emission_detect_ai/使用lightgbm对机组特征建模.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import lightgbm as lgb\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error, r2_score\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "#新增加的两行\n",
    "from pylab import mpl\n",
    "# 设置显示中文字体\n",
    "mpl.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "\n",
    "mpl.rcParams[\"axes.unicode_minus\"] = False"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "ori_data = pd.read_csv('data/unit_train_data.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "ori_data['day_of_week'] = ori_data.days.apply(lambda x: dt.datetime.strptime(x, '%Y-%m-%d').weekday() + 1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "data = ori_data.drop(columns=['days', 'day_of_year', '企业名称'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "           r_O2  temperature  发电量（万千瓦时）  供热量（吉焦）       c_smoke         c_NO2  \\\n0      9.900000    51.250000    15.6796  6536.83  2.872405e+04  3.979907e+06   \n1      9.400000    50.679167    13.3984  2484.64  2.261807e+04  2.639425e+06   \n2      8.550000    52.808333    13.4023  3020.83  1.817677e+04  3.231672e+06   \n3     10.202083    48.854167    12.4765  5599.23  9.161746e+04  2.243444e+06   \n4     11.497917    45.783333    13.4414  4702.65  2.995257e+05  3.580802e+06   \n...         ...          ...        ...      ...           ...           ...   \n1486   6.329167    44.741667   467.1060     0.00  1.020390e+06  1.173919e+07   \n1487   6.183333    45.587500   504.9000     0.00  1.169393e+06  1.393048e+07   \n1488   6.425000    45.545833   462.8220     0.00  1.124243e+06  1.267043e+07   \n1489   6.162500    45.175000   528.9600     0.00  1.296293e+06  1.442430e+07   \n1490   5.570833    46.100000   672.1800     0.00  1.554891e+06  1.758048e+07   \n\n             c_SO2           flow  燃料消耗量（吨）       生产设备类型     燃料类型  \\\n0     7.665088e+05  162345.192917     323.0  高温高压循环流化床锅炉  中高挥发分烟煤   \n1     5.183845e+05  140175.330833     218.0  高温高压循环流化床锅炉  中高挥发分烟煤   \n2     9.870800e+05  154686.184167     212.0  高温高压循环流化床锅炉  中高挥发分烟煤   \n3     2.880779e+05  120345.545833     223.0  高温高压循环流化床锅炉  中高挥发分烟煤   \n4     5.500482e+04  162533.103542     243.0  高温高压循环流化床锅炉  中高挥发分烟煤   \n...            ...            ...       ...          ...      ...   \n1486  1.101318e+07  836100.000000    2401.0         煤粉锅炉     一般烟煤   \n1487  1.335698e+07  895515.000000    2611.0         煤粉锅炉     一般烟煤   \n1488  1.129934e+07  837945.000000    2846.0         煤粉锅炉     一般烟煤   \n1489  1.434195e+07  915030.000000    2981.0         煤粉锅炉     一般烟煤   \n1490  1.593381e+07  992220.000000    3560.0         煤粉锅炉     一般烟煤   \n\n      低位发热量（GJ/t） 汽轮机类型     冷却方式   额定蒸发量 压力参数   单机容量  week_of_year  \\\n0          20.501   背压式  水冷-闭式循环   230.0   高压   30.0            39   \n1          20.501   背压式  水冷-闭式循环   230.0   高压   30.0            39   \n2          20.501   背压式  水冷-闭式循环   230.0   高压   30.0            39   \n3          20.501   背压式  水冷-闭式循环   230.0   高压   30.0            39   \n4          20.501   背压式  水冷-闭式循环   230.0   高压   30.0            39   \n...           ...   ...      ...     ...  ...    ...           ...   \n1486       14.682   抽凝式  水冷-闭式循环  1172.0  超临界  350.0            20   \n1487       14.682   抽凝式  水冷-闭式循环  1172.0  超临界  350.0            21   \n1488       14.682   抽凝式  水冷-闭式循环  1172.0  超临界  350.0            21   \n1489       14.682   抽凝式  水冷-闭式循环  1172.0  超临界  350.0            21   \n1490       14.682   抽凝式  水冷-闭式循环  1172.0  超临界  350.0            21   \n\n      day_of_week  \n0               1  \n1               2  \n2               3  \n3               4  \n4               5  \n...           ...  \n1486            5  \n1487            6  \n1488            7  \n1489            1  \n1490            2  \n\n[1254 rows x 19 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>r_O2</th>\n      <th>temperature</th>\n      <th>发电量（万千瓦时）</th>\n      <th>供热量（吉焦）</th>\n      <th>c_smoke</th>\n      <th>c_NO2</th>\n      <th>c_SO2</th>\n      <th>flow</th>\n      <th>燃料消耗量（吨）</th>\n      <th>生产设备类型</th>\n      <th>燃料类型</th>\n      <th>低位发热量（GJ/t）</th>\n      <th>汽轮机类型</th>\n      <th>冷却方式</th>\n      <th>额定蒸发量</th>\n      <th>压力参数</th>\n      <th>单机容量</th>\n      <th>week_of_year</th>\n      <th>day_of_week</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>9.900000</td>\n      <td>51.250000</td>\n      <td>15.6796</td>\n      <td>6536.83</td>\n      <td>2.872405e+04</td>\n      <td>3.979907e+06</td>\n      <td>7.665088e+05</td>\n      <td>162345.192917</td>\n      <td>323.0</td>\n      <td>高温高压循环流化床锅炉</td>\n      <td>中高挥发分烟煤</td>\n      <td>20.501</td>\n      <td>背压式</td>\n      <td>水冷-闭式循环</td>\n      <td>230.0</td>\n      <td>高压</td>\n      <td>30.0</td>\n      <td>39</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>9.400000</td>\n      <td>50.679167</td>\n      <td>13.3984</td>\n      <td>2484.64</td>\n      <td>2.261807e+04</td>\n      <td>2.639425e+06</td>\n      <td>5.183845e+05</td>\n      <td>140175.330833</td>\n      <td>218.0</td>\n      <td>高温高压循环流化床锅炉</td>\n      <td>中高挥发分烟煤</td>\n      <td>20.501</td>\n      <td>背压式</td>\n      <td>水冷-闭式循环</td>\n      <td>230.0</td>\n      <td>高压</td>\n      <td>30.0</td>\n      <td>39</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8.550000</td>\n      <td>52.808333</td>\n      <td>13.4023</td>\n      <td>3020.83</td>\n      <td>1.817677e+04</td>\n      <td>3.231672e+06</td>\n      <td>9.870800e+05</td>\n      <td>154686.184167</td>\n      <td>212.0</td>\n      <td>高温高压循环流化床锅炉</td>\n      <td>中高挥发分烟煤</td>\n      <td>20.501</td>\n      <td>背压式</td>\n      <td>水冷-闭式循环</td>\n      <td>230.0</td>\n      <td>高压</td>\n      <td>30.0</td>\n      <td>39</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10.202083</td>\n      <td>48.854167</td>\n      <td>12.4765</td>\n      <td>5599.23</td>\n      <td>9.161746e+04</td>\n      <td>2.243444e+06</td>\n      <td>2.880779e+05</td>\n      <td>120345.545833</td>\n      <td>223.0</td>\n      <td>高温高压循环流化床锅炉</td>\n      <td>中高挥发分烟煤</td>\n      <td>20.501</td>\n      <td>背压式</td>\n      <td>水冷-闭式循环</td>\n      <td>230.0</td>\n      <td>高压</td>\n      <td>30.0</td>\n      <td>39</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11.497917</td>\n      <td>45.783333</td>\n      <td>13.4414</td>\n      <td>4702.65</td>\n      <td>2.995257e+05</td>\n      <td>3.580802e+06</td>\n      <td>5.500482e+04</td>\n      <td>162533.103542</td>\n      <td>243.0</td>\n      <td>高温高压循环流化床锅炉</td>\n      <td>中高挥发分烟煤</td>\n      <td>20.501</td>\n      <td>背压式</td>\n      <td>水冷-闭式循环</td>\n      <td>230.0</td>\n      <td>高压</td>\n      <td>30.0</td>\n      <td>39</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>.
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[data['燃料消耗量（吨）']>0].dropna()\n",
    "data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "['r_O2',\n 'temperature',\n '发电量（万千瓦时）',\n '供热量（吉焦）',\n 'c_smoke',\n 'c_NO2',\n 'c_SO2',\n 'flow',\n '燃料消耗量（吨）',\n '低位发热量（GJ/t）',\n '额定蒸发量',\n '单机容量',\n 'week_of_year']"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_cols = ['生产设备类型', '燃料类型', '汽轮机类型', '冷却方式', '压力参数', 'day_of_week']\n",
    "num_cols = [x for x in data.columns if x not in object_cols]\n",
    "num_cols"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "for col in num_cols:\n",
    "    minus_index = data[data[col]<0].index.values\n",
    "    data.loc[minus_index, col] = 0\n",
    "    data[col] = np.log1p(data[col])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2000x2000 with 19 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 20))\n",
    "for index, col in enumerate(object_cols+num_cols):\n",
    "    plt.subplot(4,5,index+1)\n",
    "    plt.title(col)\n",
    "    # if col in object_cols:\n",
    "    #     plt.bar(data['燃料类型'].value_counts().index, data['燃料类型'].value_counts().values)\n",
    "    # else:\n",
    "    plt.hist(data[col])\n",
    "plt.savefig('figure/特征分析.png')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "use_data = pd.get_dummies(data.drop(columns=['r_O2', 'temperature']), columns=object_cols)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "['day_of_week_3',\n '汽轮机类型_背压式',\n 'day_of_week_6',\n 'day_of_week_5',\n '额定蒸发量',\n '低位发热量（GJ/t）',\n '生产设备类型_煤粉锅炉',\n 'day_of_week_7',\n 'day_of_week_1',\n 'day_of_week_2',\n 'c_SO2',\n '燃料类型_低挥发分烟煤',\n 'day_of_week_4',\n '生产设备类型_高温高压循环流化床锅炉',\n '燃料类型_中高挥发分烟煤',\n '单机容量',\n '压力参数_超临界',\n 'c_smoke',\n 'flow',\n 'week_of_year',\n '燃料类型_一般烟煤',\n '压力参数_超超临界',\n '汽轮机类型_抽凝式',\n '冷却方式_水冷-闭式循环',\n '压力参数_高压',\n 'c_NO2']"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# feature_cols = [x for x in use_data.columns if x != '燃料消耗量（吨）']\n",
    "feature_cols = [x for x in use_data.columns if x not in ['发电量（万千瓦时）', '供热量（吉焦）', '燃料消耗量（吨）']]\n",
    "np.random.shuffle(feature_cols)\n",
    "feature_cols"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "train_data, valid = train_test_split(use_data, test_size=0.2, shuffle=True, random_state=666)\n",
    "valid_data, test_data = train_test_split(valid, test_size=0.5, shuffle=True, random_state=666)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "X_train, Y_train = train_data[feature_cols], train_data['燃料消耗量（吨）']\n",
    "X_valid, Y_valid = valid_data[feature_cols], valid_data['燃料消耗量（吨）']\n",
    "X_test, Y_test = test_data[feature_cols], test_data['燃料消耗量（吨）']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "lgb_train = lgb.Dataset(X_train, Y_train)\n",
    "lgb_eval = lgb.Dataset(X_valid, Y_valid)\n",
    "lgb_test = lgb.Dataset(X_test, Y_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "params = {\n",
    "    'task': 'train',\n",
    "    'boosting_type': 'gbdt',  # 设置提升类型\n",
    "    'objective': 'regression_l2',  # 目标函数\n",
    "    'metric': {'rmse'},  # 评估函数\n",
    "    'max_depth': 10,\n",
    "    'num_leaves': 31,  # 叶子节点数\n",
    "    'learning_rate': 0.005,  # 学习速率\n",
    "    'feature_fraction': 0.9,  # 建树的特征选择比例\n",
    "    'bagging_fraction': 0.8,  # 建树的样本采样比例\n",
    "    'bagging_freq': 5,  # k 意味着每 k 次迭代执行bagging\n",
    "    'verbose': -1  # <0 显示致命的, =0 显示错误 (警告), >0 显示信息\n",
    "}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's rmse: 0.619783\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[2]\tvalid_0's rmse: 0.617031\n",
      "[3]\tvalid_0's rmse: 0.614294\n",
      "[4]\tvalid_0's rmse: 0.611572\n",
      "[5]\tvalid_0's rmse: 0.608865\n",
      "[6]\tvalid_0's rmse: 0.606158\n",
      "[7]\tvalid_0's rmse: 0.603466\n",
      "[8]\tvalid_0's rmse: 0.600814\n",
      "[9]\tvalid_0's rmse: 0.598151\n",
      "[10]\tvalid_0's rmse: 0.595503\n",
      "[11]\tvalid_0's rmse: 0.592866\n",
      "[12]\tvalid_0's rmse: 0.590287\n",
      "[13]\tvalid_0's rmse: 0.587678\n",
      "[14]\tvalid_0's rmse: 0.585082\n",
      "[15]\tvalid_0's rmse: 0.582545\n",
      "[16]\tvalid_0's rmse: 0.579979\n",
      "[17]\tvalid_0's rmse: 0.577427\n",
      "[18]\tvalid_0's rmse: 0.574889\n",
      "[19]\tvalid_0's rmse: 0.572365\n",
      "[20]\tvalid_0's rmse: 0.569854\n",
      "[21]\tvalid_0's rmse: 0.567376\n",
      "[22]\tvalid_0's rmse: 0.564911\n",
      "[23]\tvalid_0's rmse: 0.562463\n",
      "[24]\tvalid_0's rmse: 0.560027\n",
      "[25]\tvalid_0's rmse: 0.557602\n",
      "[26]\tvalid_0's rmse: 0.555143\n",
      "[27]\tvalid_0's rmse: 0.552698\n",
      "[28]\tvalid_0's rmse: 0.550265\n",
      "[29]\tvalid_0's rmse: 0.547893\n",
      "[30]\tvalid_0's rmse: 0.545487\n",
      "[31]\tvalid_0's rmse: 0.543116\n",
      "[32]\tvalid_0's rmse: 0.54079\n",
      "[33]\tvalid_0's rmse: 0.538434\n",
      "[34]\tvalid_0's rmse: 0.536101\n",
      "[35]\tvalid_0's rmse: 0.533775\n",
      "[36]\tvalid_0's rmse: 0.531488\n",
      "[37]\tvalid_0's rmse: 0.529221\n",
      "[38]\tvalid_0's rmse: 0.52696\n",
      "[39]\tvalid_0's rmse: 0.524734\n",
      "[40]\tvalid_0's rmse: 0.522497\n",
      "[41]\tvalid_0's rmse: 0.52024\n",
      "[42]\tvalid_0's rmse: 0.517995\n",
      "[43]\tvalid_0's rmse: 0.515746\n",
      "[44]\tvalid_0's rmse: 0.513525\n",
      "[45]\tvalid_0's rmse: 0.511317\n",
      "[46]\tvalid_0's rmse: 0.509117\n",
      "[47]\tvalid_0's rmse: 0.50695\n",
      "[48]\tvalid_0's rmse: 0.504751\n",
      "[49]\tvalid_0's rmse: 0.502583\n",
      "[50]\tvalid_0's rmse: 0.500458\n",
      "[51]\tvalid_0's rmse: 0.498381\n",
      "[52]\tvalid_0's rmse: 0.496333\n",
      "[53]\tvalid_0's rmse: 0.494242\n",
      "[54]\tvalid_0's rmse: 0.492194\n",
      "[55]\tvalid_0's rmse: 0.49018\n",
      "[56]\tvalid_0's rmse: 0.488205\n",
      "[57]\tvalid_0's rmse: 0.486212\n",
      "[58]\tvalid_0's rmse: 0.484243\n",
      "[59]\tvalid_0's rmse: 0.48227\n",
      "[60]\tvalid_0's rmse: 0.48031\n",
      "[61]\tvalid_0's rmse: 0.478283\n",
      "[62]\tvalid_0's rmse: 0.476267\n",
      "[63]\tvalid_0's rmse: 0.474262\n",
      "[64]\tvalid_0's rmse: 0.472269\n",
      "[65]\tvalid_0's rmse: 0.470281\n",
      "[66]\tvalid_0's rmse: 0.468313\n",
      "[67]\tvalid_0's rmse: 0.466414\n",
      "[68]\tvalid_0's rmse: 0.464465\n",
      "[69]\tvalid_0's rmse: 0.462515\n",
      "[70]\tvalid_0's rmse: 0.460588\n",
      "[71]\tvalid_0's rmse: 0.458701\n",
      "[72]\tvalid_0's rmse: 0.45682\n",
      "[73]\tvalid_0's rmse: 0.454947\n",
      "[74]\tvalid_0's rmse: 0.453091\n",
      "[75]\tvalid_0's rmse: 0.451269\n",
      "[76]\tvalid_0's rmse: 0.449393\n",
      "[77]\tvalid_0's rmse: 0.447497\n",
      "[78]\tvalid_0's rmse: 0.445613\n",
      "[79]\tvalid_0's rmse: 0.443803\n",
      "[80]\tvalid_0's rmse: 0.442004\n",
      "[81]\tvalid_0's rmse: 0.440201\n",
      "[82]\tvalid_0's rmse: 0.438408\n",
      "[83]\tvalid_0's rmse: 0.436629\n",
      "[84]\tvalid_0's rmse: 0.43486\n",
      "[85]\tvalid_0's rmse: 0.433098\n",
      "[86]\tvalid_0's rmse: 0.431378\n",
      "[87]\tvalid_0's rmse: 0.429625\n",
      "[88]\tvalid_0's rmse: 0.427891\n",
      "[89]\tvalid_0's rmse: 0.426163\n",
      "[90]\tvalid_0's rmse: 0.424432\n",
      "[91]\tvalid_0's rmse: 0.422746\n",
      "[92]\tvalid_0's rmse: 0.421041\n",
      "[93]\tvalid_0's rmse: 0.419345\n",
      "[94]\tvalid_0's rmse: 0.41765\n",
      "[95]\tvalid_0's rmse: 0.415965\n",
      "[96]\tvalid_0's rmse: 0.414284\n",
      "[97]\tvalid_0's rmse: 0.412607\n",
      "[98]\tvalid_0's rmse: 0.410945\n",
      "[99]\tvalid_0's rmse: 0.409287\n",
      "[100]\tvalid_0's rmse: 0.407644\n",
      "[101]\tvalid_0's rmse: 0.405998\n",
      "[102]\tvalid_0's rmse: 0.404362\n",
      "[103]\tvalid_0's rmse: 0.402735\n",
      "[104]\tvalid_0's rmse: 0.40113\n",
      "[105]\tvalid_0's rmse: 0.399534\n",
      "[106]\tvalid_0's rmse: 0.397949\n",
      "[107]\tvalid_0's rmse: 0.396416\n",
      "[108]\tvalid_0's rmse: 0.394846\n",
      "[109]\tvalid_0's rmse: 0.393286\n",
      "[110]\tvalid_0's rmse: 0.391743\n",
      "[111]\tvalid_0's rmse: 0.390222\n",
      "[112]\tvalid_0's rmse: 0.38871\n",
      "[113]\tvalid_0's rmse: 0.387208\n",
      "[114]\tvalid_0's rmse: 0.385714\n",
      "[115]\tvalid_0's rmse: 0.384217\n",
      "[116]\tvalid_0's rmse: 0.382661\n",
      "[117]\tvalid_0's rmse: 0.381114\n",
      "[118]\tvalid_0's rmse: 0.379577\n",
      "[119]\tvalid_0's rmse: 0.378049\n",
      "[120]\tvalid_0's rmse: 0.376538\n",
      "[121]\tvalid_0's rmse: 0.375084\n",
      "[122]\tvalid_0's rmse: 0.373638\n",
      "[123]\tvalid_0's rmse: 0.372202\n",
      "[124]\tvalid_0's rmse: 0.370773\n",
      "[125]\tvalid_0's rmse: 0.369354\n",
      "[126]\tvalid_0's rmse: 0.36794\n",
      "[127]\tvalid_0's rmse: 0.366524\n",
      "[128]\tvalid_0's rmse: 0.365117\n",
      "[129]\tvalid_0's rmse: 0.363727\n",
      "[130]\tvalid_0's rmse: 0.36234\n",
      "[131]\tvalid_0's rmse: 0.36101\n",
      "[132]\tvalid_0's rmse: 0.359689\n",
      "[133]\tvalid_0's rmse: 0.358416\n",
      "[134]\tvalid_0's rmse: 0.357111\n",
      "[135]\tvalid_0's rmse: 0.355822\n",
      "[136]\tvalid_0's rmse: 0.35454\n",
      "[137]\tvalid_0's rmse: 0.353265\n",
      "[138]\tvalid_0's rmse: 0.351998\n",
      "[139]\tvalid_0's rmse: 0.350739\n",
      "[140]\tvalid_0's rmse: 0.349487\n",
      "[141]\tvalid_0's rmse: 0.348259\n",
      "[142]\tvalid_0's rmse: 0.347038\n",
      "[143]\tvalid_0's rmse: 0.345869\n",
      "[144]\tvalid_0's rmse: 0.344681\n",
      "[145]\tvalid_0's rmse: 0.343526\n",
      "[146]\tvalid_0's rmse: 0.342256\n",
      "[147]\tvalid_0's rmse: 0.341045\n",
      "[148]\tvalid_0's rmse: 0.339791\n",
      "[149]\tvalid_0's rmse: 0.338543\n",
      "[150]\tvalid_0's rmse: 0.337329\n",
      "[151]\tvalid_0's rmse: 0.336155\n",
      "[152]\tvalid_0's rmse: 0.334988\n",
      "[153]\tvalid_0's rmse: 0.333828\n",
      "[154]\tvalid_0's rmse: 0.332673\n",
      "[155]\tvalid_0's rmse: 0.331528\n",
      "[156]\tvalid_0's rmse: 0.330367\n",
      "[157]\tvalid_0's rmse: 0.329181\n",
      "[158]\tvalid_0's rmse: 0.328009\n",
      "[159]\tvalid_0's rmse: 0.326837\n",
      "[160]\tvalid_0's rmse: 0.325674\n",
      "[161]\tvalid_0's rmse: 0.324545\n",
      "[162]\tvalid_0's rmse: 0.32341\n",
      "[163]\tvalid_0's rmse: 0.322245\n",
      "[164]\tvalid_0's rmse: 0.321128\n",
      "[165]\tvalid_0's rmse: 0.320027\n",
      "[166]\tvalid_0's rmse: 0.318917\n",
      "[167]\tvalid_0's rmse: 0.31785\n",
      "[168]\tvalid_0's rmse: 0.316779\n",
      "[169]\tvalid_0's rmse: 0.315706\n",
      "[170]\tvalid_0's rmse: 0.314629\n",
      "[171]\tvalid_0's rmse: 0.313557\n",
      "[172]\tvalid_0's rmse: 0.31248\n",
      "[173]\tvalid_0's rmse: 0.311406\n",
      "[174]\tvalid_0's rmse: 0.310324\n",
      "[175]\tvalid_0's rmse: 0.30925\n",
      "[176]\tvalid_0's rmse: 0.308202\n",
      "[177]\tvalid_0's rmse: 0.30716\n",
      "[178]\tvalid_0's rmse: 0.306122\n",
      "[179]\tvalid_0's rmse: 0.305093\n",
      "[180]\tvalid_0's rmse: 0.304074\n",
      "[181]\tvalid_0's rmse: 0.303092\n",
      "[182]\tvalid_0's rmse: 0.302127\n",
      "[183]\tvalid_0's rmse: 0.301168\n",
      "[184]\tvalid_0's rmse: 0.300216\n",
      "[185]\tvalid_0's rmse: 0.29927\n",
      "[186]\tvalid_0's rmse: 0.298222\n",
      "[187]\tvalid_0's rmse: 0.29718\n",
      "[188]\tvalid_0's rmse: 0.296147\n",
      "[189]\tvalid_0's rmse: 0.295143\n",
      "[190]\tvalid_0's rmse: 0.294134\n",
      "[191]\tvalid_0's rmse: 0.293147\n",
      "[192]\tvalid_0's rmse: 0.292167\n",
      "[193]\tvalid_0's rmse: 0.291193\n",
      "[194]\tvalid_0's rmse: 0.290286\n",
      "[195]\tvalid_0's rmse: 0.28937\n",
      "[196]\tvalid_0's rmse: 0.288437\n",
      "[197]\tvalid_0's rmse: 0.287519\n",
      "[198]\tvalid_0's rmse: 0.286579\n",
      "[199]\tvalid_0's rmse: 0.285642\n",
      "[200]\tvalid_0's rmse: 0.284734\n",
      "[201]\tvalid_0's rmse: 0.283841\n",
      "[202]\tvalid_0's rmse: 0.282962\n",
      "[203]\tvalid_0's rmse: 0.28209\n",
      "[204]\tvalid_0's rmse: 0.281222\n",
      "[205]\tvalid_0's rmse: 0.280369\n",
      "[206]\tvalid_0's rmse: 0.279506\n",
      "[207]\tvalid_0's rmse: 0.278653\n",
      "[208]\tvalid_0's rmse: 0.277802\n",
      "[209]\tvalid_0's rmse: 0.276953\n",
      "[210]\tvalid_0's rmse: 0.276114\n",
      "[211]\tvalid_0's rmse: 0.275285\n",
      "[212]\tvalid_0's rmse: 0.274473\n",
      "[213]\tvalid_0's rmse: 0.273674\n",
      "[214]\tvalid_0's rmse: 0.272856\n",
      "[215]\tvalid_0's rmse: 0.272068\n",
      "[216]\tvalid_0's rmse: 0.271275\n",
      "[217]\tvalid_0's rmse: 0.27047\n",
      "[218]\tvalid_0's rmse: 0.269669\n",
      "[219]\tvalid_0's rmse: 0.268874\n",
      "[220]\tvalid_0's rmse: 0.268084\n",
      "[221]\tvalid_0's rmse: 0.267302\n",
      "[222]\tvalid_0's rmse: 0.266539\n",
      "[223]\tvalid_0's rmse: 0.265767\n",
      "[224]\tvalid_0's rmse: 0.265001\n",
      "[225]\tvalid_0's rmse: 0.264239\n",
      "[226]\tvalid_0's rmse: 0.26344\n",
      "[227]\tvalid_0's rmse: 0.262706\n",
      "[228]\tvalid_0's rmse: 0.261917\n",
      "[229]\tvalid_0's rmse: 0.261132\n",
      "[230]\tvalid_0's rmse: 0.260357\n",
      "[231]\tvalid_0's rmse: 0.259586\n",
      "[232]\tvalid_0's rmse: 0.258825\n",
      "[233]\tvalid_0's rmse: 0.258087\n",
      "[234]\tvalid_0's rmse: 0.257355\n",
      "[235]\tvalid_0's rmse: 0.256589\n",
      "[236]\tvalid_0's rmse: 0.255878\n",
      "[237]\tvalid_0's rmse: 0.25519\n",
      "[238]\tvalid_0's rmse: 0.254488\n",
      "[239]\tvalid_0's rmse: 0.25381\n",
      "[240]\tvalid_0's rmse: 0.253118\n",
      "[241]\tvalid_0's rmse: 0.252457\n",
      "[242]\tvalid_0's rmse: 0.251815\n",
      "[243]\tvalid_0's rmse: 0.251124\n",
      "[244]\tvalid_0's rmse: 0.250478\n",
      "[245]\tvalid_0's rmse: 0.249796\n",
      "[246]\tvalid_0's rmse: 0.249153\n",
      "[247]\tvalid_0's rmse: 0.248502\n",
      "[248]\tvalid_0's rmse: 0.247847\n",
      "[249]\tvalid_0's rmse: 0.247176\n",
      "[250]\tvalid_0's rmse: 0.246527\n",
      "[251]\tvalid_0's rmse: 0.245838\n",
      "[252]\tvalid_0's rmse: 0.245198\n",
      "[253]\tvalid_0's rmse: 0.24455\n",
      "[254]\tvalid_0's rmse: 0.243906\n",
      "[255]\tvalid_0's rmse: 0.243267\n",
      "[256]\tvalid_0's rmse: 0.242641\n",
      "[257]\tvalid_0's rmse: 0.24202\n",
      "[258]\tvalid_0's rmse: 0.2414\n",
      "[259]\tvalid_0's rmse: 0.240788\n",
      "[260]\tvalid_0's rmse: 0.240177\n",
      "[261]\tvalid_0's rmse: 0.239581\n",
      "[262]\tvalid_0's rmse: 0.23892\n",
      "[263]\tvalid_0's rmse: 0.238264\n",
      "[264]\tvalid_0's rmse: 0.237611\n",
      "[265]\tvalid_0's rmse: 0.236964\n",
      "[266]\tvalid_0's rmse: 0.236425\n",
      "[267]\tvalid_0's rmse: 0.235845\n",
      "[268]\tvalid_0's rmse: 0.235212\n",
      "[269]\tvalid_0's rmse: 0.234584\n",
      "[270]\tvalid_0's rmse: 0.23396\n",
      "[271]\tvalid_0's rmse: 0.233365\n",
      "[272]\tvalid_0's rmse: 0.23277\n",
      "[273]\tvalid_0's rmse: 0.232181\n",
      "[274]\tvalid_0's rmse: 0.231669\n",
      "[275]\tvalid_0's rmse: 0.231094\n",
      "[276]\tvalid_0's rmse: 0.230545\n",
      "[277]\tvalid_0's rmse: 0.229999\n",
      "[278]\tvalid_0's rmse: 0.229457\n",
      "[279]\tvalid_0's rmse: 0.228919\n",
      "[280]\tvalid_0's rmse: 0.228369\n",
      "[281]\tvalid_0's rmse: 0.227856\n",
      "[282]\tvalid_0's rmse: 0.227374\n",
      "[283]\tvalid_0's rmse: 0.226837\n",
      "[284]\tvalid_0's rmse: 0.226334\n",
      "[285]\tvalid_0's rmse: 0.225805\n",
      "[286]\tvalid_0's rmse: 0.225299\n",
      "[287]\tvalid_0's rmse: 0.224793\n",
      "[288]\tvalid_0's rmse: 0.224287\n",
      "[289]\tvalid_0's rmse: 0.223792\n",
      "[290]\tvalid_0's rmse: 0.223301\n",
      "[291]\tvalid_0's rmse: 0.222791\n",
      "[292]\tvalid_0's rmse: 0.222286\n",
      "[293]\tvalid_0's rmse: 0.221828\n",
      "[294]\tvalid_0's rmse: 0.221345\n",
      "[295]\tvalid_0's rmse: 0.220867\n",
      "[296]\tvalid_0's rmse: 0.220366\n",
      "[297]\tvalid_0's rmse: 0.219868\n",
      "[298]\tvalid_0's rmse: 0.219369\n",
      "[299]\tvalid_0's rmse: 0.21892\n",
      "[300]\tvalid_0's rmse: 0.218451\n",
      "[301]\tvalid_0's rmse: 0.217973\n",
      "[302]\tvalid_0's rmse: 0.217498\n",
      "[303]\tvalid_0's rmse: 0.217026\n",
      "[304]\tvalid_0's rmse: 0.216558\n",
      "[305]\tvalid_0's rmse: 0.216093\n",
      "[306]\tvalid_0's rmse: 0.21558\n",
      "[307]\tvalid_0's rmse: 0.215071\n",
      "[308]\tvalid_0's rmse: 0.214565\n",
      "[309]\tvalid_0's rmse: 0.214063\n",
      "[310]\tvalid_0's rmse: 0.213564\n",
      "[311]\tvalid_0's rmse: 0.213094\n",
      "[312]\tvalid_0's rmse: 0.212612\n",
      "[313]\tvalid_0's rmse: 0.212136\n",
      "[314]\tvalid_0's rmse: 0.211661\n",
      "[315]\tvalid_0's rmse: 0.211191\n",
      "[316]\tvalid_0's rmse: 0.21075\n",
      "[317]\tvalid_0's rmse: 0.2103\n",
      "[318]\tvalid_0's rmse: 0.209865\n",
      "[319]\tvalid_0's rmse: 0.209421\n",
      "[320]\tvalid_0's rmse: 0.209008\n",
      "[321]\tvalid_0's rmse: 0.208552\n",
      "[322]\tvalid_0's rmse: 0.208116\n",
      "[323]\tvalid_0's rmse: 0.207668\n",
      "[324]\tvalid_0's rmse: 0.207213\n",
      "[325]\tvalid_0's rmse: 0.206771\n",
      "[326]\tvalid_0's rmse: 0.206379\n",
      "[327]\tvalid_0's rmse: 0.205998\n",
      "[328]\tvalid_0's rmse: 0.205628\n",
      "[329]\tvalid_0's rmse: 0.205268\n",
      "[330]\tvalid_0's rmse: 0.204885\n",
      "[331]\tvalid_0's rmse: 0.204505\n",
      "[332]\tvalid_0's rmse: 0.204119\n",
      "[333]\tvalid_0's rmse: 0.20374\n",
      "[334]\tvalid_0's rmse: 0.203344\n",
      "[335]\tvalid_0's rmse: 0.202957\n",
      "[336]\tvalid_0's rmse: 0.20259\n",
      "[337]\tvalid_0's rmse: 0.202226\n",
      "[338]\tvalid_0's rmse: 0.201864\n",
      "[339]\tvalid_0's rmse: 0.201506\n",
      "[340]\tvalid_0's rmse: 0.20115\n",
      "[341]\tvalid_0's rmse: 0.200774\n",
      "[342]\tvalid_0's rmse: 0.200405\n",
      "[343]\tvalid_0's rmse: 0.200039\n",
      "[344]\tvalid_0's rmse: 0.199675\n",
      "[345]\tvalid_0's rmse: 0.199315\n",
      "[346]\tvalid_0's rmse: 0.198938\n",
      "[347]\tvalid_0's rmse: 0.198563\n",
      "[348]\tvalid_0's rmse: 0.198191\n",
      "[349]\tvalid_0's rmse: 0.197823\n",
      "[350]\tvalid_0's rmse: 0.197457\n",
      "[351]\tvalid_0's rmse: 0.197116\n",
      "[352]\tvalid_0's rmse: 0.196775\n",
      "[353]\tvalid_0's rmse: 0.196428\n",
      "[354]\tvalid_0's rmse: 0.196089\n",
      "[355]\tvalid_0's rmse: 0.195754\n",
      "[356]\tvalid_0's rmse: 0.195476\n",
      "[357]\tvalid_0's rmse: 0.195165\n",
      "[358]\tvalid_0's rmse: 0.194848\n",
      "[359]\tvalid_0's rmse: 0.194578\n",
      "[360]\tvalid_0's rmse: 0.194265\n",
      "[361]\tvalid_0's rmse: 0.193964\n",
      "[362]\tvalid_0's rmse: 0.193665\n",
      "[363]\tvalid_0's rmse: 0.193355\n",
      "[364]\tvalid_0's rmse: 0.193027\n",
      "[365]\tvalid_0's rmse: 0.192736\n",
      "[366]\tvalid_0's rmse: 0.192424\n",
      "[367]\tvalid_0's rmse: 0.192154\n",
      "[368]\tvalid_0's rmse: 0.191886\n",
      "[369]\tvalid_0's rmse: 0.191645\n",
      "[370]\tvalid_0's rmse: 0.191379\n",
      "[371]\tvalid_0's rmse: 0.191069\n",
      "[372]\tvalid_0's rmse: 0.190752\n",
      "[373]\tvalid_0's rmse: 0.190382\n",
      "[374]\tvalid_0's rmse: 0.190016\n",
      "[375]\tvalid_0's rmse: 0.189714\n",
      "[376]\tvalid_0's rmse: 0.189433\n",
      "[377]\tvalid_0's rmse: 0.189196\n",
      "[378]\tvalid_0's rmse: 0.188975\n",
      "[379]\tvalid_0's rmse: 0.188741\n",
      "[380]\tvalid_0's rmse: 0.188509\n",
      "[381]\tvalid_0's rmse: 0.188247\n",
      "[382]\tvalid_0's rmse: 0.187967\n",
      "[383]\tvalid_0's rmse: 0.187689\n",
      "[384]\tvalid_0's rmse: 0.187433\n",
      "[385]\tvalid_0's rmse: 0.187179\n",
      "[386]\tvalid_0's rmse: 0.186922\n",
      "[387]\tvalid_0's rmse: 0.186674\n",
      "[388]\tvalid_0's rmse: 0.186428\n",
      "[389]\tvalid_0's rmse: 0.186182\n",
      "[390]\tvalid_0's rmse: 0.185937\n",
      "[391]\tvalid_0's rmse: 0.185662\n",
      "[392]\tvalid_0's rmse: 0.185401\n",
      "[393]\tvalid_0's rmse: 0.185129\n",
      "[394]\tvalid_0's rmse: 0.184858\n",
      "[395]\tvalid_0's rmse: 0.184597\n",
      "[396]\tvalid_0's rmse: 0.184356\n",
      "[397]\tvalid_0's rmse: 0.184062\n",
      "[398]\tvalid_0's rmse: 0.183829\n",
      "[399]\tvalid_0's rmse: 0.18354\n",
      "[400]\tvalid_0's rmse: 0.183307\n",
      "[401]\tvalid_0's rmse: 0.183066\n",
      "[402]\tvalid_0's rmse: 0.182836\n",
      "[403]\tvalid_0's rmse: 0.18258\n",
      "[404]\tvalid_0's rmse: 0.182325\n",
      "[405]\tvalid_0's rmse: 0.1821\n",
      "[406]\tvalid_0's rmse: 0.181845\n",
      "[407]\tvalid_0's rmse: 0.18161\n",
      "[408]\tvalid_0's rmse: 0.181358\n",
      "[409]\tvalid_0's rmse: 0.181108\n",
      "[410]\tvalid_0's rmse: 0.180853\n",
      "[411]\tvalid_0's rmse: 0.180626\n",
      "[412]\tvalid_0's rmse: 0.180408\n",
      "[413]\tvalid_0's rmse: 0.180192\n",
      "[414]\tvalid_0's rmse: 0.179978\n",
      "[415]\tvalid_0's rmse: 0.179733\n",
      "[416]\tvalid_0's rmse: 0.179496\n",
      "[417]\tvalid_0's rmse: 0.179264\n",
      "[418]\tvalid_0's rmse: 0.179013\n",
      "[419]\tvalid_0's rmse: 0.178782\n",
      "[420]\tvalid_0's rmse: 0.178538\n",
      "[421]\tvalid_0's rmse: 0.178333\n",
      "[422]\tvalid_0's rmse: 0.17813\n",
      "[423]\tvalid_0's rmse: 0.177934\n",
      "[424]\tvalid_0's rmse: 0.177734\n",
      "[425]\tvalid_0's rmse: 0.177535\n",
      "[426]\tvalid_0's rmse: 0.177367\n",
      "[427]\tvalid_0's rmse: 0.177148\n",
      "[428]\tvalid_0's rmse: 0.176931\n",
      "[429]\tvalid_0's rmse: 0.176715\n",
      "[430]\tvalid_0's rmse: 0.176499\n",
      "[431]\tvalid_0's rmse: 0.176319\n",
      "[432]\tvalid_0's rmse: 0.176141\n",
      "[433]\tvalid_0's rmse: 0.175955\n",
      "[434]\tvalid_0's rmse: 0.175762\n",
      "[435]\tvalid_0's rmse: 0.175613\n",
      "[436]\tvalid_0's rmse: 0.175437\n",
      "[437]\tvalid_0's rmse: 0.175248\n",
      "[438]\tvalid_0's rmse: 0.17505\n",
      "[439]\tvalid_0's rmse: 0.174853\n",
      "[440]\tvalid_0's rmse: 0.174673\n",
      "[441]\tvalid_0's rmse: 0.174474\n",
      "[442]\tvalid_0's rmse: 0.174273\n",
      "[443]\tvalid_0's rmse: 0.174106\n",
      "[444]\tvalid_0's rmse: 0.173908\n",
      "[445]\tvalid_0's rmse: 0.173716\n",
      "[446]\tvalid_0's rmse: 0.173561\n",
      "[447]\tvalid_0's rmse: 0.173408\n",
      "[448]\tvalid_0's rmse: 0.173249\n",
      "[449]\tvalid_0's rmse: 0.173107\n",
      "[450]\tvalid_0's rmse: 0.172955\n",
      "[451]\tvalid_0's rmse: 0.172746\n",
      "[452]\tvalid_0's rmse: 0.172535\n",
      "[453]\tvalid_0's rmse: 0.17233\n",
      "[454]\tvalid_0's rmse: 0.172121\n",
      "[455]\tvalid_0's rmse: 0.171919\n",
      "[456]\tvalid_0's rmse: 0.171761\n",
      "[457]\tvalid_0's rmse: 0.171602\n",
      "[458]\tvalid_0's rmse: 0.171454\n",
      "[459]\tvalid_0's rmse: 0.171307\n",
      "[460]\tvalid_0's rmse: 0.171153\n",
      "[461]\tvalid_0's rmse: 0.170961\n",
      "[462]\tvalid_0's rmse: 0.170759\n",
      "[463]\tvalid_0's rmse: 0.170558\n",
      "[464]\tvalid_0's rmse: 0.17036\n",
      "[465]\tvalid_0's rmse: 0.170163\n",
      "[466]\tvalid_0's rmse: 0.169965\n",
      "[467]\tvalid_0's rmse: 0.169769\n",
      "[468]\tvalid_0's rmse: 0.169574\n",
      "[469]\tvalid_0's rmse: 0.169381\n",
      "[470]\tvalid_0's rmse: 0.169172\n",
      "[471]\tvalid_0's rmse: 0.169051\n",
      "[472]\tvalid_0's rmse: 0.168918\n",
      "[473]\tvalid_0's rmse: 0.168822\n",
      "[474]\tvalid_0's rmse: 0.168698\n",
      "[475]\tvalid_0's rmse: 0.168575\n",
      "[476]\tvalid_0's rmse: 0.168457\n",
      "[477]\tvalid_0's rmse: 0.168306\n",
      "[478]\tvalid_0's rmse: 0.16819\n",
      "[479]\tvalid_0's rmse: 0.168027\n",
      "[480]\tvalid_0's rmse: 0.167848\n",
      "[481]\tvalid_0's rmse: 0.167696\n",
      "[482]\tvalid_0's rmse: 0.167583\n",
      "[483]\tvalid_0's rmse: 0.167468\n",
      "[484]\tvalid_0's rmse: 0.167319\n",
      "[485]\tvalid_0's rmse: 0.167172\n",
      "[486]\tvalid_0's rmse: 0.167056\n",
      "[487]\tvalid_0's rmse: 0.166944\n",
      "[488]\tvalid_0's rmse: 0.166833\n",
      "[489]\tvalid_0's rmse: 0.166736\n",
      "[490]\tvalid_0's rmse: 0.166625\n",
      "[491]\tvalid_0's rmse: 0.16646\n",
      "[492]\tvalid_0's rmse: 0.166296\n",
      "[493]\tvalid_0's rmse: 0.166178\n",
      "[494]\tvalid_0's rmse: 0.166029\n",
      "[495]\tvalid_0's rmse: 0.165943\n",
      "[496]\tvalid_0's rmse: 0.165823\n",
      "[497]\tvalid_0's rmse: 0.165704\n",
      "[498]\tvalid_0's rmse: 0.165587\n",
      "[499]\tvalid_0's rmse: 0.165474\n",
      "[500]\tvalid_0's rmse: 0.16536\n",
      "[501]\tvalid_0's rmse: 0.165277\n",
      "[502]\tvalid_0's rmse: 0.165201\n",
      "[503]\tvalid_0's rmse: 0.165125\n",
      "[504]\tvalid_0's rmse: 0.165051\n",
      "[505]\tvalid_0's rmse: 0.164976\n",
      "[506]\tvalid_0's rmse: 0.164822\n",
      "[507]\tvalid_0's rmse: 0.164701\n",
      "[508]\tvalid_0's rmse: 0.164615\n",
      "[509]\tvalid_0's rmse: 0.164525\n",
      "[510]\tvalid_0's rmse: 0.164436\n",
      "[511]\tvalid_0's rmse: 0.164322\n",
      "[512]\tvalid_0's rmse: 0.16421\n",
      "[513]\tvalid_0's rmse: 0.164099\n",
      "[514]\tvalid_0's rmse: 0.163985\n",
      "[515]\tvalid_0's rmse: 0.163903\n",
      "[516]\tvalid_0's rmse: 0.163774\n",
      "[517]\tvalid_0's rmse: 0.163648\n",
      "[518]\tvalid_0's rmse: 0.163522\n",
      "[519]\tvalid_0's rmse: 0.163399\n",
      "[520]\tvalid_0's rmse: 0.163288\n",
      "[521]\tvalid_0's rmse: 0.16316\n",
      "[522]\tvalid_0's rmse: 0.163048\n",
      "[523]\tvalid_0's rmse: 0.162938\n",
      "[524]\tvalid_0's rmse: 0.162834\n",
      "[525]\tvalid_0's rmse: 0.162771\n",
      "[526]\tvalid_0's rmse: 0.162658\n",
      "[527]\tvalid_0's rmse: 0.162548\n",
      "[528]\tvalid_0's rmse: 0.162427\n",
      "[529]\tvalid_0's rmse: 0.162331\n",
      "[530]\tvalid_0's rmse: 0.162224\n",
      "[531]\tvalid_0's rmse: 0.162099\n",
      "[532]\tvalid_0's rmse: 0.162019\n",
      "[533]\tvalid_0's rmse: 0.161896\n",
      "[534]\tvalid_0's rmse: 0.161772\n",
      "[535]\tvalid_0's rmse: 0.16165\n",
      "[536]\tvalid_0's rmse: 0.161567\n",
      "[537]\tvalid_0's rmse: 0.161483\n",
      "[538]\tvalid_0's rmse: 0.161378\n",
      "[539]\tvalid_0's rmse: 0.161269\n",
      "[540]\tvalid_0's rmse: 0.16119\n",
      "[541]\tvalid_0's rmse: 0.161061\n",
      "[542]\tvalid_0's rmse: 0.160941\n",
      "[543]\tvalid_0's rmse: 0.160814\n",
      "[544]\tvalid_0's rmse: 0.160693\n",
      "[545]\tvalid_0's rmse: 0.160582\n",
      "[546]\tvalid_0's rmse: 0.160462\n",
      "[547]\tvalid_0's rmse: 0.160407\n",
      "[548]\tvalid_0's rmse: 0.160333\n",
      "[549]\tvalid_0's rmse: 0.16026\n",
      "[550]\tvalid_0's rmse: 0.160179\n",
      "[551]\tvalid_0's rmse: 0.160086\n",
      "[552]\tvalid_0's rmse: 0.159994\n",
      "[553]\tvalid_0's rmse: 0.159903\n",
      "[554]\tvalid_0's rmse: 0.159816\n",
      "[555]\tvalid_0's rmse: 0.159729\n",
      "[556]\tvalid_0's rmse: 0.159657\n",
      "[557]\tvalid_0's rmse: 0.159583\n",
      "[558]\tvalid_0's rmse: 0.159512\n",
      "[559]\tvalid_0's rmse: 0.159431\n",
      "[560]\tvalid_0's rmse: 0.159363\n",
      "[561]\tvalid_0's rmse: 0.15929\n",
      "[562]\tvalid_0's rmse: 0.159201\n",
      "[563]\tvalid_0's rmse: 0.15913\n",
      "[564]\tvalid_0's rmse: 0.159059\n",
      "[565]\tvalid_0's rmse: 0.158994\n",
      "[566]\tvalid_0's rmse: 0.158926\n",
      "[567]\tvalid_0's rmse: 0.158859\n",
      "[568]\tvalid_0's rmse: 0.158784\n",
      "[569]\tvalid_0's rmse: 0.158718\n",
      "[570]\tvalid_0's rmse: 0.15864\n",
      "[571]\tvalid_0's rmse: 0.15854\n",
      "[572]\tvalid_0's rmse: 0.158466\n",
      "[573]\tvalid_0's rmse: 0.158377\n",
      "[574]\tvalid_0's rmse: 0.158279\n",
      "[575]\tvalid_0's rmse: 0.158175\n",
      "[576]\tvalid_0's rmse: 0.158108\n",
      "[577]\tvalid_0's rmse: 0.158043\n",
      "[578]\tvalid_0's rmse: 0.157987\n",
      "[579]\tvalid_0's rmse: 0.157923\n",
      "[580]\tvalid_0's rmse: 0.157859\n",
      "[581]\tvalid_0's rmse: 0.157801\n",
      "[582]\tvalid_0's rmse: 0.157766\n",
      "[583]\tvalid_0's rmse: 0.157713\n",
      "[584]\tvalid_0's rmse: 0.157656\n",
      "[585]\tvalid_0's rmse: 0.157601\n",
      "[586]\tvalid_0's rmse: 0.157536\n",
      "[587]\tvalid_0's rmse: 0.157471\n",
      "[588]\tvalid_0's rmse: 0.157411\n",
      "[589]\tvalid_0's rmse: 0.157344\n",
      "[590]\tvalid_0's rmse: 0.157278\n",
      "[591]\tvalid_0's rmse: 0.157254\n",
      "[592]\tvalid_0's rmse: 0.157224\n",
      "[593]\tvalid_0's rmse: 0.157201\n",
      "[594]\tvalid_0's rmse: 0.157166\n",
      "[595]\tvalid_0's rmse: 0.157144\n",
      "[596]\tvalid_0's rmse: 0.157069\n",
      "[597]\tvalid_0's rmse: 0.156995\n",
      "[598]\tvalid_0's rmse: 0.156921\n",
      "[599]\tvalid_0's rmse: 0.156846\n",
      "[600]\tvalid_0's rmse: 0.156775\n",
      "[601]\tvalid_0's rmse: 0.156688\n",
      "[602]\tvalid_0's rmse: 0.156607\n",
      "[603]\tvalid_0's rmse: 0.156525\n",
      "[604]\tvalid_0's rmse: 0.156432\n",
      "[605]\tvalid_0's rmse: 0.156355\n",
      "[606]\tvalid_0's rmse: 0.156329\n",
      "[607]\tvalid_0's rmse: 0.156249\n",
      "[608]\tvalid_0's rmse: 0.156157\n",
      "[609]\tvalid_0's rmse: 0.156061\n",
      "[610]\tvalid_0's rmse: 0.155983\n",
      "[611]\tvalid_0's rmse: 0.155912\n",
      "[612]\tvalid_0's rmse: 0.155841\n",
      "[613]\tvalid_0's rmse: 0.155761\n",
      "[614]\tvalid_0's rmse: 0.155703\n",
      "[615]\tvalid_0's rmse: 0.155649\n",
      "[616]\tvalid_0's rmse: 0.155608\n",
      "[617]\tvalid_0's rmse: 0.155591\n",
      "[618]\tvalid_0's rmse: 0.155552\n",
      "[619]\tvalid_0's rmse: 0.155526\n",
      "[620]\tvalid_0's rmse: 0.155487\n",
      "[621]\tvalid_0's rmse: 0.155432\n",
      "[622]\tvalid_0's rmse: 0.155367\n",
      "[623]\tvalid_0's rmse: 0.15532\n",
      "[624]\tvalid_0's rmse: 0.155265\n",
      "[625]\tvalid_0's rmse: 0.155232\n",
      "[626]\tvalid_0's rmse: 0.155182\n",
      "[627]\tvalid_0's rmse: 0.155132\n",
      "[628]\tvalid_0's rmse: 0.155115\n",
      "[629]\tvalid_0's rmse: 0.155051\n",
      "[630]\tvalid_0's rmse: 0.155003\n",
      "[631]\tvalid_0's rmse: 0.154952\n",
      "[632]\tvalid_0's rmse: 0.154903\n",
      "[633]\tvalid_0's rmse: 0.154854\n",
      "[634]\tvalid_0's rmse: 0.154807\n",
      "[635]\tvalid_0's rmse: 0.154764\n",
      "[636]\tvalid_0's rmse: 0.154671\n",
      "[637]\tvalid_0's rmse: 0.154588\n",
      "[638]\tvalid_0's rmse: 0.154506\n",
      "[639]\tvalid_0's rmse: 0.154425\n",
      "[640]\tvalid_0's rmse: 0.154347\n",
      "[641]\tvalid_0's rmse: 0.154288\n",
      "[642]\tvalid_0's rmse: 0.154227\n",
      "[643]\tvalid_0's rmse: 0.154169\n",
      "[644]\tvalid_0's rmse: 0.154108\n",
      "[645]\tvalid_0's rmse: 0.154048\n",
      "[646]\tvalid_0's rmse: 0.154007\n",
      "[647]\tvalid_0's rmse: 0.153966\n",
      "[648]\tvalid_0's rmse: 0.153926\n",
      "[649]\tvalid_0's rmse: 0.153923\n",
      "[650]\tvalid_0's rmse: 0.153883\n",
      "[651]\tvalid_0's rmse: 0.153857\n",
      "[652]\tvalid_0's rmse: 0.153794\n",
      "[653]\tvalid_0's rmse: 0.153768\n",
      "[654]\tvalid_0's rmse: 0.153743\n",
      "[655]\tvalid_0's rmse: 0.153685\n",
      "[656]\tvalid_0's rmse: 0.153635\n",
      "[657]\tvalid_0's rmse: 0.153615\n",
      "[658]\tvalid_0's rmse: 0.153595\n",
      "[659]\tvalid_0's rmse: 0.153569\n",
      "[660]\tvalid_0's rmse: 0.153539\n",
      "[661]\tvalid_0's rmse: 0.153494\n",
      "[662]\tvalid_0's rmse: 0.153472\n",
      "[663]\tvalid_0's rmse: 0.153427\n",
      "[664]\tvalid_0's rmse: 0.15341\n",
      "[665]\tvalid_0's rmse: 0.153373\n",
      "[666]\tvalid_0's rmse: 0.153329\n",
      "[667]\tvalid_0's rmse: 0.153287\n",
      "[668]\tvalid_0's rmse: 0.153239\n",
      "[669]\tvalid_0's rmse: 0.153198\n",
      "[670]\tvalid_0's rmse: 0.153157\n",
      "[671]\tvalid_0's rmse: 0.153097\n",
      "[672]\tvalid_0's rmse: 0.153048\n",
      "[673]\tvalid_0's rmse: 0.152994\n",
      "[674]\tvalid_0's rmse: 0.152932\n",
      "[675]\tvalid_0's rmse: 0.152884\n",
      "[676]\tvalid_0's rmse: 0.152847\n",
      "[677]\tvalid_0's rmse: 0.152817\n",
      "[678]\tvalid_0's rmse: 0.152766\n",
      "[679]\tvalid_0's rmse: 0.152716\n",
      "[680]\tvalid_0's rmse: 0.152667\n",
      "[681]\tvalid_0's rmse: 0.152594\n",
      "[682]\tvalid_0's rmse: 0.152523\n",
      "[683]\tvalid_0's rmse: 0.152434\n",
      "[684]\tvalid_0's rmse: 0.15238\n",
      "[685]\tvalid_0's rmse: 0.152307\n",
      "[686]\tvalid_0's rmse: 0.152271\n",
      "[687]\tvalid_0's rmse: 0.152279\n",
      "[688]\tvalid_0's rmse: 0.152222\n",
      "[689]\tvalid_0's rmse: 0.1522\n",
      "[690]\tvalid_0's rmse: 0.152185\n",
      "[691]\tvalid_0's rmse: 0.152157\n",
      "[692]\tvalid_0's rmse: 0.152129\n",
      "[693]\tvalid_0's rmse: 0.152099\n",
      "[694]\tvalid_0's rmse: 0.152073\n",
      "[695]\tvalid_0's rmse: 0.152044\n",
      "[696]\tvalid_0's rmse: 0.151981\n",
      "[697]\tvalid_0's rmse: 0.151922\n",
      "[698]\tvalid_0's rmse: 0.151867\n",
      "[699]\tvalid_0's rmse: 0.151799\n",
      "[700]\tvalid_0's rmse: 0.151729\n",
      "[701]\tvalid_0's rmse: 0.151686\n",
      "[702]\tvalid_0's rmse: 0.15171\n",
      "[703]\tvalid_0's rmse: 0.151691\n",
      "[704]\tvalid_0's rmse: 0.151706\n",
      "[705]\tvalid_0's rmse: 0.151722\n",
      "[706]\tvalid_0's rmse: 0.151679\n",
      "[707]\tvalid_0's rmse: 0.15163\n",
      "[708]\tvalid_0's rmse: 0.151596\n",
      "[709]\tvalid_0's rmse: 0.151555\n",
      "[710]\tvalid_0's rmse: 0.15151\n",
      "[711]\tvalid_0's rmse: 0.151444\n",
      "[712]\tvalid_0's rmse: 0.151413\n",
      "[713]\tvalid_0's rmse: 0.151383\n",
      "[714]\tvalid_0's rmse: 0.151317\n",
      "[715]\tvalid_0's rmse: 0.151253\n",
      "[716]\tvalid_0's rmse: 0.151228\n",
      "[717]\tvalid_0's rmse: 0.15119\n",
      "[718]\tvalid_0's rmse: 0.151166\n",
      "[719]\tvalid_0's rmse: 0.151113\n",
      "[720]\tvalid_0's rmse: 0.151077\n",
      "[721]\tvalid_0's rmse: 0.151058\n",
      "[722]\tvalid_0's rmse: 0.15104\n",
      "[723]\tvalid_0's rmse: 0.150994\n",
      "[724]\tvalid_0's rmse: 0.150972\n",
      "[725]\tvalid_0's rmse: 0.150956\n",
      "[726]\tvalid_0's rmse: 0.150933\n",
      "[727]\tvalid_0's rmse: 0.1509\n",
      "[728]\tvalid_0's rmse: 0.150878\n",
      "[729]\tvalid_0's rmse: 0.150858\n",
      "[730]\tvalid_0's rmse: 0.150836\n",
      "[731]\tvalid_0's rmse: 0.150799\n",
      "[732]\tvalid_0's rmse: 0.150742\n",
      "[733]\tvalid_0's rmse: 0.150679\n",
      "[734]\tvalid_0's rmse: 0.150623\n",
      "[735]\tvalid_0's rmse: 0.150563\n",
      "[736]\tvalid_0's rmse: 0.150528\n",
      "[737]\tvalid_0's rmse: 0.150481\n",
      "[738]\tvalid_0's rmse: 0.150439\n",
      "[739]\tvalid_0's rmse: 0.150397\n",
      "[740]\tvalid_0's rmse: 0.150356\n",
      "[741]\tvalid_0's rmse: 0.15036\n",
      "[742]\tvalid_0's rmse: 0.150374\n",
      "[743]\tvalid_0's rmse: 0.150396\n",
      "[744]\tvalid_0's rmse: 0.150401\n",
      "[745]\tvalid_0's rmse: 0.150429\n",
      "[746]\tvalid_0's rmse: 0.150402\n",
      "[747]\tvalid_0's rmse: 0.150365\n",
      "[748]\tvalid_0's rmse: 0.150307\n",
      "[749]\tvalid_0's rmse: 0.150285\n",
      "[750]\tvalid_0's rmse: 0.150228\n",
      "[751]\tvalid_0's rmse: 0.150187\n",
      "[752]\tvalid_0's rmse: 0.150151\n",
      "[753]\tvalid_0's rmse: 0.150093\n",
      "[754]\tvalid_0's rmse: 0.150037\n",
      "[755]\tvalid_0's rmse: 0.149979\n",
      "[756]\tvalid_0's rmse: 0.149932\n",
      "[757]\tvalid_0's rmse: 0.149904\n",
      "[758]\tvalid_0's rmse: 0.149876\n",
      "[759]\tvalid_0's rmse: 0.149846\n",
      "[760]\tvalid_0's rmse: 0.149817\n",
      "[761]\tvalid_0's rmse: 0.149764\n",
      "[762]\tvalid_0's rmse: 0.149712\n",
      "[763]\tvalid_0's rmse: 0.149656\n",
      "[764]\tvalid_0's rmse: 0.149671\n",
      "[765]\tvalid_0's rmse: 0.149688\n",
      "[766]\tvalid_0's rmse: 0.149645\n",
      "[767]\tvalid_0's rmse: 0.149606\n",
      "[768]\tvalid_0's rmse: 0.14957\n",
      "[769]\tvalid_0's rmse: 0.149524\n",
      "[770]\tvalid_0's rmse: 0.149478\n",
      "[771]\tvalid_0's rmse: 0.149489\n",
      "[772]\tvalid_0's rmse: 0.149491\n",
      "[773]\tvalid_0's rmse: 0.149503\n",
      "[774]\tvalid_0's rmse: 0.149514\n",
      "[775]\tvalid_0's rmse: 0.149529\n",
      "[776]\tvalid_0's rmse: 0.149512\n",
      "[777]\tvalid_0's rmse: 0.149499\n",
      "[778]\tvalid_0's rmse: 0.149513\n",
      "[779]\tvalid_0's rmse: 0.149507\n",
      "[780]\tvalid_0's rmse: 0.149502\n",
      "[781]\tvalid_0's rmse: 0.149483\n",
      "[782]\tvalid_0's rmse: 0.149466\n",
      "[783]\tvalid_0's rmse: 0.149445\n",
      "[784]\tvalid_0's rmse: 0.149428\n",
      "[785]\tvalid_0's rmse: 0.149411\n",
      "[786]\tvalid_0's rmse: 0.149423\n",
      "[787]\tvalid_0's rmse: 0.149401\n",
      "[788]\tvalid_0's rmse: 0.149415\n",
      "[789]\tvalid_0's rmse: 0.149426\n",
      "[790]\tvalid_0's rmse: 0.149446\n",
      "[791]\tvalid_0's rmse: 0.149456\n",
      "[792]\tvalid_0's rmse: 0.149466\n",
      "[793]\tvalid_0's rmse: 0.149472\n",
      "[794]\tvalid_0's rmse: 0.149478\n",
      "[795]\tvalid_0's rmse: 0.149483\n",
      "[796]\tvalid_0's rmse: 0.149452\n",
      "[797]\tvalid_0's rmse: 0.149422\n",
      "[798]\tvalid_0's rmse: 0.149392\n",
      "[799]\tvalid_0's rmse: 0.149367\n",
      "[800]\tvalid_0's rmse: 0.149338\n",
      "[801]\tvalid_0's rmse: 0.149311\n",
      "[802]\tvalid_0's rmse: 0.149289\n",
      "[803]\tvalid_0's rmse: 0.149268\n",
      "[804]\tvalid_0's rmse: 0.149246\n",
      "[805]\tvalid_0's rmse: 0.149224\n",
      "[806]\tvalid_0's rmse: 0.149191\n",
      "[807]\tvalid_0's rmse: 0.14917\n",
      "[808]\tvalid_0's rmse: 0.149138\n",
      "[809]\tvalid_0's rmse: 0.14913\n",
      "[810]\tvalid_0's rmse: 0.149099\n",
      "[811]\tvalid_0's rmse: 0.149082\n",
      "[812]\tvalid_0's rmse: 0.149065\n",
      "[813]\tvalid_0's rmse: 0.149055\n",
      "[814]\tvalid_0's rmse: 0.149038\n",
      "[815]\tvalid_0's rmse: 0.149023\n",
      "[816]\tvalid_0's rmse: 0.149\n",
      "[817]\tvalid_0's rmse: 0.148979\n",
      "[818]\tvalid_0's rmse: 0.148949\n",
      "[819]\tvalid_0's rmse: 0.148928\n",
      "[820]\tvalid_0's rmse: 0.148906\n",
      "[821]\tvalid_0's rmse: 0.14892\n",
      "[822]\tvalid_0's rmse: 0.148933\n",
      "[823]\tvalid_0's rmse: 0.14894\n",
      "[824]\tvalid_0's rmse: 0.148946\n",
      "[825]\tvalid_0's rmse: 0.148956\n",
      "[826]\tvalid_0's rmse: 0.148908\n",
      "[827]\tvalid_0's rmse: 0.148863\n",
      "[828]\tvalid_0's rmse: 0.148817\n",
      "[829]\tvalid_0's rmse: 0.148771\n",
      "[830]\tvalid_0's rmse: 0.148692\n",
      "[831]\tvalid_0's rmse: 0.148667\n",
      "[832]\tvalid_0's rmse: 0.148652\n",
      "[833]\tvalid_0's rmse: 0.148627\n",
      "[834]\tvalid_0's rmse: 0.148592\n",
      "[835]\tvalid_0's rmse: 0.14859\n",
      "[836]\tvalid_0's rmse: 0.148583\n",
      "[837]\tvalid_0's rmse: 0.148577\n",
      "[838]\tvalid_0's rmse: 0.148574\n",
      "[839]\tvalid_0's rmse: 0.148575\n",
      "[840]\tvalid_0's rmse: 0.14857\n",
      "[841]\tvalid_0's rmse: 0.148544\n",
      "[842]\tvalid_0's rmse: 0.148519\n",
      "[843]\tvalid_0's rmse: 0.148509\n",
      "[844]\tvalid_0's rmse: 0.148501\n",
      "[845]\tvalid_0's rmse: 0.148474\n",
      "[846]\tvalid_0's rmse: 0.148464\n",
      "[847]\tvalid_0's rmse: 0.148453\n",
      "[848]\tvalid_0's rmse: 0.148443\n",
      "[849]\tvalid_0's rmse: 0.148433\n",
      "[850]\tvalid_0's rmse: 0.148424\n",
      "[851]\tvalid_0's rmse: 0.148388\n",
      "[852]\tvalid_0's rmse: 0.148354\n",
      "[853]\tvalid_0's rmse: 0.148321\n",
      "[854]\tvalid_0's rmse: 0.148289\n",
      "[855]\tvalid_0's rmse: 0.148257\n",
      "[856]\tvalid_0's rmse: 0.148274\n",
      "[857]\tvalid_0's rmse: 0.148271\n",
      "[858]\tvalid_0's rmse: 0.148268\n",
      "[859]\tvalid_0's rmse: 0.148266\n",
      "[860]\tvalid_0's rmse: 0.148264\n",
      "[861]\tvalid_0's rmse: 0.148254\n",
      "[862]\tvalid_0's rmse: 0.148246\n",
      "[863]\tvalid_0's rmse: 0.148237\n",
      "[864]\tvalid_0's rmse: 0.148221\n",
      "[865]\tvalid_0's rmse: 0.148207\n",
      "[866]\tvalid_0's rmse: 0.148203\n",
      "[867]\tvalid_0's rmse: 0.148203\n",
      "[868]\tvalid_0's rmse: 0.148201\n",
      "[869]\tvalid_0's rmse: 0.148176\n",
      "[870]\tvalid_0's rmse: 0.148165\n",
      "[871]\tvalid_0's rmse: 0.148155\n",
      "[872]\tvalid_0's rmse: 0.148146\n",
      "[873]\tvalid_0's rmse: 0.148122\n",
      "[874]\tvalid_0's rmse: 0.148113\n",
      "[875]\tvalid_0's rmse: 0.148095\n",
      "[876]\tvalid_0's rmse: 0.148086\n",
      "[877]\tvalid_0's rmse: 0.14807\n",
      "[878]\tvalid_0's rmse: 0.148057\n",
      "[879]\tvalid_0's rmse: 0.148048\n",
      "[880]\tvalid_0's rmse: 0.148025\n",
      "[881]\tvalid_0's rmse: 0.14802\n",
      "[882]\tvalid_0's rmse: 0.148022\n",
      "[883]\tvalid_0's rmse: 0.14804\n",
      "[884]\tvalid_0's rmse: 0.148051\n",
      "[885]\tvalid_0's rmse: 0.14807\n",
      "[886]\tvalid_0's rmse: 0.148063\n",
      "[887]\tvalid_0's rmse: 0.148049\n",
      "[888]\tvalid_0's rmse: 0.148035\n",
      "[889]\tvalid_0's rmse: 0.148021\n",
      "[890]\tvalid_0's rmse: 0.148008\n",
      "[891]\tvalid_0's rmse: 0.14801\n",
      "[892]\tvalid_0's rmse: 0.14801\n",
      "[893]\tvalid_0's rmse: 0.148005\n",
      "[894]\tvalid_0's rmse: 0.147981\n",
      "[895]\tvalid_0's rmse: 0.147984\n",
      "[896]\tvalid_0's rmse: 0.147978\n",
      "[897]\tvalid_0's rmse: 0.147973\n",
      "[898]\tvalid_0's rmse: 0.147958\n",
      "[899]\tvalid_0's rmse: 0.147943\n",
      "[900]\tvalid_0's rmse: 0.147929\n",
      "[901]\tvalid_0's rmse: 0.147911\n",
      "[902]\tvalid_0's rmse: 0.147888\n",
      "[903]\tvalid_0's rmse: 0.147867\n",
      "[904]\tvalid_0's rmse: 0.147821\n",
      "[905]\tvalid_0's rmse: 0.147813\n",
      "[906]\tvalid_0's rmse: 0.147808\n",
      "[907]\tvalid_0's rmse: 0.147787\n",
      "[908]\tvalid_0's rmse: 0.147781\n",
      "[909]\tvalid_0's rmse: 0.147805\n",
      "[910]\tvalid_0's rmse: 0.147801\n",
      "[911]\tvalid_0's rmse: 0.147776\n",
      "[912]\tvalid_0's rmse: 0.14778\n",
      "[913]\tvalid_0's rmse: 0.147763\n",
      "[914]\tvalid_0's rmse: 0.147766\n",
      "[915]\tvalid_0's rmse: 0.14775\n",
      "[916]\tvalid_0's rmse: 0.147762\n",
      "[917]\tvalid_0's rmse: 0.147776\n",
      "[918]\tvalid_0's rmse: 0.147789\n",
      "[919]\tvalid_0's rmse: 0.147782\n",
      "[920]\tvalid_0's rmse: 0.147784\n",
      "[921]\tvalid_0's rmse: 0.147774\n",
      "[922]\tvalid_0's rmse: 0.14777\n",
      "[923]\tvalid_0's rmse: 0.14776\n",
      "[924]\tvalid_0's rmse: 0.147757\n",
      "[925]\tvalid_0's rmse: 0.147754\n",
      "[926]\tvalid_0's rmse: 0.147698\n",
      "[927]\tvalid_0's rmse: 0.147643\n",
      "[928]\tvalid_0's rmse: 0.147595\n",
      "[929]\tvalid_0's rmse: 0.147578\n",
      "[930]\tvalid_0's rmse: 0.147524\n",
      "[931]\tvalid_0's rmse: 0.14752\n",
      "[932]\tvalid_0's rmse: 0.147516\n",
      "[933]\tvalid_0's rmse: 0.147515\n",
      "[934]\tvalid_0's rmse: 0.147512\n",
      "[935]\tvalid_0's rmse: 0.147509\n",
      "[936]\tvalid_0's rmse: 0.1475\n",
      "[937]\tvalid_0's rmse: 0.147489\n",
      "[938]\tvalid_0's rmse: 0.147463\n",
      "[939]\tvalid_0's rmse: 0.147455\n",
      "[940]\tvalid_0's rmse: 0.147445\n",
      "[941]\tvalid_0's rmse: 0.147443\n",
      "[942]\tvalid_0's rmse: 0.147442\n",
      "[943]\tvalid_0's rmse: 0.147437\n",
      "[944]\tvalid_0's rmse: 0.147436\n",
      "[945]\tvalid_0's rmse: 0.147429\n",
      "[946]\tvalid_0's rmse: 0.147407\n",
      "[947]\tvalid_0's rmse: 0.147385\n",
      "[948]\tvalid_0's rmse: 0.147361\n",
      "[949]\tvalid_0's rmse: 0.14734\n",
      "[950]\tvalid_0's rmse: 0.147312\n",
      "[951]\tvalid_0's rmse: 0.147274\n",
      "[952]\tvalid_0's rmse: 0.147266\n",
      "[953]\tvalid_0's rmse: 0.147228\n",
      "[954]\tvalid_0's rmse: 0.147191\n",
      "[955]\tvalid_0's rmse: 0.147153\n",
      "[956]\tvalid_0's rmse: 0.14713\n",
      "[957]\tvalid_0's rmse: 0.147107\n",
      "[958]\tvalid_0's rmse: 0.147054\n",
      "[959]\tvalid_0's rmse: 0.147032\n",
      "[960]\tvalid_0's rmse: 0.146991\n",
      "[961]\tvalid_0's rmse: 0.146988\n",
      "[962]\tvalid_0's rmse: 0.146978\n",
      "[963]\tvalid_0's rmse: 0.146976\n",
      "[964]\tvalid_0's rmse: 0.146966\n",
      "[965]\tvalid_0's rmse: 0.146956\n",
      "[966]\tvalid_0's rmse: 0.146962\n",
      "[967]\tvalid_0's rmse: 0.146959\n",
      "[968]\tvalid_0's rmse: 0.146945\n",
      "[969]\tvalid_0's rmse: 0.146948\n",
      "[970]\tvalid_0's rmse: 0.146947\n",
      "[971]\tvalid_0's rmse: 0.14694\n",
      "[972]\tvalid_0's rmse: 0.146949\n",
      "[973]\tvalid_0's rmse: 0.14694\n",
      "[974]\tvalid_0's rmse: 0.146936\n",
      "[975]\tvalid_0's rmse: 0.146931\n",
      "[976]\tvalid_0's rmse: 0.146911\n",
      "[977]\tvalid_0's rmse: 0.146895\n",
      "[978]\tvalid_0's rmse: 0.146861\n",
      "[979]\tvalid_0's rmse: 0.146845\n",
      "[980]\tvalid_0's rmse: 0.14683\n",
      "[981]\tvalid_0's rmse: 0.146822\n",
      "[982]\tvalid_0's rmse: 0.146801\n",
      "[983]\tvalid_0's rmse: 0.146795\n",
      "[984]\tvalid_0's rmse: 0.146788\n",
      "[985]\tvalid_0's rmse: 0.146776\n",
      "[986]\tvalid_0's rmse: 0.146783\n",
      "[987]\tvalid_0's rmse: 0.146789\n",
      "[988]\tvalid_0's rmse: 0.146796\n",
      "[989]\tvalid_0's rmse: 0.146797\n",
      "[990]\tvalid_0's rmse: 0.146804\n",
      "[991]\tvalid_0's rmse: 0.14678\n",
      "[992]\tvalid_0's rmse: 0.146741\n",
      "[993]\tvalid_0's rmse: 0.146719\n",
      "[994]\tvalid_0's rmse: 0.14668\n",
      "[995]\tvalid_0's rmse: 0.146653\n",
      "[996]\tvalid_0's rmse: 0.146669\n",
      "[997]\tvalid_0's rmse: 0.146679\n",
      "[998]\tvalid_0's rmse: 0.146678\n",
      "[999]\tvalid_0's rmse: 0.146662\n",
      "[1000]\tvalid_0's rmse: 0.146662\n",
      "[1001]\tvalid_0's rmse: 0.146678\n",
      "[1002]\tvalid_0's rmse: 0.146694\n",
      "[1003]\tvalid_0's rmse: 0.146694\n",
      "[1004]\tvalid_0's rmse: 0.14671\n",
      "[1005]\tvalid_0's rmse: 0.146715\n",
      "[1006]\tvalid_0's rmse: 0.146716\n",
      "[1007]\tvalid_0's rmse: 0.146703\n",
      "[1008]\tvalid_0's rmse: 0.14668\n",
      "[1009]\tvalid_0's rmse: 0.146655\n",
      "[1010]\tvalid_0's rmse: 0.146641\n",
      "[1011]\tvalid_0's rmse: 0.146648\n",
      "[1012]\tvalid_0's rmse: 0.146627\n",
      "[1013]\tvalid_0's rmse: 0.146618\n",
      "[1014]\tvalid_0's rmse: 0.146598\n",
      "[1015]\tvalid_0's rmse: 0.146577\n",
      "[1016]\tvalid_0's rmse: 0.146538\n",
      "[1017]\tvalid_0's rmse: 0.146499\n",
      "[1018]\tvalid_0's rmse: 0.146461\n",
      "[1019]\tvalid_0's rmse: 0.146429\n",
      "[1020]\tvalid_0's rmse: 0.146409\n",
      "[1021]\tvalid_0's rmse: 0.146412\n",
      "[1022]\tvalid_0's rmse: 0.146427\n",
      "[1023]\tvalid_0's rmse: 0.146425\n",
      "[1024]\tvalid_0's rmse: 0.146441\n",
      "[1025]\tvalid_0's rmse: 0.146457\n",
      "[1026]\tvalid_0's rmse: 0.146421\n",
      "[1027]\tvalid_0's rmse: 0.146425\n",
      "[1028]\tvalid_0's rmse: 0.146412\n",
      "[1029]\tvalid_0's rmse: 0.14641\n",
      "[1030]\tvalid_0's rmse: 0.146412\n",
      "[1031]\tvalid_0's rmse: 0.146394\n",
      "[1032]\tvalid_0's rmse: 0.146406\n",
      "[1033]\tvalid_0's rmse: 0.146391\n",
      "[1034]\tvalid_0's rmse: 0.146375\n",
      "[1035]\tvalid_0's rmse: 0.146362\n",
      "[1036]\tvalid_0's rmse: 0.146356\n",
      "[1037]\tvalid_0's rmse: 0.146357\n",
      "[1038]\tvalid_0's rmse: 0.146358\n",
      "[1039]\tvalid_0's rmse: 0.146359\n",
      "[1040]\tvalid_0's rmse: 0.146361\n",
      "[1041]\tvalid_0's rmse: 0.146354\n",
      "[1042]\tvalid_0's rmse: 0.146347\n",
      "[1043]\tvalid_0's rmse: 0.14634\n",
      "[1044]\tvalid_0's rmse: 0.146324\n",
      "[1045]\tvalid_0's rmse: 0.146302\n",
      "[1046]\tvalid_0's rmse: 0.146291\n",
      "[1047]\tvalid_0's rmse: 0.146277\n",
      "[1048]\tvalid_0's rmse: 0.14626\n",
      "[1049]\tvalid_0's rmse: 0.146244\n",
      "[1050]\tvalid_0's rmse: 0.146231\n",
      "[1051]\tvalid_0's rmse: 0.146227\n",
      "[1052]\tvalid_0's rmse: 0.146208\n",
      "[1053]\tvalid_0's rmse: 0.146189\n",
      "[1054]\tvalid_0's rmse: 0.146173\n",
      "[1055]\tvalid_0's rmse: 0.146152\n",
      "[1056]\tvalid_0's rmse: 0.146152\n",
      "[1057]\tvalid_0's rmse: 0.146167\n",
      "[1058]\tvalid_0's rmse: 0.146168\n",
      "[1059]\tvalid_0's rmse: 0.146163\n",
      "[1060]\tvalid_0's rmse: 0.146159\n",
      "[1061]\tvalid_0's rmse: 0.146169\n",
      "[1062]\tvalid_0's rmse: 0.146179\n",
      "[1063]\tvalid_0's rmse: 0.146189\n",
      "[1064]\tvalid_0's rmse: 0.146196\n",
      "[1065]\tvalid_0's rmse: 0.146207\n",
      "[1066]\tvalid_0's rmse: 0.146199\n",
      "[1067]\tvalid_0's rmse: 0.146204\n",
      "[1068]\tvalid_0's rmse: 0.146209\n",
      "[1069]\tvalid_0's rmse: 0.146212\n",
      "[1070]\tvalid_0's rmse: 0.146216\n",
      "[1071]\tvalid_0's rmse: 0.146191\n",
      "[1072]\tvalid_0's rmse: 0.146175\n",
      "[1073]\tvalid_0's rmse: 0.146162\n",
      "[1074]\tvalid_0's rmse: 0.146146\n",
      "[1075]\tvalid_0's rmse: 0.146136\n",
      "[1076]\tvalid_0's rmse: 0.146116\n",
      "[1077]\tvalid_0's rmse: 0.146097\n",
      "[1078]\tvalid_0's rmse: 0.146073\n",
      "[1079]\tvalid_0's rmse: 0.146055\n",
      "[1080]\tvalid_0's rmse: 0.146036\n",
      "[1081]\tvalid_0's rmse: 0.146056\n",
      "[1082]\tvalid_0's rmse: 0.146086\n",
      "[1083]\tvalid_0's rmse: 0.146115\n",
      "[1084]\tvalid_0's rmse: 0.146145\n",
      "[1085]\tvalid_0's rmse: 0.146165\n",
      "[1086]\tvalid_0's rmse: 0.146145\n",
      "[1087]\tvalid_0's rmse: 0.14614\n",
      "[1088]\tvalid_0's rmse: 0.146117\n",
      "[1089]\tvalid_0's rmse: 0.146137\n",
      "[1090]\tvalid_0's rmse: 0.146113\n",
      "[1091]\tvalid_0's rmse: 0.146099\n",
      "[1092]\tvalid_0's rmse: 0.146091\n",
      "[1093]\tvalid_0's rmse: 0.146081\n",
      "[1094]\tvalid_0's rmse: 0.146057\n",
      "[1095]\tvalid_0's rmse: 0.146033\n",
      "[1096]\tvalid_0's rmse: 0.146009\n",
      "[1097]\tvalid_0's rmse: 0.145993\n",
      "[1098]\tvalid_0's rmse: 0.145964\n",
      "[1099]\tvalid_0's rmse: 0.145941\n",
      "[1100]\tvalid_0's rmse: 0.145926\n",
      "[1101]\tvalid_0's rmse: 0.145928\n",
      "[1102]\tvalid_0's rmse: 0.145882\n",
      "[1103]\tvalid_0's rmse: 0.145863\n",
      "[1104]\tvalid_0's rmse: 0.145824\n",
      "[1105]\tvalid_0's rmse: 0.14581\n",
      "[1106]\tvalid_0's rmse: 0.145787\n",
      "[1107]\tvalid_0's rmse: 0.145791\n",
      "[1108]\tvalid_0's rmse: 0.145811\n",
      "[1109]\tvalid_0's rmse: 0.145814\n",
      "[1110]\tvalid_0's rmse: 0.145793\n",
      "[1111]\tvalid_0's rmse: 0.145801\n",
      "[1112]\tvalid_0's rmse: 0.145802\n",
      "[1113]\tvalid_0's rmse: 0.145811\n",
      "[1114]\tvalid_0's rmse: 0.145811\n",
      "[1115]\tvalid_0's rmse: 0.145822\n",
      "[1116]\tvalid_0's rmse: 0.145806\n",
      "[1117]\tvalid_0's rmse: 0.145791\n",
      "[1118]\tvalid_0's rmse: 0.145777\n",
      "[1119]\tvalid_0's rmse: 0.145792\n",
      "[1120]\tvalid_0's rmse: 0.145778\n",
      "[1121]\tvalid_0's rmse: 0.145792\n",
      "[1122]\tvalid_0's rmse: 0.14577\n",
      "[1123]\tvalid_0's rmse: 0.145749\n",
      "[1124]\tvalid_0's rmse: 0.145727\n",
      "[1125]\tvalid_0's rmse: 0.145706\n",
      "[1126]\tvalid_0's rmse: 0.145681\n",
      "[1127]\tvalid_0's rmse: 0.145657\n",
      "[1128]\tvalid_0's rmse: 0.145649\n",
      "[1129]\tvalid_0's rmse: 0.145625\n",
      "[1130]\tvalid_0's rmse: 0.145602\n",
      "[1131]\tvalid_0's rmse: 0.14559\n",
      "[1132]\tvalid_0's rmse: 0.145585\n",
      "[1133]\tvalid_0's rmse: 0.14558\n",
      "[1134]\tvalid_0's rmse: 0.145575\n",
      "[1135]\tvalid_0's rmse: 0.14557\n",
      "[1136]\tvalid_0's rmse: 0.145549\n",
      "[1137]\tvalid_0's rmse: 0.145536\n",
      "[1138]\tvalid_0's rmse: 0.145521\n",
      "[1139]\tvalid_0's rmse: 0.145519\n",
      "[1140]\tvalid_0's rmse: 0.1455\n",
      "[1141]\tvalid_0's rmse: 0.145503\n",
      "[1142]\tvalid_0's rmse: 0.145501\n",
      "[1143]\tvalid_0's rmse: 0.145463\n",
      "[1144]\tvalid_0's rmse: 0.145473\n",
      "[1145]\tvalid_0's rmse: 0.145472\n",
      "[1146]\tvalid_0's rmse: 0.145466\n",
      "[1147]\tvalid_0's rmse: 0.14546\n",
      "[1148]\tvalid_0's rmse: 0.145455\n",
      "[1149]\tvalid_0's rmse: 0.145449\n",
      "[1150]\tvalid_0's rmse: 0.145437\n",
      "[1151]\tvalid_0's rmse: 0.145413\n",
      "[1152]\tvalid_0's rmse: 0.145386\n",
      "[1153]\tvalid_0's rmse: 0.145361\n",
      "[1154]\tvalid_0's rmse: 0.145366\n",
      "[1155]\tvalid_0's rmse: 0.145343\n",
      "[1156]\tvalid_0's rmse: 0.145344\n",
      "[1157]\tvalid_0's rmse: 0.145346\n",
      "[1158]\tvalid_0's rmse: 0.145348\n",
      "[1159]\tvalid_0's rmse: 0.145351\n",
      "[1160]\tvalid_0's rmse: 0.145354\n",
      "[1161]\tvalid_0's rmse: 0.145326\n",
      "[1162]\tvalid_0's rmse: 0.1453\n",
      "[1163]\tvalid_0's rmse: 0.145295\n",
      "[1164]\tvalid_0's rmse: 0.145304\n",
      "[1165]\tvalid_0's rmse: 0.145279\n",
      "[1166]\tvalid_0's rmse: 0.145267\n",
      "[1167]\tvalid_0's rmse: 0.145225\n",
      "[1168]\tvalid_0's rmse: 0.145217\n",
      "[1169]\tvalid_0's rmse: 0.145233\n",
      "[1170]\tvalid_0's rmse: 0.14525\n",
      "[1171]\tvalid_0's rmse: 0.14524\n",
      "[1172]\tvalid_0's rmse: 0.145241\n",
      "[1173]\tvalid_0's rmse: 0.14523\n",
      "[1174]\tvalid_0's rmse: 0.14522\n",
      "[1175]\tvalid_0's rmse: 0.145201\n",
      "[1176]\tvalid_0's rmse: 0.145181\n",
      "[1177]\tvalid_0's rmse: 0.145188\n",
      "[1178]\tvalid_0's rmse: 0.145194\n",
      "[1179]\tvalid_0's rmse: 0.145201\n",
      "[1180]\tvalid_0's rmse: 0.145201\n",
      "[1181]\tvalid_0's rmse: 0.145194\n",
      "[1182]\tvalid_0's rmse: 0.145175\n",
      "[1183]\tvalid_0's rmse: 0.145161\n",
      "[1184]\tvalid_0's rmse: 0.145145\n",
      "[1185]\tvalid_0's rmse: 0.145119\n",
      "[1186]\tvalid_0's rmse: 0.145115\n",
      "[1187]\tvalid_0's rmse: 0.145112\n",
      "[1188]\tvalid_0's rmse: 0.145139\n",
      "[1189]\tvalid_0's rmse: 0.145167\n",
      "[1190]\tvalid_0's rmse: 0.145195\n",
      "[1191]\tvalid_0's rmse: 0.145197\n",
      "[1192]\tvalid_0's rmse: 0.145199\n",
      "[1193]\tvalid_0's rmse: 0.145202\n",
      "[1194]\tvalid_0's rmse: 0.145205\n",
      "[1195]\tvalid_0's rmse: 0.145215\n",
      "[1196]\tvalid_0's rmse: 0.145211\n",
      "[1197]\tvalid_0's rmse: 0.145198\n",
      "[1198]\tvalid_0's rmse: 0.145174\n",
      "[1199]\tvalid_0's rmse: 0.145155\n",
      "[1200]\tvalid_0's rmse: 0.145154\n",
      "[1201]\tvalid_0's rmse: 0.145154\n",
      "[1202]\tvalid_0's rmse: 0.145148\n",
      "[1203]\tvalid_0's rmse: 0.145146\n",
      "[1204]\tvalid_0's rmse: 0.145139\n",
      "[1205]\tvalid_0's rmse: 0.145133\n",
      "[1206]\tvalid_0's rmse: 0.14512\n",
      "[1207]\tvalid_0's rmse: 0.145098\n",
      "[1208]\tvalid_0's rmse: 0.145101\n",
      "[1209]\tvalid_0's rmse: 0.145078\n",
      "[1210]\tvalid_0's rmse: 0.145083\n",
      "[1211]\tvalid_0's rmse: 0.145073\n",
      "[1212]\tvalid_0's rmse: 0.145056\n",
      "[1213]\tvalid_0's rmse: 0.145033\n",
      "[1214]\tvalid_0's rmse: 0.145016\n",
      "[1215]\tvalid_0's rmse: 0.144999\n",
      "[1216]\tvalid_0's rmse: 0.145006\n",
      "[1217]\tvalid_0's rmse: 0.144994\n",
      "[1218]\tvalid_0's rmse: 0.144999\n",
      "[1219]\tvalid_0's rmse: 0.145004\n",
      "[1220]\tvalid_0's rmse: 0.145005\n",
      "[1221]\tvalid_0's rmse: 0.144999\n",
      "[1222]\tvalid_0's rmse: 0.144964\n",
      "[1223]\tvalid_0's rmse: 0.144958\n",
      "[1224]\tvalid_0's rmse: 0.144952\n",
      "[1225]\tvalid_0's rmse: 0.144926\n",
      "[1226]\tvalid_0's rmse: 0.144926\n",
      "[1227]\tvalid_0's rmse: 0.144924\n",
      "[1228]\tvalid_0's rmse: 0.14494\n",
      "[1229]\tvalid_0's rmse: 0.144968\n",
      "[1230]\tvalid_0's rmse: 0.144992\n",
      "[1231]\tvalid_0's rmse: 0.144984\n",
      "[1232]\tvalid_0's rmse: 0.144979\n",
      "[1233]\tvalid_0's rmse: 0.144995\n",
      "[1234]\tvalid_0's rmse: 0.144976\n",
      "[1235]\tvalid_0's rmse: 0.144958\n",
      "[1236]\tvalid_0's rmse: 0.144964\n",
      "[1237]\tvalid_0's rmse: 0.144964\n",
      "[1238]\tvalid_0's rmse: 0.14496\n",
      "[1239]\tvalid_0's rmse: 0.144966\n",
      "[1240]\tvalid_0's rmse: 0.144962\n",
      "[1241]\tvalid_0's rmse: 0.144973\n",
      "[1242]\tvalid_0's rmse: 0.14495\n",
      "[1243]\tvalid_0's rmse: 0.144955\n",
      "[1244]\tvalid_0's rmse: 0.144955\n",
      "[1245]\tvalid_0's rmse: 0.144955\n",
      "[1246]\tvalid_0's rmse: 0.144929\n",
      "[1247]\tvalid_0's rmse: 0.144896\n",
      "[1248]\tvalid_0's rmse: 0.144874\n",
      "[1249]\tvalid_0's rmse: 0.144852\n",
      "[1250]\tvalid_0's rmse: 0.144869\n",
      "[1251]\tvalid_0's rmse: 0.144882\n",
      "[1252]\tvalid_0's rmse: 0.144905\n",
      "[1253]\tvalid_0's rmse: 0.144917\n",
      "[1254]\tvalid_0's rmse: 0.144935\n",
      "[1255]\tvalid_0's rmse: 0.144945\n",
      "[1256]\tvalid_0's rmse: 0.144947\n",
      "[1257]\tvalid_0's rmse: 0.144944\n",
      "[1258]\tvalid_0's rmse: 0.144951\n",
      "[1259]\tvalid_0's rmse: 0.144954\n",
      "[1260]\tvalid_0's rmse: 0.144945\n",
      "[1261]\tvalid_0's rmse: 0.14496\n",
      "[1262]\tvalid_0's rmse: 0.144975\n",
      "[1263]\tvalid_0's rmse: 0.14499\n",
      "[1264]\tvalid_0's rmse: 0.144997\n",
      "[1265]\tvalid_0's rmse: 0.145012\n",
      "[1266]\tvalid_0's rmse: 0.145021\n",
      "[1267]\tvalid_0's rmse: 0.145031\n",
      "[1268]\tvalid_0's rmse: 0.145052\n",
      "[1269]\tvalid_0's rmse: 0.145071\n",
      "[1270]\tvalid_0's rmse: 0.145079\n",
      "[1271]\tvalid_0's rmse: 0.145054\n",
      "[1272]\tvalid_0's rmse: 0.145067\n",
      "[1273]\tvalid_0's rmse: 0.145042\n",
      "[1274]\tvalid_0's rmse: 0.14502\n",
      "[1275]\tvalid_0's rmse: 0.145004\n",
      "[1276]\tvalid_0's rmse: 0.145021\n",
      "[1277]\tvalid_0's rmse: 0.145038\n",
      "[1278]\tvalid_0's rmse: 0.145055\n",
      "[1279]\tvalid_0's rmse: 0.145072\n",
      "[1280]\tvalid_0's rmse: 0.145072\n",
      "[1281]\tvalid_0's rmse: 0.145041\n",
      "[1282]\tvalid_0's rmse: 0.145004\n",
      "[1283]\tvalid_0's rmse: 0.144975\n",
      "[1284]\tvalid_0's rmse: 0.144938\n",
      "[1285]\tvalid_0's rmse: 0.144955\n",
      "[1286]\tvalid_0's rmse: 0.144984\n",
      "[1287]\tvalid_0's rmse: 0.145009\n",
      "[1288]\tvalid_0's rmse: 0.145037\n",
      "[1289]\tvalid_0's rmse: 0.145046\n",
      "[1290]\tvalid_0's rmse: 0.145071\n",
      "[1291]\tvalid_0's rmse: 0.145041\n",
      "[1292]\tvalid_0's rmse: 0.145012\n",
      "[1293]\tvalid_0's rmse: 0.144983\n",
      "[1294]\tvalid_0's rmse: 0.14497\n",
      "[1295]\tvalid_0's rmse: 0.144942\n",
      "[1296]\tvalid_0's rmse: 0.144919\n",
      "[1297]\tvalid_0's rmse: 0.144891\n",
      "[1298]\tvalid_0's rmse: 0.144877\n",
      "[1299]\tvalid_0's rmse: 0.144864\n",
      "[1300]\tvalid_0's rmse: 0.14485\n",
      "[1301]\tvalid_0's rmse: 0.144828\n",
      "[1302]\tvalid_0's rmse: 0.144782\n",
      "[1303]\tvalid_0's rmse: 0.144736\n",
      "[1304]\tvalid_0's rmse: 0.144727\n",
      "[1305]\tvalid_0's rmse: 0.144682\n",
      "[1306]\tvalid_0's rmse: 0.144676\n",
      "[1307]\tvalid_0's rmse: 0.14467\n",
      "[1308]\tvalid_0's rmse: 0.144665\n",
      "[1309]\tvalid_0's rmse: 0.14465\n",
      "[1310]\tvalid_0's rmse: 0.144624\n",
      "[1311]\tvalid_0's rmse: 0.144618\n",
      "[1312]\tvalid_0's rmse: 0.144613\n",
      "[1313]\tvalid_0's rmse: 0.144611\n",
      "[1314]\tvalid_0's rmse: 0.144602\n",
      "[1315]\tvalid_0's rmse: 0.144597\n",
      "[1316]\tvalid_0's rmse: 0.14457\n",
      "[1317]\tvalid_0's rmse: 0.14454\n",
      "[1318]\tvalid_0's rmse: 0.14451\n",
      "[1319]\tvalid_0's rmse: 0.144493\n",
      "[1320]\tvalid_0's rmse: 0.144463\n",
      "[1321]\tvalid_0's rmse: 0.144465\n",
      "[1322]\tvalid_0's rmse: 0.144465\n",
      "[1323]\tvalid_0's rmse: 0.144466\n",
      "[1324]\tvalid_0's rmse: 0.144475\n",
      "[1325]\tvalid_0's rmse: 0.144474\n",
      "[1326]\tvalid_0's rmse: 0.144456\n",
      "[1327]\tvalid_0's rmse: 0.144439\n",
      "[1328]\tvalid_0's rmse: 0.144422\n",
      "[1329]\tvalid_0's rmse: 0.144406\n",
      "[1330]\tvalid_0's rmse: 0.144403\n",
      "[1331]\tvalid_0's rmse: 0.144416\n",
      "[1332]\tvalid_0's rmse: 0.14443\n",
      "[1333]\tvalid_0's rmse: 0.144438\n",
      "[1334]\tvalid_0's rmse: 0.144451\n",
      "[1335]\tvalid_0's rmse: 0.144464\n",
      "[1336]\tvalid_0's rmse: 0.144441\n",
      "[1337]\tvalid_0's rmse: 0.144462\n",
      "[1338]\tvalid_0's rmse: 0.144437\n",
      "[1339]\tvalid_0's rmse: 0.144414\n",
      "[1340]\tvalid_0's rmse: 0.14439\n",
      "[1341]\tvalid_0's rmse: 0.144388\n",
      "[1342]\tvalid_0's rmse: 0.144385\n",
      "[1343]\tvalid_0's rmse: 0.144382\n",
      "[1344]\tvalid_0's rmse: 0.144379\n",
      "[1345]\tvalid_0's rmse: 0.144364\n",
      "[1346]\tvalid_0's rmse: 0.144367\n",
      "[1347]\tvalid_0's rmse: 0.144367\n",
      "[1348]\tvalid_0's rmse: 0.144351\n",
      "[1349]\tvalid_0's rmse: 0.144344\n",
      "[1350]\tvalid_0's rmse: 0.14434\n",
      "[1351]\tvalid_0's rmse: 0.144308\n",
      "[1352]\tvalid_0's rmse: 0.144282\n",
      "[1353]\tvalid_0's rmse: 0.144283\n",
      "[1354]\tvalid_0's rmse: 0.144251\n",
      "[1355]\tvalid_0's rmse: 0.144227\n",
      "[1356]\tvalid_0's rmse: 0.14421\n",
      "[1357]\tvalid_0's rmse: 0.144195\n",
      "[1358]\tvalid_0's rmse: 0.144169\n",
      "[1359]\tvalid_0's rmse: 0.144149\n",
      "[1360]\tvalid_0's rmse: 0.144124\n",
      "[1361]\tvalid_0's rmse: 0.144112\n",
      "[1362]\tvalid_0's rmse: 0.144073\n",
      "[1363]\tvalid_0's rmse: 0.144061\n",
      "[1364]\tvalid_0's rmse: 0.144049\n",
      "[1365]\tvalid_0's rmse: 0.144037\n",
      "[1366]\tvalid_0's rmse: 0.144027\n",
      "[1367]\tvalid_0's rmse: 0.144036\n",
      "[1368]\tvalid_0's rmse: 0.144028\n",
      "[1369]\tvalid_0's rmse: 0.14403\n",
      "[1370]\tvalid_0's rmse: 0.144018\n",
      "[1371]\tvalid_0's rmse: 0.14401\n",
      "[1372]\tvalid_0's rmse: 0.144003\n",
      "[1373]\tvalid_0's rmse: 0.143978\n",
      "[1374]\tvalid_0's rmse: 0.143972\n",
      "[1375]\tvalid_0's rmse: 0.143966\n",
      "[1376]\tvalid_0's rmse: 0.143954\n",
      "[1377]\tvalid_0's rmse: 0.143926\n",
      "[1378]\tvalid_0's rmse: 0.143911\n",
      "[1379]\tvalid_0's rmse: 0.143903\n",
      "[1380]\tvalid_0's rmse: 0.143895\n",
      "[1381]\tvalid_0's rmse: 0.143884\n",
      "[1382]\tvalid_0's rmse: 0.143874\n",
      "[1383]\tvalid_0's rmse: 0.143863\n",
      "[1384]\tvalid_0's rmse: 0.143849\n",
      "[1385]\tvalid_0's rmse: 0.143835\n",
      "[1386]\tvalid_0's rmse: 0.143832\n",
      "[1387]\tvalid_0's rmse: 0.143855\n",
      "[1388]\tvalid_0's rmse: 0.143857\n",
      "[1389]\tvalid_0's rmse: 0.143855\n",
      "[1390]\tvalid_0's rmse: 0.143855\n",
      "[1391]\tvalid_0's rmse: 0.143854\n",
      "[1392]\tvalid_0's rmse: 0.143854\n",
      "[1393]\tvalid_0's rmse: 0.143854\n",
      "[1394]\tvalid_0's rmse: 0.143884\n",
      "[1395]\tvalid_0's rmse: 0.1439\n",
      "[1396]\tvalid_0's rmse: 0.143914\n",
      "[1397]\tvalid_0's rmse: 0.143926\n",
      "[1398]\tvalid_0's rmse: 0.143939\n",
      "[1399]\tvalid_0's rmse: 0.143945\n",
      "[1400]\tvalid_0's rmse: 0.143966\n",
      "[1401]\tvalid_0's rmse: 0.143975\n",
      "[1402]\tvalid_0's rmse: 0.14398\n",
      "[1403]\tvalid_0's rmse: 0.143975\n",
      "[1404]\tvalid_0's rmse: 0.143984\n",
      "[1405]\tvalid_0's rmse: 0.143988\n",
      "[1406]\tvalid_0's rmse: 0.14399\n",
      "[1407]\tvalid_0's rmse: 0.143987\n",
      "[1408]\tvalid_0's rmse: 0.14399\n",
      "[1409]\tvalid_0's rmse: 0.14399\n",
      "[1410]\tvalid_0's rmse: 0.143983\n",
      "[1411]\tvalid_0's rmse: 0.143977\n",
      "[1412]\tvalid_0's rmse: 0.14397\n",
      "[1413]\tvalid_0's rmse: 0.143969\n",
      "[1414]\tvalid_0's rmse: 0.143964\n",
      "[1415]\tvalid_0's rmse: 0.143957\n",
      "[1416]\tvalid_0's rmse: 0.143946\n",
      "[1417]\tvalid_0's rmse: 0.143936\n",
      "[1418]\tvalid_0's rmse: 0.143918\n",
      "[1419]\tvalid_0's rmse: 0.143909\n",
      "[1420]\tvalid_0's rmse: 0.143883\n",
      "[1421]\tvalid_0's rmse: 0.143869\n",
      "[1422]\tvalid_0's rmse: 0.143865\n",
      "[1423]\tvalid_0's rmse: 0.143851\n",
      "[1424]\tvalid_0's rmse: 0.143837\n",
      "[1425]\tvalid_0's rmse: 0.143833\n",
      "[1426]\tvalid_0's rmse: 0.143827\n",
      "[1427]\tvalid_0's rmse: 0.14382\n",
      "[1428]\tvalid_0's rmse: 0.143814\n",
      "[1429]\tvalid_0's rmse: 0.143808\n",
      "[1430]\tvalid_0's rmse: 0.143787\n",
      "[1431]\tvalid_0's rmse: 0.143764\n",
      "[1432]\tvalid_0's rmse: 0.143742\n",
      "[1433]\tvalid_0's rmse: 0.14372\n",
      "[1434]\tvalid_0's rmse: 0.143698\n",
      "[1435]\tvalid_0's rmse: 0.143675\n",
      "[1436]\tvalid_0's rmse: 0.14369\n",
      "[1437]\tvalid_0's rmse: 0.143684\n",
      "[1438]\tvalid_0's rmse: 0.143688\n",
      "[1439]\tvalid_0's rmse: 0.143699\n",
      "[1440]\tvalid_0's rmse: 0.143698\n",
      "[1441]\tvalid_0's rmse: 0.143682\n",
      "[1442]\tvalid_0's rmse: 0.143672\n",
      "[1443]\tvalid_0's rmse: 0.143662\n",
      "[1444]\tvalid_0's rmse: 0.143653\n",
      "[1445]\tvalid_0's rmse: 0.143643\n",
      "[1446]\tvalid_0's rmse: 0.143603\n",
      "[1447]\tvalid_0's rmse: 0.143563\n",
      "[1448]\tvalid_0's rmse: 0.143524\n",
      "[1449]\tvalid_0's rmse: 0.143488\n",
      "[1450]\tvalid_0's rmse: 0.14345\n",
      "[1451]\tvalid_0's rmse: 0.14346\n",
      "[1452]\tvalid_0's rmse: 0.143435\n",
      "[1453]\tvalid_0's rmse: 0.143424\n",
      "[1454]\tvalid_0's rmse: 0.143401\n",
      "[1455]\tvalid_0's rmse: 0.143375\n",
      "[1456]\tvalid_0's rmse: 0.143373\n",
      "[1457]\tvalid_0's rmse: 0.143388\n",
      "[1458]\tvalid_0's rmse: 0.143397\n",
      "[1459]\tvalid_0's rmse: 0.143412\n",
      "[1460]\tvalid_0's rmse: 0.143426\n",
      "[1461]\tvalid_0's rmse: 0.143424\n",
      "[1462]\tvalid_0's rmse: 0.14342\n",
      "[1463]\tvalid_0's rmse: 0.143417\n",
      "[1464]\tvalid_0's rmse: 0.143407\n",
      "[1465]\tvalid_0's rmse: 0.143398\n",
      "[1466]\tvalid_0's rmse: 0.143389\n",
      "[1467]\tvalid_0's rmse: 0.143381\n",
      "[1468]\tvalid_0's rmse: 0.143368\n",
      "[1469]\tvalid_0's rmse: 0.143358\n",
      "[1470]\tvalid_0's rmse: 0.14336\n",
      "[1471]\tvalid_0's rmse: 0.143373\n",
      "[1472]\tvalid_0's rmse: 0.143387\n",
      "[1473]\tvalid_0's rmse: 0.143402\n",
      "[1474]\tvalid_0's rmse: 0.143421\n",
      "[1475]\tvalid_0's rmse: 0.143434\n",
      "[1476]\tvalid_0's rmse: 0.143435\n",
      "[1477]\tvalid_0's rmse: 0.143446\n",
      "[1478]\tvalid_0's rmse: 0.143447\n",
      "[1479]\tvalid_0's rmse: 0.143444\n",
      "[1480]\tvalid_0's rmse: 0.143439\n",
      "[1481]\tvalid_0's rmse: 0.143435\n",
      "[1482]\tvalid_0's rmse: 0.143432\n",
      "[1483]\tvalid_0's rmse: 0.143428\n",
      "[1484]\tvalid_0's rmse: 0.143426\n",
      "[1485]\tvalid_0's rmse: 0.143424\n",
      "[1486]\tvalid_0's rmse: 0.143405\n",
      "[1487]\tvalid_0's rmse: 0.143387\n",
      "[1488]\tvalid_0's rmse: 0.143369\n",
      "[1489]\tvalid_0's rmse: 0.143347\n",
      "[1490]\tvalid_0's rmse: 0.14333\n",
      "[1491]\tvalid_0's rmse: 0.143322\n",
      "[1492]\tvalid_0's rmse: 0.143314\n",
      "[1493]\tvalid_0's rmse: 0.14331\n",
      "[1494]\tvalid_0's rmse: 0.143323\n",
      "[1495]\tvalid_0's rmse: 0.143324\n",
      "[1496]\tvalid_0's rmse: 0.143314\n",
      "[1497]\tvalid_0's rmse: 0.143303\n",
      "[1498]\tvalid_0's rmse: 0.143288\n",
      "[1499]\tvalid_0's rmse: 0.143278\n",
      "[1500]\tvalid_0's rmse: 0.143254\n",
      "[1501]\tvalid_0's rmse: 0.143224\n",
      "[1502]\tvalid_0's rmse: 0.143206\n",
      "[1503]\tvalid_0's rmse: 0.143176\n",
      "[1504]\tvalid_0's rmse: 0.143183\n",
      "[1505]\tvalid_0's rmse: 0.14317\n",
      "[1506]\tvalid_0's rmse: 0.143176\n",
      "[1507]\tvalid_0's rmse: 0.143197\n",
      "[1508]\tvalid_0's rmse: 0.143203\n",
      "[1509]\tvalid_0's rmse: 0.14321\n",
      "[1510]\tvalid_0's rmse: 0.143217\n",
      "[1511]\tvalid_0's rmse: 0.143191\n",
      "[1512]\tvalid_0's rmse: 0.143163\n",
      "[1513]\tvalid_0's rmse: 0.143153\n",
      "[1514]\tvalid_0's rmse: 0.143125\n",
      "[1515]\tvalid_0's rmse: 0.143095\n",
      "[1516]\tvalid_0's rmse: 0.143071\n",
      "[1517]\tvalid_0's rmse: 0.143047\n",
      "[1518]\tvalid_0's rmse: 0.143024\n",
      "[1519]\tvalid_0's rmse: 0.143011\n",
      "[1520]\tvalid_0's rmse: 0.142988\n",
      "[1521]\tvalid_0's rmse: 0.142963\n",
      "[1522]\tvalid_0's rmse: 0.142942\n",
      "[1523]\tvalid_0's rmse: 0.142917\n",
      "[1524]\tvalid_0's rmse: 0.142896\n",
      "[1525]\tvalid_0's rmse: 0.14287\n",
      "[1526]\tvalid_0's rmse: 0.142861\n",
      "[1527]\tvalid_0's rmse: 0.142852\n",
      "[1528]\tvalid_0's rmse: 0.142843\n",
      "[1529]\tvalid_0's rmse: 0.142827\n",
      "[1530]\tvalid_0's rmse: 0.14281\n",
      "[1531]\tvalid_0's rmse: 0.142826\n",
      "[1532]\tvalid_0's rmse: 0.142834\n",
      "[1533]\tvalid_0's rmse: 0.142855\n",
      "[1534]\tvalid_0's rmse: 0.142881\n",
      "[1535]\tvalid_0's rmse: 0.142908\n",
      "[1536]\tvalid_0's rmse: 0.142897\n",
      "[1537]\tvalid_0's rmse: 0.142886\n",
      "[1538]\tvalid_0's rmse: 0.142878\n",
      "[1539]\tvalid_0's rmse: 0.142869\n",
      "[1540]\tvalid_0's rmse: 0.142865\n",
      "[1541]\tvalid_0's rmse: 0.142872\n",
      "[1542]\tvalid_0's rmse: 0.142902\n",
      "[1543]\tvalid_0's rmse: 0.14291\n",
      "[1544]\tvalid_0's rmse: 0.142918\n",
      "[1545]\tvalid_0's rmse: 0.142926\n",
      "[1546]\tvalid_0's rmse: 0.142934\n",
      "[1547]\tvalid_0's rmse: 0.142935\n",
      "[1548]\tvalid_0's rmse: 0.142935\n",
      "[1549]\tvalid_0's rmse: 0.142936\n",
      "[1550]\tvalid_0's rmse: 0.142936\n",
      "[1551]\tvalid_0's rmse: 0.142937\n",
      "[1552]\tvalid_0's rmse: 0.142947\n",
      "[1553]\tvalid_0's rmse: 0.142947\n",
      "[1554]\tvalid_0's rmse: 0.14295\n",
      "[1555]\tvalid_0's rmse: 0.142947\n",
      "[1556]\tvalid_0's rmse: 0.142939\n",
      "[1557]\tvalid_0's rmse: 0.142931\n",
      "[1558]\tvalid_0's rmse: 0.142916\n",
      "[1559]\tvalid_0's rmse: 0.142886\n",
      "[1560]\tvalid_0's rmse: 0.142868\n",
      "[1561]\tvalid_0's rmse: 0.142862\n",
      "[1562]\tvalid_0's rmse: 0.142856\n",
      "[1563]\tvalid_0's rmse: 0.142849\n",
      "[1564]\tvalid_0's rmse: 0.142843\n",
      "[1565]\tvalid_0's rmse: 0.142837\n",
      "[1566]\tvalid_0's rmse: 0.142816\n",
      "[1567]\tvalid_0's rmse: 0.142795\n",
      "[1568]\tvalid_0's rmse: 0.14281\n",
      "[1569]\tvalid_0's rmse: 0.142791\n",
      "[1570]\tvalid_0's rmse: 0.14281\n",
      "[1571]\tvalid_0's rmse: 0.142804\n",
      "[1572]\tvalid_0's rmse: 0.142798\n",
      "[1573]\tvalid_0's rmse: 0.142792\n",
      "[1574]\tvalid_0's rmse: 0.142774\n",
      "[1575]\tvalid_0's rmse: 0.142772\n",
      "[1576]\tvalid_0's rmse: 0.142774\n",
      "[1577]\tvalid_0's rmse: 0.142776\n",
      "[1578]\tvalid_0's rmse: 0.142778\n",
      "[1579]\tvalid_0's rmse: 0.14278\n",
      "[1580]\tvalid_0's rmse: 0.142765\n",
      "[1581]\tvalid_0's rmse: 0.142791\n",
      "[1582]\tvalid_0's rmse: 0.142806\n",
      "[1583]\tvalid_0's rmse: 0.142832\n",
      "[1584]\tvalid_0's rmse: 0.142849\n",
      "[1585]\tvalid_0's rmse: 0.142869\n",
      "[1586]\tvalid_0's rmse: 0.142892\n",
      "[1587]\tvalid_0's rmse: 0.142914\n",
      "[1588]\tvalid_0's rmse: 0.142937\n",
      "[1589]\tvalid_0's rmse: 0.142959\n",
      "[1590]\tvalid_0's rmse: 0.142981\n",
      "[1591]\tvalid_0's rmse: 0.142986\n",
      "[1592]\tvalid_0's rmse: 0.142997\n",
      "[1593]\tvalid_0's rmse: 0.143007\n",
      "[1594]\tvalid_0's rmse: 0.143017\n",
      "[1595]\tvalid_0's rmse: 0.143023\n",
      "[1596]\tvalid_0's rmse: 0.143018\n",
      "[1597]\tvalid_0's rmse: 0.143012\n",
      "[1598]\tvalid_0's rmse: 0.143\n",
      "[1599]\tvalid_0's rmse: 0.142994\n",
      "[1600]\tvalid_0's rmse: 0.14298\n",
      "[1601]\tvalid_0's rmse: 0.142961\n",
      "[1602]\tvalid_0's rmse: 0.142939\n",
      "[1603]\tvalid_0's rmse: 0.14293\n",
      "[1604]\tvalid_0's rmse: 0.142914\n",
      "[1605]\tvalid_0's rmse: 0.142897\n",
      "[1606]\tvalid_0's rmse: 0.142893\n",
      "[1607]\tvalid_0's rmse: 0.14289\n",
      "[1608]\tvalid_0's rmse: 0.142875\n",
      "[1609]\tvalid_0's rmse: 0.142876\n",
      "[1610]\tvalid_0's rmse: 0.142872\n",
      "[1611]\tvalid_0's rmse: 0.142878\n",
      "[1612]\tvalid_0's rmse: 0.142884\n",
      "[1613]\tvalid_0's rmse: 0.142897\n",
      "[1614]\tvalid_0's rmse: 0.142905\n",
      "[1615]\tvalid_0's rmse: 0.142885\n",
      "[1616]\tvalid_0's rmse: 0.142905\n",
      "[1617]\tvalid_0's rmse: 0.142924\n",
      "[1618]\tvalid_0's rmse: 0.142943\n",
      "[1619]\tvalid_0's rmse: 0.142963\n",
      "[1620]\tvalid_0's rmse: 0.142982\n",
      "[1621]\tvalid_0's rmse: 0.143007\n",
      "[1622]\tvalid_0's rmse: 0.143031\n",
      "[1623]\tvalid_0's rmse: 0.143056\n",
      "[1624]\tvalid_0's rmse: 0.143078\n",
      "[1625]\tvalid_0's rmse: 0.143087\n",
      "[1626]\tvalid_0's rmse: 0.143085\n",
      "[1627]\tvalid_0's rmse: 0.143084\n",
      "[1628]\tvalid_0's rmse: 0.143081\n",
      "[1629]\tvalid_0's rmse: 0.14308\n",
      "[1630]\tvalid_0's rmse: 0.143077\n",
      "[1631]\tvalid_0's rmse: 0.143082\n",
      "[1632]\tvalid_0's rmse: 0.143088\n",
      "[1633]\tvalid_0's rmse: 0.143081\n",
      "[1634]\tvalid_0's rmse: 0.143087\n",
      "[1635]\tvalid_0's rmse: 0.143089\n",
      "[1636]\tvalid_0's rmse: 0.143091\n",
      "[1637]\tvalid_0's rmse: 0.143099\n",
      "[1638]\tvalid_0's rmse: 0.143104\n",
      "[1639]\tvalid_0's rmse: 0.143108\n",
      "[1640]\tvalid_0's rmse: 0.143101\n",
      "[1641]\tvalid_0's rmse: 0.143104\n",
      "[1642]\tvalid_0's rmse: 0.143099\n",
      "[1643]\tvalid_0's rmse: 0.143092\n",
      "[1644]\tvalid_0's rmse: 0.143089\n",
      "[1645]\tvalid_0's rmse: 0.143084\n",
      "[1646]\tvalid_0's rmse: 0.143065\n",
      "[1647]\tvalid_0's rmse: 0.143047\n",
      "[1648]\tvalid_0's rmse: 0.14305\n",
      "[1649]\tvalid_0's rmse: 0.143024\n",
      "[1650]\tvalid_0's rmse: 0.143006\n",
      "[1651]\tvalid_0's rmse: 0.142999\n",
      "[1652]\tvalid_0's rmse: 0.142992\n",
      "[1653]\tvalid_0's rmse: 0.142985\n",
      "[1654]\tvalid_0's rmse: 0.142973\n",
      "[1655]\tvalid_0's rmse: 0.142962\n",
      "[1656]\tvalid_0's rmse: 0.142952\n",
      "[1657]\tvalid_0's rmse: 0.14294\n",
      "[1658]\tvalid_0's rmse: 0.142906\n",
      "[1659]\tvalid_0's rmse: 0.142891\n",
      "[1660]\tvalid_0's rmse: 0.142902\n",
      "[1661]\tvalid_0's rmse: 0.142894\n",
      "[1662]\tvalid_0's rmse: 0.142888\n",
      "[1663]\tvalid_0's rmse: 0.142893\n",
      "[1664]\tvalid_0's rmse: 0.142886\n",
      "[1665]\tvalid_0's rmse: 0.142882\n",
      "[1666]\tvalid_0's rmse: 0.1429\n",
      "[1667]\tvalid_0's rmse: 0.142903\n",
      "[1668]\tvalid_0's rmse: 0.142921\n",
      "[1669]\tvalid_0's rmse: 0.142943\n",
      "[1670]\tvalid_0's rmse: 0.142964\n",
      "[1671]\tvalid_0's rmse: 0.142938\n",
      "[1672]\tvalid_0's rmse: 0.142937\n",
      "[1673]\tvalid_0's rmse: 0.142911\n",
      "[1674]\tvalid_0's rmse: 0.142916\n",
      "[1675]\tvalid_0's rmse: 0.14292\n",
      "[1676]\tvalid_0's rmse: 0.142922\n",
      "[1677]\tvalid_0's rmse: 0.142918\n",
      "[1678]\tvalid_0's rmse: 0.142914\n",
      "[1679]\tvalid_0's rmse: 0.14291\n",
      "[1680]\tvalid_0's rmse: 0.142914\n",
      "Early stopping, best iteration is:\n",
      "[1580]\tvalid_0's rmse: 0.142765\n"
     ]
    }
   ],
   "source": [
    "gbm = lgb.train(params, lgb_train, num_boost_round=2000, valid_sets=lgb_eval, early_stopping_rounds=100)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "y_pred = np.expm1(gbm.predict(X_test))\n",
    "y_true = np.expm1(Y_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 1.96E+04\n",
      "RMSE: 140.1415\n",
      "MAE: 77.1526\n",
      "MAPE: 10.08%\n",
      "R_2: 0.9768\n",
      "NMB: -0.0215\n",
      "NME: 0.0784\n",
      "MFB: 0.0051\n",
      "MFE: 0.0953\n"
     ]
    }
   ],
   "source": [
    "MSE = mean_squared_error(y_true, y_pred)\n",
    "RMSE = np.sqrt(mean_squared_error(y_true, y_pred))\n",
    "MAE = mean_absolute_error(y_true, y_pred)\n",
    "MAPE = mean_absolute_percentage_error(y_true, y_pred)\n",
    "R_2 = r2_score(y_true, y_pred)\n",
    "NMB = sum(y_pred - y_true) / sum(y_true)\n",
    "NME = sum(abs(y_true-y_pred)) / sum(y_true)\n",
    "MFB = sum((y_pred - y_true)/((y_pred+y_true)/2))/len(y_true)\n",
    "MFE = sum(abs((y_pred - y_true))/((y_pred+y_true)/2))/len(y_true)\n",
    "print(f\"MSE: {format(MSE, '.2E')}\")\n",
    "print(f'RMSE: {round(RMSE, 4)}')\n",
    "print(f'MAE: {round(MAE, 4)}')\n",
    "print(f'MAPE: {round(MAPE*100, 2)}%')\n",
    "print(f'R_2: {round(R_2, 4)}')\n",
    "print(f'NMB: {round(NMB, 4)}')\n",
    "print(f'NME: {round(NME, 4)}')\n",
    "print(f'MFB: {round(MFB, 4)}')\n",
    "print(f'MFE: {round(MFE, 4)}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "test_rst = pd.DataFrame(np.array([y_pred, y_true]).T, columns=['y_pred', 'y_true'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2200x900 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(22, 9))\n",
    "smaller_rst = test_rst[test_rst.y_true <= 2000].reset_index(drop=True)\n",
    "plt.plot(smaller_rst.y_pred, 'o-', color='b', label='预测值')\n",
    "plt.plot(smaller_rst.y_true, '*-', color='r', label='真实值')\n",
    "# for index in range(smaller_rst.shape[0]):\n",
    "#     tmp_y = smaller_rst.iloc[index].y_true\n",
    "#     tmp_yhat = smaller_rst.iloc[index].y_pred\n",
    "#     plt.plot([index, index], [tmp_yhat, tmp_y], color='grey')\n",
    "#     # print(f\"从({index}, {tmp_y}) 到 ({index},{tmp_yhat})\")\n",
    "plt.legend(loc='best')\n",
    "plt.title('低煤耗机组预测结果（不添加发电量、供热量）')\n",
    "plt.savefig('./figure/unit_smaller_2.png')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2200x900 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(22, 9))\n",
    "larger_rst = test_rst[test_rst.y_true > 2000].reset_index(drop=True)\n",
    "plt.plot(larger_rst.y_pred, 'o-', color='b', label='预测值')\n",
    "plt.plot(larger_rst.y_true, '*-', color='r', label='真实值')\n",
    "# for index in range(larger_rst.shape[0]):\n",
    "#     tmp_y = larger_rst.iloc[index].y_true\n",
    "#     tmp_yhat = larger_rst.iloc[index].y_pred\n",
    "#     plt.plot([index, index], [tmp_yhat, tmp_y], color='grey')\n",
    "#     # print(f\"从({index}, {tmp_y}) 到 ({index},{tmp_yhat})\")\n",
    "plt.legend(loc='best')\n",
    "plt.title('高煤耗机组预测结果（不添加发电量、供热量）')\n",
    "plt.savefig('./figure/unit_larger_2.png')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 8.76E+04\n",
      "RMSE: 295.91786010538635\n",
      "MAE: 195.36140253946826\n",
      "MAPE: 0.0663623609341966\n",
      "R_2: 0.5412201459987633\n"
     ]
    }
   ],
   "source": [
    "y_pred_l = test_rst[test_rst.y_true > 2000].y_pred\n",
    "y_true_l = test_rst[test_rst.y_true > 2000].y_true\n",
    "MSE_l = mean_squared_error(y_true_l, y_pred_l)\n",
    "RMSE_l = np.sqrt(mean_squared_error(y_true_l, y_pred_l))\n",
    "MAE_l = mean_absolute_error(y_true_l, y_pred_l)\n",
    "MAPE_l = mean_absolute_percentage_error(y_true_l, y_pred_l)\n",
    "R_2_l = r2_score(y_true_l, y_pred_l)\n",
    "print('MSE:', format(MSE_l, '.2E'))\n",
    "print('RMSE:', RMSE_l)\n",
    "print('MAE:', MAE_l)\n",
    "print('MAPE:', MAPE_l)\n",
    "print('R_2:', R_2_l)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 5.27E+03\n",
      "RMSE: 72.59700673680008\n",
      "MAE: 52.1469378966335\n",
      "MAPE: 0.1081435925002414\n",
      "R_2: 0.6655229354965604\n"
     ]
    }
   ],
   "source": [
    "y_pred_s = test_rst[test_rst.y_true <= 2000].y_pred\n",
    "y_true_s = test_rst[test_rst.y_true <= 2000].y_true\n",
    "MSE_s = mean_squared_error(y_true_s, y_pred_s)\n",
    "RMSE_s = np.sqrt(mean_squared_error(y_true_s, y_pred_s))\n",
    "MAE_s = mean_absolute_error(y_true_s, y_pred_s)\n",
    "MAPE_s = mean_absolute_percentage_error(y_true_s, y_pred_s)\n",
    "R_2_s = r2_score(y_true_s, y_pred_s)\n",
    "print('MSE:', format(MSE_s, '.2E'))\n",
    "print('RMSE:', RMSE_s)\n",
    "print('MAE:', MAE_s)\n",
    "print('MAPE:', MAPE_s)\n",
    "print('R_2:', R_2_s)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "feature_importance = pd.DataFrame()\n",
    "feature_importance['fea_name'] = feature_cols\n",
    "feature_importance['fea_imp'] = gbm.feature_importance(importance_type='split')\n",
    "feature_importance = feature_importance.sort_values('fea_imp', ascending=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [],
   "source": [
    "import matplotlib.ticker as ticker"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2000x1200 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[20, 12])\n",
    "ax = sns.barplot(x=feature_importance['fea_name'], y=feature_importance['fea_imp'])\n",
    "ax.set_xticklabels(labels=feature_importance['fea_name'], rotation=45, fontsize=12)\n",
    "ax.yaxis.set_major_locator(ticker.MultipleLocator(2000))  # 设置y轴标签间隔\n",
    "plt.title('特征重要性（使用发电量、供热量）')\n",
    "plt.xlabel('fea_name', fontsize=12)\n",
    "plt.ylabel('fea_imp', fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.savefig('./figure/机组建模特征重要性_1.png')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "         fea_name  fea_imp  imp_scale\n8   day_of_week_1      309   0.211354\n2   day_of_week_6      292   0.199726\n12  day_of_week_4      277   0.189466\n7   day_of_week_7      215   0.147059\n9   day_of_week_2      147   0.100547\n3   day_of_week_5      139   0.095075\n0   day_of_week_3       83   0.056772",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>fea_name</th>\n      <th>fea_imp</th>\n      <th>imp_scale</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8</th>\n      <td>day_of_week_1</td>\n      <td>309</td>\n      <td>0.211354</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>day_of_week_6</td>\n      <td>292</td>\n      <td>0.199726</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>day_of_week_4</td>\n      <td>277</td>\n      <td>0.189466</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>day_of_week_7</td>\n      <td>215</td>\n      <td>0.147059</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>day_of_week_2</td>\n      <td>147</td>\n      <td>0.100547</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>day_of_week_5</td>\n      <td>139</td>\n      <td>0.095075</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>day_of_week_3</td>\n      <td>83</td>\n      <td>0.056772</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_imp = feature_importance[feature_importance.fea_name.str.contains('day_of_week')].copy()\n",
    "dow_imp['imp_scale'] = dow_imp.fea_imp / dow_imp.fea_imp.sum()\n",
    "dow_imp"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}