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practical.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from pandas.api.types import is_numeric_dtype, is_string_dtype\n",
    "from sklearn.metrics import r2_score\n",
    "import xgboost as xgb\n",
    "plt.rcParams['figure.figsize'] = 16, 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform categorical data into numerical data\n",
    "def make_cats(df):\n",
    "    for n, c in df.items():\n",
    "        if is_string_dtype(c):\n",
    "            df[n] = c.astype('category').cat.as_ordered()\n",
    "            df[n] = df[n].cat.codes + 1\n",
    "            \n",
    "# If a column has missing values, replace them with the median and add another column 'missing'\n",
    "def fix_numeric(df):\n",
    "    for n in df:\n",
    "        col = df[n]\n",
    "        if is_numeric_dtype(col) and any(pd.isnull(col)):\n",
    "            df[n + '_nan'] = pd.isnull(col) * 1\n",
    "            filler = col.median()\n",
    "            df[n] = col.fillna(filler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Id', axis='columns', inplace=True)\n",
    "make_cats(df)\n",
    "fix_numeric(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('SalePrice', axis='columns')\n",
    "Y = df.SalePrice\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)\n",
    "X_train, X_valid, Y_train, Y_valid = train_test_split(X_train, Y_train, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(817, 351, 292)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train), len(X_valid), len(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation:  0.767147865699559\n",
      "Training:  0.8158940396277206\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators=100, n_jobs=-1)\n",
    "rf.fit(X_train, Y_train)\n",
    "print('Validation: ', r2_score(Y_valid, rf.predict(X_valid)))\n",
    "print('Training: ', r2_score(Y_train, rf.predict(X_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e4d69c2320>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = np.stack([t.predict(X_valid) for t in rf.estimators_])\n",
    "plt.plot([r2_score(Y_valid, np.mean(preds[:i+1], axis=0)) for i in range(100)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e4d7c60a20>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_valid, np.mean(preds[:1], axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation:  0.8571209678586758\n",
      "Training:  0.9506715695025579\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators=128, n_jobs=-1, min_samples_leaf=3, max_depth=8, max_features=0.7)\n",
    "rf.fit(X_train, Y_train)\n",
    "print('Validation: ', r2_score(Y_valid, rf.predict(X_valid)))\n",
    "print('Training: ', r2_score(Y_train, rf.predict(X_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e4d803f978>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_valid, rf.predict(X_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost\n",
    "https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning rate and tree based parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_params = {\n",
    "    'learning_rate': 0.03,  # start high, go down when fine tuning\n",
    "    'max_depth': 4,         # up to 10\n",
    "    'min_child_weight': 2,  # this is similar to min_samples_leaf\n",
    "    'gamma': 0.028,         # minimum reduction for doing a split (something like min_delta from EarlyStopping)\n",
    "    'subsample': 0.6,\n",
    "    'colsample_bylevel': 0.55,\n",
    "    'reg_lambda': 1.1,\n",
    "    'objective': 'reg:linear',\n",
    "    'tree_method': 'exact',\n",
    "    'n_estimators': 2500,\n",
    "}\n",
    "xgb_model = xgb.XGBRegressor(**xgb_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-rmse:191323\n",
      "Will train until validation_0-rmse hasn't improved in 200 rounds.\n",
      "[1]\tvalidation_0-rmse:186160\n",
      "[2]\tvalidation_0-rmse:181042\n",
      "[3]\tvalidation_0-rmse:176299\n",
      "[4]\tvalidation_0-rmse:171708\n",
      "[5]\tvalidation_0-rmse:167089\n",
      "[6]\tvalidation_0-rmse:162592\n",
      "[7]\tvalidation_0-rmse:158187\n",
      "[8]\tvalidation_0-rmse:154014\n",
      "[9]\tvalidation_0-rmse:149908\n",
      "[10]\tvalidation_0-rmse:145989\n",
      "[11]\tvalidation_0-rmse:142232\n",
      "[12]\tvalidation_0-rmse:138393\n",
      "[13]\tvalidation_0-rmse:134812\n",
      "[14]\tvalidation_0-rmse:131295\n",
      "[15]\tvalidation_0-rmse:127841\n",
      "[16]\tvalidation_0-rmse:124398\n",
      "[17]\tvalidation_0-rmse:121273\n",
      "[18]\tvalidation_0-rmse:118292\n",
      "[19]\tvalidation_0-rmse:115278\n",
      "[20]\tvalidation_0-rmse:112378\n",
      "[21]\tvalidation_0-rmse:109604\n",
      "[22]\tvalidation_0-rmse:106859\n",
      "[23]\tvalidation_0-rmse:104231\n",
      "[24]\tvalidation_0-rmse:101655\n",
      "[25]\tvalidation_0-rmse:99185\n",
      "[26]\tvalidation_0-rmse:96839.1\n",
      "[27]\tvalidation_0-rmse:94592.5\n",
      "[28]\tvalidation_0-rmse:92375.3\n",
      "[29]\tvalidation_0-rmse:90231.2\n",
      "[30]\tvalidation_0-rmse:88154.7\n",
      "[31]\tvalidation_0-rmse:86221.3\n",
      "[32]\tvalidation_0-rmse:84287.8\n",
      "[33]\tvalidation_0-rmse:82245.7\n",
      "[34]\tvalidation_0-rmse:80445.4\n",
      "[35]\tvalidation_0-rmse:78685.1\n",
      "[36]\tvalidation_0-rmse:76931.4\n",
      "[37]\tvalidation_0-rmse:75245.5\n",
      "[38]\tvalidation_0-rmse:73694.3\n",
      "[39]\tvalidation_0-rmse:72235.4\n",
      "[40]\tvalidation_0-rmse:70887.6\n",
      "[41]\tvalidation_0-rmse:69496.1\n",
      "[42]\tvalidation_0-rmse:68165.6\n",
      "[43]\tvalidation_0-rmse:66832.6\n",
      "[44]\tvalidation_0-rmse:65539.8\n",
      "[45]\tvalidation_0-rmse:64384\n",
      "[46]\tvalidation_0-rmse:63220.9\n",
      "[47]\tvalidation_0-rmse:62230.3\n",
      "[48]\tvalidation_0-rmse:61074.3\n",
      "[49]\tvalidation_0-rmse:60010.5\n",
      "[50]\tvalidation_0-rmse:59026.5\n",
      "[51]\tvalidation_0-rmse:58132.8\n",
      "[52]\tvalidation_0-rmse:57208.7\n",
      "[53]\tvalidation_0-rmse:56337.5\n",
      "[54]\tvalidation_0-rmse:55312.8\n",
      "[55]\tvalidation_0-rmse:54608.7\n",
      "[56]\tvalidation_0-rmse:53740.2\n",
      "[57]\tvalidation_0-rmse:53002.2\n",
      "[58]\tvalidation_0-rmse:52258.2\n",
      "[59]\tvalidation_0-rmse:51590.2\n",
      "[60]\tvalidation_0-rmse:50899.6\n",
      "[61]\tvalidation_0-rmse:50107.3\n",
      "[62]\tvalidation_0-rmse:49557.5\n",
      "[63]\tvalidation_0-rmse:48881.5\n",
      "[64]\tvalidation_0-rmse:48185.5\n",
      "[65]\tvalidation_0-rmse:47582.8\n",
      "[66]\tvalidation_0-rmse:46969.9\n",
      "[67]\tvalidation_0-rmse:46494.7\n",
      "[68]\tvalidation_0-rmse:45951.8\n",
      "[69]\tvalidation_0-rmse:45433\n",
      "[70]\tvalidation_0-rmse:45040.4\n",
      "[71]\tvalidation_0-rmse:44485.6\n",
      "[72]\tvalidation_0-rmse:44072.3\n",
      "[73]\tvalidation_0-rmse:43566.8\n",
      "[74]\tvalidation_0-rmse:43193\n",
      "[75]\tvalidation_0-rmse:42879.6\n",
      "[76]\tvalidation_0-rmse:42521.7\n",
      "[77]\tvalidation_0-rmse:42099.2\n",
      "[78]\tvalidation_0-rmse:41808\n",
      "[79]\tvalidation_0-rmse:41497.1\n",
      "[80]\tvalidation_0-rmse:41212.7\n",
      "[81]\tvalidation_0-rmse:40885.5\n",
      "[82]\tvalidation_0-rmse:40641.5\n",
      "[83]\tvalidation_0-rmse:40328.7\n",
      "[84]\tvalidation_0-rmse:40036\n",
      "[85]\tvalidation_0-rmse:39816.2\n",
      "[86]\tvalidation_0-rmse:39439.8\n",
      "[87]\tvalidation_0-rmse:39183.8\n",
      "[88]\tvalidation_0-rmse:39070.9\n",
      "[89]\tvalidation_0-rmse:38773.1\n",
      "[90]\tvalidation_0-rmse:38485.7\n",
      "[91]\tvalidation_0-rmse:38222.6\n",
      "[92]\tvalidation_0-rmse:37996.9\n",
      "[93]\tvalidation_0-rmse:37831.8\n",
      "[94]\tvalidation_0-rmse:37666.6\n",
      "[95]\tvalidation_0-rmse:37567.1\n",
      "[96]\tvalidation_0-rmse:37472.9\n",
      "[97]\tvalidation_0-rmse:37189.6\n",
      "[98]\tvalidation_0-rmse:36992.6\n",
      "[99]\tvalidation_0-rmse:36854.6\n",
      "[100]\tvalidation_0-rmse:36695.2\n",
      "[101]\tvalidation_0-rmse:36559.3\n",
      "[102]\tvalidation_0-rmse:36440.5\n",
      "[103]\tvalidation_0-rmse:36282.2\n",
      "[104]\tvalidation_0-rmse:36120.9\n",
      "[105]\tvalidation_0-rmse:35911.5\n",
      "[106]\tvalidation_0-rmse:35824.4\n",
      "[107]\tvalidation_0-rmse:35781.7\n",
      "[108]\tvalidation_0-rmse:35619.9\n",
      "[109]\tvalidation_0-rmse:35470.3\n",
      "[110]\tvalidation_0-rmse:35383.5\n",
      "[111]\tvalidation_0-rmse:35202.2\n",
      "[112]\tvalidation_0-rmse:35109\n",
      "[113]\tvalidation_0-rmse:34901.5\n",
      "[114]\tvalidation_0-rmse:34819.4\n",
      "[115]\tvalidation_0-rmse:34764.5\n",
      "[116]\tvalidation_0-rmse:34651.9\n",
      "[117]\tvalidation_0-rmse:34552.4\n",
      "[118]\tvalidation_0-rmse:34541.4\n",
      "[119]\tvalidation_0-rmse:34442.2\n",
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    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1283]\tvalidation_0-rmse:31120.4\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1533]\tvalidation_0-rmse:31116.6\n",
      "[1534]\tvalidation_0-rmse:31115.5\n",
      "[1535]\tvalidation_0-rmse:31115.1\n",
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      "[1551]\tvalidation_0-rmse:31110.2\n",
      "[1552]\tvalidation_0-rmse:31110\n",
      "[1553]\tvalidation_0-rmse:31111.9\n",
      "Stopping. Best iteration:\n",
      "[1353]\tvalidation_0-rmse:31109.1\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=0.55,\n",
       "       colsample_bytree=1, gamma=0.028, learning_rate=0.03,\n",
       "       max_delta_step=0, max_depth=4, min_child_weight=2, missing=None,\n",
       "       n_estimators=2500, n_jobs=1, nthread=None, objective='reg:linear',\n",
       "       random_state=0, reg_alpha=0, reg_lambda=1.1, scale_pos_weight=1,\n",
       "       seed=None, silent=True, subsample=0.6, tree_method='exact')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model.fit(X_train, Y_train,\n",
    "          eval_set=[(X_valid, Y_valid)],\n",
    "          early_stopping_rounds=200,\n",
    "          eval_metric='rmse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e4d7fb80f0>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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grmb3+jYqKjy3OlQPsk7tvwYAoNsIuwCAuprd69uoToXqQdap/dcAAHQby5gBAIk0s9e3mWdIYoltG7FUHAAwKAi7AICe0olQPcg6tf8aAIBuYxkzAKCj5peLmphd1MjMIU3MLrJXtMNYKg4AGBRUdgEAHeM3R/L3jPrNkSRRze0QlooDAAYFYRcA0DFJztHlWJz2Y6k4AGAQEHYBAC0XFVjrNUei8gsAAFqFPbsAgJaKO8e13jm6HIsDAABahbALAGipqMB67+Mn6jZH4lgcAADQKoRdAEBLRQXTUrkiSTqwb5cK+ZxMUiGf04F9u9aWKNer/AIAACTFnl0A6JBBabwUdY6rVK36HpnZG/m5pydH1+3ZlXrrWJxB+WcIAEAaUNkFgA6I28eaNnHBtN5y5KmxQmzlt5sG6Z8hAABpQGUXADogyZE7aTE1VtB9Xz+hc+crG15Lshy5V4/FGaR/hgAApAGVXQDogHY3XppfLmpidlEjM4c0MbvY9WrjPe+6IbYRVT+ieRYAAP2FsAsAHdDOxku9uLy2l5cjN4vmWQAA9BeWMQNAB7Sz8VKvLq/t1eXIzer15lkAAGA9wi4AdIAf+trRyZfltZ3Rzn+GAACg9Qi7ANAh7ap0Rh3106ol0oS7S9JWrQYAIM3YswsAfW56crQtzaB6cS8wAABAUoRdAOhz7WoGFbcXGAAAoNexjBkAUqAdy2vZCwwAAPoZlV0AQCiO2gEAAP2MsAsACNWuvcAAAACdwDJmAEAojtoBAAD9jLALAIjEUTsAAKBfsYwZAAAAAJA6hF0AAAAAQOoQdgEAAAAAqUPYBQAAAACkTt2wa2ZXmNn3zexpMzthZvd54yNm9pSZPWtmj5jZZd745d7PJ73Xrw/ca783vmJmk4HxW7yxk2Y2ExgPfQYAAAAAAHGSVHZflbTXOfdWSbsl3WJmN0n6a0n3O+d2SDon6U7v+jslnXPOvVnS/d51MrO3SHq/pBsk3SLpb80sY2YZSZ+V9A5Jb5H0Ae9axTwDAAAAAIBIdcOuq/qV92PW+3KS9kr6qjf+kKQp7/vbvJ/lvf4HZmbe+MPOuVedc89LOinpRu/rpHPuJ8651yQ9LOk27z1RzwAA9Jn55aImZhc1MnNIE7OLml8udntKAAAgxRLt2fUqsMck/VzSE5Kek1Ryzl3wLjklyT+IsSDpRUnyXn9Z0uuD4zXviRp/fcwzaud3l5ktmdnSmTNnknwkAEAHzS8Xtf/gcRVLZTlJxVJZ+w8eJ/ACAIC2SRR2nXOrzrndkq5RtRL7O2GXeX9axGutGg+b34POuXHn3Pjw8HDYJQCALppbWFG5srpurFxZ1dzCSpdmBAAA0m5LIxc750pm9l1JN0nKm9kWr/J6jaTT3mWnJF0r6ZSZbZF0laSzgXFf8D1h47+IeQYADKT55aLmFlZ0ulTW9nxO05OjmhoLXfTSU06Xyg2NAwAAbFaSbszDZpb3vs9J+jeSfizpsKT3epfdIelr3vePez/Le33ROee88fd73ZpHJO2Q9H1JP5C0w+u8fJmqTawe994T9QwAGDj9vBR4ez7X0DgAAMBmJansvkHSQ17X5CFJjzrnvmFmP5L0sJl9UtKypM95139O0t+b2UlVK7rvlyTn3Akze1TSjyRdkPRh59yqJJnZRyQtSMpI+rxz7oR3r49HPAMABkKwkjtkplW3fjeHvxS416u705Oj2n/w+LqlzLlsRtOTo12cFQAASDNzLnQbbN8aHx93S0tL3Z4GAGyaX8mt3etayyQ9P3trZya1Cf26BBsAAPQWMzvqnBuvd11De3YBAJ0T1tQpTL8sBZ4aKxBuAQBAxyTqxgwA6LwkzZtYCgwAABCOsAsAPSqqYpsxk0kq5HM6sG8X1VIAAIAQLGMGgB4V1dSJgAsAAFAfYRcAepQfaGnqBAAA0DjCLgD0oNrOxfffvpuQCwAA0ADCLgD0mNojh4qlsvYfPC5JBF4AAICEaFAFAD0m7MihcmVVcwsrXZoRAABA/yHsAkCPiTpyKMlRRAAAAKgi7AJAj4k6cihqHAAAABsRdgGgx0xPjiqXzawby2Uzmp4c7dKMAAAA+g8NqgCgx3DkEAAAwOYRdgGgy2qPGfKDbVi4jboWAAAA6xF2AaCLGjlmiCOJAAAAkmPPLgB0USPHDHEkEQAAQHKEXQDookaOGeJIIgAAgOQIuwDQRY0cM8SRRAAAAMkRdgGgixo5ZogjiQAAAJKjQRUAdFEjxwxxJBEAAEBy5pzr9hxaanx83C0tLXV7GgAAAACANjCzo8658XrXsYwZAAAAAJA6hF0AAAAAQOqwZxcAumB+ucjeWwAAgDYi7AJAh80vF7X/4HGVK6uSpGKprP0Hj0sSgRcAAKBFWMYMAB02t7CyFnR95cqq5hZWujQjAACA9CHsAkCHnS6VGxoHAABA4wi7ANBhV+WyoePb87kOzwQAACC92LMLAB00v1zUK69d2DCeHTJNT4629bk0xAIAAIOEsAsAHTS3sKLKqtsw/rortrQtfNIQCwAADCKWMQNAk+aXi5qYXdTIzCFNzC5qfrlY9z1R+3JL5yutnt6aqIZYdz9yLPG8AQAA+g1hFwCa4FdLi6WynC5VS+sFx6h9ue3crxvX+CrpvAEAAPoNYRdAqjVTfU2i2eODpidHlctm1o3lspm27tetF6Q59ggAAKQRYRdAajVbfU2i2eODpsYKOrBvlwr5nExSIZ/TgX272rp3Nixg1+LYIwAAkDY0qAKQWnHV182Gy+35nIohATGuilrbEfn+23c3PI9muir7r88trITOud68AQAA+hGVXQCp1Wz1NYlGlyO3osq8mXtMjRV0ZGavPn377o4vowYAAOgGwi6A1GpnM6hGlyM3u8e31ffoxjJqAACAbmAZM4DUmp4cXXe+rNRcFbN26fCencM6/MyZhpYjt6LK3KpK9dRYgXALAABSj7ALILWCe1Ub2eMa5C8d9gNzsVTWl558Ye11fylx8Hlhmtnj2457AAAADArCLoBU22wVM2zpcK0kTa9aUWVuVaUaAABgEBB2ASBG0iXCSY4ckjZXZW7FPQAAAAYFYRcAYkQtHQ67rp5W7JVlvy0AAEAydGMGgBhhRwzVYikxAABA7yHsAkCMsKN6PnjTdRzdAwAA0ONYxgwAdbB0GAAAoP8QdgEMjNrzcmnuBAAAkF6EXQADIey83CTn43YLwRwAAGBzCLsABkLYeblJzsdtJT/AFktlZcy06pwKIUG234I5AABAL6JBFYCBEHUObtJzdDfLD7D+MUarzkm6FGTnl4tr18YFcwAAACRD2AUwEKLOwU1yPm4rhAVYX22Q7XYwBwAASIO6YdfMrjWzw2b2YzM7YWZ/6o3fa2ZFMzvmfb0z8J79ZnbSzFbMbDIwfos3dtLMZgLjI2b2lJk9a2aPmNll3vjl3s8nvdevb+WHBzA4ws7L7eT5uPWCarFU1sTsokZmDmnILPSaTgVzAACANEhS2b0g6WPOud+RdJOkD5vZW7zX7nfO7fa+vilJ3mvvl3SDpFsk/a2ZZcwsI+mzkt4h6S2SPhC4z19799oh6ZykO73xOyWdc869WdL93nUA0BB/r2y5sqqMFyQ7dT7u/HJRE7OLcnWuM1UDr9OlJc5BnQzmAAAAaVC3QZVz7iVJL3nf/9LMfiwp7r8Ob5P0sHPuVUnPm9lJSTd6r510zv1EkszsYUm3effbK+mPvGseknSvpAe8e93rjX9V0n82M3Mu5L8EAaDG/HJR9339hM6dr6yNrTq3Fhw7EXSDjaaimBQahjNmuugc3ZgBAACa0NCeXW8Z8Zikp7yhj5jZD83s82a2zRsrSHox8LZT3ljU+OsllZxzF2rG193Le/1l73oAiOUHzWDQ9XWq2VPcPt1ghTnqt3cXndPzs7fqyMxegi4AAECDEoddM3udpMck3e2c+xdVK69vkrRb1crvp/xLQ97umhiPu1ft3O4ysyUzWzpz5kzs5wAwGOKCptSZZk9RzzBJzx14p/7ZC7KFLjfPAgAASKNEYdfMsqoG3S875w5KknPuZ865VefcRUl/p0tLlU9Jujbw9msknY4Z/4WkvJltqRlfdy/v9askna2dn3PuQefcuHNufHh4OMlHApBy9cJsJ4Jk0g7Q3W6eBQAAkEZJujGbpM9J+rFz7m8C428IXPbvJP2T9/3jkt7vdVIekbRD0vcl/UDSDq/z8mWqNrF63Nt/e1jSe7333yHpa4F73eF9/15Ji+zXBZBEXJjtVJBMGmKnxgo6sG+XCvmcTJ1rngUAAJBmVi87mtn/IOn/kXRc0kVv+M8lfUDVJcxO0j9L+g9eMyuZ2X+U9D+r2sn5bufct7zxd0r6tKSMpM875/7KG//vJT0s6WpJy5I+6Jx71cyukPT3qu4TPivp/X6Dqyjj4+NuaWmpgb8CAK3idz0+XSp3valSVHOoXHZIV2QzKp2vdGSOvfR3AgAAkAZmdtQ5N173urQVSgm7QHeEhctcNtPVCmVt0Nyzc1iPHS321BwBAADQGMIugI6amF1UMWSfbCGf05GZvV2Y0Ub9MEcAAADESxp2656zCwBJRDWE6kTX46Si5lIslTW/XNTUWIFlxwAAAClB2AXQEtvzudCqaTu6HjcbSKPmKEn7Dx7X0k/PrlvmXCyVtf/g8bVrCMEAAAD9I/E5uwAQp1PH5/h7g4ulspwuBdL55WJTc/SVK6v6ylMvbmhoVa6s6r6vn2j6mQAAAOgOKrsAWsKvcra7+jm3sBIaSOcWVuo+3//+7keOhd57NaKHwbnzlQ1j/jOp7gIAAPQmwi6AlpkaK7Q9/MXtuw12gw4uQfbn5C9/bvdcAAAA0H0sYwbQV6L2AGfMYiu+weXPYSzieSYpn8s2NBcAAAB0H2EXQF+J2hsctQTZr76GLX/2FfI5RR3C5iTd++4bOrIfGQAAAK1D2AXQV6bGCjqwb5cK+ZxM1aDq/xzGr75GLTk2SUdm9ka+v5DPRT6znUu255eLmphd1MjMIU3MLtIMCwAAoEHs2QXQd6L2Bgf37Errq6/1jkaanhyNfX8n9iP7/CXXcfuPAQAAEI/KLoBUqFd9rXc0Ujeqt1HqdZwGAABAfeYi9rn1q/Hxcbe0tNTtaQDoQX43Zv9ooj07h3X4mTPrjiqSoo8vqn1/O45WkqSRmUOhe4hN0vOzt7b8eQAAAP3EzI4658brXccyZgADI7gUOWyp8PRXn5acVLno1sb85cOSOra0uN6SawAAANTHMmYAAylsqXBl1a0FXZ+/fLiTS4vrLbkGAABAfVR2AQykqO7MjV7byH2S8ivFnVgyDQAAkFaEXQB9rdl9tFFLhaOuldTRpcWd7P4MAACQRixjBtC3/H23xVJZTpf20SY5kzZsqXA2Y8oO2boxf/kwS4sBAAD6C5VdYIB0qptwp8Tto633uaKWCoeNBe+Vpr8/AACANOPoIWBA1HYflqqVyW6dJdsKHNEDAAAweJIePcQyZmBAdLKbcKdE7Zd1kiZmFxMtZ641v1zUxOyiRmYONX0PAAAAdB9hFxgQUV2D29FNuFP27ByWRbzWyP5d32b2AAMAAKC3EHaBARFVBW1XN+F2m18u6rGjxdBlzL5yZVUfe/TpxFXadle/qRoDAAB0DmEXGBC90k24VYEvLJiGWXUucZW2ndVvqsYAAACdRdgFBsTUWEEH9u1SIZ+TSSrkcx1vTtVI4KsXipsJoPWqtO2sfqdxzzQAAEAv4+ghYIBMjRW62nk5KvDd/cgxzS2srB3lU9s52g/F0qUjg7bncyo2EXjjQvL05Ghox+pWVL/TuGcaAACgl1HZBdAxccEuWOVNUgUNW5adzZjyuaxMUsbCW1fFVWnbWf1O255pAACAXkdlF0DH1KvG+oE2SRV0aqygpZ+e1VeeelGrziljptt/71p9cmqXpOhzhetVadtV/W5n1RgAAAAbUdkF0DFh1dhaxVJZQwmqsn435lVX7ce86pweO1pc29sbrNJK1UqvH6a70RSqF/ZMAwAADBIquwA6xg92cwsrsRVeP8AG1VZB45Y6+8/x/6y3/7dTur1nGgAAYJBQ2QXQUVNjBR2Z2atP3767bpU3YxZZBY0Ky7VLoOmux5s1AAAgAElEQVSCDAAAMJio7ALoiiRV3ovO6fnZWzeMzy8XZZI21n83NnyiCzIAAMBgorILoGv8Km+hwU7FcwsroUHXpA0Nn+iCDAAAMJio7AKI5R8FdLpU1vZ8bu0s3Fa+b8/OYX3pyRc2jF//+pwmZhc33COqKuu0cR9uO7ogx322Zv++AAAA0FqEXWCA1Qtmtcf3JG3u1Oj7Dj9zJvQ+//jc2bUKbvAeUUcYmffs4DOCy6VbEUDjPpvUnWZYBGwAAICNzIV0Pe1n4+PjbmlpqdvTAHpe1Dm0wUZQE7OLoaGykM/pyMzeyHs3+r6RmUOhy5LDFLww99FHjoW+p97cNivus0nhjbPaOack/xwBAADSxMyOOufG613Hnl1gQCXpUtxIc6f55aImZhc1MnMocadkXyP7Z0+XypoaK0SG43Y3nor7O+lGMyy6TQMAAIQj7AIDKkkwS9rcya8uFkvl2Apt1P2mJ0c3HENkde7RaFOrVon7O+lGMyy6TQMAAIQj7AIDKkkwCwuhYc2dwqqLteKaQk2NFXRg3y4V8rm1c3X/+KbrYp+ddG6tFvfcbsyJbtMAAADhaFAFDKgkXYqTNneKqyKalKhp0tRYYcPr42+8OvLZrW48lVSS53ZyTu3oNg0AAJAGNKgCBliruvg228gKrUE3ZgAAMEiSNqgi7ALYNDoCAwAAoFOShl2WMQPYtG4tKQYAAACiEHYBtETYnlsAAACgWwi7ABJL097QNH0WAAAAbETYBZBI7b7cYqms/QePS1LfhcQ0fRYAAACE45xdAImEnaVbrqxqbmGlLc+bXy5qYnZRIzOHNDG7qPnlYsvu3enPAgAAgM6jsgsgkaizdOPO2G1WuyuvnfwsAAAA6A4quwAS2Z7PhY7nt2Zb/qx2V16jPkvUOAAAAPoPYRdAItOTo8pmbMP4r359oaVLjKX2V16nJ0eVy2bWjeWyGU1Pjrbk/gAAAOi+umHXzK41s8Nm9mMzO2Fmf+qNX21mT5jZs96f27xxM7PPmNlJM/uhmf1u4F53eNc/a2Z3BMbfZmbHvfd8xsws7hkAuuPCRbdhrHLRtazi6u/T3fiUqlZVXqfGCjqwb5cK+ZxMUiGf04F9u2hOBQAAkCJJKrsXJH3MOfc7km6S9GEze4ukGUnfcc7tkPQd72dJeoekHd7XXZIekKrBVdI9kt4u6UZJ9wTC6wPetf77bvHGo54BoIP8PbQuIoUWS+VNN5Lyn1GMqN62svLKsUMAAADpV7dBlXPuJUkved//0sx+LKkg6TZJv+9d9pCk70r6uDf+Reeck/SkmeXN7A3etU84585Kkpk9IekWM/uupN9wzn3PG/+ipClJ34p5BoA2CobB/NasSuVKZND1OW2ukVTYPl2fSXrP2wqR92wkvHLsEAAAwGBoaM+umV0vaUzSU5J+2wvCfiD+Le+ygqQXA2875Y3FjZ8KGVfMMwC0SbDC6iSdO18/6AY120gqbj+uk/SNp18Kfa12vn54jaowc+wQAADAYEgcds3sdZIek3S3c+5f4i4NGXNNjCdmZneZ2ZKZLZ05c6aRtwKoEVdhTaqZRlL19uOWypXQANtoeOXYIQAAgMGQKOyaWVbVoPtl59xBb/hn3vJkeX/+3Bs/JenawNuvkXS6zvg1IeNxz1jHOfegc27cOTc+PDyc5CMBiJA09OWyGeVz4ccONdNIKqxDcq2wANtoeOXYIQAAgMFQd8+u1xn5c5J+7Jz7m8BLj0u6Q9Ks9+fXAuMfMbOHVW1G9bJz7iUzW5D0vweaUt0sab9z7qyZ/dLMblJ1efSHJP0fdZ4BIIG4vazB167KZWVWXbKcRMZMB/btkqR1+1+l8EZSSfbU+j/f9/UTkfMIC7Db87nQplZR4XV6cjTRnAEAANDf6oZdSROS/idJx83smDf256oG0EfN7E5JL0h6n/faNyW9U9JJSecl/XtJ8kLtX0r6gXfdJ/xmVZL+RNIXJOVUbUz1LW886hnAQEsSHuMaMUnrQ2qpnCzkStVgGDymZ+mnZ/WVp17UqnPKmG1oJNVIQ6ipsep7xz7x7dDAGxZgGw2v/jPpxgwAAJBu5hrpPNMHxsfH3dLSUrenAbRNbXiUNgZQSZqYXQyteBa8wBh1xE+tIZN+44qsXi5XQqvD9eYSN48jM3s39RmD1xNeAQAABoOZHXXOjde7LkllF0APiWvIFAx4rWrE5Jx07J6bm55LM/NotPrqV4QBAAAAH2EX6DNJw2O9vaxJK7txjZuSzCVqHk7Vqm9UiCXAAgAAYDMaOmcXQPcl7SYc1t3Y38s6PTmq7FDYqV8KvX4zc4nrslzvTNz55aImZhc1MnNIE7OLkdcBAAAAtQi7QJ+JC7FBU2MFHdi3S4V8TqbqHll/z+vUWEGvuyJ+Yce2rVldvmVIH33kWGTQ3LNzeMNB2bVzCc4jTNSZuP6+3WKpLKf6wRgAAAAIYhkz0Gca2c8atxS4FHG8j0m6//bddTsozy8X9djRolzNe2u7MQfnMTJzSGEt8cKWQyfdmwwAAACEIewCfagV+1nj9vRGBc2PPfq0PvrIMW3P5/TKqxc2XOMkHX7mTFPPrNWqBlsAAAAYTCxjBgZU3HLoqEC56tzakuKos3njwmjYM7NDpvOvXdiwLzfp3mQAAAAgDGEXGCDBhk9zCyt6z9sKoXt6NxMo495bu484n8tKJp07X9mwLzfp3mQAAAAgDMuYgQHhN3wK7sN97GhxLeAGrzv/2oWmnpEkjAaXYE/MLm6oEPv7co/M7JWU/KxdAAAAIIiwC6TM/HIxNCAmafhUG4h9ZpIL6ywVkDHbEJzrqbcvl7N2AQAA0CzCLpAiYdVbv4tyVLAslsoamTmk7fmczr+2semUJF11RVavvHpBlYvRifeicw0H00YaVgEAAACNYM8ukCJx1du4AOnvlz0XcRxRqVzRhTql3WYCKvtyAQAA0C6EXSBFwqqkUrWqGxYskzLFL2POZkznXnlV188c0vUzh7T7vm+vdVWOU9uwKtgkCwAAANgMljEDPSJqr20j7zdVq7S1tudza/fyn1FnC+6aqHuuvW7S6kWnyuqlq0rliv7skWOSVPczsC8XAAAA7UBlF+gB/l7bohdCg0fwJDW3sBIaSk1aWxY8NVbQkZm9en721uqxPyFy2aF1ldbYoKtqxTdsK+9FSfc+fiLx/AEAAIBWorIL9IAknZLriWpA5efQidnFdVVjs/D7XJHNrB37478vanl0vepw7bFCAAAAQKdQ2QV6QL0jeJKIahBlJt39yLENVePIZlQ142F7fSNyMgAAANAzCLtAD4gKqo10OI5qQBXWWCrseKGoZ4Y1kUq633fb1vCl0gAAAEC7sYwZ6AHTk6PrzseVGj+Cp7YB1ZCZVuscFxRmz87h0HsHl1PHLW32ZTOme951Q8PPBwAAAFqBsAu0UdIOy7VBNXhtI12ag6F0ZOZQU3M+/MyZup+nWCpv6NI8ZNWfnZMyZrr9966lyzIAAAC6hrALtInfYdmv1vp7ZaXw43jCjuBp9B5B2/O5utXXMFH7hGvn4nTpWKJ8LqtXXruwdvzQqnN67GhR42+8msALAACArmDPLtAmcR2WO3GPsOXISVwVcSRR2Fycqnt4r7x8y7pzdv15fuzRpzUyc0gTs4sNHaPUCfPLRU3MLvbs/AAAALA5hF2gTVrRYXkz94hbjhznldcuhAa/uLlEvbbqXNPnBrdTK841BgAAQG8j7AJt0ooOy5u5RyOhOqiy6kIrx3FzSTKfRqva7dSKqjsAAAB6G2EXaJOwo4Aa7bDczD385bmN92G+JCwox80l6tijJPfthlZU3QEAANDbaFAFtElch+UoYZ2XD+zblbhL89JPz+rLT76wqaArhVdxk3yeesceNVLVbqeo5l29Mj8AAABsnrkmzuHsZePj425paanb0wAaVtvtWKpWTg/s21W3S3MrmaT7b9+9qS7KjXyWbuj1+QEAACCamR11zo3Xu47KLtAj4vaR1gawsGtbxSn8WKNGz/v155m0qt1JvT4/AAAAbB5hF+gRjewjbefe0kLIUt5mzvsNOze4l/T6/AAAALA5NKgCekQjnZfbtbc0m7HQ5ld0LwYAAEC/IewCHeZ3Sx6ZOaSJ2cW1s10b6by8Z+dwW+Z25WVbQquddC8GAABAv2EZM9BBSZYDJ9lHeviZM4metzU7JCepXLmY6PqXy5XQcboXAwAAoN8QdoEOqteEKmofaW1zqLDg6TNp7eih85WLymUz+uBN1+nwM2di3yetD6/BZ+a3ZpUdMlUuXure3uiZwQAAAEAnsYwZ6KBmlgP71eBiqSynajXYYp5Re5hYubKqw8+c0ZGZvbHvM2ktvNY+89z5imRSPpeVqdrEimN6AAAA0Muo7AId1Mxy4LBqsNP6Cm49fpiOqwr/8U3XrVtKXfvMyqrTlZdv0bF7bk74VAAAAKB7qOwCHRTVhGrPzuHQplVSdNXXqVphNUkZi6vZXgrTYc83SR+86Tp9cmpX3WfSkAoAAAD9gsou0Aa1e2z9RlNhTaj27BzWY0eLkU2roqqxhXxOR2b2SpJGZg5FziW4tzZpEywaUgEAAKDfmXNJF0L2h/Hxcbe0tNTtaWCA1XZclqqBM2qP68TsYmyYjbufVA2uUUuTM2b61B++teG9tY1+BgAAAKBTzOyoc2683nVUdoEWq9dxWVpf+Y36dZO/ZDiqGitpQyANyg6Z5t7XeNCNeyZBFwAAAP2CsIuBFLXMuBWi9rUWS2Xtvu/bKpUriZpLBZcMhx1JNDG7GBl0JSm29XICUccgAQAAAP2AsIuBU7tEt3aPbKP3qg3NcR2PS+WKpPpBN5sxvfLqBY3MHIoM4/WaRVVW3bpqMgAAADBI6MaMgRO3zLgRYeff7j94XHt2Dm/oeJyUSdq2NSu5ajAO3tfv0Dy/XNTE7GKiY4fongwAAIBBRdjFwGnVsTpRoflLT76gK7KN/59WIZ/T/bfv1r+UL6hycX2U9cN4MGAnQfdkAAAADCrCLgZOVABsNBjGheNz5ysNb5m9/vU57T94XKsRHdKLpbI+9ujTkft0a58XPHIIAAAAGDSEXQyc6cnRDcuMmwmG9cKxU2M9ov7xubOxDadMigzCJun+23erkM/JVK0S1x4T5C9/Hpk5pInZxbVl0QAAAEAa0aAKA6dVx+rs2TmsLz/5QuzeWadq8DxdKmvrZRm98lp0mI27T73uzdvzudjuya1sygUAAAD0A8IuBtJmj9WZXy7qsaPFRE2iJOlfv+lq/eNzZ5t6VsYssqIrJatKJzn7FwAAAEgTwi6QUPCYoaE6ATSoWConbihVK5fN6MC+XZpbWIm8x3veVj+4t6opFwAAANAv2LMLJFB7zFDSoNuobVuzoftuw/YZ+x47Wqy7/7ZVTbkAAACAflE37JrZ583s52b2T4Gxe82saGbHvK93Bl7bb2YnzWzFzCYD47d4YyfNbCYwPmJmT5nZs2b2iJld5o1f7v180nv9+lZ9aKBRYcuA2+Ged92gIzN79fzsrToys3etYjs1VtCBfbuUsY0tr5KcEdyqplwAAABAv0hS2f2CpFtCxu93zu32vr4pSWb2Fknvl3SD956/NbOMmWUkfVbSOyS9RdIHvGsl6a+9e+2QdE7Snd74nZLOOefeLOl+7zqgK5Is980MWcPHDYWJ6po8NVbQxYiKcr35+WE5rlszAAAAkCZ19+w65/5bA1XV2yQ97Jx7VdLzZnZS0o3eayedcz+RJDN7WNJtZvZjSXsl/ZF3zUOS7pX0gHeve73xr0r6z2ZmzrVp/SgQY3s+F7pnNmOmi84pvzWrX/36gjZb+73v6yf068rFyK7JUfNIshx5s025AAAAgH6ymT27HzGzH3rLnLd5YwVJLwauOeWNRY2/XlLJOXehZnzdvbzXX/auBzouahnwp/7wrXp+9lZtvWyLKhejfw+TSVjyPXe+Etk1OW4eLEcGAAAA1ms27D4g6U2Sdkt6SdKnvPGw/6R3TYzH3WsDM7vLzJbMbOnMmTNx8waaErcMeH65WLfb8uom1yOcLpXXukGXK6tre3cbWY4ctTwaAAAASKOmjh5yzv3M/97M/k7SN7wfT0m6NnDpNZJOe9+Hjf9CUt7MtnjV2+D1/r1OmdkWSVdJCj2o1Dn3oKQHJWl8fJxlzmiLsGXAfpfmVjBF/DZHUn5rVvsPHl+r+q46t1bRTRp0g++vXR4NAAAApE1TYdfM3uCce8n78d9J8js1Py7p/zKzv5G0XdIOSd9X9b/jd5jZiKSiqk2s/sg558zssKT3SnpY0h2Svha41x2Svue9vsh+XfSaVnZpjvofdy6bkXOKXN4cFlaDZwJvz+f0yqsXGno/AAAA0O/qhl0z+4qk35f0m2Z2StI9kn7fzHar+t/n/yzpP0iSc+6EmT0q6UeSLkj6sHNu1bvPRyQtSMpI+rxz7oT3iI9LetjMPilpWdLnvPHPSfp7r8nVWVUDMtBTknRpjpPLZjRk0iuvhQfmfC6re999gz76yLHEzw+r4kbZ7PwBAACAXpWkG/MHQoY/FzLmX/9Xkv4qZPybkr4ZMv4TXerYHBz/taT31Zsf0Am1lVJ/+XBUd+R6TFq7z90RQVaSXr1wUVJ0N+ghM43MHFo3p0aqzUm6OAMAAAD9aDPdmIGB4FdKi6WynC7td51fLjbVBTmfy2p7PqfTpfJal+Uo/lLjsC7MUnXvbu2cklZr6eIMAACANCPsAnXc+/iJlu53feW1C+uCcz1+eL0ie+n/XC2kV7k/p6hq7bat2dBu0gAAAEAaNdWgChgE88tF3fv4CZXKldDXm93vWmnwHKLaTsySFNWq7XSprPtv373h+lw2o3vedUNoN+mw5dkAAABAvyPsAiFqmzyF8Suo+Vw2MhA3Iuw+UZ2Y4+bkh9V6IZbjiAAAAJBmLGMGQiRp8rRn57Ak6d5336DsUMi64gYU8jkdu+dmffr23RuWGicN0sE9uFNjBR2Z2avnZ2/VkZm9oeE17DP6S6EBAACAfkdlF6nUzPLc4HuSLDR+7GhR42+8eu2+f37whzpfudjUfIMhVbpUlb338ROR78nnsrry8i1NL0GOWobNcUQAAABIA8IuUqeZ5blJli3XKldWde/jJ9buWW4y6Jqkjz5yTHMLK9qzc1iPHS2uzSOqqmuqVpQ3s9w46jgjjiMCAABAGrCMGanTzPLcRs6mDSqVK/qL+eOaW1hJVA0O47yvYqmsLz/5QqJ5OG1+X23YcUYcRwQAAIC0oLKL1GlmeW69pbsZM61GtED+0pMv1J2TSfrXb7paP3rplzp3vrI2VnvHpIF529ZswiujJW1kBQAAAPQjwi5Sp5nluVHv8X3qD9+qux851vScrshm9L7x69YFyZGZQ03f71e/vqD55eKmg+nUWIFwCwAAgFRiGTP6zvxyUROzixqZOaSJ2UXNLxfXvR63PDfqvXFLdzNmmhoraDMNl2uXUc8vFzVk4TdM8pjKRUfXZAAAACAGYRd9xW8kVfQ6JvvNp4KBd2qsoAP7dm04wkdS5Hvjqpv+8uWLzW7K9fhLpf3PELYsOpfN6I9vum7d3OvdDwAAAMBGLGNGX4lrPhUMrGHLcydmF0Pfe/cjx3Tf10/ITArbllvI5zS/XIzdt5uEv4w6qhlWxkwH9u0KnTddkwEAAIDGUNlFX9nM2bBx15w7XwkNupJ09pVXNf3VpzcVdINdjqPmcdG50AozXZMBAACAxhF20VeiqplJqpzNVkLLlYuqrG5uDXOwYtvoZ4halk1jKQAAACAaYRd9ZTNVzrD3dsK2rdl1wbSZzzA1VtCRmb16fvZWHZnZS9AFAAAA6mDPLnrK/HIx9tzXsLNh9+wc1tzCij76yLG6Z8VekR0K3S/bTs5pXRMszrcFAAAA2s/cJvYh9qLx8XG3tLTU7WmgCX6X4mAYzWUzsUt2k74n7LpOqvc5AAAAACRjZkedc+P1rmMZM3pGXKflzb4nrgNyJ9T7HAAAAABai7CLntFMp+Wk74nrgBx3lm0rcS4uAAAA0DmE3QExv1zUxOyiRmYOaWJ2UfPLxW5PaZ355aKGIqqscV2Uk3Y2jruuU42rOBcXAAAA6BzC7gDw96sWS2U5ScVSWfsPHu+ZwOvPL+wc23pdipN2No4KtNe/PrfhaJ9tW7PKDrV2eTPn4gIAAACdRTfmARC3r7UXGibF7aet19QpaWfjqbGC/mHpBR157uy68SPPndVfzB/XJ6c2NrT6j//1uF55LXlDq2zG1p3Ha5Kcqufi0m0ZAAAA6CzC7gBoZi9sJ0XNY9W5taZO9QJvkiD55E/OhY5/5akX9cmpXRuOPbpsy1BDYXfuvW/lOCEAAACgRxB2B8D2fE7FkEDZK3tIo+YnXVpyLcUH3nrml4uhy6SlaqiuPZooaj5R8rls4tANAAAAoP04Z3cANHN+bSclOQM3Y6aLzq2rmNZWYmsrqf7rxVJ5bUlx1L3/u6uuaDjg+rJDprn3vbUn/i4BAACAtEt6zi6V3QGQdF9rtwTnFxU4/aqsX+ld+ulZPXa0uK4SG6wA1wbouF/pfODt1+pLT76QaK65bEbveVtBh585s+7vUpImZhd78u8XAAAAGERUdtFTJmYXE1VYM2ahy5LzuayuvHxL4irtjt+6Uj85cz5yibNUbTAVF2J7vXIOAAAApEnSyi5HD6GnTE+OKpupf+xPVDgtlSuJg252SHr256/EBl2pWjWOWia9+75v6+5HjkV2uwYAAADQHSxjRu9JsNggqrLbiMrF5NeGLZOe/oenVbkYPYde6XYNAAAADCIqu+gpcwsrsQFSqi4R/sDbr1Uum+nQrKqC1dok8+yVbtcAAADAIKKyi54SVw01ad1y4vE3Xr2u6db51y7o3PlKW+fnL5GuV7XNZTNrjasAAAAAdB5hFz0l6szdQj6nIzN7143Vnmub5AijzcqYxc7Tv4bmVAAAAEB3sYwZPWV6cnTD8mS/Sjq/XNTE7KJGZg5pYnZR88vFddct/fSsXr2w+aDrB9ow/j7h6clRZYc2XpfNmD71h5y5CwAAAHQbYRc9ZWqsoAP7dqmQz8lUrege2LdLkrT/4HEVS2U5XWoY5Qfev5g/ri89+YLCttFmzDTxpqtjQ6yvkM/puQPvVCFiv60/PjVW0Nz73qp8Lrv22ratWd3+e9dqbmElMpADAAAA6AzO2UXbzS8X1+2tDTurtp6483cL+ZxOv1xW2P+UM1attCZZ3hw8G7eZs3M5bxcAAABoP87ZRU/wA2BURTapuIZQxVJ40JWqy47nFlbqBl2/guyH0qgKc1xoDXsO5+0CAAAA3UGDKrRVXABspNoZ1xAqTsYs0Xm3tc2vpI0NsOqJml8z8wYAAACwOVR20RZ+M6mooJckgAaFNa5K4gNvvzbRebet2FsbtSc4yV5hAAAAAK1F2EXLBZcuR0kSQIOCy4qTyJjpgzddp09O7Up03m0zS6trrUaspY4aBwAAANA+hF20XJI9snt2Djd836mxgo7M7NUHb7ou9jqT9NyBd+qTU7vW3rdtazb2Pa3YW1uvgzMAAACAzmHPLlouyRLlw8+cWfdzsGPzVbmszKTS+Upo9+ba99YKqxrf864b6nZkbnRpda3pydHQbsxJKssAAAAAWouwi5ZL0kwqGCxrj+wplStrr/ndmyWtBd64UBoVLv33zi2sRM6t0aXVcc/YzDFLAAAAADaPc3bRcmHnzdbK57K68vItOl0qa8is7r7WQj631jE5qvGVf6ZuvXDJebgAAABA/+KcXXTN1FhB73lbQVE9iLNDpldeu7B29m6SBk7Bam5YZ+ZcNpMo6PrzCztDV6oG6ZGZQ5qYXWxJh2YAAAAA3cEyZrTF4WfOKCzCZsz0uiu26Nz5Ssir0YJLjGuXJGfM1jWYShp4g9fVVnvDlk8DAAAA6B9UdtFS9c7XveicSg0G3bB9uFNjhbUKr18Z9gNqMxXZsA7SrejQDAAAAKA7CLtomaTn60Y1gsqYyVTdz7tta3bdEuOw6morA2pU06vNdmgGAAAA0B0sY06x4HE+negMXO983WCFthUNoloZUKM6SG+2QzMAAACA7qhb2TWzz5vZz83snwJjV5vZE2b2rPfnNm/czOwzZnbSzH5oZr8beM8d3vXPmtkdgfG3mdlx7z2fMTOLewaSCVZZnTa3xDepuJAZrNBGNYhqNIhHBdFmAmpU0yvOyAUAAAD6U5JlzF+QdEvN2Iyk7zjndkj6jvezJL1D0g7v6y5JD0jV4CrpHklvl3SjpHsC4fUB71r/fbfUeQYS6MYe1KiQWXts0MjMIc0trGh6clTPz96qIzN7m6o4hwVUk7Rn53DD92pVAAcAAADQG+ouY3bO/Tczu75m+DZJv+99/5Ck70r6uDf+RVc9vPdJM8ub2Ru8a59wzp2VJDN7QtItZvZdSb/hnPueN/5FSVOSvhXzDCTQjT2o05OjocuTpydH29LteGqsoKWfntWXn3xhrfOzk/TY0aLG33h1w/et7dAMAAAAoH8126Dqt51zL0mS9+dveeMFSS8GrjvljcWNnwoZj3sGEoiqsua3Ztt2lmxcdbRdleawI47oogwAAACg1Q2qLGTMNTHe2EPN7lJ1KbSuu+66Rt+eSmFV1mzG9KtfX1g747YdZ8lGVUfbVWmmizIAAACAMM1Wdn/mLU+W9+fPvfFTkq4NXHeNpNN1xq8JGY97xgbOuQedc+POufHh4cb3a6ZRWJX1ysu2qHJx/e8SOlUFbUUzKf8M32BVupVNqgAAAACkR7Nh93FJfkflOyR9LTD+Ia8r802SXvaWIC9IutnMtnmNqW6WtOC99kszu8nrwvyhmnuFPSNVwgJcq+4lSUdm9q41gXq5XAl9XyeqoJvtdhzVXXrPzmG6KAMAAADYIMnRQ1+R9D1Jo2Z2yszulDQr6UcUHKYAAA63SURBVN+a2bOS/q33syR9U9JPJJ2U9HeS/ldJ8hpT/aWkH3hfn/CbVUn6E0n/xXvPc6o2p1LMM1KjlccDJblXN6ugm+l2PL9c1McefTp0z+/hZ87QRRkAAADABlZtnJwe4+PjbmlpqdvTSGRidlHFkKpq8KieVt6rtiOyVK2C9nI4DJtzkEl6fvbWzk4KAAAAQNeY2VHn3Hi961rdoAoNaGVzpST38gPt3MKKTpfK2p7PaXpytGNBd365mOjZweuGzLQa8wsZ9uYCAAAACEPY7aLt+VxoNbaZAJf0Xt06SzbpObu118UFXfbmAgAAAIjSbIMqtMBmmza1617tkPSc3bDrwmTMenr5NQAAAIDuorLbRa1cVtztJcr1JF2ynWQJdzZjmnvvW3vmswEAAADoPYTdLmvlsuJuLVFOIuky66jrgq68bEvPfk4AAAAAvYGwi1BJm0n519739RM6d756jm8+l9W9775h3fV7dg7rS0++sOG9e3YOr/t5enJU0//wtCoXo/fqRp0XDAAAAAA+9uxig0bO/51fLmr6q0+vBV1JKpUrmv6Hp9ddf/iZM6HP+spTL268r8XPjw7MAAAAAOoh7GKDpM2k/GsrqxursJWLbt31UXtxV51bF6Sj7ufrpaZbAAAAAHoXYRcbNHL+b1xDqeBrcdXYYJCOu18hn6MDMwAA/3979x9zV10fcPz9sS3wiOBTtJDyFAQS0kjGxlOfaEkXs+HWAjGuIZpBltA4lybOP2aWdGljMrPtD9xINkO2KGZjw2RuOgeV4LA2oP+QiRQLtA5qqyOjLVKRFY3rH4x99sf5Xri0994+v+893/t+Jd/ccz/3nPs993zgtJ+e7/keSdKsWOzqDP0K017xQUVs92e9Ho3UrVPk9vu+qckJHt15g4WuJEmSpFmx2G2p3fuPsekzj3Dlzq+z6TOP9Lyfdr5m88zeTv/9Zk5e9ZZ40/pbp6e445ZreUuf+3En37pq1n1LkiRJ0tlY7LbQXCaQmo9OYTo1OUFw5vDh7v57mZxYxZ0fOfM5uFunp7jwvFU9t8mcXd+SJEmSNBs+eqiFBk0gtRzP7O3VP7wx1HiQfo8N6o6P8vOCJUmSJLWDxW4LzWUCqcVw+jN3+13RnU3//bb3cUKSJEmSFpPDmFtoLhNILVSvIdP9HoM7m/69J1eSJEnScrDYbaHlLBh7DVlOOKPgnW3/3pMrSZIkaTk4jLmFOoVh99DiHVvWL0nB2G9octIUqvPp33tyJUmSJC01i91ldPq9rwspUJerYBx0jy7AX/32dRaukiRJkkaOw5iXyVI/Lmip9Boy3dGW3yBJkiRp/FjsLpNBjwsaZd332PbSht8gSZIkafxY7C6T5X5c0GLaOj3Foztv6DsLcxt+gyRJkqTxYrG7TJbzcUFLpYbfIEmSJGk8WOwukxqeL1vDb5AkSZI0HpyNeZks5+OClkoNv2ExLOas2pIkSZKWRmTmsPdhUc3MzOS+ffuGvRuqVGdW7e7JxiZWreCOW6614JUkSZKWQUQ8kZkzZ1vPK7vySuUcDJpV22MmSZIkjQ6L3TF3+pXKzrNzAYu3Hto8q7YkSZI0Tpygasy19fm/w+KM1JIkSVI7WOyOOa9Uzo0zUkuSJEntYLE75rxSOTdbp6e445ZrmZqcIICpyQknp5IkSZJGkPfsjrkdW9b3nF3YK5X9bZ2esriVJEmSRpzF7pjz2bmSJEmSamSxK69USpIkSaqO9+xKkiRJkqpjsStJkiRJqo7FriRJkiSpOha7kiRJkqTqWOxKkiRJkqpjsStJkiRJqo7FriRJkiSpOha7kiRJkqTqWOxKkiRJkqpjsStJkiRJqo7FriRJkiSpOha7kiRJkqTqWOxKkiRJkqpjsStJkiRJqo7FriRJkiSpOha7kiRJkqTqLKjYjYjnIuJARDwZEftK7KKI2BsRh8vr6hKPiLgrIo5ExNMRsaHre7aV9Q9HxLau+HvK9x8p28ZC9leSJEmSNB4W48rur2fmdZk5U97vBB7OzKuBh8t7gJuAq0vbDnwOmuIY+DTwPuC9wKc7BXJZZ3vXdjcuwv5KkiRJkiq3FMOYfwu4tyzfC2ztin8xG98BJiNiLbAF2JuZL2fmfwN7gRvLZxdm5r9nZgJf7PouSZIkSZL6Wmixm8A3I+KJiNheYpdk5gsA5fXiEp8Cnu/a9miJDYof7RGXJEmSJGmglQvcflNmHo+Ii4G9EfHsgHV73W+b84if+cVNob0d4PLLLx+8xyNm9/5j3LnnEMdPnuLSyQl2bFnP1mlrekmSJElaiAVd2c3M4+X1BHA/zT23L5YhyJTXE2X1o8BlXZuvA46fJb6uR7zXfnwhM2cyc2bNmjUL+UnLavf+Y+y67wDHTp4igWMnT7HrvgPs3n9s2LsmSZIkSa0272I3Is6PiAs6y8Bm4CDwANCZUXkb8LWy/ABwe5mVeSPwShnmvAfYHBGry8RUm4E95bOfR8TGMgvz7V3fVYU79xzi1KuvvSl26tXXuHPPoSHtkSRJkiTVYSHDmC8B7i9PA1oJfCkzvxERjwNfiYiPAf8FfKSs/2/AzcAR4H+AjwJk5ssR8WfA42W9P83Ml8vyx4F/ACaAh0qrxvGTp+YUlyRJkiTNzryL3cz8EfArPeI/BT7QI57AJ/p81z3APT3i+4Bfmu8+jrpLJyc41qOwvXRyYgh7I0mSJEn1WIpHD2mWdmxZz8SqFW+KTaxawY4t64e0R5IkSZJUh4XOxqwF6My67GzMkiRJkrS4LHaHbOv0lMWtJEmSJC0yhzFLkiRJkqpjsStJkiRJqo7FriRJkiSpOha7kiRJkqTqWOxKkiRJkqpjsStJkiRJqo7FriRJkiSpOha7kiRJkqTqWOxKkiRJkqpjsStJkiRJqo7FriRJkiSpOha7kiRJkqTqWOxKkiRJkqpjsStJkiRJqo7FriRJkiSpOha7kiRJkqTqWOxKkiRJkqoTmTnsfVhUEfET4BfAS8PeFy3YOzGPNTCP9TCXdTCPdTCPdTCP9TCXy+tdmbnmbCtVV+wCRMS+zJwZ9n5oYcxjHcxjPcxlHcxjHcxjHcxjPczlaHIYsyRJkiSpOha7kiRJkqTq1FrsfmHYO6BFYR7rYB7rYS7rYB7rYB7rYB7rYS5HUJX37EqSJEmSxlutV3YlSZIkSWOsqmI3Im6MiEMRcSQidg57f8ZVRNwTESci4mBX7KKI2BsRh8vr6hKPiLir5OzpiNjQtc22sv7hiNjWFX9PRBwo29wVETGoD81PRFwWEd+KiGci4vsR8Qclbi5bJiLOi4jvRsRTJZd/UuJXRsRj5Th/OSLOKfFzy/sj5fMrur5rV4kfiogtXfGe599+fWj+ImJFROyPiAfLe/PYMhHxXDn3PRkR+0rMc2vLRMRkRHw1Ip4tf1Zebx7bJyLWl/8XO+1nEfFJc1mJzKyiASuAHwJXAecATwHXDHu/xrEB7wc2AAe7Yn8B7CzLO4E/L8s3Aw8BAWwEHivxi4AfldfVZXl1+ey7wPVlm4eAmwb1YZt3HtcCG8ryBcAPgGvMZftaOb5vK8urgMdKjr4C3Frinwc+XpZ/H/h8Wb4V+HJZvqacW88Frizn3BWDzr/9+rAtKJ9/CHwJeHDQMTaPo9uA54B3nhbz3NqyBtwL/F5ZPgeYNI/tbuU8+GPgXeayjjb0HVi0H9L8B7Sn6/0uYNew92tcG3AFby52DwFry/Ja4FBZvhu47fT1gNuAu7vid5fYWuDZrvjr6/Xrw7ZoOf0a8Jvmst0NeCvwPeB9wEvAyhJ//RwK7AGuL8sry3px+nm1s16/82/Zpmcftnnnbx3wMHAD8OCgY2weR7fRu9j13NqiBlwI/Cdl/hvzWEcDNgOPmst6Wk3DmKeA57veHy0xjYZLMvMFgPJ6cYn3y9ug+NEe8UF9aIHK8MdpmiuC5rKFytDXJ4ETwF6aK3gnM/N/yyrdx//1nJXPXwHewdxz/I4BfWh+Pgv8EfB/5f2gY2weR1cC34yIJyJie4l5bm2Xq4CfAH8fzW0FfxsR52Me2+5W4J/KsrmsQE3FbvSIOdX06OuXt7nGtUQi4m3AvwKfzMyfDVq1R8xcjojMfC0zr6O5Mvhe4N29Viuvi5VLc7yIIuKDwInMfKI73GNV8zj6NmXmBuAm4BMR8f4B65qv0bSS5patz2XmNPALmmGo/ZjHEVfmIvgQ8C9nW7VHzFyOqJqK3aPAZV3v1wHHh7QvOtOLEbEWoLyeKPF+eRsUX9cjPqgPzVNErKIpdP8xM+8rYXPZYpl5Evg2zX1GkxGxsnzUffxfz1n5/O3Ay8w9xy8N6ENztwn4UEQ8B/wzzVDmz2IeWyczj5fXE8D9NP8A5bm1XY4CRzPzsfL+qzTFr3lsr5uA72Xmi+W9uaxATcXu48DV0cwYeQ7NMIQHhrxPesMDwLayvI3m/s9O/PYys91G4JUyjGMPsDkiVpeZ6TbT3CP2AvDziNhYZrK7/bTv6tWH5qEc378DnsnMv+z6yFy2TESsiYjJsjwB/AbwDPAt4MNltdNz2Tn+HwYeycws8VujmeX3SuBqmkk3ep5/yzb9+tAcZeauzFyXmVfQHONHMvN3MI+tEhHnR8QFnWWac+JBPLe2Smb+GHg+ItaX0AeA/8A8ttltvDGEGcxlHYZ90/BiNprZ0X5Acy/ap4a9P+PaaE4ULwCv0vxr1sdo7vl6GDhcXi8q6wbwNyVnB4CZru/5XeBIaR/tis/Q/MXgh8BfUyaH6NeHbd55/FWaYTZPA0+WdrO5bF8DfhnYX3J5EPjjEr+Kpsg5QjNs69wSP6+8P1I+v6rruz5V8nWIMptkifc8//brw7bgnP4ab8zGbB5b1MqxfKq073eOs+fW9jXgOmBfObfuppmB1zy2sNFM3vhT4O1dMXNZQescaEmSJEmSqlHTMGZJkiRJkgCLXUmSJElShSx2JUmSJEnVsdiVJEmSJFXHYleSJEmSVB2LXUmSJElSdSx2JUmSJEnVsdiVJEmSJFXn/wF1Z+TQpzCH8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_valid, xgb_model.predict(X_valid, ntree_limit=xgb_model.best_iteration))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, BatchNormalization, Dropout\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras import regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\programs\\miniconda3\\envs\\ai36\\lib\\site-packages\\sklearn\\preprocessing\\data.py:617: DataConversionWarning: Data with input dtype int8, int32, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "d:\\programs\\miniconda3\\envs\\ai36\\lib\\site-packages\\sklearn\\base.py:462: DataConversionWarning: Data with input dtype int8, int32, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "d:\\programs\\miniconda3\\envs\\ai36\\lib\\site-packages\\ipykernel_launcher.py:3: DataConversionWarning: Data with input dtype int8, int32, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "d:\\programs\\miniconda3\\envs\\ai36\\lib\\site-packages\\ipykernel_launcher.py:4: DataConversionWarning: Data with input dtype int8, int32, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_valid_scaled = scaler.transform(X_valid)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(200, input_dim=X_train_scaled.shape[1], activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dense(100, activation='relu', kernel_regularizer=regularizers.l2(0.4)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dense(360, activation='relu', kernel_regularizer=regularizers.l2(0.2)))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dense(1, activation='linear'))\n",
    "model.compile(loss='mse', optimizer='adam', metrics=['mse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = list()\n",
    "callbacks.append(EarlyStopping(monitor='val_loss', min_delta=0, mode='auto', patience=8))\n",
    "callbacks.append(ModelCheckpoint('nn.h5', monitor='val_acc', save_weights_only=False,\n",
    "                                             save_best_only=True, mode='max'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 817 samples, validate on 351 samples\n",
      "Epoch 1/200\n",
      "817/817 [==============================] - 2s 3ms/step - loss: 38965220353.2534 - mean_squared_error: 38965220353.2534 - val_loss: 38680775116.9459 - val_mean_squared_error: 38680775116.9459\n",
      "Epoch 2/200\n",
      "817/817 [==============================] - 0s 169us/step - loss: 38963802006.7173 - mean_squared_error: 38963802006.7173 - val_loss: 38678822608.5926 - val_mean_squared_error: 38678822608.5926\n",
      "Epoch 3/200\n",
      "416/817 [==============>...............] - ETA: 0s - loss: 39280064984.6154 - mean_squared_error: 39280064984.6154"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\programs\\miniconda3\\envs\\ai36\\lib\\site-packages\\keras\\callbacks.py:434: RuntimeWarning: Can save best model only with val_acc available, skipping.\n",
      "  'skipping.' % (self.monitor), RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "817/817 [==============================] - 0s 167us/step - loss: 38961811350.7173 - mean_squared_error: 38961811350.7173 - val_loss: 38676321387.9430 - val_mean_squared_error: 38676321387.9430\n",
      "Epoch 4/200\n",
      "817/817 [==============================] - 0s 175us/step - loss: 38958915250.6047 - mean_squared_error: 38958915250.6047 - val_loss: 38673208392.9345 - val_mean_squared_error: 38673208392.9345\n",
      "Epoch 5/200\n",
      "817/817 [==============================] - 0s 168us/step - loss: 38954706106.7515 - mean_squared_error: 38954706106.7515 - val_loss: 38668683269.8348 - val_mean_squared_error: 38668683269.8348\n",
      "Epoch 6/200\n",
      "817/817 [==============================] - 0s 171us/step - loss: 38948827201.1750 - mean_squared_error: 38948827201.1750 - val_loss: 38662651157.1510 - val_mean_squared_error: 38662651157.1510\n",
      "Epoch 7/200\n",
      "817/817 [==============================] - 0s 165us/step - loss: 38941258419.8580 - mean_squared_error: 38941258419.8580 - val_loss: 38650192467.1453 - val_mean_squared_error: 38650192467.1453\n",
      "Epoch 8/200\n",
      "817/817 [==============================] - 0s 176us/step - loss: 38931318445.5912 - mean_squared_error: 38931318445.5912 - val_loss: 38637777246.0855 - val_mean_squared_error: 38637777246.0855\n",
      "Epoch 9/200\n",
      "817/817 [==============================] - 0s 168us/step - loss: 38918655759.3537 - mean_squared_error: 38918655759.3537 - val_loss: 38632531658.7578 - val_mean_squared_error: 38632531658.7578\n",
      "Epoch 10/200\n",
      "817/817 [==============================] - 0s 159us/step - loss: 38904299780.7001 - mean_squared_error: 38904299780.7001 - val_loss: 38604180418.7350 - val_mean_squared_error: 38604180418.7350\n",
      "Epoch 11/200\n",
      "817/817 [==============================] - 0s 163us/step - loss: 38886243233.9976 - mean_squared_error: 38886243233.9976 - val_loss: 38592476758.0627 - val_mean_squared_error: 38592476758.0627\n",
      "Epoch 12/200\n",
      "817/817 [==============================] - 0s 172us/step - loss: 38865043947.3195 - mean_squared_error: 38865043947.3195 - val_loss: 38552759199.7265 - val_mean_squared_error: 38552759199.7265\n",
      "Epoch 13/200\n",
      "817/817 [==============================] - 0s 173us/step - loss: 38839323193.0282 - mean_squared_error: 38839323193.0282 - val_loss: 38527509857.0028 - val_mean_squared_error: 38527509857.0028\n",
      "Epoch 14/200\n",
      "817/817 [==============================] - 0s 168us/step - loss: 38809532523.7895 - mean_squared_error: 38809532523.7895 - val_loss: 38512636604.1709 - val_mean_squared_error: 38512636604.1709\n",
      "Epoch 15/200\n",
      "817/817 [==============================] - 0s 167us/step - loss: 38776531598.2570 - mean_squared_error: 38776531598.2570 - val_loss: 38441556603.9886 - val_mean_squared_error: 38441556603.9886\n",
      "Epoch 16/200\n",
      "817/817 [==============================] - 0s 178us/step - loss: 38738284920.0098 - mean_squared_error: 38738284920.0098 - val_loss: 38412551871.0883 - val_mean_squared_error: 38412551871.0883\n",
      "Epoch 17/200\n",
      "817/817 [==============================] - 0s 167us/step - loss: 38693247715.4859 - mean_squared_error: 38693247715.4859 - val_loss: 38419561877.5157 - val_mean_squared_error: 38419561877.5157\n",
      "Epoch 18/200\n",
      "817/817 [==============================] - 0s 181us/step - loss: 38644991242.9669 - mean_squared_error: 38644991242.9669 - val_loss: 38364839224.1595 - val_mean_squared_error: 38364839224.1595\n",
      "Epoch 19/200\n",
      "817/817 [==============================] - 0s 170us/step - loss: 38591326691.7993 - mean_squared_error: 38591326691.7993 - val_loss: 38312909926.1083 - val_mean_squared_error: 38312909926.1083\n",
      "Epoch 20/200\n",
      "817/817 [==============================] - 0s 167us/step - loss: 38529797086.1591 - mean_squared_error: 38529797086.1591 - val_loss: 38274501314.0057 - val_mean_squared_error: 38274501314.0057\n",
      "Epoch 21/200\n",
      "817/817 [==============================] - 0s 195us/step - loss: 38466892322.4676 - mean_squared_error: 38466892322.4676 - val_loss: 38153441746.7806 - val_mean_squared_error: 38153441746.7806\n",
      "Epoch 22/200\n",
      "817/817 [==============================] - 0s 199us/step - loss: 38394232516.1518 - mean_squared_error: 38394232516.1518 - val_loss: 38024690626.7350 - val_mean_squared_error: 38024690626.7350\n",
      "Epoch 23/200\n",
      "817/817 [==============================] - 0s 190us/step - loss: 38314237483.2411 - mean_squared_error: 38314237483.2411 - val_loss: 37902054309.5613 - val_mean_squared_error: 37902054309.5613\n",
      "Epoch 24/200\n",
      "817/817 [==============================] - 0s 171us/step - loss: 38229676438.0906 - mean_squared_error: 38229676438.0906 - val_loss: 37867027102.9972 - val_mean_squared_error: 37867027102.9972\n",
      "Epoch 25/200\n",
      "817/817 [==============================] - 0s 174us/step - loss: 38136252664.1665 - mean_squared_error: 38136252664.1665 - val_loss: 37792488305.0484 - val_mean_squared_error: 37792488305.0484\n",
      "Epoch 26/200\n",
      "817/817 [==============================] - 0s 190us/step - loss: 38035880916.1322 - mean_squared_error: 38035880916.1322 - val_loss: 37720049343.0883 - val_mean_squared_error: 37720049343.0883\n",
      "Epoch 27/200\n",
      "817/817 [==============================] - 0s 189us/step - loss: 37934402095.0012 - mean_squared_error: 37934402095.0012 - val_loss: 37646714687.4530 - val_mean_squared_error: 37646714687.4530\n",
      "Epoch 28/200\n",
      "817/817 [==============================] - 0s 185us/step - loss: 37819171407.5887 - mean_squared_error: 37819171407.5887 - val_loss: 37727032696.3419 - val_mean_squared_error: 37727032696.3419\n",
      "Epoch 29/200\n",
      "817/817 [==============================] - 0s 172us/step - loss: 37699926369.4492 - mean_squared_error: 37699926369.4492 - val_loss: 37395138799.2251 - val_mean_squared_error: 37395138799.2251\n",
      "Epoch 30/200\n",
      "817/817 [==============================] - 0s 171us/step - loss: 37571155581.9633 - mean_squared_error: 37571155581.9633 - val_loss: 37343887605.0598 - val_mean_squared_error: 37343887605.0598\n",
      "Epoch 31/200\n",
      "817/817 [==============================] - 0s 183us/step - loss: 37439339062.5214 - mean_squared_error: 37439339062.5214 - val_loss: 37281381454.7692 - val_mean_squared_error: 37281381454.7692\n",
      "Epoch 32/200\n",
      "817/817 [==============================] - 0s 173us/step - loss: 37289625660.1616 - mean_squared_error: 37289625660.1616 - val_loss: 37273128143.1339 - val_mean_squared_error: 37273128143.1339\n",
      "Epoch 33/200\n",
      "817/817 [==============================] - 0s 171us/step - loss: 37142383790.2179 - mean_squared_error: 37142383790.2179 - val_loss: 36985125826.7350 - val_mean_squared_error: 36985125826.7350\n",
      "Epoch 34/200\n",
      "817/817 [==============================] - 0s 196us/step - loss: 36977514443.3586 - mean_squared_error: 36977514443.3586 - val_loss: 36423696547.3732 - val_mean_squared_error: 36423696547.3732\n",
      "Epoch 35/200\n",
      "817/817 [==============================] - 0s 167us/step - loss: 36816704706.2717 - mean_squared_error: 36816704706.2717 - val_loss: 36495555966.1766 - val_mean_squared_error: 36495555966.1766\n",
      "Epoch 36/200\n",
      "817/817 [==============================] - 0s 175us/step - loss: 36643205403.2607 - mean_squared_error: 36643205403.2607 - val_loss: 36180235768.7066 - val_mean_squared_error: 36180235768.7066\n",
      "Epoch 37/200\n",
      "817/817 [==============================] - 0s 174us/step - loss: 36456082729.0477 - mean_squared_error: 36456082729.0477 - val_loss: 36420013998.3134 - val_mean_squared_error: 36420013998.3134\n",
      "Epoch 38/200\n",
      "817/817 [==============================] - 0s 162us/step - loss: 36271902113.3709 - mean_squared_error: 36271902113.3709 - val_loss: 36068241571.3732 - val_mean_squared_error: 36068241571.3732\n",
      "Epoch 39/200\n",
      "817/817 [==============================] - 0s 175us/step - loss: 36057165984.4308 - mean_squared_error: 36057165984.4308 - val_loss: 36245201666.1880 - val_mean_squared_error: 36245201666.1880\n",
      "Epoch 40/200\n",
      "817/817 [==============================] - 0s 182us/step - loss: 35859200300.8078 - mean_squared_error: 35859200300.8078 - val_loss: 35873987137.6410 - val_mean_squared_error: 35873987137.6410\n",
      "Epoch 41/200\n",
      "817/817 [==============================] - 0s 174us/step - loss: 35641262450.9963 - mean_squared_error: 35641262450.9963 - val_loss: 35537449216.7293 - val_mean_squared_error: 35537449216.7293\n",
      "Epoch 42/200\n",
      "817/817 [==============================] - 0s 175us/step - loss: 35424361871.8237 - mean_squared_error: 35424361871.8237 - val_loss: 35176874404.1026 - val_mean_squared_error: 35176874404.1026\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 43/200\n",
      "817/817 [==============================] - 0s 163us/step - loss: 35177674328.3623 - mean_squared_error: 35177674328.3623 - val_loss: 35009002831.4986 - val_mean_squared_error: 35009002831.4986\n",
      "Epoch 44/200\n",
      "817/817 [==============================] - 0s 154us/step - loss: 34937649327.4712 - mean_squared_error: 34937649327.4712 - val_loss: 34744632276.2393 - val_mean_squared_error: 34744632276.2393\n",
      "Epoch 45/200\n",
      "817/817 [==============================] - 0s 154us/step - loss: 34710120723.7405 - mean_squared_error: 34710120723.7405 - val_loss: 34380290555.6239 - val_mean_squared_error: 34380290555.6239\n",
      "Epoch 46/200\n",
      "817/817 [==============================] - 0s 159us/step - loss: 34440549011.2705 - mean_squared_error: 34440549011.2705 - val_loss: 34401110185.2080 - val_mean_squared_error: 34401110185.2080\n",
      "Epoch 47/200\n",
      "817/817 [==============================] - 0s 149us/step - loss: 34177159198.0808 - mean_squared_error: 34177159198.0808 - val_loss: 34067994151.3846 - val_mean_squared_error: 34067994151.3846\n",
      "Epoch 48/200\n",
      "817/817 [==============================] - 0s 154us/step - loss: 33885611508.0930 - mean_squared_error: 33885611508.0930 - val_loss: 34283201317.1966 - val_mean_squared_error: 34283201317.1966\n",
      "Epoch 49/200\n",
      "817/817 [==============================] - 0s 165us/step - loss: 33626073160.6952 - mean_squared_error: 33626073160.6952 - val_loss: 33240683371.2137 - val_mean_squared_error: 33240683371.2137\n",
      "Epoch 50/200\n",
      "817/817 [==============================] - 0s 150us/step - loss: 33346373669.6010 - mean_squared_error: 33346373669.6010 - val_loss: 33400555665.8689 - val_mean_squared_error: 33400555665.8689\n",
      "Epoch 51/200\n",
      "817/817 [==============================] - 0s 154us/step - loss: 33029032685.5129 - mean_squared_error: 33029032685.5129 - val_loss: 32716999186.9630 - val_mean_squared_error: 32716999186.9630\n",
      "Epoch 52/200\n",
      "817/817 [==============================] - 0s 151us/step - loss: 32717640765.4149 - mean_squared_error: 32717640765.4149 - val_loss: 32876797391.8632 - val_mean_squared_error: 32876797391.8632\n",
      "Epoch 53/200\n",
      "817/817 [==============================] - 0s 161us/step - loss: 32402788276.7980 - mean_squared_error: 32402788276.7980 - val_loss: 32371674599.2023 - val_mean_squared_error: 32371674599.2023\n",
      "Epoch 54/200\n",
      "817/817 [==============================] - 0s 157us/step - loss: 32095447167.8433 - mean_squared_error: 32095447167.8433 - val_loss: 31910406453.2422 - val_mean_squared_error: 31910406453.2422\n",
      "Epoch 55/200\n",
      "817/817 [==============================] - 0s 150us/step - loss: 31747894498.8592 - mean_squared_error: 31747894498.8592 - val_loss: 32136031322.4387 - val_mean_squared_error: 32136031322.4387\n",
      "Epoch 56/200\n",
      "817/817 [==============================] - 0s 149us/step - loss: 31457042524.7491 - mean_squared_error: 31457042524.7491 - val_loss: 31032445412.2849 - val_mean_squared_error: 31032445412.2849\n",
      "Epoch 57/200\n",
      "817/817 [==============================] - 0s 165us/step - loss: 31077280162.6242 - mean_squared_error: 31077280162.6242 - val_loss: 31555292285.4473 - val_mean_squared_error: 31555292285.4473\n",
      "Epoch 58/200\n",
      "817/817 [==============================] - 0s 157us/step - loss: 30733109949.8849 - mean_squared_error: 30733109949.8849 - val_loss: 30411307060.5128 - val_mean_squared_error: 30411307060.5128\n",
      "Epoch 59/200\n",
      "817/817 [==============================] - 0s 163us/step - loss: 30377342757.9143 - mean_squared_error: 30377342757.9143 - val_loss: 30752130972.8091 - val_mean_squared_error: 30752130972.8091\n",
      "Epoch 60/200\n",
      "817/817 [==============================] - 0s 154us/step - loss: 30043773414.3060 - mean_squared_error: 30043773414.3060 - val_loss: 31189123340.3989 - val_mean_squared_error: 31189123340.3989\n",
      "Epoch 61/200\n",
      "817/817 [==============================] - 0s 149us/step - loss: 29639812448.1958 - mean_squared_error: 29639812448.1958 - val_loss: 30588180602.5299 - val_mean_squared_error: 30588180602.5299\n",
      "Epoch 62/200\n",
      "817/817 [==============================] - 0s 148us/step - loss: 29280494971.7699 - mean_squared_error: 29280494971.7699 - val_loss: 30501093107.6011 - val_mean_squared_error: 30501093107.6011\n",
      "Epoch 63/200\n",
      "817/817 [==============================] - 0s 149us/step - loss: 28907291501.3562 - mean_squared_error: 28907291501.3562 - val_loss: 29014819492.8319 - val_mean_squared_error: 29014819492.8319\n",
      "Epoch 64/200\n",
      "817/817 [==============================] - 0s 151us/step - loss: 28518488648.0685 - mean_squared_error: 28518488648.0685 - val_loss: 29114658956.0342 - val_mean_squared_error: 29114658956.0342\n",
      "Epoch 65/200\n",
      "817/817 [==============================] - 0s 149us/step - loss: 28118483284.9155 - mean_squared_error: 28118483284.9155 - val_loss: 28494761482.2108 - val_mean_squared_error: 28494761482.2108\n",
      "Epoch 66/200\n",
      "817/817 [==============================] - 0s 171us/step - loss: 27722065300.8372 - mean_squared_error: 27722065300.8372 - val_loss: 27482636778.1197 - val_mean_squared_error: 27482636778.1197\n",
      "Epoch 67/200\n",
      "817/817 [==============================] - 0s 161us/step - loss: 27319789583.0404 - mean_squared_error: 27319789583.0404 - val_loss: 27103095221.6068 - val_mean_squared_error: 27103095221.6068\n",
      "Epoch 68/200\n",
      "817/817 [==============================] - 0s 182us/step - loss: 26964745357.6304 - mean_squared_error: 26964745357.6304 - val_loss: 27245545787.0769 - val_mean_squared_error: 27245545787.0769\n",
      "Epoch 69/200\n",
      "817/817 [==============================] - 0s 199us/step - loss: 26511326597.7968 - mean_squared_error: 26511326597.7968 - val_loss: 28224961976.5242 - val_mean_squared_error: 28224961976.5242\n",
      "Epoch 70/200\n",
      "817/817 [==============================] - 0s 176us/step - loss: 26125230440.9694 - mean_squared_error: 26125230440.9694 - val_loss: 26946543306.7578 - val_mean_squared_error: 26946543306.7578\n",
      "Epoch 71/200\n",
      "817/817 [==============================] - 0s 174us/step - loss: 25692545490.2521 - mean_squared_error: 25692545490.2521 - val_loss: 26260153516.1254 - val_mean_squared_error: 26260153516.1254\n",
      "Epoch 72/200\n",
      "817/817 [==============================] - 0s 174us/step - loss: 25241149745.8213 - mean_squared_error: 25241149745.8213 - val_loss: 25857531571.4188 - val_mean_squared_error: 25857531571.4188\n",
      "Epoch 73/200\n",
      "817/817 [==============================] - 0s 178us/step - loss: 24785883151.0404 - mean_squared_error: 24785883151.0404 - val_loss: 26270078112.4558 - val_mean_squared_error: 26270078112.4558\n",
      "Epoch 74/200\n",
      "817/817 [==============================] - 0s 171us/step - loss: 24379709412.4259 - mean_squared_error: 24379709412.4259 - val_loss: 23866075398.5641 - val_mean_squared_error: 23866075398.5641\n",
      "Epoch 75/200\n",
      "817/817 [==============================] - 0s 186us/step - loss: 23975599235.6034 - mean_squared_error: 23975599235.6034 - val_loss: 23890254664.2051 - val_mean_squared_error: 23890254664.2051\n",
      "Epoch 76/200\n",
      "817/817 [==============================] - 0s 184us/step - loss: 23530026378.8103 - mean_squared_error: 23530026378.8103 - val_loss: 23653390919.4758 - val_mean_squared_error: 23653390919.4758\n",
      "Epoch 77/200\n",
      "817/817 [==============================] - 0s 177us/step - loss: 23087016740.6610 - mean_squared_error: 23087016740.6610 - val_loss: 23357940120.4330 - val_mean_squared_error: 23357940120.4330\n",
      "Epoch 78/200\n",
      "817/817 [==============================] - 0s 187us/step - loss: 22627850093.3562 - mean_squared_error: 22627850093.3562 - val_loss: 23661087466.8490 - val_mean_squared_error: 23661087466.8490\n",
      "Epoch 79/200\n",
      "817/817 [==============================] - 0s 177us/step - loss: 22226893780.1322 - mean_squared_error: 22226893780.1322 - val_loss: 23286054270.1766 - val_mean_squared_error: 23286054270.1766\n",
      "Epoch 80/200\n",
      "817/817 [==============================] - 0s 192us/step - loss: 21820667314.9180 - mean_squared_error: 21820667314.9180 - val_loss: 22371892518.6553 - val_mean_squared_error: 22371892518.6553\n",
      "Epoch 81/200\n",
      "817/817 [==============================] - 0s 178us/step - loss: 21339423534.6879 - mean_squared_error: 21339423534.6879 - val_loss: 22653594486.8832 - val_mean_squared_error: 22653594486.8832\n",
      "Epoch 82/200\n",
      "817/817 [==============================] - 0s 185us/step - loss: 21091882654.5508 - mean_squared_error: 21091882654.5508 - val_loss: 21661098083.1909 - val_mean_squared_error: 21661098083.1909\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "Epoch 83/200\n",
      "817/817 [==============================] - 0s 183us/step - loss: 20545666603.2411 - mean_squared_error: 20545666603.2411 - val_loss: 22329766022.1994 - val_mean_squared_error: 22329766022.1994\n",
      "Epoch 84/200\n",
      "817/817 [==============================] - 0s 181us/step - loss: 20118875317.7381 - mean_squared_error: 20118875317.7381 - val_loss: 20747071890.5983 - val_mean_squared_error: 20747071890.5983\n",
      "Epoch 85/200\n",
      "817/817 [==============================] - 0s 170us/step - loss: 19612640156.9841 - mean_squared_error: 19612640156.9841 - val_loss: 20800543770.2564 - val_mean_squared_error: 20800543770.2564\n",
      "Epoch 86/200\n",
      "817/817 [==============================] - 0s 165us/step - loss: 19225021506.4284 - mean_squared_error: 19225021506.4284 - val_loss: 20867777042.9630 - val_mean_squared_error: 20867777042.9630\n",
      "Epoch 87/200\n",
      "817/817 [==============================] - 0s 167us/step - loss: 18761130905.2240 - mean_squared_error: 18761130905.2240 - val_loss: 19634819247.0427 - val_mean_squared_error: 19634819247.0427\n",
      "Epoch 88/200\n",
      "817/817 [==============================] - 0s 163us/step - loss: 18368198781.3366 - mean_squared_error: 18368198781.3366 - val_loss: 18253634737.9601 - val_mean_squared_error: 18253634737.9601\n",
      "Epoch 89/200\n",
      "817/817 [==============================] - 0s 172us/step - loss: 17852634963.0355 - mean_squared_error: 17852634963.0355 - val_loss: 18666968206.9516 - val_mean_squared_error: 18666968206.9516\n",
      "Epoch 90/200\n",
      "817/817 [==============================] - 0s 170us/step - loss: 17423038421.3856 - mean_squared_error: 17423038421.3856 - val_loss: 20230870631.5670 - val_mean_squared_error: 20230870631.5670\n",
      "Epoch 91/200\n",
      "817/817 [==============================] - 0s 177us/step - loss: 17008059112.4994 - mean_squared_error: 17008059112.4994 - val_loss: 18581642038.7009 - val_mean_squared_error: 18581642038.7009\n",
      "Epoch 92/200\n",
      "817/817 [==============================] - 0s 157us/step - loss: 16561644480.0783 - mean_squared_error: 16561644480.0783 - val_loss: 17717850666.3020 - val_mean_squared_error: 17717850666.3020\n",
      "Epoch 93/200\n",
      "817/817 [==============================] - 0s 167us/step - loss: 16164784660.6805 - mean_squared_error: 16164784660.6805 - val_loss: 17624764661.0598 - val_mean_squared_error: 17624764661.0598\n",
      "Epoch 94/200\n",
      "817/817 [==============================] - 0s 161us/step - loss: 15807637942.6781 - mean_squared_error: 15807637942.6781 - val_loss: 15860624958.7236 - val_mean_squared_error: 15860624958.7236\n",
      "Epoch 95/200\n",
      "817/817 [==============================] - 0s 168us/step - loss: 15231055160.0881 - mean_squared_error: 15231055160.0881 - val_loss: 15829250141.3561 - val_mean_squared_error: 15829250141.3561\n",
      "Epoch 96/200\n",
      "817/817 [==============================] - 0s 178us/step - loss: 14787016984.7540 - mean_squared_error: 14787016984.7540 - val_loss: 16985777767.5670 - val_mean_squared_error: 16985777767.5670\n",
      "Epoch 97/200\n",
      "817/817 [==============================] - 0s 160us/step - loss: 14395589464.0490 - mean_squared_error: 14395589464.0490 - val_loss: 16064143138.2792 - val_mean_squared_error: 16064143138.2792\n",
      "Epoch 98/200\n",
      "817/817 [==============================] - 0s 159us/step - loss: 14055862352.2154 - mean_squared_error: 14055862352.2154 - val_loss: 15212225801.4815 - val_mean_squared_error: 15212225801.4815\n",
      "Epoch 99/200\n",
      "817/817 [==============================] - 0s 160us/step - loss: 13603063167.5300 - mean_squared_error: 13603063167.5300 - val_loss: 15121374706.8718 - val_mean_squared_error: 15121374706.8718\n",
      "Epoch 100/200\n",
      "817/817 [==============================] - 0s 166us/step - loss: 13170951340.9645 - mean_squared_error: 13170951340.9645 - val_loss: 12701813914.6211 - val_mean_squared_error: 12701813914.6211\n",
      "Epoch 101/200\n",
      "817/817 [==============================] - 0s 169us/step - loss: 12801509975.7356 - mean_squared_error: 12801509975.7356 - val_loss: 14760894989.1282 - val_mean_squared_error: 14760894989.1282\n",
      "Epoch 102/200\n",
      "817/817 [==============================] - 0s 178us/step - loss: 12283487107.9168 - mean_squared_error: 12283487107.9168 - val_loss: 11826449215.4530 - val_mean_squared_error: 11826449215.4530\n",
      "Epoch 103/200\n",
      "817/817 [==============================] - 0s 180us/step - loss: 11975277935.2362 - mean_squared_error: 11975277935.2362 - val_loss: 12950089850.5299 - val_mean_squared_error: 12950089850.5299\n",
      "Epoch 104/200\n",
      "817/817 [==============================] - 0s 172us/step - loss: 11596947722.9670 - mean_squared_error: 11596947722.9670 - val_loss: 11701648229.3789 - val_mean_squared_error: 11701648229.3789\n",
      "Epoch 105/200\n",
      "817/817 [==============================] - 0s 156us/step - loss: 11180634616.4798 - mean_squared_error: 11180634616.4798 - val_loss: 12304542057.7550 - val_mean_squared_error: 12304542057.7550\n",
      "Epoch 106/200\n",
      "817/817 [==============================] - 0s 179us/step - loss: 10870021452.1420 - mean_squared_error: 10870021452.1420 - val_loss: 11485181397.6980 - val_mean_squared_error: 11485181397.6980\n",
      "Epoch 107/200\n",
      "817/817 [==============================] - 0s 169us/step - loss: 10519283129.1848 - mean_squared_error: 10519283129.1848 - val_loss: 10952792428.6724 - val_mean_squared_error: 10952792428.6724\n",
      "Epoch 108/200\n",
      "817/817 [==============================] - 0s 175us/step - loss: 10048517780.5239 - mean_squared_error: 10048517780.5239 - val_loss: 11977178161.5954 - val_mean_squared_error: 11977178161.5954\n",
      "Epoch 109/200\n",
      "817/817 [==============================] - 0s 172us/step - loss: 9764891768.3231 - mean_squared_error: 9764891768.3231 - val_loss: 10380389163.0313 - val_mean_squared_error: 10380389163.0313\n",
      "Epoch 110/200\n",
      "817/817 [==============================] - 0s 174us/step - loss: 9385006077.4933 - mean_squared_error: 9385006077.4933 - val_loss: 10212533697.2764 - val_mean_squared_error: 10212533697.2764\n",
      "Epoch 111/200\n",
      "817/817 [==============================] - 0s 169us/step - loss: 9111643797.7772 - mean_squared_error: 9111643797.7772 - val_loss: 10573937521.0484 - val_mean_squared_error: 10573937521.0484\n",
      "Epoch 112/200\n",
      "817/817 [==============================] - 0s 177us/step - loss: 8654136708.5435 - mean_squared_error: 8654136708.5435 - val_loss: 9784192513.4587 - val_mean_squared_error: 9784192513.4587\n",
      "Epoch 113/200\n",
      "817/817 [==============================] - 0s 186us/step - loss: 8360954245.1701 - mean_squared_error: 8360954245.1701 - val_loss: 9600784751.5897 - val_mean_squared_error: 9600784751.5897\n",
      "Epoch 114/200\n",
      "817/817 [==============================] - 0s 189us/step - loss: 8030866196.9939 - mean_squared_error: 8030866196.9939 - val_loss: 9841509352.6610 - val_mean_squared_error: 9841509352.6610\n",
      "Epoch 115/200\n",
      "817/817 [==============================] - 0s 180us/step - loss: 7750234855.2460 - mean_squared_error: 7750234855.2460 - val_loss: 9710083897.6182 - val_mean_squared_error: 9710083897.6182\n",
      "Epoch 116/200\n",
      "817/817 [==============================] - 0s 184us/step - loss: 7422905704.9694 - mean_squared_error: 7422905704.9694 - val_loss: 10416913145.4359 - val_mean_squared_error: 10416913145.4359\n",
      "Epoch 117/200\n",
      "817/817 [==============================] - 0s 181us/step - loss: 7108006533.4835 - mean_squared_error: 7108006533.4835 - val_loss: 9629056212.9687 - val_mean_squared_error: 9629056212.9687\n",
      "Epoch 118/200\n",
      "817/817 [==============================] - 0s 182us/step - loss: 6783282409.7528 - mean_squared_error: 6783282409.7528 - val_loss: 8798847938.7350 - val_mean_squared_error: 8798847938.7350\n",
      "Epoch 119/200\n",
      "817/817 [==============================] - 0s 196us/step - loss: 6424092442.0073 - mean_squared_error: 6424092442.0073 - val_loss: 9784226740.1481 - val_mean_squared_error: 9784226740.1481\n",
      "Epoch 120/200\n",
      "817/817 [==============================] - 0s 199us/step - loss: 6172933019.7307 - mean_squared_error: 6172933019.7307 - val_loss: 8716737731.4644 - val_mean_squared_error: 8716737731.4644\n",
      "Epoch 121/200\n",
      "817/817 [==============================] - 0s 171us/step - loss: 5941812208.3329 - mean_squared_error: 5941812203.3195 - val_loss: 8424587605.3333 - val_mean_squared_error: 8424587605.3333\n",
      "Epoch 122/200\n",
      "817/817 [==============================] - 0s 174us/step - loss: 5693514743.2264 - mean_squared_error: 5693514743.2264 - val_loss: 7933865768.1140 - val_mean_squared_error: 7933865768.1140\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 123/200\n",
      "817/817 [==============================] - 0s 168us/step - loss: 5473896951.8531 - mean_squared_error: 5473896951.8531 - val_loss: 7458467306.1197 - val_mean_squared_error: 7458467306.1197\n",
      "Epoch 124/200\n",
      "817/817 [==============================] - 0s 157us/step - loss: 5315242968.8323 - mean_squared_error: 5315242958.8054 - val_loss: 7800586817.6410 - val_mean_squared_error: 7800586817.6410\n",
      "Epoch 125/200\n",
      "817/817 [==============================] - 0s 156us/step - loss: 5020212278.5214 - mean_squared_error: 5020212265.8311 - val_loss: 7971176512.1823 - val_mean_squared_error: 7971176512.1823\n",
      "Epoch 126/200\n",
      "817/817 [==============================] - 0s 159us/step - loss: 4840313202.9963 - mean_squared_error: 4840313178.9474 - val_loss: 6619211897.0712 - val_mean_squared_error: 6619211873.7322\n",
      "Epoch 127/200\n",
      "817/817 [==============================] - 0s 152us/step - loss: 4430238272.5483 - mean_squared_error: 4430238147.2118 - val_loss: 5711287907.1909 - val_mean_squared_error: 5711287813.8348\n",
      "Epoch 128/200\n",
      "817/817 [==============================] - 0s 157us/step - loss: 4360306232.4015 - mean_squared_error: 4360306071.6573 - val_loss: 5217824563.7835 - val_mean_squared_error: 5217824400.4103\n",
      "Epoch 129/200\n",
      "817/817 [==============================] - 0s 151us/step - loss: 4188821280.5092 - mean_squared_error: 4188821176.2448 - val_loss: 5477269861.3789 - val_mean_squared_error: 5477269744.6838\n",
      "Epoch 130/200\n",
      "817/817 [==============================] - 0s 151us/step - loss: 4153152100.8960 - mean_squared_error: 4153151947.9853 - val_loss: 6204314902.6097 - val_mean_squared_error: 6204314809.2536\n",
      "Epoch 131/200\n",
      "817/817 [==============================] - 0s 151us/step - loss: 4077590817.5275 - mean_squared_error: 4077590694.3843 - val_loss: 5317964940.0342 - val_mean_squared_error: 5317964776.6610\n",
      "Epoch 132/200\n",
      "817/817 [==============================] - 0s 177us/step - loss: 3661091517.8849 - mean_squared_error: 3661091369.9878 - val_loss: 4762905007.7721 - val_mean_squared_error: 4762904844.3989\n",
      "Epoch 133/200\n",
      "817/817 [==============================] - 0s 153us/step - loss: 3439974640.9596 - mean_squared_error: 3439974460.1616 - val_loss: 4608206684.6268 - val_mean_squared_error: 4608206521.2536\n",
      "Epoch 134/200\n",
      "817/817 [==============================] - 0s 191us/step - loss: 3138719061.3856 - mean_squared_error: 3138718893.2778 - val_loss: 4753696457.2991 - val_mean_squared_error: 4753696293.9259\n",
      "Epoch 135/200\n",
      "817/817 [==============================] - 0s 179us/step - loss: 2937186163.3097 - mean_squared_error: 2937185967.4712 - val_loss: 4765713593.2536 - val_mean_squared_error: 4765713429.8803\n",
      "Epoch 136/200\n",
      "817/817 [==============================] - 0s 174us/step - loss: 2898145332.9547 - mean_squared_error: 2898145142.1297 - val_loss: 4599842474.6667 - val_mean_squared_error: 4599842311.2934\n",
      "Epoch 137/200\n",
      "817/817 [==============================] - 0s 161us/step - loss: 3005507931.2999 - mean_squared_error: 3005507804.4749 - val_loss: 6666690713.1624 - val_mean_squared_error: 6666690666.4843\n",
      "Epoch 138/200\n",
      "817/817 [==============================] - 0s 165us/step - loss: 2634296905.3219 - mean_squared_error: 2634296729.8507 - val_loss: 4400162048.7293 - val_mean_squared_error: 4400161897.0256\n",
      "Epoch 139/200\n",
      "817/817 [==============================] - 0s 167us/step - loss: 2522677942.9914 - mean_squared_error: 2522677744.9596 - val_loss: 4417248719.8632 - val_mean_squared_error: 4417248568.1595\n",
      "Epoch 140/200\n",
      "817/817 [==============================] - 0s 163us/step - loss: 2227300483.1334 - mean_squared_error: 2227300287.2950 - val_loss: 4001842460.4444 - val_mean_squared_error: 4001842297.0712\n",
      "Epoch 141/200\n",
      "817/817 [==============================] - 0s 176us/step - loss: 2134008916.1322 - mean_squared_error: 2134008682.6928 - val_loss: 3290893506.0057 - val_mean_squared_error: 3290893273.3447\n",
      "Epoch 142/200\n",
      "817/817 [==============================] - 0s 167us/step - loss: 2017727149.5912 - mean_squared_error: 2017726916.4651 - val_loss: 2869986688.3647 - val_mean_squared_error: 2869986455.7037\n",
      "Epoch 143/200\n",
      "817/817 [==============================] - 0s 166us/step - loss: 2106790708.9547 - mean_squared_error: 2106790488.0490 - val_loss: 3019660908.6724 - val_mean_squared_error: 3019660681.8462\n",
      "Epoch 144/200\n",
      "817/817 [==============================] - 0s 160us/step - loss: 1879298401.7625 - mean_squared_error: 1879298178.3501 - val_loss: 2296355934.0855 - val_mean_squared_error: 2296355730.5983\n",
      "Epoch 145/200\n",
      "817/817 [==============================] - 0s 169us/step - loss: 1712407932.2399 - mean_squared_error: 1712407693.7870 - val_loss: 4369317917.1738 - val_mean_squared_error: 4369317753.8006\n",
      "Epoch 146/200\n",
      "817/817 [==============================] - 0s 180us/step - loss: 1689605019.7111 - mean_squared_error: 1689604787.6034 - val_loss: 3983959077.9259 - val_mean_squared_error: 3983958914.5527\n",
      "Epoch 147/200\n",
      "817/817 [==============================] - 0s 179us/step - loss: 1821618578.0367 - mean_squared_error: 1821618370.9963 - val_loss: 3295860121.8917 - val_mean_squared_error: 3295859912.5698\n",
      "Epoch 148/200\n",
      "817/817 [==============================] - 0s 169us/step - loss: 1555779906.1151 - mean_squared_error: 1555779672.6756 - val_loss: 3402661074.7806 - val_mean_squared_error: 3402660865.4587\n",
      "Epoch 149/200\n",
      "817/817 [==============================] - 0s 177us/step - loss: 1453772987.3782 - mean_squared_error: 1453772768.0392 - val_loss: 2544213183.8177 - val_mean_squared_error: 2544212968.6610\n",
      "Epoch 150/200\n",
      "817/817 [==============================] - 0s 190us/step - loss: 1446410675.5055 - mean_squared_error: 1446410457.4982 - val_loss: 2414009139.7835 - val_mean_squared_error: 2414008930.4615\n",
      "Epoch 151/200\n",
      "817/817 [==============================] - 0s 191us/step - loss: 1228228594.8397 - mean_squared_error: 1228228363.2803 - val_loss: 2798573767.1111 - val_mean_squared_error: 2798573540.2849\n",
      "Epoch 152/200\n",
      "817/817 [==============================] - 0s 178us/step - loss: 1285529393.7430 - mean_squared_error: 1285529172.9155 - val_loss: 2341183099.2593 - val_mean_squared_error: 2341182895.7721\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1e4f3183550>"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled, Y_train, epochs=200, batch_size=32,  verbose=1, validation_data=(X_valid_scaled, Y_valid), callbacks=callbacks)"
   ]
  }
 ],
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rf_xgb.ipynb