alldata_process_v2_ysc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "import xgboost as xgb\n",
    "\n",
    "from sklearn.model_selection import KFold, train_test_split, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, mean_squared_error\n",
    "from sklearn.datasets import load_iris, load_digits, load_boston\n",
    "\n",
    "rng = np.random.RandomState(31337)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分割数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 756 ms, sys: 68 ms, total: 824 ms\n",
      "Wall time: 822 ms\n",
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 1.25 ms\n"
     ]
    }
   ],
   "source": [
    "% time rawdata_78 = pd.read_csv('rawdata_78.csv',index_col=1)\n",
    "\n",
    "del rawdata_78['Unnamed: 0']\n",
    "% time link_info = pd.read_csv('gy_contest_link_info.txt',sep=';') # 路段id,length,width,link_class,数据\n",
    "link_ID_list = link_info['link_ID']\n",
    "\n",
    "for i in link_ID_list:\n",
    "    exec(\"ID_%s=rawdata_78.query(\\\"link_ID=='%s'\\\")\" % (i,i))\n",
    "    exec(\"ID_%s=ID_%s.copy()\" % (i,i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 清洗数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clean_2(tt):\n",
    "    if shreshold[0]<tt<shreshold[1]:\n",
    "        return tt\n",
    "    elif tt>shreshold[1]:\n",
    "        return shreshold[1]\n",
    "    else:\n",
    "        return shreshold[0]\n",
    "for ID in link_ID_list:\n",
    "    tt_describe = eval(\"ID_%s['travel_time'].describe()\" % ID)\n",
    "    tmp =tt_describe['75%']-tt_describe['25%']\n",
    "    shreshold = [tt_describe['25%']-tmp*1.5, tt_describe['75%']+tmp*1.5]\n",
    "    exec(\"ID_%s['travel_time_clean'] = ID_%s['travel_time'].apply(clean_2)\" % (ID,ID))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 制作link_top_info(每条link的物理特征)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "link_top = pd.read_csv('gy_contest_link_top.txt',sep=';')# 路段拓扑关系数据\n",
    "link_info = pd.read_csv('gy_contest_link_info.txt',sep=';') # 路段id,length,width,link_class,数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def num_top(x):\n",
    "    try:\n",
    "        return len(x)\n",
    "    except:\n",
    "        return 0\n",
    "link_top['in_num']=link_top['in_links'].str.split('#').apply(num_top)\n",
    "link_top['out_num']=link_top['out_links'].str.split('#').apply(num_top)\n",
    "link_top[['in0','in1','in2','in3']]=link_top['in_links'].str.split('#',expand=True) \n",
    "link_top[['out0','out1','out2','out3']]=link_top['out_links'].str.split('#',expand=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def in_length(x):\n",
    "    try:\n",
    "        return link_info.query(\"link_ID=='%s'\" % x)['length'].iloc[0]\n",
    "    except:\n",
    "        return 0\n",
    "def in_width(x):\n",
    "    try:\n",
    "        return link_info.query(\"link_ID=='%s'\" % x)['width'].iloc[0]\n",
    "    except:\n",
    "        return 0\n",
    "for i in range(4):\n",
    "    exec(\"link_top['in%d_len']=link_top['in%d'].apply(in_length)\" % (i,i))\n",
    "for i in range(4):\n",
    "    exec(\"link_top['in%d_wid']=link_top['in%d'].apply(in_width)\" % (i,i))\n",
    "for i in range(4):\n",
    "    exec(\"link_top['out%d_len']=link_top['out%d'].apply(in_length)\" % (i,i))\n",
    "for i in range(4):\n",
    "    exec(\"link_top['out%d_wid']=link_top['out%d'].apply(in_width)\" % (i,i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "link_top['in_width']=link_top['in0_wid']+link_top['in1_wid']+link_top['in2_wid']+link_top['in3_wid']\n",
    "link_top['in_length']=(link_top['in0_len']+link_top['in1_len']+link_top['in2_len']+link_top['in3_len'])/link_top['in_num']\n",
    "link_top['out_width']=link_top['out0_wid']+link_top['out1_wid']+link_top['out2_wid']+link_top['out3_wid']\n",
    "link_top['out_length']=(link_top['out0_len']+link_top['out1_len']+link_top['out2_len']+link_top['out3_len'])/link_top['out_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "length_mean=link_info['length'].mean()\n",
    "link_top['in_length'].fillna(length_mean, inplace=True)\n",
    "link_top['out_length'].fillna(length_mean, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del link_top['in_links']\n",
    "del link_top['out_links']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "link_info_top = pd.merge(link_info, link_top, how='left',on='link_ID')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 把物理特征合并进入训练数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for ID in link_ID_list:\n",
    "    exec(\"ID_%s=pd.merge(ID_%s, link_info_top, how='left',on='link_ID')\" % (ID, ID))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 把时间特征合并近入训练数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#当前所有时间特征:month, day, dayofweek, hour, minute, dayofyear, minuteofday, minuteofweek, hourofweek, hourofmonth\n",
    "def link_to_datetime(ID_link):\n",
    "    ID_link['time_interval_start']=pd.to_datetime(ID_link['time_interval_start'])\n",
    "    ID_link['time_interval_end']=pd.to_datetime(ID_link['time_interval_end'])\n",
    "    return ID_link\n",
    "def link_datetime(ID_link):\n",
    "    \n",
    "    ID_link['minute']=ID_link['time_interval_start'].dt.minute\n",
    "    ID_link['time_area']=ID_link['minute']//2\n",
    "    ID_link['minuteofday']=ID_link['hour']*60+ID_link['minute']\n",
    "    ID_link['dayofweek']=ID_link['time_interval_start'].dt.weekday\n",
    "    ID_link['day']=ID_link['time_interval_start'].dt.day\n",
    "    ID_link['month']=ID_link['time_interval_start'].dt.month\n",
    "    ID_link['minuteofmonth']= ID_link['day']*24*60+ID_link['hour']*60+  ID_link['minute']\n",
    "    ID_link['minuteofyear']=ID_link['month']*31*24*60+ID_link['day']*24*60+ID_link['hour']*60+  ID_link['minute']\n",
    "    ID_link['dayofyear'] = ID_link['time_interval_start'].dt.dayofyear\n",
    "    return ID_link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for ID in link_ID_list:\n",
    "    exec(\"ID_%s=link_to_datetime(ID_%s)\" % (ID,ID))\n",
    "    exec(\"ID_%s=link_datetime(ID_%s)\" % (ID,ID))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 每条link的上一小时tt平均值特征:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prehour_ttmean(ID_link):\n",
    "    import numpy as np\n",
    "    # 输入一条link的所有数据, 输出一条link的7点到8点的数据,其中数据列'prehour_ttmean'是6点到7点的travel_time平均值\n",
    "    ID_link_hour7=ID_link.query(\"hour==7\").copy()\n",
    "    ID_link_hour8=ID_link.query(\"hour==8\").copy()\n",
    "    \n",
    "    ID_link_hour8_prehourttmean=ID_link_hour7[['dayofyear','travel_time']].groupby('dayofyear').agg(np.mean)\n",
    "    ID_link_hour8_prehourttmean.columns=['prehour_ttmean']\n",
    "    ID_link_hour8_prehourttmean.reset_index(inplace=True)\n",
    "    \n",
    "    ID_link_hour8_more=pd.merge(ID_link_hour8, ID_link_hour8_prehourttmean, how='left',on='dayofyear')\n",
    "    return ID_link_hour8_more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 每条link的上游的上一小时tt平均值特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def inPreHourTTMean(ID_link):\n",
    "    in_num = ID_link['in_num'].iloc[0]\n",
    "    if in_num==1:\n",
    "        in0_ID = ID_link['in0'].iloc[0]\n",
    "        try:\n",
    "            in0_hour6 = eval('ID_%s' % in0_ID).query('hour==7').copy()\n",
    "        except:\n",
    "            exec('ID_%s = link_datetime(ID_%s)' % (in0_ID,in0_ID))\n",
    "            in0_hour6 = eval('ID_%s' % in0_ID).query('hour==7').copy()\n",
    "        link_hour7 = ID_link.query('hour==8').copy()\n",
    "\n",
    "        link_hour7_inPreHourTTMean = in0_hour6[['dayofyear','travel_time']].groupby('dayofyear').agg(np.mean)\n",
    "        link_hour7_inPreHourTTMean.columns=['inPreHourTTMean']\n",
    "        link_hour7_inPreHourTTMean.reset_index(inplace=True)\n",
    "\n",
    "        link_hour7_more = pd.merge(link_hour7, link_hour7_inPreHourTTMean, how='left', on='dayofyear')\n",
    "    elif in_num==2:\n",
    "        in0_ID = ID_link['in0'].iloc[0]\n",
    "        in1_ID = ID_link['in1'].iloc[0]\n",
    "        try:\n",
    "            in0_hour6 = eval('ID_%s' % in0_ID).query('hour==7').copy()\n",
    "        except:\n",
    "            exec('ID_%s = link_datetime(ID_%s)' % (in0_ID,in0_ID))\n",
    "            in0_hour6 = eval('ID_%s' % in0_ID).query('hour==7').copy()\n",
    "        try:\n",
    "            in1_hour6 = eval('ID_%s' % in1_ID).query('hour==7').copy()\n",
    "        except:\n",
    "            exec('ID_%s = link_datetime(ID_%s)' % (in1_ID,in1_ID))\n",
    "            in1_hour6 = eval('ID_%s' % in0_ID).query('hour==7').copy()\n",
    "        link_hour7 = ID_link.query('hour==8').copy()\n",
    "\n",
    "        link_hour7_inPreHourTTMean = in0_hour6[['dayofyear','travel_time']].groupby('dayofyear').agg(np.mean)\n",
    "        link_hour7_inPreHourTTMean += in1_hour6[['dayofyear','travel_time']].groupby('dayofyear').agg(np.mean)\n",
    "        link_hour7_inPreHourTTMean /=2\n",
    "        link_hour7_inPreHourTTMean.columns=['inPreHourTTMean']\n",
    "        link_hour7_inPreHourTTMean.reset_index(inplace=True)\n",
    "\n",
    "        link_hour7_more = pd.merge(link_hour7, link_hour7_inPreHourTTMean, how='left', on='dayofyear')\n",
    "\n",
    "    elif in_num==3:\n",
    "        in0_ID = ID_link['in0'].iloc[0]\n",
    "        in1_ID = ID_link['in1'].iloc[0]\n",
    "        in2_ID = ID_link['in2'].iloc[0]\n",
    "        try:\n",
    "            in0_hour6 = eval('ID_%s' % in0_ID).query('hour==7').copy()\n",
    "        except:\n",
    "#             eval('ID_%s' % in0_ID) = link_datetime(eval('ID_%s' % in0_ID))\n",
    "            exec('ID_%s = link_datetime(ID_%s)' % (in0_ID,in0_ID))\n",
    "            in0_hour6 = eval('ID_%s' % in0_ID).query('hour==7').copy()\n",
    "        try:\n",
    "            in1_hour6 = eval('ID_%s' % in1_ID).query('hour==7').copy()\n",
    "        except:\n",
    "#             eval('ID_%s' % in1_ID) = link_datetime(eval('ID_%s' % in1_ID))\n",
    "            exec('ID_%s = link_datetime(ID_%s)' % (in1_ID,in1_ID))\n",
    "            in1_hour6 = eval('ID_%s' % in0_ID).query('hour==7').copy()\n",
    "        try:\n",
    "            in2_hour6 = eval('ID_%s' % in2_ID).query('hour==7').copy()\n",
    "        except:\n",
    "#             eval('ID_%s' % in2_ID) = link_datetime(eval('ID_%s' % in2_ID))\n",
    "            exec('ID_%s = link_datetime(ID_%s)' % (in2_ID,in2_ID))\n",
    "            in2_hour6 = eval('ID_%s' % in2_ID).query('hour==7').copy()\n",
    "#         in0_hour6 = eval('ID_%s' % 'in0_ID').query('hour==6').copy()\n",
    "#         in1_hour6 = eval('ID_%s' % 'in1_ID').query('hour==6').copy()\n",
    "#         in2_hour6 = eval('ID_%s' % 'in2_ID').query('hour==6').copy()\n",
    "        link_hour7 = ID_link.query('hour==8').copy()\n",
    "\n",
    "        link_hour7_inPreHourTTMean = in0_hour6[['dayofyear','travel_time']].groupby('dayofyear').agg(np.mean)\n",
    "        link_hour7_inPreHourTTMean += in1_hour6[['dayofyear','travel_time']].groupby('dayofyear').agg(np.mean)\n",
    "        link_hour7_inPreHourTTMean += in2_hour6[['dayofyear','travel_time']].groupby('dayofyear').agg(np.mean)\n",
    "        link_hour7_inPreHourTTMean /=3\n",
    "        link_hour7_inPreHourTTMean.columns=['inPreHourTTMean']\n",
    "        link_hour7_inPreHourTTMean.reset_index(inplace=True)\n",
    "\n",
    "        link_hour7_more = pd.merge(link_hour7, link_hour7_inPreHourTTMean, how='left', on='dayofyear')\n",
    "\n",
    "    elif in_num==4:\n",
    "        in0_ID = ID_link['in0'].iloc[0]\n",
    "        in1_ID = ID_link['in1'].iloc[0]\n",
    "        in2_ID = ID_link['in2'].iloc[0]\n",
    "        in3_ID = ID_link['in3'].iloc[0]\n",
    "        try:\n",
    "            in0_hour6 = eval('ID_%s' % in0_ID).query('hour==7').copy()\n",
    "        except:\n",
    "#             eval('ID_%s' % in0_ID) = link_datetime(eval('ID_%s' % in0_ID))\n",
    "            exec('ID_%s = link_datetime(ID_%s)' % (in0_ID,in0_ID))\n",
    "            in0_hour6 = eval('ID_%s' % in0_ID).query('hour==7').copy()\n",
    "        try:\n",
    "            in1_hour6 = eval('ID_%s' % in1_ID).query('hour==7').copy()\n",
    "        except:\n",
    "            exec('ID_%s = link_datetime(ID_%s)' % (in1_ID,in1_ID))\n",
    "#             eval('ID_%s' % in1_ID) = link_datetime(eval('ID_%s' % in1_ID))\n",
    "            in1_hour6 = eval('ID_%s' % in0_ID).query('hour==7').copy()\n",
    "        try:\n",
    "            in2_hour6 = eval('ID_%s' % in2_ID).query('hour==7').copy()\n",
    "        except:\n",
    "            exec('ID_%s = link_datetime(ID_%s)' % (in2_ID,in2_ID))\n",
    "#             eval('ID_%s' % in2_ID) = link_datetime(eval('ID_%s' % in2_ID))\n",
    "            in2_hour6 = eval('ID_%s' % in2_ID).query('hour==7').copy()\n",
    "        try:\n",
    "            in3_hour6 = eval('ID_%s' % in3_ID).query('hour==7').copy()\n",
    "        except:\n",
    "            exec('ID_%s = link_datetime(ID_%s)' % (in3_ID,in3_ID))\n",
    "#             eval('ID_%s' % in3_ID) = link_datetime(eval('ID_%s' % in3_ID))\n",
    "            in3_hour6 = eval('ID_%s' % in3_ID).query('hour==7').copy()\n",
    "#         in0_hour6 = eval('ID_%s' % 'in0_ID').query('hour==6').copy()\n",
    "#         in1_hour6 = eval('ID_%s' % 'in1_ID').query('hour==6').copy()\n",
    "#         in2_hour6 = eval('ID_%s' % 'in2_ID').query('hour==6').copy()\n",
    "#         in3_hour6 = eval('ID_%s' % 'in3_ID').query('hour==6').copy()\n",
    "        link_hour7 = ID_link.query('hour==8').copy()\n",
    "\n",
    "        link_hour7_inPreHourTTMean = in0_hour6[['dayofyear','travel_time']].groupby('dayofyear').agg(np.mean)\n",
    "        link_hour7_inPreHourTTMean += in1_hour6[['dayofyear','travel_time']].groupby('dayofyear').agg(np.mean)\n",
    "        link_hour7_inPreHourTTMean += in2_hour6[['dayofyear','travel_time']].groupby('dayofyear').agg(np.mean)\n",
    "        link_hour7_inPreHourTTMean += in3_hour6[['dayofyear','travel_time']].groupby('dayofyear').agg(np.mean)\n",
    "        link_hour7_inPreHourTTMean /=4\n",
    "        link_hour7_inPreHourTTMean.columns=['inPreHourTTMean']\n",
    "        link_hour7_inPreHourTTMean.reset_index(inplace=True)\n",
    "\n",
    "        link_hour7_more = pd.merge(link_hour7, link_hour7_inPreHourTTMean, how='left', on='dayofyear')\n",
    "    elif in_num==0:\n",
    "        link_hour7_more = ID_link.query('hour==8').copy()\n",
    "        link_hour7_more['inPreHourTTMean']=link_hour7_more['prehour_ttmean']\n",
    "    return link_hour7_more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 把prehour_ttmean,inPreHourTTMean 合并进入训练数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for ID in link_ID_list:\n",
    "    exec(\"tmp = prehour_ttmean(ID_%s)\" % ID)\n",
    "    exec(\"tmp1 = pd.merge(ID_%s, tmp[['minuteofyear','prehour_ttmean']], how='left',on='minuteofyear')\" % ID)\n",
    "    tmp2 = inPreHourTTMean(tmp1)\n",
    "    exec(\"ID_%s_hour8 = pd.merge(tmp, tmp2[['minuteofyear','inPreHourTTMean']], how='left',on='minuteofyear')\" % ID)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_model(data0):\n",
    "    data = data0[['hour', 'travel_time_clean', 'length', 'width',\n",
    "           'link_class', 'in_num', 'out_num',  'in0_len', 'in1_len', 'in2_len', 'in3_len',\n",
    "           'in0_wid', 'in1_wid', 'in2_wid', 'in3_wid', 'out0_len', 'out1_len',\n",
    "           'out2_len', 'out3_len', 'out0_wid', 'out1_wid', 'out2_wid', 'out3_wid',\n",
    "           'in_width', 'in_length', 'out_width', 'out_length', 'minute',\n",
    "           'time_area', 'minuteofday', 'dayofweek', 'day', 'month',\n",
    "           'minuteofmonth', 'minuteofyear', 'dayofyear', 'prehour_ttmean',\n",
    "           'inPreHourTTMean']].copy()\n",
    "    X = data[['hour', 'length', 'width',\n",
    "           'link_class', 'in_num', 'out_num',  'in0_len', 'in1_len', 'in2_len', 'in3_len',\n",
    "           'in0_wid', 'in1_wid', 'in2_wid', 'in3_wid', 'out0_len', 'out1_len',\n",
    "           'out2_len', 'out3_len', 'out0_wid', 'out1_wid', 'out2_wid', 'out3_wid',\n",
    "           'in_width', 'in_length', 'out_width', 'out_length', 'minute',\n",
    "           'time_area', 'minuteofday', 'dayofweek', 'day', 'month',\n",
    "           'minuteofmonth', 'minuteofyear', 'dayofyear', 'prehour_ttmean',\n",
    "           'inPreHourTTMean']]\n",
    "    y = data['travel_time_clean']\n",
    "    X = np.array(X)\n",
    "    y = np.array(y)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "    clf = xgb.XGBRegressor()\n",
    "    clf.fit(X_train, y_train, early_stopping_rounds=100, eval_metric=\"rmse\",\n",
    "            eval_set=[(X_test, y_test)])\n",
    "    return clf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-rmse:6.21284\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:5.61414\n",
      "[2]\tvalidation_0-rmse:5.07856\n",
      "[3]\tvalidation_0-rmse:4.60338\n",
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      "[5]\tvalidation_0-rmse:3.79758\n",
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      "[99]\tvalidation_0-rmse:1.23589\n",
      "CPU times: user 1.64 s, sys: 16 ms, total: 1.65 s\n",
      "Wall time: 258 ms\n",
      "[0]\tvalidation_0-rmse:70.8446\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:64.9219\n",
      "[2]\tvalidation_0-rmse:59.7161\n",
      "[3]\tvalidation_0-rmse:55.1656\n",
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      "CPU times: user 1.87 s, sys: 28 ms, total: 1.9 s\n",
      "Wall time: 265 ms\n",
      "[0]\tvalidation_0-rmse:63.8945\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:58.9409\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[56]\tvalidation_0-rmse:27.5008\n",
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      "CPU times: user 2.07 s, sys: 24 ms, total: 2.1 s\n",
      "Wall time: 295 ms\n",
      "[0]\tvalidation_0-rmse:104.46\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:94.7102\n",
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      "[3]\tvalidation_0-rmse:78.1577\n",
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      "CPU times: user 1.97 s, sys: 20 ms, total: 1.99 s\n",
      "Wall time: 279 ms\n",
      "[0]\tvalidation_0-rmse:9.81903\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:9.04315\n",
      "[2]\tvalidation_0-rmse:8.36241\n",
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      "[99]\tvalidation_0-rmse:4.06314\n",
      "CPU times: user 1.86 s, sys: 16 ms, total: 1.88 s\n",
      "Wall time: 263 ms\n",
      "[0]\tvalidation_0-rmse:3.65415\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:3.33338\n",
      "[2]\tvalidation_0-rmse:3.04883\n",
      "[3]\tvalidation_0-rmse:2.79441\n",
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      "[9]\tvalidation_0-rmse:1.80715\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10]\tvalidation_0-rmse:1.70895\n",
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      "CPU times: user 2.12 s, sys: 24 ms, total: 2.14 s\n",
      "Wall time: 301 ms\n",
      "[0]\tvalidation_0-rmse:13.3778\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:12.1396\n",
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      "CPU times: user 2.01 s, sys: 8 ms, total: 2.02 s\n",
      "Wall time: 283 ms\n",
      "[0]\tvalidation_0-rmse:25.9348\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:23.9672\n",
      "[2]\tvalidation_0-rmse:22.2499\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[68]\tvalidation_0-rmse:11.4992\n",
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      "CPU times: user 2.04 s, sys: 36 ms, total: 2.08 s\n",
      "Wall time: 303 ms\n",
      "[0]\tvalidation_0-rmse:9.18444\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
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      "CPU times: user 1.97 s, sys: 20 ms, total: 1.99 s\n",
      "Wall time: 283 ms\n",
      "[0]\tvalidation_0-rmse:3.28222\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
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      "CPU times: user 1.88 s, sys: 24 ms, total: 1.91 s\n",
      "Wall time: 267 ms\n",
      "[0]\tvalidation_0-rmse:35.2822\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:32.6935\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "CPU times: user 2.08 s, sys: 44 ms, total: 2.12 s\n",
      "Wall time: 307 ms\n",
      "[0]\tvalidation_0-rmse:17.8254\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:16.6468\n",
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      "[99]\tvalidation_0-rmse:8.85758\n",
      "CPU times: user 1.9 s, sys: 12 ms, total: 1.91 s\n",
      "Wall time: 265 ms\n",
      "[0]\tvalidation_0-rmse:13.2209\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:12.3572\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[79]\tvalidation_0-rmse:7.21083\n",
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      "CPU times: user 1.96 s, sys: 24 ms, total: 1.98 s\n",
      "Wall time: 278 ms\n",
      "[0]\tvalidation_0-rmse:4.11536\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:3.78526\n",
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      "CPU times: user 2.08 s, sys: 12 ms, total: 2.09 s\n",
      "Wall time: 300 ms\n",
      "[0]\tvalidation_0-rmse:8.55371\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:7.82292\n",
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      "CPU times: user 2.11 s, sys: 12 ms, total: 2.12 s\n",
      "Wall time: 306 ms\n",
      "[0]\tvalidation_0-rmse:2.82658\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:2.57344\n",
      "[2]\tvalidation_0-rmse:2.3458\n",
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      "[31]\tvalidation_0-rmse:0.884182\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[32]\tvalidation_0-rmse:0.881363\n",
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      "CPU times: user 1.99 s, sys: 12 ms, total: 2 s\n",
      "Wall time: 284 ms\n",
      "[0]\tvalidation_0-rmse:5.29099\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:4.80473\n",
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      "CPU times: user 1.92 s, sys: 40 ms, total: 1.96 s\n",
      "Wall time: 284 ms\n",
      "[0]\tvalidation_0-rmse:57.0487\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:52.0729\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[87]\tvalidation_0-rmse:21.1789\n",
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      "CPU times: user 2.01 s, sys: 28 ms, total: 2.04 s\n",
      "Wall time: 285 ms\n",
      "[0]\tvalidation_0-rmse:1.42742\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:1.29878\n",
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      "[99]\tvalidation_0-rmse:0.409536\n",
      "CPU times: user 1.91 s, sys: 24 ms, total: 1.94 s\n",
      "Wall time: 270 ms\n",
      "[0]\tvalidation_0-rmse:43.4767\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:40.2239\n",
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      "[99]\tvalidation_0-rmse:18.1453\n",
      "CPU times: user 1.86 s, sys: 16 ms, total: 1.88 s\n",
      "Wall time: 261 ms\n",
      "[0]\tvalidation_0-rmse:30.6181\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:28.6282\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[38]\tvalidation_0-rmse:16.3042\n",
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      "CPU times: user 1.85 s, sys: 12 ms, total: 1.86 s\n",
      "Wall time: 257 ms\n",
      "[0]\tvalidation_0-rmse:31.8808\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:29.901\n",
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      "CPU times: user 1.86 s, sys: 44 ms, total: 1.9 s\n",
      "Wall time: 274 ms\n",
      "[0]\tvalidation_0-rmse:4.31281\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:3.94079\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[96]\tvalidation_0-rmse:1.36213\n",
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      "CPU times: user 1.91 s, sys: 12 ms, total: 1.92 s\n",
      "Wall time: 272 ms\n",
      "[0]\tvalidation_0-rmse:3.7942\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:3.42339\n",
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      "CPU times: user 1.72 s, sys: 20 ms, total: 1.74 s\n",
      "Wall time: 240 ms\n",
      "[0]\tvalidation_0-rmse:15.0514\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:13.6507\n",
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      "CPU times: user 1.95 s, sys: 16 ms, total: 1.97 s\n",
      "Wall time: 272 ms\n",
      "[0]\tvalidation_0-rmse:145.319\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:134.27\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[47]\tvalidation_0-rmse:52.6174\n",
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      "CPU times: user 1.9 s, sys: 16 ms, total: 1.92 s\n",
      "Wall time: 268 ms\n",
      "[0]\tvalidation_0-rmse:43.4084\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:39.6201\n",
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      "CPU times: user 1.9 s, sys: 12 ms, total: 1.92 s\n",
      "Wall time: 264 ms\n",
      "[0]\tvalidation_0-rmse:21.314\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:19.4113\n",
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      "CPU times: user 1.95 s, sys: 16 ms, total: 1.96 s\n",
      "Wall time: 275 ms\n",
      "[0]\tvalidation_0-rmse:10.5729\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalidation_0-rmse:9.60443\n",
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      "CPU times: user 1.88 s, sys: 20 ms, total: 1.9 s\n",
      "Wall time: 267 ms\n",
      "[0]\tvalidation_0-rmse:5.95312\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:5.53097\n",
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      "CPU times: user 1.96 s, sys: 12 ms, total: 1.97 s\n",
      "Wall time: 271 ms\n",
      "[0]\tvalidation_0-rmse:24.1724\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:22.5673\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[59]\tvalidation_0-rmse:13.0426\n",
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      "CPU times: user 1.89 s, sys: 20 ms, total: 1.91 s\n",
      "Wall time: 263 ms\n",
      "[0]\tvalidation_0-rmse:39.8107\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:36.8154\n",
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      "CPU times: user 1.88 s, sys: 32 ms, total: 1.91 s\n",
      "Wall time: 266 ms\n",
      "[0]\tvalidation_0-rmse:22.7232\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:20.8935\n",
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      "CPU times: user 1.82 s, sys: 12 ms, total: 1.83 s\n",
      "Wall time: 261 ms\n",
      "[0]\tvalidation_0-rmse:19.7456\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:18.0193\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12]\tvalidation_0-rmse:8.61634\n",
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      "CPU times: user 1.87 s, sys: 12 ms, total: 1.88 s\n",
      "Wall time: 264 ms\n",
      "[0]\tvalidation_0-rmse:34.8438\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:32.3642\n",
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      "CPU times: user 1.88 s, sys: 12 ms, total: 1.9 s\n",
      "Wall time: 262 ms\n",
      "[0]\tvalidation_0-rmse:7.19182\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:6.7623\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[70]\tvalidation_0-rmse:4.50305\n",
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      "CPU times: user 1.85 s, sys: 28 ms, total: 1.88 s\n",
      "Wall time: 262 ms\n",
      "[0]\tvalidation_0-rmse:11.1644\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:10.0934\n",
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      "CPU times: user 1.94 s, sys: 20 ms, total: 1.96 s\n",
      "Wall time: 277 ms\n",
      "[0]\tvalidation_0-rmse:51.8731\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:48.0556\n",
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      "[99]\tvalidation_0-rmse:16.0138\n",
      "CPU times: user 1.88 s, sys: 24 ms, total: 1.91 s\n",
      "Wall time: 267 ms\n",
      "[0]\tvalidation_0-rmse:4.45\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:4.12603\n",
      "[2]\tvalidation_0-rmse:3.84427\n",
      "[3]\tvalidation_0-rmse:3.59596\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[24]\tvalidation_0-rmse:2.22467\n",
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      "CPU times: user 1.84 s, sys: 4 ms, total: 1.85 s\n",
      "Wall time: 257 ms\n",
      "[0]\tvalidation_0-rmse:7.07242\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:6.46791\n",
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      "CPU times: user 1.97 s, sys: 20 ms, total: 1.99 s\n",
      "Wall time: 281 ms\n",
      "[0]\tvalidation_0-rmse:8.84506\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:8.03935\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[82]\tvalidation_0-rmse:2.42067\n",
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      "CPU times: user 1.94 s, sys: 24 ms, total: 1.96 s\n",
      "Wall time: 271 ms\n",
      "[0]\tvalidation_0-rmse:54.8988\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
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      "CPU times: user 1.96 s, sys: 16 ms, total: 1.97 s\n",
      "Wall time: 272 ms\n",
      "[0]\tvalidation_0-rmse:19.1654\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:18.1963\n",
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      "CPU times: user 1.94 s, sys: 20 ms, total: 1.96 s\n",
      "Wall time: 271 ms\n",
      "[0]\tvalidation_0-rmse:32.6297\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:29.8383\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[36]\tvalidation_0-rmse:10.5288\n",
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      "CPU times: user 1.97 s, sys: 8 ms, total: 1.98 s\n",
      "Wall time: 277 ms\n",
      "[0]\tvalidation_0-rmse:9.17408\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:8.48992\n",
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      "[99]\tvalidation_0-rmse:3.73472\n",
      "CPU times: user 1.92 s, sys: 24 ms, total: 1.94 s\n",
      "Wall time: 267 ms\n",
      "[0]\tvalidation_0-rmse:16.8761\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:15.2467\n",
      "[2]\tvalidation_0-rmse:13.7883\n",
      "[3]\tvalidation_0-rmse:12.4814\n",
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      "[93]\tvalidation_0-rmse:2.42213\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[94]\tvalidation_0-rmse:2.42119\n",
      "[95]\tvalidation_0-rmse:2.41786\n",
      "[96]\tvalidation_0-rmse:2.41759\n",
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      "[99]\tvalidation_0-rmse:2.41401\n",
      "CPU times: user 1.84 s, sys: 32 ms, total: 1.87 s\n",
      "Wall time: 261 ms\n",
      "[0]\tvalidation_0-rmse:6.43624\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:5.92061\n",
      "[2]\tvalidation_0-rmse:5.46991\n",
      "[3]\tvalidation_0-rmse:5.07374\n",
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      "CPU times: user 1.84 s, sys: 4 ms, total: 1.84 s\n",
      "Wall time: 250 ms\n",
      "[0]\tvalidation_0-rmse:10.5928\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:9.63752\n",
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      "[99]\tvalidation_0-rmse:2.68462\n",
      "CPU times: user 1.84 s, sys: 20 ms, total: 1.86 s\n",
      "Wall time: 256 ms\n",
      "[0]\tvalidation_0-rmse:17.1221\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:15.7221\n",
      "[2]\tvalidation_0-rmse:14.479\n",
      "[3]\tvalidation_0-rmse:13.387\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[48]\tvalidation_0-rmse:6.30159\n",
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      "CPU times: user 1.83 s, sys: 20 ms, total: 1.85 s\n",
      "Wall time: 257 ms\n",
      "[0]\tvalidation_0-rmse:14.2507\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:13.0693\n",
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      "CPU times: user 1.84 s, sys: 20 ms, total: 1.86 s\n",
      "Wall time: 256 ms\n",
      "[0]\tvalidation_0-rmse:11.5209\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:10.5168\n",
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      "[99]\tvalidation_0-rmse:3.79644\n",
      "CPU times: user 1.83 s, sys: 12 ms, total: 1.84 s\n",
      "Wall time: 256 ms\n",
      "[0]\tvalidation_0-rmse:14.1664\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalidation_0-rmse:12.9623\n",
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      "[99]\tvalidation_0-rmse:4.85404\n",
      "CPU times: user 1.78 s, sys: 20 ms, total: 1.8 s\n",
      "Wall time: 251 ms\n",
      "[0]\tvalidation_0-rmse:9.23808\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:8.42652\n",
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      "[99]\tvalidation_0-rmse:2.65963\n",
      "CPU times: user 1.75 s, sys: 24 ms, total: 1.78 s\n",
      "Wall time: 249 ms\n",
      "[0]\tvalidation_0-rmse:7.44764\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:6.77581\n",
      "[2]\tvalidation_0-rmse:6.1857\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[59]\tvalidation_0-rmse:2.33676\n",
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      "CPU times: user 1.81 s, sys: 16 ms, total: 1.82 s\n",
      "Wall time: 256 ms\n",
      "[0]\tvalidation_0-rmse:59.2626\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:54.2294\n",
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      "CPU times: user 1.83 s, sys: 40 ms, total: 1.87 s\n",
      "Wall time: 258 ms\n",
      "[0]\tvalidation_0-rmse:7.75657\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:7.14175\n",
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      "[99]\tvalidation_0-rmse:3.31072\n",
      "CPU times: user 1.87 s, sys: 12 ms, total: 1.88 s\n",
      "Wall time: 261 ms\n",
      "[0]\tvalidation_0-rmse:2.45127\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:2.23258\n",
      "[2]\tvalidation_0-rmse:2.03861\n",
      "[3]\tvalidation_0-rmse:1.86639\n",
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      "[11]\tvalidation_0-rmse:1.07306\n",
      "[12]\tvalidation_0-rmse:1.02493\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13]\tvalidation_0-rmse:0.984487\n",
      "[14]\tvalidation_0-rmse:0.950406\n",
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      "[99]\tvalidation_0-rmse:0.773829\n",
      "CPU times: user 1.86 s, sys: 24 ms, total: 1.89 s\n",
      "Wall time: 260 ms\n",
      "[0]\tvalidation_0-rmse:17.7824\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:16.184\n",
      "[2]\tvalidation_0-rmse:14.7567\n",
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      "CPU times: user 1.83 s, sys: 40 ms, total: 1.87 s\n",
      "Wall time: 258 ms\n",
      "[0]\tvalidation_0-rmse:22.2266\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:20.6015\n",
      "[2]\tvalidation_0-rmse:19.1857\n",
      "[3]\tvalidation_0-rmse:17.964\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[68]\tvalidation_0-rmse:10.6862\n",
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      "CPU times: user 1.81 s, sys: 8 ms, total: 1.82 s\n",
      "Wall time: 251 ms\n",
      "[0]\tvalidation_0-rmse:1.84264\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:1.68525\n",
      "[2]\tvalidation_0-rmse:1.54543\n",
      "[3]\tvalidation_0-rmse:1.42239\n",
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      "[99]\tvalidation_0-rmse:0.588469\n",
      "CPU times: user 1.86 s, sys: 16 ms, total: 1.88 s\n",
      "Wall time: 259 ms\n",
      "[0]\tvalidation_0-rmse:24.79\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:22.6113\n",
      "[2]\tvalidation_0-rmse:20.6656\n",
      "[3]\tvalidation_0-rmse:18.9419\n",
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      "[99]\tvalidation_0-rmse:7.25756\n",
      "CPU times: user 1.87 s, sys: 12 ms, total: 1.88 s\n",
      "Wall time: 264 ms\n",
      "[0]\tvalidation_0-rmse:47.9638\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:44.0903\n",
      "[2]\tvalidation_0-rmse:40.6807\n",
      "[3]\tvalidation_0-rmse:37.7099\n",
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      "[18]\tvalidation_0-rmse:21.6376\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19]\tvalidation_0-rmse:21.3973\n",
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      "CPU times: user 1.81 s, sys: 28 ms, total: 1.84 s\n",
      "Wall time: 255 ms\n",
      "[0]\tvalidation_0-rmse:21.7892\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:19.7939\n",
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      "CPU times: user 1.82 s, sys: 32 ms, total: 1.86 s\n",
      "Wall time: 254 ms\n",
      "[0]\tvalidation_0-rmse:80.1024\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:73.9443\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[77]\tvalidation_0-rmse:32.7365\n",
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      "CPU times: user 2.35 s, sys: 20 ms, total: 2.37 s\n",
      "Wall time: 338 ms\n",
      "[0]\tvalidation_0-rmse:27.3679\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
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      "CPU times: user 1.77 s, sys: 12 ms, total: 1.78 s\n",
      "Wall time: 250 ms\n",
      "[0]\tvalidation_0-rmse:8.56151\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
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      "CPU times: user 2 s, sys: 20 ms, total: 2.02 s\n",
      "Wall time: 279 ms\n",
      "[0]\tvalidation_0-rmse:34.2068\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:31.5159\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "CPU times: user 1.94 s, sys: 16 ms, total: 1.96 s\n",
      "Wall time: 279 ms\n",
      "[0]\tvalidation_0-rmse:16.7235\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:15.4086\n",
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      "CPU times: user 1.92 s, sys: 40 ms, total: 1.96 s\n",
      "Wall time: 273 ms\n",
      "[0]\tvalidation_0-rmse:43.1754\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:40.0559\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[89]\tvalidation_0-rmse:19.488\n",
      "[90]\tvalidation_0-rmse:19.478\n",
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      "CPU times: user 1.95 s, sys: 12 ms, total: 1.96 s\n",
      "Wall time: 271 ms\n",
      "[0]\tvalidation_0-rmse:2.35597\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:2.1563\n",
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      "CPU times: user 1.88 s, sys: 24 ms, total: 1.9 s\n",
      "Wall time: 262 ms\n",
      "[0]\tvalidation_0-rmse:10.5909\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:9.8511\n",
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      "CPU times: user 1.87 s, sys: 12 ms, total: 1.88 s\n",
      "Wall time: 259 ms\n",
      "[0]\tvalidation_0-rmse:41.5248\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:38.2077\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[40]\tvalidation_0-rmse:15.1962\n",
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      "CPU times: user 1.88 s, sys: 16 ms, total: 1.9 s\n",
      "Wall time: 260 ms\n",
      "[0]\tvalidation_0-rmse:10.4065\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:9.54584\n",
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      "CPU times: user 1.81 s, sys: 24 ms, total: 1.84 s\n",
      "Wall time: 267 ms\n",
      "[0]\tvalidation_0-rmse:37.6786\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:35.1126\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[98]\tvalidation_0-rmse:19.7254\n",
      "[99]\tvalidation_0-rmse:19.7083\n",
      "CPU times: user 1.91 s, sys: 48 ms, total: 1.96 s\n",
      "Wall time: 284 ms\n",
      "[0]\tvalidation_0-rmse:0.634227\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:0.5862\n",
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      "[99]\tvalidation_0-rmse:0.282696\n",
      "CPU times: user 1.83 s, sys: 20 ms, total: 1.85 s\n",
      "Wall time: 252 ms\n",
      "[0]\tvalidation_0-rmse:15.4916\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:14.0661\n",
      "[2]\tvalidation_0-rmse:12.7957\n",
      "[3]\tvalidation_0-rmse:11.6805\n",
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      "CPU times: user 1.84 s, sys: 16 ms, total: 1.86 s\n",
      "Wall time: 258 ms\n",
      "[0]\tvalidation_0-rmse:3.58314\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:3.29917\n",
      "[2]\tvalidation_0-rmse:3.04977\n",
      "[3]\tvalidation_0-rmse:2.833\n",
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      "[48]\tvalidation_0-rmse:1.50144\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[49]\tvalidation_0-rmse:1.50042\n",
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      "CPU times: user 1.93 s, sys: 36 ms, total: 1.96 s\n",
      "Wall time: 270 ms\n",
      "[0]\tvalidation_0-rmse:43.1927\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:39.9757\n",
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      "[99]\tvalidation_0-rmse:16.177\n",
      "CPU times: user 1.89 s, sys: 20 ms, total: 1.91 s\n",
      "Wall time: 265 ms\n",
      "[0]\tvalidation_0-rmse:8.99251\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:8.48725\n",
      "[2]\tvalidation_0-rmse:8.06781\n",
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      "CPU times: user 1.8 s, sys: 16 ms, total: 1.82 s\n",
      "Wall time: 253 ms\n",
      "[0]\tvalidation_0-rmse:0.972093\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:0.88378\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\tvalidation_0-rmse:0.805036\n",
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      "CPU times: user 1.87 s, sys: 12 ms, total: 1.88 s\n",
      "Wall time: 262 ms\n",
      "[0]\tvalidation_0-rmse:10.1216\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:9.21244\n",
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      "CPU times: user 1.86 s, sys: 12 ms, total: 1.88 s\n",
      "Wall time: 260 ms\n",
      "[0]\tvalidation_0-rmse:1.58563\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:1.44009\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[55]\tvalidation_0-rmse:0.436886\n",
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      "[99]\tvalidation_0-rmse:0.429121\n",
      "CPU times: user 1.83 s, sys: 20 ms, total: 1.85 s\n",
      "Wall time: 256 ms\n",
      "[0]\tvalidation_0-rmse:5.45386\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:4.9611\n",
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      "CPU times: user 1.82 s, sys: 12 ms, total: 1.83 s\n",
      "Wall time: 251 ms\n",
      "[0]\tvalidation_0-rmse:30.9141\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:28.8773\n",
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      "[99]\tvalidation_0-rmse:15.9016\n",
      "CPU times: user 1.94 s, sys: 32 ms, total: 1.97 s\n",
      "Wall time: 276 ms\n",
      "[0]\tvalidation_0-rmse:4.77453\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:4.34087\n",
      "[2]\tvalidation_0-rmse:3.95443\n",
      "[3]\tvalidation_0-rmse:3.61063\n",
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      "[6]\tvalidation_0-rmse:2.79981\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7]\tvalidation_0-rmse:2.588\n",
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      "CPU times: user 1.95 s, sys: 20 ms, total: 1.97 s\n",
      "Wall time: 274 ms\n",
      "[0]\tvalidation_0-rmse:1.13175\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:1.03543\n",
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      "CPU times: user 1.92 s, sys: 8 ms, total: 1.93 s\n",
      "Wall time: 265 ms\n",
      "[0]\tvalidation_0-rmse:59.5637\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:54.8469\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[62]\tvalidation_0-rmse:22.8661\n",
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      "CPU times: user 1.95 s, sys: 32 ms, total: 1.98 s\n",
      "Wall time: 279 ms\n",
      "[0]\tvalidation_0-rmse:3.23166\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:2.95268\n",
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      "CPU times: user 1.88 s, sys: 12 ms, total: 1.9 s\n",
      "Wall time: 262 ms\n",
      "[0]\tvalidation_0-rmse:28.2492\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:25.7565\n",
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      "[99]\tvalidation_0-rmse:7.53598\n",
      "CPU times: user 1.82 s, sys: 28 ms, total: 1.85 s\n",
      "Wall time: 253 ms\n",
      "[0]\tvalidation_0-rmse:1.34409\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:1.23633\n",
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      "[15]\tvalidation_0-rmse:0.615555\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16]\tvalidation_0-rmse:0.604577\n",
      "[17]\tvalidation_0-rmse:0.594623\n",
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      "[99]\tvalidation_0-rmse:0.520694\n",
      "CPU times: user 1.88 s, sys: 28 ms, total: 1.91 s\n",
      "Wall time: 267 ms\n",
      "[0]\tvalidation_0-rmse:5.97269\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:5.45532\n",
      "[2]\tvalidation_0-rmse:4.99021\n",
      "[3]\tvalidation_0-rmse:4.57834\n",
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      "[99]\tvalidation_0-rmse:1.67089\n",
      "CPU times: user 1.84 s, sys: 24 ms, total: 1.86 s\n",
      "Wall time: 254 ms\n",
      "[0]\tvalidation_0-rmse:4.13878\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:3.83167\n",
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      "[3]\tvalidation_0-rmse:3.34054\n",
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      "[70]\tvalidation_0-rmse:1.94481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[71]\tvalidation_0-rmse:1.94391\n",
      "[72]\tvalidation_0-rmse:1.9425\n",
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      "[99]\tvalidation_0-rmse:1.89876\n",
      "CPU times: user 1.96 s, sys: 20 ms, total: 1.98 s\n",
      "Wall time: 280 ms\n",
      "[0]\tvalidation_0-rmse:3.80686\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:3.47216\n",
      "[2]\tvalidation_0-rmse:3.17293\n",
      "[3]\tvalidation_0-rmse:2.9084\n",
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      "[98]\tvalidation_0-rmse:1.18301\n",
      "[99]\tvalidation_0-rmse:1.18268\n",
      "CPU times: user 1.86 s, sys: 24 ms, total: 1.88 s\n",
      "Wall time: 258 ms\n",
      "[0]\tvalidation_0-rmse:5.17518\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:4.78189\n",
      "[2]\tvalidation_0-rmse:4.43568\n",
      "[3]\tvalidation_0-rmse:4.13513\n",
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      "[98]\tvalidation_0-rmse:1.9788\n",
      "[99]\tvalidation_0-rmse:1.97426\n",
      "CPU times: user 1.91 s, sys: 32 ms, total: 1.94 s\n",
      "Wall time: 265 ms\n",
      "[0]\tvalidation_0-rmse:5.16143\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:4.71842\n",
      "[2]\tvalidation_0-rmse:4.32174\n",
      "[3]\tvalidation_0-rmse:3.97422\n",
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      "[23]\tvalidation_0-rmse:1.87638\n",
      "[24]\tvalidation_0-rmse:1.86589\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[25]\tvalidation_0-rmse:1.85823\n",
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      "[99]\tvalidation_0-rmse:1.75435\n",
      "CPU times: user 2.02 s, sys: 12 ms, total: 2.03 s\n",
      "Wall time: 286 ms\n",
      "[0]\tvalidation_0-rmse:8.69294\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:7.93134\n",
      "[2]\tvalidation_0-rmse:7.2517\n",
      "[3]\tvalidation_0-rmse:6.6468\n",
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      "[99]\tvalidation_0-rmse:2.75014\n",
      "CPU times: user 1.9 s, sys: 20 ms, total: 1.92 s\n",
      "Wall time: 261 ms\n",
      "[0]\tvalidation_0-rmse:11.5028\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:10.3933\n",
      "[2]\tvalidation_0-rmse:9.39903\n",
      "[3]\tvalidation_0-rmse:8.51163\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[83]\tvalidation_0-rmse:1.98733\n",
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      "CPU times: user 2 s, sys: 16 ms, total: 2.01 s\n",
      "Wall time: 280 ms\n",
      "[0]\tvalidation_0-rmse:4.23975\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:3.85933\n",
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      "[99]\tvalidation_0-rmse:1.08893\n",
      "CPU times: user 1.98 s, sys: 16 ms, total: 2 s\n",
      "Wall time: 273 ms\n",
      "[0]\tvalidation_0-rmse:26.5641\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:24.8001\n",
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      "CPU times: user 1.98 s, sys: 24 ms, total: 2 s\n",
      "Wall time: 276 ms\n",
      "[0]\tvalidation_0-rmse:1.25448\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:1.14944\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[36]\tvalidation_0-rmse:0.429674\n",
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      "[99]\tvalidation_0-rmse:0.402616\n",
      "CPU times: user 1.89 s, sys: 16 ms, total: 1.9 s\n",
      "Wall time: 260 ms\n",
      "[0]\tvalidation_0-rmse:3.08257\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:2.80687\n",
      "[2]\tvalidation_0-rmse:2.56151\n",
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      "[99]\tvalidation_0-rmse:0.833306\n",
      "CPU times: user 1.96 s, sys: 4 ms, total: 1.97 s\n",
      "Wall time: 268 ms\n",
      "[0]\tvalidation_0-rmse:8.69159\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:7.94093\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[89]\tvalidation_0-rmse:2.35254\n",
      "[90]\tvalidation_0-rmse:2.34725\n",
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      "CPU times: user 1.85 s, sys: 32 ms, total: 1.88 s\n",
      "Wall time: 261 ms\n",
      "[0]\tvalidation_0-rmse:48.1478\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:43.8579\n",
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      "CPU times: user 1.92 s, sys: 16 ms, total: 1.94 s\n",
      "Wall time: 269 ms\n",
      "[0]\tvalidation_0-rmse:25.4901\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:23.1047\n",
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      "CPU times: user 2.03 s, sys: 20 ms, total: 2.05 s\n",
      "Wall time: 284 ms\n",
      "[0]\tvalidation_0-rmse:73.1457\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:66.868\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[43]\tvalidation_0-rmse:21.9725\n",
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      "CPU times: user 1.93 s, sys: 8 ms, total: 1.94 s\n",
      "Wall time: 264 ms\n",
      "[0]\tvalidation_0-rmse:11.0009\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
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      "CPU times: user 2.04 s, sys: 12 ms, total: 2.06 s\n",
      "Wall time: 286 ms\n",
      "[0]\tvalidation_0-rmse:3.51869\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.98 s, sys: 20 ms, total: 2 s\n",
      "Wall time: 278 ms\n",
      "[0]\tvalidation_0-rmse:1.63475\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
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      "[99]\tvalidation_0-rmse:0.603141\n",
      "CPU times: user 2.14 s, sys: 36 ms, total: 2.18 s\n",
      "Wall time: 315 ms\n",
      "[0]\tvalidation_0-rmse:29.6605\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:27.3402\n",
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      "CPU times: user 1.83 s, sys: 20 ms, total: 1.85 s\n",
      "Wall time: 260 ms\n",
      "[0]\tvalidation_0-rmse:0.423846\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:0.394333\n",
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      "[48]\tvalidation_0-rmse:0.227598\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[49]\tvalidation_0-rmse:0.227581\n",
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      "CPU times: user 1.85 s, sys: 28 ms, total: 1.88 s\n",
      "Wall time: 261 ms\n",
      "[0]\tvalidation_0-rmse:44.4626\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:41.2256\n",
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      "CPU times: user 1.82 s, sys: 12 ms, total: 1.83 s\n",
      "Wall time: 252 ms\n",
      "[0]\tvalidation_0-rmse:74.6536\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:67.495\n",
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      "[99]\tvalidation_0-rmse:13.4334\n",
      "CPU times: user 1.86 s, sys: 32 ms, total: 1.89 s\n",
      "Wall time: 262 ms\n",
      "[0]\tvalidation_0-rmse:11.5734\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalidation_0-rmse:10.4962\n",
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      "CPU times: user 1.85 s, sys: 16 ms, total: 1.87 s\n",
      "Wall time: 261 ms\n",
      "[0]\tvalidation_0-rmse:13.2449\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:12.2724\n",
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      "CPU times: user 1.81 s, sys: 32 ms, total: 1.84 s\n",
      "Wall time: 260 ms\n",
      "[0]\tvalidation_0-rmse:2.98021\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:2.75944\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[59]\tvalidation_0-rmse:1.46514\n",
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      "CPU times: user 1.88 s, sys: 8 ms, total: 1.89 s\n",
      "Wall time: 260 ms\n",
      "[0]\tvalidation_0-rmse:6.88576\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:6.25343\n",
      "[2]\tvalidation_0-rmse:5.68948\n",
      "[3]\tvalidation_0-rmse:5.18674\n",
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      "[99]\tvalidation_0-rmse:1.53962\n",
      "CPU times: user 1.96 s, sys: 28 ms, total: 1.99 s\n",
      "Wall time: 275 ms\n",
      "[0]\tvalidation_0-rmse:11.8189\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:10.7975\n",
      "[2]\tvalidation_0-rmse:9.89658\n",
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      "[99]\tvalidation_0-rmse:3.78024\n",
      "CPU times: user 2.01 s, sys: 16 ms, total: 2.02 s\n",
      "Wall time: 282 ms\n",
      "[0]\tvalidation_0-rmse:0.450025\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:0.405046\n",
      "[2]\tvalidation_0-rmse:0.364562\n",
      "[3]\tvalidation_0-rmse:0.328124\n",
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      "[11]\tvalidation_0-rmse:0.141311\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12]\tvalidation_0-rmse:0.127187\n",
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      "[99]\tvalidation_0-rmse:1.3e-05\n",
      "CPU times: user 1.73 s, sys: 20 ms, total: 1.75 s\n",
      "Wall time: 246 ms\n",
      "[0]\tvalidation_0-rmse:1.03637\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:0.95751\n",
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      "CPU times: user 1.87 s, sys: 32 ms, total: 1.9 s\n",
      "Wall time: 262 ms\n",
      "[0]\tvalidation_0-rmse:4.88074\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:4.4477\n",
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      "[63]\tvalidation_0-rmse:1.37394\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[64]\tvalidation_0-rmse:1.37346\n",
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      "CPU times: user 2.03 s, sys: 12 ms, total: 2.04 s\n",
      "Wall time: 284 ms\n",
      "[0]\tvalidation_0-rmse:18.0186\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:16.3469\n",
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      "CPU times: user 1.95 s, sys: 16 ms, total: 1.96 s\n",
      "Wall time: 283 ms\n",
      "[0]\tvalidation_0-rmse:32.7357\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:29.7629\n",
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      "[99]\tvalidation_0-rmse:7.38363\n",
      "CPU times: user 2.04 s, sys: 36 ms, total: 2.07 s\n",
      "Wall time: 294 ms\n",
      "[0]\tvalidation_0-rmse:7.05728\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:6.51306\n",
      "[2]\tvalidation_0-rmse:6.0052\n",
      "[3]\tvalidation_0-rmse:5.549\n",
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      "[17]\tvalidation_0-rmse:2.92849\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18]\tvalidation_0-rmse:2.86723\n",
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      "CPU times: user 1.8 s, sys: 32 ms, total: 1.83 s\n",
      "Wall time: 270 ms\n",
      "[0]\tvalidation_0-rmse:28.0178\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:25.4823\n",
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      "CPU times: user 2.14 s, sys: 28 ms, total: 2.17 s\n",
      "Wall time: 307 ms\n",
      "[0]\tvalidation_0-rmse:24.8763\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:23.2167\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[76]\tvalidation_0-rmse:13.0186\n",
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      "CPU times: user 1.9 s, sys: 4 ms, total: 1.9 s\n",
      "Wall time: 259 ms\n",
      "[0]\tvalidation_0-rmse:70.7899\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:65.0902\n",
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      "CPU times: user 1.94 s, sys: 8 ms, total: 1.95 s\n",
      "Wall time: 265 ms\n",
      "[0]\tvalidation_0-rmse:1.47032\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:1.36258\n",
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      "CPU times: user 1.98 s, sys: 4 ms, total: 1.98 s\n",
      "Wall time: 271 ms\n",
      "[0]\tvalidation_0-rmse:45.8483\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:41.9541\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[27]\tvalidation_0-rmse:16.2368\n",
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      "CPU times: user 1.94 s, sys: 32 ms, total: 1.97 s\n",
      "Wall time: 273 ms\n",
      "[0]\tvalidation_0-rmse:27.1314\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:24.7254\n",
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      "CPU times: user 1.8 s, sys: 20 ms, total: 1.82 s\n",
      "Wall time: 255 ms\n",
      "[0]\tvalidation_0-rmse:36.385\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:33.1109\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[85]\tvalidation_0-rmse:9.75771\n",
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      "CPU times: user 1.86 s, sys: 20 ms, total: 1.88 s\n",
      "Wall time: 257 ms\n",
      "[0]\tvalidation_0-rmse:1.66969\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:1.52022\n",
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      "[8]\tvalidation_0-rmse:0.86189\n",
      "[9]\tvalidation_0-rmse:0.808259\n",
      "[10]\tvalidation_0-rmse:0.763015\n",
      "[11]\tvalidation_0-rmse:0.723604\n",
      "[12]\tvalidation_0-rmse:0.689697\n",
      "[13]\tvalidation_0-rmse:0.661141\n",
      "[14]\tvalidation_0-rmse:0.637324\n",
      "[15]\tvalidation_0-rmse:0.616669\n",
      "[16]\tvalidation_0-rmse:0.600023\n",
      "[17]\tvalidation_0-rmse:0.584971\n",
      "[18]\tvalidation_0-rmse:0.573295\n",
      "[19]\tvalidation_0-rmse:0.563374\n",
      "[20]\tvalidation_0-rmse:0.55423\n",
      "[21]\tvalidation_0-rmse:0.548042\n",
      "[22]\tvalidation_0-rmse:0.542538\n",
      "[23]\tvalidation_0-rmse:0.538052\n",
      "[24]\tvalidation_0-rmse:0.534211\n",
      "[25]\tvalidation_0-rmse:0.529978\n",
      "[26]\tvalidation_0-rmse:0.526428\n",
      "[27]\tvalidation_0-rmse:0.523695\n",
      "[28]\tvalidation_0-rmse:0.520865\n",
      "[29]\tvalidation_0-rmse:0.518709\n",
      "[30]\tvalidation_0-rmse:0.516462\n",
      "[31]\tvalidation_0-rmse:0.514714\n",
      "[32]\tvalidation_0-rmse:0.513269\n",
      "[33]\tvalidation_0-rmse:0.512086\n",
      "[34]\tvalidation_0-rmse:0.510583\n",
      "[35]\tvalidation_0-rmse:0.509335\n",
      "[36]\tvalidation_0-rmse:0.508811\n",
      "[37]\tvalidation_0-rmse:0.507179\n",
      "[38]\tvalidation_0-rmse:0.505617\n",
      "[39]\tvalidation_0-rmse:0.504331\n",
      "[40]\tvalidation_0-rmse:0.503908\n",
      "[41]\tvalidation_0-rmse:0.503546\n",
      "[42]\tvalidation_0-rmse:0.503097\n",
      "[43]\tvalidation_0-rmse:0.501942\n",
      "[44]\tvalidation_0-rmse:0.500799\n",
      "[45]\tvalidation_0-rmse:0.499769\n",
      "[46]\tvalidation_0-rmse:0.498458\n",
      "[47]\tvalidation_0-rmse:0.497985\n",
      "[48]\tvalidation_0-rmse:0.497721\n",
      "[49]\tvalidation_0-rmse:0.496637\n",
      "[50]\tvalidation_0-rmse:0.495498\n",
      "[51]\tvalidation_0-rmse:0.49473\n",
      "[52]\tvalidation_0-rmse:0.493597\n",
      "[53]\tvalidation_0-rmse:0.492764\n",
      "[54]\tvalidation_0-rmse:0.491933\n",
      "[55]\tvalidation_0-rmse:0.491111\n",
      "[56]\tvalidation_0-rmse:0.489941\n",
      "[57]\tvalidation_0-rmse:0.48949\n",
      "[58]\tvalidation_0-rmse:0.488657\n",
      "[59]\tvalidation_0-rmse:0.487995\n",
      "[60]\tvalidation_0-rmse:0.486833\n",
      "[61]\tvalidation_0-rmse:0.486432\n",
      "[62]\tvalidation_0-rmse:0.485793\n",
      "[63]\tvalidation_0-rmse:0.484939\n",
      "[64]\tvalidation_0-rmse:0.483975\n",
      "[65]\tvalidation_0-rmse:0.48328\n",
      "[66]\tvalidation_0-rmse:0.48292\n",
      "[67]\tvalidation_0-rmse:0.482234\n",
      "[68]\tvalidation_0-rmse:0.481239\n",
      "[69]\tvalidation_0-rmse:0.480744\n",
      "[70]\tvalidation_0-rmse:0.479965\n",
      "[71]\tvalidation_0-rmse:0.4792\n",
      "[72]\tvalidation_0-rmse:0.478187\n",
      "[73]\tvalidation_0-rmse:0.477971\n",
      "[74]\tvalidation_0-rmse:0.477515\n",
      "[75]\tvalidation_0-rmse:0.477306\n",
      "[76]\tvalidation_0-rmse:0.475819\n",
      "[77]\tvalidation_0-rmse:0.475655\n",
      "[78]\tvalidation_0-rmse:0.475028\n",
      "[79]\tvalidation_0-rmse:0.474863\n",
      "[80]\tvalidation_0-rmse:0.474539\n",
      "[81]\tvalidation_0-rmse:0.473853\n",
      "[82]\tvalidation_0-rmse:0.473339\n",
      "[83]\tvalidation_0-rmse:0.47315\n",
      "[84]\tvalidation_0-rmse:0.472064\n",
      "[85]\tvalidation_0-rmse:0.471161\n",
      "[86]\tvalidation_0-rmse:0.471079\n",
      "[87]\tvalidation_0-rmse:0.469693\n",
      "[88]\tvalidation_0-rmse:0.469601\n",
      "[89]\tvalidation_0-rmse:0.46884\n",
      "[90]\tvalidation_0-rmse:0.468461\n",
      "[91]\tvalidation_0-rmse:0.468417\n",
      "[92]\tvalidation_0-rmse:0.467906\n",
      "[93]\tvalidation_0-rmse:0.46786\n",
      "[94]\tvalidation_0-rmse:0.467376\n",
      "[95]\tvalidation_0-rmse:0.467019\n",
      "[96]\tvalidation_0-rmse:0.466944\n",
      "[97]\tvalidation_0-rmse:0.466626\n",
      "[98]\tvalidation_0-rmse:0.465646\n",
      "[99]\tvalidation_0-rmse:0.465212\n",
      "CPU times: user 1.96 s, sys: 12 ms, total: 1.97 s\n",
      "Wall time: 277 ms\n",
      "[0]\tvalidation_0-rmse:26.2259\n",
      "Will train until validation_0-rmse hasn't improved in 100 rounds.\n",
      "[1]\tvalidation_0-rmse:24.4232\n",
      "[2]\tvalidation_0-rmse:22.8694\n",
      "[3]\tvalidation_0-rmse:21.5111\n",
      "[4]\tvalidation_0-rmse:20.3672\n",
      "[5]\tvalidation_0-rmse:19.3765\n",
      "[6]\tvalidation_0-rmse:18.5414\n",
      "[7]\tvalidation_0-rmse:17.8407\n",
      "[8]\tvalidation_0-rmse:17.2184\n",
      "[9]\tvalidation_0-rmse:16.7272\n",
      "[10]\tvalidation_0-rmse:16.2799\n",
      "[11]\tvalidation_0-rmse:15.9424\n",
      "[12]\tvalidation_0-rmse:15.6318\n",
      "[13]\tvalidation_0-rmse:15.3792\n",
      "[14]\tvalidation_0-rmse:15.156\n",
      "[15]\tvalidation_0-rmse:15.007\n",
      "[16]\tvalidation_0-rmse:14.8815\n",
      "[17]\tvalidation_0-rmse:14.7072\n",
      "[18]\tvalidation_0-rmse:14.6329\n",
      "[19]\tvalidation_0-rmse:14.5572\n",
      "[20]\tvalidation_0-rmse:14.5056\n",
      "[21]\tvalidation_0-rmse:14.4648\n",
      "[22]\tvalidation_0-rmse:14.3929\n",
      "[23]\tvalidation_0-rmse:14.3476\n",
      "[24]\tvalidation_0-rmse:14.3198\n",
      "[25]\tvalidation_0-rmse:14.272\n",
      "[26]\tvalidation_0-rmse:14.2588\n",
      "[27]\tvalidation_0-rmse:14.224\n",
      "[28]\tvalidation_0-rmse:14.2159\n",
      "[29]\tvalidation_0-rmse:14.1877\n",
      "[30]\tvalidation_0-rmse:14.1904\n",
      "[31]\tvalidation_0-rmse:14.1437\n",
      "[32]\tvalidation_0-rmse:14.1084\n",
      "[33]\tvalidation_0-rmse:14.1074\n",
      "[34]\tvalidation_0-rmse:14.0485\n",
      "[35]\tvalidation_0-rmse:14.0303\n",
      "[36]\tvalidation_0-rmse:14.0041\n",
      "[37]\tvalidation_0-rmse:14.0148\n",
      "[38]\tvalidation_0-rmse:14.0078\n",
      "[39]\tvalidation_0-rmse:14.0141\n",
      "[40]\tvalidation_0-rmse:13.9617\n",
      "[41]\tvalidation_0-rmse:13.9472\n",
      "[42]\tvalidation_0-rmse:13.9208\n",
      "[43]\tvalidation_0-rmse:13.9223\n",
      "[44]\tvalidation_0-rmse:13.9075\n",
      "[45]\tvalidation_0-rmse:13.9045\n",
      "[46]\tvalidation_0-rmse:13.8396\n",
      "[47]\tvalidation_0-rmse:13.7938\n",
      "[48]\tvalidation_0-rmse:13.7564\n",
      "[49]\tvalidation_0-rmse:13.7615\n",
      "[50]\tvalidation_0-rmse:13.7413\n",
      "[51]\tvalidation_0-rmse:13.7157\n",
      "[52]\tvalidation_0-rmse:13.7154\n",
      "[53]\tvalidation_0-rmse:13.676\n",
      "[54]\tvalidation_0-rmse:13.6718\n",
      "[55]\tvalidation_0-rmse:13.6531\n",
      "[56]\tvalidation_0-rmse:13.643\n",
      "[57]\tvalidation_0-rmse:13.6076\n",
      "[58]\tvalidation_0-rmse:13.6135\n",
      "[59]\tvalidation_0-rmse:13.6168\n",
      "[60]\tvalidation_0-rmse:13.6033\n",
      "[61]\tvalidation_0-rmse:13.6042\n",
      "[62]\tvalidation_0-rmse:13.6022\n",
      "[63]\tvalidation_0-rmse:13.5775\n",
      "[64]\tvalidation_0-rmse:13.5718\n",
      "[65]\tvalidation_0-rmse:13.5552\n",
      "[66]\tvalidation_0-rmse:13.5215\n",
      "[67]\tvalidation_0-rmse:13.5065\n",
      "[68]\tvalidation_0-rmse:13.5061\n",
      "[69]\tvalidation_0-rmse:13.5128\n",
      "[70]\tvalidation_0-rmse:13.4975\n",
      "[71]\tvalidation_0-rmse:13.5005\n",
      "[72]\tvalidation_0-rmse:13.5037\n",
      "[73]\tvalidation_0-rmse:13.4919\n",
      "[74]\tvalidation_0-rmse:13.4551\n",
      "[75]\tvalidation_0-rmse:13.4337\n",
      "[76]\tvalidation_0-rmse:13.4089\n",
      "[77]\tvalidation_0-rmse:13.4041\n",
      "[78]\tvalidation_0-rmse:13.4072\n",
      "[79]\tvalidation_0-rmse:13.4164\n",
      "[80]\tvalidation_0-rmse:13.3948\n",
      "[81]\tvalidation_0-rmse:13.3783\n",
      "[82]\tvalidation_0-rmse:13.3619\n",
      "[83]\tvalidation_0-rmse:13.3562\n",
      "[84]\tvalidation_0-rmse:13.364\n",
      "[85]\tvalidation_0-rmse:13.3682\n",
      "[86]\tvalidation_0-rmse:13.3584\n",
      "[87]\tvalidation_0-rmse:13.3572\n",
      "[88]\tvalidation_0-rmse:13.3626\n",
      "[89]\tvalidation_0-rmse:13.3502\n",
      "[90]\tvalidation_0-rmse:13.3485\n",
      "[91]\tvalidation_0-rmse:13.3394\n",
      "[92]\tvalidation_0-rmse:13.3269\n",
      "[93]\tvalidation_0-rmse:13.3138\n",
      "[94]\tvalidation_0-rmse:13.3109\n",
      "[95]\tvalidation_0-rmse:13.3143\n",
      "[96]\tvalidation_0-rmse:13.3145\n",
      "[97]\tvalidation_0-rmse:13.3066\n",
      "[98]\tvalidation_0-rmse:13.2992\n",
      "[99]\tvalidation_0-rmse:13.2673\n",
      "CPU times: user 1.97 s, sys: 12 ms, total: 1.98 s\n",
      "Wall time: 281 ms\n"
     ]
    }
   ],
   "source": [
    "for i in link_ID_list:\n",
    "    % time exec(\"rgr_%s = train_model(ID_%s_hour8)\" % (i,i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 制作预测特征 (ID_%s_month6_hour8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in link_ID_list:\n",
    "    exec(\"ID_%s_month6_7 = ID_%s.query('month==6 and hour==7').copy()\" % (i,i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0=pd.date_range('2016-06-01','2016-06-30 23:58:00',freq='2min')\n",
    "for i in link_ID_list:\n",
    "    exec(\"ID_%s_month6_8 = pd.DataFrame(t0,columns=['time_interval_start'])\" % i)\n",
    "    exec(\"ID_%s_month6_8['hour']=ID_%s_month6_8['time_interval_start'].dt.hour\" % (i,i))\n",
    "    exec(\"ID_%s_month6_8 = ID_%s_month6_8.query('hour==8')\" % (i,i))\n",
    "    exec(\"ID_%s_month6_8['link_ID'] = %s\" % (i,i) )\n",
    "    exec(\"ID_%s_month6_8=pd.merge(ID_%s_month6_8, link_info_top, how='left',on='link_ID').copy()\" % (i, i))\n",
    "    exec(\"ID_%s_month6_8=link_datetime(ID_%s_month6_8)\" % (i,i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# tmp = pd.concat([ID_4377906284769500514_month6_7,ID_4377906284769500514_month6_8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in link_ID_list:\n",
    "    exec(\"ID_%s_month6_78 = pd.concat([ID_%s_month6_7, ID_%s_month6_8])\" % (i,i,i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in link_ID_list:\n",
    "    exec(\"tmp = prehour_ttmean(ID_%s_month6_78)\" % i)\n",
    "    exec(\"tmp1 = pd.merge(ID_%s_month6_78, tmp[['minuteofyear','prehour_ttmean']], how='left',on='minuteofyear')\" % i)\n",
    "    tmp2 = inPreHourTTMean(tmp1)\n",
    "    exec(\"ID_%s_month6_hour8 = pd.merge(tmp, tmp2[['minuteofyear','inPreHourTTMean']], how='left',on='minuteofyear')\" % i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 代码合并, 适配所有月份"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "M = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for i in link_ID_list:\n",
    "    exec(\"ID_%s_month%d_7 = ID_%s.query('month==%d and hour==7').copy()\" % (i,M,i,M))\n",
    "exec(\"t0=pd.date_range('2016-0%d-01','2016-0%d-30 23:58:00',freq='2min')\" % (M,M))\n",
    "for i in link_ID_list:\n",
    "    exec(\"ID_%s_month%d_8 = pd.DataFrame(t0,columns=['time_interval_start'])\" % (i,M))\n",
    "    exec(\"ID_%s_month%d_8['hour']=ID_%s_month%d_8['time_interval_start'].dt.hour\" % (i,M,i,M))\n",
    "    exec(\"ID_%s_month%d_8 = ID_%s_month%d_8.query('hour==8')\" % (i,M,i,M))\n",
    "    exec(\"ID_%s_month%d_8['link_ID'] = %s\" % (i,M,i) )\n",
    "    exec(\"ID_%s_month%d_8=pd.merge(ID_%s_month%d_8, link_info_top, how='left',on='link_ID').copy()\" % (i, M, i,M))\n",
    "    exec(\"ID_%s_month%d_8=link_datetime(ID_%s_month%d_8)\" % (i,M,i,M))\n",
    "for i in link_ID_list:\n",
    "    exec(\"ID_%s_month%d_78 = pd.concat([ID_%s_month%d_7, ID_%s_month%d_8])\" % (i,M,i,M,i,M))\n",
    "for i in link_ID_list:\n",
    "    exec(\"tmp = prehour_ttmean(ID_%s_month%d_78)\" % (i,M))\n",
    "    exec(\"tmp1 = pd.merge(ID_%s_month%d_78, tmp[['minuteofyear','prehour_ttmean']], how='left',on='minuteofyear')\" % (i,M))\n",
    "    tmp2 = inPreHourTTMean(tmp1)\n",
    "    exec(\"ID_%s_month%d_hour8 = pd.merge(tmp, tmp2[['minuteofyear','inPreHourTTMean']], how='left',on='minuteofyear')\" % (i,M))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预测6月8点到9点的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rgr_%s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data0 = ID_4377906284769500514_month6_hour8\n",
    "pred_features = data0[['hour', 'length', 'width',\n",
    "           'link_class', 'in_num', 'out_num',  'in0_len', 'in1_len', 'in2_len', 'in3_len',\n",
    "           'in0_wid', 'in1_wid', 'in2_wid', 'in3_wid', 'out0_len', 'out1_len',\n",
    "           'out2_len', 'out3_len', 'out0_wid', 'out1_wid', 'out2_wid', 'out3_wid',\n",
    "           'in_width', 'in_length', 'out_width', 'out_length', 'minute',\n",
    "           'time_area', 'minuteofday', 'dayofweek', 'day', 'month',\n",
    "           'minuteofmonth', 'minuteofyear', 'dayofyear', 'prehour_ttmean',\n",
    "           'inPreHourTTMean']]\n",
    "X = np.array(pred_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data0['length'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "exec(\"predictions = rgr_%s.predict(X)\" % '4377906284769500514')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900,)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tt = pd.Series(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    900.000000\n",
       "mean       0.823585\n",
       "std        0.090732\n",
       "min        0.646614\n",
       "25%        0.757697\n",
       "50%        0.811346\n",
       "75%        0.881318\n",
       "max        1.367102\n",
       "Name: travel_time, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ans = pd.DataFrame({'time_interval_start':data0['time_interval_start'], 'travel_time':tt})\n",
    "ans['travel_time'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5月的一条link预测情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_interval_start</th>\n",
       "      <th>travel_time_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-05-01 08:00:00</td>\n",
       "      <td>0.950097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-05-01 08:02:00</td>\n",
       "      <td>0.950097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-05-01 08:04:00</td>\n",
       "      <td>0.938887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-05-01 08:06:00</td>\n",
       "      <td>0.948059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-05-01 08:08:00</td>\n",
       "      <td>1.247396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  time_interval_start  travel_time_pred\n",
       "0 2016-05-01 08:00:00          0.950097\n",
       "1 2016-05-01 08:02:00          0.950097\n",
       "2 2016-05-01 08:04:00          0.938887\n",
       "3 2016-05-01 08:06:00          0.948059\n",
       "4 2016-05-01 08:08:00          1.247396"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data0 = ID_4377906284769500514_month5_hour8\n",
    "pred_features = data0[['hour', 'length', 'width',\n",
    "           'link_class', 'in_num', 'out_num',  'in0_len', 'in1_len', 'in2_len', 'in3_len',\n",
    "           'in0_wid', 'in1_wid', 'in2_wid', 'in3_wid', 'out0_len', 'out1_len',\n",
    "           'out2_len', 'out3_len', 'out0_wid', 'out1_wid', 'out2_wid', 'out3_wid',\n",
    "           'in_width', 'in_length', 'out_width', 'out_length', 'minute',\n",
    "           'time_area', 'minuteofday', 'dayofweek', 'day', 'month',\n",
    "           'minuteofmonth', 'minuteofyear', 'dayofyear', 'prehour_ttmean',\n",
    "           'inPreHourTTMean']]\n",
    "X = np.array(pred_features)\n",
    "exec(\"predictions = rgr_%s.predict(X)\" % '4377906284769500514')\n",
    "tt = pd.Series(predictions)\n",
    "ans = pd.DataFrame({'time_interval_start':data0['time_interval_start'], 'travel_time_pred':tt})\n",
    "ans['travel_time_pred'].describe()\n",
    "ans.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ans1= pd.merge(ans, ID_4377906284769500514[['time_interval_start','travel_time']], how ='left', on='time_interval_start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_interval_start</th>\n",
       "      <th>travel_time_pred</th>\n",
       "      <th>travel_time</th>\n",
       "      <th>abs_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-05-01 08:00:00</td>\n",
       "      <td>0.950097</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.050097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-05-01 08:02:00</td>\n",
       "      <td>0.950097</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.049903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-05-01 08:04:00</td>\n",
       "      <td>0.938887</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.061113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-05-01 08:06:00</td>\n",
       "      <td>0.948059</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.148059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-05-01 08:08:00</td>\n",
       "      <td>1.247396</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.752604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  time_interval_start  travel_time_pred  travel_time  abs_error\n",
       "0 2016-05-01 08:00:00          0.950097          0.9   0.050097\n",
       "1 2016-05-01 08:02:00          0.950097          1.0   0.049903\n",
       "2 2016-05-01 08:04:00          0.938887          1.0   0.061113\n",
       "3 2016-05-01 08:06:00          0.948059          0.8   0.148059\n",
       "4 2016-05-01 08:08:00          1.247396          5.0   3.752604"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ans1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ans1['abs_error']=(ans1['travel_time_pred']-ans1['travel_time']).apply(np.abs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>travel_time_pred</th>\n",
       "      <th>travel_time</th>\n",
       "      <th>abs_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>900.000000</td>\n",
       "      <td>894.000000</td>\n",
       "      <td>894.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.915621</td>\n",
       "      <td>1.128747</td>\n",
       "      <td>0.386840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.081859</td>\n",
       "      <td>1.329791</td>\n",
       "      <td>1.265424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.674278</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.874562</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.063216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.908723</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.113849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.950987</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.248884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.264337</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>27.030472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       travel_time_pred  travel_time   abs_error\n",
       "count        900.000000   894.000000  894.000000\n",
       "mean           0.915621     1.128747    0.386840\n",
       "std            0.081859     1.329791    1.265424\n",
       "min            0.674278     0.400000    0.000012\n",
       "25%            0.874562     0.700000    0.063216\n",
       "50%            0.908723     0.900000    0.113849\n",
       "75%            0.950987     1.000000    0.248884\n",
       "max            1.264337    28.000000   27.030472"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ans1.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 所有link的5月8点预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_link_ans=pd.DataFrame()\n",
    "all_link_ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 118800 entries, 0 to 899\n",
      "Data columns (total 3 columns):\n",
      "time_interval_start    118800 non-null datetime64[ns]\n",
      "travel_time_pred       118800 non-null float32\n",
      "link_ID                118800 non-null uint64\n",
      "dtypes: datetime64[ns](1), float32(1), uint64(1)\n",
      "memory usage: 3.2 MB\n"
     ]
    }
   ],
   "source": [
    "for i in link_ID_list:\n",
    "    data0 = eval(\"ID_%s_month5_hour8\" % i)\n",
    "    pred_features = data0[['hour', 'length', 'width',\n",
    "               'link_class', 'in_num', 'out_num',  'in0_len', 'in1_len', 'in2_len', 'in3_len',\n",
    "               'in0_wid', 'in1_wid', 'in2_wid', 'in3_wid', 'out0_len', 'out1_len',\n",
    "               'out2_len', 'out3_len', 'out0_wid', 'out1_wid', 'out2_wid', 'out3_wid',\n",
    "               'in_width', 'in_length', 'out_width', 'out_length', 'minute',\n",
    "               'time_area', 'minuteofday', 'dayofweek', 'day', 'month',\n",
    "               'minuteofmonth', 'minuteofyear', 'dayofyear', 'prehour_ttmean',\n",
    "               'inPreHourTTMean']]\n",
    "    X = np.array(pred_features)\n",
    "    exec(\"predictions = rgr_%s.predict(X)\" % i)\n",
    "    tt = pd.Series(predictions)\n",
    "    ans = pd.DataFrame({'time_interval_start':data0['time_interval_start'], 'travel_time_pred':tt})\n",
    "    ans['link_ID']=i\n",
    "    all_link_ans = pd.concat([all_link_ans, ans])\n",
    "all_link_ans.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_interval_start</th>\n",
       "      <th>travel_time_pred</th>\n",
       "      <th>link_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-05-01 08:00:00</td>\n",
       "      <td>5.972211</td>\n",
       "      <td>4377906289869500514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-05-01 08:02:00</td>\n",
       "      <td>5.972211</td>\n",
       "      <td>4377906289869500514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-05-01 08:04:00</td>\n",
       "      <td>5.921382</td>\n",
       "      <td>4377906289869500514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-05-01 08:06:00</td>\n",
       "      <td>5.921382</td>\n",
       "      <td>4377906289869500514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-05-01 08:08:00</td>\n",
       "      <td>5.951692</td>\n",
       "      <td>4377906289869500514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  time_interval_start  travel_time_pred              link_ID\n",
       "0 2016-05-01 08:00:00          5.972211  4377906289869500514\n",
       "1 2016-05-01 08:02:00          5.972211  4377906289869500514\n",
       "2 2016-05-01 08:04:00          5.921382  4377906289869500514\n",
       "3 2016-05-01 08:06:00          5.921382  4377906289869500514\n",
       "4 2016-05-01 08:08:00          5.951692  4377906289869500514"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_link_ans.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 760581 entries, 7430427 to 7599675\n",
      "Data columns (total 6 columns):\n",
      "link_ID                760581 non-null uint64\n",
      "date                   760581 non-null object\n",
      "travel_time            760581 non-null float64\n",
      "time_interval_start    760581 non-null object\n",
      "time_interval_end      760581 non-null object\n",
      "hour                   760581 non-null int64\n",
      "dtypes: float64(1), int64(1), object(3), uint64(1)\n",
      "memory usage: 40.6+ MB\n"
     ]
    }
   ],
   "source": [
    "rawdata_78.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "actual = rawdata_78[['link_ID','time_interval_start','travel_time']].copy()\n",
    "actual['time_interval_start'] = pd.to_datetime(actual['time_interval_start'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_link_ans1 = pd.merge(all_link_ans, actual,how='left',on=['link_ID','time_interval_start'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_interval_start</th>\n",
       "      <th>travel_time_pred</th>\n",
       "      <th>link_ID</th>\n",
       "      <th>travel_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-05-01 08:00:00</td>\n",
       "      <td>5.972211</td>\n",
       "      <td>4377906289869500514</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-05-01 08:02:00</td>\n",
       "      <td>5.972211</td>\n",
       "      <td>4377906289869500514</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-05-01 08:04:00</td>\n",
       "      <td>5.921382</td>\n",
       "      <td>4377906289869500514</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-05-01 08:06:00</td>\n",
       "      <td>5.921382</td>\n",
       "      <td>4377906289869500514</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-05-01 08:08:00</td>\n",
       "      <td>5.951692</td>\n",
       "      <td>4377906289869500514</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  time_interval_start  travel_time_pred              link_ID  travel_time\n",
       "0 2016-05-01 08:00:00          5.972211  4377906289869500514          6.6\n",
       "1 2016-05-01 08:02:00          5.972211  4377906289869500514          6.6\n",
       "2 2016-05-01 08:04:00          5.921382  4377906289869500514          5.5\n",
       "3 2016-05-01 08:06:00          5.921382  4377906289869500514          5.5\n",
       "4 2016-05-01 08:08:00          5.951692  4377906289869500514          5.5"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_link_ans1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 118800 entries, 0 to 118799\n",
      "Data columns (total 4 columns):\n",
      "time_interval_start    118800 non-null datetime64[ns]\n",
      "travel_time_pred       118800 non-null float32\n",
      "link_ID                118800 non-null uint64\n",
      "travel_time            109965 non-null float64\n",
      "dtypes: datetime64[ns](1), float32(1), float64(1), uint64(1)\n",
      "memory usage: 4.1 MB\n"
     ]
    }
   ],
   "source": [
    "all_link_ans1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_link_ans1['abs_error']=(all_link_ans1['travel_time_pred']-all_link_ans1['travel_time']).apply(np.abs)\n",
    "all_link_ans1['ape']=all_link_ans1['abs_error']/all_link_ans1['travel_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>travel_time_pred</th>\n",
       "      <th>link_ID</th>\n",
       "      <th>travel_time</th>\n",
       "      <th>abs_error</th>\n",
       "      <th>ape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>118800.000000</td>\n",
       "      <td>1.188000e+05</td>\n",
       "      <td>109965.000000</td>\n",
       "      <td>109965.000000</td>\n",
       "      <td>109965.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.445143</td>\n",
       "      <td>4.643058e+18</td>\n",
       "      <td>24.190981</td>\n",
       "      <td>8.702458</td>\n",
       "      <td>0.330730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.324057</td>\n",
       "      <td>1.471245e+18</td>\n",
       "      <td>41.042476</td>\n",
       "      <td>25.689076</td>\n",
       "      <td>0.435582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.674278</td>\n",
       "      <td>3.377906e+18</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.217546</td>\n",
       "      <td>4.377906e+18</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>0.645049</td>\n",
       "      <td>0.093273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.332829</td>\n",
       "      <td>4.377906e+18</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>2.209776</td>\n",
       "      <td>0.212605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>25.802305</td>\n",
       "      <td>4.377906e+18</td>\n",
       "      <td>26.100000</td>\n",
       "      <td>7.350175</td>\n",
       "      <td>0.415351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>259.718048</td>\n",
       "      <td>9.377906e+18</td>\n",
       "      <td>983.000000</td>\n",
       "      <td>858.476448</td>\n",
       "      <td>19.593585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       travel_time_pred       link_ID    travel_time      abs_error  \\\n",
       "count     118800.000000  1.188000e+05  109965.000000  109965.000000   \n",
       "mean          20.445143  4.643058e+18      24.190981       8.702458   \n",
       "std           24.324057  1.471245e+18      41.042476      25.689076   \n",
       "min            0.674278  3.377906e+18       0.400000       0.000012   \n",
       "25%            5.217546  4.377906e+18       4.800000       0.645049   \n",
       "50%           12.332829  4.377906e+18      11.500000       2.209776   \n",
       "75%           25.802305  4.377906e+18      26.100000       7.350175   \n",
       "max          259.718048  9.377906e+18     983.000000     858.476448   \n",
       "\n",
       "                 ape  \n",
       "count  109965.000000  \n",
       "mean        0.330730  \n",
       "std         0.435582  \n",
       "min         0.000004  \n",
       "25%         0.093273  \n",
       "50%         0.212605  \n",
       "75%         0.415351  \n",
       "max        19.593585  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_link_ans1.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 可以看到5月份的8点到9点tt值预测的MAPE为0.330730"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 查看数据(以下与代码无关)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4377906285525800514"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "link_ID_list[55]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>link_ID</th>\n",
       "      <th>date</th>\n",
       "      <th>travel_time</th>\n",
       "      <th>time_interval_start</th>\n",
       "      <th>time_interval_end</th>\n",
       "      <th>hour</th>\n",
       "      <th>travel_time_clean</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>link_class</th>\n",
       "      <th>...</th>\n",
       "      <th>out_length</th>\n",
       "      <th>minute</th>\n",
       "      <th>time_area</th>\n",
       "      <th>minuteofday</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>minuteofmonth</th>\n",
       "      <th>minuteofyear</th>\n",
       "      <th>dayofyear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5206</th>\n",
       "      <td>4377906284769500514</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-06-01 07:00:00</td>\n",
       "      <td>2016-06-01 07:02:00</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>420</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1860</td>\n",
       "      <td>269700</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5207</th>\n",
       "      <td>4377906284769500514</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-06-01 07:02:00</td>\n",
       "      <td>2016-06-01 07:04:00</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>422</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1862</td>\n",
       "      <td>269702</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5208</th>\n",
       "      <td>4377906284769500514</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-06-01 07:04:00</td>\n",
       "      <td>2016-06-01 07:06:00</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>424</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1864</td>\n",
       "      <td>269704</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5209</th>\n",
       "      <td>4377906284769500514</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2016-06-01 07:06:00</td>\n",
       "      <td>2016-06-01 07:08:00</td>\n",
       "      <td>7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>426</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1866</td>\n",
       "      <td>269706</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5210</th>\n",
       "      <td>4377906284769500514</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2016-06-01 07:08:00</td>\n",
       "      <td>2016-06-01 07:10:00</td>\n",
       "      <td>7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>428</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1868</td>\n",
       "      <td>269708</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  link_ID        date  travel_time time_interval_start  \\\n",
       "5206  4377906284769500514  2016-06-01          1.0 2016-06-01 07:00:00   \n",
       "5207  4377906284769500514  2016-06-01          1.0 2016-06-01 07:02:00   \n",
       "5208  4377906284769500514  2016-06-01          1.0 2016-06-01 07:04:00   \n",
       "5209  4377906284769500514  2016-06-01          0.7 2016-06-01 07:06:00   \n",
       "5210  4377906284769500514  2016-06-01          0.7 2016-06-01 07:08:00   \n",
       "\n",
       "       time_interval_end  hour  travel_time_clean  length  width  link_class  \\\n",
       "5206 2016-06-01 07:02:00     7                1.0      11     12           1   \n",
       "5207 2016-06-01 07:04:00     7                1.0      11     12           1   \n",
       "5208 2016-06-01 07:06:00     7                1.0      11     12           1   \n",
       "5209 2016-06-01 07:08:00     7                0.7      11     12           1   \n",
       "5210 2016-06-01 07:10:00     7                0.7      11     12           1   \n",
       "\n",
       "        ...     out_length  minute time_area minuteofday dayofweek day month  \\\n",
       "5206    ...           15.0       0         0         420         2   1     6   \n",
       "5207    ...           15.0       2         1         422         2   1     6   \n",
       "5208    ...           15.0       4         2         424         2   1     6   \n",
       "5209    ...           15.0       6         3         426         2   1     6   \n",
       "5210    ...           15.0       8         4         428         2   1     6   \n",
       "\n",
       "     minuteofmonth minuteofyear dayofyear  \n",
       "5206          1860       269700       153  \n",
       "5207          1862       269702       153  \n",
       "5208          1864       269704       153  \n",
       "5209          1866       269706       153  \n",
       "5210          1868       269708       153  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ID_4377906284769500514_month6_7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1751 entries, 5269 to 899\n",
      "Data columns (total 49 columns):\n",
      "date                   851 non-null object\n",
      "day                    1751 non-null int64\n",
      "dayofweek              1751 non-null int64\n",
      "dayofyear              1751 non-null int64\n",
      "hour                   1751 non-null int64\n",
      "in0                    1751 non-null object\n",
      "in0_len                1751 non-null int64\n",
      "in0_wid                1751 non-null int64\n",
      "in1                    0 non-null object\n",
      "in1_len                1751 non-null int64\n",
      "in1_wid                1751 non-null int64\n",
      "in2                    0 non-null object\n",
      "in2_len                1751 non-null int64\n",
      "in2_wid                1751 non-null int64\n",
      "in3                    0 non-null object\n",
      "in3_len                1751 non-null int64\n",
      "in3_wid                1751 non-null int64\n",
      "in_length              1751 non-null float64\n",
      "in_num                 1751 non-null int64\n",
      "in_width               1751 non-null int64\n",
      "length                 1751 non-null int64\n",
      "link_ID                1751 non-null float64\n",
      "link_class             1751 non-null int64\n",
      "minute                 1751 non-null int64\n",
      "minuteofday            1751 non-null int64\n",
      "minuteofmonth          1751 non-null int64\n",
      "minuteofyear           1751 non-null int64\n",
      "month                  1751 non-null int64\n",
      "out0                   1751 non-null object\n",
      "out0_len               1751 non-null int64\n",
      "out0_wid               1751 non-null int64\n",
      "out1                   1751 non-null object\n",
      "out1_len               1751 non-null int64\n",
      "out1_wid               1751 non-null int64\n",
      "out2                   0 non-null object\n",
      "out2_len               1751 non-null int64\n",
      "out2_wid               1751 non-null int64\n",
      "out3                   0 non-null object\n",
      "out3_len               1751 non-null int64\n",
      "out3_wid               1751 non-null int64\n",
      "out_length             1751 non-null float64\n",
      "out_num                1751 non-null int64\n",
      "out_width              1751 non-null int64\n",
      "time_area              1751 non-null int64\n",
      "time_interval_end      851 non-null datetime64[ns]\n",
      "time_interval_start    1751 non-null datetime64[ns]\n",
      "travel_time            851 non-null float64\n",
      "travel_time_clean      851 non-null float64\n",
      "width                  1751 non-null int64\n",
      "dtypes: datetime64[ns](2), float64(5), int64(33), object(9)\n",
      "memory usage: 684.0+ KB\n"
     ]
    }
   ],
   "source": [
    "ID_4377906285525800514_month6_78.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1746 entries, 5206 to 899\n",
      "Data columns (total 49 columns):\n",
      "date                   846 non-null object\n",
      "day                    1746 non-null int64\n",
      "dayofweek              1746 non-null int64\n",
      "dayofyear              1746 non-null int64\n",
      "hour                   1746 non-null int64\n",
      "in0                    1746 non-null object\n",
      "in0_len                1746 non-null int64\n",
      "in0_wid                1746 non-null int64\n",
      "in1                    0 non-null object\n",
      "in1_len                1746 non-null int64\n",
      "in1_wid                1746 non-null int64\n",
      "in2                    0 non-null object\n",
      "in2_len                1746 non-null int64\n",
      "in2_wid                1746 non-null int64\n",
      "in3                    0 non-null object\n",
      "in3_len                1746 non-null int64\n",
      "in3_wid                1746 non-null int64\n",
      "in_length              1746 non-null float64\n",
      "in_num                 1746 non-null int64\n",
      "in_width               1746 non-null int64\n",
      "length                 1746 non-null int64\n",
      "link_ID                1746 non-null float64\n",
      "link_class             1746 non-null int64\n",
      "minute                 1746 non-null int64\n",
      "minuteofday            1746 non-null int64\n",
      "minuteofmonth          1746 non-null int64\n",
      "minuteofyear           1746 non-null int64\n",
      "month                  1746 non-null int64\n",
      "out0                   1746 non-null object\n",
      "out0_len               1746 non-null int64\n",
      "out0_wid               1746 non-null int64\n",
      "out1                   0 non-null object\n",
      "out1_len               1746 non-null int64\n",
      "out1_wid               1746 non-null int64\n",
      "out2                   0 non-null object\n",
      "out2_len               1746 non-null int64\n",
      "out2_wid               1746 non-null int64\n",
      "out3                   0 non-null object\n",
      "out3_len               1746 non-null int64\n",
      "out3_wid               1746 non-null int64\n",
      "out_length             1746 non-null float64\n",
      "out_num                1746 non-null int64\n",
      "out_width              1746 non-null int64\n",
      "time_area              1746 non-null int64\n",
      "time_interval_end      846 non-null datetime64[ns]\n",
      "time_interval_start    1746 non-null datetime64[ns]\n",
      "travel_time            846 non-null float64\n",
      "travel_time_clean      846 non-null float64\n",
      "width                  1746 non-null int64\n",
      "dtypes: datetime64[ns](2), float64(5), int64(33), object(9)\n",
      "memory usage: 682.0+ KB\n"
     ]
    }
   ],
   "source": [
    "ID_4377906284769500514_month6_78.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>day</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>hour</th>\n",
       "      <th>in0</th>\n",
       "      <th>in0_len</th>\n",
       "      <th>in0_wid</th>\n",
       "      <th>in1</th>\n",
       "      <th>in1_len</th>\n",
       "      <th>...</th>\n",
       "      <th>out_num</th>\n",
       "      <th>out_width</th>\n",
       "      <th>time_area</th>\n",
       "      <th>time_interval_end</th>\n",
       "      <th>time_interval_start</th>\n",
       "      <th>travel_time</th>\n",
       "      <th>travel_time_clean</th>\n",
       "      <th>width</th>\n",
       "      <th>prehour_ttmean</th>\n",
       "      <th>inPreHourTTMean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>4377906282769500514</td>\n",
       "      <td>57</td>\n",
       "      <td>12</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-06-01 08:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>1.36</td>\n",
       "      <td>13.296667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>8</td>\n",
       "      <td>4377906282769500514</td>\n",
       "      <td>57</td>\n",
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       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-06-01 08:02:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>1.36</td>\n",
       "      <td>13.296667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>8</td>\n",
       "      <td>4377906282769500514</td>\n",
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       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-06-01 08:04:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>1.36</td>\n",
       "      <td>13.296667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>8</td>\n",
       "      <td>4377906282769500514</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-06-01 08:08:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>1.36</td>\n",
       "      <td>13.296667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  date  day  dayofweek  dayofyear  hour                  in0  in0_len  \\\n",
       "0  NaN    1          2        153     8  4377906282769500514       57   \n",
       "1  NaN    1          2        153     8  4377906282769500514       57   \n",
       "2  NaN    1          2        153     8  4377906282769500514       57   \n",
       "3  NaN    1          2        153     8  4377906282769500514       57   \n",
       "4  NaN    1          2        153     8  4377906282769500514       57   \n",
       "\n",
       "   in0_wid   in1  in1_len       ...         out_num out_width  time_area  \\\n",
       "0       12  None        0       ...               1        12          0   \n",
       "1       12  None        0       ...               1        12          1   \n",
       "2       12  None        0       ...               1        12          2   \n",
       "3       12  None        0       ...               1        12          3   \n",
       "4       12  None        0       ...               1        12          4   \n",
       "\n",
       "   time_interval_end time_interval_start  travel_time  travel_time_clean  \\\n",
       "0                NaT 2016-06-01 08:00:00          NaN                NaN   \n",
       "1                NaT 2016-06-01 08:02:00          NaN                NaN   \n",
       "2                NaT 2016-06-01 08:04:00          NaN                NaN   \n",
       "3                NaT 2016-06-01 08:06:00          NaN                NaN   \n",
       "4                NaT 2016-06-01 08:08:00          NaN                NaN   \n",
       "\n",
       "   width  prehour_ttmean  inPreHourTTMean  \n",
       "0     12            1.36        13.296667  \n",
       "1     12            1.36        13.296667  \n",
       "2     12            1.36        13.296667  \n",
       "3     12            1.36        13.296667  \n",
       "4     12            1.36        13.296667  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ID_4377906284769500514_month6_hour8.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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       "      <td>4</td>\n",
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       "      <td>2016-05-01 08:08:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
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       "      <td>8.113333</td>\n",
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       "<p>5 rows × 51 columns</p>\n",
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      ],
      "text/plain": [
       "  date  day  dayofweek  dayofyear  hour                  in0  in0_len  \\\n",
       "0  NaN    1          6        122     8  4377906282769500514       57   \n",
       "1  NaN    1          6        122     8  4377906282769500514       57   \n",
       "2  NaN    1          6        122     8  4377906282769500514       57   \n",
       "3  NaN    1          6        122     8  4377906282769500514       57   \n",
       "4  NaN    1          6        122     8  4377906282769500514       57   \n",
       "\n",
       "   in0_wid   in1  in1_len       ...         out_num out_width  time_area  \\\n",
       "0       12  None        0       ...               1        12          0   \n",
       "1       12  None        0       ...               1        12          1   \n",
       "2       12  None        0       ...               1        12          2   \n",
       "3       12  None        0       ...               1        12          3   \n",
       "4       12  None        0       ...               1        12          4   \n",
       "\n",
       "   time_interval_end time_interval_start  travel_time  travel_time_clean  \\\n",
       "0                NaT 2016-05-01 08:00:00          NaN                NaN   \n",
       "1                NaT 2016-05-01 08:02:00          NaN                NaN   \n",
       "2                NaT 2016-05-01 08:04:00          NaN                NaN   \n",
       "3                NaT 2016-05-01 08:06:00          NaN                NaN   \n",
       "4                NaT 2016-05-01 08:08:00          NaN                NaN   \n",
       "\n",
       "   width  prehour_ttmean  inPreHourTTMean  \n",
       "0     12        0.732143         8.113333  \n",
       "1     12        0.732143         8.113333  \n",
       "2     12        0.732143         8.113333  \n",
       "3     12        0.732143         8.113333  \n",
       "4     12        0.732143         8.113333  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ID_4377906284769500514_month5_hour8.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 900 entries, 0 to 899\n",
      "Data columns (total 51 columns):\n",
      "date                   0 non-null object\n",
      "day                    900 non-null int64\n",
      "dayofweek              900 non-null int64\n",
      "dayofyear              900 non-null int64\n",
      "hour                   900 non-null int64\n",
      "in0                    900 non-null object\n",
      "in0_len                900 non-null int64\n",
      "in0_wid                900 non-null int64\n",
      "in1                    0 non-null object\n",
      "in1_len                900 non-null int64\n",
      "in1_wid                900 non-null int64\n",
      "in2                    0 non-null object\n",
      "in2_len                900 non-null int64\n",
      "in2_wid                900 non-null int64\n",
      "in3                    0 non-null object\n",
      "in3_len                900 non-null int64\n",
      "in3_wid                900 non-null int64\n",
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      "in_num                 900 non-null int64\n",
      "in_width               900 non-null int64\n",
      "length                 900 non-null int64\n",
      "link_ID                900 non-null float64\n",
      "link_class             900 non-null int64\n",
      "minute                 900 non-null int64\n",
      "minuteofday            900 non-null int64\n",
      "minuteofmonth          900 non-null int64\n",
      "minuteofyear           900 non-null int64\n",
      "month                  900 non-null int64\n",
      "out0                   900 non-null object\n",
      "out0_len               900 non-null int64\n",
      "out0_wid               900 non-null int64\n",
      "out1                   0 non-null object\n",
      "out1_len               900 non-null int64\n",
      "out1_wid               900 non-null int64\n",
      "out2                   0 non-null object\n",
      "out2_len               900 non-null int64\n",
      "out2_wid               900 non-null int64\n",
      "out3                   0 non-null object\n",
      "out3_len               900 non-null int64\n",
      "out3_wid               900 non-null int64\n",
      "out_length             900 non-null float64\n",
      "out_num                900 non-null int64\n",
      "out_width              900 non-null int64\n",
      "time_area              900 non-null int64\n",
      "time_interval_end      0 non-null datetime64[ns]\n",
      "time_interval_start    900 non-null datetime64[ns]\n",
      "travel_time            0 non-null float64\n",
      "travel_time_clean      0 non-null float64\n",
      "width                  900 non-null int64\n",
      "prehour_ttmean         900 non-null float64\n",
      "inPreHourTTMean        900 non-null float64\n",
      "dtypes: datetime64[ns](2), float64(7), int64(33), object(9)\n",
      "memory usage: 365.6+ KB\n"
     ]
    }
   ],
   "source": [
    "ID_4377906284769500514_month6_hour8.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 846 entries, 5206 to 6051\n",
      "Data columns (total 49 columns):\n",
      "link_ID                846 non-null uint64\n",
      "date                   846 non-null object\n",
      "travel_time            846 non-null float64\n",
      "time_interval_start    846 non-null datetime64[ns]\n",
      "time_interval_end      846 non-null datetime64[ns]\n",
      "hour                   846 non-null int64\n",
      "travel_time_clean      846 non-null float64\n",
      "length                 846 non-null int64\n",
      "width                  846 non-null int64\n",
      "link_class             846 non-null int64\n",
      "in_num                 846 non-null int64\n",
      "out_num                846 non-null int64\n",
      "in0                    846 non-null object\n",
      "in1                    0 non-null object\n",
      "in2                    0 non-null object\n",
      "in3                    0 non-null object\n",
      "out0                   846 non-null object\n",
      "out1                   0 non-null object\n",
      "out2                   0 non-null object\n",
      "out3                   0 non-null object\n",
      "in0_len                846 non-null int64\n",
      "in1_len                846 non-null int64\n",
      "in2_len                846 non-null int64\n",
      "in3_len                846 non-null int64\n",
      "in0_wid                846 non-null int64\n",
      "in1_wid                846 non-null int64\n",
      "in2_wid                846 non-null int64\n",
      "in3_wid                846 non-null int64\n",
      "out0_len               846 non-null int64\n",
      "out1_len               846 non-null int64\n",
      "out2_len               846 non-null int64\n",
      "out3_len               846 non-null int64\n",
      "out0_wid               846 non-null int64\n",
      "out1_wid               846 non-null int64\n",
      "out2_wid               846 non-null int64\n",
      "out3_wid               846 non-null int64\n",
      "in_width               846 non-null int64\n",
      "in_length              846 non-null float64\n",
      "out_width              846 non-null int64\n",
      "out_length             846 non-null float64\n",
      "minute                 846 non-null int64\n",
      "time_area              846 non-null int64\n",
      "minuteofday            846 non-null int64\n",
      "dayofweek              846 non-null int64\n",
      "day                    846 non-null int64\n",
      "month                  846 non-null int64\n",
      "minuteofmonth          846 non-null int64\n",
      "minuteofyear           846 non-null int64\n",
      "dayofyear              846 non-null int64\n",
      "dtypes: datetime64[ns](2), float64(4), int64(33), object(9), uint64(1)\n",
      "memory usage: 330.5+ KB\n"
     ]
    }
   ],
   "source": [
    "ID_4377906284769500514_month6_7.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_interval_start</th>\n",
       "      <th>hour</th>\n",
       "      <th>link_ID</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>link_class</th>\n",
       "      <th>in_num</th>\n",
       "      <th>out_num</th>\n",
       "      <th>in0</th>\n",
       "      <th>in1</th>\n",
       "      <th>...</th>\n",
       "      <th>out_length</th>\n",
       "      <th>minute</th>\n",
       "      <th>time_area</th>\n",
       "      <th>minuteofday</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>minuteofmonth</th>\n",
       "      <th>minuteofyear</th>\n",
       "      <th>dayofyear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-06-01 08:00:00</td>\n",
       "      <td>8</td>\n",
       "      <td>4377906284769500514</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4377906282769500514</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>480</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1920</td>\n",
       "      <td>269760</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-06-01 08:02:00</td>\n",
       "      <td>8</td>\n",
       "      <td>4377906284769500514</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4377906282769500514</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>482</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1922</td>\n",
       "      <td>269762</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-06-01 08:04:00</td>\n",
       "      <td>8</td>\n",
       "      <td>4377906284769500514</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4377906282769500514</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>484</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1924</td>\n",
       "      <td>269764</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-06-01 08:06:00</td>\n",
       "      <td>8</td>\n",
       "      <td>4377906284769500514</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4377906282769500514</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>486</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1926</td>\n",
       "      <td>269766</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-06-01 08:08:00</td>\n",
       "      <td>8</td>\n",
       "      <td>4377906284769500514</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4377906282769500514</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>488</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1928</td>\n",
       "      <td>269768</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  time_interval_start  hour              link_ID  length  width  link_class  \\\n",
       "0 2016-06-01 08:00:00     8  4377906284769500514      11     12           1   \n",
       "1 2016-06-01 08:02:00     8  4377906284769500514      11     12           1   \n",
       "2 2016-06-01 08:04:00     8  4377906284769500514      11     12           1   \n",
       "3 2016-06-01 08:06:00     8  4377906284769500514      11     12           1   \n",
       "4 2016-06-01 08:08:00     8  4377906284769500514      11     12           1   \n",
       "\n",
       "   in_num  out_num                  in0   in1    ...     out_length minute  \\\n",
       "0       1        1  4377906282769500514  None    ...           15.0      0   \n",
       "1       1        1  4377906282769500514  None    ...           15.0      2   \n",
       "2       1        1  4377906282769500514  None    ...           15.0      4   \n",
       "3       1        1  4377906282769500514  None    ...           15.0      6   \n",
       "4       1        1  4377906282769500514  None    ...           15.0      8   \n",
       "\n",
       "  time_area minuteofday dayofweek day  month  minuteofmonth  minuteofyear  \\\n",
       "0         0         480         2   1      6           1920        269760   \n",
       "1         1         482         2   1      6           1922        269762   \n",
       "2         2         484         2   1      6           1924        269764   \n",
       "3         3         486         2   1      6           1926        269766   \n",
       "4         4         488         2   1      6           1928        269768   \n",
       "\n",
       "   dayofyear  \n",
       "0        153  \n",
       "1        153  \n",
       "2        153  \n",
       "3        153  \n",
       "4        153  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ID_4377906284769500514_month6_8.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 900 entries, 0 to 899\n",
      "Data columns (total 45 columns):\n",
      "time_interval_start    900 non-null datetime64[ns]\n",
      "hour                   900 non-null int64\n",
      "link_ID                900 non-null int64\n",
      "length                 900 non-null int64\n",
      "width                  900 non-null int64\n",
      "link_class             900 non-null int64\n",
      "in_num                 900 non-null int64\n",
      "out_num                900 non-null int64\n",
      "in0                    900 non-null object\n",
      "in1                    0 non-null object\n",
      "in2                    0 non-null object\n",
      "in3                    0 non-null object\n",
      "out0                   900 non-null object\n",
      "out1                   0 non-null object\n",
      "out2                   0 non-null object\n",
      "out3                   0 non-null object\n",
      "in0_len                900 non-null int64\n",
      "in1_len                900 non-null int64\n",
      "in2_len                900 non-null int64\n",
      "in3_len                900 non-null int64\n",
      "in0_wid                900 non-null int64\n",
      "in1_wid                900 non-null int64\n",
      "in2_wid                900 non-null int64\n",
      "in3_wid                900 non-null int64\n",
      "out0_len               900 non-null int64\n",
      "out1_len               900 non-null int64\n",
      "out2_len               900 non-null int64\n",
      "out3_len               900 non-null int64\n",
      "out0_wid               900 non-null int64\n",
      "out1_wid               900 non-null int64\n",
      "out2_wid               900 non-null int64\n",
      "out3_wid               900 non-null int64\n",
      "in_width               900 non-null int64\n",
      "in_length              900 non-null float64\n",
      "out_width              900 non-null int64\n",
      "out_length             900 non-null float64\n",
      "minute                 900 non-null int64\n",
      "time_area              900 non-null int64\n",
      "minuteofday            900 non-null int64\n",
      "dayofweek              900 non-null int64\n",
      "day                    900 non-null int64\n",
      "month                  900 non-null int64\n",
      "minuteofmonth          900 non-null int64\n",
      "minuteofyear           900 non-null int64\n",
      "dayofyear              900 non-null int64\n",
      "dtypes: datetime64[ns](1), float64(2), int64(34), object(8)\n",
      "memory usage: 323.4+ KB\n"
     ]
    }
   ],
   "source": [
    "ID_4377906284769500514_month6_8.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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