ens_expirements.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('train_price.csv', train.price.values, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from ensembles import RandomForestMSE\n",
    "from ensembles import GradientBoostingMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('train_data.csv', index_col=0).to_numpy()\n",
    "target = pd.read_csv('train_target.csv', index_col=0).to_numpy().reshape((-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_feat = np.delete(np.arange(X_train.shape[1]), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# подготовка данных для теста на Flask\n",
    "\n",
    "data = pd.read_csv('train_data.csv', index_col=0)\n",
    "target = pd.read_csv('train_target.csv', index_col=0)\n",
    "\n",
    "num_feat = np.delete(np.arange(data.shape[1]), 1)\n",
    "data = data.iloc[:, num_feat]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.3, random_state=42)\n",
    "\n",
    "X_train.to_csv('train_sample.csv', index=False)\n",
    "X_test.to_csv('test_sample.csv', index=False)\n",
    "y_train.to_csv('train_price.csv', index=False)\n",
    "y_test.to_csv('test_price.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_features = np.random.choice(X_train.shape[1], X_train.shape[1], replace=False)\n",
    "X = X_train.to_numpy()"
   ]
  },
  {
   "source": [
    "# Random Forest"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " 1000"
     ]
    }
   ],
   "source": [
    "rf = RandomForestMSE(n_estimators=50)\n",
    "scores = []\n",
    "times = []\n",
    "iters = np.arange(50, 1001, 50)\n",
    "start = time.time()\n",
    "for i in iters:\n",
    "    print('\\r\\r\\r\\r', i, end='')\n",
    "    scores.append(rf.fit(X_train[:, num_feat], y_train, X_test[:, num_feat], y_test)**(1/2))\n",
    "    times.append(time.time() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "fig.suptitle('Number of trees in Random Forest', fontsize=14)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(iters, scores)\n",
    "plt.xlabel('Number of trees')\n",
    "plt.ylabel('RMSE')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(iters, times)\n",
    "plt.xlabel('Number of trees')\n",
    "plt.ylabel('time in seconds')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " 1.0"
     ]
    }
   ],
   "source": [
    "scores = []\n",
    "times = []\n",
    "depth = np.linspace(0.1, 1, 5)\n",
    "for i in depth:\n",
    "    print('\\r\\r\\r\\r\\r\\r\\r\\r', i, end='')\n",
    "    start = time.time()\n",
    "    rf = RandomForestMSE(n_estimators=900, feature_subsample_size=i)\n",
    "    scores.append(rf.fit(X_train[:, num_feat], y_train, X_test[:, num_feat], y_test)**(1/2))\n",
    "    times.append(time.time() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "fig.suptitle('Feature subsample size in Random Forest', fontsize=14)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(depth, scores)\n",
    "plt.xlabel('feature subsample size')\n",
    "plt.ylabel('RMSE')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(depth, times)\n",
    "plt.xlabel('feature subsample size')\n",
    "plt.ylabel('time in seconds')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " 13"
     ]
    }
   ],
   "source": [
    "scores = []\n",
    "times = []\n",
    "max_depth = np.arange(1, 15, 3)\n",
    "for i in max_depth:\n",
    "    print('\\r\\r\\r\\r\\r\\r\\r\\r', i, end='')\n",
    "    start = time.time()\n",
    "    rf = RandomForestMSE(n_estimators=900, feature_subsample_size=1, max_depth=i)\n",
    "    scores.append(rf.fit(X_train[:, num_feat], y_train, X_test[:, num_feat], y_test)**(1/2))\n",
    "    times.append(time.time() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "fig.suptitle('Max depth in Random Forest', fontsize=14)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(max_depth, scores)\n",
    "plt.xlabel('max depth')\n",
    "plt.ylabel('RMSE')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(max_depth, times)\n",
    "plt.xlabel('max depth')\n",
    "plt.ylabel('time in seconds')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(126.5194890989789, 357.32329201698303)"
      ]
     },
     "metadata": {},
     "execution_count": 252
    }
   ],
   "source": [
    "scores[-1], times[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[278.5452074375525,\n",
       " 184.02143367967278,\n",
       " 144.1010681225625,\n",
       " 129.69930841780828,\n",
       " 126.5194890989789]"
      ]
     },
     "metadata": {},
     "execution_count": 253
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[31.04021406173706,\n",
       " 86.1955018043518,\n",
       " 159.84515690803528,\n",
       " 251.48451900482178,\n",
       " 357.32329201698303]"
      ]
     },
     "metadata": {},
     "execution_count": 254
    }
   ],
   "source": [
    "times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(124.58749483837305, 143.53441286087036)"
      ]
     },
     "metadata": {},
     "execution_count": 257
    }
   ],
   "source": [
    "start = time.time()\n",
    "rf = RandomForestMSE(n_estimators=900, feature_subsample_size=1)\n",
    "rf.fit(X_train[:, num_feat], y_train, X_test[:, num_feat], y_test)**(1/2), time.time() - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(126.14783967191674, 114.52759599685669)"
      ]
     },
     "metadata": {},
     "execution_count": 259
    }
   ],
   "source": [
    "start = time.time()\n",
    "rf = RandomForestMSE(n_estimators=900, feature_subsample_size=1, max_depth=13)\n",
    "rf.fit(X_train[:, num_feat], y_train, X_test[:, num_feat], y_test)**(1/2), time.time() - start"
   ]
  },
  {
   "source": [
    "# Gradient Boosting on trees"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " 1000"
     ]
    }
   ],
   "source": [
    "gb = GradientBoostingMSE(n_estimators=50)\n",
    "scores = []\n",
    "times = []\n",
    "iters = np.arange(50, 1001, 50)\n",
    "start = time.time()\n",
    "for i in iters:\n",
    "    print('\\r\\r\\r\\r', i, end='')\n",
    "    scores.append(gb.fit(X_train[:, num_feat], y_train, X_test[:, num_feat], y_test)**(1/2))\n",
    "    times.append(time.time() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1080x360 with 2 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "fig.suptitle('Number of trees in Gradient Boosting', fontsize=14)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(iters, scores)\n",
    "plt.xlabel('Number of trees')\n",
    "plt.ylabel('RMSE')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(iters, times)\n",
    "plt.xlabel('Number of trees')\n",
    "plt.ylabel('time in seconds')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "700"
      ]
     },
     "metadata": {},
     "execution_count": 224
    }
   ],
   "source": [
    "iters[np.argmin(scores)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " 1.0"
     ]
    }
   ],
   "source": [
    "scores = []\n",
    "times = []\n",
    "depth = np.linspace(0.1, 1, 5)\n",
    "for i in depth:\n",
    "    print('\\r\\r\\r\\r\\r\\r\\r\\r', i, end='')\n",
    "    start = time.time()\n",
    "    gb = GradientBoostingMSE(n_estimators=700, feature_subsample_size=i)\n",
    "    scores.append(gb.fit(X_train[:, num_feat], y_train, X_test[:, num_feat], y_test)**(1/2))\n",
    "    times.append(time.time() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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EJIGpKBQREREREUlgKgpFREREREQSmIpCERERERGRBKaiUEREREREJIGpKMyDVdv28vLMVVHHEBERERGRBJKR4YyauoIf9h6K63GS47r3YuLVL9cwfPIyTmtandRaFaKOIyIiIiIixdzOfYf5zWvz+GzRZtIznJt7NIvbsdRSmAf9uzeldHISwyctizqKiIiIiIgUc9+s3cn5T37O50u28OeLTmLA6U3jejwVhXlQvUJpru3UiP+dt4412/dFHUdERERERIohd2fsrNVcPmIa6RnOq7d04cauTTCzuB5XRWEeDezRjCSDkVOWRx1FRERERESKmf2H0vnd6/O5761vOK1pNd67vTunNqpaIMdWUZhHdSuX5Yr2DXh19ho27zoQdRwRERERESkmVm7dy6XDvuDNuWu548zmjO7bieoVShfY8VUUHoVBPVNIS8/g2akroo4iIiIiIiLFwIRvN3Lhk1PZuOsAz/ftyF1nnUCJpPh2F81KReFRaFy9PBe1rcdLM1bFfVhYEREREREpvtLSM3j4/YXc8uIcmtYsz3u3d6d3i1qRZFFReJRu7ZXKvkPpPD9tZdRRRERERESkCNq86wC/enYmT09ZznWdG/H6oC40qFousjwqCo9SizoVObtVbUZ/sYLdBw5HHUdERERERIqQmcu3cf6TU5m/dgf/vrotf72kNaWTS0SaSUXhMRhyRiq7DqTx0ozVUUcREREREZEiwN0ZOWUZv3p2JhVLJzNucHcuPaVB1LEAFYXHpE2DKpzevAbPTV3OgcPpUccREREREZFCbNeBwwx6aQ5/e38RZ7eqzbgh3WhRp2LUsf6PisJjNKR3Klv3HOKVWWotFBERERGR7H23fhcXPTmVTxZu5v7zT2RYn1OpWKZk1LF+QkXhMTqtWXU6NqnK01OWcygtI+o4IiIiIiJSyLw+ew2XDvuCfYfSeWVgZwac3gyzgr3cRF6oKDwOg3unsmHnAd7+am3UUUREREREpJA4cDid+96az+/fmM+pjaoy/o7T6dikWtSxchS3otDMRpnZZjNbEDPvL2Y238zmmdlHZlYvnN8nnD/fzKaZWdsc9jnazFaE288zs3bxyp8XPU+oSev6lRk+aRlp6WotFBERERFJdGu27+OKEdMYO2sNt/ZK4cX+nahZsXTUsXIVz5bC0cA5WeY96u5t3L0d8B7wp3D+CqCnu7cB/gKMzGW/v3f3duFtXn6HPhpmxuDeKazcto/3F2yMMoqIiIiIiETss0WbOP+Jz1m1bR/P3NCBe85pSXKJwt85M24J3X0KsD3LvF0xk+UBD+dPc/cfwvkzgMIxNmsenN2qDs1rVWDYxKVkZHjUcUREREREpIClZziPTlhEv9GzaVC1HO/d3p2zWtWOOlaeFXjZamYPmdkaoA8/thTG6g98kMsuHgq7mf7bzCJvh01KMm7rncKijbv5dNHmqOOIiIiIiEgB2rrnIDeMmslTE5dxdYeGvHVbVxpXLx91rKNS4EWhu//B3RsCLwNDYpeZWW+CovCeHDa/D2gJdASq5bIeZjbQzGab2ewtW7bkS/acXNimHo2qlWPoxKW4q7VQRERERCQRzFm1nQuemMrslT/wyOVt+McVbShTskTUsY5alB1c/wtcnjlhZm2AZ4GL3X1bdhu4+wYPHASeBzrltHN3H+nuHdy9Q82aNfM5+k8ll0hiUM8Uvl6zgy+WZhtdRERERESKCXdn1NQVXP30DEolJ/HWbV25qmPDqGMdswItCs2seczkRcCicH4j4C3gendfnMv2dcO/BlwCLMhp3YJ2efv61K5UmqETl0QdRURERERE4mTPwTSGjP2K/3nvO3q1qMW7t3fnpHqVo451XJLjtWMzGwv0AmqY2VrgAeA8M2sBZACrgEHh6n8CqgPDwos5prl7h3A/7wMD3H098LKZ1QQMmBezfeRKJ5fg5tOb8dfxC5mzajvtGxfe65CIiIiIiMjRW7xpN4NemsPKrXu555yW3NKjGUlJhe9i9EcrbkWhu1+bzeznclh3ADAgh2Xnxdw/I3/SxcevTmvEsEnLGPrZUp7vm2PPVhERERERKWLGzVvHvW9+Q/nSybw8oDNdUqpHHSnfFP6LZhQh5Uol069bEyZ+v4UF63ZGHUdERERERI7TwbR0/jRuAb9+ZR4n16/E+Du6F6uCEFQU5rvruzShYulkhk1aGnUUERERERE5Dut27Oeqp2fwwvRV3Hx6U/57c2dqVyoTdax8F7fuo4mqctmS3NC1McMmLWPp5t2k1qoYdSQRERERETlKkxdv4c5XvuJwujO8z6mc27pu1JHiRi2FcdCvW1PKJJdg2KRlUUcREREREZGjkJHhPP7JYm56fha1KpbhnSHdinVBCCoK46J6hdJc26kR4+atZ832fVHHERGRYsjMqpjZG2a2yMwWmlkXM6tmZh+b2ZLwb9Woc4qIFCXb9x7iptFf8vgnS7i0XX3eHtyVZjUrRB0r7lQUxsnAHs0oYcbTU9RaKCIicfEf4EN3bwm0BRYC9wKfuntz4NNwWkRE8mDemh1c+ORUZizbxkOXnsxjV7WlXKnEONtORWGc1KlchsvbN+C12WvZvOtA1HFERKQYMbNKQA/CSz25+yF33wFcDIwJVxsDXBJNQhGRosPdeXH6Sq4cMQ2AN27tQp/TGhNePz0hqCiMo1t7ppCe4Tzz+fKoo4iISPHSDNgCPG9mX5nZs2ZWHqjt7hsAwr+1ogwpIlLY7TuUxm9enccfx31Lt9QavHd7d9o0qBJ1rAKnojCOGlUvx0Vt6/HyzNX8sPdQ1HFERKT4SAZOBYa7+ynAXvLYVdTMBprZbDObvWXLlnhmFBEp1JZt2cMlT33BuK/X89uzTmDUjR2pWr5U1LEioaIwzm7rlcK+Q+k8/8WKqKOIiEjxsRZY6+4zw+k3CIrETWZWFyD8uznrhu4+0t07uHuHmjVrFlhgEZHC5P1vNnDRk1PZuucQL/TrxO1nNicpKXG6i2alojDOmteuyDkn1WH0tJXsPnA46jgiIlIMuPtGYI2ZtQhnnQl8B7wD3BjOuxEYF0E8EZFC63B6Bv/z7nfc9vJcTqhTkfdu787pzfUDWWIMpxOxwb1T+fDbjbw4YxW39UqNOo6IiBQPtwMvm1kpYDnQl+DH3tfMrD+wGrgywnwiIoXKxp0HGPLfucxe9QM3dW3C/zvvREolq40MVBQWiNYNKtPjhJo89/kK+nZtStlSJaKOJCIiRZy7zwM6ZLPozILOIiJS2E1bupU7XvmKfYfSeeLaU7iobb2oIxUqKo0LyJDeqWzbe4hXvlwddRQRERERkYSQkeE8NXEp1z03kyrlSvHOkG4qCLOhorCAdGpajU5NqjFyynIOpWVEHUdEREREpFjbue8wN78wm0cnfM/5beoxbnA3UmtVjDpWoaSisAANPiOVDTsP8NbctVFHEREREREpthas28kFQz9nypItPHhhK564ph3lS+vMuZyoKCxAPZrXoHX9ygyfvIy0dLUWioiIiIjkJ3fnlVmruWz4NNLSnVdv6cJN3ZpilriXm8gLFYUFyMwY3DuVVdv2Mf6bDVHHEREREREpNvYfSuf3b8zn3re+oVOTarx3e3dObVQ16lhFgorCAnZ2q9qcULsCwyYuIyPDo44jIiIiIlLkrdy6l8uGT+ONOWu544xUxvTrRPUKpaOOVWSoKCxgSUnGbb1S+X7Tbj5ZuCnqOCIiIiIiRdqEbzdy4ZNT2bBzP8/37chdZ7egRJK6ix4NFYURuKBNXRpVK8dTE5firtZCEREREZGjlZaewcMfLOSWF+fQtGZ53h3Snd4takUdq0hSURiB5BJJ3Norha/X7mTq0q1RxxERERERKVI27z5An2dn8vTk5fQ5rRGvD+pCw2rloo5VZMW1KDSzUWa22cwWxMz7i5nNN7N5ZvaRmdUL55uZPWFmS8Plp+awz/Zm9k243hNWRIcSuuzU+tSpVIahny2NOoqIiIiISJExa8V2zn9iKl+v3cG/rmrLQ5e2pnRyiahjFWnxbikcDZyTZd6j7t7G3dsB7wF/CuefCzQPbwOB4Tnsc3i4PHPdrPsvEkonl2Bgj2bMXLGd2Su3Rx1HRERERKRQc3dGTlnGtc/MoELpZP53cDcuO7VB1LGKhbgWhe4+BdieZd6umMnyQOZJdRcDL3hgBlDFzOrGbhtOV3L36R6cjPcCcEncHkCcXdOpIdXKl2LoRLUWioiIiIjkZNeBw9z60lz+9v4izjqxNu8M6UbLOpWijlVsJEdxUDN7CLgB2An0DmfXB9bErLY2nBd7Qb/64fys6xRJ5Uol0797Ux6d8D0L1u3k5PqVo44kIiIiIlKoLNywi1tfmsOaH/Zz//kn0r+7Lkaf3yIZaMbd/+DuDYGXgSHh7Oxe2axDc+ZlnWBFs4FmNtvMZm/ZsuXYw8bZ9V0aU7FMMk+ptVBERERE5CfenLOWS4d9wb5D6Yy9uTMDTm+mgjAOoh599L/A5eH9tUDDmGUNgPVZ1l8bzs9tHQDcfaS7d3D3DjVr1synuPmvUpmS3NilCR9+u5Glm3dHHUdEREREJHIHDqdz31vf8NvXv6Zdwyq8d0d3OjWtFnWsYqvAi0Izax4zeRGwKLz/DnBDOAppZ2Cnu8d2HSWc3m1mncNRR28AxhVE7njq170pZZJLMGzisqijiIiIiIhEas32fVw5YjpjZ61mUM8UXup/GrUqlok6VrEW13MKzWws0AuoYWZrgQeA88ysBZABrAIGhau/D5wHLAX2AX1j9jMvHK0U4FaCUU3LAh+EtyKtWvlS/Oq0RoyetpI7f3ECjarrGisiIiIikngmLtrMna/OI8OdZ27owFmtakcdKSHEtSh092uzmf1cDus6MDiHZe1i7s8GTs6XgIXIwB7NeHH6KkZMWcbfLm0ddRwRERERkQKTnuH8++PFDJ24lBPrVmLEdafSuHr5qGMljKjPKZRQ7UpluKJDA96YvZZNuw5EHUdEREREpEBs23OQG0bNZOjEpVzVoQFv39ZVBWEBU1FYiNzaM4V0d56ZsjzqKCIiIiIicTdn1Q+c/8RUZq/8gUcub8MjV7SlTMkSUcdKOCoKC5GG1cpxcdt6vDxzNdv3Hoo6joiIiIhIXLg7z3+xgqufnk6p5CTevLUrV3VseOQNJS5UFBYyt/VO4UBaOs9/sSLqKCIiIiIi+W7PwTSGjP2KP7/7Hb1a1OLd27tzcv3KUcdKaCoKC5nUWhU556Q6jJ62kl0HDkcdR0REREQk3yzZtJuLh07lg282cM85LRl5fXsqly0ZdayEp6KwEBrcO5XdB9J4cfqqqKOIiIiIiOSLcfPWcfFTX7Bz/2FeGnAat/ZKISnJoo4lqCgslE6uX5meJ9Rk1NQV7D+UHnUcEREREZFjdigtgwfGLeDXr8yjVd1KjL/jdLqm1Ig6lsRQUVhIDTkjlW17DzF21uqoo4iIiIiIHJP1O/Zz1dPTGTN9FQO6N2XswM7UrlQm6liShYrCQqpjk2p0alqNkVOWczBNrYUiIiIiUrRMWbyF85/4nKWb9zCsz6ncf0ErSpZQ+VEY6VUpxIb0TmXjrgO8NXdd1FFERERERPIkI8P5zydLuPH5WdSqWIZ3hnTjvNZ1o44luVBRWIid3rwGbRpUZvikZaSlZ0QdR0REREQkVz/sPUS/MV/y708Wc0m7+rw9uCvNalaIOpYcgYrCQszMGNw7ldXb9/He/A1RxxERERERydHXa3ZwwZNTmbZ0G3+95GT+dVVbypVKjjqW5IGKwkLurBNrc0LtCjw1cSkZGR51HBERERGRnzhwOJ2nJi7lyhHTAXh9UBeu69wYM11uoqhQUVjIJSUFrYVLNu/h44Wboo4jIiIiIgIE5w6+NXctZ/xzEo9O+J7eLWvy3u3daduwStTR5CipKCwCzm9dl8bVy/HUxKW4q7VQRERERKI1bdlWLnpqKne99jXVK5Rm7M2defr6DlQtXyrqaHIM1Mm3CEgukcStPVO4961v+HzJVnqcUDPqSCIiIiKSgJZs2s3fP1jEp4s2U79KWR6/uh0Xta1HUpK6ihZlKgqLiMtObcB/Pl3C0IlLVRSKiIiISIHavPsAj3+yhFdmrYF9LwUAACAASURBVKZ8qWTuOaclfbs1oUzJElFHk3ygorCIKJWcxMAezfjzu9/x5crtdGxSLepIIiIiIlLM7TuUxrOfr2DE5GUcSsvghi5NuOPM5lRTN9FiRecUFiHXdGxE9fKlGPrZ0qijiIiIiEgxlp7hvDZ7Db3/OYl/fbyYHs1r8vFdPXnwopNUEBZDaiksQsqWKkG/7k15dML3fLN2J60bVI46koiIiIgUM1MWb+Fv7y9k0cbdtGtYhaG/OlW91Io5tRQWMdd3aUzFMsk8NVGthSIiIiKSfxZu2MUNo2Zxw6hZ7D2UxtBfncLbt3VVQZgA1FJYxFQqU5Kbujbhyc+WsmTTbprXrhh1JBEREREpwjbuPMC/Pv6e1+espVKZktx//olc36UxpZM1iEyiiFtLoZmNMrPNZrYgZt6jZrbIzOab2dtmViWc38fM5sXcMsysXTb7fNDM1sWsd1688hdmfbs1pWzJEgybtCzqKCIiIiJSRO05mMa/PvqeXv+cyP9+tZ7+3Zoy+fe9GHB6MxWECSae3UdHA+dkmfcxcLK7twEWA/cBuPvL7t7O3dsB1wMr3X1eDvv9d+a67v5+nLIXatXKl6LPaY145+v1rN62L+o4IiIiIlKEpKVn8N+Zq+n16CSe+GwpvzixNp/c1ZP7L2hFlXIaRCYRxa0odPcpwPYs8z5y97RwcgbQIJtNrwXGxitXcXFzj2aUMGP4ZLUWioiIiMiRuTufLdrEOf/5nP/39jc0rVGOt2/rytBfnUqj6uWijicRinKgmX7AB9nMv5rci8IhYffTUWZWNT7RCr/alcpwZYcGvDlnLRt3Hog6joiIiIgUYgvW7eRXz8yk3+jZpGc4I65rz2u3dOGURgn7dVpiRFIUmtkfgDTg5SzzTwP2ufuCbDeE4UAK0A7YADyWyzEGmtlsM5u9ZcuW/AleyAzqmUK6O898vjzqKCIiIiJSCK3bsZ+7Xp3HBU9OZdHGXfz5opP46Dc9OOfkOphZ1PGkkCjw0UfN7EbgAuBMd/csi68hl1ZCd98Us59ngPdyWXckMBKgQ4cOWY9TLDSsVo6L29XjvzNXM7h3qi4kKiIiIiIA7DpwmOGTlvHc1BUA3NorhVt7pVCpTMmIk0lhVKBFoZmdA9wD9HT3fVmWJQFXAj1y2b6uu28IJy8FcmpRTBi39Url7a/WMWrqCn73yxZRxxERERGRCB1Oz2DsrNU8/skStu89xKWn1Oe3Z59Ag6o6Z1ByFrei0MzGAr2AGma2FniAYLTR0sDHYXP1DHcfFG7SA1jr7suz7OdZYIS7zwYeCS9V4cBK4JZ45S8qUmtV4NyT6zBm+koG9mymX39EREREEpC789F3m/jHB4tYvnUvXZpV5/+ddyKtG1SOOpoUAXErCt392mxmP5fL+pOAztnMHxBz//p8CVfM3NYrlfe/2ciL01cxuHdq1HFEREREpADNW7ODh8Z/x5crfyC1VgVG3dSB3i1q6ZxBybMCP6dQ8t/J9SvTu0VNnpu6gr7dmlCulF5WERERkeJuzfZ9PDLhe979ej01KpTioUtP5uoODUkuEeUFBqQoUvVQTAzuncoVI6YzdtYa+ndvGnUcERHJAzMrD+x39wwzOwFoCXzg7ofzsO1KYDeQDqS5ewczqwa8CjQhOM3iKnf/IU7xRSQiO/cdZujEJYyZtoqkJLjjjFQG9kyhQml9tZdjo58RiokOTapxWtNqjJyyjINp6VHHERGRvJkClDGz+sCnQF9g9FFs39vd27l7h3D6XuBTd28e7u/e/AwrItE6mJbOs58vp8ejE3l26gouOaUek37Xm7vObqGCUI6LisJiZMgZqWzadZA356yLOoqIiOSNhaNxXwY86e6XAq2OY38XA2PC+2OAS44zn4gUAu7O+PkbOOtfU/jr+IW0aVCZ8befziNXtKVO5TJRx5NiQD8pFCPdU2vQtkFlRkxexlUdGqg/uYhI4Wdm1gXoA/QP5+X1/2YHPjIzB54Or89bO/PSTe6+wcxq5XtiESlQc1Zt56/jF/LV6h20rFORMf060fOEmlHHkmJGRWExYmYM7p3KwBfn8O789Vx6SoOoI4mISO7uJLhc09vu/q2ZNQMm5nHbbu6+Piz8PjazRXnZyMwGAgMBGjVqdCyZRaQArNy6l398uIgPFmykVsXSPHJ5Gy5v34ASSRpRVPKfisJi5hcn1qZF7YoMm7iMi9vWJ0n/cIiIFFruPhmYHDO9HLgjj9uuD/9uNrO3gU7AJjOrG7YS1gU2Z7PdSGAkQIcOHfz4H4WI5Kftew/xxKdLeGnGKkolJ3HXWScw4PSmGl1e4krvrmImKcm4rXcKv35lHh99t5FzTq4bdSQREcnCzN4l6P6ZLXe/6AjblweS3H13eP9s4H+Ad4Abgb+Hf8flW2gRiasDh9MZM20lQycuZe/BNK7u2IjfnNWcWhV1zqDEn4rCYuiCNvX498eLeWriMn55Uh1duFREpPD5Z/j3MqAO8FI4fS3BpSSOpDbwdvjvezLwX3f/0My+BF4zs/7AauDK/AwtIvkvI8N5d/56Hvnwe9bt2M8ZLWtx77ktOaF2xaijSQJRUVgMlUgybu2Vwj1vfsOUJVt1MrKISCETdhvFzP7i7j1iFr1rZlPysP1yoG0287cBZ+ZbUBGJq+nLtvG39xfyzbqdnFSvEo9e0YauqTWijiUJSMNTFlOXntKAepXL8NRnS6OOIiIiOasZDi4DgJk1BfRLnkgxt3TzHgaMmc21z8xg656D/Ouqtrw7pLsKQomMWgqLqVLJSQzs0YwH3/2OWSu206lptagjiYjIz/0GmGRmy8PpJsAt0cURkXjauucgj3+ymLGz1lC2ZAnuPqcF/bo1pUzJElFHkwSnorAYu6ZTI4ZOXMrQiUt5oWmnqOOIiEgW4XmAzYGW4axF7n4wykwikv/2H0pn1BcrGD5pGQcOp3PdaY2448zmVK9QOupoIoCKwmKtTMkS9O/ejH98uIj5a3fQpkGVqCOJiMjPtSdoIUwG2poZ7v5CtJFEJD+kZzhvf7WOf074no27DnB2q9rcc25LUmpWiDqayE+oKCzmruvciOGTlvLUxKU8fX2HqOOIiEgMM3sRSAHmAenhbAdUFIoUcVOXbOWh9xeycMMu2jaozBPXnqLTeaTQUlFYzFUsU5Kbujbhic+WsnjTbg1vLCJSuHQAWrm7LiIvUkx8v3E3D3+wkEnfb6FB1bI8ce0pXNC6LklJukSYFF4afTQB9O3WlHKlSjBsokYiFREpZBYQXKdQRIq4zbsOcO+b8zn3P1OYu+oH/nDeiXz6255c1LaeCkIp9NRSmACqli9Fn9Ma8dzUFfzmrBNoXL181JFERCRQA/jOzGYB/zfAjLtfFF0kETkaew+mMXLKckZOWU5aRgZ9uzVlSO9UqpYvFXU0kTxTUZggbj69GWOmr2LE5GU8fFmbqOOIiEjgwagDiMixSc9wXp+9hsc+XsyW3Qc5v01d7v5lC/34LkWSisIEUatSGa7q0IBXv1zDHWc2p27lslFHEhFJeO4+2cxqAx3DWbPcfXOUmUQkd+7OpMVbePj9hSzetIf2jasy4rr2tG9cNepoIsdM5xQmkFt6pJDh8MyUFVFHERERwMyuAmYBVwJXATPN7IpoU4lITr5dv5PrnptJ3+e/5FBaBsP7nMobg7qoIJQiTy2FCaRhtXJc0q4+/521isG9U3TBVBGR6P0B6JjZOmhmNYFPgDciTSUiP7Fh537+OWExb321liplS/LAha3oc1pjSiWrfUWKh7i9k81slJltNrMFMfMeNbNFZjbfzN42syrh/CZmtt/M5oW3ETnss5qZfWxmS8K/+lnmKN3WO4WDaRmM+kKthSIihUBSlu6i21AvHpFCY/eBwzw6YRG9Hp3Eu/PXM7BHMyb9vjd9uzVVQSjFSjzfzaOBc7LM+xg42d3bAIuB+2KWLXP3duFtUA77vBf41N2bA5+G03IUUmpW4LyT6/LCtFXs3H846jgiIonuQzObYGY3mdlNwHjgg4gziSS8w+kZvDhjFb0encRTE5dx7sl1+Oy3Pbnv3BOpXLZk1PFE8l3cuo+6+xQza5Jl3kcxkzOAoz1v4mKgV3h/DDAJuOeYAiaw23qnMP6bDbw4fSVDzmgedRwRkYTl7r83s8uA7oABI9397YhjiSQsd+eThZt5+IOFLN+yl9OaVuP580+kTYMqUUcTiasozynsB7waM93UzL4CdgH3u/vn2WxT2903ALj7BjOrVQA5i52T6lXmjJa1eG7qCvp1b0q5Ujq1VEQkCmbWFHjf3d8Kp8uaWRN3XxltMpHE8/WaHTz0/kJmrdhOs5rlefaGDpx5Yi3MdOF5Kf4i6QxtZn8A0oCXw1kbgEbufgpwF/BfM6t0nMcYaGazzWz2li1bji9wMTS4dyo/7DvMf2eujjqKiEgiex3IiJlOD+eJSAFZs30fv37lKy5+6guWbd7DXy45mQl39uAXrWqrIJSEkWtRaGZnxNxvmmXZZcdyQDO7EbgA6OPuDuDuB919W3h/DrAMOCGbzTeZWd1wP3WBHK/l5O4j3b2Du3eoWbPmsUQt1to3rkqXZtV55vPlHExLjzqOiEiiSnb3Q5kT4f1SEeYRSRg79x/m4fcXcua/JjPh240M6Z3KpN/34vrOjSlZQoPISGI50jv+nzH338yy7P6jPZiZnUNwDuBF7r4vZn5NMysR3m8GNAeWZ7OLd4Abw/s3AuOONoP8aHDvVDbtOsgbc9ZGHUVEJFFtMbOLMifM7GJga4R5RIq9Q2kZjJq6gp6PTmTk58u5qG09Jv6uF7/7ZQsqltEgMpKYjnQymeVwP7vpny40G0swKEwNM1sLPEAw2mhp4OOwOX5GONJoD+B/zCyNoOvMIHffHu7nWWCEu88G/g68Zmb9gdUEF/uVY9QttTptG1ZhxORlXN2hIcn6VUxEpKANAl42s6cAB9YCN0QbSaT4WrF1L/3HfMnyLXvpnlqD+85ryUn1KkcdSyRyRyoKPYf72U3/dKH7tdnMfi6Hdd/k5y2RmcsGxNzfBpyZ23El78yMIb1TufmF2bzz9XouO7VB1JFERBKKuy8DOptZBcDcfXfUmUSKq3lrdtBv9JcAPH9TR3q1qKlzBkVCRyoKm5nZOwStgpn3Caeb5ryZFBVntqxFyzoVGTZpGZe0q09Skv5xFBEpKGZWG/gbUM/dzzWzVkAXd8/2R1QROTYTv9/MbS/NpUbFUozp24lmNStEHUmkUDlSf8GLgccIzi3MvJ85fUl8o0lBSEoybuudytLNe5jw7cao44iIJJrRwASgXji9GLgzsjQixdDrs9cwYMxsUmqV561bu6kgFMlGrkWhu0+OvQHTCK4juDCclmLg/NZ1aVqjPE9NWko4IKyIiBSMGu7+GuFlKdw989x6ETlO7s7Qz5bw+zfm0zWlOq8M7ELNiqWjjiVSKB3pkhQjzOyk8H5l4GvgBeArM8vunEEpgkokGbf2TGHBul1MXqxrOoqIFKC9Zlad8Dx9M+sM7Iw2kkjRl57h/Gnct/zzo8Vcekp9nruxIxVKH+msKZHEdaTuo6e7+7fh/b7AYndvDbQH7o5rMilQl5xSn3qVy/DUxKVRRxERSSR3EVxuKcXMviD44fX2aCOJFG0HDqcz+OW5vDhjFbf0bMZjV7alVLJGWBfJzZE+IYdi7p8F/C+Au+vks2KmVHISt/RM4cuVPzBz+bao44iIJAR3nwv0BLoCtwAnufv8aFOJFF079h3i+udmMuG7jTxwYSvuO/dEDaInkgdHKgp3mNkFZnYK0A34EMDMkoGy8Q4nBevqjg2pUaE0Q9VaKCJSIMzsSqBs2CvnEuBVMzs14lgiRdK6Hfu5YsR0vl6zkyevPYW+3TRQvkheHakovAUYAjwP3BnTQngmMD6ewaTglSlZggGnN+XzJVv5es2OqOOIiCSCP7r7bjPrDvwSGAMMjziTSJGzaOMuLh82jU27DjCmXycuaFPvyBuJyP850uiji939HHdv5+6jY+ZPcPffxj2dFLjrOjemctmSOrdQRKRgZI40ej4w3N3HAaUizCNS5MxYvo0rR0zHcV4f1IUuKdWjjiRS5OQ6DJOZPZHbcne/I3/jSNQqlE7mpq5N+M+nS/h+425a1KkYdSQRkeJsnZk9DfwC+IeZlebIvXhEJDR+/gZ+8+o8GlUvx5h+nahfRWc3iRyLI/3HMwjoDqwHZgNzstykGLqpaxPKlSrBsElqLRQRibOrCC5ef4677wCqAb+PNpJI0TD6ixUMGTuXNg0q88agLioIRY7DkS7YUhe4ErgaSANeBd509x/iHUyiU7V8Ka7r3JhnP1/Ob35xAk1qlI86kohIseTu+4C3YqY3ABuiSyRS+GVkOI9M+J4Rk5dxdqvaPHHtKZQpWSLqWCJF2pHOKdzm7iPcvTdwE1AF+NbMri+IcBKdAd2bklwiiRGTl0UdRURERASAQ2kZ/Pb1rxkxeRnXdW7E8OvaqyAUyQd5Om8hHB77TuA64APUdbTYq1WpDFd3aMibc9eyfsf+qOOIiIhIgttzMI3+Y77k7a/W8buzT+AvF59MCV2DUCRf5FoUmtmfzWwOcBcwGejg7v3d/bsCSSeRuqVnM9xh5JTlUUcRkWJu36E0Nu48EHUMESmktuw+yDUjpzNt2TYeuaINQ85ojpkKQpH8cqSWwj8ClYG2wMPAXDObb2bfmNn8uKeTSDWoWo5LTqnPK1+uZuueg1HHEZFi6mBaOre8OIernp7OgcPpR96gGDGzy8xsiZntNLNdZrbbzHZFnUukMFmxdS+XD5/Gss17efaGDlzVoWHUkUSKnSMNNNO0QFJIoXVrrxTenLuWUVNXcPc5LaOOIyLFTFp6BneM/YrPl2zl0SvaJOK5QY8AF7r7wqiDiBRG89bsoN/oLwEYO7Az7RpWiTiRSPF0pIFmVmV3A9YSXKpCirmUmhU4r3VdXpy+ip37D0cdR0SKkYwM5+435zPh2008cGErrkzMX/83qSAUyd7E7zdz7cgZlC9dgjdv7aqCUCSOjnROYSUzu8/MhprZ2Ra4HVhOcG0lSQCDe6Wy+2AaL0xbGXUUESkm3J0/v/stb81dx11nnUDfbgnbMWW2mb1qZteGXUkvM7PLog4lErXXZq9hwJjZpNQqz1u3dqOpLo8lEldH6j76IvADMB0YQHBB3VLAxe4+L87ZpJBoVa8SZ7asxagvVtCve1PKlz7S20ZEJHf/+ngxY6avYkD3ptx+RmrUcaJUCdgHnB0zz4m5dqFIInF3hn62lMc+XszpzWsw/Lr2VND3DpG4O9KnrJm7twYws2eBrUAjd98d92RSqAw+I5XLhk1j7KzVDDi9WdRxRKQIGzllGU9+tpRrOjbkD+efmNAjCLp736gziBQW6RnOA+8s4KUZq7nslPr8/fI2lErO09XTROQ4Hako/L+TyNw93cxWqCBMTKc2qkrXlOqMnLKc6zo3TsTBIEQkH/x35mr+9v4iLmhTl4cubZ2wBaGZ3e3uj5jZkwQtgz/h7ndEEEskMgcOp/PrV75iwrebGNQzhXvOaZGw/z6IROFIP7+0DYfI3mVmu4E2eR0y28xGmdlmM1sQM+9RM1sUXtbibTOrEs4/y8zmhJe6mGNmZ+SwzwfNbJ2ZzQtv5x3tA5ZjN6R3Kpt3H+SNOWujjiIiRdC4eev4w/9+Q+8WNfnXVe0S/aLTmYPLzAbmZHMTSRg79h3iumdn8tF3m3jwwlbce25LFYQiBSzXlkJ3P57moNHAUOCFmHkfA/e5e5qZ/QO4D7iHoFvqhe6+3sxOBiYA9XPY77/d/Z/HkUuOUZeU6rRrWIURk5dxdceGlCyhLh0ikjefLtzEb1/7mo5NqjGsT/uE7xLm7u+Gf8dEnUUkSut27OfGUbNYvW0fQ689lfPb1I06kkhCitv/yu4+BdieZd5H7p4WTs4AGoTzv3L39eH8b4EyZlY6Xtnk2JgZQ3qnsvaH/bwzb/2RNxARAaYv28ZtL8+lVb1KPHdjB8qWUvdzEYFFG3dx+bBpbNp1gDH9OqkgFIlQlD/V9gM+yGb+5cBX7n4wh+2GhN1PR5lZ1fjFk+yceWItWtapyLBJS8nI+NlpMCIiPzFvzQ4GjPmSRtXKMaZvJyqWKRl1JBEpBKYv28aVI6bjOK8P6kKXlOpRRxJJaJEUhWb2ByANeDnL/JOAfwC35LDpcCAFaAdsAB7L5RgDzWy2mc3esmVLvuSWoLVwcO9Ulm3Zy4ffbow6jogUYt9v3M2No2ZRrUIpXhpwGlXLl4o6kogUAuPnb+DGUbOoU6kMb93WjZZ1KkUdSSThFXhRaGY3AhcAfdzdY+Y3AN4GbnD3Zdlt6+6b3D3d3TOAZ4BOOR3H3Ue6ewd371CzZs38fRAJ7rzWdWlWozxPTVxKzEsoIvJ/Vm3by3XPzaRMySRe7t+Z2pXKRB2pUDKzE8zs08xB2cysjZndH3UukXh5/osVDBk7l7YNK/P6oC7Ur1I26kgiQgEXhWZ2DsHAMhe5+76Y+VWA8QSD0HyRy/axnc0vBRbktK7ET4kkY1CvFL5dv4tJi9UKKyI/tWHnfvo8O5O09Axe6n8ajaqXizpSYfYMwaBrhwHcfT5wTV42NLMSZvaVmb0XTjc1s5lmtsTMXjUzNc1KoZGR4Tz8wUL+/O53nN2qNi/2P40q5fQWFSks4lYUmtlYYDrQwszWmll/gtFIKwIfh5eUGBGuPgRIBf4Yc7mJWuF+njWzDuF6j4SXrZgP9AZ+E6/8krtLT6lP/SpleeoztRaKyI+27TnIdc/OZMe+w7zQ7zSa164YdaTCrpy7z8oyLy3bNX/u1/x4aQsITr/4t7s3B34A+udDPpHjdigtg9++/jVPT17O9Z0bM6xPe13vWKSQOdLF64+Zu1+bzeznclj3r8Bfc1g2IOb+9fmTTo5XyRJJ3NKzGX8a9y0zV2ynczOdIC6S6HYdOMwNo2ax9of9vNCvE60bVI46UlGw1cxSCC9gb2ZXEJwzn6vwlIvzgYeAuyy4qNsZwK/CVcYADxKciy8SmT0H07j1pTl8vmQrvzv7BAb3TtU1CEUKocS+UJQcl6s6NKRGhdI8NXFp1FFEJGL7D6XTf/SXfL9xNyOua89p+qEorwYDTwMtzWwdcCdwax62exy4G8gIp6sDO2Iu+7SWnK/3K1Igtuw+yDUjpzNt2TYeuaINQ85oroJQpJBSUSjHrEzJEtx8elM+X7KVr9fsiDqOiETkUFoGg16aw5xVP/D4Ne3o3bJW1JGKDHdf7u6/AGoCLd29u7uvzG0bM7sA2Ozuc2JnZ7f7HLbX6NwSdyu27uWy4V+wbPNenr2xA1d1aBh1JBHJhYpCOS59OjemctmSDFVroUhCSkvP4M5Xv2Ly4i08fFlrLmhTL+pIRYqZVTGzO4C/AA+Z2RNm9sQRNusGXGRmK4FXCLqNPg5UMbPM00IaAOuz21ijc0u8zVuzg8uHT2PfwXReGdiZ3i30Q5FIYaeiUI5LhdLJ9O3WhI+/28SijbuijiMiBSgjw7nvrW94/5uN3H/+iVzdsVHUkYqi94EmwDfAnJhbjtz9Pndv4O5NCEYq/czd+wATgSvC1W4ExsUps0iOJi7azLUjZ1ChdDJv3tqVtg2rRB1JRPJARaEct5u6NqF8qRIMm5jt5SVFpBhyd/4y/jten7OWO85szoDTm0Udqagq4+53ufvz7j4m83aM+7qHYNCZpQTnGGY7uJtIvLw2ew0DXphNSq3yvHlrV5rUKB91JBHJIxWFctyqlCvFdZ0b89789azcujfqOCJSAB7/ZAnPf7GSvt2a8JtfNI86TlH2opndbGZ1zaxa5i2vG7v7JHe/ILy/3N07uXuqu1/p7gfjF1vkR+7Ok58u4e435tM1pTqvDOxCzYqlo44lIkdBRaHki/6nNyW5RBLDJ6m1UKS4e/bz5fzn0yVc2b4Bfzy/lUYTPD6HgEcJruub2XV0dqSJRI5Ceobzx3ELeOzjxVx2Sn2eu7EjFUrH7YpnIhInKgolX9SqWIZrOjbkra/Wsn7H/qjjiEicvPrlav46fiHnta7D3y9vQ1KSCsLjdBeQ6u5N3L1peFNfXCkSDhxO59aX5vDSjNUM6pnCY1e1pVSyvlqKFEX65Eq+uaVnCu4wcsryqKOISBy8N3899/7/9u48Pqrq/v/465MQCHuAhH1fwiZ7DDti64KKYhXcQKCiFBSptXWrtlZra6tVqyyiAiqgKBUExJUqyh7WGMK+70sChC0QspzfHxm/v5iyJJDJnWTez8djHszc3Ll5z3mEOfOZe+45M9bQIzqK1+5sS6gKwoKwFkj1OoRIfqWknmXA+Djmrj/IX25uwZM3NNOoAZEiTOf3pcDUiijNr9rVYuqyXTx0dWNdTyBSjMzbcIhHPoonpl4l3hrQgVIlQr2OVFxkAvFmNg/4v2sAnXMjvYskcmF7U04zaOIydh1OZcw97bmxVQ2vI4nIZdKZQilQw3s2Ij0zi4mLtnsdRUQKSNy2wwybspKm1cszYfCVlC6pgrAAzQT+Biwmj0tSiHhpw4Hj3DZ2EQePn2HSkFgVhCLFhM4USoFqGFWOG1vVYPKSnQzr0YiKZcK8jiQilyFhTwpD3l9B7UqlmXRfLBXC9X+6IF3G8hMihW7J1sMMnbSCsqVK8J9hnWlWvYLXkUSkgOhMoRS4h65uzMm0DN5fssPrKCJyGTYfPMGgicuoWDqMKfd3pEo5DQkvKGY2zffvGjNLyH3zOp9Ibp8n7GfQxGVUrxjOjAe7qCAUKWZ0plAKXPMaFbimeVUmLtrOkG4NKKupqUWKnF2HU+k/Po4SoSF8cH9HalQs7XWk4ua3vn97e5pCJA/eXbSd5+esI6ZeJd4ZGENEmZJeRxKRAqYzheIXD13dmJTUdD6M2+V1FBHJp4PHLwTpDQAAIABJREFUzzBgQhxpGVlMGdKR+pFlvY5U7Djn9vvuPuic25nzBjzoZTaRn2RlOV78cj3PfbaO61tUZ/KQjioIRYopFYXiF+3qVqJr4yq8vWAbZ9IzvY4jInl05FT2NPOHT6bx/n2xNK1e3utIxd2159h2Q6GnEMnlbEYWj06L560ftnFvp3qM6d+e8DBNMiVSXKkoFL956OrGJJ1I4z8r93gdRUTy4MSZdAZNXMbOI6mMH3QlbetEeB2p2DKz4Wa2Bmia63rC7YCuKRRPnUzLYMj7y5kZv4/Hrm/K831aal1SkWJOF3uJ33RuWIX2dSMY9/1W7rqyDmGh+g5CJFCdSc9kyPsrWL//OG/d24HOjap4Ham4+xD4EngReDLH9hPOuSPeRBKBQyfOcN97y1m//wQv921Nv5g6XkcSkUKgT+niN2bGQ1c3Zm/KaWbF7/M6joicx9mMLIZPWcnyHUd45Y42/LJ5Na8jFXvOuWPOuR3OubtzXVOoglA8sz35FLe/uZith04xflCMCkKRIKKiUPzqF82q0rxGBcZ+v4XMLOd1HBHJJTPL8btp8czbmMTfbm1Fn7a1vI4kIh6I353C7W8uJjUtk4+GduLqplW9jiQihUhFofhV9tnCRmxLOsVXiQe8jiMiOTjnePrTNXyesJ+nbmjGPR3reh1JRDwwb8Mh7n57KeVKlWD68C600fXEIkFHRaH43Q1X1KBhVFlGz9uCczpbKBIInHP87fP1fLR8NyOubsxvrmrkdSQR8cC05bu5f9IKGlctx/ThXbQEjUiQ8mtRaGYTzeyQmSXm2PaymW3wzbL2qZlF5PjZU2a2xcw2mtn15zlmAzOLM7PNZvaxmWnBnAAXGmIMv6oR6/cfZ97GQ17HERFg1HdbGL9wO4M61+P310V7HUdECplzjlHfbubx6Ql0bRzJR0M7EVW+lNexRMQj/j5T+B7QK9e2ucAVzrnWwCbgKQAzawHcBbT0PWesmZ1rQZx/Aq8555oAR4Eh/okuBenWdrWoFVGa0d/pbKGI1yYu3M6rczdxW/taPHtzS8w01bxIMMnMcjwzM5FXfO8DEwbFULaUJqQXCWZ+LQqdc/OBI7m2feOcy/A9XArU9t3vA3zknEtzzm0HtgCxOZ9r2Z9cfgF84tv0PnCrn+JLAQoLDWHYVQ1ZtSuFpds0uZ6IV6at2M3zc9ZxfctqvHR7a0K09phIUDmTnsnwKSv5IG4Xw3s24pV+bbRklIh4fk3hfWSv0wRQC9id42d7fNtyqgKk5Cgqz7WPBKh+MXWIKl+KMfO2eB1FJCh9uWY/T05PoHuTSN64ux0l9EFQJKikpJ5lwPg45q4/yHO3tOSJXs00UkBEAA+LQjN7GsgAPvhp0zl2yz3OMC/7/HT8oWa2wsxWJCUlXXpQKTDhYaE80L0BC7ckE787xes4IkHlh01JjPxoNe3qVuKteztQqsS5RueLSHG1N+U0fcctIWHPMcbc055BXep7HUlEAognRaGZDQJ6A/3d/7/AbA+Qc5XU2kDuFc+TgQgzK3GBfQBwzr3tnItxzsVERUUVXHi5LP071iOiTBijv9PZQpHCsnzHEX4zeQWNq5Zn4uArKVNS1w6JBJP1+49z29hFHDx+hklDYrmxVQ2vI4lIgCn0otDMegFPALc451Jz/Gg2cJeZlTKzBkATYFnO5/oKyHlAX9+mQcAs/6eWglK2VAl+3aUB/11/kA0HjnsdR6TYS9x7jPveXU7NiqWZPCSWiqXDvI4kIoVoydbD3DFuCYbxybAudGpYxetIIhKA/L0kxVRgCdDUzPaY2RBgNFAemGtm8WY2DsA5txaYBqwDvgIecs5l+o7zhZnV9B32CeBRM9tC9jWGE/z5GqTgDe5Sn3KlSjBm3lavo4gUa1sOnWTgxGVUKB3GlPs7EllO082LBJM5CfsYNHEZ1SuGM+PBLjStXt7rSCISoPw6hsg5d/c5Np+3iHPO/Q342zm235jj/jZyzUoqRUvFMmEM6FSPt+dv5dFro2mghXJFCtzuI6kMGB9HiBlT7u9IzYjSXkcSkUI0ceF2/vr5Oq6sV5l3BsZQsYxGCYjI+WnqOfHEkG4NCAsN4c3vdW2hSEE7dPwMAybEkXo2g8lDYvXFi0gQycpyvPjF+uylZ1pUZ9KQWBWEInJRKgrFE1HlS3HXlXWYsWove1NOex1HpNhIST3LvROWkXQijffui6V5jQpeRxKRQnI2I4tHp8Xz1vxt3NupHmP6tyc8TDMNi8jFqSgUzwy9qhEhIcaA8XFsPHDC6zgiRd7JtAwGvbuc7cmneGdgDO3rVvI6kogUkpNpGQx5fzkz4/fx2PVNeb5PS0JDtAahiOSNikLxTK2I0kwZ0pGTaRncOmYRM1fv9TqSSJF1Jj2TB95fQeLeY4y+px1dG0d6HUlECsmhE2e4860lLN56mJf7tuahqxtrUXoRyRcVheKp2AaV+fzhbrSqVZFHPo7nz7MSOZuR5XUskSIlPTOLER+uYsm2w/yrX2uua1nd60giUki2JZ3k9jcXsz35FBMGxdAvps7FnyQikouKQvFc1QrhfPBARx7o3oBJS3Zyx1tL2KfrDEXyJDPL8ftpP/Lf9Yf4a5+W/Kpdba8jiUghWb3rKH3HLSE1LZOpD3SiZ9OqXkcSkSJKRaEEhLDQEJ6+qQVj+7dny6GT9B61kIWbk72OJRLQnHP8aVYis3/cx+O9mnJv5/peRxKRQvLdhoPc804c5UqVYPrwLrSpE+F1JBEpwlQUSkC5sVUNZo3oSpWyJbl3Yhyjv9tMVpbzOpZIwHHO8Y+vNvBh3C6G92zEgz0bex1JRArJtOW7eWDSShpXLcf04V2or2VnROQyqSiUgNMoqhwzH+rKza1r8q9vNvHApBUcS033OpZIQBn7/Vbe+mEbAzrV5fHrm3odR0QKgXOOUd9u5vHpCXRtHMlHQzsRVb6U17FEpBhQUSgBqWypErx+V1ueu6Ul8zcn0Xv0AhL3HvM6lkhAeH/xDl7+eiO3tq3J87dcoVkGRYJAZpbjmZmJvDJ3E7e1r8WEQTGULVXC61giUkyoKJSAZWYM6lKfj4Z2Jj3Dcfubi5m2YrfXsUQ8NX3lHp6dvZZrW1Tj5X5tCNE6ZCLF3pn0TIZPWckHcbt4sGcjXunXhrBQfYQTkYKjdxQJeB3qVWLOyG50qFeJxz9J4KkZCZxJz/Q6lkih+yrxAI9PT6BLoyqMurudPhSKBIGU1LP0Hx/H3PUHee6Wljzeq5lGB4hIgdMnCikSIsuVYvKQjjx0dSOmLttN33GL2X0k1etYIoVmweYkRk5dTataFXlnYAzhYaFeRxIRP9tzNJXb31zMmr3HGHNPewZ1qe91JBEpplQUSpERGmI8dn0z3hkYw87DqfQetZB5Gw95HUvE71buPMLQSStpGFWW9359pa4jEgkC8btTuP3NxRw6kcbk+2K5sVUNryOJSDGmolCKnGtbVGPOw92oGVGa+95bzqtzN5GpZSukmFq37ziD311OtQqlmDQklogyJb2OJCJ+dCotg+c/W8dtYxcRasYnw7rQsWEVr2OJSDGnr5ulSKpXpSwzhnfhmZmJvPHtZlbvOsrrd7Wjcll9YJbiY1vSSQZOzF6cesr9HalaPtzrSCLiR99vPMTTnyayN+V09nIzvZpRITzM61giEgR0plCKrNIlQ/lXv9a8eFsr4rYd4eZRC/lxd4rXsUQKxN6U0wwYH4dzMOX+jtSuVMbrSCLiJ4dPpvHIR6sZ/O5ywsNC+M+wzrxwaysVhCJSaFQUSpFmZtwdW5dPhncGoN+4JXwQtxPnNJxUiq6kE2kMGB/HibQMJg2JpVFUOa8jiYgfOOeYsWoP17z6A5+v2c/IXzbhi99258r6lb2OJiJBRsNHpVhoXTuCOQ9345GP43n600RW7jzK325tRemSmqFRipZjqencOyGOA8fOMOX+WFrWrOh1JBHxg91HUvnjp2tYsDmZ9nUj+MftrYmuVt7rWCISpFQUSrFRqWxJ3h18JW98t5nXv93Mun3HGTegA/Ujy3odTSRPTqVlMPi9ZWxLOsWEwTF0qKezBSLFTUZmFu8t3sEr32wixOD5Pi0Z0LEeISFae1BEvKPho1KshIQYj1wTzbuDr+TA8TPcPGoh36w94HUskYs6k57J0Mkr+HF3Cm/c3ZbuTaK8jiQiBWztvmP8auxiXvh8PV0aVWHuo1cxsHN9FYQi4jkVhVIs9Wxalc9GdKN+ZFmGTl7JP77cQEZmltexRM4pPTOLh6euZtGWw7zUtw29rtB6ZCLFyZn0TP751QZuGb2I/cdOM/qedowfFEPNiNJeRxMRAfxYFJrZRDM7ZGaJObb1M7O1ZpZlZjE5tvc3s/gctywza3uOY/7FzPbm2O9Gf+WXoq9O5TL8Z1hn7o6ty7gftnLvhGUknUjzOpbIz2RlOR7/JIG56w7y3C0t6duhtteRRKQALd6STK9/z+fN77dye/ta/PfRq+jduiZmOjsoIoHDn2cK3wN65dqWCNwGzM+50Tn3gXOurXOuLXAvsMM5F3+e4772077OuS8KOrQUL+Fhobx4Wyte7tuaVbuO0nvUAlbuPOJ1LBEge+bBZ2ev5dPVe/nDddEM6lLf60giUkBSUs/y+Cc/cs/4OBzw4f0dealvGyLKaD1dEQk8fisKnXPzgSO5tq13zm28yFPvBqb6K5cEp34xdZjxYBfCw0K5862lvLtou5atEM+9/PVGJi/dydAeDXno6sZexxGRAuCcY07CPq559Qemr9rL8J6N+PqRHnRpHOl1NBGR8wrEawrv5MJF4QgzS/ANT61UWKGk6GtZsyKzR3SjZ9MonvtsHSM/iudUWobXsSRIvfn9VsZ+v5W7Y+vy1A3NNJRM8sXMws1smZn96Lss4znf9gZmFmdmm83sYzPTaalCtC/lNA9MWsGID1dTo2JpZo/oyhO9mhEepuWRRCSwBVRRaGYdgVTnXOJ5dnkTaAS0BfYDr1zgWEPNbIWZrUhKSir4sFIkVSwdxtv3xvDY9U35PGEffcYsYsuhk17HkiAzZelO/vnVBm5uU5MXbr1CBaFcijTgF865NmT3ib3MrBPwT7Ivs2gCHAWGeJgxaGRlOSYt2cG1r/7Aoi2Heeam5nz6YBetMyoiRUZAFYXAXVzgLKFz7qBzLtM5lwW8A8ReYN+3nXMxzrmYqChN7S7/X0iI8dDVjZk8pCNHT52lz+iFfJ6w3+tYEiRmxe/lT7MS+UWzqrx6RxtCNRW9XAKX7advtMJ8Nwf8AvjEt/194FYP4gWVTQdP0HfcYv48ay3t61Xim9/14P7uDSkRGmgfsUREzi9g3rHMLAToB3x0gX1yztP+K7InrhG5JF0bRzJnZDeiq5fnoQ9X8dc560jXshXiR3PXHeTRaT/SsUFlxvZvT5g+NMplMLNQM4sHDgFzga1AinPup3Hxe4BaXuUr7tIyMnl17iZuemMB25NP8dqdbZh0Xyx1KpfxOpqISL6V8NeBzWwq0BOINLM9wLNkTzwzCogCPjezeOfc9b6n9AD2OOe25TrOeGCcc24F8JJvqQoH7AB+46/8EhxqVCzNx0M78/cv1jNh4XYS9qQw+p72VKsQ7nU0KWYWbUnmoQ9XcUXNCowfdKWuMZLL5pzLBNqaWQTwKdD8XLvl3mBmQ4GhAHXr1vVrxuJqxY4jPDE9ga1Jp/hVu1o8c1NzqpQr5XUsEZFLZsEwA2NMTIxbsWKF1zEkwM2K38uT09dQtlQJRt/Tjk4Nq3gdSYqJ1buO0n98HLUrZX8JUams5v7wFzNb6ZyLufiexYuZPQukAk8A1Z1zGWbWGfhLji9f/4f6x/w5fiadl77awJSlu6gVUZq/39aKq6J1iYqIFA0X6iM1dknEp0/bWswa0ZUK4SXoPz6Ot+dv1bIVctnW7z/O4HeXE1muFFOGdFRBKAXCzKJ8Zwgxs9LANcB6YB7Q17fbIGCWNwmLn2/WHuDaV3/gw7hdDOnWgG9+10MFoYgUG34bPipSFEVXK8+sEV15/JME/v7FBlbtTOGlfq2pEB7mdTQpgrYnn+LeCcsoHRbKB/d3pKqGJUvBqQG8b2ahZH/BO805N8fM1gEfmdkLwGpggpchi4NDx8/w7Oy1fJl4gGbVy/P2vTG0qRPhdSwRkQKlolAkl/LhYYzt354JC7fz4pcb6DN6EeMGdKBp9fJeR5MiZF/KaQaMjyPLOabc31GTT0iBcs4lAO3OsX0bF5iZW/IuK8vx8Yrd/P2L9aRlZPF4r6Y80L2hJogSkWJJ72wi52Bm3N+9IVMf6MTJtAxuHbOImav3eh1Liojkk2kMmBDH8dPpTLovlsZV9YWCSFGyLekkd7+zlKdmrKFlzQp8/UgPHuzZWAWhiBRbencTuYDYBpX5/OFutKpVkUc+jufPsxJJy8j0OpYEsGOn0xk4YRn7Uk4zYfCVXFFLi1eLFBXpmVmMmbeFXq8vYP3+4/zz9lZMfaATDSLLeh1NRMSvNHxU5CKqVgjngwc68vLXG3l7/jYS9hxjbP/21Iwo7XU0CTCpZzO4773lbD50gncGxhDboLLXkUQkj+J3p/Dk9AQ2HDjBTa1q8OwtLahaXtcBi0hw0JlCkTwICw3hjzc2583+7dly6CS9Ry1k4eZkr2NJAEnLyOQ3k1eyetdRXr+rHT2bVvU6kojkwam0DJ77bC2/GruIlNR03hkYw5j+7VUQikhQUVEokg83tKrBrBFdqVK2JPdOjGP0d5vJytKyFcEuIzOL306NZ8HmZP5xW2tubFXD60gikgfzNh7iutfm897iHQzoWI+5j/bg2hbVvI4lIlLoNHxUJJ8aRZVj5kNdeWrGGv71zSZW70rh1TvaUrGMlq0IRllZjiemr+GrtQf4U+8W3HFlHa8jichFHD6ZxvNz1jErfh+Nq5bjk2Gd6VBPw71FJHipKBS5BGVLleD1u9rSoV4lXvh8Hb1HL+DN/h00qUiQcc7x/Jx1TF+1h0euacKQbg28jiQiF+CcY8aqvbzw+TpOpmXwyDVNGN6zEaVKhHodTUTEUxo+KnKJzIxBXerz0dDOpGc4bn9zMdNW7PY6lhSiV+du4r3FOxjSrQG//WUTr+OIyAXsOpzKwInL+P1/fqRhVDm+GNmdR66JVkEoIoLOFIpctg71KjFnZDdGTl3N458ksGrnUf5yS0vCw/RBozh7e/5WRn23hTtj6vDMTc0xM68jicg5ZGRmMXHRdl6du4kSISH8tU9L+nesR0iI/s+KiPxERaFIAYgsV4rJQzry6tyNjJm3lcR9x3izfwfqVC7jdTTxg6nLdvH3LzZwU6sa/P22VioIRQJU4t5jPDkjgcS9x7mmeTX+emtLalTUckIiIrlp+KhIAQkNMR67vhnvDIxh5+FUeo9ayLwNh7yOJQVs9o/7+OOna+jZNIrX7mxLqM42iASc02czefHL9fQZs4iDx9MY27897wzsoIJQROQ8dKZQpIBd26Iacx7uxrApq/j1e8sZ+YvG/PaaaBUPRVTq2QyWbjvM/E3JLNicxNakU8TWr8yb/TtQsoS+VxMJNIu2JPPHT9ew83Aqd11Zh6duaK7ZoUVELkJFoYgf1KtSlk8f7MIzMxN547strN6dwut3taNy2ZJeR5OLyMpyrD9wnPmbkpm/KYmVO49yNjOL8LAQOjaowt2xdbk7ti6lS+qaUZFAkpJ6lr99vp7/rNxDg8iyTH2gE50bVfE6lohIkaCiUMRPwsNCeblvazrUq8Szs9Zy86iFjO3fnjZ1IryOJrkknUhjweYkFmzOPhuYfPIsAM2ql2dw1/r0aBJFTP1KmjxIJAA555iTsJ/nPltLSmo6D/ZsxMhfNtH/VxGRfFBRKOJHZsbdsXVpWbMCw6esot+4Jfz55hb071hXk5N4KC0jkxU7jjJ/cxLzNyWzfv9xACq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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "fig.suptitle('Feature subsample size in Gradient Boosting', fontsize=14)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(depth, scores)\n",
    "plt.xlabel('feature subsample size')\n",
    "plt.ylabel('RMSE')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(depth, times)\n",
    "plt.xlabel('feature subsample size')\n",
    "plt.ylabel('time in seconds')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.775"
      ]
     },
     "metadata": {},
     "execution_count": 283
    }
   ],
   "source": [
    "depth[np.argmin(scores)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " 13"
     ]
    }
   ],
   "source": [
    "scores = []\n",
    "times = []\n",
    "max_depth = np.arange(1, 15, 3)\n",
    "for i in max_depth:\n",
    "    print('\\r\\r\\r\\r\\r\\r\\r\\r', i, end='')\n",
    "    start = time.time()\n",
    "    gb = GradientBoostingMSE(n_estimators=700, feature_subsample_size=0.55, max_depth=i)\n",
    "    scores.append(gb.fit(X_train[:, num_feat], y_train, X_test[:, num_feat], y_test)**(1/2))\n",
    "    times.append(time.time() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "fig.suptitle('Max depth in Gradient Boosting', fontsize=14)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(max_depth, scores)\n",
    "plt.xlabel('max depth')\n",
    "plt.ylabel('RMSE')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(max_depth, times)\n",
    "plt.xlabel('max depth')\n",
    "plt.ylabel('time in seconds')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(112.4413907099116, 45.18332505226135)"
      ]
     },
     "metadata": {},
     "execution_count": 236
    }
   ],
   "source": [
    "start = time.time()\n",
    "gb = GradientBoostingMSE(n_estimators=700, feature_subsample_size=0.55)\n",
    "gb.fit(X_train[:, num_feat], y_train, X_test[:, num_feat], y_test)**(1/2), time.time() - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(111.62956390744779, 38.95130181312561)"
      ]
     },
     "metadata": {},
     "execution_count": 243
    }
   ],
   "source": [
    "start = time.time()\n",
    "gb = GradientBoostingMSE(n_estimators=700, feature_subsample_size=0.55, max_depth=4)\n",
    "gb.fit(X_train[:, num_feat], y_train, X_test[:, num_feat], y_test)**(1/2), time.time() - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " 1.0"
     ]
    }
   ],
   "source": [
    "scores = []\n",
    "times = []\n",
    "learning_rate = np.logspace(-3, 0, 4)\n",
    "for i in learning_rate:\n",
    "    print('\\r\\r\\r\\r\\r\\r\\r\\r', i, end='')\n",
    "    start = time.time()\n",
    "    gb = GradientBoostingMSE(n_estimators=700, feature_subsample_size=0.55, max_depth=4, learning_rate=i)\n",
    "    scores.append(gb.fit(X_train[:, num_feat], y_train, X_test[:, num_feat], y_test)**(1/2))\n",
    "    times.append(time.time() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1080x360 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "fig.suptitle('Learning rate in Gradient Boosting', fontsize=14)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(learning_rate, scores)\n",
    "plt.xlabel('learning rate')\n",
    "plt.ylabel('RMSE')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(learning_rate, times)\n",
    "plt.xlabel('learning rate')\n",
    "plt.ylabel('time in seconds')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 38.7 s, sys: 468 ms, total: 39.2 s\nWall time: 41.9 s\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "189.63835189618104"
      ]
     },
     "metadata": {},
     "execution_count": 263
    }
   ],
   "source": [
    "%%time\n",
    "gb = GradientBoostingMSE(n_estimators=700, feature_subsample_size=0.55, max_depth=4, learning_rate=1)\n",
    "gb.fit(X_train[:, num_feat], y_train, X_test[:, num_feat], y_test)**(1/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 38.5 s, sys: 406 ms, total: 38.9 s\nWall time: 40 s\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "113.14996988465455"
      ]
     },
     "metadata": {},
     "execution_count": 264
    }
   ],
   "source": [
    "%%time\n",
    "gb = GradientBoostingMSE(n_estimators=700, feature_subsample_size=0.55, max_depth=4, learning_rate=0.1)\n",
    "gb.fit(X_train[:, num_feat], y_train, X_test[:, num_feat], y_test)**(1/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 35.5 s, sys: 314 ms, total: 35.8 s\nWall time: 36.7 s\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "122.7339038045814"
      ]
     },
     "metadata": {},
     "execution_count": 265
    }
   ],
   "source": [
    "%%time\n",
    "gb = GradientBoostingMSE(n_estimators=700, feature_subsample_size=0.55, max_depth=4, learning_rate=0.01)\n",
    "gb.fit(X_train[:, num_feat], y_train, X_test[:, num_feat], y_test)**(1/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 33.2 s, sys: 260 ms, total: 33.5 s\nWall time: 34.1 s\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "245.5171418590529"
      ]
     },
     "metadata": {},
     "execution_count": 266
    }
   ],
   "source": [
    "%%time\n",
    "gb = GradientBoostingMSE(n_estimators=700, feature_subsample_size=0.55, max_depth=4, learning_rate=0.001)\n",
    "gb.fit(X_train[:, num_feat], y_train, X_test[:, num_feat], y_test)**(1/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}