{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Novel hybrid staking ML model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>172786.0</td>\n",
       "      <td>-11.881118</td>\n",
       "      <td>10.071785</td>\n",
       "      <td>-9.834783</td>\n",
       "      <td>-2.066656</td>\n",
       "      <td>-5.364473</td>\n",
       "      <td>-2.606837</td>\n",
       "      <td>-4.918215</td>\n",
       "      <td>7.305334</td>\n",
       "      <td>1.914428</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>1.014480</td>\n",
       "      <td>-0.509348</td>\n",
       "      <td>1.436807</td>\n",
       "      <td>0.250034</td>\n",
       "      <td>0.943651</td>\n",
       "      <td>0.823731</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>172787.0</td>\n",
       "      <td>-0.732789</td>\n",
       "      <td>-0.055080</td>\n",
       "      <td>2.035030</td>\n",
       "      <td>-0.738589</td>\n",
       "      <td>0.868229</td>\n",
       "      <td>1.058415</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>0.294869</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214205</td>\n",
       "      <td>0.924384</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>-1.016226</td>\n",
       "      <td>-0.606624</td>\n",
       "      <td>-0.395255</td>\n",
       "      <td>0.068472</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>24.79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>1.919565</td>\n",
       "      <td>-0.301254</td>\n",
       "      <td>-3.249640</td>\n",
       "      <td>-0.557828</td>\n",
       "      <td>2.630515</td>\n",
       "      <td>3.031260</td>\n",
       "      <td>-0.296827</td>\n",
       "      <td>0.708417</td>\n",
       "      <td>0.432454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.232045</td>\n",
       "      <td>0.578229</td>\n",
       "      <td>-0.037501</td>\n",
       "      <td>0.640134</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>67.88</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>-0.240440</td>\n",
       "      <td>0.530483</td>\n",
       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
       "      <td>0.623708</td>\n",
       "      <td>-0.686180</td>\n",
       "      <td>0.679145</td>\n",
       "      <td>0.392087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.265245</td>\n",
       "      <td>0.800049</td>\n",
       "      <td>-0.163298</td>\n",
       "      <td>0.123205</td>\n",
       "      <td>-0.569159</td>\n",
       "      <td>0.546668</td>\n",
       "      <td>0.108821</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>172792.0</td>\n",
       "      <td>-0.533413</td>\n",
       "      <td>-0.189733</td>\n",
       "      <td>0.703337</td>\n",
       "      <td>-0.506271</td>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.649617</td>\n",
       "      <td>1.577006</td>\n",
       "      <td>-0.414650</td>\n",
       "      <td>0.486180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261057</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.376777</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>-0.473649</td>\n",
       "      <td>-0.818267</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>217.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284807 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time         V1         V2        V3        V4        V5  \\\n",
       "0            0.0  -1.359807  -0.072781  2.536347  1.378155 -0.338321   \n",
       "1            0.0   1.191857   0.266151  0.166480  0.448154  0.060018   \n",
       "2            1.0  -1.358354  -1.340163  1.773209  0.379780 -0.503198   \n",
       "3            1.0  -0.966272  -0.185226  1.792993 -0.863291 -0.010309   \n",
       "4            2.0  -1.158233   0.877737  1.548718  0.403034 -0.407193   \n",
       "...          ...        ...        ...       ...       ...       ...   \n",
       "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
       "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
       "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
       "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
       "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
       "\n",
       "              V6        V7        V8        V9  ...       V21       V22  \\\n",
       "0       0.462388  0.239599  0.098698  0.363787  ... -0.018307  0.277838   \n",
       "1      -0.082361 -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672   \n",
       "2       1.800499  0.791461  0.247676 -1.514654  ...  0.247998  0.771679   \n",
       "3       1.247203  0.237609  0.377436 -1.387024  ... -0.108300  0.005274   \n",
       "4       0.095921  0.592941 -0.270533  0.817739  ... -0.009431  0.798278   \n",
       "...          ...       ...       ...       ...  ...       ...       ...   \n",
       "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
       "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
       "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
       "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
       "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
       "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62   \n",
       "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69   \n",
       "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66   \n",
       "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50   \n",
       "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99   \n",
       "...          ...       ...       ...       ...       ...       ...     ...   \n",
       "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
       "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
       "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
       "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
       "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
       "\n",
       "        Class  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "...       ...  \n",
       "284802      0  \n",
       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
       "284806      0  \n",
       "\n",
       "[284807 rows x 31 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('creditcardfull.csv')\n",
    "df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total count of missing values: 0\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values in the DataFrame and sum them up\n",
    "missing_values_count = df.isnull().sum().sum()\n",
    "\n",
    "# Display the total count of missing values\n",
    "print(\"Total count of missing values:\", missing_values_count)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
       "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
       "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
       "       'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Time        V1        V2        V3        V4        V5        V6  \\\n",
      "0      -1.996583 -0.694242 -0.044075  1.672773  0.973366 -0.245117  0.347068   \n",
      "1      -1.996583  0.608496  0.161176  0.109797  0.316523  0.043483 -0.061820   \n",
      "2      -1.996562 -0.693500 -0.811578  1.169468  0.268231 -0.364572  1.351454   \n",
      "3      -1.996562 -0.493325 -0.112169  1.182516 -0.609727 -0.007469  0.936150   \n",
      "4      -1.996541 -0.591330  0.531541  1.021412  0.284655 -0.295015  0.071999   \n",
      "...          ...       ...       ...       ...       ...       ...       ...   \n",
      "284802  1.641931 -6.065842  6.099286 -6.486245 -1.459641 -3.886611 -1.956690   \n",
      "284803  1.641952 -0.374121 -0.033356  1.342145 -0.521651  0.629040  0.794446   \n",
      "284804  1.641974  0.980024 -0.182434 -2.143205 -0.393984  1.905833  2.275262   \n",
      "284805  1.641974 -0.122755  0.321250  0.463320  0.487192 -0.273836  0.468155   \n",
      "284806  1.642058 -0.272331 -0.114899  0.463866 -0.357570 -0.009089 -0.487602   \n",
      "\n",
      "              V7        V8        V9  ...       V21       V22       V23  \\\n",
      "0       0.193679  0.082637  0.331128  ... -0.024923  0.382854 -0.176911   \n",
      "1      -0.063700  0.071253 -0.232494  ... -0.307377 -0.880077  0.162201   \n",
      "2       0.639776  0.207373 -1.378675  ...  0.337632  1.063358  1.456320   \n",
      "3       0.192071  0.316018 -1.262503  ... -0.147443  0.007267 -0.304777   \n",
      "4       0.479302 -0.226510  0.744326  ... -0.012839  1.100011 -0.220123   \n",
      "...          ...       ...       ...  ...       ...       ...       ...   \n",
      "284802 -3.975628  6.116573  1.742559  ...  0.290602  0.154146  1.624574   \n",
      "284803  0.019667  0.246886  0.532299  ...  0.291625  1.273781  0.019958   \n",
      "284804 -0.239939  0.593140  0.393630  ...  0.315913  0.796788 -0.060053   \n",
      "284805 -0.554672  0.568631  0.356887  ...  0.361112  1.102451 -0.261503   \n",
      "284806  1.274769 -0.347176  0.442532  ...  0.355411  0.886149  0.603365   \n",
      "\n",
      "             V24       V25       V26       V27       V28    Amount  Class  \n",
      "0       0.110507  0.246585 -0.392170  0.330892 -0.063781  0.244964      0  \n",
      "1      -0.561131  0.320694  0.261069 -0.022256  0.044608 -0.342475      0  \n",
      "2      -1.138092 -0.628537 -0.288447 -0.137137 -0.181021  1.160686      0  \n",
      "3      -1.941027  1.241904 -0.460217  0.155396  0.186189  0.140534      0  \n",
      "4       0.233250 -0.395202  1.041611  0.543620  0.651816 -0.073403      0  \n",
      "...          ...       ...       ...       ...       ...       ...    ...  \n",
      "284802 -0.841000  2.756320  0.518500  2.337901  2.495529 -0.350151      0  \n",
      "284803 -1.677920 -1.163726 -0.819647  0.169641 -0.162164 -0.254117      0  \n",
      "284804  1.056944  0.509797 -0.181182  0.011037 -0.080467 -0.081839      0  \n",
      "284805  0.203428 -1.091855  1.133635  0.269604  0.316687 -0.313249      0  \n",
      "284806  0.014526 -0.908631 -1.696853 -0.005984  0.041350  0.514355      0  \n",
      "\n",
      "[284807 rows x 31 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv('creditcardfull.csv')\n",
    "\n",
    "# Separate the features (X) and target variable (y)\n",
    "X = df.drop('Class', axis=1)\n",
    "y = df['Class']\n",
    "\n",
    "# Initialize the StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit and transform the features (X)\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# Convert the scaled features back to a DataFrame\n",
    "X_scaled_df = pd.DataFrame(X_scaled, columns=X.columns)\n",
    "\n",
    "# Concatenate the scaled features with the target variable\n",
    "df_scaled = pd.concat([X_scaled_df, y], axis=1)\n",
    "\n",
    "# Display the scaled DataFrame\n",
    "print(df_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 15 Features and Their Importance:\n",
      "Class: 1.0\n",
      "V17: 0.32648106724371595\n",
      "V14: 0.3025436958044028\n",
      "V12: 0.26059292487722485\n",
      "V10: 0.21688294364103206\n",
      "V16: 0.19653894030401736\n",
      "V3: 0.19296082706741602\n",
      "V7: 0.18725659151430013\n",
      "V11: 0.15487564474394433\n",
      "V4: 0.13344748623900718\n",
      "V18: 0.11148525388904133\n",
      "V1: 0.10134729859508294\n",
      "V9: 0.0977326860740807\n",
      "V5: 0.09497429899144802\n",
      "V2: 0.0912886503446179\n",
      "V6: 0.04364316069996494\n",
      "V21: 0.04041338061057565\n",
      "V19: 0.034783013036515056\n",
      "V20: 0.020090324196974638\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt  # Importing matplotlib.pyplot\n",
    "\n",
    "# Calculate feature importance (you can replace this with any feature selection method)\n",
    "correlation_matrix = df.corr()\n",
    "feature_importance = correlation_matrix['Class'].abs().sort_values(ascending=False)\n",
    "\n",
    "# Plot the correlation matrix\n",
    "plt.figure(figsize=(16, 12))\n",
    "plt.title('Credit Card Transactions Features Correlation Plot (Pearson)')\n",
    "\n",
    "# Create a mask to hide the upper triangle of the plot\n",
    "mask = np.triu(np.ones_like(correlation_matrix, dtype=bool))\n",
    "\n",
    "# Use a diverging color palette and annotate the values\n",
    "sns.heatmap(correlation_matrix, annot=True, fmt=\".2f\", cmap=sns.diverging_palette(220, 10, as_cmap=True), linewidths=0.1, mask=mask)\n",
    "\n",
    "# Sort features based on their importance\n",
    "sorted_features = feature_importance.index[:19]  # Select top 19 features\n",
    "\n",
    "# Display top features and their importance\n",
    "print(\"Top 15 Features and Their Importance:\")\n",
    "for feature in sorted_features:\n",
    "    print(f\"{feature}: {feature_importance[feature]}\")\n",
    "\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Compute the correlation matrix\n",
    "correlation_matrix = df.corr()\n",
    "\n",
    "# Sort the correlation values with the target variable 'Class' in descending order\n",
    "correlation_with_class = correlation_matrix['Class'].abs()\n",
    "correlation_with_class_sorted = correlation_with_class.sort_values(ascending=False)\n",
    "\n",
    "# Select the top 15 features with the highest correlation coefficients\n",
    "top_features = correlation_with_class_sorted.head(18).index  # Top 18 features\n",
    "\n",
    "# Plot the correlation matrix\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(correlation_matrix.loc[top_features, top_features], annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5)\n",
    "plt.title('Correlation Matrix of Top 18 Features with Target Variable \"Class\"')\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
    "plt.yticks(rotation=0)   # Rotate y-axis labels for better readability\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA24AAAK9CAYAAABcjD9SAAAAP3RFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMS5wb3N0MSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8kixA/AAAACXBIWXMAAA9hAAAPYQGoP6dpAACKzklEQVR4nOzde3xUxf3/8fcmIUsgZhENQmTlEtGASEGiliANFzUREvFrNGlsG+IuplgQqdX+ul+piK0GjVqxUmsr2TViLCyh3hWxYFwLtYoNGPCWCJoIWFTcTQSiJOf3B7Jf1gQIl2QXzuv5eMyj7MycOZ/ZhD74OHPmWAzDMAQAAAAAiFhR4Q4AAAAAAHBwJG4AAAAAEOFI3AAAAAAgwpG4AQAAAECEI3EDAAAAgAhH4gYAAAAAEY7EDQAAAAAiHIkbAAAAAEQ4EjcAAAAAiHAkbgBwgiksLFT//v3DHQYAADiGSNwA4Ah5PB5ZLJZgiYmJ0emnn67CwkJ9+umn4Q4vYnz/e9q//OY3vwl3eG2666679NRTT7Wr7+bNmw84vx/+8IcdEt+WLVt0++23q6qqqkPGPxr7vo9777033KEcsRdeeEG33357uMMAgBAx4Q4AAI53d9xxhwYMGKDdu3frX//6lzwej15//XVVV1era9eu4Q4vYuz7nvY3dOjQMEVzcHfddZeuuuoqXXHFFe2+Jj8/XxMnTgypS0xMPMaR7bVlyxbNnTtX/fv31/DhwzvkHmb2wgsvaMGCBSRvACIKiRsAHKXLLrtMqampkqSpU6fq1FNP1d13361nnnlGubm5YY4ucuz/PR1LX3/9tbp3737Mxz1c5513nn7605+GO4yjsnv3bsXGxioqypwbciLldwkA2mLO/2cGgA40ZswYSVJtbW2w7ptvvtFtt92mkSNHymazqXv37hozZoxWrVoVcu3+28z+8pe/KDk5WVarVeeff77efPPNVvd66qmnNHToUHXt2lVDhw7V3//+9zZj+vrrr/WrX/1KdrtdVqtVZ599tu69914ZhhHSz2KxaMaMGfJ6vRoyZIji4uI0atQovfPOO5KkRx55RGeeeaa6du2qsWPHavPmzUfzVYVYuXKlxowZo+7du6tHjx6aPHmy3n333ZA+t99+uywWizZu3KhrrrlGJ598si666KJg+6JFizRy5EjFxcWpZ8+e+vGPf6y6urqQMT788EPl5OSod+/e6tq1q/r27asf//jH8vv9we/g66+/1mOPPRbc8lhYWHjU83vvvfd01VVXqWfPnuratatSU1P1zDPPhPT58ssvdfPNN+vcc89VfHy8EhISdNlll2ndunXBPq+++qrOP/98SdK1114bjNHj8UiS+vfv32a8Y8eO1dixY0PGsVgs+tvf/qbZs2fr9NNPV7du3RQIBCRJb7zxhjIzM2Wz2dStWzelp6frn//85xHNfd922ddff10zZ85UYmKievTooZ///Of65ptv9NVXX6mgoEAnn3yyTj75ZP36178O+d3c/+/FH/7wB/Xr109xcXFKT09XdXV1q/sdze9SYWGhFixYIEkh2173uffee5WWlqZTTjlFcXFxGjlypJYuXdoqhn1/l/b9HbVarTrnnHP00ksvter76aefyul0KikpSVarVQMGDND111+vb775Jtjnq6++0qxZs4J/h88880zdfffdamlpOfwfCIDjEituAHCM7UtmTj755GBdIBDQo48+qvz8fF133XVqaGjQwoULlZGRoX//+9+ttruVl5eroaFBP//5z2WxWHTPPffoyiuv1EcffaQuXbpIkl5++WXl5ORoyJAhKi4u1hdffKFrr71Wffv2DRnLMAxdfvnlWrVqlZxOp4YPH67ly5frlltu0aeffqo//OEPIf19Pp+eeeYZTZ8+XZJUXFysrKws/frXv9af/vQn/eIXv9COHTt0zz33yOFwaOXKle36Xvx+vz7//POQulNPPVWS9Morr+iyyy7TwIEDdfvtt2vXrl364x//qNGjR+vtt99uddjK1VdfrUGDBumuu+4K/gP/zjvv1G9/+1vl5uZq6tSp2r59u/74xz/qRz/6kf7zn/+oR48e+uabb5SRkaGmpibdcMMN6t27tz799FM999xz+uqrr2Sz2fT4449r6tSpuuCCC1RUVCRJSk5OPuT8du7c2Wp+NptNXbp00YYNGzR69Gidfvrp+s1vfqPu3btryZIluuKKK1RRUaH/+Z//kSR99NFHeuqpp3T11VdrwIAB+uyzz/TII48oPT1dGzduVFJSkgYPHqw77rhDt912m4qKioL/oSAtLa1dP4fv+93vfqfY2FjdfPPNampqUmxsrFauXKnLLrtMI0eO1Jw5cxQVFSW3263x48fL5/PpggsuOKJ77fvO586dq3/961/6y1/+oh49emj16tU644wzdNddd+mFF15QSUmJhg4dqoKCgpDry8rK1NDQoOnTp2v37t2aP3++xo8fr3feeUennXaapKP/XRoxYoS2bNmiFStW6PHHH281h/nz5+vyyy/XT37yE33zzTf629/+pquvvlrPPfecJk2aFNL39ddf17Jly/SLX/xCJ510kh588EHl5OTok08+0SmnnCJp77bXCy64QF999ZWKioqUkpKiTz/9VEuXLtXOnTsVGxurnTt3Kj09XZ9++ql+/vOf64wzztDq1avlcrm0detWPfDAA0f08wBwnDEAAEfE7XYbkoxXXnnF2L59u1FXV2csXbrUSExMNKxWq1FXVxfsu2fPHqOpqSnk+h07dhinnXaa4XA4gnWbNm0yJBmnnHKK8eWXXwbrn376aUOS8eyzzwbrhg8fbvTp08f46quvgnUvv/yyIcno169fsO6pp54yJBm///3vQ+5/1VVXGRaLxaipqQnWSTKsVquxadOmYN0jjzxiSDJ69+5tBAKBYL3L5TIkhfQ92PfUVtl/Lr169TK++OKLYN26deuMqKgoo6CgIFg3Z84cQ5KRn58fco/Nmzcb0dHRxp133hlS/8477xgxMTHB+v/85z+GJMPr9R405u7duxtTpkw5aJ999v3M2iqrVq0yDMMwJkyYYJx77rnG7t27g9e1tLQYaWlpxqBBg4J1u3fvNpqbm1uNb7VajTvuuCNY9+abbxqSDLfb3Sqefv36tRl7enq6kZ6eHvy8atUqQ5IxcOBAY+fOnSFxDRo0yMjIyDBaWlqC9Tt37jQGDBhgXHLJJe36PkpKSoJ1+34Hvj/mqFGjDIvFYkybNi1Yt2fPHqNv374hse4bMy4uzqivrw/Wv/HGG4Yk45e//GWw7mh/lwzDMKZPn24c6J9I+39XhmEY33zzjTF06FBj/PjxIfWSjNjY2JC/X+vWrTMkGX/84x+DdQUFBUZUVJTx5ptvtrrXvu/qd7/7ndG9e3fjgw8+CGn/zW9+Y0RHRxuffPJJm7ECOLGwVRIAjtLFF1+sxMRE2e12XXXVVerevbueeeaZkJWv6OhoxcbGSpJaWlr05Zdfas+ePUpNTdXbb7/dasy8vLyQFbt9qyofffSRJGnr1q2qqqrSlClTZLPZgv0uueQSDRkyJGSsF154QdHR0Zo5c2ZI/a9+9SsZhqEXX3wxpH7ChAkhqxIXXnihJCknJ0cnnXRSq/p9MR3KggULtGLFipCy/1wKCwvVs2fPYP9hw4bpkksu0QsvvNBqrGnTpoV8XrZsmVpaWpSbm6vPP/88WHr37q1BgwYFt6Tu+66WL1+unTt3tivu9ioqKmo1vx/84Af68ssvtXLlSuXm5qqhoSEY2xdffKGMjAx9+OGHwVNIrVZr8Pmy5uZmffHFF4qPj9fZZ5/d5u/JsTBlyhTFxcUFP1dVVenDDz/UNddcoy+++CIY79dff60JEybotddeO+LteU6nM2Tb4YUXXijDMOR0OoN10dHRSk1NbfP36oorrtDpp58e/HzBBRfowgsvDP6OHIvfpUPZ/7vasWOH/H6/xowZ0+bP5+KLLw5ZrR02bJgSEhKCc2tpadFTTz2l7OzsNp//3Pddeb1ejRkzRieffHLI7/fFF1+s5uZmvfbaa4c1BwDHJ7ZKAsBRWrBggc466yz5/X6Vlpbqtddek9VqbdXvscce03333af33ntP3377bbD++yctStIZZ5wR8nlfErdjxw5J0scffyxJGjRoUKtrv/+P/I8//lhJSUkhSZckDR48OGSsA917X7Jjt9vbrN8X06FccMEFbf7jdN/9zz777FZtgwcP1vLly1sdGvH97+zDDz+UYRhtfh+SgttLBwwYoJtuukn333+/nnjiCY0ZM0aXX365fvrTn4YkwEdi0KBBuvjii1vV//vf/5ZhGPrtb3+r3/72t21e+9///lenn366WlpaNH/+fP3pT3/Spk2b1NzcHOyzb2vdsdbWdyntTegOxO/3h/yHhfY6nN+ttn6v2vr5nnXWWVqyZImkY/O7dCjPPfecfv/736uqqkpNTU3B+v0T0n2+P19p79/lfXPbvn27AoHAIU9X/fDDD7V+/foDnlL63//+93CmAOA4ReIGAEdp/4Tkiiuu0EUXXaRrrrlG77//vuLj4yXtPTSjsLBQV1xxhW655Rb16tVL0dHRKi4uDjnEZJ/o6Og272V87zCRjnCge4czpu/bf9VD2rtyYbFY9OKLL7YZ576fgyTdd999Kiws1NNPP62XX35ZM2fOVHFxsf71r3+1ej7wWNi3OnXzzTcrIyOjzT5nnnmmpL2vIfjtb38rh8Oh3/3ud+rZs6eioqI0a9asdq9ytZVASHtX8Nr6btr6LiWppKTkgK8a2P/7PByH87vVWb9X35//wfh8Pl1++eX60Y9+pD/96U/q06ePunTpIrfbrfLy8lb9j9XfmZaWFl1yySX69a9/3Wb7WWeddVjjATg+kbgBwDG0LxkbN26cHnrooeALppcuXaqBAwdq2bJlIf+wnjNnzhHdp1+/fpL+b3Vkf++//36rvq+88ooaGhpCVt3ee++9kLHCZd/9vx+3tDfGU0899ZBHtCcnJ8swDA0YMKBd/4g999xzde6552r27NlavXq1Ro8erT//+c/6/e9/L+nAyc+RGDhwoKS9q35trcjtb+nSpRo3bpwWLlwYUv/VV18FD3I5VHwnn3yyvvrqq1b1H3/8cTCWg9m3tS8hIeGQ8Xa2tn7fP/jgg+DW3mPxuyQd+PutqKhQ165dtXz58pBVdbfb3Z7wW0lMTFRCQkKbJ2PuLzk5WY2NjRH38wDQuXjGDQCOsbFjx+qCCy7QAw88oN27d0v6v//yvv9/aX/jjTe0Zs2aI7pHnz59NHz4cD322GPBY+wlacWKFdq4cWNI34kTJ6q5uVkPPfRQSP0f/vAHWSwWXXbZZUcUw7Gy/1z2Tziqq6v18ssvt3qpdVuuvPJKRUdHa+7cua1WMwzD0BdffCFp7+mee/bsCWk/99xzFRUVFbLtrXv37m0mP0eiV69eGjt2rB555BFt3bq1Vfv27duDf46Ojm4Vv9frDT4Dt398ktqMMTk5Wf/6179CjpJ/7rnnWr0W4UBGjhyp5ORk3XvvvWpsbDxovJ3tqaeeCvku/v3vf+uNN94I/g4fi98l6cDfb3R0tCwWS8gW1s2bN+upp546ovlERUXpiiuu0LPPPqu33nqrVfu+34Xc3FytWbNGy5cvb9Xnq6++avU7DeDExIobAHSAW265RVdffbU8Ho+mTZumrKwsLVu2TP/zP/+jSZMmadOmTfrzn/+sIUOGtPmP4/YoLi7WpEmTdNFFF8nhcOjLL7/UH//4R51zzjkhY2ZnZ2vcuHG69dZbtXnzZv3gBz/Qyy+/rKefflqzZs1q11H3Ha2kpESXXXaZRo0aJafTGTzC3Waz6fbbbz/k9cnJyfr9738vl8ulzZs364orrtBJJ52kTZs26e9//7uKiop08803a+XKlZoxY4auvvpqnXXWWdqzZ48ef/xxRUdHKycnJzjeyJEj9corr+j+++9XUlKSBgwYEDyM5UgsWLBAF110kc4991xdd911GjhwoD777DOtWbNG9fX1wfe0ZWVl6Y477tC1116rtLQ0vfPOO3riiSdarZQlJyerR48e+vOf/6yTTjpJ3bt314UXXqgBAwZo6tSpWrp0qTIzM5Wbm6va2lotWrSo3T/nqKgoPfroo7rssst0zjnn6Nprr9Xpp5+uTz/9VKtWrVJCQoKeffbZI/4ujsaZZ56piy66SNdff72ampr0wAMP6JRTTgnZQni0v0vS3p+/JM2cOVMZGRmKjo7Wj3/8Y02aNEn333+/MjMzdc011+i///2vFixYoDPPPFPr168/ojndddddevnll5Wenq6ioiINHjxYW7duldfr1euvv64ePXrolltu0TPPPKOsrCwVFhZq5MiR+vrrr/XOO+9o6dKl2rx5c8iKLIATVDiOsgSAE8G+I87bOsa7ubnZSE5ONpKTk409e/YYLS0txl133WX069fPsFqtxogRI4znnnvOmDJlSsjR/W0dpb6PJGPOnDkhdRUVFcbgwYMNq9VqDBkyxFi2bFmrMQ3DMBoaGoxf/vKXRlJSktGlSxdj0KBBRklJScjR7PvuMX369JC6A8W07zj5Qx2tf7DvaX+vvPKKMXr0aCMuLs5ISEgwsrOzjY0bN4b02XeE+/bt29sco6KiwrjooouM7t27G927dzdSUlKM6dOnG++//75hGIbx0UcfGQ6Hw0hOTja6du1q9OzZ0xg3bpzxyiuvhIzz3nvvGT/60Y+MuLg4Q9JBXw1wsJ/Z/mpra42CggKjd+/eRpcuXYzTTz/dyMrKMpYuXRrss3v3buNXv/qV0adPHyMuLs4YPXq0sWbNmlZH+RvG3ldEDBkyxIiJiWn1aoD77rvPOP300w2r1WqMHj3aeOuttw74OoAD/fz+85//GFdeeaVxyimnGFar1ejXr5+Rm5tr/OMf/zjoPA/2OoDv/w4c6Oc5ZcoUo3v37m2Oed999xl2u92wWq3GmDFjjHXr1rWK4Wh/l/bs2WPccMMNRmJiomGxWEJeDbBw4UJj0KBBhtVqNVJSUgy32x0ca39t/V0yjLZf1/Dxxx8bBQUFwVeJDBw40Jg+fXrIK0QaGhoMl8tlnHnmmUZsbKxx6qmnGmlpaca9995rfPPNN63uA+DEYzGMMDxVDgAA0E6bN2/WgAEDVFJSoptvvjnc4QBAWPCMGwAAAABEOBI3AAAAAIhwJG4AAAAAEOF4xg0AAAAAIhwrbgAAAAAQ4UjcAAAAACDC8QLuMGhpadGWLVt00kknyWKxhDscAAAAAGFiGIYaGhqUlJSkqKgDr6uRuIXBli1bZLfbwx0GAAAAgAhRV1envn37HrCdxC0MTjrpJEl7fzgJCQlhjgYAAABAuAQCAdnt9mCOcCAkbmGwb3tkQkICiRsAAACAQz5CxeEkAAAAABDhSNwAAAAAIMKRuAEAAABAhCNxAwAAAIAIR+IGAAAAABGOxA0AAAAAIhyJGwAAAABEOBI3AAAAAIhwJG4AAAAAEOFI3AAAAAAgwpG4AQAAAECEI3EDAAAAgAhH4gYAAAAAEY7EDQAAAAAiHIkbAAAAAEQ4EjcAAAAAiHAkbgAAAAAQ4UjcAAAAACDCkbgBAAAAQIQjcQMAAACACEfiBgAAAAARjsQNAAAAACIciRsAAAAARDgSNwAAAACIcCRuAAAAABDhYsIdgJkNnbNcUdZu4Q4DAAAAMI3N8yaFO4QjwoobAAAAAEQ4EjcAAAAAiHCmSNyys7OVmZnZZpvP55PFYtH69es1c+ZMjRw5UlarVcOHD2/V9/bbb5fFYmlVunfv3sEzAAAAAGBmpkjcnE6nVqxYofr6+lZtbrdbqampGjZsmCTJ4XAoLy+vzXFuvvlmbd26NaQMGTJEV199dYfGDwAAAMDcTJG4ZWVlKTExUR6PJ6S+sbFRXq9XTqdTkvTggw9q+vTpGjhwYJvjxMfHq3fv3sHy2WefaePGjcHrAQAAAKAjmCJxi4mJUUFBgTwejwzDCNZ7vV41NzcrPz//iMZ99NFHddZZZ2nMmDEH7dfU1KRAIBBSAAAAAKC9TJG4SXu3QNbW1qqysjJY53a7lZOTI5vNdtjj7d69W0888US7VtuKi4tls9mCxW63H/b9AAAAAJiXaRK3lJQUpaWlqbS0VJJUU1Mjn893xNsc//73v6uhoUFTpkw5ZF+XyyW/3x8sdXV1R3RPAAAAAOZkmsRN2ntISUVFhRoaGuR2u5WcnKz09PQjGuvRRx9VVlaWTjvttEP2tVqtSkhICCkAAAAA0F6mStxyc3MVFRWl8vJylZWVyeFwyGKxHPY4mzZt0qpVqziUBAAAAECniAl3AJ0pPj5eeXl5crlcCgQCKiwsDGmvqalRY2Ojtm3bpl27dqmqqkqSNGTIEMXGxgb7lZaWqk+fPrrssss6MXoAAAAAZmWqxE3au11y4cKFmjhxopKSkkLapk6dGnJ4yYgRIyTtXWHr37+/JKmlpUUej0eFhYWKjo7utLgBAAAAmJfpErdRo0aFvBJgf6+++uohr4+KiuJwEQAAAACdylTPuAEAAADA8ch0K26RpHpuBidMAgAAADgkVtwAAAAAIMKRuAEAAABAhCNxAwAAAIAIxzNuYTR0znJFWbuFOwwAAIDj0uZ5k8IdAtBpWHEDAAAAgAhH4gYAAAAAEc4UiVt2drYyMzPbbPP5fLJYLFq/fr1mzpypkSNHymq1avjw4a36vvrqq5o8ebL69Omj7t27a/jw4XriiSc6OHoAAAAAZmeKxM3pdGrFihWqr69v1eZ2u5Wamqphw4ZJkhwOh/Ly8tocZ/Xq1Ro2bJgqKiq0fv16XXvttSooKNBzzz3XofEDAAAAMDdTHE6SlZWlxMREeTwezZ49O1jf2Ngor9erkpISSdKDDz4oSdq+fbvWr1/fapz//d//Dfl844036uWXX9ayZcuUlZXVgTMAAAAAYGamWHGLiYlRQUGBPB6PDMMI1nu9XjU3Nys/P/+Ix/b7/erZs+dB+zQ1NSkQCIQUAAAAAGgvUyRu0t4tkLW1taqsrAzWud1u5eTkyGazHdGYS5Ys0Ztvvqlrr732oP2Ki4tls9mCxW63H9H9AAAAAJiTaRK3lJQUpaWlqbS0VJJUU1Mjn88np9N5ROOtWrVK1157rf7617/qnHPOOWhfl8slv98fLHV1dUd0TwAAAADmZJrETdp7SElFRYUaGhrkdruVnJys9PT0wx6nsrJS2dnZ+sMf/qCCgoJD9rdarUpISAgpAAAAANBepkrccnNzFRUVpfLycpWVlcnhcMhisRzWGK+++qomTZqku+++W0VFRR0UKQAAAAD8H1OcKrlPfHy88vLy5HK5FAgEVFhYGNJeU1OjxsZGbdu2Tbt27VJVVZUkaciQIYqNjdWqVauUlZWlG2+8UTk5Odq2bZskKTY29pAHlAAAAADAkTLVipu0d7vkjh07lJGRoaSkpJC2qVOnasSIEXrkkUf0wQcfaMSIERoxYoS2bNkiSXrssce0c+dOFRcXq0+fPsFy5ZVXhmMqAAAAAEzCYux/Pj46RSAQ2Hu65KwlirJ2C3c4AAAAx6XN8yaFOwTgqO3LDfx+/0HPwjDdihsAAAAAHG9M9YxbpKmem8EJkwAAAAAOiRU3AAAAAIhwJG4AAAAAEOHYKhlGQ+cs53ASAABgehwyAhwaK24AAAAAEOFI3AAAAAAgwpG4AQAAAECEM0Xilp2drczMzDbbfD6fLBaL1q9fr5kzZ2rkyJGyWq0aPnz4QcesqanRSSedpB49ehz7gAEAAABgP6ZI3JxOp1asWKH6+vpWbW63W6mpqRo2bJgkyeFwKC8v76Djffvtt8rPz9eYMWM6JF4AAAAA2J8pEresrCwlJibK4/GE1Dc2Nsrr9crpdEqSHnzwQU2fPl0DBw486HizZ89WSkqKcnNzOypkAAAAAAgyReIWExOjgoICeTweGYYRrPd6vWpublZ+fn67x1q5cqW8Xq8WLFjQ7muampoUCARCCgAAAAC0lykSN2nvFsja2lpVVlYG69xut3JycmSz2do1xhdffKHCwkJ5PB4lJCS0+97FxcWy2WzBYrfbDzt+AAAAAOZlmsQtJSVFaWlpKi0tlbT3cBGfzxfcJtke1113na655hr96Ec/Oqx7u1wu+f3+YKmrqzus6wEAAACYm2kSN2nvISUVFRVqaGiQ2+1WcnKy0tPT2339ypUrde+99yomJkYxMTFyOp3y+/2KiYkJJoRtsVqtSkhICCkAAAAA0F4x4Q6gM+Xm5urGG29UeXm5ysrKdP3118tisbT7+jVr1qi5uTn4+emnn9bdd9+t1atX6/TTT++IkAEAAADAXIlbfHy88vLy5HK5FAgEVFhYGNJeU1OjxsZGbdu2Tbt27VJVVZUkaciQIYqNjdXgwYND+r/11luKiorS0KFDO2kGAAAAAMzIVImbtHe75MKFCzVx4kQlJSWFtE2dOjXk8JIRI0ZIkjZt2qT+/ft3ZpgAAAAAEGQx9j8fH50iEAjsPV1y1hJFWbuFOxwAAICw2jxvUrhDAMJmX27g9/sPehaG6VbcIkn13AwOKgEAAABwSKY6VRIAAAAAjkckbgAAAAAQ4UjcAAAAACDC8YxbGA2ds5zDSQCgE3DwAQDgeMeKGwAAAABEOBI3AAAAAIhwJG4AAAAAEOFMkbhlZ2crMzOzzTafzyeLxaL169dr5syZGjlypKxWq4YPH95mf8MwdO+99+qss86S1WrV6aefrjvvvLMDowcAAABgdqY4nMTpdConJ0f19fXq27dvSJvb7VZqaqqGDRsmSXI4HHrjjTe0fv36Nse68cYb9fLLL+vee+/Vueeeqy+//FJffvllh88BAAAAgHmZInHLyspSYmKiPB6PZs+eHaxvbGyU1+tVSUmJJOnBBx+UJG3fvr3NxO3dd9/Vww8/rOrqap199tmSpAEDBnTCDAAAAACYmSm2SsbExKigoEAej0eGYQTrvV6vmpublZ+f365xnn32WQ0cOFDPPfecBgwYoP79+2vq1KmHXHFrampSIBAIKQAAAADQXqZI3KS9WyBra2tVWVkZrHO73crJyZHNZmvXGB999JE+/vhjeb1elZWVyePxaO3atbrqqqsOel1xcbFsNluw2O32o5oLAAAAAHMxTeKWkpKitLQ0lZaWSpJqamrk8/nkdDrbPUZLS4uamppUVlamMWPGaOzYsVq4cKFWrVql999//4DXuVwu+f3+YKmrqzvq+QAAAAAwD9MkbtLeQ0oqKirU0NAgt9ut5ORkpaent/v6Pn36KCYmRmeddVawbvDgwZKkTz755IDXWa1WJSQkhBQAAAAAaC9TJW65ubmKiopSeXm5ysrK5HA4ZLFY2n396NGjtWfPHtXW1gbrPvjgA0lSv379jnm8AAAAACCZ5FTJfeLj45WXlyeXy6VAIKDCwsKQ9pqaGjU2Nmrbtm3atWuXqqqqJElDhgxRbGysLr74Yp133nlyOBx64IEH1NLSounTp+uSSy4JWYUDAAAAgGPJVCtu0t7tkjt27FBGRoaSkpJC2qZOnaoRI0bokUce0QcffKARI0ZoxIgR2rJliyQpKipKzz77rE499VT96Ec/0qRJkzR48GD97W9/C8dUAAAAAJiEqVbcJGnUqFEhrwTY36uvvnrI65OSklRRUXGMowIAAACAAzNd4hZJqudmcFAJAAAAgEMy3VZJAAAAADjekLgBAAAAQIQjcQMAAACACMczbmE0dM5yRVm7hTsMAIhIm+dNCncIAABEDFbcAAAAACDCkbgBAAAAQIQjcQMAAACACGeKxC07O1uZmZlttvl8PlksFq1fv14zZ87UyJEjZbVaNXz48Db7r1+/XmPGjFHXrl1lt9t1zz33dGDkAAAAAGCSxM3pdGrFihWqr69v1eZ2u5Wamqphw4ZJkhwOh/Ly8tocJxAI6NJLL1W/fv20du1alZSU6Pbbb9df/vKXDo0fAAAAgLmZ4lTJrKwsJSYmyuPxaPbs2cH6xsZGeb1elZSUSJIefPBBSdL27du1fv36VuM88cQT+uabb1RaWqrY2Fidc845qqqq0v3336+ioqID3r+pqUlNTU3Bz4FA4FhNDQAAAIAJmGLFLSYmRgUFBfJ4PDIMI1jv9XrV3Nys/Pz8do2zZs0a/ehHP1JsbGywLiMjQ++//7527NhxwOuKi4tls9mCxW63H/lkAAAAAJiOKRI3ae8WyNraWlVWVgbr3G63cnJyZLPZ2jXGtm3bdNppp4XU7fu8bdu2A17ncrnk9/uDpa6u7ghmAAAAAMCsTJO4paSkKC0tTaWlpZKkmpoa+Xw+OZ3ODr+31WpVQkJCSAEAAACA9jJN4ibtPaSkoqJCDQ0NcrvdSk5OVnp6eruv7927tz777LOQun2fe/fufUxjBQAAAIB9TJW45ebmKioqSuXl5SorK5PD4ZDFYmn39aNGjdJrr72mb7/9Nli3YsUKnX322Tr55JM7ImQAAAAAMFfiFh8fr7y8PLlcLm3dulWFhYUh7TU1NaqqqtK2bdu0a9cuVVVVqaqqSt98840k6ZprrlFsbKycTqc2bNigxYsXa/78+brpppvCMBsAAAAAZmGK1wHsz+l0auHChZo4caKSkpJC2qZOnRpyeMmIESMkSZs2bVL//v1ls9n08ssva/r06Ro5cqROPfVU3XbbbQd9FQAAAAAAHC3TJW6jRo0KeSXA/l599dVDXj9s2DD5fL5jHBUAAAAAHJjpErdIUj03gxMmAQAAABySqZ5xAwAAAIDjEYkbAAAAAEQ4EjcAAAAAiHA84xZGQ+csV5S1W7jDAPCdzfMmhTsEAACANrHiBgAAAAARjsQNAAAAACKcKRK37OxsZWZmttnm8/lksVi0fv16zZw5UyNHjpTVatXw4cNb9d29e7cKCwt17rnnKiYmRldccUXHBg4AAAAAMkni5nQ6tWLFCtXX17dqc7vdSk1N1bBhwyRJDodDeXl5bY7T3NysuLg4zZw5UxdffHGHxgwAAAAA+5giccvKylJiYqI8Hk9IfWNjo7xer5xOpyTpwQcf1PTp0zVw4MA2x+nevbsefvhhXXfdderdu3dHhw0AAAAAkkySuMXExKigoEAej0eGYQTrvV6vmpublZ+f36H3b2pqUiAQCCkAAAAA0F6mSNykvVsga2trVVlZGaxzu93KycmRzWbr0HsXFxfLZrMFi91u79D7AQAAADixmCZxS0lJUVpamkpLSyVJNTU18vl8wW2SHcnlcsnv9wdLXV1dh98TAAAAwInDNImbtPeQkoqKCjU0NMjtdis5OVnp6ekdfl+r1aqEhISQAgAAAADtZarELTc3V1FRUSovL1dZWZkcDocsFku4wwIAAACAg4oJdwCdKT4+Xnl5eXK5XAoEAiosLAxpr6mpUWNjo7Zt26Zdu3apqqpKkjRkyBDFxsZKkjZu3KhvvvlGX375pRoaGoJ92nrvGwAAAAAcC6ZK3KS92yUXLlyoiRMnKikpKaRt6tSpIYeXjBgxQpK0adMm9e/fX5I0ceJEffzxx6367H9aJQAAAAAcS6ZL3EaNGnXAJOvVV1895PWbN28+tgEBAAAAwCGY6hk3AAAAADgemW7FLZJUz83ghEkAAAAAh8SKGwAAAABEOBI3AAAAAIhwbJUMo6FzlivK2i3cYeA4tnnepHCHAAAAgE7AihsAAAAARDgSNwAAAACIcCRuAAAAABDhTJG4ZWdnKzMzs802n88ni8Wi9evXa+bMmRo5cqSsVquGDx/eZv/ly5frhz/8oU466SQlJiYqJyeHl3IDAAAA6FCmSNycTqdWrFih+vr6Vm1ut1upqakaNmyYJMnhcCgvL6/NcTZt2qTJkydr/Pjxqqqq0vLly/X555/ryiuv7ND4AQAAAJibKRK3rKwsJSYmyuPxhNQ3NjbK6/XK6XRKkh588EFNnz5dAwcObHOctWvXqrm5Wb///e+VnJys8847TzfffLOqqqr07bffdvQ0AAAAAJiUKRK3mJgYFRQUyOPxyDCMYL3X61Vzc7Py8/PbNc7IkSMVFRUlt9ut5uZm+f1+Pf7447r44ovVpUuXA17X1NSkQCAQUgAAAACgvUyRuEl7t0DW1taqsrIyWOd2u5WTkyObzdauMQYMGKCXX35Z//u//yur1aoePXqovr5eS5YsOeh1xcXFstlswWK3249qLgAAAADMxTSJW0pKitLS0lRaWipJqqmpkc/nC26TbI9t27bpuuuu05QpU/Tmm2+qsrJSsbGxuuqqq0JW8r7P5XLJ7/cHS11d3VHPBwAAAIB5xIQ7gM7kdDp1ww03aMGCBXK73UpOTlZ6enq7r1+wYIFsNpvuueeeYN2iRYtkt9v1xhtv6Ic//GGb11mtVlmt1qOOHwAAAIA5mWbFTZJyc3MVFRWl8vJylZWVyeFwyGKxtPv6nTt3Kioq9CuLjo6WJLW0tBzTWAEAAABgH1MlbvHx8crLy5PL5dLWrVtVWFgY0l5TU6Oqqipt27ZNu3btUlVVlaqqqvTNN99IkiZNmqQ333xTd9xxhz788EO9/fbbuvbaa9WvXz+NGDEiDDMCAAAAYAamStykvdsld+zYoYyMDCUlJYW0TZ06VSNGjNAjjzyiDz74QCNGjNCIESO0ZcsWSdL48eNVXl6up556SiNGjFBmZqasVqteeuklxcXFhWM6AAAAAEzAYhzsVA10iEAgsPd0yVlLFGXtFu5wcBzbPG9SuEMAAADAUdiXG/j9fiUkJBywn+lW3AAAAADgeGOqUyUjTfXcjINm1QAAAAAgseIGAAAAABGPxA0AAAAAIhxbJcNo6JzlHE4CSRwyAgAAgINjxQ0AAAAAIhyJGwAAAABEOBI3AAAAAIhwJG7fyc7OVmZmZpttPp9PFotF69ev1z/+8Q+lpaXppJNOUu/evfX//t//0549ezo5WgAAAABmQuL2HafTqRUrVqi+vr5Vm9vtVmpqqgzD0MSJE5WZman//Oc/Wrx4sZ555hn95je/CUPEAAAAAMyCxO07WVlZSkxMlMfjCalvbGyU1+uV0+nU4sWLNWzYMN12220688wzlZ6ernvuuUcLFixQQ0NDeAIHAAAAcMIjcftOTEyMCgoK5PF4ZBhGsN7r9aq5uVn5+flqampS165dQ66Li4vT7t27tXbt2gOO3dTUpEAgEFIAAAAAoL1I3PbjcDhUW1urysrKYJ3b7VZOTo5sNpsyMjK0evVqPfnkk2pubtann36qO+64Q5K0devWA45bXFwsm80WLHa7vcPnAgAAAODEQeK2n5SUFKWlpam0tFSSVFNTI5/PJ6fTKUm69NJLVVJSomnTpslqteqss87SxIkTJUlRUQf+Kl0ul/x+f7DU1dV1/GQAAAAAnDBI3L7H6XSqoqJCDQ0NcrvdSk5OVnp6erD9pptu0ldffaVPPvlEn3/+uSZPnixJGjhw4AHHtFqtSkhICCkAAAAA0F4kbt+Tm5urqKgolZeXq6ysTA6HQxaLJaSPxWJRUlKS4uLi9OSTT8put+u8884LU8QAAAAATnQx4Q4g0sTHxysvL08ul0uBQECFhYUh7SUlJcrMzFRUVJSWLVumefPmacmSJYqOjg5PwAAAAABOeKy4tcHpdGrHjh3KyMhQUlJSSNuLL76oMWPGKDU1Vc8//7yefvppXXHFFeEJFAAAAIApsOLWhlGjRoW8EmB/K1eu7ORoAAAAAJgdiVsYVc/N4KASAAAAAIfEVkkAAAAAiHAkbgAAAAAQ4UjcAAAAACDC8YxbGA2ds1xR1m7hDgMdZPO8SeEOAQAAACcIVtwAAAAAIMKRuAEAAABAhCNxAwAAAIAIR+L2nezsbGVmZrbZ5vP5ZLFY9OCDD8pisbRZ/vvf/3ZyxAAAAADMgsNJvuN0OpWTk6P6+nr17ds3pM3tdis1NVXXXXedcnNzQ9oKCwu1e/du9erVqzPDBQAAAGAirLh9JysrS4mJifJ4PCH1jY2N8nq9cjqdiouLU+/evYMlOjpaK1eulNPpDE/QAAAAAEyBxO07MTExKigokMfjkWEYwXqv16vm5mbl5+e3uqasrEzdunXTVVddddCxm5qaFAgEQgoAAAAAtBeJ234cDodqa2tVWVkZrHO73crJyZHNZmvVf+HChbrmmmsUFxd30HGLi4tls9mCxW63H/PYAQAAAJy4SNz2k5KSorS0NJWWlkqSampq5PP52twKuWbNGr377rvt2ibpcrnk9/uDpa6u7pjHDgAAAODEReL2PU6nUxUVFWpoaJDb7VZycrLS09Nb9Xv00Uc1fPhwjRw58pBjWq1WJSQkhBQAAAAAaC8St+/Jzc1VVFSUysvLVVZWJofDIYvFEtKnsbFRS5Ys4VASAAAAAJ2C1wF8T3x8vPLy8uRyuRQIBFRYWNiqz+LFi7Vnzx799Kc/7fwAAQAAAJgOK25tcDqd2rFjhzIyMpSUlNSqfeHChbryyivVo0ePzg8OAAAAgOmw4taGUaNGhbwS4PtWr17didEAAAAAMDsStzCqnpvBQSUAAAAADomtkgAAAAAQ4UjcAAAAACDCkbgBAAAAQITjGbcwGjpnuaKs3cIdBo6xzfMmhTsEAAAAnGBYcQMAAACACEfiBgAAAAARjsTtO9nZ2crMzGyzzefzyWKxaP369cG6L774Qn379pXFYtFXX33VSVECAAAAMCMSt+84nU6tWLFC9fX1rdrcbrdSU1M1bNiwkP77fwYAAACAjkLi9p2srCwlJibK4/GE1Dc2Nsrr9crpdAbrHn74YX311Ve6+eabOzlKAAAAAGZE4vadmJgYFRQUyOPxyDCMYL3X61Vzc7Py8/MlSRs3btQdd9yhsrIyRUW17+trampSIBAIKQAAAADQXiRu+3E4HKqtrVVlZWWwzu12KycnRzabTU1NTcrPz1dJSYnOOOOMdo9bXFwsm80WLHa7vSPCBwAAAHCCInHbT0pKitLS0lRaWipJqqmpkc/nC26TdLlcGjx4sH76058e1rgul0t+vz9Y6urqjnnsAAAAAE5cJG7f43Q6VVFRoYaGBrndbiUnJys9PV2StHLlSnm9XsXExCgmJkYTJkyQJJ166qmaM2fOAce0Wq1KSEgIKQAAAADQXjHhDiDS5Obm6sYbb1R5ebnKysp0/fXXy2KxSJIqKiq0a9euYN8333xTDodDPp9PycnJ4QoZAAAAwAmOxO174uPjlZeXJ5fLpUAgoMLCwmDb95Ozzz//XJI0ePBg9ejRoxOjBAAAAGAmbJVsg9Pp1I4dO5SRkaGkpKRwhwMAAADA5Fhxa8OoUaNCXglwIGPHjm1XPwAAAAA4Gqy4AQAAAECEY8UtjKrnZnDCJAAAAIBDYsUNAAAAACIciRsAAAAARDgSNwAAAACIcDzjFkZD5yxXlLVbuMMwhc3zJoU7BAAAAOCIseIGAAAAABGOxA0AAAAAIpwpErfs7GxlZma22ebz+WSxWLRu3Trl5+fLbrcrLi5OgwcP1vz581v1b2pq0q233qp+/frJarWqf//+Ki0t7egpAAAAADAxUzzj5nQ6lZOTo/r6evXt2zekze12KzU1VWvXrlWvXr20aNEi2e12rV69WkVFRYqOjtaMGTOC/XNzc/XZZ59p4cKFOvPMM7V161a1tLR09pQAAAAAmIgpEresrCwlJibK4/Fo9uzZwfrGxkZ5vV6VlJTI4XCEXDNw4ECtWbNGy5YtCyZuL730kiorK/XRRx+pZ8+ekqT+/ft32jwAAAAAmJMptkrGxMSooKBAHo9HhmEE671er5qbm5Wfn9/mdX6/P5igSdIzzzyj1NRU3XPPPTr99NN11lln6eabb9auXbsOev+mpiYFAoGQAgAAAADtZYrETZIcDodqa2tVWVkZrHO73crJyZHNZmvVf/Xq1Vq8eLGKioqCdR999JFef/11VVdX6+9//7seeOABLV26VL/4xS8Oeu/i4mLZbLZgsdvtx25iAAAAAE54pkncUlJSlJaWFjxIpKamRj6fT06ns1Xf6upqTZ48WXPmzNGll14arG9paZHFYtETTzyhCy64QBMnTtT999+vxx577KCrbi6XS36/P1jq6uqO/QQBAAAAnLBMk7hJew8pqaioUENDg9xut5KTk5Wenh7SZ+PGjZowYYKKiopCnoeTpD59+uj0008PWaEbPHiwDMNQfX39Ae9rtVqVkJAQUgAAAACgvUyVuOXm5ioqKkrl5eUqKyuTw+GQxWIJtm/YsEHjxo3TlClTdOedd7a6fvTo0dqyZYsaGxuDdR988IGioqJanVYJAAAAAMeKqRK3+Ph45eXlyeVyaevWrSosLAy2VVdXa9y4cbr00kt10003adu2bdq2bZu2b98e7HPNNdfolFNO0bXXXquNGzfqtdde0y233CKHw6G4uLgwzAgAAACAGZgqcZP2bpfcsWOHMjIylJSUFKxfunSptm/frkWLFqlPnz7Bcv755wf7xMfHa8WKFfrqq6+Umpqqn/zkJ8rOztaDDz4YjqkAAAAAMAmLsf/5+OgUgUBg7+mSs5Yoytot3OGYwuZ5k8IdAgAAANDKvtzA7/cf9CwM0624AQAAAMDxJibcAZhZ9dwMTpgEAAAAcEisuAEAAABAhCNxAwAAAIAIx1bJMBo6ZzmHk3QSDicBAADA8YwVNwAAAACIcCRuAAAAABDhSNwAAAAAIMKRuH0nOztbmZmZbbb5fD5ZLBZVVlYqMzNTSUlJslqtstvtmjFjhgKBQCdHCwAAAMBMSNy+43Q6tWLFCtXX17dqc7vdSk1N1bBhwzR58mQ988wz+uCDD+TxePTKK69o2rRpYYgYAAAAgFmQuH0nKytLiYmJ8ng8IfWNjY3yer1yOp06+eSTdf311ys1NVX9+vXThAkT9Itf/EI+ny88QQMAAAAwBRK378TExKigoEAej0eGYQTrvV6vmpublZ+f3+qaLVu2aNmyZUpPTz/o2E1NTQoEAiEFAAAAANqLxG0/DodDtbW1qqysDNa53W7l5OTIZrMF6/Lz89WtWzedfvrpSkhI0KOPPnrQcYuLi2Wz2YLFbrd32BwAAAAAnHhI3PaTkpKitLQ0lZaWSpJqamrk8/nkdDpD+v3hD3/Q22+/raefflq1tbW66aabDjquy+WS3+8Plrq6ug6bAwAAAIATD4nb9zidTlVUVKihoUFut1vJycmttkL27t1bKSkpuvzyy/XII4/o4Ycf1tatWw84ptVqVUJCQkgBAAAAgPYicfue3NxcRUVFqby8XGVlZXI4HLJYLAfs39LSImnvc2wAAAAA0BFiwh1ApImPj1deXp5cLpcCgYAKCwuDbS+88II+++wznX/++YqPj9eGDRt0yy23aPTo0erfv3/YYgYAAABwYmPFrQ1Op1M7duxQRkaGkpKSgvVxcXH661//qosuukiDBw/WL3/5S11++eV67rnnwhgtAAAAgBMdK25tGDVqVMgrAfYZN26cVq9eHYaIAAAAAJgZiVsYVc/N4KASAAAAAIfEVkkAAAAAiHAkbgAAAAAQ4UjcAAAAACDC8YxbGA2ds1xR1m7hDuO4tnnepHCHAAAAAHQ4VtwAAAAAIMKRuAEAAABAhCNxAwAAAIAIZ4rELTs7W5mZmW22+Xw+WSwWrVu3Tvn5+bLb7YqLi9PgwYM1f/78kL5bt27VNddco7POOktRUVGaNWtWJ0QPAAAAwOxMkbg5nU6tWLFC9fX1rdrcbrdSU1O1du1a9erVS4sWLdKGDRt06623yuVy6aGHHgr2bWpqUmJiombPnq0f/OAHnTkFAAAAACZmilMls7KylJiYKI/Ho9mzZwfrGxsb5fV6VVJSIofDEXLNwIEDtWbNGi1btkwzZsyQJPXv3z+4CldaWtp5EwAAAABgaqZYcYuJiVFBQYE8Ho8MwwjWe71eNTc3Kz8/v83r/H6/evbsedT3b2pqUiAQCCkAAAAA0F6mSNwkyeFwqLa2VpWVlcE6t9utnJwc2Wy2Vv1Xr16txYsXq6io6KjvXVxcLJvNFix2u/2oxwQAAABgHqZJ3FJSUpSWlhbc4lhTUyOfzyen09mqb3V1tSZPnqw5c+bo0ksvPep7u1wu+f3+YKmrqzvqMQEAAACYh2kSN2nvISUVFRVqaGiQ2+1WcnKy0tPTQ/ps3LhREyZMUFFRUcjzcEfDarUqISEhpAAAAABAe5kqccvNzVVUVJTKy8tVVlYmh8Mhi8USbN+wYYPGjRunKVOm6M477wxjpAAAAADwf0xxquQ+8fHxysvLk8vlUiAQUGFhYbCturpa48ePV0ZGhm666SZt27ZNkhQdHa3ExMRgv6qqKkl7T6Tcvn27qqqqFBsbqyFDhnTmVAAAAACYiMXY/5hFE1izZo3S0tI0ceJEPf/888H622+/XXPnzm3Vv1+/ftq8eXPw8/4rdAfqcyiBQGDvISWzlijK2u2w4keozfMmhTsEAAAA4Ijtyw38fv9BH6kyXeIWCUjcjh0SNwAAABzP2pu4mWqrZKSpnpvBQSUAAAAADslUh5MAAAAAwPGIxA0AAAAAIhyJGwAAAABEOJ5xC6Ohc5ab7nASDhMBAAAADh8rbgAAAAAQ4UjcAAAAACDCkbgBAAAAQIQjcftOdna2MjMz22zz+XyyWCxat26d8vPzZbfbFRcXp8GDB2v+/PmdHCkAAAAAs+Fwku84nU7l5OSovr5effv2DWlzu91KTU3V2rVr1atXLy1atEh2u12rV69WUVGRoqOjNWPGjDBFDgAAAOBER+L2naysLCUmJsrj8Wj27NnB+sbGRnm9XpWUlMjhcIRcM3DgQK1Zs0bLli0jcQMAAADQYdgq+Z2YmBgVFBTI4/HIMIxgvdfrVXNzs/Lz89u8zu/3q2fPngcdu6mpSYFAIKQAAAAAQHuRuO3H4XCotrZWlZWVwTq3262cnBzZbLZW/VevXq3FixerqKjooOMWFxfLZrMFi91uP+axAwAAADhxkbjtJyUlRWlpaSotLZUk1dTUyOfzyel0tupbXV2tyZMna86cObr00ksPOq7L5ZLf7w+Wurq6DokfAAAAwImJxO17nE6nKioq1NDQILfbreTkZKWnp4f02bhxoyZMmKCioqKQ5+EOxGq1KiEhIaQAAAAAQHuRuH1Pbm6uoqKiVF5errKyMjkcDlkslmD7hg0bNG7cOE2ZMkV33nlnGCMFAAAAYBacKvk98fHxysvLk8vlUiAQUGFhYbCturpa48ePV0ZGhm666SZt27ZNkhQdHa3ExMQwRQwAAADgRMeKWxucTqd27NihjIwMJSUlBeuXLl2q7du3a9GiRerTp0+wnH/++WGMFgAAAMCJ7ogTt8cff1yjR49WUlKSPv74Y0nSAw88oKeffvqYBRcuo0aNkmEYev7550Pqb7/9dhmG0aps3rw5PIECAAAAMIUj2ir58MMP67bbbtOsWbN05513qrm5WZLUo0cPPfDAA5o8efIxDfJEVT03g4NKAAAAABzSEa24/fGPf9Rf//pX3XrrrYqOjg7Wp6am6p133jlmwQEAAAAAjjBx27Rpk0aMGNGq3mq16uuvvz7qoAAAAAAA/+eIErcBAwaoqqqqVf1LL72kwYMHH21MAAAAAID9HNEzbjfddJOmT5+u3bt3yzAM/fvf/9aTTz6p4uJiPfroo8c6xhPW0DnLFWXtFu4wOsXmeZPCHQIAAABw3DqixG3q1KmKi4vT7NmztXPnTl1zzTVKSkrS/Pnz9eMf//hYxwgAAAAApnbYiduePXtUXl6ujIwM/eQnP9HOnTvV2NioXr16dUR8AAAAAGB6h/2MW0xMjKZNm6bdu3dLkrp163ZCJG3Z2dnKzMxss83n88lisWj9+vV68803NWHCBPXo0UMnn3yyMjIytG7duk6OFgAAAICZHNHhJBdccIH+85//HOtYwsrpdGrFihWqr69v1eZ2u5WamqqBAwcqMzNTZ5xxht544w29/vrrOumkk5SRkaFvv/02DFEDAAAAMIMjesbtF7/4hX71q1+pvr5eI0eOVPfu3UPahw0bdkyC60xZWVlKTEyUx+PR7Nmzg/WNjY3yer0qKSnRe++9py+//FJ33HGH7Ha7JGnOnDkaNmyYPv74Y5155pnhCh8AAADACeyIErd9B5DMnDkzWGexWGQYhiwWi5qbm49NdJ0oJiZGBQUF8ng8uvXWW2WxWCRJXq9Xzc3Nys/PV1RUlE455RQtXLhQ//u//6vm5mYtXLhQgwcPVv/+/Q84dlNTk5qamoKfA4FAR08HAAAAwAnkiF/A/f3y0UcfBf/3eOVwOFRbW6vKyspgndvtVk5Ojmw2m0466SS9+uqrWrRokeLi4hQfH6+XXnpJL774omJiDpwDFxcXy2azBcu+1ToAAAAAaA+LYRhGuIOIJKNHj1ZycrLKyspUU1OjQYMGadWqVRo7dqx27dqlsWPHKiUlRTNmzFBzc7Puvfdevffee3rzzTcVFxfX5phtrbjZ7XbZZy3hPW4AAACAiQUCAdlsNvn9fiUkJByw3xFtlSwrKztoe0FBwZEMGxGcTqduuOEGLViwQG63W8nJyUpPT5cklZeXa/PmzVqzZo2ioqKCdSeffLKefvrpA77Dzmq1ymq1dtocAAAAAJxYjihxu/HGG0M+f/vtt9q5c6diY2PVrVu34zpxy83N1Y033qjy8nKVlZXp+uuvDz7vtnPnTkVFRQU/Swp+bmlpCVfIAAAAAE5wR/SM244dO0JKY2Oj3n//fV100UV68sknj3WMnSo+Pl55eXlyuVzaunWrCgsLg22XXHKJduzYoenTp+vdd9/Vhg0bdO211yomJkbjxo0LX9AAAAAATmhHlLi1ZdCgQZo3b16r1bjjkdPp1I4dO5SRkaGkpKRgfUpKip599lmtX79eo0aN0pgxY7Rlyxa99NJL6tOnTxgjBgAAAHAiO6KtkgccLCZGW7ZsOZZDhsWoUaN0oDNbLrnkEl1yySWdHBEAAAAAMzuixO2ZZ54J+WwYhrZu3aqHHnpIo0ePPiaBAQAAAAD2OqLXAew7UTE4iMWixMREjR8/Xvfddx/bBg+hvUd+AgAAADixdejrADhBEQAAAAA6zxEdTnLHHXdo586drep37dqlO+6446iDAgAAAAD8nyPaKhkdHa2tW7eqV69eIfVffPGFevXqpebm5mMW4ImIrZIAAAAApA7eKmkYRshLqPdZt26devbseSRDmtLQOcsVZe0W7jA6xeZ5k8IdAgAAAHDcOqzE7eSTT5bFYpHFYtFZZ50Vkrw1NzersbFR06ZNO+ZBAgAAAICZHVbi9sADD8gwDDkcDs2dO1c2my3YFhsbq/79+2vUqFHHPEgAAAAAMLPDStymTJkiSRowYIDS0tLUpUuXDgnqWMvOzta3336rl156qVWbz+fTj370I1VVVWnevHl6/fXX9fnnn6t///6aNm2abrzxxmDfwsJCPfbYY63GGDJkiDZs2NChcwAAAABgXkf0jFt6enrwz7t379Y333wT0h5pB244nU7l5OSovr5effv2DWlzu91KTU3V2rVr1atXLy1atEh2u12rV69WUVGRoqOjNWPGDEnS/PnzNW/evOC1e/bs0Q9+8ANdffXVnTofAAAAAOZyRInbzp079etf/1pLlizRF1980ao90k6VzMrKUmJiojwej2bPnh2sb2xslNfrVUlJiRwOR8g1AwcO1Jo1a7Rs2bJg4maz2UK2hz711FPasWOHrr322s6ZCAAAAABTOqL3uN1yyy1auXKlHn74YVmtVj366KOaO3eukpKSVFZWdqxjPGoxMTEqKCiQx+PR/m8/8Hq9am5uVn5+fpvX+f3+g56SuXDhQl188cXq16/fQe/f1NSkQCAQUgAAAACgvY4ocXv22Wf1pz/9STk5OYqJidGYMWM0e/Zs3XXXXXriiSeOdYzHhMPhUG1trSorK4N1brdbOTk5Iato+6xevVqLFy9WUVFRm+Nt2bJFL774oqZOnXrIexcXFwdX62w2m+x2+5FPBAAAAIDpHFHi9uWXX2rgwIGS9j7P9uWXX0qSLrroIr322mvHLrpjKCUlRWlpaSotLZUk1dTUyOfzyel0tupbXV2tyZMna86cObr00kvbHO+xxx5Tjx49dMUVVxzy3i6XS36/P1jq6uqOai4AAAAAzOWIEreBAwdq06ZNkvYmREuWLJG0dyWuR48exyy4Y83pdKqiokINDQ1yu91KTk4OOWhFkjZu3KgJEyaoqKgo5Hm4/RmGodLSUv3sZz9TbGzsIe9rtVqVkJAQUgAAAACgvY4ocbv22mu1bt06SdJvfvMbLViwQF27dtUvf/lL3XLLLcc0wGMpNzdXUVFRKi8vV1lZmRwOR8hLxDds2KBx48ZpypQpuvPOOw84TmVlpWpqatpcrQMAAACAY81i7H9axxH6+OOPtXbtWp155pkaNmzYsYirw0ydOlXLli1TIBDQJ598oqSkJEl7t0eOHz9eGRkZKikpCfaPjo5WYmJiyBg/+9nP9OGHH+pf//rXEcUQCAT2Pus2a4mirN2OfDLHkc3zJoU7BAAAACDi7MsN/H7/QXfmHdGK2/52796tfv366corr4z4pE3au11yx44dysjICCZtkrR06VJt375dixYtUp8+fYLl/PPPD7ne7/eroqKC1TYAAAAAneaIVtyam5t111136c9//rM+++wzffDBBxo4cKB++9vfqn///iQ1h8CKGwAAAACpg1fc7rzzTnk8Ht1zzz0hh3MMHTpUjz766JEMCQAAAAA4gCNacTvzzDP1yCOPaMKECTrppJO0bt06DRw4UO+9955GjRqlHTt2dESsJ4z2ZtUAAAAATmwduuL26aef6swzz2xV39LSom+//fZIhgQAAAAAHMARJW5DhgyRz+drVb906VKNGDHiqIMCAAAAAPyfmCO56LbbbtOUKVP06aefqqWlRcuWLdP777+vsrIyPffcc8c6xhPW0DnLTXE4CQeTAAAAAEfnsFbcPvroIxmGocmTJ+vZZ5/VK6+8ou7du+u2227Tu+++q2effVaXXHJJR8UKAAAAAKZ0WCtugwYN0tatW9WrVy+NGTNGPXv21DvvvKPTTjuto+IDAAAAANM7rBW37x9A+eKLL+rrr78+pgEBAAAAAEId0eEk+xzBmwQiVnZ2tjIzM9ts8/l8slgsWr9+vWbOnKmRI0fKarVq+PDhnRskAAAAAFM6rMTNYrHIYrG0qjsROJ1OrVixQvX19a3a3G63UlNTNWzYMEmSw+FQXl5eZ4cIAAAAwKQO6xk3wzBUWFgoq9UqSdq9e7emTZum7t27h/RbtmzZsYuwk2RlZSkxMVEej0ezZ88O1jc2Nsrr9aqkpESS9OCDD0qStm/frvXr14clVgAAAADmcliJ25QpU0I+//SnPz2mwYRTTEyMCgoK5PF4dOuttwZXEr1er5qbm5Wfn3/EYzc1NampqSn4ORAIHHW8AAAAAMzjsBI3t9vdUXFEBIfDoZKSElVWVmrs2LGS9s45JydHNpvtiMctLi7W3Llzj1GUAAAAAMzmqA4nOdGkpKQoLS1NpaWlkqSamhr5fD45nc6jGtflcsnv9wdLXV3dsQgXAAAAgEmQuH2P0+lURUWFGhoa5Ha7lZycrPT09KMa02q1KiEhIaQAAAAAQHuRuH1Pbm6uoqKiVF5errKyMjkcjhPm5EwAAAAAx6fDesbNDOLj45WXlyeXy6VAIKDCwsKQ9pqaGjU2Nmrbtm3atWuXqqqqJElDhgxRbGxs5wcMAAAA4IRH4tYGp9OphQsXauLEiUpKSgppmzp1qiorK4OfR4wYIUnatGmT+vfv35lhAgAAADAJErc2jBo1SoZhtNn26quvdm4wAAAAAEyPxC2MqudmcFAJAAAAgEPicBIAAAAAiHAkbgAAAAAQ4UjcAAAAACDC8YxbGA2ds1xR1m7hDqNDbZ43KdwhAAAAAMc9VtwAAAAAIMKRuAEAAABAhCNxAwAAAIAIZ4rELTs7W5mZmW22+Xw+WSwWrVu3Tvn5+bLb7YqLi9PgwYM1f/78Vv1fffVVnXfeebJarTrzzDPl8Xg6OHoAAAAAZmeKxM3pdGrFihWqr69v1eZ2u5Wamqq1a9eqV69eWrRokTZs2KBbb71VLpdLDz30ULDvpk2bNGnSJI0bN05VVVWaNWuWpk6dquXLl3fmdAAAAACYjMUwDCPcQXS0PXv2qG/fvpoxY4Zmz54drG9sbFSfPn1UUlKiadOmtbpu+vTpevfdd7Vy5UpJ0v/7f/9Pzz//vKqrq4N9fvzjH+urr77SSy+91O54AoGAbDab7LOWcKokAAAAYGL7cgO/36+EhIQD9jPFiltMTIwKCgrk8Xi0f57q9XrV3Nys/Pz8Nq/z+/3q2bNn8POaNWt08cUXh/TJyMjQmjVrDnr/pqYmBQKBkAIAAAAA7WWKxE2SHA6HamtrVVlZGaxzu93KycmRzWZr1X/16tVavHixioqKgnXbtm3TaaedFtLvtNNOUyAQ0K5duw547+LiYtlstmCx2+3HYEYAAAAAzMI0iVtKSorS0tJUWloqSaqpqZHP55PT6WzVt7q6WpMnT9acOXN06aWXHvW9XS6X/H5/sNTV1R31mAAAAADMwzSJm7T3kJKKigo1NDTI7XYrOTlZ6enpIX02btyoCRMmqKioKOR5OEnq3bu3Pvvss5C6zz77TAkJCYqLizvgfa1WqxISEkIKAAAAALSXqRK33NxcRUVFqby8XGVlZXI4HLJYLMH2DRs2aNy4cZoyZYruvPPOVtePGjVK//jHP0LqVqxYoVGjRnV47AAAAADMy1SJW3x8vPLy8uRyubR161YVFhYG26qrqzVu3Dhdeumluummm7Rt2zZt27ZN27dvD/aZNm2aPvroI/3617/We++9pz/96U9asmSJfvnLX4ZhNgAAAADMwlSJm7R3u+SOHTuUkZGhpKSkYP3SpUu1fft2LVq0SH369AmW888/P9hnwIABev7557VixQr94Ac/0H333adHH31UGRkZ4ZgKAAAAAJMwxXvcIg3vcQMAAAAgtf89bjGdGBO+p3puBgeVAAAAADgk022VBAAAAIDjDYkbAAAAAEQ4EjcAAAAAiHA84xZGQ+cs53ASAAAAAIfEihsAAAAARDgSNwAAAACIcCRuAAAAABDhTJG4ZWdnKzMzs802n88ni8Wi9evXa+bMmRo5cqSsVquGDx/eZv8lS5Zo+PDh6tatm/r166eSkpIOjBwAAAAATJK4OZ1OrVixQvX19a3a3G63UlNTNWzYMEmSw+FQXl5em+O8+OKL+slPfqJp06apurpaf/rTn/SHP/xBDz30UIfGDwAAAMDcTJG4ZWVlKTExUR6PJ6S+sbFRXq9XTqdTkvTggw9q+vTpGjhwYJvjPP7447riiis0bdo0DRw4UJMmTZLL5dLdd98twzAOeP+mpiYFAoGQAgAAAADtZYrELSYmRgUFBfJ4PCEJltfrVXNzs/Lz89s1TlNTk7p27RpSFxcXp/r6en388ccHvK64uFg2my1Y7Hb7kU0EAAAAgCmZInGT9m6BrK2tVWVlZbDO7XYrJydHNputXWNkZGRo2bJl+sc//qGWlhZ98MEHuu+++yRJW7duPeB1LpdLfr8/WOrq6o5uMgAAAABMxTSJW0pKitLS0lRaWipJqqmpkc/nC26TbI/rrrtOM2bMUFZWlmJjY/XDH/5QP/7xjyVJUVEH/iqtVqsSEhJCCgAAAAC0l2kSN2nvISUVFRVqaGiQ2+1WcnKy0tPT2329xWLR3XffrcbGRn388cfatm2bLrjgAkk64HNxAAAAAHC0TJW45ebmKioqSuXl5SorK5PD4ZDFYjnscaKjo3X66acrNjZWTz75pEaNGqXExMQOiBgAAAAApJhwB9CZ4uPjlZeXJ5fLpUAgoMLCwpD2mpoaNTY2atu2bdq1a5eqqqokSUOGDFFsbKw+//xzLV26VGPHjtXu3bvldrvl9XpDnpsDAAAAgGPNVImbtHe75MKFCzVx4kQlJSWFtE2dOjUkCRsxYoQkadOmTerfv78k6bHHHtPNN98swzA0atQovfrqq8HtkgAAAADQEUyXuI0aNeqA71x79dVXD3rtqaeeqjVr1nRAVAAAAABwYKZL3CJJ9dwMTpgEAAAAcEimOpwEAAAAAI5HJG4AAAAAEOFI3AAAAAAgwvGMWxgNnbNcUdZu4Q6jw2yeNyncIQAAAAAnBFbcAAAAACDCkbgBAAAAQIQjcftOdna2MjMz22zz+XyyWCxav369JMnj8WjYsGHq2rWrevXqpenTp3dmqAAAAABMhmfcvuN0OpWTk6P6+nr17ds3pM3tdis1NVXDhg3T/fffr/vuu08lJSW68MIL9fXXX2vz5s3hCRoAAACAKZC4fScrK0uJiYnyeDyaPXt2sL6xsVFer1clJSXasWOHZs+erWeffVYTJkwI9hk2bFg4QgYAAABgEmyV/E5MTIwKCgrk8XhkGEaw3uv1qrm5Wfn5+VqxYoVaWlr06aefavDgwerbt69yc3NVV1d30LGbmpoUCARCCgAAAAC0F4nbfhwOh2pra1VZWRmsc7vdysnJkc1m00cffaSWlhbdddddeuCBB7R06VJ9+eWXuuSSS/TNN98ccNzi4mLZbLZgsdvtnTEdAAAAACcIErf9pKSkKC0tTaWlpZKkmpoa+Xw+OZ1OSVJLS4u+/fZbPfjgg8rIyNAPf/hDPfnkk/rwww+1atWqA47rcrnk9/uD5VArdAAAAACwPxK373E6naqoqFBDQ4PcbreSk5OVnp4uSerTp48kaciQIcH+iYmJOvXUU/XJJ58ccEyr1aqEhISQAgAAAADtReL2Pbm5uYqKilJ5ebnKysrkcDhksVgkSaNHj5Ykvf/++8H+X375pT7//HP169cvLPECAAAAOPGRuH1PfHy88vLy5HK5tHXrVhUWFgbbzjrrLE2ePFk33nijVq9ererqak2ZMkUpKSkaN25c+IIGAAAAcEIjcWuD0+nUjh07lJGRoaSkpJC2srIyXXjhhZo0aZLS09PVpUsXvfTSS+rSpUuYogUAAABworMY+599j04RCAT2ni45a4mirN3CHU6H2TxvUrhDAAAAACLavtzA7/cf9CwMVtwAAAAAIMLFhDsAM6uem8EJkwAAAAAOiRU3AAAAAIhwJG4AAAAAEOFI3AAAAAAgwvGMWxgNnbP8hD1VkhMlAQAAgGOHFTcAAAAAiHCmStwKCwt1xRVXhDsMAAAAADgsJ8xWSYvFctD2OXPmaP78+eJ94wAAAACONydM4rZ169bgnxcvXqzbbrtN77//frAuPj5e8fHx4QgNAAAAAI7KCbNVsnfv3sFis9lksVhC6uLj41ttlRw7dqxuuOEGzZo1SyeffLJOO+00/fWvf9XXX3+ta6+9VieddJLOPPNMvfjiiyH3qq6u1mWXXab4+Hiddtpp+tnPfqbPP/+8k2cMAAAAwCxOmMTtSD322GM69dRT9e9//1s33HCDrr/+el199dVKS0vT22+/rUsvvVQ/+9nPtHPnTknSV199pfHjx2vEiBF666239NJLL+mzzz5Tbm7uAe/R1NSkQCAQUgAAAACgvUyfuP3gBz/Q7NmzNWjQILlcLnXt2lWnnnqqrrvuOg0aNEi33XabvvjiC61fv16S9NBDD2nEiBG66667lJKSohEjRqi0tFSrVq3SBx980OY9iouLZbPZgsVut3fmFAEAAAAc50yfuA0bNiz45+joaJ1yyik699xzg3WnnXaaJOm///2vJGndunVatWpV8Jm5+Ph4paSkSJJqa2vbvIfL5ZLf7w+Wurq6jpoOAAAAgBPQCXM4yZHq0qVLyGeLxRJSt++0ypaWFklSY2OjsrOzdffdd7caq0+fPm3ew2q1ymq1HquQAQAAAJiM6RO3w3XeeeepoqJC/fv3V0wMXx8AAACAjmf6rZKHa/r06fryyy+Vn5+vN998U7W1tVq+fLmuvfZaNTc3hzs8AAAAACcgErfDlJSUpH/+859qbm7WpZdeqnPPPVezZs1Sjx49FBXF1wkAAADg2LMYhmGEOwizCQQCe0+XnLVEUdZu4Q6nQ2yeNyncIQAAAAARb19u4Pf7lZCQcMB+LBEBAAAAQITjdI0wqp6bcdCsGgAAAAAkVtwAAAAAIOKRuAEAAABAhGOrZBgNnbP8hDuchENJAAAAgGOPFTcAAAAAiHAkbgAAAAAQ4UjcAAAAACDCRUTitmbNGkVHR2vSpOPr+aixY8dq1qxZ4Q4DAAAAwAkuIhK3hQsX6oYbbtBrr72mLVu2hDscAAAAAIgoYU/cGhsbtXjxYl1//fWaNGmSPB5PsO3VV1+VxWLR8uXLNWLECMXFxWn8+PH673//qxdffFGDBw9WQkKCrrnmGu3cuTN4XVNTk2bOnKlevXqpa9euuuiii/Tmm28G2z0ej3r06BESx1NPPSWLxRL8fPvtt2v48OF6/PHH1b9/f9lsNv34xz9WQ0ODJKmwsFCVlZWaP3++LBaLLBaLNm/e3CHfEQAAAABzC3vitmTJEqWkpOjss8/WT3/6U5WWlsowjJA+t99+ux566CGtXr1adXV1ys3N1QMPPKDy8nI9//zzevnll/XHP/4x2P/Xv/61Kioq9Nhjj+ntt9/WmWeeqYyMDH355ZeHFVttba2eeuopPffcc3ruuedUWVmpefPmSZLmz5+vUaNG6brrrtPWrVu1detW2e32NsdpampSIBAIKQAAAADQXmFP3BYuXKif/vSnkqTMzEz5/X5VVlaG9Pn973+v0aNHa8SIEXI6naqsrNTDDz+sESNGaMyYMbrqqqu0atUqSdLXX3+thx9+WCUlJbrssss0ZMgQ/fWvf1VcXJwWLlx4WLG1tLTI4/Fo6NChGjNmjH72s5/pH//4hyTJZrMpNjZW3bp1U+/evdW7d29FR0e3OU5xcbFsNluwHCjBAwAAAIC2hDVxe//99/Xvf/9b+fn5kqSYmBjl5eW1SrCGDRsW/PNpp52mbt26aeDAgSF1//3vfyXtXSX79ttvNXr06GB7ly5ddMEFF+jdd989rPj69++vk046Kfi5T58+wfscDpfLJb/fHyx1dXWHPQYAAAAA84oJ580XLlyoPXv2KCkpKVhnGIasVqseeuihYF2XLl2Cf7ZYLCGf99W1tLS0+75RUVGttmN+++23rfod7X32sVqtslqth30dAAAAAEhhXHHbs2ePysrKdN9996mqqipY1q1bp6SkJD355JNHNG5ycrJiY2P1z3/+M1j37bff6s0339SQIUMkSYmJiWpoaNDXX38d7FNVVXXY94qNjVVzc/MRxQkAAAAA7RW2FbfnnntOO3bskNPplM1mC2nLycnRwoULVVJSctjjdu/eXddff71uueUW9ezZU2eccYbuuece7dy5U06nU5J04YUXqlu3bvrf//1fzZw5U2+88UbIaZbt1b9/f73xxhvavHmz4uPj1bNnT0VFhf2xQQAAAAAnmLBlGQsXLtTFF1/cKmmT9iZub731ltavX39EY8+bN085OTn62c9+pvPOO081NTVavny5Tj75ZElSz549tWjRIr3wwgs699xz9eSTT+r2228/7PvcfPPNio6O1pAhQ5SYmKhPPvnkiOIFAAAAgIOxGN9/2AsdLhAI7D1dctYSRVm7hTucY2rzvEnhDgEAAAA4buzLDfx+vxISEg7YL6yHk5hd9dyMg/5wAAAAAECKgPe4AQAAAAAOjsQNAAAAACIciRsAAAAARDiecQujoXOWnzCHk3AoCQAAANBxWHEDAAAAgAhH4gYAAAAAEY7EDQAAAAAinCkSt+zsbGVmZrbZ5vP5ZLFYtH79es2cOVMjR46U1WrV8OHDW/V9//33NW7cOJ122mnq2rWrBg4cqNmzZ+vbb7/t4BkAAAAAMDNTHE7idDqVk5Oj+vp69e3bN6TN7XYrNTVVw4YNkyQ5HA698cYbWr9+fatxunTpooKCAp133nnq0aOH1q1bp+uuu04tLS266667OmUuAAAAAMzHFIlbVlaWEhMT5fF4NHv27GB9Y2OjvF6vSkpKJEkPPvigJGn79u1tJm4DBw7UwIEDg5/79eunV199VT6fr4NnAAAAAMDMTLFVMiYmRgUFBfJ4PDIMI1jv9XrV3Nys/Pz8Ixq3pqZGL730ktLT0w/ar6mpSYFAIKQAAAAAQHuZInGT9m6BrK2tVWVlZbDO7XYrJydHNpvtsMZKS0tT165dNWjQII0ZM0Z33HHHQfsXFxfLZrMFi91uP6I5AAAAADAn0yRuKSkpSktLU2lpqaS9q2U+n09Op/Owx1q8eLHefvttlZeX6/nnn9e999570P4ul0t+vz9Y6urqjmgOAAAAAMzJFM+47eN0OnXDDTdowYIFcrvdSk5OPuQ2x7bsWzEbMmSImpubVVRUpF/96leKjo5us7/VapXVaj2q2AEAAACYl2lW3CQpNzdXUVFRKi8vV1lZmRwOhywWy1GN2dLSom+//VYtLS3HKEoAAAAACGWqFbf4+Hjl5eXJ5XIpEAiosLAwpL2mpkaNjY3atm2bdu3apaqqKkl7V9ZiY2P1xBNPqEuXLjr33HNltVr11ltvyeVyKS8vT126dOn8CQEAAAAwBVMlbtLe7ZILFy7UxIkTlZSUFNI2derUkMNLRowYIUnatGmT+vfvr5iYGN1999364IMPZBiG+vXrpxkzZuiXv/xlp84BAAAAgLlYjP3Px0enCAQCe0+XnLVEUdZu4Q7nmNg8b1K4QwAAAACOO/tyA7/fr4SEhAP2M92KWySpnptx0B8OAAAAAEgmO5wEAAAAAI5HJG4AAAAAEOFI3AAAAAAgwvGMWxgNnbP8uD+chENJAAAAgI7HihsAAAAARDgSNwAAAACIcCRuAAAAABDhTJG4ZWdnKzMzs802n88ni8Wi9evXa+bMmRo5cqSsVquGDx/equ/mzZtlsVhalX/9618dPAMAAAAAZmaKw0mcTqdycnJUX1+vvn37hrS53W6lpqZq2LBhkiSHw6E33nhD69evP+B4r7zyis4555zg51NOOaVjAgcAAAAAmSRxy8rKUmJiojwej2bPnh2sb2xslNfrVUlJiSTpwQcflCRt3779oInbKaecot69e7f7/k1NTWpqagp+DgQChzsFAAAAACZmiq2SMTExKigokMfjkWEYwXqv16vm5mbl5+cf1niXX365evXqpYsuukjPPPPMIfsXFxfLZrMFi91uP+w5AAAAADAvUyRu0t4tkLW1taqsrAzWud1u5eTkyGaztWuM+Ph43XffffJ6vXr++ed10UUX6Yorrjhk8uZyueT3+4Olrq7uqOYCAAAAwFxMsVVSklJSUpSWlqbS0lKNHTtWNTU18vl8uuOOO9o9xqmnnqqbbrop+Pn888/Xli1bVFJSossvv/yA11mtVlmt1qOKHwAAAIB5mWbFTdp7SElFRYUaGhrkdruVnJys9PT0oxrzwgsvVE1NzTGKEAAAAABaM1Xilpubq6ioKJWXl6usrEwOh0MWi+WoxqyqqlKfPn2OUYQAAAAA0JpptkpKe59Ry8vLk8vlUiAQUGFhYUh7TU2NGhsbtW3bNu3atUtVVVWSpCFDhig2NlaPPfaYYmNjNWLECEnSsmXLVFpaqkcffbSTZwIAAADATEyVuEl7t0suXLhQEydOVFJSUkjb1KlTQw4v2Zegbdq0Sf3795ck/e53v9PHH3+smJgYpaSkaPHixbrqqqs6LX4AAAAA5mMx9j8fH50iEAjsfS3ArCWKsnYLdzhHZfO8SeEOAQAAADhu7csN/H6/EhISDtjPdCtukaR6bsZBfzgAAAAAIJnscBIAAAAAOB6RuAEAAABAhCNxAwAAAIAIxzNuYTR0zvLj+nASDiYBAAAAOgcrbgAAAAAQ4UjcAAAAACDCkbh9Jzs7W5mZmW22+Xw+WSwWrV+/XhaLpVX529/+1snRAgAAADATnnH7jtPpVE5Ojurr69W3b9+QNrfbrdTUVA0bNiz4ef8kr0ePHp0ZKgAAAACTYcXtO1lZWUpMTJTH4wmpb2xslNfrldPpDNb16NFDvXv3DpauXbt2crQAAAAAzITE7TsxMTEqKCiQx+ORYRjBeq/Xq+bmZuXn5wfrpk+frlNPPVUXXHCBSktLQ/q3pampSYFAIKQAAAAAQHuRuO3H4XCotrZWlZWVwTq3262cnBzZbDZJ0h133KElS5ZoxYoVysnJ0S9+8Qv98Y9/POi4xcXFstlswWK32zt0HgAAAABOLBbjUMtFJjN69GglJyerrKxMNTU1GjRokFatWqWxY8e22f+2226T2+1WXV3dAcdsampSU1NT8HMgEJDdbpd91hLe4wYAAACYWCAQkM1mk9/vV0JCwgH7seL2PU6nUxUVFWpoaJDb7VZycrLS09MP2P/CCy9UfX19SGL2fVarVQkJCSEFAAAAANqLxO17cnNzFRUVpfLycpWVlcnhcMhisRywf1VVlU4++WRZrdZOjBIAAACAmfA6gO+Jj49XXl6eXC6XAoGACgsLg23PPvusPvvsM/3whz9U165dtWLFCt111126+eabwxcwAAAAgBMeK25tcDqd2rFjhzIyMpSUlBSs79KlixYsWKBRo0Zp+PDheuSRR3T//fdrzpw5YYwWAAAAwImOFbc2jBo1qs0j/jMzM0NevA0AAAAAnYEVNwAAAACIcKy4hVH13AxOmAQAAABwSKy4AQAAAECEI3EDAAAAgAhH4gYAAAAAEY5n3MJo6JzlirJ2C3cYh2XzvEnhDgEAAAAwHVbcAAAAACDCkbgBAAAAQIQzReKWnZ19wBdn+3w+WSwWrVu3Tvn5+bLb7YqLi9PgwYM1f/78kL7Lli3TJZdcosTERCUkJGjUqFFavnx5Z0wBAAAAgImZInFzOp1asWKF6uvrW7W53W6lpqZq7dq16tWrlxYtWqQNGzbo1ltvlcvl0kMPPRTs+9prr+mSSy7RCy+8oLVr12rcuHHKzs7Wf/7zn86cDgAAAACTsRiGYYQ7iI62Z88e9e3bVzNmzNDs2bOD9Y2NjerTp49KSko0bdq0VtdNnz5d7777rlauXHnAsc855xzl5eXptttua3c8gUBANptN9llLOJwEAAAAMLF9uYHf71dCQsIB+5lixS0mJkYFBQXyeDzaP0/1er1qbm5Wfn5+m9f5/X717NnzgOO2tLSooaHhoH0kqampSYFAIKQAAAAAQHuZInGTJIfDodraWlVWVgbr3G63cnJyZLPZWvVfvXq1Fi9erKKiogOOee+996qxsVG5ubkHvXdxcbFsNluw2O32I58IAAAAANMxTeKWkpKitLQ0lZaWSpJqamrk8/nkdDpb9a2urtbkyZM1Z84cXXrppW2OV15errlz52rJkiXq1avXQe/tcrnk9/uDpa6u7ugnBAAAAMA0TJO4SXsPKamoqFBDQ4PcbreSk5OVnp4e0mfjxo2aMGGCioqKQp6H29/f/vY3TZ06VUuWLNHFF198yPtarVYlJCSEFAAAAABoL1Mlbrm5uYqKilJ5ebnKysrkcDhksViC7Rs2bNC4ceM0ZcoU3XnnnW2O8eSTT+raa6/Vk08+qUmTOKgDAAAAQMeLCXcAnSk+Pl55eXlyuVwKBAIqLCwMtlVXV2v8+PHKyMjQTTfdpG3btkmSoqOjlZiYKGnv9sgpU6Zo/vz5uvDCC4N94uLi2nxODgAAAACOBVOtuEl7t0vu2LFDGRkZSkpKCtYvXbpU27dv16JFi9SnT59gOf/884N9/vKXv2jPnj2aPn16SJ8bb7wxHFMBAAAAYBKmeI9bpOE9bgAAAAAk3uMGAAAAACcMUz3jFmmq52ZwwiQAAACAQ2LFDQAAAAAiHIkbAAAAAEQ4tkqG0dA5y4+bw0k4lAQAAAAIH1bcAAAAACDCkbgBAAAAQIQjcQMAAACACGeKxC07O1uZmZlttvl8PlksFq1bt075+fmy2+2Ki4vT4MGDNX/+/AOO+c9//lMxMTEaPnx4B0UNAAAAAHuZ4nASp9OpnJwc1dfXq2/fviFtbrdbqampWrt2rXr16qVFixbJbrdr9erVKioqUnR0tGbMmBFyzVdffaWCggJNmDBBn332WWdOBQAAAIAJmSJxy8rKUmJiojwej2bPnh2sb2xslNfrVUlJiRwOR8g1AwcO1Jo1a7Rs2bJWidu0adN0zTXXKDo6Wk899VRnTAEAAACAiZliq2RMTIwKCgrk8XhkGEaw3uv1qrm5Wfn5+W1e5/f71bNnz5A6t9utjz76SHPmzGn3/ZuamhQIBEIKAAAAALSXKRI3SXI4HKqtrVVlZWWwzu12KycnRzabrVX/1atXa/HixSoqKgrWffjhh/rNb36jRYsWKSam/YuVxcXFstlswWK3249uMgAAAABMxTSJW0pKitLS0lRaWipJqqmpkc/nk9PpbNW3urpakydP1pw5c3TppZdKkpqbm3XNNddo7ty5Ouussw7r3i6XS36/P1jq6uqOfkIAAAAATMM0iZu095CSiooKNTQ0yO12Kzk5Wenp6SF9Nm7cqAkTJqioqCjkebiGhga99dZbmjFjhmJiYhQTE6M77rhD69atU0xMjFauXHnA+1qtViUkJIQUAAAAAGgvUyVuubm5ioqKUnl5ucrKyuRwOGSxWILtGzZs0Lhx4zRlyhTdeeedIdcmJCTonXfeUVVVVbBMmzZNZ599tqqqqnThhRd29nQAAAAAmIQpTpXcJz4+Xnl5eXK5XAoEAiosLAy2VVdXa/z48crIyNBNN92kbdu2SZKio6OVmJioqKgoDR06NGS8Xr16qWvXrq3qAQAAAOBYMtWKm7R3u+SOHTuUkZGhpKSkYP3SpUu1fft2LVq0SH369AmW888/P4zRAgAAAIBkMfY/Hx+dIhAI7D1dctYSRVm7hTucdtk8b1K4QwAAAABOOPtyA7/ff9CzMEy1VTLSVM/N4KASAAAAAIdkuq2SAAAAAHC8IXEDAAAAgAhH4gYAAAAAEY5n3MJo6JzlEX04CQeSAAAAAJGBFTcAAAAAiHAkbgAAAAAQ4UjcAAAAACDCmSJxy87OVmZmZpttPp9PFotF69atU35+vux2u+Li4jR48GDNnz+/Vf8nnnhCP/jBD9StWzf16dNHDodDX3zxRUdPAQAAAICJmSJxczqdWrFiherr61u1ud1upaamau3aterVq5cWLVqkDRs26NZbb5XL5dJDDz0U7PvPf/5TBQUFcjqd2rBhg7xer/7973/ruuuu68zpAAAAADAZU5wqmZWVpcTERHk8Hs2ePTtY39jYKK/Xq5KSEjkcjpBrBg4cqDVr1mjZsmWaMWOGJGnNmjXq37+/Zs6cKUkaMGCAfv7zn+vuu+/uvMkAAAAAMB1TrLjFxMSooKBAHo9HhmEE671er5qbm5Wfn9/mdX6/Xz179gx+HjVqlOrq6vTCCy/IMAx99tlnWrp0qSZOnHjQ+zc1NSkQCIQUAAAAAGgvUyRukuRwOFRbW6vKyspgndvtVk5Ojmw2W6v+q1ev1uLFi1VUVBSsGz16tJ544gnl5eUpNjZWvXv3ls1m04IFCw567+LiYtlstmCx2+3HbmIAAAAATnimSdxSUlKUlpam0tJSSVJNTY18Pp+cTmervtXV1Zo8ebLmzJmjSy+9NFi/ceNG3Xjjjbrtttu0du1avfTSS9q8ebOmTZt20Hu7XC75/f5gqaurO7aTAwAAAHBCsxj77x08wZWWluqGG27Qtm3bNG/ePC1evFgffvihLBZLsM/GjRs1btw4TZ06VXfeeWfI9T/72c+0e/dueb3eYN3rr7+uMWPGaMuWLerTp0+74ggEAntX3mYtUZS127GZXAfYPG9SuEMAAAAATmj7cgO/36+EhIQD9jPNipsk5ebmKioqSuXl5SorK5PD4QhJ2jZs2KBx48ZpypQprZI2Sdq5c6eiokK/sujoaEmSifJfAAAAAJ3MFKdK7hMfH6+8vDy5XC4FAgEVFhYG26qrqzV+/HhlZGTopptu0rZt2yTtTcwSExMl7X0f3HXXXaeHH35YGRkZ2rp1q2bNmqULLrhASUlJ4ZgSAAAAABMw1YqbtPedbjt27FBGRkZIsrV06VJt375dixYtUp8+fYLl/PPPD/YpLCzU/fffr4ceekhDhw7V1VdfrbPPPlvLli0Lx1QAAAAAmISpnnGLFDzjBgAAAEBq/zNuptoqGWmq52Yc9IcDAAAAAJIJt0oCAAAAwPGGxA0AAAAAIhyJGwAAAABEOJ5xC6Ohc5ZH1OEkHEYCAAAARCZW3AAAAAAgwpG4AQAAAECEI3EDAAAAgAhnisQtOztbmZmZbbb5fD5ZLBatW7dO+fn5stvtiouL0+DBgzV//vyQvq+++qosFkursm3bts6YBgAAAACTMsXhJE6nUzk5Oaqvr1ffvn1D2txut1JTU7V27Vr16tVLixYtkt1u1+rVq1VUVKTo6GjNmDEj5Jr3338/5MXZvXr16pR5AAAAADAnUyRuWVlZSkxMlMfj0ezZs4P1jY2N8nq9KikpkcPhCLlm4MCBWrNmjZYtW9YqcevVq5d69OjR7vs3NTWpqakp+DkQCBzZRAAAAACYkim2SsbExKigoEAej0eGYQTrvV6vmpublZ+f3+Z1fr9fPXv2bFU/fPhw9enTR5dccon++c9/HvL+xcXFstlswWK32498MgAAAABMxxSJmyQ5HA7V1taqsrIyWOd2u5WTkyObzdaq/+rVq7V48WIVFRUF6/r06aM///nPqqioUEVFhex2u8aOHau33377oPd2uVzy+/3BUldXd+wmBgAAAOCEZzH2X4I6wY0ePVrJyckqKytTTU2NBg0apFWrVmns2LEh/aqrqzVu3DjdeOONIVsr25Kenq4zzjhDjz/+eLvjCAQCe1feZi3hBdwAAACAie3LDfx+f8g5Gt9nmhU3ae8hJRUVFWpoaJDb7VZycrLS09ND+mzcuFETJkxQUVHRIZM2SbrgggtUU1PTUSEDAAAAgLkSt9zcXEVFRam8vFxlZWVyOByyWCzB9g0bNmjcuHGaMmWK7rzzznaNWVVVpT59+nRUyAAAAABgjlMl94mPj1deXp5cLpcCgYAKCwuDbdXV1Ro/frwyMjJ00003Bd/NFh0drcTEREnSAw88oAEDBuicc87R7t279eijj2rlypV6+eWXwzEdAAAAACZhqhU3ae92yR07digjI0NJSUnB+qVLl2r79u1atGiR+vTpEyznn39+sM8333yjX/3qVzr33HOVnp6udevW6ZVXXtGECRPCMRUAAAAAJmGqw0kiBYeTAAAAAJDafziJqbZKRprquRkH/eEAAAAAgGTCrZIAAAAAcLwhcQMAAACACEfiBgAAAAARjmfcwmjonOURczgJB5MAAAAAkYsVNwAAAACIcCRuAAAAABDhTJG4ZWdnKzMzs802n88ni8WidevWKT8/X3a7XXFxcRo8eLDmz58f0vf111/X6NGjdcoppyguLk4pKSn6wx/+0BlTAAAAAGBipnjGzel0KicnR/X19erbt29Im9vtVmpqqtauXatevXpp0aJFstvtWr16tYqKihQdHa0ZM2ZIkrp3764ZM2Zo2LBh6t69u15//XX9/Oc/V/fu3VVUVBSOqQEAAAAwAYthGEa4g+hoe/bsUd++fTVjxgzNnj07WN/Y2Kg+ffqopKRE06ZNa3Xd9OnT9e6772rlypUHHPvKK69U9+7d9fjjj7c7nn1vR7fPWsLhJAAAAICJ7csN/H6/EhISDtjPFFslY2JiVFBQII/Ho/3zVK/Xq+bmZuXn57d5nd/vV8+ePQ847n/+8x+tXr1a6enpB71/U1OTAoFASAEAAACA9jJF4iZJDodDtbW1qqysDNa53W7l5OTIZrO16r969WotXry4zS2Qffv2ldVqVWpqqqZPn66pU6ce9N7FxcWy2WzBYrfbj35CAAAAAEzDNIlbSkqK0tLSVFpaKkmqqamRz+eT0+ls1be6ulqTJ0/WnDlzdOmll7Zq9/l8euutt/TnP/9ZDzzwgJ588smD3tvlcsnv9wdLXV3dsZkUAAAAAFMwxeEk+zidTt1www1asGCB3G63kpOTW21z3LhxoyZMmKCioqKQ5+H2N2DAAEnSueeeq88++0y33377AbdbSpLVapXVaj12EwEAAABgKqZZcZOk3NxcRUVFqby8XGVlZXI4HLJYLMH2DRs2aNy4cZoyZYruvPPOdo3Z0tKipqamjgoZAAAAAMy14hYfH6+8vDy5XC4FAgEVFhYG26qrqzV+/HhlZGTopptu0rZt2yRJ0dHRSkxMlCQtWLBAZ5xxhlJSUiRJr732mu69917NnDmz0+cCAAAAwDxMlbhJe7dLLly4UBMnTlRSUlKwfunSpdq+fbsWLVqkRYsWBev79eunzZs3S9q7uuZyubRp0ybFxMQoOTlZd999t37+85939jQAAAAAmIgp3uMWaXiPGwAAAACJ97gBAAAAwAnDdFslI0n13IyDZtUAAAAAILHiBgAAAAARj8QNAAAAACIciRsAAAAARDgSNwAAAACIcCRuAAAAABDhSNwAAAAAIMKRuAEAAABAhCNxAwAAAIAIR+IGAAAAABGOxA0AAAAAIhyJGwAAAABEOBI3AAAAAIhwJG4AAAAAEOFI3AAAAAAgwpG4AQAAAECEI3EDAAAAgAhH4gYAAAAAEY7EDQAAAAAiHIkbAAAAAEQ4EjcAAAAAiHAkbgAAAAAQ4UjcAAAAACDCkbgBAAAAQIQjcQMAAACACEfiBgAAAAARjsQNAAAAACJcTLgDMCPDMCRJgUAgzJEAAAAACKd9OcG+HOFASNzC4IsvvpAk2e32MEcCAAAAIBI0NDTIZrMdsJ3ELQx69uwpSfrkk08O+sPB0QkEArLb7aqrq1NCQkK4wzlh8T13PL7jzsH33Dn4njsH33Pn4HvuHCf692wYhhoaGpSUlHTQfiRuYRAVtffRQpvNdkL+8kWahIQEvudOwPfc8fiOOwffc+fge+4cfM+dg++5c5zI33N7FnM4nAQAAAAAIhyJGwAAAABEOBK3MLBarZozZ46sVmu4Qzmh8T13Dr7njsd33Dn4njsH33Pn4HvuHHzPnYPveS+LcahzJwEAAAAAYcWKGwAAAABEOBI3AAAA4P+3d+dRUdXvH8Dfw4wwwJEhQUAilhI3BBJBwqWxXPCYW5tWhtBiZZJYSbaopaaYiZqUS1rAKQvtZC5ZmQuYEkSxqCgpplkZyKlMJU1w5vn90XG+jqI/kbnMHXm/zpkjc+czH5/n7RzmPF7mQqRyHNyIiIiIiIhUjoMbERERERGRynFws4F33nkHwcHB0Ov1iI2NRVFR0RXXf/LJJ+jUqRP0ej3Cw8PxxRdfWD0uIpg2bRratWsHV1dX9O/fH5WVlUq24BBsmXN9fT0mT56M8PBwuLu7w9/fH2PGjMHvv/+udBuqZ+vX84WeeuopaDQaLFy40MZVOx4lcq6oqMCwYcNgMBjg7u6OmJgY/PLLL0q14BBsnXNtbS2Sk5MREBAAV1dXdOnSBUuXLlWyBYfQmJz37t2Le++9F8HBwVf8ftDYf7uWwNY5p6WlISYmBq1bt4aPjw9GjBiB/fv3K9iBY1Di9XzenDlzoNFoMHHiRNsW7WCUyPjo0aN4+OGH4eXlBVdXV4SHh+OHH35QqAM7EWqSnJwccXZ2lvfff1/27t0rY8eOFU9PTzl27FiD6/Pz80Wr1crcuXNl3759MmXKFGnVqpXs2bPHsmbOnDliMBhk7dq1smvXLhk2bJiEhITImTNnmqst1bF1zn///bf0799fVq1aJT/++KMUFBRIjx49pHv37s3Zluoo8Xo+b82aNRIZGSn+/v6yYMEChTtRNyVyPnjwoLRp00ZSU1OlpKREDh48KOvWrbvsni2BEjmPHTtWbrnlFsnNzZXDhw/LsmXLRKvVyrp165qrLdVpbM5FRUUyadIk+fjjj8XPz6/B7weN3bMlUCLn+Ph4yczMlPLycikrK5PBgwdLYGCg1NbWKtyNeimR84Vrg4ODJSIiQlJSUpRpwAEokfFff/0lQUFBkpSUJN99950cOnRINm3aJAcPHlS4m+bFwa2JevToIePHj7fcN5lM4u/vL2lpaQ2uHzlypNx1111Wx2JjY+XJJ58UERGz2Sx+fn7y5ptvWh7/+++/xcXFRT7++GMFOnAMts65IUVFRQJAjhw5YpuiHZBSOf/2229y4403Snl5uQQFBbX4wU2JnEeNGiUPP/ywMgU7KCVyDgsLkxkzZlitiYqKkldeecWGlTuWxuZ8oct9P2jKntcrJXK+WE1NjQCQ7du3N6VUh6ZUzqdOnZLQ0FDZvHmzGI3GFj24KZHx5MmTpXfv3rYsU5X4o5JNUFdXh+LiYvTv399yzMnJCf3790dBQUGDzykoKLBaDwDx8fGW9YcPH0Z1dbXVGoPBgNjY2Mvueb1TIueGnDhxAhqNBp6enjap29EolbPZbEZCQgJSU1MRFhamTPEORImczWYzNm7ciA4dOiA+Ph4+Pj6IjY3F2rVrFetD7ZR6Pffs2RPr16/H0aNHISLIzc3FgQMHMHDgQGUaUblrydkeezq65srkxIkTAIA2bdrYbE9HomTO48ePx1133XXJ95iWRqmM169fj+joaNx///3w8fFBt27dsHz5cluUrCoc3Jrgjz/+gMlkgq+vr9VxX19fVFdXN/ic6urqK64//2dj9rzeKZHzxf79919MnjwZDz74IDw8PGxTuINRKuc33ngDOp0OEyZMsH3RDkiJnGtqalBbW4s5c+Zg0KBB+Prrr3H33Xfjnnvuwfbt25VpROWUej1nZGSgS5cuCAgIgLOzMwYNGoR33nkHt99+u+2bcADXkrM99nR0zZGJ2WzGxIkT0atXL3Tt2tUmezoapXLOyclBSUkJ0tLSmlqiw1Mq40OHDmHJkiUIDQ3Fpk2bMG7cOEyYMAHZ2dlNLVlVdPYugMje6uvrMXLkSIgIlixZYu9yrivFxcV46623UFJSAo1GY+9yrltmsxkAMHz4cDz77LMAgFtvvRXffvstli5dCqPRaM/yrisZGRkoLCzE+vXrERQUhG+++Qbjx4+Hv79/i/+fdHJs48ePR3l5OXbu3GnvUq4rv/76K1JSUrB582bo9Xp7l3PdMpvNiI6OxuzZswEA3bp1Q3l5OZYuXYrExEQ7V2c7POPWBN7e3tBqtTh27JjV8WPHjsHPz6/B5/j5+V1x/fk/G7Pn9U6JnM87P7QdOXIEmzdvbrFn2wBlct6xYwdqamoQGBgInU4HnU6HI0eO4Pnnn0dwcLAifaidEjl7e3tDp9OhS5cuVms6d+7cYq8qqUTOZ86cwcsvv4z58+dj6NChiIiIQHJyMkaNGoV58+Yp04jKXUvO9tjT0SmdSXJyMj7//HPk5uYiICCgyfs5KiVyLi4uRk1NDaKioizvg9u3b8eiRYug0+lgMplsUbrDUOq13K5duxbxHsjBrQmcnZ3RvXt3bN261XLMbDZj69atiIuLa/A5cXFxVusBYPPmzZb1ISEh8PPzs1pz8uRJfPfdd5fd83qnRM7A/4a2yspKbNmyBV5eXso04CCUyDkhIQG7d+9GWVmZ5ebv74/U1FRs2rRJuWZUTImcnZ2dERMTc8llvA8cOICgoCAbd+AYlMi5vr4e9fX1cHKyfuvUarWWs54tzbXkbI89HZ1SmYgIkpOT8dlnn2Hbtm0ICQmxRbkOS4mc+/Xrhz179li9D0ZHR2P06NEoKyuDVqu1VfkOQanXcq9evVrGe6CdL47i8HJycsTFxUWysrJk37598sQTT4inp6dUV1eLiEhCQoK8+OKLlvX5+fmi0+lk3rx5UlFRIa+++mqDvw7A09NT1q1bJ7t375bhw4fz1wHYOOe6ujoZNmyYBAQESFlZmVRVVVluZ8+etUuPaqDE6/livKqkMjmvWbNGWrVqJe+++65UVlZKRkaGaLVa2bFjR7P3pxZK5Gw0GiUsLExyc3Pl0KFDkpmZKXq9XhYvXtzs/alFY3M+e/aslJaWSmlpqbRr104mTZokpaWlUllZedV7tkRK5Dxu3DgxGAySl5dn9T54+vTpZu9PLZTI+WIt/aqSSmRcVFQkOp1OZs2aJZWVlbJy5Upxc3OTDz/8sNn7UxIHNxvIyMiQwMBAcXZ2lh49ekhhYaHlMaPRKImJiVbrV69eLR06dBBnZ2cJCwuTjRs3Wj1uNptl6tSp4uvrKy4uLtKvXz/Zv39/c7SiarbM+fDhwwKgwVtubm4zdaROtn49X4yD23+UyPm9996T9u3bi16vl8jISFm7dq3SbaierXOuqqqSpKQk8ff3F71eLx07dpT09HQxm83N0Y5qNSbny33/NRqNV71nS2XrnC/3PpiZmdl8TamQEq/nC7X0wU1EmYw3bNggXbt2FRcXF+nUqZO8++67zdRN89GIiCh/Xo+IiIiIiIiuFT/jRkREREREpHIc3IiIiIiIiFSOgxsREREREZHKcXAjIiIiIiJSOQ5uREREREREKsfBjYiIiIiISOU4uBEREREREakcBzciIiIiIiKV4+BGRERERESkchzciIioWSUlJUGj0VxyO3jwoE32z8rKgqenp032ulZJSUkYMWKEXWu4kp9//hkajQZlZWX2LoWIiK6Szt4FEBFRyzNo0CBkZmZaHWvbtq2dqrm8+vp6tGrVyt5l2FRdXZ29SyAiomvAM25ERNTsXFxc4OfnZ3XTarUAgHXr1iEqKgp6vR4333wzpk+fjnPnzlmeO3/+fISHh8Pd3R033XQTnn76adTW1gIA8vLy8Mgjj+DEiROWM3mvvfYaAECj0WDt2rVWdXh6eiIrKwvA/85CrVq1CkajEXq9HitXrgQArFixAp07d4Zer0enTp2wePHiRvXbt29fPPPMM5g4cSJuuOEG+Pr6Yvny5fjnn3/wyCOPoHXr1mjfvj2+/PJLy3Py8vKg0WiwceNGREREQK/X47bbbkN5ebnV3p9++inCwsLg4uKC4OBgpKenWz0eHByMmTNnYsyYMfDw8MATTzyBkJAQAEC3bt2g0WjQt29fAMD333+PAQMGwNvbGwaDAUajESUlJVb7aTQarFixAnfffTfc3NwQGhqK9evXW63Zu3cvhgwZAg8PD7Ru3Rp9+vTBTz/9ZHm8qXkSEbVEHNyIiEg1duzYgTFjxiAlJQX79u3DsmXLkJWVhVmzZlnWODk5YdGiRdi7dy+ys7Oxbds2vPDCCwCAnj17YuHChfDw8EBVVRWqqqowadKkRtXw4osvIiUlBRUVFYiPj8fKlSsxbdo0zJo1CxUVFZg9ezamTp2K7OzsRu2bnZ0Nb29vFBUV4ZlnnsG4ceNw//33o2fPnigpKcHAgQORkJCA06dPWz0vNTUV6enp+P7779G2bVsMHToU9fX1AIDi4mKMHDkSDzzwAPbs2YPXXnsNU6dOtQyj582bNw+RkZEoLS3F1KlTUVRUBADYsmULqqqqsGbNGgDAqVOnkJiYiJ07d6KwsBChoaEYPHgwTp06ZbXf9OnTMXLkSOzevRuDBw/G6NGj8ddffwEAjh49ittvvx0uLi7Ytm0biouL8eijj1qGb1vlSUTU4ggREVEzSkxMFK1WK+7u7pbbfffdJyIi/fr1k9mzZ1ut/+CDD6Rdu3aX3e+TTz4RLy8vy/3MzEwxGAyXrAMgn332mdUxg8EgmZmZIiJy+PBhASALFy60WnPLLbfIRx99ZHVs5syZEhcXd8Uehw8fbrlvNBqld+/elvvnzp0Td3d3SUhIsByrqqoSAFJQUCAiIrm5uQJAcnJyLGv+/PNPcXV1lVWrVomIyEMPPSQDBgyw+rtTU1OlS5culvtBQUEyYsQIqzXney0tLb1sDyIiJpNJWrduLRs2bLAcAyBTpkyx3K+trRUA8uWXX4qIyEsvvSQhISFSV1fX4J7XkicREYnwM25ERNTs7rjjDixZssRy393dHQCwa9cu5OfnW51hM5lM+Pfff3H69Gm4ublhy5YtSEtLw48//oiTJ0/i3LlzVo83VXR0tOXrf/75Bz/99BMee+wxjB071nL83LlzMBgMjdo3IiLC8rVWq4WXlxfCw8Mtx3x9fQEANTU1Vs+Li4uzfN2mTRt07NgRFRUVAICKigoMHz7can2vXr2wcOFCmEwmy4+fXtjTlRw7dgxTpkxBXl4eampqYDKZcPr0afzyyy+X7cXd3R0eHh6WusvKytCnT58GPxtoyzyJiFoaDm5ERNTs3N3d0b59+0uO19bWYvr06bjnnnsueUyv1+Pnn3/GkCFDMG7cOMyaNQtt2rTBzp078dhjj6Guru6Kg5tGo4GIWB07/yOHF9d2YT0AsHz5csTGxlqtOz8UXa2LBxmNRmN1TKPRAADMZnOj9r0aF/Z0JYmJifjzzz/x1ltvISgoCC4uLoiLi7vkgiYN9XK+bldX18vub8s8iYhaGg5uRESkGlFRUdi/f3+DQx3w32e6zGYz0tPT4eT038e0V69ebbXG2dkZJpPpkue2bdsWVVVVlvuVlZWXfJ7sYr6+vvD398ehQ4cwevToxrZjE4WFhQgMDAQAHD9+HAcOHEDnzp0BAJ07d0Z+fr7V+vz8fHTo0OGKg5CzszMAXJJTfn4+Fi9ejMGDBwMAfv31V/zxxx+NqjciIgLZ2dkNXpFTDXkSETkqDm5ERKQa06ZNw5AhQxAYGIj77rsPTk5O2LVrF8rLy/H666+jffv2qK+vR0ZGBoYOHYr8/HwsXbrUao/g4GDU1tZi69atiIyMhJubG9zc3HDnnXfi7bffRlxcHEwmEyZPnnxVl/qfPn06JkyYAIPBgEGDBuHs2bP44YcfcPz4cTz33HNKRWExY8YMeHl5wdfXF6+88gq8vb0tvyPu+eefR0xMDGbOnIlRo0ahoKAAb7/99v97lUYfHx+4urriq6++QkBAAPR6PQwGA0JDQ/HBBx8gOjoaJ0+eRGpq6hXPoDUkOTkZGRkZeOCBB/DSSy/BYDCgsLAQPXr0QMeOHe2eJxGRo+JVJYmISDXi4+Px+eef4+uvv0ZMTAxuu+02LFiwAEFBQQCAyMhIzJ8/H2+88Qa6du2KlStXIi0tzWqPnj174qmnnsKoUaPQtm1bzJ07FwCQnp6Om266CX369MFDDz2ESZMmXdVn4h5//HGsWLECmZmZCA8Ph9FoRFZWluWS+kqbM2cOUlJS0L17d1RXV2PDhg2WM2ZRUVFYvXo1cnJy0LVrV0ybNg0zZsxAUlLSFffU6XRYtGgRli1bBn9/f8vn5N577z0cP34cUVFRSEhIwIQJE+Dj49Ooer28vLBt2zbU1tbCaDSie/fuWL58uWVItneeRESOSiMX/8A/ERER2V1eXh7uuOMOHD9+HJ6envYuh4iI7Ixn3IiIiIiIiFSOgxsREREREZHK8UcliYiIiIiIVI5n3IiIiIiIiFSOgxsREREREZHKcXAjIiIiIiJSOQ5uREREREREKsfBjYiIiIiISOU4uBEREREREakcBzciIiIiIiKV4+BGRERERESkcv8HYIftc1h7n9EAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Split the data into features (X) and target variable (y)\n",
    "X = df.drop('Class', axis=1)\n",
    "y = df['Class']\n",
    "\n",
    "# Initialize RandomForestClassifier\n",
    "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# Fit the model to the data\n",
    "rf_model.fit(X, y)\n",
    "\n",
    "# Get feature importances\n",
    "feature_importances = rf_model.feature_importances_\n",
    "\n",
    "# Sort feature importances in descending order\n",
    "sorted_indices = np.argsort(feature_importances)[::-1]\n",
    "\n",
    "# Get all features and their importances\n",
    "all_features = X.columns[sorted_indices]\n",
    "all_importances = feature_importances[sorted_indices]\n",
    "\n",
    "# Plotting Random Forest feature importance for all features\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.barh(range(len(all_features)), all_importances, align='center')\n",
    "plt.yticks(range(len(all_features)), all_features)\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.ylabel('Feature')\n",
    "plt.title('Random Forest Feature Importance')\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to have the most important feature at the top\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.inspection import permutation_importance\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Split the data into features (X) and target variable (y)\n",
    "X = df.drop('Class', axis=1)\n",
    "y = df['Class']\n",
    "\n",
    "# Initialize your model (e.g., RandomForestClassifier, DecisionTreeClassifier, etc.)\n",
    "# Replace the following lines with the initialization of your model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# Fit the model to the data\n",
    "model.fit(X, y)\n",
    "\n",
    "# Perform permutation feature importance\n",
    "perm_importance = permutation_importance(model, X, y, n_repeats=10, random_state=42)\n",
    "perm_feature_importances = perm_importance.importances_mean\n",
    "\n",
    "# Sort feature importances in descending order\n",
    "sorted_indices = np.argsort(perm_feature_importances)[::-1]\n",
    "\n",
    "# Get all features and their importances\n",
    "all_features = X.columns[sorted_indices]\n",
    "all_importances = perm_feature_importances[sorted_indices]\n",
    "\n",
    "# Plotting permutation feature importance for all features\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.barh(range(len(all_features)), all_importances, align='center')\n",
    "plt.yticks(range(len(all_features)), all_features)\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.ylabel('Feature')\n",
    "plt.title('Permutation Feature Importance')\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to have the most important feature at the top\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Split the data into features (X) and target variable (y)\n",
    "X = df.drop('Class', axis=1)\n",
    "y = df['Class']\n",
    "\n",
    "# Initialize XGBoost Classifier\n",
    "xgb_model = xgb.XGBClassifier(random_state=42)\n",
    "\n",
    "# Fit the model to the data\n",
    "xgb_model.fit(X, y)\n",
    "\n",
    "# Get feature importances\n",
    "feature_importances = xgb_model.feature_importances_\n",
    "\n",
    "# Sort feature importances in descending order\n",
    "sorted_indices = np.argsort(feature_importances)[::-1]\n",
    "\n",
    "# Get all features and their importances\n",
    "all_features = X.columns[sorted_indices]\n",
    "all_importances = feature_importances[sorted_indices]\n",
    "\n",
    "# Plotting XGBoost feature importance for all features\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.barh(range(len(all_features)), all_importances, align='center')\n",
    "plt.yticks(range(len(all_features)), all_features)\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.ylabel('Feature')\n",
    "plt.title('XGBoost Feature Importance')\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to have the most important feature at the top\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10  ...       V21       V22       V23       V24  \\\n",
       "0  0.098698  0.363787  0.090794  ... -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425 -0.166974  ... -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  0.207643  ...  0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024 -0.054952  ... -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  0.753074  ... -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Omit the 'Time' column from the DataFrame\n",
    "df = df.drop(columns=['Time'])\n",
    "\n",
    "# Display the first few rows of the DataFrame without the 'Time' column\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Features (Manual Reordering):\n",
      "   Class        V1        V2        V3        V4        V5        V6  \\\n",
      "0      0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388   \n",
      "1      0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
      "2      0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499   \n",
      "3      0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203   \n",
      "4      0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921   \n",
      "\n",
      "         V7        V9       V10       V11       V12       V14       V16  \\\n",
      "0  0.239599  0.363787  0.090794 -0.551600 -0.617801 -0.311169 -0.470401   \n",
      "1 -0.078803 -0.255425 -0.166974  1.612727  1.065235 -0.143772  0.463917   \n",
      "2  0.791461 -1.514654  0.207643  0.624501  0.066084 -0.165946 -2.890083   \n",
      "3  0.237609 -1.387024 -0.054952 -0.226487  0.178228 -0.287924 -1.059647   \n",
      "4  0.592941  0.817739  0.753074 -0.822843  0.538196 -1.119670 -0.451449   \n",
      "\n",
      "        V17       V18       V19       V21  \n",
      "0  0.207971  0.025791  0.403993 -0.018307  \n",
      "1 -0.114805 -0.183361 -0.145783 -0.225775  \n",
      "2  1.109969 -0.121359 -2.261857  0.247998  \n",
      "3 -0.684093  1.965775 -1.232622 -0.108300  \n",
      "4 -0.237033 -0.038195  0.803487 -0.009431  \n"
     ]
    }
   ],
   "source": [
    "# Assuming your DataFrame is named df_selected_features\n",
    "# If not, replace df_selected_features with the name of your DataFrame\n",
    "\n",
    "# Selected features\n",
    "selected_features = ['Class','V1', 'V2', 'V3','V4','V5','V6','V7','V9','V10','V11','V12','V14','V16','V17','V18','V19','V21']\n",
    "df_selected_features = df[selected_features]\n",
    "\n",
    "\n",
    "# Display the selected features with manual reordering\n",
    "print(\"Selected Features (Manual Reordering):\")\n",
    "print(df_selected_features.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Features (Manual Reordering):\n",
      "   Class        V1        V2        V3        V4        V5        V6  \\\n",
      "0      0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388   \n",
      "1      0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
      "2      0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499   \n",
      "3      0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203   \n",
      "4      0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921   \n",
      "\n",
      "         V7        V9       V10       V11       V12       V14       V16  \\\n",
      "0  0.239599  0.363787  0.090794 -0.551600 -0.617801 -0.311169 -0.470401   \n",
      "1 -0.078803 -0.255425 -0.166974  1.612727  1.065235 -0.143772  0.463917   \n",
      "2  0.791461 -1.514654  0.207643  0.624501  0.066084 -0.165946 -2.890083   \n",
      "3  0.237609 -1.387024 -0.054952 -0.226487  0.178228 -0.287924 -1.059647   \n",
      "4  0.592941  0.817739  0.753074 -0.822843  0.538196 -1.119670 -0.451449   \n",
      "\n",
      "        V17       V18       V19       V21  \n",
      "0  0.207971  0.025791  0.403993 -0.018307  \n",
      "1 -0.114805 -0.183361 -0.145783 -0.225775  \n",
      "2  1.109969 -0.121359 -2.261857  0.247998  \n",
      "3 -0.684093  1.965775 -1.232622 -0.108300  \n",
      "4 -0.237033 -0.038195  0.803487 -0.009431  \n"
     ]
    }
   ],
   "source": [
    "# Assuming your DataFrame is named df_selected_features\n",
    "# If not, replace df_selected_features with the name of your DataFrame\n",
    "\n",
    "# Manually reorder the columns as needed\n",
    "df_selected_features = df_selected_features[['Class', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V9', 'V10', 'V11', 'V12', 'V14', 'V16', 'V17', 'V18', 'V19', 'V21']]\n",
    "\n",
    "# Display the selected features with manual reordering\n",
    "print(\"Selected Features (Manual Reordering):\")\n",
    "print(df_selected_features.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Features:\n",
      "Index(['Class', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V9', 'V10', 'V11',\n",
      "       'V12', 'V14', 'V16', 'V17', 'V18', 'V19', 'V21'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Display the selected features\n",
    "print(\"Selected Features:\")\n",
    "print(df_selected_features.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 18 Features and Their Importance:\n",
      "Class: 1.0\n",
      "V17: 0.3264810672437159\n",
      "V14: 0.3025436958044029\n",
      "V12: 0.2605929248772249\n",
      "V10: 0.21688294364103206\n",
      "V16: 0.19653894030401736\n",
      "V3: 0.192960827067416\n",
      "V7: 0.18725659151430013\n",
      "V11: 0.15487564474394433\n",
      "V4: 0.13344748623900715\n",
      "V18: 0.11148525388904133\n",
      "V1: 0.10134729859508294\n",
      "V9: 0.0977326860740807\n",
      "V5: 0.094974298991448\n",
      "V2: 0.09128865034461789\n",
      "V6: 0.04364316069996494\n",
      "V21: 0.040413380610575665\n",
      "V19: 0.03478301303651506\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt  # Importing matplotlib.pyplot\n",
    "\n",
    "# Calculate feature importance (you can replace this with any feature selection method)\n",
    "correlation_matrix = df_selected_features.corr()\n",
    "feature_importance = correlation_matrix['Class'].abs().sort_values(ascending=False)\n",
    "\n",
    "# Plot the correlation matrix\n",
    "plt.figure(figsize=(16, 12))\n",
    "plt.title('Credit Card Transactions Features Correlation Plot (Pearson)')\n",
    "\n",
    "# Create a mask to hide the upper triangle of the plot\n",
    "mask = np.triu(np.ones_like(correlation_matrix, dtype=bool))\n",
    "\n",
    "# Use a diverging color palette and annotate the values\n",
    "sns.heatmap(correlation_matrix, annot=True, fmt=\".2f\", cmap=sns.diverging_palette(220, 10, as_cmap=True), linewidths=0.1, mask=mask)\n",
    "\n",
    "# Sort features based on their importance\n",
    "sorted_features = feature_importance.index[:18]  # Select top 15 features\n",
    "\n",
    "# Display top features and their importance\n",
    "print(\"Top 18 Features and Their Importance:\")\n",
    "for feature in sorted_features:\n",
    "    print(f\"{feature}: {feature_importance[feature]}\")\n",
    "\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Features:\n",
      "Index(['Class', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V9', 'V10', 'V11',\n",
      "       'V12', 'V14', 'V16', 'V17', 'V18', 'V19', 'V21'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Display the selected features\n",
    "print(\"Selected Features:\")\n",
    "print(df_selected_features.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DT "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC-ROC Score: 0.9079842667933803\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Evaluation:\n",
      "Accuracy: 0.9995259997893332\n",
      "Precision: 0.898876404494382\n",
      "Recall: 0.8163265306122449\n",
      "F1 Score: 0.8556149732620321\n",
      "\n",
      "Confusion Matrix:\n",
      "[[56855     9]\n",
      " [   18    80]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56864\n",
      "           1       0.90      0.82      0.86        98\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.95      0.91      0.93     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      "\n",
      "\n",
      "Cross-Validation Scores: [0.99894667 0.99949089 0.99929777 0.99942066 0.99938554]\n",
      "Mean Cross-Validation Score: 0.9993083041465042\n",
      "CPU times: total: 42.6 s\n",
      "Wall time: 44 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import roc_auc_score, roc_curve, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Assuming df_selected_features contains the selected features and the target variable 'Class'\n",
    "\n",
    "# Split the data into features (X) and target variable (y)\n",
    "X = df_selected_features.drop('Class', axis=1)\n",
    "y = df_selected_features['Class']\n",
    "\n",
    "# Initialize the DecisionTreeClassifier with specified hyperparameters\n",
    "tree_params = {\n",
    "    'max_depth': 70,\n",
    "    'min_samples_split': 8,\n",
    "    'min_samples_leaf': 10,\n",
    "    'criterion': \"entropy\"\n",
    "}\n",
    "clf = DecisionTreeClassifier(**tree_params)\n",
    "\n",
    "# Perform 5-fold cross-validation and compute cross-validation scores\n",
    "cv_scores = cross_val_score(clf, X, y, cv=5)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Train the model\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Predict the probabilities for the positive class\n",
    "y_probs = clf.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Calculate AUC-ROC\n",
    "auc_roc = roc_auc_score(y_test, y_probs)\n",
    "print(\"AUC-ROC Score:\", auc_roc)\n",
    "\n",
    "# Plot ROC Curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_probs)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, label='ROC Curve (AUC = {:.2f})'.format(auc_roc))\n",
    "plt.plot([0, 1], [0, 1], 'k--')  # Random guessing line\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Predict the classes for the test set\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "print(\"\\nModel Evaluation:\")\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 Score:\", f1)\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(\"\\nConfusion Matrix:\")\n",
    "print(conf_matrix)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix, annot=True, cmap='Blues', fmt='g', cbar=False)\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.title('Confusion Matrix (Decision Tree)')  # Updated title with algorithm name\n",
    "plt.show()\n",
    "\n",
    "# Classification Report\n",
    "class_report = classification_report(y_test, y_pred)\n",
    "print(\"\\nClassification Report:\")\n",
    "print(class_report)\n",
    "\n",
    "# Display cross-validation scores\n",
    "print(\"\\nCross-Validation Scores:\", cv_scores)\n",
    "print(\"Mean Cross-Validation Score:\", cv_scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training data (X_train): (227845, 17)\n",
      "Shape of testing data (X_test): (56962, 17)\n",
      "Length of each sample: 17\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Assuming X_train is your input training data\n",
    "# Assuming X_test is your input testing data\n",
    "\n",
    "# Check the shape of the training data\n",
    "print(\"Shape of training data (X_train):\", X_train.shape)\n",
    "\n",
    "# Check the shape of the testing data\n",
    "print(\"Shape of testing data (X_test):\", X_test.shape)\n",
    "\n",
    "# Assuming the second dimension corresponds to the length of each sample\n",
    "length_of_sample = X_train.shape[1]\n",
    "print(\"Length of each sample:\", length_of_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation Scores (5-fold): [0.99472594 0.96406781 0.94257483 0.99494578 0.97766687]\n",
      "AUC-ROC Score: 0.9725154288642863\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Random Forest Model Evaluation:\n",
      "Accuracy: 0.9995611109160493\n",
      "Precision: 0.974025974025974\n",
      "Recall: 0.7653061224489796\n",
      "F1 Score: 0.8571428571428571\n",
      "\n",
      "Confusion Matrix:\n",
      "[[56862     2]\n",
      " [   23    75]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56864\n",
      "           1       0.97      0.77      0.86        98\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.99      0.88      0.93     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      "\n",
      "CPU times: total: 25min 6s\n",
      "Wall time: 25min 48s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Split the data into features (X) and target variable (y)\n",
    "X = df_selected_features.drop('Class', axis=1)\n",
    "y = df_selected_features['Class']\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize the RandomForestClassifier with specified hyperparameters\n",
    "rf_params = {\n",
    "    'n_estimators': 100,          # Number of trees in the forest\n",
    "    'max_depth': 10,              # Maximum depth of each tree\n",
    "    'min_samples_split': 2,       # Minimum samples required to split an internal node\n",
    "    'min_samples_leaf': 1,        # Minimum samples required at a leaf node\n",
    "    'random_state': 42            # Random state for reproducibility\n",
    "}\n",
    "clf_rf = RandomForestClassifier(**rf_params)\n",
    "\n",
    "# Cross-validation scores\n",
    "cv_scores = cross_val_score(clf_rf, X, y, cv=5, scoring='roc_auc')\n",
    "print(\"Cross-validation Scores (5-fold):\", cv_scores)\n",
    "\n",
    "# Train the model\n",
    "clf_rf.fit(X_train, y_train)\n",
    "\n",
    "# Predict the probabilities for the positive class\n",
    "y_probs_rf = clf_rf.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Calculate AUC-ROC\n",
    "auc_roc_rf = roc_auc_score(y_test, y_probs_rf)\n",
    "print(\"AUC-ROC Score:\", auc_roc_rf)\n",
    "\n",
    "# Plot ROC Curve\n",
    "fpr_rf, tpr_rf, thresholds_rf = roc_curve(y_test, y_probs_rf)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr_rf, tpr_rf, label='ROC Curve (AUC = {:.2f})'.format(auc_roc_rf))\n",
    "plt.plot([0, 1], [0, 1], 'k--')  # Random guessing line\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve - Random Forest Classifier')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Predict the classes for the test set\n",
    "y_pred_rf = clf_rf.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "accuracy_rf = accuracy_score(y_test, y_pred_rf)\n",
    "precision_rf = precision_score(y_test, y_pred_rf)\n",
    "recall_rf = recall_score(y_test, y_pred_rf)\n",
    "f1_rf = f1_score(y_test, y_pred_rf)\n",
    "\n",
    "print(\"\\nRandom Forest Model Evaluation:\")\n",
    "print(\"Accuracy:\", accuracy_rf)\n",
    "print(\"Precision:\", precision_rf)\n",
    "print(\"Recall:\", recall_rf)\n",
    "print(\"F1 Score:\", f1_rf)\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix_rf = confusion_matrix(y_test, y_pred_rf)\n",
    "print(\"\\nConfusion Matrix:\")\n",
    "print(conf_matrix_rf)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix_rf, annot=True, cmap='Blues', fmt='g', cbar=False)\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.title('Confusion Matrix - Random Forest')\n",
    "plt.show()\n",
    "\n",
    "# Classification Report\n",
    "class_report_rf = classification_report(y_test, y_pred_rf)\n",
    "print(\"\\nClassification Report:\")\n",
    "print(class_report_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC-ROC Score: 0.9512967926337672\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SVM Model Evaluation:\n",
      "Accuracy: 0.999385555282469\n",
      "Precision: 0.9701492537313433\n",
      "Recall: 0.6632653061224489\n",
      "F1 Score: 0.7878787878787878\n",
      "\n",
      "Confusion Matrix:\n",
      "[[56862     2]\n",
      " [   33    65]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56864\n",
      "           1       0.97      0.66      0.79        98\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.98      0.83      0.89     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      "\n",
      "\n",
      "Cross-Validation Scores: [0.99875356 0.99908711 0.99899932 0.99887642 0.99922754]\n",
      "Mean Cross-Validation Score: 0.9989887893798715\n",
      "CPU times: total: 13min 55s\n",
      "Wall time: 14min 31s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Split the data into features (X) and target variable (y)\n",
    "X = df_selected_features.drop('Class', axis=1)\n",
    "y = df_selected_features['Class']\n",
    "\n",
    "# Initialize the SVC (Support Vector Classifier) with specified hyperparameters\n",
    "svm_params = {\n",
    "    'kernel': 'rbf',      # Kernel type (you can try different kernels)\n",
    "    'C': 1.0,             # Regularization parameter\n",
    "    'gamma': 'scale',     # Kernel coefficient (auto, scale, or a float)\n",
    "    'random_state': 42    # Random state for reproducibility\n",
    "}\n",
    "clf_svm = SVC(**svm_params)\n",
    "\n",
    "# Perform 5-fold cross-validation and compute cross-validation scores\n",
    "cv_scores_svm = cross_val_score(clf_svm, X, y, cv=5)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Train the model\n",
    "clf_svm.fit(X_train, y_train)\n",
    "\n",
    "# Predict the probabilities for the positive class\n",
    "y_probs_svm = clf_svm.decision_function(X_test)\n",
    "\n",
    "# Calculate AUC-ROC\n",
    "auc_roc_svm = roc_auc_score(y_test, y_probs_svm)\n",
    "print(\"AUC-ROC Score:\", auc_roc_svm)\n",
    "\n",
    "# Plot ROC Curve\n",
    "fpr_svm, tpr_svm, thresholds_svm = roc_curve(y_test, y_probs_svm)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr_svm, tpr_svm, label='ROC Curve (AUC = {:.2f})'.format(auc_roc_svm))\n",
    "plt.plot([0, 1], [0, 1], 'k--')  # Random guessing line\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve - SVM')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Predict the classes for the test set\n",
    "y_pred_svm = clf_svm.predict(X_test)\n",
    "\n",
    "# Evaluate the SVM model\n",
    "accuracy_svm = accuracy_score(y_test, y_pred_svm)\n",
    "precision_svm = precision_score(y_test, y_pred_svm)\n",
    "recall_svm = recall_score(y_test, y_pred_svm)\n",
    "f1_svm = f1_score(y_test, y_pred_svm)\n",
    "\n",
    "print(\"\\nSVM Model Evaluation:\")\n",
    "print(\"Accuracy:\", accuracy_svm)\n",
    "print(\"Precision:\", precision_svm)\n",
    "print(\"Recall:\", recall_svm)\n",
    "print(\"F1 Score:\", f1_svm)\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix_svm = confusion_matrix(y_test, y_pred_svm)\n",
    "print(\"\\nConfusion Matrix:\")\n",
    "print(conf_matrix_svm)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix_svm, annot=True, cmap='Blues', fmt='g', cbar=False)\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.title('Confusion Matrix - SVM')\n",
    "plt.show()\n",
    "\n",
    "# Classification Report\n",
    "class_report_svm = classification_report(y_test, y_pred_svm)\n",
    "print(\"\\nClassification Report:\")\n",
    "print(class_report_svm)\n",
    "\n",
    "# Display cross-validation scores\n",
    "print(\"\\nCross-Validation Scores:\", cv_scores_svm)\n",
    "print(\"Mean Cross-Validation Score:\", cv_scores_svm.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC-ROC Score: 0.9783308617481883\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "XGBoost Model Evaluation:\n",
      "Accuracy: 0.9995435553526912\n",
      "Precision: 0.95\n",
      "Recall: 0.7755102040816326\n",
      "F1 Score: 0.8539325842696629\n",
      "\n",
      "Confusion Matrix:\n",
      "[[56860     4]\n",
      " [   22    76]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56864\n",
      "           1       0.95      0.78      0.85        98\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.97      0.89      0.93     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      "\n",
      "\n",
      "Cross-Validation Scores: [0.99899933 0.99954356 0.99913976 0.99957866 0.99943821]\n",
      "Mean Cross-Validation Score: 0.9993399043454707\n",
      "CPU times: total: 48.3 s\n",
      "Wall time: 7.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Split the data into features (X) and target variable (y)\n",
    "X = df_selected_features.drop('Class', axis=1)\n",
    "y = df_selected_features['Class']\n",
    "\n",
    "# Initialize the XGBoost classifier with specified hyperparameters\n",
    "xgb_params = {\n",
    "    'objective': 'binary:logistic',  # Objective function\n",
    "    'eval_metric': 'logloss',         # Evaluation metric\n",
    "    'eta': 0.1,                       # Learning rate\n",
    "    'max_depth': 6,                   # Maximum depth of each tree\n",
    "    'subsample': 0.8,                 # Subsample ratio of the training instances\n",
    "    'colsample_bytree': 0.8,          # Subsample ratio of columns when constructing each tree\n",
    "    'seed': 42                        # Random seed for reproducibility\n",
    "}\n",
    "clf_xgb = xgb.XGBClassifier(**xgb_params)\n",
    "\n",
    "# Perform 5-fold cross-validation and compute cross-validation scores\n",
    "cv_scores_xgb = cross_val_score(clf_xgb, X, y, cv=5)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Train the model\n",
    "clf_xgb.fit(X_train, y_train)\n",
    "\n",
    "# Predict the probabilities for the positive class\n",
    "y_probs_xgb = clf_xgb.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Calculate AUC-ROC\n",
    "auc_roc_xgb = roc_auc_score(y_test, y_probs_xgb)\n",
    "print(\"AUC-ROC Score:\", auc_roc_xgb)\n",
    "\n",
    "# Plot ROC Curve\n",
    "fpr_xgb, tpr_xgb, thresholds_xgb = roc_curve(y_test, y_probs_xgb)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr_xgb, tpr_xgb, label='ROC Curve (AUC = {:.2f})'.format(auc_roc_xgb))\n",
    "plt.plot([0, 1], [0, 1], 'k--')  # Random guessing line\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve - XGBoost Classifier')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Predict the classes for the test set\n",
    "y_pred_xgb = clf_xgb.predict(X_test)\n",
    "\n",
    "# Evaluate the XGBoost model\n",
    "accuracy_xgb = accuracy_score(y_test, y_pred_xgb)\n",
    "precision_xgb = precision_score(y_test, y_pred_xgb)\n",
    "recall_xgb = recall_score(y_test, y_pred_xgb)\n",
    "f1_xgb = f1_score(y_test, y_pred_xgb)\n",
    "\n",
    "print(\"\\nXGBoost Model Evaluation:\")\n",
    "print(\"Accuracy:\", accuracy_xgb)\n",
    "print(\"Precision:\", precision_xgb)\n",
    "print(\"Recall:\", recall_xgb)\n",
    "print(\"F1 Score:\", f1_xgb)\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix_xgb = confusion_matrix(y_test, y_pred_xgb)\n",
    "print(\"\\nConfusion Matrix:\")\n",
    "print(conf_matrix_xgb)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix_xgb, annot=True, cmap='Blues', fmt='g', cbar=False)\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.title('Confusion Matrix - XGBoost')\n",
    "plt.show()\n",
    "\n",
    "# Classification Report\n",
    "class_report_xgb = classification_report(y_test, y_pred_xgb)\n",
    "print(\"\\nClassification Report:\")\n",
    "print(class_report_xgb)\n",
    "\n",
    "# Display cross-validation scores\n",
    "print(\"\\nCross-Validation Scores:\", cv_scores_xgb)\n",
    "print(\"Mean Cross-Validation Score:\", cv_scores_xgb.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.6259283\ttotal: 38.6ms\tremaining: 7.3s\n",
      "1:\tlearn: 0.5674868\ttotal: 69.5ms\tremaining: 6.53s\n",
      "2:\tlearn: 0.5162636\ttotal: 97.8ms\tremaining: 6.09s\n",
      "3:\tlearn: 0.4708967\ttotal: 126ms\tremaining: 5.87s\n",
      "4:\tlearn: 0.4305669\ttotal: 156ms\tremaining: 5.77s\n",
      "5:\tlearn: 0.3944602\ttotal: 184ms\tremaining: 5.64s\n",
      "6:\tlearn: 0.3621179\ttotal: 217ms\tremaining: 5.66s\n",
      "7:\tlearn: 0.3329875\ttotal: 247ms\tremaining: 5.62s\n",
      "8:\tlearn: 0.3066159\ttotal: 280ms\tremaining: 5.62s\n",
      "9:\tlearn: 0.2825895\ttotal: 312ms\tremaining: 5.62s\n",
      "10:\tlearn: 0.2607263\ttotal: 342ms\tremaining: 5.57s\n",
      "11:\tlearn: 0.2407739\ttotal: 370ms\tremaining: 5.48s\n",
      "12:\tlearn: 0.2225690\ttotal: 397ms\tremaining: 5.4s\n",
      "13:\tlearn: 0.2059036\ttotal: 426ms\tremaining: 5.36s\n",
      "14:\tlearn: 0.1906370\ttotal: 457ms\tremaining: 5.33s\n",
      "15:\tlearn: 0.1766453\ttotal: 488ms\tremaining: 5.31s\n",
      "16:\tlearn: 0.1637938\ttotal: 519ms\tremaining: 5.28s\n",
      "17:\tlearn: 0.1519300\ttotal: 551ms\tremaining: 5.26s\n",
      "18:\tlearn: 0.1409928\ttotal: 578ms\tremaining: 5.2s\n",
      "19:\tlearn: 0.1309283\ttotal: 607ms\tremaining: 5.16s\n",
      "20:\tlearn: 0.1216261\ttotal: 634ms\tremaining: 5.1s\n",
      "21:\tlearn: 0.1130461\ttotal: 661ms\tremaining: 5.04s\n",
      "22:\tlearn: 0.1051683\ttotal: 692ms\tremaining: 5.03s\n",
      "23:\tlearn: 0.0978527\ttotal: 725ms\tremaining: 5.01s\n",
      "24:\tlearn: 0.0910676\ttotal: 754ms\tremaining: 4.97s\n",
      "25:\tlearn: 0.0847979\ttotal: 780ms\tremaining: 4.92s\n",
      "26:\tlearn: 0.0789791\ttotal: 814ms\tremaining: 4.92s\n",
      "27:\tlearn: 0.0735684\ttotal: 842ms\tremaining: 4.87s\n",
      "28:\tlearn: 0.0685770\ttotal: 874ms\tremaining: 4.85s\n",
      "29:\tlearn: 0.0639320\ttotal: 904ms\tremaining: 4.82s\n",
      "30:\tlearn: 0.0596487\ttotal: 939ms\tremaining: 4.81s\n",
      "31:\tlearn: 0.0556641\ttotal: 973ms\tremaining: 4.8s\n",
      "32:\tlearn: 0.0519432\ttotal: 1s\tremaining: 4.78s\n",
      "33:\tlearn: 0.0484873\ttotal: 1.03s\tremaining: 4.74s\n",
      "34:\tlearn: 0.0452948\ttotal: 1.06s\tremaining: 4.7s\n",
      "35:\tlearn: 0.0423369\ttotal: 1.09s\tremaining: 4.66s\n",
      "36:\tlearn: 0.0395829\ttotal: 1.12s\tremaining: 4.62s\n",
      "37:\tlearn: 0.0370035\ttotal: 1.15s\tremaining: 4.59s\n",
      "38:\tlearn: 0.0346143\ttotal: 1.18s\tremaining: 4.57s\n",
      "39:\tlearn: 0.0323847\ttotal: 1.21s\tremaining: 4.55s\n",
      "40:\tlearn: 0.0303033\ttotal: 1.24s\tremaining: 4.51s\n",
      "41:\tlearn: 0.0283709\ttotal: 1.27s\tremaining: 4.46s\n",
      "42:\tlearn: 0.0265790\ttotal: 1.29s\tremaining: 4.42s\n",
      "43:\tlearn: 0.0249142\ttotal: 1.32s\tremaining: 4.38s\n",
      "44:\tlearn: 0.0233648\ttotal: 1.34s\tremaining: 4.34s\n",
      "45:\tlearn: 0.0219179\ttotal: 1.38s\tremaining: 4.31s\n",
      "46:\tlearn: 0.0205726\ttotal: 1.41s\tremaining: 4.28s\n",
      "47:\tlearn: 0.0193190\ttotal: 1.43s\tremaining: 4.24s\n",
      "48:\tlearn: 0.0181538\ttotal: 1.46s\tremaining: 4.2s\n",
      "49:\tlearn: 0.0170727\ttotal: 1.49s\tremaining: 4.16s\n",
      "50:\tlearn: 0.0160672\ttotal: 1.51s\tremaining: 4.13s\n",
      "51:\tlearn: 0.0151340\ttotal: 1.54s\tremaining: 4.09s\n",
      "52:\tlearn: 0.0142517\ttotal: 1.57s\tremaining: 4.06s\n",
      "53:\tlearn: 0.0134370\ttotal: 1.6s\tremaining: 4.03s\n",
      "54:\tlearn: 0.0126752\ttotal: 1.63s\tremaining: 4s\n",
      "55:\tlearn: 0.0119665\ttotal: 1.65s\tremaining: 3.96s\n",
      "56:\tlearn: 0.0113087\ttotal: 1.68s\tremaining: 3.92s\n",
      "57:\tlearn: 0.0107002\ttotal: 1.71s\tremaining: 3.89s\n",
      "58:\tlearn: 0.0101295\ttotal: 1.74s\tremaining: 3.85s\n",
      "59:\tlearn: 0.0095967\ttotal: 1.76s\tremaining: 3.82s\n",
      "60:\tlearn: 0.0091035\ttotal: 1.79s\tremaining: 3.79s\n",
      "61:\tlearn: 0.0086531\ttotal: 1.82s\tremaining: 3.75s\n",
      "62:\tlearn: 0.0082247\ttotal: 1.86s\tremaining: 3.74s\n",
      "63:\tlearn: 0.0078290\ttotal: 1.88s\tremaining: 3.71s\n",
      "64:\tlearn: 0.0074533\ttotal: 1.91s\tremaining: 3.67s\n",
      "65:\tlearn: 0.0071100\ttotal: 1.93s\tremaining: 3.63s\n",
      "66:\tlearn: 0.0067865\ttotal: 1.96s\tremaining: 3.6s\n",
      "67:\tlearn: 0.0064881\ttotal: 1.99s\tremaining: 3.56s\n",
      "68:\tlearn: 0.0062029\ttotal: 2.01s\tremaining: 3.53s\n",
      "69:\tlearn: 0.0059445\ttotal: 2.04s\tremaining: 3.5s\n",
      "70:\tlearn: 0.0057045\ttotal: 2.08s\tremaining: 3.48s\n",
      "71:\tlearn: 0.0054782\ttotal: 2.1s\tremaining: 3.44s\n",
      "72:\tlearn: 0.0052664\ttotal: 2.13s\tremaining: 3.41s\n",
      "73:\tlearn: 0.0050702\ttotal: 2.15s\tremaining: 3.37s\n",
      "74:\tlearn: 0.0048857\ttotal: 2.18s\tremaining: 3.34s\n",
      "75:\tlearn: 0.0047140\ttotal: 2.21s\tremaining: 3.31s\n",
      "76:\tlearn: 0.0045508\ttotal: 2.23s\tremaining: 3.28s\n",
      "77:\tlearn: 0.0044032\ttotal: 2.26s\tremaining: 3.25s\n",
      "78:\tlearn: 0.0042649\ttotal: 2.29s\tremaining: 3.22s\n",
      "79:\tlearn: 0.0041404\ttotal: 2.32s\tremaining: 3.19s\n",
      "80:\tlearn: 0.0040238\ttotal: 2.35s\tremaining: 3.16s\n",
      "81:\tlearn: 0.0039104\ttotal: 2.37s\tremaining: 3.13s\n",
      "82:\tlearn: 0.0038067\ttotal: 2.4s\tremaining: 3.09s\n",
      "83:\tlearn: 0.0037129\ttotal: 2.42s\tremaining: 3.06s\n",
      "84:\tlearn: 0.0036270\ttotal: 2.45s\tremaining: 3.03s\n",
      "85:\tlearn: 0.0035415\ttotal: 2.48s\tremaining: 3s\n",
      "86:\tlearn: 0.0034711\ttotal: 2.51s\tremaining: 2.97s\n",
      "87:\tlearn: 0.0033884\ttotal: 2.54s\tremaining: 2.94s\n",
      "88:\tlearn: 0.0033084\ttotal: 2.57s\tremaining: 2.91s\n",
      "89:\tlearn: 0.0032461\ttotal: 2.59s\tremaining: 2.88s\n",
      "90:\tlearn: 0.0031878\ttotal: 2.62s\tremaining: 2.85s\n",
      "91:\tlearn: 0.0031267\ttotal: 2.64s\tremaining: 2.81s\n",
      "92:\tlearn: 0.0030697\ttotal: 2.67s\tremaining: 2.79s\n",
      "93:\tlearn: 0.0030218\ttotal: 2.7s\tremaining: 2.76s\n",
      "94:\tlearn: 0.0029790\ttotal: 2.73s\tremaining: 2.73s\n",
      "95:\tlearn: 0.0029370\ttotal: 2.75s\tremaining: 2.7s\n",
      "96:\tlearn: 0.0028907\ttotal: 2.78s\tremaining: 2.67s\n",
      "97:\tlearn: 0.0028485\ttotal: 2.81s\tremaining: 2.63s\n",
      "98:\tlearn: 0.0028198\ttotal: 2.83s\tremaining: 2.6s\n",
      "99:\tlearn: 0.0027858\ttotal: 2.86s\tremaining: 2.57s\n",
      "100:\tlearn: 0.0027572\ttotal: 2.88s\tremaining: 2.54s\n",
      "101:\tlearn: 0.0027257\ttotal: 2.91s\tremaining: 2.51s\n",
      "102:\tlearn: 0.0026947\ttotal: 2.94s\tremaining: 2.49s\n",
      "103:\tlearn: 0.0026627\ttotal: 2.97s\tremaining: 2.46s\n",
      "104:\tlearn: 0.0026390\ttotal: 3s\tremaining: 2.42s\n",
      "105:\tlearn: 0.0026090\ttotal: 3.02s\tremaining: 2.4s\n",
      "106:\tlearn: 0.0025815\ttotal: 3.05s\tremaining: 2.36s\n",
      "107:\tlearn: 0.0025536\ttotal: 3.07s\tremaining: 2.33s\n",
      "108:\tlearn: 0.0025309\ttotal: 3.1s\tremaining: 2.3s\n",
      "109:\tlearn: 0.0025063\ttotal: 3.13s\tremaining: 2.28s\n",
      "110:\tlearn: 0.0024866\ttotal: 3.17s\tremaining: 2.25s\n",
      "111:\tlearn: 0.0024677\ttotal: 3.21s\tremaining: 2.23s\n",
      "112:\tlearn: 0.0024540\ttotal: 3.24s\tremaining: 2.21s\n",
      "113:\tlearn: 0.0024379\ttotal: 3.27s\tremaining: 2.18s\n",
      "114:\tlearn: 0.0024300\ttotal: 3.29s\tremaining: 2.15s\n",
      "115:\tlearn: 0.0024179\ttotal: 3.32s\tremaining: 2.12s\n",
      "116:\tlearn: 0.0024058\ttotal: 3.35s\tremaining: 2.09s\n",
      "117:\tlearn: 0.0023933\ttotal: 3.37s\tremaining: 2.06s\n",
      "118:\tlearn: 0.0023790\ttotal: 3.4s\tremaining: 2.03s\n",
      "119:\tlearn: 0.0023659\ttotal: 3.43s\tremaining: 2s\n",
      "120:\tlearn: 0.0023527\ttotal: 3.46s\tremaining: 1.97s\n",
      "121:\tlearn: 0.0023446\ttotal: 3.48s\tremaining: 1.94s\n",
      "122:\tlearn: 0.0023347\ttotal: 3.51s\tremaining: 1.91s\n",
      "123:\tlearn: 0.0023230\ttotal: 3.53s\tremaining: 1.88s\n",
      "124:\tlearn: 0.0023124\ttotal: 3.56s\tremaining: 1.85s\n",
      "125:\tlearn: 0.0023087\ttotal: 3.58s\tremaining: 1.82s\n",
      "126:\tlearn: 0.0022957\ttotal: 3.61s\tremaining: 1.79s\n",
      "127:\tlearn: 0.0022851\ttotal: 3.63s\tremaining: 1.76s\n",
      "128:\tlearn: 0.0022791\ttotal: 3.66s\tremaining: 1.73s\n",
      "129:\tlearn: 0.0022735\ttotal: 3.68s\tremaining: 1.7s\n",
      "130:\tlearn: 0.0022643\ttotal: 3.71s\tremaining: 1.67s\n",
      "131:\tlearn: 0.0022514\ttotal: 3.73s\tremaining: 1.64s\n",
      "132:\tlearn: 0.0022433\ttotal: 3.76s\tremaining: 1.61s\n",
      "133:\tlearn: 0.0022340\ttotal: 3.79s\tremaining: 1.58s\n",
      "134:\tlearn: 0.0022279\ttotal: 3.82s\tremaining: 1.55s\n",
      "135:\tlearn: 0.0022209\ttotal: 3.85s\tremaining: 1.53s\n",
      "136:\tlearn: 0.0022153\ttotal: 3.87s\tremaining: 1.5s\n",
      "137:\tlearn: 0.0022026\ttotal: 3.9s\tremaining: 1.47s\n",
      "138:\tlearn: 0.0021949\ttotal: 3.92s\tremaining: 1.44s\n",
      "139:\tlearn: 0.0021904\ttotal: 3.95s\tremaining: 1.41s\n",
      "140:\tlearn: 0.0021859\ttotal: 3.98s\tremaining: 1.38s\n",
      "141:\tlearn: 0.0021805\ttotal: 4.01s\tremaining: 1.35s\n",
      "142:\tlearn: 0.0021690\ttotal: 4.03s\tremaining: 1.32s\n",
      "143:\tlearn: 0.0021642\ttotal: 4.06s\tremaining: 1.3s\n",
      "144:\tlearn: 0.0021574\ttotal: 4.08s\tremaining: 1.27s\n",
      "145:\tlearn: 0.0021540\ttotal: 4.11s\tremaining: 1.24s\n",
      "146:\tlearn: 0.0021424\ttotal: 4.13s\tremaining: 1.21s\n",
      "147:\tlearn: 0.0021376\ttotal: 4.16s\tremaining: 1.18s\n",
      "148:\tlearn: 0.0021276\ttotal: 4.2s\tremaining: 1.15s\n",
      "149:\tlearn: 0.0021235\ttotal: 4.22s\tremaining: 1.13s\n",
      "150:\tlearn: 0.0021130\ttotal: 4.25s\tremaining: 1.1s\n",
      "151:\tlearn: 0.0021092\ttotal: 4.27s\tremaining: 1.07s\n",
      "152:\tlearn: 0.0021028\ttotal: 4.3s\tremaining: 1.04s\n",
      "153:\tlearn: 0.0020938\ttotal: 4.33s\tremaining: 1.01s\n",
      "154:\tlearn: 0.0020888\ttotal: 4.35s\tremaining: 983ms\n",
      "155:\tlearn: 0.0020859\ttotal: 4.38s\tremaining: 954ms\n",
      "156:\tlearn: 0.0020799\ttotal: 4.4s\tremaining: 926ms\n",
      "157:\tlearn: 0.0020773\ttotal: 4.43s\tremaining: 898ms\n",
      "158:\tlearn: 0.0020725\ttotal: 4.46s\tremaining: 869ms\n",
      "159:\tlearn: 0.0020698\ttotal: 4.48s\tremaining: 841ms\n",
      "160:\tlearn: 0.0020669\ttotal: 4.51s\tremaining: 812ms\n",
      "161:\tlearn: 0.0020636\ttotal: 4.54s\tremaining: 784ms\n",
      "162:\tlearn: 0.0020593\ttotal: 4.56s\tremaining: 756ms\n",
      "163:\tlearn: 0.0020548\ttotal: 4.59s\tremaining: 727ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "164:\tlearn: 0.0020522\ttotal: 4.61s\tremaining: 699ms\n",
      "165:\tlearn: 0.0020475\ttotal: 4.64s\tremaining: 671ms\n",
      "166:\tlearn: 0.0020451\ttotal: 4.67s\tremaining: 643ms\n",
      "167:\tlearn: 0.0020413\ttotal: 4.69s\tremaining: 615ms\n",
      "168:\tlearn: 0.0020385\ttotal: 4.72s\tremaining: 586ms\n",
      "169:\tlearn: 0.0020341\ttotal: 4.74s\tremaining: 558ms\n",
      "170:\tlearn: 0.0020302\ttotal: 4.76s\tremaining: 530ms\n",
      "171:\tlearn: 0.0020241\ttotal: 4.79s\tremaining: 502ms\n",
      "172:\tlearn: 0.0020220\ttotal: 4.82s\tremaining: 474ms\n",
      "173:\tlearn: 0.0020198\ttotal: 4.84s\tremaining: 445ms\n",
      "174:\tlearn: 0.0020161\ttotal: 4.87s\tremaining: 418ms\n",
      "175:\tlearn: 0.0020126\ttotal: 4.9s\tremaining: 389ms\n",
      "176:\tlearn: 0.0020087\ttotal: 4.92s\tremaining: 362ms\n",
      "177:\tlearn: 0.0020049\ttotal: 4.95s\tremaining: 334ms\n",
      "178:\tlearn: 0.0020015\ttotal: 4.98s\tremaining: 306ms\n",
      "179:\tlearn: 0.0019995\ttotal: 5s\tremaining: 278ms\n",
      "180:\tlearn: 0.0019943\ttotal: 5.03s\tremaining: 250ms\n",
      "181:\tlearn: 0.0019870\ttotal: 5.05s\tremaining: 222ms\n",
      "182:\tlearn: 0.0019840\ttotal: 5.08s\tremaining: 194ms\n",
      "183:\tlearn: 0.0019804\ttotal: 5.11s\tremaining: 167ms\n",
      "184:\tlearn: 0.0019778\ttotal: 5.13s\tremaining: 139ms\n",
      "185:\tlearn: 0.0019720\ttotal: 5.16s\tremaining: 111ms\n",
      "186:\tlearn: 0.0019695\ttotal: 5.18s\tremaining: 83.2ms\n",
      "187:\tlearn: 0.0019646\ttotal: 5.21s\tremaining: 55.4ms\n",
      "188:\tlearn: 0.0019602\ttotal: 5.24s\tremaining: 27.7ms\n",
      "189:\tlearn: 0.0019566\ttotal: 5.26s\tremaining: 0us\n",
      "0:\tlearn: 0.6259545\ttotal: 55ms\tremaining: 10.4s\n",
      "1:\tlearn: 0.5675423\ttotal: 99.2ms\tremaining: 9.33s\n",
      "2:\tlearn: 0.5163138\ttotal: 130ms\tremaining: 8.13s\n",
      "3:\tlearn: 0.4709520\ttotal: 159ms\tremaining: 7.38s\n",
      "4:\tlearn: 0.4306446\ttotal: 188ms\tremaining: 6.95s\n",
      "5:\tlearn: 0.3945623\ttotal: 216ms\tremaining: 6.61s\n",
      "6:\tlearn: 0.3622276\ttotal: 244ms\tremaining: 6.37s\n",
      "7:\tlearn: 0.3331019\ttotal: 274ms\tremaining: 6.24s\n",
      "8:\tlearn: 0.3067408\ttotal: 306ms\tremaining: 6.15s\n",
      "9:\tlearn: 0.2827279\ttotal: 335ms\tremaining: 6.04s\n",
      "10:\tlearn: 0.2608817\ttotal: 364ms\tremaining: 5.92s\n",
      "11:\tlearn: 0.2409441\ttotal: 391ms\tremaining: 5.81s\n",
      "12:\tlearn: 0.2227498\ttotal: 419ms\tremaining: 5.7s\n",
      "13:\tlearn: 0.2060896\ttotal: 448ms\tremaining: 5.63s\n",
      "14:\tlearn: 0.1908056\ttotal: 476ms\tremaining: 5.55s\n",
      "15:\tlearn: 0.1768248\ttotal: 507ms\tremaining: 5.51s\n",
      "16:\tlearn: 0.1639927\ttotal: 537ms\tremaining: 5.47s\n",
      "17:\tlearn: 0.1521598\ttotal: 568ms\tremaining: 5.42s\n",
      "18:\tlearn: 0.1412250\ttotal: 595ms\tremaining: 5.36s\n",
      "19:\tlearn: 0.1311646\ttotal: 627ms\tremaining: 5.33s\n",
      "20:\tlearn: 0.1218561\ttotal: 654ms\tremaining: 5.26s\n",
      "21:\tlearn: 0.1132786\ttotal: 681ms\tremaining: 5.2s\n",
      "22:\tlearn: 0.1054026\ttotal: 711ms\tremaining: 5.16s\n",
      "23:\tlearn: 0.0980849\ttotal: 744ms\tremaining: 5.15s\n",
      "24:\tlearn: 0.0913075\ttotal: 774ms\tremaining: 5.11s\n",
      "25:\tlearn: 0.0850246\ttotal: 801ms\tremaining: 5.05s\n",
      "26:\tlearn: 0.0792050\ttotal: 829ms\tremaining: 5s\n",
      "27:\tlearn: 0.0738028\ttotal: 858ms\tremaining: 4.96s\n",
      "28:\tlearn: 0.0688136\ttotal: 887ms\tremaining: 4.93s\n",
      "29:\tlearn: 0.0641639\ttotal: 915ms\tremaining: 4.88s\n",
      "30:\tlearn: 0.0598838\ttotal: 946ms\tremaining: 4.85s\n",
      "31:\tlearn: 0.0558929\ttotal: 975ms\tremaining: 4.82s\n",
      "32:\tlearn: 0.0521751\ttotal: 1s\tremaining: 4.77s\n",
      "33:\tlearn: 0.0487191\ttotal: 1.03s\tremaining: 4.72s\n",
      "34:\tlearn: 0.0455204\ttotal: 1.06s\tremaining: 4.68s\n",
      "35:\tlearn: 0.0425538\ttotal: 1.08s\tremaining: 4.64s\n",
      "36:\tlearn: 0.0397813\ttotal: 1.11s\tremaining: 4.6s\n",
      "37:\tlearn: 0.0372123\ttotal: 1.14s\tremaining: 4.56s\n",
      "38:\tlearn: 0.0348166\ttotal: 1.17s\tremaining: 4.53s\n",
      "39:\tlearn: 0.0325792\ttotal: 1.2s\tremaining: 4.5s\n",
      "40:\tlearn: 0.0305051\ttotal: 1.23s\tremaining: 4.45s\n",
      "41:\tlearn: 0.0285696\ttotal: 1.25s\tremaining: 4.41s\n",
      "42:\tlearn: 0.0267852\ttotal: 1.28s\tremaining: 4.37s\n",
      "43:\tlearn: 0.0251154\ttotal: 1.3s\tremaining: 4.33s\n",
      "44:\tlearn: 0.0235545\ttotal: 1.33s\tremaining: 4.28s\n",
      "45:\tlearn: 0.0220940\ttotal: 1.35s\tremaining: 4.24s\n",
      "46:\tlearn: 0.0207327\ttotal: 1.38s\tremaining: 4.2s\n",
      "47:\tlearn: 0.0194695\ttotal: 1.41s\tremaining: 4.16s\n",
      "48:\tlearn: 0.0182953\ttotal: 1.43s\tremaining: 4.12s\n",
      "49:\tlearn: 0.0172015\ttotal: 1.46s\tremaining: 4.08s\n",
      "50:\tlearn: 0.0161846\ttotal: 1.48s\tremaining: 4.04s\n",
      "51:\tlearn: 0.0152468\ttotal: 1.51s\tremaining: 4s\n",
      "52:\tlearn: 0.0143682\ttotal: 1.53s\tremaining: 3.96s\n",
      "53:\tlearn: 0.0135460\ttotal: 1.56s\tremaining: 3.92s\n",
      "54:\tlearn: 0.0127857\ttotal: 1.58s\tremaining: 3.89s\n",
      "55:\tlearn: 0.0120781\ttotal: 1.61s\tremaining: 3.86s\n",
      "56:\tlearn: 0.0114213\ttotal: 1.64s\tremaining: 3.84s\n",
      "57:\tlearn: 0.0108094\ttotal: 1.67s\tremaining: 3.81s\n",
      "58:\tlearn: 0.0102352\ttotal: 1.7s\tremaining: 3.77s\n",
      "59:\tlearn: 0.0096921\ttotal: 1.72s\tremaining: 3.73s\n",
      "60:\tlearn: 0.0092005\ttotal: 1.75s\tremaining: 3.7s\n",
      "61:\tlearn: 0.0087314\ttotal: 1.77s\tremaining: 3.67s\n",
      "62:\tlearn: 0.0083005\ttotal: 1.8s\tremaining: 3.63s\n",
      "63:\tlearn: 0.0078936\ttotal: 1.83s\tremaining: 3.6s\n",
      "64:\tlearn: 0.0075197\ttotal: 1.86s\tremaining: 3.57s\n",
      "65:\tlearn: 0.0071709\ttotal: 1.88s\tremaining: 3.54s\n",
      "66:\tlearn: 0.0068434\ttotal: 1.91s\tremaining: 3.51s\n",
      "67:\tlearn: 0.0065407\ttotal: 1.94s\tremaining: 3.47s\n",
      "68:\tlearn: 0.0062514\ttotal: 1.97s\tremaining: 3.45s\n",
      "69:\tlearn: 0.0059871\ttotal: 1.99s\tremaining: 3.41s\n",
      "70:\tlearn: 0.0057492\ttotal: 2.02s\tremaining: 3.38s\n",
      "71:\tlearn: 0.0055274\ttotal: 2.05s\tremaining: 3.35s\n",
      "72:\tlearn: 0.0053077\ttotal: 2.08s\tremaining: 3.33s\n",
      "73:\tlearn: 0.0051068\ttotal: 2.1s\tremaining: 3.29s\n",
      "74:\tlearn: 0.0049122\ttotal: 2.13s\tremaining: 3.26s\n",
      "75:\tlearn: 0.0047436\ttotal: 2.15s\tremaining: 3.23s\n",
      "76:\tlearn: 0.0045731\ttotal: 2.18s\tremaining: 3.19s\n",
      "77:\tlearn: 0.0044296\ttotal: 2.2s\tremaining: 3.16s\n",
      "78:\tlearn: 0.0042871\ttotal: 2.23s\tremaining: 3.13s\n",
      "79:\tlearn: 0.0041585\ttotal: 2.25s\tremaining: 3.1s\n",
      "80:\tlearn: 0.0040375\ttotal: 2.28s\tremaining: 3.07s\n",
      "81:\tlearn: 0.0039381\ttotal: 2.31s\tremaining: 3.04s\n",
      "82:\tlearn: 0.0038299\ttotal: 2.33s\tremaining: 3s\n",
      "83:\tlearn: 0.0037345\ttotal: 2.35s\tremaining: 2.97s\n",
      "84:\tlearn: 0.0036404\ttotal: 2.38s\tremaining: 2.94s\n",
      "85:\tlearn: 0.0035581\ttotal: 2.4s\tremaining: 2.91s\n",
      "86:\tlearn: 0.0034787\ttotal: 2.43s\tremaining: 2.88s\n",
      "87:\tlearn: 0.0034068\ttotal: 2.45s\tremaining: 2.85s\n",
      "88:\tlearn: 0.0033245\ttotal: 2.48s\tremaining: 2.81s\n",
      "89:\tlearn: 0.0032629\ttotal: 2.51s\tremaining: 2.79s\n",
      "90:\tlearn: 0.0031903\ttotal: 2.53s\tremaining: 2.76s\n",
      "91:\tlearn: 0.0031248\ttotal: 2.56s\tremaining: 2.73s\n",
      "92:\tlearn: 0.0030767\ttotal: 2.59s\tremaining: 2.7s\n",
      "93:\tlearn: 0.0030266\ttotal: 2.62s\tremaining: 2.67s\n",
      "94:\tlearn: 0.0029863\ttotal: 2.65s\tremaining: 2.65s\n",
      "95:\tlearn: 0.0029435\ttotal: 2.67s\tremaining: 2.61s\n",
      "96:\tlearn: 0.0028914\ttotal: 2.7s\tremaining: 2.59s\n",
      "97:\tlearn: 0.0028581\ttotal: 2.72s\tremaining: 2.56s\n",
      "98:\tlearn: 0.0028204\ttotal: 2.75s\tremaining: 2.53s\n",
      "99:\tlearn: 0.0027903\ttotal: 2.77s\tremaining: 2.5s\n",
      "100:\tlearn: 0.0027474\ttotal: 2.8s\tremaining: 2.47s\n",
      "101:\tlearn: 0.0027214\ttotal: 2.83s\tremaining: 2.44s\n",
      "102:\tlearn: 0.0026849\ttotal: 2.86s\tremaining: 2.41s\n",
      "103:\tlearn: 0.0026521\ttotal: 2.88s\tremaining: 2.38s\n",
      "104:\tlearn: 0.0026201\ttotal: 2.91s\tremaining: 2.35s\n",
      "105:\tlearn: 0.0025985\ttotal: 2.93s\tremaining: 2.33s\n",
      "106:\tlearn: 0.0025688\ttotal: 2.96s\tremaining: 2.3s\n",
      "107:\tlearn: 0.0025379\ttotal: 2.99s\tremaining: 2.27s\n",
      "108:\tlearn: 0.0025082\ttotal: 3.02s\tremaining: 2.24s\n",
      "109:\tlearn: 0.0024804\ttotal: 3.05s\tremaining: 2.22s\n",
      "110:\tlearn: 0.0024485\ttotal: 3.08s\tremaining: 2.19s\n",
      "111:\tlearn: 0.0024316\ttotal: 3.1s\tremaining: 2.16s\n",
      "112:\tlearn: 0.0024097\ttotal: 3.13s\tremaining: 2.13s\n",
      "113:\tlearn: 0.0023888\ttotal: 3.16s\tremaining: 2.1s\n",
      "114:\tlearn: 0.0023722\ttotal: 3.18s\tremaining: 2.08s\n",
      "115:\tlearn: 0.0023603\ttotal: 3.21s\tremaining: 2.05s\n",
      "116:\tlearn: 0.0023532\ttotal: 3.23s\tremaining: 2.02s\n",
      "117:\tlearn: 0.0023415\ttotal: 3.26s\tremaining: 1.99s\n",
      "118:\tlearn: 0.0023243\ttotal: 3.29s\tremaining: 1.96s\n",
      "119:\tlearn: 0.0023026\ttotal: 3.31s\tremaining: 1.93s\n",
      "120:\tlearn: 0.0022866\ttotal: 3.34s\tremaining: 1.91s\n",
      "121:\tlearn: 0.0022670\ttotal: 3.37s\tremaining: 1.88s\n",
      "122:\tlearn: 0.0022560\ttotal: 3.4s\tremaining: 1.85s\n",
      "123:\tlearn: 0.0022421\ttotal: 3.43s\tremaining: 1.82s\n",
      "124:\tlearn: 0.0022338\ttotal: 3.46s\tremaining: 1.8s\n",
      "125:\tlearn: 0.0022216\ttotal: 3.49s\tremaining: 1.77s\n",
      "126:\tlearn: 0.0022078\ttotal: 3.51s\tremaining: 1.74s\n",
      "127:\tlearn: 0.0022002\ttotal: 3.54s\tremaining: 1.71s\n",
      "128:\tlearn: 0.0021931\ttotal: 3.56s\tremaining: 1.68s\n",
      "129:\tlearn: 0.0021816\ttotal: 3.6s\tremaining: 1.66s\n",
      "130:\tlearn: 0.0021682\ttotal: 3.63s\tremaining: 1.63s\n",
      "131:\tlearn: 0.0021555\ttotal: 3.65s\tremaining: 1.6s\n",
      "132:\tlearn: 0.0021444\ttotal: 3.68s\tremaining: 1.58s\n",
      "133:\tlearn: 0.0021373\ttotal: 3.7s\tremaining: 1.55s\n",
      "134:\tlearn: 0.0021254\ttotal: 3.73s\tremaining: 1.52s\n",
      "135:\tlearn: 0.0021193\ttotal: 3.76s\tremaining: 1.49s\n",
      "136:\tlearn: 0.0021139\ttotal: 3.78s\tremaining: 1.46s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "137:\tlearn: 0.0021057\ttotal: 3.81s\tremaining: 1.44s\n",
      "138:\tlearn: 0.0021012\ttotal: 3.83s\tremaining: 1.41s\n",
      "139:\tlearn: 0.0020905\ttotal: 3.87s\tremaining: 1.38s\n",
      "140:\tlearn: 0.0020849\ttotal: 3.89s\tremaining: 1.35s\n",
      "141:\tlearn: 0.0020739\ttotal: 3.92s\tremaining: 1.32s\n",
      "142:\tlearn: 0.0020685\ttotal: 3.94s\tremaining: 1.3s\n",
      "143:\tlearn: 0.0020619\ttotal: 3.97s\tremaining: 1.27s\n",
      "144:\tlearn: 0.0020551\ttotal: 4s\tremaining: 1.24s\n",
      "145:\tlearn: 0.0020468\ttotal: 4.02s\tremaining: 1.21s\n",
      "146:\tlearn: 0.0020401\ttotal: 4.05s\tremaining: 1.19s\n",
      "147:\tlearn: 0.0020357\ttotal: 4.08s\tremaining: 1.16s\n",
      "148:\tlearn: 0.0020282\ttotal: 4.1s\tremaining: 1.13s\n",
      "149:\tlearn: 0.0020256\ttotal: 4.13s\tremaining: 1.1s\n",
      "150:\tlearn: 0.0020181\ttotal: 4.16s\tremaining: 1.07s\n",
      "151:\tlearn: 0.0020125\ttotal: 4.18s\tremaining: 1.04s\n",
      "152:\tlearn: 0.0020073\ttotal: 4.21s\tremaining: 1.02s\n",
      "153:\tlearn: 0.0019970\ttotal: 4.23s\tremaining: 989ms\n",
      "154:\tlearn: 0.0019883\ttotal: 4.26s\tremaining: 962ms\n",
      "155:\tlearn: 0.0019859\ttotal: 4.28s\tremaining: 934ms\n",
      "156:\tlearn: 0.0019824\ttotal: 4.31s\tremaining: 906ms\n",
      "157:\tlearn: 0.0019805\ttotal: 4.33s\tremaining: 878ms\n",
      "158:\tlearn: 0.0019761\ttotal: 4.36s\tremaining: 850ms\n",
      "159:\tlearn: 0.0019736\ttotal: 4.38s\tremaining: 822ms\n",
      "160:\tlearn: 0.0019701\ttotal: 4.41s\tremaining: 794ms\n",
      "161:\tlearn: 0.0019662\ttotal: 4.43s\tremaining: 766ms\n",
      "162:\tlearn: 0.0019603\ttotal: 4.46s\tremaining: 739ms\n",
      "163:\tlearn: 0.0019575\ttotal: 4.49s\tremaining: 712ms\n",
      "164:\tlearn: 0.0019472\ttotal: 4.52s\tremaining: 685ms\n",
      "165:\tlearn: 0.0019410\ttotal: 4.54s\tremaining: 657ms\n",
      "166:\tlearn: 0.0019380\ttotal: 4.57s\tremaining: 630ms\n",
      "167:\tlearn: 0.0019345\ttotal: 4.6s\tremaining: 602ms\n",
      "168:\tlearn: 0.0019319\ttotal: 4.62s\tremaining: 575ms\n",
      "169:\tlearn: 0.0019231\ttotal: 4.65s\tremaining: 547ms\n",
      "170:\tlearn: 0.0019185\ttotal: 4.68s\tremaining: 520ms\n",
      "171:\tlearn: 0.0019123\ttotal: 4.71s\tremaining: 493ms\n",
      "172:\tlearn: 0.0019061\ttotal: 4.73s\tremaining: 465ms\n",
      "173:\tlearn: 0.0019034\ttotal: 4.76s\tremaining: 437ms\n",
      "174:\tlearn: 0.0018999\ttotal: 4.78s\tremaining: 410ms\n",
      "175:\tlearn: 0.0018959\ttotal: 4.81s\tremaining: 383ms\n",
      "176:\tlearn: 0.0018930\ttotal: 4.83s\tremaining: 355ms\n",
      "177:\tlearn: 0.0018879\ttotal: 4.86s\tremaining: 328ms\n",
      "178:\tlearn: 0.0018850\ttotal: 4.89s\tremaining: 300ms\n",
      "179:\tlearn: 0.0018795\ttotal: 4.92s\tremaining: 273ms\n",
      "180:\tlearn: 0.0018769\ttotal: 4.94s\tremaining: 246ms\n",
      "181:\tlearn: 0.0018709\ttotal: 4.97s\tremaining: 218ms\n",
      "182:\tlearn: 0.0018691\ttotal: 4.99s\tremaining: 191ms\n",
      "183:\tlearn: 0.0018671\ttotal: 5.01s\tremaining: 164ms\n",
      "184:\tlearn: 0.0018646\ttotal: 5.04s\tremaining: 136ms\n",
      "185:\tlearn: 0.0018625\ttotal: 5.07s\tremaining: 109ms\n",
      "186:\tlearn: 0.0018578\ttotal: 5.09s\tremaining: 81.7ms\n",
      "187:\tlearn: 0.0018558\ttotal: 5.12s\tremaining: 54.5ms\n",
      "188:\tlearn: 0.0018506\ttotal: 5.15s\tremaining: 27.2ms\n",
      "189:\tlearn: 0.0018482\ttotal: 5.17s\tremaining: 0us\n",
      "0:\tlearn: 0.6258298\ttotal: 36.8ms\tremaining: 6.95s\n",
      "1:\tlearn: 0.5674111\ttotal: 68.9ms\tremaining: 6.47s\n",
      "2:\tlearn: 0.5161097\ttotal: 96.8ms\tremaining: 6.04s\n",
      "3:\tlearn: 0.4707520\ttotal: 128ms\tremaining: 5.93s\n",
      "4:\tlearn: 0.4304387\ttotal: 158ms\tremaining: 5.86s\n",
      "5:\tlearn: 0.3943435\ttotal: 186ms\tremaining: 5.71s\n",
      "6:\tlearn: 0.3619996\ttotal: 216ms\tremaining: 5.63s\n",
      "7:\tlearn: 0.3328769\ttotal: 246ms\tremaining: 5.58s\n",
      "8:\tlearn: 0.3065099\ttotal: 279ms\tremaining: 5.61s\n",
      "9:\tlearn: 0.2824844\ttotal: 311ms\tremaining: 5.6s\n",
      "10:\tlearn: 0.2606344\ttotal: 339ms\tremaining: 5.52s\n",
      "11:\tlearn: 0.2406770\ttotal: 367ms\tremaining: 5.44s\n",
      "12:\tlearn: 0.2224790\ttotal: 394ms\tremaining: 5.37s\n",
      "13:\tlearn: 0.2058234\ttotal: 424ms\tremaining: 5.33s\n",
      "14:\tlearn: 0.1905632\ttotal: 455ms\tremaining: 5.3s\n",
      "15:\tlearn: 0.1765767\ttotal: 488ms\tremaining: 5.31s\n",
      "16:\tlearn: 0.1637136\ttotal: 517ms\tremaining: 5.26s\n",
      "17:\tlearn: 0.1518607\ttotal: 547ms\tremaining: 5.22s\n",
      "18:\tlearn: 0.1409428\ttotal: 575ms\tremaining: 5.17s\n",
      "19:\tlearn: 0.1309013\ttotal: 604ms\tremaining: 5.13s\n",
      "20:\tlearn: 0.1215977\ttotal: 631ms\tremaining: 5.08s\n",
      "21:\tlearn: 0.1130177\ttotal: 659ms\tremaining: 5.03s\n",
      "22:\tlearn: 0.1051375\ttotal: 692ms\tremaining: 5.03s\n",
      "23:\tlearn: 0.0978109\ttotal: 721ms\tremaining: 4.99s\n",
      "24:\tlearn: 0.0910354\ttotal: 751ms\tremaining: 4.96s\n",
      "25:\tlearn: 0.0847653\ttotal: 782ms\tremaining: 4.93s\n",
      "26:\tlearn: 0.0789564\ttotal: 813ms\tremaining: 4.91s\n",
      "27:\tlearn: 0.0735561\ttotal: 841ms\tremaining: 4.86s\n",
      "28:\tlearn: 0.0685623\ttotal: 871ms\tremaining: 4.83s\n",
      "29:\tlearn: 0.0639237\ttotal: 903ms\tremaining: 4.81s\n",
      "30:\tlearn: 0.0596207\ttotal: 931ms\tremaining: 4.78s\n",
      "31:\tlearn: 0.0556404\ttotal: 960ms\tremaining: 4.74s\n",
      "32:\tlearn: 0.0519373\ttotal: 988ms\tremaining: 4.7s\n",
      "33:\tlearn: 0.0484890\ttotal: 1.01s\tremaining: 4.66s\n",
      "34:\tlearn: 0.0452874\ttotal: 1.04s\tremaining: 4.62s\n",
      "35:\tlearn: 0.0423111\ttotal: 1.07s\tremaining: 4.59s\n",
      "36:\tlearn: 0.0395239\ttotal: 1.1s\tremaining: 4.55s\n",
      "37:\tlearn: 0.0369333\ttotal: 1.13s\tremaining: 4.53s\n",
      "38:\tlearn: 0.0345415\ttotal: 1.16s\tremaining: 4.5s\n",
      "39:\tlearn: 0.0323191\ttotal: 1.19s\tremaining: 4.46s\n",
      "40:\tlearn: 0.0302296\ttotal: 1.22s\tremaining: 4.42s\n",
      "41:\tlearn: 0.0282898\ttotal: 1.24s\tremaining: 4.37s\n",
      "42:\tlearn: 0.0264926\ttotal: 1.27s\tremaining: 4.33s\n",
      "43:\tlearn: 0.0248064\ttotal: 1.29s\tremaining: 4.29s\n",
      "44:\tlearn: 0.0232419\ttotal: 1.32s\tremaining: 4.26s\n",
      "45:\tlearn: 0.0217901\ttotal: 1.35s\tremaining: 4.22s\n",
      "46:\tlearn: 0.0204392\ttotal: 1.38s\tremaining: 4.19s\n",
      "47:\tlearn: 0.0191736\ttotal: 1.4s\tremaining: 4.15s\n",
      "48:\tlearn: 0.0179942\ttotal: 1.43s\tremaining: 4.11s\n",
      "49:\tlearn: 0.0168986\ttotal: 1.45s\tremaining: 4.07s\n",
      "50:\tlearn: 0.0158774\ttotal: 1.48s\tremaining: 4.04s\n",
      "51:\tlearn: 0.0149356\ttotal: 1.51s\tremaining: 4.01s\n",
      "52:\tlearn: 0.0140579\ttotal: 1.54s\tremaining: 3.98s\n",
      "53:\tlearn: 0.0132410\ttotal: 1.56s\tremaining: 3.94s\n",
      "54:\tlearn: 0.0124747\ttotal: 1.59s\tremaining: 3.9s\n",
      "55:\tlearn: 0.0117611\ttotal: 1.62s\tremaining: 3.87s\n",
      "56:\tlearn: 0.0110970\ttotal: 1.64s\tremaining: 3.83s\n",
      "57:\tlearn: 0.0104863\ttotal: 1.67s\tremaining: 3.8s\n",
      "58:\tlearn: 0.0099167\ttotal: 1.69s\tremaining: 3.76s\n",
      "59:\tlearn: 0.0093864\ttotal: 1.72s\tremaining: 3.73s\n",
      "60:\tlearn: 0.0088877\ttotal: 1.75s\tremaining: 3.7s\n",
      "61:\tlearn: 0.0084263\ttotal: 1.78s\tremaining: 3.67s\n",
      "62:\tlearn: 0.0079954\ttotal: 1.8s\tremaining: 3.64s\n",
      "63:\tlearn: 0.0075932\ttotal: 1.83s\tremaining: 3.6s\n",
      "64:\tlearn: 0.0072127\ttotal: 1.85s\tremaining: 3.56s\n",
      "65:\tlearn: 0.0068630\ttotal: 1.88s\tremaining: 3.53s\n",
      "66:\tlearn: 0.0065390\ttotal: 1.91s\tremaining: 3.5s\n",
      "67:\tlearn: 0.0062334\ttotal: 1.93s\tremaining: 3.47s\n",
      "68:\tlearn: 0.0059503\ttotal: 1.96s\tremaining: 3.44s\n",
      "69:\tlearn: 0.0056880\ttotal: 1.99s\tremaining: 3.4s\n",
      "70:\tlearn: 0.0054520\ttotal: 2.01s\tremaining: 3.37s\n",
      "71:\tlearn: 0.0052311\ttotal: 2.04s\tremaining: 3.34s\n",
      "72:\tlearn: 0.0050197\ttotal: 2.06s\tremaining: 3.31s\n",
      "73:\tlearn: 0.0048264\ttotal: 2.09s\tremaining: 3.27s\n",
      "74:\tlearn: 0.0046397\ttotal: 2.11s\tremaining: 3.24s\n",
      "75:\tlearn: 0.0044628\ttotal: 2.14s\tremaining: 3.21s\n",
      "76:\tlearn: 0.0043014\ttotal: 2.17s\tremaining: 3.19s\n",
      "77:\tlearn: 0.0041526\ttotal: 2.2s\tremaining: 3.15s\n",
      "78:\tlearn: 0.0040088\ttotal: 2.22s\tremaining: 3.12s\n",
      "79:\tlearn: 0.0038796\ttotal: 2.25s\tremaining: 3.09s\n",
      "80:\tlearn: 0.0037601\ttotal: 2.27s\tremaining: 3.06s\n",
      "81:\tlearn: 0.0036534\ttotal: 2.3s\tremaining: 3.03s\n",
      "82:\tlearn: 0.0035480\ttotal: 2.32s\tremaining: 3s\n",
      "83:\tlearn: 0.0034567\ttotal: 2.35s\tremaining: 2.96s\n",
      "84:\tlearn: 0.0033645\ttotal: 2.37s\tremaining: 2.93s\n",
      "85:\tlearn: 0.0032765\ttotal: 2.4s\tremaining: 2.91s\n",
      "86:\tlearn: 0.0032047\ttotal: 2.43s\tremaining: 2.88s\n",
      "87:\tlearn: 0.0031303\ttotal: 2.46s\tremaining: 2.85s\n",
      "88:\tlearn: 0.0030660\ttotal: 2.49s\tremaining: 2.83s\n",
      "89:\tlearn: 0.0030047\ttotal: 2.52s\tremaining: 2.8s\n",
      "90:\tlearn: 0.0029371\ttotal: 2.54s\tremaining: 2.77s\n",
      "91:\tlearn: 0.0028720\ttotal: 2.57s\tremaining: 2.74s\n",
      "92:\tlearn: 0.0028197\ttotal: 2.6s\tremaining: 2.71s\n",
      "93:\tlearn: 0.0027698\ttotal: 2.62s\tremaining: 2.68s\n",
      "94:\tlearn: 0.0027215\ttotal: 2.65s\tremaining: 2.65s\n",
      "95:\tlearn: 0.0026690\ttotal: 2.68s\tremaining: 2.62s\n",
      "96:\tlearn: 0.0026276\ttotal: 2.7s\tremaining: 2.59s\n",
      "97:\tlearn: 0.0025946\ttotal: 2.73s\tremaining: 2.56s\n",
      "98:\tlearn: 0.0025532\ttotal: 2.75s\tremaining: 2.53s\n",
      "99:\tlearn: 0.0025161\ttotal: 2.78s\tremaining: 2.5s\n",
      "100:\tlearn: 0.0024865\ttotal: 2.81s\tremaining: 2.47s\n",
      "101:\tlearn: 0.0024572\ttotal: 2.83s\tremaining: 2.44s\n",
      "102:\tlearn: 0.0024212\ttotal: 2.86s\tremaining: 2.42s\n",
      "103:\tlearn: 0.0024001\ttotal: 2.89s\tremaining: 2.39s\n",
      "104:\tlearn: 0.0023710\ttotal: 2.91s\tremaining: 2.36s\n",
      "105:\tlearn: 0.0023515\ttotal: 2.94s\tremaining: 2.33s\n",
      "106:\tlearn: 0.0023282\ttotal: 2.96s\tremaining: 2.3s\n",
      "107:\tlearn: 0.0023013\ttotal: 2.99s\tremaining: 2.27s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108:\tlearn: 0.0022739\ttotal: 3.02s\tremaining: 2.24s\n",
      "109:\tlearn: 0.0022521\ttotal: 3.05s\tremaining: 2.22s\n",
      "110:\tlearn: 0.0022340\ttotal: 3.08s\tremaining: 2.19s\n",
      "111:\tlearn: 0.0022133\ttotal: 3.1s\tremaining: 2.16s\n",
      "112:\tlearn: 0.0021913\ttotal: 3.13s\tremaining: 2.13s\n",
      "113:\tlearn: 0.0021773\ttotal: 3.16s\tremaining: 2.11s\n",
      "114:\tlearn: 0.0021559\ttotal: 3.19s\tremaining: 2.08s\n",
      "115:\tlearn: 0.0021427\ttotal: 3.21s\tremaining: 2.05s\n",
      "116:\tlearn: 0.0021283\ttotal: 3.24s\tremaining: 2.02s\n",
      "117:\tlearn: 0.0021187\ttotal: 3.27s\tremaining: 1.99s\n",
      "118:\tlearn: 0.0021102\ttotal: 3.29s\tremaining: 1.96s\n",
      "119:\tlearn: 0.0020952\ttotal: 3.32s\tremaining: 1.94s\n",
      "120:\tlearn: 0.0020831\ttotal: 3.35s\tremaining: 1.91s\n",
      "121:\tlearn: 0.0020682\ttotal: 3.37s\tremaining: 1.88s\n",
      "122:\tlearn: 0.0020560\ttotal: 3.4s\tremaining: 1.85s\n",
      "123:\tlearn: 0.0020490\ttotal: 3.42s\tremaining: 1.82s\n",
      "124:\tlearn: 0.0020406\ttotal: 3.45s\tremaining: 1.79s\n",
      "125:\tlearn: 0.0020303\ttotal: 3.48s\tremaining: 1.77s\n",
      "126:\tlearn: 0.0020169\ttotal: 3.5s\tremaining: 1.74s\n",
      "127:\tlearn: 0.0020049\ttotal: 3.53s\tremaining: 1.71s\n",
      "128:\tlearn: 0.0019949\ttotal: 3.56s\tremaining: 1.68s\n",
      "129:\tlearn: 0.0019851\ttotal: 3.58s\tremaining: 1.65s\n",
      "130:\tlearn: 0.0019800\ttotal: 3.61s\tremaining: 1.63s\n",
      "131:\tlearn: 0.0019701\ttotal: 3.63s\tremaining: 1.6s\n",
      "132:\tlearn: 0.0019653\ttotal: 3.66s\tremaining: 1.57s\n",
      "133:\tlearn: 0.0019573\ttotal: 3.69s\tremaining: 1.54s\n",
      "134:\tlearn: 0.0019462\ttotal: 3.72s\tremaining: 1.51s\n",
      "135:\tlearn: 0.0019373\ttotal: 3.75s\tremaining: 1.49s\n",
      "136:\tlearn: 0.0019284\ttotal: 3.78s\tremaining: 1.46s\n",
      "137:\tlearn: 0.0019231\ttotal: 3.8s\tremaining: 1.43s\n",
      "138:\tlearn: 0.0019132\ttotal: 3.83s\tremaining: 1.41s\n",
      "139:\tlearn: 0.0019073\ttotal: 3.86s\tremaining: 1.38s\n",
      "140:\tlearn: 0.0019026\ttotal: 3.88s\tremaining: 1.35s\n",
      "141:\tlearn: 0.0018964\ttotal: 3.91s\tremaining: 1.32s\n",
      "142:\tlearn: 0.0018881\ttotal: 3.94s\tremaining: 1.29s\n",
      "143:\tlearn: 0.0018850\ttotal: 3.97s\tremaining: 1.27s\n",
      "144:\tlearn: 0.0018725\ttotal: 3.99s\tremaining: 1.24s\n",
      "145:\tlearn: 0.0018646\ttotal: 4.02s\tremaining: 1.21s\n",
      "146:\tlearn: 0.0018597\ttotal: 4.05s\tremaining: 1.18s\n",
      "147:\tlearn: 0.0018514\ttotal: 4.07s\tremaining: 1.16s\n",
      "148:\tlearn: 0.0018457\ttotal: 4.1s\tremaining: 1.13s\n",
      "149:\tlearn: 0.0018402\ttotal: 4.14s\tremaining: 1.1s\n",
      "150:\tlearn: 0.0018367\ttotal: 4.16s\tremaining: 1.07s\n",
      "151:\tlearn: 0.0018343\ttotal: 4.19s\tremaining: 1.05s\n",
      "152:\tlearn: 0.0018282\ttotal: 4.21s\tremaining: 1.02s\n",
      "153:\tlearn: 0.0018182\ttotal: 4.24s\tremaining: 992ms\n",
      "154:\tlearn: 0.0018155\ttotal: 4.27s\tremaining: 964ms\n",
      "155:\tlearn: 0.0018114\ttotal: 4.29s\tremaining: 936ms\n",
      "156:\tlearn: 0.0018056\ttotal: 4.32s\tremaining: 908ms\n",
      "157:\tlearn: 0.0018005\ttotal: 4.35s\tremaining: 881ms\n",
      "158:\tlearn: 0.0017923\ttotal: 4.37s\tremaining: 853ms\n",
      "159:\tlearn: 0.0017883\ttotal: 4.4s\tremaining: 825ms\n",
      "160:\tlearn: 0.0017826\ttotal: 4.42s\tremaining: 797ms\n",
      "161:\tlearn: 0.0017796\ttotal: 4.45s\tremaining: 769ms\n",
      "162:\tlearn: 0.0017768\ttotal: 4.47s\tremaining: 741ms\n",
      "163:\tlearn: 0.0017742\ttotal: 4.5s\tremaining: 713ms\n",
      "164:\tlearn: 0.0017691\ttotal: 4.53s\tremaining: 686ms\n",
      "165:\tlearn: 0.0017638\ttotal: 4.56s\tremaining: 659ms\n",
      "166:\tlearn: 0.0017601\ttotal: 4.58s\tremaining: 631ms\n",
      "167:\tlearn: 0.0017574\ttotal: 4.61s\tremaining: 603ms\n",
      "168:\tlearn: 0.0017550\ttotal: 4.63s\tremaining: 576ms\n",
      "169:\tlearn: 0.0017499\ttotal: 4.66s\tremaining: 548ms\n",
      "170:\tlearn: 0.0017466\ttotal: 4.68s\tremaining: 521ms\n",
      "171:\tlearn: 0.0017364\ttotal: 4.71s\tremaining: 493ms\n",
      "172:\tlearn: 0.0017346\ttotal: 4.74s\tremaining: 465ms\n",
      "173:\tlearn: 0.0017327\ttotal: 4.76s\tremaining: 438ms\n",
      "174:\tlearn: 0.0017283\ttotal: 4.79s\tremaining: 411ms\n",
      "175:\tlearn: 0.0017251\ttotal: 4.81s\tremaining: 383ms\n",
      "176:\tlearn: 0.0017218\ttotal: 4.84s\tremaining: 355ms\n",
      "177:\tlearn: 0.0017152\ttotal: 4.87s\tremaining: 328ms\n",
      "178:\tlearn: 0.0017110\ttotal: 4.89s\tremaining: 301ms\n",
      "179:\tlearn: 0.0017092\ttotal: 4.92s\tremaining: 273ms\n",
      "180:\tlearn: 0.0017037\ttotal: 4.94s\tremaining: 246ms\n",
      "181:\tlearn: 0.0016929\ttotal: 4.97s\tremaining: 219ms\n",
      "182:\tlearn: 0.0016902\ttotal: 5s\tremaining: 191ms\n",
      "183:\tlearn: 0.0016844\ttotal: 5.03s\tremaining: 164ms\n",
      "184:\tlearn: 0.0016814\ttotal: 5.05s\tremaining: 137ms\n",
      "185:\tlearn: 0.0016793\ttotal: 5.08s\tremaining: 109ms\n",
      "186:\tlearn: 0.0016735\ttotal: 5.11s\tremaining: 82ms\n",
      "187:\tlearn: 0.0016703\ttotal: 5.14s\tremaining: 54.7ms\n",
      "188:\tlearn: 0.0016648\ttotal: 5.17s\tremaining: 27.3ms\n",
      "189:\tlearn: 0.0016623\ttotal: 5.19s\tremaining: 0us\n",
      "0:\tlearn: 0.6259574\ttotal: 33.2ms\tremaining: 6.27s\n",
      "1:\tlearn: 0.5675502\ttotal: 64.4ms\tremaining: 6.06s\n",
      "2:\tlearn: 0.5163403\ttotal: 93.2ms\tremaining: 5.81s\n",
      "3:\tlearn: 0.4709815\ttotal: 121ms\tremaining: 5.65s\n",
      "4:\tlearn: 0.4306683\ttotal: 151ms\tremaining: 5.57s\n",
      "5:\tlearn: 0.3945821\ttotal: 179ms\tremaining: 5.48s\n",
      "6:\tlearn: 0.3622384\ttotal: 207ms\tremaining: 5.42s\n",
      "7:\tlearn: 0.3331166\ttotal: 237ms\tremaining: 5.39s\n",
      "8:\tlearn: 0.3067365\ttotal: 273ms\tremaining: 5.5s\n",
      "9:\tlearn: 0.2827229\ttotal: 304ms\tremaining: 5.48s\n",
      "10:\tlearn: 0.2608752\ttotal: 334ms\tremaining: 5.43s\n",
      "11:\tlearn: 0.2409405\ttotal: 362ms\tremaining: 5.37s\n",
      "12:\tlearn: 0.2227448\ttotal: 393ms\tremaining: 5.35s\n",
      "13:\tlearn: 0.2060908\ttotal: 422ms\tremaining: 5.31s\n",
      "14:\tlearn: 0.1908340\ttotal: 457ms\tremaining: 5.33s\n",
      "15:\tlearn: 0.1768513\ttotal: 493ms\tremaining: 5.37s\n",
      "16:\tlearn: 0.1640192\ttotal: 522ms\tremaining: 5.31s\n",
      "17:\tlearn: 0.1521857\ttotal: 556ms\tremaining: 5.31s\n",
      "18:\tlearn: 0.1412509\ttotal: 583ms\tremaining: 5.25s\n",
      "19:\tlearn: 0.1311908\ttotal: 613ms\tremaining: 5.21s\n",
      "20:\tlearn: 0.1218829\ttotal: 651ms\tremaining: 5.24s\n",
      "21:\tlearn: 0.1133039\ttotal: 684ms\tremaining: 5.23s\n",
      "22:\tlearn: 0.1053995\ttotal: 718ms\tremaining: 5.21s\n",
      "23:\tlearn: 0.0980878\ttotal: 746ms\tremaining: 5.16s\n",
      "24:\tlearn: 0.0913092\ttotal: 775ms\tremaining: 5.12s\n",
      "25:\tlearn: 0.0850150\ttotal: 805ms\tremaining: 5.08s\n",
      "26:\tlearn: 0.0791844\ttotal: 835ms\tremaining: 5.04s\n",
      "27:\tlearn: 0.0737866\ttotal: 863ms\tremaining: 4.99s\n",
      "28:\tlearn: 0.0687995\ttotal: 893ms\tremaining: 4.96s\n",
      "29:\tlearn: 0.0641616\ttotal: 935ms\tremaining: 4.99s\n",
      "30:\tlearn: 0.0598666\ttotal: 964ms\tremaining: 4.95s\n",
      "31:\tlearn: 0.0558699\ttotal: 992ms\tremaining: 4.9s\n",
      "32:\tlearn: 0.0521567\ttotal: 1.02s\tremaining: 4.85s\n",
      "33:\tlearn: 0.0487020\ttotal: 1.05s\tremaining: 4.82s\n",
      "34:\tlearn: 0.0454819\ttotal: 1.08s\tremaining: 4.78s\n",
      "35:\tlearn: 0.0425095\ttotal: 1.12s\tremaining: 4.78s\n",
      "36:\tlearn: 0.0397496\ttotal: 1.15s\tremaining: 4.75s\n",
      "37:\tlearn: 0.0371822\ttotal: 1.18s\tremaining: 4.71s\n",
      "38:\tlearn: 0.0347966\ttotal: 1.21s\tremaining: 4.69s\n",
      "39:\tlearn: 0.0325715\ttotal: 1.24s\tremaining: 4.64s\n",
      "40:\tlearn: 0.0304813\ttotal: 1.26s\tremaining: 4.6s\n",
      "41:\tlearn: 0.0285477\ttotal: 1.29s\tremaining: 4.55s\n",
      "42:\tlearn: 0.0267707\ttotal: 1.32s\tremaining: 4.52s\n",
      "43:\tlearn: 0.0251033\ttotal: 1.36s\tremaining: 4.53s\n",
      "44:\tlearn: 0.0235362\ttotal: 1.4s\tremaining: 4.5s\n",
      "45:\tlearn: 0.0220830\ttotal: 1.42s\tremaining: 4.46s\n",
      "46:\tlearn: 0.0207302\ttotal: 1.46s\tremaining: 4.44s\n",
      "47:\tlearn: 0.0194826\ttotal: 1.5s\tremaining: 4.43s\n",
      "48:\tlearn: 0.0183014\ttotal: 1.52s\tremaining: 4.39s\n",
      "49:\tlearn: 0.0172095\ttotal: 1.56s\tremaining: 4.38s\n",
      "50:\tlearn: 0.0162057\ttotal: 1.6s\tremaining: 4.36s\n",
      "51:\tlearn: 0.0152633\ttotal: 1.64s\tremaining: 4.34s\n",
      "52:\tlearn: 0.0143798\ttotal: 1.67s\tremaining: 4.32s\n",
      "53:\tlearn: 0.0135709\ttotal: 1.7s\tremaining: 4.27s\n",
      "54:\tlearn: 0.0128117\ttotal: 1.72s\tremaining: 4.23s\n",
      "55:\tlearn: 0.0120983\ttotal: 1.77s\tremaining: 4.23s\n",
      "56:\tlearn: 0.0114373\ttotal: 1.8s\tremaining: 4.2s\n",
      "57:\tlearn: 0.0108209\ttotal: 1.83s\tremaining: 4.17s\n",
      "58:\tlearn: 0.0102464\ttotal: 1.86s\tremaining: 4.13s\n",
      "59:\tlearn: 0.0097021\ttotal: 1.89s\tremaining: 4.1s\n",
      "60:\tlearn: 0.0092013\ttotal: 1.92s\tremaining: 4.06s\n",
      "61:\tlearn: 0.0087395\ttotal: 1.95s\tremaining: 4.03s\n",
      "62:\tlearn: 0.0083084\ttotal: 1.99s\tremaining: 4.01s\n",
      "63:\tlearn: 0.0079059\ttotal: 2.02s\tremaining: 3.98s\n",
      "64:\tlearn: 0.0075275\ttotal: 2.05s\tremaining: 3.94s\n",
      "65:\tlearn: 0.0071825\ttotal: 2.08s\tremaining: 3.91s\n",
      "66:\tlearn: 0.0068577\ttotal: 2.11s\tremaining: 3.88s\n",
      "67:\tlearn: 0.0065594\ttotal: 2.15s\tremaining: 3.85s\n",
      "68:\tlearn: 0.0062721\ttotal: 2.17s\tremaining: 3.81s\n",
      "69:\tlearn: 0.0060134\ttotal: 2.21s\tremaining: 3.79s\n",
      "70:\tlearn: 0.0057696\ttotal: 2.25s\tremaining: 3.77s\n",
      "71:\tlearn: 0.0055393\ttotal: 2.28s\tremaining: 3.74s\n",
      "72:\tlearn: 0.0053238\ttotal: 2.31s\tremaining: 3.71s\n",
      "73:\tlearn: 0.0051232\ttotal: 2.34s\tremaining: 3.67s\n",
      "74:\tlearn: 0.0049368\ttotal: 2.37s\tremaining: 3.64s\n",
      "75:\tlearn: 0.0047731\ttotal: 2.41s\tremaining: 3.61s\n",
      "76:\tlearn: 0.0046078\ttotal: 2.45s\tremaining: 3.6s\n",
      "77:\tlearn: 0.0044619\ttotal: 2.48s\tremaining: 3.56s\n",
      "78:\tlearn: 0.0043231\ttotal: 2.51s\tremaining: 3.53s\n",
      "79:\tlearn: 0.0042020\ttotal: 2.54s\tremaining: 3.5s\n",
      "80:\tlearn: 0.0040787\ttotal: 2.57s\tremaining: 3.46s\n",
      "81:\tlearn: 0.0039631\ttotal: 2.6s\tremaining: 3.42s\n",
      "82:\tlearn: 0.0038611\ttotal: 2.64s\tremaining: 3.4s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83:\tlearn: 0.0037659\ttotal: 2.67s\tremaining: 3.38s\n",
      "84:\tlearn: 0.0036813\ttotal: 2.71s\tremaining: 3.35s\n",
      "85:\tlearn: 0.0035921\ttotal: 2.74s\tremaining: 3.32s\n",
      "86:\tlearn: 0.0035171\ttotal: 2.77s\tremaining: 3.28s\n",
      "87:\tlearn: 0.0034450\ttotal: 2.8s\tremaining: 3.25s\n",
      "88:\tlearn: 0.0033839\ttotal: 2.83s\tremaining: 3.21s\n",
      "89:\tlearn: 0.0033280\ttotal: 2.86s\tremaining: 3.18s\n",
      "90:\tlearn: 0.0032581\ttotal: 2.89s\tremaining: 3.14s\n",
      "91:\tlearn: 0.0031951\ttotal: 2.94s\tremaining: 3.13s\n",
      "92:\tlearn: 0.0031418\ttotal: 2.97s\tremaining: 3.1s\n",
      "93:\tlearn: 0.0030883\ttotal: 3s\tremaining: 3.06s\n",
      "94:\tlearn: 0.0030445\ttotal: 3.02s\tremaining: 3.02s\n",
      "95:\tlearn: 0.0030040\ttotal: 3.05s\tremaining: 2.98s\n",
      "96:\tlearn: 0.0029582\ttotal: 3.08s\tremaining: 2.96s\n",
      "97:\tlearn: 0.0029250\ttotal: 3.12s\tremaining: 2.93s\n",
      "98:\tlearn: 0.0028801\ttotal: 3.15s\tremaining: 2.9s\n",
      "99:\tlearn: 0.0028516\ttotal: 3.18s\tremaining: 2.86s\n",
      "100:\tlearn: 0.0028125\ttotal: 3.21s\tremaining: 2.83s\n",
      "101:\tlearn: 0.0027921\ttotal: 3.24s\tremaining: 2.8s\n",
      "102:\tlearn: 0.0027559\ttotal: 3.27s\tremaining: 2.76s\n",
      "103:\tlearn: 0.0027357\ttotal: 3.3s\tremaining: 2.73s\n",
      "104:\tlearn: 0.0027052\ttotal: 3.33s\tremaining: 2.7s\n",
      "105:\tlearn: 0.0026867\ttotal: 3.38s\tremaining: 2.68s\n",
      "106:\tlearn: 0.0026575\ttotal: 3.41s\tremaining: 2.64s\n",
      "107:\tlearn: 0.0026278\ttotal: 3.44s\tremaining: 2.62s\n",
      "108:\tlearn: 0.0026020\ttotal: 3.48s\tremaining: 2.58s\n",
      "109:\tlearn: 0.0025749\ttotal: 3.51s\tremaining: 2.55s\n",
      "110:\tlearn: 0.0025524\ttotal: 3.54s\tremaining: 2.52s\n",
      "111:\tlearn: 0.0025294\ttotal: 3.59s\tremaining: 2.5s\n",
      "112:\tlearn: 0.0025107\ttotal: 3.63s\tremaining: 2.48s\n",
      "113:\tlearn: 0.0024904\ttotal: 3.67s\tremaining: 2.44s\n",
      "114:\tlearn: 0.0024814\ttotal: 3.69s\tremaining: 2.41s\n",
      "115:\tlearn: 0.0024646\ttotal: 3.73s\tremaining: 2.38s\n",
      "116:\tlearn: 0.0024466\ttotal: 3.76s\tremaining: 2.35s\n",
      "117:\tlearn: 0.0024274\ttotal: 3.79s\tremaining: 2.32s\n",
      "118:\tlearn: 0.0024101\ttotal: 3.83s\tremaining: 2.28s\n",
      "119:\tlearn: 0.0023928\ttotal: 3.85s\tremaining: 2.25s\n",
      "120:\tlearn: 0.0023800\ttotal: 3.88s\tremaining: 2.21s\n",
      "121:\tlearn: 0.0023668\ttotal: 3.92s\tremaining: 2.18s\n",
      "122:\tlearn: 0.0023576\ttotal: 3.94s\tremaining: 2.15s\n",
      "123:\tlearn: 0.0023420\ttotal: 3.98s\tremaining: 2.12s\n",
      "124:\tlearn: 0.0023366\ttotal: 4.01s\tremaining: 2.09s\n",
      "125:\tlearn: 0.0023304\ttotal: 4.04s\tremaining: 2.05s\n",
      "126:\tlearn: 0.0023191\ttotal: 4.08s\tremaining: 2.02s\n",
      "127:\tlearn: 0.0023032\ttotal: 4.11s\tremaining: 1.99s\n",
      "128:\tlearn: 0.0022974\ttotal: 4.13s\tremaining: 1.96s\n",
      "129:\tlearn: 0.0022819\ttotal: 4.16s\tremaining: 1.92s\n",
      "130:\tlearn: 0.0022777\ttotal: 4.19s\tremaining: 1.89s\n",
      "131:\tlearn: 0.0022723\ttotal: 4.24s\tremaining: 1.86s\n",
      "132:\tlearn: 0.0022655\ttotal: 4.26s\tremaining: 1.83s\n",
      "133:\tlearn: 0.0022540\ttotal: 4.29s\tremaining: 1.79s\n",
      "134:\tlearn: 0.0022444\ttotal: 4.33s\tremaining: 1.76s\n",
      "135:\tlearn: 0.0022348\ttotal: 4.36s\tremaining: 1.73s\n",
      "136:\tlearn: 0.0022255\ttotal: 4.39s\tremaining: 1.7s\n",
      "137:\tlearn: 0.0022178\ttotal: 4.43s\tremaining: 1.67s\n",
      "138:\tlearn: 0.0022141\ttotal: 4.46s\tremaining: 1.64s\n",
      "139:\tlearn: 0.0022029\ttotal: 4.5s\tremaining: 1.6s\n",
      "140:\tlearn: 0.0021951\ttotal: 4.53s\tremaining: 1.57s\n",
      "141:\tlearn: 0.0021887\ttotal: 4.55s\tremaining: 1.54s\n",
      "142:\tlearn: 0.0021816\ttotal: 4.59s\tremaining: 1.51s\n",
      "143:\tlearn: 0.0021783\ttotal: 4.62s\tremaining: 1.48s\n",
      "144:\tlearn: 0.0021737\ttotal: 4.66s\tremaining: 1.45s\n",
      "145:\tlearn: 0.0021667\ttotal: 4.69s\tremaining: 1.41s\n",
      "146:\tlearn: 0.0021624\ttotal: 4.72s\tremaining: 1.38s\n",
      "147:\tlearn: 0.0021582\ttotal: 4.75s\tremaining: 1.35s\n",
      "148:\tlearn: 0.0021473\ttotal: 4.78s\tremaining: 1.31s\n",
      "149:\tlearn: 0.0021430\ttotal: 4.81s\tremaining: 1.28s\n",
      "150:\tlearn: 0.0021338\ttotal: 4.84s\tremaining: 1.25s\n",
      "151:\tlearn: 0.0021288\ttotal: 4.87s\tremaining: 1.22s\n",
      "152:\tlearn: 0.0021218\ttotal: 4.91s\tremaining: 1.19s\n",
      "153:\tlearn: 0.0021143\ttotal: 4.94s\tremaining: 1.15s\n",
      "154:\tlearn: 0.0021102\ttotal: 4.97s\tremaining: 1.12s\n",
      "155:\tlearn: 0.0021073\ttotal: 5s\tremaining: 1.09s\n",
      "156:\tlearn: 0.0021040\ttotal: 5.02s\tremaining: 1.05s\n",
      "157:\tlearn: 0.0020994\ttotal: 5.05s\tremaining: 1.02s\n",
      "158:\tlearn: 0.0020959\ttotal: 5.08s\tremaining: 991ms\n",
      "159:\tlearn: 0.0020918\ttotal: 5.12s\tremaining: 959ms\n",
      "160:\tlearn: 0.0020867\ttotal: 5.15s\tremaining: 928ms\n",
      "161:\tlearn: 0.0020809\ttotal: 5.18s\tremaining: 895ms\n",
      "162:\tlearn: 0.0020785\ttotal: 5.21s\tremaining: 862ms\n",
      "163:\tlearn: 0.0020705\ttotal: 5.24s\tremaining: 830ms\n",
      "164:\tlearn: 0.0020688\ttotal: 5.26s\tremaining: 797ms\n",
      "165:\tlearn: 0.0020648\ttotal: 5.29s\tremaining: 765ms\n",
      "166:\tlearn: 0.0020592\ttotal: 5.32s\tremaining: 733ms\n",
      "167:\tlearn: 0.0020564\ttotal: 5.36s\tremaining: 702ms\n",
      "168:\tlearn: 0.0020500\ttotal: 5.39s\tremaining: 670ms\n",
      "169:\tlearn: 0.0020459\ttotal: 5.42s\tremaining: 638ms\n",
      "170:\tlearn: 0.0020436\ttotal: 5.46s\tremaining: 607ms\n",
      "171:\tlearn: 0.0020369\ttotal: 5.49s\tremaining: 575ms\n",
      "172:\tlearn: 0.0020337\ttotal: 5.52s\tremaining: 543ms\n",
      "173:\tlearn: 0.0020322\ttotal: 5.56s\tremaining: 511ms\n",
      "174:\tlearn: 0.0020299\ttotal: 5.6s\tremaining: 480ms\n",
      "175:\tlearn: 0.0020211\ttotal: 5.64s\tremaining: 448ms\n",
      "176:\tlearn: 0.0020179\ttotal: 5.66s\tremaining: 416ms\n",
      "177:\tlearn: 0.0020130\ttotal: 5.69s\tremaining: 384ms\n",
      "178:\tlearn: 0.0020107\ttotal: 5.72s\tremaining: 351ms\n",
      "179:\tlearn: 0.0020061\ttotal: 5.74s\tremaining: 319ms\n",
      "180:\tlearn: 0.0019997\ttotal: 5.77s\tremaining: 287ms\n",
      "181:\tlearn: 0.0019954\ttotal: 5.82s\tremaining: 256ms\n",
      "182:\tlearn: 0.0019903\ttotal: 5.85s\tremaining: 224ms\n",
      "183:\tlearn: 0.0019867\ttotal: 5.87s\tremaining: 192ms\n",
      "184:\tlearn: 0.0019847\ttotal: 5.9s\tremaining: 160ms\n",
      "185:\tlearn: 0.0019817\ttotal: 5.93s\tremaining: 128ms\n",
      "186:\tlearn: 0.0019796\ttotal: 5.96s\tremaining: 95.6ms\n",
      "187:\tlearn: 0.0019777\ttotal: 5.98s\tremaining: 63.7ms\n",
      "188:\tlearn: 0.0019699\ttotal: 6.01s\tremaining: 31.8ms\n",
      "189:\tlearn: 0.0019681\ttotal: 6.06s\tremaining: 0us\n",
      "0:\tlearn: 0.6259333\ttotal: 35.4ms\tremaining: 6.7s\n",
      "1:\tlearn: 0.5675043\ttotal: 68.5ms\tremaining: 6.44s\n",
      "2:\tlearn: 0.5162870\ttotal: 108ms\tremaining: 6.72s\n",
      "3:\tlearn: 0.4709173\ttotal: 137ms\tremaining: 6.39s\n",
      "4:\tlearn: 0.4305980\ttotal: 167ms\tremaining: 6.16s\n",
      "5:\tlearn: 0.3944995\ttotal: 196ms\tremaining: 6.02s\n",
      "6:\tlearn: 0.3621390\ttotal: 226ms\tremaining: 5.92s\n",
      "7:\tlearn: 0.3330059\ttotal: 270ms\tremaining: 6.15s\n",
      "8:\tlearn: 0.3066241\ttotal: 305ms\tremaining: 6.14s\n",
      "9:\tlearn: 0.2826018\ttotal: 339ms\tremaining: 6.1s\n",
      "10:\tlearn: 0.2607583\ttotal: 370ms\tremaining: 6.02s\n",
      "11:\tlearn: 0.2408188\ttotal: 401ms\tremaining: 5.96s\n",
      "12:\tlearn: 0.2226336\ttotal: 432ms\tremaining: 5.88s\n",
      "13:\tlearn: 0.2059643\ttotal: 463ms\tremaining: 5.82s\n",
      "14:\tlearn: 0.1907050\ttotal: 509ms\tremaining: 5.94s\n",
      "15:\tlearn: 0.1767153\ttotal: 541ms\tremaining: 5.88s\n",
      "16:\tlearn: 0.1638732\ttotal: 572ms\tremaining: 5.82s\n",
      "17:\tlearn: 0.1520410\ttotal: 612ms\tremaining: 5.85s\n",
      "18:\tlearn: 0.1411066\ttotal: 649ms\tremaining: 5.84s\n",
      "19:\tlearn: 0.1310485\ttotal: 683ms\tremaining: 5.8s\n",
      "20:\tlearn: 0.1217413\ttotal: 711ms\tremaining: 5.72s\n",
      "21:\tlearn: 0.1131643\ttotal: 756ms\tremaining: 5.77s\n",
      "22:\tlearn: 0.1052538\ttotal: 788ms\tremaining: 5.72s\n",
      "23:\tlearn: 0.0979384\ttotal: 819ms\tremaining: 5.67s\n",
      "24:\tlearn: 0.0911454\ttotal: 852ms\tremaining: 5.62s\n",
      "25:\tlearn: 0.0848512\ttotal: 880ms\tremaining: 5.55s\n",
      "26:\tlearn: 0.0790256\ttotal: 912ms\tremaining: 5.51s\n",
      "27:\tlearn: 0.0736270\ttotal: 944ms\tremaining: 5.46s\n",
      "28:\tlearn: 0.0686403\ttotal: 994ms\tremaining: 5.52s\n",
      "29:\tlearn: 0.0639991\ttotal: 1.03s\tremaining: 5.53s\n",
      "30:\tlearn: 0.0597044\ttotal: 1.07s\tremaining: 5.47s\n",
      "31:\tlearn: 0.0557296\ttotal: 1.12s\tremaining: 5.51s\n",
      "32:\tlearn: 0.0520126\ttotal: 1.15s\tremaining: 5.47s\n",
      "33:\tlearn: 0.0485524\ttotal: 1.18s\tremaining: 5.41s\n",
      "34:\tlearn: 0.0453389\ttotal: 1.21s\tremaining: 5.38s\n",
      "35:\tlearn: 0.0423717\ttotal: 1.25s\tremaining: 5.36s\n",
      "36:\tlearn: 0.0396106\ttotal: 1.28s\tremaining: 5.31s\n",
      "37:\tlearn: 0.0370111\ttotal: 1.31s\tremaining: 5.25s\n",
      "38:\tlearn: 0.0346186\ttotal: 1.34s\tremaining: 5.21s\n",
      "39:\tlearn: 0.0323803\ttotal: 1.38s\tremaining: 5.16s\n",
      "40:\tlearn: 0.0302963\ttotal: 1.4s\tremaining: 5.1s\n",
      "41:\tlearn: 0.0283624\ttotal: 1.44s\tremaining: 5.08s\n",
      "42:\tlearn: 0.0265733\ttotal: 1.48s\tremaining: 5.05s\n",
      "43:\tlearn: 0.0249024\ttotal: 1.51s\tremaining: 5s\n",
      "44:\tlearn: 0.0233579\ttotal: 1.53s\tremaining: 4.95s\n",
      "45:\tlearn: 0.0219013\ttotal: 1.56s\tremaining: 4.89s\n",
      "46:\tlearn: 0.0205447\ttotal: 1.6s\tremaining: 4.86s\n",
      "47:\tlearn: 0.0192828\ttotal: 1.63s\tremaining: 4.82s\n",
      "48:\tlearn: 0.0181117\ttotal: 1.67s\tremaining: 4.8s\n",
      "49:\tlearn: 0.0170112\ttotal: 1.7s\tremaining: 4.77s\n",
      "50:\tlearn: 0.0160002\ttotal: 1.74s\tremaining: 4.75s\n",
      "51:\tlearn: 0.0150555\ttotal: 1.77s\tremaining: 4.7s\n",
      "52:\tlearn: 0.0141753\ttotal: 1.8s\tremaining: 4.66s\n",
      "53:\tlearn: 0.0133518\ttotal: 1.83s\tremaining: 4.61s\n",
      "54:\tlearn: 0.0125823\ttotal: 1.86s\tremaining: 4.56s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55:\tlearn: 0.0118727\ttotal: 1.9s\tremaining: 4.55s\n",
      "56:\tlearn: 0.0112135\ttotal: 1.94s\tremaining: 4.52s\n",
      "57:\tlearn: 0.0105957\ttotal: 1.96s\tremaining: 4.47s\n",
      "58:\tlearn: 0.0100251\ttotal: 2s\tremaining: 4.44s\n",
      "59:\tlearn: 0.0094923\ttotal: 2.04s\tremaining: 4.42s\n",
      "60:\tlearn: 0.0089921\ttotal: 2.08s\tremaining: 4.4s\n",
      "61:\tlearn: 0.0085346\ttotal: 2.13s\tremaining: 4.39s\n",
      "62:\tlearn: 0.0081049\ttotal: 2.17s\tremaining: 4.37s\n",
      "63:\tlearn: 0.0077061\ttotal: 2.21s\tremaining: 4.35s\n",
      "64:\tlearn: 0.0073345\ttotal: 2.24s\tremaining: 4.31s\n",
      "65:\tlearn: 0.0069845\ttotal: 2.28s\tremaining: 4.29s\n",
      "66:\tlearn: 0.0066634\ttotal: 2.32s\tremaining: 4.25s\n",
      "67:\tlearn: 0.0063560\ttotal: 2.36s\tremaining: 4.24s\n",
      "68:\tlearn: 0.0060677\ttotal: 2.4s\tremaining: 4.2s\n",
      "69:\tlearn: 0.0058044\ttotal: 2.44s\tremaining: 4.18s\n",
      "70:\tlearn: 0.0055580\ttotal: 2.47s\tremaining: 4.15s\n",
      "71:\tlearn: 0.0053302\ttotal: 2.51s\tremaining: 4.11s\n",
      "72:\tlearn: 0.0051155\ttotal: 2.54s\tremaining: 4.08s\n",
      "73:\tlearn: 0.0049217\ttotal: 2.58s\tremaining: 4.04s\n",
      "74:\tlearn: 0.0047387\ttotal: 2.62s\tremaining: 4.01s\n",
      "75:\tlearn: 0.0045643\ttotal: 2.64s\tremaining: 3.97s\n",
      "76:\tlearn: 0.0044035\ttotal: 2.67s\tremaining: 3.92s\n",
      "77:\tlearn: 0.0042578\ttotal: 2.7s\tremaining: 3.88s\n",
      "78:\tlearn: 0.0041169\ttotal: 2.73s\tremaining: 3.83s\n",
      "79:\tlearn: 0.0039874\ttotal: 2.76s\tremaining: 3.79s\n",
      "80:\tlearn: 0.0038719\ttotal: 2.79s\tremaining: 3.75s\n",
      "81:\tlearn: 0.0037617\ttotal: 2.83s\tremaining: 3.72s\n",
      "82:\tlearn: 0.0036574\ttotal: 2.85s\tremaining: 3.68s\n",
      "83:\tlearn: 0.0035600\ttotal: 2.88s\tremaining: 3.63s\n",
      "84:\tlearn: 0.0034656\ttotal: 2.91s\tremaining: 3.59s\n",
      "85:\tlearn: 0.0033805\ttotal: 2.94s\tremaining: 3.55s\n",
      "86:\tlearn: 0.0033075\ttotal: 2.96s\tremaining: 3.51s\n",
      "87:\tlearn: 0.0032328\ttotal: 2.99s\tremaining: 3.47s\n",
      "88:\tlearn: 0.0031620\ttotal: 3.03s\tremaining: 3.44s\n",
      "89:\tlearn: 0.0030979\ttotal: 3.06s\tremaining: 3.4s\n",
      "90:\tlearn: 0.0030457\ttotal: 3.1s\tremaining: 3.37s\n",
      "91:\tlearn: 0.0029816\ttotal: 3.13s\tremaining: 3.33s\n",
      "92:\tlearn: 0.0029354\ttotal: 3.16s\tremaining: 3.29s\n",
      "93:\tlearn: 0.0028871\ttotal: 3.18s\tremaining: 3.25s\n",
      "94:\tlearn: 0.0028370\ttotal: 3.21s\tremaining: 3.21s\n",
      "95:\tlearn: 0.0027945\ttotal: 3.25s\tremaining: 3.18s\n",
      "96:\tlearn: 0.0027555\ttotal: 3.28s\tremaining: 3.15s\n",
      "97:\tlearn: 0.0027245\ttotal: 3.31s\tremaining: 3.11s\n",
      "98:\tlearn: 0.0026857\ttotal: 3.34s\tremaining: 3.07s\n",
      "99:\tlearn: 0.0026605\ttotal: 3.37s\tremaining: 3.04s\n",
      "100:\tlearn: 0.0026332\ttotal: 3.4s\tremaining: 3s\n",
      "101:\tlearn: 0.0026004\ttotal: 3.43s\tremaining: 2.96s\n",
      "102:\tlearn: 0.0025719\ttotal: 3.46s\tremaining: 2.92s\n",
      "103:\tlearn: 0.0025500\ttotal: 3.49s\tremaining: 2.89s\n",
      "104:\tlearn: 0.0025219\ttotal: 3.53s\tremaining: 2.85s\n",
      "105:\tlearn: 0.0025044\ttotal: 3.56s\tremaining: 2.82s\n",
      "106:\tlearn: 0.0024772\ttotal: 3.59s\tremaining: 2.78s\n",
      "107:\tlearn: 0.0024484\ttotal: 3.62s\tremaining: 2.75s\n",
      "108:\tlearn: 0.0024304\ttotal: 3.65s\tremaining: 2.71s\n",
      "109:\tlearn: 0.0024076\ttotal: 3.68s\tremaining: 2.67s\n",
      "110:\tlearn: 0.0023934\ttotal: 3.71s\tremaining: 2.64s\n",
      "111:\tlearn: 0.0023804\ttotal: 3.75s\tremaining: 2.61s\n",
      "112:\tlearn: 0.0023605\ttotal: 3.78s\tremaining: 2.58s\n",
      "113:\tlearn: 0.0023399\ttotal: 3.81s\tremaining: 2.54s\n",
      "114:\tlearn: 0.0023303\ttotal: 3.84s\tremaining: 2.5s\n",
      "115:\tlearn: 0.0023190\ttotal: 3.87s\tremaining: 2.47s\n",
      "116:\tlearn: 0.0023002\ttotal: 3.9s\tremaining: 2.44s\n",
      "117:\tlearn: 0.0022830\ttotal: 3.95s\tremaining: 2.41s\n",
      "118:\tlearn: 0.0022637\ttotal: 3.98s\tremaining: 2.38s\n",
      "119:\tlearn: 0.0022458\ttotal: 4.01s\tremaining: 2.34s\n",
      "120:\tlearn: 0.0022282\ttotal: 4.05s\tremaining: 2.31s\n",
      "121:\tlearn: 0.0022120\ttotal: 4.1s\tremaining: 2.29s\n",
      "122:\tlearn: 0.0021990\ttotal: 4.13s\tremaining: 2.25s\n",
      "123:\tlearn: 0.0021857\ttotal: 4.17s\tremaining: 2.22s\n",
      "124:\tlearn: 0.0021819\ttotal: 4.2s\tremaining: 2.19s\n",
      "125:\tlearn: 0.0021715\ttotal: 4.24s\tremaining: 2.15s\n",
      "126:\tlearn: 0.0021576\ttotal: 4.27s\tremaining: 2.12s\n",
      "127:\tlearn: 0.0021437\ttotal: 4.3s\tremaining: 2.08s\n",
      "128:\tlearn: 0.0021292\ttotal: 4.33s\tremaining: 2.05s\n",
      "129:\tlearn: 0.0021136\ttotal: 4.36s\tremaining: 2.01s\n",
      "130:\tlearn: 0.0021022\ttotal: 4.4s\tremaining: 1.98s\n",
      "131:\tlearn: 0.0020871\ttotal: 4.43s\tremaining: 1.95s\n",
      "132:\tlearn: 0.0020707\ttotal: 4.47s\tremaining: 1.91s\n",
      "133:\tlearn: 0.0020593\ttotal: 4.5s\tremaining: 1.88s\n",
      "134:\tlearn: 0.0020472\ttotal: 4.53s\tremaining: 1.84s\n",
      "135:\tlearn: 0.0020344\ttotal: 4.56s\tremaining: 1.81s\n",
      "136:\tlearn: 0.0020237\ttotal: 4.59s\tremaining: 1.78s\n",
      "137:\tlearn: 0.0020149\ttotal: 4.64s\tremaining: 1.75s\n",
      "138:\tlearn: 0.0020117\ttotal: 4.67s\tremaining: 1.71s\n",
      "139:\tlearn: 0.0020052\ttotal: 4.7s\tremaining: 1.68s\n",
      "140:\tlearn: 0.0019950\ttotal: 4.73s\tremaining: 1.64s\n",
      "141:\tlearn: 0.0019865\ttotal: 4.76s\tremaining: 1.61s\n",
      "142:\tlearn: 0.0019771\ttotal: 4.78s\tremaining: 1.57s\n",
      "143:\tlearn: 0.0019744\ttotal: 4.81s\tremaining: 1.54s\n",
      "144:\tlearn: 0.0019676\ttotal: 4.84s\tremaining: 1.5s\n",
      "145:\tlearn: 0.0019606\ttotal: 4.88s\tremaining: 1.47s\n",
      "146:\tlearn: 0.0019562\ttotal: 4.91s\tremaining: 1.44s\n",
      "147:\tlearn: 0.0019522\ttotal: 4.93s\tremaining: 1.4s\n",
      "148:\tlearn: 0.0019422\ttotal: 4.97s\tremaining: 1.37s\n",
      "149:\tlearn: 0.0019329\ttotal: 5s\tremaining: 1.33s\n",
      "150:\tlearn: 0.0019240\ttotal: 5.03s\tremaining: 1.3s\n",
      "151:\tlearn: 0.0019100\ttotal: 5.08s\tremaining: 1.27s\n",
      "152:\tlearn: 0.0019006\ttotal: 5.12s\tremaining: 1.24s\n",
      "153:\tlearn: 0.0018914\ttotal: 5.15s\tremaining: 1.2s\n",
      "154:\tlearn: 0.0018820\ttotal: 5.18s\tremaining: 1.17s\n",
      "155:\tlearn: 0.0018728\ttotal: 5.22s\tremaining: 1.14s\n",
      "156:\tlearn: 0.0018697\ttotal: 5.24s\tremaining: 1.1s\n",
      "157:\tlearn: 0.0018642\ttotal: 5.28s\tremaining: 1.07s\n",
      "158:\tlearn: 0.0018608\ttotal: 5.3s\tremaining: 1.03s\n",
      "159:\tlearn: 0.0018542\ttotal: 5.34s\tremaining: 1s\n",
      "160:\tlearn: 0.0018508\ttotal: 5.37s\tremaining: 968ms\n",
      "161:\tlearn: 0.0018430\ttotal: 5.41s\tremaining: 934ms\n",
      "162:\tlearn: 0.0018402\ttotal: 5.43s\tremaining: 900ms\n",
      "163:\tlearn: 0.0018353\ttotal: 5.46s\tremaining: 865ms\n",
      "164:\tlearn: 0.0018263\ttotal: 5.49s\tremaining: 832ms\n",
      "165:\tlearn: 0.0018214\ttotal: 5.52s\tremaining: 798ms\n",
      "166:\tlearn: 0.0018170\ttotal: 5.56s\tremaining: 765ms\n",
      "167:\tlearn: 0.0018147\ttotal: 5.6s\tremaining: 733ms\n",
      "168:\tlearn: 0.0018099\ttotal: 5.64s\tremaining: 701ms\n",
      "169:\tlearn: 0.0018019\ttotal: 5.69s\tremaining: 669ms\n",
      "170:\tlearn: 0.0017961\ttotal: 5.72s\tremaining: 636ms\n",
      "171:\tlearn: 0.0017916\ttotal: 5.75s\tremaining: 602ms\n",
      "172:\tlearn: 0.0017899\ttotal: 5.8s\tremaining: 570ms\n",
      "173:\tlearn: 0.0017852\ttotal: 5.84s\tremaining: 537ms\n",
      "174:\tlearn: 0.0017801\ttotal: 5.88s\tremaining: 504ms\n",
      "175:\tlearn: 0.0017761\ttotal: 5.92s\tremaining: 471ms\n",
      "176:\tlearn: 0.0017722\ttotal: 5.97s\tremaining: 438ms\n",
      "177:\tlearn: 0.0017660\ttotal: 6.01s\tremaining: 405ms\n",
      "178:\tlearn: 0.0017638\ttotal: 6.06s\tremaining: 372ms\n",
      "179:\tlearn: 0.0017590\ttotal: 6.11s\tremaining: 339ms\n",
      "180:\tlearn: 0.0017548\ttotal: 6.14s\tremaining: 306ms\n",
      "181:\tlearn: 0.0017501\ttotal: 6.18s\tremaining: 272ms\n",
      "182:\tlearn: 0.0017481\ttotal: 6.22s\tremaining: 238ms\n",
      "183:\tlearn: 0.0017452\ttotal: 6.26s\tremaining: 204ms\n",
      "184:\tlearn: 0.0017423\ttotal: 6.3s\tremaining: 170ms\n",
      "185:\tlearn: 0.0017393\ttotal: 6.34s\tremaining: 136ms\n",
      "186:\tlearn: 0.0017345\ttotal: 6.37s\tremaining: 102ms\n",
      "187:\tlearn: 0.0017322\ttotal: 6.4s\tremaining: 68.1ms\n",
      "188:\tlearn: 0.0017285\ttotal: 6.43s\tremaining: 34ms\n",
      "189:\tlearn: 0.0017233\ttotal: 6.47s\tremaining: 0us\n",
      "Cross-Validation Scores: [0.99906956 0.99957867 0.99915732 0.99961377 0.99945577]\n",
      "Mean CV Accuracy: 0.9993750157187492\n",
      "0:\tlearn: 0.6259339\ttotal: 32.7ms\tremaining: 6.17s\n",
      "100:\tlearn: 0.0027757\ttotal: 3.19s\tremaining: 2.81s\n",
      "189:\tlearn: 0.0019155\ttotal: 6.07s\tremaining: 0us\n",
      "AUC: 0.9837239658102972\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CatBoost Model Evaluation:\n",
      "Accuracy: 0.9995786664794073\n",
      "Precision: 0.9743589743589743\n",
      "Recall: 0.7755102040816326\n",
      "F1 Score: 0.8636363636363636\n",
      "\n",
      "Confusion Matrix:\n",
      "[[56862     2]\n",
      " [   22    76]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56864\n",
      "           1       0.97      0.78      0.86        98\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.99      0.89      0.93     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      "\n",
      "CPU times: total: 1min 17s\n",
      "Wall time: 37.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report, roc_auc_score, roc_curve\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Split the data into features (X) and target variable (y)\n",
    "X = df_selected_features.drop('Class', axis=1)\n",
    "y = df_selected_features['Class']\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize the CatBoost classifier with specified hyperparameters\n",
    "catboost_params = {\n",
    "    'iterations': 190,       # Increase number of iterations\n",
    "    'learning_rate': 0.07,    # Lower learning rate for more stable training\n",
    "    'depth': 8,               # Increase depth for more complex trees\n",
    "    'l2_leaf_reg': 1.0,       # Regularization parameter for L2 regularization\n",
    "    'random_state': 42        # Random state for reproducibility\n",
    "}\n",
    "clf_catboost = CatBoostClassifier(**catboost_params)\n",
    "\n",
    "# Perform 5-fold cross-validation and compute cross-validation scores\n",
    "cv_scores = cross_val_score(clf_catboost, X, y, cv=5, scoring='accuracy')\n",
    "\n",
    "print(\"Cross-Validation Scores:\", cv_scores)\n",
    "print(\"Mean CV Accuracy:\", cv_scores.mean())\n",
    "\n",
    "# Fit the model on the entire training data\n",
    "clf_catboost.fit(X_train, y_train, verbose=100)  \n",
    "\n",
    "# Predict probabilities for the positive class\n",
    "y_proba_catboost = clf_catboost.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Calculate AUC score\n",
    "auc_catboost = roc_auc_score(y_test, y_proba_catboost)\n",
    "print(\"AUC:\", auc_catboost)\n",
    "\n",
    "# Plot ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_proba_catboost)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, label='ROC Curve (AUC = {:.2f})'.format(auc_catboost))\n",
    "plt.plot([0, 1], [0, 1], 'k--')  # Random guessing line\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve - CatBoost')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Predict the classes for the test set\n",
    "y_pred_catboost = clf_catboost.predict(X_test)\n",
    "\n",
    "# Evaluate the CatBoost model\n",
    "accuracy_catboost = accuracy_score(y_test, y_pred_catboost)\n",
    "precision_catboost = precision_score(y_test, y_pred_catboost)\n",
    "recall_catboost = recall_score(y_test, y_pred_catboost)\n",
    "f1_catboost = f1_score(y_test, y_pred_catboost)\n",
    "\n",
    "print(\"\\nCatBoost Model Evaluation:\")\n",
    "print(\"Accuracy:\", accuracy_catboost)\n",
    "print(\"Precision:\", precision_catboost)\n",
    "print(\"Recall:\", recall_catboost)\n",
    "print(\"F1 Score:\", f1_catboost)\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix_catboost = confusion_matrix(y_test, y_pred_catboost)\n",
    "print(\"\\nConfusion Matrix:\")\n",
    "print(conf_matrix_catboost)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix_catboost, annot=True, cmap='Blues', fmt='g', cbar=False)\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.title('Confusion Matrix - CatBoost')\n",
    "plt.show()\n",
    "\n",
    "# Classification Report\n",
    "class_report_catboost = classification_report(y_test, y_pred_catboost)\n",
    "print(\"\\nClassification Report:\")\n",
    "print(class_report_catboost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation Scores: [0.99912222 0.99929778 0.9989642  0.99931532 0.99903443]\n",
      "Mean CV Accuracy: 0.9991467901897814\n",
      "AUC: 0.9701251033615472\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Model Evaluation:\n",
      "Accuracy: 0.9991748885221726\n",
      "Precision: 0.8805970149253731\n",
      "Recall: 0.6020408163265306\n",
      "F1 Score: 0.7151515151515152\n",
      "\n",
      "Confusion Matrix:\n",
      "[[56856     8]\n",
      " [   39    59]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56864\n",
      "           1       0.88      0.60      0.72        98\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.94      0.80      0.86     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      "\n",
      "CPU times: total: 6.08 s\n",
      "Wall time: 6.41 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report, roc_auc_score, roc_curve\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Split the data into features (X) and target variable (y)\n",
    "X = df_selected_features.drop('Class', axis=1)\n",
    "y = df_selected_features['Class']\n",
    "\n",
    "# Initialize the Logistic Regression classifier with specified hyperparameters\n",
    "lr_params = {\n",
    "    'penalty': 'l2',               # Regularization penalty (l2 norm)\n",
    "    'C': 1.0,                      # Inverse regularization strength\n",
    "    'solver': 'liblinear',         # Optimization algorithm for regularization\n",
    "    'random_state': 42             # Random state for reproducibility\n",
    "}\n",
    "clf_lr = LogisticRegression(**lr_params)\n",
    "\n",
    "# Perform 5-fold cross-validation and compute cross-validation scores\n",
    "cv_scores = cross_val_score(clf_lr, X, y, cv=5, scoring='accuracy')\n",
    "\n",
    "print(\"Cross-Validation Scores:\", cv_scores)\n",
    "print(\"Mean CV Accuracy:\", cv_scores.mean())\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Train the model\n",
    "clf_lr.fit(X_train, y_train)\n",
    "\n",
    "# Predict probabilities for the positive class\n",
    "y_proba_lr = clf_lr.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Calculate AUC score\n",
    "auc_lr = roc_auc_score(y_test, y_proba_lr)\n",
    "print(\"AUC:\", auc_lr)\n",
    "\n",
    "# Plot ROC Curve\n",
    "fpr_lr, tpr_lr, thresholds_lr = roc_curve(y_test, y_proba_lr)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr_lr, tpr_lr, label='ROC Curve (AUC = {:.2f})'.format(auc_lr))\n",
    "plt.plot([0, 1], [0, 1], 'k--')  # Random guessing line\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve - Logistic Regression')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Predict the classes for the test set\n",
    "y_pred_lr = clf_lr.predict(X_test)\n",
    "\n",
    "# Evaluate the Logistic Regression model\n",
    "accuracy_lr = accuracy_score(y_test, y_pred_lr)\n",
    "precision_lr = precision_score(y_test, y_pred_lr)\n",
    "recall_lr = recall_score(y_test, y_pred_lr)\n",
    "f1_lr = f1_score(y_test, y_pred_lr)\n",
    "\n",
    "print(\"Logistic Regression Model Evaluation:\")\n",
    "print(\"Accuracy:\", accuracy_lr)\n",
    "print(\"Precision:\", precision_lr)\n",
    "print(\"Recall:\", recall_lr)\n",
    "print(\"F1 Score:\", f1_lr)\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix_lr = confusion_matrix(y_test, y_pred_lr)\n",
    "print(\"\\nConfusion Matrix:\")\n",
    "print(conf_matrix_lr)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix_lr, annot=True, cmap='Blues', fmt='g', cbar=False)\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.title('Confusion Matrix - Logistic Regression')\n",
    "plt.show()\n",
    "\n",
    "# Classification Report\n",
    "class_report_lr = classification_report(y_test, y_pred_lr)\n",
    "print(\"\\nClassification Report:\")\n",
    "print(class_report_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.4465022\ttotal: 306ms\tremaining: 1m\n",
      "1:\tlearn: 0.2814896\ttotal: 544ms\tremaining: 53.9s\n",
      "2:\tlearn: 0.1832725\ttotal: 770ms\tremaining: 50.6s\n",
      "3:\tlearn: 0.1191614\ttotal: 1.03s\tremaining: 50.3s\n",
      "4:\tlearn: 0.0779931\ttotal: 1.29s\tremaining: 50.5s\n",
      "5:\tlearn: 0.0529163\ttotal: 1.58s\tremaining: 51.2s\n",
      "6:\tlearn: 0.0368365\ttotal: 1.86s\tremaining: 51.3s\n",
      "7:\tlearn: 0.0262222\ttotal: 2.07s\tremaining: 49.7s\n",
      "8:\tlearn: 0.0194310\ttotal: 2.29s\tremaining: 48.6s\n",
      "9:\tlearn: 0.0143326\ttotal: 2.53s\tremaining: 48.1s\n",
      "10:\tlearn: 0.0111324\ttotal: 2.78s\tremaining: 47.8s\n",
      "11:\tlearn: 0.0089598\ttotal: 3.03s\tremaining: 47.4s\n",
      "12:\tlearn: 0.0072437\ttotal: 3.34s\tremaining: 48.1s\n",
      "13:\tlearn: 0.0061245\ttotal: 3.63s\tremaining: 48.2s\n",
      "14:\tlearn: 0.0052955\ttotal: 3.88s\tremaining: 47.9s\n",
      "15:\tlearn: 0.0046259\ttotal: 4.14s\tremaining: 47.7s\n",
      "16:\tlearn: 0.0041429\ttotal: 4.4s\tremaining: 47.4s\n",
      "17:\tlearn: 0.0037813\ttotal: 4.66s\tremaining: 47.1s\n",
      "18:\tlearn: 0.0034836\ttotal: 4.91s\tremaining: 46.8s\n",
      "19:\tlearn: 0.0032418\ttotal: 5.2s\tremaining: 46.8s\n",
      "20:\tlearn: 0.0030480\ttotal: 5.46s\tremaining: 46.6s\n",
      "21:\tlearn: 0.0028730\ttotal: 5.74s\tremaining: 46.4s\n",
      "22:\tlearn: 0.0027465\ttotal: 5.98s\tremaining: 46s\n",
      "23:\tlearn: 0.0026451\ttotal: 6.2s\tremaining: 45.4s\n",
      "24:\tlearn: 0.0025541\ttotal: 6.45s\tremaining: 45.1s\n",
      "25:\tlearn: 0.0024677\ttotal: 6.68s\tremaining: 44.7s\n",
      "26:\tlearn: 0.0024074\ttotal: 6.96s\tremaining: 44.6s\n",
      "27:\tlearn: 0.0023454\ttotal: 7.23s\tremaining: 44.4s\n",
      "28:\tlearn: 0.0022966\ttotal: 7.49s\tremaining: 44.1s\n",
      "29:\tlearn: 0.0022495\ttotal: 7.74s\tremaining: 43.8s\n",
      "30:\tlearn: 0.0021928\ttotal: 8s\tremaining: 43.6s\n",
      "31:\tlearn: 0.0021447\ttotal: 8.25s\tremaining: 43.3s\n",
      "32:\tlearn: 0.0021064\ttotal: 8.5s\tremaining: 43s\n",
      "33:\tlearn: 0.0020831\ttotal: 8.74s\tremaining: 42.7s\n",
      "34:\tlearn: 0.0020610\ttotal: 8.98s\tremaining: 42.3s\n",
      "35:\tlearn: 0.0020010\ttotal: 9.25s\tremaining: 42.1s\n",
      "36:\tlearn: 0.0019802\ttotal: 9.52s\tremaining: 41.9s\n",
      "37:\tlearn: 0.0019610\ttotal: 9.78s\tremaining: 41.7s\n",
      "38:\tlearn: 0.0019162\ttotal: 10.1s\tremaining: 41.5s\n",
      "39:\tlearn: 0.0018735\ttotal: 10.4s\tremaining: 41.5s\n",
      "40:\tlearn: 0.0018467\ttotal: 10.6s\tremaining: 41.2s\n",
      "41:\tlearn: 0.0018337\ttotal: 10.9s\tremaining: 40.9s\n",
      "42:\tlearn: 0.0017934\ttotal: 11.2s\tremaining: 40.8s\n",
      "43:\tlearn: 0.0017569\ttotal: 11.5s\tremaining: 40.6s\n",
      "44:\tlearn: 0.0017189\ttotal: 11.7s\tremaining: 40.4s\n",
      "45:\tlearn: 0.0016881\ttotal: 12s\tremaining: 40.1s\n",
      "46:\tlearn: 0.0016622\ttotal: 12.3s\tremaining: 39.9s\n",
      "47:\tlearn: 0.0016317\ttotal: 12.5s\tremaining: 39.6s\n",
      "48:\tlearn: 0.0015977\ttotal: 12.8s\tremaining: 39.4s\n",
      "49:\tlearn: 0.0015627\ttotal: 13.1s\tremaining: 39.2s\n",
      "50:\tlearn: 0.0015511\ttotal: 13.3s\tremaining: 38.9s\n",
      "51:\tlearn: 0.0015294\ttotal: 13.6s\tremaining: 38.7s\n",
      "52:\tlearn: 0.0014998\ttotal: 13.9s\tremaining: 38.4s\n",
      "53:\tlearn: 0.0014765\ttotal: 14.1s\tremaining: 38.3s\n",
      "54:\tlearn: 0.0014462\ttotal: 14.4s\tremaining: 38s\n",
      "55:\tlearn: 0.0014224\ttotal: 14.7s\tremaining: 37.7s\n",
      "56:\tlearn: 0.0014055\ttotal: 14.9s\tremaining: 37.5s\n",
      "57:\tlearn: 0.0013817\ttotal: 15.2s\tremaining: 37.2s\n",
      "58:\tlearn: 0.0013640\ttotal: 15.4s\tremaining: 36.9s\n",
      "59:\tlearn: 0.0013400\ttotal: 15.7s\tremaining: 36.6s\n",
      "60:\tlearn: 0.0013242\ttotal: 15.9s\tremaining: 36.3s\n",
      "61:\tlearn: 0.0013050\ttotal: 16.2s\tremaining: 36.1s\n",
      "62:\tlearn: 0.0012963\ttotal: 16.5s\tremaining: 35.8s\n",
      "63:\tlearn: 0.0012884\ttotal: 16.7s\tremaining: 35.5s\n",
      "64:\tlearn: 0.0012813\ttotal: 17s\tremaining: 35.2s\n",
      "65:\tlearn: 0.0012632\ttotal: 17.2s\tremaining: 35s\n",
      "66:\tlearn: 0.0012533\ttotal: 17.5s\tremaining: 34.7s\n",
      "67:\tlearn: 0.0012418\ttotal: 17.7s\tremaining: 34.4s\n",
      "68:\tlearn: 0.0012223\ttotal: 18s\tremaining: 34.1s\n",
      "69:\tlearn: 0.0012104\ttotal: 18.3s\tremaining: 33.9s\n",
      "70:\tlearn: 0.0011963\ttotal: 18.6s\tremaining: 33.7s\n",
      "71:\tlearn: 0.0011883\ttotal: 18.8s\tremaining: 33.4s\n",
      "72:\tlearn: 0.0011759\ttotal: 19.1s\tremaining: 33.1s\n",
      "73:\tlearn: 0.0011630\ttotal: 19.3s\tremaining: 32.9s\n",
      "74:\tlearn: 0.0011479\ttotal: 19.6s\tremaining: 32.7s\n",
      "75:\tlearn: 0.0011354\ttotal: 19.9s\tremaining: 32.5s\n",
      "76:\tlearn: 0.0011284\ttotal: 20.1s\tremaining: 32.2s\n",
      "77:\tlearn: 0.0011206\ttotal: 20.4s\tremaining: 31.9s\n",
      "78:\tlearn: 0.0011071\ttotal: 20.7s\tremaining: 31.6s\n",
      "79:\tlearn: 0.0010999\ttotal: 20.9s\tremaining: 31.3s\n",
      "80:\tlearn: 0.0010882\ttotal: 21.2s\tremaining: 31.1s\n",
      "81:\tlearn: 0.0010757\ttotal: 21.4s\tremaining: 30.8s\n",
      "82:\tlearn: 0.0010639\ttotal: 21.7s\tremaining: 30.5s\n",
      "83:\tlearn: 0.0010538\ttotal: 21.9s\tremaining: 30.3s\n",
      "84:\tlearn: 0.0010404\ttotal: 22.2s\tremaining: 30s\n",
      "85:\tlearn: 0.0010266\ttotal: 22.5s\tremaining: 29.8s\n",
      "86:\tlearn: 0.0010114\ttotal: 22.8s\tremaining: 29.6s\n",
      "87:\tlearn: 0.0009940\ttotal: 23s\tremaining: 29.3s\n",
      "88:\tlearn: 0.0009688\ttotal: 23.2s\tremaining: 29s\n",
      "89:\tlearn: 0.0009588\ttotal: 23.5s\tremaining: 28.7s\n",
      "90:\tlearn: 0.0009487\ttotal: 23.8s\tremaining: 28.5s\n",
      "91:\tlearn: 0.0009361\ttotal: 24.1s\tremaining: 28.3s\n",
      "92:\tlearn: 0.0009232\ttotal: 24.4s\tremaining: 28s\n",
      "93:\tlearn: 0.0009121\ttotal: 24.6s\tremaining: 27.8s\n",
      "94:\tlearn: 0.0009040\ttotal: 24.9s\tremaining: 27.5s\n",
      "95:\tlearn: 0.0008880\ttotal: 25.2s\tremaining: 27.3s\n",
      "96:\tlearn: 0.0008814\ttotal: 25.4s\tremaining: 27s\n",
      "97:\tlearn: 0.0008740\ttotal: 25.7s\tremaining: 26.8s\n",
      "98:\tlearn: 0.0008697\ttotal: 26s\tremaining: 26.5s\n",
      "99:\tlearn: 0.0008581\ttotal: 26.2s\tremaining: 26.2s\n",
      "100:\tlearn: 0.0008499\ttotal: 26.5s\tremaining: 26s\n",
      "101:\tlearn: 0.0008424\ttotal: 26.8s\tremaining: 25.7s\n",
      "102:\tlearn: 0.0008383\ttotal: 27s\tremaining: 25.5s\n",
      "103:\tlearn: 0.0008314\ttotal: 27.3s\tremaining: 25.2s\n",
      "104:\tlearn: 0.0008214\ttotal: 27.6s\tremaining: 24.9s\n",
      "105:\tlearn: 0.0008067\ttotal: 27.8s\tremaining: 24.7s\n",
      "106:\tlearn: 0.0007961\ttotal: 28.1s\tremaining: 24.4s\n",
      "107:\tlearn: 0.0007878\ttotal: 28.3s\tremaining: 24.1s\n",
      "108:\tlearn: 0.0007801\ttotal: 28.5s\tremaining: 23.8s\n",
      "109:\tlearn: 0.0007751\ttotal: 28.8s\tremaining: 23.6s\n",
      "110:\tlearn: 0.0007682\ttotal: 29s\tremaining: 23.3s\n",
      "111:\tlearn: 0.0007633\ttotal: 29.2s\tremaining: 23s\n",
      "112:\tlearn: 0.0007587\ttotal: 29.5s\tremaining: 22.7s\n",
      "113:\tlearn: 0.0007531\ttotal: 29.8s\tremaining: 22.5s\n",
      "114:\tlearn: 0.0007500\ttotal: 30s\tremaining: 22.2s\n",
      "115:\tlearn: 0.0007416\ttotal: 30.3s\tremaining: 21.9s\n",
      "116:\tlearn: 0.0007374\ttotal: 30.5s\tremaining: 21.7s\n",
      "117:\tlearn: 0.0007343\ttotal: 30.8s\tremaining: 21.4s\n",
      "118:\tlearn: 0.0007262\ttotal: 31.1s\tremaining: 21.1s\n",
      "119:\tlearn: 0.0007141\ttotal: 31.3s\tremaining: 20.9s\n",
      "120:\tlearn: 0.0007057\ttotal: 31.6s\tremaining: 20.6s\n",
      "121:\tlearn: 0.0006952\ttotal: 31.8s\tremaining: 20.3s\n",
      "122:\tlearn: 0.0006850\ttotal: 32.1s\tremaining: 20.1s\n",
      "123:\tlearn: 0.0006799\ttotal: 32.3s\tremaining: 19.8s\n",
      "124:\tlearn: 0.0006688\ttotal: 32.6s\tremaining: 19.6s\n",
      "125:\tlearn: 0.0006581\ttotal: 32.9s\tremaining: 19.3s\n",
      "126:\tlearn: 0.0006519\ttotal: 33.1s\tremaining: 19s\n",
      "127:\tlearn: 0.0006451\ttotal: 33.3s\tremaining: 18.8s\n",
      "128:\tlearn: 0.0006405\ttotal: 33.6s\tremaining: 18.5s\n",
      "129:\tlearn: 0.0006359\ttotal: 33.8s\tremaining: 18.2s\n",
      "130:\tlearn: 0.0006310\ttotal: 34.1s\tremaining: 18s\n",
      "131:\tlearn: 0.0006222\ttotal: 34.3s\tremaining: 17.7s\n",
      "132:\tlearn: 0.0006135\ttotal: 34.6s\tremaining: 17.4s\n",
      "133:\tlearn: 0.0006097\ttotal: 34.8s\tremaining: 17.2s\n",
      "134:\tlearn: 0.0006002\ttotal: 35.1s\tremaining: 16.9s\n",
      "135:\tlearn: 0.0005891\ttotal: 35.4s\tremaining: 16.7s\n",
      "136:\tlearn: 0.0005810\ttotal: 35.7s\tremaining: 16.4s\n",
      "137:\tlearn: 0.0005762\ttotal: 35.9s\tremaining: 16.1s\n",
      "138:\tlearn: 0.0005694\ttotal: 36.2s\tremaining: 15.9s\n",
      "139:\tlearn: 0.0005637\ttotal: 36.4s\tremaining: 15.6s\n",
      "140:\tlearn: 0.0005548\ttotal: 36.7s\tremaining: 15.4s\n",
      "141:\tlearn: 0.0005489\ttotal: 36.9s\tremaining: 15.1s\n",
      "142:\tlearn: 0.0005442\ttotal: 37.2s\tremaining: 14.8s\n",
      "143:\tlearn: 0.0005364\ttotal: 37.5s\tremaining: 14.6s\n",
      "144:\tlearn: 0.0005268\ttotal: 37.7s\tremaining: 14.3s\n",
      "145:\tlearn: 0.0005195\ttotal: 38s\tremaining: 14s\n",
      "146:\tlearn: 0.0005095\ttotal: 38.2s\tremaining: 13.8s\n",
      "147:\tlearn: 0.0005012\ttotal: 38.4s\tremaining: 13.5s\n",
      "148:\tlearn: 0.0004950\ttotal: 38.6s\tremaining: 13.2s\n",
      "149:\tlearn: 0.0004928\ttotal: 38.9s\tremaining: 13s\n",
      "150:\tlearn: 0.0004825\ttotal: 39.1s\tremaining: 12.7s\n",
      "151:\tlearn: 0.0004809\ttotal: 39.4s\tremaining: 12.4s\n",
      "152:\tlearn: 0.0004736\ttotal: 39.6s\tremaining: 12.2s\n",
      "153:\tlearn: 0.0004677\ttotal: 39.9s\tremaining: 11.9s\n",
      "154:\tlearn: 0.0004602\ttotal: 40.2s\tremaining: 11.7s\n",
      "155:\tlearn: 0.0004565\ttotal: 40.5s\tremaining: 11.4s\n",
      "156:\tlearn: 0.0004490\ttotal: 40.7s\tremaining: 11.2s\n",
      "157:\tlearn: 0.0004469\ttotal: 41s\tremaining: 10.9s\n",
      "158:\tlearn: 0.0004444\ttotal: 41.3s\tremaining: 10.6s\n",
      "159:\tlearn: 0.0004369\ttotal: 41.6s\tremaining: 10.4s\n",
      "160:\tlearn: 0.0004346\ttotal: 41.8s\tremaining: 10.1s\n",
      "161:\tlearn: 0.0004277\ttotal: 42.1s\tremaining: 9.87s\n",
      "162:\tlearn: 0.0004212\ttotal: 42.4s\tremaining: 9.62s\n",
      "163:\tlearn: 0.0004173\ttotal: 42.6s\tremaining: 9.35s\n",
      "164:\tlearn: 0.0004151\ttotal: 42.9s\tremaining: 9.09s\n",
      "165:\tlearn: 0.0004130\ttotal: 43.1s\tremaining: 8.83s\n",
      "166:\tlearn: 0.0004112\ttotal: 43.3s\tremaining: 8.56s\n",
      "167:\tlearn: 0.0004077\ttotal: 43.6s\tremaining: 8.31s\n",
      "168:\tlearn: 0.0004055\ttotal: 43.8s\tremaining: 8.04s\n",
      "169:\tlearn: 0.0004008\ttotal: 44.1s\tremaining: 7.79s\n",
      "170:\tlearn: 0.0003998\ttotal: 44.4s\tremaining: 7.53s\n",
      "171:\tlearn: 0.0003929\ttotal: 44.6s\tremaining: 7.27s\n",
      "172:\tlearn: 0.0003911\ttotal: 44.9s\tremaining: 7s\n",
      "173:\tlearn: 0.0003893\ttotal: 45.1s\tremaining: 6.75s\n",
      "174:\tlearn: 0.0003868\ttotal: 45.4s\tremaining: 6.48s\n",
      "175:\tlearn: 0.0003811\ttotal: 45.6s\tremaining: 6.22s\n",
      "176:\tlearn: 0.0003782\ttotal: 45.8s\tremaining: 5.96s\n",
      "177:\tlearn: 0.0003729\ttotal: 46.1s\tremaining: 5.69s\n",
      "178:\tlearn: 0.0003701\ttotal: 46.3s\tremaining: 5.43s\n",
      "179:\tlearn: 0.0003675\ttotal: 46.6s\tremaining: 5.17s\n",
      "180:\tlearn: 0.0003653\ttotal: 46.8s\tremaining: 4.91s\n",
      "181:\tlearn: 0.0003643\ttotal: 47s\tremaining: 4.65s\n",
      "182:\tlearn: 0.0003624\ttotal: 47.3s\tremaining: 4.4s\n",
      "183:\tlearn: 0.0003564\ttotal: 47.6s\tremaining: 4.14s\n",
      "184:\tlearn: 0.0003506\ttotal: 47.9s\tremaining: 3.89s\n",
      "185:\tlearn: 0.0003468\ttotal: 48.3s\tremaining: 3.63s\n",
      "186:\tlearn: 0.0003434\ttotal: 48.6s\tremaining: 3.38s\n",
      "187:\tlearn: 0.0003415\ttotal: 48.9s\tremaining: 3.12s\n",
      "188:\tlearn: 0.0003381\ttotal: 49.2s\tremaining: 2.86s\n",
      "189:\tlearn: 0.0003330\ttotal: 49.5s\tremaining: 2.6s\n",
      "190:\tlearn: 0.0003271\ttotal: 49.8s\tremaining: 2.35s\n",
      "191:\tlearn: 0.0003245\ttotal: 50.1s\tremaining: 2.09s\n",
      "192:\tlearn: 0.0003210\ttotal: 50.4s\tremaining: 1.83s\n",
      "193:\tlearn: 0.0003174\ttotal: 50.7s\tremaining: 1.57s\n",
      "194:\tlearn: 0.0003161\ttotal: 50.9s\tremaining: 1.3s\n",
      "195:\tlearn: 0.0003132\ttotal: 51.2s\tremaining: 1.04s\n",
      "196:\tlearn: 0.0003065\ttotal: 51.5s\tremaining: 784ms\n",
      "197:\tlearn: 0.0003053\ttotal: 51.8s\tremaining: 523ms\n",
      "198:\tlearn: 0.0003014\ttotal: 52.1s\tremaining: 262ms\n",
      "199:\tlearn: 0.0002977\ttotal: 52.3s\tremaining: 0us\n",
      "0:\tlearn: 0.4485006\ttotal: 260ms\tremaining: 51.8s\n",
      "1:\tlearn: 0.2835411\ttotal: 448ms\tremaining: 44.3s\n",
      "2:\tlearn: 0.1840580\ttotal: 682ms\tremaining: 44.8s\n",
      "3:\tlearn: 0.1192899\ttotal: 933ms\tremaining: 45.7s\n",
      "4:\tlearn: 0.0800113\ttotal: 1.18s\tremaining: 45.9s\n",
      "5:\tlearn: 0.0543802\ttotal: 1.43s\tremaining: 46.2s\n",
      "6:\tlearn: 0.0379095\ttotal: 1.67s\tremaining: 46.2s\n",
      "7:\tlearn: 0.0269892\ttotal: 1.9s\tremaining: 45.6s\n",
      "8:\tlearn: 0.0200811\ttotal: 2.15s\tremaining: 45.7s\n",
      "9:\tlearn: 0.0151728\ttotal: 2.38s\tremaining: 45.2s\n",
      "10:\tlearn: 0.0117069\ttotal: 2.61s\tremaining: 44.8s\n",
      "11:\tlearn: 0.0092228\ttotal: 2.84s\tremaining: 44.5s\n",
      "12:\tlearn: 0.0075718\ttotal: 3.07s\tremaining: 44.2s\n",
      "13:\tlearn: 0.0063112\ttotal: 3.31s\tremaining: 44s\n",
      "14:\tlearn: 0.0054363\ttotal: 3.53s\tremaining: 43.5s\n",
      "15:\tlearn: 0.0047821\ttotal: 3.75s\tremaining: 43.2s\n",
      "16:\tlearn: 0.0042026\ttotal: 3.93s\tremaining: 42.3s\n",
      "17:\tlearn: 0.0038107\ttotal: 4.16s\tremaining: 42.1s\n",
      "18:\tlearn: 0.0034605\ttotal: 4.41s\tremaining: 42s\n",
      "19:\tlearn: 0.0032312\ttotal: 4.63s\tremaining: 41.7s\n",
      "20:\tlearn: 0.0030040\ttotal: 4.86s\tremaining: 41.4s\n",
      "21:\tlearn: 0.0028431\ttotal: 5.09s\tremaining: 41.2s\n",
      "22:\tlearn: 0.0027110\ttotal: 5.31s\tremaining: 40.9s\n",
      "23:\tlearn: 0.0026176\ttotal: 5.54s\tremaining: 40.6s\n",
      "24:\tlearn: 0.0025116\ttotal: 5.76s\tremaining: 40.3s\n",
      "25:\tlearn: 0.0024206\ttotal: 5.99s\tremaining: 40.1s\n",
      "26:\tlearn: 0.0023490\ttotal: 6.21s\tremaining: 39.8s\n",
      "27:\tlearn: 0.0023021\ttotal: 6.44s\tremaining: 39.5s\n",
      "28:\tlearn: 0.0022444\ttotal: 6.67s\tremaining: 39.3s\n",
      "29:\tlearn: 0.0021982\ttotal: 6.91s\tremaining: 39.1s\n",
      "30:\tlearn: 0.0021540\ttotal: 7.13s\tremaining: 38.9s\n",
      "31:\tlearn: 0.0020963\ttotal: 7.36s\tremaining: 38.6s\n",
      "32:\tlearn: 0.0020457\ttotal: 7.6s\tremaining: 38.5s\n",
      "33:\tlearn: 0.0019801\ttotal: 7.84s\tremaining: 38.3s\n",
      "34:\tlearn: 0.0019332\ttotal: 8.07s\tremaining: 38s\n",
      "35:\tlearn: 0.0018861\ttotal: 8.3s\tremaining: 37.8s\n",
      "36:\tlearn: 0.0018408\ttotal: 8.51s\tremaining: 37.5s\n",
      "37:\tlearn: 0.0018251\ttotal: 8.73s\tremaining: 37.2s\n",
      "38:\tlearn: 0.0017879\ttotal: 8.96s\tremaining: 37s\n",
      "39:\tlearn: 0.0017470\ttotal: 9.18s\tremaining: 36.7s\n",
      "40:\tlearn: 0.0017253\ttotal: 9.41s\tremaining: 36.5s\n",
      "41:\tlearn: 0.0016885\ttotal: 9.61s\tremaining: 36.1s\n",
      "42:\tlearn: 0.0016658\ttotal: 9.84s\tremaining: 35.9s\n",
      "43:\tlearn: 0.0016396\ttotal: 10.1s\tremaining: 35.8s\n",
      "44:\tlearn: 0.0015964\ttotal: 10.3s\tremaining: 35.6s\n",
      "45:\tlearn: 0.0015686\ttotal: 10.6s\tremaining: 35.4s\n",
      "46:\tlearn: 0.0015535\ttotal: 10.8s\tremaining: 35.1s\n",
      "47:\tlearn: 0.0015391\ttotal: 11s\tremaining: 34.9s\n",
      "48:\tlearn: 0.0015191\ttotal: 11.2s\tremaining: 34.6s\n",
      "49:\tlearn: 0.0015095\ttotal: 11.5s\tremaining: 34.4s\n",
      "50:\tlearn: 0.0014950\ttotal: 11.7s\tremaining: 34.2s\n",
      "51:\tlearn: 0.0014752\ttotal: 11.9s\tremaining: 33.9s\n",
      "52:\tlearn: 0.0014504\ttotal: 12.2s\tremaining: 33.7s\n",
      "53:\tlearn: 0.0014327\ttotal: 12.4s\tremaining: 33.5s\n",
      "54:\tlearn: 0.0014205\ttotal: 12.6s\tremaining: 33.2s\n",
      "55:\tlearn: 0.0014088\ttotal: 12.8s\tremaining: 33s\n",
      "56:\tlearn: 0.0013875\ttotal: 13.1s\tremaining: 32.8s\n",
      "57:\tlearn: 0.0013684\ttotal: 13.3s\tremaining: 32.5s\n",
      "58:\tlearn: 0.0013484\ttotal: 13.5s\tremaining: 32.3s\n",
      "59:\tlearn: 0.0013305\ttotal: 13.7s\tremaining: 32.1s\n",
      "60:\tlearn: 0.0013207\ttotal: 14s\tremaining: 31.8s\n",
      "61:\tlearn: 0.0012983\ttotal: 14.2s\tremaining: 31.6s\n",
      "62:\tlearn: 0.0012812\ttotal: 14.4s\tremaining: 31.4s\n",
      "63:\tlearn: 0.0012559\ttotal: 14.7s\tremaining: 31.2s\n",
      "64:\tlearn: 0.0012498\ttotal: 14.9s\tremaining: 30.9s\n",
      "65:\tlearn: 0.0012387\ttotal: 15.1s\tremaining: 30.7s\n",
      "66:\tlearn: 0.0012240\ttotal: 15.4s\tremaining: 30.5s\n",
      "67:\tlearn: 0.0012125\ttotal: 15.6s\tremaining: 30.2s\n",
      "68:\tlearn: 0.0011919\ttotal: 15.8s\tremaining: 30s\n",
      "69:\tlearn: 0.0011782\ttotal: 16.1s\tremaining: 29.8s\n",
      "70:\tlearn: 0.0011632\ttotal: 16.3s\tremaining: 29.6s\n",
      "71:\tlearn: 0.0011497\ttotal: 16.5s\tremaining: 29.4s\n",
      "72:\tlearn: 0.0011339\ttotal: 16.8s\tremaining: 29.1s\n",
      "73:\tlearn: 0.0011194\ttotal: 17s\tremaining: 28.9s\n",
      "74:\tlearn: 0.0011101\ttotal: 17.2s\tremaining: 28.7s\n",
      "75:\tlearn: 0.0010867\ttotal: 17.5s\tremaining: 28.5s\n",
      "76:\tlearn: 0.0010764\ttotal: 17.7s\tremaining: 28.3s\n",
      "77:\tlearn: 0.0010568\ttotal: 17.9s\tremaining: 28s\n",
      "78:\tlearn: 0.0010425\ttotal: 18.2s\tremaining: 27.8s\n",
      "79:\tlearn: 0.0010225\ttotal: 18.4s\tremaining: 27.6s\n",
      "80:\tlearn: 0.0010077\ttotal: 18.6s\tremaining: 27.4s\n",
      "81:\tlearn: 0.0009978\ttotal: 18.9s\tremaining: 27.2s\n",
      "82:\tlearn: 0.0009791\ttotal: 19.2s\tremaining: 27s\n",
      "83:\tlearn: 0.0009692\ttotal: 19.4s\tremaining: 26.8s\n",
      "84:\tlearn: 0.0009608\ttotal: 19.6s\tremaining: 26.6s\n",
      "85:\tlearn: 0.0009489\ttotal: 19.9s\tremaining: 26.3s\n",
      "86:\tlearn: 0.0009422\ttotal: 20.1s\tremaining: 26.1s\n",
      "87:\tlearn: 0.0009288\ttotal: 20.3s\tremaining: 25.9s\n",
      "88:\tlearn: 0.0009199\ttotal: 20.6s\tremaining: 25.6s\n",
      "89:\tlearn: 0.0009042\ttotal: 20.8s\tremaining: 25.4s\n",
      "90:\tlearn: 0.0008938\ttotal: 21s\tremaining: 25.2s\n",
      "91:\tlearn: 0.0008860\ttotal: 21.3s\tremaining: 25s\n",
      "92:\tlearn: 0.0008783\ttotal: 21.5s\tremaining: 24.7s\n",
      "93:\tlearn: 0.0008664\ttotal: 21.7s\tremaining: 24.5s\n",
      "94:\tlearn: 0.0008585\ttotal: 21.9s\tremaining: 24.2s\n",
      "95:\tlearn: 0.0008523\ttotal: 22.1s\tremaining: 24s\n",
      "96:\tlearn: 0.0008410\ttotal: 22.4s\tremaining: 23.8s\n",
      "97:\tlearn: 0.0008246\ttotal: 22.6s\tremaining: 23.5s\n",
      "98:\tlearn: 0.0008178\ttotal: 22.8s\tremaining: 23.3s\n",
      "99:\tlearn: 0.0008069\ttotal: 23.1s\tremaining: 23.1s\n",
      "100:\tlearn: 0.0007967\ttotal: 23.3s\tremaining: 22.8s\n",
      "101:\tlearn: 0.0007898\ttotal: 23.5s\tremaining: 22.6s\n",
      "102:\tlearn: 0.0007816\ttotal: 23.8s\tremaining: 22.4s\n",
      "103:\tlearn: 0.0007723\ttotal: 24s\tremaining: 22.2s\n",
      "104:\tlearn: 0.0007646\ttotal: 24.2s\tremaining: 21.9s\n",
      "105:\tlearn: 0.0007558\ttotal: 24.5s\tremaining: 21.7s\n",
      "106:\tlearn: 0.0007446\ttotal: 24.7s\tremaining: 21.5s\n",
      "107:\tlearn: 0.0007260\ttotal: 25s\tremaining: 21.3s\n",
      "108:\tlearn: 0.0007161\ttotal: 25.2s\tremaining: 21s\n",
      "109:\tlearn: 0.0007083\ttotal: 25.4s\tremaining: 20.8s\n",
      "110:\tlearn: 0.0007012\ttotal: 25.7s\tremaining: 20.6s\n",
      "111:\tlearn: 0.0006897\ttotal: 25.9s\tremaining: 20.4s\n",
      "112:\tlearn: 0.0006843\ttotal: 26.1s\tremaining: 20.1s\n",
      "113:\tlearn: 0.0006686\ttotal: 26.4s\tremaining: 19.9s\n",
      "114:\tlearn: 0.0006543\ttotal: 26.6s\tremaining: 19.7s\n",
      "115:\tlearn: 0.0006465\ttotal: 26.9s\tremaining: 19.5s\n",
      "116:\tlearn: 0.0006360\ttotal: 27.1s\tremaining: 19.2s\n",
      "117:\tlearn: 0.0006290\ttotal: 27.3s\tremaining: 19s\n",
      "118:\tlearn: 0.0006217\ttotal: 27.6s\tremaining: 18.8s\n",
      "119:\tlearn: 0.0006181\ttotal: 27.8s\tremaining: 18.5s\n",
      "120:\tlearn: 0.0006081\ttotal: 28s\tremaining: 18.3s\n",
      "121:\tlearn: 0.0005984\ttotal: 28.3s\tremaining: 18.1s\n",
      "122:\tlearn: 0.0005924\ttotal: 28.5s\tremaining: 17.8s\n",
      "123:\tlearn: 0.0005840\ttotal: 28.7s\tremaining: 17.6s\n",
      "124:\tlearn: 0.0005803\ttotal: 28.9s\tremaining: 17.4s\n",
      "125:\tlearn: 0.0005689\ttotal: 29.2s\tremaining: 17.1s\n",
      "126:\tlearn: 0.0005623\ttotal: 29.4s\tremaining: 16.9s\n",
      "127:\tlearn: 0.0005560\ttotal: 29.7s\tremaining: 16.7s\n",
      "128:\tlearn: 0.0005438\ttotal: 29.9s\tremaining: 16.5s\n",
      "129:\tlearn: 0.0005323\ttotal: 30.1s\tremaining: 16.2s\n",
      "130:\tlearn: 0.0005235\ttotal: 30.4s\tremaining: 16s\n",
      "131:\tlearn: 0.0005194\ttotal: 30.6s\tremaining: 15.8s\n",
      "132:\tlearn: 0.0005123\ttotal: 30.8s\tremaining: 15.5s\n",
      "133:\tlearn: 0.0005092\ttotal: 31.1s\tremaining: 15.3s\n",
      "134:\tlearn: 0.0005055\ttotal: 31.3s\tremaining: 15.1s\n",
      "135:\tlearn: 0.0004968\ttotal: 31.5s\tremaining: 14.8s\n",
      "136:\tlearn: 0.0004909\ttotal: 31.8s\tremaining: 14.6s\n",
      "137:\tlearn: 0.0004856\ttotal: 32s\tremaining: 14.4s\n",
      "138:\tlearn: 0.0004749\ttotal: 32.2s\tremaining: 14.1s\n",
      "139:\tlearn: 0.0004662\ttotal: 32.5s\tremaining: 13.9s\n",
      "140:\tlearn: 0.0004604\ttotal: 32.7s\tremaining: 13.7s\n",
      "141:\tlearn: 0.0004582\ttotal: 32.9s\tremaining: 13.5s\n",
      "142:\tlearn: 0.0004523\ttotal: 33.2s\tremaining: 13.2s\n",
      "143:\tlearn: 0.0004488\ttotal: 33.4s\tremaining: 13s\n",
      "144:\tlearn: 0.0004399\ttotal: 33.7s\tremaining: 12.8s\n",
      "145:\tlearn: 0.0004313\ttotal: 33.9s\tremaining: 12.5s\n",
      "146:\tlearn: 0.0004257\ttotal: 34.1s\tremaining: 12.3s\n",
      "147:\tlearn: 0.0004162\ttotal: 34.4s\tremaining: 12.1s\n",
      "148:\tlearn: 0.0004086\ttotal: 34.6s\tremaining: 11.8s\n",
      "149:\tlearn: 0.0003968\ttotal: 34.9s\tremaining: 11.6s\n",
      "150:\tlearn: 0.0003900\ttotal: 35.1s\tremaining: 11.4s\n",
      "151:\tlearn: 0.0003856\ttotal: 35.3s\tremaining: 11.2s\n",
      "152:\tlearn: 0.0003823\ttotal: 35.6s\tremaining: 10.9s\n",
      "153:\tlearn: 0.0003778\ttotal: 35.8s\tremaining: 10.7s\n",
      "154:\tlearn: 0.0003715\ttotal: 36s\tremaining: 10.5s\n",
      "155:\tlearn: 0.0003658\ttotal: 36.3s\tremaining: 10.2s\n",
      "156:\tlearn: 0.0003568\ttotal: 36.5s\tremaining: 10s\n",
      "157:\tlearn: 0.0003532\ttotal: 36.7s\tremaining: 9.77s\n",
      "158:\tlearn: 0.0003502\ttotal: 37s\tremaining: 9.54s\n",
      "159:\tlearn: 0.0003452\ttotal: 37.2s\tremaining: 9.3s\n",
      "160:\tlearn: 0.0003408\ttotal: 37.4s\tremaining: 9.07s\n",
      "161:\tlearn: 0.0003324\ttotal: 37.7s\tremaining: 8.84s\n",
      "162:\tlearn: 0.0003308\ttotal: 37.9s\tremaining: 8.6s\n",
      "163:\tlearn: 0.0003249\ttotal: 38.1s\tremaining: 8.37s\n",
      "164:\tlearn: 0.0003186\ttotal: 38.4s\tremaining: 8.14s\n",
      "165:\tlearn: 0.0003163\ttotal: 38.6s\tremaining: 7.91s\n",
      "166:\tlearn: 0.0003098\ttotal: 38.8s\tremaining: 7.67s\n",
      "167:\tlearn: 0.0003076\ttotal: 39.1s\tremaining: 7.44s\n",
      "168:\tlearn: 0.0003003\ttotal: 39.3s\tremaining: 7.21s\n",
      "169:\tlearn: 0.0002964\ttotal: 39.5s\tremaining: 6.97s\n",
      "170:\tlearn: 0.0002947\ttotal: 39.8s\tremaining: 6.74s\n",
      "171:\tlearn: 0.0002911\ttotal: 40s\tremaining: 6.51s\n",
      "172:\tlearn: 0.0002842\ttotal: 40.2s\tremaining: 6.28s\n",
      "173:\tlearn: 0.0002828\ttotal: 40.4s\tremaining: 6.04s\n",
      "174:\tlearn: 0.0002804\ttotal: 40.7s\tremaining: 5.81s\n",
      "175:\tlearn: 0.0002739\ttotal: 40.9s\tremaining: 5.58s\n",
      "176:\tlearn: 0.0002691\ttotal: 41.2s\tremaining: 5.35s\n",
      "177:\tlearn: 0.0002673\ttotal: 41.4s\tremaining: 5.12s\n",
      "178:\tlearn: 0.0002658\ttotal: 41.6s\tremaining: 4.88s\n",
      "179:\tlearn: 0.0002636\ttotal: 41.8s\tremaining: 4.65s\n",
      "180:\tlearn: 0.0002578\ttotal: 42.1s\tremaining: 4.42s\n",
      "181:\tlearn: 0.0002534\ttotal: 42.3s\tremaining: 4.18s\n",
      "182:\tlearn: 0.0002493\ttotal: 42.5s\tremaining: 3.95s\n",
      "183:\tlearn: 0.0002478\ttotal: 42.8s\tremaining: 3.72s\n",
      "184:\tlearn: 0.0002444\ttotal: 43s\tremaining: 3.49s\n",
      "185:\tlearn: 0.0002412\ttotal: 43.2s\tremaining: 3.25s\n",
      "186:\tlearn: 0.0002352\ttotal: 43.5s\tremaining: 3.02s\n",
      "187:\tlearn: 0.0002328\ttotal: 43.7s\tremaining: 2.79s\n",
      "188:\tlearn: 0.0002307\ttotal: 43.9s\tremaining: 2.56s\n",
      "189:\tlearn: 0.0002295\ttotal: 44.1s\tremaining: 2.32s\n",
      "190:\tlearn: 0.0002279\ttotal: 44.4s\tremaining: 2.09s\n",
      "191:\tlearn: 0.0002268\ttotal: 44.6s\tremaining: 1.86s\n",
      "192:\tlearn: 0.0002218\ttotal: 44.8s\tremaining: 1.63s\n",
      "193:\tlearn: 0.0002174\ttotal: 45s\tremaining: 1.39s\n",
      "194:\tlearn: 0.0002165\ttotal: 45.3s\tremaining: 1.16s\n",
      "195:\tlearn: 0.0002153\ttotal: 45.5s\tremaining: 929ms\n",
      "196:\tlearn: 0.0002136\ttotal: 45.7s\tremaining: 697ms\n",
      "197:\tlearn: 0.0002122\ttotal: 46s\tremaining: 464ms\n",
      "198:\tlearn: 0.0002114\ttotal: 46.2s\tremaining: 232ms\n",
      "199:\tlearn: 0.0002103\ttotal: 46.3s\tremaining: 0us\n",
      "0:\tlearn: 0.4457329\ttotal: 243ms\tremaining: 48.3s\n",
      "1:\tlearn: 0.2807743\ttotal: 476ms\tremaining: 47.2s\n",
      "2:\tlearn: 0.1819943\ttotal: 724ms\tremaining: 47.6s\n",
      "3:\tlearn: 0.1169922\ttotal: 980ms\tremaining: 48s\n",
      "4:\tlearn: 0.0782190\ttotal: 1.24s\tremaining: 48.2s\n",
      "5:\tlearn: 0.0529193\ttotal: 1.47s\tremaining: 47.6s\n",
      "6:\tlearn: 0.0370578\ttotal: 1.71s\tremaining: 47.2s\n",
      "7:\tlearn: 0.0261801\ttotal: 1.95s\tremaining: 46.9s\n",
      "8:\tlearn: 0.0186919\ttotal: 2.18s\tremaining: 46.4s\n",
      "9:\tlearn: 0.0139658\ttotal: 2.42s\tremaining: 46s\n",
      "10:\tlearn: 0.0106987\ttotal: 2.65s\tremaining: 45.5s\n",
      "11:\tlearn: 0.0086654\ttotal: 2.88s\tremaining: 45s\n",
      "12:\tlearn: 0.0071672\ttotal: 3.11s\tremaining: 44.7s\n",
      "13:\tlearn: 0.0059867\ttotal: 3.35s\tremaining: 44.5s\n",
      "14:\tlearn: 0.0051784\ttotal: 3.58s\tremaining: 44.1s\n",
      "15:\tlearn: 0.0045730\ttotal: 3.8s\tremaining: 43.8s\n",
      "16:\tlearn: 0.0041185\ttotal: 4.03s\tremaining: 43.4s\n",
      "17:\tlearn: 0.0037084\ttotal: 4.26s\tremaining: 43.1s\n",
      "18:\tlearn: 0.0034271\ttotal: 4.49s\tremaining: 42.8s\n",
      "19:\tlearn: 0.0031676\ttotal: 4.73s\tremaining: 42.6s\n",
      "20:\tlearn: 0.0029452\ttotal: 4.95s\tremaining: 42.2s\n",
      "21:\tlearn: 0.0028006\ttotal: 5.18s\tremaining: 41.9s\n",
      "22:\tlearn: 0.0026996\ttotal: 5.4s\tremaining: 41.6s\n",
      "23:\tlearn: 0.0025583\ttotal: 5.63s\tremaining: 41.3s\n",
      "24:\tlearn: 0.0024481\ttotal: 5.88s\tremaining: 41.2s\n",
      "25:\tlearn: 0.0023737\ttotal: 6.1s\tremaining: 40.8s\n",
      "26:\tlearn: 0.0022816\ttotal: 6.34s\tremaining: 40.6s\n",
      "27:\tlearn: 0.0022118\ttotal: 6.58s\tremaining: 40.4s\n",
      "28:\tlearn: 0.0021390\ttotal: 6.81s\tremaining: 40.2s\n",
      "29:\tlearn: 0.0021050\ttotal: 7.03s\tremaining: 39.8s\n",
      "30:\tlearn: 0.0020626\ttotal: 7.26s\tremaining: 39.6s\n",
      "31:\tlearn: 0.0020311\ttotal: 7.47s\tremaining: 39.2s\n",
      "32:\tlearn: 0.0020038\ttotal: 7.68s\tremaining: 38.9s\n",
      "33:\tlearn: 0.0019763\ttotal: 7.86s\tremaining: 38.4s\n",
      "34:\tlearn: 0.0019239\ttotal: 8.11s\tremaining: 38.2s\n",
      "35:\tlearn: 0.0019045\ttotal: 8.33s\tremaining: 38s\n",
      "36:\tlearn: 0.0018541\ttotal: 8.54s\tremaining: 37.6s\n",
      "37:\tlearn: 0.0018337\ttotal: 8.76s\tremaining: 37.3s\n",
      "38:\tlearn: 0.0017809\ttotal: 8.99s\tremaining: 37.1s\n",
      "39:\tlearn: 0.0017392\ttotal: 9.21s\tremaining: 36.8s\n",
      "40:\tlearn: 0.0016987\ttotal: 9.44s\tremaining: 36.6s\n",
      "41:\tlearn: 0.0016573\ttotal: 9.67s\tremaining: 36.4s\n",
      "42:\tlearn: 0.0016319\ttotal: 9.9s\tremaining: 36.2s\n",
      "43:\tlearn: 0.0015889\ttotal: 10.1s\tremaining: 36s\n",
      "44:\tlearn: 0.0015596\ttotal: 10.4s\tremaining: 35.8s\n",
      "45:\tlearn: 0.0015197\ttotal: 10.6s\tremaining: 35.6s\n",
      "46:\tlearn: 0.0015064\ttotal: 10.9s\tremaining: 35.4s\n",
      "47:\tlearn: 0.0014846\ttotal: 11.1s\tremaining: 35.1s\n",
      "48:\tlearn: 0.0014717\ttotal: 11.3s\tremaining: 34.9s\n",
      "49:\tlearn: 0.0014422\ttotal: 11.6s\tremaining: 34.7s\n",
      "50:\tlearn: 0.0014122\ttotal: 11.8s\tremaining: 34.5s\n",
      "51:\tlearn: 0.0013810\ttotal: 12s\tremaining: 34.3s\n",
      "52:\tlearn: 0.0013495\ttotal: 12.3s\tremaining: 34.1s\n",
      "53:\tlearn: 0.0013244\ttotal: 12.5s\tremaining: 33.7s\n",
      "54:\tlearn: 0.0012967\ttotal: 12.7s\tremaining: 33.4s\n",
      "55:\tlearn: 0.0012875\ttotal: 12.9s\tremaining: 33.2s\n",
      "56:\tlearn: 0.0012639\ttotal: 13.1s\tremaining: 33s\n",
      "57:\tlearn: 0.0012495\ttotal: 13.4s\tremaining: 32.8s\n",
      "58:\tlearn: 0.0012336\ttotal: 13.6s\tremaining: 32.5s\n",
      "59:\tlearn: 0.0012045\ttotal: 13.9s\tremaining: 32.3s\n",
      "60:\tlearn: 0.0011830\ttotal: 14.1s\tremaining: 32.1s\n",
      "61:\tlearn: 0.0011593\ttotal: 14.3s\tremaining: 31.9s\n",
      "62:\tlearn: 0.0011397\ttotal: 14.6s\tremaining: 31.7s\n",
      "63:\tlearn: 0.0011324\ttotal: 14.8s\tremaining: 31.5s\n",
      "64:\tlearn: 0.0011210\ttotal: 15.1s\tremaining: 31.3s\n",
      "65:\tlearn: 0.0011006\ttotal: 15.3s\tremaining: 31.1s\n",
      "66:\tlearn: 0.0010891\ttotal: 15.6s\tremaining: 30.9s\n",
      "67:\tlearn: 0.0010736\ttotal: 15.8s\tremaining: 30.7s\n",
      "68:\tlearn: 0.0010611\ttotal: 16s\tremaining: 30.4s\n",
      "69:\tlearn: 0.0010528\ttotal: 16.3s\tremaining: 30.2s\n",
      "70:\tlearn: 0.0010289\ttotal: 16.5s\tremaining: 30s\n",
      "71:\tlearn: 0.0010194\ttotal: 16.7s\tremaining: 29.8s\n",
      "72:\tlearn: 0.0010048\ttotal: 17s\tremaining: 29.5s\n",
      "73:\tlearn: 0.0009830\ttotal: 17.2s\tremaining: 29.3s\n",
      "74:\tlearn: 0.0009735\ttotal: 17.5s\tremaining: 29.1s\n",
      "75:\tlearn: 0.0009660\ttotal: 17.7s\tremaining: 28.9s\n",
      "76:\tlearn: 0.0009545\ttotal: 17.9s\tremaining: 28.7s\n",
      "77:\tlearn: 0.0009372\ttotal: 18.2s\tremaining: 28.4s\n",
      "78:\tlearn: 0.0009215\ttotal: 18.4s\tremaining: 28.2s\n",
      "79:\tlearn: 0.0009170\ttotal: 18.6s\tremaining: 27.9s\n",
      "80:\tlearn: 0.0009032\ttotal: 18.9s\tremaining: 27.7s\n",
      "81:\tlearn: 0.0008936\ttotal: 19.1s\tremaining: 27.5s\n",
      "82:\tlearn: 0.0008824\ttotal: 19.3s\tremaining: 27.3s\n",
      "83:\tlearn: 0.0008746\ttotal: 19.6s\tremaining: 27s\n",
      "84:\tlearn: 0.0008650\ttotal: 19.8s\tremaining: 26.8s\n",
      "85:\tlearn: 0.0008575\ttotal: 20s\tremaining: 26.6s\n",
      "86:\tlearn: 0.0008509\ttotal: 20.3s\tremaining: 26.3s\n",
      "87:\tlearn: 0.0008328\ttotal: 20.5s\tremaining: 26.1s\n",
      "88:\tlearn: 0.0008084\ttotal: 20.7s\tremaining: 25.9s\n",
      "89:\tlearn: 0.0007961\ttotal: 21s\tremaining: 25.6s\n",
      "90:\tlearn: 0.0007825\ttotal: 21.2s\tremaining: 25.4s\n",
      "91:\tlearn: 0.0007727\ttotal: 21.5s\tremaining: 25.2s\n",
      "92:\tlearn: 0.0007681\ttotal: 21.7s\tremaining: 25s\n",
      "93:\tlearn: 0.0007623\ttotal: 21.9s\tremaining: 24.7s\n",
      "94:\tlearn: 0.0007569\ttotal: 22.2s\tremaining: 24.5s\n",
      "95:\tlearn: 0.0007450\ttotal: 22.4s\tremaining: 24.3s\n",
      "96:\tlearn: 0.0007355\ttotal: 22.6s\tremaining: 24s\n",
      "97:\tlearn: 0.0007267\ttotal: 22.9s\tremaining: 23.8s\n",
      "98:\tlearn: 0.0007130\ttotal: 23.1s\tremaining: 23.6s\n",
      "99:\tlearn: 0.0007054\ttotal: 23.3s\tremaining: 23.3s\n",
      "100:\tlearn: 0.0007022\ttotal: 23.6s\tremaining: 23.1s\n",
      "101:\tlearn: 0.0006862\ttotal: 23.8s\tremaining: 22.9s\n",
      "102:\tlearn: 0.0006773\ttotal: 24s\tremaining: 22.6s\n",
      "103:\tlearn: 0.0006687\ttotal: 24.3s\tremaining: 22.4s\n",
      "104:\tlearn: 0.0006651\ttotal: 24.5s\tremaining: 22.2s\n",
      "105:\tlearn: 0.0006577\ttotal: 24.7s\tremaining: 21.9s\n",
      "106:\tlearn: 0.0006484\ttotal: 25s\tremaining: 21.7s\n",
      "107:\tlearn: 0.0006365\ttotal: 25.2s\tremaining: 21.5s\n",
      "108:\tlearn: 0.0006212\ttotal: 25.5s\tremaining: 21.3s\n",
      "109:\tlearn: 0.0006181\ttotal: 25.7s\tremaining: 21s\n",
      "110:\tlearn: 0.0006084\ttotal: 26s\tremaining: 20.8s\n",
      "111:\tlearn: 0.0006039\ttotal: 26.2s\tremaining: 20.6s\n",
      "112:\tlearn: 0.0005993\ttotal: 26.4s\tremaining: 20.3s\n",
      "113:\tlearn: 0.0005937\ttotal: 26.6s\tremaining: 20.1s\n",
      "114:\tlearn: 0.0005910\ttotal: 26.9s\tremaining: 19.9s\n",
      "115:\tlearn: 0.0005863\ttotal: 27.1s\tremaining: 19.6s\n",
      "116:\tlearn: 0.0005803\ttotal: 27.3s\tremaining: 19.4s\n",
      "117:\tlearn: 0.0005717\ttotal: 27.6s\tremaining: 19.2s\n",
      "118:\tlearn: 0.0005621\ttotal: 27.8s\tremaining: 18.9s\n",
      "119:\tlearn: 0.0005569\ttotal: 28s\tremaining: 18.7s\n",
      "120:\tlearn: 0.0005469\ttotal: 28.3s\tremaining: 18.5s\n",
      "121:\tlearn: 0.0005378\ttotal: 28.5s\tremaining: 18.2s\n",
      "122:\tlearn: 0.0005338\ttotal: 28.7s\tremaining: 18s\n",
      "123:\tlearn: 0.0005237\ttotal: 29s\tremaining: 17.7s\n",
      "124:\tlearn: 0.0005213\ttotal: 29.2s\tremaining: 17.5s\n",
      "125:\tlearn: 0.0005134\ttotal: 29.4s\tremaining: 17.3s\n",
      "126:\tlearn: 0.0005070\ttotal: 29.7s\tremaining: 17.1s\n",
      "127:\tlearn: 0.0005042\ttotal: 29.9s\tremaining: 16.8s\n",
      "128:\tlearn: 0.0005015\ttotal: 30.1s\tremaining: 16.6s\n",
      "129:\tlearn: 0.0004991\ttotal: 30.4s\tremaining: 16.3s\n",
      "130:\tlearn: 0.0004908\ttotal: 30.6s\tremaining: 16.1s\n",
      "131:\tlearn: 0.0004852\ttotal: 30.8s\tremaining: 15.9s\n",
      "132:\tlearn: 0.0004819\ttotal: 31.1s\tremaining: 15.7s\n",
      "133:\tlearn: 0.0004790\ttotal: 31.3s\tremaining: 15.4s\n",
      "134:\tlearn: 0.0004680\ttotal: 31.6s\tremaining: 15.2s\n",
      "135:\tlearn: 0.0004583\ttotal: 31.8s\tremaining: 15s\n",
      "136:\tlearn: 0.0004472\ttotal: 32s\tremaining: 14.7s\n",
      "137:\tlearn: 0.0004461\ttotal: 32.3s\tremaining: 14.5s\n",
      "138:\tlearn: 0.0004390\ttotal: 32.5s\tremaining: 14.3s\n",
      "139:\tlearn: 0.0004304\ttotal: 32.8s\tremaining: 14s\n",
      "140:\tlearn: 0.0004236\ttotal: 32.9s\tremaining: 13.8s\n",
      "141:\tlearn: 0.0004178\ttotal: 33.1s\tremaining: 13.5s\n",
      "142:\tlearn: 0.0004127\ttotal: 33.4s\tremaining: 13.3s\n",
      "143:\tlearn: 0.0004057\ttotal: 33.6s\tremaining: 13.1s\n",
      "144:\tlearn: 0.0004034\ttotal: 33.9s\tremaining: 12.8s\n",
      "145:\tlearn: 0.0003958\ttotal: 34.1s\tremaining: 12.6s\n",
      "146:\tlearn: 0.0003886\ttotal: 34.4s\tremaining: 12.4s\n",
      "147:\tlearn: 0.0003862\ttotal: 34.6s\tremaining: 12.2s\n",
      "148:\tlearn: 0.0003794\ttotal: 34.8s\tremaining: 11.9s\n",
      "149:\tlearn: 0.0003707\ttotal: 35s\tremaining: 11.7s\n",
      "150:\tlearn: 0.0003633\ttotal: 35.3s\tremaining: 11.4s\n",
      "151:\tlearn: 0.0003616\ttotal: 35.5s\tremaining: 11.2s\n",
      "152:\tlearn: 0.0003601\ttotal: 35.7s\tremaining: 11s\n",
      "153:\tlearn: 0.0003566\ttotal: 35.9s\tremaining: 10.7s\n",
      "154:\tlearn: 0.0003535\ttotal: 36.2s\tremaining: 10.5s\n",
      "155:\tlearn: 0.0003505\ttotal: 36.4s\tremaining: 10.3s\n",
      "156:\tlearn: 0.0003445\ttotal: 36.6s\tremaining: 10s\n",
      "157:\tlearn: 0.0003372\ttotal: 36.9s\tremaining: 9.8s\n",
      "158:\tlearn: 0.0003349\ttotal: 37.1s\tremaining: 9.57s\n",
      "159:\tlearn: 0.0003327\ttotal: 37.3s\tremaining: 9.33s\n",
      "160:\tlearn: 0.0003265\ttotal: 37.6s\tremaining: 9.1s\n",
      "161:\tlearn: 0.0003223\ttotal: 37.8s\tremaining: 8.87s\n",
      "162:\tlearn: 0.0003200\ttotal: 38s\tremaining: 8.63s\n",
      "163:\tlearn: 0.0003168\ttotal: 38.2s\tremaining: 8.39s\n",
      "164:\tlearn: 0.0003155\ttotal: 38.5s\tremaining: 8.16s\n",
      "165:\tlearn: 0.0003116\ttotal: 38.7s\tremaining: 7.93s\n",
      "166:\tlearn: 0.0003048\ttotal: 38.9s\tremaining: 7.7s\n",
      "167:\tlearn: 0.0003022\ttotal: 39.2s\tremaining: 7.46s\n",
      "168:\tlearn: 0.0002983\ttotal: 39.4s\tremaining: 7.23s\n",
      "169:\tlearn: 0.0002940\ttotal: 39.7s\tremaining: 7s\n",
      "170:\tlearn: 0.0002906\ttotal: 39.9s\tremaining: 6.76s\n",
      "171:\tlearn: 0.0002846\ttotal: 40.1s\tremaining: 6.53s\n",
      "172:\tlearn: 0.0002797\ttotal: 40.4s\tremaining: 6.3s\n",
      "173:\tlearn: 0.0002735\ttotal: 40.6s\tremaining: 6.07s\n",
      "174:\tlearn: 0.0002726\ttotal: 40.8s\tremaining: 5.83s\n",
      "175:\tlearn: 0.0002666\ttotal: 41.1s\tremaining: 5.6s\n",
      "176:\tlearn: 0.0002640\ttotal: 41.3s\tremaining: 5.37s\n",
      "177:\tlearn: 0.0002626\ttotal: 41.5s\tremaining: 5.13s\n",
      "178:\tlearn: 0.0002595\ttotal: 41.8s\tremaining: 4.9s\n",
      "179:\tlearn: 0.0002584\ttotal: 42s\tremaining: 4.67s\n",
      "180:\tlearn: 0.0002574\ttotal: 42.3s\tremaining: 4.43s\n",
      "181:\tlearn: 0.0002562\ttotal: 42.5s\tremaining: 4.2s\n",
      "182:\tlearn: 0.0002544\ttotal: 42.7s\tremaining: 3.97s\n",
      "183:\tlearn: 0.0002533\ttotal: 43s\tremaining: 3.73s\n",
      "184:\tlearn: 0.0002484\ttotal: 43.2s\tremaining: 3.5s\n",
      "185:\tlearn: 0.0002475\ttotal: 43.4s\tremaining: 3.27s\n",
      "186:\tlearn: 0.0002421\ttotal: 43.7s\tremaining: 3.04s\n",
      "187:\tlearn: 0.0002380\ttotal: 43.9s\tremaining: 2.8s\n",
      "188:\tlearn: 0.0002370\ttotal: 44.1s\tremaining: 2.57s\n",
      "189:\tlearn: 0.0002337\ttotal: 44.4s\tremaining: 2.34s\n",
      "190:\tlearn: 0.0002310\ttotal: 44.6s\tremaining: 2.1s\n",
      "191:\tlearn: 0.0002291\ttotal: 44.9s\tremaining: 1.87s\n",
      "192:\tlearn: 0.0002253\ttotal: 45.1s\tremaining: 1.64s\n",
      "193:\tlearn: 0.0002228\ttotal: 45.4s\tremaining: 1.4s\n",
      "194:\tlearn: 0.0002213\ttotal: 45.6s\tremaining: 1.17s\n",
      "195:\tlearn: 0.0002193\ttotal: 45.8s\tremaining: 935ms\n",
      "196:\tlearn: 0.0002171\ttotal: 46s\tremaining: 701ms\n",
      "197:\tlearn: 0.0002157\ttotal: 46.3s\tremaining: 467ms\n",
      "198:\tlearn: 0.0002131\ttotal: 46.5s\tremaining: 234ms\n",
      "199:\tlearn: 0.0002111\ttotal: 46.8s\tremaining: 0us\n",
      "0:\tlearn: 0.4477999\ttotal: 274ms\tremaining: 54.5s\n",
      "1:\tlearn: 0.2835264\ttotal: 515ms\tremaining: 51s\n",
      "2:\tlearn: 0.1828922\ttotal: 768ms\tremaining: 50.4s\n",
      "3:\tlearn: 0.1184327\ttotal: 1.02s\tremaining: 49.8s\n",
      "4:\tlearn: 0.0790999\ttotal: 1.26s\tremaining: 49.1s\n",
      "5:\tlearn: 0.0533976\ttotal: 1.5s\tremaining: 48.4s\n",
      "6:\tlearn: 0.0373394\ttotal: 1.74s\tremaining: 47.9s\n",
      "7:\tlearn: 0.0261221\ttotal: 1.96s\tremaining: 47.2s\n",
      "8:\tlearn: 0.0188170\ttotal: 2.2s\tremaining: 46.8s\n",
      "9:\tlearn: 0.0141111\ttotal: 2.43s\tremaining: 46.2s\n",
      "10:\tlearn: 0.0110527\ttotal: 2.66s\tremaining: 45.6s\n",
      "11:\tlearn: 0.0088909\ttotal: 2.88s\tremaining: 45.1s\n",
      "12:\tlearn: 0.0073029\ttotal: 3.11s\tremaining: 44.7s\n",
      "13:\tlearn: 0.0060865\ttotal: 3.34s\tremaining: 44.3s\n",
      "14:\tlearn: 0.0052205\ttotal: 3.56s\tremaining: 43.9s\n",
      "15:\tlearn: 0.0045919\ttotal: 3.79s\tremaining: 43.6s\n",
      "16:\tlearn: 0.0040565\ttotal: 4.03s\tremaining: 43.3s\n",
      "17:\tlearn: 0.0036691\ttotal: 4.23s\tremaining: 42.8s\n",
      "18:\tlearn: 0.0033643\ttotal: 4.46s\tremaining: 42.5s\n",
      "19:\tlearn: 0.0031437\ttotal: 4.67s\tremaining: 42s\n",
      "20:\tlearn: 0.0029354\ttotal: 4.9s\tremaining: 41.8s\n",
      "21:\tlearn: 0.0027810\ttotal: 5.13s\tremaining: 41.5s\n",
      "22:\tlearn: 0.0026722\ttotal: 5.35s\tremaining: 41.2s\n",
      "23:\tlearn: 0.0025827\ttotal: 5.57s\tremaining: 40.9s\n",
      "24:\tlearn: 0.0024807\ttotal: 5.8s\tremaining: 40.6s\n",
      "25:\tlearn: 0.0023823\ttotal: 6.04s\tremaining: 40.4s\n",
      "26:\tlearn: 0.0023081\ttotal: 6.27s\tremaining: 40.2s\n",
      "27:\tlearn: 0.0022560\ttotal: 6.49s\tremaining: 39.9s\n",
      "28:\tlearn: 0.0022005\ttotal: 6.72s\tremaining: 39.6s\n",
      "29:\tlearn: 0.0021199\ttotal: 6.95s\tremaining: 39.4s\n",
      "30:\tlearn: 0.0020722\ttotal: 7.18s\tremaining: 39.1s\n",
      "31:\tlearn: 0.0020405\ttotal: 7.4s\tremaining: 38.9s\n",
      "32:\tlearn: 0.0019893\ttotal: 7.63s\tremaining: 38.6s\n",
      "33:\tlearn: 0.0019195\ttotal: 7.9s\tremaining: 38.6s\n",
      "34:\tlearn: 0.0018888\ttotal: 8.13s\tremaining: 38.3s\n",
      "35:\tlearn: 0.0018451\ttotal: 8.36s\tremaining: 38.1s\n",
      "36:\tlearn: 0.0018285\ttotal: 8.59s\tremaining: 37.8s\n",
      "37:\tlearn: 0.0018084\ttotal: 8.82s\tremaining: 37.6s\n",
      "38:\tlearn: 0.0017606\ttotal: 9.01s\tremaining: 37.2s\n",
      "39:\tlearn: 0.0017443\ttotal: 9.2s\tremaining: 36.8s\n",
      "40:\tlearn: 0.0017348\ttotal: 9.43s\tremaining: 36.6s\n",
      "41:\tlearn: 0.0016982\ttotal: 9.66s\tremaining: 36.3s\n",
      "42:\tlearn: 0.0016758\ttotal: 9.91s\tremaining: 36.2s\n",
      "43:\tlearn: 0.0016544\ttotal: 10.1s\tremaining: 35.9s\n",
      "44:\tlearn: 0.0016402\ttotal: 10.4s\tremaining: 35.7s\n",
      "45:\tlearn: 0.0016243\ttotal: 10.6s\tremaining: 35.5s\n",
      "46:\tlearn: 0.0016107\ttotal: 10.8s\tremaining: 35.2s\n",
      "47:\tlearn: 0.0015756\ttotal: 11.1s\tremaining: 35s\n",
      "48:\tlearn: 0.0015380\ttotal: 11.3s\tremaining: 34.8s\n",
      "49:\tlearn: 0.0015274\ttotal: 11.5s\tremaining: 34.5s\n",
      "50:\tlearn: 0.0015004\ttotal: 11.8s\tremaining: 34.3s\n",
      "51:\tlearn: 0.0014693\ttotal: 12s\tremaining: 34.1s\n",
      "52:\tlearn: 0.0014411\ttotal: 12.2s\tremaining: 33.9s\n",
      "53:\tlearn: 0.0014129\ttotal: 12.5s\tremaining: 33.7s\n",
      "54:\tlearn: 0.0013858\ttotal: 12.7s\tremaining: 33.5s\n",
      "55:\tlearn: 0.0013717\ttotal: 12.9s\tremaining: 33.1s\n",
      "56:\tlearn: 0.0013475\ttotal: 13.1s\tremaining: 32.9s\n",
      "57:\tlearn: 0.0013394\ttotal: 13.3s\tremaining: 32.6s\n",
      "58:\tlearn: 0.0013154\ttotal: 13.6s\tremaining: 32.4s\n",
      "59:\tlearn: 0.0012823\ttotal: 13.8s\tremaining: 32.2s\n",
      "60:\tlearn: 0.0012580\ttotal: 14.1s\tremaining: 32s\n",
      "61:\tlearn: 0.0012412\ttotal: 14.3s\tremaining: 31.8s\n",
      "62:\tlearn: 0.0012320\ttotal: 14.5s\tremaining: 31.6s\n",
      "63:\tlearn: 0.0012136\ttotal: 14.8s\tremaining: 31.4s\n",
      "64:\tlearn: 0.0012066\ttotal: 15s\tremaining: 31.1s\n",
      "65:\tlearn: 0.0011846\ttotal: 15.2s\tremaining: 30.9s\n",
      "66:\tlearn: 0.0011652\ttotal: 15.5s\tremaining: 30.7s\n",
      "67:\tlearn: 0.0011567\ttotal: 15.7s\tremaining: 30.4s\n",
      "68:\tlearn: 0.0011313\ttotal: 15.9s\tremaining: 30.2s\n",
      "69:\tlearn: 0.0011136\ttotal: 16.1s\tremaining: 30s\n",
      "70:\tlearn: 0.0010989\ttotal: 16.4s\tremaining: 29.8s\n",
      "71:\tlearn: 0.0010863\ttotal: 16.6s\tremaining: 29.5s\n",
      "72:\tlearn: 0.0010749\ttotal: 16.8s\tremaining: 29.3s\n",
      "73:\tlearn: 0.0010619\ttotal: 17.1s\tremaining: 29.1s\n",
      "74:\tlearn: 0.0010482\ttotal: 17.3s\tremaining: 28.9s\n",
      "75:\tlearn: 0.0010412\ttotal: 17.5s\tremaining: 28.6s\n",
      "76:\tlearn: 0.0010270\ttotal: 17.8s\tremaining: 28.4s\n",
      "77:\tlearn: 0.0010188\ttotal: 18s\tremaining: 28.2s\n",
      "78:\tlearn: 0.0010083\ttotal: 18.2s\tremaining: 27.9s\n",
      "79:\tlearn: 0.0010007\ttotal: 18.5s\tremaining: 27.7s\n",
      "80:\tlearn: 0.0009925\ttotal: 18.7s\tremaining: 27.5s\n",
      "81:\tlearn: 0.0009735\ttotal: 18.9s\tremaining: 27.3s\n",
      "82:\tlearn: 0.0009641\ttotal: 19.2s\tremaining: 27s\n",
      "83:\tlearn: 0.0009518\ttotal: 19.4s\tremaining: 26.8s\n",
      "84:\tlearn: 0.0009438\ttotal: 19.6s\tremaining: 26.6s\n",
      "85:\tlearn: 0.0009301\ttotal: 19.9s\tremaining: 26.3s\n",
      "86:\tlearn: 0.0009245\ttotal: 20.1s\tremaining: 26.1s\n",
      "87:\tlearn: 0.0009142\ttotal: 20.3s\tremaining: 25.9s\n",
      "88:\tlearn: 0.0009099\ttotal: 20.6s\tremaining: 25.6s\n",
      "89:\tlearn: 0.0009005\ttotal: 20.8s\tremaining: 25.4s\n",
      "90:\tlearn: 0.0008871\ttotal: 21.1s\tremaining: 25.2s\n",
      "91:\tlearn: 0.0008741\ttotal: 21.3s\tremaining: 25s\n",
      "92:\tlearn: 0.0008695\ttotal: 21.5s\tremaining: 24.8s\n",
      "93:\tlearn: 0.0008558\ttotal: 21.8s\tremaining: 24.6s\n",
      "94:\tlearn: 0.0008429\ttotal: 22s\tremaining: 24.3s\n",
      "95:\tlearn: 0.0008339\ttotal: 22.2s\tremaining: 24.1s\n",
      "96:\tlearn: 0.0008229\ttotal: 22.4s\tremaining: 23.8s\n",
      "97:\tlearn: 0.0008131\ttotal: 22.6s\tremaining: 23.6s\n",
      "98:\tlearn: 0.0008070\ttotal: 22.9s\tremaining: 23.3s\n",
      "99:\tlearn: 0.0008005\ttotal: 23.1s\tremaining: 23.1s\n",
      "100:\tlearn: 0.0007923\ttotal: 23.4s\tremaining: 22.9s\n",
      "101:\tlearn: 0.0007861\ttotal: 23.6s\tremaining: 22.7s\n",
      "102:\tlearn: 0.0007715\ttotal: 23.8s\tremaining: 22.4s\n",
      "103:\tlearn: 0.0007663\ttotal: 24.1s\tremaining: 22.2s\n",
      "104:\tlearn: 0.0007546\ttotal: 24.3s\tremaining: 22s\n",
      "105:\tlearn: 0.0007502\ttotal: 24.5s\tremaining: 21.8s\n",
      "106:\tlearn: 0.0007445\ttotal: 24.8s\tremaining: 21.5s\n",
      "107:\tlearn: 0.0007399\ttotal: 25s\tremaining: 21.3s\n",
      "108:\tlearn: 0.0007330\ttotal: 25.2s\tremaining: 21.1s\n",
      "109:\tlearn: 0.0007276\ttotal: 25.5s\tremaining: 20.8s\n",
      "110:\tlearn: 0.0007172\ttotal: 25.7s\tremaining: 20.6s\n",
      "111:\tlearn: 0.0007062\ttotal: 25.9s\tremaining: 20.4s\n",
      "112:\tlearn: 0.0007014\ttotal: 26.2s\tremaining: 20.1s\n",
      "113:\tlearn: 0.0006879\ttotal: 26.4s\tremaining: 19.9s\n",
      "114:\tlearn: 0.0006855\ttotal: 26.6s\tremaining: 19.7s\n",
      "115:\tlearn: 0.0006805\ttotal: 26.9s\tremaining: 19.5s\n",
      "116:\tlearn: 0.0006727\ttotal: 27.1s\tremaining: 19.2s\n",
      "117:\tlearn: 0.0006653\ttotal: 27.4s\tremaining: 19s\n",
      "118:\tlearn: 0.0006503\ttotal: 27.6s\tremaining: 18.8s\n",
      "119:\tlearn: 0.0006417\ttotal: 27.8s\tremaining: 18.5s\n",
      "120:\tlearn: 0.0006388\ttotal: 28s\tremaining: 18.3s\n",
      "121:\tlearn: 0.0006319\ttotal: 28.3s\tremaining: 18.1s\n",
      "122:\tlearn: 0.0006252\ttotal: 28.5s\tremaining: 17.8s\n",
      "123:\tlearn: 0.0006208\ttotal: 28.7s\tremaining: 17.6s\n",
      "124:\tlearn: 0.0006183\ttotal: 28.9s\tremaining: 17.4s\n",
      "125:\tlearn: 0.0006131\ttotal: 29.2s\tremaining: 17.1s\n",
      "126:\tlearn: 0.0006047\ttotal: 29.5s\tremaining: 16.9s\n",
      "127:\tlearn: 0.0005966\ttotal: 29.7s\tremaining: 16.7s\n",
      "128:\tlearn: 0.0005897\ttotal: 29.9s\tremaining: 16.5s\n",
      "129:\tlearn: 0.0005865\ttotal: 30.1s\tremaining: 16.2s\n",
      "130:\tlearn: 0.0005800\ttotal: 30.4s\tremaining: 16s\n",
      "131:\tlearn: 0.0005741\ttotal: 30.6s\tremaining: 15.8s\n",
      "132:\tlearn: 0.0005691\ttotal: 30.8s\tremaining: 15.5s\n",
      "133:\tlearn: 0.0005603\ttotal: 31s\tremaining: 15.3s\n",
      "134:\tlearn: 0.0005566\ttotal: 31.1s\tremaining: 15s\n",
      "135:\tlearn: 0.0005436\ttotal: 31.4s\tremaining: 14.8s\n",
      "136:\tlearn: 0.0005349\ttotal: 31.6s\tremaining: 14.5s\n",
      "137:\tlearn: 0.0005330\ttotal: 31.8s\tremaining: 14.3s\n",
      "138:\tlearn: 0.0005292\ttotal: 32s\tremaining: 14.1s\n",
      "139:\tlearn: 0.0005254\ttotal: 32.3s\tremaining: 13.8s\n",
      "140:\tlearn: 0.0005189\ttotal: 32.5s\tremaining: 13.6s\n",
      "141:\tlearn: 0.0005161\ttotal: 32.7s\tremaining: 13.4s\n",
      "142:\tlearn: 0.0005134\ttotal: 32.9s\tremaining: 13.1s\n",
      "143:\tlearn: 0.0005082\ttotal: 33.2s\tremaining: 12.9s\n",
      "144:\tlearn: 0.0005000\ttotal: 33.4s\tremaining: 12.7s\n",
      "145:\tlearn: 0.0004946\ttotal: 33.6s\tremaining: 12.4s\n",
      "146:\tlearn: 0.0004862\ttotal: 33.8s\tremaining: 12.2s\n",
      "147:\tlearn: 0.0004798\ttotal: 34s\tremaining: 11.9s\n",
      "148:\tlearn: 0.0004734\ttotal: 34.2s\tremaining: 11.7s\n",
      "149:\tlearn: 0.0004686\ttotal: 34.5s\tremaining: 11.5s\n",
      "150:\tlearn: 0.0004655\ttotal: 34.7s\tremaining: 11.3s\n",
      "151:\tlearn: 0.0004584\ttotal: 34.9s\tremaining: 11s\n",
      "152:\tlearn: 0.0004552\ttotal: 35.2s\tremaining: 10.8s\n",
      "153:\tlearn: 0.0004528\ttotal: 35.4s\tremaining: 10.6s\n",
      "154:\tlearn: 0.0004490\ttotal: 35.6s\tremaining: 10.3s\n",
      "155:\tlearn: 0.0004434\ttotal: 35.9s\tremaining: 10.1s\n",
      "156:\tlearn: 0.0004346\ttotal: 36.1s\tremaining: 9.89s\n",
      "157:\tlearn: 0.0004288\ttotal: 36.4s\tremaining: 9.66s\n",
      "158:\tlearn: 0.0004250\ttotal: 36.6s\tremaining: 9.44s\n",
      "159:\tlearn: 0.0004228\ttotal: 36.8s\tremaining: 9.2s\n",
      "160:\tlearn: 0.0004168\ttotal: 37s\tremaining: 8.97s\n",
      "161:\tlearn: 0.0004078\ttotal: 37.3s\tremaining: 8.75s\n",
      "162:\tlearn: 0.0004018\ttotal: 37.5s\tremaining: 8.52s\n",
      "163:\tlearn: 0.0003910\ttotal: 37.8s\tremaining: 8.29s\n",
      "164:\tlearn: 0.0003852\ttotal: 38s\tremaining: 8.06s\n",
      "165:\tlearn: 0.0003769\ttotal: 38.2s\tremaining: 7.83s\n",
      "166:\tlearn: 0.0003702\ttotal: 38.5s\tremaining: 7.6s\n",
      "167:\tlearn: 0.0003586\ttotal: 38.7s\tremaining: 7.38s\n",
      "168:\tlearn: 0.0003522\ttotal: 39s\tremaining: 7.15s\n",
      "169:\tlearn: 0.0003483\ttotal: 39.2s\tremaining: 6.92s\n",
      "170:\tlearn: 0.0003466\ttotal: 39.4s\tremaining: 6.69s\n",
      "171:\tlearn: 0.0003450\ttotal: 39.7s\tremaining: 6.46s\n",
      "172:\tlearn: 0.0003397\ttotal: 39.9s\tremaining: 6.23s\n",
      "173:\tlearn: 0.0003360\ttotal: 40.2s\tremaining: 6s\n",
      "174:\tlearn: 0.0003315\ttotal: 40.4s\tremaining: 5.77s\n",
      "175:\tlearn: 0.0003301\ttotal: 40.6s\tremaining: 5.54s\n",
      "176:\tlearn: 0.0003273\ttotal: 40.8s\tremaining: 5.31s\n",
      "177:\tlearn: 0.0003262\ttotal: 41.1s\tremaining: 5.08s\n",
      "178:\tlearn: 0.0003219\ttotal: 41.3s\tremaining: 4.85s\n",
      "179:\tlearn: 0.0003202\ttotal: 41.5s\tremaining: 4.62s\n",
      "180:\tlearn: 0.0003160\ttotal: 41.8s\tremaining: 4.38s\n",
      "181:\tlearn: 0.0003105\ttotal: 42s\tremaining: 4.16s\n",
      "182:\tlearn: 0.0003080\ttotal: 42.3s\tremaining: 3.92s\n",
      "183:\tlearn: 0.0003030\ttotal: 42.5s\tremaining: 3.69s\n",
      "184:\tlearn: 0.0003009\ttotal: 42.7s\tremaining: 3.46s\n",
      "185:\tlearn: 0.0002997\ttotal: 42.9s\tremaining: 3.23s\n",
      "186:\tlearn: 0.0002971\ttotal: 43.2s\tremaining: 3s\n",
      "187:\tlearn: 0.0002939\ttotal: 43.4s\tremaining: 2.77s\n",
      "188:\tlearn: 0.0002895\ttotal: 43.6s\tremaining: 2.54s\n",
      "189:\tlearn: 0.0002841\ttotal: 43.9s\tremaining: 2.31s\n",
      "190:\tlearn: 0.0002814\ttotal: 44.1s\tremaining: 2.08s\n",
      "191:\tlearn: 0.0002781\ttotal: 44.3s\tremaining: 1.85s\n",
      "192:\tlearn: 0.0002748\ttotal: 44.6s\tremaining: 1.62s\n",
      "193:\tlearn: 0.0002732\ttotal: 44.8s\tremaining: 1.39s\n",
      "194:\tlearn: 0.0002699\ttotal: 45s\tremaining: 1.16s\n",
      "195:\tlearn: 0.0002619\ttotal: 45.3s\tremaining: 924ms\n",
      "196:\tlearn: 0.0002607\ttotal: 45.5s\tremaining: 693ms\n",
      "197:\tlearn: 0.0002598\ttotal: 45.7s\tremaining: 462ms\n",
      "198:\tlearn: 0.0002578\ttotal: 46s\tremaining: 231ms\n",
      "199:\tlearn: 0.0002551\ttotal: 46.2s\tremaining: 0us\n",
      "0:\tlearn: 0.4483260\ttotal: 236ms\tremaining: 47s\n",
      "1:\tlearn: 0.2827043\ttotal: 467ms\tremaining: 46.3s\n",
      "2:\tlearn: 0.1834980\ttotal: 715ms\tremaining: 47s\n",
      "3:\tlearn: 0.1188722\ttotal: 965ms\tremaining: 47.3s\n",
      "4:\tlearn: 0.0794388\ttotal: 1.2s\tremaining: 46.9s\n",
      "5:\tlearn: 0.0540687\ttotal: 1.44s\tremaining: 46.6s\n",
      "6:\tlearn: 0.0379381\ttotal: 1.68s\tremaining: 46.2s\n",
      "7:\tlearn: 0.0264835\ttotal: 1.91s\tremaining: 45.8s\n",
      "8:\tlearn: 0.0188828\ttotal: 2.14s\tremaining: 45.5s\n",
      "9:\tlearn: 0.0142085\ttotal: 2.38s\tremaining: 45.2s\n",
      "10:\tlearn: 0.0109171\ttotal: 2.61s\tremaining: 44.9s\n",
      "11:\tlearn: 0.0087107\ttotal: 2.85s\tremaining: 44.7s\n",
      "12:\tlearn: 0.0071705\ttotal: 3.08s\tremaining: 44.3s\n",
      "13:\tlearn: 0.0060879\ttotal: 3.3s\tremaining: 43.9s\n",
      "14:\tlearn: 0.0052545\ttotal: 3.54s\tremaining: 43.6s\n",
      "15:\tlearn: 0.0046573\ttotal: 3.76s\tremaining: 43.3s\n",
      "16:\tlearn: 0.0041632\ttotal: 3.99s\tremaining: 43s\n",
      "17:\tlearn: 0.0037974\ttotal: 4.22s\tremaining: 42.6s\n",
      "18:\tlearn: 0.0035106\ttotal: 4.43s\tremaining: 42.3s\n",
      "19:\tlearn: 0.0032780\ttotal: 4.66s\tremaining: 42s\n",
      "20:\tlearn: 0.0030902\ttotal: 4.88s\tremaining: 41.6s\n",
      "21:\tlearn: 0.0029278\ttotal: 5.12s\tremaining: 41.4s\n",
      "22:\tlearn: 0.0028040\ttotal: 5.33s\tremaining: 41.1s\n",
      "23:\tlearn: 0.0026741\ttotal: 5.56s\tremaining: 40.8s\n",
      "24:\tlearn: 0.0025714\ttotal: 5.79s\tremaining: 40.5s\n",
      "25:\tlearn: 0.0024989\ttotal: 6.01s\tremaining: 40.2s\n",
      "26:\tlearn: 0.0024330\ttotal: 6.23s\tremaining: 39.9s\n",
      "27:\tlearn: 0.0023767\ttotal: 6.45s\tremaining: 39.6s\n",
      "28:\tlearn: 0.0023268\ttotal: 6.68s\tremaining: 39.4s\n",
      "29:\tlearn: 0.0022855\ttotal: 6.9s\tremaining: 39.1s\n",
      "30:\tlearn: 0.0022289\ttotal: 7.14s\tremaining: 38.9s\n",
      "31:\tlearn: 0.0021962\ttotal: 7.38s\tremaining: 38.7s\n",
      "32:\tlearn: 0.0021685\ttotal: 7.6s\tremaining: 38.4s\n",
      "33:\tlearn: 0.0021371\ttotal: 7.82s\tremaining: 38.2s\n",
      "34:\tlearn: 0.0020884\ttotal: 8.07s\tremaining: 38s\n",
      "35:\tlearn: 0.0020595\ttotal: 8.3s\tremaining: 37.8s\n",
      "36:\tlearn: 0.0020054\ttotal: 8.55s\tremaining: 37.7s\n",
      "37:\tlearn: 0.0019874\ttotal: 8.77s\tremaining: 37.4s\n",
      "38:\tlearn: 0.0019547\ttotal: 9s\tremaining: 37.2s\n",
      "39:\tlearn: 0.0019079\ttotal: 9.23s\tremaining: 36.9s\n",
      "40:\tlearn: 0.0018777\ttotal: 9.47s\tremaining: 36.7s\n",
      "41:\tlearn: 0.0018638\ttotal: 9.7s\tremaining: 36.5s\n",
      "42:\tlearn: 0.0018338\ttotal: 9.95s\tremaining: 36.3s\n",
      "43:\tlearn: 0.0018140\ttotal: 10.2s\tremaining: 36.1s\n",
      "44:\tlearn: 0.0017854\ttotal: 10.4s\tremaining: 35.8s\n",
      "45:\tlearn: 0.0017522\ttotal: 10.6s\tremaining: 35.6s\n",
      "46:\tlearn: 0.0017392\ttotal: 10.9s\tremaining: 35.4s\n",
      "47:\tlearn: 0.0017265\ttotal: 11.1s\tremaining: 35.2s\n",
      "48:\tlearn: 0.0016992\ttotal: 11.3s\tremaining: 34.7s\n",
      "49:\tlearn: 0.0016801\ttotal: 11.5s\tremaining: 34.5s\n",
      "50:\tlearn: 0.0016613\ttotal: 11.7s\tremaining: 34.2s\n",
      "51:\tlearn: 0.0016479\ttotal: 12s\tremaining: 34s\n",
      "52:\tlearn: 0.0016204\ttotal: 12.2s\tremaining: 33.8s\n",
      "53:\tlearn: 0.0015869\ttotal: 12.4s\tremaining: 33.6s\n",
      "54:\tlearn: 0.0015607\ttotal: 12.7s\tremaining: 33.4s\n",
      "55:\tlearn: 0.0015435\ttotal: 12.9s\tremaining: 33.2s\n",
      "56:\tlearn: 0.0015227\ttotal: 13.1s\tremaining: 33s\n",
      "57:\tlearn: 0.0015112\ttotal: 13.4s\tremaining: 32.7s\n",
      "58:\tlearn: 0.0014953\ttotal: 13.6s\tremaining: 32.5s\n",
      "59:\tlearn: 0.0014693\ttotal: 13.9s\tremaining: 32.3s\n",
      "60:\tlearn: 0.0014469\ttotal: 14.1s\tremaining: 32.1s\n",
      "61:\tlearn: 0.0014277\ttotal: 14.3s\tremaining: 31.9s\n",
      "62:\tlearn: 0.0014118\ttotal: 14.6s\tremaining: 31.7s\n",
      "63:\tlearn: 0.0013995\ttotal: 14.8s\tremaining: 31.4s\n",
      "64:\tlearn: 0.0013797\ttotal: 15s\tremaining: 31.2s\n",
      "65:\tlearn: 0.0013700\ttotal: 15.3s\tremaining: 31s\n",
      "66:\tlearn: 0.0013496\ttotal: 15.5s\tremaining: 30.8s\n",
      "67:\tlearn: 0.0013347\ttotal: 15.7s\tremaining: 30.5s\n",
      "68:\tlearn: 0.0013220\ttotal: 15.9s\tremaining: 30.3s\n",
      "69:\tlearn: 0.0013125\ttotal: 16.2s\tremaining: 30s\n",
      "70:\tlearn: 0.0012949\ttotal: 16.4s\tremaining: 29.8s\n",
      "71:\tlearn: 0.0012727\ttotal: 16.7s\tremaining: 29.6s\n",
      "72:\tlearn: 0.0012613\ttotal: 16.9s\tremaining: 29.4s\n",
      "73:\tlearn: 0.0012531\ttotal: 17.1s\tremaining: 29.1s\n",
      "74:\tlearn: 0.0012306\ttotal: 17.4s\tremaining: 28.9s\n",
      "75:\tlearn: 0.0012146\ttotal: 17.6s\tremaining: 28.7s\n",
      "76:\tlearn: 0.0012006\ttotal: 17.8s\tremaining: 28.5s\n",
      "77:\tlearn: 0.0011892\ttotal: 18s\tremaining: 28.2s\n",
      "78:\tlearn: 0.0011695\ttotal: 18.3s\tremaining: 28s\n",
      "79:\tlearn: 0.0011506\ttotal: 18.5s\tremaining: 27.8s\n",
      "80:\tlearn: 0.0011375\ttotal: 18.7s\tremaining: 27.5s\n",
      "81:\tlearn: 0.0011232\ttotal: 19s\tremaining: 27.3s\n",
      "82:\tlearn: 0.0011065\ttotal: 19.2s\tremaining: 27.1s\n",
      "83:\tlearn: 0.0010922\ttotal: 19.5s\tremaining: 26.9s\n",
      "84:\tlearn: 0.0010792\ttotal: 19.7s\tremaining: 26.6s\n",
      "85:\tlearn: 0.0010623\ttotal: 19.9s\tremaining: 26.4s\n",
      "86:\tlearn: 0.0010557\ttotal: 20.2s\tremaining: 26.2s\n",
      "87:\tlearn: 0.0010383\ttotal: 20.4s\tremaining: 26s\n",
      "88:\tlearn: 0.0010291\ttotal: 20.6s\tremaining: 25.7s\n",
      "89:\tlearn: 0.0010234\ttotal: 20.9s\tremaining: 25.5s\n",
      "90:\tlearn: 0.0010068\ttotal: 21.1s\tremaining: 25.3s\n",
      "91:\tlearn: 0.0009951\ttotal: 21.4s\tremaining: 25.1s\n",
      "92:\tlearn: 0.0009793\ttotal: 21.6s\tremaining: 24.9s\n",
      "93:\tlearn: 0.0009578\ttotal: 21.9s\tremaining: 24.7s\n",
      "94:\tlearn: 0.0009399\ttotal: 22.1s\tremaining: 24.5s\n",
      "95:\tlearn: 0.0009330\ttotal: 22.4s\tremaining: 24.2s\n",
      "96:\tlearn: 0.0009220\ttotal: 22.6s\tremaining: 24s\n",
      "97:\tlearn: 0.0009114\ttotal: 22.8s\tremaining: 23.8s\n",
      "98:\tlearn: 0.0009013\ttotal: 23.1s\tremaining: 23.5s\n",
      "99:\tlearn: 0.0008930\ttotal: 23.3s\tremaining: 23.3s\n",
      "100:\tlearn: 0.0008808\ttotal: 23.5s\tremaining: 23.1s\n",
      "101:\tlearn: 0.0008712\ttotal: 23.8s\tremaining: 22.8s\n",
      "102:\tlearn: 0.0008534\ttotal: 24s\tremaining: 22.6s\n",
      "103:\tlearn: 0.0008444\ttotal: 24.2s\tremaining: 22.4s\n",
      "104:\tlearn: 0.0008315\ttotal: 24.5s\tremaining: 22.2s\n",
      "105:\tlearn: 0.0008247\ttotal: 24.7s\tremaining: 21.9s\n",
      "106:\tlearn: 0.0008175\ttotal: 25s\tremaining: 21.7s\n",
      "107:\tlearn: 0.0008088\ttotal: 25.2s\tremaining: 21.5s\n",
      "108:\tlearn: 0.0008008\ttotal: 25.4s\tremaining: 21.2s\n",
      "109:\tlearn: 0.0007922\ttotal: 25.7s\tremaining: 21s\n",
      "110:\tlearn: 0.0007821\ttotal: 25.9s\tremaining: 20.8s\n",
      "111:\tlearn: 0.0007774\ttotal: 26.1s\tremaining: 20.5s\n",
      "112:\tlearn: 0.0007635\ttotal: 26.3s\tremaining: 20.3s\n",
      "113:\tlearn: 0.0007496\ttotal: 26.6s\tremaining: 20.1s\n",
      "114:\tlearn: 0.0007465\ttotal: 26.8s\tremaining: 19.8s\n",
      "115:\tlearn: 0.0007439\ttotal: 27s\tremaining: 19.6s\n",
      "116:\tlearn: 0.0007343\ttotal: 27.3s\tremaining: 19.3s\n",
      "117:\tlearn: 0.0007216\ttotal: 27.5s\tremaining: 19.1s\n",
      "118:\tlearn: 0.0007160\ttotal: 27.7s\tremaining: 18.9s\n",
      "119:\tlearn: 0.0007127\ttotal: 28s\tremaining: 18.6s\n",
      "120:\tlearn: 0.0007032\ttotal: 28.2s\tremaining: 18.4s\n",
      "121:\tlearn: 0.0006935\ttotal: 28.4s\tremaining: 18.2s\n",
      "122:\tlearn: 0.0006807\ttotal: 28.7s\tremaining: 18s\n",
      "123:\tlearn: 0.0006729\ttotal: 28.9s\tremaining: 17.7s\n",
      "124:\tlearn: 0.0006685\ttotal: 29.2s\tremaining: 17.5s\n",
      "125:\tlearn: 0.0006636\ttotal: 29.4s\tremaining: 17.3s\n",
      "126:\tlearn: 0.0006550\ttotal: 29.6s\tremaining: 17s\n",
      "127:\tlearn: 0.0006428\ttotal: 29.9s\tremaining: 16.8s\n",
      "128:\tlearn: 0.0006266\ttotal: 30.1s\tremaining: 16.6s\n",
      "129:\tlearn: 0.0006208\ttotal: 30.4s\tremaining: 16.3s\n",
      "130:\tlearn: 0.0006160\ttotal: 30.6s\tremaining: 16.1s\n",
      "131:\tlearn: 0.0006059\ttotal: 30.8s\tremaining: 15.9s\n",
      "132:\tlearn: 0.0005992\ttotal: 31.1s\tremaining: 15.7s\n",
      "133:\tlearn: 0.0005899\ttotal: 31.3s\tremaining: 15.4s\n",
      "134:\tlearn: 0.0005834\ttotal: 31.6s\tremaining: 15.2s\n",
      "135:\tlearn: 0.0005764\ttotal: 31.8s\tremaining: 15s\n",
      "136:\tlearn: 0.0005695\ttotal: 32.1s\tremaining: 14.7s\n",
      "137:\tlearn: 0.0005664\ttotal: 32.3s\tremaining: 14.5s\n",
      "138:\tlearn: 0.0005538\ttotal: 32.5s\tremaining: 14.3s\n",
      "139:\tlearn: 0.0005464\ttotal: 32.8s\tremaining: 14.1s\n",
      "140:\tlearn: 0.0005374\ttotal: 33s\tremaining: 13.8s\n",
      "141:\tlearn: 0.0005328\ttotal: 33.3s\tremaining: 13.6s\n",
      "142:\tlearn: 0.0005263\ttotal: 33.5s\tremaining: 13.4s\n",
      "143:\tlearn: 0.0005170\ttotal: 33.7s\tremaining: 13.1s\n",
      "144:\tlearn: 0.0005139\ttotal: 34s\tremaining: 12.9s\n",
      "145:\tlearn: 0.0005083\ttotal: 34.2s\tremaining: 12.6s\n",
      "146:\tlearn: 0.0005004\ttotal: 34.4s\tremaining: 12.4s\n",
      "147:\tlearn: 0.0004889\ttotal: 34.7s\tremaining: 12.2s\n",
      "148:\tlearn: 0.0004802\ttotal: 34.9s\tremaining: 11.9s\n",
      "149:\tlearn: 0.0004702\ttotal: 35.2s\tremaining: 11.7s\n",
      "150:\tlearn: 0.0004609\ttotal: 35.4s\tremaining: 11.5s\n",
      "151:\tlearn: 0.0004562\ttotal: 35.6s\tremaining: 11.3s\n",
      "152:\tlearn: 0.0004494\ttotal: 35.9s\tremaining: 11s\n",
      "153:\tlearn: 0.0004470\ttotal: 36.1s\tremaining: 10.8s\n",
      "154:\tlearn: 0.0004431\ttotal: 36.3s\tremaining: 10.5s\n",
      "155:\tlearn: 0.0004356\ttotal: 36.6s\tremaining: 10.3s\n",
      "156:\tlearn: 0.0004274\ttotal: 36.8s\tremaining: 10.1s\n",
      "157:\tlearn: 0.0004216\ttotal: 37.1s\tremaining: 9.85s\n",
      "158:\tlearn: 0.0004100\ttotal: 37.3s\tremaining: 9.62s\n",
      "159:\tlearn: 0.0004073\ttotal: 37.5s\tremaining: 9.38s\n",
      "160:\tlearn: 0.0003990\ttotal: 37.8s\tremaining: 9.15s\n",
      "161:\tlearn: 0.0003902\ttotal: 38s\tremaining: 8.92s\n",
      "162:\tlearn: 0.0003864\ttotal: 38.3s\tremaining: 8.69s\n",
      "163:\tlearn: 0.0003828\ttotal: 38.5s\tremaining: 8.45s\n",
      "164:\tlearn: 0.0003808\ttotal: 38.7s\tremaining: 8.21s\n",
      "165:\tlearn: 0.0003753\ttotal: 39s\tremaining: 7.98s\n",
      "166:\tlearn: 0.0003682\ttotal: 39.2s\tremaining: 7.75s\n",
      "167:\tlearn: 0.0003651\ttotal: 39.4s\tremaining: 7.51s\n",
      "168:\tlearn: 0.0003582\ttotal: 39.7s\tremaining: 7.28s\n",
      "169:\tlearn: 0.0003531\ttotal: 39.9s\tremaining: 7.04s\n",
      "170:\tlearn: 0.0003512\ttotal: 40.1s\tremaining: 6.81s\n",
      "171:\tlearn: 0.0003491\ttotal: 40.4s\tremaining: 6.57s\n",
      "172:\tlearn: 0.0003453\ttotal: 40.6s\tremaining: 6.34s\n",
      "173:\tlearn: 0.0003395\ttotal: 40.9s\tremaining: 6.11s\n",
      "174:\tlearn: 0.0003350\ttotal: 41.1s\tremaining: 5.87s\n",
      "175:\tlearn: 0.0003278\ttotal: 41.3s\tremaining: 5.64s\n",
      "176:\tlearn: 0.0003237\ttotal: 41.6s\tremaining: 5.4s\n",
      "177:\tlearn: 0.0003222\ttotal: 41.8s\tremaining: 5.17s\n",
      "178:\tlearn: 0.0003182\ttotal: 42s\tremaining: 4.93s\n",
      "179:\tlearn: 0.0003166\ttotal: 42.3s\tremaining: 4.7s\n",
      "180:\tlearn: 0.0003153\ttotal: 42.5s\tremaining: 4.46s\n",
      "181:\tlearn: 0.0003136\ttotal: 42.7s\tremaining: 4.22s\n",
      "182:\tlearn: 0.0003096\ttotal: 43s\tremaining: 3.99s\n",
      "183:\tlearn: 0.0003049\ttotal: 43.2s\tremaining: 3.75s\n",
      "184:\tlearn: 0.0003019\ttotal: 43.4s\tremaining: 3.52s\n",
      "185:\tlearn: 0.0003010\ttotal: 43.6s\tremaining: 3.28s\n",
      "186:\tlearn: 0.0002984\ttotal: 43.9s\tremaining: 3.05s\n",
      "187:\tlearn: 0.0002950\ttotal: 44.1s\tremaining: 2.81s\n",
      "188:\tlearn: 0.0002910\ttotal: 44.3s\tremaining: 2.58s\n",
      "189:\tlearn: 0.0002898\ttotal: 44.6s\tremaining: 2.35s\n",
      "190:\tlearn: 0.0002886\ttotal: 44.8s\tremaining: 2.11s\n",
      "191:\tlearn: 0.0002842\ttotal: 45s\tremaining: 1.88s\n",
      "192:\tlearn: 0.0002797\ttotal: 45.3s\tremaining: 1.64s\n",
      "193:\tlearn: 0.0002747\ttotal: 45.5s\tremaining: 1.41s\n",
      "194:\tlearn: 0.0002732\ttotal: 45.7s\tremaining: 1.17s\n",
      "195:\tlearn: 0.0002723\ttotal: 46s\tremaining: 938ms\n",
      "196:\tlearn: 0.0002690\ttotal: 46.2s\tremaining: 704ms\n",
      "197:\tlearn: 0.0002668\ttotal: 46.4s\tremaining: 469ms\n",
      "198:\tlearn: 0.0002638\ttotal: 46.7s\tremaining: 234ms\n",
      "199:\tlearn: 0.0002622\ttotal: 46.9s\tremaining: 0us\n",
      "0:\tlearn: 0.4472335\ttotal: 258ms\tremaining: 51.4s\n",
      "1:\tlearn: 0.2832542\ttotal: 494ms\tremaining: 48.9s\n",
      "2:\tlearn: 0.1840287\ttotal: 734ms\tremaining: 48.2s\n",
      "3:\tlearn: 0.1184804\ttotal: 988ms\tremaining: 48.4s\n",
      "4:\tlearn: 0.0775724\ttotal: 1.23s\tremaining: 48s\n",
      "5:\tlearn: 0.0524385\ttotal: 1.47s\tremaining: 47.5s\n",
      "6:\tlearn: 0.0371847\ttotal: 1.7s\tremaining: 47s\n",
      "7:\tlearn: 0.0261983\ttotal: 1.95s\tremaining: 46.9s\n",
      "8:\tlearn: 0.0189761\ttotal: 2.19s\tremaining: 46.5s\n",
      "9:\tlearn: 0.0141101\ttotal: 2.44s\tremaining: 46.3s\n",
      "10:\tlearn: 0.0110604\ttotal: 2.67s\tremaining: 45.8s\n",
      "11:\tlearn: 0.0088103\ttotal: 2.9s\tremaining: 45.4s\n",
      "12:\tlearn: 0.0072168\ttotal: 3.13s\tremaining: 45s\n",
      "13:\tlearn: 0.0061211\ttotal: 3.36s\tremaining: 44.6s\n",
      "14:\tlearn: 0.0053008\ttotal: 3.58s\tremaining: 44.2s\n",
      "15:\tlearn: 0.0047244\ttotal: 3.8s\tremaining: 43.7s\n",
      "16:\tlearn: 0.0041877\ttotal: 4.04s\tremaining: 43.5s\n",
      "17:\tlearn: 0.0038232\ttotal: 4.27s\tremaining: 43.2s\n",
      "18:\tlearn: 0.0034684\ttotal: 4.51s\tremaining: 42.9s\n",
      "19:\tlearn: 0.0032270\ttotal: 4.73s\tremaining: 42.6s\n",
      "20:\tlearn: 0.0030420\ttotal: 4.96s\tremaining: 42.3s\n",
      "21:\tlearn: 0.0028981\ttotal: 5.18s\tremaining: 42s\n",
      "22:\tlearn: 0.0027160\ttotal: 5.41s\tremaining: 41.6s\n",
      "23:\tlearn: 0.0026005\ttotal: 5.64s\tremaining: 41.3s\n",
      "24:\tlearn: 0.0025149\ttotal: 5.87s\tremaining: 41.1s\n",
      "25:\tlearn: 0.0024435\ttotal: 6.1s\tremaining: 40.8s\n",
      "26:\tlearn: 0.0023728\ttotal: 6.34s\tremaining: 40.6s\n",
      "27:\tlearn: 0.0022895\ttotal: 6.57s\tremaining: 40.3s\n",
      "28:\tlearn: 0.0022407\ttotal: 6.79s\tremaining: 40s\n",
      "29:\tlearn: 0.0022081\ttotal: 7.01s\tremaining: 39.7s\n",
      "30:\tlearn: 0.0021697\ttotal: 7.24s\tremaining: 39.5s\n",
      "31:\tlearn: 0.0021347\ttotal: 7.46s\tremaining: 39.2s\n",
      "32:\tlearn: 0.0020951\ttotal: 7.63s\tremaining: 38.6s\n",
      "33:\tlearn: 0.0020403\ttotal: 7.88s\tremaining: 38.5s\n",
      "34:\tlearn: 0.0020029\ttotal: 8.11s\tremaining: 38.2s\n",
      "35:\tlearn: 0.0019852\ttotal: 8.34s\tremaining: 38s\n",
      "36:\tlearn: 0.0019534\ttotal: 8.58s\tremaining: 37.8s\n",
      "37:\tlearn: 0.0019342\ttotal: 8.81s\tremaining: 37.6s\n",
      "38:\tlearn: 0.0018970\ttotal: 9.05s\tremaining: 37.3s\n",
      "39:\tlearn: 0.0018476\ttotal: 9.29s\tremaining: 37.2s\n",
      "40:\tlearn: 0.0018230\ttotal: 9.53s\tremaining: 37s\n",
      "41:\tlearn: 0.0017758\ttotal: 9.77s\tremaining: 36.8s\n",
      "42:\tlearn: 0.0017401\ttotal: 10s\tremaining: 36.5s\n",
      "43:\tlearn: 0.0017091\ttotal: 10.2s\tremaining: 36.3s\n",
      "44:\tlearn: 0.0016937\ttotal: 10.5s\tremaining: 36.1s\n",
      "45:\tlearn: 0.0016810\ttotal: 10.7s\tremaining: 35.8s\n",
      "46:\tlearn: 0.0016715\ttotal: 10.9s\tremaining: 35.6s\n",
      "47:\tlearn: 0.0016584\ttotal: 11.2s\tremaining: 35.3s\n",
      "48:\tlearn: 0.0016403\ttotal: 11.4s\tremaining: 35.1s\n",
      "49:\tlearn: 0.0016158\ttotal: 11.6s\tremaining: 34.8s\n",
      "50:\tlearn: 0.0015971\ttotal: 11.8s\tremaining: 34.6s\n",
      "51:\tlearn: 0.0015697\ttotal: 12.1s\tremaining: 34.4s\n",
      "52:\tlearn: 0.0015344\ttotal: 12.3s\tremaining: 34.2s\n",
      "53:\tlearn: 0.0015093\ttotal: 12.6s\tremaining: 34s\n",
      "54:\tlearn: 0.0014974\ttotal: 12.8s\tremaining: 33.8s\n",
      "55:\tlearn: 0.0014838\ttotal: 13.1s\tremaining: 33.6s\n",
      "56:\tlearn: 0.0014609\ttotal: 13.3s\tremaining: 33.3s\n",
      "57:\tlearn: 0.0014468\ttotal: 13.5s\tremaining: 33.1s\n",
      "58:\tlearn: 0.0014264\ttotal: 13.7s\tremaining: 32.8s\n",
      "59:\tlearn: 0.0014102\ttotal: 14s\tremaining: 32.6s\n",
      "60:\tlearn: 0.0013826\ttotal: 14.2s\tremaining: 32.4s\n",
      "61:\tlearn: 0.0013588\ttotal: 14.4s\tremaining: 32.2s\n",
      "62:\tlearn: 0.0013414\ttotal: 14.7s\tremaining: 32s\n",
      "63:\tlearn: 0.0013234\ttotal: 14.9s\tremaining: 31.7s\n",
      "64:\tlearn: 0.0013094\ttotal: 15.2s\tremaining: 31.5s\n",
      "65:\tlearn: 0.0012912\ttotal: 15.4s\tremaining: 31.3s\n",
      "66:\tlearn: 0.0012791\ttotal: 15.6s\tremaining: 31s\n",
      "67:\tlearn: 0.0012549\ttotal: 15.9s\tremaining: 30.8s\n",
      "68:\tlearn: 0.0012457\ttotal: 16.1s\tremaining: 30.6s\n",
      "69:\tlearn: 0.0012354\ttotal: 16.3s\tremaining: 30.3s\n",
      "70:\tlearn: 0.0012093\ttotal: 16.6s\tremaining: 30.1s\n",
      "71:\tlearn: 0.0012016\ttotal: 16.8s\tremaining: 29.9s\n",
      "72:\tlearn: 0.0011899\ttotal: 17s\tremaining: 29.6s\n",
      "73:\tlearn: 0.0011839\ttotal: 17.3s\tremaining: 29.4s\n",
      "74:\tlearn: 0.0011689\ttotal: 17.5s\tremaining: 29.2s\n",
      "75:\tlearn: 0.0011586\ttotal: 17.7s\tremaining: 28.9s\n",
      "76:\tlearn: 0.0011463\ttotal: 18s\tremaining: 28.7s\n",
      "77:\tlearn: 0.0011311\ttotal: 18.2s\tremaining: 28.5s\n",
      "78:\tlearn: 0.0011167\ttotal: 18.4s\tremaining: 28.2s\n",
      "79:\tlearn: 0.0011018\ttotal: 18.7s\tremaining: 28s\n",
      "80:\tlearn: 0.0010897\ttotal: 18.9s\tremaining: 27.7s\n",
      "81:\tlearn: 0.0010693\ttotal: 19.1s\tremaining: 27.5s\n",
      "82:\tlearn: 0.0010544\ttotal: 19.3s\tremaining: 27.3s\n",
      "83:\tlearn: 0.0010439\ttotal: 19.6s\tremaining: 27.1s\n",
      "84:\tlearn: 0.0010372\ttotal: 19.8s\tremaining: 26.8s\n",
      "85:\tlearn: 0.0010310\ttotal: 20s\tremaining: 26.5s\n",
      "86:\tlearn: 0.0010263\ttotal: 20.3s\tremaining: 26.3s\n",
      "87:\tlearn: 0.0010005\ttotal: 20.5s\tremaining: 26.1s\n",
      "88:\tlearn: 0.0009808\ttotal: 20.8s\tremaining: 25.9s\n",
      "89:\tlearn: 0.0009701\ttotal: 21s\tremaining: 25.7s\n",
      "90:\tlearn: 0.0009541\ttotal: 21.3s\tremaining: 25.5s\n",
      "91:\tlearn: 0.0009305\ttotal: 21.5s\tremaining: 25.3s\n",
      "92:\tlearn: 0.0009155\ttotal: 21.8s\tremaining: 25s\n",
      "93:\tlearn: 0.0008989\ttotal: 22s\tremaining: 24.8s\n",
      "94:\tlearn: 0.0008824\ttotal: 22.2s\tremaining: 24.6s\n",
      "95:\tlearn: 0.0008784\ttotal: 22.5s\tremaining: 24.4s\n",
      "96:\tlearn: 0.0008653\ttotal: 22.7s\tremaining: 24.1s\n",
      "97:\tlearn: 0.0008583\ttotal: 22.9s\tremaining: 23.9s\n",
      "98:\tlearn: 0.0008482\ttotal: 23.2s\tremaining: 23.7s\n",
      "99:\tlearn: 0.0008425\ttotal: 23.4s\tremaining: 23.4s\n",
      "100:\tlearn: 0.0008365\ttotal: 23.6s\tremaining: 23.2s\n",
      "101:\tlearn: 0.0008214\ttotal: 23.9s\tremaining: 23s\n",
      "102:\tlearn: 0.0008111\ttotal: 24.1s\tremaining: 22.7s\n",
      "103:\tlearn: 0.0008071\ttotal: 24.4s\tremaining: 22.5s\n",
      "104:\tlearn: 0.0007935\ttotal: 24.6s\tremaining: 22.3s\n",
      "105:\tlearn: 0.0007821\ttotal: 24.8s\tremaining: 22s\n",
      "106:\tlearn: 0.0007686\ttotal: 25.1s\tremaining: 21.8s\n",
      "107:\tlearn: 0.0007572\ttotal: 25.3s\tremaining: 21.6s\n",
      "108:\tlearn: 0.0007508\ttotal: 25.6s\tremaining: 21.3s\n",
      "109:\tlearn: 0.0007435\ttotal: 25.8s\tremaining: 21.1s\n",
      "110:\tlearn: 0.0007384\ttotal: 26s\tremaining: 20.9s\n",
      "111:\tlearn: 0.0007337\ttotal: 26.2s\tremaining: 20.6s\n",
      "112:\tlearn: 0.0007294\ttotal: 26.4s\tremaining: 20.4s\n",
      "113:\tlearn: 0.0007207\ttotal: 26.7s\tremaining: 20.1s\n",
      "114:\tlearn: 0.0007174\ttotal: 26.9s\tremaining: 19.9s\n",
      "115:\tlearn: 0.0007128\ttotal: 27.1s\tremaining: 19.7s\n",
      "116:\tlearn: 0.0007001\ttotal: 27.4s\tremaining: 19.4s\n",
      "117:\tlearn: 0.0006861\ttotal: 27.6s\tremaining: 19.2s\n",
      "118:\tlearn: 0.0006759\ttotal: 27.9s\tremaining: 19s\n",
      "119:\tlearn: 0.0006714\ttotal: 28.1s\tremaining: 18.7s\n",
      "120:\tlearn: 0.0006604\ttotal: 28.3s\tremaining: 18.5s\n",
      "121:\tlearn: 0.0006520\ttotal: 28.6s\tremaining: 18.3s\n",
      "122:\tlearn: 0.0006456\ttotal: 28.8s\tremaining: 18s\n",
      "123:\tlearn: 0.0006370\ttotal: 29.1s\tremaining: 17.8s\n",
      "124:\tlearn: 0.0006340\ttotal: 29.3s\tremaining: 17.6s\n",
      "125:\tlearn: 0.0006238\ttotal: 29.5s\tremaining: 17.3s\n",
      "126:\tlearn: 0.0006147\ttotal: 29.8s\tremaining: 17.1s\n",
      "127:\tlearn: 0.0005963\ttotal: 30s\tremaining: 16.9s\n",
      "128:\tlearn: 0.0005817\ttotal: 30.2s\tremaining: 16.6s\n",
      "129:\tlearn: 0.0005713\ttotal: 30.5s\tremaining: 16.4s\n",
      "130:\tlearn: 0.0005632\ttotal: 30.7s\tremaining: 16.2s\n",
      "131:\tlearn: 0.0005575\ttotal: 31s\tremaining: 16s\n",
      "132:\tlearn: 0.0005506\ttotal: 31.2s\tremaining: 15.7s\n",
      "133:\tlearn: 0.0005484\ttotal: 31.5s\tremaining: 15.5s\n",
      "134:\tlearn: 0.0005354\ttotal: 31.7s\tremaining: 15.3s\n",
      "135:\tlearn: 0.0005272\ttotal: 31.9s\tremaining: 15s\n",
      "136:\tlearn: 0.0005192\ttotal: 32.2s\tremaining: 14.8s\n",
      "137:\tlearn: 0.0005094\ttotal: 32.4s\tremaining: 14.6s\n",
      "138:\tlearn: 0.0005005\ttotal: 32.7s\tremaining: 14.3s\n",
      "139:\tlearn: 0.0004925\ttotal: 32.9s\tremaining: 14.1s\n",
      "140:\tlearn: 0.0004849\ttotal: 33.2s\tremaining: 13.9s\n",
      "141:\tlearn: 0.0004771\ttotal: 33.4s\tremaining: 13.7s\n",
      "142:\tlearn: 0.0004714\ttotal: 33.7s\tremaining: 13.4s\n",
      "143:\tlearn: 0.0004697\ttotal: 33.9s\tremaining: 13.2s\n",
      "144:\tlearn: 0.0004674\ttotal: 34.1s\tremaining: 12.9s\n",
      "145:\tlearn: 0.0004631\ttotal: 34.3s\tremaining: 12.7s\n",
      "146:\tlearn: 0.0004546\ttotal: 34.6s\tremaining: 12.5s\n",
      "147:\tlearn: 0.0004460\ttotal: 34.8s\tremaining: 12.2s\n",
      "148:\tlearn: 0.0004423\ttotal: 35s\tremaining: 12s\n",
      "149:\tlearn: 0.0004395\ttotal: 35.3s\tremaining: 11.8s\n",
      "150:\tlearn: 0.0004377\ttotal: 35.5s\tremaining: 11.5s\n",
      "151:\tlearn: 0.0004315\ttotal: 35.7s\tremaining: 11.3s\n",
      "152:\tlearn: 0.0004298\ttotal: 36s\tremaining: 11s\n",
      "153:\tlearn: 0.0004255\ttotal: 36.2s\tremaining: 10.8s\n",
      "154:\tlearn: 0.0004210\ttotal: 36.4s\tremaining: 10.6s\n",
      "155:\tlearn: 0.0004140\ttotal: 36.7s\tremaining: 10.3s\n",
      "156:\tlearn: 0.0004070\ttotal: 36.9s\tremaining: 10.1s\n",
      "157:\tlearn: 0.0004039\ttotal: 37.1s\tremaining: 9.87s\n",
      "158:\tlearn: 0.0004001\ttotal: 37.4s\tremaining: 9.64s\n",
      "159:\tlearn: 0.0003984\ttotal: 37.6s\tremaining: 9.4s\n",
      "160:\tlearn: 0.0003931\ttotal: 37.8s\tremaining: 9.17s\n",
      "161:\tlearn: 0.0003845\ttotal: 38.1s\tremaining: 8.93s\n",
      "162:\tlearn: 0.0003820\ttotal: 38.3s\tremaining: 8.7s\n",
      "163:\tlearn: 0.0003780\ttotal: 38.5s\tremaining: 8.46s\n",
      "164:\tlearn: 0.0003758\ttotal: 38.8s\tremaining: 8.23s\n",
      "165:\tlearn: 0.0003695\ttotal: 39.1s\tremaining: 8s\n",
      "166:\tlearn: 0.0003660\ttotal: 39.3s\tremaining: 7.76s\n",
      "167:\tlearn: 0.0003627\ttotal: 39.5s\tremaining: 7.53s\n",
      "168:\tlearn: 0.0003556\ttotal: 39.8s\tremaining: 7.29s\n",
      "169:\tlearn: 0.0003509\ttotal: 40s\tremaining: 7.06s\n",
      "170:\tlearn: 0.0003467\ttotal: 40.3s\tremaining: 6.83s\n",
      "171:\tlearn: 0.0003454\ttotal: 40.5s\tremaining: 6.59s\n",
      "172:\tlearn: 0.0003388\ttotal: 40.7s\tremaining: 6.36s\n",
      "173:\tlearn: 0.0003350\ttotal: 41s\tremaining: 6.12s\n",
      "174:\tlearn: 0.0003287\ttotal: 41.2s\tremaining: 5.89s\n",
      "175:\tlearn: 0.0003256\ttotal: 41.4s\tremaining: 5.65s\n",
      "176:\tlearn: 0.0003206\ttotal: 41.7s\tremaining: 5.42s\n",
      "177:\tlearn: 0.0003134\ttotal: 41.9s\tremaining: 5.18s\n",
      "178:\tlearn: 0.0003050\ttotal: 42.2s\tremaining: 4.95s\n",
      "179:\tlearn: 0.0003012\ttotal: 42.4s\tremaining: 4.71s\n",
      "180:\tlearn: 0.0002996\ttotal: 42.6s\tremaining: 4.47s\n",
      "181:\tlearn: 0.0002982\ttotal: 42.9s\tremaining: 4.24s\n",
      "182:\tlearn: 0.0002924\ttotal: 43.1s\tremaining: 4s\n",
      "183:\tlearn: 0.0002901\ttotal: 43.3s\tremaining: 3.76s\n",
      "184:\tlearn: 0.0002888\ttotal: 43.5s\tremaining: 3.53s\n",
      "185:\tlearn: 0.0002880\ttotal: 43.7s\tremaining: 3.29s\n",
      "186:\tlearn: 0.0002838\ttotal: 43.9s\tremaining: 3.05s\n",
      "187:\tlearn: 0.0002802\ttotal: 44.1s\tremaining: 2.82s\n",
      "188:\tlearn: 0.0002762\ttotal: 44.4s\tremaining: 2.58s\n",
      "189:\tlearn: 0.0002709\ttotal: 44.6s\tremaining: 2.35s\n",
      "190:\tlearn: 0.0002700\ttotal: 44.8s\tremaining: 2.11s\n",
      "191:\tlearn: 0.0002656\ttotal: 45.1s\tremaining: 1.88s\n",
      "192:\tlearn: 0.0002614\ttotal: 45.3s\tremaining: 1.64s\n",
      "193:\tlearn: 0.0002605\ttotal: 45.5s\tremaining: 1.41s\n",
      "194:\tlearn: 0.0002585\ttotal: 45.8s\tremaining: 1.17s\n",
      "195:\tlearn: 0.0002518\ttotal: 46s\tremaining: 939ms\n",
      "196:\tlearn: 0.0002468\ttotal: 46.2s\tremaining: 704ms\n",
      "197:\tlearn: 0.0002427\ttotal: 46.5s\tremaining: 469ms\n",
      "198:\tlearn: 0.0002394\ttotal: 46.7s\tremaining: 235ms\n",
      "199:\tlearn: 0.0002360\ttotal: 46.9s\tremaining: 0us\n",
      "Accuracy: 0.9996313331694814\n",
      "Precision: 0.9873417721518988\n",
      "Recall: 0.7959183673469388\n",
      "F1 Score: 0.8813559322033898\n",
      "ROC AUC Score: 0.8979503907640715\n",
      "\n",
      "Confusion Matrix:\n",
      " [[56863     1]\n",
      " [   20    78]]\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56864\n",
      "           1       0.99      0.80      0.88        98\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.99      0.90      0.94     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      "\n",
      "CPU times: total: 52min 34s\n",
      "Wall time: 1h 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier, StackingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import xgboost as xgb\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, classification_report\n",
    "\n",
    "# Split the data into features (X) and target variable (y) using selected features\n",
    "X = df_selected_features.drop('Class', axis=1)\n",
    "y = df_selected_features['Class']\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "X_test = X_test.dropna()\n",
    "y_test = y_test.dropna()\n",
    "\n",
    "# Normalize the data (optional but recommended for some algorithms)\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Create individual classifiers\n",
    "rf_params = {\n",
    "    'n_estimators': 100,          # Number of trees in the forest\n",
    "    'max_depth': 10,              # Maximum depth of each tree\n",
    "    'min_samples_split': 2,       # Minimum samples required to split an internal node\n",
    "    'min_samples_leaf': 1,        # Minimum samples required at a leaf node\n",
    "    'random_state': 42            # Random state for reproducibility\n",
    "}\n",
    "rf_classifier = RandomForestClassifier(**rf_params)\n",
    "svm_params = {\n",
    "    'kernel': 'rbf',      # Kernel type (you can try different kernels)\n",
    "    'C': 1.0,             # Regularization parameter\n",
    "    'gamma': 'scale',     # Kernel coefficient (auto, scale, or a float)\n",
    "    'random_state': 42    # Random state for reproducibility\n",
    "}\n",
    "svm_classifier = SVC(**svm_params)\n",
    "lr_params = {\n",
    "    'penalty': 'l2',               # Regularization penalty (l2 norm)\n",
    "    'C': 1.0,                      # Inverse regularization strength\n",
    "    'solver': 'liblinear',         # Optimization algorithm for regularization\n",
    "    'random_state': 42             # Random state for reproducibility\n",
    "}\n",
    "lr_classifier = LogisticRegression(**lr_params)\n",
    "dt_params = {\n",
    "    'max_depth': 70,\n",
    "    'min_samples_split': 8,\n",
    "    'min_samples_leaf': 10,\n",
    "    'criterion': \"entropy\"\n",
    "}\n",
    "dt_classifier = DecisionTreeClassifier(**dt_params)\n",
    "xgb_params = {\n",
    "    'objective': 'binary:logistic',   # Objective function\n",
    "    'eval_metric': 'logloss',         # Evaluation metric\n",
    "    'eta': 0.1,                       # Learning rate\n",
    "    'max_depth': 6,                   # Maximum depth of each tree\n",
    "    'subsample': 0.8,                 # Subsample ratio of the training instances\n",
    "    'colsample_bytree': 0.8,          # Subsample ratio of columns when constructing each tree\n",
    "    'seed': 42                        # Random seed for reproducibility\n",
    "}\n",
    "xgb_classifier = xgb.XGBClassifier(**xgb_params)\n",
    "catboost_params = {\n",
    "    'iterations': 200,       # Increase number of iterations\n",
    "    'learning_rate': 0.07,    # Lower learning rate for more stable training\n",
    "    'depth': 10,               # Increase depth for more complex trees\n",
    "    'l2_leaf_reg': 1.0,       # Regularization parameter for L2 regularization\n",
    "    'random_state': 42        # Random state for reproducibility\n",
    "}\n",
    "catboost_classifier = CatBoostClassifier(**catboost_params)\n",
    "\n",
    "# Create a stacking classifier with Random Forest as the meta-classifier\n",
    "stacking_classifier = StackingClassifier(\n",
    "    estimators=[\n",
    "        ('Random Forest', rf_classifier),\n",
    "        ('SVM', svm_classifier),\n",
    "        ('Logistic Regression', lr_classifier),\n",
    "        ('Decision Tree', dt_classifier),\n",
    "        ('XGBoost', xgb_classifier),\n",
    "        ('CatBoost', catboost_classifier)\n",
    "    ],\n",
    "    final_estimator=RandomForestClassifier(n_estimators=100, random_state=42),\n",
    "    cv=StratifiedKFold(n_splits=5),\n",
    ")\n",
    "\n",
    "# Fit the stacking classifier on the training data\n",
    "stacking_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = stacking_classifier.predict(X_test)\n",
    "\n",
    "# Evaluate the stacking model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "roc_auc = roc_auc_score(y_test, y_pred)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "class_report = classification_report(y_test, y_pred)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 Score:\", f1)\n",
    "print(\"ROC AUC Score:\", roc_auc)\n",
    "print(\"\\nConfusion Matrix:\\n\", conf_matrix)\n",
    "print(\"\\nClassification Report:\\n\", class_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.6259490\ttotal: 51.1ms\tremaining: 9.65s\n",
      "1:\tlearn: 0.5674825\ttotal: 126ms\tremaining: 11.8s\n",
      "2:\tlearn: 0.5161010\ttotal: 191ms\tremaining: 11.9s\n",
      "3:\tlearn: 0.4707822\ttotal: 256ms\tremaining: 11.9s\n",
      "4:\tlearn: 0.4304371\ttotal: 334ms\tremaining: 12.4s\n",
      "5:\tlearn: 0.3943831\ttotal: 402ms\tremaining: 12.3s\n",
      "6:\tlearn: 0.3619308\ttotal: 472ms\tremaining: 12.4s\n",
      "7:\tlearn: 0.3326775\ttotal: 554ms\tremaining: 12.6s\n",
      "8:\tlearn: 0.3062646\ttotal: 634ms\tremaining: 12.7s\n",
      "9:\tlearn: 0.2822379\ttotal: 707ms\tremaining: 12.7s\n",
      "10:\tlearn: 0.2604288\ttotal: 784ms\tremaining: 12.8s\n",
      "11:\tlearn: 0.2405025\ttotal: 861ms\tremaining: 12.8s\n",
      "12:\tlearn: 0.2223148\ttotal: 934ms\tremaining: 12.7s\n",
      "13:\tlearn: 0.2056795\ttotal: 1.01s\tremaining: 12.7s\n",
      "14:\tlearn: 0.1905053\ttotal: 1.08s\tremaining: 12.6s\n",
      "15:\tlearn: 0.1765321\ttotal: 1.16s\tremaining: 12.6s\n",
      "16:\tlearn: 0.1636697\ttotal: 1.24s\tremaining: 12.6s\n",
      "17:\tlearn: 0.1518499\ttotal: 1.33s\tremaining: 12.7s\n",
      "18:\tlearn: 0.1409477\ttotal: 1.42s\tremaining: 12.7s\n",
      "19:\tlearn: 0.1309127\ttotal: 1.48s\tremaining: 12.6s\n",
      "20:\tlearn: 0.1216337\ttotal: 1.55s\tremaining: 12.5s\n",
      "21:\tlearn: 0.1130682\ttotal: 1.62s\tremaining: 12.4s\n",
      "22:\tlearn: 0.1051645\ttotal: 1.7s\tremaining: 12.4s\n",
      "23:\tlearn: 0.0978410\ttotal: 1.77s\tremaining: 12.3s\n",
      "24:\tlearn: 0.0910496\ttotal: 1.84s\tremaining: 12.2s\n",
      "25:\tlearn: 0.0847890\ttotal: 1.92s\tremaining: 12.1s\n",
      "26:\tlearn: 0.0789847\ttotal: 2s\tremaining: 12.1s\n",
      "27:\tlearn: 0.0735996\ttotal: 2.07s\tremaining: 12s\n",
      "28:\tlearn: 0.0686068\ttotal: 2.17s\tremaining: 12.1s\n",
      "29:\tlearn: 0.0639598\ttotal: 2.25s\tremaining: 12s\n",
      "30:\tlearn: 0.0596694\ttotal: 2.33s\tremaining: 11.9s\n",
      "31:\tlearn: 0.0557009\ttotal: 2.39s\tremaining: 11.8s\n",
      "32:\tlearn: 0.0519913\ttotal: 2.45s\tremaining: 11.7s\n",
      "33:\tlearn: 0.0485358\ttotal: 2.52s\tremaining: 11.6s\n",
      "34:\tlearn: 0.0453350\ttotal: 2.59s\tremaining: 11.5s\n",
      "35:\tlearn: 0.0423558\ttotal: 2.66s\tremaining: 11.4s\n",
      "36:\tlearn: 0.0395812\ttotal: 2.73s\tremaining: 11.3s\n",
      "37:\tlearn: 0.0370062\ttotal: 2.8s\tremaining: 11.2s\n",
      "38:\tlearn: 0.0345953\ttotal: 2.87s\tremaining: 11.1s\n",
      "39:\tlearn: 0.0323793\ttotal: 2.93s\tremaining: 11s\n",
      "40:\tlearn: 0.0303083\ttotal: 3s\tremaining: 10.9s\n",
      "41:\tlearn: 0.0283635\ttotal: 3.06s\tremaining: 10.8s\n",
      "42:\tlearn: 0.0265621\ttotal: 3.12s\tremaining: 10.7s\n",
      "43:\tlearn: 0.0248940\ttotal: 3.19s\tremaining: 10.6s\n",
      "44:\tlearn: 0.0233281\ttotal: 3.25s\tremaining: 10.5s\n",
      "45:\tlearn: 0.0218759\ttotal: 3.32s\tremaining: 10.4s\n",
      "46:\tlearn: 0.0205219\ttotal: 3.38s\tremaining: 10.3s\n",
      "47:\tlearn: 0.0192663\ttotal: 3.45s\tremaining: 10.2s\n",
      "48:\tlearn: 0.0181028\ttotal: 3.51s\tremaining: 10.1s\n",
      "49:\tlearn: 0.0170141\ttotal: 3.57s\tremaining: 10s\n",
      "50:\tlearn: 0.0160056\ttotal: 3.64s\tremaining: 9.93s\n",
      "51:\tlearn: 0.0150669\ttotal: 3.71s\tremaining: 9.85s\n",
      "52:\tlearn: 0.0141892\ttotal: 3.77s\tremaining: 9.75s\n",
      "53:\tlearn: 0.0133649\ttotal: 3.83s\tremaining: 9.66s\n",
      "54:\tlearn: 0.0126029\ttotal: 3.89s\tremaining: 9.56s\n",
      "55:\tlearn: 0.0118900\ttotal: 3.96s\tremaining: 9.47s\n",
      "56:\tlearn: 0.0112350\ttotal: 4.02s\tremaining: 9.39s\n",
      "57:\tlearn: 0.0106181\ttotal: 4.09s\tremaining: 9.31s\n",
      "58:\tlearn: 0.0100481\ttotal: 4.15s\tremaining: 9.22s\n",
      "59:\tlearn: 0.0095139\ttotal: 4.22s\tremaining: 9.14s\n",
      "60:\tlearn: 0.0090155\ttotal: 4.29s\tremaining: 9.07s\n",
      "61:\tlearn: 0.0085449\ttotal: 4.35s\tremaining: 8.98s\n",
      "62:\tlearn: 0.0081139\ttotal: 4.41s\tremaining: 8.9s\n",
      "63:\tlearn: 0.0077055\ttotal: 4.47s\tremaining: 8.8s\n",
      "64:\tlearn: 0.0073301\ttotal: 4.53s\tremaining: 8.71s\n",
      "65:\tlearn: 0.0069801\ttotal: 4.59s\tremaining: 8.63s\n",
      "66:\tlearn: 0.0066592\ttotal: 4.66s\tremaining: 8.56s\n",
      "67:\tlearn: 0.0063582\ttotal: 4.72s\tremaining: 8.47s\n",
      "68:\tlearn: 0.0060833\ttotal: 4.79s\tremaining: 8.4s\n",
      "69:\tlearn: 0.0058205\ttotal: 4.85s\tremaining: 8.32s\n",
      "70:\tlearn: 0.0055805\ttotal: 4.92s\tremaining: 8.24s\n",
      "71:\tlearn: 0.0053502\ttotal: 4.98s\tremaining: 8.17s\n",
      "72:\tlearn: 0.0051376\ttotal: 5.06s\tremaining: 8.11s\n",
      "73:\tlearn: 0.0049423\ttotal: 5.12s\tremaining: 8.03s\n",
      "74:\tlearn: 0.0047515\ttotal: 5.19s\tremaining: 7.96s\n",
      "75:\tlearn: 0.0045835\ttotal: 5.25s\tremaining: 7.87s\n",
      "76:\tlearn: 0.0044255\ttotal: 5.32s\tremaining: 7.8s\n",
      "77:\tlearn: 0.0042772\ttotal: 5.39s\tremaining: 7.74s\n",
      "78:\tlearn: 0.0041379\ttotal: 5.45s\tremaining: 7.66s\n",
      "79:\tlearn: 0.0040087\ttotal: 5.51s\tremaining: 7.58s\n",
      "80:\tlearn: 0.0038834\ttotal: 5.58s\tremaining: 7.51s\n",
      "81:\tlearn: 0.0037731\ttotal: 5.65s\tremaining: 7.44s\n",
      "82:\tlearn: 0.0036691\ttotal: 5.71s\tremaining: 7.36s\n",
      "83:\tlearn: 0.0035733\ttotal: 5.78s\tremaining: 7.29s\n",
      "84:\tlearn: 0.0034810\ttotal: 5.84s\tremaining: 7.22s\n",
      "85:\tlearn: 0.0033881\ttotal: 5.91s\tremaining: 7.14s\n",
      "86:\tlearn: 0.0033134\ttotal: 5.97s\tremaining: 7.06s\n",
      "87:\tlearn: 0.0032329\ttotal: 6.03s\tremaining: 7s\n",
      "88:\tlearn: 0.0031572\ttotal: 6.1s\tremaining: 6.92s\n",
      "89:\tlearn: 0.0030961\ttotal: 6.17s\tremaining: 6.85s\n",
      "90:\tlearn: 0.0030366\ttotal: 6.23s\tremaining: 6.77s\n",
      "91:\tlearn: 0.0029741\ttotal: 6.29s\tremaining: 6.7s\n",
      "92:\tlearn: 0.0029184\ttotal: 6.36s\tremaining: 6.63s\n",
      "93:\tlearn: 0.0028731\ttotal: 6.42s\tremaining: 6.56s\n",
      "94:\tlearn: 0.0028224\ttotal: 6.49s\tremaining: 6.49s\n",
      "95:\tlearn: 0.0027827\ttotal: 6.54s\tremaining: 6.41s\n",
      "96:\tlearn: 0.0027520\ttotal: 6.61s\tremaining: 6.34s\n",
      "97:\tlearn: 0.0027039\ttotal: 6.67s\tremaining: 6.26s\n",
      "98:\tlearn: 0.0026691\ttotal: 6.74s\tremaining: 6.19s\n",
      "99:\tlearn: 0.0026323\ttotal: 6.8s\tremaining: 6.12s\n",
      "100:\tlearn: 0.0025969\ttotal: 6.86s\tremaining: 6.05s\n",
      "101:\tlearn: 0.0025701\ttotal: 6.93s\tremaining: 5.98s\n",
      "102:\tlearn: 0.0025378\ttotal: 7s\tremaining: 5.91s\n",
      "103:\tlearn: 0.0025159\ttotal: 7.05s\tremaining: 5.83s\n",
      "104:\tlearn: 0.0024880\ttotal: 7.12s\tremaining: 5.76s\n",
      "105:\tlearn: 0.0024687\ttotal: 7.17s\tremaining: 5.69s\n",
      "106:\tlearn: 0.0024409\ttotal: 7.24s\tremaining: 5.62s\n",
      "107:\tlearn: 0.0024255\ttotal: 7.3s\tremaining: 5.54s\n",
      "108:\tlearn: 0.0024004\ttotal: 7.37s\tremaining: 5.48s\n",
      "109:\tlearn: 0.0023766\ttotal: 7.45s\tremaining: 5.42s\n",
      "110:\tlearn: 0.0023507\ttotal: 7.51s\tremaining: 5.34s\n",
      "111:\tlearn: 0.0023383\ttotal: 7.58s\tremaining: 5.28s\n",
      "112:\tlearn: 0.0023143\ttotal: 7.64s\tremaining: 5.2s\n",
      "113:\tlearn: 0.0022967\ttotal: 7.7s\tremaining: 5.13s\n",
      "114:\tlearn: 0.0022799\ttotal: 7.76s\tremaining: 5.06s\n",
      "115:\tlearn: 0.0022684\ttotal: 7.82s\tremaining: 4.99s\n",
      "116:\tlearn: 0.0022538\ttotal: 7.88s\tremaining: 4.92s\n",
      "117:\tlearn: 0.0022399\ttotal: 7.94s\tremaining: 4.84s\n",
      "118:\tlearn: 0.0022244\ttotal: 8.01s\tremaining: 4.78s\n",
      "119:\tlearn: 0.0022128\ttotal: 8.07s\tremaining: 4.71s\n",
      "120:\tlearn: 0.0022023\ttotal: 8.14s\tremaining: 4.64s\n",
      "121:\tlearn: 0.0021861\ttotal: 8.21s\tremaining: 4.57s\n",
      "122:\tlearn: 0.0021796\ttotal: 8.26s\tremaining: 4.5s\n",
      "123:\tlearn: 0.0021731\ttotal: 8.32s\tremaining: 4.43s\n",
      "124:\tlearn: 0.0021683\ttotal: 8.38s\tremaining: 4.36s\n",
      "125:\tlearn: 0.0021568\ttotal: 8.45s\tremaining: 4.29s\n",
      "126:\tlearn: 0.0021437\ttotal: 8.51s\tremaining: 4.22s\n",
      "127:\tlearn: 0.0021316\ttotal: 8.58s\tremaining: 4.16s\n",
      "128:\tlearn: 0.0021220\ttotal: 8.64s\tremaining: 4.08s\n",
      "129:\tlearn: 0.0021172\ttotal: 8.7s\tremaining: 4.01s\n",
      "130:\tlearn: 0.0021045\ttotal: 8.77s\tremaining: 3.95s\n",
      "131:\tlearn: 0.0020920\ttotal: 8.83s\tremaining: 3.88s\n",
      "132:\tlearn: 0.0020820\ttotal: 8.89s\tremaining: 3.81s\n",
      "133:\tlearn: 0.0020754\ttotal: 8.96s\tremaining: 3.74s\n",
      "134:\tlearn: 0.0020643\ttotal: 9.02s\tremaining: 3.67s\n",
      "135:\tlearn: 0.0020584\ttotal: 9.09s\tremaining: 3.61s\n",
      "136:\tlearn: 0.0020518\ttotal: 9.15s\tremaining: 3.54s\n",
      "137:\tlearn: 0.0020476\ttotal: 9.22s\tremaining: 3.47s\n",
      "138:\tlearn: 0.0020403\ttotal: 9.29s\tremaining: 3.41s\n",
      "139:\tlearn: 0.0020305\ttotal: 9.35s\tremaining: 3.34s\n",
      "140:\tlearn: 0.0020220\ttotal: 9.43s\tremaining: 3.27s\n",
      "141:\tlearn: 0.0020165\ttotal: 9.49s\tremaining: 3.21s\n",
      "142:\tlearn: 0.0020058\ttotal: 9.55s\tremaining: 3.14s\n",
      "143:\tlearn: 0.0020005\ttotal: 9.61s\tremaining: 3.07s\n",
      "144:\tlearn: 0.0019966\ttotal: 9.68s\tremaining: 3s\n",
      "145:\tlearn: 0.0019918\ttotal: 9.73s\tremaining: 2.93s\n",
      "146:\tlearn: 0.0019877\ttotal: 9.79s\tremaining: 2.86s\n",
      "147:\tlearn: 0.0019795\ttotal: 9.85s\tremaining: 2.8s\n",
      "148:\tlearn: 0.0019751\ttotal: 9.91s\tremaining: 2.73s\n",
      "149:\tlearn: 0.0019703\ttotal: 9.98s\tremaining: 2.66s\n",
      "150:\tlearn: 0.0019650\ttotal: 10s\tremaining: 2.59s\n",
      "151:\tlearn: 0.0019561\ttotal: 10.1s\tremaining: 2.53s\n",
      "152:\tlearn: 0.0019474\ttotal: 10.2s\tremaining: 2.46s\n",
      "153:\tlearn: 0.0019426\ttotal: 10.2s\tremaining: 2.39s\n",
      "154:\tlearn: 0.0019392\ttotal: 10.3s\tremaining: 2.32s\n",
      "155:\tlearn: 0.0019362\ttotal: 10.4s\tremaining: 2.26s\n",
      "156:\tlearn: 0.0019258\ttotal: 10.4s\tremaining: 2.19s\n",
      "157:\tlearn: 0.0019186\ttotal: 10.5s\tremaining: 2.12s\n",
      "158:\tlearn: 0.0019161\ttotal: 10.6s\tremaining: 2.06s\n",
      "159:\tlearn: 0.0019128\ttotal: 10.6s\tremaining: 1.99s\n",
      "160:\tlearn: 0.0019099\ttotal: 10.7s\tremaining: 1.92s\n",
      "161:\tlearn: 0.0019048\ttotal: 10.7s\tremaining: 1.86s\n",
      "162:\tlearn: 0.0018994\ttotal: 10.8s\tremaining: 1.79s\n",
      "163:\tlearn: 0.0018968\ttotal: 10.9s\tremaining: 1.72s\n",
      "164:\tlearn: 0.0018930\ttotal: 10.9s\tremaining: 1.66s\n",
      "165:\tlearn: 0.0018867\ttotal: 11s\tremaining: 1.59s\n",
      "166:\tlearn: 0.0018836\ttotal: 11.1s\tremaining: 1.52s\n",
      "167:\tlearn: 0.0018800\ttotal: 11.1s\tremaining: 1.46s\n",
      "168:\tlearn: 0.0018756\ttotal: 11.2s\tremaining: 1.39s\n",
      "169:\tlearn: 0.0018725\ttotal: 11.2s\tremaining: 1.32s\n",
      "170:\tlearn: 0.0018694\ttotal: 11.3s\tremaining: 1.25s\n",
      "171:\tlearn: 0.0018651\ttotal: 11.4s\tremaining: 1.19s\n",
      "172:\tlearn: 0.0018603\ttotal: 11.4s\tremaining: 1.12s\n",
      "173:\tlearn: 0.0018573\ttotal: 11.5s\tremaining: 1.06s\n",
      "174:\tlearn: 0.0018541\ttotal: 11.5s\tremaining: 989ms\n",
      "175:\tlearn: 0.0018504\ttotal: 11.6s\tremaining: 923ms\n",
      "176:\tlearn: 0.0018476\ttotal: 11.7s\tremaining: 857ms\n",
      "177:\tlearn: 0.0018444\ttotal: 11.7s\tremaining: 790ms\n",
      "178:\tlearn: 0.0018391\ttotal: 11.8s\tremaining: 724ms\n",
      "179:\tlearn: 0.0018349\ttotal: 11.8s\tremaining: 658ms\n",
      "180:\tlearn: 0.0018324\ttotal: 11.9s\tremaining: 592ms\n",
      "181:\tlearn: 0.0018295\ttotal: 12s\tremaining: 526ms\n",
      "182:\tlearn: 0.0018268\ttotal: 12s\tremaining: 460ms\n",
      "183:\tlearn: 0.0018228\ttotal: 12.1s\tremaining: 395ms\n",
      "184:\tlearn: 0.0018201\ttotal: 12.2s\tremaining: 329ms\n",
      "185:\tlearn: 0.0018165\ttotal: 12.2s\tremaining: 263ms\n",
      "186:\tlearn: 0.0018104\ttotal: 12.3s\tremaining: 197ms\n",
      "187:\tlearn: 0.0018084\ttotal: 12.4s\tremaining: 131ms\n",
      "188:\tlearn: 0.0018066\ttotal: 12.4s\tremaining: 65.7ms\n",
      "189:\tlearn: 0.0018035\ttotal: 12.5s\tremaining: 0us\n",
      "0:\tlearn: 0.6259490\ttotal: 73.4ms\tremaining: 13.9s\n",
      "1:\tlearn: 0.5674825\ttotal: 147ms\tremaining: 13.8s\n",
      "2:\tlearn: 0.5161010\ttotal: 209ms\tremaining: 13.1s\n",
      "3:\tlearn: 0.4707822\ttotal: 276ms\tremaining: 12.8s\n",
      "4:\tlearn: 0.4304371\ttotal: 346ms\tremaining: 12.8s\n",
      "5:\tlearn: 0.3943831\ttotal: 416ms\tremaining: 12.7s\n",
      "6:\tlearn: 0.3619308\ttotal: 485ms\tremaining: 12.7s\n",
      "7:\tlearn: 0.3326775\ttotal: 558ms\tremaining: 12.7s\n",
      "8:\tlearn: 0.3062646\ttotal: 639ms\tremaining: 12.9s\n",
      "9:\tlearn: 0.2822379\ttotal: 711ms\tremaining: 12.8s\n",
      "10:\tlearn: 0.2604288\ttotal: 785ms\tremaining: 12.8s\n",
      "11:\tlearn: 0.2405025\ttotal: 856ms\tremaining: 12.7s\n",
      "12:\tlearn: 0.2223148\ttotal: 927ms\tremaining: 12.6s\n",
      "13:\tlearn: 0.2056795\ttotal: 1.01s\tremaining: 12.7s\n",
      "14:\tlearn: 0.1905053\ttotal: 1.09s\tremaining: 12.7s\n",
      "15:\tlearn: 0.1765321\ttotal: 1.17s\tremaining: 12.7s\n",
      "16:\tlearn: 0.1636697\ttotal: 1.23s\tremaining: 12.5s\n",
      "17:\tlearn: 0.1518499\ttotal: 1.31s\tremaining: 12.5s\n",
      "18:\tlearn: 0.1409477\ttotal: 1.39s\tremaining: 12.5s\n",
      "19:\tlearn: 0.1309127\ttotal: 1.46s\tremaining: 12.4s\n",
      "20:\tlearn: 0.1216337\ttotal: 1.53s\tremaining: 12.3s\n",
      "21:\tlearn: 0.1130682\ttotal: 1.6s\tremaining: 12.2s\n",
      "22:\tlearn: 0.1051645\ttotal: 1.67s\tremaining: 12.1s\n",
      "23:\tlearn: 0.0978410\ttotal: 1.74s\tremaining: 12s\n",
      "24:\tlearn: 0.0910496\ttotal: 1.81s\tremaining: 12s\n",
      "25:\tlearn: 0.0847890\ttotal: 1.88s\tremaining: 11.9s\n",
      "26:\tlearn: 0.0789847\ttotal: 1.95s\tremaining: 11.8s\n",
      "27:\tlearn: 0.0735996\ttotal: 2.02s\tremaining: 11.7s\n",
      "28:\tlearn: 0.0686068\ttotal: 2.09s\tremaining: 11.6s\n",
      "29:\tlearn: 0.0639598\ttotal: 2.16s\tremaining: 11.5s\n",
      "30:\tlearn: 0.0596694\ttotal: 2.24s\tremaining: 11.5s\n",
      "31:\tlearn: 0.0557009\ttotal: 2.31s\tremaining: 11.4s\n",
      "32:\tlearn: 0.0519913\ttotal: 2.37s\tremaining: 11.3s\n",
      "33:\tlearn: 0.0485358\ttotal: 2.44s\tremaining: 11.2s\n",
      "34:\tlearn: 0.0453350\ttotal: 2.51s\tremaining: 11.1s\n",
      "35:\tlearn: 0.0423558\ttotal: 2.58s\tremaining: 11s\n",
      "36:\tlearn: 0.0395812\ttotal: 2.65s\tremaining: 11s\n",
      "37:\tlearn: 0.0370062\ttotal: 2.72s\tremaining: 10.9s\n",
      "38:\tlearn: 0.0345953\ttotal: 2.78s\tremaining: 10.8s\n",
      "39:\tlearn: 0.0323793\ttotal: 2.85s\tremaining: 10.7s\n",
      "40:\tlearn: 0.0303083\ttotal: 2.91s\tremaining: 10.6s\n",
      "41:\tlearn: 0.0283635\ttotal: 2.97s\tremaining: 10.5s\n",
      "42:\tlearn: 0.0265621\ttotal: 3.04s\tremaining: 10.4s\n",
      "43:\tlearn: 0.0248940\ttotal: 3.1s\tremaining: 10.3s\n",
      "44:\tlearn: 0.0233281\ttotal: 3.17s\tremaining: 10.2s\n",
      "45:\tlearn: 0.0218759\ttotal: 3.23s\tremaining: 10.1s\n",
      "46:\tlearn: 0.0205219\ttotal: 3.3s\tremaining: 10s\n",
      "47:\tlearn: 0.0192663\ttotal: 3.36s\tremaining: 9.95s\n",
      "48:\tlearn: 0.0181028\ttotal: 3.43s\tremaining: 9.86s\n",
      "49:\tlearn: 0.0170141\ttotal: 3.49s\tremaining: 9.77s\n",
      "50:\tlearn: 0.0160056\ttotal: 3.56s\tremaining: 9.71s\n",
      "51:\tlearn: 0.0150669\ttotal: 3.63s\tremaining: 9.62s\n",
      "52:\tlearn: 0.0141892\ttotal: 3.68s\tremaining: 9.52s\n",
      "53:\tlearn: 0.0133649\ttotal: 3.74s\tremaining: 9.43s\n",
      "54:\tlearn: 0.0126029\ttotal: 3.8s\tremaining: 9.33s\n",
      "55:\tlearn: 0.0118900\ttotal: 3.86s\tremaining: 9.24s\n",
      "56:\tlearn: 0.0112350\ttotal: 3.92s\tremaining: 9.15s\n",
      "57:\tlearn: 0.0106181\ttotal: 3.99s\tremaining: 9.08s\n",
      "58:\tlearn: 0.0100481\ttotal: 4.06s\tremaining: 9.01s\n",
      "59:\tlearn: 0.0095139\ttotal: 4.13s\tremaining: 8.94s\n",
      "60:\tlearn: 0.0090155\ttotal: 4.19s\tremaining: 8.86s\n",
      "61:\tlearn: 0.0085449\ttotal: 4.25s\tremaining: 8.78s\n",
      "62:\tlearn: 0.0081139\ttotal: 4.32s\tremaining: 8.7s\n",
      "63:\tlearn: 0.0077055\ttotal: 4.37s\tremaining: 8.61s\n",
      "64:\tlearn: 0.0073301\ttotal: 4.43s\tremaining: 8.52s\n",
      "65:\tlearn: 0.0069801\ttotal: 4.49s\tremaining: 8.44s\n",
      "66:\tlearn: 0.0066592\ttotal: 4.56s\tremaining: 8.37s\n",
      "67:\tlearn: 0.0063582\ttotal: 4.62s\tremaining: 8.29s\n",
      "68:\tlearn: 0.0060833\ttotal: 4.68s\tremaining: 8.21s\n",
      "69:\tlearn: 0.0058205\ttotal: 4.74s\tremaining: 8.13s\n",
      "70:\tlearn: 0.0055805\ttotal: 4.8s\tremaining: 8.05s\n",
      "71:\tlearn: 0.0053502\ttotal: 4.86s\tremaining: 7.97s\n",
      "72:\tlearn: 0.0051376\ttotal: 4.92s\tremaining: 7.89s\n",
      "73:\tlearn: 0.0049423\ttotal: 4.99s\tremaining: 7.82s\n",
      "74:\tlearn: 0.0047515\ttotal: 5.05s\tremaining: 7.75s\n",
      "75:\tlearn: 0.0045835\ttotal: 5.11s\tremaining: 7.66s\n",
      "76:\tlearn: 0.0044255\ttotal: 5.17s\tremaining: 7.58s\n",
      "77:\tlearn: 0.0042772\ttotal: 5.24s\tremaining: 7.52s\n",
      "78:\tlearn: 0.0041379\ttotal: 5.3s\tremaining: 7.45s\n",
      "79:\tlearn: 0.0040087\ttotal: 5.37s\tremaining: 7.39s\n",
      "80:\tlearn: 0.0038834\ttotal: 5.44s\tremaining: 7.32s\n",
      "81:\tlearn: 0.0037731\ttotal: 5.5s\tremaining: 7.25s\n",
      "82:\tlearn: 0.0036691\ttotal: 5.57s\tremaining: 7.18s\n",
      "83:\tlearn: 0.0035733\ttotal: 5.62s\tremaining: 7.09s\n",
      "84:\tlearn: 0.0034810\ttotal: 5.68s\tremaining: 7.02s\n",
      "85:\tlearn: 0.0033881\ttotal: 5.75s\tremaining: 6.95s\n",
      "86:\tlearn: 0.0033134\ttotal: 5.81s\tremaining: 6.87s\n",
      "87:\tlearn: 0.0032329\ttotal: 5.87s\tremaining: 6.81s\n",
      "88:\tlearn: 0.0031572\ttotal: 5.93s\tremaining: 6.74s\n",
      "89:\tlearn: 0.0030961\ttotal: 6s\tremaining: 6.66s\n",
      "90:\tlearn: 0.0030366\ttotal: 6.05s\tremaining: 6.59s\n",
      "91:\tlearn: 0.0029741\ttotal: 6.12s\tremaining: 6.52s\n",
      "92:\tlearn: 0.0029184\ttotal: 6.19s\tremaining: 6.45s\n",
      "93:\tlearn: 0.0028731\ttotal: 6.25s\tremaining: 6.38s\n",
      "94:\tlearn: 0.0028224\ttotal: 6.32s\tremaining: 6.32s\n",
      "95:\tlearn: 0.0027827\ttotal: 6.38s\tremaining: 6.25s\n",
      "96:\tlearn: 0.0027520\ttotal: 6.45s\tremaining: 6.18s\n",
      "97:\tlearn: 0.0027039\ttotal: 6.51s\tremaining: 6.11s\n",
      "98:\tlearn: 0.0026691\ttotal: 6.58s\tremaining: 6.04s\n",
      "99:\tlearn: 0.0026323\ttotal: 6.64s\tremaining: 5.98s\n",
      "100:\tlearn: 0.0025969\ttotal: 6.71s\tremaining: 5.91s\n",
      "101:\tlearn: 0.0025701\ttotal: 6.78s\tremaining: 5.85s\n",
      "102:\tlearn: 0.0025378\ttotal: 6.84s\tremaining: 5.78s\n",
      "103:\tlearn: 0.0025159\ttotal: 6.9s\tremaining: 5.71s\n",
      "104:\tlearn: 0.0024880\ttotal: 6.97s\tremaining: 5.64s\n",
      "105:\tlearn: 0.0024687\ttotal: 7.03s\tremaining: 5.57s\n",
      "106:\tlearn: 0.0024409\ttotal: 7.1s\tremaining: 5.51s\n",
      "107:\tlearn: 0.0024255\ttotal: 7.16s\tremaining: 5.44s\n",
      "108:\tlearn: 0.0024004\ttotal: 7.24s\tremaining: 5.38s\n",
      "109:\tlearn: 0.0023766\ttotal: 7.3s\tremaining: 5.31s\n",
      "110:\tlearn: 0.0023507\ttotal: 7.38s\tremaining: 5.25s\n",
      "111:\tlearn: 0.0023383\ttotal: 7.44s\tremaining: 5.18s\n",
      "112:\tlearn: 0.0023143\ttotal: 7.5s\tremaining: 5.11s\n",
      "113:\tlearn: 0.0022967\ttotal: 7.57s\tremaining: 5.04s\n",
      "114:\tlearn: 0.0022799\ttotal: 7.63s\tremaining: 4.98s\n",
      "115:\tlearn: 0.0022684\ttotal: 7.71s\tremaining: 4.92s\n",
      "116:\tlearn: 0.0022538\ttotal: 7.78s\tremaining: 4.86s\n",
      "117:\tlearn: 0.0022399\ttotal: 7.85s\tremaining: 4.79s\n",
      "118:\tlearn: 0.0022244\ttotal: 7.93s\tremaining: 4.73s\n",
      "119:\tlearn: 0.0022128\ttotal: 7.99s\tremaining: 4.66s\n",
      "120:\tlearn: 0.0022023\ttotal: 8.05s\tremaining: 4.59s\n",
      "121:\tlearn: 0.0021861\ttotal: 8.12s\tremaining: 4.52s\n",
      "122:\tlearn: 0.0021796\ttotal: 8.17s\tremaining: 4.45s\n",
      "123:\tlearn: 0.0021731\ttotal: 8.23s\tremaining: 4.38s\n",
      "124:\tlearn: 0.0021683\ttotal: 8.29s\tremaining: 4.31s\n",
      "125:\tlearn: 0.0021568\ttotal: 8.36s\tremaining: 4.25s\n",
      "126:\tlearn: 0.0021437\ttotal: 8.43s\tremaining: 4.18s\n",
      "127:\tlearn: 0.0021316\ttotal: 8.5s\tremaining: 4.12s\n",
      "128:\tlearn: 0.0021220\ttotal: 8.56s\tremaining: 4.05s\n",
      "129:\tlearn: 0.0021172\ttotal: 8.63s\tremaining: 3.98s\n",
      "130:\tlearn: 0.0021045\ttotal: 8.7s\tremaining: 3.92s\n",
      "131:\tlearn: 0.0020920\ttotal: 8.77s\tremaining: 3.85s\n",
      "132:\tlearn: 0.0020820\ttotal: 8.83s\tremaining: 3.79s\n",
      "133:\tlearn: 0.0020754\ttotal: 8.9s\tremaining: 3.72s\n",
      "134:\tlearn: 0.0020643\ttotal: 8.96s\tremaining: 3.65s\n",
      "135:\tlearn: 0.0020584\ttotal: 9.03s\tremaining: 3.59s\n",
      "136:\tlearn: 0.0020518\ttotal: 9.09s\tremaining: 3.52s\n",
      "137:\tlearn: 0.0020476\ttotal: 9.16s\tremaining: 3.45s\n",
      "138:\tlearn: 0.0020403\ttotal: 9.23s\tremaining: 3.39s\n",
      "139:\tlearn: 0.0020305\ttotal: 9.33s\tremaining: 3.33s\n",
      "140:\tlearn: 0.0020220\ttotal: 9.45s\tremaining: 3.28s\n",
      "141:\tlearn: 0.0020165\ttotal: 9.54s\tremaining: 3.23s\n",
      "142:\tlearn: 0.0020058\ttotal: 9.63s\tremaining: 3.17s\n",
      "143:\tlearn: 0.0020005\ttotal: 9.7s\tremaining: 3.1s\n",
      "144:\tlearn: 0.0019966\ttotal: 9.77s\tremaining: 3.03s\n",
      "145:\tlearn: 0.0019918\ttotal: 9.84s\tremaining: 2.97s\n",
      "146:\tlearn: 0.0019877\ttotal: 9.9s\tremaining: 2.9s\n",
      "147:\tlearn: 0.0019795\ttotal: 9.97s\tremaining: 2.83s\n",
      "148:\tlearn: 0.0019751\ttotal: 10.1s\tremaining: 2.77s\n",
      "149:\tlearn: 0.0019703\ttotal: 10.2s\tremaining: 2.71s\n",
      "150:\tlearn: 0.0019650\ttotal: 10.2s\tremaining: 2.65s\n",
      "151:\tlearn: 0.0019561\ttotal: 10.3s\tremaining: 2.58s\n",
      "152:\tlearn: 0.0019474\ttotal: 10.4s\tremaining: 2.52s\n",
      "153:\tlearn: 0.0019426\ttotal: 10.5s\tremaining: 2.45s\n",
      "154:\tlearn: 0.0019392\ttotal: 10.5s\tremaining: 2.38s\n",
      "155:\tlearn: 0.0019362\ttotal: 10.6s\tremaining: 2.31s\n",
      "156:\tlearn: 0.0019258\ttotal: 10.7s\tremaining: 2.24s\n",
      "157:\tlearn: 0.0019186\ttotal: 10.7s\tremaining: 2.17s\n",
      "158:\tlearn: 0.0019161\ttotal: 10.8s\tremaining: 2.1s\n",
      "159:\tlearn: 0.0019128\ttotal: 10.9s\tremaining: 2.04s\n",
      "160:\tlearn: 0.0019099\ttotal: 10.9s\tremaining: 1.97s\n",
      "161:\tlearn: 0.0019048\ttotal: 11s\tremaining: 1.9s\n",
      "162:\tlearn: 0.0018994\ttotal: 11.1s\tremaining: 1.83s\n",
      "163:\tlearn: 0.0018968\ttotal: 11.1s\tremaining: 1.76s\n",
      "164:\tlearn: 0.0018930\ttotal: 11.2s\tremaining: 1.69s\n",
      "165:\tlearn: 0.0018867\ttotal: 11.2s\tremaining: 1.63s\n",
      "166:\tlearn: 0.0018836\ttotal: 11.3s\tremaining: 1.56s\n",
      "167:\tlearn: 0.0018800\ttotal: 11.4s\tremaining: 1.49s\n",
      "168:\tlearn: 0.0018756\ttotal: 11.4s\tremaining: 1.42s\n",
      "169:\tlearn: 0.0018725\ttotal: 11.5s\tremaining: 1.35s\n",
      "170:\tlearn: 0.0018694\ttotal: 11.6s\tremaining: 1.28s\n",
      "171:\tlearn: 0.0018651\ttotal: 11.6s\tremaining: 1.22s\n",
      "172:\tlearn: 0.0018603\ttotal: 11.7s\tremaining: 1.15s\n",
      "173:\tlearn: 0.0018573\ttotal: 11.8s\tremaining: 1.08s\n",
      "174:\tlearn: 0.0018541\ttotal: 11.8s\tremaining: 1.01s\n",
      "175:\tlearn: 0.0018504\ttotal: 11.9s\tremaining: 944ms\n",
      "176:\tlearn: 0.0018476\ttotal: 11.9s\tremaining: 876ms\n",
      "177:\tlearn: 0.0018444\ttotal: 12s\tremaining: 808ms\n",
      "178:\tlearn: 0.0018391\ttotal: 12.1s\tremaining: 741ms\n",
      "179:\tlearn: 0.0018349\ttotal: 12.1s\tremaining: 674ms\n",
      "180:\tlearn: 0.0018324\ttotal: 12.2s\tremaining: 607ms\n",
      "181:\tlearn: 0.0018295\ttotal: 12.3s\tremaining: 539ms\n",
      "182:\tlearn: 0.0018268\ttotal: 12.3s\tremaining: 471ms\n",
      "183:\tlearn: 0.0018228\ttotal: 12.4s\tremaining: 404ms\n",
      "184:\tlearn: 0.0018201\ttotal: 12.4s\tremaining: 336ms\n",
      "185:\tlearn: 0.0018165\ttotal: 12.5s\tremaining: 269ms\n",
      "186:\tlearn: 0.0018104\ttotal: 12.6s\tremaining: 202ms\n",
      "187:\tlearn: 0.0018084\ttotal: 12.6s\tremaining: 134ms\n",
      "188:\tlearn: 0.0018066\ttotal: 12.7s\tremaining: 67.1ms\n",
      "189:\tlearn: 0.0018035\ttotal: 12.7s\tremaining: 0us\n",
      "0:\tlearn: 0.6258671\ttotal: 70.6ms\tremaining: 13.3s\n",
      "1:\tlearn: 0.5674876\ttotal: 131ms\tremaining: 12.3s\n",
      "2:\tlearn: 0.5162874\ttotal: 191ms\tremaining: 11.9s\n",
      "3:\tlearn: 0.4710435\ttotal: 249ms\tremaining: 11.6s\n",
      "4:\tlearn: 0.4307627\ttotal: 312ms\tremaining: 11.5s\n",
      "5:\tlearn: 0.3946996\ttotal: 379ms\tremaining: 11.6s\n",
      "6:\tlearn: 0.3622861\ttotal: 440ms\tremaining: 11.5s\n",
      "7:\tlearn: 0.3330103\ttotal: 503ms\tremaining: 11.4s\n",
      "8:\tlearn: 0.3065245\ttotal: 566ms\tremaining: 11.4s\n",
      "9:\tlearn: 0.2825006\ttotal: 627ms\tremaining: 11.3s\n",
      "10:\tlearn: 0.2607120\ttotal: 670ms\tremaining: 10.9s\n",
      "11:\tlearn: 0.2407281\ttotal: 717ms\tremaining: 10.6s\n",
      "12:\tlearn: 0.2225190\ttotal: 783ms\tremaining: 10.7s\n",
      "13:\tlearn: 0.2058658\ttotal: 848ms\tremaining: 10.7s\n",
      "14:\tlearn: 0.1906439\ttotal: 908ms\tremaining: 10.6s\n",
      "15:\tlearn: 0.1766765\ttotal: 971ms\tremaining: 10.6s\n",
      "16:\tlearn: 0.1638340\ttotal: 1.04s\tremaining: 10.6s\n",
      "17:\tlearn: 0.1519678\ttotal: 1.11s\tremaining: 10.6s\n",
      "18:\tlearn: 0.1410924\ttotal: 1.17s\tremaining: 10.6s\n",
      "19:\tlearn: 0.1310007\ttotal: 1.23s\tremaining: 10.5s\n",
      "20:\tlearn: 0.1216933\ttotal: 1.29s\tremaining: 10.4s\n",
      "21:\tlearn: 0.1131114\ttotal: 1.36s\tremaining: 10.4s\n",
      "22:\tlearn: 0.1051847\ttotal: 1.42s\tremaining: 10.3s\n",
      "23:\tlearn: 0.0978541\ttotal: 1.48s\tremaining: 10.2s\n",
      "24:\tlearn: 0.0910574\ttotal: 1.53s\tremaining: 10.1s\n",
      "25:\tlearn: 0.0847960\ttotal: 1.59s\tremaining: 10.1s\n",
      "26:\tlearn: 0.0789427\ttotal: 1.65s\tremaining: 9.98s\n",
      "27:\tlearn: 0.0735575\ttotal: 1.71s\tremaining: 9.92s\n",
      "28:\tlearn: 0.0685577\ttotal: 1.78s\tremaining: 9.87s\n",
      "29:\tlearn: 0.0639377\ttotal: 1.84s\tremaining: 9.81s\n",
      "30:\tlearn: 0.0596278\ttotal: 1.9s\tremaining: 9.74s\n",
      "31:\tlearn: 0.0556424\ttotal: 1.96s\tremaining: 9.69s\n",
      "32:\tlearn: 0.0519320\ttotal: 2.02s\tremaining: 9.63s\n",
      "33:\tlearn: 0.0484768\ttotal: 2.08s\tremaining: 9.57s\n",
      "34:\tlearn: 0.0452815\ttotal: 2.14s\tremaining: 9.49s\n",
      "35:\tlearn: 0.0423125\ttotal: 2.2s\tremaining: 9.43s\n",
      "36:\tlearn: 0.0395469\ttotal: 2.26s\tremaining: 9.35s\n",
      "37:\tlearn: 0.0369754\ttotal: 2.33s\tremaining: 9.31s\n",
      "38:\tlearn: 0.0345725\ttotal: 2.38s\tremaining: 9.23s\n",
      "39:\tlearn: 0.0323503\ttotal: 2.44s\tremaining: 9.17s\n",
      "40:\tlearn: 0.0302728\ttotal: 2.5s\tremaining: 9.1s\n",
      "41:\tlearn: 0.0283367\ttotal: 2.56s\tremaining: 9.01s\n",
      "42:\tlearn: 0.0265352\ttotal: 2.61s\tremaining: 8.94s\n",
      "43:\tlearn: 0.0248663\ttotal: 2.67s\tremaining: 8.87s\n",
      "44:\tlearn: 0.0233153\ttotal: 2.73s\tremaining: 8.81s\n",
      "45:\tlearn: 0.0218592\ttotal: 2.79s\tremaining: 8.74s\n",
      "46:\tlearn: 0.0205078\ttotal: 2.85s\tremaining: 8.68s\n",
      "47:\tlearn: 0.0192599\ttotal: 2.91s\tremaining: 8.62s\n",
      "48:\tlearn: 0.0180845\ttotal: 2.97s\tremaining: 8.54s\n",
      "49:\tlearn: 0.0169966\ttotal: 3.03s\tremaining: 8.48s\n",
      "50:\tlearn: 0.0159759\ttotal: 3.08s\tremaining: 8.4s\n",
      "51:\tlearn: 0.0150291\ttotal: 3.12s\tremaining: 8.28s\n",
      "52:\tlearn: 0.0141593\ttotal: 3.17s\tremaining: 8.18s\n",
      "53:\tlearn: 0.0133424\ttotal: 3.22s\tremaining: 8.12s\n",
      "54:\tlearn: 0.0125769\ttotal: 3.28s\tremaining: 8.05s\n",
      "55:\tlearn: 0.0118653\ttotal: 3.34s\tremaining: 7.98s\n",
      "56:\tlearn: 0.0112011\ttotal: 3.39s\tremaining: 7.91s\n",
      "57:\tlearn: 0.0105931\ttotal: 3.45s\tremaining: 7.86s\n",
      "58:\tlearn: 0.0100226\ttotal: 3.51s\tremaining: 7.79s\n",
      "59:\tlearn: 0.0094900\ttotal: 3.56s\tremaining: 7.72s\n",
      "60:\tlearn: 0.0089895\ttotal: 3.62s\tremaining: 7.65s\n",
      "61:\tlearn: 0.0085235\ttotal: 3.67s\tremaining: 7.58s\n",
      "62:\tlearn: 0.0080859\ttotal: 3.73s\tremaining: 7.51s\n",
      "63:\tlearn: 0.0076892\ttotal: 3.77s\tremaining: 7.42s\n",
      "64:\tlearn: 0.0073144\ttotal: 3.83s\tremaining: 7.36s\n",
      "65:\tlearn: 0.0069651\ttotal: 3.88s\tremaining: 7.29s\n",
      "66:\tlearn: 0.0066375\ttotal: 3.94s\tremaining: 7.24s\n",
      "67:\tlearn: 0.0063383\ttotal: 4s\tremaining: 7.17s\n",
      "68:\tlearn: 0.0060572\ttotal: 4.03s\tremaining: 7.08s\n",
      "69:\tlearn: 0.0058002\ttotal: 4.07s\tremaining: 6.98s\n",
      "70:\tlearn: 0.0055543\ttotal: 4.12s\tremaining: 6.91s\n",
      "71:\tlearn: 0.0053303\ttotal: 4.18s\tremaining: 6.85s\n",
      "72:\tlearn: 0.0051177\ttotal: 4.24s\tremaining: 6.79s\n",
      "73:\tlearn: 0.0049198\ttotal: 4.29s\tremaining: 6.73s\n",
      "74:\tlearn: 0.0047371\ttotal: 4.36s\tremaining: 6.68s\n",
      "75:\tlearn: 0.0045684\ttotal: 4.42s\tremaining: 6.62s\n",
      "76:\tlearn: 0.0044137\ttotal: 4.47s\tremaining: 6.56s\n",
      "77:\tlearn: 0.0042654\ttotal: 4.53s\tremaining: 6.5s\n",
      "78:\tlearn: 0.0041255\ttotal: 4.59s\tremaining: 6.45s\n",
      "79:\tlearn: 0.0040001\ttotal: 4.65s\tremaining: 6.39s\n",
      "80:\tlearn: 0.0038850\ttotal: 4.71s\tremaining: 6.33s\n",
      "81:\tlearn: 0.0037783\ttotal: 4.74s\tremaining: 6.25s\n",
      "82:\tlearn: 0.0036764\ttotal: 4.79s\tremaining: 6.17s\n",
      "83:\tlearn: 0.0035847\ttotal: 4.84s\tremaining: 6.11s\n",
      "84:\tlearn: 0.0034959\ttotal: 4.9s\tremaining: 6.06s\n",
      "85:\tlearn: 0.0034116\ttotal: 4.97s\tremaining: 6s\n",
      "86:\tlearn: 0.0033242\ttotal: 5.03s\tremaining: 5.95s\n",
      "87:\tlearn: 0.0032548\ttotal: 5.08s\tremaining: 5.89s\n",
      "88:\tlearn: 0.0031807\ttotal: 5.14s\tremaining: 5.83s\n",
      "89:\tlearn: 0.0031131\ttotal: 5.2s\tremaining: 5.78s\n",
      "90:\tlearn: 0.0030519\ttotal: 5.26s\tremaining: 5.72s\n",
      "91:\tlearn: 0.0029915\ttotal: 5.32s\tremaining: 5.66s\n",
      "92:\tlearn: 0.0029445\ttotal: 5.37s\tremaining: 5.6s\n",
      "93:\tlearn: 0.0028982\ttotal: 5.42s\tremaining: 5.54s\n",
      "94:\tlearn: 0.0028499\ttotal: 5.48s\tremaining: 5.48s\n",
      "95:\tlearn: 0.0028038\ttotal: 5.53s\tremaining: 5.42s\n",
      "96:\tlearn: 0.0027643\ttotal: 5.58s\tremaining: 5.35s\n",
      "97:\tlearn: 0.0027251\ttotal: 5.62s\tremaining: 5.28s\n",
      "98:\tlearn: 0.0026910\ttotal: 5.67s\tremaining: 5.21s\n",
      "99:\tlearn: 0.0026650\ttotal: 5.7s\tremaining: 5.13s\n",
      "100:\tlearn: 0.0026379\ttotal: 5.76s\tremaining: 5.08s\n",
      "101:\tlearn: 0.0026045\ttotal: 5.82s\tremaining: 5.02s\n",
      "102:\tlearn: 0.0025827\ttotal: 5.88s\tremaining: 4.97s\n",
      "103:\tlearn: 0.0025527\ttotal: 5.94s\tremaining: 4.91s\n",
      "104:\tlearn: 0.0025339\ttotal: 5.99s\tremaining: 4.85s\n",
      "105:\tlearn: 0.0025013\ttotal: 6.05s\tremaining: 4.8s\n",
      "106:\tlearn: 0.0024856\ttotal: 6.11s\tremaining: 4.74s\n",
      "107:\tlearn: 0.0024624\ttotal: 6.17s\tremaining: 4.68s\n",
      "108:\tlearn: 0.0024359\ttotal: 6.22s\tremaining: 4.63s\n",
      "109:\tlearn: 0.0024174\ttotal: 6.28s\tremaining: 4.57s\n",
      "110:\tlearn: 0.0023960\ttotal: 6.33s\tremaining: 4.51s\n",
      "111:\tlearn: 0.0023771\ttotal: 6.39s\tremaining: 4.45s\n",
      "112:\tlearn: 0.0023559\ttotal: 6.45s\tremaining: 4.39s\n",
      "113:\tlearn: 0.0023379\ttotal: 6.51s\tremaining: 4.34s\n",
      "114:\tlearn: 0.0023156\ttotal: 6.57s\tremaining: 4.28s\n",
      "115:\tlearn: 0.0023058\ttotal: 6.62s\tremaining: 4.22s\n",
      "116:\tlearn: 0.0022973\ttotal: 6.68s\tremaining: 4.17s\n",
      "117:\tlearn: 0.0022803\ttotal: 6.74s\tremaining: 4.11s\n",
      "118:\tlearn: 0.0022697\ttotal: 6.79s\tremaining: 4.05s\n",
      "119:\tlearn: 0.0022591\ttotal: 6.86s\tremaining: 4s\n",
      "120:\tlearn: 0.0022458\ttotal: 6.91s\tremaining: 3.94s\n",
      "121:\tlearn: 0.0022375\ttotal: 6.97s\tremaining: 3.88s\n",
      "122:\tlearn: 0.0022244\ttotal: 7.02s\tremaining: 3.83s\n",
      "123:\tlearn: 0.0022162\ttotal: 7.08s\tremaining: 3.77s\n",
      "124:\tlearn: 0.0022029\ttotal: 7.14s\tremaining: 3.71s\n",
      "125:\tlearn: 0.0021923\ttotal: 7.21s\tremaining: 3.66s\n",
      "126:\tlearn: 0.0021793\ttotal: 7.27s\tremaining: 3.61s\n",
      "127:\tlearn: 0.0021697\ttotal: 7.33s\tremaining: 3.55s\n",
      "128:\tlearn: 0.0021576\ttotal: 7.38s\tremaining: 3.49s\n",
      "129:\tlearn: 0.0021514\ttotal: 7.43s\tremaining: 3.43s\n",
      "130:\tlearn: 0.0021415\ttotal: 7.5s\tremaining: 3.38s\n",
      "131:\tlearn: 0.0021283\ttotal: 7.55s\tremaining: 3.32s\n",
      "132:\tlearn: 0.0021229\ttotal: 7.6s\tremaining: 3.26s\n",
      "133:\tlearn: 0.0021181\ttotal: 7.65s\tremaining: 3.2s\n",
      "134:\tlearn: 0.0021080\ttotal: 7.71s\tremaining: 3.14s\n",
      "135:\tlearn: 0.0021050\ttotal: 7.76s\tremaining: 3.08s\n",
      "136:\tlearn: 0.0020966\ttotal: 7.82s\tremaining: 3.02s\n",
      "137:\tlearn: 0.0020919\ttotal: 7.88s\tremaining: 2.97s\n",
      "138:\tlearn: 0.0020821\ttotal: 7.93s\tremaining: 2.91s\n",
      "139:\tlearn: 0.0020767\ttotal: 7.99s\tremaining: 2.85s\n",
      "140:\tlearn: 0.0020728\ttotal: 8.04s\tremaining: 2.79s\n",
      "141:\tlearn: 0.0020666\ttotal: 8.1s\tremaining: 2.74s\n",
      "142:\tlearn: 0.0020571\ttotal: 8.15s\tremaining: 2.68s\n",
      "143:\tlearn: 0.0020478\ttotal: 8.21s\tremaining: 2.62s\n",
      "144:\tlearn: 0.0020436\ttotal: 8.26s\tremaining: 2.56s\n",
      "145:\tlearn: 0.0020405\ttotal: 8.31s\tremaining: 2.5s\n",
      "146:\tlearn: 0.0020319\ttotal: 8.37s\tremaining: 2.45s\n",
      "147:\tlearn: 0.0020196\ttotal: 8.43s\tremaining: 2.39s\n",
      "148:\tlearn: 0.0020089\ttotal: 8.48s\tremaining: 2.33s\n",
      "149:\tlearn: 0.0019972\ttotal: 8.54s\tremaining: 2.28s\n",
      "150:\tlearn: 0.0019945\ttotal: 8.6s\tremaining: 2.22s\n",
      "151:\tlearn: 0.0019857\ttotal: 8.66s\tremaining: 2.16s\n",
      "152:\tlearn: 0.0019809\ttotal: 8.7s\tremaining: 2.1s\n",
      "153:\tlearn: 0.0019761\ttotal: 8.75s\tremaining: 2.04s\n",
      "154:\tlearn: 0.0019689\ttotal: 8.8s\tremaining: 1.99s\n",
      "155:\tlearn: 0.0019645\ttotal: 8.86s\tremaining: 1.93s\n",
      "156:\tlearn: 0.0019598\ttotal: 8.92s\tremaining: 1.87s\n",
      "157:\tlearn: 0.0019548\ttotal: 8.97s\tremaining: 1.82s\n",
      "158:\tlearn: 0.0019516\ttotal: 9.03s\tremaining: 1.76s\n",
      "159:\tlearn: 0.0019475\ttotal: 9.09s\tremaining: 1.7s\n",
      "160:\tlearn: 0.0019450\ttotal: 9.14s\tremaining: 1.65s\n",
      "161:\tlearn: 0.0019426\ttotal: 9.2s\tremaining: 1.59s\n",
      "162:\tlearn: 0.0019374\ttotal: 9.25s\tremaining: 1.53s\n",
      "163:\tlearn: 0.0019304\ttotal: 9.31s\tremaining: 1.48s\n",
      "164:\tlearn: 0.0019258\ttotal: 9.37s\tremaining: 1.42s\n",
      "165:\tlearn: 0.0019223\ttotal: 9.42s\tremaining: 1.36s\n",
      "166:\tlearn: 0.0019186\ttotal: 9.47s\tremaining: 1.3s\n",
      "167:\tlearn: 0.0019098\ttotal: 9.53s\tremaining: 1.25s\n",
      "168:\tlearn: 0.0019035\ttotal: 9.59s\tremaining: 1.19s\n",
      "169:\tlearn: 0.0018943\ttotal: 9.64s\tremaining: 1.13s\n",
      "170:\tlearn: 0.0018914\ttotal: 9.7s\tremaining: 1.08s\n",
      "171:\tlearn: 0.0018855\ttotal: 9.76s\tremaining: 1.02s\n",
      "172:\tlearn: 0.0018821\ttotal: 9.81s\tremaining: 964ms\n",
      "173:\tlearn: 0.0018798\ttotal: 9.87s\tremaining: 908ms\n",
      "174:\tlearn: 0.0018760\ttotal: 9.93s\tremaining: 851ms\n",
      "175:\tlearn: 0.0018726\ttotal: 9.99s\tremaining: 794ms\n",
      "176:\tlearn: 0.0018709\ttotal: 10s\tremaining: 737ms\n",
      "177:\tlearn: 0.0018686\ttotal: 10.1s\tremaining: 681ms\n",
      "178:\tlearn: 0.0018661\ttotal: 10.1s\tremaining: 624ms\n",
      "179:\tlearn: 0.0018628\ttotal: 10.2s\tremaining: 567ms\n",
      "180:\tlearn: 0.0018594\ttotal: 10.3s\tremaining: 510ms\n",
      "181:\tlearn: 0.0018539\ttotal: 10.3s\tremaining: 453ms\n",
      "182:\tlearn: 0.0018520\ttotal: 10.4s\tremaining: 397ms\n",
      "183:\tlearn: 0.0018498\ttotal: 10.4s\tremaining: 340ms\n",
      "184:\tlearn: 0.0018454\ttotal: 10.5s\tremaining: 283ms\n",
      "185:\tlearn: 0.0018426\ttotal: 10.5s\tremaining: 226ms\n",
      "186:\tlearn: 0.0018396\ttotal: 10.6s\tremaining: 170ms\n",
      "187:\tlearn: 0.0018347\ttotal: 10.6s\tremaining: 113ms\n",
      "188:\tlearn: 0.0018308\ttotal: 10.7s\tremaining: 56.6ms\n",
      "189:\tlearn: 0.0018281\ttotal: 10.7s\tremaining: 0us\n",
      "0:\tlearn: 0.6258964\ttotal: 56.9ms\tremaining: 10.8s\n",
      "1:\tlearn: 0.5675169\ttotal: 118ms\tremaining: 11.1s\n",
      "2:\tlearn: 0.5163998\ttotal: 183ms\tremaining: 11.4s\n",
      "3:\tlearn: 0.4711547\ttotal: 249ms\tremaining: 11.6s\n",
      "4:\tlearn: 0.4308908\ttotal: 313ms\tremaining: 11.6s\n",
      "5:\tlearn: 0.3948506\ttotal: 374ms\tremaining: 11.5s\n",
      "6:\tlearn: 0.3624463\ttotal: 434ms\tremaining: 11.4s\n",
      "7:\tlearn: 0.3331817\ttotal: 498ms\tremaining: 11.3s\n",
      "8:\tlearn: 0.3067152\ttotal: 565ms\tremaining: 11.4s\n",
      "9:\tlearn: 0.2826996\ttotal: 627ms\tremaining: 11.3s\n",
      "10:\tlearn: 0.2609151\ttotal: 691ms\tremaining: 11.2s\n",
      "11:\tlearn: 0.2409495\ttotal: 750ms\tremaining: 11.1s\n",
      "12:\tlearn: 0.2227506\ttotal: 812ms\tremaining: 11.1s\n",
      "13:\tlearn: 0.2061073\ttotal: 874ms\tremaining: 11s\n",
      "14:\tlearn: 0.1908934\ttotal: 934ms\tremaining: 10.9s\n",
      "15:\tlearn: 0.1769299\ttotal: 995ms\tremaining: 10.8s\n",
      "16:\tlearn: 0.1640992\ttotal: 1.05s\tremaining: 10.7s\n",
      "17:\tlearn: 0.1522261\ttotal: 1.11s\tremaining: 10.6s\n",
      "18:\tlearn: 0.1413208\ttotal: 1.17s\tremaining: 10.5s\n",
      "19:\tlearn: 0.1312454\ttotal: 1.23s\tremaining: 10.5s\n",
      "20:\tlearn: 0.1219464\ttotal: 1.29s\tremaining: 10.4s\n",
      "21:\tlearn: 0.1133720\ttotal: 1.35s\tremaining: 10.3s\n",
      "22:\tlearn: 0.1054497\ttotal: 1.41s\tremaining: 10.2s\n",
      "23:\tlearn: 0.0981281\ttotal: 1.47s\tremaining: 10.2s\n",
      "24:\tlearn: 0.0913307\ttotal: 1.53s\tremaining: 10.1s\n",
      "25:\tlearn: 0.0850696\ttotal: 1.6s\tremaining: 10.1s\n",
      "26:\tlearn: 0.0792236\ttotal: 1.65s\tremaining: 9.99s\n",
      "27:\tlearn: 0.0738396\ttotal: 1.7s\tremaining: 9.81s\n",
      "28:\tlearn: 0.0688455\ttotal: 1.76s\tremaining: 9.76s\n",
      "29:\tlearn: 0.0642230\ttotal: 1.82s\tremaining: 9.69s\n",
      "30:\tlearn: 0.0599310\ttotal: 1.88s\tremaining: 9.62s\n",
      "31:\tlearn: 0.0559613\ttotal: 1.94s\tremaining: 9.56s\n",
      "32:\tlearn: 0.0522488\ttotal: 1.99s\tremaining: 9.49s\n",
      "33:\tlearn: 0.0488027\ttotal: 2.06s\tremaining: 9.43s\n",
      "34:\tlearn: 0.0456057\ttotal: 2.11s\tremaining: 9.36s\n",
      "35:\tlearn: 0.0426102\ttotal: 2.17s\tremaining: 9.27s\n",
      "36:\tlearn: 0.0398444\ttotal: 2.22s\tremaining: 9.2s\n",
      "37:\tlearn: 0.0372900\ttotal: 2.29s\tremaining: 9.14s\n",
      "38:\tlearn: 0.0348623\ttotal: 2.34s\tremaining: 9.07s\n",
      "39:\tlearn: 0.0326406\ttotal: 2.4s\tremaining: 9.02s\n",
      "40:\tlearn: 0.0305630\ttotal: 2.46s\tremaining: 8.94s\n",
      "41:\tlearn: 0.0286409\ttotal: 2.52s\tremaining: 8.88s\n",
      "42:\tlearn: 0.0268429\ttotal: 2.58s\tremaining: 8.82s\n",
      "43:\tlearn: 0.0251643\ttotal: 2.64s\tremaining: 8.77s\n",
      "44:\tlearn: 0.0235875\ttotal: 2.7s\tremaining: 8.69s\n",
      "45:\tlearn: 0.0221301\ttotal: 2.75s\tremaining: 8.61s\n",
      "46:\tlearn: 0.0207770\ttotal: 2.81s\tremaining: 8.54s\n",
      "47:\tlearn: 0.0195048\ttotal: 2.86s\tremaining: 8.47s\n",
      "48:\tlearn: 0.0183327\ttotal: 2.92s\tremaining: 8.4s\n",
      "49:\tlearn: 0.0172304\ttotal: 2.97s\tremaining: 8.32s\n",
      "50:\tlearn: 0.0162148\ttotal: 3.03s\tremaining: 8.25s\n",
      "51:\tlearn: 0.0152701\ttotal: 3.09s\tremaining: 8.19s\n",
      "52:\tlearn: 0.0143963\ttotal: 3.15s\tremaining: 8.13s\n",
      "53:\tlearn: 0.0135718\ttotal: 3.2s\tremaining: 8.06s\n",
      "54:\tlearn: 0.0128109\ttotal: 3.26s\tremaining: 8s\n",
      "55:\tlearn: 0.0121012\ttotal: 3.32s\tremaining: 7.94s\n",
      "56:\tlearn: 0.0114382\ttotal: 3.38s\tremaining: 7.88s\n",
      "57:\tlearn: 0.0108189\ttotal: 3.43s\tremaining: 7.81s\n",
      "58:\tlearn: 0.0102386\ttotal: 3.49s\tremaining: 7.75s\n",
      "59:\tlearn: 0.0096930\ttotal: 3.54s\tremaining: 7.67s\n",
      "60:\tlearn: 0.0091880\ttotal: 3.6s\tremaining: 7.6s\n",
      "61:\tlearn: 0.0087147\ttotal: 3.65s\tremaining: 7.53s\n",
      "62:\tlearn: 0.0082721\ttotal: 3.7s\tremaining: 7.46s\n",
      "63:\tlearn: 0.0078656\ttotal: 3.75s\tremaining: 7.39s\n",
      "64:\tlearn: 0.0074853\ttotal: 3.81s\tremaining: 7.32s\n",
      "65:\tlearn: 0.0071258\ttotal: 3.86s\tremaining: 7.25s\n",
      "66:\tlearn: 0.0068068\ttotal: 3.92s\tremaining: 7.2s\n",
      "67:\tlearn: 0.0064994\ttotal: 3.97s\tremaining: 7.13s\n",
      "68:\tlearn: 0.0062192\ttotal: 4.03s\tremaining: 7.06s\n",
      "69:\tlearn: 0.0059548\ttotal: 4.08s\tremaining: 6.99s\n",
      "70:\tlearn: 0.0057146\ttotal: 4.13s\tremaining: 6.92s\n",
      "71:\tlearn: 0.0054883\ttotal: 4.18s\tremaining: 6.86s\n",
      "72:\tlearn: 0.0052784\ttotal: 4.24s\tremaining: 6.79s\n",
      "73:\tlearn: 0.0050832\ttotal: 4.28s\tremaining: 6.71s\n",
      "74:\tlearn: 0.0048974\ttotal: 4.32s\tremaining: 6.62s\n",
      "75:\tlearn: 0.0047210\ttotal: 4.35s\tremaining: 6.53s\n",
      "76:\tlearn: 0.0045640\ttotal: 4.39s\tremaining: 6.45s\n",
      "77:\tlearn: 0.0044122\ttotal: 4.43s\tremaining: 6.36s\n",
      "78:\tlearn: 0.0042771\ttotal: 4.46s\tremaining: 6.27s\n",
      "79:\tlearn: 0.0041498\ttotal: 4.5s\tremaining: 6.19s\n",
      "80:\tlearn: 0.0040261\ttotal: 4.54s\tremaining: 6.11s\n",
      "81:\tlearn: 0.0039193\ttotal: 4.59s\tremaining: 6.04s\n",
      "82:\tlearn: 0.0038132\ttotal: 4.64s\tremaining: 5.98s\n",
      "83:\tlearn: 0.0037072\ttotal: 4.7s\tremaining: 5.93s\n",
      "84:\tlearn: 0.0036103\ttotal: 4.76s\tremaining: 5.88s\n",
      "85:\tlearn: 0.0035249\ttotal: 4.82s\tremaining: 5.82s\n",
      "86:\tlearn: 0.0034405\ttotal: 4.88s\tremaining: 5.77s\n",
      "87:\tlearn: 0.0033558\ttotal: 4.93s\tremaining: 5.71s\n",
      "88:\tlearn: 0.0032844\ttotal: 4.98s\tremaining: 5.65s\n",
      "89:\tlearn: 0.0032175\ttotal: 5.04s\tremaining: 5.6s\n",
      "90:\tlearn: 0.0031631\ttotal: 5.09s\tremaining: 5.54s\n",
      "91:\tlearn: 0.0031035\ttotal: 5.15s\tremaining: 5.48s\n",
      "92:\tlearn: 0.0030442\ttotal: 5.2s\tremaining: 5.43s\n",
      "93:\tlearn: 0.0029944\ttotal: 5.26s\tremaining: 5.37s\n",
      "94:\tlearn: 0.0029408\ttotal: 5.31s\tremaining: 5.31s\n",
      "95:\tlearn: 0.0029013\ttotal: 5.36s\tremaining: 5.25s\n",
      "96:\tlearn: 0.0028662\ttotal: 5.42s\tremaining: 5.19s\n",
      "97:\tlearn: 0.0028333\ttotal: 5.45s\tremaining: 5.12s\n",
      "98:\tlearn: 0.0028014\ttotal: 5.51s\tremaining: 5.06s\n",
      "99:\tlearn: 0.0027643\ttotal: 5.57s\tremaining: 5.01s\n",
      "100:\tlearn: 0.0027363\ttotal: 5.62s\tremaining: 4.95s\n",
      "101:\tlearn: 0.0027030\ttotal: 5.67s\tremaining: 4.89s\n",
      "102:\tlearn: 0.0026693\ttotal: 5.73s\tremaining: 4.84s\n",
      "103:\tlearn: 0.0026446\ttotal: 5.79s\tremaining: 4.79s\n",
      "104:\tlearn: 0.0026212\ttotal: 5.84s\tremaining: 4.73s\n",
      "105:\tlearn: 0.0025883\ttotal: 5.89s\tremaining: 4.67s\n",
      "106:\tlearn: 0.0025610\ttotal: 5.95s\tremaining: 4.62s\n",
      "107:\tlearn: 0.0025319\ttotal: 6.01s\tremaining: 4.56s\n",
      "108:\tlearn: 0.0025115\ttotal: 6.06s\tremaining: 4.51s\n",
      "109:\tlearn: 0.0024946\ttotal: 6.12s\tremaining: 4.45s\n",
      "110:\tlearn: 0.0024803\ttotal: 6.17s\tremaining: 4.39s\n",
      "111:\tlearn: 0.0024666\ttotal: 6.22s\tremaining: 4.33s\n",
      "112:\tlearn: 0.0024557\ttotal: 6.28s\tremaining: 4.28s\n",
      "113:\tlearn: 0.0024445\ttotal: 6.33s\tremaining: 4.22s\n",
      "114:\tlearn: 0.0024272\ttotal: 6.38s\tremaining: 4.16s\n",
      "115:\tlearn: 0.0024138\ttotal: 6.44s\tremaining: 4.11s\n",
      "116:\tlearn: 0.0023993\ttotal: 6.49s\tremaining: 4.05s\n",
      "117:\tlearn: 0.0023800\ttotal: 6.55s\tremaining: 4s\n",
      "118:\tlearn: 0.0023664\ttotal: 6.61s\tremaining: 3.94s\n",
      "119:\tlearn: 0.0023486\ttotal: 6.67s\tremaining: 3.89s\n",
      "120:\tlearn: 0.0023399\ttotal: 6.72s\tremaining: 3.83s\n",
      "121:\tlearn: 0.0023248\ttotal: 6.77s\tremaining: 3.77s\n",
      "122:\tlearn: 0.0023132\ttotal: 6.83s\tremaining: 3.72s\n",
      "123:\tlearn: 0.0023012\ttotal: 6.89s\tremaining: 3.67s\n",
      "124:\tlearn: 0.0022865\ttotal: 6.94s\tremaining: 3.61s\n",
      "125:\tlearn: 0.0022805\ttotal: 7s\tremaining: 3.55s\n",
      "126:\tlearn: 0.0022670\ttotal: 7.05s\tremaining: 3.5s\n",
      "127:\tlearn: 0.0022574\ttotal: 7.11s\tremaining: 3.44s\n",
      "128:\tlearn: 0.0022523\ttotal: 7.16s\tremaining: 3.38s\n",
      "129:\tlearn: 0.0022471\ttotal: 7.21s\tremaining: 3.33s\n",
      "130:\tlearn: 0.0022366\ttotal: 7.27s\tremaining: 3.27s\n",
      "131:\tlearn: 0.0022233\ttotal: 7.32s\tremaining: 3.22s\n",
      "132:\tlearn: 0.0022115\ttotal: 7.38s\tremaining: 3.16s\n",
      "133:\tlearn: 0.0022068\ttotal: 7.44s\tremaining: 3.11s\n",
      "134:\tlearn: 0.0021959\ttotal: 7.49s\tremaining: 3.05s\n",
      "135:\tlearn: 0.0021914\ttotal: 7.55s\tremaining: 3s\n",
      "136:\tlearn: 0.0021859\ttotal: 7.6s\tremaining: 2.94s\n",
      "137:\tlearn: 0.0021743\ttotal: 7.66s\tremaining: 2.88s\n",
      "138:\tlearn: 0.0021660\ttotal: 7.71s\tremaining: 2.83s\n",
      "139:\tlearn: 0.0021533\ttotal: 7.77s\tremaining: 2.77s\n",
      "140:\tlearn: 0.0021480\ttotal: 7.83s\tremaining: 2.72s\n",
      "141:\tlearn: 0.0021434\ttotal: 7.88s\tremaining: 2.66s\n",
      "142:\tlearn: 0.0021309\ttotal: 7.93s\tremaining: 2.61s\n",
      "143:\tlearn: 0.0021266\ttotal: 7.99s\tremaining: 2.55s\n",
      "144:\tlearn: 0.0021218\ttotal: 8.05s\tremaining: 2.5s\n",
      "145:\tlearn: 0.0021123\ttotal: 8.1s\tremaining: 2.44s\n",
      "146:\tlearn: 0.0021094\ttotal: 8.16s\tremaining: 2.38s\n",
      "147:\tlearn: 0.0021005\ttotal: 8.2s\tremaining: 2.33s\n",
      "148:\tlearn: 0.0020957\ttotal: 8.26s\tremaining: 2.27s\n",
      "149:\tlearn: 0.0020905\ttotal: 8.31s\tremaining: 2.22s\n",
      "150:\tlearn: 0.0020874\ttotal: 8.37s\tremaining: 2.16s\n",
      "151:\tlearn: 0.0020761\ttotal: 8.43s\tremaining: 2.11s\n",
      "152:\tlearn: 0.0020671\ttotal: 8.47s\tremaining: 2.05s\n",
      "153:\tlearn: 0.0020623\ttotal: 8.51s\tremaining: 1.99s\n",
      "154:\tlearn: 0.0020539\ttotal: 8.57s\tremaining: 1.94s\n",
      "155:\tlearn: 0.0020515\ttotal: 8.63s\tremaining: 1.88s\n",
      "156:\tlearn: 0.0020484\ttotal: 8.68s\tremaining: 1.82s\n",
      "157:\tlearn: 0.0020407\ttotal: 8.74s\tremaining: 1.77s\n",
      "158:\tlearn: 0.0020371\ttotal: 8.8s\tremaining: 1.71s\n",
      "159:\tlearn: 0.0020355\ttotal: 8.85s\tremaining: 1.66s\n",
      "160:\tlearn: 0.0020316\ttotal: 8.9s\tremaining: 1.6s\n",
      "161:\tlearn: 0.0020284\ttotal: 8.94s\tremaining: 1.55s\n",
      "162:\tlearn: 0.0020211\ttotal: 9s\tremaining: 1.49s\n",
      "163:\tlearn: 0.0020146\ttotal: 9.05s\tremaining: 1.44s\n",
      "164:\tlearn: 0.0020120\ttotal: 9.11s\tremaining: 1.38s\n",
      "165:\tlearn: 0.0020085\ttotal: 9.16s\tremaining: 1.32s\n",
      "166:\tlearn: 0.0020061\ttotal: 9.22s\tremaining: 1.27s\n",
      "167:\tlearn: 0.0020000\ttotal: 9.28s\tremaining: 1.22s\n",
      "168:\tlearn: 0.0019952\ttotal: 9.34s\tremaining: 1.16s\n",
      "169:\tlearn: 0.0019911\ttotal: 9.38s\tremaining: 1.1s\n",
      "170:\tlearn: 0.0019890\ttotal: 9.43s\tremaining: 1.05s\n",
      "171:\tlearn: 0.0019844\ttotal: 9.49s\tremaining: 993ms\n",
      "172:\tlearn: 0.0019817\ttotal: 9.55s\tremaining: 938ms\n",
      "173:\tlearn: 0.0019790\ttotal: 9.6s\tremaining: 883ms\n",
      "174:\tlearn: 0.0019760\ttotal: 9.66s\tremaining: 828ms\n",
      "175:\tlearn: 0.0019729\ttotal: 9.71s\tremaining: 773ms\n",
      "176:\tlearn: 0.0019662\ttotal: 9.77s\tremaining: 718ms\n",
      "177:\tlearn: 0.0019643\ttotal: 9.82s\tremaining: 662ms\n",
      "178:\tlearn: 0.0019608\ttotal: 9.88s\tremaining: 607ms\n",
      "179:\tlearn: 0.0019554\ttotal: 9.93s\tremaining: 552ms\n",
      "180:\tlearn: 0.0019516\ttotal: 9.99s\tremaining: 497ms\n",
      "181:\tlearn: 0.0019471\ttotal: 10s\tremaining: 442ms\n",
      "182:\tlearn: 0.0019449\ttotal: 10.1s\tremaining: 386ms\n",
      "183:\tlearn: 0.0019387\ttotal: 10.2s\tremaining: 331ms\n",
      "184:\tlearn: 0.0019349\ttotal: 10.2s\tremaining: 276ms\n",
      "185:\tlearn: 0.0019321\ttotal: 10.3s\tremaining: 221ms\n",
      "186:\tlearn: 0.0019295\ttotal: 10.3s\tremaining: 166ms\n",
      "187:\tlearn: 0.0019277\ttotal: 10.4s\tremaining: 110ms\n",
      "188:\tlearn: 0.0019208\ttotal: 10.4s\tremaining: 55.2ms\n",
      "189:\tlearn: 0.0019185\ttotal: 10.5s\tremaining: 0us\n",
      "0:\tlearn: 0.6258809\ttotal: 43.8ms\tremaining: 8.28s\n",
      "1:\tlearn: 0.5674968\ttotal: 98.3ms\tremaining: 9.24s\n",
      "2:\tlearn: 0.5162985\ttotal: 161ms\tremaining: 10s\n",
      "3:\tlearn: 0.4710422\ttotal: 231ms\tremaining: 10.7s\n",
      "4:\tlearn: 0.4307717\ttotal: 290ms\tremaining: 10.7s\n",
      "5:\tlearn: 0.3947070\ttotal: 347ms\tremaining: 10.6s\n",
      "6:\tlearn: 0.3622760\ttotal: 409ms\tremaining: 10.7s\n",
      "7:\tlearn: 0.3329980\ttotal: 471ms\tremaining: 10.7s\n",
      "8:\tlearn: 0.3065227\ttotal: 534ms\tremaining: 10.7s\n",
      "9:\tlearn: 0.2825290\ttotal: 598ms\tremaining: 10.8s\n",
      "10:\tlearn: 0.2607327\ttotal: 661ms\tremaining: 10.8s\n",
      "11:\tlearn: 0.2407531\ttotal: 720ms\tremaining: 10.7s\n",
      "12:\tlearn: 0.2225442\ttotal: 773ms\tremaining: 10.5s\n",
      "13:\tlearn: 0.2058916\ttotal: 816ms\tremaining: 10.3s\n",
      "14:\tlearn: 0.1906720\ttotal: 856ms\tremaining: 9.99s\n",
      "15:\tlearn: 0.1766986\ttotal: 902ms\tremaining: 9.8s\n",
      "16:\tlearn: 0.1638603\ttotal: 945ms\tremaining: 9.62s\n",
      "17:\tlearn: 0.1519919\ttotal: 1s\tremaining: 9.6s\n",
      "18:\tlearn: 0.1410795\ttotal: 1.07s\tremaining: 9.62s\n",
      "19:\tlearn: 0.1309967\ttotal: 1.13s\tremaining: 9.6s\n",
      "20:\tlearn: 0.1217008\ttotal: 1.19s\tremaining: 9.56s\n",
      "21:\tlearn: 0.1131198\ttotal: 1.25s\tremaining: 9.55s\n",
      "22:\tlearn: 0.1051913\ttotal: 1.31s\tremaining: 9.54s\n",
      "23:\tlearn: 0.0978683\ttotal: 1.38s\tremaining: 9.52s\n",
      "24:\tlearn: 0.0910884\ttotal: 1.44s\tremaining: 9.48s\n",
      "25:\tlearn: 0.0848216\ttotal: 1.5s\tremaining: 9.48s\n",
      "26:\tlearn: 0.0789555\ttotal: 1.56s\tremaining: 9.42s\n",
      "27:\tlearn: 0.0735661\ttotal: 1.62s\tremaining: 9.37s\n",
      "28:\tlearn: 0.0685843\ttotal: 1.68s\tremaining: 9.33s\n",
      "29:\tlearn: 0.0639484\ttotal: 1.73s\tremaining: 9.25s\n",
      "30:\tlearn: 0.0596303\ttotal: 1.77s\tremaining: 9.1s\n",
      "31:\tlearn: 0.0556450\ttotal: 1.81s\tremaining: 8.96s\n",
      "32:\tlearn: 0.0519177\ttotal: 1.86s\tremaining: 8.83s\n",
      "33:\tlearn: 0.0484556\ttotal: 1.9s\tremaining: 8.71s\n",
      "34:\tlearn: 0.0452587\ttotal: 1.94s\tremaining: 8.58s\n",
      "35:\tlearn: 0.0422693\ttotal: 1.97s\tremaining: 8.44s\n",
      "36:\tlearn: 0.0394924\ttotal: 2.01s\tremaining: 8.31s\n",
      "37:\tlearn: 0.0369258\ttotal: 2.05s\tremaining: 8.2s\n",
      "38:\tlearn: 0.0345251\ttotal: 2.09s\tremaining: 8.09s\n",
      "39:\tlearn: 0.0322997\ttotal: 2.13s\tremaining: 8s\n",
      "40:\tlearn: 0.0302097\ttotal: 2.17s\tremaining: 7.89s\n",
      "41:\tlearn: 0.0282765\ttotal: 2.21s\tremaining: 7.79s\n",
      "42:\tlearn: 0.0264527\ttotal: 2.25s\tremaining: 7.69s\n",
      "43:\tlearn: 0.0247796\ttotal: 2.29s\tremaining: 7.59s\n",
      "44:\tlearn: 0.0232262\ttotal: 2.34s\tremaining: 7.55s\n",
      "45:\tlearn: 0.0217749\ttotal: 2.4s\tremaining: 7.5s\n",
      "46:\tlearn: 0.0204240\ttotal: 2.45s\tremaining: 7.45s\n",
      "47:\tlearn: 0.0191556\ttotal: 2.51s\tremaining: 7.42s\n",
      "48:\tlearn: 0.0179759\ttotal: 2.56s\tremaining: 7.37s\n",
      "49:\tlearn: 0.0168807\ttotal: 2.62s\tremaining: 7.33s\n",
      "50:\tlearn: 0.0158639\ttotal: 2.67s\tremaining: 7.29s\n",
      "51:\tlearn: 0.0149053\ttotal: 2.73s\tremaining: 7.24s\n",
      "52:\tlearn: 0.0140241\ttotal: 2.79s\tremaining: 7.2s\n",
      "53:\tlearn: 0.0132022\ttotal: 2.85s\tremaining: 7.17s\n",
      "54:\tlearn: 0.0124322\ttotal: 2.9s\tremaining: 7.12s\n",
      "55:\tlearn: 0.0117164\ttotal: 2.96s\tremaining: 7.08s\n",
      "56:\tlearn: 0.0110480\ttotal: 3.01s\tremaining: 7.02s\n",
      "57:\tlearn: 0.0104269\ttotal: 3.06s\tremaining: 6.97s\n",
      "58:\tlearn: 0.0098460\ttotal: 3.11s\tremaining: 6.91s\n",
      "59:\tlearn: 0.0093083\ttotal: 3.17s\tremaining: 6.86s\n",
      "60:\tlearn: 0.0088056\ttotal: 3.21s\tremaining: 6.8s\n",
      "61:\tlearn: 0.0083392\ttotal: 3.27s\tremaining: 6.75s\n",
      "62:\tlearn: 0.0078998\ttotal: 3.33s\tremaining: 6.71s\n",
      "63:\tlearn: 0.0074964\ttotal: 3.38s\tremaining: 6.66s\n",
      "64:\tlearn: 0.0071158\ttotal: 3.44s\tremaining: 6.61s\n",
      "65:\tlearn: 0.0067630\ttotal: 3.49s\tremaining: 6.56s\n",
      "66:\tlearn: 0.0064383\ttotal: 3.55s\tremaining: 6.51s\n",
      "67:\tlearn: 0.0061408\ttotal: 3.6s\tremaining: 6.47s\n",
      "68:\tlearn: 0.0058571\ttotal: 3.66s\tremaining: 6.42s\n",
      "69:\tlearn: 0.0055935\ttotal: 3.72s\tremaining: 6.38s\n",
      "70:\tlearn: 0.0053510\ttotal: 3.78s\tremaining: 6.33s\n",
      "71:\tlearn: 0.0051276\ttotal: 3.83s\tremaining: 6.28s\n",
      "72:\tlearn: 0.0049131\ttotal: 3.88s\tremaining: 6.23s\n",
      "73:\tlearn: 0.0047089\ttotal: 3.94s\tremaining: 6.18s\n",
      "74:\tlearn: 0.0045225\ttotal: 4s\tremaining: 6.13s\n",
      "75:\tlearn: 0.0043473\ttotal: 4.05s\tremaining: 6.08s\n",
      "76:\tlearn: 0.0041878\ttotal: 4.11s\tremaining: 6.03s\n",
      "77:\tlearn: 0.0040341\ttotal: 4.16s\tremaining: 5.98s\n",
      "78:\tlearn: 0.0038955\ttotal: 4.22s\tremaining: 5.92s\n",
      "79:\tlearn: 0.0037684\ttotal: 4.28s\tremaining: 5.88s\n",
      "80:\tlearn: 0.0036469\ttotal: 4.33s\tremaining: 5.83s\n",
      "81:\tlearn: 0.0035350\ttotal: 4.39s\tremaining: 5.78s\n",
      "82:\tlearn: 0.0034312\ttotal: 4.44s\tremaining: 5.73s\n",
      "83:\tlearn: 0.0033253\ttotal: 4.5s\tremaining: 5.68s\n",
      "84:\tlearn: 0.0032381\ttotal: 4.56s\tremaining: 5.63s\n",
      "85:\tlearn: 0.0031558\ttotal: 4.62s\tremaining: 5.59s\n",
      "86:\tlearn: 0.0030685\ttotal: 4.68s\tremaining: 5.54s\n",
      "87:\tlearn: 0.0029861\ttotal: 4.74s\tremaining: 5.49s\n",
      "88:\tlearn: 0.0029180\ttotal: 4.79s\tremaining: 5.43s\n",
      "89:\tlearn: 0.0028458\ttotal: 4.83s\tremaining: 5.37s\n",
      "90:\tlearn: 0.0027887\ttotal: 4.87s\tremaining: 5.3s\n",
      "91:\tlearn: 0.0027245\ttotal: 4.93s\tremaining: 5.25s\n",
      "92:\tlearn: 0.0026664\ttotal: 4.99s\tremaining: 5.21s\n",
      "93:\tlearn: 0.0026113\ttotal: 5.05s\tremaining: 5.16s\n",
      "94:\tlearn: 0.0025603\ttotal: 5.11s\tremaining: 5.11s\n",
      "95:\tlearn: 0.0025133\ttotal: 5.17s\tremaining: 5.06s\n",
      "96:\tlearn: 0.0024752\ttotal: 5.22s\tremaining: 5s\n",
      "97:\tlearn: 0.0024392\ttotal: 5.27s\tremaining: 4.95s\n",
      "98:\tlearn: 0.0024101\ttotal: 5.33s\tremaining: 4.9s\n",
      "99:\tlearn: 0.0023664\ttotal: 5.39s\tremaining: 4.85s\n",
      "100:\tlearn: 0.0023372\ttotal: 5.44s\tremaining: 4.79s\n",
      "101:\tlearn: 0.0023059\ttotal: 5.5s\tremaining: 4.75s\n",
      "102:\tlearn: 0.0022825\ttotal: 5.55s\tremaining: 4.69s\n",
      "103:\tlearn: 0.0022483\ttotal: 5.61s\tremaining: 4.64s\n",
      "104:\tlearn: 0.0022171\ttotal: 5.67s\tremaining: 4.59s\n",
      "105:\tlearn: 0.0021865\ttotal: 5.73s\tremaining: 4.54s\n",
      "106:\tlearn: 0.0021550\ttotal: 5.78s\tremaining: 4.49s\n",
      "107:\tlearn: 0.0021313\ttotal: 5.84s\tremaining: 4.43s\n",
      "108:\tlearn: 0.0021158\ttotal: 5.89s\tremaining: 4.38s\n",
      "109:\tlearn: 0.0020931\ttotal: 5.95s\tremaining: 4.33s\n",
      "110:\tlearn: 0.0020761\ttotal: 6s\tremaining: 4.27s\n",
      "111:\tlearn: 0.0020633\ttotal: 6.05s\tremaining: 4.22s\n",
      "112:\tlearn: 0.0020421\ttotal: 6.12s\tremaining: 4.17s\n",
      "113:\tlearn: 0.0020219\ttotal: 6.18s\tremaining: 4.12s\n",
      "114:\tlearn: 0.0020029\ttotal: 6.23s\tremaining: 4.06s\n",
      "115:\tlearn: 0.0019848\ttotal: 6.28s\tremaining: 4s\n",
      "116:\tlearn: 0.0019657\ttotal: 6.33s\tremaining: 3.95s\n",
      "117:\tlearn: 0.0019495\ttotal: 6.39s\tremaining: 3.9s\n",
      "118:\tlearn: 0.0019318\ttotal: 6.45s\tremaining: 3.85s\n",
      "119:\tlearn: 0.0019217\ttotal: 6.51s\tremaining: 3.8s\n",
      "120:\tlearn: 0.0019122\ttotal: 6.56s\tremaining: 3.74s\n",
      "121:\tlearn: 0.0019050\ttotal: 6.62s\tremaining: 3.69s\n",
      "122:\tlearn: 0.0018968\ttotal: 6.68s\tremaining: 3.64s\n",
      "123:\tlearn: 0.0018850\ttotal: 6.73s\tremaining: 3.58s\n",
      "124:\tlearn: 0.0018783\ttotal: 6.77s\tremaining: 3.52s\n",
      "125:\tlearn: 0.0018669\ttotal: 6.8s\tremaining: 3.46s\n",
      "126:\tlearn: 0.0018574\ttotal: 6.85s\tremaining: 3.4s\n",
      "127:\tlearn: 0.0018434\ttotal: 6.91s\tremaining: 3.35s\n",
      "128:\tlearn: 0.0018320\ttotal: 6.97s\tremaining: 3.29s\n",
      "129:\tlearn: 0.0018231\ttotal: 7.03s\tremaining: 3.24s\n",
      "130:\tlearn: 0.0018120\ttotal: 7.08s\tremaining: 3.19s\n",
      "131:\tlearn: 0.0018003\ttotal: 7.14s\tremaining: 3.13s\n",
      "132:\tlearn: 0.0017910\ttotal: 7.19s\tremaining: 3.08s\n",
      "133:\tlearn: 0.0017839\ttotal: 7.25s\tremaining: 3.03s\n",
      "134:\tlearn: 0.0017732\ttotal: 7.3s\tremaining: 2.98s\n",
      "135:\tlearn: 0.0017676\ttotal: 7.36s\tremaining: 2.92s\n",
      "136:\tlearn: 0.0017624\ttotal: 7.41s\tremaining: 2.87s\n",
      "137:\tlearn: 0.0017559\ttotal: 7.46s\tremaining: 2.81s\n",
      "138:\tlearn: 0.0017483\ttotal: 7.52s\tremaining: 2.76s\n",
      "139:\tlearn: 0.0017383\ttotal: 7.58s\tremaining: 2.71s\n",
      "140:\tlearn: 0.0017320\ttotal: 7.63s\tremaining: 2.65s\n",
      "141:\tlearn: 0.0017268\ttotal: 7.69s\tremaining: 2.6s\n",
      "142:\tlearn: 0.0017196\ttotal: 7.74s\tremaining: 2.54s\n",
      "143:\tlearn: 0.0017154\ttotal: 7.79s\tremaining: 2.49s\n",
      "144:\tlearn: 0.0017080\ttotal: 7.85s\tremaining: 2.44s\n",
      "145:\tlearn: 0.0017037\ttotal: 7.9s\tremaining: 2.38s\n",
      "146:\tlearn: 0.0016965\ttotal: 7.96s\tremaining: 2.33s\n",
      "147:\tlearn: 0.0016892\ttotal: 8.02s\tremaining: 2.27s\n",
      "148:\tlearn: 0.0016840\ttotal: 8.07s\tremaining: 2.22s\n",
      "149:\tlearn: 0.0016795\ttotal: 8.13s\tremaining: 2.17s\n",
      "150:\tlearn: 0.0016760\ttotal: 8.18s\tremaining: 2.11s\n",
      "151:\tlearn: 0.0016712\ttotal: 8.23s\tremaining: 2.06s\n",
      "152:\tlearn: 0.0016609\ttotal: 8.3s\tremaining: 2.01s\n",
      "153:\tlearn: 0.0016552\ttotal: 8.34s\tremaining: 1.95s\n",
      "154:\tlearn: 0.0016500\ttotal: 8.4s\tremaining: 1.9s\n",
      "155:\tlearn: 0.0016457\ttotal: 8.46s\tremaining: 1.84s\n",
      "156:\tlearn: 0.0016409\ttotal: 8.52s\tremaining: 1.79s\n",
      "157:\tlearn: 0.0016300\ttotal: 8.57s\tremaining: 1.74s\n",
      "158:\tlearn: 0.0016272\ttotal: 8.63s\tremaining: 1.68s\n",
      "159:\tlearn: 0.0016234\ttotal: 8.69s\tremaining: 1.63s\n",
      "160:\tlearn: 0.0016125\ttotal: 8.75s\tremaining: 1.58s\n",
      "161:\tlearn: 0.0016073\ttotal: 8.81s\tremaining: 1.52s\n",
      "162:\tlearn: 0.0016047\ttotal: 8.86s\tremaining: 1.47s\n",
      "163:\tlearn: 0.0015972\ttotal: 8.92s\tremaining: 1.41s\n",
      "164:\tlearn: 0.0015908\ttotal: 8.98s\tremaining: 1.36s\n",
      "165:\tlearn: 0.0015888\ttotal: 9.03s\tremaining: 1.3s\n",
      "166:\tlearn: 0.0015840\ttotal: 9.09s\tremaining: 1.25s\n",
      "167:\tlearn: 0.0015801\ttotal: 9.15s\tremaining: 1.2s\n",
      "168:\tlearn: 0.0015698\ttotal: 9.21s\tremaining: 1.14s\n",
      "169:\tlearn: 0.0015606\ttotal: 9.27s\tremaining: 1.09s\n",
      "170:\tlearn: 0.0015585\ttotal: 9.32s\tremaining: 1.03s\n",
      "171:\tlearn: 0.0015550\ttotal: 9.38s\tremaining: 982ms\n",
      "172:\tlearn: 0.0015493\ttotal: 9.44s\tremaining: 928ms\n",
      "173:\tlearn: 0.0015448\ttotal: 9.5s\tremaining: 874ms\n",
      "174:\tlearn: 0.0015394\ttotal: 9.57s\tremaining: 820ms\n",
      "175:\tlearn: 0.0015349\ttotal: 9.63s\tremaining: 766ms\n",
      "176:\tlearn: 0.0015302\ttotal: 9.68s\tremaining: 711ms\n",
      "177:\tlearn: 0.0015286\ttotal: 9.73s\tremaining: 656ms\n",
      "178:\tlearn: 0.0015252\ttotal: 9.78s\tremaining: 601ms\n",
      "179:\tlearn: 0.0015202\ttotal: 9.84s\tremaining: 547ms\n",
      "180:\tlearn: 0.0015173\ttotal: 9.89s\tremaining: 492ms\n",
      "181:\tlearn: 0.0015145\ttotal: 9.95s\tremaining: 437ms\n",
      "182:\tlearn: 0.0015113\ttotal: 10s\tremaining: 383ms\n",
      "183:\tlearn: 0.0015093\ttotal: 10.1s\tremaining: 328ms\n",
      "184:\tlearn: 0.0015067\ttotal: 10.1s\tremaining: 274ms\n",
      "185:\tlearn: 0.0015042\ttotal: 10.2s\tremaining: 219ms\n",
      "186:\tlearn: 0.0015024\ttotal: 10.2s\tremaining: 164ms\n",
      "187:\tlearn: 0.0015001\ttotal: 10.3s\tremaining: 109ms\n",
      "188:\tlearn: 0.0014963\ttotal: 10.3s\tremaining: 54.6ms\n",
      "189:\tlearn: 0.0014914\ttotal: 10.4s\tremaining: 0us\n",
      "0:\tlearn: 0.6258777\ttotal: 67.3ms\tremaining: 12.7s\n",
      "1:\tlearn: 0.5675060\ttotal: 125ms\tremaining: 11.8s\n",
      "2:\tlearn: 0.5163905\ttotal: 188ms\tremaining: 11.7s\n",
      "3:\tlearn: 0.4711479\ttotal: 257ms\tremaining: 12s\n",
      "4:\tlearn: 0.4308782\ttotal: 322ms\tremaining: 11.9s\n",
      "5:\tlearn: 0.3948237\ttotal: 388ms\tremaining: 11.9s\n",
      "6:\tlearn: 0.3624279\ttotal: 455ms\tremaining: 11.9s\n",
      "7:\tlearn: 0.3332427\ttotal: 519ms\tremaining: 11.8s\n",
      "8:\tlearn: 0.3067668\ttotal: 584ms\tremaining: 11.7s\n",
      "9:\tlearn: 0.2827480\ttotal: 650ms\tremaining: 11.7s\n",
      "10:\tlearn: 0.2609592\ttotal: 716ms\tremaining: 11.7s\n",
      "11:\tlearn: 0.2410101\ttotal: 777ms\tremaining: 11.5s\n",
      "12:\tlearn: 0.2228088\ttotal: 845ms\tremaining: 11.5s\n",
      "13:\tlearn: 0.2061617\ttotal: 912ms\tremaining: 11.5s\n",
      "14:\tlearn: 0.1909422\ttotal: 980ms\tremaining: 11.4s\n",
      "15:\tlearn: 0.1769692\ttotal: 1.05s\tremaining: 11.4s\n",
      "16:\tlearn: 0.1641335\ttotal: 1.11s\tremaining: 11.3s\n",
      "17:\tlearn: 0.1522500\ttotal: 1.17s\tremaining: 11.2s\n",
      "18:\tlearn: 0.1413421\ttotal: 1.23s\tremaining: 11.1s\n",
      "19:\tlearn: 0.1312507\ttotal: 1.29s\tremaining: 11s\n",
      "20:\tlearn: 0.1219857\ttotal: 1.35s\tremaining: 10.9s\n",
      "21:\tlearn: 0.1134103\ttotal: 1.41s\tremaining: 10.8s\n",
      "22:\tlearn: 0.1054702\ttotal: 1.46s\tremaining: 10.6s\n",
      "23:\tlearn: 0.0981484\ttotal: 1.51s\tremaining: 10.4s\n",
      "24:\tlearn: 0.0913485\ttotal: 1.57s\tremaining: 10.4s\n",
      "25:\tlearn: 0.0850870\ttotal: 1.64s\tremaining: 10.3s\n",
      "26:\tlearn: 0.0792393\ttotal: 1.7s\tremaining: 10.2s\n",
      "27:\tlearn: 0.0738562\ttotal: 1.76s\tremaining: 10.2s\n",
      "28:\tlearn: 0.0688465\ttotal: 1.82s\tremaining: 10.1s\n",
      "29:\tlearn: 0.0642199\ttotal: 1.89s\tremaining: 10.1s\n",
      "30:\tlearn: 0.0599319\ttotal: 1.94s\tremaining: 9.97s\n",
      "31:\tlearn: 0.0559459\ttotal: 2s\tremaining: 9.87s\n",
      "32:\tlearn: 0.0522324\ttotal: 2.06s\tremaining: 9.8s\n",
      "33:\tlearn: 0.0487872\ttotal: 2.12s\tremaining: 9.74s\n",
      "34:\tlearn: 0.0455888\ttotal: 2.18s\tremaining: 9.68s\n",
      "35:\tlearn: 0.0425988\ttotal: 2.24s\tremaining: 9.6s\n",
      "36:\tlearn: 0.0398289\ttotal: 2.3s\tremaining: 9.52s\n",
      "37:\tlearn: 0.0372529\ttotal: 2.36s\tremaining: 9.44s\n",
      "38:\tlearn: 0.0348597\ttotal: 2.42s\tremaining: 9.37s\n",
      "39:\tlearn: 0.0326047\ttotal: 2.47s\tremaining: 9.28s\n",
      "40:\tlearn: 0.0305295\ttotal: 2.54s\tremaining: 9.21s\n",
      "41:\tlearn: 0.0285912\ttotal: 2.59s\tremaining: 9.14s\n",
      "42:\tlearn: 0.0267887\ttotal: 2.65s\tremaining: 9.07s\n",
      "43:\tlearn: 0.0251098\ttotal: 2.72s\tremaining: 9.02s\n",
      "44:\tlearn: 0.0235392\ttotal: 2.79s\tremaining: 8.97s\n",
      "45:\tlearn: 0.0220945\ttotal: 2.84s\tremaining: 8.9s\n",
      "46:\tlearn: 0.0207407\ttotal: 2.9s\tremaining: 8.84s\n",
      "47:\tlearn: 0.0194847\ttotal: 2.97s\tremaining: 8.78s\n",
      "48:\tlearn: 0.0183254\ttotal: 3.02s\tremaining: 8.7s\n",
      "49:\tlearn: 0.0172263\ttotal: 3.08s\tremaining: 8.62s\n",
      "50:\tlearn: 0.0162176\ttotal: 3.14s\tremaining: 8.55s\n",
      "51:\tlearn: 0.0152592\ttotal: 3.19s\tremaining: 8.47s\n",
      "52:\tlearn: 0.0143734\ttotal: 3.25s\tremaining: 8.4s\n",
      "53:\tlearn: 0.0135547\ttotal: 3.31s\tremaining: 8.32s\n",
      "54:\tlearn: 0.0127911\ttotal: 3.36s\tremaining: 8.26s\n",
      "55:\tlearn: 0.0120782\ttotal: 3.42s\tremaining: 8.18s\n",
      "56:\tlearn: 0.0114224\ttotal: 3.47s\tremaining: 8.1s\n",
      "57:\tlearn: 0.0108099\ttotal: 3.53s\tremaining: 8.03s\n",
      "58:\tlearn: 0.0102293\ttotal: 3.58s\tremaining: 7.96s\n",
      "59:\tlearn: 0.0096921\ttotal: 3.64s\tremaining: 7.88s\n",
      "60:\tlearn: 0.0091963\ttotal: 3.7s\tremaining: 7.82s\n",
      "61:\tlearn: 0.0087341\ttotal: 3.76s\tremaining: 7.76s\n",
      "62:\tlearn: 0.0082923\ttotal: 3.81s\tremaining: 7.68s\n",
      "63:\tlearn: 0.0078929\ttotal: 3.87s\tremaining: 7.62s\n",
      "64:\tlearn: 0.0075205\ttotal: 3.91s\tremaining: 7.51s\n",
      "65:\tlearn: 0.0071724\ttotal: 3.95s\tremaining: 7.43s\n",
      "66:\tlearn: 0.0068433\ttotal: 4.01s\tremaining: 7.37s\n",
      "67:\tlearn: 0.0065401\ttotal: 4.07s\tremaining: 7.3s\n",
      "68:\tlearn: 0.0062488\ttotal: 4.13s\tremaining: 7.24s\n",
      "69:\tlearn: 0.0059827\ttotal: 4.18s\tremaining: 7.17s\n",
      "70:\tlearn: 0.0057318\ttotal: 4.24s\tremaining: 7.1s\n",
      "71:\tlearn: 0.0055051\ttotal: 4.29s\tremaining: 7.03s\n",
      "72:\tlearn: 0.0052956\ttotal: 4.34s\tremaining: 6.96s\n",
      "73:\tlearn: 0.0051016\ttotal: 4.4s\tremaining: 6.9s\n",
      "74:\tlearn: 0.0049172\ttotal: 4.46s\tremaining: 6.83s\n",
      "75:\tlearn: 0.0047448\ttotal: 4.51s\tremaining: 6.76s\n",
      "76:\tlearn: 0.0045866\ttotal: 4.56s\tremaining: 6.7s\n",
      "77:\tlearn: 0.0044387\ttotal: 4.61s\tremaining: 6.62s\n",
      "78:\tlearn: 0.0043032\ttotal: 4.67s\tremaining: 6.56s\n",
      "79:\tlearn: 0.0041771\ttotal: 4.73s\tremaining: 6.5s\n",
      "80:\tlearn: 0.0040500\ttotal: 4.78s\tremaining: 6.43s\n",
      "81:\tlearn: 0.0039401\ttotal: 4.83s\tremaining: 6.36s\n",
      "82:\tlearn: 0.0038334\ttotal: 4.89s\tremaining: 6.3s\n",
      "83:\tlearn: 0.0037315\ttotal: 4.94s\tremaining: 6.24s\n",
      "84:\tlearn: 0.0036415\ttotal: 5s\tremaining: 6.18s\n",
      "85:\tlearn: 0.0035478\ttotal: 5.07s\tremaining: 6.13s\n",
      "86:\tlearn: 0.0034640\ttotal: 5.12s\tremaining: 6.07s\n",
      "87:\tlearn: 0.0033985\ttotal: 5.18s\tremaining: 6.01s\n",
      "88:\tlearn: 0.0033343\ttotal: 5.24s\tremaining: 5.94s\n",
      "89:\tlearn: 0.0032642\ttotal: 5.29s\tremaining: 5.88s\n",
      "90:\tlearn: 0.0032011\ttotal: 5.35s\tremaining: 5.82s\n",
      "91:\tlearn: 0.0031412\ttotal: 5.4s\tremaining: 5.75s\n",
      "92:\tlearn: 0.0030811\ttotal: 5.46s\tremaining: 5.7s\n",
      "93:\tlearn: 0.0030276\ttotal: 5.52s\tremaining: 5.64s\n",
      "94:\tlearn: 0.0029785\ttotal: 5.58s\tremaining: 5.58s\n",
      "95:\tlearn: 0.0029355\ttotal: 5.63s\tremaining: 5.51s\n",
      "96:\tlearn: 0.0028981\ttotal: 5.68s\tremaining: 5.45s\n",
      "97:\tlearn: 0.0028610\ttotal: 5.73s\tremaining: 5.38s\n",
      "98:\tlearn: 0.0028298\ttotal: 5.79s\tremaining: 5.32s\n",
      "99:\tlearn: 0.0027916\ttotal: 5.84s\tremaining: 5.26s\n",
      "100:\tlearn: 0.0027531\ttotal: 5.9s\tremaining: 5.2s\n",
      "101:\tlearn: 0.0027195\ttotal: 5.96s\tremaining: 5.14s\n",
      "102:\tlearn: 0.0026849\ttotal: 6.02s\tremaining: 5.08s\n",
      "103:\tlearn: 0.0026553\ttotal: 6.08s\tremaining: 5.03s\n",
      "104:\tlearn: 0.0026212\ttotal: 6.14s\tremaining: 4.97s\n",
      "105:\tlearn: 0.0025894\ttotal: 6.19s\tremaining: 4.91s\n",
      "106:\tlearn: 0.0025640\ttotal: 6.25s\tremaining: 4.84s\n",
      "107:\tlearn: 0.0025364\ttotal: 6.31s\tremaining: 4.79s\n",
      "108:\tlearn: 0.0025166\ttotal: 6.37s\tremaining: 4.73s\n",
      "109:\tlearn: 0.0024938\ttotal: 6.43s\tremaining: 4.68s\n",
      "110:\tlearn: 0.0024753\ttotal: 6.5s\tremaining: 4.62s\n",
      "111:\tlearn: 0.0024628\ttotal: 6.55s\tremaining: 4.56s\n",
      "112:\tlearn: 0.0024522\ttotal: 6.6s\tremaining: 4.5s\n",
      "113:\tlearn: 0.0024332\ttotal: 6.67s\tremaining: 4.44s\n",
      "114:\tlearn: 0.0024226\ttotal: 6.72s\tremaining: 4.38s\n",
      "115:\tlearn: 0.0024007\ttotal: 6.78s\tremaining: 4.33s\n",
      "116:\tlearn: 0.0023857\ttotal: 6.85s\tremaining: 4.27s\n",
      "117:\tlearn: 0.0023681\ttotal: 6.91s\tremaining: 4.21s\n",
      "118:\tlearn: 0.0023510\ttotal: 6.96s\tremaining: 4.16s\n",
      "119:\tlearn: 0.0023339\ttotal: 7.02s\tremaining: 4.09s\n",
      "120:\tlearn: 0.0023261\ttotal: 7.07s\tremaining: 4.03s\n",
      "121:\tlearn: 0.0023098\ttotal: 7.13s\tremaining: 3.97s\n",
      "122:\tlearn: 0.0022917\ttotal: 7.17s\tremaining: 3.91s\n",
      "123:\tlearn: 0.0022741\ttotal: 7.23s\tremaining: 3.85s\n",
      "124:\tlearn: 0.0022674\ttotal: 7.28s\tremaining: 3.79s\n",
      "125:\tlearn: 0.0022596\ttotal: 7.34s\tremaining: 3.73s\n",
      "126:\tlearn: 0.0022531\ttotal: 7.39s\tremaining: 3.67s\n",
      "127:\tlearn: 0.0022418\ttotal: 7.44s\tremaining: 3.6s\n",
      "128:\tlearn: 0.0022346\ttotal: 7.5s\tremaining: 3.54s\n",
      "129:\tlearn: 0.0022305\ttotal: 7.55s\tremaining: 3.48s\n",
      "130:\tlearn: 0.0022183\ttotal: 7.61s\tremaining: 3.42s\n",
      "131:\tlearn: 0.0022072\ttotal: 7.67s\tremaining: 3.37s\n",
      "132:\tlearn: 0.0022021\ttotal: 7.73s\tremaining: 3.31s\n",
      "133:\tlearn: 0.0021972\ttotal: 7.79s\tremaining: 3.26s\n",
      "134:\tlearn: 0.0021886\ttotal: 7.86s\tremaining: 3.2s\n",
      "135:\tlearn: 0.0021769\ttotal: 7.92s\tremaining: 3.14s\n",
      "136:\tlearn: 0.0021716\ttotal: 7.98s\tremaining: 3.09s\n",
      "137:\tlearn: 0.0021590\ttotal: 8.04s\tremaining: 3.03s\n",
      "138:\tlearn: 0.0021502\ttotal: 8.1s\tremaining: 2.97s\n",
      "139:\tlearn: 0.0021389\ttotal: 8.16s\tremaining: 2.92s\n",
      "140:\tlearn: 0.0021355\ttotal: 8.21s\tremaining: 2.85s\n",
      "141:\tlearn: 0.0021257\ttotal: 8.27s\tremaining: 2.79s\n",
      "142:\tlearn: 0.0021169\ttotal: 8.32s\tremaining: 2.74s\n",
      "143:\tlearn: 0.0021104\ttotal: 8.38s\tremaining: 2.68s\n",
      "144:\tlearn: 0.0021073\ttotal: 8.43s\tremaining: 2.62s\n",
      "145:\tlearn: 0.0020986\ttotal: 8.49s\tremaining: 2.56s\n",
      "146:\tlearn: 0.0020943\ttotal: 8.55s\tremaining: 2.5s\n",
      "147:\tlearn: 0.0020856\ttotal: 8.61s\tremaining: 2.44s\n",
      "148:\tlearn: 0.0020811\ttotal: 8.66s\tremaining: 2.38s\n",
      "149:\tlearn: 0.0020770\ttotal: 8.71s\tremaining: 2.32s\n",
      "150:\tlearn: 0.0020727\ttotal: 8.77s\tremaining: 2.27s\n",
      "151:\tlearn: 0.0020652\ttotal: 8.83s\tremaining: 2.21s\n",
      "152:\tlearn: 0.0020553\ttotal: 8.89s\tremaining: 2.15s\n",
      "153:\tlearn: 0.0020465\ttotal: 8.96s\tremaining: 2.1s\n",
      "154:\tlearn: 0.0020382\ttotal: 9.02s\tremaining: 2.04s\n",
      "155:\tlearn: 0.0020359\ttotal: 9.08s\tremaining: 1.98s\n",
      "156:\tlearn: 0.0020308\ttotal: 9.11s\tremaining: 1.92s\n",
      "157:\tlearn: 0.0020229\ttotal: 9.15s\tremaining: 1.85s\n",
      "158:\tlearn: 0.0020189\ttotal: 9.19s\tremaining: 1.79s\n",
      "159:\tlearn: 0.0020128\ttotal: 9.23s\tremaining: 1.73s\n",
      "160:\tlearn: 0.0020054\ttotal: 9.27s\tremaining: 1.67s\n",
      "161:\tlearn: 0.0020023\ttotal: 9.3s\tremaining: 1.61s\n",
      "162:\tlearn: 0.0020002\ttotal: 9.34s\tremaining: 1.55s\n",
      "163:\tlearn: 0.0019944\ttotal: 9.38s\tremaining: 1.49s\n",
      "164:\tlearn: 0.0019914\ttotal: 9.42s\tremaining: 1.43s\n",
      "165:\tlearn: 0.0019867\ttotal: 9.48s\tremaining: 1.37s\n",
      "166:\tlearn: 0.0019821\ttotal: 9.54s\tremaining: 1.31s\n",
      "167:\tlearn: 0.0019751\ttotal: 9.59s\tremaining: 1.25s\n",
      "168:\tlearn: 0.0019698\ttotal: 9.65s\tremaining: 1.2s\n",
      "169:\tlearn: 0.0019651\ttotal: 9.7s\tremaining: 1.14s\n",
      "170:\tlearn: 0.0019630\ttotal: 9.76s\tremaining: 1.08s\n",
      "171:\tlearn: 0.0019593\ttotal: 9.81s\tremaining: 1.03s\n",
      "172:\tlearn: 0.0019548\ttotal: 9.87s\tremaining: 970ms\n",
      "173:\tlearn: 0.0019502\ttotal: 9.93s\tremaining: 913ms\n",
      "174:\tlearn: 0.0019461\ttotal: 9.98s\tremaining: 855ms\n",
      "175:\tlearn: 0.0019427\ttotal: 10s\tremaining: 798ms\n",
      "176:\tlearn: 0.0019405\ttotal: 10.1s\tremaining: 741ms\n",
      "177:\tlearn: 0.0019376\ttotal: 10.2s\tremaining: 684ms\n",
      "178:\tlearn: 0.0019314\ttotal: 10.2s\tremaining: 627ms\n",
      "179:\tlearn: 0.0019281\ttotal: 10.3s\tremaining: 570ms\n",
      "180:\tlearn: 0.0019250\ttotal: 10.3s\tremaining: 513ms\n",
      "181:\tlearn: 0.0019191\ttotal: 10.4s\tremaining: 456ms\n",
      "182:\tlearn: 0.0019154\ttotal: 10.4s\tremaining: 399ms\n",
      "183:\tlearn: 0.0019121\ttotal: 10.5s\tremaining: 342ms\n",
      "184:\tlearn: 0.0019060\ttotal: 10.5s\tremaining: 285ms\n",
      "185:\tlearn: 0.0019036\ttotal: 10.6s\tremaining: 228ms\n",
      "186:\tlearn: 0.0018964\ttotal: 10.6s\tremaining: 171ms\n",
      "187:\tlearn: 0.0018936\ttotal: 10.7s\tremaining: 114ms\n",
      "188:\tlearn: 0.0018915\ttotal: 10.7s\tremaining: 56.7ms\n",
      "189:\tlearn: 0.0018881\ttotal: 10.8s\tremaining: 0us\n",
      "0:\tlearn: 0.6258638\ttotal: 45.4ms\tremaining: 8.57s\n",
      "1:\tlearn: 0.5674788\ttotal: 92.2ms\tremaining: 8.66s\n",
      "2:\tlearn: 0.5162906\ttotal: 147ms\tremaining: 9.19s\n",
      "3:\tlearn: 0.4710258\ttotal: 219ms\tremaining: 10.2s\n",
      "4:\tlearn: 0.4307554\ttotal: 282ms\tremaining: 10.4s\n",
      "5:\tlearn: 0.3946963\ttotal: 347ms\tremaining: 10.6s\n",
      "6:\tlearn: 0.3622946\ttotal: 410ms\tremaining: 10.7s\n",
      "7:\tlearn: 0.3330893\ttotal: 471ms\tremaining: 10.7s\n",
      "8:\tlearn: 0.3066131\ttotal: 536ms\tremaining: 10.8s\n",
      "9:\tlearn: 0.2826109\ttotal: 603ms\tremaining: 10.8s\n",
      "10:\tlearn: 0.2608149\ttotal: 669ms\tremaining: 10.9s\n",
      "11:\tlearn: 0.2408416\ttotal: 730ms\tremaining: 10.8s\n",
      "12:\tlearn: 0.2226219\ttotal: 791ms\tremaining: 10.8s\n",
      "13:\tlearn: 0.2059749\ttotal: 858ms\tremaining: 10.8s\n",
      "14:\tlearn: 0.1907520\ttotal: 919ms\tremaining: 10.7s\n",
      "15:\tlearn: 0.1767794\ttotal: 987ms\tremaining: 10.7s\n",
      "16:\tlearn: 0.1639366\ttotal: 1.05s\tremaining: 10.7s\n",
      "17:\tlearn: 0.1520717\ttotal: 1.1s\tremaining: 10.5s\n",
      "18:\tlearn: 0.1412066\ttotal: 1.17s\tremaining: 10.5s\n",
      "19:\tlearn: 0.1311221\ttotal: 1.23s\tremaining: 10.4s\n",
      "20:\tlearn: 0.1218222\ttotal: 1.29s\tremaining: 10.4s\n",
      "21:\tlearn: 0.1132366\ttotal: 1.35s\tremaining: 10.3s\n",
      "22:\tlearn: 0.1053117\ttotal: 1.42s\tremaining: 10.3s\n",
      "23:\tlearn: 0.0979868\ttotal: 1.48s\tremaining: 10.3s\n",
      "24:\tlearn: 0.0911838\ttotal: 1.55s\tremaining: 10.2s\n",
      "25:\tlearn: 0.0849164\ttotal: 1.61s\tremaining: 10.2s\n",
      "26:\tlearn: 0.0790696\ttotal: 1.67s\tremaining: 10.1s\n",
      "27:\tlearn: 0.0737039\ttotal: 1.74s\tremaining: 10s\n",
      "28:\tlearn: 0.0686911\ttotal: 1.8s\tremaining: 9.98s\n",
      "29:\tlearn: 0.0640648\ttotal: 1.86s\tremaining: 9.94s\n",
      "30:\tlearn: 0.0597692\ttotal: 1.93s\tremaining: 9.89s\n",
      "31:\tlearn: 0.0557816\ttotal: 1.99s\tremaining: 9.81s\n",
      "32:\tlearn: 0.0520676\ttotal: 2.04s\tremaining: 9.73s\n",
      "33:\tlearn: 0.0486103\ttotal: 2.11s\tremaining: 9.68s\n",
      "34:\tlearn: 0.0454048\ttotal: 2.17s\tremaining: 9.61s\n",
      "35:\tlearn: 0.0424030\ttotal: 2.22s\tremaining: 9.52s\n",
      "36:\tlearn: 0.0396238\ttotal: 2.28s\tremaining: 9.44s\n",
      "37:\tlearn: 0.0370343\ttotal: 2.34s\tremaining: 9.36s\n",
      "38:\tlearn: 0.0346385\ttotal: 2.4s\tremaining: 9.3s\n",
      "39:\tlearn: 0.0323840\ttotal: 2.46s\tremaining: 9.22s\n",
      "40:\tlearn: 0.0302854\ttotal: 2.51s\tremaining: 9.14s\n",
      "41:\tlearn: 0.0283622\ttotal: 2.57s\tremaining: 9.06s\n",
      "42:\tlearn: 0.0265482\ttotal: 2.63s\tremaining: 9.01s\n",
      "43:\tlearn: 0.0248649\ttotal: 2.69s\tremaining: 8.94s\n",
      "44:\tlearn: 0.0233092\ttotal: 2.75s\tremaining: 8.87s\n",
      "45:\tlearn: 0.0218474\ttotal: 2.81s\tremaining: 8.8s\n",
      "46:\tlearn: 0.0204997\ttotal: 2.87s\tremaining: 8.74s\n",
      "47:\tlearn: 0.0192188\ttotal: 2.93s\tremaining: 8.66s\n",
      "48:\tlearn: 0.0180504\ttotal: 2.98s\tremaining: 8.59s\n",
      "49:\tlearn: 0.0169668\ttotal: 3.04s\tremaining: 8.51s\n",
      "50:\tlearn: 0.0159407\ttotal: 3.1s\tremaining: 8.44s\n",
      "51:\tlearn: 0.0149969\ttotal: 3.16s\tremaining: 8.39s\n",
      "52:\tlearn: 0.0141125\ttotal: 3.21s\tremaining: 8.31s\n",
      "53:\tlearn: 0.0132815\ttotal: 3.27s\tremaining: 8.24s\n",
      "54:\tlearn: 0.0125180\ttotal: 3.33s\tremaining: 8.18s\n",
      "55:\tlearn: 0.0118033\ttotal: 3.4s\tremaining: 8.14s\n",
      "56:\tlearn: 0.0111378\ttotal: 3.46s\tremaining: 8.07s\n",
      "57:\tlearn: 0.0105170\ttotal: 3.52s\tremaining: 8.01s\n",
      "58:\tlearn: 0.0099421\ttotal: 3.57s\tremaining: 7.93s\n",
      "59:\tlearn: 0.0094101\ttotal: 3.63s\tremaining: 7.87s\n",
      "60:\tlearn: 0.0089000\ttotal: 3.69s\tremaining: 7.79s\n",
      "61:\tlearn: 0.0084338\ttotal: 3.74s\tremaining: 7.73s\n",
      "62:\tlearn: 0.0079872\ttotal: 3.8s\tremaining: 7.66s\n",
      "63:\tlearn: 0.0075788\ttotal: 3.86s\tremaining: 7.6s\n",
      "64:\tlearn: 0.0071995\ttotal: 3.92s\tremaining: 7.53s\n",
      "65:\tlearn: 0.0068405\ttotal: 3.97s\tremaining: 7.46s\n",
      "66:\tlearn: 0.0065157\ttotal: 4.03s\tremaining: 7.39s\n",
      "67:\tlearn: 0.0062103\ttotal: 4.09s\tremaining: 7.34s\n",
      "68:\tlearn: 0.0059291\ttotal: 4.15s\tremaining: 7.28s\n",
      "69:\tlearn: 0.0056650\ttotal: 4.19s\tremaining: 7.18s\n",
      "70:\tlearn: 0.0054234\ttotal: 4.23s\tremaining: 7.09s\n",
      "71:\tlearn: 0.0051927\ttotal: 4.27s\tremaining: 6.99s\n",
      "72:\tlearn: 0.0049818\ttotal: 4.31s\tremaining: 6.91s\n",
      "73:\tlearn: 0.0047805\ttotal: 4.35s\tremaining: 6.82s\n",
      "74:\tlearn: 0.0045936\ttotal: 4.39s\tremaining: 6.73s\n",
      "75:\tlearn: 0.0044247\ttotal: 4.43s\tremaining: 6.65s\n",
      "76:\tlearn: 0.0042649\ttotal: 4.49s\tremaining: 6.58s\n",
      "77:\tlearn: 0.0041104\ttotal: 4.54s\tremaining: 6.52s\n",
      "78:\tlearn: 0.0039772\ttotal: 4.59s\tremaining: 6.45s\n",
      "79:\tlearn: 0.0038423\ttotal: 4.63s\tremaining: 6.36s\n",
      "80:\tlearn: 0.0037202\ttotal: 4.68s\tremaining: 6.3s\n",
      "81:\tlearn: 0.0036052\ttotal: 4.74s\tremaining: 6.24s\n",
      "82:\tlearn: 0.0034997\ttotal: 4.79s\tremaining: 6.18s\n",
      "83:\tlearn: 0.0034034\ttotal: 4.84s\tremaining: 6.11s\n",
      "84:\tlearn: 0.0033150\ttotal: 4.9s\tremaining: 6.05s\n",
      "85:\tlearn: 0.0032299\ttotal: 4.95s\tremaining: 5.99s\n",
      "86:\tlearn: 0.0031458\ttotal: 5.01s\tremaining: 5.93s\n",
      "87:\tlearn: 0.0030695\ttotal: 5.07s\tremaining: 5.87s\n",
      "88:\tlearn: 0.0030017\ttotal: 5.12s\tremaining: 5.81s\n",
      "89:\tlearn: 0.0029299\ttotal: 5.19s\tremaining: 5.77s\n",
      "90:\tlearn: 0.0028708\ttotal: 5.25s\tremaining: 5.71s\n",
      "91:\tlearn: 0.0028180\ttotal: 5.31s\tremaining: 5.65s\n",
      "92:\tlearn: 0.0027638\ttotal: 5.36s\tremaining: 5.59s\n",
      "93:\tlearn: 0.0027154\ttotal: 5.42s\tremaining: 5.53s\n",
      "94:\tlearn: 0.0026637\ttotal: 5.47s\tremaining: 5.47s\n",
      "95:\tlearn: 0.0026179\ttotal: 5.53s\tremaining: 5.41s\n",
      "96:\tlearn: 0.0025761\ttotal: 5.58s\tremaining: 5.35s\n",
      "97:\tlearn: 0.0025364\ttotal: 5.64s\tremaining: 5.29s\n",
      "98:\tlearn: 0.0025049\ttotal: 5.69s\tremaining: 5.23s\n",
      "99:\tlearn: 0.0024690\ttotal: 5.75s\tremaining: 5.17s\n",
      "100:\tlearn: 0.0024372\ttotal: 5.8s\tremaining: 5.12s\n",
      "101:\tlearn: 0.0024054\ttotal: 5.86s\tremaining: 5.06s\n",
      "102:\tlearn: 0.0023810\ttotal: 5.92s\tremaining: 5s\n",
      "103:\tlearn: 0.0023578\ttotal: 5.97s\tremaining: 4.94s\n",
      "104:\tlearn: 0.0023295\ttotal: 6.04s\tremaining: 4.88s\n",
      "105:\tlearn: 0.0023095\ttotal: 6.09s\tremaining: 4.83s\n",
      "106:\tlearn: 0.0022930\ttotal: 6.14s\tremaining: 4.76s\n",
      "107:\tlearn: 0.0022677\ttotal: 6.18s\tremaining: 4.69s\n",
      "108:\tlearn: 0.0022493\ttotal: 6.22s\tremaining: 4.62s\n",
      "109:\tlearn: 0.0022358\ttotal: 6.28s\tremaining: 4.57s\n",
      "110:\tlearn: 0.0022129\ttotal: 6.33s\tremaining: 4.51s\n",
      "111:\tlearn: 0.0022005\ttotal: 6.39s\tremaining: 4.45s\n",
      "112:\tlearn: 0.0021795\ttotal: 6.45s\tremaining: 4.39s\n",
      "113:\tlearn: 0.0021616\ttotal: 6.5s\tremaining: 4.33s\n",
      "114:\tlearn: 0.0021430\ttotal: 6.57s\tremaining: 4.28s\n",
      "115:\tlearn: 0.0021346\ttotal: 6.62s\tremaining: 4.22s\n",
      "116:\tlearn: 0.0021250\ttotal: 6.67s\tremaining: 4.16s\n",
      "117:\tlearn: 0.0021088\ttotal: 6.73s\tremaining: 4.1s\n",
      "118:\tlearn: 0.0020938\ttotal: 6.78s\tremaining: 4.04s\n",
      "119:\tlearn: 0.0020796\ttotal: 6.84s\tremaining: 3.99s\n",
      "120:\tlearn: 0.0020738\ttotal: 6.89s\tremaining: 3.93s\n",
      "121:\tlearn: 0.0020679\ttotal: 6.95s\tremaining: 3.87s\n",
      "122:\tlearn: 0.0020506\ttotal: 7.01s\tremaining: 3.82s\n",
      "123:\tlearn: 0.0020444\ttotal: 7.06s\tremaining: 3.76s\n",
      "124:\tlearn: 0.0020318\ttotal: 7.12s\tremaining: 3.7s\n",
      "125:\tlearn: 0.0020209\ttotal: 7.18s\tremaining: 3.65s\n",
      "126:\tlearn: 0.0020098\ttotal: 7.24s\tremaining: 3.59s\n",
      "127:\tlearn: 0.0019982\ttotal: 7.3s\tremaining: 3.54s\n",
      "128:\tlearn: 0.0019838\ttotal: 7.36s\tremaining: 3.48s\n",
      "129:\tlearn: 0.0019730\ttotal: 7.41s\tremaining: 3.42s\n",
      "130:\tlearn: 0.0019573\ttotal: 7.47s\tremaining: 3.37s\n",
      "131:\tlearn: 0.0019465\ttotal: 7.54s\tremaining: 3.31s\n",
      "132:\tlearn: 0.0019430\ttotal: 7.59s\tremaining: 3.25s\n",
      "133:\tlearn: 0.0019306\ttotal: 7.65s\tremaining: 3.2s\n",
      "134:\tlearn: 0.0019166\ttotal: 7.71s\tremaining: 3.14s\n",
      "135:\tlearn: 0.0019059\ttotal: 7.77s\tremaining: 3.08s\n",
      "136:\tlearn: 0.0018980\ttotal: 7.83s\tremaining: 3.03s\n",
      "137:\tlearn: 0.0018951\ttotal: 7.88s\tremaining: 2.97s\n",
      "138:\tlearn: 0.0018803\ttotal: 7.95s\tremaining: 2.92s\n",
      "139:\tlearn: 0.0018717\ttotal: 8s\tremaining: 2.86s\n",
      "140:\tlearn: 0.0018631\ttotal: 8.07s\tremaining: 2.8s\n",
      "141:\tlearn: 0.0018526\ttotal: 8.13s\tremaining: 2.75s\n",
      "142:\tlearn: 0.0018432\ttotal: 8.19s\tremaining: 2.69s\n",
      "143:\tlearn: 0.0018293\ttotal: 8.24s\tremaining: 2.63s\n",
      "144:\tlearn: 0.0018261\ttotal: 8.29s\tremaining: 2.57s\n",
      "145:\tlearn: 0.0018185\ttotal: 8.36s\tremaining: 2.52s\n",
      "146:\tlearn: 0.0018121\ttotal: 8.4s\tremaining: 2.46s\n",
      "147:\tlearn: 0.0018024\ttotal: 8.44s\tremaining: 2.4s\n",
      "148:\tlearn: 0.0017916\ttotal: 8.48s\tremaining: 2.33s\n",
      "149:\tlearn: 0.0017822\ttotal: 8.52s\tremaining: 2.27s\n",
      "150:\tlearn: 0.0017772\ttotal: 8.57s\tremaining: 2.21s\n",
      "151:\tlearn: 0.0017697\ttotal: 8.63s\tremaining: 2.16s\n",
      "152:\tlearn: 0.0017587\ttotal: 8.69s\tremaining: 2.1s\n",
      "153:\tlearn: 0.0017490\ttotal: 8.75s\tremaining: 2.04s\n",
      "154:\tlearn: 0.0017424\ttotal: 8.81s\tremaining: 1.99s\n",
      "155:\tlearn: 0.0017351\ttotal: 8.87s\tremaining: 1.93s\n",
      "156:\tlearn: 0.0017314\ttotal: 8.92s\tremaining: 1.88s\n",
      "157:\tlearn: 0.0017276\ttotal: 8.97s\tremaining: 1.82s\n",
      "158:\tlearn: 0.0017243\ttotal: 9.03s\tremaining: 1.76s\n",
      "159:\tlearn: 0.0017215\ttotal: 9.08s\tremaining: 1.7s\n",
      "160:\tlearn: 0.0017184\ttotal: 9.14s\tremaining: 1.65s\n",
      "161:\tlearn: 0.0017135\ttotal: 9.19s\tremaining: 1.59s\n",
      "162:\tlearn: 0.0017102\ttotal: 9.25s\tremaining: 1.53s\n",
      "163:\tlearn: 0.0016994\ttotal: 9.3s\tremaining: 1.48s\n",
      "164:\tlearn: 0.0016958\ttotal: 9.36s\tremaining: 1.42s\n",
      "165:\tlearn: 0.0016934\ttotal: 9.41s\tremaining: 1.36s\n",
      "166:\tlearn: 0.0016902\ttotal: 9.47s\tremaining: 1.3s\n",
      "167:\tlearn: 0.0016831\ttotal: 9.53s\tremaining: 1.25s\n",
      "168:\tlearn: 0.0016749\ttotal: 9.59s\tremaining: 1.19s\n",
      "169:\tlearn: 0.0016656\ttotal: 9.65s\tremaining: 1.14s\n",
      "170:\tlearn: 0.0016614\ttotal: 9.71s\tremaining: 1.08s\n",
      "171:\tlearn: 0.0016579\ttotal: 9.76s\tremaining: 1.02s\n",
      "172:\tlearn: 0.0016557\ttotal: 9.81s\tremaining: 964ms\n",
      "173:\tlearn: 0.0016519\ttotal: 9.87s\tremaining: 908ms\n",
      "174:\tlearn: 0.0016469\ttotal: 9.92s\tremaining: 851ms\n",
      "175:\tlearn: 0.0016438\ttotal: 9.98s\tremaining: 794ms\n",
      "176:\tlearn: 0.0016405\ttotal: 10s\tremaining: 737ms\n",
      "177:\tlearn: 0.0016352\ttotal: 10.1s\tremaining: 681ms\n",
      "178:\tlearn: 0.0016334\ttotal: 10.2s\tremaining: 624ms\n",
      "179:\tlearn: 0.0016296\ttotal: 10.2s\tremaining: 568ms\n",
      "180:\tlearn: 0.0016279\ttotal: 10.3s\tremaining: 511ms\n",
      "181:\tlearn: 0.0016251\ttotal: 10.3s\tremaining: 454ms\n",
      "182:\tlearn: 0.0016230\ttotal: 10.4s\tremaining: 397ms\n",
      "183:\tlearn: 0.0016195\ttotal: 10.4s\tremaining: 340ms\n",
      "184:\tlearn: 0.0016168\ttotal: 10.5s\tremaining: 284ms\n",
      "185:\tlearn: 0.0016149\ttotal: 10.6s\tremaining: 227ms\n",
      "186:\tlearn: 0.0016134\ttotal: 10.6s\tremaining: 170ms\n",
      "187:\tlearn: 0.0016113\ttotal: 10.7s\tremaining: 113ms\n",
      "188:\tlearn: 0.0016094\ttotal: 10.7s\tremaining: 56.7ms\n",
      "189:\tlearn: 0.0016072\ttotal: 10.8s\tremaining: 0us\n",
      "0:\tlearn: 0.6259551\ttotal: 75.2ms\tremaining: 14.2s\n",
      "1:\tlearn: 0.5674978\ttotal: 147ms\tremaining: 13.8s\n",
      "2:\tlearn: 0.5161331\ttotal: 220ms\tremaining: 13.7s\n",
      "3:\tlearn: 0.4707878\ttotal: 289ms\tremaining: 13.4s\n",
      "4:\tlearn: 0.4304522\ttotal: 358ms\tremaining: 13.2s\n",
      "5:\tlearn: 0.3944026\ttotal: 427ms\tremaining: 13.1s\n",
      "6:\tlearn: 0.3619516\ttotal: 496ms\tremaining: 13s\n",
      "7:\tlearn: 0.3327074\ttotal: 571ms\tremaining: 13s\n",
      "8:\tlearn: 0.3063003\ttotal: 649ms\tremaining: 13s\n",
      "9:\tlearn: 0.2822726\ttotal: 728ms\tremaining: 13.1s\n",
      "10:\tlearn: 0.2604679\ttotal: 806ms\tremaining: 13.1s\n",
      "11:\tlearn: 0.2405419\ttotal: 877ms\tremaining: 13s\n",
      "12:\tlearn: 0.2223583\ttotal: 951ms\tremaining: 12.9s\n",
      "13:\tlearn: 0.2057231\ttotal: 1.02s\tremaining: 12.8s\n",
      "14:\tlearn: 0.1905486\ttotal: 1.09s\tremaining: 12.8s\n",
      "15:\tlearn: 0.1765799\ttotal: 1.17s\tremaining: 12.7s\n",
      "16:\tlearn: 0.1637222\ttotal: 1.23s\tremaining: 12.6s\n",
      "17:\tlearn: 0.1519079\ttotal: 1.31s\tremaining: 12.5s\n",
      "18:\tlearn: 0.1410062\ttotal: 1.39s\tremaining: 12.5s\n",
      "19:\tlearn: 0.1309685\ttotal: 1.46s\tremaining: 12.4s\n",
      "20:\tlearn: 0.1216929\ttotal: 1.53s\tremaining: 12.3s\n",
      "21:\tlearn: 0.1131281\ttotal: 1.6s\tremaining: 12.2s\n",
      "22:\tlearn: 0.1052251\ttotal: 1.67s\tremaining: 12.2s\n",
      "23:\tlearn: 0.0979056\ttotal: 1.75s\tremaining: 12.1s\n",
      "24:\tlearn: 0.0911188\ttotal: 1.82s\tremaining: 12s\n",
      "25:\tlearn: 0.0848592\ttotal: 1.89s\tremaining: 11.9s\n",
      "26:\tlearn: 0.0790581\ttotal: 1.96s\tremaining: 11.8s\n",
      "27:\tlearn: 0.0736740\ttotal: 2.03s\tremaining: 11.8s\n",
      "28:\tlearn: 0.0686902\ttotal: 2.11s\tremaining: 11.7s\n",
      "29:\tlearn: 0.0640548\ttotal: 2.18s\tremaining: 11.6s\n",
      "30:\tlearn: 0.0597609\ttotal: 2.25s\tremaining: 11.5s\n",
      "31:\tlearn: 0.0557905\ttotal: 2.3s\tremaining: 11.4s\n",
      "32:\tlearn: 0.0520851\ttotal: 2.37s\tremaining: 11.3s\n",
      "33:\tlearn: 0.0486309\ttotal: 2.43s\tremaining: 11.2s\n",
      "34:\tlearn: 0.0454517\ttotal: 2.51s\tremaining: 11.1s\n",
      "35:\tlearn: 0.0424703\ttotal: 2.58s\tremaining: 11s\n",
      "36:\tlearn: 0.0396965\ttotal: 2.65s\tremaining: 10.9s\n",
      "37:\tlearn: 0.0371272\ttotal: 2.72s\tremaining: 10.9s\n",
      "38:\tlearn: 0.0347333\ttotal: 2.79s\tremaining: 10.8s\n",
      "39:\tlearn: 0.0325122\ttotal: 2.85s\tremaining: 10.7s\n",
      "40:\tlearn: 0.0304489\ttotal: 2.92s\tremaining: 10.6s\n",
      "41:\tlearn: 0.0285088\ttotal: 2.98s\tremaining: 10.5s\n",
      "42:\tlearn: 0.0267078\ttotal: 3.05s\tremaining: 10.4s\n",
      "43:\tlearn: 0.0250401\ttotal: 3.11s\tremaining: 10.3s\n",
      "44:\tlearn: 0.0234817\ttotal: 3.18s\tremaining: 10.2s\n",
      "45:\tlearn: 0.0220335\ttotal: 3.24s\tremaining: 10.1s\n",
      "46:\tlearn: 0.0206893\ttotal: 3.31s\tremaining: 10.1s\n",
      "47:\tlearn: 0.0194265\ttotal: 3.38s\tremaining: 9.99s\n",
      "48:\tlearn: 0.0182556\ttotal: 3.44s\tremaining: 9.89s\n",
      "49:\tlearn: 0.0171634\ttotal: 3.5s\tremaining: 9.79s\n",
      "50:\tlearn: 0.0161545\ttotal: 3.57s\tremaining: 9.73s\n",
      "51:\tlearn: 0.0152179\ttotal: 3.63s\tremaining: 9.64s\n",
      "52:\tlearn: 0.0143441\ttotal: 3.7s\tremaining: 9.55s\n",
      "53:\tlearn: 0.0135200\ttotal: 3.76s\tremaining: 9.46s\n",
      "54:\tlearn: 0.0127555\ttotal: 3.83s\tremaining: 9.39s\n",
      "55:\tlearn: 0.0120495\ttotal: 3.89s\tremaining: 9.31s\n",
      "56:\tlearn: 0.0113872\ttotal: 3.95s\tremaining: 9.22s\n",
      "57:\tlearn: 0.0107653\ttotal: 4.01s\tremaining: 9.13s\n",
      "58:\tlearn: 0.0101994\ttotal: 4.08s\tremaining: 9.06s\n",
      "59:\tlearn: 0.0096730\ttotal: 4.15s\tremaining: 9s\n",
      "60:\tlearn: 0.0091830\ttotal: 4.23s\tremaining: 8.94s\n",
      "61:\tlearn: 0.0087187\ttotal: 4.29s\tremaining: 8.85s\n",
      "62:\tlearn: 0.0082890\ttotal: 4.35s\tremaining: 8.78s\n",
      "63:\tlearn: 0.0078898\ttotal: 4.43s\tremaining: 8.71s\n",
      "64:\tlearn: 0.0075094\ttotal: 4.49s\tremaining: 8.63s\n",
      "65:\tlearn: 0.0071652\ttotal: 4.55s\tremaining: 8.56s\n",
      "66:\tlearn: 0.0068423\ttotal: 4.63s\tremaining: 8.49s\n",
      "67:\tlearn: 0.0065383\ttotal: 4.69s\tremaining: 8.42s\n",
      "68:\tlearn: 0.0062589\ttotal: 4.75s\tremaining: 8.34s\n",
      "69:\tlearn: 0.0059969\ttotal: 4.81s\tremaining: 8.25s\n",
      "70:\tlearn: 0.0057541\ttotal: 4.88s\tremaining: 8.17s\n",
      "71:\tlearn: 0.0055216\ttotal: 4.93s\tremaining: 8.09s\n",
      "72:\tlearn: 0.0053145\ttotal: 5s\tremaining: 8.02s\n",
      "73:\tlearn: 0.0051172\ttotal: 5.06s\tremaining: 7.94s\n",
      "74:\tlearn: 0.0049276\ttotal: 5.13s\tremaining: 7.87s\n",
      "75:\tlearn: 0.0047606\ttotal: 5.18s\tremaining: 7.78s\n",
      "76:\tlearn: 0.0045963\ttotal: 5.25s\tremaining: 7.7s\n",
      "77:\tlearn: 0.0044557\ttotal: 5.32s\tremaining: 7.64s\n",
      "78:\tlearn: 0.0043145\ttotal: 5.38s\tremaining: 7.56s\n",
      "79:\tlearn: 0.0041923\ttotal: 5.45s\tremaining: 7.5s\n",
      "80:\tlearn: 0.0040732\ttotal: 5.52s\tremaining: 7.42s\n",
      "81:\tlearn: 0.0039662\ttotal: 5.58s\tremaining: 7.35s\n",
      "82:\tlearn: 0.0038572\ttotal: 5.65s\tremaining: 7.28s\n",
      "83:\tlearn: 0.0037657\ttotal: 5.71s\tremaining: 7.21s\n",
      "84:\tlearn: 0.0036809\ttotal: 5.77s\tremaining: 7.13s\n",
      "85:\tlearn: 0.0035969\ttotal: 5.84s\tremaining: 7.06s\n",
      "86:\tlearn: 0.0035184\ttotal: 5.91s\tremaining: 7s\n",
      "87:\tlearn: 0.0034466\ttotal: 5.97s\tremaining: 6.92s\n",
      "88:\tlearn: 0.0033700\ttotal: 6.03s\tremaining: 6.84s\n",
      "89:\tlearn: 0.0033051\ttotal: 6.09s\tremaining: 6.76s\n",
      "90:\tlearn: 0.0032408\ttotal: 6.14s\tremaining: 6.68s\n",
      "91:\tlearn: 0.0031852\ttotal: 6.2s\tremaining: 6.6s\n",
      "92:\tlearn: 0.0031375\ttotal: 6.26s\tremaining: 6.53s\n",
      "93:\tlearn: 0.0030921\ttotal: 6.32s\tremaining: 6.46s\n",
      "94:\tlearn: 0.0030465\ttotal: 6.39s\tremaining: 6.39s\n",
      "95:\tlearn: 0.0030087\ttotal: 6.45s\tremaining: 6.32s\n",
      "96:\tlearn: 0.0029743\ttotal: 6.51s\tremaining: 6.24s\n",
      "97:\tlearn: 0.0029335\ttotal: 6.59s\tremaining: 6.18s\n",
      "98:\tlearn: 0.0028961\ttotal: 6.66s\tremaining: 6.12s\n",
      "99:\tlearn: 0.0028550\ttotal: 6.72s\tremaining: 6.05s\n",
      "100:\tlearn: 0.0028158\ttotal: 6.79s\tremaining: 5.98s\n",
      "101:\tlearn: 0.0027902\ttotal: 6.85s\tremaining: 5.91s\n",
      "102:\tlearn: 0.0027599\ttotal: 6.92s\tremaining: 5.84s\n",
      "103:\tlearn: 0.0027393\ttotal: 6.98s\tremaining: 5.77s\n",
      "104:\tlearn: 0.0027104\ttotal: 7.04s\tremaining: 5.7s\n",
      "105:\tlearn: 0.0026860\ttotal: 7.1s\tremaining: 5.63s\n",
      "106:\tlearn: 0.0026665\ttotal: 7.16s\tremaining: 5.55s\n",
      "107:\tlearn: 0.0026363\ttotal: 7.23s\tremaining: 5.49s\n",
      "108:\tlearn: 0.0026138\ttotal: 7.3s\tremaining: 5.42s\n",
      "109:\tlearn: 0.0025922\ttotal: 7.36s\tremaining: 5.35s\n",
      "110:\tlearn: 0.0025752\ttotal: 7.43s\tremaining: 5.29s\n",
      "111:\tlearn: 0.0025562\ttotal: 7.47s\tremaining: 5.2s\n",
      "112:\tlearn: 0.0025428\ttotal: 7.53s\tremaining: 5.13s\n",
      "113:\tlearn: 0.0025259\ttotal: 7.59s\tremaining: 5.06s\n",
      "114:\tlearn: 0.0025059\ttotal: 7.66s\tremaining: 4.99s\n",
      "115:\tlearn: 0.0024973\ttotal: 7.72s\tremaining: 4.92s\n",
      "116:\tlearn: 0.0024802\ttotal: 7.79s\tremaining: 4.86s\n",
      "117:\tlearn: 0.0024633\ttotal: 7.86s\tremaining: 4.79s\n",
      "118:\tlearn: 0.0024393\ttotal: 7.93s\tremaining: 4.73s\n",
      "119:\tlearn: 0.0024227\ttotal: 8s\tremaining: 4.67s\n",
      "120:\tlearn: 0.0024053\ttotal: 8.07s\tremaining: 4.6s\n",
      "121:\tlearn: 0.0023850\ttotal: 8.14s\tremaining: 4.54s\n",
      "122:\tlearn: 0.0023786\ttotal: 8.2s\tremaining: 4.47s\n",
      "123:\tlearn: 0.0023629\ttotal: 8.27s\tremaining: 4.4s\n",
      "124:\tlearn: 0.0023506\ttotal: 8.33s\tremaining: 4.33s\n",
      "125:\tlearn: 0.0023426\ttotal: 8.4s\tremaining: 4.26s\n",
      "126:\tlearn: 0.0023325\ttotal: 8.46s\tremaining: 4.2s\n",
      "127:\tlearn: 0.0023208\ttotal: 8.53s\tremaining: 4.13s\n",
      "128:\tlearn: 0.0023104\ttotal: 8.59s\tremaining: 4.06s\n",
      "129:\tlearn: 0.0022991\ttotal: 8.66s\tremaining: 4s\n",
      "130:\tlearn: 0.0022864\ttotal: 8.73s\tremaining: 3.93s\n",
      "131:\tlearn: 0.0022784\ttotal: 8.79s\tremaining: 3.86s\n",
      "132:\tlearn: 0.0022652\ttotal: 8.87s\tremaining: 3.8s\n",
      "133:\tlearn: 0.0022602\ttotal: 8.93s\tremaining: 3.73s\n",
      "134:\tlearn: 0.0022571\ttotal: 8.99s\tremaining: 3.66s\n",
      "135:\tlearn: 0.0022437\ttotal: 9.05s\tremaining: 3.59s\n",
      "136:\tlearn: 0.0022403\ttotal: 9.11s\tremaining: 3.52s\n",
      "137:\tlearn: 0.0022342\ttotal: 9.17s\tremaining: 3.46s\n",
      "138:\tlearn: 0.0022304\ttotal: 9.23s\tremaining: 3.38s\n",
      "139:\tlearn: 0.0022168\ttotal: 9.3s\tremaining: 3.32s\n",
      "140:\tlearn: 0.0022146\ttotal: 9.36s\tremaining: 3.25s\n",
      "141:\tlearn: 0.0022071\ttotal: 9.42s\tremaining: 3.18s\n",
      "142:\tlearn: 0.0022012\ttotal: 9.48s\tremaining: 3.12s\n",
      "143:\tlearn: 0.0021925\ttotal: 9.54s\tremaining: 3.05s\n",
      "144:\tlearn: 0.0021886\ttotal: 9.6s\tremaining: 2.98s\n",
      "145:\tlearn: 0.0021815\ttotal: 9.67s\tremaining: 2.91s\n",
      "146:\tlearn: 0.0021789\ttotal: 9.72s\tremaining: 2.84s\n",
      "147:\tlearn: 0.0021691\ttotal: 9.79s\tremaining: 2.78s\n",
      "148:\tlearn: 0.0021657\ttotal: 9.86s\tremaining: 2.71s\n",
      "149:\tlearn: 0.0021622\ttotal: 9.91s\tremaining: 2.64s\n",
      "150:\tlearn: 0.0021535\ttotal: 9.97s\tremaining: 2.58s\n",
      "151:\tlearn: 0.0021475\ttotal: 10s\tremaining: 2.51s\n",
      "152:\tlearn: 0.0021411\ttotal: 10.1s\tremaining: 2.44s\n",
      "153:\tlearn: 0.0021372\ttotal: 10.2s\tremaining: 2.37s\n",
      "154:\tlearn: 0.0021340\ttotal: 10.2s\tremaining: 2.31s\n",
      "155:\tlearn: 0.0021310\ttotal: 10.3s\tremaining: 2.24s\n",
      "156:\tlearn: 0.0021204\ttotal: 10.3s\tremaining: 2.17s\n",
      "157:\tlearn: 0.0021157\ttotal: 10.4s\tremaining: 2.1s\n",
      "158:\tlearn: 0.0021072\ttotal: 10.4s\tremaining: 2.03s\n",
      "159:\tlearn: 0.0021030\ttotal: 10.5s\tremaining: 1.96s\n",
      "160:\tlearn: 0.0020999\ttotal: 10.5s\tremaining: 1.9s\n",
      "161:\tlearn: 0.0020900\ttotal: 10.6s\tremaining: 1.83s\n",
      "162:\tlearn: 0.0020847\ttotal: 10.7s\tremaining: 1.77s\n",
      "163:\tlearn: 0.0020768\ttotal: 10.7s\tremaining: 1.7s\n",
      "164:\tlearn: 0.0020731\ttotal: 10.8s\tremaining: 1.63s\n",
      "165:\tlearn: 0.0020639\ttotal: 10.9s\tremaining: 1.57s\n",
      "166:\tlearn: 0.0020610\ttotal: 10.9s\tremaining: 1.5s\n",
      "167:\tlearn: 0.0020567\ttotal: 11s\tremaining: 1.44s\n",
      "168:\tlearn: 0.0020515\ttotal: 11s\tremaining: 1.37s\n",
      "169:\tlearn: 0.0020420\ttotal: 11.1s\tremaining: 1.31s\n",
      "170:\tlearn: 0.0020329\ttotal: 11.2s\tremaining: 1.24s\n",
      "171:\tlearn: 0.0020282\ttotal: 11.3s\tremaining: 1.18s\n",
      "172:\tlearn: 0.0020253\ttotal: 11.3s\tremaining: 1.11s\n",
      "173:\tlearn: 0.0020228\ttotal: 11.4s\tremaining: 1.04s\n",
      "174:\tlearn: 0.0020190\ttotal: 11.4s\tremaining: 980ms\n",
      "175:\tlearn: 0.0020153\ttotal: 11.5s\tremaining: 914ms\n",
      "176:\tlearn: 0.0020068\ttotal: 11.6s\tremaining: 849ms\n",
      "177:\tlearn: 0.0020041\ttotal: 11.6s\tremaining: 783ms\n",
      "178:\tlearn: 0.0019979\ttotal: 11.7s\tremaining: 718ms\n",
      "179:\tlearn: 0.0019906\ttotal: 11.7s\tremaining: 652ms\n",
      "180:\tlearn: 0.0019882\ttotal: 11.8s\tremaining: 587ms\n",
      "181:\tlearn: 0.0019861\ttotal: 11.9s\tremaining: 521ms\n",
      "182:\tlearn: 0.0019826\ttotal: 11.9s\tremaining: 456ms\n",
      "183:\tlearn: 0.0019774\ttotal: 12s\tremaining: 391ms\n",
      "184:\tlearn: 0.0019728\ttotal: 12s\tremaining: 325ms\n",
      "185:\tlearn: 0.0019702\ttotal: 12.1s\tremaining: 260ms\n",
      "186:\tlearn: 0.0019673\ttotal: 12.2s\tremaining: 195ms\n",
      "187:\tlearn: 0.0019642\ttotal: 12.2s\tremaining: 130ms\n",
      "188:\tlearn: 0.0019617\ttotal: 12.3s\tremaining: 64.9ms\n",
      "189:\tlearn: 0.0019591\ttotal: 12.3s\tremaining: 0us\n",
      "0:\tlearn: 0.6259551\ttotal: 73.7ms\tremaining: 13.9s\n",
      "1:\tlearn: 0.5674978\ttotal: 146ms\tremaining: 13.8s\n",
      "2:\tlearn: 0.5161331\ttotal: 213ms\tremaining: 13.3s\n",
      "3:\tlearn: 0.4707878\ttotal: 279ms\tremaining: 13s\n",
      "4:\tlearn: 0.4304522\ttotal: 349ms\tremaining: 12.9s\n",
      "5:\tlearn: 0.3944026\ttotal: 415ms\tremaining: 12.7s\n",
      "6:\tlearn: 0.3619516\ttotal: 484ms\tremaining: 12.7s\n",
      "7:\tlearn: 0.3327074\ttotal: 559ms\tremaining: 12.7s\n",
      "8:\tlearn: 0.3063003\ttotal: 635ms\tremaining: 12.8s\n",
      "9:\tlearn: 0.2822726\ttotal: 709ms\tremaining: 12.8s\n",
      "10:\tlearn: 0.2604679\ttotal: 783ms\tremaining: 12.7s\n",
      "11:\tlearn: 0.2405419\ttotal: 856ms\tremaining: 12.7s\n",
      "12:\tlearn: 0.2223583\ttotal: 929ms\tremaining: 12.7s\n",
      "13:\tlearn: 0.2057231\ttotal: 1.01s\tremaining: 12.7s\n",
      "14:\tlearn: 0.1905486\ttotal: 1.11s\tremaining: 12.9s\n",
      "15:\tlearn: 0.1765799\ttotal: 1.19s\tremaining: 12.9s\n",
      "16:\tlearn: 0.1637222\ttotal: 1.25s\tremaining: 12.7s\n",
      "17:\tlearn: 0.1519079\ttotal: 1.32s\tremaining: 12.7s\n",
      "18:\tlearn: 0.1410062\ttotal: 1.4s\tremaining: 12.6s\n",
      "19:\tlearn: 0.1309685\ttotal: 1.47s\tremaining: 12.5s\n",
      "20:\tlearn: 0.1216929\ttotal: 1.54s\tremaining: 12.4s\n",
      "21:\tlearn: 0.1131281\ttotal: 1.61s\tremaining: 12.3s\n",
      "22:\tlearn: 0.1052251\ttotal: 1.68s\tremaining: 12.2s\n",
      "23:\tlearn: 0.0979056\ttotal: 1.76s\tremaining: 12.2s\n",
      "24:\tlearn: 0.0911188\ttotal: 1.82s\tremaining: 12s\n",
      "25:\tlearn: 0.0848592\ttotal: 1.89s\tremaining: 11.9s\n",
      "26:\tlearn: 0.0790581\ttotal: 1.96s\tremaining: 11.9s\n",
      "27:\tlearn: 0.0736740\ttotal: 2.04s\tremaining: 11.8s\n",
      "28:\tlearn: 0.0686902\ttotal: 2.11s\tremaining: 11.7s\n",
      "29:\tlearn: 0.0640548\ttotal: 2.18s\tremaining: 11.7s\n",
      "30:\tlearn: 0.0597609\ttotal: 2.25s\tremaining: 11.6s\n",
      "31:\tlearn: 0.0557905\ttotal: 2.32s\tremaining: 11.5s\n",
      "32:\tlearn: 0.0520851\ttotal: 2.39s\tremaining: 11.4s\n",
      "33:\tlearn: 0.0486309\ttotal: 2.46s\tremaining: 11.3s\n",
      "34:\tlearn: 0.0454517\ttotal: 2.52s\tremaining: 11.2s\n",
      "35:\tlearn: 0.0424703\ttotal: 2.59s\tremaining: 11.1s\n",
      "36:\tlearn: 0.0396965\ttotal: 2.66s\tremaining: 11s\n",
      "37:\tlearn: 0.0371272\ttotal: 2.73s\tremaining: 10.9s\n",
      "38:\tlearn: 0.0347333\ttotal: 2.79s\tremaining: 10.8s\n",
      "39:\tlearn: 0.0325122\ttotal: 2.85s\tremaining: 10.7s\n",
      "40:\tlearn: 0.0304489\ttotal: 2.92s\tremaining: 10.6s\n",
      "41:\tlearn: 0.0285088\ttotal: 2.99s\tremaining: 10.5s\n",
      "42:\tlearn: 0.0267078\ttotal: 3.05s\tremaining: 10.4s\n",
      "43:\tlearn: 0.0250401\ttotal: 3.12s\tremaining: 10.4s\n",
      "44:\tlearn: 0.0234817\ttotal: 3.18s\tremaining: 10.3s\n",
      "45:\tlearn: 0.0220335\ttotal: 3.25s\tremaining: 10.2s\n",
      "46:\tlearn: 0.0206893\ttotal: 3.31s\tremaining: 10.1s\n",
      "47:\tlearn: 0.0194265\ttotal: 3.4s\tremaining: 10.1s\n",
      "48:\tlearn: 0.0182556\ttotal: 3.46s\tremaining: 9.95s\n",
      "49:\tlearn: 0.0171634\ttotal: 3.52s\tremaining: 9.85s\n",
      "50:\tlearn: 0.0161545\ttotal: 3.59s\tremaining: 9.78s\n",
      "51:\tlearn: 0.0152179\ttotal: 3.65s\tremaining: 9.68s\n",
      "52:\tlearn: 0.0143441\ttotal: 3.71s\tremaining: 9.59s\n",
      "53:\tlearn: 0.0135200\ttotal: 3.77s\tremaining: 9.49s\n",
      "54:\tlearn: 0.0127555\ttotal: 3.84s\tremaining: 9.42s\n",
      "55:\tlearn: 0.0120495\ttotal: 3.9s\tremaining: 9.34s\n",
      "56:\tlearn: 0.0113872\ttotal: 3.96s\tremaining: 9.24s\n",
      "57:\tlearn: 0.0107653\ttotal: 4.02s\tremaining: 9.15s\n",
      "58:\tlearn: 0.0101994\ttotal: 4.09s\tremaining: 9.08s\n",
      "59:\tlearn: 0.0096730\ttotal: 4.15s\tremaining: 9s\n",
      "60:\tlearn: 0.0091830\ttotal: 4.22s\tremaining: 8.93s\n",
      "61:\tlearn: 0.0087187\ttotal: 4.28s\tremaining: 8.83s\n",
      "62:\tlearn: 0.0082890\ttotal: 4.34s\tremaining: 8.76s\n",
      "63:\tlearn: 0.0078898\ttotal: 4.41s\tremaining: 8.69s\n",
      "64:\tlearn: 0.0075094\ttotal: 4.47s\tremaining: 8.6s\n",
      "65:\tlearn: 0.0071652\ttotal: 4.54s\tremaining: 8.54s\n",
      "66:\tlearn: 0.0068423\ttotal: 4.62s\tremaining: 8.47s\n",
      "67:\tlearn: 0.0065383\ttotal: 4.68s\tremaining: 8.39s\n",
      "68:\tlearn: 0.0062589\ttotal: 4.74s\tremaining: 8.31s\n",
      "69:\tlearn: 0.0059969\ttotal: 4.8s\tremaining: 8.23s\n",
      "70:\tlearn: 0.0057541\ttotal: 4.86s\tremaining: 8.14s\n",
      "71:\tlearn: 0.0055216\ttotal: 4.92s\tremaining: 8.06s\n",
      "72:\tlearn: 0.0053145\ttotal: 4.99s\tremaining: 7.99s\n",
      "73:\tlearn: 0.0051172\ttotal: 5.05s\tremaining: 7.91s\n",
      "74:\tlearn: 0.0049276\ttotal: 5.11s\tremaining: 7.84s\n",
      "75:\tlearn: 0.0047606\ttotal: 5.17s\tremaining: 7.75s\n",
      "76:\tlearn: 0.0045963\ttotal: 5.23s\tremaining: 7.68s\n",
      "77:\tlearn: 0.0044557\ttotal: 5.32s\tremaining: 7.64s\n",
      "78:\tlearn: 0.0043145\ttotal: 5.38s\tremaining: 7.56s\n",
      "79:\tlearn: 0.0041923\ttotal: 5.45s\tremaining: 7.5s\n",
      "80:\tlearn: 0.0040732\ttotal: 5.51s\tremaining: 7.42s\n",
      "81:\tlearn: 0.0039662\ttotal: 5.57s\tremaining: 7.34s\n",
      "82:\tlearn: 0.0038572\ttotal: 5.63s\tremaining: 7.26s\n",
      "83:\tlearn: 0.0037657\ttotal: 5.69s\tremaining: 7.19s\n",
      "84:\tlearn: 0.0036809\ttotal: 5.75s\tremaining: 7.11s\n",
      "85:\tlearn: 0.0035969\ttotal: 5.82s\tremaining: 7.04s\n",
      "86:\tlearn: 0.0035184\ttotal: 5.89s\tremaining: 6.97s\n",
      "87:\tlearn: 0.0034466\ttotal: 5.94s\tremaining: 6.89s\n",
      "88:\tlearn: 0.0033700\ttotal: 6s\tremaining: 6.81s\n",
      "89:\tlearn: 0.0033051\ttotal: 6.06s\tremaining: 6.74s\n",
      "90:\tlearn: 0.0032408\ttotal: 6.12s\tremaining: 6.66s\n",
      "91:\tlearn: 0.0031852\ttotal: 6.18s\tremaining: 6.58s\n",
      "92:\tlearn: 0.0031375\ttotal: 6.24s\tremaining: 6.51s\n",
      "93:\tlearn: 0.0030921\ttotal: 6.3s\tremaining: 6.43s\n",
      "94:\tlearn: 0.0030465\ttotal: 6.37s\tremaining: 6.37s\n",
      "95:\tlearn: 0.0030087\ttotal: 6.42s\tremaining: 6.29s\n",
      "96:\tlearn: 0.0029743\ttotal: 6.49s\tremaining: 6.22s\n",
      "97:\tlearn: 0.0029335\ttotal: 6.56s\tremaining: 6.16s\n",
      "98:\tlearn: 0.0028961\ttotal: 6.63s\tremaining: 6.1s\n",
      "99:\tlearn: 0.0028550\ttotal: 6.7s\tremaining: 6.03s\n",
      "100:\tlearn: 0.0028158\ttotal: 6.76s\tremaining: 5.96s\n",
      "101:\tlearn: 0.0027902\ttotal: 6.82s\tremaining: 5.88s\n",
      "102:\tlearn: 0.0027599\ttotal: 6.89s\tremaining: 5.82s\n",
      "103:\tlearn: 0.0027393\ttotal: 6.95s\tremaining: 5.75s\n",
      "104:\tlearn: 0.0027104\ttotal: 7.02s\tremaining: 5.68s\n",
      "105:\tlearn: 0.0026860\ttotal: 7.07s\tremaining: 5.61s\n",
      "106:\tlearn: 0.0026665\ttotal: 7.14s\tremaining: 5.54s\n",
      "107:\tlearn: 0.0026363\ttotal: 7.21s\tremaining: 5.47s\n",
      "108:\tlearn: 0.0026138\ttotal: 7.27s\tremaining: 5.41s\n",
      "109:\tlearn: 0.0025922\ttotal: 7.34s\tremaining: 5.33s\n",
      "110:\tlearn: 0.0025752\ttotal: 7.41s\tremaining: 5.27s\n",
      "111:\tlearn: 0.0025562\ttotal: 7.47s\tremaining: 5.2s\n",
      "112:\tlearn: 0.0025428\ttotal: 7.53s\tremaining: 5.13s\n",
      "113:\tlearn: 0.0025259\ttotal: 7.59s\tremaining: 5.06s\n",
      "114:\tlearn: 0.0025059\ttotal: 7.66s\tremaining: 4.99s\n",
      "115:\tlearn: 0.0024973\ttotal: 7.71s\tremaining: 4.92s\n",
      "116:\tlearn: 0.0024802\ttotal: 7.78s\tremaining: 4.86s\n",
      "117:\tlearn: 0.0024633\ttotal: 7.85s\tremaining: 4.79s\n",
      "118:\tlearn: 0.0024393\ttotal: 7.92s\tremaining: 4.72s\n",
      "119:\tlearn: 0.0024227\ttotal: 7.99s\tremaining: 4.66s\n",
      "120:\tlearn: 0.0024053\ttotal: 8.05s\tremaining: 4.59s\n",
      "121:\tlearn: 0.0023850\ttotal: 8.12s\tremaining: 4.53s\n",
      "122:\tlearn: 0.0023786\ttotal: 8.18s\tremaining: 4.45s\n",
      "123:\tlearn: 0.0023629\ttotal: 8.24s\tremaining: 4.38s\n",
      "124:\tlearn: 0.0023506\ttotal: 8.3s\tremaining: 4.32s\n",
      "125:\tlearn: 0.0023426\ttotal: 8.37s\tremaining: 4.25s\n",
      "126:\tlearn: 0.0023325\ttotal: 8.43s\tremaining: 4.18s\n",
      "127:\tlearn: 0.0023208\ttotal: 8.5s\tremaining: 4.12s\n",
      "128:\tlearn: 0.0023104\ttotal: 8.56s\tremaining: 4.05s\n",
      "129:\tlearn: 0.0022991\ttotal: 8.63s\tremaining: 3.98s\n",
      "130:\tlearn: 0.0022864\ttotal: 8.69s\tremaining: 3.91s\n",
      "131:\tlearn: 0.0022784\ttotal: 8.75s\tremaining: 3.85s\n",
      "132:\tlearn: 0.0022652\ttotal: 8.81s\tremaining: 3.78s\n",
      "133:\tlearn: 0.0022602\ttotal: 8.88s\tremaining: 3.71s\n",
      "134:\tlearn: 0.0022571\ttotal: 8.93s\tremaining: 3.64s\n",
      "135:\tlearn: 0.0022437\ttotal: 9s\tremaining: 3.57s\n",
      "136:\tlearn: 0.0022403\ttotal: 9.05s\tremaining: 3.5s\n",
      "137:\tlearn: 0.0022342\ttotal: 9.12s\tremaining: 3.43s\n",
      "138:\tlearn: 0.0022304\ttotal: 9.17s\tremaining: 3.37s\n",
      "139:\tlearn: 0.0022168\ttotal: 9.24s\tremaining: 3.3s\n",
      "140:\tlearn: 0.0022146\ttotal: 9.3s\tremaining: 3.23s\n",
      "141:\tlearn: 0.0022071\ttotal: 9.37s\tremaining: 3.17s\n",
      "142:\tlearn: 0.0022012\ttotal: 9.46s\tremaining: 3.11s\n",
      "143:\tlearn: 0.0021925\ttotal: 9.53s\tremaining: 3.04s\n",
      "144:\tlearn: 0.0021886\ttotal: 9.59s\tremaining: 2.98s\n",
      "145:\tlearn: 0.0021815\ttotal: 9.66s\tremaining: 2.91s\n",
      "146:\tlearn: 0.0021789\ttotal: 9.71s\tremaining: 2.84s\n",
      "147:\tlearn: 0.0021691\ttotal: 9.78s\tremaining: 2.77s\n",
      "148:\tlearn: 0.0021657\ttotal: 9.89s\tremaining: 2.72s\n",
      "149:\tlearn: 0.0021622\ttotal: 9.96s\tremaining: 2.65s\n",
      "150:\tlearn: 0.0021535\ttotal: 10s\tremaining: 2.59s\n",
      "151:\tlearn: 0.0021475\ttotal: 10.1s\tremaining: 2.52s\n",
      "152:\tlearn: 0.0021411\ttotal: 10.1s\tremaining: 2.45s\n",
      "153:\tlearn: 0.0021372\ttotal: 10.2s\tremaining: 2.38s\n",
      "154:\tlearn: 0.0021340\ttotal: 10.3s\tremaining: 2.32s\n",
      "155:\tlearn: 0.0021310\ttotal: 10.3s\tremaining: 2.25s\n",
      "156:\tlearn: 0.0021204\ttotal: 10.4s\tremaining: 2.18s\n",
      "157:\tlearn: 0.0021157\ttotal: 10.4s\tremaining: 2.12s\n",
      "158:\tlearn: 0.0021072\ttotal: 10.5s\tremaining: 2.05s\n",
      "159:\tlearn: 0.0021030\ttotal: 10.6s\tremaining: 1.98s\n",
      "160:\tlearn: 0.0020999\ttotal: 10.6s\tremaining: 1.92s\n",
      "161:\tlearn: 0.0020900\ttotal: 10.7s\tremaining: 1.85s\n",
      "162:\tlearn: 0.0020847\ttotal: 10.8s\tremaining: 1.78s\n",
      "163:\tlearn: 0.0020768\ttotal: 10.8s\tremaining: 1.72s\n",
      "164:\tlearn: 0.0020731\ttotal: 10.9s\tremaining: 1.65s\n",
      "165:\tlearn: 0.0020639\ttotal: 11s\tremaining: 1.58s\n",
      "166:\tlearn: 0.0020610\ttotal: 11s\tremaining: 1.52s\n",
      "167:\tlearn: 0.0020567\ttotal: 11.1s\tremaining: 1.45s\n",
      "168:\tlearn: 0.0020515\ttotal: 11.2s\tremaining: 1.39s\n",
      "169:\tlearn: 0.0020420\ttotal: 11.2s\tremaining: 1.32s\n",
      "170:\tlearn: 0.0020329\ttotal: 11.3s\tremaining: 1.25s\n",
      "171:\tlearn: 0.0020282\ttotal: 11.4s\tremaining: 1.19s\n",
      "172:\tlearn: 0.0020253\ttotal: 11.4s\tremaining: 1.12s\n",
      "173:\tlearn: 0.0020228\ttotal: 11.5s\tremaining: 1.05s\n",
      "174:\tlearn: 0.0020190\ttotal: 11.5s\tremaining: 989ms\n",
      "175:\tlearn: 0.0020153\ttotal: 11.6s\tremaining: 923ms\n",
      "176:\tlearn: 0.0020068\ttotal: 11.7s\tremaining: 857ms\n",
      "177:\tlearn: 0.0020041\ttotal: 11.7s\tremaining: 791ms\n",
      "178:\tlearn: 0.0019979\ttotal: 11.8s\tremaining: 725ms\n",
      "179:\tlearn: 0.0019906\ttotal: 11.9s\tremaining: 659ms\n",
      "180:\tlearn: 0.0019882\ttotal: 11.9s\tremaining: 593ms\n",
      "181:\tlearn: 0.0019861\ttotal: 12s\tremaining: 527ms\n",
      "182:\tlearn: 0.0019826\ttotal: 12s\tremaining: 461ms\n",
      "183:\tlearn: 0.0019774\ttotal: 12.1s\tremaining: 395ms\n",
      "184:\tlearn: 0.0019728\ttotal: 12.2s\tremaining: 329ms\n",
      "185:\tlearn: 0.0019702\ttotal: 12.2s\tremaining: 263ms\n",
      "186:\tlearn: 0.0019673\ttotal: 12.3s\tremaining: 197ms\n",
      "187:\tlearn: 0.0019642\ttotal: 12.3s\tremaining: 131ms\n",
      "188:\tlearn: 0.0019617\ttotal: 12.4s\tremaining: 65.6ms\n",
      "189:\tlearn: 0.0019591\ttotal: 12.5s\tremaining: 0us\n",
      "0:\tlearn: 0.6258807\ttotal: 66.1ms\tremaining: 12.5s\n",
      "1:\tlearn: 0.5675176\ttotal: 129ms\tremaining: 12.2s\n",
      "2:\tlearn: 0.5163255\ttotal: 191ms\tremaining: 11.9s\n",
      "3:\tlearn: 0.4710804\ttotal: 257ms\tremaining: 11.9s\n",
      "4:\tlearn: 0.4308067\ttotal: 322ms\tremaining: 11.9s\n",
      "5:\tlearn: 0.3947541\ttotal: 397ms\tremaining: 12.2s\n",
      "6:\tlearn: 0.3623457\ttotal: 462ms\tremaining: 12.1s\n",
      "7:\tlearn: 0.3330751\ttotal: 529ms\tremaining: 12s\n",
      "8:\tlearn: 0.3065946\ttotal: 591ms\tremaining: 11.9s\n",
      "9:\tlearn: 0.2825748\ttotal: 653ms\tremaining: 11.7s\n",
      "10:\tlearn: 0.2607906\ttotal: 720ms\tremaining: 11.7s\n",
      "11:\tlearn: 0.2408069\ttotal: 781ms\tremaining: 11.6s\n",
      "12:\tlearn: 0.2226006\ttotal: 846ms\tremaining: 11.5s\n",
      "13:\tlearn: 0.2059471\ttotal: 912ms\tremaining: 11.5s\n",
      "14:\tlearn: 0.1907286\ttotal: 986ms\tremaining: 11.5s\n",
      "15:\tlearn: 0.1767641\ttotal: 1.06s\tremaining: 11.6s\n",
      "16:\tlearn: 0.1639272\ttotal: 1.13s\tremaining: 11.5s\n",
      "17:\tlearn: 0.1520595\ttotal: 1.2s\tremaining: 11.4s\n",
      "18:\tlearn: 0.1411844\ttotal: 1.26s\tremaining: 11.4s\n",
      "19:\tlearn: 0.1310970\ttotal: 1.33s\tremaining: 11.3s\n",
      "20:\tlearn: 0.1218090\ttotal: 1.4s\tremaining: 11.2s\n",
      "21:\tlearn: 0.1132252\ttotal: 1.46s\tremaining: 11.2s\n",
      "22:\tlearn: 0.1052976\ttotal: 1.52s\tremaining: 11.1s\n",
      "23:\tlearn: 0.0979695\ttotal: 1.58s\tremaining: 11s\n",
      "24:\tlearn: 0.0911760\ttotal: 1.64s\tremaining: 10.8s\n",
      "25:\tlearn: 0.0849157\ttotal: 1.71s\tremaining: 10.8s\n",
      "26:\tlearn: 0.0790713\ttotal: 1.76s\tremaining: 10.6s\n",
      "27:\tlearn: 0.0736857\ttotal: 1.82s\tremaining: 10.6s\n",
      "28:\tlearn: 0.0686892\ttotal: 1.88s\tremaining: 10.5s\n",
      "29:\tlearn: 0.0640705\ttotal: 1.94s\tremaining: 10.4s\n",
      "30:\tlearn: 0.0597603\ttotal: 2s\tremaining: 10.3s\n",
      "31:\tlearn: 0.0557701\ttotal: 2.06s\tremaining: 10.2s\n",
      "32:\tlearn: 0.0520617\ttotal: 2.12s\tremaining: 10.1s\n",
      "33:\tlearn: 0.0486119\ttotal: 2.19s\tremaining: 10s\n",
      "34:\tlearn: 0.0454190\ttotal: 2.24s\tremaining: 9.93s\n",
      "35:\tlearn: 0.0424429\ttotal: 2.3s\tremaining: 9.83s\n",
      "36:\tlearn: 0.0396711\ttotal: 2.36s\tremaining: 9.75s\n",
      "37:\tlearn: 0.0371000\ttotal: 2.42s\tremaining: 9.67s\n",
      "38:\tlearn: 0.0347117\ttotal: 2.48s\tremaining: 9.6s\n",
      "39:\tlearn: 0.0324946\ttotal: 2.54s\tremaining: 9.53s\n",
      "40:\tlearn: 0.0304201\ttotal: 2.59s\tremaining: 9.43s\n",
      "41:\tlearn: 0.0285002\ttotal: 2.65s\tremaining: 9.35s\n",
      "42:\tlearn: 0.0267033\ttotal: 2.71s\tremaining: 9.26s\n",
      "43:\tlearn: 0.0250379\ttotal: 2.77s\tremaining: 9.19s\n",
      "44:\tlearn: 0.0234862\ttotal: 2.83s\tremaining: 9.11s\n",
      "45:\tlearn: 0.0220409\ttotal: 2.88s\tremaining: 9.03s\n",
      "46:\tlearn: 0.0206935\ttotal: 2.94s\tremaining: 8.95s\n",
      "47:\tlearn: 0.0194435\ttotal: 3s\tremaining: 8.88s\n",
      "48:\tlearn: 0.0182768\ttotal: 3.06s\tremaining: 8.79s\n",
      "49:\tlearn: 0.0171946\ttotal: 3.12s\tremaining: 8.73s\n",
      "50:\tlearn: 0.0161883\ttotal: 3.17s\tremaining: 8.65s\n",
      "51:\tlearn: 0.0152651\ttotal: 3.24s\tremaining: 8.6s\n",
      "52:\tlearn: 0.0143966\ttotal: 3.3s\tremaining: 8.53s\n",
      "53:\tlearn: 0.0135860\ttotal: 3.36s\tremaining: 8.46s\n",
      "54:\tlearn: 0.0128334\ttotal: 3.42s\tremaining: 8.39s\n",
      "55:\tlearn: 0.0121236\ttotal: 3.48s\tremaining: 8.31s\n",
      "56:\tlearn: 0.0114600\ttotal: 3.53s\tremaining: 8.24s\n",
      "57:\tlearn: 0.0108459\ttotal: 3.59s\tremaining: 8.17s\n",
      "58:\tlearn: 0.0102678\ttotal: 3.64s\tremaining: 8.09s\n",
      "59:\tlearn: 0.0097368\ttotal: 3.7s\tremaining: 8.02s\n",
      "60:\tlearn: 0.0092373\ttotal: 3.76s\tremaining: 7.95s\n",
      "61:\tlearn: 0.0087713\ttotal: 3.81s\tremaining: 7.88s\n",
      "62:\tlearn: 0.0083429\ttotal: 3.87s\tremaining: 7.8s\n",
      "63:\tlearn: 0.0079455\ttotal: 3.92s\tremaining: 7.73s\n",
      "64:\tlearn: 0.0075707\ttotal: 3.98s\tremaining: 7.66s\n",
      "65:\tlearn: 0.0072214\ttotal: 4.04s\tremaining: 7.59s\n",
      "66:\tlearn: 0.0068988\ttotal: 4.09s\tremaining: 7.52s\n",
      "67:\tlearn: 0.0066023\ttotal: 4.16s\tremaining: 7.46s\n",
      "68:\tlearn: 0.0063221\ttotal: 4.21s\tremaining: 7.39s\n",
      "69:\tlearn: 0.0060600\ttotal: 4.27s\tremaining: 7.32s\n",
      "70:\tlearn: 0.0058208\ttotal: 4.33s\tremaining: 7.26s\n",
      "71:\tlearn: 0.0055950\ttotal: 4.39s\tremaining: 7.19s\n",
      "72:\tlearn: 0.0053811\ttotal: 4.45s\tremaining: 7.13s\n",
      "73:\tlearn: 0.0051909\ttotal: 4.5s\tremaining: 7.06s\n",
      "74:\tlearn: 0.0050023\ttotal: 4.56s\tremaining: 7s\n",
      "75:\tlearn: 0.0048360\ttotal: 4.62s\tremaining: 6.93s\n",
      "76:\tlearn: 0.0046784\ttotal: 4.67s\tremaining: 6.86s\n",
      "77:\tlearn: 0.0045259\ttotal: 4.75s\tremaining: 6.82s\n",
      "78:\tlearn: 0.0043864\ttotal: 4.81s\tremaining: 6.76s\n",
      "79:\tlearn: 0.0042617\ttotal: 4.87s\tremaining: 6.7s\n",
      "80:\tlearn: 0.0041441\ttotal: 4.93s\tremaining: 6.63s\n",
      "81:\tlearn: 0.0040369\ttotal: 4.99s\tremaining: 6.57s\n",
      "82:\tlearn: 0.0039338\ttotal: 5.04s\tremaining: 6.5s\n",
      "83:\tlearn: 0.0038285\ttotal: 5.1s\tremaining: 6.43s\n",
      "84:\tlearn: 0.0037352\ttotal: 5.16s\tremaining: 6.37s\n",
      "85:\tlearn: 0.0036491\ttotal: 5.21s\tremaining: 6.3s\n",
      "86:\tlearn: 0.0035674\ttotal: 5.27s\tremaining: 6.24s\n",
      "87:\tlearn: 0.0034938\ttotal: 5.32s\tremaining: 6.17s\n",
      "88:\tlearn: 0.0034264\ttotal: 5.38s\tremaining: 6.11s\n",
      "89:\tlearn: 0.0033698\ttotal: 5.44s\tremaining: 6.04s\n",
      "90:\tlearn: 0.0033098\ttotal: 5.49s\tremaining: 5.97s\n",
      "91:\tlearn: 0.0032490\ttotal: 5.55s\tremaining: 5.91s\n",
      "92:\tlearn: 0.0032049\ttotal: 5.61s\tremaining: 5.85s\n",
      "93:\tlearn: 0.0031553\ttotal: 5.67s\tremaining: 5.79s\n",
      "94:\tlearn: 0.0031003\ttotal: 5.73s\tremaining: 5.73s\n",
      "95:\tlearn: 0.0030538\ttotal: 5.79s\tremaining: 5.67s\n",
      "96:\tlearn: 0.0030152\ttotal: 5.85s\tremaining: 5.61s\n",
      "97:\tlearn: 0.0029777\ttotal: 5.91s\tremaining: 5.54s\n",
      "98:\tlearn: 0.0029460\ttotal: 5.97s\tremaining: 5.49s\n",
      "99:\tlearn: 0.0029152\ttotal: 6.03s\tremaining: 5.42s\n",
      "100:\tlearn: 0.0028871\ttotal: 6.09s\tremaining: 5.36s\n",
      "101:\tlearn: 0.0028588\ttotal: 6.14s\tremaining: 5.3s\n",
      "102:\tlearn: 0.0028274\ttotal: 6.2s\tremaining: 5.23s\n",
      "103:\tlearn: 0.0028014\ttotal: 6.25s\tremaining: 5.17s\n",
      "104:\tlearn: 0.0027725\ttotal: 6.31s\tremaining: 5.11s\n",
      "105:\tlearn: 0.0027494\ttotal: 6.37s\tremaining: 5.05s\n",
      "106:\tlearn: 0.0027311\ttotal: 6.43s\tremaining: 4.99s\n",
      "107:\tlearn: 0.0027045\ttotal: 6.49s\tremaining: 4.92s\n",
      "108:\tlearn: 0.0026840\ttotal: 6.54s\tremaining: 4.86s\n",
      "109:\tlearn: 0.0026663\ttotal: 6.6s\tremaining: 4.8s\n",
      "110:\tlearn: 0.0026491\ttotal: 6.66s\tremaining: 4.74s\n",
      "111:\tlearn: 0.0026354\ttotal: 6.71s\tremaining: 4.67s\n",
      "112:\tlearn: 0.0026232\ttotal: 6.76s\tremaining: 4.61s\n",
      "113:\tlearn: 0.0026047\ttotal: 6.83s\tremaining: 4.55s\n",
      "114:\tlearn: 0.0025849\ttotal: 6.88s\tremaining: 4.49s\n",
      "115:\tlearn: 0.0025701\ttotal: 6.95s\tremaining: 4.43s\n",
      "116:\tlearn: 0.0025642\ttotal: 7.01s\tremaining: 4.37s\n",
      "117:\tlearn: 0.0025464\ttotal: 7.06s\tremaining: 4.31s\n",
      "118:\tlearn: 0.0025330\ttotal: 7.12s\tremaining: 4.25s\n",
      "119:\tlearn: 0.0025145\ttotal: 7.17s\tremaining: 4.18s\n",
      "120:\tlearn: 0.0025052\ttotal: 7.24s\tremaining: 4.13s\n",
      "121:\tlearn: 0.0024940\ttotal: 7.33s\tremaining: 4.08s\n",
      "122:\tlearn: 0.0024772\ttotal: 7.38s\tremaining: 4.02s\n",
      "123:\tlearn: 0.0024709\ttotal: 7.44s\tremaining: 3.96s\n",
      "124:\tlearn: 0.0024643\ttotal: 7.49s\tremaining: 3.9s\n",
      "125:\tlearn: 0.0024536\ttotal: 7.54s\tremaining: 3.83s\n",
      "126:\tlearn: 0.0024419\ttotal: 7.6s\tremaining: 3.77s\n",
      "127:\tlearn: 0.0024380\ttotal: 7.65s\tremaining: 3.71s\n",
      "128:\tlearn: 0.0024287\ttotal: 7.71s\tremaining: 3.65s\n",
      "129:\tlearn: 0.0024204\ttotal: 7.76s\tremaining: 3.58s\n",
      "130:\tlearn: 0.0024077\ttotal: 7.82s\tremaining: 3.52s\n",
      "131:\tlearn: 0.0023956\ttotal: 7.88s\tremaining: 3.46s\n",
      "132:\tlearn: 0.0023852\ttotal: 7.94s\tremaining: 3.4s\n",
      "133:\tlearn: 0.0023798\ttotal: 7.99s\tremaining: 3.34s\n",
      "134:\tlearn: 0.0023710\ttotal: 8.05s\tremaining: 3.28s\n",
      "135:\tlearn: 0.0023656\ttotal: 8.1s\tremaining: 3.22s\n",
      "136:\tlearn: 0.0023593\ttotal: 8.16s\tremaining: 3.16s\n",
      "137:\tlearn: 0.0023496\ttotal: 8.22s\tremaining: 3.1s\n",
      "138:\tlearn: 0.0023403\ttotal: 8.28s\tremaining: 3.04s\n",
      "139:\tlearn: 0.0023352\ttotal: 8.34s\tremaining: 2.98s\n",
      "140:\tlearn: 0.0023298\ttotal: 8.39s\tremaining: 2.92s\n",
      "141:\tlearn: 0.0023192\ttotal: 8.45s\tremaining: 2.86s\n",
      "142:\tlearn: 0.0023101\ttotal: 8.51s\tremaining: 2.8s\n",
      "143:\tlearn: 0.0023015\ttotal: 8.56s\tremaining: 2.73s\n",
      "144:\tlearn: 0.0022934\ttotal: 8.62s\tremaining: 2.67s\n",
      "145:\tlearn: 0.0022891\ttotal: 8.68s\tremaining: 2.62s\n",
      "146:\tlearn: 0.0022779\ttotal: 8.74s\tremaining: 2.56s\n",
      "147:\tlearn: 0.0022713\ttotal: 8.82s\tremaining: 2.5s\n",
      "148:\tlearn: 0.0022686\ttotal: 8.88s\tremaining: 2.44s\n",
      "149:\tlearn: 0.0022592\ttotal: 8.94s\tremaining: 2.38s\n",
      "150:\tlearn: 0.0022511\ttotal: 9s\tremaining: 2.32s\n",
      "151:\tlearn: 0.0022413\ttotal: 9.06s\tremaining: 2.27s\n",
      "152:\tlearn: 0.0022314\ttotal: 9.12s\tremaining: 2.21s\n",
      "153:\tlearn: 0.0022282\ttotal: 9.17s\tremaining: 2.14s\n",
      "154:\tlearn: 0.0022213\ttotal: 9.23s\tremaining: 2.08s\n",
      "155:\tlearn: 0.0022175\ttotal: 9.29s\tremaining: 2.02s\n",
      "156:\tlearn: 0.0022094\ttotal: 9.35s\tremaining: 1.97s\n",
      "157:\tlearn: 0.0022033\ttotal: 9.41s\tremaining: 1.91s\n",
      "158:\tlearn: 0.0021941\ttotal: 9.46s\tremaining: 1.84s\n",
      "159:\tlearn: 0.0021814\ttotal: 9.52s\tremaining: 1.78s\n",
      "160:\tlearn: 0.0021737\ttotal: 9.58s\tremaining: 1.73s\n",
      "161:\tlearn: 0.0021654\ttotal: 9.64s\tremaining: 1.67s\n",
      "162:\tlearn: 0.0021593\ttotal: 9.7s\tremaining: 1.61s\n",
      "163:\tlearn: 0.0021540\ttotal: 9.76s\tremaining: 1.55s\n",
      "164:\tlearn: 0.0021486\ttotal: 9.82s\tremaining: 1.49s\n",
      "165:\tlearn: 0.0021407\ttotal: 9.87s\tremaining: 1.43s\n",
      "166:\tlearn: 0.0021360\ttotal: 9.93s\tremaining: 1.37s\n",
      "167:\tlearn: 0.0021323\ttotal: 9.99s\tremaining: 1.31s\n",
      "168:\tlearn: 0.0021276\ttotal: 10s\tremaining: 1.25s\n",
      "169:\tlearn: 0.0021250\ttotal: 10.1s\tremaining: 1.19s\n",
      "170:\tlearn: 0.0021189\ttotal: 10.1s\tremaining: 1.13s\n",
      "171:\tlearn: 0.0021153\ttotal: 10.2s\tremaining: 1.07s\n",
      "172:\tlearn: 0.0021135\ttotal: 10.3s\tremaining: 1.01s\n",
      "173:\tlearn: 0.0021110\ttotal: 10.3s\tremaining: 948ms\n",
      "174:\tlearn: 0.0021067\ttotal: 10.4s\tremaining: 889ms\n",
      "175:\tlearn: 0.0021028\ttotal: 10.4s\tremaining: 829ms\n",
      "176:\tlearn: 0.0021008\ttotal: 10.5s\tremaining: 770ms\n",
      "177:\tlearn: 0.0020962\ttotal: 10.5s\tremaining: 710ms\n",
      "178:\tlearn: 0.0020942\ttotal: 10.6s\tremaining: 651ms\n",
      "179:\tlearn: 0.0020896\ttotal: 10.6s\tremaining: 592ms\n",
      "180:\tlearn: 0.0020879\ttotal: 10.7s\tremaining: 532ms\n",
      "181:\tlearn: 0.0020845\ttotal: 10.8s\tremaining: 473ms\n",
      "182:\tlearn: 0.0020824\ttotal: 10.8s\tremaining: 413ms\n",
      "183:\tlearn: 0.0020797\ttotal: 10.9s\tremaining: 354ms\n",
      "184:\tlearn: 0.0020771\ttotal: 10.9s\tremaining: 295ms\n",
      "185:\tlearn: 0.0020691\ttotal: 11s\tremaining: 236ms\n",
      "186:\tlearn: 0.0020646\ttotal: 11s\tremaining: 177ms\n",
      "187:\tlearn: 0.0020608\ttotal: 11.1s\tremaining: 118ms\n",
      "188:\tlearn: 0.0020589\ttotal: 11.2s\tremaining: 59ms\n",
      "189:\tlearn: 0.0020539\ttotal: 11.2s\tremaining: 0us\n",
      "0:\tlearn: 0.6258952\ttotal: 65.6ms\tremaining: 12.4s\n",
      "1:\tlearn: 0.5675258\ttotal: 127ms\tremaining: 11.9s\n",
      "2:\tlearn: 0.5164093\ttotal: 190ms\tremaining: 11.8s\n",
      "3:\tlearn: 0.4711538\ttotal: 255ms\tremaining: 11.8s\n",
      "4:\tlearn: 0.4308944\ttotal: 317ms\tremaining: 11.7s\n",
      "5:\tlearn: 0.3948611\ttotal: 383ms\tremaining: 11.7s\n",
      "6:\tlearn: 0.3624584\ttotal: 446ms\tremaining: 11.7s\n",
      "7:\tlearn: 0.3332769\ttotal: 513ms\tremaining: 11.7s\n",
      "8:\tlearn: 0.3068023\ttotal: 592ms\tremaining: 11.9s\n",
      "9:\tlearn: 0.2827891\ttotal: 657ms\tremaining: 11.8s\n",
      "10:\tlearn: 0.2610023\ttotal: 726ms\tremaining: 11.8s\n",
      "11:\tlearn: 0.2410304\ttotal: 789ms\tremaining: 11.7s\n",
      "12:\tlearn: 0.2228360\ttotal: 851ms\tremaining: 11.6s\n",
      "13:\tlearn: 0.2061892\ttotal: 915ms\tremaining: 11.5s\n",
      "14:\tlearn: 0.1909780\ttotal: 978ms\tremaining: 11.4s\n",
      "15:\tlearn: 0.1770116\ttotal: 1.04s\tremaining: 11.4s\n",
      "16:\tlearn: 0.1641811\ttotal: 1.1s\tremaining: 11.2s\n",
      "17:\tlearn: 0.1523188\ttotal: 1.16s\tremaining: 11.1s\n",
      "18:\tlearn: 0.1414120\ttotal: 1.22s\tremaining: 11s\n",
      "19:\tlearn: 0.1313210\ttotal: 1.28s\tremaining: 10.9s\n",
      "20:\tlearn: 0.1220185\ttotal: 1.34s\tremaining: 10.8s\n",
      "21:\tlearn: 0.1134422\ttotal: 1.4s\tremaining: 10.7s\n",
      "22:\tlearn: 0.1055183\ttotal: 1.47s\tremaining: 10.6s\n",
      "23:\tlearn: 0.0981962\ttotal: 1.53s\tremaining: 10.6s\n",
      "24:\tlearn: 0.0913989\ttotal: 1.59s\tremaining: 10.5s\n",
      "25:\tlearn: 0.0851385\ttotal: 1.65s\tremaining: 10.4s\n",
      "26:\tlearn: 0.0793060\ttotal: 1.71s\tremaining: 10.3s\n",
      "27:\tlearn: 0.0739233\ttotal: 1.77s\tremaining: 10.3s\n",
      "28:\tlearn: 0.0689299\ttotal: 1.84s\tremaining: 10.2s\n",
      "29:\tlearn: 0.0643096\ttotal: 1.9s\tremaining: 10.1s\n",
      "30:\tlearn: 0.0600031\ttotal: 1.97s\tremaining: 10.1s\n",
      "31:\tlearn: 0.0560243\ttotal: 2.03s\tremaining: 10s\n",
      "32:\tlearn: 0.0523067\ttotal: 2.09s\tremaining: 9.93s\n",
      "33:\tlearn: 0.0488943\ttotal: 2.15s\tremaining: 9.87s\n",
      "34:\tlearn: 0.0456985\ttotal: 2.21s\tremaining: 9.79s\n",
      "35:\tlearn: 0.0427087\ttotal: 2.27s\tremaining: 9.7s\n",
      "36:\tlearn: 0.0399270\ttotal: 2.32s\tremaining: 9.59s\n",
      "37:\tlearn: 0.0373730\ttotal: 2.38s\tremaining: 9.52s\n",
      "38:\tlearn: 0.0349675\ttotal: 2.44s\tremaining: 9.45s\n",
      "39:\tlearn: 0.0327478\ttotal: 2.5s\tremaining: 9.39s\n",
      "40:\tlearn: 0.0306640\ttotal: 2.56s\tremaining: 9.29s\n",
      "41:\tlearn: 0.0287304\ttotal: 2.61s\tremaining: 9.21s\n",
      "42:\tlearn: 0.0269249\ttotal: 2.67s\tremaining: 9.12s\n",
      "43:\tlearn: 0.0252505\ttotal: 2.72s\tremaining: 9.04s\n",
      "44:\tlearn: 0.0236991\ttotal: 2.78s\tremaining: 8.97s\n",
      "45:\tlearn: 0.0222639\ttotal: 2.85s\tremaining: 8.91s\n",
      "46:\tlearn: 0.0209222\ttotal: 2.9s\tremaining: 8.84s\n",
      "47:\tlearn: 0.0196551\ttotal: 2.96s\tremaining: 8.76s\n",
      "48:\tlearn: 0.0184836\ttotal: 3.02s\tremaining: 8.69s\n",
      "49:\tlearn: 0.0173797\ttotal: 3.07s\tremaining: 8.61s\n",
      "50:\tlearn: 0.0163725\ttotal: 3.13s\tremaining: 8.53s\n",
      "51:\tlearn: 0.0154272\ttotal: 3.19s\tremaining: 8.47s\n",
      "52:\tlearn: 0.0145514\ttotal: 3.25s\tremaining: 8.4s\n",
      "53:\tlearn: 0.0137225\ttotal: 3.3s\tremaining: 8.32s\n",
      "54:\tlearn: 0.0129472\ttotal: 3.35s\tremaining: 8.23s\n",
      "55:\tlearn: 0.0122291\ttotal: 3.41s\tremaining: 8.16s\n",
      "56:\tlearn: 0.0115696\ttotal: 3.47s\tremaining: 8.1s\n",
      "57:\tlearn: 0.0109403\ttotal: 3.52s\tremaining: 8.01s\n",
      "58:\tlearn: 0.0103666\ttotal: 3.57s\tremaining: 7.93s\n",
      "59:\tlearn: 0.0098327\ttotal: 3.63s\tremaining: 7.86s\n",
      "60:\tlearn: 0.0093288\ttotal: 3.68s\tremaining: 7.79s\n",
      "61:\tlearn: 0.0088724\ttotal: 3.75s\tremaining: 7.73s\n",
      "62:\tlearn: 0.0084358\ttotal: 3.8s\tremaining: 7.65s\n",
      "63:\tlearn: 0.0080248\ttotal: 3.85s\tremaining: 7.58s\n",
      "64:\tlearn: 0.0076636\ttotal: 3.91s\tremaining: 7.52s\n",
      "65:\tlearn: 0.0073082\ttotal: 3.97s\tremaining: 7.45s\n",
      "66:\tlearn: 0.0069790\ttotal: 4.02s\tremaining: 7.38s\n",
      "67:\tlearn: 0.0066755\ttotal: 4.08s\tremaining: 7.32s\n",
      "68:\tlearn: 0.0063927\ttotal: 4.13s\tremaining: 7.25s\n",
      "69:\tlearn: 0.0061312\ttotal: 4.19s\tremaining: 7.18s\n",
      "70:\tlearn: 0.0058793\ttotal: 4.24s\tremaining: 7.11s\n",
      "71:\tlearn: 0.0056523\ttotal: 4.3s\tremaining: 7.05s\n",
      "72:\tlearn: 0.0054408\ttotal: 4.38s\tremaining: 7.01s\n",
      "73:\tlearn: 0.0052411\ttotal: 4.43s\tremaining: 6.95s\n",
      "74:\tlearn: 0.0050522\ttotal: 4.49s\tremaining: 6.88s\n",
      "75:\tlearn: 0.0048816\ttotal: 4.55s\tremaining: 6.82s\n",
      "76:\tlearn: 0.0047205\ttotal: 4.6s\tremaining: 6.75s\n",
      "77:\tlearn: 0.0045731\ttotal: 4.66s\tremaining: 6.68s\n",
      "78:\tlearn: 0.0044365\ttotal: 4.71s\tremaining: 6.62s\n",
      "79:\tlearn: 0.0043070\ttotal: 4.76s\tremaining: 6.55s\n",
      "80:\tlearn: 0.0041947\ttotal: 4.82s\tremaining: 6.49s\n",
      "81:\tlearn: 0.0040902\ttotal: 4.88s\tremaining: 6.42s\n",
      "82:\tlearn: 0.0039821\ttotal: 4.93s\tremaining: 6.36s\n",
      "83:\tlearn: 0.0038719\ttotal: 4.99s\tremaining: 6.29s\n",
      "84:\tlearn: 0.0037777\ttotal: 5.04s\tremaining: 6.23s\n",
      "85:\tlearn: 0.0036927\ttotal: 5.1s\tremaining: 6.17s\n",
      "86:\tlearn: 0.0036127\ttotal: 5.15s\tremaining: 6.1s\n",
      "87:\tlearn: 0.0035325\ttotal: 5.21s\tremaining: 6.04s\n",
      "88:\tlearn: 0.0034667\ttotal: 5.26s\tremaining: 5.97s\n",
      "89:\tlearn: 0.0034021\ttotal: 5.32s\tremaining: 5.91s\n",
      "90:\tlearn: 0.0033447\ttotal: 5.37s\tremaining: 5.85s\n",
      "91:\tlearn: 0.0032923\ttotal: 5.43s\tremaining: 5.78s\n",
      "92:\tlearn: 0.0032451\ttotal: 5.48s\tremaining: 5.72s\n",
      "93:\tlearn: 0.0031892\ttotal: 5.53s\tremaining: 5.65s\n",
      "94:\tlearn: 0.0031413\ttotal: 5.59s\tremaining: 5.59s\n",
      "95:\tlearn: 0.0031009\ttotal: 5.64s\tremaining: 5.52s\n",
      "96:\tlearn: 0.0030617\ttotal: 5.69s\tremaining: 5.46s\n",
      "97:\tlearn: 0.0030228\ttotal: 5.75s\tremaining: 5.39s\n",
      "98:\tlearn: 0.0029920\ttotal: 5.8s\tremaining: 5.33s\n",
      "99:\tlearn: 0.0029633\ttotal: 5.85s\tremaining: 5.27s\n",
      "100:\tlearn: 0.0029369\ttotal: 5.91s\tremaining: 5.21s\n",
      "101:\tlearn: 0.0029038\ttotal: 5.96s\tremaining: 5.14s\n",
      "102:\tlearn: 0.0028704\ttotal: 6.01s\tremaining: 5.08s\n",
      "103:\tlearn: 0.0028480\ttotal: 6.07s\tremaining: 5.02s\n",
      "104:\tlearn: 0.0028280\ttotal: 6.13s\tremaining: 4.96s\n",
      "105:\tlearn: 0.0028024\ttotal: 6.18s\tremaining: 4.9s\n",
      "106:\tlearn: 0.0027831\ttotal: 6.24s\tremaining: 4.84s\n",
      "107:\tlearn: 0.0027523\ttotal: 6.29s\tremaining: 4.78s\n",
      "108:\tlearn: 0.0027266\ttotal: 6.35s\tremaining: 4.72s\n",
      "109:\tlearn: 0.0027003\ttotal: 6.41s\tremaining: 4.66s\n",
      "110:\tlearn: 0.0026853\ttotal: 6.46s\tremaining: 4.6s\n",
      "111:\tlearn: 0.0026685\ttotal: 6.51s\tremaining: 4.54s\n",
      "112:\tlearn: 0.0026535\ttotal: 6.57s\tremaining: 4.48s\n",
      "113:\tlearn: 0.0026412\ttotal: 6.63s\tremaining: 4.42s\n",
      "114:\tlearn: 0.0026151\ttotal: 6.69s\tremaining: 4.36s\n",
      "115:\tlearn: 0.0026018\ttotal: 6.75s\tremaining: 4.3s\n",
      "116:\tlearn: 0.0025938\ttotal: 6.8s\tremaining: 4.24s\n",
      "117:\tlearn: 0.0025700\ttotal: 6.85s\tremaining: 4.18s\n",
      "118:\tlearn: 0.0025611\ttotal: 6.91s\tremaining: 4.12s\n",
      "119:\tlearn: 0.0025398\ttotal: 6.96s\tremaining: 4.06s\n",
      "120:\tlearn: 0.0025220\ttotal: 7.02s\tremaining: 4s\n",
      "121:\tlearn: 0.0025050\ttotal: 7.08s\tremaining: 3.95s\n",
      "122:\tlearn: 0.0024916\ttotal: 7.13s\tremaining: 3.88s\n",
      "123:\tlearn: 0.0024745\ttotal: 7.19s\tremaining: 3.83s\n",
      "124:\tlearn: 0.0024580\ttotal: 7.25s\tremaining: 3.77s\n",
      "125:\tlearn: 0.0024444\ttotal: 7.31s\tremaining: 3.71s\n",
      "126:\tlearn: 0.0024307\ttotal: 7.36s\tremaining: 3.65s\n",
      "127:\tlearn: 0.0024241\ttotal: 7.42s\tremaining: 3.59s\n",
      "128:\tlearn: 0.0024179\ttotal: 7.48s\tremaining: 3.54s\n",
      "129:\tlearn: 0.0023968\ttotal: 7.54s\tremaining: 3.48s\n",
      "130:\tlearn: 0.0023814\ttotal: 7.59s\tremaining: 3.42s\n",
      "131:\tlearn: 0.0023671\ttotal: 7.66s\tremaining: 3.36s\n",
      "132:\tlearn: 0.0023561\ttotal: 7.71s\tremaining: 3.31s\n",
      "133:\tlearn: 0.0023495\ttotal: 7.77s\tremaining: 3.25s\n",
      "134:\tlearn: 0.0023335\ttotal: 7.83s\tremaining: 3.19s\n",
      "135:\tlearn: 0.0023294\ttotal: 7.88s\tremaining: 3.13s\n",
      "136:\tlearn: 0.0023158\ttotal: 7.94s\tremaining: 3.07s\n",
      "137:\tlearn: 0.0023007\ttotal: 8s\tremaining: 3.01s\n",
      "138:\tlearn: 0.0022899\ttotal: 8.05s\tremaining: 2.95s\n",
      "139:\tlearn: 0.0022845\ttotal: 8.1s\tremaining: 2.89s\n",
      "140:\tlearn: 0.0022818\ttotal: 8.15s\tremaining: 2.83s\n",
      "141:\tlearn: 0.0022775\ttotal: 8.21s\tremaining: 2.77s\n",
      "142:\tlearn: 0.0022628\ttotal: 8.27s\tremaining: 2.72s\n",
      "143:\tlearn: 0.0022532\ttotal: 8.32s\tremaining: 2.66s\n",
      "144:\tlearn: 0.0022506\ttotal: 8.37s\tremaining: 2.6s\n",
      "145:\tlearn: 0.0022358\ttotal: 8.43s\tremaining: 2.54s\n",
      "146:\tlearn: 0.0022228\ttotal: 8.49s\tremaining: 2.48s\n",
      "147:\tlearn: 0.0022101\ttotal: 8.55s\tremaining: 2.43s\n",
      "148:\tlearn: 0.0022035\ttotal: 8.61s\tremaining: 2.37s\n",
      "149:\tlearn: 0.0022001\ttotal: 8.66s\tremaining: 2.31s\n",
      "150:\tlearn: 0.0021964\ttotal: 8.71s\tremaining: 2.25s\n",
      "151:\tlearn: 0.0021894\ttotal: 8.77s\tremaining: 2.19s\n",
      "152:\tlearn: 0.0021745\ttotal: 8.83s\tremaining: 2.13s\n",
      "153:\tlearn: 0.0021685\ttotal: 8.89s\tremaining: 2.08s\n",
      "154:\tlearn: 0.0021561\ttotal: 8.95s\tremaining: 2.02s\n",
      "155:\tlearn: 0.0021535\ttotal: 9s\tremaining: 1.96s\n",
      "156:\tlearn: 0.0021435\ttotal: 9.05s\tremaining: 1.9s\n",
      "157:\tlearn: 0.0021339\ttotal: 9.11s\tremaining: 1.84s\n",
      "158:\tlearn: 0.0021314\ttotal: 9.15s\tremaining: 1.78s\n",
      "159:\tlearn: 0.0021247\ttotal: 9.21s\tremaining: 1.73s\n",
      "160:\tlearn: 0.0021197\ttotal: 9.27s\tremaining: 1.67s\n",
      "161:\tlearn: 0.0021150\ttotal: 9.32s\tremaining: 1.61s\n",
      "162:\tlearn: 0.0021055\ttotal: 9.38s\tremaining: 1.55s\n",
      "163:\tlearn: 0.0020946\ttotal: 9.44s\tremaining: 1.5s\n",
      "164:\tlearn: 0.0020897\ttotal: 9.51s\tremaining: 1.44s\n",
      "165:\tlearn: 0.0020841\ttotal: 9.56s\tremaining: 1.38s\n",
      "166:\tlearn: 0.0020813\ttotal: 9.61s\tremaining: 1.32s\n",
      "167:\tlearn: 0.0020729\ttotal: 9.68s\tremaining: 1.27s\n",
      "168:\tlearn: 0.0020707\ttotal: 9.73s\tremaining: 1.21s\n",
      "169:\tlearn: 0.0020665\ttotal: 9.78s\tremaining: 1.15s\n",
      "170:\tlearn: 0.0020632\ttotal: 9.84s\tremaining: 1.09s\n",
      "171:\tlearn: 0.0020593\ttotal: 9.89s\tremaining: 1.03s\n",
      "172:\tlearn: 0.0020563\ttotal: 9.94s\tremaining: 977ms\n",
      "173:\tlearn: 0.0020521\ttotal: 10s\tremaining: 919ms\n",
      "174:\tlearn: 0.0020482\ttotal: 10s\tremaining: 861ms\n",
      "175:\tlearn: 0.0020452\ttotal: 10.1s\tremaining: 804ms\n",
      "176:\tlearn: 0.0020417\ttotal: 10.2s\tremaining: 746ms\n",
      "177:\tlearn: 0.0020384\ttotal: 10.2s\tremaining: 688ms\n",
      "178:\tlearn: 0.0020364\ttotal: 10.3s\tremaining: 631ms\n",
      "179:\tlearn: 0.0020302\ttotal: 10.3s\tremaining: 573ms\n",
      "180:\tlearn: 0.0020264\ttotal: 10.4s\tremaining: 516ms\n",
      "181:\tlearn: 0.0020236\ttotal: 10.4s\tremaining: 458ms\n",
      "182:\tlearn: 0.0020209\ttotal: 10.5s\tremaining: 401ms\n",
      "183:\tlearn: 0.0020186\ttotal: 10.5s\tremaining: 343ms\n",
      "184:\tlearn: 0.0020117\ttotal: 10.6s\tremaining: 286ms\n",
      "185:\tlearn: 0.0020102\ttotal: 10.6s\tremaining: 229ms\n",
      "186:\tlearn: 0.0020038\ttotal: 10.7s\tremaining: 172ms\n",
      "187:\tlearn: 0.0020014\ttotal: 10.7s\tremaining: 114ms\n",
      "188:\tlearn: 0.0019985\ttotal: 10.8s\tremaining: 57.1ms\n",
      "189:\tlearn: 0.0019911\ttotal: 10.9s\tremaining: 0us\n",
      "0:\tlearn: 0.6258830\ttotal: 64.8ms\tremaining: 12.3s\n",
      "1:\tlearn: 0.5675078\ttotal: 130ms\tremaining: 12.2s\n",
      "2:\tlearn: 0.5163771\ttotal: 195ms\tremaining: 12.1s\n",
      "3:\tlearn: 0.4711150\ttotal: 259ms\tremaining: 12s\n",
      "4:\tlearn: 0.4308365\ttotal: 319ms\tremaining: 11.8s\n",
      "5:\tlearn: 0.3947732\ttotal: 382ms\tremaining: 11.7s\n",
      "6:\tlearn: 0.3623488\ttotal: 446ms\tremaining: 11.7s\n",
      "7:\tlearn: 0.3330673\ttotal: 510ms\tremaining: 11.6s\n",
      "8:\tlearn: 0.3065802\ttotal: 571ms\tremaining: 11.5s\n",
      "9:\tlearn: 0.2825854\ttotal: 636ms\tremaining: 11.4s\n",
      "10:\tlearn: 0.2607887\ttotal: 701ms\tremaining: 11.4s\n",
      "11:\tlearn: 0.2408024\ttotal: 763ms\tremaining: 11.3s\n",
      "12:\tlearn: 0.2225928\ttotal: 827ms\tremaining: 11.3s\n",
      "13:\tlearn: 0.2059371\ttotal: 892ms\tremaining: 11.2s\n",
      "14:\tlearn: 0.1907193\ttotal: 950ms\tremaining: 11.1s\n",
      "15:\tlearn: 0.1767457\ttotal: 1.02s\tremaining: 11.1s\n",
      "16:\tlearn: 0.1639095\ttotal: 1.08s\tremaining: 11s\n",
      "17:\tlearn: 0.1520365\ttotal: 1.14s\tremaining: 10.9s\n",
      "18:\tlearn: 0.1411082\ttotal: 1.2s\tremaining: 10.8s\n",
      "19:\tlearn: 0.1310278\ttotal: 1.26s\tremaining: 10.7s\n",
      "20:\tlearn: 0.1217328\ttotal: 1.32s\tremaining: 10.7s\n",
      "21:\tlearn: 0.1131483\ttotal: 1.39s\tremaining: 10.6s\n",
      "22:\tlearn: 0.1052185\ttotal: 1.45s\tremaining: 10.5s\n",
      "23:\tlearn: 0.0978972\ttotal: 1.51s\tremaining: 10.4s\n",
      "24:\tlearn: 0.0911037\ttotal: 1.57s\tremaining: 10.4s\n",
      "25:\tlearn: 0.0848088\ttotal: 1.63s\tremaining: 10.3s\n",
      "26:\tlearn: 0.0789771\ttotal: 1.69s\tremaining: 10.2s\n",
      "27:\tlearn: 0.0735927\ttotal: 1.75s\tremaining: 10.1s\n",
      "28:\tlearn: 0.0686134\ttotal: 1.81s\tremaining: 10s\n",
      "29:\tlearn: 0.0639794\ttotal: 1.87s\tremaining: 9.97s\n",
      "30:\tlearn: 0.0596771\ttotal: 1.93s\tremaining: 9.89s\n",
      "31:\tlearn: 0.0556908\ttotal: 1.99s\tremaining: 9.82s\n",
      "32:\tlearn: 0.0519633\ttotal: 2.05s\tremaining: 9.74s\n",
      "33:\tlearn: 0.0485282\ttotal: 2.11s\tremaining: 9.7s\n",
      "34:\tlearn: 0.0453303\ttotal: 2.17s\tremaining: 9.62s\n",
      "35:\tlearn: 0.0423327\ttotal: 2.23s\tremaining: 9.54s\n",
      "36:\tlearn: 0.0395582\ttotal: 2.29s\tremaining: 9.45s\n",
      "37:\tlearn: 0.0369903\ttotal: 2.35s\tremaining: 9.39s\n",
      "38:\tlearn: 0.0346016\ttotal: 2.41s\tremaining: 9.33s\n",
      "39:\tlearn: 0.0323834\ttotal: 2.47s\tremaining: 9.27s\n",
      "40:\tlearn: 0.0303023\ttotal: 2.53s\tremaining: 9.19s\n",
      "41:\tlearn: 0.0283731\ttotal: 2.59s\tremaining: 9.13s\n",
      "42:\tlearn: 0.0265487\ttotal: 2.64s\tremaining: 9.04s\n",
      "43:\tlearn: 0.0248977\ttotal: 2.71s\tremaining: 8.98s\n",
      "44:\tlearn: 0.0233447\ttotal: 2.76s\tremaining: 8.91s\n",
      "45:\tlearn: 0.0218914\ttotal: 2.82s\tremaining: 8.83s\n",
      "46:\tlearn: 0.0205250\ttotal: 2.88s\tremaining: 8.75s\n",
      "47:\tlearn: 0.0192489\ttotal: 2.93s\tremaining: 8.67s\n",
      "48:\tlearn: 0.0180631\ttotal: 2.98s\tremaining: 8.59s\n",
      "49:\tlearn: 0.0169608\ttotal: 3.04s\tremaining: 8.5s\n",
      "50:\tlearn: 0.0159352\ttotal: 3.09s\tremaining: 8.42s\n",
      "51:\tlearn: 0.0149803\ttotal: 3.15s\tremaining: 8.35s\n",
      "52:\tlearn: 0.0141004\ttotal: 3.2s\tremaining: 8.28s\n",
      "53:\tlearn: 0.0132843\ttotal: 3.26s\tremaining: 8.21s\n",
      "54:\tlearn: 0.0125161\ttotal: 3.31s\tremaining: 8.13s\n",
      "55:\tlearn: 0.0118057\ttotal: 3.37s\tremaining: 8.07s\n",
      "56:\tlearn: 0.0111370\ttotal: 3.42s\tremaining: 7.99s\n",
      "57:\tlearn: 0.0105259\ttotal: 3.48s\tremaining: 7.93s\n",
      "58:\tlearn: 0.0099457\ttotal: 3.54s\tremaining: 7.85s\n",
      "59:\tlearn: 0.0094053\ttotal: 3.59s\tremaining: 7.78s\n",
      "60:\tlearn: 0.0089054\ttotal: 3.65s\tremaining: 7.71s\n",
      "61:\tlearn: 0.0084382\ttotal: 3.7s\tremaining: 7.64s\n",
      "62:\tlearn: 0.0080049\ttotal: 3.75s\tremaining: 7.57s\n",
      "63:\tlearn: 0.0075980\ttotal: 3.81s\tremaining: 7.5s\n",
      "64:\tlearn: 0.0072438\ttotal: 3.87s\tremaining: 7.45s\n",
      "65:\tlearn: 0.0068907\ttotal: 3.92s\tremaining: 7.37s\n",
      "66:\tlearn: 0.0065637\ttotal: 3.98s\tremaining: 7.31s\n",
      "67:\tlearn: 0.0062554\ttotal: 4.03s\tremaining: 7.24s\n",
      "68:\tlearn: 0.0059760\ttotal: 4.09s\tremaining: 7.17s\n",
      "69:\tlearn: 0.0057103\ttotal: 4.14s\tremaining: 7.1s\n",
      "70:\tlearn: 0.0054636\ttotal: 4.2s\tremaining: 7.04s\n",
      "71:\tlearn: 0.0052441\ttotal: 4.25s\tremaining: 6.97s\n",
      "72:\tlearn: 0.0050321\ttotal: 4.31s\tremaining: 6.91s\n",
      "73:\tlearn: 0.0048348\ttotal: 4.37s\tremaining: 6.85s\n",
      "74:\tlearn: 0.0046504\ttotal: 4.42s\tremaining: 6.78s\n",
      "75:\tlearn: 0.0044749\ttotal: 4.48s\tremaining: 6.72s\n",
      "76:\tlearn: 0.0043177\ttotal: 4.54s\tremaining: 6.66s\n",
      "77:\tlearn: 0.0041683\ttotal: 4.59s\tremaining: 6.59s\n",
      "78:\tlearn: 0.0040262\ttotal: 4.65s\tremaining: 6.54s\n",
      "79:\tlearn: 0.0038999\ttotal: 4.71s\tremaining: 6.47s\n",
      "80:\tlearn: 0.0037723\ttotal: 4.76s\tremaining: 6.41s\n",
      "81:\tlearn: 0.0036621\ttotal: 4.82s\tremaining: 6.34s\n",
      "82:\tlearn: 0.0035543\ttotal: 4.87s\tremaining: 6.28s\n",
      "83:\tlearn: 0.0034517\ttotal: 4.92s\tremaining: 6.21s\n",
      "84:\tlearn: 0.0033537\ttotal: 4.98s\tremaining: 6.15s\n",
      "85:\tlearn: 0.0032637\ttotal: 5.03s\tremaining: 6.09s\n",
      "86:\tlearn: 0.0031863\ttotal: 5.08s\tremaining: 6.02s\n",
      "87:\tlearn: 0.0031077\ttotal: 5.14s\tremaining: 5.96s\n",
      "88:\tlearn: 0.0030437\ttotal: 5.2s\tremaining: 5.9s\n",
      "89:\tlearn: 0.0029860\ttotal: 5.25s\tremaining: 5.84s\n",
      "90:\tlearn: 0.0029298\ttotal: 5.31s\tremaining: 5.77s\n",
      "91:\tlearn: 0.0028752\ttotal: 5.37s\tremaining: 5.71s\n",
      "92:\tlearn: 0.0028188\ttotal: 5.42s\tremaining: 5.65s\n",
      "93:\tlearn: 0.0027769\ttotal: 5.48s\tremaining: 5.59s\n",
      "94:\tlearn: 0.0027243\ttotal: 5.53s\tremaining: 5.53s\n",
      "95:\tlearn: 0.0026740\ttotal: 5.59s\tremaining: 5.47s\n",
      "96:\tlearn: 0.0026387\ttotal: 5.64s\tremaining: 5.41s\n",
      "97:\tlearn: 0.0026071\ttotal: 5.7s\tremaining: 5.36s\n",
      "98:\tlearn: 0.0025715\ttotal: 5.76s\tremaining: 5.3s\n",
      "99:\tlearn: 0.0025445\ttotal: 5.83s\tremaining: 5.24s\n",
      "100:\tlearn: 0.0025146\ttotal: 5.88s\tremaining: 5.18s\n",
      "101:\tlearn: 0.0024787\ttotal: 5.93s\tremaining: 5.12s\n",
      "102:\tlearn: 0.0024440\ttotal: 5.99s\tremaining: 5.06s\n",
      "103:\tlearn: 0.0024166\ttotal: 6.04s\tremaining: 5s\n",
      "104:\tlearn: 0.0023943\ttotal: 6.1s\tremaining: 4.94s\n",
      "105:\tlearn: 0.0023716\ttotal: 6.16s\tremaining: 4.88s\n",
      "106:\tlearn: 0.0023522\ttotal: 6.21s\tremaining: 4.81s\n",
      "107:\tlearn: 0.0023345\ttotal: 6.26s\tremaining: 4.75s\n",
      "108:\tlearn: 0.0023179\ttotal: 6.31s\tremaining: 4.69s\n",
      "109:\tlearn: 0.0022977\ttotal: 6.37s\tremaining: 4.63s\n",
      "110:\tlearn: 0.0022742\ttotal: 6.42s\tremaining: 4.57s\n",
      "111:\tlearn: 0.0022576\ttotal: 6.47s\tremaining: 4.51s\n",
      "112:\tlearn: 0.0022479\ttotal: 6.53s\tremaining: 4.45s\n",
      "113:\tlearn: 0.0022384\ttotal: 6.58s\tremaining: 4.39s\n",
      "114:\tlearn: 0.0022201\ttotal: 6.64s\tremaining: 4.33s\n",
      "115:\tlearn: 0.0022126\ttotal: 6.7s\tremaining: 4.27s\n",
      "116:\tlearn: 0.0022041\ttotal: 6.75s\tremaining: 4.21s\n",
      "117:\tlearn: 0.0021928\ttotal: 6.82s\tremaining: 4.16s\n",
      "118:\tlearn: 0.0021768\ttotal: 6.87s\tremaining: 4.1s\n",
      "119:\tlearn: 0.0021581\ttotal: 6.93s\tremaining: 4.04s\n",
      "120:\tlearn: 0.0021406\ttotal: 6.99s\tremaining: 3.98s\n",
      "121:\tlearn: 0.0021278\ttotal: 7.04s\tremaining: 3.92s\n",
      "122:\tlearn: 0.0021193\ttotal: 7.1s\tremaining: 3.87s\n",
      "123:\tlearn: 0.0021047\ttotal: 7.16s\tremaining: 3.81s\n",
      "124:\tlearn: 0.0020925\ttotal: 7.21s\tremaining: 3.75s\n",
      "125:\tlearn: 0.0020799\ttotal: 7.27s\tremaining: 3.69s\n",
      "126:\tlearn: 0.0020665\ttotal: 7.33s\tremaining: 3.64s\n",
      "127:\tlearn: 0.0020566\ttotal: 7.39s\tremaining: 3.58s\n",
      "128:\tlearn: 0.0020453\ttotal: 7.45s\tremaining: 3.52s\n",
      "129:\tlearn: 0.0020318\ttotal: 7.51s\tremaining: 3.46s\n",
      "130:\tlearn: 0.0020211\ttotal: 7.56s\tremaining: 3.4s\n",
      "131:\tlearn: 0.0020124\ttotal: 7.61s\tremaining: 3.35s\n",
      "132:\tlearn: 0.0020007\ttotal: 7.67s\tremaining: 3.29s\n",
      "133:\tlearn: 0.0019880\ttotal: 7.73s\tremaining: 3.23s\n",
      "134:\tlearn: 0.0019766\ttotal: 7.79s\tremaining: 3.17s\n",
      "135:\tlearn: 0.0019692\ttotal: 7.84s\tremaining: 3.12s\n",
      "136:\tlearn: 0.0019601\ttotal: 7.9s\tremaining: 3.06s\n",
      "137:\tlearn: 0.0019525\ttotal: 7.96s\tremaining: 3s\n",
      "138:\tlearn: 0.0019445\ttotal: 8.02s\tremaining: 2.94s\n",
      "139:\tlearn: 0.0019365\ttotal: 8.08s\tremaining: 2.88s\n",
      "140:\tlearn: 0.0019320\ttotal: 8.13s\tremaining: 2.82s\n",
      "141:\tlearn: 0.0019268\ttotal: 8.19s\tremaining: 2.77s\n",
      "142:\tlearn: 0.0019207\ttotal: 8.24s\tremaining: 2.71s\n",
      "143:\tlearn: 0.0019111\ttotal: 8.29s\tremaining: 2.65s\n",
      "144:\tlearn: 0.0019044\ttotal: 8.35s\tremaining: 2.59s\n",
      "145:\tlearn: 0.0018917\ttotal: 8.41s\tremaining: 2.53s\n",
      "146:\tlearn: 0.0018806\ttotal: 8.46s\tremaining: 2.48s\n",
      "147:\tlearn: 0.0018715\ttotal: 8.52s\tremaining: 2.42s\n",
      "148:\tlearn: 0.0018667\ttotal: 8.57s\tremaining: 2.36s\n",
      "149:\tlearn: 0.0018630\ttotal: 8.63s\tremaining: 2.3s\n",
      "150:\tlearn: 0.0018566\ttotal: 8.68s\tremaining: 2.24s\n",
      "151:\tlearn: 0.0018443\ttotal: 8.74s\tremaining: 2.19s\n",
      "152:\tlearn: 0.0018347\ttotal: 8.8s\tremaining: 2.13s\n",
      "153:\tlearn: 0.0018285\ttotal: 8.86s\tremaining: 2.07s\n",
      "154:\tlearn: 0.0018236\ttotal: 8.92s\tremaining: 2.01s\n",
      "155:\tlearn: 0.0018169\ttotal: 8.97s\tremaining: 1.96s\n",
      "156:\tlearn: 0.0018119\ttotal: 9.03s\tremaining: 1.9s\n",
      "157:\tlearn: 0.0018008\ttotal: 9.09s\tremaining: 1.84s\n",
      "158:\tlearn: 0.0017888\ttotal: 9.14s\tremaining: 1.78s\n",
      "159:\tlearn: 0.0017799\ttotal: 9.21s\tremaining: 1.73s\n",
      "160:\tlearn: 0.0017726\ttotal: 9.26s\tremaining: 1.67s\n",
      "161:\tlearn: 0.0017675\ttotal: 9.32s\tremaining: 1.61s\n",
      "162:\tlearn: 0.0017616\ttotal: 9.38s\tremaining: 1.55s\n",
      "163:\tlearn: 0.0017559\ttotal: 9.43s\tremaining: 1.49s\n",
      "164:\tlearn: 0.0017511\ttotal: 9.48s\tremaining: 1.44s\n",
      "165:\tlearn: 0.0017466\ttotal: 9.54s\tremaining: 1.38s\n",
      "166:\tlearn: 0.0017409\ttotal: 9.59s\tremaining: 1.32s\n",
      "167:\tlearn: 0.0017373\ttotal: 9.65s\tremaining: 1.26s\n",
      "168:\tlearn: 0.0017329\ttotal: 9.7s\tremaining: 1.21s\n",
      "169:\tlearn: 0.0017281\ttotal: 9.76s\tremaining: 1.15s\n",
      "170:\tlearn: 0.0017213\ttotal: 9.81s\tremaining: 1.09s\n",
      "171:\tlearn: 0.0017175\ttotal: 9.87s\tremaining: 1.03s\n",
      "172:\tlearn: 0.0017154\ttotal: 9.92s\tremaining: 975ms\n",
      "173:\tlearn: 0.0017132\ttotal: 9.97s\tremaining: 917ms\n",
      "174:\tlearn: 0.0017083\ttotal: 10s\tremaining: 860ms\n",
      "175:\tlearn: 0.0017060\ttotal: 10.1s\tremaining: 802ms\n",
      "176:\tlearn: 0.0017015\ttotal: 10.1s\tremaining: 744ms\n",
      "177:\tlearn: 0.0016981\ttotal: 10.2s\tremaining: 687ms\n",
      "178:\tlearn: 0.0016965\ttotal: 10.2s\tremaining: 629ms\n",
      "179:\tlearn: 0.0016900\ttotal: 10.3s\tremaining: 573ms\n",
      "180:\tlearn: 0.0016868\ttotal: 10.4s\tremaining: 515ms\n",
      "181:\tlearn: 0.0016839\ttotal: 10.4s\tremaining: 458ms\n",
      "182:\tlearn: 0.0016823\ttotal: 10.5s\tremaining: 400ms\n",
      "183:\tlearn: 0.0016791\ttotal: 10.5s\tremaining: 343ms\n",
      "184:\tlearn: 0.0016750\ttotal: 10.6s\tremaining: 286ms\n",
      "185:\tlearn: 0.0016720\ttotal: 10.6s\tremaining: 229ms\n",
      "186:\tlearn: 0.0016687\ttotal: 10.7s\tremaining: 171ms\n",
      "187:\tlearn: 0.0016592\ttotal: 10.7s\tremaining: 114ms\n",
      "188:\tlearn: 0.0016560\ttotal: 10.8s\tremaining: 57.1ms\n",
      "189:\tlearn: 0.0016498\ttotal: 10.9s\tremaining: 0us\n",
      "0:\tlearn: 0.6258841\ttotal: 65.9ms\tremaining: 12.5s\n",
      "1:\tlearn: 0.5675228\ttotal: 129ms\tremaining: 12.1s\n",
      "2:\tlearn: 0.5164128\ttotal: 193ms\tremaining: 12s\n",
      "3:\tlearn: 0.4711594\ttotal: 256ms\tremaining: 11.9s\n",
      "4:\tlearn: 0.4308955\ttotal: 316ms\tremaining: 11.7s\n",
      "5:\tlearn: 0.3948520\ttotal: 378ms\tremaining: 11.6s\n",
      "6:\tlearn: 0.3624620\ttotal: 441ms\tremaining: 11.5s\n",
      "7:\tlearn: 0.3332709\ttotal: 505ms\tremaining: 11.5s\n",
      "8:\tlearn: 0.3067922\ttotal: 566ms\tremaining: 11.4s\n",
      "9:\tlearn: 0.2827777\ttotal: 631ms\tremaining: 11.4s\n",
      "10:\tlearn: 0.2609935\ttotal: 695ms\tremaining: 11.3s\n",
      "11:\tlearn: 0.2410188\ttotal: 754ms\tremaining: 11.2s\n",
      "12:\tlearn: 0.2228233\ttotal: 816ms\tremaining: 11.1s\n",
      "13:\tlearn: 0.2061730\ttotal: 880ms\tremaining: 11.1s\n",
      "14:\tlearn: 0.1909582\ttotal: 940ms\tremaining: 11s\n",
      "15:\tlearn: 0.1769892\ttotal: 1s\tremaining: 10.9s\n",
      "16:\tlearn: 0.1641572\ttotal: 1.06s\tremaining: 10.8s\n",
      "17:\tlearn: 0.1522758\ttotal: 1.12s\tremaining: 10.7s\n",
      "18:\tlearn: 0.1413721\ttotal: 1.18s\tremaining: 10.6s\n",
      "19:\tlearn: 0.1312796\ttotal: 1.24s\tremaining: 10.5s\n",
      "20:\tlearn: 0.1219814\ttotal: 1.29s\tremaining: 10.4s\n",
      "21:\tlearn: 0.1134049\ttotal: 1.36s\tremaining: 10.4s\n",
      "22:\tlearn: 0.1054673\ttotal: 1.42s\tremaining: 10.3s\n",
      "23:\tlearn: 0.0981483\ttotal: 1.48s\tremaining: 10.2s\n",
      "24:\tlearn: 0.0913518\ttotal: 1.53s\tremaining: 10.1s\n",
      "25:\tlearn: 0.0850933\ttotal: 1.6s\tremaining: 10.1s\n",
      "26:\tlearn: 0.0792542\ttotal: 1.65s\tremaining: 9.98s\n",
      "27:\tlearn: 0.0738793\ttotal: 1.71s\tremaining: 9.92s\n",
      "28:\tlearn: 0.0688848\ttotal: 1.78s\tremaining: 9.86s\n",
      "29:\tlearn: 0.0642937\ttotal: 1.84s\tremaining: 9.84s\n",
      "30:\tlearn: 0.0599855\ttotal: 1.9s\tremaining: 9.76s\n",
      "31:\tlearn: 0.0560048\ttotal: 1.96s\tremaining: 9.68s\n",
      "32:\tlearn: 0.0522932\ttotal: 2.02s\tremaining: 9.61s\n",
      "33:\tlearn: 0.0488503\ttotal: 2.08s\tremaining: 9.55s\n",
      "34:\tlearn: 0.0456512\ttotal: 2.14s\tremaining: 9.47s\n",
      "35:\tlearn: 0.0426455\ttotal: 2.19s\tremaining: 9.38s\n",
      "36:\tlearn: 0.0398755\ttotal: 2.25s\tremaining: 9.3s\n",
      "37:\tlearn: 0.0372834\ttotal: 2.3s\tremaining: 9.21s\n",
      "38:\tlearn: 0.0348859\ttotal: 2.36s\tremaining: 9.14s\n",
      "39:\tlearn: 0.0326555\ttotal: 2.42s\tremaining: 9.08s\n",
      "40:\tlearn: 0.0305681\ttotal: 2.48s\tremaining: 9s\n",
      "41:\tlearn: 0.0286466\ttotal: 2.53s\tremaining: 8.93s\n",
      "42:\tlearn: 0.0268529\ttotal: 2.59s\tremaining: 8.86s\n",
      "43:\tlearn: 0.0251793\ttotal: 2.65s\tremaining: 8.78s\n",
      "44:\tlearn: 0.0236238\ttotal: 2.7s\tremaining: 8.71s\n",
      "45:\tlearn: 0.0221733\ttotal: 2.76s\tremaining: 8.65s\n",
      "46:\tlearn: 0.0208356\ttotal: 2.82s\tremaining: 8.59s\n",
      "47:\tlearn: 0.0195658\ttotal: 2.88s\tremaining: 8.52s\n",
      "48:\tlearn: 0.0183934\ttotal: 2.94s\tremaining: 8.45s\n",
      "49:\tlearn: 0.0172881\ttotal: 2.99s\tremaining: 8.37s\n",
      "50:\tlearn: 0.0162618\ttotal: 3.04s\tremaining: 8.28s\n",
      "51:\tlearn: 0.0153162\ttotal: 3.1s\tremaining: 8.22s\n",
      "52:\tlearn: 0.0144375\ttotal: 3.15s\tremaining: 8.15s\n",
      "53:\tlearn: 0.0136236\ttotal: 3.21s\tremaining: 8.09s\n",
      "54:\tlearn: 0.0128554\ttotal: 3.27s\tremaining: 8.02s\n",
      "55:\tlearn: 0.0121486\ttotal: 3.32s\tremaining: 7.95s\n",
      "56:\tlearn: 0.0114896\ttotal: 3.38s\tremaining: 7.89s\n",
      "57:\tlearn: 0.0108745\ttotal: 3.44s\tremaining: 7.82s\n",
      "58:\tlearn: 0.0102989\ttotal: 3.49s\tremaining: 7.75s\n",
      "59:\tlearn: 0.0097742\ttotal: 3.54s\tremaining: 7.68s\n",
      "60:\tlearn: 0.0092796\ttotal: 3.6s\tremaining: 7.62s\n",
      "61:\tlearn: 0.0088258\ttotal: 3.67s\tremaining: 7.57s\n",
      "62:\tlearn: 0.0083853\ttotal: 3.72s\tremaining: 7.5s\n",
      "63:\tlearn: 0.0079801\ttotal: 3.77s\tremaining: 7.43s\n",
      "64:\tlearn: 0.0076106\ttotal: 3.83s\tremaining: 7.37s\n",
      "65:\tlearn: 0.0072581\ttotal: 3.89s\tremaining: 7.3s\n",
      "66:\tlearn: 0.0069379\ttotal: 3.94s\tremaining: 7.24s\n",
      "67:\tlearn: 0.0066352\ttotal: 4s\tremaining: 7.18s\n",
      "68:\tlearn: 0.0063506\ttotal: 4.05s\tremaining: 7.11s\n",
      "69:\tlearn: 0.0060957\ttotal: 4.11s\tremaining: 7.05s\n",
      "70:\tlearn: 0.0058551\ttotal: 4.17s\tremaining: 6.99s\n",
      "71:\tlearn: 0.0056311\ttotal: 4.22s\tremaining: 6.92s\n",
      "72:\tlearn: 0.0054262\ttotal: 4.28s\tremaining: 6.86s\n",
      "73:\tlearn: 0.0052246\ttotal: 4.34s\tremaining: 6.8s\n",
      "74:\tlearn: 0.0050335\ttotal: 4.39s\tremaining: 6.74s\n",
      "75:\tlearn: 0.0048590\ttotal: 4.45s\tremaining: 6.67s\n",
      "76:\tlearn: 0.0047044\ttotal: 4.5s\tremaining: 6.61s\n",
      "77:\tlearn: 0.0045648\ttotal: 4.56s\tremaining: 6.55s\n",
      "78:\tlearn: 0.0044242\ttotal: 4.62s\tremaining: 6.49s\n",
      "79:\tlearn: 0.0042933\ttotal: 4.67s\tremaining: 6.42s\n",
      "80:\tlearn: 0.0041711\ttotal: 4.72s\tremaining: 6.36s\n",
      "81:\tlearn: 0.0040572\ttotal: 4.78s\tremaining: 6.29s\n",
      "82:\tlearn: 0.0039555\ttotal: 4.83s\tremaining: 6.23s\n",
      "83:\tlearn: 0.0038492\ttotal: 4.89s\tremaining: 6.18s\n",
      "84:\tlearn: 0.0037661\ttotal: 4.96s\tremaining: 6.12s\n",
      "85:\tlearn: 0.0036873\ttotal: 5.02s\tremaining: 6.07s\n",
      "86:\tlearn: 0.0036013\ttotal: 5.08s\tremaining: 6.01s\n",
      "87:\tlearn: 0.0035169\ttotal: 5.13s\tremaining: 5.95s\n",
      "88:\tlearn: 0.0034503\ttotal: 5.18s\tremaining: 5.88s\n",
      "89:\tlearn: 0.0033725\ttotal: 5.24s\tremaining: 5.83s\n",
      "90:\tlearn: 0.0033147\ttotal: 5.3s\tremaining: 5.76s\n",
      "91:\tlearn: 0.0032603\ttotal: 5.35s\tremaining: 5.7s\n",
      "92:\tlearn: 0.0031959\ttotal: 5.41s\tremaining: 5.64s\n",
      "93:\tlearn: 0.0031523\ttotal: 5.47s\tremaining: 5.58s\n",
      "94:\tlearn: 0.0031007\ttotal: 5.52s\tremaining: 5.52s\n",
      "95:\tlearn: 0.0030570\ttotal: 5.58s\tremaining: 5.46s\n",
      "96:\tlearn: 0.0030157\ttotal: 5.63s\tremaining: 5.4s\n",
      "97:\tlearn: 0.0029819\ttotal: 5.68s\tremaining: 5.34s\n",
      "98:\tlearn: 0.0029395\ttotal: 5.74s\tremaining: 5.28s\n",
      "99:\tlearn: 0.0029116\ttotal: 5.79s\tremaining: 5.21s\n",
      "100:\tlearn: 0.0028740\ttotal: 5.86s\tremaining: 5.16s\n",
      "101:\tlearn: 0.0028376\ttotal: 5.92s\tremaining: 5.11s\n",
      "102:\tlearn: 0.0028000\ttotal: 5.97s\tremaining: 5.05s\n",
      "103:\tlearn: 0.0027797\ttotal: 6.03s\tremaining: 4.99s\n",
      "104:\tlearn: 0.0027585\ttotal: 6.09s\tremaining: 4.93s\n",
      "105:\tlearn: 0.0027265\ttotal: 6.15s\tremaining: 4.87s\n",
      "106:\tlearn: 0.0027129\ttotal: 6.21s\tremaining: 4.81s\n",
      "107:\tlearn: 0.0026854\ttotal: 6.26s\tremaining: 4.75s\n",
      "108:\tlearn: 0.0026697\ttotal: 6.31s\tremaining: 4.69s\n",
      "109:\tlearn: 0.0026460\ttotal: 6.37s\tremaining: 4.63s\n",
      "110:\tlearn: 0.0026274\ttotal: 6.42s\tremaining: 4.57s\n",
      "111:\tlearn: 0.0026134\ttotal: 6.47s\tremaining: 4.51s\n",
      "112:\tlearn: 0.0025928\ttotal: 6.53s\tremaining: 4.45s\n",
      "113:\tlearn: 0.0025725\ttotal: 6.59s\tremaining: 4.39s\n",
      "114:\tlearn: 0.0025499\ttotal: 6.65s\tremaining: 4.34s\n",
      "115:\tlearn: 0.0025279\ttotal: 6.71s\tremaining: 4.28s\n",
      "116:\tlearn: 0.0025193\ttotal: 6.76s\tremaining: 4.22s\n",
      "117:\tlearn: 0.0024978\ttotal: 6.82s\tremaining: 4.16s\n",
      "118:\tlearn: 0.0024768\ttotal: 6.88s\tremaining: 4.11s\n",
      "119:\tlearn: 0.0024613\ttotal: 6.94s\tremaining: 4.05s\n",
      "120:\tlearn: 0.0024438\ttotal: 7s\tremaining: 3.99s\n",
      "121:\tlearn: 0.0024293\ttotal: 7.05s\tremaining: 3.93s\n",
      "122:\tlearn: 0.0024236\ttotal: 7.11s\tremaining: 3.87s\n",
      "123:\tlearn: 0.0024149\ttotal: 7.16s\tremaining: 3.81s\n",
      "124:\tlearn: 0.0024069\ttotal: 7.21s\tremaining: 3.75s\n",
      "125:\tlearn: 0.0023970\ttotal: 7.27s\tremaining: 3.69s\n",
      "126:\tlearn: 0.0023919\ttotal: 7.32s\tremaining: 3.63s\n",
      "127:\tlearn: 0.0023859\ttotal: 7.37s\tremaining: 3.57s\n",
      "128:\tlearn: 0.0023682\ttotal: 7.43s\tremaining: 3.52s\n",
      "129:\tlearn: 0.0023494\ttotal: 7.5s\tremaining: 3.46s\n",
      "130:\tlearn: 0.0023373\ttotal: 7.56s\tremaining: 3.41s\n",
      "131:\tlearn: 0.0023233\ttotal: 7.62s\tremaining: 3.35s\n",
      "132:\tlearn: 0.0023190\ttotal: 7.67s\tremaining: 3.29s\n",
      "133:\tlearn: 0.0023156\ttotal: 7.73s\tremaining: 3.23s\n",
      "134:\tlearn: 0.0023113\ttotal: 7.78s\tremaining: 3.17s\n",
      "135:\tlearn: 0.0023070\ttotal: 7.84s\tremaining: 3.11s\n",
      "136:\tlearn: 0.0022934\ttotal: 7.9s\tremaining: 3.06s\n",
      "137:\tlearn: 0.0022888\ttotal: 7.96s\tremaining: 3s\n",
      "138:\tlearn: 0.0022751\ttotal: 8.02s\tremaining: 2.94s\n",
      "139:\tlearn: 0.0022637\ttotal: 8.08s\tremaining: 2.89s\n",
      "140:\tlearn: 0.0022587\ttotal: 8.14s\tremaining: 2.83s\n",
      "141:\tlearn: 0.0022524\ttotal: 8.19s\tremaining: 2.77s\n",
      "142:\tlearn: 0.0022480\ttotal: 8.24s\tremaining: 2.71s\n",
      "143:\tlearn: 0.0022371\ttotal: 8.3s\tremaining: 2.65s\n",
      "144:\tlearn: 0.0022330\ttotal: 8.35s\tremaining: 2.59s\n",
      "145:\tlearn: 0.0022171\ttotal: 8.41s\tremaining: 2.54s\n",
      "146:\tlearn: 0.0022128\ttotal: 8.46s\tremaining: 2.48s\n",
      "147:\tlearn: 0.0021983\ttotal: 8.53s\tremaining: 2.42s\n",
      "148:\tlearn: 0.0021953\ttotal: 8.58s\tremaining: 2.36s\n",
      "149:\tlearn: 0.0021823\ttotal: 8.64s\tremaining: 2.31s\n",
      "150:\tlearn: 0.0021699\ttotal: 8.71s\tremaining: 2.25s\n",
      "151:\tlearn: 0.0021638\ttotal: 8.76s\tremaining: 2.19s\n",
      "152:\tlearn: 0.0021526\ttotal: 8.82s\tremaining: 2.13s\n",
      "153:\tlearn: 0.0021507\ttotal: 8.87s\tremaining: 2.07s\n",
      "154:\tlearn: 0.0021390\ttotal: 8.93s\tremaining: 2.02s\n",
      "155:\tlearn: 0.0021363\ttotal: 8.98s\tremaining: 1.96s\n",
      "156:\tlearn: 0.0021283\ttotal: 9.04s\tremaining: 1.9s\n",
      "157:\tlearn: 0.0021256\ttotal: 9.09s\tremaining: 1.84s\n",
      "158:\tlearn: 0.0021211\ttotal: 9.14s\tremaining: 1.78s\n",
      "159:\tlearn: 0.0021128\ttotal: 9.2s\tremaining: 1.73s\n",
      "160:\tlearn: 0.0021038\ttotal: 9.26s\tremaining: 1.67s\n",
      "161:\tlearn: 0.0021003\ttotal: 9.32s\tremaining: 1.61s\n",
      "162:\tlearn: 0.0020935\ttotal: 9.37s\tremaining: 1.55s\n",
      "163:\tlearn: 0.0020905\ttotal: 9.43s\tremaining: 1.49s\n",
      "164:\tlearn: 0.0020855\ttotal: 9.48s\tremaining: 1.44s\n",
      "165:\tlearn: 0.0020822\ttotal: 9.54s\tremaining: 1.38s\n",
      "166:\tlearn: 0.0020791\ttotal: 9.59s\tremaining: 1.32s\n",
      "167:\tlearn: 0.0020760\ttotal: 9.64s\tremaining: 1.26s\n",
      "168:\tlearn: 0.0020733\ttotal: 9.7s\tremaining: 1.2s\n",
      "169:\tlearn: 0.0020710\ttotal: 9.74s\tremaining: 1.15s\n",
      "170:\tlearn: 0.0020686\ttotal: 9.8s\tremaining: 1.09s\n",
      "171:\tlearn: 0.0020655\ttotal: 9.85s\tremaining: 1.03s\n",
      "172:\tlearn: 0.0020631\ttotal: 9.91s\tremaining: 974ms\n",
      "173:\tlearn: 0.0020604\ttotal: 9.96s\tremaining: 916ms\n",
      "174:\tlearn: 0.0020579\ttotal: 10s\tremaining: 858ms\n",
      "175:\tlearn: 0.0020473\ttotal: 10.1s\tremaining: 801ms\n",
      "176:\tlearn: 0.0020457\ttotal: 10.1s\tremaining: 744ms\n",
      "177:\tlearn: 0.0020431\ttotal: 10.2s\tremaining: 687ms\n",
      "178:\tlearn: 0.0020407\ttotal: 10.2s\tremaining: 629ms\n",
      "179:\tlearn: 0.0020305\ttotal: 10.3s\tremaining: 572ms\n",
      "180:\tlearn: 0.0020287\ttotal: 10.3s\tremaining: 515ms\n",
      "181:\tlearn: 0.0020211\ttotal: 10.4s\tremaining: 458ms\n",
      "182:\tlearn: 0.0020194\ttotal: 10.5s\tremaining: 400ms\n",
      "183:\tlearn: 0.0020173\ttotal: 10.5s\tremaining: 343ms\n",
      "184:\tlearn: 0.0020098\ttotal: 10.6s\tremaining: 286ms\n",
      "185:\tlearn: 0.0020074\ttotal: 10.6s\tremaining: 229ms\n",
      "186:\tlearn: 0.0020049\ttotal: 10.7s\tremaining: 172ms\n",
      "187:\tlearn: 0.0020024\ttotal: 10.7s\tremaining: 114ms\n",
      "188:\tlearn: 0.0020001\ttotal: 10.8s\tremaining: 57.2ms\n",
      "189:\tlearn: 0.0019971\ttotal: 10.9s\tremaining: 0us\n",
      "0:\tlearn: 0.6258838\ttotal: 62.5ms\tremaining: 11.8s\n",
      "1:\tlearn: 0.5675065\ttotal: 125ms\tremaining: 11.7s\n",
      "2:\tlearn: 0.5163842\ttotal: 187ms\tremaining: 11.6s\n",
      "3:\tlearn: 0.4711200\ttotal: 249ms\tremaining: 11.6s\n",
      "4:\tlearn: 0.4308503\ttotal: 311ms\tremaining: 11.5s\n",
      "5:\tlearn: 0.3947993\ttotal: 373ms\tremaining: 11.4s\n",
      "6:\tlearn: 0.3624047\ttotal: 436ms\tremaining: 11.4s\n",
      "7:\tlearn: 0.3332017\ttotal: 499ms\tremaining: 11.3s\n",
      "8:\tlearn: 0.3067226\ttotal: 564ms\tremaining: 11.3s\n",
      "9:\tlearn: 0.2827230\ttotal: 625ms\tremaining: 11.2s\n",
      "10:\tlearn: 0.2609287\ttotal: 687ms\tremaining: 11.2s\n",
      "11:\tlearn: 0.2409519\ttotal: 748ms\tremaining: 11.1s\n",
      "12:\tlearn: 0.2227495\ttotal: 808ms\tremaining: 11s\n",
      "13:\tlearn: 0.2060984\ttotal: 873ms\tremaining: 11s\n",
      "14:\tlearn: 0.1908786\ttotal: 936ms\tremaining: 10.9s\n",
      "15:\tlearn: 0.1769086\ttotal: 1s\tremaining: 10.9s\n",
      "16:\tlearn: 0.1640708\ttotal: 1.06s\tremaining: 10.8s\n",
      "17:\tlearn: 0.1522070\ttotal: 1.12s\tremaining: 10.7s\n",
      "18:\tlearn: 0.1413304\ttotal: 1.18s\tremaining: 10.7s\n",
      "19:\tlearn: 0.1312356\ttotal: 1.24s\tremaining: 10.5s\n",
      "20:\tlearn: 0.1219528\ttotal: 1.3s\tremaining: 10.4s\n",
      "21:\tlearn: 0.1133655\ttotal: 1.36s\tremaining: 10.4s\n",
      "22:\tlearn: 0.1054263\ttotal: 1.42s\tremaining: 10.3s\n",
      "23:\tlearn: 0.0981047\ttotal: 1.48s\tremaining: 10.2s\n",
      "24:\tlearn: 0.0913054\ttotal: 1.54s\tremaining: 10.2s\n",
      "25:\tlearn: 0.0850429\ttotal: 1.6s\tremaining: 10.1s\n",
      "26:\tlearn: 0.0791978\ttotal: 1.66s\tremaining: 10s\n",
      "27:\tlearn: 0.0738173\ttotal: 1.72s\tremaining: 9.96s\n",
      "28:\tlearn: 0.0688129\ttotal: 1.78s\tremaining: 9.89s\n",
      "29:\tlearn: 0.0642170\ttotal: 1.84s\tremaining: 9.83s\n",
      "30:\tlearn: 0.0599224\ttotal: 1.9s\tremaining: 9.77s\n",
      "31:\tlearn: 0.0559359\ttotal: 1.97s\tremaining: 9.71s\n",
      "32:\tlearn: 0.0522133\ttotal: 2.02s\tremaining: 9.64s\n",
      "33:\tlearn: 0.0487637\ttotal: 2.09s\tremaining: 9.58s\n",
      "34:\tlearn: 0.0455594\ttotal: 2.14s\tremaining: 9.49s\n",
      "35:\tlearn: 0.0425680\ttotal: 2.2s\tremaining: 9.41s\n",
      "36:\tlearn: 0.0397918\ttotal: 2.25s\tremaining: 9.32s\n",
      "37:\tlearn: 0.0372293\ttotal: 2.31s\tremaining: 9.26s\n",
      "38:\tlearn: 0.0348334\ttotal: 2.37s\tremaining: 9.19s\n",
      "39:\tlearn: 0.0326077\ttotal: 2.43s\tremaining: 9.12s\n",
      "40:\tlearn: 0.0305266\ttotal: 2.49s\tremaining: 9.04s\n",
      "41:\tlearn: 0.0285915\ttotal: 2.55s\tremaining: 8.98s\n",
      "42:\tlearn: 0.0267826\ttotal: 2.61s\tremaining: 8.92s\n",
      "43:\tlearn: 0.0251117\ttotal: 2.67s\tremaining: 8.84s\n",
      "44:\tlearn: 0.0235598\ttotal: 2.72s\tremaining: 8.78s\n",
      "45:\tlearn: 0.0221099\ttotal: 2.78s\tremaining: 8.71s\n",
      "46:\tlearn: 0.0207619\ttotal: 2.84s\tremaining: 8.64s\n",
      "47:\tlearn: 0.0194877\ttotal: 2.9s\tremaining: 8.57s\n",
      "48:\tlearn: 0.0183205\ttotal: 2.95s\tremaining: 8.49s\n",
      "49:\tlearn: 0.0172234\ttotal: 3s\tremaining: 8.41s\n",
      "50:\tlearn: 0.0162079\ttotal: 3.06s\tremaining: 8.33s\n",
      "51:\tlearn: 0.0152638\ttotal: 3.12s\tremaining: 8.28s\n",
      "52:\tlearn: 0.0143788\ttotal: 3.17s\tremaining: 8.2s\n",
      "53:\tlearn: 0.0135543\ttotal: 3.23s\tremaining: 8.14s\n",
      "54:\tlearn: 0.0127913\ttotal: 3.29s\tremaining: 8.07s\n",
      "55:\tlearn: 0.0120651\ttotal: 3.34s\tremaining: 8s\n",
      "56:\tlearn: 0.0114008\ttotal: 3.4s\tremaining: 7.93s\n",
      "57:\tlearn: 0.0107836\ttotal: 3.45s\tremaining: 7.86s\n",
      "58:\tlearn: 0.0102077\ttotal: 3.51s\tremaining: 7.79s\n",
      "59:\tlearn: 0.0096634\ttotal: 3.56s\tremaining: 7.72s\n",
      "60:\tlearn: 0.0091560\ttotal: 3.62s\tremaining: 7.65s\n",
      "61:\tlearn: 0.0086855\ttotal: 3.67s\tremaining: 7.59s\n",
      "62:\tlearn: 0.0082416\ttotal: 3.73s\tremaining: 7.51s\n",
      "63:\tlearn: 0.0078378\ttotal: 3.78s\tremaining: 7.45s\n",
      "64:\tlearn: 0.0074614\ttotal: 3.84s\tremaining: 7.38s\n",
      "65:\tlearn: 0.0071142\ttotal: 3.9s\tremaining: 7.32s\n",
      "66:\tlearn: 0.0067889\ttotal: 3.95s\tremaining: 7.25s\n",
      "67:\tlearn: 0.0064904\ttotal: 4s\tremaining: 7.19s\n",
      "68:\tlearn: 0.0062130\ttotal: 4.06s\tremaining: 7.12s\n",
      "69:\tlearn: 0.0059508\ttotal: 4.12s\tremaining: 7.06s\n",
      "70:\tlearn: 0.0057066\ttotal: 4.17s\tremaining: 6.99s\n",
      "71:\tlearn: 0.0054776\ttotal: 4.22s\tremaining: 6.92s\n",
      "72:\tlearn: 0.0052646\ttotal: 4.28s\tremaining: 6.85s\n",
      "73:\tlearn: 0.0050612\ttotal: 4.34s\tremaining: 6.8s\n",
      "74:\tlearn: 0.0048730\ttotal: 4.39s\tremaining: 6.73s\n",
      "75:\tlearn: 0.0047042\ttotal: 4.44s\tremaining: 6.66s\n",
      "76:\tlearn: 0.0045475\ttotal: 4.49s\tremaining: 6.6s\n",
      "77:\tlearn: 0.0044016\ttotal: 4.55s\tremaining: 6.53s\n",
      "78:\tlearn: 0.0042642\ttotal: 4.61s\tremaining: 6.47s\n",
      "79:\tlearn: 0.0041347\ttotal: 4.67s\tremaining: 6.42s\n",
      "80:\tlearn: 0.0040102\ttotal: 4.72s\tremaining: 6.35s\n",
      "81:\tlearn: 0.0039022\ttotal: 4.77s\tremaining: 6.28s\n",
      "82:\tlearn: 0.0037930\ttotal: 4.82s\tremaining: 6.22s\n",
      "83:\tlearn: 0.0036972\ttotal: 4.88s\tremaining: 6.15s\n",
      "84:\tlearn: 0.0036004\ttotal: 4.93s\tremaining: 6.09s\n",
      "85:\tlearn: 0.0035198\ttotal: 4.98s\tremaining: 6.03s\n",
      "86:\tlearn: 0.0034377\ttotal: 5.04s\tremaining: 5.97s\n",
      "87:\tlearn: 0.0033623\ttotal: 5.09s\tremaining: 5.91s\n",
      "88:\tlearn: 0.0032964\ttotal: 5.15s\tremaining: 5.85s\n",
      "89:\tlearn: 0.0032381\ttotal: 5.21s\tremaining: 5.79s\n",
      "90:\tlearn: 0.0031811\ttotal: 5.27s\tremaining: 5.73s\n",
      "91:\tlearn: 0.0031154\ttotal: 5.32s\tremaining: 5.67s\n",
      "92:\tlearn: 0.0030642\ttotal: 5.38s\tremaining: 5.61s\n",
      "93:\tlearn: 0.0030112\ttotal: 5.43s\tremaining: 5.55s\n",
      "94:\tlearn: 0.0029698\ttotal: 5.49s\tremaining: 5.49s\n",
      "95:\tlearn: 0.0029284\ttotal: 5.54s\tremaining: 5.42s\n",
      "96:\tlearn: 0.0028850\ttotal: 5.59s\tremaining: 5.36s\n",
      "97:\tlearn: 0.0028449\ttotal: 5.66s\tremaining: 5.31s\n",
      "98:\tlearn: 0.0028122\ttotal: 5.71s\tremaining: 5.25s\n",
      "99:\tlearn: 0.0027767\ttotal: 5.77s\tremaining: 5.2s\n",
      "100:\tlearn: 0.0027519\ttotal: 5.83s\tremaining: 5.13s\n",
      "101:\tlearn: 0.0027223\ttotal: 5.88s\tremaining: 5.07s\n",
      "102:\tlearn: 0.0026998\ttotal: 5.93s\tremaining: 5.01s\n",
      "103:\tlearn: 0.0026724\ttotal: 5.99s\tremaining: 4.95s\n",
      "104:\tlearn: 0.0026459\ttotal: 6.05s\tremaining: 4.89s\n",
      "105:\tlearn: 0.0026251\ttotal: 6.1s\tremaining: 4.84s\n",
      "106:\tlearn: 0.0026066\ttotal: 6.16s\tremaining: 4.78s\n",
      "107:\tlearn: 0.0025872\ttotal: 6.21s\tremaining: 4.71s\n",
      "108:\tlearn: 0.0025689\ttotal: 6.26s\tremaining: 4.66s\n",
      "109:\tlearn: 0.0025507\ttotal: 6.32s\tremaining: 4.6s\n",
      "110:\tlearn: 0.0025321\ttotal: 6.37s\tremaining: 4.54s\n",
      "111:\tlearn: 0.0025181\ttotal: 6.42s\tremaining: 4.47s\n",
      "112:\tlearn: 0.0025052\ttotal: 6.48s\tremaining: 4.41s\n",
      "113:\tlearn: 0.0024950\ttotal: 6.53s\tremaining: 4.36s\n",
      "114:\tlearn: 0.0024693\ttotal: 6.59s\tremaining: 4.3s\n",
      "115:\tlearn: 0.0024478\ttotal: 6.65s\tremaining: 4.24s\n",
      "116:\tlearn: 0.0024387\ttotal: 6.7s\tremaining: 4.18s\n",
      "117:\tlearn: 0.0024143\ttotal: 6.76s\tremaining: 4.13s\n",
      "118:\tlearn: 0.0023941\ttotal: 6.82s\tremaining: 4.07s\n",
      "119:\tlearn: 0.0023729\ttotal: 6.88s\tremaining: 4.01s\n",
      "120:\tlearn: 0.0023657\ttotal: 6.93s\tremaining: 3.95s\n",
      "121:\tlearn: 0.0023461\ttotal: 6.99s\tremaining: 3.9s\n",
      "122:\tlearn: 0.0023243\ttotal: 7.05s\tremaining: 3.84s\n",
      "123:\tlearn: 0.0023089\ttotal: 7.11s\tremaining: 3.78s\n",
      "124:\tlearn: 0.0022940\ttotal: 7.16s\tremaining: 3.72s\n",
      "125:\tlearn: 0.0022823\ttotal: 7.22s\tremaining: 3.67s\n",
      "126:\tlearn: 0.0022664\ttotal: 7.3s\tremaining: 3.62s\n",
      "127:\tlearn: 0.0022509\ttotal: 7.37s\tremaining: 3.57s\n",
      "128:\tlearn: 0.0022350\ttotal: 7.45s\tremaining: 3.52s\n",
      "129:\tlearn: 0.0022240\ttotal: 7.52s\tremaining: 3.47s\n",
      "130:\tlearn: 0.0022114\ttotal: 7.59s\tremaining: 3.42s\n",
      "131:\tlearn: 0.0021950\ttotal: 7.66s\tremaining: 3.37s\n",
      "132:\tlearn: 0.0021909\ttotal: 7.72s\tremaining: 3.31s\n",
      "133:\tlearn: 0.0021767\ttotal: 7.79s\tremaining: 3.26s\n",
      "134:\tlearn: 0.0021730\ttotal: 7.86s\tremaining: 3.2s\n",
      "135:\tlearn: 0.0021615\ttotal: 7.94s\tremaining: 3.15s\n",
      "136:\tlearn: 0.0021511\ttotal: 8.03s\tremaining: 3.11s\n",
      "137:\tlearn: 0.0021405\ttotal: 8.12s\tremaining: 3.06s\n",
      "138:\tlearn: 0.0021300\ttotal: 8.19s\tremaining: 3s\n",
      "139:\tlearn: 0.0021247\ttotal: 8.24s\tremaining: 2.94s\n",
      "140:\tlearn: 0.0021109\ttotal: 8.31s\tremaining: 2.89s\n",
      "141:\tlearn: 0.0021014\ttotal: 8.37s\tremaining: 2.83s\n",
      "142:\tlearn: 0.0020897\ttotal: 8.43s\tremaining: 2.77s\n",
      "143:\tlearn: 0.0020785\ttotal: 8.49s\tremaining: 2.71s\n",
      "144:\tlearn: 0.0020674\ttotal: 8.55s\tremaining: 2.65s\n",
      "145:\tlearn: 0.0020638\ttotal: 8.61s\tremaining: 2.59s\n",
      "146:\tlearn: 0.0020545\ttotal: 8.67s\tremaining: 2.54s\n",
      "147:\tlearn: 0.0020479\ttotal: 8.73s\tremaining: 2.48s\n",
      "148:\tlearn: 0.0020460\ttotal: 8.78s\tremaining: 2.42s\n",
      "149:\tlearn: 0.0020420\ttotal: 8.83s\tremaining: 2.35s\n",
      "150:\tlearn: 0.0020337\ttotal: 8.89s\tremaining: 2.29s\n",
      "151:\tlearn: 0.0020234\ttotal: 8.95s\tremaining: 2.24s\n",
      "152:\tlearn: 0.0020113\ttotal: 9.01s\tremaining: 2.18s\n",
      "153:\tlearn: 0.0019983\ttotal: 9.07s\tremaining: 2.12s\n",
      "154:\tlearn: 0.0019893\ttotal: 9.13s\tremaining: 2.06s\n",
      "155:\tlearn: 0.0019869\ttotal: 9.18s\tremaining: 2s\n",
      "156:\tlearn: 0.0019828\ttotal: 9.24s\tremaining: 1.94s\n",
      "157:\tlearn: 0.0019734\ttotal: 9.3s\tremaining: 1.88s\n",
      "158:\tlearn: 0.0019703\ttotal: 9.36s\tremaining: 1.82s\n",
      "159:\tlearn: 0.0019673\ttotal: 9.4s\tremaining: 1.76s\n",
      "160:\tlearn: 0.0019640\ttotal: 9.46s\tremaining: 1.7s\n",
      "161:\tlearn: 0.0019565\ttotal: 9.52s\tremaining: 1.65s\n",
      "162:\tlearn: 0.0019523\ttotal: 9.57s\tremaining: 1.59s\n",
      "163:\tlearn: 0.0019478\ttotal: 9.63s\tremaining: 1.53s\n",
      "164:\tlearn: 0.0019457\ttotal: 9.68s\tremaining: 1.47s\n",
      "165:\tlearn: 0.0019416\ttotal: 9.74s\tremaining: 1.41s\n",
      "166:\tlearn: 0.0019384\ttotal: 9.79s\tremaining: 1.35s\n",
      "167:\tlearn: 0.0019361\ttotal: 9.85s\tremaining: 1.29s\n",
      "168:\tlearn: 0.0019335\ttotal: 9.9s\tremaining: 1.23s\n",
      "169:\tlearn: 0.0019266\ttotal: 9.96s\tremaining: 1.17s\n",
      "170:\tlearn: 0.0019245\ttotal: 10s\tremaining: 1.11s\n",
      "171:\tlearn: 0.0019226\ttotal: 10.1s\tremaining: 1.05s\n",
      "172:\tlearn: 0.0019203\ttotal: 10.1s\tremaining: 995ms\n",
      "173:\tlearn: 0.0019135\ttotal: 10.2s\tremaining: 937ms\n",
      "174:\tlearn: 0.0019085\ttotal: 10.2s\tremaining: 878ms\n",
      "175:\tlearn: 0.0019056\ttotal: 10.3s\tremaining: 819ms\n",
      "176:\tlearn: 0.0019023\ttotal: 10.4s\tremaining: 760ms\n",
      "177:\tlearn: 0.0018990\ttotal: 10.4s\tremaining: 702ms\n",
      "178:\tlearn: 0.0018955\ttotal: 10.5s\tremaining: 643ms\n",
      "179:\tlearn: 0.0018913\ttotal: 10.5s\tremaining: 585ms\n",
      "180:\tlearn: 0.0018899\ttotal: 10.6s\tremaining: 526ms\n",
      "181:\tlearn: 0.0018839\ttotal: 10.6s\tremaining: 468ms\n",
      "182:\tlearn: 0.0018771\ttotal: 10.7s\tremaining: 409ms\n",
      "183:\tlearn: 0.0018664\ttotal: 10.8s\tremaining: 351ms\n",
      "184:\tlearn: 0.0018653\ttotal: 10.8s\tremaining: 292ms\n",
      "185:\tlearn: 0.0018638\ttotal: 10.9s\tremaining: 234ms\n",
      "186:\tlearn: 0.0018612\ttotal: 10.9s\tremaining: 175ms\n",
      "187:\tlearn: 0.0018563\ttotal: 11s\tremaining: 117ms\n",
      "188:\tlearn: 0.0018552\ttotal: 11s\tremaining: 58.4ms\n",
      "189:\tlearn: 0.0018502\ttotal: 11.1s\tremaining: 0us\n",
      "0:\tlearn: 0.6259590\ttotal: 72.4ms\tremaining: 13.7s\n",
      "1:\tlearn: 0.5675066\ttotal: 146ms\tremaining: 13.8s\n",
      "2:\tlearn: 0.5161679\ttotal: 222ms\tremaining: 13.8s\n",
      "3:\tlearn: 0.4708660\ttotal: 312ms\tremaining: 14.5s\n",
      "4:\tlearn: 0.4305226\ttotal: 388ms\tremaining: 14.4s\n",
      "5:\tlearn: 0.3944696\ttotal: 463ms\tremaining: 14.2s\n",
      "6:\tlearn: 0.3620109\ttotal: 531ms\tremaining: 13.9s\n",
      "7:\tlearn: 0.3327626\ttotal: 611ms\tremaining: 13.9s\n",
      "8:\tlearn: 0.3063512\ttotal: 688ms\tremaining: 13.8s\n",
      "9:\tlearn: 0.2824521\ttotal: 772ms\tremaining: 13.9s\n",
      "10:\tlearn: 0.2606340\ttotal: 846ms\tremaining: 13.8s\n",
      "11:\tlearn: 0.2406952\ttotal: 917ms\tremaining: 13.6s\n",
      "12:\tlearn: 0.2224999\ttotal: 987ms\tremaining: 13.4s\n",
      "13:\tlearn: 0.2058528\ttotal: 1.06s\tremaining: 13.3s\n",
      "14:\tlearn: 0.1906786\ttotal: 1.14s\tremaining: 13.3s\n",
      "15:\tlearn: 0.1767029\ttotal: 1.21s\tremaining: 13.2s\n",
      "16:\tlearn: 0.1638378\ttotal: 1.28s\tremaining: 13s\n",
      "17:\tlearn: 0.1520164\ttotal: 1.36s\tremaining: 13s\n",
      "18:\tlearn: 0.1411177\ttotal: 1.43s\tremaining: 12.9s\n",
      "19:\tlearn: 0.1310565\ttotal: 1.5s\tremaining: 12.8s\n",
      "20:\tlearn: 0.1217761\ttotal: 1.57s\tremaining: 12.7s\n",
      "21:\tlearn: 0.1132120\ttotal: 1.64s\tremaining: 12.6s\n",
      "22:\tlearn: 0.1053075\ttotal: 1.71s\tremaining: 12.5s\n",
      "23:\tlearn: 0.0979820\ttotal: 1.79s\tremaining: 12.4s\n",
      "24:\tlearn: 0.0911900\ttotal: 1.86s\tremaining: 12.3s\n",
      "25:\tlearn: 0.0849284\ttotal: 1.93s\tremaining: 12.2s\n",
      "26:\tlearn: 0.0791233\ttotal: 2s\tremaining: 12.1s\n",
      "27:\tlearn: 0.0737390\ttotal: 2.07s\tremaining: 12s\n",
      "28:\tlearn: 0.0687472\ttotal: 2.15s\tremaining: 11.9s\n",
      "29:\tlearn: 0.0641097\ttotal: 2.22s\tremaining: 11.9s\n",
      "30:\tlearn: 0.0598160\ttotal: 2.29s\tremaining: 11.8s\n",
      "31:\tlearn: 0.0558262\ttotal: 2.36s\tremaining: 11.7s\n",
      "32:\tlearn: 0.0521150\ttotal: 2.43s\tremaining: 11.6s\n",
      "33:\tlearn: 0.0486577\ttotal: 2.49s\tremaining: 11.4s\n",
      "34:\tlearn: 0.0454600\ttotal: 2.56s\tremaining: 11.3s\n",
      "35:\tlearn: 0.0424888\ttotal: 2.63s\tremaining: 11.2s\n",
      "36:\tlearn: 0.0397133\ttotal: 2.69s\tremaining: 11.1s\n",
      "37:\tlearn: 0.0371432\ttotal: 2.76s\tremaining: 11.1s\n",
      "38:\tlearn: 0.0347559\ttotal: 2.83s\tremaining: 10.9s\n",
      "39:\tlearn: 0.0325331\ttotal: 2.89s\tremaining: 10.8s\n",
      "40:\tlearn: 0.0304604\ttotal: 2.96s\tremaining: 10.7s\n",
      "41:\tlearn: 0.0285204\ttotal: 3.02s\tremaining: 10.6s\n",
      "42:\tlearn: 0.0267192\ttotal: 3.08s\tremaining: 10.5s\n",
      "43:\tlearn: 0.0250450\ttotal: 3.14s\tremaining: 10.4s\n",
      "44:\tlearn: 0.0234907\ttotal: 3.21s\tremaining: 10.3s\n",
      "45:\tlearn: 0.0220438\ttotal: 3.27s\tremaining: 10.2s\n",
      "46:\tlearn: 0.0206874\ttotal: 3.34s\tremaining: 10.2s\n",
      "47:\tlearn: 0.0194323\ttotal: 3.4s\tremaining: 10.1s\n",
      "48:\tlearn: 0.0182638\ttotal: 3.46s\tremaining: 9.96s\n",
      "49:\tlearn: 0.0171751\ttotal: 3.52s\tremaining: 9.86s\n",
      "50:\tlearn: 0.0161697\ttotal: 3.59s\tremaining: 9.78s\n",
      "51:\tlearn: 0.0152299\ttotal: 3.66s\tremaining: 9.71s\n",
      "52:\tlearn: 0.0143597\ttotal: 3.72s\tremaining: 9.63s\n",
      "53:\tlearn: 0.0135393\ttotal: 3.79s\tremaining: 9.53s\n",
      "54:\tlearn: 0.0127796\ttotal: 3.85s\tremaining: 9.45s\n",
      "55:\tlearn: 0.0120804\ttotal: 3.91s\tremaining: 9.35s\n",
      "56:\tlearn: 0.0114156\ttotal: 3.97s\tremaining: 9.26s\n",
      "57:\tlearn: 0.0108077\ttotal: 4.03s\tremaining: 9.17s\n",
      "58:\tlearn: 0.0102383\ttotal: 4.09s\tremaining: 9.09s\n",
      "59:\tlearn: 0.0096997\ttotal: 4.15s\tremaining: 9s\n",
      "60:\tlearn: 0.0092003\ttotal: 4.22s\tremaining: 8.92s\n",
      "61:\tlearn: 0.0087295\ttotal: 4.28s\tremaining: 8.83s\n",
      "62:\tlearn: 0.0083017\ttotal: 4.34s\tremaining: 8.74s\n",
      "63:\tlearn: 0.0079055\ttotal: 4.39s\tremaining: 8.65s\n",
      "64:\tlearn: 0.0075254\ttotal: 4.46s\tremaining: 8.57s\n",
      "65:\tlearn: 0.0071711\ttotal: 4.52s\tremaining: 8.5s\n",
      "66:\tlearn: 0.0068517\ttotal: 4.6s\tremaining: 8.44s\n",
      "67:\tlearn: 0.0065532\ttotal: 4.66s\tremaining: 8.36s\n",
      "68:\tlearn: 0.0062702\ttotal: 4.72s\tremaining: 8.27s\n",
      "69:\tlearn: 0.0060038\ttotal: 4.79s\tremaining: 8.21s\n",
      "70:\tlearn: 0.0057592\ttotal: 4.85s\tremaining: 8.13s\n",
      "71:\tlearn: 0.0055208\ttotal: 4.92s\tremaining: 8.06s\n",
      "72:\tlearn: 0.0053121\ttotal: 4.98s\tremaining: 7.98s\n",
      "73:\tlearn: 0.0051144\ttotal: 5.04s\tremaining: 7.89s\n",
      "74:\tlearn: 0.0049228\ttotal: 5.11s\tremaining: 7.83s\n",
      "75:\tlearn: 0.0047531\ttotal: 5.17s\tremaining: 7.75s\n",
      "76:\tlearn: 0.0045912\ttotal: 5.23s\tremaining: 7.67s\n",
      "77:\tlearn: 0.0044361\ttotal: 5.29s\tremaining: 7.59s\n",
      "78:\tlearn: 0.0043005\ttotal: 5.36s\tremaining: 7.52s\n",
      "79:\tlearn: 0.0041752\ttotal: 5.42s\tremaining: 7.45s\n",
      "80:\tlearn: 0.0040565\ttotal: 5.48s\tremaining: 7.37s\n",
      "81:\tlearn: 0.0039447\ttotal: 5.54s\tremaining: 7.3s\n",
      "82:\tlearn: 0.0038404\ttotal: 5.6s\tremaining: 7.22s\n",
      "83:\tlearn: 0.0037448\ttotal: 5.66s\tremaining: 7.14s\n",
      "84:\tlearn: 0.0036585\ttotal: 5.72s\tremaining: 7.07s\n",
      "85:\tlearn: 0.0035775\ttotal: 5.79s\tremaining: 7s\n",
      "86:\tlearn: 0.0035024\ttotal: 5.84s\tremaining: 6.92s\n",
      "87:\tlearn: 0.0034330\ttotal: 5.9s\tremaining: 6.84s\n",
      "88:\tlearn: 0.0033594\ttotal: 5.97s\tremaining: 6.78s\n",
      "89:\tlearn: 0.0032882\ttotal: 6.05s\tremaining: 6.72s\n",
      "90:\tlearn: 0.0032323\ttotal: 6.12s\tremaining: 6.66s\n",
      "91:\tlearn: 0.0031734\ttotal: 6.18s\tremaining: 6.58s\n",
      "92:\tlearn: 0.0031186\ttotal: 6.24s\tremaining: 6.51s\n",
      "93:\tlearn: 0.0030748\ttotal: 6.3s\tremaining: 6.43s\n",
      "94:\tlearn: 0.0030188\ttotal: 6.37s\tremaining: 6.37s\n",
      "95:\tlearn: 0.0029678\ttotal: 6.44s\tremaining: 6.31s\n",
      "96:\tlearn: 0.0029267\ttotal: 6.51s\tremaining: 6.24s\n",
      "97:\tlearn: 0.0028818\ttotal: 6.58s\tremaining: 6.18s\n",
      "98:\tlearn: 0.0028482\ttotal: 6.64s\tremaining: 6.11s\n",
      "99:\tlearn: 0.0028105\ttotal: 6.71s\tremaining: 6.04s\n",
      "100:\tlearn: 0.0027826\ttotal: 6.77s\tremaining: 5.96s\n",
      "101:\tlearn: 0.0027466\ttotal: 6.84s\tremaining: 5.9s\n",
      "102:\tlearn: 0.0027220\ttotal: 6.9s\tremaining: 5.83s\n",
      "103:\tlearn: 0.0026983\ttotal: 6.96s\tremaining: 5.76s\n",
      "104:\tlearn: 0.0026663\ttotal: 7.03s\tremaining: 5.69s\n",
      "105:\tlearn: 0.0026469\ttotal: 7.09s\tremaining: 5.62s\n",
      "106:\tlearn: 0.0026201\ttotal: 7.17s\tremaining: 5.56s\n",
      "107:\tlearn: 0.0025867\ttotal: 7.24s\tremaining: 5.5s\n",
      "108:\tlearn: 0.0025580\ttotal: 7.31s\tremaining: 5.43s\n",
      "109:\tlearn: 0.0025340\ttotal: 7.39s\tremaining: 5.38s\n",
      "110:\tlearn: 0.0025042\ttotal: 7.46s\tremaining: 5.31s\n",
      "111:\tlearn: 0.0024848\ttotal: 7.52s\tremaining: 5.24s\n",
      "112:\tlearn: 0.0024731\ttotal: 7.58s\tremaining: 5.16s\n",
      "113:\tlearn: 0.0024624\ttotal: 7.64s\tremaining: 5.09s\n",
      "114:\tlearn: 0.0024423\ttotal: 7.71s\tremaining: 5.03s\n",
      "115:\tlearn: 0.0024246\ttotal: 7.77s\tremaining: 4.96s\n",
      "116:\tlearn: 0.0024082\ttotal: 7.84s\tremaining: 4.89s\n",
      "117:\tlearn: 0.0023920\ttotal: 7.9s\tremaining: 4.82s\n",
      "118:\tlearn: 0.0023715\ttotal: 7.98s\tremaining: 4.76s\n",
      "119:\tlearn: 0.0023503\ttotal: 8.05s\tremaining: 4.7s\n",
      "120:\tlearn: 0.0023318\ttotal: 8.12s\tremaining: 4.63s\n",
      "121:\tlearn: 0.0023218\ttotal: 8.19s\tremaining: 4.57s\n",
      "122:\tlearn: 0.0023068\ttotal: 8.26s\tremaining: 4.5s\n",
      "123:\tlearn: 0.0022918\ttotal: 8.33s\tremaining: 4.43s\n",
      "124:\tlearn: 0.0022755\ttotal: 8.41s\tremaining: 4.37s\n",
      "125:\tlearn: 0.0022689\ttotal: 8.46s\tremaining: 4.3s\n",
      "126:\tlearn: 0.0022508\ttotal: 8.54s\tremaining: 4.24s\n",
      "127:\tlearn: 0.0022357\ttotal: 8.61s\tremaining: 4.17s\n",
      "128:\tlearn: 0.0022225\ttotal: 8.67s\tremaining: 4.1s\n",
      "129:\tlearn: 0.0022110\ttotal: 8.75s\tremaining: 4.04s\n",
      "130:\tlearn: 0.0022035\ttotal: 8.81s\tremaining: 3.97s\n",
      "131:\tlearn: 0.0021894\ttotal: 8.88s\tremaining: 3.9s\n",
      "132:\tlearn: 0.0021791\ttotal: 8.94s\tremaining: 3.83s\n",
      "133:\tlearn: 0.0021732\ttotal: 9.01s\tremaining: 3.76s\n",
      "134:\tlearn: 0.0021606\ttotal: 9.07s\tremaining: 3.69s\n",
      "135:\tlearn: 0.0021513\ttotal: 9.13s\tremaining: 3.63s\n",
      "136:\tlearn: 0.0021468\ttotal: 9.19s\tremaining: 3.56s\n",
      "137:\tlearn: 0.0021426\ttotal: 9.26s\tremaining: 3.49s\n",
      "138:\tlearn: 0.0021384\ttotal: 9.32s\tremaining: 3.42s\n",
      "139:\tlearn: 0.0021250\ttotal: 9.4s\tremaining: 3.35s\n",
      "140:\tlearn: 0.0021200\ttotal: 9.45s\tremaining: 3.29s\n",
      "141:\tlearn: 0.0021066\ttotal: 9.53s\tremaining: 3.22s\n",
      "142:\tlearn: 0.0020949\ttotal: 9.6s\tremaining: 3.15s\n",
      "143:\tlearn: 0.0020843\ttotal: 9.67s\tremaining: 3.09s\n",
      "144:\tlearn: 0.0020793\ttotal: 9.73s\tremaining: 3.02s\n",
      "145:\tlearn: 0.0020694\ttotal: 9.8s\tremaining: 2.95s\n",
      "146:\tlearn: 0.0020658\ttotal: 9.86s\tremaining: 2.88s\n",
      "147:\tlearn: 0.0020586\ttotal: 9.93s\tremaining: 2.82s\n",
      "148:\tlearn: 0.0020530\ttotal: 9.99s\tremaining: 2.75s\n",
      "149:\tlearn: 0.0020482\ttotal: 10.1s\tremaining: 2.68s\n",
      "150:\tlearn: 0.0020419\ttotal: 10.1s\tremaining: 2.61s\n",
      "151:\tlearn: 0.0020384\ttotal: 10.2s\tremaining: 2.55s\n",
      "152:\tlearn: 0.0020313\ttotal: 10.3s\tremaining: 2.48s\n",
      "153:\tlearn: 0.0020267\ttotal: 10.3s\tremaining: 2.41s\n",
      "154:\tlearn: 0.0020228\ttotal: 10.4s\tremaining: 2.34s\n",
      "155:\tlearn: 0.0020124\ttotal: 10.4s\tremaining: 2.27s\n",
      "156:\tlearn: 0.0020064\ttotal: 10.5s\tremaining: 2.21s\n",
      "157:\tlearn: 0.0020018\ttotal: 10.6s\tremaining: 2.14s\n",
      "158:\tlearn: 0.0019949\ttotal: 10.6s\tremaining: 2.07s\n",
      "159:\tlearn: 0.0019871\ttotal: 10.7s\tremaining: 2s\n",
      "160:\tlearn: 0.0019829\ttotal: 10.8s\tremaining: 1.94s\n",
      "161:\tlearn: 0.0019756\ttotal: 10.8s\tremaining: 1.87s\n",
      "162:\tlearn: 0.0019706\ttotal: 10.9s\tremaining: 1.8s\n",
      "163:\tlearn: 0.0019629\ttotal: 11s\tremaining: 1.74s\n",
      "164:\tlearn: 0.0019549\ttotal: 11s\tremaining: 1.67s\n",
      "165:\tlearn: 0.0019497\ttotal: 11.1s\tremaining: 1.6s\n",
      "166:\tlearn: 0.0019461\ttotal: 11.2s\tremaining: 1.54s\n",
      "167:\tlearn: 0.0019430\ttotal: 11.2s\tremaining: 1.47s\n",
      "168:\tlearn: 0.0019382\ttotal: 11.3s\tremaining: 1.4s\n",
      "169:\tlearn: 0.0019325\ttotal: 11.4s\tremaining: 1.33s\n",
      "170:\tlearn: 0.0019278\ttotal: 11.4s\tremaining: 1.27s\n",
      "171:\tlearn: 0.0019218\ttotal: 11.5s\tremaining: 1.2s\n",
      "172:\tlearn: 0.0019185\ttotal: 11.6s\tremaining: 1.14s\n",
      "173:\tlearn: 0.0019162\ttotal: 11.6s\tremaining: 1.07s\n",
      "174:\tlearn: 0.0019141\ttotal: 11.7s\tremaining: 1s\n",
      "175:\tlearn: 0.0019096\ttotal: 11.7s\tremaining: 934ms\n",
      "176:\tlearn: 0.0019028\ttotal: 11.8s\tremaining: 868ms\n",
      "177:\tlearn: 0.0018977\ttotal: 11.9s\tremaining: 801ms\n",
      "178:\tlearn: 0.0018918\ttotal: 11.9s\tremaining: 734ms\n",
      "179:\tlearn: 0.0018878\ttotal: 12s\tremaining: 668ms\n",
      "180:\tlearn: 0.0018836\ttotal: 12.1s\tremaining: 601ms\n",
      "181:\tlearn: 0.0018799\ttotal: 12.1s\tremaining: 534ms\n",
      "182:\tlearn: 0.0018758\ttotal: 12.2s\tremaining: 467ms\n",
      "183:\tlearn: 0.0018726\ttotal: 12.3s\tremaining: 400ms\n",
      "184:\tlearn: 0.0018701\ttotal: 12.3s\tremaining: 333ms\n",
      "185:\tlearn: 0.0018675\ttotal: 12.4s\tremaining: 267ms\n",
      "186:\tlearn: 0.0018632\ttotal: 12.5s\tremaining: 200ms\n",
      "187:\tlearn: 0.0018604\ttotal: 12.5s\tremaining: 133ms\n",
      "188:\tlearn: 0.0018576\ttotal: 12.6s\tremaining: 66.6ms\n",
      "189:\tlearn: 0.0018546\ttotal: 12.7s\tremaining: 0us\n",
      "0:\tlearn: 0.6259590\ttotal: 74.2ms\tremaining: 14s\n",
      "1:\tlearn: 0.5675066\ttotal: 146ms\tremaining: 13.7s\n",
      "2:\tlearn: 0.5161679\ttotal: 211ms\tremaining: 13.1s\n",
      "3:\tlearn: 0.4708660\ttotal: 280ms\tremaining: 13s\n",
      "4:\tlearn: 0.4305226\ttotal: 348ms\tremaining: 12.9s\n",
      "5:\tlearn: 0.3944696\ttotal: 415ms\tremaining: 12.7s\n",
      "6:\tlearn: 0.3620109\ttotal: 483ms\tremaining: 12.6s\n",
      "7:\tlearn: 0.3327626\ttotal: 555ms\tremaining: 12.6s\n",
      "8:\tlearn: 0.3063512\ttotal: 630ms\tremaining: 12.7s\n",
      "9:\tlearn: 0.2824521\ttotal: 710ms\tremaining: 12.8s\n",
      "10:\tlearn: 0.2606340\ttotal: 786ms\tremaining: 12.8s\n",
      "11:\tlearn: 0.2406952\ttotal: 861ms\tremaining: 12.8s\n",
      "12:\tlearn: 0.2224999\ttotal: 935ms\tremaining: 12.7s\n",
      "13:\tlearn: 0.2058528\ttotal: 1.03s\tremaining: 13s\n",
      "14:\tlearn: 0.1906786\ttotal: 1.12s\tremaining: 13.1s\n",
      "15:\tlearn: 0.1767029\ttotal: 1.2s\tremaining: 13s\n",
      "16:\tlearn: 0.1638378\ttotal: 1.27s\tremaining: 12.9s\n",
      "17:\tlearn: 0.1520164\ttotal: 1.35s\tremaining: 12.9s\n",
      "18:\tlearn: 0.1411177\ttotal: 1.43s\tremaining: 12.8s\n",
      "19:\tlearn: 0.1310565\ttotal: 1.5s\tremaining: 12.8s\n",
      "20:\tlearn: 0.1217761\ttotal: 1.6s\tremaining: 12.9s\n",
      "21:\tlearn: 0.1132120\ttotal: 1.67s\tremaining: 12.8s\n",
      "22:\tlearn: 0.1053075\ttotal: 1.74s\tremaining: 12.7s\n",
      "23:\tlearn: 0.0979820\ttotal: 1.81s\tremaining: 12.6s\n",
      "24:\tlearn: 0.0911900\ttotal: 1.88s\tremaining: 12.4s\n",
      "25:\tlearn: 0.0849284\ttotal: 1.95s\tremaining: 12.3s\n",
      "26:\tlearn: 0.0791233\ttotal: 2.03s\tremaining: 12.2s\n",
      "27:\tlearn: 0.0737390\ttotal: 2.1s\tremaining: 12.2s\n",
      "28:\tlearn: 0.0687472\ttotal: 2.18s\tremaining: 12.1s\n",
      "29:\tlearn: 0.0641097\ttotal: 2.25s\tremaining: 12s\n",
      "30:\tlearn: 0.0598160\ttotal: 2.32s\tremaining: 11.9s\n",
      "31:\tlearn: 0.0558262\ttotal: 2.39s\tremaining: 11.8s\n",
      "32:\tlearn: 0.0521150\ttotal: 2.45s\tremaining: 11.7s\n",
      "33:\tlearn: 0.0486577\ttotal: 2.52s\tremaining: 11.6s\n",
      "34:\tlearn: 0.0454600\ttotal: 2.58s\tremaining: 11.4s\n",
      "35:\tlearn: 0.0424888\ttotal: 2.65s\tremaining: 11.4s\n",
      "36:\tlearn: 0.0397133\ttotal: 2.72s\tremaining: 11.3s\n",
      "37:\tlearn: 0.0371432\ttotal: 2.79s\tremaining: 11.2s\n",
      "38:\tlearn: 0.0347559\ttotal: 2.85s\tremaining: 11.1s\n",
      "39:\tlearn: 0.0325331\ttotal: 2.92s\tremaining: 11s\n",
      "40:\tlearn: 0.0304604\ttotal: 2.98s\tremaining: 10.9s\n",
      "41:\tlearn: 0.0285204\ttotal: 3.05s\tremaining: 10.8s\n",
      "42:\tlearn: 0.0267192\ttotal: 3.11s\tremaining: 10.6s\n",
      "43:\tlearn: 0.0250450\ttotal: 3.17s\tremaining: 10.5s\n",
      "44:\tlearn: 0.0234907\ttotal: 3.24s\tremaining: 10.4s\n",
      "45:\tlearn: 0.0220438\ttotal: 3.3s\tremaining: 10.3s\n",
      "46:\tlearn: 0.0206874\ttotal: 3.37s\tremaining: 10.2s\n",
      "47:\tlearn: 0.0194323\ttotal: 3.43s\tremaining: 10.2s\n",
      "48:\tlearn: 0.0182638\ttotal: 3.49s\tremaining: 10s\n",
      "49:\tlearn: 0.0171751\ttotal: 3.55s\tremaining: 9.94s\n",
      "50:\tlearn: 0.0161697\ttotal: 3.62s\tremaining: 9.86s\n",
      "51:\tlearn: 0.0152299\ttotal: 3.68s\tremaining: 9.77s\n",
      "52:\tlearn: 0.0143597\ttotal: 3.75s\tremaining: 9.7s\n",
      "53:\tlearn: 0.0135393\ttotal: 3.82s\tremaining: 9.62s\n",
      "54:\tlearn: 0.0127796\ttotal: 3.88s\tremaining: 9.53s\n",
      "55:\tlearn: 0.0120804\ttotal: 3.95s\tremaining: 9.44s\n",
      "56:\tlearn: 0.0114156\ttotal: 4.01s\tremaining: 9.35s\n",
      "57:\tlearn: 0.0108077\ttotal: 4.07s\tremaining: 9.27s\n",
      "58:\tlearn: 0.0102383\ttotal: 4.14s\tremaining: 9.19s\n",
      "59:\tlearn: 0.0096997\ttotal: 4.2s\tremaining: 9.1s\n",
      "60:\tlearn: 0.0092003\ttotal: 4.26s\tremaining: 9.02s\n",
      "61:\tlearn: 0.0087295\ttotal: 4.32s\tremaining: 8.92s\n",
      "62:\tlearn: 0.0083017\ttotal: 4.38s\tremaining: 8.83s\n",
      "63:\tlearn: 0.0079055\ttotal: 4.45s\tremaining: 8.76s\n",
      "64:\tlearn: 0.0075254\ttotal: 4.51s\tremaining: 8.67s\n",
      "65:\tlearn: 0.0071711\ttotal: 4.58s\tremaining: 8.6s\n",
      "66:\tlearn: 0.0068517\ttotal: 4.65s\tremaining: 8.53s\n",
      "67:\tlearn: 0.0065532\ttotal: 4.71s\tremaining: 8.45s\n",
      "68:\tlearn: 0.0062702\ttotal: 4.78s\tremaining: 8.38s\n",
      "69:\tlearn: 0.0060038\ttotal: 4.85s\tremaining: 8.31s\n",
      "70:\tlearn: 0.0057592\ttotal: 4.91s\tremaining: 8.23s\n",
      "71:\tlearn: 0.0055208\ttotal: 4.98s\tremaining: 8.16s\n",
      "72:\tlearn: 0.0053121\ttotal: 5.04s\tremaining: 8.08s\n",
      "73:\tlearn: 0.0051144\ttotal: 5.1s\tremaining: 7.99s\n",
      "74:\tlearn: 0.0049228\ttotal: 5.17s\tremaining: 7.93s\n",
      "75:\tlearn: 0.0047531\ttotal: 5.23s\tremaining: 7.84s\n",
      "76:\tlearn: 0.0045912\ttotal: 5.29s\tremaining: 7.76s\n",
      "77:\tlearn: 0.0044361\ttotal: 5.35s\tremaining: 7.68s\n",
      "78:\tlearn: 0.0043005\ttotal: 5.42s\tremaining: 7.61s\n",
      "79:\tlearn: 0.0041752\ttotal: 5.48s\tremaining: 7.53s\n",
      "80:\tlearn: 0.0040565\ttotal: 5.54s\tremaining: 7.46s\n",
      "81:\tlearn: 0.0039447\ttotal: 5.61s\tremaining: 7.38s\n",
      "82:\tlearn: 0.0038404\ttotal: 5.67s\tremaining: 7.31s\n",
      "83:\tlearn: 0.0037448\ttotal: 5.73s\tremaining: 7.23s\n",
      "84:\tlearn: 0.0036585\ttotal: 5.79s\tremaining: 7.15s\n",
      "85:\tlearn: 0.0035775\ttotal: 5.86s\tremaining: 7.08s\n",
      "86:\tlearn: 0.0035024\ttotal: 5.92s\tremaining: 7.01s\n",
      "87:\tlearn: 0.0034330\ttotal: 5.98s\tremaining: 6.93s\n",
      "88:\tlearn: 0.0033594\ttotal: 6.05s\tremaining: 6.87s\n",
      "89:\tlearn: 0.0032882\ttotal: 6.13s\tremaining: 6.81s\n",
      "90:\tlearn: 0.0032323\ttotal: 6.2s\tremaining: 6.74s\n",
      "91:\tlearn: 0.0031734\ttotal: 6.26s\tremaining: 6.67s\n",
      "92:\tlearn: 0.0031186\ttotal: 6.32s\tremaining: 6.59s\n",
      "93:\tlearn: 0.0030748\ttotal: 6.38s\tremaining: 6.52s\n",
      "94:\tlearn: 0.0030188\ttotal: 6.45s\tremaining: 6.45s\n",
      "95:\tlearn: 0.0029678\ttotal: 6.52s\tremaining: 6.39s\n",
      "96:\tlearn: 0.0029267\ttotal: 6.6s\tremaining: 6.33s\n",
      "97:\tlearn: 0.0028818\ttotal: 6.67s\tremaining: 6.26s\n",
      "98:\tlearn: 0.0028482\ttotal: 6.73s\tremaining: 6.18s\n",
      "99:\tlearn: 0.0028105\ttotal: 6.8s\tremaining: 6.12s\n",
      "100:\tlearn: 0.0027826\ttotal: 6.86s\tremaining: 6.05s\n",
      "101:\tlearn: 0.0027466\ttotal: 6.93s\tremaining: 5.98s\n",
      "102:\tlearn: 0.0027220\ttotal: 7s\tremaining: 5.91s\n",
      "103:\tlearn: 0.0026983\ttotal: 7.05s\tremaining: 5.83s\n",
      "104:\tlearn: 0.0026663\ttotal: 7.13s\tremaining: 5.77s\n",
      "105:\tlearn: 0.0026469\ttotal: 7.19s\tremaining: 5.7s\n",
      "106:\tlearn: 0.0026201\ttotal: 7.26s\tremaining: 5.63s\n",
      "107:\tlearn: 0.0025867\ttotal: 7.33s\tremaining: 5.56s\n",
      "108:\tlearn: 0.0025580\ttotal: 7.4s\tremaining: 5.5s\n",
      "109:\tlearn: 0.0025340\ttotal: 7.47s\tremaining: 5.43s\n",
      "110:\tlearn: 0.0025042\ttotal: 7.54s\tremaining: 5.37s\n",
      "111:\tlearn: 0.0024848\ttotal: 7.6s\tremaining: 5.29s\n",
      "112:\tlearn: 0.0024731\ttotal: 7.66s\tremaining: 5.22s\n",
      "113:\tlearn: 0.0024624\ttotal: 7.71s\tremaining: 5.14s\n",
      "114:\tlearn: 0.0024423\ttotal: 7.78s\tremaining: 5.07s\n",
      "115:\tlearn: 0.0024246\ttotal: 7.85s\tremaining: 5.01s\n",
      "116:\tlearn: 0.0024082\ttotal: 7.91s\tremaining: 4.94s\n",
      "117:\tlearn: 0.0023920\ttotal: 7.97s\tremaining: 4.87s\n",
      "118:\tlearn: 0.0023715\ttotal: 8.04s\tremaining: 4.8s\n",
      "119:\tlearn: 0.0023503\ttotal: 8.11s\tremaining: 4.73s\n",
      "120:\tlearn: 0.0023318\ttotal: 8.19s\tremaining: 4.67s\n",
      "121:\tlearn: 0.0023218\ttotal: 8.25s\tremaining: 4.6s\n",
      "122:\tlearn: 0.0023068\ttotal: 8.31s\tremaining: 4.53s\n",
      "123:\tlearn: 0.0022918\ttotal: 8.38s\tremaining: 4.46s\n",
      "124:\tlearn: 0.0022755\ttotal: 8.46s\tremaining: 4.4s\n",
      "125:\tlearn: 0.0022689\ttotal: 8.51s\tremaining: 4.32s\n",
      "126:\tlearn: 0.0022508\ttotal: 8.58s\tremaining: 4.26s\n",
      "127:\tlearn: 0.0022357\ttotal: 8.65s\tremaining: 4.19s\n",
      "128:\tlearn: 0.0022225\ttotal: 8.71s\tremaining: 4.12s\n",
      "129:\tlearn: 0.0022110\ttotal: 8.78s\tremaining: 4.05s\n",
      "130:\tlearn: 0.0022035\ttotal: 8.84s\tremaining: 3.98s\n",
      "131:\tlearn: 0.0021894\ttotal: 8.91s\tremaining: 3.92s\n",
      "132:\tlearn: 0.0021791\ttotal: 8.97s\tremaining: 3.85s\n",
      "133:\tlearn: 0.0021732\ttotal: 9.04s\tremaining: 3.78s\n",
      "134:\tlearn: 0.0021606\ttotal: 9.1s\tremaining: 3.71s\n",
      "135:\tlearn: 0.0021513\ttotal: 9.16s\tremaining: 3.64s\n",
      "136:\tlearn: 0.0021468\ttotal: 9.22s\tremaining: 3.57s\n",
      "137:\tlearn: 0.0021426\ttotal: 9.29s\tremaining: 3.5s\n",
      "138:\tlearn: 0.0021384\ttotal: 9.35s\tremaining: 3.43s\n",
      "139:\tlearn: 0.0021250\ttotal: 9.42s\tremaining: 3.36s\n",
      "140:\tlearn: 0.0021200\ttotal: 9.48s\tremaining: 3.29s\n",
      "141:\tlearn: 0.0021066\ttotal: 9.55s\tremaining: 3.23s\n",
      "142:\tlearn: 0.0020949\ttotal: 9.63s\tremaining: 3.16s\n",
      "143:\tlearn: 0.0020843\ttotal: 9.7s\tremaining: 3.1s\n",
      "144:\tlearn: 0.0020793\ttotal: 9.76s\tremaining: 3.03s\n",
      "145:\tlearn: 0.0020694\ttotal: 9.83s\tremaining: 2.96s\n",
      "146:\tlearn: 0.0020658\ttotal: 9.89s\tremaining: 2.89s\n",
      "147:\tlearn: 0.0020586\ttotal: 9.96s\tremaining: 2.83s\n",
      "148:\tlearn: 0.0020530\ttotal: 10s\tremaining: 2.76s\n",
      "149:\tlearn: 0.0020482\ttotal: 10.1s\tremaining: 2.69s\n",
      "150:\tlearn: 0.0020419\ttotal: 10.2s\tremaining: 2.62s\n",
      "151:\tlearn: 0.0020384\ttotal: 10.2s\tremaining: 2.56s\n",
      "152:\tlearn: 0.0020313\ttotal: 10.3s\tremaining: 2.49s\n",
      "153:\tlearn: 0.0020267\ttotal: 10.3s\tremaining: 2.42s\n",
      "154:\tlearn: 0.0020228\ttotal: 10.4s\tremaining: 2.35s\n",
      "155:\tlearn: 0.0020124\ttotal: 10.5s\tremaining: 2.28s\n",
      "156:\tlearn: 0.0020064\ttotal: 10.5s\tremaining: 2.21s\n",
      "157:\tlearn: 0.0020018\ttotal: 10.6s\tremaining: 2.15s\n",
      "158:\tlearn: 0.0019949\ttotal: 10.7s\tremaining: 2.08s\n",
      "159:\tlearn: 0.0019871\ttotal: 10.7s\tremaining: 2.01s\n",
      "160:\tlearn: 0.0019829\ttotal: 10.8s\tremaining: 1.94s\n",
      "161:\tlearn: 0.0019756\ttotal: 10.9s\tremaining: 1.88s\n",
      "162:\tlearn: 0.0019706\ttotal: 10.9s\tremaining: 1.81s\n",
      "163:\tlearn: 0.0019629\ttotal: 11s\tremaining: 1.74s\n",
      "164:\tlearn: 0.0019549\ttotal: 11.1s\tremaining: 1.68s\n",
      "165:\tlearn: 0.0019497\ttotal: 11.1s\tremaining: 1.61s\n",
      "166:\tlearn: 0.0019461\ttotal: 11.2s\tremaining: 1.54s\n",
      "167:\tlearn: 0.0019430\ttotal: 11.2s\tremaining: 1.47s\n",
      "168:\tlearn: 0.0019382\ttotal: 11.3s\tremaining: 1.4s\n",
      "169:\tlearn: 0.0019325\ttotal: 11.4s\tremaining: 1.34s\n",
      "170:\tlearn: 0.0019278\ttotal: 11.4s\tremaining: 1.27s\n",
      "171:\tlearn: 0.0019218\ttotal: 11.5s\tremaining: 1.2s\n",
      "172:\tlearn: 0.0019185\ttotal: 11.6s\tremaining: 1.14s\n",
      "173:\tlearn: 0.0019162\ttotal: 11.6s\tremaining: 1.07s\n",
      "174:\tlearn: 0.0019141\ttotal: 11.7s\tremaining: 1s\n",
      "175:\tlearn: 0.0019096\ttotal: 11.8s\tremaining: 935ms\n",
      "176:\tlearn: 0.0019028\ttotal: 11.8s\tremaining: 868ms\n",
      "177:\tlearn: 0.0018977\ttotal: 11.9s\tremaining: 801ms\n",
      "178:\tlearn: 0.0018918\ttotal: 12s\tremaining: 735ms\n",
      "179:\tlearn: 0.0018878\ttotal: 12s\tremaining: 668ms\n",
      "180:\tlearn: 0.0018836\ttotal: 12.1s\tremaining: 601ms\n",
      "181:\tlearn: 0.0018799\ttotal: 12.1s\tremaining: 534ms\n",
      "182:\tlearn: 0.0018758\ttotal: 12.2s\tremaining: 467ms\n",
      "183:\tlearn: 0.0018726\ttotal: 12.3s\tremaining: 400ms\n",
      "184:\tlearn: 0.0018701\ttotal: 12.3s\tremaining: 333ms\n",
      "185:\tlearn: 0.0018675\ttotal: 12.4s\tremaining: 266ms\n",
      "186:\tlearn: 0.0018632\ttotal: 12.5s\tremaining: 200ms\n",
      "187:\tlearn: 0.0018604\ttotal: 12.5s\tremaining: 133ms\n",
      "188:\tlearn: 0.0018576\ttotal: 12.6s\tremaining: 66.5ms\n",
      "189:\tlearn: 0.0018546\ttotal: 12.6s\tremaining: 0us\n",
      "0:\tlearn: 0.6258755\ttotal: 83.1ms\tremaining: 15.7s\n",
      "1:\tlearn: 0.5675098\ttotal: 157ms\tremaining: 14.8s\n",
      "2:\tlearn: 0.5163634\ttotal: 225ms\tremaining: 14.1s\n",
      "3:\tlearn: 0.4711065\ttotal: 309ms\tremaining: 14.4s\n",
      "4:\tlearn: 0.4308264\ttotal: 375ms\tremaining: 13.9s\n",
      "5:\tlearn: 0.3947722\ttotal: 442ms\tremaining: 13.6s\n",
      "6:\tlearn: 0.3623592\ttotal: 499ms\tremaining: 13.1s\n",
      "7:\tlearn: 0.3330855\ttotal: 563ms\tremaining: 12.8s\n",
      "8:\tlearn: 0.3066027\ttotal: 626ms\tremaining: 12.6s\n",
      "9:\tlearn: 0.2825831\ttotal: 694ms\tremaining: 12.5s\n",
      "10:\tlearn: 0.2608002\ttotal: 762ms\tremaining: 12.4s\n",
      "11:\tlearn: 0.2408132\ttotal: 823ms\tremaining: 12.2s\n",
      "12:\tlearn: 0.2226071\ttotal: 888ms\tremaining: 12.1s\n",
      "13:\tlearn: 0.2059537\ttotal: 951ms\tremaining: 12s\n",
      "14:\tlearn: 0.1907316\ttotal: 1s\tremaining: 11.7s\n",
      "15:\tlearn: 0.1767664\ttotal: 1.06s\tremaining: 11.5s\n",
      "16:\tlearn: 0.1639268\ttotal: 1.12s\tremaining: 11.4s\n",
      "17:\tlearn: 0.1520591\ttotal: 1.18s\tremaining: 11.3s\n",
      "18:\tlearn: 0.1411816\ttotal: 1.24s\tremaining: 11.1s\n",
      "19:\tlearn: 0.1310908\ttotal: 1.3s\tremaining: 11s\n",
      "20:\tlearn: 0.1217867\ttotal: 1.36s\tremaining: 10.9s\n",
      "21:\tlearn: 0.1132059\ttotal: 1.42s\tremaining: 10.9s\n",
      "22:\tlearn: 0.1052795\ttotal: 1.49s\tremaining: 10.8s\n",
      "23:\tlearn: 0.0979520\ttotal: 1.55s\tremaining: 10.7s\n",
      "24:\tlearn: 0.0911570\ttotal: 1.59s\tremaining: 10.5s\n",
      "25:\tlearn: 0.0848966\ttotal: 1.64s\tremaining: 10.3s\n",
      "26:\tlearn: 0.0790565\ttotal: 1.7s\tremaining: 10.2s\n",
      "27:\tlearn: 0.0736693\ttotal: 1.76s\tremaining: 10.2s\n",
      "28:\tlearn: 0.0686650\ttotal: 1.82s\tremaining: 10.1s\n",
      "29:\tlearn: 0.0640449\ttotal: 1.89s\tremaining: 10.1s\n",
      "30:\tlearn: 0.0597355\ttotal: 1.95s\tremaining: 9.98s\n",
      "31:\tlearn: 0.0557448\ttotal: 2.01s\tremaining: 9.93s\n",
      "32:\tlearn: 0.0520231\ttotal: 2.07s\tremaining: 9.87s\n",
      "33:\tlearn: 0.0485735\ttotal: 2.14s\tremaining: 9.8s\n",
      "34:\tlearn: 0.0453810\ttotal: 2.2s\tremaining: 9.73s\n",
      "35:\tlearn: 0.0424098\ttotal: 2.26s\tremaining: 9.65s\n",
      "36:\tlearn: 0.0396377\ttotal: 2.32s\tremaining: 9.58s\n",
      "37:\tlearn: 0.0370661\ttotal: 2.38s\tremaining: 9.51s\n",
      "38:\tlearn: 0.0346767\ttotal: 2.44s\tremaining: 9.46s\n",
      "39:\tlearn: 0.0324561\ttotal: 2.51s\tremaining: 9.42s\n",
      "40:\tlearn: 0.0303822\ttotal: 2.57s\tremaining: 9.33s\n",
      "41:\tlearn: 0.0284512\ttotal: 2.63s\tremaining: 9.25s\n",
      "42:\tlearn: 0.0266539\ttotal: 2.68s\tremaining: 9.17s\n",
      "43:\tlearn: 0.0249888\ttotal: 2.74s\tremaining: 9.08s\n",
      "44:\tlearn: 0.0234435\ttotal: 2.79s\tremaining: 8.99s\n",
      "45:\tlearn: 0.0220024\ttotal: 2.85s\tremaining: 8.91s\n",
      "46:\tlearn: 0.0206625\ttotal: 2.91s\tremaining: 8.85s\n",
      "47:\tlearn: 0.0194169\ttotal: 2.97s\tremaining: 8.78s\n",
      "48:\tlearn: 0.0182505\ttotal: 3.02s\tremaining: 8.7s\n",
      "49:\tlearn: 0.0171691\ttotal: 3.08s\tremaining: 8.63s\n",
      "50:\tlearn: 0.0161577\ttotal: 3.14s\tremaining: 8.56s\n",
      "51:\tlearn: 0.0152142\ttotal: 3.2s\tremaining: 8.5s\n",
      "52:\tlearn: 0.0143521\ttotal: 3.27s\tremaining: 8.46s\n",
      "53:\tlearn: 0.0135285\ttotal: 3.34s\tremaining: 8.42s\n",
      "54:\tlearn: 0.0127701\ttotal: 3.41s\tremaining: 8.38s\n",
      "55:\tlearn: 0.0120673\ttotal: 3.48s\tremaining: 8.32s\n",
      "56:\tlearn: 0.0114065\ttotal: 3.54s\tremaining: 8.25s\n",
      "57:\tlearn: 0.0107962\ttotal: 3.59s\tremaining: 8.17s\n",
      "58:\tlearn: 0.0102285\ttotal: 3.64s\tremaining: 8.09s\n",
      "59:\tlearn: 0.0097059\ttotal: 3.69s\tremaining: 8s\n",
      "60:\tlearn: 0.0092255\ttotal: 3.75s\tremaining: 7.94s\n",
      "61:\tlearn: 0.0087601\ttotal: 3.81s\tremaining: 7.86s\n",
      "62:\tlearn: 0.0083295\ttotal: 3.87s\tremaining: 7.79s\n",
      "63:\tlearn: 0.0079251\ttotal: 3.92s\tremaining: 7.72s\n",
      "64:\tlearn: 0.0075557\ttotal: 3.98s\tremaining: 7.66s\n",
      "65:\tlearn: 0.0072108\ttotal: 4.04s\tremaining: 7.6s\n",
      "66:\tlearn: 0.0068887\ttotal: 4.1s\tremaining: 7.53s\n",
      "67:\tlearn: 0.0065844\ttotal: 4.16s\tremaining: 7.46s\n",
      "68:\tlearn: 0.0063031\ttotal: 4.22s\tremaining: 7.39s\n",
      "69:\tlearn: 0.0060412\ttotal: 4.27s\tremaining: 7.32s\n",
      "70:\tlearn: 0.0058020\ttotal: 4.33s\tremaining: 7.25s\n",
      "71:\tlearn: 0.0055812\ttotal: 4.38s\tremaining: 7.19s\n",
      "72:\tlearn: 0.0053674\ttotal: 4.45s\tremaining: 7.13s\n",
      "73:\tlearn: 0.0051723\ttotal: 4.51s\tremaining: 7.07s\n",
      "74:\tlearn: 0.0049856\ttotal: 4.56s\tremaining: 6.99s\n",
      "75:\tlearn: 0.0048144\ttotal: 4.62s\tremaining: 6.93s\n",
      "76:\tlearn: 0.0046560\ttotal: 4.68s\tremaining: 6.87s\n",
      "77:\tlearn: 0.0045108\ttotal: 4.74s\tremaining: 6.8s\n",
      "78:\tlearn: 0.0043780\ttotal: 4.8s\tremaining: 6.75s\n",
      "79:\tlearn: 0.0042525\ttotal: 4.86s\tremaining: 6.68s\n",
      "80:\tlearn: 0.0041351\ttotal: 4.92s\tremaining: 6.62s\n",
      "81:\tlearn: 0.0040289\ttotal: 4.98s\tremaining: 6.56s\n",
      "82:\tlearn: 0.0039202\ttotal: 5.04s\tremaining: 6.5s\n",
      "83:\tlearn: 0.0038159\ttotal: 5.11s\tremaining: 6.45s\n",
      "84:\tlearn: 0.0037287\ttotal: 5.18s\tremaining: 6.4s\n",
      "85:\tlearn: 0.0036438\ttotal: 5.25s\tremaining: 6.34s\n",
      "86:\tlearn: 0.0035574\ttotal: 5.3s\tremaining: 6.27s\n",
      "87:\tlearn: 0.0034820\ttotal: 5.36s\tremaining: 6.21s\n",
      "88:\tlearn: 0.0034170\ttotal: 5.42s\tremaining: 6.15s\n",
      "89:\tlearn: 0.0033480\ttotal: 5.47s\tremaining: 6.08s\n",
      "90:\tlearn: 0.0032904\ttotal: 5.53s\tremaining: 6.02s\n",
      "91:\tlearn: 0.0032197\ttotal: 5.59s\tremaining: 5.96s\n",
      "92:\tlearn: 0.0031723\ttotal: 5.65s\tremaining: 5.89s\n",
      "93:\tlearn: 0.0031283\ttotal: 5.71s\tremaining: 5.83s\n",
      "94:\tlearn: 0.0030786\ttotal: 5.76s\tremaining: 5.76s\n",
      "95:\tlearn: 0.0030389\ttotal: 5.82s\tremaining: 5.7s\n",
      "96:\tlearn: 0.0030015\ttotal: 5.88s\tremaining: 5.63s\n",
      "97:\tlearn: 0.0029593\ttotal: 5.93s\tremaining: 5.57s\n",
      "98:\tlearn: 0.0029289\ttotal: 5.98s\tremaining: 5.5s\n",
      "99:\tlearn: 0.0028964\ttotal: 6.02s\tremaining: 5.42s\n",
      "100:\tlearn: 0.0028720\ttotal: 6.06s\tremaining: 5.34s\n",
      "101:\tlearn: 0.0028340\ttotal: 6.11s\tremaining: 5.27s\n",
      "102:\tlearn: 0.0028111\ttotal: 6.16s\tremaining: 5.2s\n",
      "103:\tlearn: 0.0027839\ttotal: 6.21s\tremaining: 5.13s\n",
      "104:\tlearn: 0.0027600\ttotal: 6.26s\tremaining: 5.07s\n",
      "105:\tlearn: 0.0027397\ttotal: 6.32s\tremaining: 5.01s\n",
      "106:\tlearn: 0.0027134\ttotal: 6.38s\tremaining: 4.95s\n",
      "107:\tlearn: 0.0026821\ttotal: 6.45s\tremaining: 4.89s\n",
      "108:\tlearn: 0.0026577\ttotal: 6.5s\tremaining: 4.83s\n",
      "109:\tlearn: 0.0026363\ttotal: 6.56s\tremaining: 4.77s\n",
      "110:\tlearn: 0.0026175\ttotal: 6.62s\tremaining: 4.71s\n",
      "111:\tlearn: 0.0025971\ttotal: 6.68s\tremaining: 4.65s\n",
      "112:\tlearn: 0.0025759\ttotal: 6.74s\tremaining: 4.59s\n",
      "113:\tlearn: 0.0025518\ttotal: 6.8s\tremaining: 4.54s\n",
      "114:\tlearn: 0.0025350\ttotal: 6.87s\tremaining: 4.48s\n",
      "115:\tlearn: 0.0025159\ttotal: 6.93s\tremaining: 4.42s\n",
      "116:\tlearn: 0.0024925\ttotal: 6.99s\tremaining: 4.36s\n",
      "117:\tlearn: 0.0024711\ttotal: 7.05s\tremaining: 4.3s\n",
      "118:\tlearn: 0.0024569\ttotal: 7.11s\tremaining: 4.24s\n",
      "119:\tlearn: 0.0024484\ttotal: 7.17s\tremaining: 4.18s\n",
      "120:\tlearn: 0.0024355\ttotal: 7.22s\tremaining: 4.12s\n",
      "121:\tlearn: 0.0024201\ttotal: 7.29s\tremaining: 4.07s\n",
      "122:\tlearn: 0.0024100\ttotal: 7.35s\tremaining: 4s\n",
      "123:\tlearn: 0.0023927\ttotal: 7.41s\tremaining: 3.94s\n",
      "124:\tlearn: 0.0023863\ttotal: 7.46s\tremaining: 3.88s\n",
      "125:\tlearn: 0.0023751\ttotal: 7.51s\tremaining: 3.82s\n",
      "126:\tlearn: 0.0023658\ttotal: 7.57s\tremaining: 3.75s\n",
      "127:\tlearn: 0.0023532\ttotal: 7.63s\tremaining: 3.7s\n",
      "128:\tlearn: 0.0023457\ttotal: 7.69s\tremaining: 3.63s\n",
      "129:\tlearn: 0.0023390\ttotal: 7.74s\tremaining: 3.57s\n",
      "130:\tlearn: 0.0023277\ttotal: 7.79s\tremaining: 3.51s\n",
      "131:\tlearn: 0.0023103\ttotal: 7.86s\tremaining: 3.45s\n",
      "132:\tlearn: 0.0023035\ttotal: 7.92s\tremaining: 3.39s\n",
      "133:\tlearn: 0.0022913\ttotal: 7.98s\tremaining: 3.33s\n",
      "134:\tlearn: 0.0022858\ttotal: 8.04s\tremaining: 3.27s\n",
      "135:\tlearn: 0.0022772\ttotal: 8.09s\tremaining: 3.21s\n",
      "136:\tlearn: 0.0022729\ttotal: 8.15s\tremaining: 3.15s\n",
      "137:\tlearn: 0.0022604\ttotal: 8.21s\tremaining: 3.09s\n",
      "138:\tlearn: 0.0022527\ttotal: 8.26s\tremaining: 3.03s\n",
      "139:\tlearn: 0.0022400\ttotal: 8.32s\tremaining: 2.97s\n",
      "140:\tlearn: 0.0022217\ttotal: 8.39s\tremaining: 2.92s\n",
      "141:\tlearn: 0.0022169\ttotal: 8.45s\tremaining: 2.86s\n",
      "142:\tlearn: 0.0022095\ttotal: 8.52s\tremaining: 2.8s\n",
      "143:\tlearn: 0.0022016\ttotal: 8.58s\tremaining: 2.74s\n",
      "144:\tlearn: 0.0021969\ttotal: 8.63s\tremaining: 2.68s\n",
      "145:\tlearn: 0.0021937\ttotal: 8.68s\tremaining: 2.62s\n",
      "146:\tlearn: 0.0021827\ttotal: 8.74s\tremaining: 2.56s\n",
      "147:\tlearn: 0.0021736\ttotal: 8.78s\tremaining: 2.49s\n",
      "148:\tlearn: 0.0021627\ttotal: 8.83s\tremaining: 2.43s\n",
      "149:\tlearn: 0.0021540\ttotal: 8.89s\tremaining: 2.37s\n",
      "150:\tlearn: 0.0021484\ttotal: 8.95s\tremaining: 2.31s\n",
      "151:\tlearn: 0.0021344\ttotal: 9s\tremaining: 2.25s\n",
      "152:\tlearn: 0.0021306\ttotal: 9.05s\tremaining: 2.19s\n",
      "153:\tlearn: 0.0021225\ttotal: 9.11s\tremaining: 2.13s\n",
      "154:\tlearn: 0.0021125\ttotal: 9.16s\tremaining: 2.07s\n",
      "155:\tlearn: 0.0021046\ttotal: 9.22s\tremaining: 2.01s\n",
      "156:\tlearn: 0.0020985\ttotal: 9.28s\tremaining: 1.95s\n",
      "157:\tlearn: 0.0020913\ttotal: 9.34s\tremaining: 1.89s\n",
      "158:\tlearn: 0.0020797\ttotal: 9.4s\tremaining: 1.83s\n",
      "159:\tlearn: 0.0020755\ttotal: 9.46s\tremaining: 1.77s\n",
      "160:\tlearn: 0.0020667\ttotal: 9.51s\tremaining: 1.71s\n",
      "161:\tlearn: 0.0020609\ttotal: 9.57s\tremaining: 1.65s\n",
      "162:\tlearn: 0.0020573\ttotal: 9.63s\tremaining: 1.59s\n",
      "163:\tlearn: 0.0020494\ttotal: 9.69s\tremaining: 1.53s\n",
      "164:\tlearn: 0.0020452\ttotal: 9.74s\tremaining: 1.48s\n",
      "165:\tlearn: 0.0020408\ttotal: 9.8s\tremaining: 1.42s\n",
      "166:\tlearn: 0.0020373\ttotal: 9.86s\tremaining: 1.36s\n",
      "167:\tlearn: 0.0020337\ttotal: 9.91s\tremaining: 1.3s\n",
      "168:\tlearn: 0.0020272\ttotal: 9.97s\tremaining: 1.24s\n",
      "169:\tlearn: 0.0020189\ttotal: 10s\tremaining: 1.18s\n",
      "170:\tlearn: 0.0020162\ttotal: 10.1s\tremaining: 1.12s\n",
      "171:\tlearn: 0.0020141\ttotal: 10.1s\tremaining: 1.06s\n",
      "172:\tlearn: 0.0020061\ttotal: 10.2s\tremaining: 1s\n",
      "173:\tlearn: 0.0020031\ttotal: 10.2s\tremaining: 942ms\n",
      "174:\tlearn: 0.0020004\ttotal: 10.3s\tremaining: 883ms\n",
      "175:\tlearn: 0.0019944\ttotal: 10.3s\tremaining: 823ms\n",
      "176:\tlearn: 0.0019907\ttotal: 10.4s\tremaining: 764ms\n",
      "177:\tlearn: 0.0019870\ttotal: 10.5s\tremaining: 705ms\n",
      "178:\tlearn: 0.0019835\ttotal: 10.5s\tremaining: 646ms\n",
      "179:\tlearn: 0.0019734\ttotal: 10.6s\tremaining: 588ms\n",
      "180:\tlearn: 0.0019692\ttotal: 10.6s\tremaining: 529ms\n",
      "181:\tlearn: 0.0019653\ttotal: 10.7s\tremaining: 470ms\n",
      "182:\tlearn: 0.0019632\ttotal: 10.8s\tremaining: 411ms\n",
      "183:\tlearn: 0.0019586\ttotal: 10.8s\tremaining: 352ms\n",
      "184:\tlearn: 0.0019540\ttotal: 10.9s\tremaining: 294ms\n",
      "185:\tlearn: 0.0019508\ttotal: 10.9s\tremaining: 235ms\n",
      "186:\tlearn: 0.0019479\ttotal: 11s\tremaining: 176ms\n",
      "187:\tlearn: 0.0019438\ttotal: 11s\tremaining: 117ms\n",
      "188:\tlearn: 0.0019404\ttotal: 11.1s\tremaining: 58.6ms\n",
      "189:\tlearn: 0.0019344\ttotal: 11.1s\tremaining: 0us\n",
      "0:\tlearn: 0.6258995\ttotal: 66.2ms\tremaining: 12.5s\n",
      "1:\tlearn: 0.5675210\ttotal: 130ms\tremaining: 12.2s\n",
      "2:\tlearn: 0.5164108\ttotal: 201ms\tremaining: 12.5s\n",
      "3:\tlearn: 0.4711685\ttotal: 255ms\tremaining: 11.9s\n",
      "4:\tlearn: 0.4309096\ttotal: 308ms\tremaining: 11.4s\n",
      "5:\tlearn: 0.3948802\ttotal: 374ms\tremaining: 11.5s\n",
      "6:\tlearn: 0.3624802\ttotal: 438ms\tremaining: 11.5s\n",
      "7:\tlearn: 0.3332160\ttotal: 503ms\tremaining: 11.4s\n",
      "8:\tlearn: 0.3067481\ttotal: 568ms\tremaining: 11.4s\n",
      "9:\tlearn: 0.2827416\ttotal: 634ms\tremaining: 11.4s\n",
      "10:\tlearn: 0.2609608\ttotal: 706ms\tremaining: 11.5s\n",
      "11:\tlearn: 0.2409945\ttotal: 768ms\tremaining: 11.4s\n",
      "12:\tlearn: 0.2227978\ttotal: 831ms\tremaining: 11.3s\n",
      "13:\tlearn: 0.2061566\ttotal: 898ms\tremaining: 11.3s\n",
      "14:\tlearn: 0.1909427\ttotal: 960ms\tremaining: 11.2s\n",
      "15:\tlearn: 0.1769805\ttotal: 1.02s\tremaining: 11.2s\n",
      "16:\tlearn: 0.1641523\ttotal: 1.09s\tremaining: 11.1s\n",
      "17:\tlearn: 0.1523004\ttotal: 1.15s\tremaining: 11s\n",
      "18:\tlearn: 0.1413936\ttotal: 1.22s\tremaining: 10.9s\n",
      "19:\tlearn: 0.1313015\ttotal: 1.28s\tremaining: 10.8s\n",
      "20:\tlearn: 0.1220357\ttotal: 1.34s\tremaining: 10.8s\n",
      "21:\tlearn: 0.1134525\ttotal: 1.41s\tremaining: 10.8s\n",
      "22:\tlearn: 0.1055283\ttotal: 1.47s\tremaining: 10.7s\n",
      "23:\tlearn: 0.0982067\ttotal: 1.54s\tremaining: 10.6s\n",
      "24:\tlearn: 0.0914077\ttotal: 1.6s\tremaining: 10.6s\n",
      "25:\tlearn: 0.0851470\ttotal: 1.67s\tremaining: 10.5s\n",
      "26:\tlearn: 0.0792996\ttotal: 1.71s\tremaining: 10.3s\n",
      "27:\tlearn: 0.0739183\ttotal: 1.78s\tremaining: 10.3s\n",
      "28:\tlearn: 0.0689212\ttotal: 1.84s\tremaining: 10.2s\n",
      "29:\tlearn: 0.0643015\ttotal: 1.9s\tremaining: 10.1s\n",
      "30:\tlearn: 0.0600039\ttotal: 1.97s\tremaining: 10.1s\n",
      "31:\tlearn: 0.0560174\ttotal: 2.02s\tremaining: 10s\n",
      "32:\tlearn: 0.0522869\ttotal: 2.09s\tremaining: 9.93s\n",
      "33:\tlearn: 0.0488383\ttotal: 2.15s\tremaining: 9.85s\n",
      "34:\tlearn: 0.0456348\ttotal: 2.2s\tremaining: 9.74s\n",
      "35:\tlearn: 0.0426350\ttotal: 2.26s\tremaining: 9.69s\n",
      "36:\tlearn: 0.0398689\ttotal: 2.32s\tremaining: 9.59s\n",
      "37:\tlearn: 0.0373106\ttotal: 2.38s\tremaining: 9.53s\n",
      "38:\tlearn: 0.0348875\ttotal: 2.44s\tremaining: 9.44s\n",
      "39:\tlearn: 0.0326674\ttotal: 2.51s\tremaining: 9.41s\n",
      "40:\tlearn: 0.0305818\ttotal: 2.56s\tremaining: 9.31s\n",
      "41:\tlearn: 0.0286392\ttotal: 2.61s\tremaining: 9.21s\n",
      "42:\tlearn: 0.0268309\ttotal: 2.67s\tremaining: 9.13s\n",
      "43:\tlearn: 0.0251609\ttotal: 2.73s\tremaining: 9.06s\n",
      "44:\tlearn: 0.0236067\ttotal: 2.79s\tremaining: 8.99s\n",
      "45:\tlearn: 0.0221618\ttotal: 2.84s\tremaining: 8.89s\n",
      "46:\tlearn: 0.0208096\ttotal: 2.9s\tremaining: 8.82s\n",
      "47:\tlearn: 0.0195408\ttotal: 2.96s\tremaining: 8.74s\n",
      "48:\tlearn: 0.0183724\ttotal: 3.02s\tremaining: 8.69s\n",
      "49:\tlearn: 0.0172731\ttotal: 3.07s\tremaining: 8.61s\n",
      "50:\tlearn: 0.0162567\ttotal: 3.13s\tremaining: 8.53s\n",
      "51:\tlearn: 0.0153154\ttotal: 3.19s\tremaining: 8.46s\n",
      "52:\tlearn: 0.0144447\ttotal: 3.25s\tremaining: 8.39s\n",
      "53:\tlearn: 0.0136182\ttotal: 3.3s\tremaining: 8.32s\n",
      "54:\tlearn: 0.0128530\ttotal: 3.36s\tremaining: 8.25s\n",
      "55:\tlearn: 0.0121392\ttotal: 3.42s\tremaining: 8.19s\n",
      "56:\tlearn: 0.0114769\ttotal: 3.48s\tremaining: 8.12s\n",
      "57:\tlearn: 0.0108516\ttotal: 3.53s\tremaining: 8.04s\n",
      "58:\tlearn: 0.0102785\ttotal: 3.58s\tremaining: 7.96s\n",
      "59:\tlearn: 0.0097504\ttotal: 3.64s\tremaining: 7.89s\n",
      "60:\tlearn: 0.0092424\ttotal: 3.7s\tremaining: 7.82s\n",
      "61:\tlearn: 0.0087783\ttotal: 3.76s\tremaining: 7.76s\n",
      "62:\tlearn: 0.0083393\ttotal: 3.8s\tremaining: 7.67s\n",
      "63:\tlearn: 0.0079412\ttotal: 3.86s\tremaining: 7.61s\n",
      "64:\tlearn: 0.0075632\ttotal: 3.92s\tremaining: 7.54s\n",
      "65:\tlearn: 0.0072100\ttotal: 3.98s\tremaining: 7.47s\n",
      "66:\tlearn: 0.0068820\ttotal: 4.04s\tremaining: 7.41s\n",
      "67:\tlearn: 0.0065780\ttotal: 4.09s\tremaining: 7.34s\n",
      "68:\tlearn: 0.0062969\ttotal: 4.14s\tremaining: 7.27s\n",
      "69:\tlearn: 0.0060395\ttotal: 4.2s\tremaining: 7.21s\n",
      "70:\tlearn: 0.0057964\ttotal: 4.26s\tremaining: 7.13s\n",
      "71:\tlearn: 0.0055716\ttotal: 4.32s\tremaining: 7.08s\n",
      "72:\tlearn: 0.0053543\ttotal: 4.39s\tremaining: 7.03s\n",
      "73:\tlearn: 0.0051487\ttotal: 4.44s\tremaining: 6.97s\n",
      "74:\tlearn: 0.0049687\ttotal: 4.5s\tremaining: 6.91s\n",
      "75:\tlearn: 0.0047983\ttotal: 4.56s\tremaining: 6.84s\n",
      "76:\tlearn: 0.0046405\ttotal: 4.62s\tremaining: 6.78s\n",
      "77:\tlearn: 0.0044934\ttotal: 4.68s\tremaining: 6.71s\n",
      "78:\tlearn: 0.0043539\ttotal: 4.73s\tremaining: 6.65s\n",
      "79:\tlearn: 0.0042280\ttotal: 4.79s\tremaining: 6.58s\n",
      "80:\tlearn: 0.0041038\ttotal: 4.84s\tremaining: 6.52s\n",
      "81:\tlearn: 0.0039910\ttotal: 4.9s\tremaining: 6.45s\n",
      "82:\tlearn: 0.0038865\ttotal: 4.95s\tremaining: 6.38s\n",
      "83:\tlearn: 0.0037819\ttotal: 5.01s\tremaining: 6.33s\n",
      "84:\tlearn: 0.0036827\ttotal: 5.08s\tremaining: 6.27s\n",
      "85:\tlearn: 0.0035910\ttotal: 5.14s\tremaining: 6.21s\n",
      "86:\tlearn: 0.0035101\ttotal: 5.2s\tremaining: 6.16s\n",
      "87:\tlearn: 0.0034270\ttotal: 5.26s\tremaining: 6.09s\n",
      "88:\tlearn: 0.0033421\ttotal: 5.31s\tremaining: 6.03s\n",
      "89:\tlearn: 0.0032722\ttotal: 5.39s\tremaining: 5.99s\n",
      "90:\tlearn: 0.0032160\ttotal: 5.44s\tremaining: 5.92s\n",
      "91:\tlearn: 0.0031485\ttotal: 5.5s\tremaining: 5.86s\n",
      "92:\tlearn: 0.0030893\ttotal: 5.56s\tremaining: 5.8s\n",
      "93:\tlearn: 0.0030448\ttotal: 5.62s\tremaining: 5.74s\n",
      "94:\tlearn: 0.0029986\ttotal: 5.67s\tremaining: 5.67s\n",
      "95:\tlearn: 0.0029517\ttotal: 5.73s\tremaining: 5.61s\n",
      "96:\tlearn: 0.0029131\ttotal: 5.78s\tremaining: 5.54s\n",
      "97:\tlearn: 0.0028781\ttotal: 5.84s\tremaining: 5.48s\n",
      "98:\tlearn: 0.0028445\ttotal: 5.89s\tremaining: 5.42s\n",
      "99:\tlearn: 0.0028145\ttotal: 5.95s\tremaining: 5.36s\n",
      "100:\tlearn: 0.0027769\ttotal: 6.01s\tremaining: 5.3s\n",
      "101:\tlearn: 0.0027359\ttotal: 6.08s\tremaining: 5.24s\n",
      "102:\tlearn: 0.0027015\ttotal: 6.13s\tremaining: 5.18s\n",
      "103:\tlearn: 0.0026770\ttotal: 6.19s\tremaining: 5.12s\n",
      "104:\tlearn: 0.0026567\ttotal: 6.25s\tremaining: 5.05s\n",
      "105:\tlearn: 0.0026358\ttotal: 6.3s\tremaining: 5s\n",
      "106:\tlearn: 0.0026176\ttotal: 6.37s\tremaining: 4.94s\n",
      "107:\tlearn: 0.0025809\ttotal: 6.43s\tremaining: 4.89s\n",
      "108:\tlearn: 0.0025651\ttotal: 6.49s\tremaining: 4.82s\n",
      "109:\tlearn: 0.0025337\ttotal: 6.56s\tremaining: 4.77s\n",
      "110:\tlearn: 0.0025093\ttotal: 6.62s\tremaining: 4.71s\n",
      "111:\tlearn: 0.0024909\ttotal: 6.67s\tremaining: 4.64s\n",
      "112:\tlearn: 0.0024735\ttotal: 6.73s\tremaining: 4.58s\n",
      "113:\tlearn: 0.0024611\ttotal: 6.77s\tremaining: 4.51s\n",
      "114:\tlearn: 0.0024270\ttotal: 6.84s\tremaining: 4.46s\n",
      "115:\tlearn: 0.0023975\ttotal: 6.9s\tremaining: 4.4s\n",
      "116:\tlearn: 0.0023892\ttotal: 6.95s\tremaining: 4.34s\n",
      "117:\tlearn: 0.0023727\ttotal: 7.01s\tremaining: 4.28s\n",
      "118:\tlearn: 0.0023492\ttotal: 7.07s\tremaining: 4.21s\n",
      "119:\tlearn: 0.0023234\ttotal: 7.13s\tremaining: 4.16s\n",
      "120:\tlearn: 0.0023035\ttotal: 7.2s\tremaining: 4.11s\n",
      "121:\tlearn: 0.0022853\ttotal: 7.26s\tremaining: 4.05s\n",
      "122:\tlearn: 0.0022654\ttotal: 7.32s\tremaining: 3.99s\n",
      "123:\tlearn: 0.0022464\ttotal: 7.38s\tremaining: 3.93s\n",
      "124:\tlearn: 0.0022365\ttotal: 7.44s\tremaining: 3.87s\n",
      "125:\tlearn: 0.0022253\ttotal: 7.49s\tremaining: 3.81s\n",
      "126:\tlearn: 0.0022186\ttotal: 7.54s\tremaining: 3.74s\n",
      "127:\tlearn: 0.0022129\ttotal: 7.59s\tremaining: 3.68s\n",
      "128:\tlearn: 0.0022018\ttotal: 7.66s\tremaining: 3.62s\n",
      "129:\tlearn: 0.0021919\ttotal: 7.71s\tremaining: 3.56s\n",
      "130:\tlearn: 0.0021763\ttotal: 7.77s\tremaining: 3.5s\n",
      "131:\tlearn: 0.0021569\ttotal: 7.83s\tremaining: 3.44s\n",
      "132:\tlearn: 0.0021433\ttotal: 7.89s\tremaining: 3.38s\n",
      "133:\tlearn: 0.0021360\ttotal: 7.95s\tremaining: 3.32s\n",
      "134:\tlearn: 0.0021247\ttotal: 8.03s\tremaining: 3.27s\n",
      "135:\tlearn: 0.0021198\ttotal: 8.09s\tremaining: 3.21s\n",
      "136:\tlearn: 0.0021109\ttotal: 8.15s\tremaining: 3.15s\n",
      "137:\tlearn: 0.0020927\ttotal: 8.21s\tremaining: 3.09s\n",
      "138:\tlearn: 0.0020864\ttotal: 8.26s\tremaining: 3.03s\n",
      "139:\tlearn: 0.0020818\ttotal: 8.32s\tremaining: 2.97s\n",
      "140:\tlearn: 0.0020727\ttotal: 8.39s\tremaining: 2.91s\n",
      "141:\tlearn: 0.0020680\ttotal: 8.45s\tremaining: 2.86s\n",
      "142:\tlearn: 0.0020569\ttotal: 8.51s\tremaining: 2.8s\n",
      "143:\tlearn: 0.0020431\ttotal: 8.58s\tremaining: 2.74s\n",
      "144:\tlearn: 0.0020384\ttotal: 8.64s\tremaining: 2.68s\n",
      "145:\tlearn: 0.0020263\ttotal: 8.7s\tremaining: 2.62s\n",
      "146:\tlearn: 0.0020132\ttotal: 8.77s\tremaining: 2.56s\n",
      "147:\tlearn: 0.0020034\ttotal: 8.83s\tremaining: 2.5s\n",
      "148:\tlearn: 0.0019955\ttotal: 8.89s\tremaining: 2.44s\n",
      "149:\tlearn: 0.0019889\ttotal: 8.96s\tremaining: 2.39s\n",
      "150:\tlearn: 0.0019825\ttotal: 9.02s\tremaining: 2.33s\n",
      "151:\tlearn: 0.0019782\ttotal: 9.08s\tremaining: 2.27s\n",
      "152:\tlearn: 0.0019679\ttotal: 9.14s\tremaining: 2.21s\n",
      "153:\tlearn: 0.0019621\ttotal: 9.2s\tremaining: 2.15s\n",
      "154:\tlearn: 0.0019521\ttotal: 9.27s\tremaining: 2.09s\n",
      "155:\tlearn: 0.0019505\ttotal: 9.32s\tremaining: 2.03s\n",
      "156:\tlearn: 0.0019478\ttotal: 9.37s\tremaining: 1.97s\n",
      "157:\tlearn: 0.0019413\ttotal: 9.41s\tremaining: 1.91s\n",
      "158:\tlearn: 0.0019375\ttotal: 9.46s\tremaining: 1.84s\n",
      "159:\tlearn: 0.0019345\ttotal: 9.51s\tremaining: 1.78s\n",
      "160:\tlearn: 0.0019276\ttotal: 9.56s\tremaining: 1.72s\n",
      "161:\tlearn: 0.0019249\ttotal: 9.62s\tremaining: 1.66s\n",
      "162:\tlearn: 0.0019147\ttotal: 9.69s\tremaining: 1.6s\n",
      "163:\tlearn: 0.0019069\ttotal: 9.76s\tremaining: 1.55s\n",
      "164:\tlearn: 0.0018947\ttotal: 9.82s\tremaining: 1.49s\n",
      "165:\tlearn: 0.0018903\ttotal: 9.88s\tremaining: 1.43s\n",
      "166:\tlearn: 0.0018840\ttotal: 9.94s\tremaining: 1.37s\n",
      "167:\tlearn: 0.0018795\ttotal: 10s\tremaining: 1.31s\n",
      "168:\tlearn: 0.0018768\ttotal: 10.1s\tremaining: 1.25s\n",
      "169:\tlearn: 0.0018743\ttotal: 10.1s\tremaining: 1.19s\n",
      "170:\tlearn: 0.0018697\ttotal: 10.2s\tremaining: 1.13s\n",
      "171:\tlearn: 0.0018665\ttotal: 10.2s\tremaining: 1.07s\n",
      "172:\tlearn: 0.0018642\ttotal: 10.3s\tremaining: 1.01s\n",
      "173:\tlearn: 0.0018618\ttotal: 10.3s\tremaining: 950ms\n",
      "174:\tlearn: 0.0018560\ttotal: 10.4s\tremaining: 891ms\n",
      "175:\tlearn: 0.0018534\ttotal: 10.4s\tremaining: 831ms\n",
      "176:\tlearn: 0.0018509\ttotal: 10.5s\tremaining: 772ms\n",
      "177:\tlearn: 0.0018488\ttotal: 10.6s\tremaining: 712ms\n",
      "178:\tlearn: 0.0018463\ttotal: 10.6s\tremaining: 653ms\n",
      "179:\tlearn: 0.0018419\ttotal: 10.7s\tremaining: 593ms\n",
      "180:\tlearn: 0.0018380\ttotal: 10.7s\tremaining: 534ms\n",
      "181:\tlearn: 0.0018308\ttotal: 10.8s\tremaining: 474ms\n",
      "182:\tlearn: 0.0018282\ttotal: 10.8s\tremaining: 415ms\n",
      "183:\tlearn: 0.0018220\ttotal: 10.9s\tremaining: 356ms\n",
      "184:\tlearn: 0.0018177\ttotal: 11s\tremaining: 296ms\n",
      "185:\tlearn: 0.0018152\ttotal: 11s\tremaining: 237ms\n",
      "186:\tlearn: 0.0018128\ttotal: 11.1s\tremaining: 177ms\n",
      "187:\tlearn: 0.0018084\ttotal: 11.1s\tremaining: 118ms\n",
      "188:\tlearn: 0.0018059\ttotal: 11.2s\tremaining: 59.1ms\n",
      "189:\tlearn: 0.0017994\ttotal: 11.2s\tremaining: 0us\n",
      "0:\tlearn: 0.6258850\ttotal: 57.7ms\tremaining: 10.9s\n",
      "1:\tlearn: 0.5675084\ttotal: 112ms\tremaining: 10.5s\n",
      "2:\tlearn: 0.5163779\ttotal: 165ms\tremaining: 10.3s\n",
      "3:\tlearn: 0.4711171\ttotal: 226ms\tremaining: 10.5s\n",
      "4:\tlearn: 0.4308445\ttotal: 288ms\tremaining: 10.6s\n",
      "5:\tlearn: 0.3947824\ttotal: 355ms\tremaining: 10.9s\n",
      "6:\tlearn: 0.3623559\ttotal: 421ms\tremaining: 11s\n",
      "7:\tlearn: 0.3330764\ttotal: 488ms\tremaining: 11.1s\n",
      "8:\tlearn: 0.3065929\ttotal: 529ms\tremaining: 10.6s\n",
      "9:\tlearn: 0.2825927\ttotal: 576ms\tremaining: 10.4s\n",
      "10:\tlearn: 0.2607998\ttotal: 640ms\tremaining: 10.4s\n",
      "11:\tlearn: 0.2408181\ttotal: 700ms\tremaining: 10.4s\n",
      "12:\tlearn: 0.2226053\ttotal: 764ms\tremaining: 10.4s\n",
      "13:\tlearn: 0.2059548\ttotal: 830ms\tremaining: 10.4s\n",
      "14:\tlearn: 0.1907371\ttotal: 891ms\tremaining: 10.4s\n",
      "15:\tlearn: 0.1767695\ttotal: 957ms\tremaining: 10.4s\n",
      "16:\tlearn: 0.1639354\ttotal: 1.02s\tremaining: 10.4s\n",
      "17:\tlearn: 0.1520582\ttotal: 1.08s\tremaining: 10.3s\n",
      "18:\tlearn: 0.1411294\ttotal: 1.15s\tremaining: 10.3s\n",
      "19:\tlearn: 0.1310482\ttotal: 1.21s\tremaining: 10.3s\n",
      "20:\tlearn: 0.1217545\ttotal: 1.28s\tremaining: 10.3s\n",
      "21:\tlearn: 0.1131760\ttotal: 1.35s\tremaining: 10.3s\n",
      "22:\tlearn: 0.1052490\ttotal: 1.41s\tremaining: 10.2s\n",
      "23:\tlearn: 0.0979297\ttotal: 1.47s\tremaining: 10.2s\n",
      "24:\tlearn: 0.0911321\ttotal: 1.53s\tremaining: 10.1s\n",
      "25:\tlearn: 0.0848682\ttotal: 1.6s\tremaining: 10.1s\n",
      "26:\tlearn: 0.0790222\ttotal: 1.66s\tremaining: 10s\n",
      "27:\tlearn: 0.0736355\ttotal: 1.72s\tremaining: 9.95s\n",
      "28:\tlearn: 0.0686542\ttotal: 1.78s\tremaining: 9.9s\n",
      "29:\tlearn: 0.0640200\ttotal: 1.85s\tremaining: 9.87s\n",
      "30:\tlearn: 0.0597101\ttotal: 1.91s\tremaining: 9.79s\n",
      "31:\tlearn: 0.0557212\ttotal: 1.97s\tremaining: 9.71s\n",
      "32:\tlearn: 0.0519918\ttotal: 2.02s\tremaining: 9.63s\n",
      "33:\tlearn: 0.0485357\ttotal: 2.08s\tremaining: 9.54s\n",
      "34:\tlearn: 0.0453395\ttotal: 2.12s\tremaining: 9.4s\n",
      "35:\tlearn: 0.0423446\ttotal: 2.17s\tremaining: 9.31s\n",
      "36:\tlearn: 0.0395640\ttotal: 2.23s\tremaining: 9.22s\n",
      "37:\tlearn: 0.0369989\ttotal: 2.29s\tremaining: 9.16s\n",
      "38:\tlearn: 0.0346024\ttotal: 2.35s\tremaining: 9.09s\n",
      "39:\tlearn: 0.0323823\ttotal: 2.41s\tremaining: 9.03s\n",
      "40:\tlearn: 0.0303082\ttotal: 2.46s\tremaining: 8.95s\n",
      "41:\tlearn: 0.0283612\ttotal: 2.52s\tremaining: 8.87s\n",
      "42:\tlearn: 0.0265468\ttotal: 2.57s\tremaining: 8.79s\n",
      "43:\tlearn: 0.0248742\ttotal: 2.63s\tremaining: 8.73s\n",
      "44:\tlearn: 0.0233197\ttotal: 2.69s\tremaining: 8.66s\n",
      "45:\tlearn: 0.0218692\ttotal: 2.74s\tremaining: 8.58s\n",
      "46:\tlearn: 0.0205227\ttotal: 2.8s\tremaining: 8.51s\n",
      "47:\tlearn: 0.0192535\ttotal: 2.85s\tremaining: 8.44s\n",
      "48:\tlearn: 0.0180731\ttotal: 2.91s\tremaining: 8.38s\n",
      "49:\tlearn: 0.0169739\ttotal: 2.96s\tremaining: 8.3s\n",
      "50:\tlearn: 0.0159525\ttotal: 3.02s\tremaining: 8.22s\n",
      "51:\tlearn: 0.0150004\ttotal: 3.07s\tremaining: 8.16s\n",
      "52:\tlearn: 0.0141179\ttotal: 3.13s\tremaining: 8.09s\n",
      "53:\tlearn: 0.0133051\ttotal: 3.19s\tremaining: 8.04s\n",
      "54:\tlearn: 0.0125366\ttotal: 3.25s\tremaining: 7.97s\n",
      "55:\tlearn: 0.0118281\ttotal: 3.32s\tremaining: 7.94s\n",
      "56:\tlearn: 0.0111648\ttotal: 3.37s\tremaining: 7.87s\n",
      "57:\tlearn: 0.0105434\ttotal: 3.42s\tremaining: 7.79s\n",
      "58:\tlearn: 0.0099649\ttotal: 3.48s\tremaining: 7.72s\n",
      "59:\tlearn: 0.0094266\ttotal: 3.53s\tremaining: 7.65s\n",
      "60:\tlearn: 0.0089324\ttotal: 3.59s\tremaining: 7.59s\n",
      "61:\tlearn: 0.0084688\ttotal: 3.65s\tremaining: 7.54s\n",
      "62:\tlearn: 0.0080347\ttotal: 3.71s\tremaining: 7.47s\n",
      "63:\tlearn: 0.0076313\ttotal: 3.76s\tremaining: 7.41s\n",
      "64:\tlearn: 0.0072546\ttotal: 3.82s\tremaining: 7.34s\n",
      "65:\tlearn: 0.0069026\ttotal: 3.85s\tremaining: 7.24s\n",
      "66:\tlearn: 0.0065863\ttotal: 3.9s\tremaining: 7.16s\n",
      "67:\tlearn: 0.0062892\ttotal: 3.96s\tremaining: 7.1s\n",
      "68:\tlearn: 0.0060076\ttotal: 4.02s\tremaining: 7.04s\n",
      "69:\tlearn: 0.0057465\ttotal: 4.07s\tremaining: 6.98s\n",
      "70:\tlearn: 0.0055024\ttotal: 4.13s\tremaining: 6.91s\n",
      "71:\tlearn: 0.0052741\ttotal: 4.18s\tremaining: 6.85s\n",
      "72:\tlearn: 0.0050588\ttotal: 4.23s\tremaining: 6.79s\n",
      "73:\tlearn: 0.0048590\ttotal: 4.28s\tremaining: 6.7s\n",
      "74:\tlearn: 0.0046711\ttotal: 4.31s\tremaining: 6.61s\n",
      "75:\tlearn: 0.0044996\ttotal: 4.37s\tremaining: 6.55s\n",
      "76:\tlearn: 0.0043404\ttotal: 4.43s\tremaining: 6.5s\n",
      "77:\tlearn: 0.0041900\ttotal: 4.48s\tremaining: 6.43s\n",
      "78:\tlearn: 0.0040430\ttotal: 4.54s\tremaining: 6.37s\n",
      "79:\tlearn: 0.0039168\ttotal: 4.59s\tremaining: 6.31s\n",
      "80:\tlearn: 0.0037981\ttotal: 4.65s\tremaining: 6.25s\n",
      "81:\tlearn: 0.0036803\ttotal: 4.71s\tremaining: 6.2s\n",
      "82:\tlearn: 0.0035799\ttotal: 4.76s\tremaining: 6.14s\n",
      "83:\tlearn: 0.0034747\ttotal: 4.82s\tremaining: 6.08s\n",
      "84:\tlearn: 0.0033820\ttotal: 4.88s\tremaining: 6.02s\n",
      "85:\tlearn: 0.0033034\ttotal: 4.93s\tremaining: 5.96s\n",
      "86:\tlearn: 0.0032246\ttotal: 4.99s\tremaining: 5.91s\n",
      "87:\tlearn: 0.0031458\ttotal: 5.05s\tremaining: 5.85s\n",
      "88:\tlearn: 0.0030767\ttotal: 5.11s\tremaining: 5.79s\n",
      "89:\tlearn: 0.0030149\ttotal: 5.16s\tremaining: 5.73s\n",
      "90:\tlearn: 0.0029487\ttotal: 5.21s\tremaining: 5.67s\n",
      "91:\tlearn: 0.0028880\ttotal: 5.27s\tremaining: 5.61s\n",
      "92:\tlearn: 0.0028351\ttotal: 5.33s\tremaining: 5.56s\n",
      "93:\tlearn: 0.0027898\ttotal: 5.39s\tremaining: 5.51s\n",
      "94:\tlearn: 0.0027485\ttotal: 5.45s\tremaining: 5.45s\n",
      "95:\tlearn: 0.0027002\ttotal: 5.51s\tremaining: 5.39s\n",
      "96:\tlearn: 0.0026549\ttotal: 5.56s\tremaining: 5.33s\n",
      "97:\tlearn: 0.0026127\ttotal: 5.64s\tremaining: 5.3s\n",
      "98:\tlearn: 0.0025795\ttotal: 5.7s\tremaining: 5.24s\n",
      "99:\tlearn: 0.0025509\ttotal: 5.75s\tremaining: 5.18s\n",
      "100:\tlearn: 0.0025128\ttotal: 5.82s\tremaining: 5.13s\n",
      "101:\tlearn: 0.0024767\ttotal: 5.88s\tremaining: 5.07s\n",
      "102:\tlearn: 0.0024467\ttotal: 5.93s\tremaining: 5.01s\n",
      "103:\tlearn: 0.0024238\ttotal: 5.99s\tremaining: 4.95s\n",
      "104:\tlearn: 0.0024031\ttotal: 6.04s\tremaining: 4.89s\n",
      "105:\tlearn: 0.0023861\ttotal: 6.09s\tremaining: 4.83s\n",
      "106:\tlearn: 0.0023549\ttotal: 6.15s\tremaining: 4.77s\n",
      "107:\tlearn: 0.0023240\ttotal: 6.21s\tremaining: 4.72s\n",
      "108:\tlearn: 0.0023097\ttotal: 6.26s\tremaining: 4.65s\n",
      "109:\tlearn: 0.0022788\ttotal: 6.32s\tremaining: 4.6s\n",
      "110:\tlearn: 0.0022514\ttotal: 6.38s\tremaining: 4.54s\n",
      "111:\tlearn: 0.0022355\ttotal: 6.43s\tremaining: 4.48s\n",
      "112:\tlearn: 0.0022202\ttotal: 6.49s\tremaining: 4.42s\n",
      "113:\tlearn: 0.0022042\ttotal: 6.55s\tremaining: 4.37s\n",
      "114:\tlearn: 0.0021792\ttotal: 6.62s\tremaining: 4.32s\n",
      "115:\tlearn: 0.0021601\ttotal: 6.68s\tremaining: 4.26s\n",
      "116:\tlearn: 0.0021414\ttotal: 6.75s\tremaining: 4.21s\n",
      "117:\tlearn: 0.0021214\ttotal: 6.81s\tremaining: 4.15s\n",
      "118:\tlearn: 0.0021082\ttotal: 6.86s\tremaining: 4.09s\n",
      "119:\tlearn: 0.0020890\ttotal: 6.92s\tremaining: 4.04s\n",
      "120:\tlearn: 0.0020744\ttotal: 6.98s\tremaining: 3.98s\n",
      "121:\tlearn: 0.0020592\ttotal: 7.04s\tremaining: 3.92s\n",
      "122:\tlearn: 0.0020462\ttotal: 7.09s\tremaining: 3.86s\n",
      "123:\tlearn: 0.0020324\ttotal: 7.16s\tremaining: 3.81s\n",
      "124:\tlearn: 0.0020216\ttotal: 7.23s\tremaining: 3.76s\n",
      "125:\tlearn: 0.0020097\ttotal: 7.28s\tremaining: 3.7s\n",
      "126:\tlearn: 0.0019963\ttotal: 7.35s\tremaining: 3.64s\n",
      "127:\tlearn: 0.0019826\ttotal: 7.41s\tremaining: 3.59s\n",
      "128:\tlearn: 0.0019674\ttotal: 7.47s\tremaining: 3.53s\n",
      "129:\tlearn: 0.0019593\ttotal: 7.53s\tremaining: 3.47s\n",
      "130:\tlearn: 0.0019472\ttotal: 7.59s\tremaining: 3.42s\n",
      "131:\tlearn: 0.0019319\ttotal: 7.65s\tremaining: 3.36s\n",
      "132:\tlearn: 0.0019245\ttotal: 7.71s\tremaining: 3.3s\n",
      "133:\tlearn: 0.0019166\ttotal: 7.76s\tremaining: 3.24s\n",
      "134:\tlearn: 0.0019078\ttotal: 7.82s\tremaining: 3.19s\n",
      "135:\tlearn: 0.0019030\ttotal: 7.87s\tremaining: 3.13s\n",
      "136:\tlearn: 0.0018978\ttotal: 7.92s\tremaining: 3.07s\n",
      "137:\tlearn: 0.0018836\ttotal: 7.99s\tremaining: 3.01s\n",
      "138:\tlearn: 0.0018703\ttotal: 8.03s\tremaining: 2.94s\n",
      "139:\tlearn: 0.0018567\ttotal: 8.08s\tremaining: 2.89s\n",
      "140:\tlearn: 0.0018471\ttotal: 8.14s\tremaining: 2.83s\n",
      "141:\tlearn: 0.0018379\ttotal: 8.2s\tremaining: 2.77s\n",
      "142:\tlearn: 0.0018341\ttotal: 8.25s\tremaining: 2.71s\n",
      "143:\tlearn: 0.0018262\ttotal: 8.3s\tremaining: 2.65s\n",
      "144:\tlearn: 0.0018212\ttotal: 8.35s\tremaining: 2.59s\n",
      "145:\tlearn: 0.0018168\ttotal: 8.41s\tremaining: 2.54s\n",
      "146:\tlearn: 0.0018104\ttotal: 8.47s\tremaining: 2.48s\n",
      "147:\tlearn: 0.0017990\ttotal: 8.53s\tremaining: 2.42s\n",
      "148:\tlearn: 0.0017927\ttotal: 8.59s\tremaining: 2.36s\n",
      "149:\tlearn: 0.0017840\ttotal: 8.65s\tremaining: 2.31s\n",
      "150:\tlearn: 0.0017756\ttotal: 8.71s\tremaining: 2.25s\n",
      "151:\tlearn: 0.0017694\ttotal: 8.78s\tremaining: 2.19s\n",
      "152:\tlearn: 0.0017584\ttotal: 8.84s\tremaining: 2.14s\n",
      "153:\tlearn: 0.0017550\ttotal: 8.9s\tremaining: 2.08s\n",
      "154:\tlearn: 0.0017525\ttotal: 8.95s\tremaining: 2.02s\n",
      "155:\tlearn: 0.0017470\ttotal: 9.01s\tremaining: 1.96s\n",
      "156:\tlearn: 0.0017432\ttotal: 9.06s\tremaining: 1.9s\n",
      "157:\tlearn: 0.0017325\ttotal: 9.12s\tremaining: 1.85s\n",
      "158:\tlearn: 0.0017225\ttotal: 9.19s\tremaining: 1.79s\n",
      "159:\tlearn: 0.0017161\ttotal: 9.24s\tremaining: 1.73s\n",
      "160:\tlearn: 0.0017106\ttotal: 9.3s\tremaining: 1.67s\n",
      "161:\tlearn: 0.0017028\ttotal: 9.35s\tremaining: 1.62s\n",
      "162:\tlearn: 0.0016974\ttotal: 9.41s\tremaining: 1.56s\n",
      "163:\tlearn: 0.0016870\ttotal: 9.48s\tremaining: 1.5s\n",
      "164:\tlearn: 0.0016792\ttotal: 9.54s\tremaining: 1.45s\n",
      "165:\tlearn: 0.0016715\ttotal: 9.61s\tremaining: 1.39s\n",
      "166:\tlearn: 0.0016660\ttotal: 9.66s\tremaining: 1.33s\n",
      "167:\tlearn: 0.0016593\ttotal: 9.73s\tremaining: 1.27s\n",
      "168:\tlearn: 0.0016568\ttotal: 9.79s\tremaining: 1.22s\n",
      "169:\tlearn: 0.0016501\ttotal: 9.85s\tremaining: 1.16s\n",
      "170:\tlearn: 0.0016420\ttotal: 9.92s\tremaining: 1.1s\n",
      "171:\tlearn: 0.0016387\ttotal: 9.97s\tremaining: 1.04s\n",
      "172:\tlearn: 0.0016362\ttotal: 10s\tremaining: 986ms\n",
      "173:\tlearn: 0.0016275\ttotal: 10.1s\tremaining: 927ms\n",
      "174:\tlearn: 0.0016201\ttotal: 10.1s\tremaining: 868ms\n",
      "175:\tlearn: 0.0016145\ttotal: 10.2s\tremaining: 811ms\n",
      "176:\tlearn: 0.0016110\ttotal: 10.2s\tremaining: 753ms\n",
      "177:\tlearn: 0.0016079\ttotal: 10.3s\tremaining: 694ms\n",
      "178:\tlearn: 0.0016052\ttotal: 10.4s\tremaining: 636ms\n",
      "179:\tlearn: 0.0016014\ttotal: 10.4s\tremaining: 578ms\n",
      "180:\tlearn: 0.0015980\ttotal: 10.5s\tremaining: 520ms\n",
      "181:\tlearn: 0.0015923\ttotal: 10.5s\tremaining: 463ms\n",
      "182:\tlearn: 0.0015905\ttotal: 10.6s\tremaining: 404ms\n",
      "183:\tlearn: 0.0015878\ttotal: 10.6s\tremaining: 346ms\n",
      "184:\tlearn: 0.0015854\ttotal: 10.7s\tremaining: 289ms\n",
      "185:\tlearn: 0.0015802\ttotal: 10.7s\tremaining: 231ms\n",
      "186:\tlearn: 0.0015783\ttotal: 10.8s\tremaining: 173ms\n",
      "187:\tlearn: 0.0015761\ttotal: 10.8s\tremaining: 115ms\n",
      "188:\tlearn: 0.0015694\ttotal: 10.9s\tremaining: 57.7ms\n",
      "189:\tlearn: 0.0015668\ttotal: 11s\tremaining: 0us\n",
      "0:\tlearn: 0.6258873\ttotal: 220ms\tremaining: 41.5s\n",
      "1:\tlearn: 0.5675231\ttotal: 340ms\tremaining: 31.9s\n",
      "2:\tlearn: 0.5164158\ttotal: 456ms\tremaining: 28.4s\n",
      "3:\tlearn: 0.4711728\ttotal: 567ms\tremaining: 26.4s\n",
      "4:\tlearn: 0.4309090\ttotal: 660ms\tremaining: 24.4s\n",
      "5:\tlearn: 0.3948643\ttotal: 782ms\tremaining: 24s\n",
      "6:\tlearn: 0.3624734\ttotal: 882ms\tremaining: 23s\n",
      "7:\tlearn: 0.3332148\ttotal: 1.01s\tremaining: 22.9s\n",
      "8:\tlearn: 0.3067476\ttotal: 1.1s\tremaining: 22.1s\n",
      "9:\tlearn: 0.2827408\ttotal: 1.19s\tremaining: 21.5s\n",
      "10:\tlearn: 0.2609601\ttotal: 1.28s\tremaining: 20.8s\n",
      "11:\tlearn: 0.2409983\ttotal: 1.35s\tremaining: 20.1s\n",
      "12:\tlearn: 0.2228069\ttotal: 1.43s\tremaining: 19.5s\n",
      "13:\tlearn: 0.2061661\ttotal: 1.51s\tremaining: 19s\n",
      "14:\tlearn: 0.1909493\ttotal: 1.6s\tremaining: 18.6s\n",
      "15:\tlearn: 0.1769841\ttotal: 1.68s\tremaining: 18.3s\n",
      "16:\tlearn: 0.1641526\ttotal: 1.75s\tremaining: 17.8s\n",
      "17:\tlearn: 0.1522791\ttotal: 1.82s\tremaining: 17.4s\n",
      "18:\tlearn: 0.1413789\ttotal: 1.9s\tremaining: 17.1s\n",
      "19:\tlearn: 0.1312886\ttotal: 1.97s\tremaining: 16.8s\n",
      "20:\tlearn: 0.1220308\ttotal: 2.06s\tremaining: 16.6s\n",
      "21:\tlearn: 0.1134526\ttotal: 2.14s\tremaining: 16.3s\n",
      "22:\tlearn: 0.1055304\ttotal: 2.22s\tremaining: 16.1s\n",
      "23:\tlearn: 0.0982147\ttotal: 2.31s\tremaining: 16s\n",
      "24:\tlearn: 0.0914246\ttotal: 2.39s\tremaining: 15.8s\n",
      "25:\tlearn: 0.0851679\ttotal: 2.47s\tremaining: 15.6s\n",
      "26:\tlearn: 0.0793276\ttotal: 2.54s\tremaining: 15.3s\n",
      "27:\tlearn: 0.0739627\ttotal: 2.62s\tremaining: 15.2s\n",
      "28:\tlearn: 0.0689684\ttotal: 2.7s\tremaining: 15s\n",
      "29:\tlearn: 0.0643740\ttotal: 2.77s\tremaining: 14.8s\n",
      "30:\tlearn: 0.0600704\ttotal: 2.84s\tremaining: 14.6s\n",
      "31:\tlearn: 0.0560816\ttotal: 2.91s\tremaining: 14.4s\n",
      "32:\tlearn: 0.0523703\ttotal: 2.98s\tremaining: 14.2s\n",
      "33:\tlearn: 0.0489284\ttotal: 3.06s\tremaining: 14s\n",
      "34:\tlearn: 0.0457272\ttotal: 3.13s\tremaining: 13.9s\n",
      "35:\tlearn: 0.0427563\ttotal: 3.2s\tremaining: 13.7s\n",
      "36:\tlearn: 0.0399870\ttotal: 3.27s\tremaining: 13.5s\n",
      "37:\tlearn: 0.0373990\ttotal: 3.34s\tremaining: 13.3s\n",
      "38:\tlearn: 0.0350075\ttotal: 3.41s\tremaining: 13.2s\n",
      "39:\tlearn: 0.0327793\ttotal: 3.49s\tremaining: 13.1s\n",
      "40:\tlearn: 0.0307077\ttotal: 3.56s\tremaining: 12.9s\n",
      "41:\tlearn: 0.0287526\ttotal: 3.62s\tremaining: 12.8s\n",
      "42:\tlearn: 0.0269406\ttotal: 3.69s\tremaining: 12.6s\n",
      "43:\tlearn: 0.0252680\ttotal: 3.76s\tremaining: 12.5s\n",
      "44:\tlearn: 0.0237146\ttotal: 3.84s\tremaining: 12.4s\n",
      "45:\tlearn: 0.0222642\ttotal: 3.91s\tremaining: 12.2s\n",
      "46:\tlearn: 0.0209219\ttotal: 3.98s\tremaining: 12.1s\n",
      "47:\tlearn: 0.0196670\ttotal: 4.09s\tremaining: 12.1s\n",
      "48:\tlearn: 0.0184825\ttotal: 4.17s\tremaining: 12s\n",
      "49:\tlearn: 0.0173832\ttotal: 4.23s\tremaining: 11.9s\n",
      "50:\tlearn: 0.0163665\ttotal: 4.3s\tremaining: 11.7s\n",
      "51:\tlearn: 0.0154256\ttotal: 4.38s\tremaining: 11.6s\n",
      "52:\tlearn: 0.0145454\ttotal: 4.45s\tremaining: 11.5s\n",
      "53:\tlearn: 0.0137163\ttotal: 4.52s\tremaining: 11.4s\n",
      "54:\tlearn: 0.0129540\ttotal: 4.59s\tremaining: 11.3s\n",
      "55:\tlearn: 0.0122376\ttotal: 4.66s\tremaining: 11.1s\n",
      "56:\tlearn: 0.0115712\ttotal: 4.72s\tremaining: 11s\n",
      "57:\tlearn: 0.0109503\ttotal: 4.79s\tremaining: 10.9s\n",
      "58:\tlearn: 0.0103737\ttotal: 4.86s\tremaining: 10.8s\n",
      "59:\tlearn: 0.0098393\ttotal: 4.92s\tremaining: 10.7s\n",
      "60:\tlearn: 0.0093448\ttotal: 5s\tremaining: 10.6s\n",
      "61:\tlearn: 0.0088812\ttotal: 5.07s\tremaining: 10.5s\n",
      "62:\tlearn: 0.0084393\ttotal: 5.13s\tremaining: 10.4s\n",
      "63:\tlearn: 0.0080410\ttotal: 5.21s\tremaining: 10.3s\n",
      "64:\tlearn: 0.0076685\ttotal: 5.28s\tremaining: 10.2s\n",
      "65:\tlearn: 0.0073169\ttotal: 5.36s\tremaining: 10.1s\n",
      "66:\tlearn: 0.0069923\ttotal: 5.43s\tremaining: 9.97s\n",
      "67:\tlearn: 0.0066887\ttotal: 5.5s\tremaining: 9.86s\n",
      "68:\tlearn: 0.0064072\ttotal: 5.56s\tremaining: 9.75s\n",
      "69:\tlearn: 0.0061453\ttotal: 5.62s\tremaining: 9.63s\n",
      "70:\tlearn: 0.0058961\ttotal: 5.69s\tremaining: 9.53s\n",
      "71:\tlearn: 0.0056724\ttotal: 5.74s\tremaining: 9.41s\n",
      "72:\tlearn: 0.0054582\ttotal: 5.81s\tremaining: 9.31s\n",
      "73:\tlearn: 0.0052625\ttotal: 5.89s\tremaining: 9.24s\n",
      "74:\tlearn: 0.0050795\ttotal: 5.97s\tremaining: 9.16s\n",
      "75:\tlearn: 0.0049133\ttotal: 6.05s\tremaining: 9.08s\n",
      "76:\tlearn: 0.0047601\ttotal: 6.13s\tremaining: 8.99s\n",
      "77:\tlearn: 0.0046141\ttotal: 6.2s\tremaining: 8.9s\n",
      "78:\tlearn: 0.0044738\ttotal: 6.27s\tremaining: 8.81s\n",
      "79:\tlearn: 0.0043483\ttotal: 6.33s\tremaining: 8.71s\n",
      "80:\tlearn: 0.0042315\ttotal: 6.41s\tremaining: 8.63s\n",
      "81:\tlearn: 0.0041229\ttotal: 6.49s\tremaining: 8.54s\n",
      "82:\tlearn: 0.0040188\ttotal: 6.56s\tremaining: 8.46s\n",
      "83:\tlearn: 0.0039121\ttotal: 6.64s\tremaining: 8.38s\n",
      "84:\tlearn: 0.0038209\ttotal: 6.71s\tremaining: 8.28s\n",
      "85:\tlearn: 0.0037404\ttotal: 6.78s\tremaining: 8.2s\n",
      "86:\tlearn: 0.0036556\ttotal: 6.86s\tremaining: 8.12s\n",
      "87:\tlearn: 0.0035753\ttotal: 6.92s\tremaining: 8.03s\n",
      "88:\tlearn: 0.0035063\ttotal: 6.99s\tremaining: 7.93s\n",
      "89:\tlearn: 0.0034384\ttotal: 7.06s\tremaining: 7.84s\n",
      "90:\tlearn: 0.0033797\ttotal: 7.12s\tremaining: 7.75s\n",
      "91:\tlearn: 0.0033121\ttotal: 7.2s\tremaining: 7.67s\n",
      "92:\tlearn: 0.0032485\ttotal: 7.27s\tremaining: 7.58s\n",
      "93:\tlearn: 0.0031939\ttotal: 7.35s\tremaining: 7.5s\n",
      "94:\tlearn: 0.0031490\ttotal: 7.41s\tremaining: 7.41s\n",
      "95:\tlearn: 0.0031118\ttotal: 7.48s\tremaining: 7.32s\n",
      "96:\tlearn: 0.0030729\ttotal: 7.54s\tremaining: 7.23s\n",
      "97:\tlearn: 0.0030388\ttotal: 7.61s\tremaining: 7.14s\n",
      "98:\tlearn: 0.0030075\ttotal: 7.67s\tremaining: 7.05s\n",
      "99:\tlearn: 0.0029667\ttotal: 7.75s\tremaining: 6.97s\n",
      "100:\tlearn: 0.0029397\ttotal: 7.81s\tremaining: 6.88s\n",
      "101:\tlearn: 0.0029044\ttotal: 7.89s\tremaining: 6.81s\n",
      "102:\tlearn: 0.0028654\ttotal: 7.97s\tremaining: 6.73s\n",
      "103:\tlearn: 0.0028462\ttotal: 8.03s\tremaining: 6.64s\n",
      "104:\tlearn: 0.0028146\ttotal: 8.12s\tremaining: 6.57s\n",
      "105:\tlearn: 0.0027833\ttotal: 8.19s\tremaining: 6.49s\n",
      "106:\tlearn: 0.0027554\ttotal: 8.28s\tremaining: 6.42s\n",
      "107:\tlearn: 0.0027299\ttotal: 8.34s\tremaining: 6.33s\n",
      "108:\tlearn: 0.0027142\ttotal: 8.41s\tremaining: 6.25s\n",
      "109:\tlearn: 0.0026986\ttotal: 8.48s\tremaining: 6.17s\n",
      "110:\tlearn: 0.0026700\ttotal: 8.55s\tremaining: 6.09s\n",
      "111:\tlearn: 0.0026442\ttotal: 8.63s\tremaining: 6.01s\n",
      "112:\tlearn: 0.0026223\ttotal: 8.71s\tremaining: 5.93s\n",
      "113:\tlearn: 0.0025958\ttotal: 8.78s\tremaining: 5.86s\n",
      "114:\tlearn: 0.0025701\ttotal: 8.86s\tremaining: 5.78s\n",
      "115:\tlearn: 0.0025487\ttotal: 8.94s\tremaining: 5.7s\n",
      "116:\tlearn: 0.0025352\ttotal: 9.01s\tremaining: 5.62s\n",
      "117:\tlearn: 0.0025058\ttotal: 9.08s\tremaining: 5.54s\n",
      "118:\tlearn: 0.0024859\ttotal: 9.18s\tremaining: 5.47s\n",
      "119:\tlearn: 0.0024675\ttotal: 9.25s\tremaining: 5.39s\n",
      "120:\tlearn: 0.0024604\ttotal: 9.31s\tremaining: 5.31s\n",
      "121:\tlearn: 0.0024445\ttotal: 9.38s\tremaining: 5.23s\n",
      "122:\tlearn: 0.0024254\ttotal: 9.45s\tremaining: 5.15s\n",
      "123:\tlearn: 0.0024084\ttotal: 9.53s\tremaining: 5.07s\n",
      "124:\tlearn: 0.0023898\ttotal: 9.61s\tremaining: 5s\n",
      "125:\tlearn: 0.0023806\ttotal: 9.67s\tremaining: 4.91s\n",
      "126:\tlearn: 0.0023717\ttotal: 9.74s\tremaining: 4.83s\n",
      "127:\tlearn: 0.0023611\ttotal: 9.81s\tremaining: 4.75s\n",
      "128:\tlearn: 0.0023450\ttotal: 9.89s\tremaining: 4.68s\n",
      "129:\tlearn: 0.0023286\ttotal: 9.97s\tremaining: 4.6s\n",
      "130:\tlearn: 0.0023132\ttotal: 10s\tremaining: 4.52s\n",
      "131:\tlearn: 0.0022994\ttotal: 10.1s\tremaining: 4.45s\n",
      "132:\tlearn: 0.0022970\ttotal: 10.2s\tremaining: 4.36s\n",
      "133:\tlearn: 0.0022858\ttotal: 10.2s\tremaining: 4.28s\n",
      "134:\tlearn: 0.0022799\ttotal: 10.3s\tremaining: 4.2s\n",
      "135:\tlearn: 0.0022764\ttotal: 10.4s\tremaining: 4.12s\n",
      "136:\tlearn: 0.0022724\ttotal: 10.4s\tremaining: 4.04s\n",
      "137:\tlearn: 0.0022604\ttotal: 10.5s\tremaining: 3.96s\n",
      "138:\tlearn: 0.0022524\ttotal: 10.6s\tremaining: 3.88s\n",
      "139:\tlearn: 0.0022428\ttotal: 10.7s\tremaining: 3.8s\n",
      "140:\tlearn: 0.0022391\ttotal: 10.7s\tremaining: 3.73s\n",
      "141:\tlearn: 0.0022258\ttotal: 10.8s\tremaining: 3.65s\n",
      "142:\tlearn: 0.0022228\ttotal: 10.9s\tremaining: 3.57s\n",
      "143:\tlearn: 0.0022114\ttotal: 10.9s\tremaining: 3.49s\n",
      "144:\tlearn: 0.0022073\ttotal: 11s\tremaining: 3.42s\n",
      "145:\tlearn: 0.0021994\ttotal: 11.1s\tremaining: 3.34s\n",
      "146:\tlearn: 0.0021949\ttotal: 11.1s\tremaining: 3.26s\n",
      "147:\tlearn: 0.0021863\ttotal: 11.2s\tremaining: 3.19s\n",
      "148:\tlearn: 0.0021790\ttotal: 11.3s\tremaining: 3.11s\n",
      "149:\tlearn: 0.0021759\ttotal: 11.4s\tremaining: 3.03s\n",
      "150:\tlearn: 0.0021693\ttotal: 11.4s\tremaining: 2.96s\n",
      "151:\tlearn: 0.0021589\ttotal: 11.5s\tremaining: 2.88s\n",
      "152:\tlearn: 0.0021500\ttotal: 11.6s\tremaining: 2.8s\n",
      "153:\tlearn: 0.0021463\ttotal: 11.6s\tremaining: 2.72s\n",
      "154:\tlearn: 0.0021361\ttotal: 11.7s\tremaining: 2.65s\n",
      "155:\tlearn: 0.0021301\ttotal: 11.8s\tremaining: 2.57s\n",
      "156:\tlearn: 0.0021252\ttotal: 11.9s\tremaining: 2.49s\n",
      "157:\tlearn: 0.0021173\ttotal: 11.9s\tremaining: 2.42s\n",
      "158:\tlearn: 0.0021143\ttotal: 12s\tremaining: 2.34s\n",
      "159:\tlearn: 0.0021047\ttotal: 12.1s\tremaining: 2.26s\n",
      "160:\tlearn: 0.0021022\ttotal: 12.1s\tremaining: 2.19s\n",
      "161:\tlearn: 0.0020975\ttotal: 12.2s\tremaining: 2.11s\n",
      "162:\tlearn: 0.0020947\ttotal: 12.3s\tremaining: 2.03s\n",
      "163:\tlearn: 0.0020850\ttotal: 12.3s\tremaining: 1.96s\n",
      "164:\tlearn: 0.0020727\ttotal: 12.4s\tremaining: 1.88s\n",
      "165:\tlearn: 0.0020696\ttotal: 12.5s\tremaining: 1.81s\n",
      "166:\tlearn: 0.0020676\ttotal: 12.6s\tremaining: 1.73s\n",
      "167:\tlearn: 0.0020644\ttotal: 12.6s\tremaining: 1.65s\n",
      "168:\tlearn: 0.0020621\ttotal: 12.7s\tremaining: 1.58s\n",
      "169:\tlearn: 0.0020534\ttotal: 12.8s\tremaining: 1.5s\n",
      "170:\tlearn: 0.0020508\ttotal: 12.8s\tremaining: 1.43s\n",
      "171:\tlearn: 0.0020482\ttotal: 12.9s\tremaining: 1.35s\n",
      "172:\tlearn: 0.0020454\ttotal: 13s\tremaining: 1.27s\n",
      "173:\tlearn: 0.0020413\ttotal: 13s\tremaining: 1.2s\n",
      "174:\tlearn: 0.0020386\ttotal: 13.1s\tremaining: 1.12s\n",
      "175:\tlearn: 0.0020327\ttotal: 13.2s\tremaining: 1.05s\n",
      "176:\tlearn: 0.0020307\ttotal: 13.2s\tremaining: 972ms\n",
      "177:\tlearn: 0.0020284\ttotal: 13.3s\tremaining: 897ms\n",
      "178:\tlearn: 0.0020261\ttotal: 13.4s\tremaining: 822ms\n",
      "179:\tlearn: 0.0020237\ttotal: 13.4s\tremaining: 747ms\n",
      "180:\tlearn: 0.0020124\ttotal: 13.5s\tremaining: 672ms\n",
      "181:\tlearn: 0.0020078\ttotal: 13.6s\tremaining: 597ms\n",
      "182:\tlearn: 0.0020056\ttotal: 13.6s\tremaining: 522ms\n",
      "183:\tlearn: 0.0020019\ttotal: 13.7s\tremaining: 447ms\n",
      "184:\tlearn: 0.0019932\ttotal: 13.8s\tremaining: 373ms\n",
      "185:\tlearn: 0.0019912\ttotal: 13.9s\tremaining: 298ms\n",
      "186:\tlearn: 0.0019886\ttotal: 13.9s\tremaining: 223ms\n",
      "187:\tlearn: 0.0019867\ttotal: 14s\tremaining: 149ms\n",
      "188:\tlearn: 0.0019847\ttotal: 14s\tremaining: 74.3ms\n",
      "189:\tlearn: 0.0019785\ttotal: 14.1s\tremaining: 0us\n",
      "0:\tlearn: 0.6258811\ttotal: 78.3ms\tremaining: 14.8s\n",
      "1:\tlearn: 0.5674992\ttotal: 147ms\tremaining: 13.8s\n",
      "2:\tlearn: 0.5163788\ttotal: 228ms\tremaining: 14.2s\n",
      "3:\tlearn: 0.4711155\ttotal: 313ms\tremaining: 14.6s\n",
      "4:\tlearn: 0.4308431\ttotal: 386ms\tremaining: 14.3s\n",
      "5:\tlearn: 0.3947890\ttotal: 468ms\tremaining: 14.3s\n",
      "6:\tlearn: 0.3623928\ttotal: 540ms\tremaining: 14.1s\n",
      "7:\tlearn: 0.3331896\ttotal: 619ms\tremaining: 14.1s\n",
      "8:\tlearn: 0.3067091\ttotal: 699ms\tremaining: 14.1s\n",
      "9:\tlearn: 0.2827080\ttotal: 772ms\tremaining: 13.9s\n",
      "10:\tlearn: 0.2609167\ttotal: 826ms\tremaining: 13.4s\n",
      "11:\tlearn: 0.2409405\ttotal: 903ms\tremaining: 13.4s\n",
      "12:\tlearn: 0.2227234\ttotal: 981ms\tremaining: 13.4s\n",
      "13:\tlearn: 0.2060735\ttotal: 1.06s\tremaining: 13.3s\n",
      "14:\tlearn: 0.1908527\ttotal: 1.14s\tremaining: 13.3s\n",
      "15:\tlearn: 0.1768827\ttotal: 1.22s\tremaining: 13.3s\n",
      "16:\tlearn: 0.1640425\ttotal: 1.29s\tremaining: 13.2s\n",
      "17:\tlearn: 0.1521833\ttotal: 1.36s\tremaining: 13.1s\n",
      "18:\tlearn: 0.1413207\ttotal: 1.44s\tremaining: 13s\n",
      "19:\tlearn: 0.1312217\ttotal: 1.52s\tremaining: 12.9s\n",
      "20:\tlearn: 0.1219368\ttotal: 1.59s\tremaining: 12.8s\n",
      "21:\tlearn: 0.1133489\ttotal: 1.67s\tremaining: 12.7s\n",
      "22:\tlearn: 0.1054079\ttotal: 1.74s\tremaining: 12.6s\n",
      "23:\tlearn: 0.0980855\ttotal: 1.81s\tremaining: 12.5s\n",
      "24:\tlearn: 0.0912831\ttotal: 1.88s\tremaining: 12.4s\n",
      "25:\tlearn: 0.0850179\ttotal: 1.96s\tremaining: 12.4s\n",
      "26:\tlearn: 0.0791744\ttotal: 2.03s\tremaining: 12.3s\n",
      "27:\tlearn: 0.0737916\ttotal: 2.11s\tremaining: 12.2s\n",
      "28:\tlearn: 0.0687837\ttotal: 2.18s\tremaining: 12.1s\n",
      "29:\tlearn: 0.0641693\ttotal: 2.26s\tremaining: 12.1s\n",
      "30:\tlearn: 0.0598569\ttotal: 2.34s\tremaining: 12s\n",
      "31:\tlearn: 0.0558698\ttotal: 2.41s\tremaining: 11.9s\n",
      "32:\tlearn: 0.0521637\ttotal: 2.49s\tremaining: 11.8s\n",
      "33:\tlearn: 0.0487097\ttotal: 2.57s\tremaining: 11.8s\n",
      "34:\tlearn: 0.0455034\ttotal: 2.64s\tremaining: 11.7s\n",
      "35:\tlearn: 0.0425069\ttotal: 2.7s\tremaining: 11.6s\n",
      "36:\tlearn: 0.0397348\ttotal: 2.77s\tremaining: 11.4s\n",
      "37:\tlearn: 0.0371701\ttotal: 2.83s\tremaining: 11.3s\n",
      "38:\tlearn: 0.0347434\ttotal: 2.91s\tremaining: 11.3s\n",
      "39:\tlearn: 0.0325140\ttotal: 2.98s\tremaining: 11.2s\n",
      "40:\tlearn: 0.0304291\ttotal: 3.07s\tremaining: 11.2s\n",
      "41:\tlearn: 0.0284967\ttotal: 3.14s\tremaining: 11.1s\n",
      "42:\tlearn: 0.0266836\ttotal: 3.21s\tremaining: 11s\n",
      "43:\tlearn: 0.0250035\ttotal: 3.28s\tremaining: 10.9s\n",
      "44:\tlearn: 0.0234345\ttotal: 3.34s\tremaining: 10.8s\n",
      "45:\tlearn: 0.0219855\ttotal: 3.4s\tremaining: 10.7s\n",
      "46:\tlearn: 0.0206377\ttotal: 3.48s\tremaining: 10.6s\n",
      "47:\tlearn: 0.0193613\ttotal: 3.54s\tremaining: 10.5s\n",
      "48:\tlearn: 0.0181959\ttotal: 3.62s\tremaining: 10.4s\n",
      "49:\tlearn: 0.0170986\ttotal: 3.68s\tremaining: 10.3s\n",
      "50:\tlearn: 0.0160798\ttotal: 3.75s\tremaining: 10.2s\n",
      "51:\tlearn: 0.0151313\ttotal: 3.82s\tremaining: 10.1s\n",
      "52:\tlearn: 0.0142489\ttotal: 3.9s\tremaining: 10.1s\n",
      "53:\tlearn: 0.0134275\ttotal: 3.96s\tremaining: 9.98s\n",
      "54:\tlearn: 0.0126655\ttotal: 4.03s\tremaining: 9.9s\n",
      "55:\tlearn: 0.0119480\ttotal: 4.1s\tremaining: 9.81s\n",
      "56:\tlearn: 0.0112812\ttotal: 4.17s\tremaining: 9.72s\n",
      "57:\tlearn: 0.0106663\ttotal: 4.23s\tremaining: 9.64s\n",
      "58:\tlearn: 0.0100930\ttotal: 4.3s\tremaining: 9.55s\n",
      "59:\tlearn: 0.0095606\ttotal: 4.37s\tremaining: 9.46s\n",
      "60:\tlearn: 0.0090558\ttotal: 4.44s\tremaining: 9.38s\n",
      "61:\tlearn: 0.0085915\ttotal: 4.51s\tremaining: 9.3s\n",
      "62:\tlearn: 0.0081497\ttotal: 4.57s\tremaining: 9.22s\n",
      "63:\tlearn: 0.0077511\ttotal: 4.65s\tremaining: 9.15s\n",
      "64:\tlearn: 0.0073763\ttotal: 4.72s\tremaining: 9.08s\n",
      "65:\tlearn: 0.0070301\ttotal: 4.79s\tremaining: 9.01s\n",
      "66:\tlearn: 0.0067022\ttotal: 4.86s\tremaining: 8.92s\n",
      "67:\tlearn: 0.0063920\ttotal: 4.92s\tremaining: 8.83s\n",
      "68:\tlearn: 0.0061096\ttotal: 4.99s\tremaining: 8.74s\n",
      "69:\tlearn: 0.0058482\ttotal: 5.06s\tremaining: 8.67s\n",
      "70:\tlearn: 0.0056028\ttotal: 5.12s\tremaining: 8.59s\n",
      "71:\tlearn: 0.0053714\ttotal: 5.19s\tremaining: 8.51s\n",
      "72:\tlearn: 0.0051578\ttotal: 5.26s\tremaining: 8.43s\n",
      "73:\tlearn: 0.0049597\ttotal: 5.33s\tremaining: 8.36s\n",
      "74:\tlearn: 0.0047741\ttotal: 5.4s\tremaining: 8.28s\n",
      "75:\tlearn: 0.0046038\ttotal: 5.47s\tremaining: 8.2s\n",
      "76:\tlearn: 0.0044486\ttotal: 5.54s\tremaining: 8.13s\n",
      "77:\tlearn: 0.0042957\ttotal: 5.61s\tremaining: 8.05s\n",
      "78:\tlearn: 0.0041553\ttotal: 5.68s\tremaining: 7.98s\n",
      "79:\tlearn: 0.0040254\ttotal: 5.75s\tremaining: 7.91s\n",
      "80:\tlearn: 0.0039023\ttotal: 5.83s\tremaining: 7.84s\n",
      "81:\tlearn: 0.0037915\ttotal: 5.9s\tremaining: 7.78s\n",
      "82:\tlearn: 0.0036921\ttotal: 5.98s\tremaining: 7.7s\n",
      "83:\tlearn: 0.0035917\ttotal: 6.04s\tremaining: 7.62s\n",
      "84:\tlearn: 0.0035030\ttotal: 6.12s\tremaining: 7.55s\n",
      "85:\tlearn: 0.0034153\ttotal: 6.19s\tremaining: 7.49s\n",
      "86:\tlearn: 0.0033393\ttotal: 6.27s\tremaining: 7.42s\n",
      "87:\tlearn: 0.0032654\ttotal: 6.34s\tremaining: 7.35s\n",
      "88:\tlearn: 0.0031993\ttotal: 6.41s\tremaining: 7.27s\n",
      "89:\tlearn: 0.0031405\ttotal: 6.47s\tremaining: 7.19s\n",
      "90:\tlearn: 0.0030877\ttotal: 6.54s\tremaining: 7.12s\n",
      "91:\tlearn: 0.0030331\ttotal: 6.61s\tremaining: 7.04s\n",
      "92:\tlearn: 0.0029769\ttotal: 6.68s\tremaining: 6.96s\n",
      "93:\tlearn: 0.0029289\ttotal: 6.75s\tremaining: 6.89s\n",
      "94:\tlearn: 0.0028887\ttotal: 6.81s\tremaining: 6.81s\n",
      "95:\tlearn: 0.0028381\ttotal: 6.91s\tremaining: 6.77s\n",
      "96:\tlearn: 0.0028033\ttotal: 6.98s\tremaining: 6.69s\n",
      "97:\tlearn: 0.0027663\ttotal: 7.04s\tremaining: 6.61s\n",
      "98:\tlearn: 0.0027348\ttotal: 7.11s\tremaining: 6.54s\n",
      "99:\tlearn: 0.0026979\ttotal: 7.19s\tremaining: 6.47s\n",
      "100:\tlearn: 0.0026649\ttotal: 7.26s\tremaining: 6.4s\n",
      "101:\tlearn: 0.0026374\ttotal: 7.35s\tremaining: 6.34s\n",
      "102:\tlearn: 0.0026049\ttotal: 7.42s\tremaining: 6.27s\n",
      "103:\tlearn: 0.0025744\ttotal: 7.5s\tremaining: 6.2s\n",
      "104:\tlearn: 0.0025481\ttotal: 7.56s\tremaining: 6.12s\n",
      "105:\tlearn: 0.0025147\ttotal: 7.63s\tremaining: 6.04s\n",
      "106:\tlearn: 0.0024971\ttotal: 7.69s\tremaining: 5.97s\n",
      "107:\tlearn: 0.0024806\ttotal: 7.76s\tremaining: 5.89s\n",
      "108:\tlearn: 0.0024683\ttotal: 7.82s\tremaining: 5.81s\n",
      "109:\tlearn: 0.0024472\ttotal: 7.91s\tremaining: 5.75s\n",
      "110:\tlearn: 0.0024232\ttotal: 7.98s\tremaining: 5.68s\n",
      "111:\tlearn: 0.0024107\ttotal: 8.04s\tremaining: 5.6s\n",
      "112:\tlearn: 0.0023926\ttotal: 8.11s\tremaining: 5.53s\n",
      "113:\tlearn: 0.0023823\ttotal: 8.18s\tremaining: 5.45s\n",
      "114:\tlearn: 0.0023492\ttotal: 8.24s\tremaining: 5.38s\n",
      "115:\tlearn: 0.0023302\ttotal: 8.32s\tremaining: 5.3s\n",
      "116:\tlearn: 0.0023052\ttotal: 8.39s\tremaining: 5.24s\n",
      "117:\tlearn: 0.0022871\ttotal: 8.47s\tremaining: 5.17s\n",
      "118:\tlearn: 0.0022706\ttotal: 8.54s\tremaining: 5.1s\n",
      "119:\tlearn: 0.0022518\ttotal: 8.62s\tremaining: 5.03s\n",
      "120:\tlearn: 0.0022320\ttotal: 8.69s\tremaining: 4.96s\n",
      "121:\tlearn: 0.0022175\ttotal: 8.77s\tremaining: 4.89s\n",
      "122:\tlearn: 0.0022097\ttotal: 8.83s\tremaining: 4.81s\n",
      "123:\tlearn: 0.0021893\ttotal: 8.9s\tremaining: 4.74s\n",
      "124:\tlearn: 0.0021732\ttotal: 8.97s\tremaining: 4.66s\n",
      "125:\tlearn: 0.0021585\ttotal: 9.04s\tremaining: 4.59s\n",
      "126:\tlearn: 0.0021453\ttotal: 9.12s\tremaining: 4.52s\n",
      "127:\tlearn: 0.0021312\ttotal: 9.19s\tremaining: 4.45s\n",
      "128:\tlearn: 0.0021180\ttotal: 9.26s\tremaining: 4.38s\n",
      "129:\tlearn: 0.0021134\ttotal: 9.32s\tremaining: 4.3s\n",
      "130:\tlearn: 0.0020990\ttotal: 9.4s\tremaining: 4.24s\n",
      "131:\tlearn: 0.0020853\ttotal: 9.49s\tremaining: 4.17s\n",
      "132:\tlearn: 0.0020811\ttotal: 9.55s\tremaining: 4.09s\n",
      "133:\tlearn: 0.0020665\ttotal: 9.62s\tremaining: 4.02s\n",
      "134:\tlearn: 0.0020528\ttotal: 9.69s\tremaining: 3.95s\n",
      "135:\tlearn: 0.0020455\ttotal: 9.76s\tremaining: 3.88s\n",
      "136:\tlearn: 0.0020336\ttotal: 9.83s\tremaining: 3.8s\n",
      "137:\tlearn: 0.0020175\ttotal: 9.9s\tremaining: 3.73s\n",
      "138:\tlearn: 0.0020082\ttotal: 9.97s\tremaining: 3.66s\n",
      "139:\tlearn: 0.0019958\ttotal: 10s\tremaining: 3.59s\n",
      "140:\tlearn: 0.0019924\ttotal: 10.1s\tremaining: 3.51s\n",
      "141:\tlearn: 0.0019829\ttotal: 10.2s\tremaining: 3.44s\n",
      "142:\tlearn: 0.0019707\ttotal: 10.3s\tremaining: 3.37s\n",
      "143:\tlearn: 0.0019632\ttotal: 10.3s\tremaining: 3.3s\n",
      "144:\tlearn: 0.0019597\ttotal: 10.4s\tremaining: 3.23s\n",
      "145:\tlearn: 0.0019558\ttotal: 10.5s\tremaining: 3.15s\n",
      "146:\tlearn: 0.0019526\ttotal: 10.5s\tremaining: 3.08s\n",
      "147:\tlearn: 0.0019415\ttotal: 10.6s\tremaining: 3.01s\n",
      "148:\tlearn: 0.0019379\ttotal: 10.7s\tremaining: 2.94s\n",
      "149:\tlearn: 0.0019279\ttotal: 10.7s\tremaining: 2.87s\n",
      "150:\tlearn: 0.0019245\ttotal: 10.8s\tremaining: 2.79s\n",
      "151:\tlearn: 0.0019140\ttotal: 10.9s\tremaining: 2.72s\n",
      "152:\tlearn: 0.0019028\ttotal: 11s\tremaining: 2.65s\n",
      "153:\tlearn: 0.0019001\ttotal: 11s\tremaining: 2.58s\n",
      "154:\tlearn: 0.0018894\ttotal: 11.1s\tremaining: 2.51s\n",
      "155:\tlearn: 0.0018785\ttotal: 11.2s\tremaining: 2.44s\n",
      "156:\tlearn: 0.0018724\ttotal: 11.2s\tremaining: 2.36s\n",
      "157:\tlearn: 0.0018694\ttotal: 11.3s\tremaining: 2.29s\n",
      "158:\tlearn: 0.0018653\ttotal: 11.3s\tremaining: 2.21s\n",
      "159:\tlearn: 0.0018624\ttotal: 11.4s\tremaining: 2.14s\n",
      "160:\tlearn: 0.0018556\ttotal: 11.5s\tremaining: 2.07s\n",
      "161:\tlearn: 0.0018431\ttotal: 11.5s\tremaining: 2s\n",
      "162:\tlearn: 0.0018377\ttotal: 11.6s\tremaining: 1.92s\n",
      "163:\tlearn: 0.0018262\ttotal: 11.7s\tremaining: 1.85s\n",
      "164:\tlearn: 0.0018237\ttotal: 11.8s\tremaining: 1.78s\n",
      "165:\tlearn: 0.0018194\ttotal: 11.8s\tremaining: 1.71s\n",
      "166:\tlearn: 0.0018139\ttotal: 11.9s\tremaining: 1.64s\n",
      "167:\tlearn: 0.0018055\ttotal: 12s\tremaining: 1.57s\n",
      "168:\tlearn: 0.0018017\ttotal: 12.1s\tremaining: 1.5s\n",
      "169:\tlearn: 0.0017995\ttotal: 12.1s\tremaining: 1.43s\n",
      "170:\tlearn: 0.0017920\ttotal: 12.2s\tremaining: 1.35s\n",
      "171:\tlearn: 0.0017900\ttotal: 12.2s\tremaining: 1.28s\n",
      "172:\tlearn: 0.0017870\ttotal: 12.3s\tremaining: 1.21s\n",
      "173:\tlearn: 0.0017843\ttotal: 12.4s\tremaining: 1.14s\n",
      "174:\tlearn: 0.0017805\ttotal: 12.5s\tremaining: 1.07s\n",
      "175:\tlearn: 0.0017775\ttotal: 12.5s\tremaining: 997ms\n",
      "176:\tlearn: 0.0017742\ttotal: 12.6s\tremaining: 925ms\n",
      "177:\tlearn: 0.0017690\ttotal: 12.7s\tremaining: 854ms\n",
      "178:\tlearn: 0.0017662\ttotal: 12.7s\tremaining: 782ms\n",
      "179:\tlearn: 0.0017607\ttotal: 12.8s\tremaining: 711ms\n",
      "180:\tlearn: 0.0017516\ttotal: 12.9s\tremaining: 640ms\n",
      "181:\tlearn: 0.0017492\ttotal: 12.9s\tremaining: 568ms\n",
      "182:\tlearn: 0.0017468\ttotal: 13s\tremaining: 497ms\n",
      "183:\tlearn: 0.0017424\ttotal: 13.1s\tremaining: 426ms\n",
      "184:\tlearn: 0.0017406\ttotal: 13.1s\tremaining: 355ms\n",
      "185:\tlearn: 0.0017382\ttotal: 13.2s\tremaining: 284ms\n",
      "186:\tlearn: 0.0017310\ttotal: 13.3s\tremaining: 213ms\n",
      "187:\tlearn: 0.0017288\ttotal: 13.3s\tremaining: 142ms\n",
      "188:\tlearn: 0.0017270\ttotal: 13.4s\tremaining: 70.9ms\n",
      "189:\tlearn: 0.0017229\ttotal: 13.5s\tremaining: 0us\n",
      "0:\tlearn: 0.6259488\ttotal: 76.4ms\tremaining: 14.4s\n",
      "1:\tlearn: 0.5674872\ttotal: 152ms\tremaining: 14.3s\n",
      "2:\tlearn: 0.5161101\ttotal: 215ms\tremaining: 13.4s\n",
      "3:\tlearn: 0.4708140\ttotal: 285ms\tremaining: 13.2s\n",
      "4:\tlearn: 0.4304753\ttotal: 354ms\tremaining: 13.1s\n",
      "5:\tlearn: 0.3944237\ttotal: 421ms\tremaining: 12.9s\n",
      "6:\tlearn: 0.3619721\ttotal: 493ms\tremaining: 12.9s\n",
      "7:\tlearn: 0.3327238\ttotal: 568ms\tremaining: 12.9s\n",
      "8:\tlearn: 0.3063172\ttotal: 643ms\tremaining: 12.9s\n",
      "9:\tlearn: 0.2822910\ttotal: 718ms\tremaining: 12.9s\n",
      "10:\tlearn: 0.2604838\ttotal: 795ms\tremaining: 12.9s\n",
      "11:\tlearn: 0.2405573\ttotal: 871ms\tremaining: 12.9s\n",
      "12:\tlearn: 0.2223714\ttotal: 943ms\tremaining: 12.8s\n",
      "13:\tlearn: 0.2057369\ttotal: 1.02s\tremaining: 12.9s\n",
      "14:\tlearn: 0.1905621\ttotal: 1.11s\tremaining: 13s\n",
      "15:\tlearn: 0.1765906\ttotal: 1.18s\tremaining: 12.9s\n",
      "16:\tlearn: 0.1637320\ttotal: 1.26s\tremaining: 12.8s\n",
      "17:\tlearn: 0.1519156\ttotal: 1.33s\tremaining: 12.7s\n",
      "18:\tlearn: 0.1410154\ttotal: 1.41s\tremaining: 12.7s\n",
      "19:\tlearn: 0.1309787\ttotal: 1.48s\tremaining: 12.6s\n",
      "20:\tlearn: 0.1217031\ttotal: 1.55s\tremaining: 12.5s\n",
      "21:\tlearn: 0.1131386\ttotal: 1.62s\tremaining: 12.4s\n",
      "22:\tlearn: 0.1052365\ttotal: 1.69s\tremaining: 12.3s\n",
      "23:\tlearn: 0.0979167\ttotal: 1.76s\tremaining: 12.2s\n",
      "24:\tlearn: 0.0911265\ttotal: 1.83s\tremaining: 12.1s\n",
      "25:\tlearn: 0.0848651\ttotal: 1.9s\tremaining: 12s\n",
      "26:\tlearn: 0.0790617\ttotal: 1.97s\tremaining: 11.9s\n",
      "27:\tlearn: 0.0736675\ttotal: 2.04s\tremaining: 11.8s\n",
      "28:\tlearn: 0.0686802\ttotal: 2.12s\tremaining: 11.8s\n",
      "29:\tlearn: 0.0640675\ttotal: 2.2s\tremaining: 11.7s\n",
      "30:\tlearn: 0.0597718\ttotal: 2.27s\tremaining: 11.6s\n",
      "31:\tlearn: 0.0557818\ttotal: 2.33s\tremaining: 11.5s\n",
      "32:\tlearn: 0.0520709\ttotal: 2.4s\tremaining: 11.4s\n",
      "33:\tlearn: 0.0486174\ttotal: 2.46s\tremaining: 11.3s\n",
      "34:\tlearn: 0.0454054\ttotal: 2.53s\tremaining: 11.2s\n",
      "35:\tlearn: 0.0424207\ttotal: 2.59s\tremaining: 11.1s\n",
      "36:\tlearn: 0.0396468\ttotal: 2.66s\tremaining: 11s\n",
      "37:\tlearn: 0.0370877\ttotal: 2.73s\tremaining: 10.9s\n",
      "38:\tlearn: 0.0346969\ttotal: 2.8s\tremaining: 10.8s\n",
      "39:\tlearn: 0.0324771\ttotal: 2.87s\tremaining: 10.8s\n",
      "40:\tlearn: 0.0303879\ttotal: 2.93s\tremaining: 10.6s\n",
      "41:\tlearn: 0.0284617\ttotal: 3s\tremaining: 10.6s\n",
      "42:\tlearn: 0.0266752\ttotal: 3.06s\tremaining: 10.5s\n",
      "43:\tlearn: 0.0249893\ttotal: 3.13s\tremaining: 10.4s\n",
      "44:\tlearn: 0.0234193\ttotal: 3.19s\tremaining: 10.3s\n",
      "45:\tlearn: 0.0219798\ttotal: 3.26s\tremaining: 10.2s\n",
      "46:\tlearn: 0.0206406\ttotal: 3.33s\tremaining: 10.1s\n",
      "47:\tlearn: 0.0193766\ttotal: 3.39s\tremaining: 10s\n",
      "48:\tlearn: 0.0182099\ttotal: 3.45s\tremaining: 9.94s\n",
      "49:\tlearn: 0.0171181\ttotal: 3.51s\tremaining: 9.84s\n",
      "50:\tlearn: 0.0161042\ttotal: 3.58s\tremaining: 9.75s\n",
      "51:\tlearn: 0.0151614\ttotal: 3.64s\tremaining: 9.67s\n",
      "52:\tlearn: 0.0142811\ttotal: 3.7s\tremaining: 9.57s\n",
      "53:\tlearn: 0.0134686\ttotal: 3.77s\tremaining: 9.5s\n",
      "54:\tlearn: 0.0127033\ttotal: 3.83s\tremaining: 9.41s\n",
      "55:\tlearn: 0.0119941\ttotal: 3.9s\tremaining: 9.33s\n",
      "56:\tlearn: 0.0113277\ttotal: 3.96s\tremaining: 9.24s\n",
      "57:\tlearn: 0.0107142\ttotal: 4.03s\tremaining: 9.16s\n",
      "58:\tlearn: 0.0101394\ttotal: 4.09s\tremaining: 9.08s\n",
      "59:\tlearn: 0.0096026\ttotal: 4.15s\tremaining: 8.99s\n",
      "60:\tlearn: 0.0090980\ttotal: 4.21s\tremaining: 8.9s\n",
      "61:\tlearn: 0.0086287\ttotal: 4.27s\tremaining: 8.81s\n",
      "62:\tlearn: 0.0081957\ttotal: 4.33s\tremaining: 8.73s\n",
      "63:\tlearn: 0.0077956\ttotal: 4.4s\tremaining: 8.66s\n",
      "64:\tlearn: 0.0074195\ttotal: 4.46s\tremaining: 8.58s\n",
      "65:\tlearn: 0.0070727\ttotal: 4.51s\tremaining: 8.47s\n",
      "66:\tlearn: 0.0067574\ttotal: 4.56s\tremaining: 8.37s\n",
      "67:\tlearn: 0.0064541\ttotal: 4.6s\tremaining: 8.26s\n",
      "68:\tlearn: 0.0061707\ttotal: 4.67s\tremaining: 8.18s\n",
      "69:\tlearn: 0.0059083\ttotal: 4.73s\tremaining: 8.11s\n",
      "70:\tlearn: 0.0056636\ttotal: 4.79s\tremaining: 8.03s\n",
      "71:\tlearn: 0.0054319\ttotal: 4.86s\tremaining: 7.96s\n",
      "72:\tlearn: 0.0052201\ttotal: 4.92s\tremaining: 7.88s\n",
      "73:\tlearn: 0.0050223\ttotal: 4.98s\tremaining: 7.8s\n",
      "74:\tlearn: 0.0048280\ttotal: 5.04s\tremaining: 7.72s\n",
      "75:\tlearn: 0.0046555\ttotal: 5.1s\tremaining: 7.64s\n",
      "76:\tlearn: 0.0044900\ttotal: 5.16s\tremaining: 7.58s\n",
      "77:\tlearn: 0.0043410\ttotal: 5.24s\tremaining: 7.52s\n",
      "78:\tlearn: 0.0042044\ttotal: 5.3s\tremaining: 7.45s\n",
      "79:\tlearn: 0.0040730\ttotal: 5.37s\tremaining: 7.38s\n",
      "80:\tlearn: 0.0039524\ttotal: 5.43s\tremaining: 7.31s\n",
      "81:\tlearn: 0.0038376\ttotal: 5.5s\tremaining: 7.24s\n",
      "82:\tlearn: 0.0037326\ttotal: 5.56s\tremaining: 7.16s\n",
      "83:\tlearn: 0.0036352\ttotal: 5.62s\tremaining: 7.09s\n",
      "84:\tlearn: 0.0035448\ttotal: 5.69s\tremaining: 7.03s\n",
      "85:\tlearn: 0.0034539\ttotal: 5.75s\tremaining: 6.96s\n",
      "86:\tlearn: 0.0033774\ttotal: 5.82s\tremaining: 6.88s\n",
      "87:\tlearn: 0.0033089\ttotal: 5.87s\tremaining: 6.81s\n",
      "88:\tlearn: 0.0032430\ttotal: 5.93s\tremaining: 6.73s\n",
      "89:\tlearn: 0.0031802\ttotal: 5.99s\tremaining: 6.65s\n",
      "90:\tlearn: 0.0031159\ttotal: 6.05s\tremaining: 6.58s\n",
      "91:\tlearn: 0.0030609\ttotal: 6.11s\tremaining: 6.51s\n",
      "92:\tlearn: 0.0029980\ttotal: 6.18s\tremaining: 6.45s\n",
      "93:\tlearn: 0.0029526\ttotal: 6.24s\tremaining: 6.37s\n",
      "94:\tlearn: 0.0029032\ttotal: 6.31s\tremaining: 6.31s\n",
      "95:\tlearn: 0.0028659\ttotal: 6.37s\tremaining: 6.24s\n",
      "96:\tlearn: 0.0028244\ttotal: 6.44s\tremaining: 6.17s\n",
      "97:\tlearn: 0.0027774\ttotal: 6.51s\tremaining: 6.11s\n",
      "98:\tlearn: 0.0027388\ttotal: 6.58s\tremaining: 6.05s\n",
      "99:\tlearn: 0.0026994\ttotal: 6.65s\tremaining: 5.98s\n",
      "100:\tlearn: 0.0026619\ttotal: 6.71s\tremaining: 5.92s\n",
      "101:\tlearn: 0.0026275\ttotal: 6.78s\tremaining: 5.85s\n",
      "102:\tlearn: 0.0025972\ttotal: 6.85s\tremaining: 5.78s\n",
      "103:\tlearn: 0.0025645\ttotal: 6.92s\tremaining: 5.72s\n",
      "104:\tlearn: 0.0025356\ttotal: 6.99s\tremaining: 5.65s\n",
      "105:\tlearn: 0.0025093\ttotal: 7.05s\tremaining: 5.59s\n",
      "106:\tlearn: 0.0024849\ttotal: 7.11s\tremaining: 5.52s\n",
      "107:\tlearn: 0.0024592\ttotal: 7.18s\tremaining: 5.45s\n",
      "108:\tlearn: 0.0024372\ttotal: 7.22s\tremaining: 5.37s\n",
      "109:\tlearn: 0.0024142\ttotal: 7.27s\tremaining: 5.29s\n",
      "110:\tlearn: 0.0023936\ttotal: 7.32s\tremaining: 5.21s\n",
      "111:\tlearn: 0.0023758\ttotal: 7.38s\tremaining: 5.14s\n",
      "112:\tlearn: 0.0023649\ttotal: 7.44s\tremaining: 5.07s\n",
      "113:\tlearn: 0.0023539\ttotal: 7.5s\tremaining: 5s\n",
      "114:\tlearn: 0.0023361\ttotal: 7.56s\tremaining: 4.93s\n",
      "115:\tlearn: 0.0023250\ttotal: 7.63s\tremaining: 4.86s\n",
      "116:\tlearn: 0.0023081\ttotal: 7.69s\tremaining: 4.8s\n",
      "117:\tlearn: 0.0022925\ttotal: 7.76s\tremaining: 4.73s\n",
      "118:\tlearn: 0.0022800\ttotal: 7.82s\tremaining: 4.67s\n",
      "119:\tlearn: 0.0022701\ttotal: 7.88s\tremaining: 4.6s\n",
      "120:\tlearn: 0.0022546\ttotal: 7.95s\tremaining: 4.53s\n",
      "121:\tlearn: 0.0022398\ttotal: 8.02s\tremaining: 4.47s\n",
      "122:\tlearn: 0.0022330\ttotal: 8.08s\tremaining: 4.4s\n",
      "123:\tlearn: 0.0022226\ttotal: 8.15s\tremaining: 4.34s\n",
      "124:\tlearn: 0.0022116\ttotal: 8.21s\tremaining: 4.27s\n",
      "125:\tlearn: 0.0022021\ttotal: 8.28s\tremaining: 4.21s\n",
      "126:\tlearn: 0.0021911\ttotal: 8.35s\tremaining: 4.14s\n",
      "127:\tlearn: 0.0021773\ttotal: 8.42s\tremaining: 4.08s\n",
      "128:\tlearn: 0.0021675\ttotal: 8.49s\tremaining: 4.01s\n",
      "129:\tlearn: 0.0021585\ttotal: 8.55s\tremaining: 3.95s\n",
      "130:\tlearn: 0.0021467\ttotal: 8.62s\tremaining: 3.88s\n",
      "131:\tlearn: 0.0021368\ttotal: 8.69s\tremaining: 3.82s\n",
      "132:\tlearn: 0.0021252\ttotal: 8.76s\tremaining: 3.75s\n",
      "133:\tlearn: 0.0021140\ttotal: 8.83s\tremaining: 3.69s\n",
      "134:\tlearn: 0.0021043\ttotal: 8.89s\tremaining: 3.62s\n",
      "135:\tlearn: 0.0020965\ttotal: 8.96s\tremaining: 3.56s\n",
      "136:\tlearn: 0.0020877\ttotal: 9.03s\tremaining: 3.49s\n",
      "137:\tlearn: 0.0020769\ttotal: 9.11s\tremaining: 3.43s\n",
      "138:\tlearn: 0.0020726\ttotal: 9.17s\tremaining: 3.37s\n",
      "139:\tlearn: 0.0020658\ttotal: 9.24s\tremaining: 3.3s\n",
      "140:\tlearn: 0.0020617\ttotal: 9.3s\tremaining: 3.23s\n",
      "141:\tlearn: 0.0020583\ttotal: 9.36s\tremaining: 3.17s\n",
      "142:\tlearn: 0.0020490\ttotal: 9.43s\tremaining: 3.1s\n",
      "143:\tlearn: 0.0020418\ttotal: 9.5s\tremaining: 3.03s\n",
      "144:\tlearn: 0.0020373\ttotal: 9.56s\tremaining: 2.97s\n",
      "145:\tlearn: 0.0020316\ttotal: 9.63s\tremaining: 2.9s\n",
      "146:\tlearn: 0.0020283\ttotal: 9.69s\tremaining: 2.83s\n",
      "147:\tlearn: 0.0020217\ttotal: 9.76s\tremaining: 2.77s\n",
      "148:\tlearn: 0.0020189\ttotal: 9.82s\tremaining: 2.7s\n",
      "149:\tlearn: 0.0020139\ttotal: 9.88s\tremaining: 2.63s\n",
      "150:\tlearn: 0.0020034\ttotal: 9.94s\tremaining: 2.57s\n",
      "151:\tlearn: 0.0019947\ttotal: 10s\tremaining: 2.5s\n",
      "152:\tlearn: 0.0019873\ttotal: 10.1s\tremaining: 2.44s\n",
      "153:\tlearn: 0.0019781\ttotal: 10.1s\tremaining: 2.37s\n",
      "154:\tlearn: 0.0019707\ttotal: 10.2s\tremaining: 2.3s\n",
      "155:\tlearn: 0.0019656\ttotal: 10.3s\tremaining: 2.24s\n",
      "156:\tlearn: 0.0019578\ttotal: 10.3s\tremaining: 2.17s\n",
      "157:\tlearn: 0.0019532\ttotal: 10.4s\tremaining: 2.1s\n",
      "158:\tlearn: 0.0019504\ttotal: 10.4s\tremaining: 2.04s\n",
      "159:\tlearn: 0.0019477\ttotal: 10.5s\tremaining: 1.97s\n",
      "160:\tlearn: 0.0019412\ttotal: 10.6s\tremaining: 1.91s\n",
      "161:\tlearn: 0.0019334\ttotal: 10.6s\tremaining: 1.84s\n",
      "162:\tlearn: 0.0019302\ttotal: 10.7s\tremaining: 1.77s\n",
      "163:\tlearn: 0.0019225\ttotal: 10.8s\tremaining: 1.71s\n",
      "164:\tlearn: 0.0019196\ttotal: 10.8s\tremaining: 1.64s\n",
      "165:\tlearn: 0.0019111\ttotal: 10.9s\tremaining: 1.58s\n",
      "166:\tlearn: 0.0019038\ttotal: 11s\tremaining: 1.51s\n",
      "167:\tlearn: 0.0019001\ttotal: 11s\tremaining: 1.44s\n",
      "168:\tlearn: 0.0018945\ttotal: 11.1s\tremaining: 1.38s\n",
      "169:\tlearn: 0.0018900\ttotal: 11.2s\tremaining: 1.31s\n",
      "170:\tlearn: 0.0018814\ttotal: 11.2s\tremaining: 1.25s\n",
      "171:\tlearn: 0.0018776\ttotal: 11.3s\tremaining: 1.18s\n",
      "172:\tlearn: 0.0018721\ttotal: 11.4s\tremaining: 1.12s\n",
      "173:\tlearn: 0.0018666\ttotal: 11.4s\tremaining: 1.05s\n",
      "174:\tlearn: 0.0018634\ttotal: 11.5s\tremaining: 985ms\n",
      "175:\tlearn: 0.0018596\ttotal: 11.6s\tremaining: 920ms\n",
      "176:\tlearn: 0.0018563\ttotal: 11.6s\tremaining: 853ms\n",
      "177:\tlearn: 0.0018533\ttotal: 11.7s\tremaining: 787ms\n",
      "178:\tlearn: 0.0018480\ttotal: 11.7s\tremaining: 722ms\n",
      "179:\tlearn: 0.0018389\ttotal: 11.8s\tremaining: 656ms\n",
      "180:\tlearn: 0.0018362\ttotal: 11.9s\tremaining: 590ms\n",
      "181:\tlearn: 0.0018335\ttotal: 11.9s\tremaining: 525ms\n",
      "182:\tlearn: 0.0018275\ttotal: 12s\tremaining: 459ms\n",
      "183:\tlearn: 0.0018248\ttotal: 12.1s\tremaining: 393ms\n",
      "184:\tlearn: 0.0018214\ttotal: 12.1s\tremaining: 328ms\n",
      "185:\tlearn: 0.0018190\ttotal: 12.2s\tremaining: 262ms\n",
      "186:\tlearn: 0.0018166\ttotal: 12.2s\tremaining: 196ms\n",
      "187:\tlearn: 0.0018142\ttotal: 12.3s\tremaining: 131ms\n",
      "188:\tlearn: 0.0018122\ttotal: 12.4s\tremaining: 65.4ms\n",
      "189:\tlearn: 0.0018099\ttotal: 12.4s\tremaining: 0us\n",
      "0:\tlearn: 0.6259488\ttotal: 72.3ms\tremaining: 13.7s\n",
      "1:\tlearn: 0.5674872\ttotal: 148ms\tremaining: 13.9s\n",
      "2:\tlearn: 0.5161101\ttotal: 214ms\tremaining: 13.4s\n",
      "3:\tlearn: 0.4708140\ttotal: 282ms\tremaining: 13.1s\n",
      "4:\tlearn: 0.4304753\ttotal: 356ms\tremaining: 13.2s\n",
      "5:\tlearn: 0.3944237\ttotal: 427ms\tremaining: 13.1s\n",
      "6:\tlearn: 0.3619721\ttotal: 499ms\tremaining: 13s\n",
      "7:\tlearn: 0.3327238\ttotal: 575ms\tremaining: 13.1s\n",
      "8:\tlearn: 0.3063172\ttotal: 651ms\tremaining: 13.1s\n",
      "9:\tlearn: 0.2822910\ttotal: 726ms\tremaining: 13.1s\n",
      "10:\tlearn: 0.2604838\ttotal: 799ms\tremaining: 13s\n",
      "11:\tlearn: 0.2405573\ttotal: 867ms\tremaining: 12.9s\n",
      "12:\tlearn: 0.2223714\ttotal: 939ms\tremaining: 12.8s\n",
      "13:\tlearn: 0.2057369\ttotal: 1.01s\tremaining: 12.8s\n",
      "14:\tlearn: 0.1905621\ttotal: 1.1s\tremaining: 12.9s\n",
      "15:\tlearn: 0.1765906\ttotal: 1.18s\tremaining: 12.8s\n",
      "16:\tlearn: 0.1637320\ttotal: 1.24s\tremaining: 12.7s\n",
      "17:\tlearn: 0.1519156\ttotal: 1.32s\tremaining: 12.6s\n",
      "18:\tlearn: 0.1410154\ttotal: 1.4s\tremaining: 12.6s\n",
      "19:\tlearn: 0.1309787\ttotal: 1.47s\tremaining: 12.5s\n",
      "20:\tlearn: 0.1217031\ttotal: 1.54s\tremaining: 12.4s\n",
      "21:\tlearn: 0.1131386\ttotal: 1.61s\tremaining: 12.3s\n",
      "22:\tlearn: 0.1052365\ttotal: 1.68s\tremaining: 12.2s\n",
      "23:\tlearn: 0.0979167\ttotal: 1.75s\tremaining: 12.1s\n",
      "24:\tlearn: 0.0911265\ttotal: 1.81s\tremaining: 12s\n",
      "25:\tlearn: 0.0848651\ttotal: 1.88s\tremaining: 11.9s\n",
      "26:\tlearn: 0.0790617\ttotal: 1.95s\tremaining: 11.8s\n",
      "27:\tlearn: 0.0736675\ttotal: 2.02s\tremaining: 11.7s\n",
      "28:\tlearn: 0.0686802\ttotal: 2.09s\tremaining: 11.6s\n",
      "29:\tlearn: 0.0640675\ttotal: 2.17s\tremaining: 11.6s\n",
      "30:\tlearn: 0.0597718\ttotal: 2.24s\tremaining: 11.5s\n",
      "31:\tlearn: 0.0557818\ttotal: 2.31s\tremaining: 11.4s\n",
      "32:\tlearn: 0.0520709\ttotal: 2.38s\tremaining: 11.3s\n",
      "33:\tlearn: 0.0486174\ttotal: 2.44s\tremaining: 11.2s\n",
      "34:\tlearn: 0.0454054\ttotal: 2.5s\tremaining: 11.1s\n",
      "35:\tlearn: 0.0424207\ttotal: 2.57s\tremaining: 11s\n",
      "36:\tlearn: 0.0396468\ttotal: 2.63s\tremaining: 10.9s\n",
      "37:\tlearn: 0.0370877\ttotal: 2.7s\tremaining: 10.8s\n",
      "38:\tlearn: 0.0346969\ttotal: 2.77s\tremaining: 10.7s\n",
      "39:\tlearn: 0.0324771\ttotal: 2.84s\tremaining: 10.6s\n",
      "40:\tlearn: 0.0303879\ttotal: 2.9s\tremaining: 10.5s\n",
      "41:\tlearn: 0.0284617\ttotal: 2.97s\tremaining: 10.5s\n",
      "42:\tlearn: 0.0266752\ttotal: 3.04s\tremaining: 10.4s\n",
      "43:\tlearn: 0.0249893\ttotal: 3.1s\tremaining: 10.3s\n",
      "44:\tlearn: 0.0234193\ttotal: 3.16s\tremaining: 10.2s\n",
      "45:\tlearn: 0.0219798\ttotal: 3.22s\tremaining: 10.1s\n",
      "46:\tlearn: 0.0206406\ttotal: 3.29s\tremaining: 10s\n",
      "47:\tlearn: 0.0193766\ttotal: 3.35s\tremaining: 9.92s\n",
      "48:\tlearn: 0.0182099\ttotal: 3.42s\tremaining: 9.85s\n",
      "49:\tlearn: 0.0171181\ttotal: 3.48s\tremaining: 9.75s\n",
      "50:\tlearn: 0.0161042\ttotal: 3.55s\tremaining: 9.68s\n",
      "51:\tlearn: 0.0151614\ttotal: 3.62s\tremaining: 9.59s\n",
      "52:\tlearn: 0.0142811\ttotal: 3.67s\tremaining: 9.49s\n",
      "53:\tlearn: 0.0134686\ttotal: 3.74s\tremaining: 9.41s\n",
      "54:\tlearn: 0.0127033\ttotal: 3.8s\tremaining: 9.32s\n",
      "55:\tlearn: 0.0119941\ttotal: 3.86s\tremaining: 9.23s\n",
      "56:\tlearn: 0.0113277\ttotal: 3.92s\tremaining: 9.14s\n",
      "57:\tlearn: 0.0107142\ttotal: 3.98s\tremaining: 9.07s\n",
      "58:\tlearn: 0.0101394\ttotal: 4.05s\tremaining: 8.98s\n",
      "59:\tlearn: 0.0096026\ttotal: 4.12s\tremaining: 8.93s\n",
      "60:\tlearn: 0.0090980\ttotal: 4.19s\tremaining: 8.86s\n",
      "61:\tlearn: 0.0086287\ttotal: 4.25s\tremaining: 8.79s\n",
      "62:\tlearn: 0.0081957\ttotal: 4.32s\tremaining: 8.71s\n",
      "63:\tlearn: 0.0077956\ttotal: 4.4s\tremaining: 8.67s\n",
      "64:\tlearn: 0.0074195\ttotal: 4.47s\tremaining: 8.6s\n",
      "65:\tlearn: 0.0070727\ttotal: 4.55s\tremaining: 8.54s\n",
      "66:\tlearn: 0.0067574\ttotal: 4.63s\tremaining: 8.5s\n",
      "67:\tlearn: 0.0064541\ttotal: 4.7s\tremaining: 8.44s\n",
      "68:\tlearn: 0.0061707\ttotal: 4.78s\tremaining: 8.38s\n",
      "69:\tlearn: 0.0059083\ttotal: 4.84s\tremaining: 8.3s\n",
      "70:\tlearn: 0.0056636\ttotal: 4.9s\tremaining: 8.22s\n",
      "71:\tlearn: 0.0054319\ttotal: 4.97s\tremaining: 8.15s\n",
      "72:\tlearn: 0.0052201\ttotal: 5.03s\tremaining: 8.06s\n",
      "73:\tlearn: 0.0050223\ttotal: 5.09s\tremaining: 7.98s\n",
      "74:\tlearn: 0.0048280\ttotal: 5.15s\tremaining: 7.89s\n",
      "75:\tlearn: 0.0046555\ttotal: 5.21s\tremaining: 7.82s\n",
      "76:\tlearn: 0.0044900\ttotal: 5.27s\tremaining: 7.74s\n",
      "77:\tlearn: 0.0043410\ttotal: 5.34s\tremaining: 7.66s\n",
      "78:\tlearn: 0.0042044\ttotal: 5.4s\tremaining: 7.59s\n",
      "79:\tlearn: 0.0040730\ttotal: 5.47s\tremaining: 7.52s\n",
      "80:\tlearn: 0.0039524\ttotal: 5.53s\tremaining: 7.44s\n",
      "81:\tlearn: 0.0038376\ttotal: 5.59s\tremaining: 7.37s\n",
      "82:\tlearn: 0.0037326\ttotal: 5.65s\tremaining: 7.29s\n",
      "83:\tlearn: 0.0036352\ttotal: 5.72s\tremaining: 7.22s\n",
      "84:\tlearn: 0.0035448\ttotal: 5.78s\tremaining: 7.14s\n",
      "85:\tlearn: 0.0034539\ttotal: 5.84s\tremaining: 7.06s\n",
      "86:\tlearn: 0.0033774\ttotal: 5.9s\tremaining: 6.98s\n",
      "87:\tlearn: 0.0033089\ttotal: 5.96s\tremaining: 6.9s\n",
      "88:\tlearn: 0.0032430\ttotal: 6.01s\tremaining: 6.83s\n",
      "89:\tlearn: 0.0031802\ttotal: 6.07s\tremaining: 6.75s\n",
      "90:\tlearn: 0.0031159\ttotal: 6.14s\tremaining: 6.68s\n",
      "91:\tlearn: 0.0030609\ttotal: 6.2s\tremaining: 6.6s\n",
      "92:\tlearn: 0.0029980\ttotal: 6.27s\tremaining: 6.54s\n",
      "93:\tlearn: 0.0029526\ttotal: 6.33s\tremaining: 6.46s\n",
      "94:\tlearn: 0.0029032\ttotal: 6.39s\tremaining: 6.39s\n",
      "95:\tlearn: 0.0028659\ttotal: 6.46s\tremaining: 6.32s\n",
      "96:\tlearn: 0.0028244\ttotal: 6.52s\tremaining: 6.25s\n",
      "97:\tlearn: 0.0027774\ttotal: 6.58s\tremaining: 6.18s\n",
      "98:\tlearn: 0.0027388\ttotal: 6.65s\tremaining: 6.12s\n",
      "99:\tlearn: 0.0026994\ttotal: 6.71s\tremaining: 6.04s\n",
      "100:\tlearn: 0.0026619\ttotal: 6.78s\tremaining: 5.97s\n",
      "101:\tlearn: 0.0026275\ttotal: 6.84s\tremaining: 5.9s\n",
      "102:\tlearn: 0.0025972\ttotal: 6.9s\tremaining: 5.83s\n",
      "103:\tlearn: 0.0025645\ttotal: 6.97s\tremaining: 5.77s\n",
      "104:\tlearn: 0.0025356\ttotal: 7.04s\tremaining: 5.7s\n",
      "105:\tlearn: 0.0025093\ttotal: 7.1s\tremaining: 5.63s\n",
      "106:\tlearn: 0.0024849\ttotal: 7.16s\tremaining: 5.55s\n",
      "107:\tlearn: 0.0024592\ttotal: 7.23s\tremaining: 5.49s\n",
      "108:\tlearn: 0.0024372\ttotal: 7.29s\tremaining: 5.42s\n",
      "109:\tlearn: 0.0024142\ttotal: 7.35s\tremaining: 5.35s\n",
      "110:\tlearn: 0.0023936\ttotal: 7.43s\tremaining: 5.29s\n",
      "111:\tlearn: 0.0023758\ttotal: 7.5s\tremaining: 5.23s\n",
      "112:\tlearn: 0.0023649\ttotal: 7.56s\tremaining: 5.15s\n",
      "113:\tlearn: 0.0023539\ttotal: 7.62s\tremaining: 5.08s\n",
      "114:\tlearn: 0.0023361\ttotal: 7.69s\tremaining: 5.01s\n",
      "115:\tlearn: 0.0023250\ttotal: 7.75s\tremaining: 4.94s\n",
      "116:\tlearn: 0.0023081\ttotal: 7.82s\tremaining: 4.88s\n",
      "117:\tlearn: 0.0022925\ttotal: 7.88s\tremaining: 4.81s\n",
      "118:\tlearn: 0.0022800\ttotal: 7.95s\tremaining: 4.74s\n",
      "119:\tlearn: 0.0022701\ttotal: 8s\tremaining: 4.67s\n",
      "120:\tlearn: 0.0022546\ttotal: 8.07s\tremaining: 4.6s\n",
      "121:\tlearn: 0.0022398\ttotal: 8.13s\tremaining: 4.53s\n",
      "122:\tlearn: 0.0022330\ttotal: 8.19s\tremaining: 4.46s\n",
      "123:\tlearn: 0.0022226\ttotal: 8.26s\tremaining: 4.39s\n",
      "124:\tlearn: 0.0022116\ttotal: 8.32s\tremaining: 4.33s\n",
      "125:\tlearn: 0.0022021\ttotal: 8.38s\tremaining: 4.26s\n",
      "126:\tlearn: 0.0021911\ttotal: 8.45s\tremaining: 4.19s\n",
      "127:\tlearn: 0.0021773\ttotal: 8.53s\tremaining: 4.13s\n",
      "128:\tlearn: 0.0021675\ttotal: 8.59s\tremaining: 4.06s\n",
      "129:\tlearn: 0.0021585\ttotal: 8.65s\tremaining: 3.99s\n",
      "130:\tlearn: 0.0021467\ttotal: 8.72s\tremaining: 3.93s\n",
      "131:\tlearn: 0.0021368\ttotal: 8.78s\tremaining: 3.86s\n",
      "132:\tlearn: 0.0021252\ttotal: 8.84s\tremaining: 3.79s\n",
      "133:\tlearn: 0.0021140\ttotal: 8.9s\tremaining: 3.72s\n",
      "134:\tlearn: 0.0021043\ttotal: 8.97s\tremaining: 3.65s\n",
      "135:\tlearn: 0.0020965\ttotal: 9.04s\tremaining: 3.59s\n",
      "136:\tlearn: 0.0020877\ttotal: 9.1s\tremaining: 3.52s\n",
      "137:\tlearn: 0.0020769\ttotal: 9.17s\tremaining: 3.46s\n",
      "138:\tlearn: 0.0020726\ttotal: 9.23s\tremaining: 3.39s\n",
      "139:\tlearn: 0.0020658\ttotal: 9.3s\tremaining: 3.32s\n",
      "140:\tlearn: 0.0020617\ttotal: 9.36s\tremaining: 3.25s\n",
      "141:\tlearn: 0.0020583\ttotal: 9.42s\tremaining: 3.19s\n",
      "142:\tlearn: 0.0020490\ttotal: 9.49s\tremaining: 3.12s\n",
      "143:\tlearn: 0.0020418\ttotal: 9.55s\tremaining: 3.05s\n",
      "144:\tlearn: 0.0020373\ttotal: 9.62s\tremaining: 2.98s\n",
      "145:\tlearn: 0.0020316\ttotal: 9.68s\tremaining: 2.92s\n",
      "146:\tlearn: 0.0020283\ttotal: 9.74s\tremaining: 2.85s\n",
      "147:\tlearn: 0.0020217\ttotal: 9.8s\tremaining: 2.78s\n",
      "148:\tlearn: 0.0020189\ttotal: 9.86s\tremaining: 2.71s\n",
      "149:\tlearn: 0.0020139\ttotal: 9.92s\tremaining: 2.65s\n",
      "150:\tlearn: 0.0020034\ttotal: 9.99s\tremaining: 2.58s\n",
      "151:\tlearn: 0.0019947\ttotal: 10s\tremaining: 2.51s\n",
      "152:\tlearn: 0.0019873\ttotal: 10.1s\tremaining: 2.44s\n",
      "153:\tlearn: 0.0019781\ttotal: 10.2s\tremaining: 2.38s\n",
      "154:\tlearn: 0.0019707\ttotal: 10.2s\tremaining: 2.31s\n",
      "155:\tlearn: 0.0019656\ttotal: 10.3s\tremaining: 2.24s\n",
      "156:\tlearn: 0.0019578\ttotal: 10.4s\tremaining: 2.18s\n",
      "157:\tlearn: 0.0019532\ttotal: 10.4s\tremaining: 2.11s\n",
      "158:\tlearn: 0.0019504\ttotal: 10.5s\tremaining: 2.04s\n",
      "159:\tlearn: 0.0019477\ttotal: 10.5s\tremaining: 1.98s\n",
      "160:\tlearn: 0.0019412\ttotal: 10.6s\tremaining: 1.91s\n",
      "161:\tlearn: 0.0019334\ttotal: 10.7s\tremaining: 1.84s\n",
      "162:\tlearn: 0.0019302\ttotal: 10.7s\tremaining: 1.78s\n",
      "163:\tlearn: 0.0019225\ttotal: 10.8s\tremaining: 1.71s\n",
      "164:\tlearn: 0.0019196\ttotal: 10.9s\tremaining: 1.65s\n",
      "165:\tlearn: 0.0019111\ttotal: 10.9s\tremaining: 1.58s\n",
      "166:\tlearn: 0.0019038\ttotal: 11s\tremaining: 1.51s\n",
      "167:\tlearn: 0.0019001\ttotal: 11s\tremaining: 1.45s\n",
      "168:\tlearn: 0.0018945\ttotal: 11.1s\tremaining: 1.38s\n",
      "169:\tlearn: 0.0018900\ttotal: 11.2s\tremaining: 1.31s\n",
      "170:\tlearn: 0.0018814\ttotal: 11.2s\tremaining: 1.25s\n",
      "171:\tlearn: 0.0018776\ttotal: 11.3s\tremaining: 1.18s\n",
      "172:\tlearn: 0.0018721\ttotal: 11.4s\tremaining: 1.12s\n",
      "173:\tlearn: 0.0018666\ttotal: 11.4s\tremaining: 1.05s\n",
      "174:\tlearn: 0.0018634\ttotal: 11.5s\tremaining: 987ms\n",
      "175:\tlearn: 0.0018596\ttotal: 11.6s\tremaining: 921ms\n",
      "176:\tlearn: 0.0018563\ttotal: 11.6s\tremaining: 855ms\n",
      "177:\tlearn: 0.0018533\ttotal: 11.7s\tremaining: 788ms\n",
      "178:\tlearn: 0.0018480\ttotal: 11.8s\tremaining: 722ms\n",
      "179:\tlearn: 0.0018389\ttotal: 11.8s\tremaining: 657ms\n",
      "180:\tlearn: 0.0018362\ttotal: 11.9s\tremaining: 591ms\n",
      "181:\tlearn: 0.0018335\ttotal: 11.9s\tremaining: 525ms\n",
      "182:\tlearn: 0.0018275\ttotal: 12s\tremaining: 459ms\n",
      "183:\tlearn: 0.0018248\ttotal: 12.1s\tremaining: 393ms\n",
      "184:\tlearn: 0.0018214\ttotal: 12.1s\tremaining: 328ms\n",
      "185:\tlearn: 0.0018190\ttotal: 12.2s\tremaining: 262ms\n",
      "186:\tlearn: 0.0018166\ttotal: 12.3s\tremaining: 197ms\n",
      "187:\tlearn: 0.0018142\ttotal: 12.3s\tremaining: 131ms\n",
      "188:\tlearn: 0.0018122\ttotal: 12.4s\tremaining: 65.5ms\n",
      "189:\tlearn: 0.0018099\ttotal: 12.4s\tremaining: 0us\n",
      "0:\tlearn: 0.6258735\ttotal: 64.5ms\tremaining: 12.2s\n",
      "1:\tlearn: 0.5675050\ttotal: 130ms\tremaining: 12.3s\n",
      "2:\tlearn: 0.5163071\ttotal: 192ms\tremaining: 12s\n",
      "3:\tlearn: 0.4710536\ttotal: 255ms\tremaining: 11.8s\n",
      "4:\tlearn: 0.4307778\ttotal: 319ms\tremaining: 11.8s\n",
      "5:\tlearn: 0.3947232\ttotal: 382ms\tremaining: 11.7s\n",
      "6:\tlearn: 0.3623150\ttotal: 442ms\tremaining: 11.6s\n",
      "7:\tlearn: 0.3330402\ttotal: 506ms\tremaining: 11.5s\n",
      "8:\tlearn: 0.3065637\ttotal: 572ms\tremaining: 11.5s\n",
      "9:\tlearn: 0.2825463\ttotal: 638ms\tremaining: 11.5s\n",
      "10:\tlearn: 0.2607648\ttotal: 704ms\tremaining: 11.5s\n",
      "11:\tlearn: 0.2407795\ttotal: 764ms\tremaining: 11.3s\n",
      "12:\tlearn: 0.2225715\ttotal: 826ms\tremaining: 11.2s\n",
      "13:\tlearn: 0.2059183\ttotal: 893ms\tremaining: 11.2s\n",
      "14:\tlearn: 0.1906978\ttotal: 955ms\tremaining: 11.1s\n",
      "15:\tlearn: 0.1767351\ttotal: 1.03s\tremaining: 11.2s\n",
      "16:\tlearn: 0.1639018\ttotal: 1.1s\tremaining: 11.2s\n",
      "17:\tlearn: 0.1520357\ttotal: 1.16s\tremaining: 11.1s\n",
      "18:\tlearn: 0.1411245\ttotal: 1.21s\tremaining: 10.9s\n",
      "19:\tlearn: 0.1310365\ttotal: 1.25s\tremaining: 10.6s\n",
      "20:\tlearn: 0.1217320\ttotal: 1.29s\tremaining: 10.4s\n",
      "21:\tlearn: 0.1131511\ttotal: 1.34s\tremaining: 10.2s\n",
      "22:\tlearn: 0.1052119\ttotal: 1.38s\tremaining: 10s\n",
      "23:\tlearn: 0.0978862\ttotal: 1.42s\tremaining: 9.84s\n",
      "24:\tlearn: 0.0911044\ttotal: 1.47s\tremaining: 9.69s\n",
      "25:\tlearn: 0.0848465\ttotal: 1.52s\tremaining: 9.57s\n",
      "26:\tlearn: 0.0790033\ttotal: 1.58s\tremaining: 9.52s\n",
      "27:\tlearn: 0.0736176\ttotal: 1.64s\tremaining: 9.48s\n",
      "28:\tlearn: 0.0686208\ttotal: 1.7s\tremaining: 9.43s\n",
      "29:\tlearn: 0.0640029\ttotal: 1.76s\tremaining: 9.4s\n",
      "30:\tlearn: 0.0596941\ttotal: 1.82s\tremaining: 9.34s\n",
      "31:\tlearn: 0.0557024\ttotal: 1.88s\tremaining: 9.3s\n",
      "32:\tlearn: 0.0520035\ttotal: 1.94s\tremaining: 9.25s\n",
      "33:\tlearn: 0.0485601\ttotal: 2.02s\tremaining: 9.25s\n",
      "34:\tlearn: 0.0453778\ttotal: 2.07s\tremaining: 9.18s\n",
      "35:\tlearn: 0.0424042\ttotal: 2.13s\tremaining: 9.11s\n",
      "36:\tlearn: 0.0396286\ttotal: 2.19s\tremaining: 9.04s\n",
      "37:\tlearn: 0.0370702\ttotal: 2.24s\tremaining: 8.98s\n",
      "38:\tlearn: 0.0346770\ttotal: 2.31s\tremaining: 8.93s\n",
      "39:\tlearn: 0.0324566\ttotal: 2.37s\tremaining: 8.89s\n",
      "40:\tlearn: 0.0303814\ttotal: 2.43s\tremaining: 8.82s\n",
      "41:\tlearn: 0.0284521\ttotal: 2.49s\tremaining: 8.77s\n",
      "42:\tlearn: 0.0266614\ttotal: 2.55s\tremaining: 8.71s\n",
      "43:\tlearn: 0.0249959\ttotal: 2.61s\tremaining: 8.66s\n",
      "44:\tlearn: 0.0234432\ttotal: 2.67s\tremaining: 8.6s\n",
      "45:\tlearn: 0.0219933\ttotal: 2.73s\tremaining: 8.54s\n",
      "46:\tlearn: 0.0206509\ttotal: 2.79s\tremaining: 8.48s\n",
      "47:\tlearn: 0.0193943\ttotal: 2.85s\tremaining: 8.42s\n",
      "48:\tlearn: 0.0182231\ttotal: 2.9s\tremaining: 8.34s\n",
      "49:\tlearn: 0.0171427\ttotal: 2.95s\tremaining: 8.26s\n",
      "50:\tlearn: 0.0161335\ttotal: 3.01s\tremaining: 8.21s\n",
      "51:\tlearn: 0.0151899\ttotal: 3.07s\tremaining: 8.16s\n",
      "52:\tlearn: 0.0143158\ttotal: 3.13s\tremaining: 8.11s\n",
      "53:\tlearn: 0.0134982\ttotal: 3.19s\tremaining: 8.04s\n",
      "54:\tlearn: 0.0127328\ttotal: 3.25s\tremaining: 7.98s\n",
      "55:\tlearn: 0.0120247\ttotal: 3.31s\tremaining: 7.93s\n",
      "56:\tlearn: 0.0113610\ttotal: 3.37s\tremaining: 7.86s\n",
      "57:\tlearn: 0.0107366\ttotal: 3.43s\tremaining: 7.8s\n",
      "58:\tlearn: 0.0101659\ttotal: 3.48s\tremaining: 7.73s\n",
      "59:\tlearn: 0.0096299\ttotal: 3.54s\tremaining: 7.67s\n",
      "60:\tlearn: 0.0091271\ttotal: 3.6s\tremaining: 7.6s\n",
      "61:\tlearn: 0.0086611\ttotal: 3.65s\tremaining: 7.54s\n",
      "62:\tlearn: 0.0082237\ttotal: 3.71s\tremaining: 7.47s\n",
      "63:\tlearn: 0.0078171\ttotal: 3.76s\tremaining: 7.4s\n",
      "64:\tlearn: 0.0074541\ttotal: 3.82s\tremaining: 7.35s\n",
      "65:\tlearn: 0.0071080\ttotal: 3.88s\tremaining: 7.29s\n",
      "66:\tlearn: 0.0067851\ttotal: 3.94s\tremaining: 7.23s\n",
      "67:\tlearn: 0.0064806\ttotal: 3.99s\tremaining: 7.17s\n",
      "68:\tlearn: 0.0061970\ttotal: 4.05s\tremaining: 7.11s\n",
      "69:\tlearn: 0.0059312\ttotal: 4.11s\tremaining: 7.05s\n",
      "70:\tlearn: 0.0056834\ttotal: 4.17s\tremaining: 6.99s\n",
      "71:\tlearn: 0.0054611\ttotal: 4.22s\tremaining: 6.92s\n",
      "72:\tlearn: 0.0052465\ttotal: 4.25s\tremaining: 6.82s\n",
      "73:\tlearn: 0.0050456\ttotal: 4.29s\tremaining: 6.73s\n",
      "74:\tlearn: 0.0048583\ttotal: 4.34s\tremaining: 6.66s\n",
      "75:\tlearn: 0.0046937\ttotal: 4.4s\tremaining: 6.6s\n",
      "76:\tlearn: 0.0045350\ttotal: 4.46s\tremaining: 6.54s\n",
      "77:\tlearn: 0.0043850\ttotal: 4.51s\tremaining: 6.48s\n",
      "78:\tlearn: 0.0042520\ttotal: 4.57s\tremaining: 6.43s\n",
      "79:\tlearn: 0.0041254\ttotal: 4.63s\tremaining: 6.37s\n",
      "80:\tlearn: 0.0040073\ttotal: 4.69s\tremaining: 6.31s\n",
      "81:\tlearn: 0.0039010\ttotal: 4.75s\tremaining: 6.25s\n",
      "82:\tlearn: 0.0037969\ttotal: 4.81s\tremaining: 6.2s\n",
      "83:\tlearn: 0.0037007\ttotal: 4.87s\tremaining: 6.14s\n",
      "84:\tlearn: 0.0036133\ttotal: 4.93s\tremaining: 6.09s\n",
      "85:\tlearn: 0.0035233\ttotal: 4.99s\tremaining: 6.03s\n",
      "86:\tlearn: 0.0034401\ttotal: 5.04s\tremaining: 5.97s\n",
      "87:\tlearn: 0.0033683\ttotal: 5.1s\tremaining: 5.91s\n",
      "88:\tlearn: 0.0033010\ttotal: 5.16s\tremaining: 5.85s\n",
      "89:\tlearn: 0.0032308\ttotal: 5.21s\tremaining: 5.79s\n",
      "90:\tlearn: 0.0031753\ttotal: 5.27s\tremaining: 5.73s\n",
      "91:\tlearn: 0.0031203\ttotal: 5.32s\tremaining: 5.67s\n",
      "92:\tlearn: 0.0030602\ttotal: 5.38s\tremaining: 5.61s\n",
      "93:\tlearn: 0.0030074\ttotal: 5.44s\tremaining: 5.55s\n",
      "94:\tlearn: 0.0029576\ttotal: 5.5s\tremaining: 5.5s\n",
      "95:\tlearn: 0.0029158\ttotal: 5.56s\tremaining: 5.45s\n",
      "96:\tlearn: 0.0028683\ttotal: 5.62s\tremaining: 5.39s\n",
      "97:\tlearn: 0.0028313\ttotal: 5.66s\tremaining: 5.31s\n",
      "98:\tlearn: 0.0027939\ttotal: 5.7s\tremaining: 5.24s\n",
      "99:\tlearn: 0.0027588\ttotal: 5.75s\tremaining: 5.18s\n",
      "100:\tlearn: 0.0027314\ttotal: 5.81s\tremaining: 5.12s\n",
      "101:\tlearn: 0.0027020\ttotal: 5.87s\tremaining: 5.06s\n",
      "102:\tlearn: 0.0026790\ttotal: 5.92s\tremaining: 5s\n",
      "103:\tlearn: 0.0026450\ttotal: 5.96s\tremaining: 4.93s\n",
      "104:\tlearn: 0.0026213\ttotal: 6s\tremaining: 4.86s\n",
      "105:\tlearn: 0.0025943\ttotal: 6.04s\tremaining: 4.79s\n",
      "106:\tlearn: 0.0025688\ttotal: 6.1s\tremaining: 4.73s\n",
      "107:\tlearn: 0.0025474\ttotal: 6.17s\tremaining: 4.68s\n",
      "108:\tlearn: 0.0025305\ttotal: 6.22s\tremaining: 4.62s\n",
      "109:\tlearn: 0.0025092\ttotal: 6.28s\tremaining: 4.56s\n",
      "110:\tlearn: 0.0024838\ttotal: 6.34s\tremaining: 4.51s\n",
      "111:\tlearn: 0.0024678\ttotal: 6.39s\tremaining: 4.45s\n",
      "112:\tlearn: 0.0024490\ttotal: 6.46s\tremaining: 4.4s\n",
      "113:\tlearn: 0.0024242\ttotal: 6.52s\tremaining: 4.35s\n",
      "114:\tlearn: 0.0024070\ttotal: 6.58s\tremaining: 4.29s\n",
      "115:\tlearn: 0.0023885\ttotal: 6.64s\tremaining: 4.24s\n",
      "116:\tlearn: 0.0023799\ttotal: 6.7s\tremaining: 4.18s\n",
      "117:\tlearn: 0.0023553\ttotal: 6.75s\tremaining: 4.12s\n",
      "118:\tlearn: 0.0023445\ttotal: 6.81s\tremaining: 4.06s\n",
      "119:\tlearn: 0.0023324\ttotal: 6.86s\tremaining: 4s\n",
      "120:\tlearn: 0.0023111\ttotal: 6.92s\tremaining: 3.95s\n",
      "121:\tlearn: 0.0022962\ttotal: 6.98s\tremaining: 3.89s\n",
      "122:\tlearn: 0.0022813\ttotal: 7.04s\tremaining: 3.83s\n",
      "123:\tlearn: 0.0022714\ttotal: 7.1s\tremaining: 3.78s\n",
      "124:\tlearn: 0.0022668\ttotal: 7.15s\tremaining: 3.72s\n",
      "125:\tlearn: 0.0022519\ttotal: 7.21s\tremaining: 3.66s\n",
      "126:\tlearn: 0.0022421\ttotal: 7.26s\tremaining: 3.6s\n",
      "127:\tlearn: 0.0022269\ttotal: 7.32s\tremaining: 3.54s\n",
      "128:\tlearn: 0.0022224\ttotal: 7.37s\tremaining: 3.49s\n",
      "129:\tlearn: 0.0022094\ttotal: 7.43s\tremaining: 3.43s\n",
      "130:\tlearn: 0.0021995\ttotal: 7.49s\tremaining: 3.37s\n",
      "131:\tlearn: 0.0021886\ttotal: 7.55s\tremaining: 3.32s\n",
      "132:\tlearn: 0.0021741\ttotal: 7.61s\tremaining: 3.26s\n",
      "133:\tlearn: 0.0021715\ttotal: 7.66s\tremaining: 3.2s\n",
      "134:\tlearn: 0.0021634\ttotal: 7.72s\tremaining: 3.15s\n",
      "135:\tlearn: 0.0021602\ttotal: 7.77s\tremaining: 3.09s\n",
      "136:\tlearn: 0.0021569\ttotal: 7.83s\tremaining: 3.03s\n",
      "137:\tlearn: 0.0021511\ttotal: 7.88s\tremaining: 2.97s\n",
      "138:\tlearn: 0.0021425\ttotal: 7.94s\tremaining: 2.91s\n",
      "139:\tlearn: 0.0021314\ttotal: 8s\tremaining: 2.86s\n",
      "140:\tlearn: 0.0021290\ttotal: 8.06s\tremaining: 2.8s\n",
      "141:\tlearn: 0.0021150\ttotal: 8.12s\tremaining: 2.74s\n",
      "142:\tlearn: 0.0021056\ttotal: 8.17s\tremaining: 2.69s\n",
      "143:\tlearn: 0.0021026\ttotal: 8.23s\tremaining: 2.63s\n",
      "144:\tlearn: 0.0020978\ttotal: 8.29s\tremaining: 2.57s\n",
      "145:\tlearn: 0.0020920\ttotal: 8.34s\tremaining: 2.51s\n",
      "146:\tlearn: 0.0020872\ttotal: 8.4s\tremaining: 2.46s\n",
      "147:\tlearn: 0.0020804\ttotal: 8.46s\tremaining: 2.4s\n",
      "148:\tlearn: 0.0020648\ttotal: 8.51s\tremaining: 2.34s\n",
      "149:\tlearn: 0.0020595\ttotal: 8.57s\tremaining: 2.28s\n",
      "150:\tlearn: 0.0020532\ttotal: 8.62s\tremaining: 2.23s\n",
      "151:\tlearn: 0.0020453\ttotal: 8.69s\tremaining: 2.17s\n",
      "152:\tlearn: 0.0020356\ttotal: 8.74s\tremaining: 2.11s\n",
      "153:\tlearn: 0.0020300\ttotal: 8.8s\tremaining: 2.06s\n",
      "154:\tlearn: 0.0020226\ttotal: 8.86s\tremaining: 2s\n",
      "155:\tlearn: 0.0020196\ttotal: 8.92s\tremaining: 1.94s\n",
      "156:\tlearn: 0.0020059\ttotal: 8.98s\tremaining: 1.89s\n",
      "157:\tlearn: 0.0019999\ttotal: 9.04s\tremaining: 1.83s\n",
      "158:\tlearn: 0.0019973\ttotal: 9.09s\tremaining: 1.77s\n",
      "159:\tlearn: 0.0019915\ttotal: 9.14s\tremaining: 1.71s\n",
      "160:\tlearn: 0.0019846\ttotal: 9.2s\tremaining: 1.66s\n",
      "161:\tlearn: 0.0019798\ttotal: 9.26s\tremaining: 1.6s\n",
      "162:\tlearn: 0.0019756\ttotal: 9.31s\tremaining: 1.54s\n",
      "163:\tlearn: 0.0019714\ttotal: 9.37s\tremaining: 1.48s\n",
      "164:\tlearn: 0.0019661\ttotal: 9.42s\tremaining: 1.43s\n",
      "165:\tlearn: 0.0019632\ttotal: 9.48s\tremaining: 1.37s\n",
      "166:\tlearn: 0.0019592\ttotal: 9.53s\tremaining: 1.31s\n",
      "167:\tlearn: 0.0019522\ttotal: 9.59s\tremaining: 1.25s\n",
      "168:\tlearn: 0.0019497\ttotal: 9.65s\tremaining: 1.2s\n",
      "169:\tlearn: 0.0019464\ttotal: 9.71s\tremaining: 1.14s\n",
      "170:\tlearn: 0.0019424\ttotal: 9.76s\tremaining: 1.08s\n",
      "171:\tlearn: 0.0019387\ttotal: 9.82s\tremaining: 1.03s\n",
      "172:\tlearn: 0.0019370\ttotal: 9.87s\tremaining: 970ms\n",
      "173:\tlearn: 0.0019355\ttotal: 9.93s\tremaining: 913ms\n",
      "174:\tlearn: 0.0019298\ttotal: 9.98s\tremaining: 856ms\n",
      "175:\tlearn: 0.0019271\ttotal: 10s\tremaining: 799ms\n",
      "176:\tlearn: 0.0019256\ttotal: 10.1s\tremaining: 742ms\n",
      "177:\tlearn: 0.0019223\ttotal: 10.2s\tremaining: 685ms\n",
      "178:\tlearn: 0.0019203\ttotal: 10.2s\tremaining: 627ms\n",
      "179:\tlearn: 0.0019101\ttotal: 10.3s\tremaining: 571ms\n",
      "180:\tlearn: 0.0019049\ttotal: 10.3s\tremaining: 514ms\n",
      "181:\tlearn: 0.0018985\ttotal: 10.4s\tremaining: 457ms\n",
      "182:\tlearn: 0.0018960\ttotal: 10.4s\tremaining: 400ms\n",
      "183:\tlearn: 0.0018929\ttotal: 10.5s\tremaining: 342ms\n",
      "184:\tlearn: 0.0018896\ttotal: 10.6s\tremaining: 285ms\n",
      "185:\tlearn: 0.0018876\ttotal: 10.6s\tremaining: 228ms\n",
      "186:\tlearn: 0.0018824\ttotal: 10.7s\tremaining: 171ms\n",
      "187:\tlearn: 0.0018801\ttotal: 10.7s\tremaining: 114ms\n",
      "188:\tlearn: 0.0018785\ttotal: 10.8s\tremaining: 57ms\n",
      "189:\tlearn: 0.0018749\ttotal: 10.8s\tremaining: 0us\n",
      "0:\tlearn: 0.6258931\ttotal: 45.2ms\tremaining: 8.54s\n",
      "1:\tlearn: 0.5675209\ttotal: 109ms\tremaining: 10.2s\n",
      "2:\tlearn: 0.5164076\ttotal: 176ms\tremaining: 11s\n",
      "3:\tlearn: 0.4711541\ttotal: 239ms\tremaining: 11.1s\n",
      "4:\tlearn: 0.4308963\ttotal: 302ms\tremaining: 11.2s\n",
      "5:\tlearn: 0.3948641\ttotal: 365ms\tremaining: 11.2s\n",
      "6:\tlearn: 0.3624624\ttotal: 427ms\tremaining: 11.2s\n",
      "7:\tlearn: 0.3332011\ttotal: 488ms\tremaining: 11.1s\n",
      "8:\tlearn: 0.3067335\ttotal: 550ms\tremaining: 11.1s\n",
      "9:\tlearn: 0.2827196\ttotal: 615ms\tremaining: 11.1s\n",
      "10:\tlearn: 0.2609393\ttotal: 678ms\tremaining: 11s\n",
      "11:\tlearn: 0.2409723\ttotal: 739ms\tremaining: 11s\n",
      "12:\tlearn: 0.2227791\ttotal: 800ms\tremaining: 10.9s\n",
      "13:\tlearn: 0.2061406\ttotal: 865ms\tremaining: 10.9s\n",
      "14:\tlearn: 0.1909283\ttotal: 926ms\tremaining: 10.8s\n",
      "15:\tlearn: 0.1769654\ttotal: 991ms\tremaining: 10.8s\n",
      "16:\tlearn: 0.1641049\ttotal: 1.05s\tremaining: 10.7s\n",
      "17:\tlearn: 0.1522603\ttotal: 1.11s\tremaining: 10.7s\n",
      "18:\tlearn: 0.1413542\ttotal: 1.18s\tremaining: 10.6s\n",
      "19:\tlearn: 0.1312656\ttotal: 1.24s\tremaining: 10.5s\n",
      "20:\tlearn: 0.1220013\ttotal: 1.28s\tremaining: 10.3s\n",
      "21:\tlearn: 0.1134282\ttotal: 1.35s\tremaining: 10.3s\n",
      "22:\tlearn: 0.1055062\ttotal: 1.41s\tremaining: 10.2s\n",
      "23:\tlearn: 0.0981847\ttotal: 1.47s\tremaining: 10.2s\n",
      "24:\tlearn: 0.0914077\ttotal: 1.53s\tremaining: 10.1s\n",
      "25:\tlearn: 0.0851482\ttotal: 1.59s\tremaining: 10.1s\n",
      "26:\tlearn: 0.0793363\ttotal: 1.65s\tremaining: 9.98s\n",
      "27:\tlearn: 0.0739562\ttotal: 1.72s\tremaining: 9.93s\n",
      "28:\tlearn: 0.0689768\ttotal: 1.78s\tremaining: 9.88s\n",
      "29:\tlearn: 0.0643536\ttotal: 1.84s\tremaining: 9.82s\n",
      "30:\tlearn: 0.0600467\ttotal: 1.9s\tremaining: 9.74s\n",
      "31:\tlearn: 0.0560605\ttotal: 1.96s\tremaining: 9.67s\n",
      "32:\tlearn: 0.0523513\ttotal: 2.02s\tremaining: 9.6s\n",
      "33:\tlearn: 0.0488940\ttotal: 2.08s\tremaining: 9.54s\n",
      "34:\tlearn: 0.0456980\ttotal: 2.14s\tremaining: 9.46s\n",
      "35:\tlearn: 0.0427284\ttotal: 2.19s\tremaining: 9.39s\n",
      "36:\tlearn: 0.0399498\ttotal: 2.25s\tremaining: 9.3s\n",
      "37:\tlearn: 0.0373919\ttotal: 2.31s\tremaining: 9.24s\n",
      "38:\tlearn: 0.0349903\ttotal: 2.37s\tremaining: 9.19s\n",
      "39:\tlearn: 0.0327640\ttotal: 2.43s\tremaining: 9.12s\n",
      "40:\tlearn: 0.0306737\ttotal: 2.49s\tremaining: 9.05s\n",
      "41:\tlearn: 0.0287346\ttotal: 2.55s\tremaining: 8.97s\n",
      "42:\tlearn: 0.0269347\ttotal: 2.6s\tremaining: 8.9s\n",
      "43:\tlearn: 0.0252517\ttotal: 2.66s\tremaining: 8.84s\n",
      "44:\tlearn: 0.0236996\ttotal: 2.73s\tremaining: 8.79s\n",
      "45:\tlearn: 0.0222401\ttotal: 2.78s\tremaining: 8.71s\n",
      "46:\tlearn: 0.0208765\ttotal: 2.84s\tremaining: 8.65s\n",
      "47:\tlearn: 0.0196217\ttotal: 2.9s\tremaining: 8.58s\n",
      "48:\tlearn: 0.0184575\ttotal: 2.96s\tremaining: 8.51s\n",
      "49:\tlearn: 0.0173593\ttotal: 3.01s\tremaining: 8.43s\n",
      "50:\tlearn: 0.0163471\ttotal: 3.07s\tremaining: 8.36s\n",
      "51:\tlearn: 0.0154053\ttotal: 3.12s\tremaining: 8.29s\n",
      "52:\tlearn: 0.0145340\ttotal: 3.18s\tremaining: 8.23s\n",
      "53:\tlearn: 0.0137114\ttotal: 3.24s\tremaining: 8.15s\n",
      "54:\tlearn: 0.0129297\ttotal: 3.29s\tremaining: 8.08s\n",
      "55:\tlearn: 0.0122136\ttotal: 3.35s\tremaining: 8.03s\n",
      "56:\tlearn: 0.0115448\ttotal: 3.41s\tremaining: 7.95s\n",
      "57:\tlearn: 0.0109269\ttotal: 3.47s\tremaining: 7.89s\n",
      "58:\tlearn: 0.0103513\ttotal: 3.52s\tremaining: 7.81s\n",
      "59:\tlearn: 0.0098163\ttotal: 3.58s\tremaining: 7.75s\n",
      "60:\tlearn: 0.0093067\ttotal: 3.63s\tremaining: 7.67s\n",
      "61:\tlearn: 0.0088368\ttotal: 3.66s\tremaining: 7.57s\n",
      "62:\tlearn: 0.0083958\ttotal: 3.72s\tremaining: 7.51s\n",
      "63:\tlearn: 0.0079837\ttotal: 3.78s\tremaining: 7.45s\n",
      "64:\tlearn: 0.0076106\ttotal: 3.84s\tremaining: 7.39s\n",
      "65:\tlearn: 0.0072631\ttotal: 3.9s\tremaining: 7.33s\n",
      "66:\tlearn: 0.0069448\ttotal: 3.96s\tremaining: 7.28s\n",
      "67:\tlearn: 0.0066301\ttotal: 4.02s\tremaining: 7.21s\n",
      "68:\tlearn: 0.0063429\ttotal: 4.07s\tremaining: 7.14s\n",
      "69:\tlearn: 0.0060733\ttotal: 4.13s\tremaining: 7.07s\n",
      "70:\tlearn: 0.0058245\ttotal: 4.18s\tremaining: 7.01s\n",
      "71:\tlearn: 0.0055963\ttotal: 4.24s\tremaining: 6.94s\n",
      "72:\tlearn: 0.0053726\ttotal: 4.29s\tremaining: 6.88s\n",
      "73:\tlearn: 0.0051754\ttotal: 4.35s\tremaining: 6.82s\n",
      "74:\tlearn: 0.0049784\ttotal: 4.41s\tremaining: 6.77s\n",
      "75:\tlearn: 0.0048028\ttotal: 4.47s\tremaining: 6.71s\n",
      "76:\tlearn: 0.0046390\ttotal: 4.53s\tremaining: 6.65s\n",
      "77:\tlearn: 0.0044930\ttotal: 4.59s\tremaining: 6.59s\n",
      "78:\tlearn: 0.0043536\ttotal: 4.64s\tremaining: 6.53s\n",
      "79:\tlearn: 0.0042185\ttotal: 4.7s\tremaining: 6.46s\n",
      "80:\tlearn: 0.0040948\ttotal: 4.75s\tremaining: 6.4s\n",
      "81:\tlearn: 0.0039876\ttotal: 4.81s\tremaining: 6.34s\n",
      "82:\tlearn: 0.0038858\ttotal: 4.87s\tremaining: 6.28s\n",
      "83:\tlearn: 0.0037767\ttotal: 4.93s\tremaining: 6.22s\n",
      "84:\tlearn: 0.0036884\ttotal: 4.99s\tremaining: 6.16s\n",
      "85:\tlearn: 0.0036043\ttotal: 5.04s\tremaining: 6.1s\n",
      "86:\tlearn: 0.0035143\ttotal: 5.1s\tremaining: 6.04s\n",
      "87:\tlearn: 0.0034331\ttotal: 5.16s\tremaining: 5.98s\n",
      "88:\tlearn: 0.0033643\ttotal: 5.21s\tremaining: 5.91s\n",
      "89:\tlearn: 0.0032862\ttotal: 5.27s\tremaining: 5.85s\n",
      "90:\tlearn: 0.0032303\ttotal: 5.32s\tremaining: 5.79s\n",
      "91:\tlearn: 0.0031592\ttotal: 5.38s\tremaining: 5.73s\n",
      "92:\tlearn: 0.0030977\ttotal: 5.43s\tremaining: 5.67s\n",
      "93:\tlearn: 0.0030374\ttotal: 5.5s\tremaining: 5.61s\n",
      "94:\tlearn: 0.0029803\ttotal: 5.56s\tremaining: 5.56s\n",
      "95:\tlearn: 0.0029394\ttotal: 5.61s\tremaining: 5.5s\n",
      "96:\tlearn: 0.0028928\ttotal: 5.67s\tremaining: 5.43s\n",
      "97:\tlearn: 0.0028583\ttotal: 5.72s\tremaining: 5.37s\n",
      "98:\tlearn: 0.0028180\ttotal: 5.78s\tremaining: 5.31s\n",
      "99:\tlearn: 0.0027920\ttotal: 5.83s\tremaining: 5.25s\n",
      "100:\tlearn: 0.0027502\ttotal: 5.89s\tremaining: 5.19s\n",
      "101:\tlearn: 0.0027179\ttotal: 5.95s\tremaining: 5.13s\n",
      "102:\tlearn: 0.0026791\ttotal: 6s\tremaining: 5.07s\n",
      "103:\tlearn: 0.0026484\ttotal: 6.06s\tremaining: 5.01s\n",
      "104:\tlearn: 0.0026115\ttotal: 6.12s\tremaining: 4.96s\n",
      "105:\tlearn: 0.0025917\ttotal: 6.17s\tremaining: 4.89s\n",
      "106:\tlearn: 0.0025634\ttotal: 6.23s\tremaining: 4.83s\n",
      "107:\tlearn: 0.0025463\ttotal: 6.29s\tremaining: 4.77s\n",
      "108:\tlearn: 0.0025326\ttotal: 6.34s\tremaining: 4.71s\n",
      "109:\tlearn: 0.0025029\ttotal: 6.38s\tremaining: 4.64s\n",
      "110:\tlearn: 0.0024847\ttotal: 6.42s\tremaining: 4.57s\n",
      "111:\tlearn: 0.0024693\ttotal: 6.48s\tremaining: 4.51s\n",
      "112:\tlearn: 0.0024467\ttotal: 6.54s\tremaining: 4.46s\n",
      "113:\tlearn: 0.0024257\ttotal: 6.6s\tremaining: 4.4s\n",
      "114:\tlearn: 0.0024035\ttotal: 6.66s\tremaining: 4.34s\n",
      "115:\tlearn: 0.0023829\ttotal: 6.72s\tremaining: 4.29s\n",
      "116:\tlearn: 0.0023687\ttotal: 6.78s\tremaining: 4.23s\n",
      "117:\tlearn: 0.0023573\ttotal: 6.84s\tremaining: 4.17s\n",
      "118:\tlearn: 0.0023444\ttotal: 6.9s\tremaining: 4.12s\n",
      "119:\tlearn: 0.0023227\ttotal: 6.96s\tremaining: 4.06s\n",
      "120:\tlearn: 0.0023077\ttotal: 7.02s\tremaining: 4s\n",
      "121:\tlearn: 0.0022887\ttotal: 7.08s\tremaining: 3.94s\n",
      "122:\tlearn: 0.0022760\ttotal: 7.13s\tremaining: 3.89s\n",
      "123:\tlearn: 0.0022573\ttotal: 7.2s\tremaining: 3.83s\n",
      "124:\tlearn: 0.0022452\ttotal: 7.26s\tremaining: 3.77s\n",
      "125:\tlearn: 0.0022348\ttotal: 7.32s\tremaining: 3.72s\n",
      "126:\tlearn: 0.0022202\ttotal: 7.38s\tremaining: 3.66s\n",
      "127:\tlearn: 0.0022130\ttotal: 7.44s\tremaining: 3.6s\n",
      "128:\tlearn: 0.0022024\ttotal: 7.5s\tremaining: 3.54s\n",
      "129:\tlearn: 0.0021902\ttotal: 7.54s\tremaining: 3.48s\n",
      "130:\tlearn: 0.0021741\ttotal: 7.6s\tremaining: 3.42s\n",
      "131:\tlearn: 0.0021684\ttotal: 7.66s\tremaining: 3.37s\n",
      "132:\tlearn: 0.0021634\ttotal: 7.72s\tremaining: 3.31s\n",
      "133:\tlearn: 0.0021469\ttotal: 7.78s\tremaining: 3.25s\n",
      "134:\tlearn: 0.0021363\ttotal: 7.84s\tremaining: 3.19s\n",
      "135:\tlearn: 0.0021312\ttotal: 7.9s\tremaining: 3.14s\n",
      "136:\tlearn: 0.0021265\ttotal: 7.96s\tremaining: 3.08s\n",
      "137:\tlearn: 0.0021148\ttotal: 8.02s\tremaining: 3.02s\n",
      "138:\tlearn: 0.0021097\ttotal: 8.07s\tremaining: 2.96s\n",
      "139:\tlearn: 0.0021036\ttotal: 8.13s\tremaining: 2.9s\n",
      "140:\tlearn: 0.0020930\ttotal: 8.19s\tremaining: 2.85s\n",
      "141:\tlearn: 0.0020886\ttotal: 8.26s\tremaining: 2.79s\n",
      "142:\tlearn: 0.0020827\ttotal: 8.31s\tremaining: 2.73s\n",
      "143:\tlearn: 0.0020749\ttotal: 8.37s\tremaining: 2.67s\n",
      "144:\tlearn: 0.0020706\ttotal: 8.43s\tremaining: 2.62s\n",
      "145:\tlearn: 0.0020656\ttotal: 8.49s\tremaining: 2.56s\n",
      "146:\tlearn: 0.0020622\ttotal: 8.54s\tremaining: 2.5s\n",
      "147:\tlearn: 0.0020523\ttotal: 8.61s\tremaining: 2.44s\n",
      "148:\tlearn: 0.0020476\ttotal: 8.67s\tremaining: 2.39s\n",
      "149:\tlearn: 0.0020435\ttotal: 8.73s\tremaining: 2.33s\n",
      "150:\tlearn: 0.0020378\ttotal: 8.78s\tremaining: 2.27s\n",
      "151:\tlearn: 0.0020346\ttotal: 8.84s\tremaining: 2.21s\n",
      "152:\tlearn: 0.0020214\ttotal: 8.89s\tremaining: 2.15s\n",
      "153:\tlearn: 0.0020144\ttotal: 8.95s\tremaining: 2.09s\n",
      "154:\tlearn: 0.0020085\ttotal: 9.01s\tremaining: 2.04s\n",
      "155:\tlearn: 0.0020052\ttotal: 9.07s\tremaining: 1.98s\n",
      "156:\tlearn: 0.0019999\ttotal: 9.13s\tremaining: 1.92s\n",
      "157:\tlearn: 0.0019915\ttotal: 9.19s\tremaining: 1.86s\n",
      "158:\tlearn: 0.0019871\ttotal: 9.24s\tremaining: 1.8s\n",
      "159:\tlearn: 0.0019795\ttotal: 9.3s\tremaining: 1.74s\n",
      "160:\tlearn: 0.0019752\ttotal: 9.36s\tremaining: 1.69s\n",
      "161:\tlearn: 0.0019661\ttotal: 9.42s\tremaining: 1.63s\n",
      "162:\tlearn: 0.0019610\ttotal: 9.48s\tremaining: 1.57s\n",
      "163:\tlearn: 0.0019521\ttotal: 9.54s\tremaining: 1.51s\n",
      "164:\tlearn: 0.0019471\ttotal: 9.6s\tremaining: 1.45s\n",
      "165:\tlearn: 0.0019448\ttotal: 9.65s\tremaining: 1.4s\n",
      "166:\tlearn: 0.0019419\ttotal: 9.7s\tremaining: 1.34s\n",
      "167:\tlearn: 0.0019361\ttotal: 9.76s\tremaining: 1.28s\n",
      "168:\tlearn: 0.0019319\ttotal: 9.82s\tremaining: 1.22s\n",
      "169:\tlearn: 0.0019192\ttotal: 9.88s\tremaining: 1.16s\n",
      "170:\tlearn: 0.0019167\ttotal: 9.93s\tremaining: 1.1s\n",
      "171:\tlearn: 0.0019110\ttotal: 9.99s\tremaining: 1.04s\n",
      "172:\tlearn: 0.0019078\ttotal: 10s\tremaining: 987ms\n",
      "173:\tlearn: 0.0019055\ttotal: 10.1s\tremaining: 929ms\n",
      "174:\tlearn: 0.0019003\ttotal: 10.2s\tremaining: 871ms\n",
      "175:\tlearn: 0.0018963\ttotal: 10.2s\tremaining: 813ms\n",
      "176:\tlearn: 0.0018943\ttotal: 10.3s\tremaining: 754ms\n",
      "177:\tlearn: 0.0018922\ttotal: 10.3s\tremaining: 696ms\n",
      "178:\tlearn: 0.0018893\ttotal: 10.4s\tremaining: 638ms\n",
      "179:\tlearn: 0.0018823\ttotal: 10.4s\tremaining: 580ms\n",
      "180:\tlearn: 0.0018803\ttotal: 10.5s\tremaining: 522ms\n",
      "181:\tlearn: 0.0018754\ttotal: 10.5s\tremaining: 463ms\n",
      "182:\tlearn: 0.0018719\ttotal: 10.6s\tremaining: 405ms\n",
      "183:\tlearn: 0.0018698\ttotal: 10.6s\tremaining: 347ms\n",
      "184:\tlearn: 0.0018679\ttotal: 10.7s\tremaining: 289ms\n",
      "185:\tlearn: 0.0018636\ttotal: 10.8s\tremaining: 231ms\n",
      "186:\tlearn: 0.0018583\ttotal: 10.8s\tremaining: 173ms\n",
      "187:\tlearn: 0.0018547\ttotal: 10.9s\tremaining: 116ms\n",
      "188:\tlearn: 0.0018512\ttotal: 10.9s\tremaining: 57.8ms\n",
      "189:\tlearn: 0.0018474\ttotal: 11s\tremaining: 0us\n",
      "0:\tlearn: 0.6258924\ttotal: 45ms\tremaining: 8.5s\n",
      "1:\tlearn: 0.5675307\ttotal: 91.4ms\tremaining: 8.59s\n",
      "2:\tlearn: 0.5163431\ttotal: 158ms\tremaining: 9.86s\n",
      "3:\tlearn: 0.4710884\ttotal: 225ms\tremaining: 10.5s\n",
      "4:\tlearn: 0.4308248\ttotal: 286ms\tremaining: 10.6s\n",
      "5:\tlearn: 0.3947715\ttotal: 349ms\tremaining: 10.7s\n",
      "6:\tlearn: 0.3623532\ttotal: 394ms\tremaining: 10.3s\n",
      "7:\tlearn: 0.3330820\ttotal: 440ms\tremaining: 10s\n",
      "8:\tlearn: 0.3066045\ttotal: 480ms\tremaining: 9.66s\n",
      "9:\tlearn: 0.2826206\ttotal: 527ms\tremaining: 9.48s\n",
      "10:\tlearn: 0.2608313\ttotal: 571ms\tremaining: 9.28s\n",
      "11:\tlearn: 0.2408540\ttotal: 633ms\tremaining: 9.38s\n",
      "12:\tlearn: 0.2226488\ttotal: 697ms\tremaining: 9.48s\n",
      "13:\tlearn: 0.2060010\ttotal: 762ms\tremaining: 9.58s\n",
      "14:\tlearn: 0.1907881\ttotal: 815ms\tremaining: 9.51s\n",
      "15:\tlearn: 0.1768200\ttotal: 879ms\tremaining: 9.56s\n",
      "16:\tlearn: 0.1639904\ttotal: 941ms\tremaining: 9.58s\n",
      "17:\tlearn: 0.1521141\ttotal: 1s\tremaining: 9.57s\n",
      "18:\tlearn: 0.1412048\ttotal: 1.06s\tremaining: 9.59s\n",
      "19:\tlearn: 0.1311233\ttotal: 1.13s\tremaining: 9.57s\n",
      "20:\tlearn: 0.1218530\ttotal: 1.19s\tremaining: 9.59s\n",
      "21:\tlearn: 0.1132726\ttotal: 1.25s\tremaining: 9.59s\n",
      "22:\tlearn: 0.1053424\ttotal: 1.32s\tremaining: 9.57s\n",
      "23:\tlearn: 0.0980071\ttotal: 1.38s\tremaining: 9.55s\n",
      "24:\tlearn: 0.0912151\ttotal: 1.45s\tremaining: 9.54s\n",
      "25:\tlearn: 0.0849523\ttotal: 1.51s\tremaining: 9.55s\n",
      "26:\tlearn: 0.0791117\ttotal: 1.58s\tremaining: 9.54s\n",
      "27:\tlearn: 0.0737242\ttotal: 1.64s\tremaining: 9.51s\n",
      "28:\tlearn: 0.0687417\ttotal: 1.71s\tremaining: 9.48s\n",
      "29:\tlearn: 0.0641170\ttotal: 1.77s\tremaining: 9.44s\n",
      "30:\tlearn: 0.0598135\ttotal: 1.83s\tremaining: 9.4s\n",
      "31:\tlearn: 0.0558231\ttotal: 1.89s\tremaining: 9.34s\n",
      "32:\tlearn: 0.0520939\ttotal: 1.93s\tremaining: 9.2s\n",
      "33:\tlearn: 0.0486079\ttotal: 1.97s\tremaining: 9.05s\n",
      "34:\tlearn: 0.0453963\ttotal: 2.03s\tremaining: 8.99s\n",
      "35:\tlearn: 0.0424090\ttotal: 2.07s\tremaining: 8.85s\n",
      "36:\tlearn: 0.0396218\ttotal: 2.13s\tremaining: 8.79s\n",
      "37:\tlearn: 0.0370548\ttotal: 2.19s\tremaining: 8.74s\n",
      "38:\tlearn: 0.0346569\ttotal: 2.25s\tremaining: 8.7s\n",
      "39:\tlearn: 0.0324309\ttotal: 2.3s\tremaining: 8.64s\n",
      "40:\tlearn: 0.0303363\ttotal: 2.36s\tremaining: 8.57s\n",
      "41:\tlearn: 0.0283964\ttotal: 2.42s\tremaining: 8.52s\n",
      "42:\tlearn: 0.0265739\ttotal: 2.47s\tremaining: 8.44s\n",
      "43:\tlearn: 0.0248817\ttotal: 2.52s\tremaining: 8.36s\n",
      "44:\tlearn: 0.0233312\ttotal: 2.58s\tremaining: 8.31s\n",
      "45:\tlearn: 0.0218814\ttotal: 2.65s\tremaining: 8.28s\n",
      "46:\tlearn: 0.0205381\ttotal: 2.69s\tremaining: 8.2s\n",
      "47:\tlearn: 0.0192684\ttotal: 2.75s\tremaining: 8.13s\n",
      "48:\tlearn: 0.0180955\ttotal: 2.79s\tremaining: 8.03s\n",
      "49:\tlearn: 0.0170062\ttotal: 2.85s\tremaining: 7.99s\n",
      "50:\tlearn: 0.0159841\ttotal: 2.91s\tremaining: 7.93s\n",
      "51:\tlearn: 0.0150375\ttotal: 2.97s\tremaining: 7.89s\n",
      "52:\tlearn: 0.0141475\ttotal: 3.03s\tremaining: 7.83s\n",
      "53:\tlearn: 0.0133298\ttotal: 3.09s\tremaining: 7.78s\n",
      "54:\tlearn: 0.0125618\ttotal: 3.15s\tremaining: 7.72s\n",
      "55:\tlearn: 0.0118508\ttotal: 3.21s\tremaining: 7.67s\n",
      "56:\tlearn: 0.0111915\ttotal: 3.26s\tremaining: 7.62s\n",
      "57:\tlearn: 0.0105691\ttotal: 3.32s\tremaining: 7.55s\n",
      "58:\tlearn: 0.0099910\ttotal: 3.38s\tremaining: 7.5s\n",
      "59:\tlearn: 0.0094537\ttotal: 3.43s\tremaining: 7.43s\n",
      "60:\tlearn: 0.0089546\ttotal: 3.47s\tremaining: 7.35s\n",
      "61:\tlearn: 0.0084933\ttotal: 3.55s\tremaining: 7.33s\n",
      "62:\tlearn: 0.0080571\ttotal: 3.6s\tremaining: 7.26s\n",
      "63:\tlearn: 0.0076509\ttotal: 3.66s\tremaining: 7.21s\n",
      "64:\tlearn: 0.0072805\ttotal: 3.71s\tremaining: 7.14s\n",
      "65:\tlearn: 0.0069269\ttotal: 3.75s\tremaining: 7.04s\n",
      "66:\tlearn: 0.0066064\ttotal: 3.79s\tremaining: 6.96s\n",
      "67:\tlearn: 0.0063017\ttotal: 3.83s\tremaining: 6.87s\n",
      "68:\tlearn: 0.0060201\ttotal: 3.87s\tremaining: 6.78s\n",
      "69:\tlearn: 0.0057579\ttotal: 3.9s\tremaining: 6.69s\n",
      "70:\tlearn: 0.0055100\ttotal: 3.94s\tremaining: 6.6s\n",
      "71:\tlearn: 0.0052815\ttotal: 3.96s\tremaining: 6.5s\n",
      "72:\tlearn: 0.0050674\ttotal: 4s\tremaining: 6.41s\n",
      "73:\tlearn: 0.0048660\ttotal: 4.03s\tremaining: 6.31s\n",
      "74:\tlearn: 0.0046757\ttotal: 4.05s\tremaining: 6.22s\n",
      "75:\tlearn: 0.0045047\ttotal: 4.08s\tremaining: 6.12s\n",
      "76:\tlearn: 0.0043448\ttotal: 4.11s\tremaining: 6.03s\n",
      "77:\tlearn: 0.0041932\ttotal: 4.14s\tremaining: 5.94s\n",
      "78:\tlearn: 0.0040557\ttotal: 4.17s\tremaining: 5.85s\n",
      "79:\tlearn: 0.0039301\ttotal: 4.19s\tremaining: 5.77s\n",
      "80:\tlearn: 0.0038085\ttotal: 4.23s\tremaining: 5.69s\n",
      "81:\tlearn: 0.0036977\ttotal: 4.28s\tremaining: 5.64s\n",
      "82:\tlearn: 0.0035964\ttotal: 4.34s\tremaining: 5.59s\n",
      "83:\tlearn: 0.0034917\ttotal: 4.38s\tremaining: 5.53s\n",
      "84:\tlearn: 0.0034015\ttotal: 4.44s\tremaining: 5.48s\n",
      "85:\tlearn: 0.0033185\ttotal: 4.49s\tremaining: 5.43s\n",
      "86:\tlearn: 0.0032319\ttotal: 4.54s\tremaining: 5.37s\n",
      "87:\tlearn: 0.0031547\ttotal: 4.59s\tremaining: 5.32s\n",
      "88:\tlearn: 0.0030824\ttotal: 4.64s\tremaining: 5.27s\n",
      "89:\tlearn: 0.0030182\ttotal: 4.7s\tremaining: 5.22s\n",
      "90:\tlearn: 0.0029602\ttotal: 4.75s\tremaining: 5.17s\n",
      "91:\tlearn: 0.0028987\ttotal: 4.81s\tremaining: 5.12s\n",
      "92:\tlearn: 0.0028413\ttotal: 4.87s\tremaining: 5.08s\n",
      "93:\tlearn: 0.0027857\ttotal: 4.92s\tremaining: 5.03s\n",
      "94:\tlearn: 0.0027425\ttotal: 4.97s\tremaining: 4.97s\n",
      "95:\tlearn: 0.0026981\ttotal: 5.03s\tremaining: 4.93s\n",
      "96:\tlearn: 0.0026614\ttotal: 5.08s\tremaining: 4.87s\n",
      "97:\tlearn: 0.0026244\ttotal: 5.13s\tremaining: 4.82s\n",
      "98:\tlearn: 0.0025916\ttotal: 5.19s\tremaining: 4.77s\n",
      "99:\tlearn: 0.0025599\ttotal: 5.25s\tremaining: 4.72s\n",
      "100:\tlearn: 0.0025283\ttotal: 5.3s\tremaining: 4.67s\n",
      "101:\tlearn: 0.0024977\ttotal: 5.36s\tremaining: 4.62s\n",
      "102:\tlearn: 0.0024688\ttotal: 5.42s\tremaining: 4.58s\n",
      "103:\tlearn: 0.0024362\ttotal: 5.48s\tremaining: 4.53s\n",
      "104:\tlearn: 0.0024038\ttotal: 5.54s\tremaining: 4.49s\n",
      "105:\tlearn: 0.0023758\ttotal: 5.6s\tremaining: 4.44s\n",
      "106:\tlearn: 0.0023549\ttotal: 5.64s\tremaining: 4.37s\n",
      "107:\tlearn: 0.0023325\ttotal: 5.69s\tremaining: 4.32s\n",
      "108:\tlearn: 0.0023163\ttotal: 5.74s\tremaining: 4.26s\n",
      "109:\tlearn: 0.0022937\ttotal: 5.79s\tremaining: 4.21s\n",
      "110:\tlearn: 0.0022774\ttotal: 5.85s\tremaining: 4.17s\n",
      "111:\tlearn: 0.0022613\ttotal: 5.91s\tremaining: 4.11s\n",
      "112:\tlearn: 0.0022417\ttotal: 5.97s\tremaining: 4.07s\n",
      "113:\tlearn: 0.0022218\ttotal: 6.03s\tremaining: 4.02s\n",
      "114:\tlearn: 0.0022030\ttotal: 6.08s\tremaining: 3.97s\n",
      "115:\tlearn: 0.0021866\ttotal: 6.14s\tremaining: 3.92s\n",
      "116:\tlearn: 0.0021696\ttotal: 6.2s\tremaining: 3.87s\n",
      "117:\tlearn: 0.0021470\ttotal: 6.26s\tremaining: 3.82s\n",
      "118:\tlearn: 0.0021345\ttotal: 6.31s\tremaining: 3.77s\n",
      "119:\tlearn: 0.0021251\ttotal: 6.38s\tremaining: 3.72s\n",
      "120:\tlearn: 0.0021130\ttotal: 6.43s\tremaining: 3.67s\n",
      "121:\tlearn: 0.0021030\ttotal: 6.49s\tremaining: 3.62s\n",
      "122:\tlearn: 0.0020911\ttotal: 6.55s\tremaining: 3.57s\n",
      "123:\tlearn: 0.0020726\ttotal: 6.61s\tremaining: 3.52s\n",
      "124:\tlearn: 0.0020578\ttotal: 6.67s\tremaining: 3.47s\n",
      "125:\tlearn: 0.0020481\ttotal: 6.73s\tremaining: 3.42s\n",
      "126:\tlearn: 0.0020362\ttotal: 6.79s\tremaining: 3.37s\n",
      "127:\tlearn: 0.0020240\ttotal: 6.85s\tremaining: 3.32s\n",
      "128:\tlearn: 0.0020165\ttotal: 6.9s\tremaining: 3.26s\n",
      "129:\tlearn: 0.0020079\ttotal: 6.96s\tremaining: 3.21s\n",
      "130:\tlearn: 0.0019969\ttotal: 7.02s\tremaining: 3.16s\n",
      "131:\tlearn: 0.0019849\ttotal: 7.08s\tremaining: 3.11s\n",
      "132:\tlearn: 0.0019769\ttotal: 7.14s\tremaining: 3.06s\n",
      "133:\tlearn: 0.0019733\ttotal: 7.19s\tremaining: 3.01s\n",
      "134:\tlearn: 0.0019669\ttotal: 7.26s\tremaining: 2.96s\n",
      "135:\tlearn: 0.0019626\ttotal: 7.31s\tremaining: 2.9s\n",
      "136:\tlearn: 0.0019590\ttotal: 7.36s\tremaining: 2.85s\n",
      "137:\tlearn: 0.0019529\ttotal: 7.42s\tremaining: 2.8s\n",
      "138:\tlearn: 0.0019455\ttotal: 7.47s\tremaining: 2.74s\n",
      "139:\tlearn: 0.0019350\ttotal: 7.53s\tremaining: 2.69s\n",
      "140:\tlearn: 0.0019255\ttotal: 7.59s\tremaining: 2.64s\n",
      "141:\tlearn: 0.0019075\ttotal: 7.65s\tremaining: 2.58s\n",
      "142:\tlearn: 0.0018962\ttotal: 7.71s\tremaining: 2.53s\n",
      "143:\tlearn: 0.0018921\ttotal: 7.77s\tremaining: 2.48s\n",
      "144:\tlearn: 0.0018852\ttotal: 7.82s\tremaining: 2.43s\n",
      "145:\tlearn: 0.0018787\ttotal: 7.88s\tremaining: 2.38s\n",
      "146:\tlearn: 0.0018701\ttotal: 7.94s\tremaining: 2.32s\n",
      "147:\tlearn: 0.0018662\ttotal: 8s\tremaining: 2.27s\n",
      "148:\tlearn: 0.0018609\ttotal: 8.06s\tremaining: 2.22s\n",
      "149:\tlearn: 0.0018543\ttotal: 8.11s\tremaining: 2.16s\n",
      "150:\tlearn: 0.0018482\ttotal: 8.17s\tremaining: 2.11s\n",
      "151:\tlearn: 0.0018440\ttotal: 8.23s\tremaining: 2.06s\n",
      "152:\tlearn: 0.0018344\ttotal: 8.29s\tremaining: 2s\n",
      "153:\tlearn: 0.0018243\ttotal: 8.35s\tremaining: 1.95s\n",
      "154:\tlearn: 0.0018216\ttotal: 8.43s\tremaining: 1.9s\n",
      "155:\tlearn: 0.0018151\ttotal: 8.48s\tremaining: 1.85s\n",
      "156:\tlearn: 0.0018098\ttotal: 8.54s\tremaining: 1.79s\n",
      "157:\tlearn: 0.0018029\ttotal: 8.6s\tremaining: 1.74s\n",
      "158:\tlearn: 0.0017956\ttotal: 8.66s\tremaining: 1.69s\n",
      "159:\tlearn: 0.0017896\ttotal: 8.73s\tremaining: 1.64s\n",
      "160:\tlearn: 0.0017851\ttotal: 8.78s\tremaining: 1.58s\n",
      "161:\tlearn: 0.0017808\ttotal: 8.85s\tremaining: 1.53s\n",
      "162:\tlearn: 0.0017769\ttotal: 8.92s\tremaining: 1.48s\n",
      "163:\tlearn: 0.0017718\ttotal: 8.97s\tremaining: 1.42s\n",
      "164:\tlearn: 0.0017682\ttotal: 9.03s\tremaining: 1.37s\n",
      "165:\tlearn: 0.0017630\ttotal: 9.09s\tremaining: 1.31s\n",
      "166:\tlearn: 0.0017590\ttotal: 9.14s\tremaining: 1.26s\n",
      "167:\tlearn: 0.0017503\ttotal: 9.19s\tremaining: 1.2s\n",
      "168:\tlearn: 0.0017406\ttotal: 9.24s\tremaining: 1.15s\n",
      "169:\tlearn: 0.0017363\ttotal: 9.3s\tremaining: 1.09s\n",
      "170:\tlearn: 0.0017320\ttotal: 9.36s\tremaining: 1.04s\n",
      "171:\tlearn: 0.0017248\ttotal: 9.42s\tremaining: 986ms\n",
      "172:\tlearn: 0.0017210\ttotal: 9.48s\tremaining: 931ms\n",
      "173:\tlearn: 0.0017181\ttotal: 9.53s\tremaining: 877ms\n",
      "174:\tlearn: 0.0017138\ttotal: 9.59s\tremaining: 822ms\n",
      "175:\tlearn: 0.0017110\ttotal: 9.65s\tremaining: 767ms\n",
      "176:\tlearn: 0.0017094\ttotal: 9.7s\tremaining: 712ms\n",
      "177:\tlearn: 0.0017015\ttotal: 9.76s\tremaining: 658ms\n",
      "178:\tlearn: 0.0016999\ttotal: 9.82s\tremaining: 603ms\n",
      "179:\tlearn: 0.0016906\ttotal: 9.88s\tremaining: 549ms\n",
      "180:\tlearn: 0.0016852\ttotal: 9.93s\tremaining: 494ms\n",
      "181:\tlearn: 0.0016821\ttotal: 9.98s\tremaining: 439ms\n",
      "182:\tlearn: 0.0016767\ttotal: 10s\tremaining: 384ms\n",
      "183:\tlearn: 0.0016722\ttotal: 10.1s\tremaining: 329ms\n",
      "184:\tlearn: 0.0016695\ttotal: 10.2s\tremaining: 274ms\n",
      "185:\tlearn: 0.0016663\ttotal: 10.2s\tremaining: 219ms\n",
      "186:\tlearn: 0.0016604\ttotal: 10.3s\tremaining: 165ms\n",
      "187:\tlearn: 0.0016555\ttotal: 10.3s\tremaining: 110ms\n",
      "188:\tlearn: 0.0016525\ttotal: 10.4s\tremaining: 54.9ms\n",
      "189:\tlearn: 0.0016476\ttotal: 10.4s\tremaining: 0us\n",
      "0:\tlearn: 0.6258796\ttotal: 45.9ms\tremaining: 8.67s\n",
      "1:\tlearn: 0.5675108\ttotal: 106ms\tremaining: 9.97s\n",
      "2:\tlearn: 0.5163549\ttotal: 167ms\tremaining: 10.4s\n",
      "3:\tlearn: 0.4710960\ttotal: 232ms\tremaining: 10.8s\n",
      "4:\tlearn: 0.4308321\ttotal: 296ms\tremaining: 11s\n",
      "5:\tlearn: 0.3947877\ttotal: 360ms\tremaining: 11.1s\n",
      "6:\tlearn: 0.3623958\ttotal: 427ms\tremaining: 11.2s\n",
      "7:\tlearn: 0.3332029\ttotal: 489ms\tremaining: 11.1s\n",
      "8:\tlearn: 0.3067277\ttotal: 552ms\tremaining: 11.1s\n",
      "9:\tlearn: 0.2827135\ttotal: 618ms\tremaining: 11.1s\n",
      "10:\tlearn: 0.2609279\ttotal: 682ms\tremaining: 11.1s\n",
      "11:\tlearn: 0.2409588\ttotal: 741ms\tremaining: 11s\n",
      "12:\tlearn: 0.2227590\ttotal: 805ms\tremaining: 11s\n",
      "13:\tlearn: 0.2061138\ttotal: 872ms\tremaining: 11s\n",
      "14:\tlearn: 0.1908941\ttotal: 935ms\tremaining: 10.9s\n",
      "15:\tlearn: 0.1769294\ttotal: 1s\tremaining: 10.9s\n",
      "16:\tlearn: 0.1641013\ttotal: 1.07s\tremaining: 10.9s\n",
      "17:\tlearn: 0.1522216\ttotal: 1.13s\tremaining: 10.8s\n",
      "18:\tlearn: 0.1413173\ttotal: 1.19s\tremaining: 10.7s\n",
      "19:\tlearn: 0.1312230\ttotal: 1.25s\tremaining: 10.6s\n",
      "20:\tlearn: 0.1219602\ttotal: 1.31s\tremaining: 10.6s\n",
      "21:\tlearn: 0.1133845\ttotal: 1.38s\tremaining: 10.5s\n",
      "22:\tlearn: 0.1054597\ttotal: 1.44s\tremaining: 10.5s\n",
      "23:\tlearn: 0.0981386\ttotal: 1.5s\tremaining: 10.4s\n",
      "24:\tlearn: 0.0913457\ttotal: 1.56s\tremaining: 10.3s\n",
      "25:\tlearn: 0.0850861\ttotal: 1.63s\tremaining: 10.3s\n",
      "26:\tlearn: 0.0792431\ttotal: 1.69s\tremaining: 10.2s\n",
      "27:\tlearn: 0.0738645\ttotal: 1.75s\tremaining: 10.1s\n",
      "28:\tlearn: 0.0688682\ttotal: 1.81s\tremaining: 10.1s\n",
      "29:\tlearn: 0.0642686\ttotal: 1.87s\tremaining: 9.99s\n",
      "30:\tlearn: 0.0599523\ttotal: 1.93s\tremaining: 9.91s\n",
      "31:\tlearn: 0.0559712\ttotal: 1.99s\tremaining: 9.83s\n",
      "32:\tlearn: 0.0522639\ttotal: 2.05s\tremaining: 9.76s\n",
      "33:\tlearn: 0.0488091\ttotal: 2.11s\tremaining: 9.69s\n",
      "34:\tlearn: 0.0455925\ttotal: 2.17s\tremaining: 9.6s\n",
      "35:\tlearn: 0.0425971\ttotal: 2.22s\tremaining: 9.51s\n",
      "36:\tlearn: 0.0398257\ttotal: 2.27s\tremaining: 9.41s\n",
      "37:\tlearn: 0.0372396\ttotal: 2.33s\tremaining: 9.32s\n",
      "38:\tlearn: 0.0348520\ttotal: 2.39s\tremaining: 9.27s\n",
      "39:\tlearn: 0.0326306\ttotal: 2.46s\tremaining: 9.21s\n",
      "40:\tlearn: 0.0305595\ttotal: 2.51s\tremaining: 9.13s\n",
      "41:\tlearn: 0.0286195\ttotal: 2.57s\tremaining: 9.06s\n",
      "42:\tlearn: 0.0267994\ttotal: 2.62s\tremaining: 8.96s\n",
      "43:\tlearn: 0.0251138\ttotal: 2.68s\tremaining: 8.88s\n",
      "44:\tlearn: 0.0235415\ttotal: 2.73s\tremaining: 8.79s\n",
      "45:\tlearn: 0.0221043\ttotal: 2.79s\tremaining: 8.73s\n",
      "46:\tlearn: 0.0207492\ttotal: 2.85s\tremaining: 8.66s\n",
      "47:\tlearn: 0.0194906\ttotal: 2.91s\tremaining: 8.6s\n",
      "48:\tlearn: 0.0183282\ttotal: 2.97s\tremaining: 8.54s\n",
      "49:\tlearn: 0.0172289\ttotal: 3.02s\tremaining: 8.47s\n",
      "50:\tlearn: 0.0162055\ttotal: 3.08s\tremaining: 8.39s\n",
      "51:\tlearn: 0.0152641\ttotal: 3.14s\tremaining: 8.33s\n",
      "52:\tlearn: 0.0143826\ttotal: 3.19s\tremaining: 8.25s\n",
      "53:\tlearn: 0.0135561\ttotal: 3.25s\tremaining: 8.18s\n",
      "54:\tlearn: 0.0127850\ttotal: 3.3s\tremaining: 8.1s\n",
      "55:\tlearn: 0.0120708\ttotal: 3.36s\tremaining: 8.03s\n",
      "56:\tlearn: 0.0114072\ttotal: 3.42s\tremaining: 7.97s\n",
      "57:\tlearn: 0.0107913\ttotal: 3.48s\tremaining: 7.91s\n",
      "58:\tlearn: 0.0102072\ttotal: 3.53s\tremaining: 7.84s\n",
      "59:\tlearn: 0.0096758\ttotal: 3.59s\tremaining: 7.77s\n",
      "60:\tlearn: 0.0091769\ttotal: 3.64s\tremaining: 7.7s\n",
      "61:\tlearn: 0.0087092\ttotal: 3.7s\tremaining: 7.63s\n",
      "62:\tlearn: 0.0082709\ttotal: 3.75s\tremaining: 7.57s\n",
      "63:\tlearn: 0.0078714\ttotal: 3.81s\tremaining: 7.51s\n",
      "64:\tlearn: 0.0074960\ttotal: 3.87s\tremaining: 7.44s\n",
      "65:\tlearn: 0.0071431\ttotal: 3.92s\tremaining: 7.38s\n",
      "66:\tlearn: 0.0068130\ttotal: 3.98s\tremaining: 7.31s\n",
      "67:\tlearn: 0.0065046\ttotal: 4.03s\tremaining: 7.24s\n",
      "68:\tlearn: 0.0062211\ttotal: 4.09s\tremaining: 7.17s\n",
      "69:\tlearn: 0.0059575\ttotal: 4.15s\tremaining: 7.11s\n",
      "70:\tlearn: 0.0057127\ttotal: 4.2s\tremaining: 7.04s\n",
      "71:\tlearn: 0.0054820\ttotal: 4.26s\tremaining: 6.98s\n",
      "72:\tlearn: 0.0052715\ttotal: 4.32s\tremaining: 6.92s\n",
      "73:\tlearn: 0.0050638\ttotal: 4.36s\tremaining: 6.83s\n",
      "74:\tlearn: 0.0048730\ttotal: 4.41s\tremaining: 6.77s\n",
      "75:\tlearn: 0.0047004\ttotal: 4.47s\tremaining: 6.71s\n",
      "76:\tlearn: 0.0045388\ttotal: 4.53s\tremaining: 6.65s\n",
      "77:\tlearn: 0.0043923\ttotal: 4.58s\tremaining: 6.58s\n",
      "78:\tlearn: 0.0042506\ttotal: 4.64s\tremaining: 6.52s\n",
      "79:\tlearn: 0.0041219\ttotal: 4.69s\tremaining: 6.45s\n",
      "80:\tlearn: 0.0039976\ttotal: 4.74s\tremaining: 6.38s\n",
      "81:\tlearn: 0.0038889\ttotal: 4.8s\tremaining: 6.32s\n",
      "82:\tlearn: 0.0037795\ttotal: 4.86s\tremaining: 6.26s\n",
      "83:\tlearn: 0.0036798\ttotal: 4.91s\tremaining: 6.2s\n",
      "84:\tlearn: 0.0035912\ttotal: 4.97s\tremaining: 6.14s\n",
      "85:\tlearn: 0.0035088\ttotal: 5.02s\tremaining: 6.08s\n",
      "86:\tlearn: 0.0034238\ttotal: 5.08s\tremaining: 6.01s\n",
      "87:\tlearn: 0.0033455\ttotal: 5.14s\tremaining: 5.95s\n",
      "88:\tlearn: 0.0032783\ttotal: 5.2s\tremaining: 5.9s\n",
      "89:\tlearn: 0.0032120\ttotal: 5.24s\tremaining: 5.82s\n",
      "90:\tlearn: 0.0031505\ttotal: 5.28s\tremaining: 5.74s\n",
      "91:\tlearn: 0.0030865\ttotal: 5.32s\tremaining: 5.66s\n",
      "92:\tlearn: 0.0030269\ttotal: 5.36s\tremaining: 5.59s\n",
      "93:\tlearn: 0.0029703\ttotal: 5.4s\tremaining: 5.51s\n",
      "94:\tlearn: 0.0029151\ttotal: 5.45s\tremaining: 5.45s\n",
      "95:\tlearn: 0.0028681\ttotal: 5.5s\tremaining: 5.38s\n",
      "96:\tlearn: 0.0028327\ttotal: 5.55s\tremaining: 5.32s\n",
      "97:\tlearn: 0.0027989\ttotal: 5.61s\tremaining: 5.27s\n",
      "98:\tlearn: 0.0027537\ttotal: 5.67s\tremaining: 5.21s\n",
      "99:\tlearn: 0.0027131\ttotal: 5.73s\tremaining: 5.15s\n",
      "100:\tlearn: 0.0026870\ttotal: 5.78s\tremaining: 5.09s\n",
      "101:\tlearn: 0.0026512\ttotal: 5.84s\tremaining: 5.04s\n",
      "102:\tlearn: 0.0026254\ttotal: 5.9s\tremaining: 4.98s\n",
      "103:\tlearn: 0.0026029\ttotal: 5.96s\tremaining: 4.92s\n",
      "104:\tlearn: 0.0025726\ttotal: 6.02s\tremaining: 4.87s\n",
      "105:\tlearn: 0.0025434\ttotal: 6.07s\tremaining: 4.81s\n",
      "106:\tlearn: 0.0025200\ttotal: 6.13s\tremaining: 4.75s\n",
      "107:\tlearn: 0.0024933\ttotal: 6.18s\tremaining: 4.69s\n",
      "108:\tlearn: 0.0024784\ttotal: 6.24s\tremaining: 4.63s\n",
      "109:\tlearn: 0.0024565\ttotal: 6.29s\tremaining: 4.58s\n",
      "110:\tlearn: 0.0024425\ttotal: 6.34s\tremaining: 4.51s\n",
      "111:\tlearn: 0.0024211\ttotal: 6.4s\tremaining: 4.46s\n",
      "112:\tlearn: 0.0023982\ttotal: 6.46s\tremaining: 4.41s\n",
      "113:\tlearn: 0.0023742\ttotal: 6.53s\tremaining: 4.35s\n",
      "114:\tlearn: 0.0023547\ttotal: 6.58s\tremaining: 4.29s\n",
      "115:\tlearn: 0.0023384\ttotal: 6.64s\tremaining: 4.24s\n",
      "116:\tlearn: 0.0023201\ttotal: 6.7s\tremaining: 4.18s\n",
      "117:\tlearn: 0.0023053\ttotal: 6.76s\tremaining: 4.12s\n",
      "118:\tlearn: 0.0022922\ttotal: 6.8s\tremaining: 4.06s\n",
      "119:\tlearn: 0.0022800\ttotal: 6.84s\tremaining: 3.99s\n",
      "120:\tlearn: 0.0022726\ttotal: 6.88s\tremaining: 3.92s\n",
      "121:\tlearn: 0.0022613\ttotal: 6.92s\tremaining: 3.86s\n",
      "122:\tlearn: 0.0022441\ttotal: 6.97s\tremaining: 3.8s\n",
      "123:\tlearn: 0.0022375\ttotal: 7.03s\tremaining: 3.74s\n",
      "124:\tlearn: 0.0022322\ttotal: 7.08s\tremaining: 3.68s\n",
      "125:\tlearn: 0.0022173\ttotal: 7.14s\tremaining: 3.63s\n",
      "126:\tlearn: 0.0022094\ttotal: 7.19s\tremaining: 3.57s\n",
      "127:\tlearn: 0.0021995\ttotal: 7.24s\tremaining: 3.51s\n",
      "128:\tlearn: 0.0021865\ttotal: 7.3s\tremaining: 3.45s\n",
      "129:\tlearn: 0.0021764\ttotal: 7.36s\tremaining: 3.4s\n",
      "130:\tlearn: 0.0021719\ttotal: 7.41s\tremaining: 3.34s\n",
      "131:\tlearn: 0.0021625\ttotal: 7.47s\tremaining: 3.28s\n",
      "132:\tlearn: 0.0021590\ttotal: 7.53s\tremaining: 3.23s\n",
      "133:\tlearn: 0.0021522\ttotal: 7.58s\tremaining: 3.17s\n",
      "134:\tlearn: 0.0021411\ttotal: 7.64s\tremaining: 3.11s\n",
      "135:\tlearn: 0.0021371\ttotal: 7.69s\tremaining: 3.05s\n",
      "136:\tlearn: 0.0021340\ttotal: 7.74s\tremaining: 3s\n",
      "137:\tlearn: 0.0021241\ttotal: 7.8s\tremaining: 2.94s\n",
      "138:\tlearn: 0.0021127\ttotal: 7.86s\tremaining: 2.88s\n",
      "139:\tlearn: 0.0021013\ttotal: 7.91s\tremaining: 2.83s\n",
      "140:\tlearn: 0.0020986\ttotal: 7.96s\tremaining: 2.77s\n",
      "141:\tlearn: 0.0020867\ttotal: 8.02s\tremaining: 2.71s\n",
      "142:\tlearn: 0.0020747\ttotal: 8.08s\tremaining: 2.66s\n",
      "143:\tlearn: 0.0020638\ttotal: 8.14s\tremaining: 2.6s\n",
      "144:\tlearn: 0.0020608\ttotal: 8.2s\tremaining: 2.54s\n",
      "145:\tlearn: 0.0020521\ttotal: 8.25s\tremaining: 2.49s\n",
      "146:\tlearn: 0.0020466\ttotal: 8.31s\tremaining: 2.43s\n",
      "147:\tlearn: 0.0020428\ttotal: 8.36s\tremaining: 2.37s\n",
      "148:\tlearn: 0.0020310\ttotal: 8.43s\tremaining: 2.32s\n",
      "149:\tlearn: 0.0020199\ttotal: 8.49s\tremaining: 2.27s\n",
      "150:\tlearn: 0.0020108\ttotal: 8.56s\tremaining: 2.21s\n",
      "151:\tlearn: 0.0020078\ttotal: 8.61s\tremaining: 2.15s\n",
      "152:\tlearn: 0.0019996\ttotal: 8.68s\tremaining: 2.1s\n",
      "153:\tlearn: 0.0019941\ttotal: 8.73s\tremaining: 2.04s\n",
      "154:\tlearn: 0.0019870\ttotal: 8.79s\tremaining: 1.99s\n",
      "155:\tlearn: 0.0019760\ttotal: 8.86s\tremaining: 1.93s\n",
      "156:\tlearn: 0.0019734\ttotal: 8.92s\tremaining: 1.87s\n",
      "157:\tlearn: 0.0019667\ttotal: 8.98s\tremaining: 1.82s\n",
      "158:\tlearn: 0.0019626\ttotal: 9.04s\tremaining: 1.76s\n",
      "159:\tlearn: 0.0019550\ttotal: 9.09s\tremaining: 1.7s\n",
      "160:\tlearn: 0.0019509\ttotal: 9.14s\tremaining: 1.65s\n",
      "161:\tlearn: 0.0019456\ttotal: 9.2s\tremaining: 1.59s\n",
      "162:\tlearn: 0.0019379\ttotal: 9.26s\tremaining: 1.53s\n",
      "163:\tlearn: 0.0019300\ttotal: 9.31s\tremaining: 1.48s\n",
      "164:\tlearn: 0.0019236\ttotal: 9.38s\tremaining: 1.42s\n",
      "165:\tlearn: 0.0019208\ttotal: 9.43s\tremaining: 1.36s\n",
      "166:\tlearn: 0.0019188\ttotal: 9.48s\tremaining: 1.31s\n",
      "167:\tlearn: 0.0019167\ttotal: 9.54s\tremaining: 1.25s\n",
      "168:\tlearn: 0.0019139\ttotal: 9.59s\tremaining: 1.19s\n",
      "169:\tlearn: 0.0019054\ttotal: 9.66s\tremaining: 1.14s\n",
      "170:\tlearn: 0.0018985\ttotal: 9.72s\tremaining: 1.08s\n",
      "171:\tlearn: 0.0018820\ttotal: 9.78s\tremaining: 1.02s\n",
      "172:\tlearn: 0.0018773\ttotal: 9.83s\tremaining: 966ms\n",
      "173:\tlearn: 0.0018750\ttotal: 9.88s\tremaining: 909ms\n",
      "174:\tlearn: 0.0018716\ttotal: 9.93s\tremaining: 851ms\n",
      "175:\tlearn: 0.0018643\ttotal: 9.98s\tremaining: 794ms\n",
      "176:\tlearn: 0.0018620\ttotal: 10s\tremaining: 737ms\n",
      "177:\tlearn: 0.0018564\ttotal: 10.1s\tremaining: 681ms\n",
      "178:\tlearn: 0.0018505\ttotal: 10.2s\tremaining: 624ms\n",
      "179:\tlearn: 0.0018401\ttotal: 10.2s\tremaining: 567ms\n",
      "180:\tlearn: 0.0018334\ttotal: 10.3s\tremaining: 511ms\n",
      "181:\tlearn: 0.0018270\ttotal: 10.3s\tremaining: 454ms\n",
      "182:\tlearn: 0.0018248\ttotal: 10.4s\tremaining: 397ms\n",
      "183:\tlearn: 0.0018218\ttotal: 10.4s\tremaining: 340ms\n",
      "184:\tlearn: 0.0018110\ttotal: 10.5s\tremaining: 284ms\n",
      "185:\tlearn: 0.0018092\ttotal: 10.6s\tremaining: 227ms\n",
      "186:\tlearn: 0.0018075\ttotal: 10.6s\tremaining: 170ms\n",
      "187:\tlearn: 0.0018055\ttotal: 10.7s\tremaining: 113ms\n",
      "188:\tlearn: 0.0018036\ttotal: 10.7s\tremaining: 56.7ms\n",
      "189:\tlearn: 0.0017987\ttotal: 10.8s\tremaining: 0us\n",
      "0:\tlearn: 0.6258728\ttotal: 65.8ms\tremaining: 12.4s\n",
      "1:\tlearn: 0.5674943\ttotal: 129ms\tremaining: 12.1s\n",
      "2:\tlearn: 0.5163127\ttotal: 194ms\tremaining: 12.1s\n",
      "3:\tlearn: 0.4710505\ttotal: 259ms\tremaining: 12.1s\n",
      "4:\tlearn: 0.4307850\ttotal: 323ms\tremaining: 11.9s\n",
      "5:\tlearn: 0.3947382\ttotal: 388ms\tremaining: 11.9s\n",
      "6:\tlearn: 0.3623432\ttotal: 451ms\tremaining: 11.8s\n",
      "7:\tlearn: 0.3331405\ttotal: 514ms\tremaining: 11.7s\n",
      "8:\tlearn: 0.3066619\ttotal: 576ms\tremaining: 11.6s\n",
      "9:\tlearn: 0.2826440\ttotal: 643ms\tremaining: 11.6s\n",
      "10:\tlearn: 0.2608544\ttotal: 701ms\tremaining: 11.4s\n",
      "11:\tlearn: 0.2408847\ttotal: 751ms\tremaining: 11.1s\n",
      "12:\tlearn: 0.2226647\ttotal: 815ms\tremaining: 11.1s\n",
      "13:\tlearn: 0.2060203\ttotal: 878ms\tremaining: 11s\n",
      "14:\tlearn: 0.1908017\ttotal: 939ms\tremaining: 10.9s\n",
      "15:\tlearn: 0.1768335\ttotal: 1s\tremaining: 10.9s\n",
      "16:\tlearn: 0.1639975\ttotal: 1.06s\tremaining: 10.8s\n",
      "17:\tlearn: 0.1521364\ttotal: 1.1s\tremaining: 10.5s\n",
      "18:\tlearn: 0.1412211\ttotal: 1.16s\tremaining: 10.4s\n",
      "19:\tlearn: 0.1311268\ttotal: 1.22s\tremaining: 10.3s\n",
      "20:\tlearn: 0.1218617\ttotal: 1.27s\tremaining: 10.3s\n",
      "21:\tlearn: 0.1132800\ttotal: 1.34s\tremaining: 10.2s\n",
      "22:\tlearn: 0.1053597\ttotal: 1.41s\tremaining: 10.3s\n",
      "23:\tlearn: 0.0980397\ttotal: 1.49s\tremaining: 10.3s\n",
      "24:\tlearn: 0.0912418\ttotal: 1.55s\tremaining: 10.3s\n",
      "25:\tlearn: 0.0849793\ttotal: 1.62s\tremaining: 10.2s\n",
      "26:\tlearn: 0.0791394\ttotal: 1.68s\tremaining: 10.2s\n",
      "27:\tlearn: 0.0737574\ttotal: 1.75s\tremaining: 10.1s\n",
      "28:\tlearn: 0.0687608\ttotal: 1.81s\tremaining: 10.1s\n",
      "29:\tlearn: 0.0641724\ttotal: 1.88s\tremaining: 10s\n",
      "30:\tlearn: 0.0598610\ttotal: 1.94s\tremaining: 9.94s\n",
      "31:\tlearn: 0.0558751\ttotal: 2s\tremaining: 9.88s\n",
      "32:\tlearn: 0.0521686\ttotal: 2.06s\tremaining: 9.81s\n",
      "33:\tlearn: 0.0487199\ttotal: 2.13s\tremaining: 9.78s\n",
      "34:\tlearn: 0.0455061\ttotal: 2.2s\tremaining: 9.73s\n",
      "35:\tlearn: 0.0425061\ttotal: 2.26s\tremaining: 9.68s\n",
      "36:\tlearn: 0.0397316\ttotal: 2.33s\tremaining: 9.62s\n",
      "37:\tlearn: 0.0371480\ttotal: 2.39s\tremaining: 9.55s\n",
      "38:\tlearn: 0.0347557\ttotal: 2.46s\tremaining: 9.52s\n",
      "39:\tlearn: 0.0325009\ttotal: 2.52s\tremaining: 9.44s\n",
      "40:\tlearn: 0.0304196\ttotal: 2.57s\tremaining: 9.34s\n",
      "41:\tlearn: 0.0284855\ttotal: 2.63s\tremaining: 9.27s\n",
      "42:\tlearn: 0.0266900\ttotal: 2.69s\tremaining: 9.18s\n",
      "43:\tlearn: 0.0250090\ttotal: 2.73s\tremaining: 9.08s\n",
      "44:\tlearn: 0.0234406\ttotal: 2.79s\tremaining: 8.98s\n",
      "45:\tlearn: 0.0219728\ttotal: 2.83s\tremaining: 8.87s\n",
      "46:\tlearn: 0.0206266\ttotal: 2.9s\tremaining: 8.82s\n",
      "47:\tlearn: 0.0193651\ttotal: 2.96s\tremaining: 8.75s\n",
      "48:\tlearn: 0.0181907\ttotal: 3.02s\tremaining: 8.68s\n",
      "49:\tlearn: 0.0170902\ttotal: 3.07s\tremaining: 8.6s\n",
      "50:\tlearn: 0.0160750\ttotal: 3.12s\tremaining: 8.51s\n",
      "51:\tlearn: 0.0151351\ttotal: 3.18s\tremaining: 8.45s\n",
      "52:\tlearn: 0.0142574\ttotal: 3.24s\tremaining: 8.38s\n",
      "53:\tlearn: 0.0134384\ttotal: 3.31s\tremaining: 8.34s\n",
      "54:\tlearn: 0.0126690\ttotal: 3.37s\tremaining: 8.27s\n",
      "55:\tlearn: 0.0119478\ttotal: 3.43s\tremaining: 8.2s\n",
      "56:\tlearn: 0.0112830\ttotal: 3.49s\tremaining: 8.14s\n",
      "57:\tlearn: 0.0106670\ttotal: 3.55s\tremaining: 8.09s\n",
      "58:\tlearn: 0.0100876\ttotal: 3.6s\tremaining: 8.01s\n",
      "59:\tlearn: 0.0095561\ttotal: 3.66s\tremaining: 7.93s\n",
      "60:\tlearn: 0.0090548\ttotal: 3.72s\tremaining: 7.87s\n",
      "61:\tlearn: 0.0085891\ttotal: 3.78s\tremaining: 7.8s\n",
      "62:\tlearn: 0.0081460\ttotal: 3.83s\tremaining: 7.73s\n",
      "63:\tlearn: 0.0077343\ttotal: 3.89s\tremaining: 7.66s\n",
      "64:\tlearn: 0.0073582\ttotal: 3.94s\tremaining: 7.58s\n",
      "65:\tlearn: 0.0070028\ttotal: 3.99s\tremaining: 7.5s\n",
      "66:\tlearn: 0.0066793\ttotal: 4.05s\tremaining: 7.43s\n",
      "67:\tlearn: 0.0063758\ttotal: 4.1s\tremaining: 7.36s\n",
      "68:\tlearn: 0.0060948\ttotal: 4.16s\tremaining: 7.3s\n",
      "69:\tlearn: 0.0058279\ttotal: 4.22s\tremaining: 7.23s\n",
      "70:\tlearn: 0.0055774\ttotal: 4.27s\tremaining: 7.16s\n",
      "71:\tlearn: 0.0053474\ttotal: 4.33s\tremaining: 7.09s\n",
      "72:\tlearn: 0.0051297\ttotal: 4.38s\tremaining: 7.02s\n",
      "73:\tlearn: 0.0049309\ttotal: 4.42s\tremaining: 6.93s\n",
      "74:\tlearn: 0.0047431\ttotal: 4.48s\tremaining: 6.87s\n",
      "75:\tlearn: 0.0045746\ttotal: 4.54s\tremaining: 6.81s\n",
      "76:\tlearn: 0.0044143\ttotal: 4.6s\tremaining: 6.75s\n",
      "77:\tlearn: 0.0042577\ttotal: 4.66s\tremaining: 6.69s\n",
      "78:\tlearn: 0.0041173\ttotal: 4.71s\tremaining: 6.62s\n",
      "79:\tlearn: 0.0039871\ttotal: 4.77s\tremaining: 6.56s\n",
      "80:\tlearn: 0.0038642\ttotal: 4.83s\tremaining: 6.5s\n",
      "81:\tlearn: 0.0037485\ttotal: 4.89s\tremaining: 6.44s\n",
      "82:\tlearn: 0.0036435\ttotal: 4.95s\tremaining: 6.38s\n",
      "83:\tlearn: 0.0035414\ttotal: 5.01s\tremaining: 6.32s\n",
      "84:\tlearn: 0.0034524\ttotal: 5.06s\tremaining: 6.25s\n",
      "85:\tlearn: 0.0033665\ttotal: 5.11s\tremaining: 6.18s\n",
      "86:\tlearn: 0.0032907\ttotal: 5.18s\tremaining: 6.13s\n",
      "87:\tlearn: 0.0032161\ttotal: 5.24s\tremaining: 6.07s\n",
      "88:\tlearn: 0.0031529\ttotal: 5.29s\tremaining: 6.01s\n",
      "89:\tlearn: 0.0030823\ttotal: 5.35s\tremaining: 5.95s\n",
      "90:\tlearn: 0.0030250\ttotal: 5.41s\tremaining: 5.88s\n",
      "91:\tlearn: 0.0029737\ttotal: 5.46s\tremaining: 5.82s\n",
      "92:\tlearn: 0.0029199\ttotal: 5.52s\tremaining: 5.75s\n",
      "93:\tlearn: 0.0028761\ttotal: 5.57s\tremaining: 5.69s\n",
      "94:\tlearn: 0.0028247\ttotal: 5.63s\tremaining: 5.63s\n",
      "95:\tlearn: 0.0027837\ttotal: 5.69s\tremaining: 5.57s\n",
      "96:\tlearn: 0.0027441\ttotal: 5.74s\tremaining: 5.51s\n",
      "97:\tlearn: 0.0027072\ttotal: 5.8s\tremaining: 5.44s\n",
      "98:\tlearn: 0.0026668\ttotal: 5.85s\tremaining: 5.38s\n",
      "99:\tlearn: 0.0026368\ttotal: 5.91s\tremaining: 5.32s\n",
      "100:\tlearn: 0.0026037\ttotal: 5.96s\tremaining: 5.25s\n",
      "101:\tlearn: 0.0025706\ttotal: 6.03s\tremaining: 5.2s\n",
      "102:\tlearn: 0.0025409\ttotal: 6.08s\tremaining: 5.14s\n",
      "103:\tlearn: 0.0025174\ttotal: 6.14s\tremaining: 5.08s\n",
      "104:\tlearn: 0.0024900\ttotal: 6.21s\tremaining: 5.02s\n",
      "105:\tlearn: 0.0024623\ttotal: 6.26s\tremaining: 4.96s\n",
      "106:\tlearn: 0.0024364\ttotal: 6.32s\tremaining: 4.9s\n",
      "107:\tlearn: 0.0024124\ttotal: 6.38s\tremaining: 4.84s\n",
      "108:\tlearn: 0.0023910\ttotal: 6.44s\tremaining: 4.78s\n",
      "109:\tlearn: 0.0023770\ttotal: 6.49s\tremaining: 4.72s\n",
      "110:\tlearn: 0.0023566\ttotal: 6.54s\tremaining: 4.66s\n",
      "111:\tlearn: 0.0023435\ttotal: 6.59s\tremaining: 4.59s\n",
      "112:\tlearn: 0.0023290\ttotal: 6.65s\tremaining: 4.53s\n",
      "113:\tlearn: 0.0023094\ttotal: 6.71s\tremaining: 4.47s\n",
      "114:\tlearn: 0.0022911\ttotal: 6.76s\tremaining: 4.41s\n",
      "115:\tlearn: 0.0022724\ttotal: 6.82s\tremaining: 4.35s\n",
      "116:\tlearn: 0.0022562\ttotal: 6.87s\tremaining: 4.29s\n",
      "117:\tlearn: 0.0022348\ttotal: 6.94s\tremaining: 4.23s\n",
      "118:\tlearn: 0.0022204\ttotal: 6.99s\tremaining: 4.17s\n",
      "119:\tlearn: 0.0022054\ttotal: 7.06s\tremaining: 4.12s\n",
      "120:\tlearn: 0.0021913\ttotal: 7.13s\tremaining: 4.06s\n",
      "121:\tlearn: 0.0021698\ttotal: 7.19s\tremaining: 4.01s\n",
      "122:\tlearn: 0.0021589\ttotal: 7.26s\tremaining: 3.95s\n",
      "123:\tlearn: 0.0021417\ttotal: 7.32s\tremaining: 3.9s\n",
      "124:\tlearn: 0.0021315\ttotal: 7.39s\tremaining: 3.84s\n",
      "125:\tlearn: 0.0021186\ttotal: 7.46s\tremaining: 3.79s\n",
      "126:\tlearn: 0.0021066\ttotal: 7.52s\tremaining: 3.73s\n",
      "127:\tlearn: 0.0020933\ttotal: 7.59s\tremaining: 3.68s\n",
      "128:\tlearn: 0.0020868\ttotal: 7.65s\tremaining: 3.62s\n",
      "129:\tlearn: 0.0020709\ttotal: 7.71s\tremaining: 3.56s\n",
      "130:\tlearn: 0.0020581\ttotal: 7.76s\tremaining: 3.5s\n",
      "131:\tlearn: 0.0020473\ttotal: 7.83s\tremaining: 3.44s\n",
      "132:\tlearn: 0.0020410\ttotal: 7.88s\tremaining: 3.38s\n",
      "133:\tlearn: 0.0020317\ttotal: 7.93s\tremaining: 3.31s\n",
      "134:\tlearn: 0.0020273\ttotal: 7.98s\tremaining: 3.25s\n",
      "135:\tlearn: 0.0020180\ttotal: 8.04s\tremaining: 3.19s\n",
      "136:\tlearn: 0.0020113\ttotal: 8.1s\tremaining: 3.13s\n",
      "137:\tlearn: 0.0020021\ttotal: 8.16s\tremaining: 3.07s\n",
      "138:\tlearn: 0.0019925\ttotal: 8.22s\tremaining: 3.02s\n",
      "139:\tlearn: 0.0019826\ttotal: 8.28s\tremaining: 2.96s\n",
      "140:\tlearn: 0.0019741\ttotal: 8.34s\tremaining: 2.9s\n",
      "141:\tlearn: 0.0019645\ttotal: 8.39s\tremaining: 2.84s\n",
      "142:\tlearn: 0.0019572\ttotal: 8.45s\tremaining: 2.78s\n",
      "143:\tlearn: 0.0019533\ttotal: 8.5s\tremaining: 2.71s\n",
      "144:\tlearn: 0.0019481\ttotal: 8.55s\tremaining: 2.65s\n",
      "145:\tlearn: 0.0019425\ttotal: 8.6s\tremaining: 2.59s\n",
      "146:\tlearn: 0.0019362\ttotal: 8.65s\tremaining: 2.53s\n",
      "147:\tlearn: 0.0019336\ttotal: 8.71s\tremaining: 2.47s\n",
      "148:\tlearn: 0.0019314\ttotal: 8.76s\tremaining: 2.41s\n",
      "149:\tlearn: 0.0019276\ttotal: 8.81s\tremaining: 2.35s\n",
      "150:\tlearn: 0.0019242\ttotal: 8.84s\tremaining: 2.28s\n",
      "151:\tlearn: 0.0019158\ttotal: 8.89s\tremaining: 2.22s\n",
      "152:\tlearn: 0.0019010\ttotal: 8.96s\tremaining: 2.17s\n",
      "153:\tlearn: 0.0018935\ttotal: 9.01s\tremaining: 2.11s\n",
      "154:\tlearn: 0.0018823\ttotal: 9.07s\tremaining: 2.05s\n",
      "155:\tlearn: 0.0018776\ttotal: 9.13s\tremaining: 1.99s\n",
      "156:\tlearn: 0.0018725\ttotal: 9.19s\tremaining: 1.93s\n",
      "157:\tlearn: 0.0018624\ttotal: 9.25s\tremaining: 1.87s\n",
      "158:\tlearn: 0.0018585\ttotal: 9.31s\tremaining: 1.81s\n",
      "159:\tlearn: 0.0018519\ttotal: 9.36s\tremaining: 1.75s\n",
      "160:\tlearn: 0.0018489\ttotal: 9.41s\tremaining: 1.7s\n",
      "161:\tlearn: 0.0018425\ttotal: 9.47s\tremaining: 1.64s\n",
      "162:\tlearn: 0.0018334\ttotal: 9.53s\tremaining: 1.58s\n",
      "163:\tlearn: 0.0018303\ttotal: 9.58s\tremaining: 1.52s\n",
      "164:\tlearn: 0.0018273\ttotal: 9.62s\tremaining: 1.46s\n",
      "165:\tlearn: 0.0018241\ttotal: 9.66s\tremaining: 1.4s\n",
      "166:\tlearn: 0.0018191\ttotal: 9.7s\tremaining: 1.34s\n",
      "167:\tlearn: 0.0018104\ttotal: 9.75s\tremaining: 1.28s\n",
      "168:\tlearn: 0.0018042\ttotal: 9.79s\tremaining: 1.22s\n",
      "169:\tlearn: 0.0017981\ttotal: 9.84s\tremaining: 1.16s\n",
      "170:\tlearn: 0.0017892\ttotal: 9.88s\tremaining: 1.1s\n",
      "171:\tlearn: 0.0017846\ttotal: 9.92s\tremaining: 1.04s\n",
      "172:\tlearn: 0.0017814\ttotal: 9.97s\tremaining: 980ms\n",
      "173:\tlearn: 0.0017787\ttotal: 10s\tremaining: 922ms\n",
      "174:\tlearn: 0.0017749\ttotal: 10.1s\tremaining: 865ms\n",
      "175:\tlearn: 0.0017681\ttotal: 10.1s\tremaining: 807ms\n",
      "176:\tlearn: 0.0017644\ttotal: 10.2s\tremaining: 749ms\n",
      "177:\tlearn: 0.0017617\ttotal: 10.2s\tremaining: 691ms\n",
      "178:\tlearn: 0.0017586\ttotal: 10.3s\tremaining: 634ms\n",
      "179:\tlearn: 0.0017560\ttotal: 10.4s\tremaining: 576ms\n",
      "180:\tlearn: 0.0017535\ttotal: 10.4s\tremaining: 518ms\n",
      "181:\tlearn: 0.0017374\ttotal: 10.5s\tremaining: 460ms\n",
      "182:\tlearn: 0.0017342\ttotal: 10.5s\tremaining: 403ms\n",
      "183:\tlearn: 0.0017317\ttotal: 10.6s\tremaining: 345ms\n",
      "184:\tlearn: 0.0017248\ttotal: 10.6s\tremaining: 288ms\n",
      "185:\tlearn: 0.0017187\ttotal: 10.7s\tremaining: 230ms\n",
      "186:\tlearn: 0.0017152\ttotal: 10.8s\tremaining: 172ms\n",
      "187:\tlearn: 0.0017112\ttotal: 10.8s\tremaining: 115ms\n",
      "188:\tlearn: 0.0017081\ttotal: 10.9s\tremaining: 57.5ms\n",
      "189:\tlearn: 0.0017044\ttotal: 10.9s\tremaining: 0us\n",
      "0:\tlearn: 0.6259431\ttotal: 65.8ms\tremaining: 12.4s\n",
      "1:\tlearn: 0.5674785\ttotal: 130ms\tremaining: 12.2s\n",
      "2:\tlearn: 0.5161125\ttotal: 185ms\tremaining: 11.5s\n",
      "3:\tlearn: 0.4708070\ttotal: 257ms\tremaining: 11.9s\n",
      "4:\tlearn: 0.4304642\ttotal: 333ms\tremaining: 12.3s\n",
      "5:\tlearn: 0.3944105\ttotal: 404ms\tremaining: 12.4s\n",
      "6:\tlearn: 0.3619587\ttotal: 475ms\tremaining: 12.4s\n",
      "7:\tlearn: 0.3327081\ttotal: 550ms\tremaining: 12.5s\n",
      "8:\tlearn: 0.3062942\ttotal: 630ms\tremaining: 12.7s\n",
      "9:\tlearn: 0.2822625\ttotal: 709ms\tremaining: 12.8s\n",
      "10:\tlearn: 0.2604515\ttotal: 785ms\tremaining: 12.8s\n",
      "11:\tlearn: 0.2405281\ttotal: 856ms\tremaining: 12.7s\n",
      "12:\tlearn: 0.2223456\ttotal: 929ms\tremaining: 12.6s\n",
      "13:\tlearn: 0.2057092\ttotal: 1s\tremaining: 12.6s\n",
      "14:\tlearn: 0.1905317\ttotal: 1.09s\tremaining: 12.7s\n",
      "15:\tlearn: 0.1765585\ttotal: 1.16s\tremaining: 12.6s\n",
      "16:\tlearn: 0.1636959\ttotal: 1.23s\tremaining: 12.5s\n",
      "17:\tlearn: 0.1518806\ttotal: 1.3s\tremaining: 12.5s\n",
      "18:\tlearn: 0.1409771\ttotal: 1.38s\tremaining: 12.4s\n",
      "19:\tlearn: 0.1309442\ttotal: 1.46s\tremaining: 12.4s\n",
      "20:\tlearn: 0.1216730\ttotal: 1.53s\tremaining: 12.3s\n",
      "21:\tlearn: 0.1131091\ttotal: 1.6s\tremaining: 12.2s\n",
      "22:\tlearn: 0.1052098\ttotal: 1.67s\tremaining: 12.2s\n",
      "23:\tlearn: 0.0978918\ttotal: 1.81s\tremaining: 12.6s\n",
      "24:\tlearn: 0.0911039\ttotal: 1.9s\tremaining: 12.6s\n",
      "25:\tlearn: 0.0848413\ttotal: 1.99s\tremaining: 12.5s\n",
      "26:\tlearn: 0.0790425\ttotal: 2.07s\tremaining: 12.5s\n",
      "27:\tlearn: 0.0736449\ttotal: 2.15s\tremaining: 12.5s\n",
      "28:\tlearn: 0.0686609\ttotal: 2.24s\tremaining: 12.4s\n",
      "29:\tlearn: 0.0640250\ttotal: 2.32s\tremaining: 12.4s\n",
      "30:\tlearn: 0.0597321\ttotal: 2.38s\tremaining: 12.2s\n",
      "31:\tlearn: 0.0557651\ttotal: 2.45s\tremaining: 12.1s\n",
      "32:\tlearn: 0.0520571\ttotal: 2.52s\tremaining: 12s\n",
      "33:\tlearn: 0.0486005\ttotal: 2.58s\tremaining: 11.9s\n",
      "34:\tlearn: 0.0454010\ttotal: 2.65s\tremaining: 11.7s\n",
      "35:\tlearn: 0.0424147\ttotal: 2.69s\tremaining: 11.5s\n",
      "36:\tlearn: 0.0396392\ttotal: 2.74s\tremaining: 11.3s\n",
      "37:\tlearn: 0.0370590\ttotal: 2.81s\tremaining: 11.2s\n",
      "38:\tlearn: 0.0346647\ttotal: 2.87s\tremaining: 11.1s\n",
      "39:\tlearn: 0.0324506\ttotal: 2.92s\tremaining: 11s\n",
      "40:\tlearn: 0.0303824\ttotal: 2.99s\tremaining: 10.9s\n",
      "41:\tlearn: 0.0284650\ttotal: 3.06s\tremaining: 10.8s\n",
      "42:\tlearn: 0.0266644\ttotal: 3.12s\tremaining: 10.7s\n",
      "43:\tlearn: 0.0249793\ttotal: 3.19s\tremaining: 10.6s\n",
      "44:\tlearn: 0.0234089\ttotal: 3.25s\tremaining: 10.5s\n",
      "45:\tlearn: 0.0219709\ttotal: 3.33s\tremaining: 10.4s\n",
      "46:\tlearn: 0.0206145\ttotal: 3.39s\tremaining: 10.3s\n",
      "47:\tlearn: 0.0193533\ttotal: 3.46s\tremaining: 10.2s\n",
      "48:\tlearn: 0.0181863\ttotal: 3.53s\tremaining: 10.2s\n",
      "49:\tlearn: 0.0170954\ttotal: 3.59s\tremaining: 10.1s\n",
      "50:\tlearn: 0.0160822\ttotal: 3.65s\tremaining: 9.96s\n",
      "51:\tlearn: 0.0151432\ttotal: 3.73s\tremaining: 9.89s\n",
      "52:\tlearn: 0.0142715\ttotal: 3.8s\tremaining: 9.82s\n",
      "53:\tlearn: 0.0134569\ttotal: 3.87s\tremaining: 9.75s\n",
      "54:\tlearn: 0.0126903\ttotal: 3.94s\tremaining: 9.66s\n",
      "55:\tlearn: 0.0119852\ttotal: 4s\tremaining: 9.58s\n",
      "56:\tlearn: 0.0113193\ttotal: 4.07s\tremaining: 9.49s\n",
      "57:\tlearn: 0.0107042\ttotal: 4.14s\tremaining: 9.41s\n",
      "58:\tlearn: 0.0101349\ttotal: 4.21s\tremaining: 9.34s\n",
      "59:\tlearn: 0.0095989\ttotal: 4.27s\tremaining: 9.26s\n",
      "60:\tlearn: 0.0090946\ttotal: 4.33s\tremaining: 9.16s\n",
      "61:\tlearn: 0.0086261\ttotal: 4.4s\tremaining: 9.08s\n",
      "62:\tlearn: 0.0081967\ttotal: 4.47s\tremaining: 9.01s\n",
      "63:\tlearn: 0.0077888\ttotal: 4.53s\tremaining: 8.92s\n",
      "64:\tlearn: 0.0074139\ttotal: 4.6s\tremaining: 8.84s\n",
      "65:\tlearn: 0.0070586\ttotal: 4.66s\tremaining: 8.76s\n",
      "66:\tlearn: 0.0067339\ttotal: 4.72s\tremaining: 8.67s\n",
      "67:\tlearn: 0.0064286\ttotal: 4.79s\tremaining: 8.59s\n",
      "68:\tlearn: 0.0061495\ttotal: 4.85s\tremaining: 8.51s\n",
      "69:\tlearn: 0.0058797\ttotal: 4.92s\tremaining: 8.43s\n",
      "70:\tlearn: 0.0056349\ttotal: 4.97s\tremaining: 8.34s\n",
      "71:\tlearn: 0.0054035\ttotal: 5.02s\tremaining: 8.22s\n",
      "72:\tlearn: 0.0051900\ttotal: 5.09s\tremaining: 8.15s\n",
      "73:\tlearn: 0.0049907\ttotal: 5.17s\tremaining: 8.1s\n",
      "74:\tlearn: 0.0047976\ttotal: 5.24s\tremaining: 8.03s\n",
      "75:\tlearn: 0.0046280\ttotal: 5.3s\tremaining: 7.95s\n",
      "76:\tlearn: 0.0044655\ttotal: 5.38s\tremaining: 7.89s\n",
      "77:\tlearn: 0.0043117\ttotal: 5.45s\tremaining: 7.82s\n",
      "78:\tlearn: 0.0041701\ttotal: 5.52s\tremaining: 7.75s\n",
      "79:\tlearn: 0.0040393\ttotal: 5.58s\tremaining: 7.68s\n",
      "80:\tlearn: 0.0039170\ttotal: 5.65s\tremaining: 7.61s\n",
      "81:\tlearn: 0.0038069\ttotal: 5.72s\tremaining: 7.54s\n",
      "82:\tlearn: 0.0037044\ttotal: 5.79s\tremaining: 7.47s\n",
      "83:\tlearn: 0.0036124\ttotal: 5.86s\tremaining: 7.4s\n",
      "84:\tlearn: 0.0035182\ttotal: 5.93s\tremaining: 7.33s\n",
      "85:\tlearn: 0.0034324\ttotal: 6s\tremaining: 7.25s\n",
      "86:\tlearn: 0.0033532\ttotal: 6.07s\tremaining: 7.18s\n",
      "87:\tlearn: 0.0032823\ttotal: 6.13s\tremaining: 7.11s\n",
      "88:\tlearn: 0.0032197\ttotal: 6.2s\tremaining: 7.03s\n",
      "89:\tlearn: 0.0031452\ttotal: 6.27s\tremaining: 6.97s\n",
      "90:\tlearn: 0.0030895\ttotal: 6.33s\tremaining: 6.89s\n",
      "91:\tlearn: 0.0030351\ttotal: 6.39s\tremaining: 6.8s\n",
      "92:\tlearn: 0.0029797\ttotal: 6.45s\tremaining: 6.73s\n",
      "93:\tlearn: 0.0029340\ttotal: 6.5s\tremaining: 6.63s\n",
      "94:\tlearn: 0.0028827\ttotal: 6.57s\tremaining: 6.57s\n",
      "95:\tlearn: 0.0028376\ttotal: 6.64s\tremaining: 6.51s\n",
      "96:\tlearn: 0.0028020\ttotal: 6.71s\tremaining: 6.43s\n",
      "97:\tlearn: 0.0027577\ttotal: 6.78s\tremaining: 6.36s\n",
      "98:\tlearn: 0.0027233\ttotal: 6.85s\tremaining: 6.3s\n",
      "99:\tlearn: 0.0026802\ttotal: 6.92s\tremaining: 6.23s\n",
      "100:\tlearn: 0.0026392\ttotal: 7s\tremaining: 6.17s\n",
      "101:\tlearn: 0.0026055\ttotal: 7.06s\tremaining: 6.09s\n",
      "102:\tlearn: 0.0025743\ttotal: 7.14s\tremaining: 6.03s\n",
      "103:\tlearn: 0.0025469\ttotal: 7.21s\tremaining: 5.96s\n",
      "104:\tlearn: 0.0025191\ttotal: 7.27s\tremaining: 5.89s\n",
      "105:\tlearn: 0.0024960\ttotal: 7.34s\tremaining: 5.82s\n",
      "106:\tlearn: 0.0024730\ttotal: 7.41s\tremaining: 5.75s\n",
      "107:\tlearn: 0.0024434\ttotal: 7.48s\tremaining: 5.68s\n",
      "108:\tlearn: 0.0024179\ttotal: 7.54s\tremaining: 5.61s\n",
      "109:\tlearn: 0.0023949\ttotal: 7.62s\tremaining: 5.54s\n",
      "110:\tlearn: 0.0023715\ttotal: 7.7s\tremaining: 5.48s\n",
      "111:\tlearn: 0.0023502\ttotal: 7.76s\tremaining: 5.4s\n",
      "112:\tlearn: 0.0023409\ttotal: 7.82s\tremaining: 5.33s\n",
      "113:\tlearn: 0.0023294\ttotal: 7.88s\tremaining: 5.25s\n",
      "114:\tlearn: 0.0023079\ttotal: 7.95s\tremaining: 5.18s\n",
      "115:\tlearn: 0.0022980\ttotal: 8.01s\tremaining: 5.11s\n",
      "116:\tlearn: 0.0022778\ttotal: 8.08s\tremaining: 5.04s\n",
      "117:\tlearn: 0.0022618\ttotal: 8.15s\tremaining: 4.97s\n",
      "118:\tlearn: 0.0022450\ttotal: 8.24s\tremaining: 4.91s\n",
      "119:\tlearn: 0.0022368\ttotal: 8.31s\tremaining: 4.85s\n",
      "120:\tlearn: 0.0022166\ttotal: 8.39s\tremaining: 4.79s\n",
      "121:\tlearn: 0.0022027\ttotal: 8.48s\tremaining: 4.73s\n",
      "122:\tlearn: 0.0021950\ttotal: 8.54s\tremaining: 4.65s\n",
      "123:\tlearn: 0.0021879\ttotal: 8.6s\tremaining: 4.58s\n",
      "124:\tlearn: 0.0021703\ttotal: 8.67s\tremaining: 4.51s\n",
      "125:\tlearn: 0.0021623\ttotal: 8.73s\tremaining: 4.43s\n",
      "126:\tlearn: 0.0021505\ttotal: 8.81s\tremaining: 4.37s\n",
      "127:\tlearn: 0.0021368\ttotal: 8.88s\tremaining: 4.3s\n",
      "128:\tlearn: 0.0021262\ttotal: 8.94s\tremaining: 4.23s\n",
      "129:\tlearn: 0.0021145\ttotal: 9.02s\tremaining: 4.16s\n",
      "130:\tlearn: 0.0021043\ttotal: 9.09s\tremaining: 4.09s\n",
      "131:\tlearn: 0.0020933\ttotal: 9.15s\tremaining: 4.02s\n",
      "132:\tlearn: 0.0020775\ttotal: 9.22s\tremaining: 3.95s\n",
      "133:\tlearn: 0.0020672\ttotal: 9.29s\tremaining: 3.88s\n",
      "134:\tlearn: 0.0020554\ttotal: 9.36s\tremaining: 3.81s\n",
      "135:\tlearn: 0.0020504\ttotal: 9.43s\tremaining: 3.74s\n",
      "136:\tlearn: 0.0020454\ttotal: 9.49s\tremaining: 3.67s\n",
      "137:\tlearn: 0.0020381\ttotal: 9.54s\tremaining: 3.6s\n",
      "138:\tlearn: 0.0020331\ttotal: 9.59s\tremaining: 3.52s\n",
      "139:\tlearn: 0.0020244\ttotal: 9.65s\tremaining: 3.45s\n",
      "140:\tlearn: 0.0020154\ttotal: 9.71s\tremaining: 3.38s\n",
      "141:\tlearn: 0.0020055\ttotal: 9.79s\tremaining: 3.31s\n",
      "142:\tlearn: 0.0020001\ttotal: 9.85s\tremaining: 3.24s\n",
      "143:\tlearn: 0.0019894\ttotal: 9.92s\tremaining: 3.17s\n",
      "144:\tlearn: 0.0019866\ttotal: 9.97s\tremaining: 3.1s\n",
      "145:\tlearn: 0.0019799\ttotal: 10s\tremaining: 3.02s\n",
      "146:\tlearn: 0.0019721\ttotal: 10.1s\tremaining: 2.96s\n",
      "147:\tlearn: 0.0019652\ttotal: 10.2s\tremaining: 2.89s\n",
      "148:\tlearn: 0.0019612\ttotal: 10.2s\tremaining: 2.82s\n",
      "149:\tlearn: 0.0019573\ttotal: 10.3s\tremaining: 2.75s\n",
      "150:\tlearn: 0.0019494\ttotal: 10.4s\tremaining: 2.68s\n",
      "151:\tlearn: 0.0019408\ttotal: 10.4s\tremaining: 2.61s\n",
      "152:\tlearn: 0.0019297\ttotal: 10.5s\tremaining: 2.54s\n",
      "153:\tlearn: 0.0019242\ttotal: 10.6s\tremaining: 2.47s\n",
      "154:\tlearn: 0.0019187\ttotal: 10.6s\tremaining: 2.4s\n",
      "155:\tlearn: 0.0019133\ttotal: 10.7s\tremaining: 2.33s\n",
      "156:\tlearn: 0.0019045\ttotal: 10.8s\tremaining: 2.26s\n",
      "157:\tlearn: 0.0019014\ttotal: 10.8s\tremaining: 2.19s\n",
      "158:\tlearn: 0.0018922\ttotal: 10.9s\tremaining: 2.12s\n",
      "159:\tlearn: 0.0018814\ttotal: 11s\tremaining: 2.06s\n",
      "160:\tlearn: 0.0018776\ttotal: 11s\tremaining: 1.99s\n",
      "161:\tlearn: 0.0018704\ttotal: 11.1s\tremaining: 1.92s\n",
      "162:\tlearn: 0.0018665\ttotal: 11.2s\tremaining: 1.85s\n",
      "163:\tlearn: 0.0018605\ttotal: 11.2s\tremaining: 1.78s\n",
      "164:\tlearn: 0.0018576\ttotal: 11.3s\tremaining: 1.71s\n",
      "165:\tlearn: 0.0018524\ttotal: 11.4s\tremaining: 1.64s\n",
      "166:\tlearn: 0.0018487\ttotal: 11.4s\tremaining: 1.57s\n",
      "167:\tlearn: 0.0018453\ttotal: 11.5s\tremaining: 1.5s\n",
      "168:\tlearn: 0.0018406\ttotal: 11.6s\tremaining: 1.44s\n",
      "169:\tlearn: 0.0018351\ttotal: 11.6s\tremaining: 1.37s\n",
      "170:\tlearn: 0.0018265\ttotal: 11.7s\tremaining: 1.3s\n",
      "171:\tlearn: 0.0018197\ttotal: 11.8s\tremaining: 1.23s\n",
      "172:\tlearn: 0.0018158\ttotal: 11.9s\tremaining: 1.17s\n",
      "173:\tlearn: 0.0018135\ttotal: 11.9s\tremaining: 1.1s\n",
      "174:\tlearn: 0.0018078\ttotal: 12s\tremaining: 1.03s\n",
      "175:\tlearn: 0.0018045\ttotal: 12.1s\tremaining: 959ms\n",
      "176:\tlearn: 0.0018010\ttotal: 12.1s\tremaining: 890ms\n",
      "177:\tlearn: 0.0017971\ttotal: 12.2s\tremaining: 821ms\n",
      "178:\tlearn: 0.0017922\ttotal: 12.2s\tremaining: 752ms\n",
      "179:\tlearn: 0.0017876\ttotal: 12.3s\tremaining: 684ms\n",
      "180:\tlearn: 0.0017844\ttotal: 12.4s\tremaining: 615ms\n",
      "181:\tlearn: 0.0017781\ttotal: 12.4s\tremaining: 547ms\n",
      "182:\tlearn: 0.0017762\ttotal: 12.5s\tremaining: 478ms\n",
      "183:\tlearn: 0.0017738\ttotal: 12.6s\tremaining: 410ms\n",
      "184:\tlearn: 0.0017690\ttotal: 12.7s\tremaining: 342ms\n",
      "185:\tlearn: 0.0017644\ttotal: 12.7s\tremaining: 274ms\n",
      "186:\tlearn: 0.0017588\ttotal: 12.8s\tremaining: 205ms\n",
      "187:\tlearn: 0.0017547\ttotal: 12.9s\tremaining: 137ms\n",
      "188:\tlearn: 0.0017495\ttotal: 12.9s\tremaining: 68.5ms\n",
      "189:\tlearn: 0.0017461\ttotal: 13s\tremaining: 0us\n",
      "0:\tlearn: 0.6259431\ttotal: 67.6ms\tremaining: 12.8s\n",
      "1:\tlearn: 0.5674785\ttotal: 136ms\tremaining: 12.8s\n",
      "2:\tlearn: 0.5161125\ttotal: 198ms\tremaining: 12.3s\n",
      "3:\tlearn: 0.4708070\ttotal: 268ms\tremaining: 12.4s\n",
      "4:\tlearn: 0.4304642\ttotal: 340ms\tremaining: 12.6s\n",
      "5:\tlearn: 0.3944105\ttotal: 412ms\tremaining: 12.6s\n",
      "6:\tlearn: 0.3619587\ttotal: 459ms\tremaining: 12s\n",
      "7:\tlearn: 0.3327081\ttotal: 513ms\tremaining: 11.7s\n",
      "8:\tlearn: 0.3062942\ttotal: 567ms\tremaining: 11.4s\n",
      "9:\tlearn: 0.2822625\ttotal: 619ms\tremaining: 11.1s\n",
      "10:\tlearn: 0.2604515\ttotal: 688ms\tremaining: 11.2s\n",
      "11:\tlearn: 0.2405281\ttotal: 759ms\tremaining: 11.3s\n",
      "12:\tlearn: 0.2223456\ttotal: 831ms\tremaining: 11.3s\n",
      "13:\tlearn: 0.2057092\ttotal: 909ms\tremaining: 11.4s\n",
      "14:\tlearn: 0.1905317\ttotal: 988ms\tremaining: 11.5s\n",
      "15:\tlearn: 0.1765585\ttotal: 1.05s\tremaining: 11.4s\n",
      "16:\tlearn: 0.1636959\ttotal: 1.11s\tremaining: 11.3s\n",
      "17:\tlearn: 0.1518806\ttotal: 1.19s\tremaining: 11.4s\n",
      "18:\tlearn: 0.1409771\ttotal: 1.27s\tremaining: 11.4s\n",
      "19:\tlearn: 0.1309442\ttotal: 1.34s\tremaining: 11.4s\n",
      "20:\tlearn: 0.1216730\ttotal: 1.41s\tremaining: 11.4s\n",
      "21:\tlearn: 0.1131091\ttotal: 1.48s\tremaining: 11.3s\n",
      "22:\tlearn: 0.1052098\ttotal: 1.56s\tremaining: 11.3s\n",
      "23:\tlearn: 0.0978918\ttotal: 1.63s\tremaining: 11.3s\n",
      "24:\tlearn: 0.0911039\ttotal: 1.7s\tremaining: 11.2s\n",
      "25:\tlearn: 0.0848413\ttotal: 1.77s\tremaining: 11.2s\n",
      "26:\tlearn: 0.0790425\ttotal: 1.85s\tremaining: 11.2s\n",
      "27:\tlearn: 0.0736449\ttotal: 1.92s\tremaining: 11.1s\n",
      "28:\tlearn: 0.0686609\ttotal: 2s\tremaining: 11.1s\n",
      "29:\tlearn: 0.0640250\ttotal: 2.08s\tremaining: 11.1s\n",
      "30:\tlearn: 0.0597321\ttotal: 2.14s\tremaining: 11s\n",
      "31:\tlearn: 0.0557651\ttotal: 2.19s\tremaining: 10.8s\n",
      "32:\tlearn: 0.0520571\ttotal: 2.23s\tremaining: 10.6s\n",
      "33:\tlearn: 0.0486005\ttotal: 2.28s\tremaining: 10.5s\n",
      "34:\tlearn: 0.0454010\ttotal: 2.33s\tremaining: 10.3s\n",
      "35:\tlearn: 0.0424147\ttotal: 2.38s\tremaining: 10.2s\n",
      "36:\tlearn: 0.0396392\ttotal: 2.43s\tremaining: 10s\n",
      "37:\tlearn: 0.0370590\ttotal: 2.48s\tremaining: 9.91s\n",
      "38:\tlearn: 0.0346647\ttotal: 2.52s\tremaining: 9.77s\n",
      "39:\tlearn: 0.0324506\ttotal: 2.57s\tremaining: 9.64s\n",
      "40:\tlearn: 0.0303824\ttotal: 2.64s\tremaining: 9.59s\n",
      "41:\tlearn: 0.0284650\ttotal: 2.71s\tremaining: 9.54s\n",
      "42:\tlearn: 0.0266644\ttotal: 2.77s\tremaining: 9.48s\n",
      "43:\tlearn: 0.0249793\ttotal: 2.84s\tremaining: 9.41s\n",
      "44:\tlearn: 0.0234089\ttotal: 2.9s\tremaining: 9.35s\n",
      "45:\tlearn: 0.0219709\ttotal: 2.98s\tremaining: 9.32s\n",
      "46:\tlearn: 0.0206145\ttotal: 3.04s\tremaining: 9.27s\n",
      "47:\tlearn: 0.0193533\ttotal: 3.1s\tremaining: 9.19s\n",
      "48:\tlearn: 0.0181863\ttotal: 3.18s\tremaining: 9.14s\n",
      "49:\tlearn: 0.0170954\ttotal: 3.24s\tremaining: 9.07s\n",
      "50:\tlearn: 0.0160822\ttotal: 3.3s\tremaining: 8.98s\n",
      "51:\tlearn: 0.0151432\ttotal: 3.35s\tremaining: 8.9s\n",
      "52:\tlearn: 0.0142715\ttotal: 3.42s\tremaining: 8.84s\n",
      "53:\tlearn: 0.0134569\ttotal: 3.49s\tremaining: 8.78s\n",
      "54:\tlearn: 0.0126903\ttotal: 3.56s\tremaining: 8.73s\n",
      "55:\tlearn: 0.0119852\ttotal: 3.62s\tremaining: 8.66s\n",
      "56:\tlearn: 0.0113193\ttotal: 3.68s\tremaining: 8.59s\n",
      "57:\tlearn: 0.0107042\ttotal: 3.75s\tremaining: 8.53s\n",
      "58:\tlearn: 0.0101349\ttotal: 3.81s\tremaining: 8.47s\n",
      "59:\tlearn: 0.0095989\ttotal: 3.88s\tremaining: 8.4s\n",
      "60:\tlearn: 0.0090946\ttotal: 3.94s\tremaining: 8.33s\n",
      "61:\tlearn: 0.0086261\ttotal: 4s\tremaining: 8.26s\n",
      "62:\tlearn: 0.0081967\ttotal: 4.07s\tremaining: 8.21s\n",
      "63:\tlearn: 0.0077888\ttotal: 4.13s\tremaining: 8.13s\n",
      "64:\tlearn: 0.0074139\ttotal: 4.2s\tremaining: 8.07s\n",
      "65:\tlearn: 0.0070586\ttotal: 4.25s\tremaining: 7.99s\n",
      "66:\tlearn: 0.0067339\ttotal: 4.32s\tremaining: 7.92s\n",
      "67:\tlearn: 0.0064286\ttotal: 4.38s\tremaining: 7.85s\n",
      "68:\tlearn: 0.0061495\ttotal: 4.44s\tremaining: 7.79s\n",
      "69:\tlearn: 0.0058797\ttotal: 4.5s\tremaining: 7.72s\n",
      "70:\tlearn: 0.0056349\ttotal: 4.57s\tremaining: 7.66s\n",
      "71:\tlearn: 0.0054035\ttotal: 4.63s\tremaining: 7.59s\n",
      "72:\tlearn: 0.0051900\ttotal: 4.69s\tremaining: 7.52s\n",
      "73:\tlearn: 0.0049907\ttotal: 4.76s\tremaining: 7.46s\n",
      "74:\tlearn: 0.0047976\ttotal: 4.82s\tremaining: 7.4s\n",
      "75:\tlearn: 0.0046280\ttotal: 4.88s\tremaining: 7.32s\n",
      "76:\tlearn: 0.0044655\ttotal: 4.95s\tremaining: 7.27s\n",
      "77:\tlearn: 0.0043117\ttotal: 5.01s\tremaining: 7.2s\n",
      "78:\tlearn: 0.0041701\ttotal: 5.08s\tremaining: 7.13s\n",
      "79:\tlearn: 0.0040393\ttotal: 5.13s\tremaining: 7.06s\n",
      "80:\tlearn: 0.0039170\ttotal: 5.2s\tremaining: 7s\n",
      "81:\tlearn: 0.0038069\ttotal: 5.26s\tremaining: 6.93s\n",
      "82:\tlearn: 0.0037044\ttotal: 5.33s\tremaining: 6.87s\n",
      "83:\tlearn: 0.0036124\ttotal: 5.39s\tremaining: 6.8s\n",
      "84:\tlearn: 0.0035182\ttotal: 5.46s\tremaining: 6.75s\n",
      "85:\tlearn: 0.0034324\ttotal: 5.52s\tremaining: 6.68s\n",
      "86:\tlearn: 0.0033532\ttotal: 5.58s\tremaining: 6.61s\n",
      "87:\tlearn: 0.0032823\ttotal: 5.65s\tremaining: 6.54s\n",
      "88:\tlearn: 0.0032197\ttotal: 5.71s\tremaining: 6.48s\n",
      "89:\tlearn: 0.0031452\ttotal: 5.78s\tremaining: 6.42s\n",
      "90:\tlearn: 0.0030895\ttotal: 5.84s\tremaining: 6.35s\n",
      "91:\tlearn: 0.0030351\ttotal: 5.9s\tremaining: 6.28s\n",
      "92:\tlearn: 0.0029797\ttotal: 5.96s\tremaining: 6.21s\n",
      "93:\tlearn: 0.0029340\ttotal: 6.02s\tremaining: 6.14s\n",
      "94:\tlearn: 0.0028827\ttotal: 6.09s\tremaining: 6.09s\n",
      "95:\tlearn: 0.0028376\ttotal: 6.15s\tremaining: 6.03s\n",
      "96:\tlearn: 0.0028020\ttotal: 6.22s\tremaining: 5.96s\n",
      "97:\tlearn: 0.0027577\ttotal: 6.29s\tremaining: 5.9s\n",
      "98:\tlearn: 0.0027233\ttotal: 6.35s\tremaining: 5.84s\n",
      "99:\tlearn: 0.0026802\ttotal: 6.42s\tremaining: 5.78s\n",
      "100:\tlearn: 0.0026392\ttotal: 6.49s\tremaining: 5.71s\n",
      "101:\tlearn: 0.0026055\ttotal: 6.54s\tremaining: 5.65s\n",
      "102:\tlearn: 0.0025743\ttotal: 6.61s\tremaining: 5.59s\n",
      "103:\tlearn: 0.0025469\ttotal: 6.67s\tremaining: 5.52s\n",
      "104:\tlearn: 0.0025191\ttotal: 6.73s\tremaining: 5.45s\n",
      "105:\tlearn: 0.0024960\ttotal: 6.8s\tremaining: 5.39s\n",
      "106:\tlearn: 0.0024730\ttotal: 6.87s\tremaining: 5.33s\n",
      "107:\tlearn: 0.0024434\ttotal: 6.93s\tremaining: 5.26s\n",
      "108:\tlearn: 0.0024179\ttotal: 7s\tremaining: 5.2s\n",
      "109:\tlearn: 0.0023949\ttotal: 7.07s\tremaining: 5.14s\n",
      "110:\tlearn: 0.0023715\ttotal: 7.14s\tremaining: 5.08s\n",
      "111:\tlearn: 0.0023502\ttotal: 7.21s\tremaining: 5.02s\n",
      "112:\tlearn: 0.0023409\ttotal: 7.27s\tremaining: 4.95s\n",
      "113:\tlearn: 0.0023294\ttotal: 7.33s\tremaining: 4.89s\n",
      "114:\tlearn: 0.0023079\ttotal: 7.4s\tremaining: 4.82s\n",
      "115:\tlearn: 0.0022980\ttotal: 7.46s\tremaining: 4.76s\n",
      "116:\tlearn: 0.0022778\ttotal: 7.53s\tremaining: 4.7s\n",
      "117:\tlearn: 0.0022618\ttotal: 7.59s\tremaining: 4.63s\n",
      "118:\tlearn: 0.0022450\ttotal: 7.66s\tremaining: 4.57s\n",
      "119:\tlearn: 0.0022368\ttotal: 7.73s\tremaining: 4.51s\n",
      "120:\tlearn: 0.0022166\ttotal: 7.8s\tremaining: 4.45s\n",
      "121:\tlearn: 0.0022027\ttotal: 7.87s\tremaining: 4.39s\n",
      "122:\tlearn: 0.0021950\ttotal: 7.93s\tremaining: 4.32s\n",
      "123:\tlearn: 0.0021879\ttotal: 7.99s\tremaining: 4.25s\n",
      "124:\tlearn: 0.0021703\ttotal: 8.05s\tremaining: 4.19s\n",
      "125:\tlearn: 0.0021623\ttotal: 8.11s\tremaining: 4.12s\n",
      "126:\tlearn: 0.0021505\ttotal: 8.18s\tremaining: 4.05s\n",
      "127:\tlearn: 0.0021368\ttotal: 8.24s\tremaining: 3.99s\n",
      "128:\tlearn: 0.0021262\ttotal: 8.3s\tremaining: 3.92s\n",
      "129:\tlearn: 0.0021145\ttotal: 8.37s\tremaining: 3.86s\n",
      "130:\tlearn: 0.0021043\ttotal: 8.44s\tremaining: 3.8s\n",
      "131:\tlearn: 0.0020933\ttotal: 8.51s\tremaining: 3.74s\n",
      "132:\tlearn: 0.0020775\ttotal: 8.58s\tremaining: 3.68s\n",
      "133:\tlearn: 0.0020672\ttotal: 8.66s\tremaining: 3.62s\n",
      "134:\tlearn: 0.0020554\ttotal: 8.72s\tremaining: 3.55s\n",
      "135:\tlearn: 0.0020504\ttotal: 8.79s\tremaining: 3.49s\n",
      "136:\tlearn: 0.0020454\ttotal: 8.85s\tremaining: 3.42s\n",
      "137:\tlearn: 0.0020381\ttotal: 8.9s\tremaining: 3.35s\n",
      "138:\tlearn: 0.0020331\ttotal: 8.96s\tremaining: 3.29s\n",
      "139:\tlearn: 0.0020244\ttotal: 9.03s\tremaining: 3.23s\n",
      "140:\tlearn: 0.0020154\ttotal: 9.09s\tremaining: 3.16s\n",
      "141:\tlearn: 0.0020055\ttotal: 9.16s\tremaining: 3.1s\n",
      "142:\tlearn: 0.0020001\ttotal: 9.22s\tremaining: 3.03s\n",
      "143:\tlearn: 0.0019894\ttotal: 9.29s\tremaining: 2.97s\n",
      "144:\tlearn: 0.0019866\ttotal: 9.35s\tremaining: 2.9s\n",
      "145:\tlearn: 0.0019799\ttotal: 9.41s\tremaining: 2.84s\n",
      "146:\tlearn: 0.0019721\ttotal: 9.47s\tremaining: 2.77s\n",
      "147:\tlearn: 0.0019652\ttotal: 9.54s\tremaining: 2.71s\n",
      "148:\tlearn: 0.0019612\ttotal: 9.61s\tremaining: 2.64s\n",
      "149:\tlearn: 0.0019573\ttotal: 9.67s\tremaining: 2.58s\n",
      "150:\tlearn: 0.0019494\ttotal: 9.73s\tremaining: 2.51s\n",
      "151:\tlearn: 0.0019408\ttotal: 9.8s\tremaining: 2.45s\n",
      "152:\tlearn: 0.0019297\ttotal: 9.87s\tremaining: 2.39s\n",
      "153:\tlearn: 0.0019242\ttotal: 9.93s\tremaining: 2.32s\n",
      "154:\tlearn: 0.0019187\ttotal: 9.99s\tremaining: 2.26s\n",
      "155:\tlearn: 0.0019133\ttotal: 10.1s\tremaining: 2.19s\n",
      "156:\tlearn: 0.0019045\ttotal: 10.1s\tremaining: 2.13s\n",
      "157:\tlearn: 0.0019014\ttotal: 10.2s\tremaining: 2.06s\n",
      "158:\tlearn: 0.0018922\ttotal: 10.3s\tremaining: 2s\n",
      "159:\tlearn: 0.0018814\ttotal: 10.3s\tremaining: 1.94s\n",
      "160:\tlearn: 0.0018776\ttotal: 10.4s\tremaining: 1.87s\n",
      "161:\tlearn: 0.0018704\ttotal: 10.4s\tremaining: 1.8s\n",
      "162:\tlearn: 0.0018665\ttotal: 10.5s\tremaining: 1.74s\n",
      "163:\tlearn: 0.0018605\ttotal: 10.6s\tremaining: 1.68s\n",
      "164:\tlearn: 0.0018576\ttotal: 10.6s\tremaining: 1.61s\n",
      "165:\tlearn: 0.0018524\ttotal: 10.7s\tremaining: 1.55s\n",
      "166:\tlearn: 0.0018487\ttotal: 10.8s\tremaining: 1.48s\n",
      "167:\tlearn: 0.0018453\ttotal: 10.8s\tremaining: 1.42s\n",
      "168:\tlearn: 0.0018406\ttotal: 10.9s\tremaining: 1.35s\n",
      "169:\tlearn: 0.0018351\ttotal: 11s\tremaining: 1.29s\n",
      "170:\tlearn: 0.0018265\ttotal: 11s\tremaining: 1.22s\n",
      "171:\tlearn: 0.0018197\ttotal: 11.1s\tremaining: 1.16s\n",
      "172:\tlearn: 0.0018158\ttotal: 11.2s\tremaining: 1.1s\n",
      "173:\tlearn: 0.0018135\ttotal: 11.2s\tremaining: 1.03s\n",
      "174:\tlearn: 0.0018078\ttotal: 11.3s\tremaining: 967ms\n",
      "175:\tlearn: 0.0018045\ttotal: 11.3s\tremaining: 903ms\n",
      "176:\tlearn: 0.0018010\ttotal: 11.4s\tremaining: 838ms\n",
      "177:\tlearn: 0.0017971\ttotal: 11.5s\tremaining: 773ms\n",
      "178:\tlearn: 0.0017922\ttotal: 11.5s\tremaining: 708ms\n",
      "179:\tlearn: 0.0017876\ttotal: 11.6s\tremaining: 644ms\n",
      "180:\tlearn: 0.0017844\ttotal: 11.7s\tremaining: 580ms\n",
      "181:\tlearn: 0.0017781\ttotal: 11.7s\tremaining: 516ms\n",
      "182:\tlearn: 0.0017762\ttotal: 11.8s\tremaining: 451ms\n",
      "183:\tlearn: 0.0017738\ttotal: 11.8s\tremaining: 386ms\n",
      "184:\tlearn: 0.0017690\ttotal: 11.9s\tremaining: 322ms\n",
      "185:\tlearn: 0.0017644\ttotal: 12s\tremaining: 258ms\n",
      "186:\tlearn: 0.0017588\ttotal: 12s\tremaining: 193ms\n",
      "187:\tlearn: 0.0017547\ttotal: 12.1s\tremaining: 129ms\n",
      "188:\tlearn: 0.0017495\ttotal: 12.2s\tremaining: 64.4ms\n",
      "189:\tlearn: 0.0017461\ttotal: 12.2s\tremaining: 0us\n",
      "0:\tlearn: 0.6258701\ttotal: 69.3ms\tremaining: 13.1s\n",
      "1:\tlearn: 0.5674976\ttotal: 134ms\tremaining: 12.6s\n",
      "2:\tlearn: 0.5162978\ttotal: 192ms\tremaining: 11.9s\n",
      "3:\tlearn: 0.4710416\ttotal: 256ms\tremaining: 11.9s\n",
      "4:\tlearn: 0.4307574\ttotal: 319ms\tremaining: 11.8s\n",
      "5:\tlearn: 0.3947001\ttotal: 379ms\tremaining: 11.6s\n",
      "6:\tlearn: 0.3622938\ttotal: 442ms\tremaining: 11.6s\n",
      "7:\tlearn: 0.3330179\ttotal: 501ms\tremaining: 11.4s\n",
      "8:\tlearn: 0.3065385\ttotal: 561ms\tremaining: 11.3s\n",
      "9:\tlearn: 0.2825188\ttotal: 624ms\tremaining: 11.2s\n",
      "10:\tlearn: 0.2607338\ttotal: 690ms\tremaining: 11.2s\n",
      "11:\tlearn: 0.2407534\ttotal: 748ms\tremaining: 11.1s\n",
      "12:\tlearn: 0.2225491\ttotal: 809ms\tremaining: 11s\n",
      "13:\tlearn: 0.2058975\ttotal: 874ms\tremaining: 11s\n",
      "14:\tlearn: 0.1906750\ttotal: 932ms\tremaining: 10.9s\n",
      "15:\tlearn: 0.1767084\ttotal: 990ms\tremaining: 10.8s\n",
      "16:\tlearn: 0.1638702\ttotal: 1.06s\tremaining: 10.8s\n",
      "17:\tlearn: 0.1520039\ttotal: 1.13s\tremaining: 10.8s\n",
      "18:\tlearn: 0.1411306\ttotal: 1.2s\tremaining: 10.8s\n",
      "19:\tlearn: 0.1310394\ttotal: 1.25s\tremaining: 10.7s\n",
      "20:\tlearn: 0.1217304\ttotal: 1.32s\tremaining: 10.6s\n",
      "21:\tlearn: 0.1131499\ttotal: 1.38s\tremaining: 10.5s\n",
      "22:\tlearn: 0.1052221\ttotal: 1.44s\tremaining: 10.4s\n",
      "23:\tlearn: 0.0978995\ttotal: 1.5s\tremaining: 10.4s\n",
      "24:\tlearn: 0.0910988\ttotal: 1.56s\tremaining: 10.3s\n",
      "25:\tlearn: 0.0848381\ttotal: 1.62s\tremaining: 10.2s\n",
      "26:\tlearn: 0.0789964\ttotal: 1.68s\tremaining: 10.2s\n",
      "27:\tlearn: 0.0736110\ttotal: 1.75s\tremaining: 10.1s\n",
      "28:\tlearn: 0.0686026\ttotal: 1.81s\tremaining: 10s\n",
      "29:\tlearn: 0.0639840\ttotal: 1.86s\tremaining: 9.95s\n",
      "30:\tlearn: 0.0596750\ttotal: 1.91s\tremaining: 9.82s\n",
      "31:\tlearn: 0.0556818\ttotal: 1.97s\tremaining: 9.74s\n",
      "32:\tlearn: 0.0519855\ttotal: 2.03s\tremaining: 9.67s\n",
      "33:\tlearn: 0.0485256\ttotal: 2.09s\tremaining: 9.59s\n",
      "34:\tlearn: 0.0453333\ttotal: 2.15s\tremaining: 9.52s\n",
      "35:\tlearn: 0.0423589\ttotal: 2.21s\tremaining: 9.44s\n",
      "36:\tlearn: 0.0395949\ttotal: 2.26s\tremaining: 9.36s\n",
      "37:\tlearn: 0.0370254\ttotal: 2.32s\tremaining: 9.3s\n",
      "38:\tlearn: 0.0346315\ttotal: 2.38s\tremaining: 9.23s\n",
      "39:\tlearn: 0.0324125\ttotal: 2.44s\tremaining: 9.17s\n",
      "40:\tlearn: 0.0303333\ttotal: 2.5s\tremaining: 9.09s\n",
      "41:\tlearn: 0.0284120\ttotal: 2.56s\tremaining: 9.01s\n",
      "42:\tlearn: 0.0266126\ttotal: 2.62s\tremaining: 8.96s\n",
      "43:\tlearn: 0.0249413\ttotal: 2.67s\tremaining: 8.86s\n",
      "44:\tlearn: 0.0233941\ttotal: 2.73s\tremaining: 8.79s\n",
      "45:\tlearn: 0.0219439\ttotal: 2.79s\tremaining: 8.72s\n",
      "46:\tlearn: 0.0205962\ttotal: 2.84s\tremaining: 8.65s\n",
      "47:\tlearn: 0.0193377\ttotal: 2.9s\tremaining: 8.59s\n",
      "48:\tlearn: 0.0181676\ttotal: 2.96s\tremaining: 8.51s\n",
      "49:\tlearn: 0.0170894\ttotal: 3.02s\tremaining: 8.46s\n",
      "50:\tlearn: 0.0160669\ttotal: 3.07s\tremaining: 8.38s\n",
      "51:\tlearn: 0.0151250\ttotal: 3.13s\tremaining: 8.32s\n",
      "52:\tlearn: 0.0142552\ttotal: 3.19s\tremaining: 8.25s\n",
      "53:\tlearn: 0.0134401\ttotal: 3.25s\tremaining: 8.19s\n",
      "54:\tlearn: 0.0126768\ttotal: 3.31s\tremaining: 8.11s\n",
      "55:\tlearn: 0.0119715\ttotal: 3.37s\tremaining: 8.05s\n",
      "56:\tlearn: 0.0113124\ttotal: 3.42s\tremaining: 7.98s\n",
      "57:\tlearn: 0.0106935\ttotal: 3.47s\tremaining: 7.9s\n",
      "58:\tlearn: 0.0101217\ttotal: 3.53s\tremaining: 7.83s\n",
      "59:\tlearn: 0.0096040\ttotal: 3.59s\tremaining: 7.77s\n",
      "60:\tlearn: 0.0091012\ttotal: 3.65s\tremaining: 7.71s\n",
      "61:\tlearn: 0.0086312\ttotal: 3.7s\tremaining: 7.65s\n",
      "62:\tlearn: 0.0081958\ttotal: 3.76s\tremaining: 7.58s\n",
      "63:\tlearn: 0.0077924\ttotal: 3.81s\tremaining: 7.5s\n",
      "64:\tlearn: 0.0074363\ttotal: 3.87s\tremaining: 7.44s\n",
      "65:\tlearn: 0.0070881\ttotal: 3.93s\tremaining: 7.38s\n",
      "66:\tlearn: 0.0067630\ttotal: 3.99s\tremaining: 7.32s\n",
      "67:\tlearn: 0.0064600\ttotal: 4.04s\tremaining: 7.25s\n",
      "68:\tlearn: 0.0061772\ttotal: 4.1s\tremaining: 7.18s\n",
      "69:\tlearn: 0.0059137\ttotal: 4.15s\tremaining: 7.12s\n",
      "70:\tlearn: 0.0056660\ttotal: 4.21s\tremaining: 7.05s\n",
      "71:\tlearn: 0.0054290\ttotal: 4.26s\tremaining: 6.98s\n",
      "72:\tlearn: 0.0052145\ttotal: 4.32s\tremaining: 6.92s\n",
      "73:\tlearn: 0.0050191\ttotal: 4.38s\tremaining: 6.86s\n",
      "74:\tlearn: 0.0048313\ttotal: 4.43s\tremaining: 6.79s\n",
      "75:\tlearn: 0.0046639\ttotal: 4.49s\tremaining: 6.73s\n",
      "76:\tlearn: 0.0045094\ttotal: 4.54s\tremaining: 6.67s\n",
      "77:\tlearn: 0.0043621\ttotal: 4.6s\tremaining: 6.6s\n",
      "78:\tlearn: 0.0042254\ttotal: 4.65s\tremaining: 6.54s\n",
      "79:\tlearn: 0.0040948\ttotal: 4.71s\tremaining: 6.48s\n",
      "80:\tlearn: 0.0039793\ttotal: 4.77s\tremaining: 6.41s\n",
      "81:\tlearn: 0.0038746\ttotal: 4.83s\tremaining: 6.36s\n",
      "82:\tlearn: 0.0037677\ttotal: 4.88s\tremaining: 6.29s\n",
      "83:\tlearn: 0.0036677\ttotal: 4.94s\tremaining: 6.23s\n",
      "84:\tlearn: 0.0035751\ttotal: 5s\tremaining: 6.17s\n",
      "85:\tlearn: 0.0034866\ttotal: 5.05s\tremaining: 6.11s\n",
      "86:\tlearn: 0.0033988\ttotal: 5.11s\tremaining: 6.04s\n",
      "87:\tlearn: 0.0033240\ttotal: 5.16s\tremaining: 5.98s\n",
      "88:\tlearn: 0.0032490\ttotal: 5.21s\tremaining: 5.92s\n",
      "89:\tlearn: 0.0031813\ttotal: 5.27s\tremaining: 5.86s\n",
      "90:\tlearn: 0.0031249\ttotal: 5.33s\tremaining: 5.8s\n",
      "91:\tlearn: 0.0030606\ttotal: 5.39s\tremaining: 5.74s\n",
      "92:\tlearn: 0.0030067\ttotal: 5.44s\tremaining: 5.68s\n",
      "93:\tlearn: 0.0029593\ttotal: 5.5s\tremaining: 5.61s\n",
      "94:\tlearn: 0.0029074\ttotal: 5.55s\tremaining: 5.55s\n",
      "95:\tlearn: 0.0028634\ttotal: 5.61s\tremaining: 5.49s\n",
      "96:\tlearn: 0.0028302\ttotal: 5.67s\tremaining: 5.43s\n",
      "97:\tlearn: 0.0027970\ttotal: 5.73s\tremaining: 5.38s\n",
      "98:\tlearn: 0.0027653\ttotal: 5.79s\tremaining: 5.33s\n",
      "99:\tlearn: 0.0027371\ttotal: 5.85s\tremaining: 5.26s\n",
      "100:\tlearn: 0.0027072\ttotal: 5.9s\tremaining: 5.2s\n",
      "101:\tlearn: 0.0026678\ttotal: 5.96s\tremaining: 5.14s\n",
      "102:\tlearn: 0.0026371\ttotal: 6.02s\tremaining: 5.08s\n",
      "103:\tlearn: 0.0026107\ttotal: 6.07s\tremaining: 5.02s\n",
      "104:\tlearn: 0.0025866\ttotal: 6.13s\tremaining: 4.96s\n",
      "105:\tlearn: 0.0025657\ttotal: 6.18s\tremaining: 4.9s\n",
      "106:\tlearn: 0.0025406\ttotal: 6.23s\tremaining: 4.84s\n",
      "107:\tlearn: 0.0025126\ttotal: 6.29s\tremaining: 4.78s\n",
      "108:\tlearn: 0.0024963\ttotal: 6.34s\tremaining: 4.71s\n",
      "109:\tlearn: 0.0024820\ttotal: 6.4s\tremaining: 4.66s\n",
      "110:\tlearn: 0.0024651\ttotal: 6.46s\tremaining: 4.6s\n",
      "111:\tlearn: 0.0024529\ttotal: 6.51s\tremaining: 4.54s\n",
      "112:\tlearn: 0.0024301\ttotal: 6.57s\tremaining: 4.48s\n",
      "113:\tlearn: 0.0024161\ttotal: 6.64s\tremaining: 4.42s\n",
      "114:\tlearn: 0.0023954\ttotal: 6.69s\tremaining: 4.37s\n",
      "115:\tlearn: 0.0023808\ttotal: 6.75s\tremaining: 4.31s\n",
      "116:\tlearn: 0.0023706\ttotal: 6.81s\tremaining: 4.25s\n",
      "117:\tlearn: 0.0023501\ttotal: 6.87s\tremaining: 4.19s\n",
      "118:\tlearn: 0.0023329\ttotal: 6.93s\tremaining: 4.13s\n",
      "119:\tlearn: 0.0023229\ttotal: 6.99s\tremaining: 4.08s\n",
      "120:\tlearn: 0.0023168\ttotal: 7.04s\tremaining: 4.02s\n",
      "121:\tlearn: 0.0023015\ttotal: 7.1s\tremaining: 3.96s\n",
      "122:\tlearn: 0.0022932\ttotal: 7.16s\tremaining: 3.9s\n",
      "123:\tlearn: 0.0022836\ttotal: 7.22s\tremaining: 3.85s\n",
      "124:\tlearn: 0.0022726\ttotal: 7.28s\tremaining: 3.79s\n",
      "125:\tlearn: 0.0022604\ttotal: 7.34s\tremaining: 3.73s\n",
      "126:\tlearn: 0.0022521\ttotal: 7.4s\tremaining: 3.67s\n",
      "127:\tlearn: 0.0022388\ttotal: 7.46s\tremaining: 3.61s\n",
      "128:\tlearn: 0.0022283\ttotal: 7.51s\tremaining: 3.55s\n",
      "129:\tlearn: 0.0022169\ttotal: 7.57s\tremaining: 3.49s\n",
      "130:\tlearn: 0.0022101\ttotal: 7.62s\tremaining: 3.43s\n",
      "131:\tlearn: 0.0021994\ttotal: 7.68s\tremaining: 3.37s\n",
      "132:\tlearn: 0.0021939\ttotal: 7.73s\tremaining: 3.31s\n",
      "133:\tlearn: 0.0021867\ttotal: 7.79s\tremaining: 3.26s\n",
      "134:\tlearn: 0.0021813\ttotal: 7.85s\tremaining: 3.2s\n",
      "135:\tlearn: 0.0021696\ttotal: 7.91s\tremaining: 3.14s\n",
      "136:\tlearn: 0.0021660\ttotal: 7.96s\tremaining: 3.08s\n",
      "137:\tlearn: 0.0021601\ttotal: 8.02s\tremaining: 3.02s\n",
      "138:\tlearn: 0.0021496\ttotal: 8.07s\tremaining: 2.96s\n",
      "139:\tlearn: 0.0021347\ttotal: 8.14s\tremaining: 2.91s\n",
      "140:\tlearn: 0.0021305\ttotal: 8.19s\tremaining: 2.85s\n",
      "141:\tlearn: 0.0021195\ttotal: 8.26s\tremaining: 2.79s\n",
      "142:\tlearn: 0.0021086\ttotal: 8.31s\tremaining: 2.73s\n",
      "143:\tlearn: 0.0021019\ttotal: 8.37s\tremaining: 2.67s\n",
      "144:\tlearn: 0.0020963\ttotal: 8.43s\tremaining: 2.61s\n",
      "145:\tlearn: 0.0020885\ttotal: 8.48s\tremaining: 2.56s\n",
      "146:\tlearn: 0.0020797\ttotal: 8.55s\tremaining: 2.5s\n",
      "147:\tlearn: 0.0020747\ttotal: 8.6s\tremaining: 2.44s\n",
      "148:\tlearn: 0.0020638\ttotal: 8.66s\tremaining: 2.38s\n",
      "149:\tlearn: 0.0020577\ttotal: 8.72s\tremaining: 2.33s\n",
      "150:\tlearn: 0.0020537\ttotal: 8.77s\tremaining: 2.27s\n",
      "151:\tlearn: 0.0020508\ttotal: 8.83s\tremaining: 2.21s\n",
      "152:\tlearn: 0.0020436\ttotal: 8.88s\tremaining: 2.15s\n",
      "153:\tlearn: 0.0020295\ttotal: 8.95s\tremaining: 2.09s\n",
      "154:\tlearn: 0.0020214\ttotal: 9.01s\tremaining: 2.03s\n",
      "155:\tlearn: 0.0020089\ttotal: 9.07s\tremaining: 1.98s\n",
      "156:\tlearn: 0.0020041\ttotal: 9.13s\tremaining: 1.92s\n",
      "157:\tlearn: 0.0019951\ttotal: 9.18s\tremaining: 1.86s\n",
      "158:\tlearn: 0.0019883\ttotal: 9.23s\tremaining: 1.8s\n",
      "159:\tlearn: 0.0019782\ttotal: 9.28s\tremaining: 1.74s\n",
      "160:\tlearn: 0.0019763\ttotal: 9.33s\tremaining: 1.68s\n",
      "161:\tlearn: 0.0019711\ttotal: 9.39s\tremaining: 1.62s\n",
      "162:\tlearn: 0.0019673\ttotal: 9.45s\tremaining: 1.56s\n",
      "163:\tlearn: 0.0019649\ttotal: 9.5s\tremaining: 1.51s\n",
      "164:\tlearn: 0.0019613\ttotal: 9.56s\tremaining: 1.45s\n",
      "165:\tlearn: 0.0019554\ttotal: 9.61s\tremaining: 1.39s\n",
      "166:\tlearn: 0.0019533\ttotal: 9.66s\tremaining: 1.33s\n",
      "167:\tlearn: 0.0019457\ttotal: 9.72s\tremaining: 1.27s\n",
      "168:\tlearn: 0.0019413\ttotal: 9.77s\tremaining: 1.21s\n",
      "169:\tlearn: 0.0019353\ttotal: 9.82s\tremaining: 1.16s\n",
      "170:\tlearn: 0.0019327\ttotal: 9.87s\tremaining: 1.1s\n",
      "171:\tlearn: 0.0019306\ttotal: 9.92s\tremaining: 1.04s\n",
      "172:\tlearn: 0.0019265\ttotal: 9.97s\tremaining: 980ms\n",
      "173:\tlearn: 0.0019230\ttotal: 10s\tremaining: 922ms\n",
      "174:\tlearn: 0.0019176\ttotal: 10.1s\tremaining: 864ms\n",
      "175:\tlearn: 0.0019149\ttotal: 10.1s\tremaining: 807ms\n",
      "176:\tlearn: 0.0019102\ttotal: 10.2s\tremaining: 749ms\n",
      "177:\tlearn: 0.0019073\ttotal: 10.2s\tremaining: 691ms\n",
      "178:\tlearn: 0.0019024\ttotal: 10.3s\tremaining: 633ms\n",
      "179:\tlearn: 0.0018955\ttotal: 10.4s\tremaining: 576ms\n",
      "180:\tlearn: 0.0018931\ttotal: 10.4s\tremaining: 518ms\n",
      "181:\tlearn: 0.0018849\ttotal: 10.5s\tremaining: 460ms\n",
      "182:\tlearn: 0.0018825\ttotal: 10.5s\tremaining: 402ms\n",
      "183:\tlearn: 0.0018807\ttotal: 10.6s\tremaining: 345ms\n",
      "184:\tlearn: 0.0018757\ttotal: 10.6s\tremaining: 287ms\n",
      "185:\tlearn: 0.0018722\ttotal: 10.7s\tremaining: 230ms\n",
      "186:\tlearn: 0.0018636\ttotal: 10.7s\tremaining: 172ms\n",
      "187:\tlearn: 0.0018591\ttotal: 10.8s\tremaining: 115ms\n",
      "188:\tlearn: 0.0018564\ttotal: 10.8s\tremaining: 57.4ms\n",
      "189:\tlearn: 0.0018512\ttotal: 10.9s\tremaining: 0us\n",
      "0:\tlearn: 0.6258811\ttotal: 42.9ms\tremaining: 8.12s\n",
      "1:\tlearn: 0.5675057\ttotal: 87.9ms\tremaining: 8.26s\n",
      "2:\tlearn: 0.5163877\ttotal: 130ms\tremaining: 8.12s\n",
      "3:\tlearn: 0.4711402\ttotal: 173ms\tremaining: 8.05s\n",
      "4:\tlearn: 0.4308724\ttotal: 213ms\tremaining: 7.87s\n",
      "5:\tlearn: 0.3948364\ttotal: 254ms\tremaining: 7.8s\n",
      "6:\tlearn: 0.3624374\ttotal: 312ms\tremaining: 8.14s\n",
      "7:\tlearn: 0.3331680\ttotal: 369ms\tremaining: 8.38s\n",
      "8:\tlearn: 0.3066982\ttotal: 428ms\tremaining: 8.6s\n",
      "9:\tlearn: 0.2826842\ttotal: 491ms\tremaining: 8.85s\n",
      "10:\tlearn: 0.2608995\ttotal: 554ms\tremaining: 9.02s\n",
      "11:\tlearn: 0.2409367\ttotal: 615ms\tremaining: 9.12s\n",
      "12:\tlearn: 0.2227418\ttotal: 670ms\tremaining: 9.13s\n",
      "13:\tlearn: 0.2061016\ttotal: 733ms\tremaining: 9.22s\n",
      "14:\tlearn: 0.1908857\ttotal: 791ms\tremaining: 9.22s\n",
      "15:\tlearn: 0.1769216\ttotal: 851ms\tremaining: 9.25s\n",
      "16:\tlearn: 0.1640913\ttotal: 906ms\tremaining: 9.22s\n",
      "17:\tlearn: 0.1522388\ttotal: 966ms\tremaining: 9.23s\n",
      "18:\tlearn: 0.1413284\ttotal: 1.02s\tremaining: 9.23s\n",
      "19:\tlearn: 0.1312385\ttotal: 1.08s\tremaining: 9.17s\n",
      "20:\tlearn: 0.1219364\ttotal: 1.14s\tremaining: 9.15s\n",
      "21:\tlearn: 0.1133642\ttotal: 1.2s\tremaining: 9.14s\n",
      "22:\tlearn: 0.1054261\ttotal: 1.25s\tremaining: 9.11s\n",
      "23:\tlearn: 0.0981062\ttotal: 1.31s\tremaining: 9.06s\n",
      "24:\tlearn: 0.0913160\ttotal: 1.37s\tremaining: 9.03s\n",
      "25:\tlearn: 0.0850561\ttotal: 1.43s\tremaining: 8.99s\n",
      "26:\tlearn: 0.0792058\ttotal: 1.48s\tremaining: 8.92s\n",
      "27:\tlearn: 0.0738249\ttotal: 1.53s\tremaining: 8.88s\n",
      "28:\tlearn: 0.0688236\ttotal: 1.59s\tremaining: 8.84s\n",
      "29:\tlearn: 0.0642040\ttotal: 1.65s\tremaining: 8.79s\n",
      "30:\tlearn: 0.0599000\ttotal: 1.7s\tremaining: 8.74s\n",
      "31:\tlearn: 0.0559321\ttotal: 1.76s\tremaining: 8.69s\n",
      "32:\tlearn: 0.0522183\ttotal: 1.82s\tremaining: 8.64s\n",
      "33:\tlearn: 0.0487748\ttotal: 1.87s\tremaining: 8.6s\n",
      "34:\tlearn: 0.0455743\ttotal: 1.93s\tremaining: 8.54s\n",
      "35:\tlearn: 0.0425902\ttotal: 1.98s\tremaining: 8.48s\n",
      "36:\tlearn: 0.0398065\ttotal: 2.04s\tremaining: 8.43s\n",
      "37:\tlearn: 0.0372488\ttotal: 2.1s\tremaining: 8.39s\n",
      "38:\tlearn: 0.0348300\ttotal: 2.15s\tremaining: 8.32s\n",
      "39:\tlearn: 0.0325954\ttotal: 2.2s\tremaining: 8.27s\n",
      "40:\tlearn: 0.0305172\ttotal: 2.25s\tremaining: 8.2s\n",
      "41:\tlearn: 0.0285877\ttotal: 2.3s\tremaining: 8.11s\n",
      "42:\tlearn: 0.0267902\ttotal: 2.35s\tremaining: 8.03s\n",
      "43:\tlearn: 0.0251043\ttotal: 2.41s\tremaining: 8s\n",
      "44:\tlearn: 0.0235513\ttotal: 2.46s\tremaining: 7.93s\n",
      "45:\tlearn: 0.0221043\ttotal: 2.52s\tremaining: 7.88s\n",
      "46:\tlearn: 0.0207495\ttotal: 2.57s\tremaining: 7.83s\n",
      "47:\tlearn: 0.0194824\ttotal: 2.63s\tremaining: 7.79s\n",
      "48:\tlearn: 0.0183235\ttotal: 2.68s\tremaining: 7.72s\n",
      "49:\tlearn: 0.0172289\ttotal: 2.74s\tremaining: 7.67s\n",
      "50:\tlearn: 0.0162193\ttotal: 2.79s\tremaining: 7.61s\n",
      "51:\tlearn: 0.0152815\ttotal: 2.83s\tremaining: 7.51s\n",
      "52:\tlearn: 0.0143989\ttotal: 2.88s\tremaining: 7.45s\n",
      "53:\tlearn: 0.0135669\ttotal: 2.93s\tremaining: 7.38s\n",
      "54:\tlearn: 0.0127970\ttotal: 2.98s\tremaining: 7.31s\n",
      "55:\tlearn: 0.0120819\ttotal: 3.04s\tremaining: 7.26s\n",
      "56:\tlearn: 0.0114091\ttotal: 3.09s\tremaining: 7.21s\n",
      "57:\tlearn: 0.0107845\ttotal: 3.14s\tremaining: 7.14s\n",
      "58:\tlearn: 0.0101991\ttotal: 3.19s\tremaining: 7.09s\n",
      "59:\tlearn: 0.0096647\ttotal: 3.25s\tremaining: 7.03s\n",
      "60:\tlearn: 0.0091570\ttotal: 3.29s\tremaining: 6.97s\n",
      "61:\tlearn: 0.0086944\ttotal: 3.35s\tremaining: 6.92s\n",
      "62:\tlearn: 0.0082476\ttotal: 3.4s\tremaining: 6.87s\n",
      "63:\tlearn: 0.0078395\ttotal: 3.46s\tremaining: 6.82s\n",
      "64:\tlearn: 0.0074637\ttotal: 3.53s\tremaining: 6.79s\n",
      "65:\tlearn: 0.0071156\ttotal: 3.58s\tremaining: 6.73s\n",
      "66:\tlearn: 0.0067815\ttotal: 3.63s\tremaining: 6.67s\n",
      "67:\tlearn: 0.0064780\ttotal: 3.69s\tremaining: 6.62s\n",
      "68:\tlearn: 0.0061888\ttotal: 3.74s\tremaining: 6.56s\n",
      "69:\tlearn: 0.0059228\ttotal: 3.79s\tremaining: 6.5s\n",
      "70:\tlearn: 0.0056622\ttotal: 3.84s\tremaining: 6.44s\n",
      "71:\tlearn: 0.0054296\ttotal: 3.89s\tremaining: 6.38s\n",
      "72:\tlearn: 0.0052217\ttotal: 3.94s\tremaining: 6.32s\n",
      "73:\tlearn: 0.0050255\ttotal: 4s\tremaining: 6.26s\n",
      "74:\tlearn: 0.0048361\ttotal: 4.05s\tremaining: 6.21s\n",
      "75:\tlearn: 0.0046654\ttotal: 4.1s\tremaining: 6.15s\n",
      "76:\tlearn: 0.0045104\ttotal: 4.16s\tremaining: 6.11s\n",
      "77:\tlearn: 0.0043569\ttotal: 4.21s\tremaining: 6.05s\n",
      "78:\tlearn: 0.0042135\ttotal: 4.26s\tremaining: 5.99s\n",
      "79:\tlearn: 0.0040867\ttotal: 4.32s\tremaining: 5.94s\n",
      "80:\tlearn: 0.0039616\ttotal: 4.37s\tremaining: 5.88s\n",
      "81:\tlearn: 0.0038472\ttotal: 4.42s\tremaining: 5.82s\n",
      "82:\tlearn: 0.0037426\ttotal: 4.47s\tremaining: 5.77s\n",
      "83:\tlearn: 0.0036311\ttotal: 4.53s\tremaining: 5.71s\n",
      "84:\tlearn: 0.0035342\ttotal: 4.58s\tremaining: 5.66s\n",
      "85:\tlearn: 0.0034472\ttotal: 4.64s\tremaining: 5.61s\n",
      "86:\tlearn: 0.0033558\ttotal: 4.69s\tremaining: 5.55s\n",
      "87:\tlearn: 0.0032757\ttotal: 4.74s\tremaining: 5.5s\n",
      "88:\tlearn: 0.0032089\ttotal: 4.79s\tremaining: 5.44s\n",
      "89:\tlearn: 0.0031439\ttotal: 4.85s\tremaining: 5.38s\n",
      "90:\tlearn: 0.0030819\ttotal: 4.9s\tremaining: 5.33s\n",
      "91:\tlearn: 0.0030272\ttotal: 4.96s\tremaining: 5.28s\n",
      "92:\tlearn: 0.0029758\ttotal: 5s\tremaining: 5.22s\n",
      "93:\tlearn: 0.0029308\ttotal: 5.06s\tremaining: 5.16s\n",
      "94:\tlearn: 0.0028811\ttotal: 5.09s\tremaining: 5.09s\n",
      "95:\tlearn: 0.0028437\ttotal: 5.13s\tremaining: 5.02s\n",
      "96:\tlearn: 0.0028035\ttotal: 5.18s\tremaining: 4.96s\n",
      "97:\tlearn: 0.0027596\ttotal: 5.23s\tremaining: 4.91s\n",
      "98:\tlearn: 0.0027190\ttotal: 5.28s\tremaining: 4.85s\n",
      "99:\tlearn: 0.0026786\ttotal: 5.33s\tremaining: 4.8s\n",
      "100:\tlearn: 0.0026500\ttotal: 5.38s\tremaining: 4.74s\n",
      "101:\tlearn: 0.0026073\ttotal: 5.44s\tremaining: 4.69s\n",
      "102:\tlearn: 0.0025786\ttotal: 5.49s\tremaining: 4.63s\n",
      "103:\tlearn: 0.0025487\ttotal: 5.54s\tremaining: 4.58s\n",
      "104:\tlearn: 0.0025244\ttotal: 5.59s\tremaining: 4.52s\n",
      "105:\tlearn: 0.0024890\ttotal: 5.64s\tremaining: 4.47s\n",
      "106:\tlearn: 0.0024540\ttotal: 5.71s\tremaining: 4.43s\n",
      "107:\tlearn: 0.0024193\ttotal: 5.76s\tremaining: 4.38s\n",
      "108:\tlearn: 0.0023986\ttotal: 5.81s\tremaining: 4.32s\n",
      "109:\tlearn: 0.0023784\ttotal: 5.86s\tremaining: 4.26s\n",
      "110:\tlearn: 0.0023526\ttotal: 5.92s\tremaining: 4.21s\n",
      "111:\tlearn: 0.0023348\ttotal: 5.97s\tremaining: 4.16s\n",
      "112:\tlearn: 0.0023076\ttotal: 6.02s\tremaining: 4.1s\n",
      "113:\tlearn: 0.0022852\ttotal: 6.08s\tremaining: 4.05s\n",
      "114:\tlearn: 0.0022627\ttotal: 6.13s\tremaining: 4s\n",
      "115:\tlearn: 0.0022397\ttotal: 6.19s\tremaining: 3.95s\n",
      "116:\tlearn: 0.0022171\ttotal: 6.25s\tremaining: 3.9s\n",
      "117:\tlearn: 0.0021975\ttotal: 6.3s\tremaining: 3.85s\n",
      "118:\tlearn: 0.0021794\ttotal: 6.36s\tremaining: 3.79s\n",
      "119:\tlearn: 0.0021605\ttotal: 6.41s\tremaining: 3.74s\n",
      "120:\tlearn: 0.0021541\ttotal: 6.46s\tremaining: 3.68s\n",
      "121:\tlearn: 0.0021351\ttotal: 6.51s\tremaining: 3.63s\n",
      "122:\tlearn: 0.0021201\ttotal: 6.57s\tremaining: 3.58s\n",
      "123:\tlearn: 0.0021031\ttotal: 6.62s\tremaining: 3.52s\n",
      "124:\tlearn: 0.0020851\ttotal: 6.68s\tremaining: 3.47s\n",
      "125:\tlearn: 0.0020803\ttotal: 6.73s\tremaining: 3.42s\n",
      "126:\tlearn: 0.0020703\ttotal: 6.78s\tremaining: 3.36s\n",
      "127:\tlearn: 0.0020648\ttotal: 6.83s\tremaining: 3.31s\n",
      "128:\tlearn: 0.0020557\ttotal: 6.88s\tremaining: 3.25s\n",
      "129:\tlearn: 0.0020432\ttotal: 6.94s\tremaining: 3.2s\n",
      "130:\tlearn: 0.0020330\ttotal: 7s\tremaining: 3.15s\n",
      "131:\tlearn: 0.0020196\ttotal: 7.05s\tremaining: 3.1s\n",
      "132:\tlearn: 0.0020068\ttotal: 7.11s\tremaining: 3.05s\n",
      "133:\tlearn: 0.0019951\ttotal: 7.17s\tremaining: 2.99s\n",
      "134:\tlearn: 0.0019814\ttotal: 7.22s\tremaining: 2.94s\n",
      "135:\tlearn: 0.0019711\ttotal: 7.28s\tremaining: 2.89s\n",
      "136:\tlearn: 0.0019646\ttotal: 7.33s\tremaining: 2.84s\n",
      "137:\tlearn: 0.0019543\ttotal: 7.39s\tremaining: 2.78s\n",
      "138:\tlearn: 0.0019452\ttotal: 7.44s\tremaining: 2.73s\n",
      "139:\tlearn: 0.0019362\ttotal: 7.49s\tremaining: 2.67s\n",
      "140:\tlearn: 0.0019291\ttotal: 7.54s\tremaining: 2.62s\n",
      "141:\tlearn: 0.0019232\ttotal: 7.58s\tremaining: 2.56s\n",
      "142:\tlearn: 0.0019117\ttotal: 7.64s\tremaining: 2.51s\n",
      "143:\tlearn: 0.0019071\ttotal: 7.69s\tremaining: 2.46s\n",
      "144:\tlearn: 0.0019038\ttotal: 7.74s\tremaining: 2.4s\n",
      "145:\tlearn: 0.0018953\ttotal: 7.8s\tremaining: 2.35s\n",
      "146:\tlearn: 0.0018914\ttotal: 7.85s\tremaining: 2.3s\n",
      "147:\tlearn: 0.0018814\ttotal: 7.91s\tremaining: 2.24s\n",
      "148:\tlearn: 0.0018755\ttotal: 7.96s\tremaining: 2.19s\n",
      "149:\tlearn: 0.0018702\ttotal: 8.01s\tremaining: 2.14s\n",
      "150:\tlearn: 0.0018636\ttotal: 8.07s\tremaining: 2.08s\n",
      "151:\tlearn: 0.0018501\ttotal: 8.12s\tremaining: 2.03s\n",
      "152:\tlearn: 0.0018429\ttotal: 8.18s\tremaining: 1.98s\n",
      "153:\tlearn: 0.0018362\ttotal: 8.23s\tremaining: 1.92s\n",
      "154:\tlearn: 0.0018276\ttotal: 8.28s\tremaining: 1.87s\n",
      "155:\tlearn: 0.0018206\ttotal: 8.34s\tremaining: 1.82s\n",
      "156:\tlearn: 0.0018129\ttotal: 8.4s\tremaining: 1.76s\n",
      "157:\tlearn: 0.0018008\ttotal: 8.45s\tremaining: 1.71s\n",
      "158:\tlearn: 0.0017965\ttotal: 8.5s\tremaining: 1.66s\n",
      "159:\tlearn: 0.0017910\ttotal: 8.54s\tremaining: 1.6s\n",
      "160:\tlearn: 0.0017871\ttotal: 8.59s\tremaining: 1.55s\n",
      "161:\tlearn: 0.0017831\ttotal: 8.64s\tremaining: 1.49s\n",
      "162:\tlearn: 0.0017766\ttotal: 8.7s\tremaining: 1.44s\n",
      "163:\tlearn: 0.0017726\ttotal: 8.75s\tremaining: 1.39s\n",
      "164:\tlearn: 0.0017698\ttotal: 8.8s\tremaining: 1.33s\n",
      "165:\tlearn: 0.0017674\ttotal: 8.85s\tremaining: 1.28s\n",
      "166:\tlearn: 0.0017645\ttotal: 8.9s\tremaining: 1.23s\n",
      "167:\tlearn: 0.0017571\ttotal: 8.96s\tremaining: 1.17s\n",
      "168:\tlearn: 0.0017524\ttotal: 9.01s\tremaining: 1.12s\n",
      "169:\tlearn: 0.0017452\ttotal: 9.07s\tremaining: 1.07s\n",
      "170:\tlearn: 0.0017389\ttotal: 9.12s\tremaining: 1.01s\n",
      "171:\tlearn: 0.0017361\ttotal: 9.18s\tremaining: 960ms\n",
      "172:\tlearn: 0.0017332\ttotal: 9.23s\tremaining: 907ms\n",
      "173:\tlearn: 0.0017307\ttotal: 9.28s\tremaining: 853ms\n",
      "174:\tlearn: 0.0017259\ttotal: 9.33s\tremaining: 800ms\n",
      "175:\tlearn: 0.0017230\ttotal: 9.38s\tremaining: 747ms\n",
      "176:\tlearn: 0.0017208\ttotal: 9.43s\tremaining: 693ms\n",
      "177:\tlearn: 0.0017150\ttotal: 9.49s\tremaining: 640ms\n",
      "178:\tlearn: 0.0017129\ttotal: 9.54s\tremaining: 586ms\n",
      "179:\tlearn: 0.0017050\ttotal: 9.59s\tremaining: 533ms\n",
      "180:\tlearn: 0.0017019\ttotal: 9.64s\tremaining: 479ms\n",
      "181:\tlearn: 0.0016948\ttotal: 9.69s\tremaining: 426ms\n",
      "182:\tlearn: 0.0016847\ttotal: 9.75s\tremaining: 373ms\n",
      "183:\tlearn: 0.0016803\ttotal: 9.81s\tremaining: 320ms\n",
      "184:\tlearn: 0.0016765\ttotal: 9.86s\tremaining: 267ms\n",
      "185:\tlearn: 0.0016703\ttotal: 9.91s\tremaining: 213ms\n",
      "186:\tlearn: 0.0016683\ttotal: 9.97s\tremaining: 160ms\n",
      "187:\tlearn: 0.0016625\ttotal: 10s\tremaining: 107ms\n",
      "188:\tlearn: 0.0016575\ttotal: 10.1s\tremaining: 53.3ms\n",
      "189:\tlearn: 0.0016536\ttotal: 10.1s\tremaining: 0us\n",
      "0:\tlearn: 0.6258849\ttotal: 44.1ms\tremaining: 8.34s\n",
      "1:\tlearn: 0.5675058\ttotal: 87.7ms\tremaining: 8.24s\n",
      "2:\tlearn: 0.5163722\ttotal: 132ms\tremaining: 8.2s\n",
      "3:\tlearn: 0.4711092\ttotal: 176ms\tremaining: 8.18s\n",
      "4:\tlearn: 0.4308306\ttotal: 218ms\tremaining: 8.05s\n",
      "5:\tlearn: 0.3947634\ttotal: 260ms\tremaining: 7.96s\n",
      "6:\tlearn: 0.3623358\ttotal: 303ms\tremaining: 7.92s\n",
      "7:\tlearn: 0.3330543\ttotal: 348ms\tremaining: 7.91s\n",
      "8:\tlearn: 0.3065681\ttotal: 408ms\tremaining: 8.2s\n",
      "9:\tlearn: 0.2825771\ttotal: 474ms\tremaining: 8.54s\n",
      "10:\tlearn: 0.2607825\ttotal: 539ms\tremaining: 8.76s\n",
      "11:\tlearn: 0.2408046\ttotal: 601ms\tremaining: 8.92s\n",
      "12:\tlearn: 0.2225998\ttotal: 666ms\tremaining: 9.06s\n",
      "13:\tlearn: 0.2059526\ttotal: 729ms\tremaining: 9.16s\n",
      "14:\tlearn: 0.1907317\ttotal: 790ms\tremaining: 9.21s\n",
      "15:\tlearn: 0.1767610\ttotal: 853ms\tremaining: 9.28s\n",
      "16:\tlearn: 0.1639270\ttotal: 916ms\tremaining: 9.32s\n",
      "17:\tlearn: 0.1520588\ttotal: 977ms\tremaining: 9.34s\n",
      "18:\tlearn: 0.1411431\ttotal: 1.04s\tremaining: 9.36s\n",
      "19:\tlearn: 0.1310645\ttotal: 1.1s\tremaining: 9.36s\n",
      "20:\tlearn: 0.1217719\ttotal: 1.16s\tremaining: 9.36s\n",
      "21:\tlearn: 0.1131935\ttotal: 1.22s\tremaining: 9.35s\n",
      "22:\tlearn: 0.1052508\ttotal: 1.29s\tremaining: 9.34s\n",
      "23:\tlearn: 0.0979300\ttotal: 1.35s\tremaining: 9.32s\n",
      "24:\tlearn: 0.0911311\ttotal: 1.41s\tremaining: 9.28s\n",
      "25:\tlearn: 0.0848660\ttotal: 1.47s\tremaining: 9.27s\n",
      "26:\tlearn: 0.0790244\ttotal: 1.53s\tremaining: 9.23s\n",
      "27:\tlearn: 0.0736390\ttotal: 1.59s\tremaining: 9.19s\n",
      "28:\tlearn: 0.0686412\ttotal: 1.65s\tremaining: 9.13s\n",
      "29:\tlearn: 0.0640151\ttotal: 1.71s\tremaining: 9.11s\n",
      "30:\tlearn: 0.0596992\ttotal: 1.77s\tremaining: 9.06s\n",
      "31:\tlearn: 0.0557118\ttotal: 1.82s\tremaining: 9.01s\n",
      "32:\tlearn: 0.0519880\ttotal: 1.88s\tremaining: 8.96s\n",
      "33:\tlearn: 0.0485430\ttotal: 1.95s\tremaining: 8.94s\n",
      "34:\tlearn: 0.0453478\ttotal: 2.01s\tremaining: 8.88s\n",
      "35:\tlearn: 0.0423572\ttotal: 2.06s\tremaining: 8.81s\n",
      "36:\tlearn: 0.0395717\ttotal: 2.11s\tremaining: 8.74s\n",
      "37:\tlearn: 0.0370049\ttotal: 2.17s\tremaining: 8.7s\n",
      "38:\tlearn: 0.0346131\ttotal: 2.24s\tremaining: 8.66s\n",
      "39:\tlearn: 0.0323881\ttotal: 2.29s\tremaining: 8.61s\n",
      "40:\tlearn: 0.0303013\ttotal: 2.36s\tremaining: 8.57s\n",
      "41:\tlearn: 0.0283611\ttotal: 2.41s\tremaining: 8.49s\n",
      "42:\tlearn: 0.0265416\ttotal: 2.46s\tremaining: 8.41s\n",
      "43:\tlearn: 0.0248719\ttotal: 2.52s\tremaining: 8.37s\n",
      "44:\tlearn: 0.0233056\ttotal: 2.58s\tremaining: 8.32s\n",
      "45:\tlearn: 0.0218444\ttotal: 2.63s\tremaining: 8.23s\n",
      "46:\tlearn: 0.0204940\ttotal: 2.68s\tremaining: 8.16s\n",
      "47:\tlearn: 0.0192284\ttotal: 2.73s\tremaining: 8.09s\n",
      "48:\tlearn: 0.0180522\ttotal: 2.78s\tremaining: 8s\n",
      "49:\tlearn: 0.0169566\ttotal: 2.83s\tremaining: 7.94s\n",
      "50:\tlearn: 0.0159332\ttotal: 2.88s\tremaining: 7.86s\n",
      "51:\tlearn: 0.0149812\ttotal: 2.94s\tremaining: 7.8s\n",
      "52:\tlearn: 0.0141032\ttotal: 3s\tremaining: 7.75s\n",
      "53:\tlearn: 0.0132901\ttotal: 3.06s\tremaining: 7.7s\n",
      "54:\tlearn: 0.0125190\ttotal: 3.11s\tremaining: 7.64s\n",
      "55:\tlearn: 0.0118112\ttotal: 3.17s\tremaining: 7.6s\n",
      "56:\tlearn: 0.0111444\ttotal: 3.23s\tremaining: 7.53s\n",
      "57:\tlearn: 0.0105202\ttotal: 3.28s\tremaining: 7.47s\n",
      "58:\tlearn: 0.0099386\ttotal: 3.33s\tremaining: 7.4s\n",
      "59:\tlearn: 0.0093976\ttotal: 3.39s\tremaining: 7.34s\n",
      "60:\tlearn: 0.0088920\ttotal: 3.44s\tremaining: 7.28s\n",
      "61:\tlearn: 0.0084236\ttotal: 3.5s\tremaining: 7.22s\n",
      "62:\tlearn: 0.0079829\ttotal: 3.55s\tremaining: 7.16s\n",
      "63:\tlearn: 0.0075792\ttotal: 3.61s\tremaining: 7.11s\n",
      "64:\tlearn: 0.0072102\ttotal: 3.67s\tremaining: 7.06s\n",
      "65:\tlearn: 0.0068557\ttotal: 3.73s\tremaining: 7s\n",
      "66:\tlearn: 0.0065269\ttotal: 3.79s\tremaining: 6.95s\n",
      "67:\tlearn: 0.0062303\ttotal: 3.84s\tremaining: 6.9s\n",
      "68:\tlearn: 0.0059414\ttotal: 3.9s\tremaining: 6.84s\n",
      "69:\tlearn: 0.0056811\ttotal: 3.96s\tremaining: 6.79s\n",
      "70:\tlearn: 0.0054324\ttotal: 4.02s\tremaining: 6.74s\n",
      "71:\tlearn: 0.0052075\ttotal: 4.07s\tremaining: 6.68s\n",
      "72:\tlearn: 0.0049890\ttotal: 4.13s\tremaining: 6.63s\n",
      "73:\tlearn: 0.0047912\ttotal: 4.19s\tremaining: 6.57s\n",
      "74:\tlearn: 0.0046008\ttotal: 4.24s\tremaining: 6.5s\n",
      "75:\tlearn: 0.0044244\ttotal: 4.29s\tremaining: 6.44s\n",
      "76:\tlearn: 0.0042645\ttotal: 4.35s\tremaining: 6.38s\n",
      "77:\tlearn: 0.0041170\ttotal: 4.41s\tremaining: 6.33s\n",
      "78:\tlearn: 0.0039767\ttotal: 4.46s\tremaining: 6.27s\n",
      "79:\tlearn: 0.0038449\ttotal: 4.52s\tremaining: 6.21s\n",
      "80:\tlearn: 0.0037216\ttotal: 4.57s\tremaining: 6.16s\n",
      "81:\tlearn: 0.0036132\ttotal: 4.63s\tremaining: 6.1s\n",
      "82:\tlearn: 0.0035064\ttotal: 4.68s\tremaining: 6.04s\n",
      "83:\tlearn: 0.0033993\ttotal: 4.74s\tremaining: 5.98s\n",
      "84:\tlearn: 0.0033074\ttotal: 4.79s\tremaining: 5.92s\n",
      "85:\tlearn: 0.0032203\ttotal: 4.85s\tremaining: 5.86s\n",
      "86:\tlearn: 0.0031335\ttotal: 4.9s\tremaining: 5.8s\n",
      "87:\tlearn: 0.0030529\ttotal: 4.96s\tremaining: 5.75s\n",
      "88:\tlearn: 0.0029897\ttotal: 5.03s\tremaining: 5.7s\n",
      "89:\tlearn: 0.0029227\ttotal: 5.1s\tremaining: 5.67s\n",
      "90:\tlearn: 0.0028634\ttotal: 5.16s\tremaining: 5.61s\n",
      "91:\tlearn: 0.0028046\ttotal: 5.21s\tremaining: 5.55s\n",
      "92:\tlearn: 0.0027571\ttotal: 5.28s\tremaining: 5.5s\n",
      "93:\tlearn: 0.0027119\ttotal: 5.33s\tremaining: 5.45s\n",
      "94:\tlearn: 0.0026592\ttotal: 5.39s\tremaining: 5.39s\n",
      "95:\tlearn: 0.0026153\ttotal: 5.44s\tremaining: 5.33s\n",
      "96:\tlearn: 0.0025782\ttotal: 5.49s\tremaining: 5.27s\n",
      "97:\tlearn: 0.0025359\ttotal: 5.55s\tremaining: 5.21s\n",
      "98:\tlearn: 0.0024966\ttotal: 5.61s\tremaining: 5.16s\n",
      "99:\tlearn: 0.0024619\ttotal: 5.66s\tremaining: 5.1s\n",
      "100:\tlearn: 0.0024338\ttotal: 5.72s\tremaining: 5.04s\n",
      "101:\tlearn: 0.0024034\ttotal: 5.76s\tremaining: 4.97s\n",
      "102:\tlearn: 0.0023734\ttotal: 5.81s\tremaining: 4.91s\n",
      "103:\tlearn: 0.0023452\ttotal: 5.87s\tremaining: 4.85s\n",
      "104:\tlearn: 0.0023192\ttotal: 5.92s\tremaining: 4.79s\n",
      "105:\tlearn: 0.0022967\ttotal: 5.97s\tremaining: 4.73s\n",
      "106:\tlearn: 0.0022697\ttotal: 6.03s\tremaining: 4.68s\n",
      "107:\tlearn: 0.0022405\ttotal: 6.08s\tremaining: 4.62s\n",
      "108:\tlearn: 0.0022240\ttotal: 6.14s\tremaining: 4.56s\n",
      "109:\tlearn: 0.0021979\ttotal: 6.2s\tremaining: 4.5s\n",
      "110:\tlearn: 0.0021764\ttotal: 6.26s\tremaining: 4.45s\n",
      "111:\tlearn: 0.0021625\ttotal: 6.31s\tremaining: 4.39s\n",
      "112:\tlearn: 0.0021444\ttotal: 6.37s\tremaining: 4.34s\n",
      "113:\tlearn: 0.0021253\ttotal: 6.43s\tremaining: 4.28s\n",
      "114:\tlearn: 0.0021027\ttotal: 6.48s\tremaining: 4.23s\n",
      "115:\tlearn: 0.0020827\ttotal: 6.55s\tremaining: 4.18s\n",
      "116:\tlearn: 0.0020638\ttotal: 6.62s\tremaining: 4.13s\n",
      "117:\tlearn: 0.0020541\ttotal: 6.67s\tremaining: 4.07s\n",
      "118:\tlearn: 0.0020427\ttotal: 6.73s\tremaining: 4.01s\n",
      "119:\tlearn: 0.0020347\ttotal: 6.79s\tremaining: 3.96s\n",
      "120:\tlearn: 0.0020249\ttotal: 6.84s\tremaining: 3.9s\n",
      "121:\tlearn: 0.0020106\ttotal: 6.9s\tremaining: 3.85s\n",
      "122:\tlearn: 0.0020005\ttotal: 6.96s\tremaining: 3.79s\n",
      "123:\tlearn: 0.0019888\ttotal: 7.01s\tremaining: 3.73s\n",
      "124:\tlearn: 0.0019780\ttotal: 7.07s\tremaining: 3.68s\n",
      "125:\tlearn: 0.0019675\ttotal: 7.13s\tremaining: 3.62s\n",
      "126:\tlearn: 0.0019598\ttotal: 7.18s\tremaining: 3.56s\n",
      "127:\tlearn: 0.0019478\ttotal: 7.24s\tremaining: 3.51s\n",
      "128:\tlearn: 0.0019383\ttotal: 7.3s\tremaining: 3.45s\n",
      "129:\tlearn: 0.0019274\ttotal: 7.36s\tremaining: 3.4s\n",
      "130:\tlearn: 0.0019184\ttotal: 7.41s\tremaining: 3.34s\n",
      "131:\tlearn: 0.0019009\ttotal: 7.46s\tremaining: 3.28s\n",
      "132:\tlearn: 0.0018921\ttotal: 7.51s\tremaining: 3.22s\n",
      "133:\tlearn: 0.0018886\ttotal: 7.56s\tremaining: 3.16s\n",
      "134:\tlearn: 0.0018778\ttotal: 7.62s\tremaining: 3.1s\n",
      "135:\tlearn: 0.0018657\ttotal: 7.68s\tremaining: 3.05s\n",
      "136:\tlearn: 0.0018542\ttotal: 7.73s\tremaining: 2.99s\n",
      "137:\tlearn: 0.0018417\ttotal: 7.79s\tremaining: 2.94s\n",
      "138:\tlearn: 0.0018309\ttotal: 7.85s\tremaining: 2.88s\n",
      "139:\tlearn: 0.0018212\ttotal: 7.91s\tremaining: 2.82s\n",
      "140:\tlearn: 0.0018145\ttotal: 7.96s\tremaining: 2.77s\n",
      "141:\tlearn: 0.0018093\ttotal: 8.02s\tremaining: 2.71s\n",
      "142:\tlearn: 0.0018033\ttotal: 8.07s\tremaining: 2.65s\n",
      "143:\tlearn: 0.0017958\ttotal: 8.12s\tremaining: 2.6s\n",
      "144:\tlearn: 0.0017924\ttotal: 8.18s\tremaining: 2.54s\n",
      "145:\tlearn: 0.0017846\ttotal: 8.23s\tremaining: 2.48s\n",
      "146:\tlearn: 0.0017805\ttotal: 8.29s\tremaining: 2.42s\n",
      "147:\tlearn: 0.0017719\ttotal: 8.34s\tremaining: 2.37s\n",
      "148:\tlearn: 0.0017589\ttotal: 8.4s\tremaining: 2.31s\n",
      "149:\tlearn: 0.0017465\ttotal: 8.46s\tremaining: 2.26s\n",
      "150:\tlearn: 0.0017382\ttotal: 8.52s\tremaining: 2.2s\n",
      "151:\tlearn: 0.0017279\ttotal: 8.58s\tremaining: 2.15s\n",
      "152:\tlearn: 0.0017228\ttotal: 8.63s\tremaining: 2.09s\n",
      "153:\tlearn: 0.0017195\ttotal: 8.69s\tremaining: 2.03s\n",
      "154:\tlearn: 0.0017164\ttotal: 8.74s\tremaining: 1.97s\n",
      "155:\tlearn: 0.0017090\ttotal: 8.79s\tremaining: 1.92s\n",
      "156:\tlearn: 0.0017059\ttotal: 8.85s\tremaining: 1.86s\n",
      "157:\tlearn: 0.0016992\ttotal: 8.9s\tremaining: 1.8s\n",
      "158:\tlearn: 0.0016937\ttotal: 8.96s\tremaining: 1.75s\n",
      "159:\tlearn: 0.0016902\ttotal: 9.01s\tremaining: 1.69s\n",
      "160:\tlearn: 0.0016824\ttotal: 9.06s\tremaining: 1.63s\n",
      "161:\tlearn: 0.0016759\ttotal: 9.13s\tremaining: 1.58s\n",
      "162:\tlearn: 0.0016686\ttotal: 9.18s\tremaining: 1.52s\n",
      "163:\tlearn: 0.0016615\ttotal: 9.24s\tremaining: 1.46s\n",
      "164:\tlearn: 0.0016587\ttotal: 9.29s\tremaining: 1.41s\n",
      "165:\tlearn: 0.0016527\ttotal: 9.34s\tremaining: 1.35s\n",
      "166:\tlearn: 0.0016492\ttotal: 9.39s\tremaining: 1.29s\n",
      "167:\tlearn: 0.0016470\ttotal: 9.45s\tremaining: 1.24s\n",
      "168:\tlearn: 0.0016441\ttotal: 9.5s\tremaining: 1.18s\n",
      "169:\tlearn: 0.0016394\ttotal: 9.55s\tremaining: 1.12s\n",
      "170:\tlearn: 0.0016337\ttotal: 9.61s\tremaining: 1.07s\n",
      "171:\tlearn: 0.0016305\ttotal: 9.66s\tremaining: 1.01s\n",
      "172:\tlearn: 0.0016272\ttotal: 9.72s\tremaining: 955ms\n",
      "173:\tlearn: 0.0016221\ttotal: 9.78s\tremaining: 899ms\n",
      "174:\tlearn: 0.0016190\ttotal: 9.83s\tremaining: 843ms\n",
      "175:\tlearn: 0.0016130\ttotal: 9.89s\tremaining: 787ms\n",
      "176:\tlearn: 0.0016095\ttotal: 9.94s\tremaining: 730ms\n",
      "177:\tlearn: 0.0016063\ttotal: 9.99s\tremaining: 674ms\n",
      "178:\tlearn: 0.0016042\ttotal: 10.1s\tremaining: 618ms\n",
      "179:\tlearn: 0.0016016\ttotal: 10.1s\tremaining: 561ms\n",
      "180:\tlearn: 0.0015994\ttotal: 10.2s\tremaining: 505ms\n",
      "181:\tlearn: 0.0015953\ttotal: 10.2s\tremaining: 450ms\n",
      "182:\tlearn: 0.0015912\ttotal: 10.3s\tremaining: 393ms\n",
      "183:\tlearn: 0.0015872\ttotal: 10.3s\tremaining: 337ms\n",
      "184:\tlearn: 0.0015835\ttotal: 10.4s\tremaining: 281ms\n",
      "185:\tlearn: 0.0015771\ttotal: 10.4s\tremaining: 225ms\n",
      "186:\tlearn: 0.0015753\ttotal: 10.5s\tremaining: 168ms\n",
      "187:\tlearn: 0.0015663\ttotal: 10.6s\tremaining: 112ms\n",
      "188:\tlearn: 0.0015639\ttotal: 10.6s\tremaining: 56.1ms\n",
      "189:\tlearn: 0.0015567\ttotal: 10.7s\tremaining: 0us\n",
      "0:\tlearn: 0.6258809\ttotal: 68.1ms\tremaining: 12.9s\n",
      "1:\tlearn: 0.5675133\ttotal: 131ms\tremaining: 12.3s\n",
      "2:\tlearn: 0.5163993\ttotal: 198ms\tremaining: 12.3s\n",
      "3:\tlearn: 0.4711403\ttotal: 266ms\tremaining: 12.4s\n",
      "4:\tlearn: 0.4308722\ttotal: 328ms\tremaining: 12.1s\n",
      "5:\tlearn: 0.3948257\ttotal: 398ms\tremaining: 12.2s\n",
      "6:\tlearn: 0.3624360\ttotal: 463ms\tremaining: 12.1s\n",
      "7:\tlearn: 0.3332462\ttotal: 528ms\tremaining: 12s\n",
      "8:\tlearn: 0.3067712\ttotal: 597ms\tremaining: 12s\n",
      "9:\tlearn: 0.2827539\ttotal: 665ms\tremaining: 12s\n",
      "10:\tlearn: 0.2609666\ttotal: 730ms\tremaining: 11.9s\n",
      "11:\tlearn: 0.2409988\ttotal: 788ms\tremaining: 11.7s\n",
      "12:\tlearn: 0.2228036\ttotal: 850ms\tremaining: 11.6s\n",
      "13:\tlearn: 0.2061575\ttotal: 916ms\tremaining: 11.5s\n",
      "14:\tlearn: 0.1909383\ttotal: 977ms\tremaining: 11.4s\n",
      "15:\tlearn: 0.1769680\ttotal: 1.03s\tremaining: 11.3s\n",
      "16:\tlearn: 0.1641360\ttotal: 1.1s\tremaining: 11.2s\n",
      "17:\tlearn: 0.1522537\ttotal: 1.16s\tremaining: 11.1s\n",
      "18:\tlearn: 0.1413473\ttotal: 1.22s\tremaining: 11s\n",
      "19:\tlearn: 0.1312568\ttotal: 1.28s\tremaining: 10.9s\n",
      "20:\tlearn: 0.1219581\ttotal: 1.35s\tremaining: 10.8s\n",
      "21:\tlearn: 0.1133833\ttotal: 1.41s\tremaining: 10.8s\n",
      "22:\tlearn: 0.1054445\ttotal: 1.47s\tremaining: 10.7s\n",
      "23:\tlearn: 0.0981253\ttotal: 1.53s\tremaining: 10.6s\n",
      "24:\tlearn: 0.0913328\ttotal: 1.6s\tremaining: 10.6s\n",
      "25:\tlearn: 0.0850740\ttotal: 1.66s\tremaining: 10.5s\n",
      "26:\tlearn: 0.0792287\ttotal: 1.72s\tremaining: 10.4s\n",
      "27:\tlearn: 0.0738538\ttotal: 1.78s\tremaining: 10.3s\n",
      "28:\tlearn: 0.0688446\ttotal: 1.84s\tremaining: 10.2s\n",
      "29:\tlearn: 0.0642181\ttotal: 1.9s\tremaining: 10.1s\n",
      "30:\tlearn: 0.0599053\ttotal: 1.96s\tremaining: 10s\n",
      "31:\tlearn: 0.0559242\ttotal: 2.02s\tremaining: 9.98s\n",
      "32:\tlearn: 0.0522109\ttotal: 2.08s\tremaining: 9.92s\n",
      "33:\tlearn: 0.0487725\ttotal: 2.15s\tremaining: 9.85s\n",
      "34:\tlearn: 0.0455763\ttotal: 2.2s\tremaining: 9.76s\n",
      "35:\tlearn: 0.0425703\ttotal: 2.25s\tremaining: 9.65s\n",
      "36:\tlearn: 0.0398031\ttotal: 2.32s\tremaining: 9.59s\n",
      "37:\tlearn: 0.0372170\ttotal: 2.38s\tremaining: 9.51s\n",
      "38:\tlearn: 0.0348184\ttotal: 2.44s\tremaining: 9.44s\n",
      "39:\tlearn: 0.0326004\ttotal: 2.5s\tremaining: 9.37s\n",
      "40:\tlearn: 0.0305102\ttotal: 2.55s\tremaining: 9.27s\n",
      "41:\tlearn: 0.0285773\ttotal: 2.61s\tremaining: 9.2s\n",
      "42:\tlearn: 0.0267759\ttotal: 2.67s\tremaining: 9.13s\n",
      "43:\tlearn: 0.0251024\ttotal: 2.73s\tremaining: 9.04s\n",
      "44:\tlearn: 0.0235331\ttotal: 2.78s\tremaining: 8.96s\n",
      "45:\tlearn: 0.0220787\ttotal: 2.84s\tremaining: 8.88s\n",
      "46:\tlearn: 0.0207270\ttotal: 2.89s\tremaining: 8.81s\n",
      "47:\tlearn: 0.0194574\ttotal: 2.95s\tremaining: 8.73s\n",
      "48:\tlearn: 0.0182793\ttotal: 3.01s\tremaining: 8.65s\n",
      "49:\tlearn: 0.0171809\ttotal: 3.06s\tremaining: 8.57s\n",
      "50:\tlearn: 0.0161701\ttotal: 3.12s\tremaining: 8.5s\n",
      "51:\tlearn: 0.0152229\ttotal: 3.18s\tremaining: 8.45s\n",
      "52:\tlearn: 0.0143444\ttotal: 3.24s\tremaining: 8.38s\n",
      "53:\tlearn: 0.0135218\ttotal: 3.29s\tremaining: 8.3s\n",
      "54:\tlearn: 0.0127507\ttotal: 3.35s\tremaining: 8.22s\n",
      "55:\tlearn: 0.0120438\ttotal: 3.41s\tremaining: 8.15s\n",
      "56:\tlearn: 0.0113814\ttotal: 3.46s\tremaining: 8.09s\n",
      "57:\tlearn: 0.0107606\ttotal: 3.52s\tremaining: 8.01s\n",
      "58:\tlearn: 0.0101876\ttotal: 3.57s\tremaining: 7.93s\n",
      "59:\tlearn: 0.0096545\ttotal: 3.63s\tremaining: 7.86s\n",
      "60:\tlearn: 0.0091554\ttotal: 3.69s\tremaining: 7.79s\n",
      "61:\tlearn: 0.0086925\ttotal: 3.74s\tremaining: 7.73s\n",
      "62:\tlearn: 0.0082556\ttotal: 3.8s\tremaining: 7.67s\n",
      "63:\tlearn: 0.0078518\ttotal: 3.86s\tremaining: 7.59s\n",
      "64:\tlearn: 0.0074783\ttotal: 3.92s\tremaining: 7.53s\n",
      "65:\tlearn: 0.0071291\ttotal: 3.98s\tremaining: 7.47s\n",
      "66:\tlearn: 0.0068010\ttotal: 4.03s\tremaining: 7.4s\n",
      "67:\tlearn: 0.0065020\ttotal: 4.09s\tremaining: 7.33s\n",
      "68:\tlearn: 0.0062224\ttotal: 4.15s\tremaining: 7.28s\n",
      "69:\tlearn: 0.0059607\ttotal: 4.21s\tremaining: 7.22s\n",
      "70:\tlearn: 0.0057158\ttotal: 4.27s\tremaining: 7.16s\n",
      "71:\tlearn: 0.0054844\ttotal: 4.32s\tremaining: 7.09s\n",
      "72:\tlearn: 0.0052778\ttotal: 4.39s\tremaining: 7.04s\n",
      "73:\tlearn: 0.0050759\ttotal: 4.45s\tremaining: 6.97s\n",
      "74:\tlearn: 0.0048899\ttotal: 4.5s\tremaining: 6.91s\n",
      "75:\tlearn: 0.0047169\ttotal: 4.56s\tremaining: 6.84s\n",
      "76:\tlearn: 0.0045540\ttotal: 4.62s\tremaining: 6.78s\n",
      "77:\tlearn: 0.0044019\ttotal: 4.67s\tremaining: 6.71s\n",
      "78:\tlearn: 0.0042705\ttotal: 4.74s\tremaining: 6.65s\n",
      "79:\tlearn: 0.0041448\ttotal: 4.8s\tremaining: 6.59s\n",
      "80:\tlearn: 0.0040254\ttotal: 4.86s\tremaining: 6.54s\n",
      "81:\tlearn: 0.0039071\ttotal: 4.91s\tremaining: 6.47s\n",
      "82:\tlearn: 0.0038074\ttotal: 4.97s\tremaining: 6.41s\n",
      "83:\tlearn: 0.0037000\ttotal: 5.01s\tremaining: 6.33s\n",
      "84:\tlearn: 0.0036058\ttotal: 5.05s\tremaining: 6.25s\n",
      "85:\tlearn: 0.0035226\ttotal: 5.11s\tremaining: 6.18s\n",
      "86:\tlearn: 0.0034352\ttotal: 5.15s\tremaining: 6.1s\n",
      "87:\tlearn: 0.0033597\ttotal: 5.2s\tremaining: 6.03s\n",
      "88:\tlearn: 0.0032857\ttotal: 5.25s\tremaining: 5.96s\n",
      "89:\tlearn: 0.0032106\ttotal: 5.32s\tremaining: 5.91s\n",
      "90:\tlearn: 0.0031478\ttotal: 5.37s\tremaining: 5.84s\n",
      "91:\tlearn: 0.0030853\ttotal: 5.42s\tremaining: 5.78s\n",
      "92:\tlearn: 0.0030365\ttotal: 5.48s\tremaining: 5.71s\n",
      "93:\tlearn: 0.0029894\ttotal: 5.53s\tremaining: 5.65s\n",
      "94:\tlearn: 0.0029324\ttotal: 5.59s\tremaining: 5.59s\n",
      "95:\tlearn: 0.0028912\ttotal: 5.65s\tremaining: 5.53s\n",
      "96:\tlearn: 0.0028537\ttotal: 5.7s\tremaining: 5.46s\n",
      "97:\tlearn: 0.0028178\ttotal: 5.75s\tremaining: 5.4s\n",
      "98:\tlearn: 0.0027832\ttotal: 5.81s\tremaining: 5.34s\n",
      "99:\tlearn: 0.0027465\ttotal: 5.86s\tremaining: 5.27s\n",
      "100:\tlearn: 0.0027076\ttotal: 5.92s\tremaining: 5.21s\n",
      "101:\tlearn: 0.0026741\ttotal: 5.97s\tremaining: 5.16s\n",
      "102:\tlearn: 0.0026372\ttotal: 6.03s\tremaining: 5.1s\n",
      "103:\tlearn: 0.0026152\ttotal: 6.08s\tremaining: 5.03s\n",
      "104:\tlearn: 0.0025842\ttotal: 6.16s\tremaining: 4.98s\n",
      "105:\tlearn: 0.0025530\ttotal: 6.22s\tremaining: 4.93s\n",
      "106:\tlearn: 0.0025273\ttotal: 6.28s\tremaining: 4.87s\n",
      "107:\tlearn: 0.0024991\ttotal: 6.33s\tremaining: 4.81s\n",
      "108:\tlearn: 0.0024761\ttotal: 6.39s\tremaining: 4.75s\n",
      "109:\tlearn: 0.0024610\ttotal: 6.45s\tremaining: 4.69s\n",
      "110:\tlearn: 0.0024381\ttotal: 6.5s\tremaining: 4.63s\n",
      "111:\tlearn: 0.0024216\ttotal: 6.55s\tremaining: 4.56s\n",
      "112:\tlearn: 0.0024003\ttotal: 6.61s\tremaining: 4.51s\n",
      "113:\tlearn: 0.0023777\ttotal: 6.67s\tremaining: 4.45s\n",
      "114:\tlearn: 0.0023585\ttotal: 6.73s\tremaining: 4.39s\n",
      "115:\tlearn: 0.0023426\ttotal: 6.79s\tremaining: 4.33s\n",
      "116:\tlearn: 0.0023329\ttotal: 6.84s\tremaining: 4.27s\n",
      "117:\tlearn: 0.0023117\ttotal: 6.9s\tremaining: 4.21s\n",
      "118:\tlearn: 0.0022924\ttotal: 6.96s\tremaining: 4.15s\n",
      "119:\tlearn: 0.0022750\ttotal: 7.01s\tremaining: 4.09s\n",
      "120:\tlearn: 0.0022665\ttotal: 7.06s\tremaining: 4.03s\n",
      "121:\tlearn: 0.0022472\ttotal: 7.12s\tremaining: 3.97s\n",
      "122:\tlearn: 0.0022270\ttotal: 7.16s\tremaining: 3.9s\n",
      "123:\tlearn: 0.0022219\ttotal: 7.21s\tremaining: 3.84s\n",
      "124:\tlearn: 0.0022069\ttotal: 7.27s\tremaining: 3.78s\n",
      "125:\tlearn: 0.0021931\ttotal: 7.32s\tremaining: 3.72s\n",
      "126:\tlearn: 0.0021852\ttotal: 7.38s\tremaining: 3.66s\n",
      "127:\tlearn: 0.0021704\ttotal: 7.43s\tremaining: 3.6s\n",
      "128:\tlearn: 0.0021539\ttotal: 7.47s\tremaining: 3.54s\n",
      "129:\tlearn: 0.0021407\ttotal: 7.54s\tremaining: 3.48s\n",
      "130:\tlearn: 0.0021280\ttotal: 7.6s\tremaining: 3.42s\n",
      "131:\tlearn: 0.0021164\ttotal: 7.66s\tremaining: 3.36s\n",
      "132:\tlearn: 0.0021049\ttotal: 7.71s\tremaining: 3.31s\n",
      "133:\tlearn: 0.0021000\ttotal: 7.77s\tremaining: 3.25s\n",
      "134:\tlearn: 0.0020912\ttotal: 7.82s\tremaining: 3.19s\n",
      "135:\tlearn: 0.0020835\ttotal: 7.88s\tremaining: 3.13s\n",
      "136:\tlearn: 0.0020763\ttotal: 7.93s\tremaining: 3.07s\n",
      "137:\tlearn: 0.0020675\ttotal: 7.99s\tremaining: 3.01s\n",
      "138:\tlearn: 0.0020574\ttotal: 8.05s\tremaining: 2.95s\n",
      "139:\tlearn: 0.0020511\ttotal: 8.1s\tremaining: 2.89s\n",
      "140:\tlearn: 0.0020440\ttotal: 8.16s\tremaining: 2.84s\n",
      "141:\tlearn: 0.0020360\ttotal: 8.22s\tremaining: 2.78s\n",
      "142:\tlearn: 0.0020282\ttotal: 8.28s\tremaining: 2.72s\n",
      "143:\tlearn: 0.0020178\ttotal: 8.34s\tremaining: 2.66s\n",
      "144:\tlearn: 0.0020138\ttotal: 8.39s\tremaining: 2.6s\n",
      "145:\tlearn: 0.0020045\ttotal: 8.45s\tremaining: 2.55s\n",
      "146:\tlearn: 0.0020011\ttotal: 8.51s\tremaining: 2.49s\n",
      "147:\tlearn: 0.0019915\ttotal: 8.56s\tremaining: 2.43s\n",
      "148:\tlearn: 0.0019860\ttotal: 8.62s\tremaining: 2.37s\n",
      "149:\tlearn: 0.0019812\ttotal: 8.68s\tremaining: 2.31s\n",
      "150:\tlearn: 0.0019776\ttotal: 8.73s\tremaining: 2.26s\n",
      "151:\tlearn: 0.0019724\ttotal: 8.79s\tremaining: 2.2s\n",
      "152:\tlearn: 0.0019629\ttotal: 8.85s\tremaining: 2.14s\n",
      "153:\tlearn: 0.0019594\ttotal: 8.9s\tremaining: 2.08s\n",
      "154:\tlearn: 0.0019507\ttotal: 8.96s\tremaining: 2.02s\n",
      "155:\tlearn: 0.0019462\ttotal: 9.01s\tremaining: 1.96s\n",
      "156:\tlearn: 0.0019408\ttotal: 9.05s\tremaining: 1.9s\n",
      "157:\tlearn: 0.0019343\ttotal: 9.09s\tremaining: 1.84s\n",
      "158:\tlearn: 0.0019279\ttotal: 9.12s\tremaining: 1.78s\n",
      "159:\tlearn: 0.0019184\ttotal: 9.16s\tremaining: 1.72s\n",
      "160:\tlearn: 0.0019079\ttotal: 9.21s\tremaining: 1.66s\n",
      "161:\tlearn: 0.0019033\ttotal: 9.24s\tremaining: 1.6s\n",
      "162:\tlearn: 0.0018969\ttotal: 9.28s\tremaining: 1.54s\n",
      "163:\tlearn: 0.0018859\ttotal: 9.32s\tremaining: 1.48s\n",
      "164:\tlearn: 0.0018821\ttotal: 9.37s\tremaining: 1.42s\n",
      "165:\tlearn: 0.0018783\ttotal: 9.42s\tremaining: 1.36s\n",
      "166:\tlearn: 0.0018765\ttotal: 9.48s\tremaining: 1.3s\n",
      "167:\tlearn: 0.0018748\ttotal: 9.53s\tremaining: 1.25s\n",
      "168:\tlearn: 0.0018705\ttotal: 9.56s\tremaining: 1.19s\n",
      "169:\tlearn: 0.0018661\ttotal: 9.62s\tremaining: 1.13s\n",
      "170:\tlearn: 0.0018644\ttotal: 9.67s\tremaining: 1.07s\n",
      "171:\tlearn: 0.0018611\ttotal: 9.73s\tremaining: 1.02s\n",
      "172:\tlearn: 0.0018547\ttotal: 9.79s\tremaining: 962ms\n",
      "173:\tlearn: 0.0018487\ttotal: 9.83s\tremaining: 904ms\n",
      "174:\tlearn: 0.0018459\ttotal: 9.87s\tremaining: 846ms\n",
      "175:\tlearn: 0.0018439\ttotal: 9.91s\tremaining: 788ms\n",
      "176:\tlearn: 0.0018412\ttotal: 9.94s\tremaining: 730ms\n",
      "177:\tlearn: 0.0018355\ttotal: 9.98s\tremaining: 673ms\n",
      "178:\tlearn: 0.0018333\ttotal: 10s\tremaining: 616ms\n",
      "179:\tlearn: 0.0018262\ttotal: 10.1s\tremaining: 559ms\n",
      "180:\tlearn: 0.0018238\ttotal: 10.1s\tremaining: 502ms\n",
      "181:\tlearn: 0.0018210\ttotal: 10.2s\tremaining: 446ms\n",
      "182:\tlearn: 0.0018192\ttotal: 10.2s\tremaining: 391ms\n",
      "183:\tlearn: 0.0018158\ttotal: 10.3s\tremaining: 335ms\n",
      "184:\tlearn: 0.0018123\ttotal: 10.3s\tremaining: 279ms\n",
      "185:\tlearn: 0.0018084\ttotal: 10.4s\tremaining: 223ms\n",
      "186:\tlearn: 0.0018063\ttotal: 10.4s\tremaining: 167ms\n",
      "187:\tlearn: 0.0018043\ttotal: 10.5s\tremaining: 112ms\n",
      "188:\tlearn: 0.0018026\ttotal: 10.5s\tremaining: 55.8ms\n",
      "189:\tlearn: 0.0018007\ttotal: 10.6s\tremaining: 0us\n",
      "0:\tlearn: 0.6258977\ttotal: 45.9ms\tremaining: 8.68s\n",
      "1:\tlearn: 0.5675195\ttotal: 108ms\tremaining: 10.2s\n",
      "2:\tlearn: 0.5163914\ttotal: 173ms\tremaining: 10.8s\n",
      "3:\tlearn: 0.4711202\ttotal: 238ms\tremaining: 11.1s\n",
      "4:\tlearn: 0.4308462\ttotal: 300ms\tremaining: 11.1s\n",
      "5:\tlearn: 0.3947920\ttotal: 363ms\tremaining: 11.1s\n",
      "6:\tlearn: 0.3623934\ttotal: 428ms\tremaining: 11.2s\n",
      "7:\tlearn: 0.3331847\ttotal: 492ms\tremaining: 11.2s\n",
      "8:\tlearn: 0.3067072\ttotal: 556ms\tremaining: 11.2s\n",
      "9:\tlearn: 0.2827090\ttotal: 618ms\tremaining: 11.1s\n",
      "10:\tlearn: 0.2609132\ttotal: 686ms\tremaining: 11.2s\n",
      "11:\tlearn: 0.2409392\ttotal: 747ms\tremaining: 11.1s\n",
      "12:\tlearn: 0.2227385\ttotal: 812ms\tremaining: 11s\n",
      "13:\tlearn: 0.2060894\ttotal: 875ms\tremaining: 11s\n",
      "14:\tlearn: 0.1908663\ttotal: 935ms\tremaining: 10.9s\n",
      "15:\tlearn: 0.1768940\ttotal: 999ms\tremaining: 10.9s\n",
      "16:\tlearn: 0.1640545\ttotal: 1.06s\tremaining: 10.8s\n",
      "17:\tlearn: 0.1521889\ttotal: 1.12s\tremaining: 10.7s\n",
      "18:\tlearn: 0.1413258\ttotal: 1.18s\tremaining: 10.6s\n",
      "19:\tlearn: 0.1312310\ttotal: 1.24s\tremaining: 10.5s\n",
      "20:\tlearn: 0.1219436\ttotal: 1.3s\tremaining: 10.5s\n",
      "21:\tlearn: 0.1133600\ttotal: 1.36s\tremaining: 10.4s\n",
      "22:\tlearn: 0.1054182\ttotal: 1.42s\tremaining: 10.3s\n",
      "23:\tlearn: 0.0980968\ttotal: 1.48s\tremaining: 10.3s\n",
      "24:\tlearn: 0.0913003\ttotal: 1.54s\tremaining: 10.2s\n",
      "25:\tlearn: 0.0850335\ttotal: 1.61s\tremaining: 10.1s\n",
      "26:\tlearn: 0.0791858\ttotal: 1.67s\tremaining: 10.1s\n",
      "27:\tlearn: 0.0738050\ttotal: 1.73s\tremaining: 9.99s\n",
      "28:\tlearn: 0.0687940\ttotal: 1.79s\tremaining: 9.92s\n",
      "29:\tlearn: 0.0641953\ttotal: 1.85s\tremaining: 9.89s\n",
      "30:\tlearn: 0.0599023\ttotal: 1.92s\tremaining: 9.84s\n",
      "31:\tlearn: 0.0559318\ttotal: 1.98s\tremaining: 9.79s\n",
      "32:\tlearn: 0.0522141\ttotal: 2.03s\tremaining: 9.67s\n",
      "33:\tlearn: 0.0487504\ttotal: 2.08s\tremaining: 9.52s\n",
      "34:\tlearn: 0.0455467\ttotal: 2.11s\tremaining: 9.36s\n",
      "35:\tlearn: 0.0425551\ttotal: 2.15s\tremaining: 9.22s\n",
      "36:\tlearn: 0.0397853\ttotal: 2.2s\tremaining: 9.09s\n",
      "37:\tlearn: 0.0371969\ttotal: 2.24s\tremaining: 8.95s\n",
      "38:\tlearn: 0.0347950\ttotal: 2.28s\tremaining: 8.83s\n",
      "39:\tlearn: 0.0325620\ttotal: 2.32s\tremaining: 8.7s\n",
      "40:\tlearn: 0.0304734\ttotal: 2.37s\tremaining: 8.61s\n",
      "41:\tlearn: 0.0285442\ttotal: 2.43s\tremaining: 8.55s\n",
      "42:\tlearn: 0.0267331\ttotal: 2.48s\tremaining: 8.48s\n",
      "43:\tlearn: 0.0250498\ttotal: 2.54s\tremaining: 8.42s\n",
      "44:\tlearn: 0.0234931\ttotal: 2.6s\tremaining: 8.36s\n",
      "45:\tlearn: 0.0220382\ttotal: 2.65s\tremaining: 8.3s\n",
      "46:\tlearn: 0.0206979\ttotal: 2.71s\tremaining: 8.24s\n",
      "47:\tlearn: 0.0194278\ttotal: 2.76s\tremaining: 8.18s\n",
      "48:\tlearn: 0.0182577\ttotal: 2.82s\tremaining: 8.13s\n",
      "49:\tlearn: 0.0171610\ttotal: 2.88s\tremaining: 8.07s\n",
      "50:\tlearn: 0.0161372\ttotal: 2.94s\tremaining: 8s\n",
      "51:\tlearn: 0.0151930\ttotal: 2.99s\tremaining: 7.95s\n",
      "52:\tlearn: 0.0143075\ttotal: 3.05s\tremaining: 7.88s\n",
      "53:\tlearn: 0.0134848\ttotal: 3.1s\tremaining: 7.82s\n",
      "54:\tlearn: 0.0127196\ttotal: 3.16s\tremaining: 7.77s\n",
      "55:\tlearn: 0.0120021\ttotal: 3.22s\tremaining: 7.71s\n",
      "56:\tlearn: 0.0113329\ttotal: 3.27s\tremaining: 7.64s\n",
      "57:\tlearn: 0.0107108\ttotal: 3.33s\tremaining: 7.58s\n",
      "58:\tlearn: 0.0101352\ttotal: 3.38s\tremaining: 7.51s\n",
      "59:\tlearn: 0.0096009\ttotal: 3.44s\tremaining: 7.45s\n",
      "60:\tlearn: 0.0090995\ttotal: 3.5s\tremaining: 7.4s\n",
      "61:\tlearn: 0.0086298\ttotal: 3.56s\tremaining: 7.34s\n",
      "62:\tlearn: 0.0081849\ttotal: 3.61s\tremaining: 7.27s\n",
      "63:\tlearn: 0.0077733\ttotal: 3.66s\tremaining: 7.21s\n",
      "64:\tlearn: 0.0073947\ttotal: 3.71s\tremaining: 7.14s\n",
      "65:\tlearn: 0.0070442\ttotal: 3.77s\tremaining: 7.09s\n",
      "66:\tlearn: 0.0067131\ttotal: 3.83s\tremaining: 7.03s\n",
      "67:\tlearn: 0.0064109\ttotal: 3.89s\tremaining: 6.97s\n",
      "68:\tlearn: 0.0061274\ttotal: 3.94s\tremaining: 6.91s\n",
      "69:\tlearn: 0.0058585\ttotal: 4s\tremaining: 6.85s\n",
      "70:\tlearn: 0.0056142\ttotal: 4.05s\tremaining: 6.79s\n",
      "71:\tlearn: 0.0053872\ttotal: 4.1s\tremaining: 6.72s\n",
      "72:\tlearn: 0.0051731\ttotal: 4.16s\tremaining: 6.67s\n",
      "73:\tlearn: 0.0049764\ttotal: 4.21s\tremaining: 6.61s\n",
      "74:\tlearn: 0.0047830\ttotal: 4.27s\tremaining: 6.54s\n",
      "75:\tlearn: 0.0046084\ttotal: 4.32s\tremaining: 6.48s\n",
      "76:\tlearn: 0.0044507\ttotal: 4.38s\tremaining: 6.43s\n",
      "77:\tlearn: 0.0042996\ttotal: 4.44s\tremaining: 6.37s\n",
      "78:\tlearn: 0.0041617\ttotal: 4.5s\tremaining: 6.32s\n",
      "79:\tlearn: 0.0040295\ttotal: 4.55s\tremaining: 6.26s\n",
      "80:\tlearn: 0.0038994\ttotal: 4.61s\tremaining: 6.21s\n",
      "81:\tlearn: 0.0037904\ttotal: 4.67s\tremaining: 6.15s\n",
      "82:\tlearn: 0.0036797\ttotal: 4.73s\tremaining: 6.1s\n",
      "83:\tlearn: 0.0035716\ttotal: 4.79s\tremaining: 6.04s\n",
      "84:\tlearn: 0.0034787\ttotal: 4.84s\tremaining: 5.98s\n",
      "85:\tlearn: 0.0033887\ttotal: 4.9s\tremaining: 5.92s\n",
      "86:\tlearn: 0.0033023\ttotal: 4.96s\tremaining: 5.87s\n",
      "87:\tlearn: 0.0032279\ttotal: 5.02s\tremaining: 5.81s\n",
      "88:\tlearn: 0.0031610\ttotal: 5.07s\tremaining: 5.75s\n",
      "89:\tlearn: 0.0030947\ttotal: 5.13s\tremaining: 5.7s\n",
      "90:\tlearn: 0.0030304\ttotal: 5.18s\tremaining: 5.63s\n",
      "91:\tlearn: 0.0029711\ttotal: 5.24s\tremaining: 5.58s\n",
      "92:\tlearn: 0.0029224\ttotal: 5.3s\tremaining: 5.52s\n",
      "93:\tlearn: 0.0028706\ttotal: 5.35s\tremaining: 5.47s\n",
      "94:\tlearn: 0.0028212\ttotal: 5.4s\tremaining: 5.4s\n",
      "95:\tlearn: 0.0027784\ttotal: 5.46s\tremaining: 5.35s\n",
      "96:\tlearn: 0.0027366\ttotal: 5.52s\tremaining: 5.29s\n",
      "97:\tlearn: 0.0026945\ttotal: 5.57s\tremaining: 5.23s\n",
      "98:\tlearn: 0.0026605\ttotal: 5.63s\tremaining: 5.17s\n",
      "99:\tlearn: 0.0026286\ttotal: 5.69s\tremaining: 5.12s\n",
      "100:\tlearn: 0.0025972\ttotal: 5.74s\tremaining: 5.06s\n",
      "101:\tlearn: 0.0025596\ttotal: 5.8s\tremaining: 5.01s\n",
      "102:\tlearn: 0.0025351\ttotal: 5.86s\tremaining: 4.95s\n",
      "103:\tlearn: 0.0025121\ttotal: 5.92s\tremaining: 4.89s\n",
      "104:\tlearn: 0.0024849\ttotal: 5.97s\tremaining: 4.83s\n",
      "105:\tlearn: 0.0024637\ttotal: 6.02s\tremaining: 4.77s\n",
      "106:\tlearn: 0.0024336\ttotal: 6.08s\tremaining: 4.72s\n",
      "107:\tlearn: 0.0024093\ttotal: 6.14s\tremaining: 4.66s\n",
      "108:\tlearn: 0.0023934\ttotal: 6.19s\tremaining: 4.6s\n",
      "109:\tlearn: 0.0023720\ttotal: 6.24s\tremaining: 4.54s\n",
      "110:\tlearn: 0.0023586\ttotal: 6.3s\tremaining: 4.49s\n",
      "111:\tlearn: 0.0023392\ttotal: 6.36s\tremaining: 4.43s\n",
      "112:\tlearn: 0.0023223\ttotal: 6.41s\tremaining: 4.37s\n",
      "113:\tlearn: 0.0023005\ttotal: 6.47s\tremaining: 4.31s\n",
      "114:\tlearn: 0.0022724\ttotal: 6.53s\tremaining: 4.26s\n",
      "115:\tlearn: 0.0022482\ttotal: 6.59s\tremaining: 4.21s\n",
      "116:\tlearn: 0.0022230\ttotal: 6.66s\tremaining: 4.15s\n",
      "117:\tlearn: 0.0022025\ttotal: 6.72s\tremaining: 4.1s\n",
      "118:\tlearn: 0.0021927\ttotal: 6.77s\tremaining: 4.04s\n",
      "119:\tlearn: 0.0021767\ttotal: 6.84s\tremaining: 3.99s\n",
      "120:\tlearn: 0.0021704\ttotal: 6.89s\tremaining: 3.93s\n",
      "121:\tlearn: 0.0021557\ttotal: 6.94s\tremaining: 3.87s\n",
      "122:\tlearn: 0.0021447\ttotal: 6.99s\tremaining: 3.81s\n",
      "123:\tlearn: 0.0021244\ttotal: 7.04s\tremaining: 3.74s\n",
      "124:\tlearn: 0.0021090\ttotal: 7.09s\tremaining: 3.69s\n",
      "125:\tlearn: 0.0020968\ttotal: 7.15s\tremaining: 3.63s\n",
      "126:\tlearn: 0.0020860\ttotal: 7.22s\tremaining: 3.58s\n",
      "127:\tlearn: 0.0020739\ttotal: 7.27s\tremaining: 3.52s\n",
      "128:\tlearn: 0.0020631\ttotal: 7.32s\tremaining: 3.46s\n",
      "129:\tlearn: 0.0020472\ttotal: 7.38s\tremaining: 3.41s\n",
      "130:\tlearn: 0.0020349\ttotal: 7.44s\tremaining: 3.35s\n",
      "131:\tlearn: 0.0020220\ttotal: 7.5s\tremaining: 3.3s\n",
      "132:\tlearn: 0.0020127\ttotal: 7.56s\tremaining: 3.24s\n",
      "133:\tlearn: 0.0020025\ttotal: 7.62s\tremaining: 3.18s\n",
      "134:\tlearn: 0.0019912\ttotal: 7.68s\tremaining: 3.13s\n",
      "135:\tlearn: 0.0019777\ttotal: 7.74s\tremaining: 3.07s\n",
      "136:\tlearn: 0.0019711\ttotal: 7.8s\tremaining: 3.02s\n",
      "137:\tlearn: 0.0019604\ttotal: 7.86s\tremaining: 2.96s\n",
      "138:\tlearn: 0.0019495\ttotal: 7.92s\tremaining: 2.9s\n",
      "139:\tlearn: 0.0019401\ttotal: 7.97s\tremaining: 2.85s\n",
      "140:\tlearn: 0.0019296\ttotal: 8.02s\tremaining: 2.79s\n",
      "141:\tlearn: 0.0019250\ttotal: 8.05s\tremaining: 2.72s\n",
      "142:\tlearn: 0.0019185\ttotal: 8.11s\tremaining: 2.66s\n",
      "143:\tlearn: 0.0019092\ttotal: 8.17s\tremaining: 2.61s\n",
      "144:\tlearn: 0.0019036\ttotal: 8.22s\tremaining: 2.55s\n",
      "145:\tlearn: 0.0018973\ttotal: 8.28s\tremaining: 2.49s\n",
      "146:\tlearn: 0.0018889\ttotal: 8.33s\tremaining: 2.44s\n",
      "147:\tlearn: 0.0018800\ttotal: 8.39s\tremaining: 2.38s\n",
      "148:\tlearn: 0.0018768\ttotal: 8.44s\tremaining: 2.32s\n",
      "149:\tlearn: 0.0018721\ttotal: 8.5s\tremaining: 2.27s\n",
      "150:\tlearn: 0.0018690\ttotal: 8.55s\tremaining: 2.21s\n",
      "151:\tlearn: 0.0018587\ttotal: 8.61s\tremaining: 2.15s\n",
      "152:\tlearn: 0.0018540\ttotal: 8.66s\tremaining: 2.1s\n",
      "153:\tlearn: 0.0018477\ttotal: 8.71s\tremaining: 2.04s\n",
      "154:\tlearn: 0.0018394\ttotal: 8.77s\tremaining: 1.98s\n",
      "155:\tlearn: 0.0018336\ttotal: 8.82s\tremaining: 1.92s\n",
      "156:\tlearn: 0.0018268\ttotal: 8.88s\tremaining: 1.87s\n",
      "157:\tlearn: 0.0018222\ttotal: 8.94s\tremaining: 1.81s\n",
      "158:\tlearn: 0.0018190\ttotal: 8.99s\tremaining: 1.75s\n",
      "159:\tlearn: 0.0018144\ttotal: 9.05s\tremaining: 1.7s\n",
      "160:\tlearn: 0.0018123\ttotal: 9.1s\tremaining: 1.64s\n",
      "161:\tlearn: 0.0018068\ttotal: 9.15s\tremaining: 1.58s\n",
      "162:\tlearn: 0.0018005\ttotal: 9.21s\tremaining: 1.53s\n",
      "163:\tlearn: 0.0017936\ttotal: 9.28s\tremaining: 1.47s\n",
      "164:\tlearn: 0.0017897\ttotal: 9.33s\tremaining: 1.41s\n",
      "165:\tlearn: 0.0017873\ttotal: 9.38s\tremaining: 1.36s\n",
      "166:\tlearn: 0.0017797\ttotal: 9.44s\tremaining: 1.3s\n",
      "167:\tlearn: 0.0017711\ttotal: 9.5s\tremaining: 1.24s\n",
      "168:\tlearn: 0.0017686\ttotal: 9.55s\tremaining: 1.19s\n",
      "169:\tlearn: 0.0017649\ttotal: 9.61s\tremaining: 1.13s\n",
      "170:\tlearn: 0.0017624\ttotal: 9.66s\tremaining: 1.07s\n",
      "171:\tlearn: 0.0017591\ttotal: 9.71s\tremaining: 1.02s\n",
      "172:\tlearn: 0.0017548\ttotal: 9.77s\tremaining: 960ms\n",
      "173:\tlearn: 0.0017486\ttotal: 9.82s\tremaining: 903ms\n",
      "174:\tlearn: 0.0017441\ttotal: 9.88s\tremaining: 847ms\n",
      "175:\tlearn: 0.0017406\ttotal: 9.93s\tremaining: 790ms\n",
      "176:\tlearn: 0.0017369\ttotal: 9.98s\tremaining: 733ms\n",
      "177:\tlearn: 0.0017319\ttotal: 10s\tremaining: 677ms\n",
      "178:\tlearn: 0.0017288\ttotal: 10.1s\tremaining: 620ms\n",
      "179:\tlearn: 0.0017232\ttotal: 10.1s\tremaining: 564ms\n",
      "180:\tlearn: 0.0017188\ttotal: 10.2s\tremaining: 507ms\n",
      "181:\tlearn: 0.0017156\ttotal: 10.3s\tremaining: 451ms\n",
      "182:\tlearn: 0.0017108\ttotal: 10.3s\tremaining: 395ms\n",
      "183:\tlearn: 0.0017092\ttotal: 10.4s\tremaining: 338ms\n",
      "184:\tlearn: 0.0017065\ttotal: 10.4s\tremaining: 282ms\n",
      "185:\tlearn: 0.0017043\ttotal: 10.5s\tremaining: 225ms\n",
      "186:\tlearn: 0.0016996\ttotal: 10.5s\tremaining: 169ms\n",
      "187:\tlearn: 0.0016951\ttotal: 10.6s\tremaining: 113ms\n",
      "188:\tlearn: 0.0016914\ttotal: 10.6s\tremaining: 56.3ms\n",
      "189:\tlearn: 0.0016877\ttotal: 10.7s\tremaining: 0us\n",
      "Paired t-test between Stacking and Random Forest:\n",
      "t-statistic = -3.7506, p-value = 0.0199\n",
      "Paired t-test between Stacking and Random Forest: t-statistic = -3.7506, p-value = 0.0199\n",
      "Paired t-test between Stacking and SVM: t-statistic = 3.6147, p-value = 0.0225\n",
      "Paired t-test between Stacking and Logistic Regression: t-statistic = 5.6783, p-value = 0.0047\n",
      "Paired t-test between Stacking and Decision Tree: t-statistic = 1.5217, p-value = 0.2027\n",
      "Paired t-test between Stacking and XGBoost: t-statistic = -2.5573, p-value = 0.0628\n",
      "Paired t-test between Stacking and CatBoost: t-statistic = -3.2461, p-value = 0.0315\n",
      "95% CI of F1 difference (Stacking - Random Forest): (-0.04500662689665477, -0.0067174794611843495)\n",
      "95% CI of F1 difference (Stacking - SVM): (0.008334052739946692, 0.06353842072619371)\n",
      "95% CI of F1 difference (Stacking - Logistic Regression): (0.05925766619006016, 0.172650609310888)\n",
      "95% CI of F1 difference (Stacking - Decision Tree): (-0.01606866395226563, 0.05504159662374102)\n",
      "95% CI of F1 difference (Stacking - XGBoost): (-0.02605772160389088, 0.0010705936911795215)\n",
      "95% CI of F1 difference (Stacking - CatBoost): (-0.042253848382105344, -0.003295035969870897)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from scipy.stats import ttest_rel\n",
    "\n",
    "# Define classifiers\n",
    "classifiers = {\n",
    "    'Random Forest': rf_classifier,\n",
    "    'SVM': svm_classifier,\n",
    "    'Logistic Regression': lr_classifier,\n",
    "    'Decision Tree': dt_classifier,\n",
    "    'XGBoost': xgb_classifier,\n",
    "    'CatBoost': catboost_classifier,\n",
    "    'Stacking': stacking_classifier\n",
    "}\n",
    "\n",
    "# Initialize storage\n",
    "f1_scores = {name: [] for name in classifiers.keys()}\n",
    "\n",
    "# 5-Fold Stratified Cross-Validation\n",
    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "for train_index, test_index in skf.split(X, y):\n",
    "    X_fold_train, X_fold_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_fold_train, y_fold_test = y.iloc[train_index], y.iloc[test_index]\n",
    "    \n",
    "    # Scaling\n",
    "    scaler = StandardScaler()\n",
    "    X_fold_train = scaler.fit_transform(X_fold_train)\n",
    "    X_fold_test = scaler.transform(X_fold_test)\n",
    "    \n",
    "    for name, clf in classifiers.items():\n",
    "        clf.fit(X_fold_train, y_fold_train)\n",
    "        y_pred = clf.predict(X_fold_test)\n",
    "        f1 = f1_score(y_fold_test, y_pred)\n",
    "        f1_scores[name].append(f1)\n",
    "\n",
    "# Convert to arrays\n",
    "for name in f1_scores:\n",
    "    f1_scores[name] = np.array(f1_scores[name])\n",
    "\n",
    "# Example: Paired t-test between Stacking and Random Forest\n",
    "t_stat, p_value = ttest_rel(f1_scores['Stacking'], f1_scores['Random Forest'])\n",
    "\n",
    "print(\"Paired t-test between Stacking and Random Forest:\")\n",
    "print(f\"t-statistic = {t_stat:.4f}, p-value = {p_value:.4f}\")\n",
    "\n",
    "# Repeat for other models\n",
    "for model_name in classifiers.keys():\n",
    "    if model_name != 'Stacking':\n",
    "        t_stat, p_value = ttest_rel(f1_scores['Stacking'], f1_scores[model_name])\n",
    "        print(f\"Paired t-test between Stacking and {model_name}: t-statistic = {t_stat:.4f}, p-value = {p_value:.4f}\")\n",
    "\n",
    "# Optional: print 95% confidence interval of difference\n",
    "import scipy.stats as stats\n",
    "\n",
    "def confidence_interval(data1, data2, confidence=0.95):\n",
    "    differences = data1 - data2\n",
    "    mean_diff = np.mean(differences)\n",
    "    se_diff = stats.sem(differences)\n",
    "    ci = stats.t.interval(confidence, len(differences)-1, loc=mean_diff, scale=se_diff)\n",
    "    return ci\n",
    "\n",
    "for model_name in classifiers.keys():\n",
    "    if model_name != 'Stacking':\n",
    "        ci = confidence_interval(f1_scores['Stacking'], f1_scores[model_name])\n",
    "        print(f\"95% CI of F1 difference (Stacking - {model_name}): {ci}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.5129918\ttotal: 50.7ms\tremaining: 50.6s\n",
      "1:\tlearn: 0.3735797\ttotal: 112ms\tremaining: 55.8s\n",
      "2:\tlearn: 0.2738084\ttotal: 158ms\tremaining: 52.5s\n",
      "3:\tlearn: 0.1971949\ttotal: 202ms\tremaining: 50.4s\n",
      "4:\tlearn: 0.1451994\ttotal: 237ms\tremaining: 47.2s\n",
      "5:\tlearn: 0.1070354\ttotal: 281ms\tremaining: 46.5s\n",
      "6:\tlearn: 0.0802669\ttotal: 317ms\tremaining: 44.9s\n",
      "7:\tlearn: 0.0605938\ttotal: 352ms\tremaining: 43.7s\n",
      "8:\tlearn: 0.0462398\ttotal: 392ms\tremaining: 43.1s\n",
      "9:\tlearn: 0.0358447\ttotal: 431ms\tremaining: 42.7s\n",
      "10:\tlearn: 0.0284430\ttotal: 466ms\tremaining: 41.9s\n",
      "11:\tlearn: 0.0227888\ttotal: 505ms\tremaining: 41.6s\n",
      "12:\tlearn: 0.0185375\ttotal: 543ms\tremaining: 41.2s\n",
      "13:\tlearn: 0.0151407\ttotal: 583ms\tremaining: 41s\n",
      "14:\tlearn: 0.0126837\ttotal: 623ms\tremaining: 40.9s\n",
      "15:\tlearn: 0.0108550\ttotal: 655ms\tremaining: 40.3s\n",
      "16:\tlearn: 0.0093500\ttotal: 687ms\tremaining: 39.7s\n",
      "17:\tlearn: 0.0082604\ttotal: 718ms\tremaining: 39.1s\n",
      "18:\tlearn: 0.0073103\ttotal: 749ms\tremaining: 38.7s\n",
      "19:\tlearn: 0.0065736\ttotal: 780ms\tremaining: 38.2s\n",
      "20:\tlearn: 0.0059274\ttotal: 812ms\tremaining: 37.8s\n",
      "21:\tlearn: 0.0054177\ttotal: 842ms\tremaining: 37.4s\n",
      "22:\tlearn: 0.0049423\ttotal: 873ms\tremaining: 37.1s\n",
      "23:\tlearn: 0.0046140\ttotal: 905ms\tremaining: 36.8s\n",
      "24:\tlearn: 0.0042737\ttotal: 936ms\tremaining: 36.5s\n",
      "25:\tlearn: 0.0040245\ttotal: 967ms\tremaining: 36.2s\n",
      "26:\tlearn: 0.0037678\ttotal: 1000ms\tremaining: 36s\n",
      "27:\tlearn: 0.0035910\ttotal: 1.03s\tremaining: 35.8s\n",
      "28:\tlearn: 0.0034379\ttotal: 1.06s\tremaining: 35.6s\n",
      "29:\tlearn: 0.0033071\ttotal: 1.1s\tremaining: 35.5s\n",
      "30:\tlearn: 0.0032076\ttotal: 1.13s\tremaining: 35.2s\n",
      "31:\tlearn: 0.0031155\ttotal: 1.16s\tremaining: 35s\n",
      "32:\tlearn: 0.0030328\ttotal: 1.2s\tremaining: 35.3s\n",
      "33:\tlearn: 0.0029634\ttotal: 1.24s\tremaining: 35.4s\n",
      "34:\tlearn: 0.0028701\ttotal: 1.28s\tremaining: 35.3s\n",
      "35:\tlearn: 0.0027983\ttotal: 1.31s\tremaining: 35.1s\n",
      "36:\tlearn: 0.0027475\ttotal: 1.34s\tremaining: 35s\n",
      "37:\tlearn: 0.0027082\ttotal: 1.38s\tremaining: 34.9s\n",
      "38:\tlearn: 0.0026569\ttotal: 1.41s\tremaining: 34.8s\n",
      "39:\tlearn: 0.0026146\ttotal: 1.44s\tremaining: 34.7s\n",
      "40:\tlearn: 0.0025708\ttotal: 1.48s\tremaining: 34.5s\n",
      "41:\tlearn: 0.0025447\ttotal: 1.5s\tremaining: 34.4s\n",
      "42:\tlearn: 0.0025159\ttotal: 1.54s\tremaining: 34.2s\n",
      "43:\tlearn: 0.0024620\ttotal: 1.57s\tremaining: 34.2s\n",
      "44:\tlearn: 0.0024231\ttotal: 1.6s\tremaining: 34s\n",
      "45:\tlearn: 0.0024036\ttotal: 1.64s\tremaining: 33.9s\n",
      "46:\tlearn: 0.0023911\ttotal: 1.67s\tremaining: 33.8s\n",
      "47:\tlearn: 0.0023563\ttotal: 1.7s\tremaining: 33.7s\n",
      "48:\tlearn: 0.0023300\ttotal: 1.74s\tremaining: 33.7s\n",
      "49:\tlearn: 0.0022998\ttotal: 1.77s\tremaining: 33.7s\n",
      "50:\tlearn: 0.0022864\ttotal: 1.8s\tremaining: 33.6s\n",
      "51:\tlearn: 0.0022619\ttotal: 1.84s\tremaining: 33.5s\n",
      "52:\tlearn: 0.0022282\ttotal: 1.88s\tremaining: 33.5s\n",
      "53:\tlearn: 0.0021972\ttotal: 1.92s\tremaining: 33.6s\n",
      "54:\tlearn: 0.0021679\ttotal: 1.95s\tremaining: 33.5s\n",
      "55:\tlearn: 0.0021531\ttotal: 1.99s\tremaining: 33.5s\n",
      "56:\tlearn: 0.0021323\ttotal: 2.02s\tremaining: 33.4s\n",
      "57:\tlearn: 0.0021171\ttotal: 2.05s\tremaining: 33.3s\n",
      "58:\tlearn: 0.0020935\ttotal: 2.08s\tremaining: 33.2s\n",
      "59:\tlearn: 0.0020735\ttotal: 2.12s\tremaining: 33.2s\n",
      "60:\tlearn: 0.0020475\ttotal: 2.15s\tremaining: 33.1s\n",
      "61:\tlearn: 0.0020263\ttotal: 2.19s\tremaining: 33.1s\n",
      "62:\tlearn: 0.0020195\ttotal: 2.22s\tremaining: 33s\n",
      "63:\tlearn: 0.0020025\ttotal: 2.25s\tremaining: 32.9s\n",
      "64:\tlearn: 0.0019907\ttotal: 2.28s\tremaining: 32.8s\n",
      "65:\tlearn: 0.0019844\ttotal: 2.31s\tremaining: 32.7s\n",
      "66:\tlearn: 0.0019630\ttotal: 2.35s\tremaining: 32.7s\n",
      "67:\tlearn: 0.0019461\ttotal: 2.38s\tremaining: 32.6s\n",
      "68:\tlearn: 0.0019391\ttotal: 2.41s\tremaining: 32.5s\n",
      "69:\tlearn: 0.0019258\ttotal: 2.44s\tremaining: 32.5s\n",
      "70:\tlearn: 0.0019030\ttotal: 2.48s\tremaining: 32.5s\n",
      "71:\tlearn: 0.0018822\ttotal: 2.51s\tremaining: 32.4s\n",
      "72:\tlearn: 0.0018686\ttotal: 2.55s\tremaining: 32.4s\n",
      "73:\tlearn: 0.0018542\ttotal: 2.58s\tremaining: 32.3s\n",
      "74:\tlearn: 0.0018360\ttotal: 2.61s\tremaining: 32.2s\n",
      "75:\tlearn: 0.0018232\ttotal: 2.65s\tremaining: 32.2s\n",
      "76:\tlearn: 0.0018091\ttotal: 2.68s\tremaining: 32.1s\n",
      "77:\tlearn: 0.0017987\ttotal: 2.71s\tremaining: 32s\n",
      "78:\tlearn: 0.0017860\ttotal: 2.74s\tremaining: 32s\n",
      "79:\tlearn: 0.0017754\ttotal: 2.77s\tremaining: 31.9s\n",
      "80:\tlearn: 0.0017583\ttotal: 2.81s\tremaining: 31.9s\n",
      "81:\tlearn: 0.0017471\ttotal: 2.85s\tremaining: 31.9s\n",
      "82:\tlearn: 0.0017381\ttotal: 2.89s\tremaining: 31.9s\n",
      "83:\tlearn: 0.0017263\ttotal: 2.93s\tremaining: 32s\n",
      "84:\tlearn: 0.0017172\ttotal: 2.99s\tremaining: 32.2s\n",
      "85:\tlearn: 0.0017115\ttotal: 3.04s\tremaining: 32.3s\n",
      "86:\tlearn: 0.0017068\ttotal: 3.07s\tremaining: 32.2s\n",
      "87:\tlearn: 0.0017000\ttotal: 3.1s\tremaining: 32.2s\n",
      "88:\tlearn: 0.0016937\ttotal: 3.14s\tremaining: 32.1s\n",
      "89:\tlearn: 0.0016791\ttotal: 3.17s\tremaining: 32.1s\n",
      "90:\tlearn: 0.0016722\ttotal: 3.2s\tremaining: 32s\n",
      "91:\tlearn: 0.0016610\ttotal: 3.24s\tremaining: 32s\n",
      "92:\tlearn: 0.0016563\ttotal: 3.27s\tremaining: 31.9s\n",
      "93:\tlearn: 0.0016517\ttotal: 3.31s\tremaining: 31.9s\n",
      "94:\tlearn: 0.0016417\ttotal: 3.34s\tremaining: 31.8s\n",
      "95:\tlearn: 0.0016367\ttotal: 3.37s\tremaining: 31.8s\n",
      "96:\tlearn: 0.0016307\ttotal: 3.41s\tremaining: 31.7s\n",
      "97:\tlearn: 0.0016212\ttotal: 3.44s\tremaining: 31.7s\n",
      "98:\tlearn: 0.0016096\ttotal: 3.48s\tremaining: 31.7s\n",
      "99:\tlearn: 0.0015976\ttotal: 3.52s\tremaining: 31.7s\n",
      "100:\tlearn: 0.0015931\ttotal: 3.56s\tremaining: 31.7s\n",
      "101:\tlearn: 0.0015886\ttotal: 3.59s\tremaining: 31.6s\n",
      "102:\tlearn: 0.0015818\ttotal: 3.62s\tremaining: 31.5s\n",
      "103:\tlearn: 0.0015733\ttotal: 3.65s\tremaining: 31.5s\n",
      "104:\tlearn: 0.0015665\ttotal: 3.69s\tremaining: 31.4s\n",
      "105:\tlearn: 0.0015619\ttotal: 3.72s\tremaining: 31.4s\n",
      "106:\tlearn: 0.0015547\ttotal: 3.75s\tremaining: 31.3s\n",
      "107:\tlearn: 0.0015436\ttotal: 3.79s\tremaining: 31.3s\n",
      "108:\tlearn: 0.0015397\ttotal: 3.83s\tremaining: 31.3s\n",
      "109:\tlearn: 0.0015353\ttotal: 3.86s\tremaining: 31.2s\n",
      "110:\tlearn: 0.0015317\ttotal: 3.89s\tremaining: 31.2s\n",
      "111:\tlearn: 0.0015274\ttotal: 3.92s\tremaining: 31.1s\n",
      "112:\tlearn: 0.0015233\ttotal: 3.95s\tremaining: 31s\n",
      "113:\tlearn: 0.0015120\ttotal: 3.99s\tremaining: 31s\n",
      "114:\tlearn: 0.0015051\ttotal: 4.02s\tremaining: 31s\n",
      "115:\tlearn: 0.0014983\ttotal: 4.06s\tremaining: 30.9s\n",
      "116:\tlearn: 0.0014898\ttotal: 4.09s\tremaining: 30.9s\n",
      "117:\tlearn: 0.0014871\ttotal: 4.12s\tremaining: 30.8s\n",
      "118:\tlearn: 0.0014816\ttotal: 4.15s\tremaining: 30.8s\n",
      "119:\tlearn: 0.0014747\ttotal: 4.19s\tremaining: 30.7s\n",
      "120:\tlearn: 0.0014662\ttotal: 4.22s\tremaining: 30.7s\n",
      "121:\tlearn: 0.0014616\ttotal: 4.25s\tremaining: 30.6s\n",
      "122:\tlearn: 0.0014572\ttotal: 4.28s\tremaining: 30.5s\n",
      "123:\tlearn: 0.0014515\ttotal: 4.31s\tremaining: 30.5s\n",
      "124:\tlearn: 0.0014422\ttotal: 4.35s\tremaining: 30.4s\n",
      "125:\tlearn: 0.0014371\ttotal: 4.38s\tremaining: 30.4s\n",
      "126:\tlearn: 0.0014301\ttotal: 4.42s\tremaining: 30.4s\n",
      "127:\tlearn: 0.0014204\ttotal: 4.46s\tremaining: 30.4s\n",
      "128:\tlearn: 0.0014149\ttotal: 4.49s\tremaining: 30.3s\n",
      "129:\tlearn: 0.0014082\ttotal: 4.52s\tremaining: 30.3s\n",
      "130:\tlearn: 0.0014039\ttotal: 4.55s\tremaining: 30.2s\n",
      "131:\tlearn: 0.0013990\ttotal: 4.59s\tremaining: 30.2s\n",
      "132:\tlearn: 0.0013928\ttotal: 4.62s\tremaining: 30.1s\n",
      "133:\tlearn: 0.0013856\ttotal: 4.66s\tremaining: 30.1s\n",
      "134:\tlearn: 0.0013794\ttotal: 4.69s\tremaining: 30s\n",
      "135:\tlearn: 0.0013713\ttotal: 4.72s\tremaining: 30s\n",
      "136:\tlearn: 0.0013644\ttotal: 4.76s\tremaining: 30s\n",
      "137:\tlearn: 0.0013565\ttotal: 4.79s\tremaining: 29.9s\n",
      "138:\tlearn: 0.0013480\ttotal: 4.83s\tremaining: 29.9s\n",
      "139:\tlearn: 0.0013452\ttotal: 4.87s\tremaining: 29.9s\n",
      "140:\tlearn: 0.0013386\ttotal: 4.9s\tremaining: 29.9s\n",
      "141:\tlearn: 0.0013317\ttotal: 4.94s\tremaining: 29.8s\n",
      "142:\tlearn: 0.0013236\ttotal: 4.98s\tremaining: 29.9s\n",
      "143:\tlearn: 0.0013180\ttotal: 5.05s\tremaining: 30s\n",
      "144:\tlearn: 0.0013119\ttotal: 5.1s\tremaining: 30.1s\n",
      "145:\tlearn: 0.0013048\ttotal: 5.14s\tremaining: 30.1s\n",
      "146:\tlearn: 0.0012996\ttotal: 5.17s\tremaining: 30s\n",
      "147:\tlearn: 0.0012917\ttotal: 5.21s\tremaining: 30s\n",
      "148:\tlearn: 0.0012873\ttotal: 5.25s\tremaining: 30s\n",
      "149:\tlearn: 0.0012814\ttotal: 5.29s\tremaining: 30s\n",
      "150:\tlearn: 0.0012750\ttotal: 5.32s\tremaining: 29.9s\n",
      "151:\tlearn: 0.0012689\ttotal: 5.36s\tremaining: 29.9s\n",
      "152:\tlearn: 0.0012664\ttotal: 5.39s\tremaining: 29.9s\n",
      "153:\tlearn: 0.0012564\ttotal: 5.43s\tremaining: 29.8s\n",
      "154:\tlearn: 0.0012491\ttotal: 5.47s\tremaining: 29.8s\n",
      "155:\tlearn: 0.0012415\ttotal: 5.5s\tremaining: 29.8s\n",
      "156:\tlearn: 0.0012375\ttotal: 5.54s\tremaining: 29.7s\n",
      "157:\tlearn: 0.0012332\ttotal: 5.57s\tremaining: 29.7s\n",
      "158:\tlearn: 0.0012299\ttotal: 5.61s\tremaining: 29.7s\n",
      "159:\tlearn: 0.0012232\ttotal: 5.64s\tremaining: 29.6s\n",
      "160:\tlearn: 0.0012163\ttotal: 5.68s\tremaining: 29.6s\n",
      "161:\tlearn: 0.0012079\ttotal: 5.71s\tremaining: 29.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162:\tlearn: 0.0011976\ttotal: 5.75s\tremaining: 29.5s\n",
      "163:\tlearn: 0.0011922\ttotal: 5.78s\tremaining: 29.5s\n",
      "164:\tlearn: 0.0011879\ttotal: 5.81s\tremaining: 29.4s\n",
      "165:\tlearn: 0.0011809\ttotal: 5.85s\tremaining: 29.4s\n",
      "166:\tlearn: 0.0011719\ttotal: 5.89s\tremaining: 29.4s\n",
      "167:\tlearn: 0.0011650\ttotal: 5.92s\tremaining: 29.3s\n",
      "168:\tlearn: 0.0011611\ttotal: 5.95s\tremaining: 29.3s\n",
      "169:\tlearn: 0.0011537\ttotal: 6s\tremaining: 29.3s\n",
      "170:\tlearn: 0.0011512\ttotal: 6.04s\tremaining: 29.3s\n",
      "171:\tlearn: 0.0011448\ttotal: 6.08s\tremaining: 29.2s\n",
      "172:\tlearn: 0.0011360\ttotal: 6.11s\tremaining: 29.2s\n",
      "173:\tlearn: 0.0011312\ttotal: 6.14s\tremaining: 29.2s\n",
      "174:\tlearn: 0.0011256\ttotal: 6.18s\tremaining: 29.1s\n",
      "175:\tlearn: 0.0011224\ttotal: 6.21s\tremaining: 29.1s\n",
      "176:\tlearn: 0.0011175\ttotal: 6.24s\tremaining: 29s\n",
      "177:\tlearn: 0.0011128\ttotal: 6.28s\tremaining: 29s\n",
      "178:\tlearn: 0.0011099\ttotal: 6.31s\tremaining: 28.9s\n",
      "179:\tlearn: 0.0010993\ttotal: 6.35s\tremaining: 28.9s\n",
      "180:\tlearn: 0.0010969\ttotal: 6.38s\tremaining: 28.9s\n",
      "181:\tlearn: 0.0010940\ttotal: 6.41s\tremaining: 28.8s\n",
      "182:\tlearn: 0.0010841\ttotal: 6.46s\tremaining: 28.8s\n",
      "183:\tlearn: 0.0010774\ttotal: 6.51s\tremaining: 28.9s\n",
      "184:\tlearn: 0.0010712\ttotal: 6.54s\tremaining: 28.8s\n",
      "185:\tlearn: 0.0010630\ttotal: 6.59s\tremaining: 28.9s\n",
      "186:\tlearn: 0.0010543\ttotal: 6.65s\tremaining: 28.9s\n",
      "187:\tlearn: 0.0010478\ttotal: 6.69s\tremaining: 28.9s\n",
      "188:\tlearn: 0.0010462\ttotal: 6.72s\tremaining: 28.9s\n",
      "189:\tlearn: 0.0010398\ttotal: 6.76s\tremaining: 28.8s\n",
      "190:\tlearn: 0.0010373\ttotal: 6.8s\tremaining: 28.8s\n",
      "191:\tlearn: 0.0010292\ttotal: 6.84s\tremaining: 28.8s\n",
      "192:\tlearn: 0.0010267\ttotal: 6.87s\tremaining: 28.7s\n",
      "193:\tlearn: 0.0010225\ttotal: 6.91s\tremaining: 28.7s\n",
      "194:\tlearn: 0.0010149\ttotal: 6.95s\tremaining: 28.7s\n",
      "195:\tlearn: 0.0010123\ttotal: 6.98s\tremaining: 28.6s\n",
      "196:\tlearn: 0.0010103\ttotal: 7.01s\tremaining: 28.6s\n",
      "197:\tlearn: 0.0010040\ttotal: 7.05s\tremaining: 28.5s\n",
      "198:\tlearn: 0.0009975\ttotal: 7.08s\tremaining: 28.5s\n",
      "199:\tlearn: 0.0009933\ttotal: 7.12s\tremaining: 28.5s\n",
      "200:\tlearn: 0.0009885\ttotal: 7.16s\tremaining: 28.4s\n",
      "201:\tlearn: 0.0009852\ttotal: 7.19s\tremaining: 28.4s\n",
      "202:\tlearn: 0.0009799\ttotal: 7.22s\tremaining: 28.4s\n",
      "203:\tlearn: 0.0009755\ttotal: 7.26s\tremaining: 28.3s\n",
      "204:\tlearn: 0.0009699\ttotal: 7.3s\tremaining: 28.3s\n",
      "205:\tlearn: 0.0009679\ttotal: 7.33s\tremaining: 28.3s\n",
      "206:\tlearn: 0.0009608\ttotal: 7.37s\tremaining: 28.2s\n",
      "207:\tlearn: 0.0009579\ttotal: 7.4s\tremaining: 28.2s\n",
      "208:\tlearn: 0.0009505\ttotal: 7.43s\tremaining: 28.1s\n",
      "209:\tlearn: 0.0009431\ttotal: 7.47s\tremaining: 28.1s\n",
      "210:\tlearn: 0.0009387\ttotal: 7.5s\tremaining: 28.1s\n",
      "211:\tlearn: 0.0009357\ttotal: 7.54s\tremaining: 28s\n",
      "212:\tlearn: 0.0009329\ttotal: 7.58s\tremaining: 28s\n",
      "213:\tlearn: 0.0009303\ttotal: 7.61s\tremaining: 27.9s\n",
      "214:\tlearn: 0.0009244\ttotal: 7.65s\tremaining: 27.9s\n",
      "215:\tlearn: 0.0009197\ttotal: 7.68s\tremaining: 27.9s\n",
      "216:\tlearn: 0.0009147\ttotal: 7.72s\tremaining: 27.8s\n",
      "217:\tlearn: 0.0009131\ttotal: 7.75s\tremaining: 27.8s\n",
      "218:\tlearn: 0.0009095\ttotal: 7.78s\tremaining: 27.8s\n",
      "219:\tlearn: 0.0009046\ttotal: 7.81s\tremaining: 27.7s\n",
      "220:\tlearn: 0.0008980\ttotal: 7.85s\tremaining: 27.7s\n",
      "221:\tlearn: 0.0008923\ttotal: 7.88s\tremaining: 27.6s\n",
      "222:\tlearn: 0.0008890\ttotal: 7.92s\tremaining: 27.6s\n",
      "223:\tlearn: 0.0008855\ttotal: 7.95s\tremaining: 27.6s\n",
      "224:\tlearn: 0.0008827\ttotal: 7.99s\tremaining: 27.5s\n",
      "225:\tlearn: 0.0008789\ttotal: 8.02s\tremaining: 27.5s\n",
      "226:\tlearn: 0.0008739\ttotal: 8.06s\tremaining: 27.4s\n",
      "227:\tlearn: 0.0008693\ttotal: 8.1s\tremaining: 27.4s\n",
      "228:\tlearn: 0.0008616\ttotal: 8.14s\tremaining: 27.4s\n",
      "229:\tlearn: 0.0008568\ttotal: 8.17s\tremaining: 27.4s\n",
      "230:\tlearn: 0.0008528\ttotal: 8.21s\tremaining: 27.3s\n",
      "231:\tlearn: 0.0008495\ttotal: 8.24s\tremaining: 27.3s\n",
      "232:\tlearn: 0.0008447\ttotal: 8.28s\tremaining: 27.2s\n",
      "233:\tlearn: 0.0008379\ttotal: 8.31s\tremaining: 27.2s\n",
      "234:\tlearn: 0.0008330\ttotal: 8.35s\tremaining: 27.2s\n",
      "235:\tlearn: 0.0008275\ttotal: 8.38s\tremaining: 27.1s\n",
      "236:\tlearn: 0.0008255\ttotal: 8.41s\tremaining: 27.1s\n",
      "237:\tlearn: 0.0008222\ttotal: 8.45s\tremaining: 27.1s\n",
      "238:\tlearn: 0.0008196\ttotal: 8.49s\tremaining: 27s\n",
      "239:\tlearn: 0.0008159\ttotal: 8.52s\tremaining: 27s\n",
      "240:\tlearn: 0.0008111\ttotal: 8.56s\tremaining: 27s\n",
      "241:\tlearn: 0.0008059\ttotal: 8.59s\tremaining: 26.9s\n",
      "242:\tlearn: 0.0008041\ttotal: 8.62s\tremaining: 26.9s\n",
      "243:\tlearn: 0.0007943\ttotal: 8.66s\tremaining: 26.8s\n",
      "244:\tlearn: 0.0007920\ttotal: 8.69s\tremaining: 26.8s\n",
      "245:\tlearn: 0.0007836\ttotal: 8.73s\tremaining: 26.8s\n",
      "246:\tlearn: 0.0007810\ttotal: 8.77s\tremaining: 26.7s\n",
      "247:\tlearn: 0.0007777\ttotal: 8.8s\tremaining: 26.7s\n",
      "248:\tlearn: 0.0007736\ttotal: 8.84s\tremaining: 26.7s\n",
      "249:\tlearn: 0.0007682\ttotal: 8.88s\tremaining: 26.6s\n",
      "250:\tlearn: 0.0007643\ttotal: 8.92s\tremaining: 26.6s\n",
      "251:\tlearn: 0.0007604\ttotal: 8.95s\tremaining: 26.6s\n",
      "252:\tlearn: 0.0007563\ttotal: 8.99s\tremaining: 26.5s\n",
      "253:\tlearn: 0.0007515\ttotal: 9.02s\tremaining: 26.5s\n",
      "254:\tlearn: 0.0007458\ttotal: 9.06s\tremaining: 26.5s\n",
      "255:\tlearn: 0.0007354\ttotal: 9.09s\tremaining: 26.4s\n",
      "256:\tlearn: 0.0007338\ttotal: 9.13s\tremaining: 26.4s\n",
      "257:\tlearn: 0.0007266\ttotal: 9.16s\tremaining: 26.4s\n",
      "258:\tlearn: 0.0007213\ttotal: 9.2s\tremaining: 26.3s\n",
      "259:\tlearn: 0.0007180\ttotal: 9.23s\tremaining: 26.3s\n",
      "260:\tlearn: 0.0007147\ttotal: 9.27s\tremaining: 26.2s\n",
      "261:\tlearn: 0.0007111\ttotal: 9.3s\tremaining: 26.2s\n",
      "262:\tlearn: 0.0007074\ttotal: 9.33s\tremaining: 26.2s\n",
      "263:\tlearn: 0.0007056\ttotal: 9.37s\tremaining: 26.1s\n",
      "264:\tlearn: 0.0007032\ttotal: 9.45s\tremaining: 26.2s\n",
      "265:\tlearn: 0.0007003\ttotal: 9.5s\tremaining: 26.2s\n",
      "266:\tlearn: 0.0006955\ttotal: 9.55s\tremaining: 26.2s\n",
      "267:\tlearn: 0.0006893\ttotal: 9.59s\tremaining: 26.2s\n",
      "268:\tlearn: 0.0006872\ttotal: 9.62s\tremaining: 26.1s\n",
      "269:\tlearn: 0.0006832\ttotal: 9.66s\tremaining: 26.1s\n",
      "270:\tlearn: 0.0006773\ttotal: 9.7s\tremaining: 26.1s\n",
      "271:\tlearn: 0.0006730\ttotal: 9.74s\tremaining: 26.1s\n",
      "272:\tlearn: 0.0006713\ttotal: 9.78s\tremaining: 26.1s\n",
      "273:\tlearn: 0.0006685\ttotal: 9.82s\tremaining: 26s\n",
      "274:\tlearn: 0.0006637\ttotal: 9.86s\tremaining: 26s\n",
      "275:\tlearn: 0.0006613\ttotal: 9.9s\tremaining: 26s\n",
      "276:\tlearn: 0.0006580\ttotal: 9.94s\tremaining: 25.9s\n",
      "277:\tlearn: 0.0006568\ttotal: 9.97s\tremaining: 25.9s\n",
      "278:\tlearn: 0.0006556\ttotal: 10s\tremaining: 25.9s\n",
      "279:\tlearn: 0.0006514\ttotal: 10s\tremaining: 25.8s\n",
      "280:\tlearn: 0.0006486\ttotal: 10.1s\tremaining: 25.8s\n",
      "281:\tlearn: 0.0006449\ttotal: 10.1s\tremaining: 25.7s\n",
      "282:\tlearn: 0.0006412\ttotal: 10.1s\tremaining: 25.7s\n",
      "283:\tlearn: 0.0006396\ttotal: 10.2s\tremaining: 25.7s\n",
      "284:\tlearn: 0.0006361\ttotal: 10.2s\tremaining: 25.6s\n",
      "285:\tlearn: 0.0006313\ttotal: 10.3s\tremaining: 25.6s\n",
      "286:\tlearn: 0.0006265\ttotal: 10.3s\tremaining: 25.6s\n",
      "287:\tlearn: 0.0006225\ttotal: 10.3s\tremaining: 25.5s\n",
      "288:\tlearn: 0.0006188\ttotal: 10.4s\tremaining: 25.5s\n",
      "289:\tlearn: 0.0006138\ttotal: 10.4s\tremaining: 25.5s\n",
      "290:\tlearn: 0.0006096\ttotal: 10.4s\tremaining: 25.4s\n",
      "291:\tlearn: 0.0006058\ttotal: 10.5s\tremaining: 25.4s\n",
      "292:\tlearn: 0.0006003\ttotal: 10.5s\tremaining: 25.3s\n",
      "293:\tlearn: 0.0005970\ttotal: 10.5s\tremaining: 25.3s\n",
      "294:\tlearn: 0.0005906\ttotal: 10.6s\tremaining: 25.3s\n",
      "295:\tlearn: 0.0005881\ttotal: 10.6s\tremaining: 25.2s\n",
      "296:\tlearn: 0.0005850\ttotal: 10.6s\tremaining: 25.2s\n",
      "297:\tlearn: 0.0005834\ttotal: 10.7s\tremaining: 25.1s\n",
      "298:\tlearn: 0.0005805\ttotal: 10.7s\tremaining: 25.1s\n",
      "299:\tlearn: 0.0005785\ttotal: 10.7s\tremaining: 25.1s\n",
      "300:\tlearn: 0.0005737\ttotal: 10.8s\tremaining: 25s\n",
      "301:\tlearn: 0.0005697\ttotal: 10.8s\tremaining: 25s\n",
      "302:\tlearn: 0.0005642\ttotal: 10.8s\tremaining: 24.9s\n",
      "303:\tlearn: 0.0005592\ttotal: 10.9s\tremaining: 24.9s\n",
      "304:\tlearn: 0.0005579\ttotal: 10.9s\tremaining: 24.9s\n",
      "305:\tlearn: 0.0005541\ttotal: 10.9s\tremaining: 24.8s\n",
      "306:\tlearn: 0.0005517\ttotal: 11s\tremaining: 24.8s\n",
      "307:\tlearn: 0.0005467\ttotal: 11s\tremaining: 24.8s\n",
      "308:\tlearn: 0.0005432\ttotal: 11.1s\tremaining: 24.7s\n",
      "309:\tlearn: 0.0005413\ttotal: 11.1s\tremaining: 24.7s\n",
      "310:\tlearn: 0.0005403\ttotal: 11.1s\tremaining: 24.6s\n",
      "311:\tlearn: 0.0005370\ttotal: 11.2s\tremaining: 24.6s\n",
      "312:\tlearn: 0.0005360\ttotal: 11.2s\tremaining: 24.6s\n",
      "313:\tlearn: 0.0005317\ttotal: 11.2s\tremaining: 24.5s\n",
      "314:\tlearn: 0.0005306\ttotal: 11.3s\tremaining: 24.5s\n",
      "315:\tlearn: 0.0005271\ttotal: 11.3s\tremaining: 24.5s\n",
      "316:\tlearn: 0.0005241\ttotal: 11.3s\tremaining: 24.4s\n",
      "317:\tlearn: 0.0005198\ttotal: 11.4s\tremaining: 24.4s\n",
      "318:\tlearn: 0.0005181\ttotal: 11.4s\tremaining: 24.4s\n",
      "319:\tlearn: 0.0005174\ttotal: 11.4s\tremaining: 24.3s\n",
      "320:\tlearn: 0.0005155\ttotal: 11.5s\tremaining: 24.3s\n",
      "321:\tlearn: 0.0005142\ttotal: 11.5s\tremaining: 24.2s\n",
      "322:\tlearn: 0.0005119\ttotal: 11.6s\tremaining: 24.2s\n",
      "323:\tlearn: 0.0005108\ttotal: 11.6s\tremaining: 24.2s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "324:\tlearn: 0.0005075\ttotal: 11.6s\tremaining: 24.2s\n",
      "325:\tlearn: 0.0005047\ttotal: 11.7s\tremaining: 24.2s\n",
      "326:\tlearn: 0.0005028\ttotal: 11.7s\tremaining: 24.1s\n",
      "327:\tlearn: 0.0004991\ttotal: 11.8s\tremaining: 24.1s\n",
      "328:\tlearn: 0.0004985\ttotal: 11.8s\tremaining: 24.1s\n",
      "329:\tlearn: 0.0004952\ttotal: 11.8s\tremaining: 24s\n",
      "330:\tlearn: 0.0004945\ttotal: 11.9s\tremaining: 24s\n",
      "331:\tlearn: 0.0004911\ttotal: 11.9s\tremaining: 23.9s\n",
      "332:\tlearn: 0.0004879\ttotal: 11.9s\tremaining: 23.9s\n",
      "333:\tlearn: 0.0004837\ttotal: 12s\tremaining: 23.9s\n",
      "334:\tlearn: 0.0004816\ttotal: 12s\tremaining: 23.8s\n",
      "335:\tlearn: 0.0004810\ttotal: 12s\tremaining: 23.8s\n",
      "336:\tlearn: 0.0004803\ttotal: 12.1s\tremaining: 23.7s\n",
      "337:\tlearn: 0.0004786\ttotal: 12.1s\tremaining: 23.7s\n",
      "338:\tlearn: 0.0004752\ttotal: 12.1s\tremaining: 23.7s\n",
      "339:\tlearn: 0.0004745\ttotal: 12.2s\tremaining: 23.6s\n",
      "340:\tlearn: 0.0004716\ttotal: 12.2s\tremaining: 23.6s\n",
      "341:\tlearn: 0.0004711\ttotal: 12.2s\tremaining: 23.5s\n",
      "342:\tlearn: 0.0004680\ttotal: 12.3s\tremaining: 23.5s\n",
      "343:\tlearn: 0.0004649\ttotal: 12.3s\tremaining: 23.5s\n",
      "344:\tlearn: 0.0004619\ttotal: 12.3s\tremaining: 23.4s\n",
      "345:\tlearn: 0.0004586\ttotal: 12.4s\tremaining: 23.4s\n",
      "346:\tlearn: 0.0004547\ttotal: 12.4s\tremaining: 23.4s\n",
      "347:\tlearn: 0.0004542\ttotal: 12.4s\tremaining: 23.3s\n",
      "348:\tlearn: 0.0004506\ttotal: 12.5s\tremaining: 23.3s\n",
      "349:\tlearn: 0.0004501\ttotal: 12.5s\tremaining: 23.2s\n",
      "350:\tlearn: 0.0004482\ttotal: 12.6s\tremaining: 23.2s\n",
      "351:\tlearn: 0.0004451\ttotal: 12.6s\tremaining: 23.2s\n",
      "352:\tlearn: 0.0004435\ttotal: 12.6s\tremaining: 23.1s\n",
      "353:\tlearn: 0.0004426\ttotal: 12.7s\tremaining: 23.1s\n",
      "354:\tlearn: 0.0004415\ttotal: 12.7s\tremaining: 23s\n",
      "355:\tlearn: 0.0004389\ttotal: 12.7s\tremaining: 23s\n",
      "356:\tlearn: 0.0004372\ttotal: 12.8s\tremaining: 23s\n",
      "357:\tlearn: 0.0004308\ttotal: 12.8s\tremaining: 22.9s\n",
      "358:\tlearn: 0.0004291\ttotal: 12.8s\tremaining: 22.9s\n",
      "359:\tlearn: 0.0004237\ttotal: 12.9s\tremaining: 22.9s\n",
      "360:\tlearn: 0.0004219\ttotal: 12.9s\tremaining: 22.8s\n",
      "361:\tlearn: 0.0004184\ttotal: 12.9s\tremaining: 22.8s\n",
      "362:\tlearn: 0.0004160\ttotal: 13s\tremaining: 22.8s\n",
      "363:\tlearn: 0.0004151\ttotal: 13s\tremaining: 22.7s\n",
      "364:\tlearn: 0.0004144\ttotal: 13s\tremaining: 22.7s\n",
      "365:\tlearn: 0.0004098\ttotal: 13.1s\tremaining: 22.6s\n",
      "366:\tlearn: 0.0004077\ttotal: 13.1s\tremaining: 22.6s\n",
      "367:\tlearn: 0.0004072\ttotal: 13.1s\tremaining: 22.6s\n",
      "368:\tlearn: 0.0004028\ttotal: 13.2s\tremaining: 22.6s\n",
      "369:\tlearn: 0.0003997\ttotal: 13.2s\tremaining: 22.6s\n",
      "370:\tlearn: 0.0003983\ttotal: 13.3s\tremaining: 22.5s\n",
      "371:\tlearn: 0.0003957\ttotal: 13.3s\tremaining: 22.5s\n",
      "372:\tlearn: 0.0003927\ttotal: 13.4s\tremaining: 22.5s\n",
      "373:\tlearn: 0.0003908\ttotal: 13.4s\tremaining: 22.4s\n",
      "374:\tlearn: 0.0003882\ttotal: 13.4s\tremaining: 22.4s\n",
      "375:\tlearn: 0.0003865\ttotal: 13.5s\tremaining: 22.4s\n",
      "376:\tlearn: 0.0003840\ttotal: 13.5s\tremaining: 22.3s\n",
      "377:\tlearn: 0.0003814\ttotal: 13.5s\tremaining: 22.3s\n",
      "378:\tlearn: 0.0003805\ttotal: 13.6s\tremaining: 22.2s\n",
      "379:\tlearn: 0.0003796\ttotal: 13.6s\tremaining: 22.2s\n",
      "380:\tlearn: 0.0003770\ttotal: 13.6s\tremaining: 22.2s\n",
      "381:\tlearn: 0.0003751\ttotal: 13.7s\tremaining: 22.1s\n",
      "382:\tlearn: 0.0003743\ttotal: 13.7s\tremaining: 22.1s\n",
      "383:\tlearn: 0.0003712\ttotal: 13.8s\tremaining: 22.1s\n",
      "384:\tlearn: 0.0003696\ttotal: 13.8s\tremaining: 22s\n",
      "385:\tlearn: 0.0003671\ttotal: 13.8s\tremaining: 22s\n",
      "386:\tlearn: 0.0003661\ttotal: 13.9s\tremaining: 22s\n",
      "387:\tlearn: 0.0003656\ttotal: 13.9s\tremaining: 21.9s\n",
      "388:\tlearn: 0.0003635\ttotal: 13.9s\tremaining: 21.9s\n",
      "389:\tlearn: 0.0003613\ttotal: 14s\tremaining: 21.9s\n",
      "390:\tlearn: 0.0003607\ttotal: 14s\tremaining: 21.8s\n",
      "391:\tlearn: 0.0003570\ttotal: 14s\tremaining: 21.8s\n",
      "392:\tlearn: 0.0003559\ttotal: 14.1s\tremaining: 21.7s\n",
      "393:\tlearn: 0.0003552\ttotal: 14.1s\tremaining: 21.7s\n",
      "394:\tlearn: 0.0003535\ttotal: 14.1s\tremaining: 21.7s\n",
      "395:\tlearn: 0.0003530\ttotal: 14.2s\tremaining: 21.6s\n",
      "396:\tlearn: 0.0003525\ttotal: 14.2s\tremaining: 21.6s\n",
      "397:\tlearn: 0.0003481\ttotal: 14.2s\tremaining: 21.5s\n",
      "398:\tlearn: 0.0003474\ttotal: 14.3s\tremaining: 21.5s\n",
      "399:\tlearn: 0.0003463\ttotal: 14.3s\tremaining: 21.5s\n",
      "400:\tlearn: 0.0003452\ttotal: 14.3s\tremaining: 21.4s\n",
      "401:\tlearn: 0.0003447\ttotal: 14.4s\tremaining: 21.4s\n",
      "402:\tlearn: 0.0003432\ttotal: 14.4s\tremaining: 21.3s\n",
      "403:\tlearn: 0.0003418\ttotal: 14.4s\tremaining: 21.3s\n",
      "404:\tlearn: 0.0003395\ttotal: 14.5s\tremaining: 21.3s\n",
      "405:\tlearn: 0.0003389\ttotal: 14.5s\tremaining: 21.2s\n",
      "406:\tlearn: 0.0003374\ttotal: 14.5s\tremaining: 21.2s\n",
      "407:\tlearn: 0.0003360\ttotal: 14.6s\tremaining: 21.2s\n",
      "408:\tlearn: 0.0003337\ttotal: 14.6s\tremaining: 21.1s\n",
      "409:\tlearn: 0.0003309\ttotal: 14.6s\tremaining: 21.1s\n",
      "410:\tlearn: 0.0003297\ttotal: 14.7s\tremaining: 21.1s\n",
      "411:\tlearn: 0.0003278\ttotal: 14.8s\tremaining: 21.1s\n",
      "412:\tlearn: 0.0003265\ttotal: 14.8s\tremaining: 21s\n",
      "413:\tlearn: 0.0003244\ttotal: 14.9s\tremaining: 21s\n",
      "414:\tlearn: 0.0003219\ttotal: 14.9s\tremaining: 21s\n",
      "415:\tlearn: 0.0003192\ttotal: 14.9s\tremaining: 21s\n",
      "416:\tlearn: 0.0003181\ttotal: 15s\tremaining: 20.9s\n",
      "417:\tlearn: 0.0003170\ttotal: 15s\tremaining: 20.9s\n",
      "418:\tlearn: 0.0003153\ttotal: 15s\tremaining: 20.8s\n",
      "419:\tlearn: 0.0003137\ttotal: 15.1s\tremaining: 20.8s\n",
      "420:\tlearn: 0.0003097\ttotal: 15.1s\tremaining: 20.8s\n",
      "421:\tlearn: 0.0003072\ttotal: 15.1s\tremaining: 20.7s\n",
      "422:\tlearn: 0.0003064\ttotal: 15.2s\tremaining: 20.7s\n",
      "423:\tlearn: 0.0003055\ttotal: 15.2s\tremaining: 20.7s\n",
      "424:\tlearn: 0.0003044\ttotal: 15.3s\tremaining: 20.6s\n",
      "425:\tlearn: 0.0003020\ttotal: 15.3s\tremaining: 20.6s\n",
      "426:\tlearn: 0.0003010\ttotal: 15.3s\tremaining: 20.6s\n",
      "427:\tlearn: 0.0002986\ttotal: 15.4s\tremaining: 20.5s\n",
      "428:\tlearn: 0.0002978\ttotal: 15.4s\tremaining: 20.5s\n",
      "429:\tlearn: 0.0002945\ttotal: 15.4s\tremaining: 20.5s\n",
      "430:\tlearn: 0.0002935\ttotal: 15.5s\tremaining: 20.4s\n",
      "431:\tlearn: 0.0002913\ttotal: 15.5s\tremaining: 20.4s\n",
      "432:\tlearn: 0.0002892\ttotal: 15.6s\tremaining: 20.4s\n",
      "433:\tlearn: 0.0002881\ttotal: 15.6s\tremaining: 20.3s\n",
      "434:\tlearn: 0.0002868\ttotal: 15.6s\tremaining: 20.3s\n",
      "435:\tlearn: 0.0002843\ttotal: 15.7s\tremaining: 20.3s\n",
      "436:\tlearn: 0.0002825\ttotal: 15.7s\tremaining: 20.2s\n",
      "437:\tlearn: 0.0002809\ttotal: 15.7s\tremaining: 20.2s\n",
      "438:\tlearn: 0.0002796\ttotal: 15.8s\tremaining: 20.2s\n",
      "439:\tlearn: 0.0002788\ttotal: 15.8s\tremaining: 20.1s\n",
      "440:\tlearn: 0.0002777\ttotal: 15.9s\tremaining: 20.1s\n",
      "441:\tlearn: 0.0002765\ttotal: 15.9s\tremaining: 20.1s\n",
      "442:\tlearn: 0.0002749\ttotal: 15.9s\tremaining: 20s\n",
      "443:\tlearn: 0.0002722\ttotal: 16s\tremaining: 20s\n",
      "444:\tlearn: 0.0002714\ttotal: 16s\tremaining: 19.9s\n",
      "445:\tlearn: 0.0002710\ttotal: 16s\tremaining: 19.9s\n",
      "446:\tlearn: 0.0002672\ttotal: 16.1s\tremaining: 19.9s\n",
      "447:\tlearn: 0.0002655\ttotal: 16.1s\tremaining: 19.8s\n",
      "448:\tlearn: 0.0002647\ttotal: 16.1s\tremaining: 19.8s\n",
      "449:\tlearn: 0.0002633\ttotal: 16.2s\tremaining: 19.8s\n",
      "450:\tlearn: 0.0002610\ttotal: 16.2s\tremaining: 19.7s\n",
      "451:\tlearn: 0.0002606\ttotal: 16.2s\tremaining: 19.7s\n",
      "452:\tlearn: 0.0002602\ttotal: 16.3s\tremaining: 19.6s\n",
      "453:\tlearn: 0.0002591\ttotal: 16.3s\tremaining: 19.6s\n",
      "454:\tlearn: 0.0002587\ttotal: 16.3s\tremaining: 19.6s\n",
      "455:\tlearn: 0.0002577\ttotal: 16.4s\tremaining: 19.5s\n",
      "456:\tlearn: 0.0002567\ttotal: 16.4s\tremaining: 19.5s\n",
      "457:\tlearn: 0.0002564\ttotal: 16.4s\tremaining: 19.5s\n",
      "458:\tlearn: 0.0002549\ttotal: 16.5s\tremaining: 19.4s\n",
      "459:\tlearn: 0.0002537\ttotal: 16.5s\tremaining: 19.4s\n",
      "460:\tlearn: 0.0002527\ttotal: 16.5s\tremaining: 19.3s\n",
      "461:\tlearn: 0.0002523\ttotal: 16.6s\tremaining: 19.3s\n",
      "462:\tlearn: 0.0002520\ttotal: 16.6s\tremaining: 19.3s\n",
      "463:\tlearn: 0.0002511\ttotal: 16.7s\tremaining: 19.2s\n",
      "464:\tlearn: 0.0002508\ttotal: 16.7s\tremaining: 19.2s\n",
      "465:\tlearn: 0.0002500\ttotal: 16.7s\tremaining: 19.2s\n",
      "466:\tlearn: 0.0002497\ttotal: 16.8s\tremaining: 19.1s\n",
      "467:\tlearn: 0.0002493\ttotal: 16.8s\tremaining: 19.1s\n",
      "468:\tlearn: 0.0002487\ttotal: 16.8s\tremaining: 19s\n",
      "469:\tlearn: 0.0002479\ttotal: 16.9s\tremaining: 19s\n",
      "470:\tlearn: 0.0002473\ttotal: 16.9s\tremaining: 19s\n",
      "471:\tlearn: 0.0002460\ttotal: 16.9s\tremaining: 19s\n",
      "472:\tlearn: 0.0002451\ttotal: 17s\tremaining: 18.9s\n",
      "473:\tlearn: 0.0002430\ttotal: 17s\tremaining: 18.9s\n",
      "474:\tlearn: 0.0002419\ttotal: 17s\tremaining: 18.8s\n",
      "475:\tlearn: 0.0002402\ttotal: 17.1s\tremaining: 18.8s\n",
      "476:\tlearn: 0.0002395\ttotal: 17.1s\tremaining: 18.8s\n",
      "477:\tlearn: 0.0002385\ttotal: 17.1s\tremaining: 18.7s\n",
      "478:\tlearn: 0.0002373\ttotal: 17.2s\tremaining: 18.7s\n",
      "479:\tlearn: 0.0002357\ttotal: 17.2s\tremaining: 18.7s\n",
      "480:\tlearn: 0.0002343\ttotal: 17.3s\tremaining: 18.6s\n",
      "481:\tlearn: 0.0002334\ttotal: 17.3s\tremaining: 18.6s\n",
      "482:\tlearn: 0.0002315\ttotal: 17.3s\tremaining: 18.6s\n",
      "483:\tlearn: 0.0002307\ttotal: 17.4s\tremaining: 18.5s\n",
      "484:\tlearn: 0.0002286\ttotal: 17.4s\tremaining: 18.5s\n",
      "485:\tlearn: 0.0002273\ttotal: 17.4s\tremaining: 18.4s\n",
      "486:\tlearn: 0.0002261\ttotal: 17.5s\tremaining: 18.4s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "487:\tlearn: 0.0002251\ttotal: 17.5s\tremaining: 18.4s\n",
      "488:\tlearn: 0.0002232\ttotal: 17.5s\tremaining: 18.3s\n",
      "489:\tlearn: 0.0002223\ttotal: 17.6s\tremaining: 18.3s\n",
      "490:\tlearn: 0.0002214\ttotal: 17.6s\tremaining: 18.3s\n",
      "491:\tlearn: 0.0002208\ttotal: 17.6s\tremaining: 18.2s\n",
      "492:\tlearn: 0.0002195\ttotal: 17.7s\tremaining: 18.2s\n",
      "493:\tlearn: 0.0002189\ttotal: 17.7s\tremaining: 18.1s\n",
      "494:\tlearn: 0.0002185\ttotal: 17.7s\tremaining: 18.1s\n",
      "495:\tlearn: 0.0002183\ttotal: 17.8s\tremaining: 18.1s\n",
      "496:\tlearn: 0.0002167\ttotal: 17.8s\tremaining: 18s\n",
      "497:\tlearn: 0.0002164\ttotal: 17.8s\tremaining: 18s\n",
      "498:\tlearn: 0.0002154\ttotal: 17.9s\tremaining: 18s\n",
      "499:\tlearn: 0.0002149\ttotal: 17.9s\tremaining: 17.9s\n",
      "500:\tlearn: 0.0002142\ttotal: 18s\tremaining: 17.9s\n",
      "501:\tlearn: 0.0002113\ttotal: 18s\tremaining: 17.8s\n",
      "502:\tlearn: 0.0002105\ttotal: 18s\tremaining: 17.8s\n",
      "503:\tlearn: 0.0002100\ttotal: 18.1s\tremaining: 17.8s\n",
      "504:\tlearn: 0.0002072\ttotal: 18.1s\tremaining: 17.7s\n",
      "505:\tlearn: 0.0002063\ttotal: 18.1s\tremaining: 17.7s\n",
      "506:\tlearn: 0.0002045\ttotal: 18.2s\tremaining: 17.7s\n",
      "507:\tlearn: 0.0002043\ttotal: 18.2s\tremaining: 17.6s\n",
      "508:\tlearn: 0.0002037\ttotal: 18.2s\tremaining: 17.6s\n",
      "509:\tlearn: 0.0002031\ttotal: 18.3s\tremaining: 17.6s\n",
      "510:\tlearn: 0.0002019\ttotal: 18.3s\tremaining: 17.5s\n",
      "511:\tlearn: 0.0002016\ttotal: 18.4s\tremaining: 17.5s\n",
      "512:\tlearn: 0.0002008\ttotal: 18.4s\tremaining: 17.5s\n",
      "513:\tlearn: 0.0002002\ttotal: 18.4s\tremaining: 17.4s\n",
      "514:\tlearn: 0.0002000\ttotal: 18.5s\tremaining: 17.4s\n",
      "515:\tlearn: 0.0001991\ttotal: 18.5s\tremaining: 17.4s\n",
      "516:\tlearn: 0.0001971\ttotal: 18.6s\tremaining: 17.3s\n",
      "517:\tlearn: 0.0001970\ttotal: 18.6s\tremaining: 17.3s\n",
      "518:\tlearn: 0.0001962\ttotal: 18.6s\tremaining: 17.3s\n",
      "519:\tlearn: 0.0001951\ttotal: 18.7s\tremaining: 17.2s\n",
      "520:\tlearn: 0.0001945\ttotal: 18.7s\tremaining: 17.2s\n",
      "521:\tlearn: 0.0001938\ttotal: 18.7s\tremaining: 17.2s\n",
      "522:\tlearn: 0.0001932\ttotal: 18.8s\tremaining: 17.1s\n",
      "523:\tlearn: 0.0001928\ttotal: 18.8s\tremaining: 17.1s\n",
      "524:\tlearn: 0.0001925\ttotal: 18.9s\tremaining: 17.1s\n",
      "525:\tlearn: 0.0001903\ttotal: 18.9s\tremaining: 17s\n",
      "526:\tlearn: 0.0001895\ttotal: 19s\tremaining: 17s\n",
      "527:\tlearn: 0.0001892\ttotal: 19s\tremaining: 17s\n",
      "528:\tlearn: 0.0001885\ttotal: 19.1s\tremaining: 17s\n",
      "529:\tlearn: 0.0001881\ttotal: 19.1s\tremaining: 16.9s\n",
      "530:\tlearn: 0.0001877\ttotal: 19.1s\tremaining: 16.9s\n",
      "531:\tlearn: 0.0001865\ttotal: 19.2s\tremaining: 16.9s\n",
      "532:\tlearn: 0.0001860\ttotal: 19.2s\tremaining: 16.8s\n",
      "533:\tlearn: 0.0001850\ttotal: 19.3s\tremaining: 16.8s\n",
      "534:\tlearn: 0.0001844\ttotal: 19.3s\tremaining: 16.8s\n",
      "535:\tlearn: 0.0001831\ttotal: 19.4s\tremaining: 16.8s\n",
      "536:\tlearn: 0.0001829\ttotal: 19.4s\tremaining: 16.8s\n",
      "537:\tlearn: 0.0001827\ttotal: 19.5s\tremaining: 16.7s\n",
      "538:\tlearn: 0.0001819\ttotal: 19.6s\tremaining: 16.7s\n",
      "539:\tlearn: 0.0001815\ttotal: 19.6s\tremaining: 16.7s\n",
      "540:\tlearn: 0.0001805\ttotal: 19.7s\tremaining: 16.7s\n",
      "541:\tlearn: 0.0001802\ttotal: 19.7s\tremaining: 16.7s\n",
      "542:\tlearn: 0.0001780\ttotal: 19.8s\tremaining: 16.6s\n",
      "543:\tlearn: 0.0001764\ttotal: 19.8s\tremaining: 16.6s\n",
      "544:\tlearn: 0.0001755\ttotal: 19.9s\tremaining: 16.6s\n",
      "545:\tlearn: 0.0001751\ttotal: 19.9s\tremaining: 16.6s\n",
      "546:\tlearn: 0.0001740\ttotal: 20s\tremaining: 16.5s\n",
      "547:\tlearn: 0.0001731\ttotal: 20s\tremaining: 16.5s\n",
      "548:\tlearn: 0.0001723\ttotal: 20s\tremaining: 16.5s\n",
      "549:\tlearn: 0.0001718\ttotal: 20.1s\tremaining: 16.4s\n",
      "550:\tlearn: 0.0001715\ttotal: 20.1s\tremaining: 16.4s\n",
      "551:\tlearn: 0.0001712\ttotal: 20.2s\tremaining: 16.4s\n",
      "552:\tlearn: 0.0001706\ttotal: 20.2s\tremaining: 16.3s\n",
      "553:\tlearn: 0.0001696\ttotal: 20.2s\tremaining: 16.3s\n",
      "554:\tlearn: 0.0001691\ttotal: 20.3s\tremaining: 16.3s\n",
      "555:\tlearn: 0.0001688\ttotal: 20.3s\tremaining: 16.2s\n",
      "556:\tlearn: 0.0001683\ttotal: 20.4s\tremaining: 16.2s\n",
      "557:\tlearn: 0.0001673\ttotal: 20.4s\tremaining: 16.2s\n",
      "558:\tlearn: 0.0001665\ttotal: 20.5s\tremaining: 16.2s\n",
      "559:\tlearn: 0.0001658\ttotal: 20.5s\tremaining: 16.1s\n",
      "560:\tlearn: 0.0001641\ttotal: 20.6s\tremaining: 16.1s\n",
      "561:\tlearn: 0.0001637\ttotal: 20.6s\tremaining: 16.1s\n",
      "562:\tlearn: 0.0001627\ttotal: 20.7s\tremaining: 16s\n",
      "563:\tlearn: 0.0001625\ttotal: 20.7s\tremaining: 16s\n",
      "564:\tlearn: 0.0001619\ttotal: 20.8s\tremaining: 16s\n",
      "565:\tlearn: 0.0001609\ttotal: 20.8s\tremaining: 16s\n",
      "566:\tlearn: 0.0001599\ttotal: 20.9s\tremaining: 15.9s\n",
      "567:\tlearn: 0.0001595\ttotal: 20.9s\tremaining: 15.9s\n",
      "568:\tlearn: 0.0001580\ttotal: 21s\tremaining: 15.9s\n",
      "569:\tlearn: 0.0001576\ttotal: 21s\tremaining: 15.9s\n",
      "570:\tlearn: 0.0001571\ttotal: 21.1s\tremaining: 15.8s\n",
      "571:\tlearn: 0.0001569\ttotal: 21.1s\tremaining: 15.8s\n",
      "572:\tlearn: 0.0001564\ttotal: 21.2s\tremaining: 15.8s\n",
      "573:\tlearn: 0.0001559\ttotal: 21.2s\tremaining: 15.7s\n",
      "574:\tlearn: 0.0001552\ttotal: 21.3s\tremaining: 15.7s\n",
      "575:\tlearn: 0.0001548\ttotal: 21.3s\tremaining: 15.7s\n",
      "576:\tlearn: 0.0001543\ttotal: 21.4s\tremaining: 15.7s\n",
      "577:\tlearn: 0.0001541\ttotal: 21.5s\tremaining: 15.7s\n",
      "578:\tlearn: 0.0001538\ttotal: 21.6s\tremaining: 15.7s\n",
      "579:\tlearn: 0.0001531\ttotal: 21.6s\tremaining: 15.7s\n",
      "580:\tlearn: 0.0001527\ttotal: 21.7s\tremaining: 15.6s\n",
      "581:\tlearn: 0.0001519\ttotal: 21.8s\tremaining: 15.6s\n",
      "582:\tlearn: 0.0001507\ttotal: 21.9s\tremaining: 15.6s\n",
      "583:\tlearn: 0.0001504\ttotal: 21.9s\tremaining: 15.6s\n",
      "584:\tlearn: 0.0001497\ttotal: 22s\tremaining: 15.6s\n",
      "585:\tlearn: 0.0001488\ttotal: 22s\tremaining: 15.6s\n",
      "586:\tlearn: 0.0001486\ttotal: 22.1s\tremaining: 15.5s\n",
      "587:\tlearn: 0.0001478\ttotal: 22.1s\tremaining: 15.5s\n",
      "588:\tlearn: 0.0001473\ttotal: 22.2s\tremaining: 15.5s\n",
      "589:\tlearn: 0.0001468\ttotal: 22.2s\tremaining: 15.4s\n",
      "590:\tlearn: 0.0001462\ttotal: 22.3s\tremaining: 15.4s\n",
      "591:\tlearn: 0.0001454\ttotal: 22.3s\tremaining: 15.4s\n",
      "592:\tlearn: 0.0001452\ttotal: 22.4s\tremaining: 15.3s\n",
      "593:\tlearn: 0.0001448\ttotal: 22.4s\tremaining: 15.3s\n",
      "594:\tlearn: 0.0001442\ttotal: 22.4s\tremaining: 15.3s\n",
      "595:\tlearn: 0.0001439\ttotal: 22.5s\tremaining: 15.2s\n",
      "596:\tlearn: 0.0001432\ttotal: 22.5s\tremaining: 15.2s\n",
      "597:\tlearn: 0.0001425\ttotal: 22.6s\tremaining: 15.2s\n",
      "598:\tlearn: 0.0001422\ttotal: 22.6s\tremaining: 15.1s\n",
      "599:\tlearn: 0.0001418\ttotal: 22.6s\tremaining: 15.1s\n",
      "600:\tlearn: 0.0001414\ttotal: 22.7s\tremaining: 15.1s\n",
      "601:\tlearn: 0.0001412\ttotal: 22.7s\tremaining: 15s\n",
      "602:\tlearn: 0.0001410\ttotal: 22.8s\tremaining: 15s\n",
      "603:\tlearn: 0.0001407\ttotal: 22.8s\tremaining: 15s\n",
      "604:\tlearn: 0.0001401\ttotal: 22.9s\tremaining: 14.9s\n",
      "605:\tlearn: 0.0001395\ttotal: 22.9s\tremaining: 14.9s\n",
      "606:\tlearn: 0.0001391\ttotal: 23s\tremaining: 14.9s\n",
      "607:\tlearn: 0.0001388\ttotal: 23s\tremaining: 14.9s\n",
      "608:\tlearn: 0.0001385\ttotal: 23.1s\tremaining: 14.8s\n",
      "609:\tlearn: 0.0001382\ttotal: 23.1s\tremaining: 14.8s\n",
      "610:\tlearn: 0.0001375\ttotal: 23.2s\tremaining: 14.8s\n",
      "611:\tlearn: 0.0001370\ttotal: 23.3s\tremaining: 14.7s\n",
      "612:\tlearn: 0.0001362\ttotal: 23.3s\tremaining: 14.7s\n",
      "613:\tlearn: 0.0001358\ttotal: 23.4s\tremaining: 14.7s\n",
      "614:\tlearn: 0.0001355\ttotal: 23.4s\tremaining: 14.7s\n",
      "615:\tlearn: 0.0001354\ttotal: 23.5s\tremaining: 14.6s\n",
      "616:\tlearn: 0.0001352\ttotal: 23.5s\tremaining: 14.6s\n",
      "617:\tlearn: 0.0001346\ttotal: 23.5s\tremaining: 14.5s\n",
      "618:\tlearn: 0.0001343\ttotal: 23.6s\tremaining: 14.5s\n",
      "619:\tlearn: 0.0001337\ttotal: 23.6s\tremaining: 14.5s\n",
      "620:\tlearn: 0.0001329\ttotal: 23.7s\tremaining: 14.5s\n",
      "621:\tlearn: 0.0001326\ttotal: 23.8s\tremaining: 14.5s\n",
      "622:\tlearn: 0.0001323\ttotal: 23.8s\tremaining: 14.4s\n",
      "623:\tlearn: 0.0001320\ttotal: 23.9s\tremaining: 14.4s\n",
      "624:\tlearn: 0.0001311\ttotal: 24s\tremaining: 14.4s\n",
      "625:\tlearn: 0.0001304\ttotal: 24s\tremaining: 14.4s\n",
      "626:\tlearn: 0.0001302\ttotal: 24.1s\tremaining: 14.3s\n",
      "627:\tlearn: 0.0001299\ttotal: 24.2s\tremaining: 14.3s\n",
      "628:\tlearn: 0.0001294\ttotal: 24.2s\tremaining: 14.3s\n",
      "629:\tlearn: 0.0001290\ttotal: 24.3s\tremaining: 14.2s\n",
      "630:\tlearn: 0.0001285\ttotal: 24.3s\tremaining: 14.2s\n",
      "631:\tlearn: 0.0001284\ttotal: 24.4s\tremaining: 14.2s\n",
      "632:\tlearn: 0.0001281\ttotal: 24.4s\tremaining: 14.1s\n",
      "633:\tlearn: 0.0001273\ttotal: 24.4s\tremaining: 14.1s\n",
      "634:\tlearn: 0.0001270\ttotal: 24.5s\tremaining: 14.1s\n",
      "635:\tlearn: 0.0001266\ttotal: 24.5s\tremaining: 14s\n",
      "636:\tlearn: 0.0001256\ttotal: 24.6s\tremaining: 14s\n",
      "637:\tlearn: 0.0001255\ttotal: 24.6s\tremaining: 14s\n",
      "638:\tlearn: 0.0001247\ttotal: 24.6s\tremaining: 13.9s\n",
      "639:\tlearn: 0.0001243\ttotal: 24.7s\tremaining: 13.9s\n",
      "640:\tlearn: 0.0001241\ttotal: 24.7s\tremaining: 13.9s\n",
      "641:\tlearn: 0.0001235\ttotal: 24.8s\tremaining: 13.8s\n",
      "642:\tlearn: 0.0001232\ttotal: 24.8s\tremaining: 13.8s\n",
      "643:\tlearn: 0.0001223\ttotal: 24.9s\tremaining: 13.7s\n",
      "644:\tlearn: 0.0001217\ttotal: 24.9s\tremaining: 13.7s\n",
      "645:\tlearn: 0.0001212\ttotal: 25s\tremaining: 13.7s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "646:\tlearn: 0.0001205\ttotal: 25s\tremaining: 13.6s\n",
      "647:\tlearn: 0.0001202\ttotal: 25s\tremaining: 13.6s\n",
      "648:\tlearn: 0.0001199\ttotal: 25.1s\tremaining: 13.6s\n",
      "649:\tlearn: 0.0001194\ttotal: 25.1s\tremaining: 13.5s\n",
      "650:\tlearn: 0.0001193\ttotal: 25.2s\tremaining: 13.5s\n",
      "651:\tlearn: 0.0001191\ttotal: 25.2s\tremaining: 13.4s\n",
      "652:\tlearn: 0.0001188\ttotal: 25.2s\tremaining: 13.4s\n",
      "653:\tlearn: 0.0001181\ttotal: 25.3s\tremaining: 13.4s\n",
      "654:\tlearn: 0.0001178\ttotal: 25.3s\tremaining: 13.3s\n",
      "655:\tlearn: 0.0001170\ttotal: 25.4s\tremaining: 13.3s\n",
      "656:\tlearn: 0.0001166\ttotal: 25.5s\tremaining: 13.3s\n",
      "657:\tlearn: 0.0001163\ttotal: 25.5s\tremaining: 13.3s\n",
      "658:\tlearn: 0.0001161\ttotal: 25.6s\tremaining: 13.2s\n",
      "659:\tlearn: 0.0001160\ttotal: 25.6s\tremaining: 13.2s\n",
      "660:\tlearn: 0.0001158\ttotal: 25.7s\tremaining: 13.2s\n",
      "661:\tlearn: 0.0001154\ttotal: 25.7s\tremaining: 13.1s\n",
      "662:\tlearn: 0.0001147\ttotal: 25.7s\tremaining: 13.1s\n",
      "663:\tlearn: 0.0001146\ttotal: 25.8s\tremaining: 13s\n",
      "664:\tlearn: 0.0001145\ttotal: 25.8s\tremaining: 13s\n",
      "665:\tlearn: 0.0001142\ttotal: 25.9s\tremaining: 13s\n",
      "666:\tlearn: 0.0001138\ttotal: 25.9s\tremaining: 12.9s\n",
      "667:\tlearn: 0.0001136\ttotal: 26s\tremaining: 12.9s\n",
      "668:\tlearn: 0.0001134\ttotal: 26s\tremaining: 12.9s\n",
      "669:\tlearn: 0.0001132\ttotal: 26s\tremaining: 12.8s\n",
      "670:\tlearn: 0.0001130\ttotal: 26.1s\tremaining: 12.8s\n",
      "671:\tlearn: 0.0001129\ttotal: 26.1s\tremaining: 12.7s\n",
      "672:\tlearn: 0.0001123\ttotal: 26.2s\tremaining: 12.7s\n",
      "673:\tlearn: 0.0001116\ttotal: 26.2s\tremaining: 12.7s\n",
      "674:\tlearn: 0.0001110\ttotal: 26.3s\tremaining: 12.6s\n",
      "675:\tlearn: 0.0001109\ttotal: 26.3s\tremaining: 12.6s\n",
      "676:\tlearn: 0.0001101\ttotal: 26.3s\tremaining: 12.6s\n",
      "677:\tlearn: 0.0001100\ttotal: 26.4s\tremaining: 12.5s\n",
      "678:\tlearn: 0.0001097\ttotal: 26.4s\tremaining: 12.5s\n",
      "679:\tlearn: 0.0001090\ttotal: 26.5s\tremaining: 12.5s\n",
      "680:\tlearn: 0.0001089\ttotal: 26.5s\tremaining: 12.4s\n",
      "681:\tlearn: 0.0001087\ttotal: 26.6s\tremaining: 12.4s\n",
      "682:\tlearn: 0.0001083\ttotal: 26.6s\tremaining: 12.3s\n",
      "683:\tlearn: 0.0001081\ttotal: 26.7s\tremaining: 12.3s\n",
      "684:\tlearn: 0.0001080\ttotal: 26.7s\tremaining: 12.3s\n",
      "685:\tlearn: 0.0001079\ttotal: 26.8s\tremaining: 12.2s\n",
      "686:\tlearn: 0.0001071\ttotal: 26.8s\tremaining: 12.2s\n",
      "687:\tlearn: 0.0001067\ttotal: 26.9s\tremaining: 12.2s\n",
      "688:\tlearn: 0.0001065\ttotal: 26.9s\tremaining: 12.1s\n",
      "689:\tlearn: 0.0001062\ttotal: 27s\tremaining: 12.1s\n",
      "690:\tlearn: 0.0001060\ttotal: 27s\tremaining: 12.1s\n",
      "691:\tlearn: 0.0001056\ttotal: 27s\tremaining: 12s\n",
      "692:\tlearn: 0.0001049\ttotal: 27.1s\tremaining: 12s\n",
      "693:\tlearn: 0.0001047\ttotal: 27.1s\tremaining: 12s\n",
      "694:\tlearn: 0.0001046\ttotal: 27.2s\tremaining: 11.9s\n",
      "695:\tlearn: 0.0001044\ttotal: 27.2s\tremaining: 11.9s\n",
      "696:\tlearn: 0.0001037\ttotal: 27.2s\tremaining: 11.8s\n",
      "697:\tlearn: 0.0001035\ttotal: 27.3s\tremaining: 11.8s\n",
      "698:\tlearn: 0.0001033\ttotal: 27.3s\tremaining: 11.8s\n",
      "699:\tlearn: 0.0001031\ttotal: 27.4s\tremaining: 11.7s\n",
      "700:\tlearn: 0.0001026\ttotal: 27.4s\tremaining: 11.7s\n",
      "701:\tlearn: 0.0001024\ttotal: 27.5s\tremaining: 11.7s\n",
      "702:\tlearn: 0.0001020\ttotal: 27.5s\tremaining: 11.6s\n",
      "703:\tlearn: 0.0001017\ttotal: 27.5s\tremaining: 11.6s\n",
      "704:\tlearn: 0.0001012\ttotal: 27.6s\tremaining: 11.5s\n",
      "705:\tlearn: 0.0001005\ttotal: 27.6s\tremaining: 11.5s\n",
      "706:\tlearn: 0.0001004\ttotal: 27.7s\tremaining: 11.5s\n",
      "707:\tlearn: 0.0001002\ttotal: 27.7s\tremaining: 11.4s\n",
      "708:\tlearn: 0.0000999\ttotal: 27.8s\tremaining: 11.4s\n",
      "709:\tlearn: 0.0000998\ttotal: 27.8s\tremaining: 11.4s\n",
      "710:\tlearn: 0.0000997\ttotal: 27.9s\tremaining: 11.3s\n",
      "711:\tlearn: 0.0000991\ttotal: 27.9s\tremaining: 11.3s\n",
      "712:\tlearn: 0.0000989\ttotal: 27.9s\tremaining: 11.2s\n",
      "713:\tlearn: 0.0000987\ttotal: 28s\tremaining: 11.2s\n",
      "714:\tlearn: 0.0000986\ttotal: 28s\tremaining: 11.2s\n",
      "715:\tlearn: 0.0000980\ttotal: 28s\tremaining: 11.1s\n",
      "716:\tlearn: 0.0000979\ttotal: 28.1s\tremaining: 11.1s\n",
      "717:\tlearn: 0.0000977\ttotal: 28.1s\tremaining: 11s\n",
      "718:\tlearn: 0.0000976\ttotal: 28.2s\tremaining: 11s\n",
      "719:\tlearn: 0.0000973\ttotal: 28.2s\tremaining: 11s\n",
      "720:\tlearn: 0.0000973\ttotal: 28.2s\tremaining: 10.9s\n",
      "721:\tlearn: 0.0000971\ttotal: 28.3s\tremaining: 10.9s\n",
      "722:\tlearn: 0.0000969\ttotal: 28.3s\tremaining: 10.8s\n",
      "723:\tlearn: 0.0000967\ttotal: 28.4s\tremaining: 10.8s\n",
      "724:\tlearn: 0.0000965\ttotal: 28.4s\tremaining: 10.8s\n",
      "725:\tlearn: 0.0000964\ttotal: 28.4s\tremaining: 10.7s\n",
      "726:\tlearn: 0.0000953\ttotal: 28.5s\tremaining: 10.7s\n",
      "727:\tlearn: 0.0000952\ttotal: 28.5s\tremaining: 10.7s\n",
      "728:\tlearn: 0.0000949\ttotal: 28.6s\tremaining: 10.6s\n",
      "729:\tlearn: 0.0000945\ttotal: 28.7s\tremaining: 10.6s\n",
      "730:\tlearn: 0.0000942\ttotal: 28.7s\tremaining: 10.6s\n",
      "731:\tlearn: 0.0000940\ttotal: 28.8s\tremaining: 10.5s\n",
      "732:\tlearn: 0.0000937\ttotal: 28.8s\tremaining: 10.5s\n",
      "733:\tlearn: 0.0000934\ttotal: 28.8s\tremaining: 10.5s\n",
      "734:\tlearn: 0.0000928\ttotal: 28.9s\tremaining: 10.4s\n",
      "735:\tlearn: 0.0000923\ttotal: 28.9s\tremaining: 10.4s\n",
      "736:\tlearn: 0.0000919\ttotal: 29s\tremaining: 10.3s\n",
      "737:\tlearn: 0.0000917\ttotal: 29s\tremaining: 10.3s\n",
      "738:\tlearn: 0.0000916\ttotal: 29.1s\tremaining: 10.3s\n",
      "739:\tlearn: 0.0000915\ttotal: 29.1s\tremaining: 10.2s\n",
      "740:\tlearn: 0.0000914\ttotal: 29.2s\tremaining: 10.2s\n",
      "741:\tlearn: 0.0000909\ttotal: 29.2s\tremaining: 10.2s\n",
      "742:\tlearn: 0.0000908\ttotal: 29.3s\tremaining: 10.1s\n",
      "743:\tlearn: 0.0000904\ttotal: 29.3s\tremaining: 10.1s\n",
      "744:\tlearn: 0.0000903\ttotal: 29.4s\tremaining: 10.1s\n",
      "745:\tlearn: 0.0000901\ttotal: 29.4s\tremaining: 10s\n",
      "746:\tlearn: 0.0000897\ttotal: 29.5s\tremaining: 9.99s\n",
      "747:\tlearn: 0.0000895\ttotal: 29.5s\tremaining: 9.95s\n",
      "748:\tlearn: 0.0000893\ttotal: 29.6s\tremaining: 9.91s\n",
      "749:\tlearn: 0.0000892\ttotal: 29.6s\tremaining: 9.87s\n",
      "750:\tlearn: 0.0000889\ttotal: 29.7s\tremaining: 9.84s\n",
      "751:\tlearn: 0.0000886\ttotal: 29.7s\tremaining: 9.8s\n",
      "752:\tlearn: 0.0000885\ttotal: 29.8s\tremaining: 9.77s\n",
      "753:\tlearn: 0.0000884\ttotal: 29.8s\tremaining: 9.73s\n",
      "754:\tlearn: 0.0000882\ttotal: 29.9s\tremaining: 9.69s\n",
      "755:\tlearn: 0.0000881\ttotal: 29.9s\tremaining: 9.65s\n",
      "756:\tlearn: 0.0000878\ttotal: 29.9s\tremaining: 9.61s\n",
      "757:\tlearn: 0.0000878\ttotal: 30s\tremaining: 9.57s\n",
      "758:\tlearn: 0.0000876\ttotal: 30s\tremaining: 9.53s\n",
      "759:\tlearn: 0.0000875\ttotal: 30s\tremaining: 9.49s\n",
      "760:\tlearn: 0.0000874\ttotal: 30.1s\tremaining: 9.45s\n",
      "761:\tlearn: 0.0000872\ttotal: 30.1s\tremaining: 9.41s\n",
      "762:\tlearn: 0.0000871\ttotal: 30.2s\tremaining: 9.37s\n",
      "763:\tlearn: 0.0000868\ttotal: 30.2s\tremaining: 9.33s\n",
      "764:\tlearn: 0.0000864\ttotal: 30.2s\tremaining: 9.29s\n",
      "765:\tlearn: 0.0000863\ttotal: 30.3s\tremaining: 9.25s\n",
      "766:\tlearn: 0.0000862\ttotal: 30.3s\tremaining: 9.21s\n",
      "767:\tlearn: 0.0000860\ttotal: 30.4s\tremaining: 9.17s\n",
      "768:\tlearn: 0.0000858\ttotal: 30.4s\tremaining: 9.13s\n",
      "769:\tlearn: 0.0000857\ttotal: 30.4s\tremaining: 9.09s\n",
      "770:\tlearn: 0.0000856\ttotal: 30.5s\tremaining: 9.06s\n",
      "771:\tlearn: 0.0000855\ttotal: 30.6s\tremaining: 9.02s\n",
      "772:\tlearn: 0.0000854\ttotal: 30.6s\tremaining: 8.99s\n",
      "773:\tlearn: 0.0000851\ttotal: 30.7s\tremaining: 8.96s\n",
      "774:\tlearn: 0.0000848\ttotal: 30.8s\tremaining: 8.93s\n",
      "775:\tlearn: 0.0000847\ttotal: 30.8s\tremaining: 8.89s\n",
      "776:\tlearn: 0.0000845\ttotal: 30.8s\tremaining: 8.85s\n",
      "777:\tlearn: 0.0000844\ttotal: 30.9s\tremaining: 8.81s\n",
      "778:\tlearn: 0.0000843\ttotal: 30.9s\tremaining: 8.77s\n",
      "779:\tlearn: 0.0000841\ttotal: 31s\tremaining: 8.73s\n",
      "780:\tlearn: 0.0000840\ttotal: 31s\tremaining: 8.69s\n",
      "781:\tlearn: 0.0000839\ttotal: 31s\tremaining: 8.65s\n",
      "782:\tlearn: 0.0000837\ttotal: 31.1s\tremaining: 8.61s\n",
      "783:\tlearn: 0.0000837\ttotal: 31.1s\tremaining: 8.57s\n",
      "784:\tlearn: 0.0000835\ttotal: 31.2s\tremaining: 8.54s\n",
      "785:\tlearn: 0.0000834\ttotal: 31.2s\tremaining: 8.5s\n",
      "786:\tlearn: 0.0000833\ttotal: 31.3s\tremaining: 8.47s\n",
      "787:\tlearn: 0.0000832\ttotal: 31.3s\tremaining: 8.43s\n",
      "788:\tlearn: 0.0000831\ttotal: 31.4s\tremaining: 8.39s\n",
      "789:\tlearn: 0.0000829\ttotal: 31.4s\tremaining: 8.36s\n",
      "790:\tlearn: 0.0000828\ttotal: 31.5s\tremaining: 8.32s\n",
      "791:\tlearn: 0.0000826\ttotal: 31.6s\tremaining: 8.29s\n",
      "792:\tlearn: 0.0000825\ttotal: 31.6s\tremaining: 8.26s\n",
      "793:\tlearn: 0.0000824\ttotal: 31.7s\tremaining: 8.22s\n",
      "794:\tlearn: 0.0000822\ttotal: 31.7s\tremaining: 8.18s\n",
      "795:\tlearn: 0.0000821\ttotal: 31.8s\tremaining: 8.15s\n",
      "796:\tlearn: 0.0000820\ttotal: 31.9s\tremaining: 8.11s\n",
      "797:\tlearn: 0.0000818\ttotal: 31.9s\tremaining: 8.07s\n",
      "798:\tlearn: 0.0000817\ttotal: 32s\tremaining: 8.04s\n",
      "799:\tlearn: 0.0000816\ttotal: 32s\tremaining: 8s\n",
      "800:\tlearn: 0.0000815\ttotal: 32s\tremaining: 7.96s\n",
      "801:\tlearn: 0.0000814\ttotal: 32.1s\tremaining: 7.92s\n",
      "802:\tlearn: 0.0000813\ttotal: 32.1s\tremaining: 7.87s\n",
      "803:\tlearn: 0.0000811\ttotal: 32.2s\tremaining: 7.84s\n",
      "804:\tlearn: 0.0000809\ttotal: 32.2s\tremaining: 7.8s\n",
      "805:\tlearn: 0.0000808\ttotal: 32.3s\tremaining: 7.77s\n",
      "806:\tlearn: 0.0000807\ttotal: 32.3s\tremaining: 7.73s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "807:\tlearn: 0.0000806\ttotal: 32.4s\tremaining: 7.69s\n",
      "808:\tlearn: 0.0000802\ttotal: 32.4s\tremaining: 7.65s\n",
      "809:\tlearn: 0.0000801\ttotal: 32.5s\tremaining: 7.61s\n",
      "810:\tlearn: 0.0000800\ttotal: 32.5s\tremaining: 7.57s\n",
      "811:\tlearn: 0.0000795\ttotal: 32.6s\tremaining: 7.54s\n",
      "812:\tlearn: 0.0000794\ttotal: 32.6s\tremaining: 7.5s\n",
      "813:\tlearn: 0.0000793\ttotal: 32.7s\tremaining: 7.46s\n",
      "814:\tlearn: 0.0000792\ttotal: 32.7s\tremaining: 7.43s\n",
      "815:\tlearn: 0.0000791\ttotal: 32.8s\tremaining: 7.39s\n",
      "816:\tlearn: 0.0000790\ttotal: 32.8s\tremaining: 7.35s\n",
      "817:\tlearn: 0.0000788\ttotal: 32.9s\tremaining: 7.31s\n",
      "818:\tlearn: 0.0000787\ttotal: 32.9s\tremaining: 7.27s\n",
      "819:\tlearn: 0.0000786\ttotal: 33s\tremaining: 7.23s\n",
      "820:\tlearn: 0.0000783\ttotal: 33s\tremaining: 7.2s\n",
      "821:\tlearn: 0.0000783\ttotal: 33.1s\tremaining: 7.16s\n",
      "822:\tlearn: 0.0000782\ttotal: 33.1s\tremaining: 7.12s\n",
      "823:\tlearn: 0.0000780\ttotal: 33.2s\tremaining: 7.08s\n",
      "824:\tlearn: 0.0000778\ttotal: 33.2s\tremaining: 7.04s\n",
      "825:\tlearn: 0.0000777\ttotal: 33.3s\tremaining: 7s\n",
      "826:\tlearn: 0.0000777\ttotal: 33.3s\tremaining: 6.96s\n",
      "827:\tlearn: 0.0000773\ttotal: 33.3s\tremaining: 6.92s\n",
      "828:\tlearn: 0.0000771\ttotal: 33.4s\tremaining: 6.88s\n",
      "829:\tlearn: 0.0000770\ttotal: 33.4s\tremaining: 6.84s\n",
      "830:\tlearn: 0.0000767\ttotal: 33.4s\tremaining: 6.8s\n",
      "831:\tlearn: 0.0000759\ttotal: 33.5s\tremaining: 6.76s\n",
      "832:\tlearn: 0.0000758\ttotal: 33.5s\tremaining: 6.72s\n",
      "833:\tlearn: 0.0000757\ttotal: 33.6s\tremaining: 6.68s\n",
      "834:\tlearn: 0.0000755\ttotal: 33.6s\tremaining: 6.64s\n",
      "835:\tlearn: 0.0000753\ttotal: 33.7s\tremaining: 6.6s\n",
      "836:\tlearn: 0.0000749\ttotal: 33.7s\tremaining: 6.57s\n",
      "837:\tlearn: 0.0000748\ttotal: 33.8s\tremaining: 6.53s\n",
      "838:\tlearn: 0.0000745\ttotal: 33.8s\tremaining: 6.49s\n",
      "839:\tlearn: 0.0000743\ttotal: 33.9s\tremaining: 6.45s\n",
      "840:\tlearn: 0.0000741\ttotal: 33.9s\tremaining: 6.41s\n",
      "841:\tlearn: 0.0000739\ttotal: 33.9s\tremaining: 6.37s\n",
      "842:\tlearn: 0.0000737\ttotal: 34s\tremaining: 6.33s\n",
      "843:\tlearn: 0.0000736\ttotal: 34s\tremaining: 6.29s\n",
      "844:\tlearn: 0.0000733\ttotal: 34.1s\tremaining: 6.25s\n",
      "845:\tlearn: 0.0000731\ttotal: 34.1s\tremaining: 6.21s\n",
      "846:\tlearn: 0.0000730\ttotal: 34.2s\tremaining: 6.17s\n",
      "847:\tlearn: 0.0000727\ttotal: 34.2s\tremaining: 6.13s\n",
      "848:\tlearn: 0.0000725\ttotal: 34.2s\tremaining: 6.09s\n",
      "849:\tlearn: 0.0000722\ttotal: 34.3s\tremaining: 6.05s\n",
      "850:\tlearn: 0.0000721\ttotal: 34.3s\tremaining: 6.01s\n",
      "851:\tlearn: 0.0000720\ttotal: 34.4s\tremaining: 5.97s\n",
      "852:\tlearn: 0.0000718\ttotal: 34.4s\tremaining: 5.93s\n",
      "853:\tlearn: 0.0000718\ttotal: 34.4s\tremaining: 5.89s\n",
      "854:\tlearn: 0.0000716\ttotal: 34.5s\tremaining: 5.85s\n",
      "855:\tlearn: 0.0000715\ttotal: 34.5s\tremaining: 5.81s\n",
      "856:\tlearn: 0.0000714\ttotal: 34.6s\tremaining: 5.77s\n",
      "857:\tlearn: 0.0000712\ttotal: 34.6s\tremaining: 5.73s\n",
      "858:\tlearn: 0.0000708\ttotal: 34.6s\tremaining: 5.69s\n",
      "859:\tlearn: 0.0000705\ttotal: 34.7s\tremaining: 5.64s\n",
      "860:\tlearn: 0.0000705\ttotal: 34.7s\tremaining: 5.61s\n",
      "861:\tlearn: 0.0000702\ttotal: 34.8s\tremaining: 5.57s\n",
      "862:\tlearn: 0.0000701\ttotal: 34.8s\tremaining: 5.53s\n",
      "863:\tlearn: 0.0000700\ttotal: 34.8s\tremaining: 5.49s\n",
      "864:\tlearn: 0.0000699\ttotal: 34.9s\tremaining: 5.44s\n",
      "865:\tlearn: 0.0000697\ttotal: 34.9s\tremaining: 5.4s\n",
      "866:\tlearn: 0.0000697\ttotal: 35s\tremaining: 5.37s\n",
      "867:\tlearn: 0.0000694\ttotal: 35s\tremaining: 5.33s\n",
      "868:\tlearn: 0.0000693\ttotal: 35.1s\tremaining: 5.29s\n",
      "869:\tlearn: 0.0000692\ttotal: 35.1s\tremaining: 5.25s\n",
      "870:\tlearn: 0.0000690\ttotal: 35.1s\tremaining: 5.21s\n",
      "871:\tlearn: 0.0000689\ttotal: 35.2s\tremaining: 5.16s\n",
      "872:\tlearn: 0.0000687\ttotal: 35.2s\tremaining: 5.13s\n",
      "873:\tlearn: 0.0000685\ttotal: 35.3s\tremaining: 5.08s\n",
      "874:\tlearn: 0.0000684\ttotal: 35.3s\tremaining: 5.04s\n",
      "875:\tlearn: 0.0000681\ttotal: 35.4s\tremaining: 5s\n",
      "876:\tlearn: 0.0000681\ttotal: 35.4s\tremaining: 4.96s\n",
      "877:\tlearn: 0.0000680\ttotal: 35.4s\tremaining: 4.92s\n",
      "878:\tlearn: 0.0000679\ttotal: 35.5s\tremaining: 4.88s\n",
      "879:\tlearn: 0.0000678\ttotal: 35.5s\tremaining: 4.84s\n",
      "880:\tlearn: 0.0000676\ttotal: 35.5s\tremaining: 4.8s\n",
      "881:\tlearn: 0.0000674\ttotal: 35.6s\tremaining: 4.76s\n",
      "882:\tlearn: 0.0000673\ttotal: 35.6s\tremaining: 4.72s\n",
      "883:\tlearn: 0.0000673\ttotal: 35.7s\tremaining: 4.68s\n",
      "884:\tlearn: 0.0000670\ttotal: 35.7s\tremaining: 4.64s\n",
      "885:\tlearn: 0.0000669\ttotal: 35.8s\tremaining: 4.6s\n",
      "886:\tlearn: 0.0000668\ttotal: 35.8s\tremaining: 4.56s\n",
      "887:\tlearn: 0.0000666\ttotal: 35.8s\tremaining: 4.52s\n",
      "888:\tlearn: 0.0000665\ttotal: 35.9s\tremaining: 4.48s\n",
      "889:\tlearn: 0.0000663\ttotal: 35.9s\tremaining: 4.44s\n",
      "890:\tlearn: 0.0000661\ttotal: 36s\tremaining: 4.4s\n",
      "891:\tlearn: 0.0000660\ttotal: 36s\tremaining: 4.36s\n",
      "892:\tlearn: 0.0000659\ttotal: 36s\tremaining: 4.32s\n",
      "893:\tlearn: 0.0000658\ttotal: 36.1s\tremaining: 4.28s\n",
      "894:\tlearn: 0.0000657\ttotal: 36.1s\tremaining: 4.24s\n",
      "895:\tlearn: 0.0000656\ttotal: 36.2s\tremaining: 4.2s\n",
      "896:\tlearn: 0.0000655\ttotal: 36.2s\tremaining: 4.16s\n",
      "897:\tlearn: 0.0000654\ttotal: 36.3s\tremaining: 4.12s\n",
      "898:\tlearn: 0.0000654\ttotal: 36.3s\tremaining: 4.08s\n",
      "899:\tlearn: 0.0000652\ttotal: 36.4s\tremaining: 4.04s\n",
      "900:\tlearn: 0.0000651\ttotal: 36.4s\tremaining: 4s\n",
      "901:\tlearn: 0.0000650\ttotal: 36.5s\tremaining: 3.96s\n",
      "902:\tlearn: 0.0000649\ttotal: 36.5s\tremaining: 3.92s\n",
      "903:\tlearn: 0.0000648\ttotal: 36.6s\tremaining: 3.88s\n",
      "904:\tlearn: 0.0000647\ttotal: 36.6s\tremaining: 3.84s\n",
      "905:\tlearn: 0.0000646\ttotal: 36.6s\tremaining: 3.8s\n",
      "906:\tlearn: 0.0000645\ttotal: 36.7s\tremaining: 3.76s\n",
      "907:\tlearn: 0.0000644\ttotal: 36.7s\tremaining: 3.72s\n",
      "908:\tlearn: 0.0000643\ttotal: 36.8s\tremaining: 3.68s\n",
      "909:\tlearn: 0.0000642\ttotal: 36.8s\tremaining: 3.64s\n",
      "910:\tlearn: 0.0000642\ttotal: 36.9s\tremaining: 3.6s\n",
      "911:\tlearn: 0.0000640\ttotal: 36.9s\tremaining: 3.56s\n",
      "912:\tlearn: 0.0000638\ttotal: 36.9s\tremaining: 3.52s\n",
      "913:\tlearn: 0.0000638\ttotal: 37s\tremaining: 3.48s\n",
      "914:\tlearn: 0.0000637\ttotal: 37s\tremaining: 3.44s\n",
      "915:\tlearn: 0.0000636\ttotal: 37.1s\tremaining: 3.4s\n",
      "916:\tlearn: 0.0000634\ttotal: 37.1s\tremaining: 3.36s\n",
      "917:\tlearn: 0.0000633\ttotal: 37.1s\tremaining: 3.32s\n",
      "918:\tlearn: 0.0000631\ttotal: 37.2s\tremaining: 3.28s\n",
      "919:\tlearn: 0.0000630\ttotal: 37.2s\tremaining: 3.24s\n",
      "920:\tlearn: 0.0000628\ttotal: 37.3s\tremaining: 3.2s\n",
      "921:\tlearn: 0.0000627\ttotal: 37.3s\tremaining: 3.16s\n",
      "922:\tlearn: 0.0000626\ttotal: 37.4s\tremaining: 3.12s\n",
      "923:\tlearn: 0.0000624\ttotal: 37.5s\tremaining: 3.08s\n",
      "924:\tlearn: 0.0000621\ttotal: 37.5s\tremaining: 3.04s\n",
      "925:\tlearn: 0.0000621\ttotal: 37.6s\tremaining: 3s\n",
      "926:\tlearn: 0.0000620\ttotal: 37.6s\tremaining: 2.96s\n",
      "927:\tlearn: 0.0000618\ttotal: 37.6s\tremaining: 2.92s\n",
      "928:\tlearn: 0.0000617\ttotal: 37.7s\tremaining: 2.88s\n",
      "929:\tlearn: 0.0000617\ttotal: 37.7s\tremaining: 2.84s\n",
      "930:\tlearn: 0.0000616\ttotal: 37.8s\tremaining: 2.8s\n",
      "931:\tlearn: 0.0000614\ttotal: 37.8s\tremaining: 2.76s\n",
      "932:\tlearn: 0.0000613\ttotal: 37.8s\tremaining: 2.72s\n",
      "933:\tlearn: 0.0000609\ttotal: 37.9s\tremaining: 2.67s\n",
      "934:\tlearn: 0.0000609\ttotal: 37.9s\tremaining: 2.63s\n",
      "935:\tlearn: 0.0000608\ttotal: 37.9s\tremaining: 2.59s\n",
      "936:\tlearn: 0.0000606\ttotal: 38s\tremaining: 2.55s\n",
      "937:\tlearn: 0.0000605\ttotal: 38s\tremaining: 2.51s\n",
      "938:\tlearn: 0.0000604\ttotal: 38.1s\tremaining: 2.47s\n",
      "939:\tlearn: 0.0000604\ttotal: 38.1s\tremaining: 2.43s\n",
      "940:\tlearn: 0.0000601\ttotal: 38.2s\tremaining: 2.39s\n",
      "941:\tlearn: 0.0000599\ttotal: 38.2s\tremaining: 2.35s\n",
      "942:\tlearn: 0.0000598\ttotal: 38.3s\tremaining: 2.31s\n",
      "943:\tlearn: 0.0000597\ttotal: 38.3s\tremaining: 2.27s\n",
      "944:\tlearn: 0.0000596\ttotal: 38.3s\tremaining: 2.23s\n",
      "945:\tlearn: 0.0000596\ttotal: 38.4s\tremaining: 2.19s\n",
      "946:\tlearn: 0.0000593\ttotal: 38.4s\tremaining: 2.15s\n",
      "947:\tlearn: 0.0000591\ttotal: 38.5s\tremaining: 2.11s\n",
      "948:\tlearn: 0.0000590\ttotal: 38.5s\tremaining: 2.07s\n",
      "949:\tlearn: 0.0000589\ttotal: 38.6s\tremaining: 2.03s\n",
      "950:\tlearn: 0.0000588\ttotal: 38.6s\tremaining: 1.99s\n",
      "951:\tlearn: 0.0000587\ttotal: 38.6s\tremaining: 1.95s\n",
      "952:\tlearn: 0.0000586\ttotal: 38.7s\tremaining: 1.91s\n",
      "953:\tlearn: 0.0000585\ttotal: 38.7s\tremaining: 1.87s\n",
      "954:\tlearn: 0.0000582\ttotal: 38.8s\tremaining: 1.83s\n",
      "955:\tlearn: 0.0000581\ttotal: 38.8s\tremaining: 1.79s\n",
      "956:\tlearn: 0.0000581\ttotal: 38.9s\tremaining: 1.75s\n",
      "957:\tlearn: 0.0000579\ttotal: 38.9s\tremaining: 1.71s\n",
      "958:\tlearn: 0.0000578\ttotal: 38.9s\tremaining: 1.66s\n",
      "959:\tlearn: 0.0000577\ttotal: 39s\tremaining: 1.62s\n",
      "960:\tlearn: 0.0000576\ttotal: 39s\tremaining: 1.58s\n",
      "961:\tlearn: 0.0000576\ttotal: 39.1s\tremaining: 1.54s\n",
      "962:\tlearn: 0.0000574\ttotal: 39.1s\tremaining: 1.5s\n",
      "963:\tlearn: 0.0000572\ttotal: 39.2s\tremaining: 1.46s\n",
      "964:\tlearn: 0.0000570\ttotal: 39.2s\tremaining: 1.42s\n",
      "965:\tlearn: 0.0000569\ttotal: 39.3s\tremaining: 1.38s\n",
      "966:\tlearn: 0.0000567\ttotal: 39.4s\tremaining: 1.34s\n",
      "967:\tlearn: 0.0000566\ttotal: 39.4s\tremaining: 1.3s\n",
      "968:\tlearn: 0.0000566\ttotal: 39.5s\tremaining: 1.26s\n",
      "969:\tlearn: 0.0000564\ttotal: 39.5s\tremaining: 1.22s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "970:\tlearn: 0.0000562\ttotal: 39.5s\tremaining: 1.18s\n",
      "971:\tlearn: 0.0000562\ttotal: 39.6s\tremaining: 1.14s\n",
      "972:\tlearn: 0.0000559\ttotal: 39.6s\tremaining: 1.1s\n",
      "973:\tlearn: 0.0000558\ttotal: 39.7s\tremaining: 1.06s\n",
      "974:\tlearn: 0.0000557\ttotal: 39.7s\tremaining: 1.02s\n",
      "975:\tlearn: 0.0000556\ttotal: 39.8s\tremaining: 978ms\n",
      "976:\tlearn: 0.0000555\ttotal: 39.8s\tremaining: 938ms\n",
      "977:\tlearn: 0.0000554\ttotal: 39.9s\tremaining: 897ms\n",
      "978:\tlearn: 0.0000552\ttotal: 39.9s\tremaining: 856ms\n",
      "979:\tlearn: 0.0000551\ttotal: 40s\tremaining: 816ms\n",
      "980:\tlearn: 0.0000550\ttotal: 40s\tremaining: 775ms\n",
      "981:\tlearn: 0.0000548\ttotal: 40s\tremaining: 734ms\n",
      "982:\tlearn: 0.0000547\ttotal: 40.1s\tremaining: 693ms\n",
      "983:\tlearn: 0.0000547\ttotal: 40.1s\tremaining: 652ms\n",
      "984:\tlearn: 0.0000546\ttotal: 40.2s\tremaining: 611ms\n",
      "985:\tlearn: 0.0000544\ttotal: 40.2s\tremaining: 571ms\n",
      "986:\tlearn: 0.0000544\ttotal: 40.2s\tremaining: 530ms\n",
      "987:\tlearn: 0.0000543\ttotal: 40.3s\tremaining: 489ms\n",
      "988:\tlearn: 0.0000541\ttotal: 40.3s\tremaining: 449ms\n",
      "989:\tlearn: 0.0000540\ttotal: 40.4s\tremaining: 408ms\n",
      "990:\tlearn: 0.0000540\ttotal: 40.4s\tremaining: 367ms\n",
      "991:\tlearn: 0.0000540\ttotal: 40.4s\tremaining: 326ms\n",
      "992:\tlearn: 0.0000539\ttotal: 40.5s\tremaining: 285ms\n",
      "993:\tlearn: 0.0000538\ttotal: 40.5s\tremaining: 245ms\n",
      "994:\tlearn: 0.0000536\ttotal: 40.6s\tremaining: 204ms\n",
      "995:\tlearn: 0.0000536\ttotal: 40.6s\tremaining: 163ms\n",
      "996:\tlearn: 0.0000534\ttotal: 40.6s\tremaining: 122ms\n",
      "997:\tlearn: 0.0000534\ttotal: 40.7s\tremaining: 81.5ms\n",
      "998:\tlearn: 0.0000533\ttotal: 40.7s\tremaining: 40.8ms\n",
      "999:\tlearn: 0.0000533\ttotal: 40.8s\tremaining: 0us\n",
      "AUC-ROC Score (Ensemble): 0.9745396822206653\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Ensemble Model Evaluation:\n",
      "Accuracy: 0.9995435553526912\n",
      "Precision: 0.9736842105263158\n",
      "Recall: 0.7551020408163265\n",
      "F1 Score: 0.8505747126436782\n",
      "Selected Features:\n",
      "Index(['Class', 'V17', 'V14', 'V12', 'V10', 'V16', 'V3', 'V7', 'V11', 'V4',\n",
      "       'V18', 'V1', 'V9', 'V5', 'V2', 'V6', 'V21', 'V19'],\n",
      "      dtype='object')\n",
      "\n",
      "Confusion Matrix:\n",
      "[[56862     2]\n",
      " [   24    74]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqsAAAIjCAYAAAAk+FJEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA4BklEQVR4nO3de3zP9f//8ft7Y+djznPYMERhLPkiRk4JYdXK4WOEklI5hU+JIStEUVKRU3xSkUJCDqn4FDKpj2TMIefTsGFm79fvD7+9623D3ow9227Xy2WXi/fr/Xq/Xo/32y5vN6+93q/ZLMuyBAAAABjILa8HAAAAAK6GWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFkOt27typFi1aKDAwUDabTYsWLcrV7e/Zs0c2m00zZ87M1e3+kzVu3FiNGzfO6zHypbCwMLVp0+a6661du1Y2m01r16699UMBBQixCuRTu3bt0lNPPaUKFSrIy8tLAQEBatCggd566y2dP3/+lu47NjZW27Zt06uvvqo5c+bonnvuuaX7u526desmm82mgICAbF/HnTt3ymazyWazafz48S5v/+DBgxoxYoQSEhJyYdrbIywszPGcr/x64IEH8no8AP9whfJ6AAC5b+nSpXr00Ufl6emprl276u6779bFixf1/fffa9CgQfrtt9/0/vvv35J9nz9/Xhs2bNBLL72kZ5999pbsIzQ0VOfPn1fhwoVvyfavp1ChQjp37pwWL16smJgYp/vmzp0rLy8vXbhw4Ya2ffDgQcXFxSksLEwRERE5ftyKFStuaH+5JSIiQgMGDMiyPCQkJA+mAZCfEKtAPpOUlKTHH39coaGhWr16tUqVKuW475lnnlFiYqKWLl16y/Z/7NgxSVJQUNAt24fNZpOXl9ct2/71eHp6qkGDBvrPf/6TJVbnzZun1q1ba8GCBbdllnPnzsnHx0ceHh63ZX9XU7p0aXXp0iVPZwCQP3EaAJDPjB07VikpKZo+fbpTqGYKDw/X888/77h96dIljRo1ShUrVpSnp6fCwsL073//W2lpaU6Pyzxv7/vvv9e9994rLy8vVahQQbNnz3asM2LECIWGhkqSBg0aJJvNprCwMEmXf3ye+ee/GzFihGw2m9OylStX6r777lNQUJD8/PxUpUoV/fvf/3bcf7VzVlevXq2GDRvK19dXQUFBateunbZv357t/hITE9WtWzcFBQUpMDBQ3bt317lz567+wl6hU6dOWrZsmZKTkx3LNm7cqJ07d6pTp05Z1j958qQGDhyo6tWry8/PTwEBAWrVqpW2bt3qWGft2rWqU6eOJKl79+6OH6VnPs/GjRvr7rvv1ubNm9WoUSP5+Pg4Xpcrz1mNjY2Vl5dXluffsmVLBQcH6+DBgzl+rrmlW7du8vPz04EDB9S+fXv5+fmpWLFiGjhwoDIyMpzW/fjjjxUZGSl/f38FBASoevXqeuutt5zWSU5O1gsvvKCyZcvK09NT4eHhev3112W32x3rZH6vjB8/Xu+8844qVKggHx8ftWjRQvv375dlWRo1apTKlCkjb29vtWvXTidPnsx2/hUrVigiIkJeXl6qVq2aFi5cmKPn/eOPP+qBBx5QYGCgfHx8FBUVpR9++MHFVw8ouIhVIJ9ZvHixKlSooPr16+do/Z49e+qVV15R7dq1NXHiREVFRSk+Pl6PP/54lnUTExP1yCOPqHnz5nrjjTcUHBysbt266bfffpMkRUdHa+LEiZKkjh07as6cOXrzzTddmv+3335TmzZtlJaWppEjR+qNN97QQw89dN1/3L/55hu1bNlSR48e1YgRI9S/f3+tX79eDRo00J49e7KsHxMTo7Nnzyo+Pl4xMTGaOXOm4uLicjxndHS0bDabU7DMmzdPd955p2rXrp1l/d27d2vRokVq06aNJkyYoEGDBmnbtm2KiopyhGPVqlU1cuRISdKTTz6pOXPmaM6cOWrUqJFjOydOnFCrVq0UERGhN998U02aNMl2vrfeekvFihVTbGysIwTfe+89rVixQpMnT871H8+np6fr+PHjWb6uPK83IyNDLVu2VJEiRTR+/HhFRUXpjTfecDotZeXKlerYsaOCg4P1+uuv67XXXlPjxo2dvgfOnTunqKgoffTRR+ratasmTZqkBg0aaOjQoerfv3+W+ebOnaspU6aob9++GjBggL799lvFxMTo5Zdf1tdff63BgwfrySef1OLFizVw4MAsj9+5c6cee+wxtWrVSvHx8SpUqJAeffRRrVy58pqvy+rVq9WoUSOdOXNGw4cP15gxY5ScnKz7779fP/30k6svM1AwWQDyjdOnT1uSrHbt2uVo/YSEBEuS1bNnT6flAwcOtCRZq1evdiwLDQ21JFnr1q1zLDt69Kjl6elpDRgwwLEsKSnJkmSNGzfOaZuxsbFWaGholhmGDx9u/f2taOLEiZYk69ixY1edO3MfM2bMcCyLiIiwihcvbp04ccKxbOvWrZabm5vVtWvXLPt74oknnLbZoUMHq0iRIlfd59+fh6+vr2VZlvXII49YTZs2tSzLsjIyMqySJUtacXFx2b4GFy5csDIyMrI8D09PT2vkyJGOZRs3bszy3DJFRUVZkqypU6dme19UVJTTsuXLl1uSrNGjR1u7d++2/Pz8rPbt21/3Oboq83sju6/4+HjHerGxsZYkp+drWZZVq1YtKzIy0nH7+eeftwICAqxLly5ddZ+jRo2yfH19rT/++MNp+ZAhQyx3d3dr3759lmX99b1SrFgxKzk52bHe0KFDLUlWzZo1rfT0dMfyjh07Wh4eHtaFCxeyPL8FCxY4lp0+fdoqVaqUVatWLceyNWvWWJKsNWvWWJZlWXa73apUqZLVsmVLy263O9Y7d+6cVb58eat58+ZXfX4A/sKRVSAfOXPmjCTJ398/R+t/9dVXkpTlSFTmB2WuPLe1WrVqatiwoeN2sWLFVKVKFe3evfuGZ75S5rmuX3zxhdOPc6/l0KFDSkhIULdu3XTHHXc4lteoUUPNmzd3PM+/6927t9Pthg0b6sSJE47XMCc6deqktWvX6vDhw1q9erUOHz6c7SkA0uXzXN3cLr/lZmRk6MSJE45THH7++ecc79PT01Pdu3fP0botWrTQU089pZEjRyo6OlpeXl567733crwvV9StW1crV67M8tWxY8cs62b32v/9eygoKEipqanXPGr56aefqmHDhgoODnY6ktusWTNlZGRo3bp1Tus/+uijCgwMdJpXkrp06aJChQo5Lb948aIOHDjg9PiQkBB16NDBcTsgIEBdu3bVli1bdPjw4WxnTEhIcJwWcuLECceMqampatq0qdatW5fj73GgIOMDVkA+EhAQIEk6e/Zsjtbfu3ev3NzcFB4e7rS8ZMmSCgoK0t69e52WlytXLss2goODderUqRucOKvHHntM06ZNU8+ePTVkyBA1bdpU0dHReuSRRxyxl93zkKQqVapkua9q1apavny5UlNT5evr61h+5XMJDg6WJJ06dcrxOl7Pgw8+KH9/f82fP18JCQmqU6eOwsPDsz3twG6366233tKUKVOUlJTkdI5mkSJFcrQ/6fIHmVz5MNX48eP1xRdfKCEhQfPmzVPx4sWv+5hjx445zefn5yc/P79rPqZo0aJq1qzZdbft5eWlYsWKOS278nuoT58++uSTT9SqVSuVLl1aLVq0UExMjNNlsHbu3Klffvkly7YyHT161On2lX/fmeFatmzZbJdf+T0dHh6e5dzqypUrS7p8XmzJkiWzzLBz505Jl88fvprTp087vvcAZI9YBfKRgIAAhYSE6Ndff3XpcVf+I3w17u7u2S63LOuG93HlB2u8vb21bt06rVmzRkuXLtXXX3+t+fPn6/7779eKFSuuOoOrbua5ZPL09FR0dLRmzZql3bt3a8SIEVddd8yYMRo2bJieeOIJjRo1SnfccYfc3Nz0wgsvuHR0zdvbO8frStKWLVsc4bZt27Zsj3ReqU6dOk7/URk+fPg1n5srcvL3V7x4cSUkJGj58uVatmyZli1bphkzZqhr166aNWuWpMvx37x5c7344ovZbiMzJK+339z4PriazL/XcePGXfUyZNf7TwAAYhXId9q0aaP3339fGzZsUL169a65bmhoqOx2u3bu3KmqVas6lh85ckTJycmOT/bnhuDgYKdPzme68uitJLm5ualp06Zq2rSpJkyYoDFjxuill17SmjVrsj16lznnjh07stz3+++/q2jRok5HVXNTp06d9OGHH8rNzS3bD6Vl+uyzz9SkSRNNnz7daXlycrKKFi3quJ3T/zjkRGpqqrp3765q1aqpfv36Gjt2rDp06OC44sDVzJ071+mDURUqVMi1mXLKw8NDbdu2Vdu2bWW329WnTx+99957GjZsmMLDw1WxYkWlpKTk6GhubkhMTJRlWU5/P3/88YckZXuVC0mqWLGipMv/ibxdcwL5EeesAvnMiy++KF9fX/Xs2VNHjhzJcv+uXbsclwB68MEHJSnLJ/YnTJggSWrdunWuzVWxYkWdPn1av/zyi2PZoUOH9Pnnnzutl91lgzKPSl15Oa1MpUqVUkREhGbNmuUUxL/++qtWrFjheJ63QpMmTTRq1Ci9/fbb2f4oOJO7u3uWo3WffvpplnMjM6M6u7B31eDBg7Vv3z7NmjVLEyZMUFhYmGJjY6/6OmZq0KCBmjVr5vi63bF64sQJp9tubm6qUaOGpL++B2JiYrRhwwYtX748y+OTk5N16dKlXJ3p4MGDTt+rZ86c0ezZsxUREXHVv/fIyEhVrFhR48ePV0pKSpb7M69JDODaOLIK5DMVK1bUvHnz9Nhjj6lq1apOv8Fq/fr1+vTTT9WtWzdJUs2aNRUbG6v3339fycnJioqK0k8//aRZs2apffv2V70s0o14/PHHNXjwYHXo0EHPPfeczp07p3fffVeVK1d2+oDRyJEjtW7dOrVu3VqhoaE6evSopkyZojJlyui+++676vbHjRunVq1aqV69eurRo4fOnz+vyZMnKzAwMNd+hJ0dNzc3vfzyy9ddr02bNho5cqS6d++u+vXra9u2bZo7d26WEKxYsaKCgoI0depU+fv7y9fXV3Xr1lX58uVdmmv16tWaMmWKhg8f7riU1owZM9S4cWMNGzZMY8eOdWl713PgwAF99NFHWZb7+fmpffv2Lm2rZ8+eOnnypO6//36VKVNGe/fu1eTJkxUREeH4CcCgQYP05Zdfqk2bNurWrZsiIyOVmpqqbdu26bPPPtOePXucjljfrMqVK6tHjx7auHGjSpQooQ8//FBHjhzRjBkzrvoYNzc3TZs2Ta1atdJdd92l7t27q3Tp0jpw4IDWrFmjgIAALV68ONdmBPKtPL0WAYBb5o8//rB69eplhYWFWR4eHpa/v7/VoEEDa/LkyU6X5UlPT7fi4uKs8uXLW4ULF7bKli1rDR061Gkdy7p8+Z7WrVtn2c+Vl0y62qWrLMuyVqxYYd19992Wh4eHVaVKFeujjz7KcumqVatWWe3atbNCQkIsDw8PKyQkxOrYsaPTJYqyu3SVZVnWN998YzVo0MDy9va2AgICrLZt21r/+9//nNbJ3N+Vl8aaMWOGJclKSkq66mtqWc6Xrrqaq126asCAAVapUqUsb29vq0GDBtaGDRuyveTUF198YVWrVs0qVKiQ0/OMioqy7rrrrmz3+fftnDlzxgoNDbVq167tdFkmy7Ksfv36WW5ubtaGDRuu+Rxcca1LV/39cmVXe+2u/B747LPPrBYtWljFixe3PDw8rHLlyllPPfWUdejQIafHnT171ho6dKgVHh5ueXh4WEWLFrXq169vjR8/3rp48aJlWVf/fsy8zNSnn37qtDzz+2Djxo1Oz69169bW8uXLrRo1alienp7WnXfemeWxV166KtOWLVus6Ohoq0iRIpanp6cVGhpqxcTEWKtWrbr+iwvAsllWLpxFDgAAANwCnLMKAAAAYxGrAAAAMBaxCgAAAGMRqwAAADAWsQoAAABjEasAAAAwFrEKAAAAY+XL32DlXevZvB4BAHLVqY1v5/UIAJCrvHJYoRxZBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLEK5fUAwO3y0lMP6uXeDzot25F0WBHRox2369YorxHPtFGd6mHKyLDrlz8OqG2fd3QhLV2SFF6uuMb0a696NSvIo7C7ft15UHFTlmjdpp1O2+3Stq6e63K/KoUW15nUC1q4cov6vfaJJKlhZCX17dJE99wVqgA/LyXuO6Y3Z32jj5dtusWvAABkb/oH72nVyhVKStotTy8vRUTU0gv9ByqsfIW8Hg0gVlGw/JZ4UK17T3bcvpRhd/y5bo3y+uLtPho/Y4X6v/6pLmXYVaNyadntlmOdhZN6K3HfUbV6apLOp6Xr2U5NtHBSb93VdoSOnDgrSXquy/16/l/3698TF+mnX/fI19tDoSFFHNv4v5rl9evOA5owc6WOnDirBxverWmjuup0ygUt++7X2/AqAICzTRt/0mMdO+uu6tWVcSlDk9+aoN69emjhl0vl4+OT1+OhgLNZlmVdf7V/Fu9az+b1CDDQS089qLZNauj/Hn8t2/u/nTVAq378XSOnLM32/iJBvvpzzetq9sRE/bBllyTJz8dTx354Qw/2nqw1P+5QkL+3di1/VQ+/MFVrf/ojx7MtnNRbR0+cVe+4ua4/MRQIpza+ndcjoAA5efKkmjSspw9nfaTIe+rk9TjIp7xyeMg0T4+sHj9+XB9++KE2bNigw4cPS5JKliyp+vXrq1u3bipWrFhejod8KLxcMe1e8aoupKXrx1+S9MrkL7X/8CkVC/bTvTXK6+Nlm7RmZn+VL1NUf+w5ohFvL9b6hN2SpBPJqdqRdFid2tyrLdv3Ky39kno+fJ+OnDijLf/bJ0lq+n93ys3NppDiQdqy4GX5+3rqv1uTNGTCQv15JPmqcwX6eWtH0pHb8RIAwHWlnL38k6KAwMA8ngTIwyOrGzduVMuWLeXj46NmzZqpRIkSkqQjR45o1apVOnfunJYvX6577rnnmttJS0tTWlqa07LiDQfL5uZ+y2bHP1OLBtXk5+2pP/YeUcmigXrpqVYKKR6kyEdeVbWKpfTt7IE6kZyqoRM/1y87/lTnNvfqyZiGinx0jHbtOyZJKl08SPMnPqlad5aR3W7p2KkUdej7rrbu+FOSNLB7cw17urWS/jyugeMW6EzKeQ1/po1KFw9SnZh4pV/KyDLXw81rafrorqrX8XVt3334tr4m+OfgyCpuF7vdrueefVpnz5zRrI/+k9fjIB8z/shq37599eijj2rq1Kmy2WxO91mWpd69e6tv377asGHDNbcTHx+vuLg4p2XuJeqocKl7c31m/LOt+OF/jj//uvOgNm7box1fjdTDLWprR9LlSJy+4HvN+fK/kqStO/5U43urKLZdPb0y+UtJ0sShMTp28qyaPfGmzqddVLcO9bXgrad0X5dxOnz8jGw2mzwKF9KAsZ9p1X9/lyTFDp2pPSvHKKpOZX2zYbvTTI3uqaT34rqoz6j/EKoAjDBmdJx27dypmXPm5fUogKQ8vHTV1q1b1a9fvyyhKkk2m039+vVTQkLCdbczdOhQnT592umrUInIWzAx8pvTKeeVuO+oKpYtpkPHzkhSlmDckXRYZUsGS5Ia31tZDza8W12HzNCGrbuV8PufeiH+E51PS1eXtnUlSYePX97O73/bzvFTKTqenOLYTqb7IsO14K3eenH8Qs1b8tMte54AkFNjRo/Uum/X6oMZs1SiZMm8HgeQlIexWrJkSf3009X/gf7pp58cpwZci6enpwICApy+OAUAOeHr7aHyZYrq8PHT2nvwhA4eTVblsOJO64SHFte+QyclST5eHpIu/4js7+x2y/Gfrg3///zWSn/bTnCAj4oG+Tm2I12+fNXnk57Wy299oQ8X/pD7Tw4AXGBZlsaMHqnVq1bqgw9nqUyZsnk9EuCQZ6cBDBw4UE8++aQ2b96spk2bZjln9YMPPtD48ePzajzkQ/H9Omjpum3ad/CkQooH6uXerZVht+uTrzdLkibO+kYv926tbX8c0NYdf6pL27qqElZCnQZNlyT9+EuSTp05p2mjumrM+8t0/kK6noiur7DSRfT1979JkhL3HdXiNVs1ftAjenb0f3Qm5YJG9n1IO/Yc0bebLl8doNE9lbRwUm+9M2+tFq3aohJF/CVJF9MzdOrMuTx4ZQAUdGNGxWnZV0v05uQp8vXx1fFjl8/T9/P3l5eXVx5Ph4IuTy9dNX/+fE2cOFGbN29WRsblD564u7srMjJS/fv3V0xMzA1tl0tXITuzX+uu+2qH645AHx0/laL1Cbs1/O3FSvrzuGOdgd2b66mYRgoO9NG2Pw7opTcXOa4GIEm1q5XTiGfaqna1cipcyE3bdx/WmPeXOZ0P6+/rpbEDo9Xu/gjZ7Za+37xTA8d95rgawPtxXfSvh/4vy3zrNu1Uy15v3boXAP9ofMAKt1LNu6pku3zk6Hi16xB9m6dBQZHTD1gZcZ3V9PR0HT9+ORiKFi2qwoUL39T2iFUA+Q2xCiC/Mf5qAH9XuHBhlSpVKq/HAAAAgGHy7ANWAAAAwPUQqwAAADAWsQoAAABjEasAAAAwFrEKAAAAYxGrAAAAMBaxCgAAAGMRqwAAADAWsQoAAABjEasAAAAwFrEKAAAAYxGrAAAAMBaxCgAAAGMRqwAAADAWsQoAAABjEasAAAAwFrEKAAAAYxGrAAAAMBaxCgAAAGMRqwAAADAWsQoAAABjEasAAAAwFrEKAAAAYxGrAAAAMBaxCgAAAGMRqwAAADAWsQoAAABjEasAAAAwFrEKAAAAYxGrAAAAMBaxCgAAAGMRqwAAADAWsQoAAABjEasAAAAwFrEKAAAAYxGrAAAAMBaxCgAAAGMRqwAAADAWsQoAAABjEasAAAAwFrEKAAAAYxGrAAAAMBaxCgAAAGMRqwAAADAWsQoAAABjEasAAAAwFrEKAAAAYxGrAAAAMBaxCgAAAGMRqwAAADAWsQoAAABjEasAAAAwFrEKAAAAYxGrAAAAMBaxCgAAAGMRqwAAADAWsQoAAABjEasAAAAwFrEKAAAAYxGrAAAAMBaxCgAAAGMRqwAAADAWsQoAAABjEasAAAAwFrEKAAAAYxGrAAAAMBaxCgAAAGMRqwAAADAWsQoAAABjEasAAAAwFrEKAAAAYxGrAAAAMBaxCgAAAGPlSqwmJyfnxmYAAAAAJy7H6uuvv6758+c7bsfExKhIkSIqXbq0tm7dmqvDAQAAoGBzOVanTp2qsmXLSpJWrlyplStXatmyZWrVqpUGDRqU6wMCAACg4Crk6gMOHz7siNUlS5YoJiZGLVq0UFhYmOrWrZvrAwIAAKDgcvnIanBwsPbv3y9J+vrrr9WsWTNJkmVZysjIyN3pAAAAUKC5fGQ1OjpanTp1UqVKlXTixAm1atVKkrRlyxaFh4fn+oAAAAAouFyO1YkTJyosLEz79+/X2LFj5efnJ0k6dOiQ+vTpk+sDAgAAoOCyWZZl5fUQuc271rN5PQIA5KpTG9/O6xEAIFd55fCQaY5W+/LLL3O844ceeijH6wIAAADXkqNYbd++fY42ZrPZ+JAVAAAAck2OYtVut9/qOQAAAIAsburXrV64cCG35gAAAACycDlWMzIyNGrUKJUuXVp+fn7avXu3JGnYsGGaPn16rg8IAACAgsvlWH311Vc1c+ZMjR07Vh4eHo7ld999t6ZNm5arwwEAAKBgczlWZ8+erffff1+dO3eWu7u7Y3nNmjX1+++/5+pwAAAAKNhcjtUDBw5k+5uq7Ha70tPTc2UoAAAAQLqBWK1WrZq+++67LMs/++wz1apVK1eGAgAAAKQb+HWrr7zyimJjY3XgwAHZ7XYtXLhQO3bs0OzZs7VkyZJbMSMAAAAKKJePrLZr106LFy/WN998I19fX73yyivavn27Fi9erObNm9+KGQEAAFBA2SzLsvJ6iNzmXevZvB4BAHLVqY1v5/UIAJCrvHL4832XTwPItGnTJm3fvl3S5fNYIyMjb3RTAAAAQLZcjtU///xTHTt21A8//KCgoCBJUnJysurXr6+PP/5YZcqUye0ZAQAAUEC5fM5qz549lZ6eru3bt+vkyZM6efKktm/fLrvdrp49e96KGQEAAFBAuXzOqre3t9avX5/lMlWbN29Ww4YNde7cuVwd8EZwziqA/IZzVgHkNzk9Z9XlI6tly5bN9uL/GRkZCgkJcXVzAAAAwFW5HKvjxo1T3759tWnTJseyTZs26fnnn9f48eNzdTgAAAAUbDk6DSA4OFg2m81xOzU1VZcuXVKhQpeP32b+2dfXVydPnrx10+YQpwEAyG84DQBAfpOrl6568803b2IUAAAA4MbkKFZjY2Nv9RwAAABAFjf8SwEk6cKFC7p48aLTsoCAgJsaCAAAAMjk8gesUlNT9eyzz6p48eLy9fVVcHCw0xcAAACQW1yO1RdffFGrV6/Wu+++K09PT02bNk1xcXEKCQnR7Nmzb8WMAAAAKKBcPg1g8eLFmj17tho3bqzu3burYcOGCg8PV2hoqObOnavOnTvfijkBAABQALl8ZPXkyZOqUKGCpMvnp2Zequq+++7TunXrcnc6AAAAFGgux2qFChWUlJQkSbrzzjv1ySefSLp8xDUoKChXhwMAAEDB5nKsdu/eXVu3bpUkDRkyRO+88468vLzUr18/DRo0KNcHBAAAQMGVo99gdS179+7V5s2bFR4erho1auTWXDflwqW8ngAAAADXktPfYHXTsWoiYhUAAMBsufrrVidNmpTjHT/33HM5XhcAAAC4lhwdWS1fvnzONmazaffu3Tc91M3iyCoAAIDZOA0AAAAAxspprLp8NQAAAADgdiFWAQAAYCxiFQAAAMYiVgEAAGAsYhUAAADGuqFY/e6779SlSxfVq1dPBw4ckCTNmTNH33//fa4OBwAAgILN5VhdsGCBWrZsKW9vb23ZskVpaWmSpNOnT2vMmDG5PiAAAAAKLpdjdfTo0Zo6dao++OADFS5c2LG8QYMG+vnnn3N1OAAAABRsLsfqjh071KhRoyzLAwMDlZycnBszAQAAAJJuIFZLliypxMTELMu///57VahQIVeGAgAAAKQbiNVevXrp+eef148//iibzaaDBw9q7ty5GjhwoJ5++ulbMSMAAAAKqBz+Vta/DBkyRHa7XU2bNtW5c+fUqFEjeXp6auDAgerbt++tmBEAAAAFlM2yLOtGHnjx4kUlJiYqJSVF1apVk5+fX27PdsMuXMrrCQAAAHAtXjk8ZHrDsWoyYhUAAMBsOY1Vl08DaNKkiWw221XvX716taubBAAAALLlcqxGREQ43U5PT1dCQoJ+/fVXxcbG5tZcAAAAgOuxOnHixGyXjxgxQikpKTc9EAAAAJAp185ZTUxM1L333quTJ0/mxuZuCuesAgAAmC2n56y6fJ3Vq9mwYYO8vLxya3MAAACA66cBREdHO922LEuHDh3Spk2bNGzYsFwbDAAAAHA5VgMDA51uu7m5qUqVKho5cqRatGiRa4MBAAAALp2zmpGRoR9++EHVq1dXcHDwrZzrpnDOKgAAgNluyTmr7u7uatGihZKTk29gJAAAAMA1Ln/A6u6779bu3btvxSwAAACAE5djdfTo0Ro4cKCWLFmiQ4cO6cyZM05fAAAAQG7J8TmrI0eO1IABA+Tv7//Xg//2a1cty5LNZlNGRkbuT+kizlkFAAAwW07PWc1xrLq7u+vQoUPavn37NdeLiorK2Z5vIWIVAADAbLkeq25ubjp8+LCKFy9+M3PdFsQqAACA2W7J1QD+/mN/AAAA4FZz6chqYGDgdYP15MmTuTLYzeDIKgAAgNlyemTVpd9gFRcXl+U3WAEAAAC3CuesAgAA4LbL9XNWOV8VAAAAt1uOYzWHB2ABAACAXJPjc1btdvutnAMAAADIwuVftwoAAADcLsQqAAAAjEWsAgAAwFjEKgAAAIxFrAIAAMBYxCoAAACMRawCAADAWMQqAAAAjEWsAgAAwFjEKgAAAIxFrAIAAMBYxCoAAACMRawCAADAWMQqAAAAjEWsAgAAwFjEKgAAAIxFrAIAAMBYxCoAAACMRawCAADAWMQqAAAAjEWsAgAAwFjEKgAAAIxFrAIAAMBYxCoAAACMRawCAADAWMQqAAAAjEWsAgAAwFjEKgAAAIxFrAIAAMBYxCoAAACMRawCAADAWMQqAAAAjEWsAgAAwFjEKgAAAIxFrAIAAMBYxCoAAACMRawCAADAWMQqAAAAjEWsAgAAwFjEKgAAAIxFrAIAAMBYxCoAAACMRawCAADAWMQqAAAAjEWsAgAAwFjEKgAAAIxFrAIAAMBYxCoAAACMRawCAADAWMQqAAAAjEWsAgAAwFjEKgAAAIxFrAIAAMBYxCoAAACMRawCAADAWMQqAAAAjEWsAgAAwFjEKgAAAIxFrAIAAMBYxCoAAACMRawCAADAWMQqAAAAjEWsAgAAwFjEKgAAAIxFrAIAAMBYxCoAAACMRawCAADAWMQqAAAAjEWsAgAAwFjEKgAAAIxFrALXMf2D99Qp5mHVq1NLjRvW0wt9+2hP0u5s17UsS32e6qmad1XR6lXf3OZJASBnWjW/XzXvqpLla8yoOKf1eE+DCQrl9QCA6TZt/EmPdeysu6pXV8alDE1+a4J69+qhhV8ulY+Pj9O6H82eJZvNlkeTAkDOzJ3/mewZGY7biYk79VTP7mre8gGn9XhPgwmIVeA63n1/utPtka++piYN62n7/35T5D11HMt/375ds2d9qP/MX6Cmje+73WMCQI7dcccdTrc/nPa+ypYtp3vq3OtYxnsaTMFpAICLUs6elSQFBAY6lp0/f15DXxygf7/8iooWK5ZXowGAy9IvXtTSJV+qffTDjqOovKfBJEbH6v79+/XEE09cc520tDSdOXPG6SstLe02TYiCxm63a+zrYxRRq7YqVarsWD7u9XjVrFVLTe5vlofTAYDrVq/+RmfPntVD7Ts4lvGeBpMYHasnT57UrFmzrrlOfHy8AgMDnb7GvR5/myZEQTNmdJx27dypseMnOpatXb1KG3/8r14c/O88nAwAbsznCxaowX2NVLx4CUm8p8E8NsuyrLza+ZdffnnN+3fv3q0BAwYo428ngV8pLS0ty5FUy91Tnp6euTIjkGnM6JFau2aVPpz1kcqUKetYPjb+Vc2bO0dubn/93y8jI0Nubm6qHXmPps+ckxfjAsB1HTx4QK1bNtOEtyY7jqLynobbxSuHn5zK01h1c3OTzWbTtUaw2WzXjNXsXLh0s5MBf7EsS/GvjtLqVSs1feYchYaGOd1//NgxnUo+5bTskfZt9eLQlxTVuIlT2AKASd59Z7I++2S+lq9aq0KFLpcD72m4XXIaq3l6NYBSpUppypQpateuXbb3JyQkKDIy8jZPBTgbMypOy75aojcnT5Gvj6+OHzsmSfLz95eXl5eKFiuW7QcQSpUK4U0dgLHsdru++Hyh2rZr7whVSbynwTh5es5qZGSkNm/efNX7r3fUFbgdPpn/H509e1Y9uv1LTRvf5/havuyrvB4NAG7Yfzes16FDB9U++uG8HgW4pjw9DeC7775TamqqHnjggWzvT01N1aZNmxQVFeXSdjkNAAAAwGz/iHNWbxViFQAAwGw5jVWjL10FAACAgo1YBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGItYBQAAgLGIVQAAABiLWAUAAICxiFUAAAAYi1gFAACAsYhVAAAAGMtmWZaV10MA/0RpaWmKj4/X0KFD5enpmdfjAMBN430NJiJWgRt05swZBQYG6vTp0woICMjrcQDgpvG+BhNxGgAAAACMRawCAADAWMQqAAAAjEWsAjfI09NTw4cP50MIAPIN3tdgIj5gBQAAAGNxZBUAAADGIlYBAABgLGIVAAAAxiJWAQAAYCxiFbhB77zzjsLCwuTl5aW6devqp59+yuuRAOCGrFu3Tm3btlVISIhsNpsWLVqU1yMBDsQqcAPmz5+v/v37a/jw4fr5559Vs2ZNtWzZUkePHs3r0QDAZampqapZs6beeeedvB4FyIJLVwE3oG7duqpTp47efvttSZLdblfZsmXVt29fDRkyJI+nA4AbZ7PZ9Pnnn6t9+/Z5PQogiSOrgMsuXryozZs3q1mzZo5lbm5uatasmTZs2JCHkwEAkP8Qq4CLjh8/royMDJUoUcJpeYkSJXT48OE8mgoAgPyJWAUAAICxiFXARUWLFpW7u7uOHDnitPzIkSMqWbJkHk0FAED+RKwCLvLw8FBkZKRWrVrlWGa327Vq1SrVq1cvDycDACD/KZTXAwD/RP3791dsbKzuuece3XvvvXrzzTeVmpqq7t275/VoAOCylJQUJSYmOm4nJSUpISFBd9xxh8qVK5eHkwFcugq4YW+//bbGjRunw4cPKyIiQpMmTVLdunXzeiwAcNnatWvVpEmTLMtjY2M1c+bM2z8Q8DfEKgAAAIzFOasAAAAwFrEKAAAAYxGrAAAAMBaxCgAAAGMRqwAAADAWsQoAAABjEasAAAAwFrEKAAAAYxGrAHCDunXrpvbt2ztuN27cWC+88MJtn2Pt2rWy2WxKTk6+6jo2m02LFi3K8TZHjBihiIiIm5prz549stlsSkhIuKntACjYiFUA+Uq3bt1ks9lks9nk4eGh8PBwjRw5UpcuXbrl+164cKFGjRqVo3VzEpgAAKlQXg8AALntgQce0IwZM5SWlqavvvpKzzzzjAoXLqyhQ4dmWffixYvy8PDIlf3ecccdubIdAMBfOLIKIN/x9PRUyZIlFRoaqqefflrNmjXTl19+KemvH92/+uqrCgkJUZUqVSRJ+/fvV0xMjIKCgnTHHXeoXbt22rNnj2ObGRkZ6t+/v4KCglSkSBG9+OKLsizLab9XngaQlpamwYMHq2zZsvL09FR4eLimT5+uPXv2qEmTJpKk4OBg2Ww2devWTZJkt9sVHx+v8uXLy9vbWzVr1tRnn33mtJ+vvvpKlStXlre3t5o0aeI0Z04NHjxYlStXlo+PjypUqKBhw4YpPT09y3rvvfeeypYtKx8fH8XExOj06dNO90+bNk1Vq1aVl5eX7rzzTk2ZMuWq+zx16pQ6d+6sYsWKydvbW5UqVdKMGTNcnh1AwcKRVQD5nre3t06cOOG4vWrVKgUEBGjlypWSpPT0dLVs2VL16tXTd999p0KFCmn06NF64IEH9Msvv8jDw0NvvPGGZs6cqQ8//FBVq1bVG2+8oc8//1z333//VffbtWtXbdiwQZMmTVLNmjWVlJSk48ePq2zZslqwYIEefvhh7dixQwEBAfL29pYkxcfH66OPPtLUqVNVqVIlrVu3Tl26dFGxYsUUFRWl/fv3Kzo6Ws8884yefPJJbdq0SQMGDHD5NfH399fMmTMVEhKibdu2qVevXvL399eLL77oWCcxMVGffPKJFi9erDNnzqhHjx7q06eP5s6dK0maO3euXnnlFb399tuqVauWtmzZol69esnX11exsbFZ9jls2DD973//07Jly1S0aFElJibq/PnzLs8OoICxACAfiY2Ntdq1a2dZlmXZ7XZr5cqVlqenpzVw4EDH/SVKlLDS0tIcj5kzZ45VpUoVy263O5alpaVZ3t7e1vLlyy3LsqxSpUpZY8eOddyfnp5ulSlTxrEvy7KsqKgo6/nnn7csy7J27NhhSbJWrlyZ7Zxr1qyxJFmnTp1yLLtw4YLl4+NjrV+/3mndHj16WB07drQsy7KGDh1qVatWzen+wYMHZ9nWlSRZn3/++VXvHzdunBUZGem4PXz4cMvd3d36888/HcuWLVtmubm5WYcOHbIsy7IqVqxozZs3z2k7o0aNsurVq2dZlmUlJSVZkqwtW7ZYlmVZbdu2tbp3737VGQAgOxxZBZDvLFmyRH5+fkpPT5fdblenTp00YsQIx/3Vq1d3Ok9169atSkxMlL+/v9N2Lly4oF27dun06dM6dOiQ6tat67ivUKFCuueee7KcCpApISFB7u7uioqKyvHciYmJOnfunJo3b+60/OLFi6pVq5Ykafv27U5zSFK9evVyvI9M8+fP16RJk7Rr1y6lpKTo0qVLCggIcFqnXLlyKl26tNN+7Ha7duzYIX9/f+3atUs9evRQr169HOtcunRJgYGB2e7z6aef1sMPP6yff/5ZLVq0UPv27VW/fn2XZwdQsBCrAPKdJk2a6N1335WHh4dCQkJUqJDzW52vr6/T7ZSUFEVGRjp+vP13xYoVu6EZMn+s74qUlBRJ0tKlS50iUbp8Hm5u2bBhgzp37qy4uDi1bNlSgYGB+vjjj/XGG2+4POsHH3yQJZ7d3d2zfUyrVq20d+9effXVV1q5cqWaNm2qZ555RuPHj7/xJwMg3yNWAeQ7vr6+Cg8Pz/H6tWvX1vz581W8ePEsRxczlSpVSj/++KMaNWok6fIRxM2bN6t27drZrl+9enXZ7XZ9++23atasWZb7M4/sZmRkOJZVq1ZNnp6e2rdv31WPyFatWtXxYbFM//3vf6//JP9m/fr1Cg0N1UsvveRYtnfv3izr7du3TwcPHlRISIhjP25ubqpSpYpKlCihkJAQ7d69W507d87xvosVK6bY2FjFxsaqYcOGGjRoELEK4Jq4GgCAAq9z584qWrSo2rVrp++++05JSUlau3atnnvuOf3555+SpOeff16vvfaaFi1apN9//119+vS55jVSw8LCFBsbqyeeeEKLFi1ybPOTTz6RJIWGhspms2nJkiU6duyYUlJS5O/vr4EDB6pfv36aNWuWdu3apZ9//lmTJ0/WrFmzJEm9e/fWzp07NWjQIO3YsUPz5s3TzJkzXXq+lSpV0r59+/Txxx9r165dmjRpkj7//PMs63l5eSk2NlZbt27Vd999p+eee04xMTEqWbKkJCkuLk7x8fGaNGmS/vjjD23btk0zZszQhAkTst3vK6+8oi+++EKJiYn67bfftGTJElWtWtWl2QEUPMQqgALPx8dH69atU7ly5RQdHa2qVauqR48eunDhguNI64ABA/Svf/1LsbGxqlevnvz9/dWhQ4drbvfdd9/VI488oj59+ujOO+9Ur169lJqaKkkqXbq04uLiNGTIEJUoUULPPvusJGnUqFEaNmyY4uPjVbVqVT3wwANaunSpypcvL+nyeaQLFizQokWLVLNmTU2dOlVjxoxx6fk+9NBD6tevn5599llFRERo/fr1GjZsWJb1wsPDFR0drQcffFAtWrRQjRo1nC5N1bNnT02bNk0zZsxQ9erVFRUVpZkzZzpmvZKHh4eGDh2qGjVqqFGjRnJ3d9fHH3/s0uwACh6bdbVPBwAAAAB5jCOrAAAAMBaxCgAAAGMRqwAAADAWsQoAAABjEasAAAAwFrEKAAAAYxGrAAAAMBaxCgAAAGMRqwAAADAWsQoAAABjEasAAAAw1v8D9lyPAB2EA/YAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56864\n",
      "           1       0.97      0.76      0.85        98\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.99      0.88      0.93     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Assuming df_selected_features contains the selected features and the target variable 'Class'\n",
    "\n",
    "# Split the data into features (X) and target variable (y)\n",
    "X = df_selected_features.drop('Class', axis=1)\n",
    "y = df_selected_features['Class']\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize base models with probability=True\n",
    "base_models = [\n",
    "    ('DT', clf),\n",
    "    ('RF', clf_rf),\n",
    "    ('SVM', SVC(probability=True)),  # Specify probability=True for SVC\n",
    "    ('XGBoost', clf_xgb),\n",
    "    ('CatBoost', clf_catboost),\n",
    "    ('LR', clf_lr)\n",
    "]\n",
    "\n",
    "# Initialize Voting Classifier\n",
    "voting_clf = VotingClassifier(estimators=base_models, voting='soft')\n",
    "\n",
    "# Train the Voting Classifier\n",
    "voting_clf.fit(X_train, y_train)\n",
    "\n",
    "# Predict probabilities for the positive class\n",
    "y_probs_ensemble = voting_clf.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Calculate AUC-ROC\n",
    "auc_roc_ensemble = roc_auc_score(y_test, y_probs_ensemble)\n",
    "print(\"AUC-ROC Score (Ensemble):\", auc_roc_ensemble)\n",
    "\n",
    "# Plot ROC Curve\n",
    "fpr_ensemble, tpr_ensemble, thresholds_ensemble = roc_curve(y_test, y_probs_ensemble)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr_ensemble, tpr_ensemble, label='ROC Curve (AUC = {:.2f})'.format(auc_roc_ensemble))\n",
    "plt.plot([0, 1], [0, 1], 'k--')  # Random guessing line\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve - Ensemble')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Predict the classes for the test set\n",
    "y_pred_ensemble = voting_clf.predict(X_test)\n",
    "\n",
    "# Evaluate the ensemble model\n",
    "accuracy_ensemble = accuracy_score(y_test, y_pred_ensemble)\n",
    "precision_ensemble = precision_score(y_test, y_pred_ensemble)\n",
    "recall_ensemble = recall_score(y_test, y_pred_ensemble)\n",
    "f1_ensemble = f1_score(y_test, y_pred_ensemble)\n",
    "\n",
    "print(\"\\nEnsemble Model Evaluation:\")\n",
    "print(\"Accuracy:\", accuracy_ensemble)\n",
    "print(\"Precision:\", precision_ensemble)\n",
    "print(\"Recall:\", recall_ensemble)\n",
    "print(\"F1 Score:\", f1_ensemble)\n",
    "\n",
    "# Display the selected features\n",
    "print(\"Selected Features:\")\n",
    "print(df_selected_features.columns)\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix_ensemble = confusion_matrix(y_test, y_pred_ensemble)\n",
    "print(\"\\nConfusion Matrix:\")\n",
    "print(conf_matrix_ensemble)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix_ensemble, annot=True, cmap='Blues', fmt='g', cbar=False)\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.title('Confusion Matrix - Ensemble')\n",
    "plt.show()\n",
    "\n",
    "# Classification Report\n",
    "class_report_ensemble = classification_report(y_test, y_pred_ensemble)\n",
    "print(\"\\nClassification Report:\")\n",
    "print(class_report_ensemble)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subspace Random ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jawad\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\ensemble\\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9994733330992591\n",
      "Precision: 0.9594594594594594\n",
      "Recall: 0.7244897959183674\n",
      "F1 Score: 0.8255813953488371\n",
      "ROC AUC Score: 0.86221851923099\n",
      "\n",
      "Confusion Matrix:\n",
      " [[56861     3]\n",
      " [   27    71]]\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56864\n",
      "           1       0.96      0.72      0.83        98\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.98      0.86      0.91     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier, BaggingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import xgboost as xgb\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, classification_report\n",
    "\n",
    "# Split the data into features (X) and target variable (y) using selected features\n",
    "X = df_selected_features.drop('Class', axis=1)\n",
    "y = df_selected_features['Class']\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "X_test = X_test.dropna()\n",
    "y_test = y_test.dropna()\n",
    "\n",
    "# Normalize the data (optional but recommended for some algorithms)\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Create individual classifiers\n",
    "rf_params = {\n",
    "    'n_estimators': 100,          # Number of trees in the forest\n",
    "    'max_depth': 10,              # Maximum depth of each tree\n",
    "    'min_samples_split': 2,       # Minimum samples required to split an internal node\n",
    "    'min_samples_leaf': 1,        # Minimum samples required at a leaf node\n",
    "    'random_state': 42            # Random state for reproducibility\n",
    "}\n",
    "rf_classifier = RandomForestClassifier(**rf_params)\n",
    "\n",
    "svm_params = {\n",
    "    'kernel': 'rbf',      # Kernel type (you can try different kernels)\n",
    "    'C': 1.0,             # Regularization parameter\n",
    "    'gamma': 'scale',     # Kernel coefficient (auto, scale, or a float)\n",
    "    'random_state': 42    # Random state for reproducibility\n",
    "}\n",
    "svm_classifier = SVC(**svm_params)\n",
    "\n",
    "lr_params = {\n",
    "    'penalty': 'l2',               # Regularization penalty (l2 norm)\n",
    "    'C': 1.0,                      # Inverse regularization strength\n",
    "    'solver': 'liblinear',         # Optimization algorithm for regularization\n",
    "    'random_state': 42             # Random state for reproducibility\n",
    "}\n",
    "lr_classifier = LogisticRegression(**lr_params)\n",
    "\n",
    "dt_params = {\n",
    "    'max_depth': 70,\n",
    "    'min_samples_split': 8,\n",
    "    'min_samples_leaf': 10,\n",
    "    'criterion': \"entropy\"\n",
    "}\n",
    "dt_classifier = DecisionTreeClassifier(**dt_params)\n",
    "\n",
    "xgb_params = {\n",
    "    'objective': 'binary:logistic',   # Objective function\n",
    "    'eval_metric': 'logloss',         # Evaluation metric\n",
    "    'eta': 0.1,                       # Learning rate\n",
    "    'max_depth': 6,                   # Maximum depth of each tree\n",
    "    'subsample': 0.8,                 # Subsample ratio of the training instances\n",
    "    'colsample_bytree': 0.8,          # Subsample ratio of columns when constructing each tree\n",
    "    'seed': 42                        # Random seed for reproducibility\n",
    "}\n",
    "xgb_classifier = xgb.XGBClassifier(**xgb_params)\n",
    "\n",
    "catboost_params = {\n",
    "    'iterations': 1000,       # Increase number of iterations\n",
    "    'learning_rate': 0.05,    # Lower learning rate for more stable training\n",
    "    'depth': 8,               # Increase depth for more complex trees\n",
    "    'l2_leaf_reg': 1.0,       # Regularization parameter for L2 regularization\n",
    "    'random_state': 42        # Random state for reproducibility\n",
    "}\n",
    "catboost_classifier = CatBoostClassifier(**catboost_params)\n",
    "\n",
    "# Create a Bagging classifier with Random Forest as the base estimator\n",
    "random_subspace_classifier = BaggingClassifier(\n",
    "    base_estimator=rf_classifier,\n",
    "    n_estimators=10,           # Number of base estimators\n",
    "    max_samples=1.0,            # Sample ratio of training instances\n",
    "    max_features=0.5,           # Feature ratio (random subspace)\n",
    "    random_state=42            # Random state for reproducibility\n",
    ")\n",
    "\n",
    "# Fit the random subspace classifier on the training data\n",
    "random_subspace_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = random_subspace_classifier.predict(X_test)\n",
    "\n",
    "# Evaluate the random subspace model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "roc_auc = roc_auc_score(y_test, y_pred)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "class_report = classification_report(y_test, y_pred)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 Score:\", f1)\n",
    "print(\"ROC AUC Score:\", roc_auc)\n",
    "print(\"\\nConfusion Matrix:\\n\", conf_matrix)\n",
    "print(\"\\nClassification Report:\\n\", class_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bagging "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jawad\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\ensemble\\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9995084442259752\n",
      "Precision: 0.972972972972973\n",
      "Recall: 0.7346938775510204\n",
      "F1 Score: 0.8372093023255813\n",
      "ROC AUC Score: 0.8673293529567144\n",
      "\n",
      "Confusion Matrix:\n",
      " [[56862     2]\n",
      " [   26    72]]\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56864\n",
      "           1       0.97      0.73      0.84        98\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.99      0.87      0.92     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier, BaggingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import xgboost as xgb\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, classification_report\n",
    "\n",
    "# Split the data into features (X) and target variable (y) using selected features\n",
    "X = df_selected_features.drop('Class', axis=1)\n",
    "y = df_selected_features['Class']\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "X_test = X_test.dropna()\n",
    "y_test = y_test.dropna()\n",
    "\n",
    "# Normalize the data (optional but recommended for some algorithms)\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Create individual classifiers\n",
    "rf_params = {\n",
    "    'n_estimators': 100,          # Number of trees in the forest\n",
    "    'max_depth': 10,              # Maximum depth of each tree\n",
    "    'min_samples_split': 2,       # Minimum samples required to split an internal node\n",
    "    'min_samples_leaf': 1,        # Minimum samples required at a leaf node\n",
    "    'random_state': 42            # Random state for reproducibility\n",
    "}\n",
    "rf_classifier = RandomForestClassifier(**rf_params)\n",
    "\n",
    "svm_params = {\n",
    "    'kernel': 'rbf',      # Kernel type (you can try different kernels)\n",
    "    'C': 1.0,             # Regularization parameter\n",
    "    'gamma': 'scale',     # Kernel coefficient (auto, scale, or a float)\n",
    "    'random_state': 42    # Random state for reproducibility\n",
    "}\n",
    "svm_classifier = SVC(**svm_params)\n",
    "\n",
    "lr_params = {\n",
    "    'penalty': 'l2',               # Regularization penalty (l2 norm)\n",
    "    'C': 1.0,                      # Inverse regularization strength\n",
    "    'solver': 'liblinear',         # Optimization algorithm for regularization\n",
    "    'random_state': 42             # Random state for reproducibility\n",
    "}\n",
    "lr_classifier = LogisticRegression(**lr_params)\n",
    "\n",
    "dt_params = {\n",
    "    'max_depth': 70,\n",
    "    'min_samples_split': 8,\n",
    "    'min_samples_leaf': 10,\n",
    "    'criterion': \"entropy\"\n",
    "}\n",
    "dt_classifier = DecisionTreeClassifier(**dt_params)\n",
    "\n",
    "xgb_params = {\n",
    "    'objective': 'binary:logistic',   # Objective function\n",
    "    'eval_metric': 'logloss',         # Evaluation metric\n",
    "    'eta': 0.1,                       # Learning rate\n",
    "    'max_depth': 6,                   # Maximum depth of each tree\n",
    "    'subsample': 0.8,                 # Subsample ratio of the training instances\n",
    "    'colsample_bytree': 0.8,          # Subsample ratio of columns when constructing each tree\n",
    "    'seed': 42                        # Random seed for reproducibility\n",
    "}\n",
    "xgb_classifier = xgb.XGBClassifier(**xgb_params)\n",
    "\n",
    "catboost_params = {\n",
    "    'iterations': 1000,       # Increase number of iterations\n",
    "    'learning_rate': 0.05,    # Lower learning rate for more stable training\n",
    "    'depth': 8,               # Increase depth for more complex trees\n",
    "    'l2_leaf_reg': 1.0,       # Regularization parameter for L2 regularization\n",
    "    'random_state': 42        # Random state for reproducibility\n",
    "}\n",
    "catboost_classifier = CatBoostClassifier(**catboost_params)\n",
    "\n",
    "# Create a Bagging classifier with Decision Tree as the base estimator\n",
    "bagging_classifier = BaggingClassifier(\n",
    "    base_estimator=rf_classifier,\n",
    "    n_estimators=10,           # Number of base estimators\n",
    "    max_samples=1.0,            # Sample ratio of training instances\n",
    "    max_features=1.0,           # Feature ratio (use all features)\n",
    "    random_state=42            # Random state for reproducibility\n",
    ")\n",
    "\n",
    "# Fit the bagging classifier on the training data\n",
    "bagging_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = bagging_classifier.predict(X_test)\n",
    "\n",
    "# Evaluate the bagging model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "roc_auc = roc_auc_score(y_test, y_pred)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "class_report = classification_report(y_test, y_pred)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 Score:\", f1)\n",
    "print(\"ROC AUC Score:\", roc_auc)\n",
    "print(\"\\nConfusion Matrix:\\n\", conf_matrix)\n",
    "print(\"\\nClassification Report:\\n\", class_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}