model.acc <- xgb.train(data = xgb.data.train\n",
    "\t\t, params = list(objective = \"binary:logistic\"\n",
    "\t\t\t, eta = 0.1\n",
    "\t\t\t, max.depth = 7\n",
    "\t\t\t, min_child_weight = 100\n",
    "\t\t\t, subsample = 1\n",
    "\t\t\t, colsample_bytree = 1\n",
    "\t\t\t, nthread = 3\n",
    "\t\t\t, eval_metric = \"auc\"\n",
    "\t\t\t)\n",
    "\t\t, watchlist = list(test = xgb.data.test)\n",
    "\t\t, nrounds = 500\n",
    "\t\t, early_stopping_rounds = 40\n",
    "\t\t, print_every_n = 20\n",
    "\t\t)\n",
    "    , times = 5L\n",
    ")\n",
    "print(xgb.bench.acc)\n",
    "print(xgb.model.acc$bestScore)\n",
    "\n",
    "#Get feature importance\n",
    "xgb.feature.imp = xgb.importance(model = xgb.model.acc)\n",
    "\n",
    "# Make predictions on test set for ROC curve\n",
    "xgb.test.acc = predict(xgb.model.acc\n",
    "                   , newdata = as.matrix(test[, colnames(test) != \"Class\"])\n",
    "                   , ntreelimit = xgb.model.acc$bestInd)\n",
    "auc.xgb.acc = roc(test$Class, xgb.test.acc, plot = TRUE, col = \"blue\")\n",
    "print(auc.xgb.acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "afbb958b-8943-4d3a-a883-e15751a6f7b5",
    "_uuid": "9d69ae1b9a8187eac4d98754402fb5270c877a05"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17:28:27] Tree method is selected to be 'hist', which uses a single updater grow_fast_histmaker.\n",
      "[1]\ttest-auc:0.922328 \n",
      "Will train until test_auc hasn't improved in 40 rounds.\n",
      "\n",
      "[21]\ttest-auc:0.942289 \n",
      "[41]\ttest-auc:0.946525 \n",
      "[61]\ttest-auc:0.962809 \n",
      "[81]\ttest-auc:0.963911 \n",
      "[101]\ttest-auc:0.963911 \n",
      "Stopping. Best iteration:\n",
      "[70]\ttest-auc:0.965380\n",
      "\n",
      "[17:28:29] Tree method is selected to be 'hist', which uses a single updater grow_fast_histmaker.\n",
      "[1]\ttest-auc:0.922328 \n",
      "Will train until test_auc hasn't improved in 40 rounds.\n",
      "\n",
      "[21]\ttest-auc:0.942289 \n",
      "[41]\ttest-auc:0.946525 \n",
      "[61]\ttest-auc:0.962809 \n",
      "[81]\ttest-auc:0.963911 \n",
      "[101]\ttest-auc:0.963911 \n",
      "Stopping. Best iteration:\n",
      "[70]\ttest-auc:0.965380\n",
      "\n",
      "[17:28:32] Tree method is selected to be 'hist', which uses a single updater grow_fast_histmaker.\n",
      "[1]\ttest-auc:0.922328 \n",
      "Will train until test_auc hasn't improved in 40 rounds.\n",
      "\n",
      "[21]\ttest-auc:0.942289 \n",
      "[41]\ttest-auc:0.946525 \n",
      "[61]\ttest-auc:0.962809 \n",
      "[81]\ttest-auc:0.963911 \n",
      "[101]\ttest-auc:0.963911 \n",
      "Stopping. Best iteration:\n",
      "[70]\ttest-auc:0.965380\n",
      "\n",
      "[17:28:34] Tree method is selected to be 'hist', which uses a single updater grow_fast_histmaker.\n",
      "[1]\ttest-auc:0.922328 \n",
      "Will train until test_auc hasn't improved in 40 rounds.\n",
      "\n",
      "[21]\ttest-auc:0.942289 \n",
      "[41]\ttest-auc:0.946525 \n",
      "[61]\ttest-auc:0.962809 \n",
      "[81]\ttest-auc:0.963911 \n",
      "[101]\ttest-auc:0.963911 \n",
      "Stopping. Best iteration:\n",
      "[70]\ttest-auc:0.965380\n",
      "\n",
      "[17:28:37] Tree method is selected to be 'hist', which uses a single updater grow_fast_histmaker.\n",
      "[1]\ttest-auc:0.922328 \n",
      "Will train until test_auc hasn't improved in 40 rounds.\n",
      "\n",
      "[21]\ttest-auc:0.942289 \n",
      "[41]\ttest-auc:0.946525 \n",
      "[61]\ttest-auc:0.962809 \n",
      "[81]\ttest-auc:0.963911 \n",
      "[101]\ttest-auc:0.963911 \n",
      "Stopping. Best iteration:\n",
      "[70]\ttest-auc:0.965380\n",
      "\n",
      "Unit: seconds\n",
      "                                                                                                                                                                                                                                                                                                                                                                                                  expr\n",
      " xgb.model.hist <- xgb.train(data = xgb.data.train, params = list(objective = \"binary:logistic\",      eta = 0.1, max.depth = 7, min_child_weight = 100, subsample = 1,      colsample_bytree = 1, nthread = 3, eval_metric = \"auc\", tree_method = \"hist\",      grow_policy = \"lossguide\"), watchlist = list(test = xgb.data.test),      nrounds = 500, early_stopping_rounds = 40, print_every_n = 20)\n",
      "      min       lq     mean   median       uq      max neval\n",
      " 2.505573 2.548369 2.573963 2.576947 2.618865 2.620061     5\n",
      "NULL\n",
      "\n",
      "Call:\n",
      "roc.default(response = test$Class, predictor = xgb.test.hist,     plot = TRUE, col = \"blue\")\n",
      "\n",
      "Data: xgb.test.hist in 85504 controls (test$Class 0) < 164 cases (test$Class 1).\n",
      "Area under the curve: 0.9654\n"
     ]
    },
    {
     "data": {
      "image/png": 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3f3WcQA9aLqIN2S91QsAA2j6iD1En/GbgOrlk3pufNxIYR58+r1UXbV1dUTJkyo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\nEWjsAAAAABiBxg4AAACAEf8Hr5T4YU32d80AAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# xgBoost with Histogram\n",
    "xgb.bench.hist = microbenchmark(\n",
    "\txgb.model.hist <- xgb.train(data = xgb.data.train\n",
    "\t\t, params = list(objective = \"binary:logistic\"\n",
    "\t\t\t, eta = 0.1\n",
    "\t\t\t, max.depth = 7\n",
    "\t\t\t, min_child_weight = 100\n",
    "\t\t\t, subsample = 1\n",
    "\t\t\t, colsample_bytree = 1\n",
    "\t\t\t, nthread = 3\n",
    "\t\t\t, eval_metric = \"auc\"\n",
    "            , tree_method = \"hist\"\n",
    "            , grow_policy = \"lossguide\"\n",
    "\t\t\t)\n",
    "\t\t, watchlist = list(test = xgb.data.test)\n",
    "\t\t, nrounds = 500\n",
    "\t\t, early_stopping_rounds = 40\n",
    "\t\t, print_every_n = 20\n",
    "\t\t)\n",
    "    , times = 5L\n",
    ")\n",
    "print(xgb.bench.hist)\n",
    "print(xgb.model.hist$bestScore)\n",
    "\n",
    "#Get feature importance\n",
    "xgb.feature.imp = xgb.importance(model = xgb.model.hist)\n",
    "\n",
    "# Make predictions on test set for ROC curve\n",
    "xgb.test.hist = predict(xgb.model.hist\n",
    "                   , newdata = as.matrix(test[, colnames(test) != \"Class\"])\n",
    "                   , ntreelimit = xgb.model.hist$bestInd)\n",
    "auc.xgb.hist = roc(test$Class, xgb.test.hist, plot = TRUE, col = \"blue\")\n",
    "print(auc.xgb.hist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "59fec7ed-727a-f92b-d59d-1ffa447629b2",
    "_uuid": "0948d8eadf22b1b7fa72ece6756ae22c88e238b1"
   },
   "source": [
    "## lightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "964fa5d7-597f-1a15-d666-34d7a4abc12d",
    "_uuid": "3c2deddfd747795ca730e62c78b9671c1c163f99"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘lightgbm’\n",
      "\n",
      "The following objects are masked from ‘package:xgboost’:\n",
      "\n",
      "    getinfo, setinfo, slice\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]:\ttest's auc:0.9262 \n",
      "[21]:\ttest's auc:0.94341 \n",
      "[41]:\ttest's auc:0.964942 \n",
      "[61]:\ttest's auc:0.965916 \n",
      "[1]:\ttest's auc:0.9262 \n",
      "[21]:\ttest's auc:0.94341 \n",
      "[41]:\ttest's auc:0.964942 \n",
      "[61]:\ttest's auc:0.965916 \n",
      "[1]:\ttest's auc:0.9262 \n",
      "[21]:\ttest's auc:0.94341 \n",
      "[41]:\ttest's auc:0.964942 \n",
      "[61]:\ttest's auc:0.965916 \n",
      "[1]:\ttest's auc:0.9262 \n",
      "[21]:\ttest's auc:0.94341 \n",
      "[41]:\ttest's auc:0.964942 \n",
      "[61]:\ttest's auc:0.965916 \n",
      "[1]:\ttest's auc:0.9262 \n",
      "[21]:\ttest's auc:0.94341 \n",
      "[41]:\ttest's auc:0.964942 \n",
      "[61]:\ttest's auc:0.965916 \n",
      "Unit: seconds\n",
      "                                                                                                                                                                                                                          expr\n",
      " lgb.model <- lgb.train(params = params.lgb, data = lgb.train,      valids = list(test = lgb.test), learning_rate = 0.1, num_leaves = 7,      num_threads = 2, nrounds = 500, early_stopping_rounds = 40,      eval_freq = 20)\n",
      "      min       lq     mean   median       uq      max neval\n",
      " 1.491331 1.535797 1.787964 1.647181 1.702615 2.562893     5\n",
      "[1] 0.9666542\n",
      "\n",
      "Call:\n",
      "roc.default(response = test$Class, predictor = lgb.test, plot = TRUE,     col = \"green\")\n",
      "\n",
      "Data: lgb.test in 85504 controls (test$Class 0) < 164 cases (test$Class 1).\n",
      "Area under the curve: 0.9667\n"
     ]
    },
    {
     "data": {
      "image/png": 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Hju8+cmOypAiqg6yIaUHYq97e33t915fEPVRcTPq1Ztt9st/1d1ERE9djv7x7tt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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(lightgbm, quietly=TRUE)\n",
    "lgb.train = lgb.Dataset(as.matrix(train[, colnames(train) != \"Class\"]), label = train$Class)\n",
    "lgb.test = lgb.Dataset(as.matrix(test[, colnames(test) != \"Class\"]), label = test$Class)\n",
    "\n",
    "params.lgb = list(\n",
    "\tobjective = \"binary\"\n",
    "\t, metric = \"auc\"\n",
    "\t, min_data_in_leaf = 1\n",
    "\t, min_sum_hessian_in_leaf = 100\n",
    "\t, feature_fraction = 1\n",
    "\t, bagging_fraction = 1\n",
    "\t, bagging_freq = 0\n",
    "\t)\n",
    "\n",
    "# Get the time to train the lightGBM model\n",
    "lgb.bench = microbenchmark(\n",
    "\tlgb.model <- lgb.train(\n",
    "\t\tparams = params.lgb\n",
    "\t\t, data = lgb.train\n",
    "\t\t, valids = list(test = lgb.test)\n",
    "\t\t, learning_rate = 0.1\n",
    "\t\t, num_leaves = 7\n",
    "\t\t, num_threads = 2\n",
    "\t\t, nrounds = 500\n",
    "\t\t, early_stopping_rounds = 40\n",
    "\t\t, eval_freq = 20\n",
    "\t\t)\n",
    "\t\t, times = 5L\n",
    ")\n",
    "print(lgb.bench)\n",
    "print(max(unlist(lgb.model$record_evals[[\"test\"]][[\"auc\"]][[\"eval\"]])))\n",
    "\n",
    "# get feature importance\n",
    "lgb.feature.imp = lgb.importance(lgb.model, percentage = TRUE)\n",
    "\n",
    "# make test predictions\n",
    "lgb.test = predict(lgb.model, data = as.matrix(test[, colnames(test) != \"Class\"]), n = lgb.model$best_iter)\n",
    "auc.lgb = roc(test$Class, lgb.test, plot = TRUE, col = \"green\")\n",
    "print(auc.lgb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6b627a20-fe3e-1b39-8818-4047fdb0e656",
    "_uuid": "4565fd2ae49868d6b95f9ca5e1fd6ca98e766936"
   },
   "source": [
    "# Results\n",
    "\n",
    "## Speed\n",
    "\n",
    "-----\n",
    "\n",
    "The following shows the estimated **GBM** benchmark (see above for actual) and the microbenchmark results for the **xgboost** and **lightgbm** models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "4a8bc848-1841-7ac9-d81e-401b3e5aeae2",
    "_uuid": "a26bac004e697d5623cd42a81215cf4b387c1343"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"GBM = ~243s\"\n",
      "Unit: seconds\n",
      "                                                                                                                                                                                                                                                                                                                                                  expr\n",
      " xgb.model.speed <- xgb.train(data = xgb.data.train, params = list(objective = \"binary:logistic\",      eta = 0.1, max.depth = 3, min_child_weight = 100, subsample = 1,      colsample_bytree = 1, nthread = 3, eval_metric = \"auc\"),      watchlist = list(test = xgb.data.test), nrounds = 500, early_stopping_rounds = 40,      print_every_n = 20)\n",
      "      min       lq    mean   median       uq      max neval\n",
      " 11.81437 11.85168 11.8613 11.85613 11.86979 11.91451     5\n",
      "Unit: seconds\n",
      "                                                                                                                                                                                                                                                                                                                                                                                                  expr\n",
      " xgb.model.hist <- xgb.train(data = xgb.data.train, params = list(objective = \"binary:logistic\",      eta = 0.1, max.depth = 7, min_child_weight = 100, subsample = 1,      colsample_bytree = 1, nthread = 3, eval_metric = \"auc\", tree_method = \"hist\",      grow_policy = \"lossguide\"), watchlist = list(test = xgb.data.test),      nrounds = 500, early_stopping_rounds = 40, print_every_n = 20)\n",
      "      min       lq     mean   median       uq      max neval\n",
      " 2.505573 2.548369 2.573963 2.576947 2.618865 2.620061     5\n",
      "Unit: seconds\n",
      "                                                                                                                                                                                                                          expr\n",
      " lgb.model <- lgb.train(params = params.lgb, data = lgb.train,      valids = list(test = lgb.test), learning_rate = 0.1, num_leaves = 7,      num_threads = 2, nrounds = 500, early_stopping_rounds = 40,      eval_freq = 20)\n",
      "      min       lq     mean   median       uq      max neval\n",
      " 1.491331 1.535797 1.787964 1.647181 1.702615 2.562893     5\n"
     ]
    }
   ],
   "source": [
    "print(\"GBM = ~243s\")\n",
    "print(xgb.bench.speed)\n",
    "print(xgb.bench.hist)\n",
    "print(lgb.bench)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "37d069b2-5a6e-b383-27c9-4a1a993d8ada",
    "_uuid": "cdd6dcfcc4926571ae7fcaa4537997352478fa8c"
   },
   "source": [
    "## Accuracy\n",
    "\n",
    "-----\n",
    "\n",
    "The following are the *AUC* results for the test set. \n",
    "\n",
    "\n",
    "### GBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "82bbcf8f-96fb-80dd-09e9-1b279666a8f4",
    "_uuid": "1633a06c6c5c20b77a28effa3c1d11f2180dc1e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      "roc.default(response = test$Class, predictor = gbm.test, plot = TRUE,     col = \"red\")\n",
      "\n",
      "Data: gbm.test in 85504 controls (test$Class 0) < 164 cases (test$Class 1).\n",
      "Area under the curve: 0.9498\n"
     ]
    }
   ],
   "source": [
    "print(auc.gbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "7f977fd5-5963-29cf-57cd-6e7bf2b61423",
    "_uuid": "6e443b82b8f0394aa3622058c33775bde43ef2e4"
   },
   "source": [
    "## xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "e80c7564-19b7-9fa8-d0a1-277ac07f12c0",
    "_uuid": "d2aa8341542b1ff0aad51ec92235b5bf0994de7f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      "roc.default(response = test$Class, predictor = xgb.test.acc,     plot = TRUE, col = \"blue\")\n",
      "\n",
      "Data: xgb.test.acc in 85504 controls (test$Class 0) < 164 cases (test$Class 1).\n",
      "Area under the curve: 0.963\n",
      "\n",
      "Call:\n",
      "roc.default(response = test$Class, predictor = xgb.test.hist,     plot = TRUE, col = \"blue\")\n",
      "\n",
      "Data: xgb.test.hist in 85504 controls (test$Class 0) < 164 cases (test$Class 1).\n",
      "Area under the curve: 0.9654\n"
     ]
    }
   ],
   "source": [
    "print(auc.xgb.acc)\n",
    "print(auc.xgb.hist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ba775a11-0d5b-aa78-8bb5-8c75ef068870",
    "_uuid": "81da63611f4224cc9c96f5430a2f3f9c2b8f6983"
   },
   "source": [
    "## lightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "4bcc82db-8b51-b8da-6813-202598c1976e",
    "_uuid": "3154a0b24582206168a8d820190ee24d8e960758"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      "roc.default(response = test$Class, predictor = lgb.test, plot = TRUE,     col = \"green\")\n",
      "\n",
      "Data: lgb.test in 85504 controls (test$Class 0) < 164 cases (test$Class 1).\n",
      "Area under the curve: 0.9667\n"
     ]
    }
   ],
   "source": [
    "print(auc.lgb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6e8f528d-1035-9b8d-5c67-a022a1104354",
    "_uuid": "63c9d6cbc53abd215861d7ae7a97af68c06d493b"
   },
   "source": [
    "## Feature Importance\n",
    "\n",
    "-----\n",
    "\n",
    "The top features selected by all three models were very similar. Although, my understanding is that GBM is only based on frequency. The top 5 features were the same expect for GBM selecting v20 as an important feature. It is interesting that xgboost selects so few features. \n",
    "\n",
    "### GBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "33a77ad3-7dca-2628-f1a2-04386e2f89d4",
    "_uuid": "c6a20be791d88a8ece53fd8baa18857ce50307f5",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          var      rel.inf\n",
      "V17       V17 36.689849893\n",
      "V12       V12 27.376980007\n",
      "V14       V14 20.585668687\n",
      "V10       V10  5.033576807\n",
      "V20       V20  4.893272164\n",
      "V9         V9  2.007747143\n",
      "V4         V4  0.890157856\n",
      "V7         V7  0.633520610\n",
      "V3         V3  0.379193323\n",
      "V11       V11  0.363774708\n",
      "V26       V26  0.341230776\n",
      "V16       V16  0.277737163\n",
      "V18       V18  0.188276172\n",
      "V1         V1  0.166799644\n",
      "V24       V24  0.048701815\n",
      "V28       V28  0.032858291\n",
      "V8         V8  0.031456165\n",
      "Time     Time  0.020266668\n",
      "V25       V25  0.015560439\n",
      "V22       V22  0.014221940\n",
      "V27       V27  0.004592544\n",
      "V2         V2  0.004557184\n",
      "V5         V5  0.000000000\n",
      "V6         V6  0.000000000\n",
      "V13       V13  0.000000000\n",
      "V15       V15  0.000000000\n",
      "V19       V19  0.000000000\n",
      "V21       V21  0.000000000\n",
      "V23       V23  0.000000000\n",
      "Amount Amount  0.000000000\n"
     ]
    }
   ],
   "source": [
    "print(gbm.feature.imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "71123f33-f448-4faf-c8ca-3b826ef1896c",
    "_uuid": "aa3cd2b3126f3c6a50183a31dff231e32761f550"
   },
   "source": [
    "### xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "28ac3cd1-85f9-d33b-d111-119494e75b37",
    "_uuid": "78e827b3b4e5ebf050d88c0426f165e93a9e7931",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Feature         Gain        Cover   Frequency\n",
      "1:     V14 3.919509e-01 0.3672567716 0.385964912\n",
      "2:     V10 2.836024e-01 0.2238627706 0.146198830\n",
      "3:     V12 1.429214e-01 0.1300624239 0.076023392\n",
      "4:     V17 1.090377e-01 0.2005589750 0.070175439\n",
      "5:      V4 7.130479e-02 0.0506007506 0.274853801\n",
      "6:      V3 1.053690e-03 0.0010699484 0.035087719\n",
      "7:     V11 1.243299e-04 0.0003748567 0.005847953\n",
      "8:     V16 4.822091e-06 0.0262135033 0.005847953\n"
     ]
    }
   ],
   "source": [
    "print(xgb.feature.imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a20f7b26-6067-ecaf-3de2-9fbbf99058a9",
    "_uuid": "57c5e061685bd040556102e7f391a8aaa284b272"
   },
   "source": [
    "### lightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "c2c16d4e-11d5-7d6f-5706-b0f7aa9906ef",
    "_uuid": "3a8e4cc9a4cc0d87872c54e26e4adca7ce69892a",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Feature         Gain        Cover   Frequency\n",
      " 1:     V14 3.680682e-01 0.3954538938 0.303418803\n",
      " 2:     V10 3.196521e-01 0.2110461596 0.158119658\n",
      " 3:     V17 1.550632e-01 0.0975745317 0.076923077\n",
      " 4:     V12 1.465647e-01 0.0469655281 0.047008547\n",
      " 5:      V4 6.439478e-03 0.1974808694 0.217948718\n",
      " 6:      V3 2.139756e-03 0.0026903761 0.034188034\n",
      " 7:     V11 7.348094e-04 0.0017239487 0.021367521\n",
      " 8:  Amount 3.564204e-04 0.0081624980 0.068376068\n",
      " 9:      V7 2.836991e-04 0.0152463511 0.017094017\n",
      "10:      V5 2.193674e-04 0.0002689428 0.008547009\n",
      "11:     V16 1.637813e-04 0.0060633498 0.004273504\n",
      "12:      V9 1.243448e-04 0.0060429300 0.008547009\n",
      "13:      V1 9.903499e-05 0.0003893221 0.012820513\n",
      "14:      V8 4.369346e-05 0.0003089878 0.004273504\n",
      "15:     V26 3.166212e-05 0.0002214086 0.008547009\n",
      "16:     V13 8.871171e-06 0.0055226212 0.004273504\n",
      "17:     V20 6.823963e-06 0.0048382812 0.004273504\n"
     ]
    }
   ],
   "source": [
    "print(lgb.feature.imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "170a3f49-33cb-d066-d8dd-f87a71f5f8ed",
    "_uuid": "ebcf8dc86e1bb3ec6a6478ae47f6ed7972d563ca"
   },
   "source": [
    "# Additional Observations\n",
    "\n",
    "## GBM\n",
    "\n",
    "Advantages:\n",
    "\n",
    "* None\n",
    "\n",
    "Disadvantages:\n",
    "\n",
    "* No early exit\n",
    "* Slower training\n",
    "* Less accurate\n",
    "\n",
    "## xgboost\n",
    "\n",
    "Advantages:\n",
    "\n",
    "* Proven success (on kaggle)\n",
    "* Now with Histogram Binning\n",
    "\n",
    "Disadvantages:\n",
    "\n",
    "* Traditionally slower than lightGBM, but `tree_method = 'hist'` is a big improvement.\n",
    "\n",
    "## lightGBM\n",
    "\n",
    "Advantages:\n",
    "\n",
    "* Fast training efficiency\n",
    "* Low memory usage\n",
    "* Better accuracy\n",
    "* Parallel learning supported\n",
    "* Deal with large scale data\n",
    "* Corporate supported\n",
    "\n",
    "Disadvantages:\n",
    "\n",
    "* Newer, so less community documentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "77d9178c-6446-640d-c8a9-f834a9697a6a",
    "_uuid": "7b418651f091fa89ddea011db459d7b259a759d5"
   },
   "source": [
    "## Post Script\n",
    "\n",
    "In this example lightGBM completed in 15% of the time it took xgboost. That seems too extreme to me. Any one have feedback on how I may not be fairly parameterizing xgboost in this comparison? **Update:** Switching xgboost to `tree_method = 'hist'` makes the comparison much closer.\n",
    "\n",
    "## Release Notes\n",
    "\n",
    "### - version(17)\n",
    "\n",
    "#### major changes\n",
    "\n",
    "* Added a section for `xgboost` using `tree_method = 'hist'`\n",
    "\n",
    "### - version(16)\n",
    "\n",
    "#### Major changes\n",
    "\n",
    "* Added feature importance comparison\n",
    "\n",
    "#### Minor changes\n",
    "\n",
    "* some code comments\n",
    "\n",
    "### - version(12)\n",
    "\n",
    "#### Major changes\n",
    "\n",
    "* Added second xgboost model to compare accuracy (allows for deeper tree growth)\n",
    "\n",
    "#### Minor changes\n",
    "\n",
    "* Reduced warning levels on depreciated parameters in xgboost\n",
    "* Updated GBM run time based on latest kernel run."
   ]
  }
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