demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Run ADBench \n",
    "- Here we provide a demo for testing AD algorithms on the datasets proposed in ADBench.\n",
    "- Feel free to evaluate any customized algorithm in ADBench.\n",
    "- For reproducing the complete experiment results in ADBench, please run the code in the run.py file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T07:41:53.643140Z",
     "start_time": "2022-07-08T07:41:41.552946Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# import basic package\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# import the necessary package\n",
    "from data_generator import DataGenerator\n",
    "from myutils import Utils\n",
    "\n",
    "datagenerator = DataGenerator() # data generator\n",
    "utils = Utils() # utils function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We include all the datasets of ADBench in the \"datasets\" folder, as the \"number_data_class.npz\" filename. Please see the table in the markdown for details.\n",
    "    - You can specify the dataset name by removing the filename \".npz\" suffix in the data generator, e.g., \"10_cover.npz\" as \"10_cover\". \n",
    "    \n",
    "    \n",
    "- All the algorithms included in the ADBench are illustrated in the table of markdown.\n",
    "    - You need to specify the model name when initialization, as some algorithms (e.g., supervised algorithms) are integrated in one class, please see the table in the markdown for details.\n",
    "    - You can also test your own AD algorithms on our generated dataset, as long as the algorithm can output anomaly score for evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T07:41:53.675055Z",
     "start_time": "2022-07-08T07:41:53.648127Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['10_cover.npz',\n",
       " '11_donors.npz',\n",
       " '12_fault.npz',\n",
       " '13_fraud.npz',\n",
       " '14_glass.npz',\n",
       " '15_Hepatitis.npz',\n",
       " '16_http.npz',\n",
       " '17_InternetAds.npz',\n",
       " '18_Ionosphere.npz',\n",
       " '19_landsat.npz',\n",
       " '1_ALOI.npz',\n",
       " '20_letter.npz',\n",
       " '21_Lymphography.npz',\n",
       " '22_magic.gamma.npz',\n",
       " '23_mammography.npz',\n",
       " '24_mnist.npz',\n",
       " '25_musk.npz',\n",
       " '26_optdigits.npz',\n",
       " '27_PageBlocks.npz',\n",
       " '28_pendigits.npz',\n",
       " '29_Pima.npz',\n",
       " '2_annthyroid.npz',\n",
       " '30_satellite.npz',\n",
       " '31_satimage-2.npz',\n",
       " '32_shuttle.npz',\n",
       " '33_skin.npz',\n",
       " '34_smtp.npz',\n",
       " '35_SpamBase.npz',\n",
       " '36_speech.npz',\n",
       " '37_Stamps.npz',\n",
       " '38_thyroid.npz',\n",
       " '39_vertebral.npz',\n",
       " '3_backdoor.npz',\n",
       " '40_vowels.npz',\n",
       " '41_Waveform.npz',\n",
       " '42_WBC.npz',\n",
       " '43_WDBC.npz',\n",
       " '44_Wilt.npz',\n",
       " '45_wine.npz',\n",
       " '46_WPBC.npz',\n",
       " '47_yeast.npz',\n",
       " '4_breastw.npz',\n",
       " '5_campaign.npz',\n",
       " '6_cardio.npz',\n",
       " '7_Cardiotocography.npz',\n",
       " '8_celeba.npz',\n",
       " '9_census.npz']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('datasets/Classical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T07:41:55.627834Z",
     "start_time": "2022-07-08T07:41:53.682035Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from baseline.PyOD import PYOD\n",
    "from baseline.DevNet.run import DevNet\n",
    "from baseline.Supervised import supervised\n",
    "\n",
    "# dataset and model list / dict\n",
    "dataset_list = ['6_cardio', '25_musk', '26_optdigits', '36_speech', '40_vowels']\n",
    "model_dict = {'IForest': PYOD, 'DeepSVDD': PYOD, 'DevNet': DevNet, 'RF': supervised, 'CatB': supervised}\n",
    "\n",
    "# save the results\n",
    "df_AUCROC = pd.DataFrame(data=None, index=dataset_list, columns = model_dict.keys())\n",
    "df_AUCPR = pd.DataFrame(data=None, index=dataset_list, columns = model_dict.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T07:50:14.507244Z",
     "start_time": "2022-07-08T07:41:55.631823Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current noise type: None\n",
      "{'Samples': 1831, 'Features': 21, 'Anomalies': 176, 'Anomalies Ratio(%)': 9.61}\n",
      "best param: None\n",
      "best param: None\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A85DB64F78> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A85DB64F78> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 21)]              0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 64)                1344      \n",
      "                                                                 \n",
      " net_output (Dense)          (None, 32)                2048      \n",
      "                                                                 \n",
      " tf.math.subtract_1 (TFOpLam  (None, 32)               0         \n",
      " bda)                                                            \n",
      "                                                                 \n",
      " tf.math.pow_1 (TFOpLambda)  (None, 32)                0         \n",
      "                                                                 \n",
      " tf.math.reduce_sum_1 (TFOpL  (None,)                  0         \n",
      " ambda)                                                          \n",
      "                                                                 \n",
      " tf.math.reduce_mean_1 (TFOp  ()                       0         \n",
      " Lambda)                                                         \n",
      "                                                                 \n",
      " tf.__operators__.add_1 (TFO  ()                       0         \n",
      " pLambda)                                                        \n",
      "                                                                 \n",
      " add_loss_1 (AddLoss)        ()                        0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3,392\n",
      "Trainable params: 3,392\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/100\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000002A864D2B948> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000002A864D2B948> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "26/36 [====================>.........] - ETA: 0s - loss: 3.7731 WARNING:tensorflow:AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x000002A85D984D38> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x000002A85D984D38> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "36/36 [==============================] - 1s 15ms/step - loss: 3.5793 - val_loss: 2.5642\n",
      "Epoch 2/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 2.3332 - val_loss: 1.9590\n",
      "Epoch 3/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 1.8247 - val_loss: 1.5770\n",
      "Epoch 4/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 1.4952 - val_loss: 1.3395\n",
      "Epoch 5/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 1.2543 - val_loss: 1.1465\n",
      "Epoch 6/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 1.0795 - val_loss: 0.9893\n",
      "Epoch 7/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.9447 - val_loss: 0.8691\n",
      "Epoch 8/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.8356 - val_loss: 0.7674\n",
      "Epoch 9/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.7454 - val_loss: 0.6875\n",
      "Epoch 10/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.6720 - val_loss: 0.6246\n",
      "Epoch 11/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.6102 - val_loss: 0.5674\n",
      "Epoch 12/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.5600 - val_loss: 0.5262\n",
      "Epoch 13/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.5220 - val_loss: 0.4894\n",
      "Epoch 14/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.4907 - val_loss: 0.4613\n",
      "Epoch 15/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.4636 - val_loss: 0.4324\n",
      "Epoch 16/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.4382 - val_loss: 0.4101\n",
      "Epoch 17/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.4174 - val_loss: 0.3914\n",
      "Epoch 18/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.3981 - val_loss: 0.3754\n",
      "Epoch 19/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.3813 - val_loss: 0.3613\n",
      "Epoch 20/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.3672 - val_loss: 0.3516\n",
      "Epoch 21/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.3537 - val_loss: 0.3365\n",
      "Epoch 22/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.3409 - val_loss: 0.3217\n",
      "Epoch 23/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.3295 - val_loss: 0.3116\n",
      "Epoch 24/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.3186 - val_loss: 0.3050\n",
      "Epoch 25/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.3084 - val_loss: 0.2920\n",
      "Epoch 26/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.3004 - val_loss: 0.2907\n",
      "Epoch 27/100\n",
      "36/36 [==============================] - 0s 4ms/step - loss: 0.2938 - val_loss: 0.2819\n",
      "Epoch 28/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2893 - val_loss: 0.2761\n",
      "Epoch 29/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2832 - val_loss: 0.2725\n",
      "Epoch 30/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2783 - val_loss: 0.2691\n",
      "Epoch 31/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2747 - val_loss: 0.2694\n",
      "Epoch 32/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2704 - val_loss: 0.2648\n",
      "Epoch 33/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2670 - val_loss: 0.2610\n",
      "Epoch 34/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2631 - val_loss: 0.2573\n",
      "Epoch 35/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2612 - val_loss: 0.2568\n",
      "Epoch 36/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2592 - val_loss: 0.2537\n",
      "Epoch 37/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2563 - val_loss: 0.2512\n",
      "Epoch 38/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2546 - val_loss: 0.2507\n",
      "Epoch 39/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2528 - val_loss: 0.2485\n",
      "Epoch 40/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2516 - val_loss: 0.2480\n",
      "Epoch 41/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2488 - val_loss: 0.2445\n",
      "Epoch 42/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2463 - val_loss: 0.2473\n",
      "Epoch 43/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2449 - val_loss: 0.2445\n",
      "Epoch 44/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2451 - val_loss: 0.2428\n",
      "Epoch 45/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2436 - val_loss: 0.2424\n",
      "Epoch 46/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2418 - val_loss: 0.2382\n",
      "Epoch 47/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2403 - val_loss: 0.2393\n",
      "Epoch 48/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2404 - val_loss: 0.2370\n",
      "Epoch 49/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2388 - val_loss: 0.2376\n",
      "Epoch 50/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2376 - val_loss: 0.2373\n",
      "Epoch 51/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2368 - val_loss: 0.2361\n",
      "Epoch 52/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2367 - val_loss: 0.2374\n",
      "Epoch 53/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2374 - val_loss: 0.2367\n",
      "Epoch 54/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2356 - val_loss: 0.2333\n",
      "Epoch 55/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2336 - val_loss: 0.2339\n",
      "Epoch 56/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2328 - val_loss: 0.2324\n",
      "Epoch 57/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2324 - val_loss: 0.2337\n",
      "Epoch 58/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2323 - val_loss: 0.2346\n",
      "Epoch 59/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2323 - val_loss: 0.2336\n",
      "Epoch 60/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2313 - val_loss: 0.2334\n",
      "Epoch 61/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2301 - val_loss: 0.2337\n",
      "Epoch 62/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2300 - val_loss: 0.2299\n",
      "Epoch 63/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2290 - val_loss: 0.2303\n",
      "Epoch 64/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2296 - val_loss: 0.2318\n",
      "Epoch 65/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2299 - val_loss: 0.2330\n",
      "Epoch 66/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2289 - val_loss: 0.2313\n",
      "Epoch 67/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2278 - val_loss: 0.2310\n",
      "Epoch 68/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2295 - val_loss: 0.2297\n",
      "Epoch 69/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2271 - val_loss: 0.2297\n",
      "Epoch 70/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2268 - val_loss: 0.2306\n",
      "Epoch 71/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2271 - val_loss: 0.2304\n",
      "Epoch 72/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2264 - val_loss: 0.2272\n",
      "Epoch 73/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2254 - val_loss: 0.2293\n",
      "Epoch 74/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2261 - val_loss: 0.2300\n",
      "Epoch 75/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2269 - val_loss: 0.2290\n",
      "Epoch 76/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2262 - val_loss: 0.2284\n",
      "Epoch 77/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2248 - val_loss: 0.2257\n",
      "Epoch 78/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2238 - val_loss: 0.2263\n",
      "Epoch 79/100\n",
      "36/36 [==============================] - 0s 2ms/step - loss: 0.2240 - val_loss: 0.2266\n",
      "Epoch 80/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2240 - val_loss: 0.2260\n",
      "Epoch 81/100\n",
      "36/36 [==============================] - 0s 4ms/step - loss: 0.2243 - val_loss: 0.2290\n",
      "Epoch 82/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2245 - val_loss: 0.2257\n",
      "Epoch 83/100\n",
      "36/36 [==============================] - 0s 4ms/step - loss: 0.2249 - val_loss: 0.2266\n",
      "Epoch 84/100\n",
      "36/36 [==============================] - 0s 4ms/step - loss: 0.2235 - val_loss: 0.2274\n",
      "Epoch 85/100\n",
      "36/36 [==============================] - 0s 4ms/step - loss: 0.2239 - val_loss: 0.2266\n",
      "Epoch 86/100\n",
      "36/36 [==============================] - 0s 4ms/step - loss: 0.2231 - val_loss: 0.2243\n",
      "Epoch 87/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2234 - val_loss: 0.2249\n",
      "Epoch 88/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2237 - val_loss: 0.2245\n",
      "Epoch 89/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2219 - val_loss: 0.2287\n",
      "Epoch 90/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2231 - val_loss: 0.2225\n",
      "Epoch 91/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2212 - val_loss: 0.2224\n",
      "Epoch 92/100\n",
      "36/36 [==============================] - 0s 4ms/step - loss: 0.2209 - val_loss: 0.2245\n",
      "Epoch 93/100\n",
      "36/36 [==============================] - 0s 4ms/step - loss: 0.2223 - val_loss: 0.2217\n",
      "Epoch 94/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2213 - val_loss: 0.2234\n",
      "Epoch 95/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2210 - val_loss: 0.2239\n",
      "Epoch 96/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2208 - val_loss: 0.2216\n",
      "Epoch 97/100\n",
      "36/36 [==============================] - 0s 4ms/step - loss: 0.2195 - val_loss: 0.2251\n",
      "Epoch 98/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2213 - val_loss: 0.2210\n",
      "Epoch 99/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2204 - val_loss: 0.2239\n",
      "Epoch 100/100\n",
      "36/36 [==============================] - 0s 3ms/step - loss: 0.2201 - val_loss: 0.2254\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A85DC31DC8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A85DC31DC8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "Training size: 1281, No. outliers: 12\n",
      "Epoch 1/50\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000002A86623A5E8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000002A86623A5E8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <bound method DevNet.deviation_loss of <baseline.DevNet.run.DevNet object at 0x000002A85D912788>> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <bound method DevNet.deviation_loss of <baseline.DevNet.run.DevNet object at 0x000002A85D912788>> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "20/20 [==============================] - 2s 19ms/step - loss: 2.6113\n",
      "Epoch 2/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 2.5092\n",
      "Epoch 3/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 2.4254\n",
      "Epoch 4/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 2.3352\n",
      "Epoch 5/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 2.2528\n",
      "Epoch 6/50\n",
      "20/20 [==============================] - 0s 24ms/step - loss: 2.1757\n",
      "Epoch 7/50\n",
      "20/20 [==============================] - 0s 25ms/step - loss: 2.0780\n",
      "Epoch 8/50\n",
      "20/20 [==============================] - 0s 23ms/step - loss: 1.9833\n",
      "Epoch 9/50\n",
      "20/20 [==============================] - 0s 23ms/step - loss: 1.8887\n",
      "Epoch 10/50\n",
      "20/20 [==============================] - 0s 24ms/step - loss: 1.7759\n",
      "Epoch 11/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 1.6884\n",
      "Epoch 12/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 1.6021\n",
      "Epoch 13/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 1.5143\n",
      "Epoch 14/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 1.4463\n",
      "Epoch 15/50\n",
      "20/20 [==============================] - 0s 24ms/step - loss: 1.3844\n",
      "Epoch 16/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 1.3041\n",
      "Epoch 17/50\n",
      "20/20 [==============================] - 0s 17ms/step - loss: 1.2343\n",
      "Epoch 18/50\n",
      "20/20 [==============================] - 0s 17ms/step - loss: 1.1601\n",
      "Epoch 19/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 1.1050\n",
      "Epoch 20/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 1.0670\n",
      "Epoch 21/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 1.0465\n",
      "Epoch 22/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 1.0341\n",
      "Epoch 23/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 1.0117\n",
      "Epoch 24/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.9782\n",
      "Epoch 25/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.9769\n",
      "Epoch 26/50\n",
      "20/20 [==============================] - 0s 23ms/step - loss: 0.9716\n",
      "Epoch 27/50\n",
      "20/20 [==============================] - 0s 24ms/step - loss: 0.9261\n",
      "Epoch 28/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.9384\n",
      "Epoch 29/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.9182\n",
      "Epoch 30/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 0.9306\n",
      "Epoch 31/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.9137\n",
      "Epoch 32/50\n",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.9005\n",
      "Epoch 33/50\n",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.9003\n",
      "Epoch 34/50\n",
      "20/20 [==============================] - 1s 32ms/step - loss: 0.8745\n",
      "Epoch 35/50\n",
      "20/20 [==============================] - 1s 28ms/step - loss: 0.8753\n",
      "Epoch 36/50\n",
      "20/20 [==============================] - 1s 30ms/step - loss: 0.8713\n",
      "Epoch 37/50\n",
      "20/20 [==============================] - 1s 30ms/step - loss: 0.8690\n",
      "Epoch 38/50\n",
      "20/20 [==============================] - 1s 30ms/step - loss: 0.8483\n",
      "Epoch 39/50\n",
      "20/20 [==============================] - 0s 24ms/step - loss: 0.8555\n",
      "Epoch 40/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.8416\n",
      "Epoch 41/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 0.8228\n",
      "Epoch 42/50\n",
      "20/20 [==============================] - 0s 17ms/step - loss: 0.8439\n",
      "Epoch 43/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.8245\n",
      "Epoch 44/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 0.8181\n",
      "Epoch 45/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 0.8054\n",
      "Epoch 46/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.8058\n",
      "Epoch 47/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 0.7976\n",
      "Epoch 48/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.7972\n",
      "Epoch 49/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.7974\n",
      "Epoch 50/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.7631\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A86628BD38> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A86628BD38> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "Learning rate set to 0.011451\n",
      "0:\tlearn: 0.6660157\ttotal: 151ms\tremaining: 2m 30s\n",
      "1:\tlearn: 0.6365578\ttotal: 153ms\tremaining: 1m 16s\n",
      "2:\tlearn: 0.6118627\ttotal: 156ms\tremaining: 51.9s\n",
      "3:\tlearn: 0.5888814\ttotal: 158ms\tremaining: 39.5s\n",
      "4:\tlearn: 0.5661308\ttotal: 161ms\tremaining: 32s\n",
      "5:\tlearn: 0.5448045\ttotal: 163ms\tremaining: 27s\n",
      "6:\tlearn: 0.5247282\ttotal: 165ms\tremaining: 23.4s\n",
      "7:\tlearn: 0.5033319\ttotal: 167ms\tremaining: 20.7s\n",
      "8:\tlearn: 0.4836736\ttotal: 171ms\tremaining: 18.8s\n",
      "9:\tlearn: 0.4663022\ttotal: 173ms\tremaining: 17.2s\n",
      "10:\tlearn: 0.4496327\ttotal: 176ms\tremaining: 15.8s\n",
      "11:\tlearn: 0.4299769\ttotal: 178ms\tremaining: 14.7s\n",
      "12:\tlearn: 0.4130788\ttotal: 181ms\tremaining: 13.7s\n",
      "13:\tlearn: 0.3979479\ttotal: 183ms\tremaining: 12.9s\n",
      "14:\tlearn: 0.3835299\ttotal: 187ms\tremaining: 12.3s\n",
      "15:\tlearn: 0.3691349\ttotal: 191ms\tremaining: 11.8s\n",
      "16:\tlearn: 0.3561152\ttotal: 199ms\tremaining: 11.5s\n",
      "17:\tlearn: 0.3428338\ttotal: 204ms\tremaining: 11.1s\n",
      "18:\tlearn: 0.3308655\ttotal: 207ms\tremaining: 10.7s\n",
      "19:\tlearn: 0.3196190\ttotal: 212ms\tremaining: 10.4s\n",
      "20:\tlearn: 0.3070250\ttotal: 217ms\tremaining: 10.1s\n",
      "21:\tlearn: 0.2956027\ttotal: 221ms\tremaining: 9.81s\n",
      "22:\tlearn: 0.2856615\ttotal: 223ms\tremaining: 9.49s\n",
      "23:\tlearn: 0.2759252\ttotal: 226ms\tremaining: 9.21s\n",
      "24:\tlearn: 0.2669707\ttotal: 232ms\tremaining: 9.03s\n",
      "25:\tlearn: 0.2578723\ttotal: 235ms\tremaining: 8.79s\n",
      "26:\tlearn: 0.2478459\ttotal: 238ms\tremaining: 8.57s\n",
      "27:\tlearn: 0.2394358\ttotal: 241ms\tremaining: 8.37s\n",
      "28:\tlearn: 0.2298494\ttotal: 245ms\tremaining: 8.2s\n",
      "29:\tlearn: 0.2229783\ttotal: 248ms\tremaining: 8.01s\n",
      "30:\tlearn: 0.2158239\ttotal: 250ms\tremaining: 7.82s\n",
      "31:\tlearn: 0.2099653\ttotal: 252ms\tremaining: 7.64s\n",
      "32:\tlearn: 0.2028758\ttotal: 255ms\tremaining: 7.47s\n",
      "33:\tlearn: 0.1963324\ttotal: 258ms\tremaining: 7.33s\n",
      "34:\tlearn: 0.1910135\ttotal: 262ms\tremaining: 7.22s\n",
      "35:\tlearn: 0.1847418\ttotal: 265ms\tremaining: 7.1s\n",
      "36:\tlearn: 0.1788221\ttotal: 269ms\tremaining: 7.01s\n",
      "37:\tlearn: 0.1734762\ttotal: 273ms\tremaining: 6.91s\n",
      "38:\tlearn: 0.1681208\ttotal: 276ms\tremaining: 6.81s\n",
      "39:\tlearn: 0.1626311\ttotal: 279ms\tremaining: 6.69s\n",
      "40:\tlearn: 0.1583310\ttotal: 281ms\tremaining: 6.58s\n",
      "41:\tlearn: 0.1538517\ttotal: 284ms\tremaining: 6.47s\n",
      "42:\tlearn: 0.1499068\ttotal: 286ms\tremaining: 6.37s\n",
      "43:\tlearn: 0.1454193\ttotal: 289ms\tremaining: 6.29s\n",
      "44:\tlearn: 0.1417341\ttotal: 292ms\tremaining: 6.2s\n",
      "45:\tlearn: 0.1381711\ttotal: 295ms\tremaining: 6.12s\n",
      "46:\tlearn: 0.1343979\ttotal: 298ms\tremaining: 6.03s\n",
      "47:\tlearn: 0.1302370\ttotal: 300ms\tremaining: 5.95s\n",
      "48:\tlearn: 0.1268341\ttotal: 304ms\tremaining: 5.89s\n",
      "49:\tlearn: 0.1239061\ttotal: 306ms\tremaining: 5.82s\n",
      "50:\tlearn: 0.1208857\ttotal: 309ms\tremaining: 5.75s\n",
      "51:\tlearn: 0.1181752\ttotal: 312ms\tremaining: 5.68s\n",
      "52:\tlearn: 0.1151706\ttotal: 314ms\tremaining: 5.61s\n",
      "53:\tlearn: 0.1119817\ttotal: 317ms\tremaining: 5.55s\n",
      "54:\tlearn: 0.1097389\ttotal: 320ms\tremaining: 5.49s\n",
      "55:\tlearn: 0.1070659\ttotal: 323ms\tremaining: 5.44s\n",
      "56:\tlearn: 0.1046100\ttotal: 325ms\tremaining: 5.38s\n",
      "57:\tlearn: 0.1019902\ttotal: 328ms\tremaining: 5.33s\n",
      "58:\tlearn: 0.1000335\ttotal: 331ms\tremaining: 5.27s\n",
      "59:\tlearn: 0.0979094\ttotal: 334ms\tremaining: 5.23s\n",
      "60:\tlearn: 0.0957460\ttotal: 336ms\tremaining: 5.18s\n",
      "61:\tlearn: 0.0934519\ttotal: 339ms\tremaining: 5.13s\n",
      "62:\tlearn: 0.0916409\ttotal: 341ms\tremaining: 5.07s\n",
      "63:\tlearn: 0.0899037\ttotal: 344ms\tremaining: 5.03s\n",
      "64:\tlearn: 0.0883397\ttotal: 347ms\tremaining: 5s\n",
      "65:\tlearn: 0.0857897\ttotal: 350ms\tremaining: 4.95s\n",
      "66:\tlearn: 0.0842692\ttotal: 353ms\tremaining: 4.91s\n",
      "67:\tlearn: 0.0826653\ttotal: 356ms\tremaining: 4.88s\n",
      "68:\tlearn: 0.0812548\ttotal: 359ms\tremaining: 4.85s\n",
      "69:\tlearn: 0.0797492\ttotal: 364ms\tremaining: 4.84s\n",
      "70:\tlearn: 0.0777173\ttotal: 367ms\tremaining: 4.8s\n",
      "71:\tlearn: 0.0765012\ttotal: 370ms\tremaining: 4.77s\n",
      "72:\tlearn: 0.0751282\ttotal: 373ms\tremaining: 4.73s\n",
      "73:\tlearn: 0.0735202\ttotal: 377ms\tremaining: 4.72s\n",
      "74:\tlearn: 0.0723487\ttotal: 380ms\tremaining: 4.68s\n",
      "75:\tlearn: 0.0709706\ttotal: 383ms\tremaining: 4.65s\n",
      "76:\tlearn: 0.0697607\ttotal: 385ms\tremaining: 4.62s\n",
      "77:\tlearn: 0.0684008\ttotal: 388ms\tremaining: 4.59s\n",
      "78:\tlearn: 0.0666412\ttotal: 391ms\tremaining: 4.56s\n",
      "79:\tlearn: 0.0654194\ttotal: 393ms\tremaining: 4.52s\n",
      "80:\tlearn: 0.0644364\ttotal: 396ms\tremaining: 4.49s\n",
      "81:\tlearn: 0.0634194\ttotal: 398ms\tremaining: 4.46s\n",
      "82:\tlearn: 0.0625443\ttotal: 401ms\tremaining: 4.43s\n",
      "83:\tlearn: 0.0614441\ttotal: 423ms\tremaining: 4.61s\n",
      "84:\tlearn: 0.0604291\ttotal: 426ms\tremaining: 4.58s\n",
      "85:\tlearn: 0.0597700\ttotal: 428ms\tremaining: 4.55s\n",
      "86:\tlearn: 0.0588004\ttotal: 431ms\tremaining: 4.52s\n",
      "87:\tlearn: 0.0580271\ttotal: 435ms\tremaining: 4.5s\n",
      "88:\tlearn: 0.0570925\ttotal: 437ms\tremaining: 4.48s\n",
      "89:\tlearn: 0.0564227\ttotal: 440ms\tremaining: 4.45s\n",
      "90:\tlearn: 0.0557418\ttotal: 442ms\tremaining: 4.42s\n",
      "91:\tlearn: 0.0546394\ttotal: 444ms\tremaining: 4.39s\n",
      "92:\tlearn: 0.0540091\ttotal: 447ms\tremaining: 4.36s\n",
      "93:\tlearn: 0.0530624\ttotal: 451ms\tremaining: 4.35s\n",
      "94:\tlearn: 0.0523389\ttotal: 454ms\tremaining: 4.32s\n",
      "95:\tlearn: 0.0516195\ttotal: 456ms\tremaining: 4.3s\n",
      "96:\tlearn: 0.0507570\ttotal: 460ms\tremaining: 4.28s\n",
      "97:\tlearn: 0.0500821\ttotal: 463ms\tremaining: 4.26s\n",
      "98:\tlearn: 0.0493586\ttotal: 466ms\tremaining: 4.24s\n",
      "99:\tlearn: 0.0488157\ttotal: 468ms\tremaining: 4.21s\n",
      "100:\tlearn: 0.0482844\ttotal: 471ms\tremaining: 4.19s\n",
      "101:\tlearn: 0.0476518\ttotal: 473ms\tremaining: 4.17s\n",
      "102:\tlearn: 0.0469734\ttotal: 477ms\tremaining: 4.16s\n",
      "103:\tlearn: 0.0464291\ttotal: 489ms\tremaining: 4.21s\n",
      "104:\tlearn: 0.0459916\ttotal: 495ms\tremaining: 4.22s\n",
      "105:\tlearn: 0.0454906\ttotal: 499ms\tremaining: 4.21s\n",
      "106:\tlearn: 0.0449245\ttotal: 504ms\tremaining: 4.21s\n",
      "107:\tlearn: 0.0444684\ttotal: 509ms\tremaining: 4.2s\n",
      "108:\tlearn: 0.0438666\ttotal: 512ms\tremaining: 4.19s\n",
      "109:\tlearn: 0.0433194\ttotal: 517ms\tremaining: 4.18s\n",
      "110:\tlearn: 0.0427553\ttotal: 520ms\tremaining: 4.16s\n",
      "111:\tlearn: 0.0421684\ttotal: 525ms\tremaining: 4.16s\n",
      "112:\tlearn: 0.0417930\ttotal: 528ms\tremaining: 4.14s\n",
      "113:\tlearn: 0.0414166\ttotal: 531ms\tremaining: 4.13s\n",
      "114:\tlearn: 0.0409933\ttotal: 535ms\tremaining: 4.11s\n",
      "115:\tlearn: 0.0405202\ttotal: 538ms\tremaining: 4.1s\n",
      "116:\tlearn: 0.0400966\ttotal: 541ms\tremaining: 4.08s\n",
      "117:\tlearn: 0.0396424\ttotal: 545ms\tremaining: 4.08s\n",
      "118:\tlearn: 0.0393196\ttotal: 548ms\tremaining: 4.06s\n",
      "119:\tlearn: 0.0389754\ttotal: 552ms\tremaining: 4.04s\n",
      "120:\tlearn: 0.0384517\ttotal: 556ms\tremaining: 4.04s\n",
      "121:\tlearn: 0.0379211\ttotal: 560ms\tremaining: 4.03s\n",
      "122:\tlearn: 0.0376159\ttotal: 563ms\tremaining: 4.01s\n",
      "123:\tlearn: 0.0372547\ttotal: 566ms\tremaining: 4s\n",
      "124:\tlearn: 0.0367420\ttotal: 571ms\tremaining: 4s\n",
      "125:\tlearn: 0.0362307\ttotal: 574ms\tremaining: 3.98s\n",
      "126:\tlearn: 0.0359378\ttotal: 577ms\tremaining: 3.96s\n",
      "127:\tlearn: 0.0356446\ttotal: 579ms\tremaining: 3.95s\n",
      "128:\tlearn: 0.0352492\ttotal: 583ms\tremaining: 3.93s\n",
      "129:\tlearn: 0.0349378\ttotal: 586ms\tremaining: 3.92s\n",
      "130:\tlearn: 0.0346847\ttotal: 590ms\tremaining: 3.91s\n",
      "131:\tlearn: 0.0343889\ttotal: 593ms\tremaining: 3.9s\n",
      "132:\tlearn: 0.0340682\ttotal: 596ms\tremaining: 3.88s\n",
      "133:\tlearn: 0.0338070\ttotal: 600ms\tremaining: 3.88s\n",
      "134:\tlearn: 0.0334765\ttotal: 604ms\tremaining: 3.87s\n",
      "135:\tlearn: 0.0332056\ttotal: 607ms\tremaining: 3.85s\n",
      "136:\tlearn: 0.0328764\ttotal: 610ms\tremaining: 3.84s\n",
      "137:\tlearn: 0.0325778\ttotal: 615ms\tremaining: 3.84s\n",
      "138:\tlearn: 0.0323473\ttotal: 619ms\tremaining: 3.83s\n",
      "139:\tlearn: 0.0321128\ttotal: 623ms\tremaining: 3.83s\n",
      "140:\tlearn: 0.0318799\ttotal: 626ms\tremaining: 3.81s\n",
      "141:\tlearn: 0.0316967\ttotal: 631ms\tremaining: 3.81s\n",
      "142:\tlearn: 0.0314729\ttotal: 634ms\tremaining: 3.8s\n",
      "143:\tlearn: 0.0312320\ttotal: 638ms\tremaining: 3.79s\n",
      "144:\tlearn: 0.0310843\ttotal: 641ms\tremaining: 3.78s\n",
      "145:\tlearn: 0.0308554\ttotal: 646ms\tremaining: 3.78s\n",
      "146:\tlearn: 0.0306791\ttotal: 649ms\tremaining: 3.77s\n",
      "147:\tlearn: 0.0304375\ttotal: 652ms\tremaining: 3.75s\n",
      "148:\tlearn: 0.0302389\ttotal: 655ms\tremaining: 3.74s\n",
      "149:\tlearn: 0.0299652\ttotal: 660ms\tremaining: 3.74s\n",
      "150:\tlearn: 0.0298484\ttotal: 663ms\tremaining: 3.73s\n",
      "151:\tlearn: 0.0296226\ttotal: 666ms\tremaining: 3.71s\n",
      "152:\tlearn: 0.0294530\ttotal: 669ms\tremaining: 3.7s\n",
      "153:\tlearn: 0.0291845\ttotal: 674ms\tremaining: 3.7s\n",
      "154:\tlearn: 0.0288962\ttotal: 677ms\tremaining: 3.69s\n",
      "155:\tlearn: 0.0285515\ttotal: 680ms\tremaining: 3.68s\n",
      "156:\tlearn: 0.0283865\ttotal: 685ms\tremaining: 3.68s\n",
      "157:\tlearn: 0.0281959\ttotal: 690ms\tremaining: 3.67s\n",
      "158:\tlearn: 0.0278903\ttotal: 693ms\tremaining: 3.67s\n",
      "159:\tlearn: 0.0277109\ttotal: 696ms\tremaining: 3.65s\n",
      "160:\tlearn: 0.0275503\ttotal: 700ms\tremaining: 3.65s\n",
      "161:\tlearn: 0.0274004\ttotal: 704ms\tremaining: 3.64s\n",
      "162:\tlearn: 0.0271978\ttotal: 707ms\tremaining: 3.63s\n",
      "163:\tlearn: 0.0270501\ttotal: 710ms\tremaining: 3.62s\n",
      "164:\tlearn: 0.0269573\ttotal: 713ms\tremaining: 3.61s\n",
      "165:\tlearn: 0.0268431\ttotal: 717ms\tremaining: 3.6s\n",
      "166:\tlearn: 0.0265507\ttotal: 720ms\tremaining: 3.59s\n",
      "167:\tlearn: 0.0263867\ttotal: 722ms\tremaining: 3.58s\n",
      "168:\tlearn: 0.0261640\ttotal: 726ms\tremaining: 3.57s\n",
      "169:\tlearn: 0.0260732\ttotal: 729ms\tremaining: 3.56s\n",
      "170:\tlearn: 0.0259308\ttotal: 733ms\tremaining: 3.55s\n",
      "171:\tlearn: 0.0257331\ttotal: 735ms\tremaining: 3.54s\n",
      "172:\tlearn: 0.0255270\ttotal: 739ms\tremaining: 3.53s\n",
      "173:\tlearn: 0.0253897\ttotal: 742ms\tremaining: 3.52s\n",
      "174:\tlearn: 0.0252172\ttotal: 745ms\tremaining: 3.51s\n",
      "175:\tlearn: 0.0250884\ttotal: 749ms\tremaining: 3.5s\n",
      "176:\tlearn: 0.0249793\ttotal: 751ms\tremaining: 3.49s\n",
      "177:\tlearn: 0.0248345\ttotal: 754ms\tremaining: 3.48s\n",
      "178:\tlearn: 0.0246663\ttotal: 756ms\tremaining: 3.47s\n",
      "179:\tlearn: 0.0246044\ttotal: 760ms\tremaining: 3.46s\n",
      "180:\tlearn: 0.0244754\ttotal: 764ms\tremaining: 3.46s\n",
      "181:\tlearn: 0.0243526\ttotal: 769ms\tremaining: 3.46s\n",
      "182:\tlearn: 0.0242861\ttotal: 777ms\tremaining: 3.47s\n",
      "183:\tlearn: 0.0241582\ttotal: 782ms\tremaining: 3.47s\n",
      "184:\tlearn: 0.0239011\ttotal: 788ms\tremaining: 3.47s\n",
      "185:\tlearn: 0.0237468\ttotal: 796ms\tremaining: 3.48s\n",
      "186:\tlearn: 0.0236465\ttotal: 801ms\tremaining: 3.48s\n",
      "187:\tlearn: 0.0235140\ttotal: 806ms\tremaining: 3.48s\n",
      "188:\tlearn: 0.0233975\ttotal: 809ms\tremaining: 3.47s\n",
      "189:\tlearn: 0.0233189\ttotal: 813ms\tremaining: 3.46s\n",
      "190:\tlearn: 0.0230978\ttotal: 816ms\tremaining: 3.46s\n",
      "191:\tlearn: 0.0230001\ttotal: 821ms\tremaining: 3.46s\n",
      "192:\tlearn: 0.0229027\ttotal: 824ms\tremaining: 3.44s\n",
      "193:\tlearn: 0.0227592\ttotal: 827ms\tremaining: 3.43s\n",
      "194:\tlearn: 0.0226318\ttotal: 829ms\tremaining: 3.42s\n",
      "195:\tlearn: 0.0225102\ttotal: 834ms\tremaining: 3.42s\n",
      "196:\tlearn: 0.0224266\ttotal: 837ms\tremaining: 3.41s\n",
      "197:\tlearn: 0.0222838\ttotal: 842ms\tremaining: 3.41s\n",
      "198:\tlearn: 0.0221276\ttotal: 845ms\tremaining: 3.4s\n",
      "199:\tlearn: 0.0220378\ttotal: 850ms\tremaining: 3.4s\n",
      "200:\tlearn: 0.0219262\ttotal: 854ms\tremaining: 3.39s\n",
      "201:\tlearn: 0.0217797\ttotal: 857ms\tremaining: 3.39s\n",
      "202:\tlearn: 0.0216423\ttotal: 861ms\tremaining: 3.38s\n",
      "203:\tlearn: 0.0215260\ttotal: 865ms\tremaining: 3.38s\n",
      "204:\tlearn: 0.0214196\ttotal: 869ms\tremaining: 3.37s\n",
      "205:\tlearn: 0.0212981\ttotal: 872ms\tremaining: 3.36s\n",
      "206:\tlearn: 0.0212670\ttotal: 875ms\tremaining: 3.35s\n",
      "207:\tlearn: 0.0211703\ttotal: 879ms\tremaining: 3.35s\n",
      "208:\tlearn: 0.0210970\ttotal: 882ms\tremaining: 3.34s\n",
      "209:\tlearn: 0.0210490\ttotal: 885ms\tremaining: 3.33s\n",
      "210:\tlearn: 0.0209402\ttotal: 888ms\tremaining: 3.32s\n",
      "211:\tlearn: 0.0209245\ttotal: 891ms\tremaining: 3.31s\n",
      "212:\tlearn: 0.0208195\ttotal: 895ms\tremaining: 3.31s\n",
      "213:\tlearn: 0.0207885\ttotal: 898ms\tremaining: 3.3s\n",
      "214:\tlearn: 0.0207173\ttotal: 900ms\tremaining: 3.29s\n",
      "215:\tlearn: 0.0206847\ttotal: 904ms\tremaining: 3.28s\n",
      "216:\tlearn: 0.0205810\ttotal: 907ms\tremaining: 3.27s\n",
      "217:\tlearn: 0.0204705\ttotal: 910ms\tremaining: 3.27s\n",
      "218:\tlearn: 0.0203476\ttotal: 913ms\tremaining: 3.26s\n",
      "219:\tlearn: 0.0202495\ttotal: 918ms\tremaining: 3.25s\n",
      "220:\tlearn: 0.0201091\ttotal: 921ms\tremaining: 3.25s\n",
      "221:\tlearn: 0.0200692\ttotal: 925ms\tremaining: 3.24s\n",
      "222:\tlearn: 0.0200130\ttotal: 928ms\tremaining: 3.23s\n",
      "223:\tlearn: 0.0199843\ttotal: 933ms\tremaining: 3.23s\n",
      "224:\tlearn: 0.0199313\ttotal: 937ms\tremaining: 3.23s\n",
      "225:\tlearn: 0.0198580\ttotal: 940ms\tremaining: 3.22s\n",
      "226:\tlearn: 0.0197504\ttotal: 945ms\tremaining: 3.22s\n",
      "227:\tlearn: 0.0197377\ttotal: 948ms\tremaining: 3.21s\n",
      "228:\tlearn: 0.0195674\ttotal: 954ms\tremaining: 3.21s\n",
      "229:\tlearn: 0.0195047\ttotal: 957ms\tremaining: 3.2s\n",
      "230:\tlearn: 0.0194007\ttotal: 962ms\tremaining: 3.2s\n",
      "231:\tlearn: 0.0193780\ttotal: 965ms\tremaining: 3.19s\n",
      "232:\tlearn: 0.0192984\ttotal: 970ms\tremaining: 3.19s\n",
      "233:\tlearn: 0.0192073\ttotal: 973ms\tremaining: 3.19s\n",
      "234:\tlearn: 0.0191118\ttotal: 976ms\tremaining: 3.18s\n",
      "235:\tlearn: 0.0189925\ttotal: 979ms\tremaining: 3.17s\n",
      "236:\tlearn: 0.0189271\ttotal: 984ms\tremaining: 3.17s\n",
      "237:\tlearn: 0.0189074\ttotal: 987ms\tremaining: 3.16s\n",
      "238:\tlearn: 0.0188918\ttotal: 990ms\tremaining: 3.15s\n",
      "239:\tlearn: 0.0188687\ttotal: 993ms\tremaining: 3.15s\n",
      "240:\tlearn: 0.0188288\ttotal: 1000ms\tremaining: 3.15s\n",
      "241:\tlearn: 0.0188027\ttotal: 1.01s\tremaining: 3.15s\n",
      "242:\tlearn: 0.0187059\ttotal: 1.01s\tremaining: 3.15s\n",
      "243:\tlearn: 0.0186106\ttotal: 1.01s\tremaining: 3.14s\n",
      "244:\tlearn: 0.0185155\ttotal: 1.02s\tremaining: 3.13s\n",
      "245:\tlearn: 0.0184377\ttotal: 1.02s\tremaining: 3.13s\n",
      "246:\tlearn: 0.0183646\ttotal: 1.02s\tremaining: 3.12s\n",
      "247:\tlearn: 0.0182364\ttotal: 1.03s\tremaining: 3.11s\n",
      "248:\tlearn: 0.0181628\ttotal: 1.03s\tremaining: 3.1s\n",
      "249:\tlearn: 0.0181168\ttotal: 1.03s\tremaining: 3.1s\n",
      "250:\tlearn: 0.0180449\ttotal: 1.03s\tremaining: 3.09s\n",
      "251:\tlearn: 0.0179119\ttotal: 1.04s\tremaining: 3.08s\n",
      "252:\tlearn: 0.0178113\ttotal: 1.04s\tremaining: 3.07s\n",
      "253:\tlearn: 0.0177860\ttotal: 1.04s\tremaining: 3.06s\n",
      "254:\tlearn: 0.0177563\ttotal: 1.04s\tremaining: 3.06s\n",
      "255:\tlearn: 0.0176909\ttotal: 1.05s\tremaining: 3.05s\n",
      "256:\tlearn: 0.0176583\ttotal: 1.05s\tremaining: 3.04s\n",
      "257:\tlearn: 0.0176348\ttotal: 1.05s\tremaining: 3.03s\n",
      "258:\tlearn: 0.0175543\ttotal: 1.06s\tremaining: 3.03s\n",
      "259:\tlearn: 0.0174951\ttotal: 1.07s\tremaining: 3.04s\n",
      "260:\tlearn: 0.0174421\ttotal: 1.07s\tremaining: 3.04s\n",
      "261:\tlearn: 0.0173668\ttotal: 1.08s\tremaining: 3.04s\n",
      "262:\tlearn: 0.0172894\ttotal: 1.08s\tremaining: 3.03s\n",
      "263:\tlearn: 0.0171555\ttotal: 1.09s\tremaining: 3.03s\n",
      "264:\tlearn: 0.0171243\ttotal: 1.09s\tremaining: 3.03s\n",
      "265:\tlearn: 0.0170296\ttotal: 1.1s\tremaining: 3.03s\n",
      "266:\tlearn: 0.0169472\ttotal: 1.1s\tremaining: 3.02s\n",
      "267:\tlearn: 0.0168935\ttotal: 1.1s\tremaining: 3.02s\n",
      "268:\tlearn: 0.0168263\ttotal: 1.11s\tremaining: 3.02s\n",
      "269:\tlearn: 0.0167209\ttotal: 1.11s\tremaining: 3.01s\n",
      "270:\tlearn: 0.0166792\ttotal: 1.12s\tremaining: 3s\n",
      "271:\tlearn: 0.0166571\ttotal: 1.12s\tremaining: 3s\n",
      "272:\tlearn: 0.0165880\ttotal: 1.12s\tremaining: 3s\n",
      "273:\tlearn: 0.0165208\ttotal: 1.13s\tremaining: 2.99s\n",
      "274:\tlearn: 0.0164861\ttotal: 1.13s\tremaining: 2.99s\n",
      "275:\tlearn: 0.0164395\ttotal: 1.14s\tremaining: 2.98s\n",
      "276:\tlearn: 0.0163610\ttotal: 1.14s\tremaining: 2.98s\n",
      "277:\tlearn: 0.0162778\ttotal: 1.15s\tremaining: 2.98s\n",
      "278:\tlearn: 0.0162210\ttotal: 1.15s\tremaining: 2.98s\n",
      "279:\tlearn: 0.0161739\ttotal: 1.16s\tremaining: 2.98s\n",
      "280:\tlearn: 0.0161156\ttotal: 1.17s\tremaining: 2.99s\n",
      "281:\tlearn: 0.0160713\ttotal: 1.18s\tremaining: 2.99s\n",
      "282:\tlearn: 0.0160023\ttotal: 1.18s\tremaining: 3s\n",
      "283:\tlearn: 0.0159403\ttotal: 1.19s\tremaining: 3s\n",
      "284:\tlearn: 0.0158765\ttotal: 1.2s\tremaining: 3s\n",
      "285:\tlearn: 0.0158101\ttotal: 1.2s\tremaining: 3s\n",
      "286:\tlearn: 0.0157451\ttotal: 1.21s\tremaining: 2.99s\n",
      "287:\tlearn: 0.0156695\ttotal: 1.21s\tremaining: 2.99s\n",
      "288:\tlearn: 0.0156319\ttotal: 1.21s\tremaining: 2.98s\n",
      "289:\tlearn: 0.0156024\ttotal: 1.21s\tremaining: 2.97s\n",
      "290:\tlearn: 0.0155599\ttotal: 1.22s\tremaining: 2.97s\n",
      "291:\tlearn: 0.0154601\ttotal: 1.22s\tremaining: 2.96s\n",
      "292:\tlearn: 0.0153869\ttotal: 1.23s\tremaining: 2.96s\n",
      "293:\tlearn: 0.0153352\ttotal: 1.23s\tremaining: 2.95s\n",
      "294:\tlearn: 0.0152648\ttotal: 1.23s\tremaining: 2.94s\n",
      "295:\tlearn: 0.0152264\ttotal: 1.24s\tremaining: 2.94s\n",
      "296:\tlearn: 0.0151716\ttotal: 1.24s\tremaining: 2.93s\n",
      "297:\tlearn: 0.0151107\ttotal: 1.24s\tremaining: 2.93s\n",
      "298:\tlearn: 0.0150860\ttotal: 1.25s\tremaining: 2.92s\n",
      "299:\tlearn: 0.0150473\ttotal: 1.25s\tremaining: 2.92s\n",
      "300:\tlearn: 0.0149903\ttotal: 1.25s\tremaining: 2.91s\n",
      "301:\tlearn: 0.0149336\ttotal: 1.25s\tremaining: 2.9s\n",
      "302:\tlearn: 0.0148875\ttotal: 1.26s\tremaining: 2.89s\n",
      "303:\tlearn: 0.0148410\ttotal: 1.26s\tremaining: 2.89s\n",
      "304:\tlearn: 0.0148194\ttotal: 1.26s\tremaining: 2.88s\n",
      "305:\tlearn: 0.0147988\ttotal: 1.27s\tremaining: 2.87s\n",
      "306:\tlearn: 0.0147801\ttotal: 1.27s\tremaining: 2.87s\n",
      "307:\tlearn: 0.0147040\ttotal: 1.27s\tremaining: 2.86s\n",
      "308:\tlearn: 0.0145930\ttotal: 1.28s\tremaining: 2.86s\n",
      "309:\tlearn: 0.0145409\ttotal: 1.28s\tremaining: 2.85s\n",
      "310:\tlearn: 0.0145012\ttotal: 1.28s\tremaining: 2.84s\n",
      "311:\tlearn: 0.0144630\ttotal: 1.29s\tremaining: 2.84s\n",
      "312:\tlearn: 0.0143995\ttotal: 1.29s\tremaining: 2.84s\n",
      "313:\tlearn: 0.0143607\ttotal: 1.29s\tremaining: 2.83s\n",
      "314:\tlearn: 0.0143475\ttotal: 1.3s\tremaining: 2.83s\n",
      "315:\tlearn: 0.0142756\ttotal: 1.3s\tremaining: 2.82s\n",
      "316:\tlearn: 0.0142304\ttotal: 1.3s\tremaining: 2.81s\n",
      "317:\tlearn: 0.0141811\ttotal: 1.31s\tremaining: 2.81s\n",
      "318:\tlearn: 0.0141195\ttotal: 1.31s\tremaining: 2.8s\n",
      "319:\tlearn: 0.0140717\ttotal: 1.31s\tremaining: 2.79s\n",
      "320:\tlearn: 0.0140186\ttotal: 1.32s\tremaining: 2.78s\n",
      "321:\tlearn: 0.0139816\ttotal: 1.32s\tremaining: 2.78s\n",
      "322:\tlearn: 0.0139347\ttotal: 1.32s\tremaining: 2.77s\n",
      "323:\tlearn: 0.0138825\ttotal: 1.32s\tremaining: 2.76s\n",
      "324:\tlearn: 0.0138041\ttotal: 1.33s\tremaining: 2.76s\n",
      "325:\tlearn: 0.0137500\ttotal: 1.33s\tremaining: 2.75s\n",
      "326:\tlearn: 0.0136988\ttotal: 1.33s\tremaining: 2.75s\n",
      "327:\tlearn: 0.0136520\ttotal: 1.34s\tremaining: 2.74s\n",
      "328:\tlearn: 0.0135885\ttotal: 1.34s\tremaining: 2.73s\n",
      "329:\tlearn: 0.0135457\ttotal: 1.34s\tremaining: 2.73s\n",
      "330:\tlearn: 0.0135103\ttotal: 1.35s\tremaining: 2.72s\n",
      "331:\tlearn: 0.0134629\ttotal: 1.35s\tremaining: 2.72s\n",
      "332:\tlearn: 0.0133973\ttotal: 1.36s\tremaining: 2.73s\n",
      "333:\tlearn: 0.0133574\ttotal: 1.37s\tremaining: 2.72s\n",
      "334:\tlearn: 0.0133363\ttotal: 1.37s\tremaining: 2.72s\n",
      "335:\tlearn: 0.0132894\ttotal: 1.38s\tremaining: 2.72s\n",
      "336:\tlearn: 0.0132648\ttotal: 1.38s\tremaining: 2.71s\n",
      "337:\tlearn: 0.0132381\ttotal: 1.38s\tremaining: 2.71s\n",
      "338:\tlearn: 0.0131745\ttotal: 1.39s\tremaining: 2.7s\n",
      "339:\tlearn: 0.0131305\ttotal: 1.39s\tremaining: 2.7s\n",
      "340:\tlearn: 0.0131059\ttotal: 1.4s\tremaining: 2.7s\n",
      "341:\tlearn: 0.0130123\ttotal: 1.4s\tremaining: 2.69s\n",
      "342:\tlearn: 0.0129495\ttotal: 1.4s\tremaining: 2.69s\n",
      "343:\tlearn: 0.0129138\ttotal: 1.41s\tremaining: 2.68s\n",
      "344:\tlearn: 0.0129075\ttotal: 1.41s\tremaining: 2.67s\n",
      "345:\tlearn: 0.0128803\ttotal: 1.41s\tremaining: 2.67s\n",
      "346:\tlearn: 0.0128562\ttotal: 1.42s\tremaining: 2.67s\n",
      "347:\tlearn: 0.0128176\ttotal: 1.42s\tremaining: 2.66s\n",
      "348:\tlearn: 0.0127849\ttotal: 1.42s\tremaining: 2.66s\n",
      "349:\tlearn: 0.0127746\ttotal: 1.43s\tremaining: 2.65s\n",
      "350:\tlearn: 0.0126921\ttotal: 1.43s\tremaining: 2.65s\n",
      "351:\tlearn: 0.0126533\ttotal: 1.43s\tremaining: 2.64s\n",
      "352:\tlearn: 0.0125844\ttotal: 1.44s\tremaining: 2.64s\n",
      "353:\tlearn: 0.0125733\ttotal: 1.44s\tremaining: 2.63s\n",
      "354:\tlearn: 0.0125193\ttotal: 1.45s\tremaining: 2.63s\n",
      "355:\tlearn: 0.0125100\ttotal: 1.45s\tremaining: 2.63s\n",
      "356:\tlearn: 0.0124683\ttotal: 1.46s\tremaining: 2.62s\n",
      "357:\tlearn: 0.0124635\ttotal: 1.48s\tremaining: 2.65s\n",
      "358:\tlearn: 0.0124202\ttotal: 1.48s\tremaining: 2.64s\n",
      "359:\tlearn: 0.0123710\ttotal: 1.48s\tremaining: 2.64s\n",
      "360:\tlearn: 0.0123376\ttotal: 1.49s\tremaining: 2.63s\n",
      "361:\tlearn: 0.0122949\ttotal: 1.49s\tremaining: 2.63s\n",
      "362:\tlearn: 0.0122720\ttotal: 1.5s\tremaining: 2.62s\n",
      "363:\tlearn: 0.0122237\ttotal: 1.5s\tremaining: 2.62s\n",
      "364:\tlearn: 0.0121953\ttotal: 1.5s\tremaining: 2.62s\n",
      "365:\tlearn: 0.0121447\ttotal: 1.51s\tremaining: 2.61s\n",
      "366:\tlearn: 0.0120938\ttotal: 1.51s\tremaining: 2.6s\n",
      "367:\tlearn: 0.0120588\ttotal: 1.51s\tremaining: 2.6s\n",
      "368:\tlearn: 0.0120090\ttotal: 1.52s\tremaining: 2.59s\n",
      "369:\tlearn: 0.0119615\ttotal: 1.52s\tremaining: 2.59s\n",
      "370:\tlearn: 0.0119460\ttotal: 1.52s\tremaining: 2.58s\n",
      "371:\tlearn: 0.0119055\ttotal: 1.53s\tremaining: 2.58s\n",
      "372:\tlearn: 0.0118730\ttotal: 1.53s\tremaining: 2.58s\n",
      "373:\tlearn: 0.0118536\ttotal: 1.54s\tremaining: 2.57s\n",
      "374:\tlearn: 0.0118053\ttotal: 1.54s\tremaining: 2.57s\n",
      "375:\tlearn: 0.0117382\ttotal: 1.54s\tremaining: 2.56s\n",
      "376:\tlearn: 0.0117232\ttotal: 1.55s\tremaining: 2.56s\n",
      "377:\tlearn: 0.0116790\ttotal: 1.55s\tremaining: 2.55s\n",
      "378:\tlearn: 0.0116494\ttotal: 1.55s\tremaining: 2.55s\n",
      "379:\tlearn: 0.0116041\ttotal: 1.56s\tremaining: 2.54s\n",
      "380:\tlearn: 0.0115324\ttotal: 1.56s\tremaining: 2.54s\n",
      "381:\tlearn: 0.0115043\ttotal: 1.57s\tremaining: 2.54s\n",
      "382:\tlearn: 0.0114549\ttotal: 1.57s\tremaining: 2.53s\n",
      "383:\tlearn: 0.0114350\ttotal: 1.57s\tremaining: 2.52s\n",
      "384:\tlearn: 0.0114206\ttotal: 1.58s\tremaining: 2.52s\n",
      "385:\tlearn: 0.0114017\ttotal: 1.58s\tremaining: 2.52s\n",
      "386:\tlearn: 0.0113911\ttotal: 1.59s\tremaining: 2.51s\n",
      "387:\tlearn: 0.0113503\ttotal: 1.59s\tremaining: 2.51s\n",
      "388:\tlearn: 0.0113025\ttotal: 1.59s\tremaining: 2.51s\n",
      "389:\tlearn: 0.0112729\ttotal: 1.6s\tremaining: 2.5s\n",
      "390:\tlearn: 0.0111882\ttotal: 1.61s\tremaining: 2.51s\n",
      "391:\tlearn: 0.0111593\ttotal: 1.61s\tremaining: 2.51s\n",
      "392:\tlearn: 0.0111096\ttotal: 1.62s\tremaining: 2.5s\n",
      "393:\tlearn: 0.0110830\ttotal: 1.63s\tremaining: 2.5s\n",
      "394:\tlearn: 0.0110295\ttotal: 1.63s\tremaining: 2.5s\n",
      "395:\tlearn: 0.0110056\ttotal: 1.64s\tremaining: 2.5s\n",
      "396:\tlearn: 0.0109942\ttotal: 1.64s\tremaining: 2.5s\n",
      "397:\tlearn: 0.0109842\ttotal: 1.65s\tremaining: 2.49s\n",
      "398:\tlearn: 0.0109269\ttotal: 1.65s\tremaining: 2.49s\n",
      "399:\tlearn: 0.0108895\ttotal: 1.66s\tremaining: 2.49s\n",
      "400:\tlearn: 0.0108371\ttotal: 1.66s\tremaining: 2.48s\n",
      "401:\tlearn: 0.0108298\ttotal: 1.67s\tremaining: 2.48s\n",
      "402:\tlearn: 0.0107810\ttotal: 1.67s\tremaining: 2.48s\n",
      "403:\tlearn: 0.0107639\ttotal: 1.68s\tremaining: 2.48s\n",
      "404:\tlearn: 0.0107423\ttotal: 1.68s\tremaining: 2.47s\n",
      "405:\tlearn: 0.0107307\ttotal: 1.69s\tremaining: 2.47s\n",
      "406:\tlearn: 0.0107171\ttotal: 1.69s\tremaining: 2.46s\n",
      "407:\tlearn: 0.0106882\ttotal: 1.7s\tremaining: 2.46s\n",
      "408:\tlearn: 0.0106727\ttotal: 1.7s\tremaining: 2.46s\n",
      "409:\tlearn: 0.0106314\ttotal: 1.7s\tremaining: 2.45s\n",
      "410:\tlearn: 0.0105876\ttotal: 1.71s\tremaining: 2.45s\n",
      "411:\tlearn: 0.0105684\ttotal: 1.71s\tremaining: 2.44s\n",
      "412:\tlearn: 0.0105372\ttotal: 1.72s\tremaining: 2.44s\n",
      "413:\tlearn: 0.0105113\ttotal: 1.72s\tremaining: 2.44s\n",
      "414:\tlearn: 0.0104619\ttotal: 1.73s\tremaining: 2.43s\n",
      "415:\tlearn: 0.0104141\ttotal: 1.73s\tremaining: 2.43s\n",
      "416:\tlearn: 0.0104096\ttotal: 1.73s\tremaining: 2.42s\n",
      "417:\tlearn: 0.0103851\ttotal: 1.74s\tremaining: 2.42s\n",
      "418:\tlearn: 0.0103551\ttotal: 1.74s\tremaining: 2.41s\n",
      "419:\tlearn: 0.0103274\ttotal: 1.74s\tremaining: 2.41s\n",
      "420:\tlearn: 0.0102885\ttotal: 1.75s\tremaining: 2.4s\n",
      "421:\tlearn: 0.0102851\ttotal: 1.75s\tremaining: 2.4s\n",
      "422:\tlearn: 0.0102378\ttotal: 1.76s\tremaining: 2.4s\n",
      "423:\tlearn: 0.0101941\ttotal: 1.76s\tremaining: 2.39s\n",
      "424:\tlearn: 0.0101450\ttotal: 1.77s\tremaining: 2.39s\n",
      "425:\tlearn: 0.0101277\ttotal: 1.77s\tremaining: 2.39s\n",
      "426:\tlearn: 0.0101027\ttotal: 1.77s\tremaining: 2.38s\n",
      "427:\tlearn: 0.0100743\ttotal: 1.78s\tremaining: 2.38s\n",
      "428:\tlearn: 0.0100669\ttotal: 1.78s\tremaining: 2.37s\n",
      "429:\tlearn: 0.0100412\ttotal: 1.79s\tremaining: 2.37s\n",
      "430:\tlearn: 0.0100152\ttotal: 1.79s\tremaining: 2.37s\n",
      "431:\tlearn: 0.0099943\ttotal: 1.8s\tremaining: 2.36s\n",
      "432:\tlearn: 0.0099912\ttotal: 1.8s\tremaining: 2.36s\n",
      "433:\tlearn: 0.0099652\ttotal: 1.8s\tremaining: 2.35s\n",
      "434:\tlearn: 0.0099486\ttotal: 1.81s\tremaining: 2.35s\n",
      "435:\tlearn: 0.0099243\ttotal: 1.81s\tremaining: 2.35s\n",
      "436:\tlearn: 0.0099068\ttotal: 1.82s\tremaining: 2.34s\n",
      "437:\tlearn: 0.0098802\ttotal: 1.82s\tremaining: 2.34s\n",
      "438:\tlearn: 0.0098770\ttotal: 1.83s\tremaining: 2.33s\n",
      "439:\tlearn: 0.0098462\ttotal: 1.83s\tremaining: 2.33s\n",
      "440:\tlearn: 0.0098145\ttotal: 1.84s\tremaining: 2.33s\n",
      "441:\tlearn: 0.0097908\ttotal: 1.84s\tremaining: 2.33s\n",
      "442:\tlearn: 0.0097724\ttotal: 1.85s\tremaining: 2.32s\n",
      "443:\tlearn: 0.0097489\ttotal: 1.85s\tremaining: 2.32s\n",
      "444:\tlearn: 0.0097085\ttotal: 1.86s\tremaining: 2.31s\n",
      "445:\tlearn: 0.0096822\ttotal: 1.86s\tremaining: 2.31s\n",
      "446:\tlearn: 0.0096593\ttotal: 1.87s\tremaining: 2.31s\n",
      "447:\tlearn: 0.0096552\ttotal: 1.87s\tremaining: 2.3s\n",
      "448:\tlearn: 0.0096177\ttotal: 1.87s\tremaining: 2.3s\n",
      "449:\tlearn: 0.0095970\ttotal: 1.88s\tremaining: 2.29s\n",
      "450:\tlearn: 0.0095800\ttotal: 1.88s\tremaining: 2.29s\n",
      "451:\tlearn: 0.0095762\ttotal: 1.89s\tremaining: 2.29s\n",
      "452:\tlearn: 0.0095666\ttotal: 1.9s\tremaining: 2.29s\n",
      "453:\tlearn: 0.0095436\ttotal: 1.91s\tremaining: 2.29s\n",
      "454:\tlearn: 0.0095071\ttotal: 1.91s\tremaining: 2.29s\n",
      "455:\tlearn: 0.0094791\ttotal: 1.93s\tremaining: 2.3s\n",
      "456:\tlearn: 0.0094698\ttotal: 1.93s\tremaining: 2.3s\n",
      "457:\tlearn: 0.0094488\ttotal: 1.94s\tremaining: 2.29s\n",
      "458:\tlearn: 0.0094211\ttotal: 1.95s\tremaining: 2.3s\n",
      "459:\tlearn: 0.0093905\ttotal: 1.96s\tremaining: 2.3s\n",
      "460:\tlearn: 0.0093570\ttotal: 1.97s\tremaining: 2.3s\n",
      "461:\tlearn: 0.0093429\ttotal: 1.97s\tremaining: 2.3s\n",
      "462:\tlearn: 0.0093279\ttotal: 1.98s\tremaining: 2.3s\n",
      "463:\tlearn: 0.0093132\ttotal: 1.99s\tremaining: 2.29s\n",
      "464:\tlearn: 0.0092841\ttotal: 1.99s\tremaining: 2.29s\n",
      "465:\tlearn: 0.0092690\ttotal: 2s\tremaining: 2.29s\n",
      "466:\tlearn: 0.0092482\ttotal: 2s\tremaining: 2.29s\n",
      "467:\tlearn: 0.0092293\ttotal: 2.01s\tremaining: 2.28s\n",
      "468:\tlearn: 0.0092151\ttotal: 2.01s\tremaining: 2.28s\n",
      "469:\tlearn: 0.0091947\ttotal: 2.02s\tremaining: 2.27s\n",
      "470:\tlearn: 0.0091748\ttotal: 2.02s\tremaining: 2.27s\n",
      "471:\tlearn: 0.0091596\ttotal: 2.02s\tremaining: 2.26s\n",
      "472:\tlearn: 0.0091296\ttotal: 2.03s\tremaining: 2.26s\n",
      "473:\tlearn: 0.0091063\ttotal: 2.03s\tremaining: 2.26s\n",
      "474:\tlearn: 0.0090716\ttotal: 2.04s\tremaining: 2.25s\n",
      "475:\tlearn: 0.0090457\ttotal: 2.04s\tremaining: 2.25s\n",
      "476:\tlearn: 0.0090346\ttotal: 2.04s\tremaining: 2.24s\n",
      "477:\tlearn: 0.0090164\ttotal: 2.05s\tremaining: 2.24s\n",
      "478:\tlearn: 0.0090034\ttotal: 2.06s\tremaining: 2.23s\n",
      "479:\tlearn: 0.0089852\ttotal: 2.06s\tremaining: 2.23s\n",
      "480:\tlearn: 0.0089595\ttotal: 2.06s\tremaining: 2.23s\n",
      "481:\tlearn: 0.0089073\ttotal: 2.07s\tremaining: 2.23s\n",
      "482:\tlearn: 0.0088914\ttotal: 2.08s\tremaining: 2.22s\n",
      "483:\tlearn: 0.0088583\ttotal: 2.08s\tremaining: 2.22s\n",
      "484:\tlearn: 0.0088388\ttotal: 2.09s\tremaining: 2.21s\n",
      "485:\tlearn: 0.0088115\ttotal: 2.09s\tremaining: 2.21s\n",
      "486:\tlearn: 0.0087894\ttotal: 2.09s\tremaining: 2.21s\n",
      "487:\tlearn: 0.0087805\ttotal: 2.1s\tremaining: 2.2s\n",
      "488:\tlearn: 0.0087638\ttotal: 2.1s\tremaining: 2.2s\n",
      "489:\tlearn: 0.0087419\ttotal: 2.11s\tremaining: 2.19s\n",
      "490:\tlearn: 0.0087260\ttotal: 2.11s\tremaining: 2.19s\n",
      "491:\tlearn: 0.0087085\ttotal: 2.11s\tremaining: 2.18s\n",
      "492:\tlearn: 0.0086921\ttotal: 2.12s\tremaining: 2.18s\n",
      "493:\tlearn: 0.0086746\ttotal: 2.12s\tremaining: 2.17s\n",
      "494:\tlearn: 0.0086378\ttotal: 2.13s\tremaining: 2.17s\n",
      "495:\tlearn: 0.0086244\ttotal: 2.13s\tremaining: 2.16s\n",
      "496:\tlearn: 0.0086167\ttotal: 2.13s\tremaining: 2.16s\n",
      "497:\tlearn: 0.0086137\ttotal: 2.14s\tremaining: 2.15s\n",
      "498:\tlearn: 0.0085958\ttotal: 2.14s\tremaining: 2.15s\n",
      "499:\tlearn: 0.0085793\ttotal: 2.14s\tremaining: 2.14s\n",
      "500:\tlearn: 0.0085559\ttotal: 2.15s\tremaining: 2.14s\n",
      "501:\tlearn: 0.0085312\ttotal: 2.15s\tremaining: 2.13s\n",
      "502:\tlearn: 0.0085220\ttotal: 2.16s\tremaining: 2.13s\n",
      "503:\tlearn: 0.0085081\ttotal: 2.16s\tremaining: 2.13s\n",
      "504:\tlearn: 0.0084911\ttotal: 2.17s\tremaining: 2.12s\n",
      "505:\tlearn: 0.0084671\ttotal: 2.17s\tremaining: 2.12s\n",
      "506:\tlearn: 0.0084421\ttotal: 2.18s\tremaining: 2.12s\n",
      "507:\tlearn: 0.0084210\ttotal: 2.18s\tremaining: 2.11s\n",
      "508:\tlearn: 0.0084169\ttotal: 2.19s\tremaining: 2.11s\n",
      "509:\tlearn: 0.0083894\ttotal: 2.19s\tremaining: 2.1s\n",
      "510:\tlearn: 0.0083691\ttotal: 2.19s\tremaining: 2.1s\n",
      "511:\tlearn: 0.0083397\ttotal: 2.2s\tremaining: 2.1s\n",
      "512:\tlearn: 0.0083279\ttotal: 2.2s\tremaining: 2.09s\n",
      "513:\tlearn: 0.0083197\ttotal: 2.21s\tremaining: 2.08s\n",
      "514:\tlearn: 0.0083004\ttotal: 2.21s\tremaining: 2.08s\n",
      "515:\tlearn: 0.0082792\ttotal: 2.21s\tremaining: 2.08s\n",
      "516:\tlearn: 0.0082713\ttotal: 2.21s\tremaining: 2.07s\n",
      "517:\tlearn: 0.0082622\ttotal: 2.22s\tremaining: 2.06s\n",
      "518:\tlearn: 0.0082463\ttotal: 2.22s\tremaining: 2.06s\n",
      "519:\tlearn: 0.0082257\ttotal: 2.23s\tremaining: 2.06s\n",
      "520:\tlearn: 0.0082064\ttotal: 2.23s\tremaining: 2.05s\n",
      "521:\tlearn: 0.0081926\ttotal: 2.23s\tremaining: 2.04s\n",
      "522:\tlearn: 0.0081576\ttotal: 2.24s\tremaining: 2.04s\n",
      "523:\tlearn: 0.0081408\ttotal: 2.24s\tremaining: 2.03s\n",
      "524:\tlearn: 0.0081169\ttotal: 2.24s\tremaining: 2.03s\n",
      "525:\tlearn: 0.0081135\ttotal: 2.25s\tremaining: 2.02s\n",
      "526:\tlearn: 0.0080954\ttotal: 2.25s\tremaining: 2.02s\n",
      "527:\tlearn: 0.0080847\ttotal: 2.26s\tremaining: 2.02s\n",
      "528:\tlearn: 0.0080694\ttotal: 2.26s\tremaining: 2.02s\n",
      "529:\tlearn: 0.0080542\ttotal: 2.27s\tremaining: 2.01s\n",
      "530:\tlearn: 0.0080231\ttotal: 2.27s\tremaining: 2.01s\n",
      "531:\tlearn: 0.0079944\ttotal: 2.28s\tremaining: 2.01s\n",
      "532:\tlearn: 0.0079563\ttotal: 2.29s\tremaining: 2s\n",
      "533:\tlearn: 0.0079383\ttotal: 2.29s\tremaining: 2s\n",
      "534:\tlearn: 0.0078955\ttotal: 2.3s\tremaining: 2s\n",
      "535:\tlearn: 0.0078602\ttotal: 2.3s\tremaining: 1.99s\n",
      "536:\tlearn: 0.0078563\ttotal: 2.3s\tremaining: 1.99s\n",
      "537:\tlearn: 0.0078180\ttotal: 2.31s\tremaining: 1.98s\n",
      "538:\tlearn: 0.0078132\ttotal: 2.31s\tremaining: 1.98s\n",
      "539:\tlearn: 0.0077807\ttotal: 2.32s\tremaining: 1.97s\n",
      "540:\tlearn: 0.0077588\ttotal: 2.32s\tremaining: 1.97s\n",
      "541:\tlearn: 0.0077404\ttotal: 2.33s\tremaining: 1.97s\n",
      "542:\tlearn: 0.0077177\ttotal: 2.33s\tremaining: 1.96s\n",
      "543:\tlearn: 0.0077000\ttotal: 2.33s\tremaining: 1.96s\n",
      "544:\tlearn: 0.0076799\ttotal: 2.34s\tremaining: 1.95s\n",
      "545:\tlearn: 0.0076600\ttotal: 2.34s\tremaining: 1.95s\n",
      "546:\tlearn: 0.0076339\ttotal: 2.35s\tremaining: 1.94s\n",
      "547:\tlearn: 0.0076254\ttotal: 2.35s\tremaining: 1.94s\n",
      "548:\tlearn: 0.0076066\ttotal: 2.36s\tremaining: 1.94s\n",
      "549:\tlearn: 0.0075944\ttotal: 2.36s\tremaining: 1.93s\n",
      "550:\tlearn: 0.0075753\ttotal: 2.37s\tremaining: 1.93s\n",
      "551:\tlearn: 0.0075602\ttotal: 2.37s\tremaining: 1.93s\n",
      "552:\tlearn: 0.0075465\ttotal: 2.38s\tremaining: 1.92s\n",
      "553:\tlearn: 0.0075222\ttotal: 2.38s\tremaining: 1.92s\n",
      "554:\tlearn: 0.0075110\ttotal: 2.38s\tremaining: 1.91s\n",
      "555:\tlearn: 0.0075041\ttotal: 2.39s\tremaining: 1.91s\n",
      "556:\tlearn: 0.0074905\ttotal: 2.39s\tremaining: 1.9s\n",
      "557:\tlearn: 0.0074740\ttotal: 2.39s\tremaining: 1.9s\n",
      "558:\tlearn: 0.0074574\ttotal: 2.4s\tremaining: 1.89s\n",
      "559:\tlearn: 0.0074345\ttotal: 2.4s\tremaining: 1.89s\n",
      "560:\tlearn: 0.0074258\ttotal: 2.4s\tremaining: 1.88s\n",
      "561:\tlearn: 0.0074147\ttotal: 2.41s\tremaining: 1.88s\n",
      "562:\tlearn: 0.0073950\ttotal: 2.41s\tremaining: 1.87s\n",
      "563:\tlearn: 0.0073783\ttotal: 2.41s\tremaining: 1.87s\n",
      "564:\tlearn: 0.0073749\ttotal: 2.42s\tremaining: 1.86s\n",
      "565:\tlearn: 0.0073657\ttotal: 2.42s\tremaining: 1.86s\n",
      "566:\tlearn: 0.0073527\ttotal: 2.42s\tremaining: 1.85s\n",
      "567:\tlearn: 0.0073294\ttotal: 2.43s\tremaining: 1.85s\n",
      "568:\tlearn: 0.0073012\ttotal: 2.43s\tremaining: 1.84s\n",
      "569:\tlearn: 0.0072884\ttotal: 2.44s\tremaining: 1.84s\n",
      "570:\tlearn: 0.0072669\ttotal: 2.44s\tremaining: 1.83s\n",
      "571:\tlearn: 0.0072289\ttotal: 2.44s\tremaining: 1.83s\n",
      "572:\tlearn: 0.0072112\ttotal: 2.45s\tremaining: 1.82s\n",
      "573:\tlearn: 0.0071984\ttotal: 2.45s\tremaining: 1.82s\n",
      "574:\tlearn: 0.0071908\ttotal: 2.45s\tremaining: 1.81s\n",
      "575:\tlearn: 0.0071741\ttotal: 2.46s\tremaining: 1.81s\n",
      "576:\tlearn: 0.0071532\ttotal: 2.46s\tremaining: 1.81s\n",
      "577:\tlearn: 0.0071298\ttotal: 2.47s\tremaining: 1.8s\n",
      "578:\tlearn: 0.0071244\ttotal: 2.47s\tremaining: 1.8s\n",
      "579:\tlearn: 0.0071099\ttotal: 2.48s\tremaining: 1.79s\n",
      "580:\tlearn: 0.0070880\ttotal: 2.48s\tremaining: 1.79s\n",
      "581:\tlearn: 0.0070691\ttotal: 2.49s\tremaining: 1.78s\n",
      "582:\tlearn: 0.0070633\ttotal: 2.49s\tremaining: 1.78s\n",
      "583:\tlearn: 0.0070507\ttotal: 2.5s\tremaining: 1.78s\n",
      "584:\tlearn: 0.0070304\ttotal: 2.5s\tremaining: 1.77s\n",
      "585:\tlearn: 0.0070179\ttotal: 2.5s\tremaining: 1.77s\n",
      "586:\tlearn: 0.0069955\ttotal: 2.51s\tremaining: 1.76s\n",
      "587:\tlearn: 0.0069763\ttotal: 2.51s\tremaining: 1.76s\n",
      "588:\tlearn: 0.0069656\ttotal: 2.52s\tremaining: 1.75s\n",
      "589:\tlearn: 0.0069526\ttotal: 2.52s\tremaining: 1.75s\n",
      "590:\tlearn: 0.0069371\ttotal: 2.52s\tremaining: 1.75s\n",
      "591:\tlearn: 0.0069331\ttotal: 2.53s\tremaining: 1.74s\n",
      "592:\tlearn: 0.0069266\ttotal: 2.53s\tremaining: 1.74s\n",
      "593:\tlearn: 0.0069227\ttotal: 2.53s\tremaining: 1.73s\n",
      "594:\tlearn: 0.0069065\ttotal: 2.54s\tremaining: 1.73s\n",
      "595:\tlearn: 0.0068939\ttotal: 2.55s\tremaining: 1.73s\n",
      "596:\tlearn: 0.0068922\ttotal: 2.55s\tremaining: 1.72s\n",
      "597:\tlearn: 0.0068841\ttotal: 2.57s\tremaining: 1.72s\n",
      "598:\tlearn: 0.0068638\ttotal: 2.57s\tremaining: 1.72s\n",
      "599:\tlearn: 0.0068518\ttotal: 2.58s\tremaining: 1.72s\n",
      "600:\tlearn: 0.0068183\ttotal: 2.58s\tremaining: 1.71s\n",
      "601:\tlearn: 0.0068082\ttotal: 2.58s\tremaining: 1.71s\n",
      "602:\tlearn: 0.0067979\ttotal: 2.59s\tremaining: 1.7s\n",
      "603:\tlearn: 0.0067880\ttotal: 2.59s\tremaining: 1.7s\n",
      "604:\tlearn: 0.0067858\ttotal: 2.59s\tremaining: 1.69s\n",
      "605:\tlearn: 0.0067681\ttotal: 2.6s\tremaining: 1.69s\n",
      "606:\tlearn: 0.0067523\ttotal: 2.6s\tremaining: 1.69s\n",
      "607:\tlearn: 0.0067280\ttotal: 2.61s\tremaining: 1.68s\n",
      "608:\tlearn: 0.0067119\ttotal: 2.61s\tremaining: 1.68s\n",
      "609:\tlearn: 0.0066972\ttotal: 2.61s\tremaining: 1.67s\n",
      "610:\tlearn: 0.0066769\ttotal: 2.62s\tremaining: 1.67s\n",
      "611:\tlearn: 0.0066690\ttotal: 2.62s\tremaining: 1.66s\n",
      "612:\tlearn: 0.0066329\ttotal: 2.63s\tremaining: 1.66s\n",
      "613:\tlearn: 0.0066231\ttotal: 2.63s\tremaining: 1.65s\n",
      "614:\tlearn: 0.0066164\ttotal: 2.64s\tremaining: 1.65s\n",
      "615:\tlearn: 0.0066016\ttotal: 2.64s\tremaining: 1.65s\n",
      "616:\tlearn: 0.0065890\ttotal: 2.65s\tremaining: 1.64s\n",
      "617:\tlearn: 0.0065816\ttotal: 2.65s\tremaining: 1.64s\n",
      "618:\tlearn: 0.0065710\ttotal: 2.65s\tremaining: 1.63s\n",
      "619:\tlearn: 0.0065659\ttotal: 2.66s\tremaining: 1.63s\n",
      "620:\tlearn: 0.0065542\ttotal: 2.66s\tremaining: 1.63s\n",
      "621:\tlearn: 0.0065488\ttotal: 2.67s\tremaining: 1.62s\n",
      "622:\tlearn: 0.0065326\ttotal: 2.67s\tremaining: 1.62s\n",
      "623:\tlearn: 0.0065218\ttotal: 2.68s\tremaining: 1.61s\n",
      "624:\tlearn: 0.0065049\ttotal: 2.69s\tremaining: 1.61s\n",
      "625:\tlearn: 0.0064952\ttotal: 2.69s\tremaining: 1.61s\n",
      "626:\tlearn: 0.0064894\ttotal: 2.69s\tremaining: 1.6s\n",
      "627:\tlearn: 0.0064755\ttotal: 2.7s\tremaining: 1.6s\n",
      "628:\tlearn: 0.0064669\ttotal: 2.7s\tremaining: 1.59s\n",
      "629:\tlearn: 0.0064483\ttotal: 2.71s\tremaining: 1.59s\n",
      "630:\tlearn: 0.0064179\ttotal: 2.71s\tremaining: 1.58s\n",
      "631:\tlearn: 0.0064006\ttotal: 2.71s\tremaining: 1.58s\n",
      "632:\tlearn: 0.0063954\ttotal: 2.72s\tremaining: 1.57s\n",
      "633:\tlearn: 0.0063938\ttotal: 2.72s\tremaining: 1.57s\n",
      "634:\tlearn: 0.0063909\ttotal: 2.73s\tremaining: 1.57s\n",
      "635:\tlearn: 0.0063759\ttotal: 2.73s\tremaining: 1.56s\n",
      "636:\tlearn: 0.0063730\ttotal: 2.73s\tremaining: 1.56s\n",
      "637:\tlearn: 0.0063637\ttotal: 2.73s\tremaining: 1.55s\n",
      "638:\tlearn: 0.0063501\ttotal: 2.74s\tremaining: 1.55s\n",
      "639:\tlearn: 0.0063301\ttotal: 2.74s\tremaining: 1.54s\n",
      "640:\tlearn: 0.0063164\ttotal: 2.75s\tremaining: 1.54s\n",
      "641:\tlearn: 0.0063141\ttotal: 2.75s\tremaining: 1.53s\n",
      "642:\tlearn: 0.0063098\ttotal: 2.75s\tremaining: 1.53s\n",
      "643:\tlearn: 0.0062931\ttotal: 2.76s\tremaining: 1.52s\n",
      "644:\tlearn: 0.0062712\ttotal: 2.76s\tremaining: 1.52s\n",
      "645:\tlearn: 0.0062650\ttotal: 2.77s\tremaining: 1.52s\n",
      "646:\tlearn: 0.0062505\ttotal: 2.77s\tremaining: 1.51s\n",
      "647:\tlearn: 0.0062472\ttotal: 2.77s\tremaining: 1.51s\n",
      "648:\tlearn: 0.0062382\ttotal: 2.78s\tremaining: 1.5s\n",
      "649:\tlearn: 0.0062186\ttotal: 2.78s\tremaining: 1.5s\n",
      "650:\tlearn: 0.0062078\ttotal: 2.79s\tremaining: 1.49s\n",
      "651:\tlearn: 0.0061900\ttotal: 2.79s\tremaining: 1.49s\n",
      "652:\tlearn: 0.0061855\ttotal: 2.79s\tremaining: 1.48s\n",
      "653:\tlearn: 0.0061841\ttotal: 2.8s\tremaining: 1.48s\n",
      "654:\tlearn: 0.0061754\ttotal: 2.81s\tremaining: 1.48s\n",
      "655:\tlearn: 0.0061697\ttotal: 2.81s\tremaining: 1.48s\n",
      "656:\tlearn: 0.0061639\ttotal: 2.82s\tremaining: 1.47s\n",
      "657:\tlearn: 0.0061612\ttotal: 2.83s\tremaining: 1.47s\n",
      "658:\tlearn: 0.0061567\ttotal: 2.83s\tremaining: 1.47s\n",
      "659:\tlearn: 0.0061485\ttotal: 2.84s\tremaining: 1.46s\n",
      "660:\tlearn: 0.0061337\ttotal: 2.84s\tremaining: 1.46s\n",
      "661:\tlearn: 0.0061205\ttotal: 2.85s\tremaining: 1.45s\n",
      "662:\tlearn: 0.0061062\ttotal: 2.85s\tremaining: 1.45s\n",
      "663:\tlearn: 0.0060898\ttotal: 2.85s\tremaining: 1.44s\n",
      "664:\tlearn: 0.0060811\ttotal: 2.86s\tremaining: 1.44s\n",
      "665:\tlearn: 0.0060739\ttotal: 2.87s\tremaining: 1.44s\n",
      "666:\tlearn: 0.0060621\ttotal: 2.87s\tremaining: 1.43s\n",
      "667:\tlearn: 0.0060415\ttotal: 2.88s\tremaining: 1.43s\n",
      "668:\tlearn: 0.0060250\ttotal: 2.88s\tremaining: 1.43s\n",
      "669:\tlearn: 0.0060175\ttotal: 2.89s\tremaining: 1.42s\n",
      "670:\tlearn: 0.0060042\ttotal: 2.89s\tremaining: 1.42s\n",
      "671:\tlearn: 0.0059918\ttotal: 2.9s\tremaining: 1.41s\n",
      "672:\tlearn: 0.0059829\ttotal: 2.9s\tremaining: 1.41s\n",
      "673:\tlearn: 0.0059808\ttotal: 2.9s\tremaining: 1.4s\n",
      "674:\tlearn: 0.0059722\ttotal: 2.91s\tremaining: 1.4s\n",
      "675:\tlearn: 0.0059694\ttotal: 2.91s\tremaining: 1.4s\n",
      "676:\tlearn: 0.0059626\ttotal: 2.91s\tremaining: 1.39s\n",
      "677:\tlearn: 0.0059570\ttotal: 2.92s\tremaining: 1.39s\n",
      "678:\tlearn: 0.0059446\ttotal: 2.92s\tremaining: 1.38s\n",
      "679:\tlearn: 0.0059280\ttotal: 2.93s\tremaining: 1.38s\n",
      "680:\tlearn: 0.0059210\ttotal: 2.93s\tremaining: 1.37s\n",
      "681:\tlearn: 0.0059093\ttotal: 2.94s\tremaining: 1.37s\n",
      "682:\tlearn: 0.0058990\ttotal: 2.94s\tremaining: 1.36s\n",
      "683:\tlearn: 0.0058917\ttotal: 2.94s\tremaining: 1.36s\n",
      "684:\tlearn: 0.0058798\ttotal: 2.95s\tremaining: 1.36s\n",
      "685:\tlearn: 0.0058737\ttotal: 2.95s\tremaining: 1.35s\n",
      "686:\tlearn: 0.0058706\ttotal: 2.96s\tremaining: 1.35s\n",
      "687:\tlearn: 0.0058692\ttotal: 2.97s\tremaining: 1.34s\n",
      "688:\tlearn: 0.0058612\ttotal: 2.97s\tremaining: 1.34s\n",
      "689:\tlearn: 0.0058598\ttotal: 2.98s\tremaining: 1.34s\n",
      "690:\tlearn: 0.0058500\ttotal: 2.98s\tremaining: 1.33s\n",
      "691:\tlearn: 0.0058434\ttotal: 2.98s\tremaining: 1.33s\n",
      "692:\tlearn: 0.0058354\ttotal: 2.99s\tremaining: 1.32s\n",
      "693:\tlearn: 0.0058284\ttotal: 2.99s\tremaining: 1.32s\n",
      "694:\tlearn: 0.0058144\ttotal: 3s\tremaining: 1.31s\n",
      "695:\tlearn: 0.0058130\ttotal: 3s\tremaining: 1.31s\n",
      "696:\tlearn: 0.0057973\ttotal: 3s\tremaining: 1.3s\n",
      "697:\tlearn: 0.0057928\ttotal: 3.01s\tremaining: 1.3s\n",
      "698:\tlearn: 0.0057917\ttotal: 3.01s\tremaining: 1.29s\n",
      "699:\tlearn: 0.0057770\ttotal: 3.02s\tremaining: 1.29s\n",
      "700:\tlearn: 0.0057759\ttotal: 3.02s\tremaining: 1.29s\n",
      "701:\tlearn: 0.0057541\ttotal: 3.02s\tremaining: 1.28s\n",
      "702:\tlearn: 0.0057380\ttotal: 3.02s\tremaining: 1.28s\n",
      "703:\tlearn: 0.0057312\ttotal: 3.03s\tremaining: 1.27s\n",
      "704:\tlearn: 0.0057214\ttotal: 3.03s\tremaining: 1.27s\n",
      "705:\tlearn: 0.0057148\ttotal: 3.04s\tremaining: 1.26s\n",
      "706:\tlearn: 0.0057018\ttotal: 3.04s\tremaining: 1.26s\n",
      "707:\tlearn: 0.0056953\ttotal: 3.05s\tremaining: 1.26s\n",
      "708:\tlearn: 0.0056932\ttotal: 3.05s\tremaining: 1.25s\n",
      "709:\tlearn: 0.0056702\ttotal: 3.06s\tremaining: 1.25s\n",
      "710:\tlearn: 0.0056673\ttotal: 3.06s\tremaining: 1.24s\n",
      "711:\tlearn: 0.0056532\ttotal: 3.06s\tremaining: 1.24s\n",
      "712:\tlearn: 0.0056505\ttotal: 3.06s\tremaining: 1.23s\n",
      "713:\tlearn: 0.0056436\ttotal: 3.07s\tremaining: 1.23s\n",
      "714:\tlearn: 0.0056299\ttotal: 3.07s\tremaining: 1.22s\n",
      "715:\tlearn: 0.0056236\ttotal: 3.07s\tremaining: 1.22s\n",
      "716:\tlearn: 0.0056085\ttotal: 3.08s\tremaining: 1.21s\n",
      "717:\tlearn: 0.0056008\ttotal: 3.08s\tremaining: 1.21s\n",
      "718:\tlearn: 0.0055819\ttotal: 3.08s\tremaining: 1.2s\n",
      "719:\tlearn: 0.0055760\ttotal: 3.08s\tremaining: 1.2s\n",
      "720:\tlearn: 0.0055697\ttotal: 3.09s\tremaining: 1.2s\n",
      "721:\tlearn: 0.0055631\ttotal: 3.09s\tremaining: 1.19s\n",
      "722:\tlearn: 0.0055607\ttotal: 3.1s\tremaining: 1.19s\n",
      "723:\tlearn: 0.0055543\ttotal: 3.1s\tremaining: 1.18s\n",
      "724:\tlearn: 0.0055450\ttotal: 3.1s\tremaining: 1.18s\n",
      "725:\tlearn: 0.0055296\ttotal: 3.11s\tremaining: 1.17s\n",
      "726:\tlearn: 0.0055124\ttotal: 3.11s\tremaining: 1.17s\n",
      "727:\tlearn: 0.0054998\ttotal: 3.12s\tremaining: 1.16s\n",
      "728:\tlearn: 0.0054868\ttotal: 3.12s\tremaining: 1.16s\n",
      "729:\tlearn: 0.0054811\ttotal: 3.12s\tremaining: 1.15s\n",
      "730:\tlearn: 0.0054753\ttotal: 3.12s\tremaining: 1.15s\n",
      "731:\tlearn: 0.0054695\ttotal: 3.13s\tremaining: 1.15s\n",
      "732:\tlearn: 0.0054549\ttotal: 3.13s\tremaining: 1.14s\n",
      "733:\tlearn: 0.0054428\ttotal: 3.13s\tremaining: 1.14s\n",
      "734:\tlearn: 0.0054309\ttotal: 3.14s\tremaining: 1.13s\n",
      "735:\tlearn: 0.0054273\ttotal: 3.14s\tremaining: 1.13s\n",
      "736:\tlearn: 0.0054207\ttotal: 3.14s\tremaining: 1.12s\n",
      "737:\tlearn: 0.0054150\ttotal: 3.15s\tremaining: 1.12s\n",
      "738:\tlearn: 0.0054029\ttotal: 3.15s\tremaining: 1.11s\n",
      "739:\tlearn: 0.0053936\ttotal: 3.16s\tremaining: 1.11s\n",
      "740:\tlearn: 0.0053807\ttotal: 3.17s\tremaining: 1.11s\n",
      "741:\tlearn: 0.0053746\ttotal: 3.17s\tremaining: 1.1s\n",
      "742:\tlearn: 0.0053680\ttotal: 3.18s\tremaining: 1.1s\n",
      "743:\tlearn: 0.0053619\ttotal: 3.18s\tremaining: 1.09s\n",
      "744:\tlearn: 0.0053560\ttotal: 3.18s\tremaining: 1.09s\n",
      "745:\tlearn: 0.0053502\ttotal: 3.19s\tremaining: 1.08s\n",
      "746:\tlearn: 0.0053445\ttotal: 3.19s\tremaining: 1.08s\n",
      "747:\tlearn: 0.0053356\ttotal: 3.2s\tremaining: 1.08s\n",
      "748:\tlearn: 0.0053294\ttotal: 3.2s\tremaining: 1.07s\n",
      "749:\tlearn: 0.0053283\ttotal: 3.2s\tremaining: 1.07s\n",
      "750:\tlearn: 0.0053215\ttotal: 3.21s\tremaining: 1.06s\n",
      "751:\tlearn: 0.0053099\ttotal: 3.21s\tremaining: 1.06s\n",
      "752:\tlearn: 0.0052991\ttotal: 3.21s\tremaining: 1.05s\n",
      "753:\tlearn: 0.0052850\ttotal: 3.22s\tremaining: 1.05s\n",
      "754:\tlearn: 0.0052808\ttotal: 3.22s\tremaining: 1.04s\n",
      "755:\tlearn: 0.0052789\ttotal: 3.22s\tremaining: 1.04s\n",
      "756:\tlearn: 0.0052718\ttotal: 3.23s\tremaining: 1.03s\n",
      "757:\tlearn: 0.0052583\ttotal: 3.23s\tremaining: 1.03s\n",
      "758:\tlearn: 0.0052560\ttotal: 3.23s\tremaining: 1.03s\n",
      "759:\tlearn: 0.0052527\ttotal: 3.23s\tremaining: 1.02s\n",
      "760:\tlearn: 0.0052449\ttotal: 3.24s\tremaining: 1.02s\n",
      "761:\tlearn: 0.0052306\ttotal: 3.24s\tremaining: 1.01s\n",
      "762:\tlearn: 0.0052207\ttotal: 3.25s\tremaining: 1.01s\n",
      "763:\tlearn: 0.0052093\ttotal: 3.25s\tremaining: 1s\n",
      "764:\tlearn: 0.0052043\ttotal: 3.25s\tremaining: 999ms\n",
      "765:\tlearn: 0.0051958\ttotal: 3.25s\tremaining: 994ms\n",
      "766:\tlearn: 0.0051858\ttotal: 3.26s\tremaining: 990ms\n",
      "767:\tlearn: 0.0051737\ttotal: 3.26s\tremaining: 985ms\n",
      "768:\tlearn: 0.0051677\ttotal: 3.26s\tremaining: 981ms\n",
      "769:\tlearn: 0.0051627\ttotal: 3.27s\tremaining: 976ms\n",
      "770:\tlearn: 0.0051524\ttotal: 3.27s\tremaining: 971ms\n",
      "771:\tlearn: 0.0051475\ttotal: 3.27s\tremaining: 967ms\n",
      "772:\tlearn: 0.0051402\ttotal: 3.28s\tremaining: 963ms\n",
      "773:\tlearn: 0.0051352\ttotal: 3.28s\tremaining: 958ms\n",
      "774:\tlearn: 0.0051286\ttotal: 3.28s\tremaining: 953ms\n",
      "775:\tlearn: 0.0051199\ttotal: 3.29s\tremaining: 949ms\n",
      "776:\tlearn: 0.0051138\ttotal: 3.29s\tremaining: 944ms\n",
      "777:\tlearn: 0.0050984\ttotal: 3.29s\tremaining: 939ms\n",
      "778:\tlearn: 0.0050857\ttotal: 3.29s\tremaining: 935ms\n",
      "779:\tlearn: 0.0050791\ttotal: 3.3s\tremaining: 930ms\n",
      "780:\tlearn: 0.0050618\ttotal: 3.3s\tremaining: 926ms\n",
      "781:\tlearn: 0.0050570\ttotal: 3.3s\tremaining: 921ms\n",
      "782:\tlearn: 0.0050526\ttotal: 3.31s\tremaining: 917ms\n",
      "783:\tlearn: 0.0050503\ttotal: 3.31s\tremaining: 912ms\n",
      "784:\tlearn: 0.0050373\ttotal: 3.31s\tremaining: 907ms\n",
      "785:\tlearn: 0.0050201\ttotal: 3.32s\tremaining: 903ms\n",
      "786:\tlearn: 0.0050155\ttotal: 3.32s\tremaining: 899ms\n",
      "787:\tlearn: 0.0050044\ttotal: 3.32s\tremaining: 894ms\n",
      "788:\tlearn: 0.0049950\ttotal: 3.33s\tremaining: 890ms\n",
      "789:\tlearn: 0.0049904\ttotal: 3.33s\tremaining: 885ms\n",
      "790:\tlearn: 0.0049865\ttotal: 3.33s\tremaining: 881ms\n",
      "791:\tlearn: 0.0049754\ttotal: 3.34s\tremaining: 877ms\n",
      "792:\tlearn: 0.0049655\ttotal: 3.34s\tremaining: 872ms\n",
      "793:\tlearn: 0.0049609\ttotal: 3.35s\tremaining: 868ms\n",
      "794:\tlearn: 0.0049533\ttotal: 3.35s\tremaining: 864ms\n",
      "795:\tlearn: 0.0049461\ttotal: 3.35s\tremaining: 860ms\n",
      "796:\tlearn: 0.0049423\ttotal: 3.36s\tremaining: 855ms\n",
      "797:\tlearn: 0.0049300\ttotal: 3.36s\tremaining: 852ms\n",
      "798:\tlearn: 0.0049184\ttotal: 3.37s\tremaining: 847ms\n",
      "799:\tlearn: 0.0049165\ttotal: 3.37s\tremaining: 843ms\n",
      "800:\tlearn: 0.0049111\ttotal: 3.37s\tremaining: 838ms\n",
      "801:\tlearn: 0.0049055\ttotal: 3.38s\tremaining: 834ms\n",
      "802:\tlearn: 0.0049034\ttotal: 3.38s\tremaining: 829ms\n",
      "803:\tlearn: 0.0048983\ttotal: 3.38s\tremaining: 825ms\n",
      "804:\tlearn: 0.0048929\ttotal: 3.39s\tremaining: 820ms\n",
      "805:\tlearn: 0.0048813\ttotal: 3.39s\tremaining: 816ms\n",
      "806:\tlearn: 0.0048758\ttotal: 3.4s\tremaining: 812ms\n",
      "807:\tlearn: 0.0048699\ttotal: 3.4s\tremaining: 808ms\n",
      "808:\tlearn: 0.0048593\ttotal: 3.4s\tremaining: 803ms\n",
      "809:\tlearn: 0.0048537\ttotal: 3.41s\tremaining: 799ms\n",
      "810:\tlearn: 0.0048431\ttotal: 3.41s\tremaining: 795ms\n",
      "811:\tlearn: 0.0048391\ttotal: 3.41s\tremaining: 790ms\n",
      "812:\tlearn: 0.0048339\ttotal: 3.42s\tremaining: 786ms\n",
      "813:\tlearn: 0.0048216\ttotal: 3.42s\tremaining: 781ms\n",
      "814:\tlearn: 0.0048169\ttotal: 3.42s\tremaining: 777ms\n",
      "815:\tlearn: 0.0048115\ttotal: 3.42s\tremaining: 772ms\n",
      "816:\tlearn: 0.0047990\ttotal: 3.43s\tremaining: 768ms\n",
      "817:\tlearn: 0.0047927\ttotal: 3.43s\tremaining: 763ms\n",
      "818:\tlearn: 0.0047886\ttotal: 3.44s\tremaining: 759ms\n",
      "819:\tlearn: 0.0047785\ttotal: 3.44s\tremaining: 756ms\n",
      "820:\tlearn: 0.0047643\ttotal: 3.45s\tremaining: 752ms\n",
      "821:\tlearn: 0.0047588\ttotal: 3.45s\tremaining: 748ms\n",
      "822:\tlearn: 0.0047537\ttotal: 3.46s\tremaining: 743ms\n",
      "823:\tlearn: 0.0047431\ttotal: 3.46s\tremaining: 740ms\n",
      "824:\tlearn: 0.0047394\ttotal: 3.47s\tremaining: 735ms\n",
      "825:\tlearn: 0.0047256\ttotal: 3.47s\tremaining: 731ms\n",
      "826:\tlearn: 0.0047206\ttotal: 3.47s\tremaining: 727ms\n",
      "827:\tlearn: 0.0047144\ttotal: 3.48s\tremaining: 722ms\n",
      "828:\tlearn: 0.0047042\ttotal: 3.48s\tremaining: 718ms\n",
      "829:\tlearn: 0.0046943\ttotal: 3.48s\tremaining: 713ms\n",
      "830:\tlearn: 0.0046842\ttotal: 3.49s\tremaining: 709ms\n",
      "831:\tlearn: 0.0046823\ttotal: 3.49s\tremaining: 705ms\n",
      "832:\tlearn: 0.0046780\ttotal: 3.49s\tremaining: 701ms\n",
      "833:\tlearn: 0.0046677\ttotal: 3.5s\tremaining: 696ms\n",
      "834:\tlearn: 0.0046590\ttotal: 3.5s\tremaining: 692ms\n",
      "835:\tlearn: 0.0046475\ttotal: 3.51s\tremaining: 688ms\n",
      "836:\tlearn: 0.0046423\ttotal: 3.51s\tremaining: 684ms\n",
      "837:\tlearn: 0.0046372\ttotal: 3.52s\tremaining: 680ms\n",
      "838:\tlearn: 0.0046324\ttotal: 3.52s\tremaining: 675ms\n",
      "839:\tlearn: 0.0046225\ttotal: 3.52s\tremaining: 671ms\n",
      "840:\tlearn: 0.0046124\ttotal: 3.52s\tremaining: 667ms\n",
      "841:\tlearn: 0.0046011\ttotal: 3.53s\tremaining: 662ms\n",
      "842:\tlearn: 0.0045899\ttotal: 3.53s\tremaining: 658ms\n",
      "843:\tlearn: 0.0045801\ttotal: 3.54s\tremaining: 654ms\n",
      "844:\tlearn: 0.0045690\ttotal: 3.54s\tremaining: 650ms\n",
      "845:\tlearn: 0.0045563\ttotal: 3.54s\tremaining: 645ms\n",
      "846:\tlearn: 0.0045513\ttotal: 3.55s\tremaining: 641ms\n",
      "847:\tlearn: 0.0045405\ttotal: 3.55s\tremaining: 637ms\n",
      "848:\tlearn: 0.0045300\ttotal: 3.56s\tremaining: 633ms\n",
      "849:\tlearn: 0.0045272\ttotal: 3.56s\tremaining: 628ms\n",
      "850:\tlearn: 0.0045178\ttotal: 3.56s\tremaining: 624ms\n",
      "851:\tlearn: 0.0045040\ttotal: 3.57s\tremaining: 620ms\n",
      "852:\tlearn: 0.0044944\ttotal: 3.57s\tremaining: 616ms\n",
      "853:\tlearn: 0.0044851\ttotal: 3.58s\tremaining: 611ms\n",
      "854:\tlearn: 0.0044804\ttotal: 3.58s\tremaining: 607ms\n",
      "855:\tlearn: 0.0044717\ttotal: 3.58s\tremaining: 602ms\n",
      "856:\tlearn: 0.0044700\ttotal: 3.6s\tremaining: 600ms\n",
      "857:\tlearn: 0.0044612\ttotal: 3.6s\tremaining: 596ms\n",
      "858:\tlearn: 0.0044493\ttotal: 3.6s\tremaining: 592ms\n",
      "859:\tlearn: 0.0044455\ttotal: 3.61s\tremaining: 588ms\n",
      "860:\tlearn: 0.0044362\ttotal: 3.61s\tremaining: 583ms\n",
      "861:\tlearn: 0.0044313\ttotal: 3.62s\tremaining: 579ms\n",
      "862:\tlearn: 0.0044227\ttotal: 3.62s\tremaining: 575ms\n",
      "863:\tlearn: 0.0044146\ttotal: 3.62s\tremaining: 571ms\n",
      "864:\tlearn: 0.0044101\ttotal: 3.63s\tremaining: 566ms\n",
      "865:\tlearn: 0.0044015\ttotal: 3.63s\tremaining: 562ms\n",
      "866:\tlearn: 0.0043971\ttotal: 3.63s\tremaining: 558ms\n",
      "867:\tlearn: 0.0043871\ttotal: 3.64s\tremaining: 553ms\n",
      "868:\tlearn: 0.0043825\ttotal: 3.66s\tremaining: 552ms\n",
      "869:\tlearn: 0.0043777\ttotal: 3.66s\tremaining: 547ms\n",
      "870:\tlearn: 0.0043689\ttotal: 3.67s\tremaining: 543ms\n",
      "871:\tlearn: 0.0043599\ttotal: 3.67s\tremaining: 539ms\n",
      "872:\tlearn: 0.0043472\ttotal: 3.67s\tremaining: 535ms\n",
      "873:\tlearn: 0.0043425\ttotal: 3.68s\tremaining: 530ms\n",
      "874:\tlearn: 0.0043379\ttotal: 3.68s\tremaining: 526ms\n",
      "875:\tlearn: 0.0043248\ttotal: 3.69s\tremaining: 522ms\n",
      "876:\tlearn: 0.0043157\ttotal: 3.69s\tremaining: 518ms\n",
      "877:\tlearn: 0.0043073\ttotal: 3.7s\tremaining: 514ms\n",
      "878:\tlearn: 0.0043030\ttotal: 3.7s\tremaining: 510ms\n",
      "879:\tlearn: 0.0042948\ttotal: 3.7s\tremaining: 505ms\n",
      "880:\tlearn: 0.0042857\ttotal: 3.71s\tremaining: 501ms\n",
      "881:\tlearn: 0.0042740\ttotal: 3.71s\tremaining: 497ms\n",
      "882:\tlearn: 0.0042696\ttotal: 3.72s\tremaining: 493ms\n",
      "883:\tlearn: 0.0042568\ttotal: 3.72s\tremaining: 488ms\n",
      "884:\tlearn: 0.0042536\ttotal: 3.72s\tremaining: 484ms\n",
      "885:\tlearn: 0.0042508\ttotal: 3.73s\tremaining: 480ms\n",
      "886:\tlearn: 0.0042424\ttotal: 3.74s\tremaining: 477ms\n",
      "887:\tlearn: 0.0042340\ttotal: 3.75s\tremaining: 473ms\n",
      "888:\tlearn: 0.0042257\ttotal: 3.75s\tremaining: 469ms\n",
      "889:\tlearn: 0.0042218\ttotal: 3.76s\tremaining: 465ms\n",
      "890:\tlearn: 0.0042100\ttotal: 3.76s\tremaining: 460ms\n",
      "891:\tlearn: 0.0042079\ttotal: 3.77s\tremaining: 456ms\n",
      "892:\tlearn: 0.0041995\ttotal: 3.77s\tremaining: 452ms\n",
      "893:\tlearn: 0.0041899\ttotal: 3.78s\tremaining: 448ms\n",
      "894:\tlearn: 0.0041817\ttotal: 3.78s\tremaining: 443ms\n",
      "895:\tlearn: 0.0041723\ttotal: 3.78s\tremaining: 439ms\n",
      "896:\tlearn: 0.0041645\ttotal: 3.79s\tremaining: 435ms\n",
      "897:\tlearn: 0.0041602\ttotal: 3.79s\tremaining: 431ms\n",
      "898:\tlearn: 0.0041520\ttotal: 3.79s\tremaining: 426ms\n",
      "899:\tlearn: 0.0041443\ttotal: 3.8s\tremaining: 422ms\n",
      "900:\tlearn: 0.0041350\ttotal: 3.8s\tremaining: 418ms\n",
      "901:\tlearn: 0.0041271\ttotal: 3.8s\tremaining: 413ms\n",
      "902:\tlearn: 0.0041178\ttotal: 3.81s\tremaining: 409ms\n",
      "903:\tlearn: 0.0041134\ttotal: 3.81s\tremaining: 405ms\n",
      "904:\tlearn: 0.0041113\ttotal: 3.82s\tremaining: 401ms\n",
      "905:\tlearn: 0.0041074\ttotal: 3.83s\tremaining: 397ms\n",
      "906:\tlearn: 0.0040998\ttotal: 3.83s\tremaining: 393ms\n",
      "907:\tlearn: 0.0040955\ttotal: 3.83s\tremaining: 388ms\n",
      "908:\tlearn: 0.0040914\ttotal: 3.84s\tremaining: 384ms\n",
      "909:\tlearn: 0.0040876\ttotal: 3.84s\tremaining: 380ms\n",
      "910:\tlearn: 0.0040798\ttotal: 3.85s\tremaining: 376ms\n",
      "911:\tlearn: 0.0040746\ttotal: 3.85s\tremaining: 372ms\n",
      "912:\tlearn: 0.0040707\ttotal: 3.86s\tremaining: 368ms\n",
      "913:\tlearn: 0.0040597\ttotal: 3.86s\tremaining: 363ms\n",
      "914:\tlearn: 0.0040560\ttotal: 3.86s\tremaining: 359ms\n",
      "915:\tlearn: 0.0040523\ttotal: 3.87s\tremaining: 355ms\n",
      "916:\tlearn: 0.0040419\ttotal: 3.87s\tremaining: 350ms\n",
      "917:\tlearn: 0.0040391\ttotal: 3.88s\tremaining: 346ms\n",
      "918:\tlearn: 0.0040318\ttotal: 3.88s\tremaining: 342ms\n",
      "919:\tlearn: 0.0040242\ttotal: 3.88s\tremaining: 338ms\n",
      "920:\tlearn: 0.0040168\ttotal: 3.89s\tremaining: 333ms\n",
      "921:\tlearn: 0.0040081\ttotal: 3.89s\tremaining: 329ms\n",
      "922:\tlearn: 0.0040009\ttotal: 3.9s\tremaining: 325ms\n",
      "923:\tlearn: 0.0039935\ttotal: 3.9s\tremaining: 321ms\n",
      "924:\tlearn: 0.0039922\ttotal: 3.9s\tremaining: 317ms\n",
      "925:\tlearn: 0.0039836\ttotal: 3.91s\tremaining: 312ms\n",
      "926:\tlearn: 0.0039764\ttotal: 3.91s\tremaining: 308ms\n",
      "927:\tlearn: 0.0039725\ttotal: 3.92s\tremaining: 304ms\n",
      "928:\tlearn: 0.0039650\ttotal: 3.92s\tremaining: 300ms\n",
      "929:\tlearn: 0.0039613\ttotal: 3.92s\tremaining: 295ms\n",
      "930:\tlearn: 0.0039577\ttotal: 3.93s\tremaining: 291ms\n",
      "931:\tlearn: 0.0039537\ttotal: 3.93s\tremaining: 287ms\n",
      "932:\tlearn: 0.0039499\ttotal: 3.94s\tremaining: 283ms\n",
      "933:\tlearn: 0.0039426\ttotal: 3.94s\tremaining: 278ms\n",
      "934:\tlearn: 0.0039335\ttotal: 3.94s\tremaining: 274ms\n",
      "935:\tlearn: 0.0039263\ttotal: 3.95s\tremaining: 270ms\n",
      "936:\tlearn: 0.0039159\ttotal: 3.95s\tremaining: 266ms\n",
      "937:\tlearn: 0.0039095\ttotal: 3.95s\tremaining: 261ms\n",
      "938:\tlearn: 0.0039026\ttotal: 3.96s\tremaining: 257ms\n",
      "939:\tlearn: 0.0039007\ttotal: 3.96s\tremaining: 253ms\n",
      "940:\tlearn: 0.0038944\ttotal: 3.97s\tremaining: 249ms\n",
      "941:\tlearn: 0.0038918\ttotal: 3.98s\tremaining: 245ms\n",
      "942:\tlearn: 0.0038849\ttotal: 3.98s\tremaining: 240ms\n",
      "943:\tlearn: 0.0038837\ttotal: 3.98s\tremaining: 236ms\n",
      "944:\tlearn: 0.0038825\ttotal: 3.98s\tremaining: 232ms\n",
      "945:\tlearn: 0.0038756\ttotal: 3.99s\tremaining: 228ms\n",
      "946:\tlearn: 0.0038717\ttotal: 3.99s\tremaining: 223ms\n",
      "947:\tlearn: 0.0038649\ttotal: 3.99s\tremaining: 219ms\n",
      "948:\tlearn: 0.0038623\ttotal: 4s\tremaining: 215ms\n",
      "949:\tlearn: 0.0038553\ttotal: 4s\tremaining: 211ms\n",
      "950:\tlearn: 0.0038517\ttotal: 4s\tremaining: 206ms\n",
      "951:\tlearn: 0.0038480\ttotal: 4.01s\tremaining: 202ms\n",
      "952:\tlearn: 0.0038451\ttotal: 4.01s\tremaining: 198ms\n",
      "953:\tlearn: 0.0038390\ttotal: 4.01s\tremaining: 193ms\n",
      "954:\tlearn: 0.0038324\ttotal: 4.02s\tremaining: 189ms\n",
      "955:\tlearn: 0.0038256\ttotal: 4.02s\tremaining: 185ms\n",
      "956:\tlearn: 0.0038221\ttotal: 4.04s\tremaining: 181ms\n",
      "957:\tlearn: 0.0038184\ttotal: 4.04s\tremaining: 177ms\n",
      "958:\tlearn: 0.0038119\ttotal: 4.05s\tremaining: 173ms\n",
      "959:\tlearn: 0.0038052\ttotal: 4.05s\tremaining: 169ms\n",
      "960:\tlearn: 0.0037958\ttotal: 4.05s\tremaining: 165ms\n",
      "961:\tlearn: 0.0037917\ttotal: 4.06s\tremaining: 160ms\n",
      "962:\tlearn: 0.0037851\ttotal: 4.06s\tremaining: 156ms\n",
      "963:\tlearn: 0.0037786\ttotal: 4.07s\tremaining: 152ms\n",
      "964:\tlearn: 0.0037725\ttotal: 4.07s\tremaining: 148ms\n",
      "965:\tlearn: 0.0037691\ttotal: 4.07s\tremaining: 143ms\n",
      "966:\tlearn: 0.0037628\ttotal: 4.08s\tremaining: 139ms\n",
      "967:\tlearn: 0.0037561\ttotal: 4.08s\tremaining: 135ms\n",
      "968:\tlearn: 0.0037497\ttotal: 4.08s\tremaining: 131ms\n",
      "969:\tlearn: 0.0037479\ttotal: 4.09s\tremaining: 126ms\n",
      "970:\tlearn: 0.0037413\ttotal: 4.09s\tremaining: 122ms\n",
      "971:\tlearn: 0.0037378\ttotal: 4.09s\tremaining: 118ms\n",
      "972:\tlearn: 0.0037342\ttotal: 4.1s\tremaining: 114ms\n",
      "973:\tlearn: 0.0037319\ttotal: 4.1s\tremaining: 110ms\n",
      "974:\tlearn: 0.0037252\ttotal: 4.11s\tremaining: 105ms\n",
      "975:\tlearn: 0.0037177\ttotal: 4.11s\tremaining: 101ms\n",
      "976:\tlearn: 0.0037112\ttotal: 4.12s\tremaining: 96.9ms\n",
      "977:\tlearn: 0.0037077\ttotal: 4.12s\tremaining: 92.7ms\n",
      "978:\tlearn: 0.0037002\ttotal: 4.12s\tremaining: 88.4ms\n",
      "979:\tlearn: 0.0036918\ttotal: 4.13s\tremaining: 84.2ms\n",
      "980:\tlearn: 0.0036856\ttotal: 4.13s\tremaining: 80ms\n",
      "981:\tlearn: 0.0036792\ttotal: 4.13s\tremaining: 75.8ms\n",
      "982:\tlearn: 0.0036756\ttotal: 4.14s\tremaining: 71.6ms\n",
      "983:\tlearn: 0.0036723\ttotal: 4.14s\tremaining: 67.3ms\n",
      "984:\tlearn: 0.0036662\ttotal: 4.14s\tremaining: 63.1ms\n",
      "985:\tlearn: 0.0036597\ttotal: 4.15s\tremaining: 58.9ms\n",
      "986:\tlearn: 0.0036535\ttotal: 4.15s\tremaining: 54.7ms\n",
      "987:\tlearn: 0.0036503\ttotal: 4.16s\tremaining: 50.5ms\n",
      "988:\tlearn: 0.0036440\ttotal: 4.16s\tremaining: 46.3ms\n",
      "989:\tlearn: 0.0036414\ttotal: 4.16s\tremaining: 42.1ms\n",
      "990:\tlearn: 0.0036358\ttotal: 4.17s\tremaining: 37.9ms\n",
      "991:\tlearn: 0.0036299\ttotal: 4.17s\tremaining: 33.7ms\n",
      "992:\tlearn: 0.0036265\ttotal: 4.18s\tremaining: 29.5ms\n",
      "993:\tlearn: 0.0036183\ttotal: 4.18s\tremaining: 25.3ms\n",
      "994:\tlearn: 0.0036123\ttotal: 4.19s\tremaining: 21.1ms\n",
      "995:\tlearn: 0.0036064\ttotal: 4.19s\tremaining: 16.8ms\n",
      "996:\tlearn: 0.0036031\ttotal: 4.2s\tremaining: 12.6ms\n",
      "997:\tlearn: 0.0035998\ttotal: 4.21s\tremaining: 8.44ms\n",
      "998:\tlearn: 0.0035980\ttotal: 4.22s\tremaining: 4.22ms\n",
      "999:\tlearn: 0.0035922\ttotal: 4.23s\tremaining: 0us\n",
      "current noise type: None\n",
      "{'Samples': 3062, 'Features': 166, 'Anomalies': 97, 'Anomalies Ratio(%)': 3.17}\n",
      "best param: None\n",
      "best param: None\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A8678738B8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A8678738B8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "Model: \"model_8\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_6 (InputLayer)        [(None, 166)]             0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 64)                10624     \n",
      "                                                                 \n",
      " net_output (Dense)          (None, 32)                2048      \n",
      "                                                                 \n",
      " tf.math.subtract_3 (TFOpLam  (None, 32)               0         \n",
      " bda)                                                            \n",
      "                                                                 \n",
      " tf.math.pow_3 (TFOpLambda)  (None, 32)                0         \n",
      "                                                                 \n",
      " tf.math.reduce_sum_3 (TFOpL  (None,)                  0         \n",
      " ambda)                                                          \n",
      "                                                                 \n",
      " tf.math.reduce_mean_3 (TFOp  ()                       0         \n",
      " Lambda)                                                         \n",
      "                                                                 \n",
      " tf.__operators__.add_3 (TFO  ()                       0         \n",
      " pLambda)                                                        \n",
      "                                                                 \n",
      " add_loss_3 (AddLoss)        ()                        0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 12,672\n",
      "Trainable params: 12,672\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/100\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000002A867C44EE8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000002A867C44EE8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "31/61 [==============>...............] - ETA: 0s - loss: 9.1377  WARNING:tensorflow:AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x000002A868631D38> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x000002A868631D38> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "61/61 [==============================] - 1s 4ms/step - loss: 7.3430 - val_loss: 4.5381\n",
      "Epoch 2/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 3.5348 - val_loss: 2.9142\n",
      "Epoch 3/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 2.4972 - val_loss: 2.2388\n",
      "Epoch 4/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 2.0142 - val_loss: 1.8898\n",
      "Epoch 5/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 1.7264 - val_loss: 1.6607\n",
      "Epoch 6/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 1.5529 - val_loss: 1.5061\n",
      "Epoch 7/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 1.4164 - val_loss: 1.4221\n",
      "Epoch 8/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 1.3415 - val_loss: 1.3473\n",
      "Epoch 9/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 1.2798 - val_loss: 1.3169\n",
      "Epoch 10/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 1.2383 - val_loss: 1.2506\n",
      "Epoch 11/100\n",
      "61/61 [==============================] - 0s 4ms/step - loss: 1.1884 - val_loss: 1.1987\n",
      "Epoch 12/100\n",
      "61/61 [==============================] - 0s 4ms/step - loss: 1.1548 - val_loss: 1.1640\n",
      "Epoch 13/100\n",
      "61/61 [==============================] - 0s 4ms/step - loss: 1.1323 - val_loss: 1.1441\n",
      "Epoch 14/100\n",
      "61/61 [==============================] - 0s 4ms/step - loss: 1.1134 - val_loss: 1.1812\n",
      "Epoch 15/100\n",
      "61/61 [==============================] - 0s 4ms/step - loss: 1.0945 - val_loss: 1.1438\n",
      "Epoch 16/100\n",
      "61/61 [==============================] - 0s 4ms/step - loss: 1.0778 - val_loss: 1.0961\n",
      "Epoch 17/100\n",
      "61/61 [==============================] - 0s 4ms/step - loss: 1.0742 - val_loss: 1.1112\n",
      "Epoch 18/100\n",
      "61/61 [==============================] - 0s 4ms/step - loss: 1.0841 - val_loss: 1.1242\n",
      "Epoch 19/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 1.0569 - val_loss: 1.0845\n",
      "Epoch 20/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 1.0465 - val_loss: 1.0588\n",
      "Epoch 21/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 1.0592 - val_loss: 1.0854\n",
      "Epoch 22/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 1.0369 - val_loss: 1.0560\n",
      "Epoch 23/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 1.0146 - val_loss: 1.0421\n",
      "Epoch 24/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 1.0063 - val_loss: 1.0391\n",
      "Epoch 25/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 1.0211 - val_loss: 1.0982\n",
      "Epoch 26/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 1.0717 - val_loss: 1.1014\n",
      "Epoch 27/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 1.0419 - val_loss: 1.0301\n",
      "Epoch 28/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 1.0257 - val_loss: 1.0376\n",
      "Epoch 29/100\n",
      "61/61 [==============================] - 0s 4ms/step - loss: 1.1032 - val_loss: 1.1237\n",
      "Epoch 30/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 1.0428 - val_loss: 1.0123\n",
      "Epoch 31/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 0.9827 - val_loss: 1.0054\n",
      "Epoch 32/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 0.9798 - val_loss: 1.0104\n",
      "Epoch 33/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 0.9676 - val_loss: 0.9793\n",
      "Epoch 34/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 0.9577 - val_loss: 0.9804\n",
      "Epoch 35/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 0.9664 - val_loss: 1.0045\n",
      "Epoch 36/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 0.9832 - val_loss: 1.0272\n",
      "Epoch 37/100\n",
      "61/61 [==============================] - 0s 4ms/step - loss: 1.0178 - val_loss: 1.0454\n",
      "Epoch 38/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 0.9934 - val_loss: 0.9906\n",
      "Epoch 39/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 0.9998 - val_loss: 0.9643\n",
      "Epoch 40/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 0.9568 - val_loss: 0.9843\n",
      "Epoch 41/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9828 - val_loss: 0.9986\n",
      "Epoch 42/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9866 - val_loss: 1.0020\n",
      "Epoch 43/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9863 - val_loss: 0.9834\n",
      "Epoch 44/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9541 - val_loss: 1.1236\n",
      "Epoch 45/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 0.9623 - val_loss: 0.9612\n",
      "Epoch 46/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9585 - val_loss: 0.9885\n",
      "Epoch 47/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 0.9754 - val_loss: 1.0569\n",
      "Epoch 48/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 1.0243 - val_loss: 1.0181\n",
      "Epoch 49/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9979 - val_loss: 1.0629\n",
      "Epoch 50/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9641 - val_loss: 0.9533\n",
      "Epoch 51/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9367 - val_loss: 0.9571\n",
      "Epoch 52/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 0.9318 - val_loss: 0.9465\n",
      "Epoch 53/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9336 - val_loss: 0.9719\n",
      "Epoch 54/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 0.9759 - val_loss: 0.9958\n",
      "Epoch 55/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9536 - val_loss: 0.9588\n",
      "Epoch 56/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9326 - val_loss: 0.9630\n",
      "Epoch 57/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9860 - val_loss: 1.0026\n",
      "Epoch 58/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9372 - val_loss: 0.9441\n",
      "Epoch 59/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 0.9292 - val_loss: 0.9503\n",
      "Epoch 60/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9453 - val_loss: 1.0259\n",
      "Epoch 61/100\n",
      "61/61 [==============================] - 0s 3ms/step - loss: 0.9903 - val_loss: 1.0764\n",
      "Epoch 62/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9826 - val_loss: 0.9982\n",
      "Epoch 63/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9299 - val_loss: 0.9224\n",
      "Epoch 64/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9298 - val_loss: 0.9307\n",
      "Epoch 65/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9531 - val_loss: 0.9755\n",
      "Epoch 66/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9402 - val_loss: 0.9479\n",
      "Epoch 67/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9510 - val_loss: 0.9945\n",
      "Epoch 68/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9907 - val_loss: 1.0216\n",
      "Epoch 69/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9867 - val_loss: 0.9697\n",
      "Epoch 70/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9294 - val_loss: 0.9218\n",
      "Epoch 71/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9130 - val_loss: 0.9152\n",
      "Epoch 72/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9033 - val_loss: 0.9012\n",
      "Epoch 73/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9158 - val_loss: 0.9185\n",
      "Epoch 74/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9397 - val_loss: 0.9508\n",
      "Epoch 75/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9327 - val_loss: 0.9242\n",
      "Epoch 76/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9578 - val_loss: 0.9641\n",
      "Epoch 77/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9847 - val_loss: 0.9523\n",
      "Epoch 78/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9605 - val_loss: 0.9749\n",
      "Epoch 79/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9520 - val_loss: 0.9704\n",
      "Epoch 80/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9311 - val_loss: 0.9219\n",
      "Epoch 81/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9069 - val_loss: 0.9272\n",
      "Epoch 82/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9024 - val_loss: 0.9209\n",
      "Epoch 83/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9135 - val_loss: 0.9219\n",
      "Epoch 84/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9208 - val_loss: 0.9444\n",
      "Epoch 85/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9283 - val_loss: 0.9526\n",
      "Epoch 86/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9629 - val_loss: 0.9561\n",
      "Epoch 87/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9693 - val_loss: 0.9242\n",
      "Epoch 88/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9173 - val_loss: 0.9445\n",
      "Epoch 89/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9071 - val_loss: 0.9127\n",
      "Epoch 90/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9093 - val_loss: 0.9188\n",
      "Epoch 91/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9230 - val_loss: 0.9890\n",
      "Epoch 92/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9426 - val_loss: 0.9365\n",
      "Epoch 93/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9113 - val_loss: 0.9079\n",
      "Epoch 94/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9152 - val_loss: 0.9133\n",
      "Epoch 95/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9020 - val_loss: 0.9143\n",
      "Epoch 96/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9466 - val_loss: 0.9412\n",
      "Epoch 97/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9639 - val_loss: 0.9513\n",
      "Epoch 98/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9192 - val_loss: 0.9432\n",
      "Epoch 99/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9044 - val_loss: 0.9352\n",
      "Epoch 100/100\n",
      "61/61 [==============================] - 0s 2ms/step - loss: 0.9381 - val_loss: 0.9360\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A868779EE8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A868779EE8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "Training size: 2143, No. outliers: 6\n",
      "Epoch 1/50\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000002A867B9D1F8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000002A867B9D1F8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "20/20 [==============================] - 1s 19ms/step - loss: 1.8955\n",
      "Epoch 2/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.8923\n",
      "Epoch 3/50\n",
      "20/20 [==============================] - 0s 17ms/step - loss: 0.6108\n",
      "Epoch 4/50\n",
      "20/20 [==============================] - 0s 17ms/step - loss: 0.5346\n",
      "Epoch 5/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.4875\n",
      "Epoch 6/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.4527\n",
      "Epoch 7/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.4334\n",
      "Epoch 8/50\n",
      "20/20 [==============================] - 0s 17ms/step - loss: 0.4186\n",
      "Epoch 9/50\n",
      "20/20 [==============================] - 0s 17ms/step - loss: 0.4163\n",
      "Epoch 10/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.3969\n",
      "Epoch 11/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.3735\n",
      "Epoch 12/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.3723\n",
      "Epoch 13/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.3701\n",
      "Epoch 14/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.3588\n",
      "Epoch 15/50\n",
      "20/20 [==============================] - 0s 16ms/step - loss: 0.3657\n",
      "Epoch 16/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.3378\n",
      "Epoch 17/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.3374\n",
      "Epoch 18/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 0.3329\n",
      "Epoch 19/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.3283\n",
      "Epoch 20/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.3247\n",
      "Epoch 21/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.3220\n",
      "Epoch 22/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.3075\n",
      "Epoch 23/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.3104\n",
      "Epoch 24/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.3123\n",
      "Epoch 25/50\n",
      "20/20 [==============================] - 0s 24ms/step - loss: 0.2949\n",
      "Epoch 26/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.2951\n",
      "Epoch 27/50\n",
      "20/20 [==============================] - 1s 25ms/step - loss: 0.2940\n",
      "Epoch 28/50\n",
      "20/20 [==============================] - 0s 23ms/step - loss: 0.2898\n",
      "Epoch 29/50\n",
      "20/20 [==============================] - 0s 23ms/step - loss: 0.2802\n",
      "Epoch 30/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.2794\n",
      "Epoch 31/50\n",
      "20/20 [==============================] - 1s 26ms/step - loss: 0.2720\n",
      "Epoch 32/50\n",
      "20/20 [==============================] - 1s 25ms/step - loss: 0.2749\n",
      "Epoch 33/50\n",
      "20/20 [==============================] - 0s 24ms/step - loss: 0.2591\n",
      "Epoch 34/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.2615\n",
      "Epoch 35/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.2547\n",
      "Epoch 36/50\n",
      "20/20 [==============================] - 0s 17ms/step - loss: 0.2599\n",
      "Epoch 37/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.2574\n",
      "Epoch 38/50\n",
      "20/20 [==============================] - 0s 16ms/step - loss: 0.2573\n",
      "Epoch 39/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.2499\n",
      "Epoch 40/50\n",
      "20/20 [==============================] - 0s 17ms/step - loss: 0.2463\n",
      "Epoch 41/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.2441\n",
      "Epoch 42/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.2425\n",
      "Epoch 43/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.2394\n",
      "Epoch 44/50\n",
      "20/20 [==============================] - 0s 17ms/step - loss: 0.2404\n",
      "Epoch 45/50\n",
      "20/20 [==============================] - 0s 16ms/step - loss: 0.2384\n",
      "Epoch 46/50\n",
      "20/20 [==============================] - 0s 17ms/step - loss: 0.2255\n",
      "Epoch 47/50\n",
      "20/20 [==============================] - 0s 17ms/step - loss: 0.2279\n",
      "Epoch 48/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.2291\n",
      "Epoch 49/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.2181\n",
      "Epoch 50/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.2172\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A869E08D38> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A869E08D38> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "Learning rate set to 0.014265\n",
      "0:\tlearn: 0.6553672\ttotal: 24.6ms\tremaining: 24.6s\n",
      "1:\tlearn: 0.6146052\ttotal: 43.4ms\tremaining: 21.7s\n",
      "2:\tlearn: 0.5831875\ttotal: 65.2ms\tremaining: 21.7s\n",
      "3:\tlearn: 0.5503776\ttotal: 90.6ms\tremaining: 22.6s\n",
      "4:\tlearn: 0.5173647\ttotal: 113ms\tremaining: 22.5s\n",
      "5:\tlearn: 0.4902149\ttotal: 134ms\tremaining: 22.1s\n",
      "6:\tlearn: 0.4615770\ttotal: 157ms\tremaining: 22.2s\n",
      "7:\tlearn: 0.4375522\ttotal: 181ms\tremaining: 22.4s\n",
      "8:\tlearn: 0.4133432\ttotal: 207ms\tremaining: 22.8s\n",
      "9:\tlearn: 0.3864447\ttotal: 231ms\tremaining: 22.8s\n",
      "10:\tlearn: 0.3646034\ttotal: 253ms\tremaining: 22.8s\n",
      "11:\tlearn: 0.3456357\ttotal: 280ms\tremaining: 23.1s\n",
      "12:\tlearn: 0.3239671\ttotal: 299ms\tremaining: 22.7s\n",
      "13:\tlearn: 0.3081483\ttotal: 333ms\tremaining: 23.4s\n",
      "14:\tlearn: 0.2887304\ttotal: 357ms\tremaining: 23.4s\n",
      "15:\tlearn: 0.2726823\ttotal: 379ms\tremaining: 23.3s\n",
      "16:\tlearn: 0.2578070\ttotal: 404ms\tremaining: 23.4s\n",
      "17:\tlearn: 0.2424564\ttotal: 431ms\tremaining: 23.5s\n",
      "18:\tlearn: 0.2285918\ttotal: 454ms\tremaining: 23.4s\n",
      "19:\tlearn: 0.2157181\ttotal: 480ms\tremaining: 23.5s\n",
      "20:\tlearn: 0.2045497\ttotal: 503ms\tremaining: 23.4s\n",
      "21:\tlearn: 0.1945441\ttotal: 525ms\tremaining: 23.3s\n",
      "22:\tlearn: 0.1842078\ttotal: 544ms\tremaining: 23.1s\n",
      "23:\tlearn: 0.1744037\ttotal: 563ms\tremaining: 22.9s\n",
      "24:\tlearn: 0.1646712\ttotal: 581ms\tremaining: 22.7s\n",
      "25:\tlearn: 0.1564027\ttotal: 603ms\tremaining: 22.6s\n",
      "26:\tlearn: 0.1485479\ttotal: 623ms\tremaining: 22.4s\n",
      "27:\tlearn: 0.1419064\ttotal: 652ms\tremaining: 22.6s\n",
      "28:\tlearn: 0.1353463\ttotal: 674ms\tremaining: 22.6s\n",
      "29:\tlearn: 0.1294532\ttotal: 694ms\tremaining: 22.4s\n",
      "30:\tlearn: 0.1240576\ttotal: 715ms\tremaining: 22.4s\n",
      "31:\tlearn: 0.1180925\ttotal: 739ms\tremaining: 22.4s\n",
      "32:\tlearn: 0.1128812\ttotal: 763ms\tremaining: 22.4s\n",
      "33:\tlearn: 0.1080922\ttotal: 785ms\tremaining: 22.3s\n",
      "34:\tlearn: 0.1035535\ttotal: 807ms\tremaining: 22.3s\n",
      "35:\tlearn: 0.0990375\ttotal: 830ms\tremaining: 22.2s\n",
      "36:\tlearn: 0.0939039\ttotal: 853ms\tremaining: 22.2s\n",
      "37:\tlearn: 0.0901351\ttotal: 877ms\tremaining: 22.2s\n",
      "38:\tlearn: 0.0865098\ttotal: 902ms\tremaining: 22.2s\n",
      "39:\tlearn: 0.0818925\ttotal: 926ms\tremaining: 22.2s\n",
      "40:\tlearn: 0.0777529\ttotal: 954ms\tremaining: 22.3s\n",
      "41:\tlearn: 0.0741591\ttotal: 994ms\tremaining: 22.7s\n",
      "42:\tlearn: 0.0701713\ttotal: 1.03s\tremaining: 23s\n",
      "43:\tlearn: 0.0675083\ttotal: 1.06s\tremaining: 23s\n",
      "44:\tlearn: 0.0649747\ttotal: 1.08s\tremaining: 22.9s\n",
      "45:\tlearn: 0.0622053\ttotal: 1.1s\tremaining: 22.8s\n",
      "46:\tlearn: 0.0595263\ttotal: 1.12s\tremaining: 22.8s\n",
      "47:\tlearn: 0.0570617\ttotal: 1.15s\tremaining: 22.7s\n",
      "48:\tlearn: 0.0547726\ttotal: 1.17s\tremaining: 22.7s\n",
      "49:\tlearn: 0.0521307\ttotal: 1.19s\tremaining: 22.7s\n",
      "50:\tlearn: 0.0502516\ttotal: 1.22s\tremaining: 22.7s\n",
      "51:\tlearn: 0.0479926\ttotal: 1.24s\tremaining: 22.6s\n",
      "52:\tlearn: 0.0460092\ttotal: 1.26s\tremaining: 22.6s\n",
      "53:\tlearn: 0.0445778\ttotal: 1.29s\tremaining: 22.5s\n",
      "54:\tlearn: 0.0427388\ttotal: 1.33s\tremaining: 22.9s\n",
      "55:\tlearn: 0.0409985\ttotal: 1.36s\tremaining: 23s\n",
      "56:\tlearn: 0.0395633\ttotal: 1.39s\tremaining: 23s\n",
      "57:\tlearn: 0.0379314\ttotal: 1.42s\tremaining: 23.1s\n",
      "58:\tlearn: 0.0363460\ttotal: 1.44s\tremaining: 23s\n",
      "59:\tlearn: 0.0347115\ttotal: 1.47s\tremaining: 23s\n",
      "60:\tlearn: 0.0333099\ttotal: 1.49s\tremaining: 23s\n",
      "61:\tlearn: 0.0319451\ttotal: 1.52s\tremaining: 23s\n",
      "62:\tlearn: 0.0306977\ttotal: 1.54s\tremaining: 22.9s\n",
      "63:\tlearn: 0.0294211\ttotal: 1.57s\tremaining: 22.9s\n",
      "64:\tlearn: 0.0283153\ttotal: 1.59s\tremaining: 22.9s\n",
      "65:\tlearn: 0.0271366\ttotal: 1.62s\tremaining: 22.9s\n",
      "66:\tlearn: 0.0261122\ttotal: 1.65s\tremaining: 23s\n",
      "67:\tlearn: 0.0246952\ttotal: 1.68s\tremaining: 23.1s\n",
      "68:\tlearn: 0.0238712\ttotal: 1.71s\tremaining: 23.1s\n",
      "69:\tlearn: 0.0228899\ttotal: 1.74s\tremaining: 23.2s\n",
      "70:\tlearn: 0.0221060\ttotal: 1.77s\tremaining: 23.1s\n",
      "71:\tlearn: 0.0213141\ttotal: 1.79s\tremaining: 23.1s\n",
      "72:\tlearn: 0.0205771\ttotal: 1.82s\tremaining: 23.1s\n",
      "73:\tlearn: 0.0198354\ttotal: 1.84s\tremaining: 23.1s\n",
      "74:\tlearn: 0.0192583\ttotal: 1.86s\tremaining: 23s\n",
      "75:\tlearn: 0.0186401\ttotal: 1.89s\tremaining: 22.9s\n",
      "76:\tlearn: 0.0179719\ttotal: 1.91s\tremaining: 22.9s\n",
      "77:\tlearn: 0.0173661\ttotal: 1.93s\tremaining: 22.8s\n",
      "78:\tlearn: 0.0167062\ttotal: 1.95s\tremaining: 22.7s\n",
      "79:\tlearn: 0.0161677\ttotal: 1.97s\tremaining: 22.7s\n",
      "80:\tlearn: 0.0157035\ttotal: 2.01s\tremaining: 22.8s\n",
      "81:\tlearn: 0.0150900\ttotal: 2.04s\tremaining: 22.8s\n",
      "82:\tlearn: 0.0146161\ttotal: 2.07s\tremaining: 22.8s\n",
      "83:\tlearn: 0.0141627\ttotal: 2.09s\tremaining: 22.8s\n",
      "84:\tlearn: 0.0136851\ttotal: 2.12s\tremaining: 22.9s\n",
      "85:\tlearn: 0.0133049\ttotal: 2.15s\tremaining: 22.9s\n",
      "86:\tlearn: 0.0129382\ttotal: 2.17s\tremaining: 22.8s\n",
      "87:\tlearn: 0.0124847\ttotal: 2.2s\tremaining: 22.8s\n",
      "88:\tlearn: 0.0121112\ttotal: 2.22s\tremaining: 22.8s\n",
      "89:\tlearn: 0.0117974\ttotal: 2.25s\tremaining: 22.7s\n",
      "90:\tlearn: 0.0114960\ttotal: 2.27s\tremaining: 22.7s\n",
      "91:\tlearn: 0.0112029\ttotal: 2.3s\tremaining: 22.7s\n",
      "92:\tlearn: 0.0109431\ttotal: 2.32s\tremaining: 22.7s\n",
      "93:\tlearn: 0.0106842\ttotal: 2.35s\tremaining: 22.7s\n",
      "94:\tlearn: 0.0104402\ttotal: 2.38s\tremaining: 22.7s\n",
      "95:\tlearn: 0.0101396\ttotal: 2.41s\tremaining: 22.7s\n",
      "96:\tlearn: 0.0098956\ttotal: 2.43s\tremaining: 22.7s\n",
      "97:\tlearn: 0.0096968\ttotal: 2.46s\tremaining: 22.6s\n",
      "98:\tlearn: 0.0093874\ttotal: 2.48s\tremaining: 22.6s\n",
      "99:\tlearn: 0.0091206\ttotal: 2.51s\tremaining: 22.6s\n",
      "100:\tlearn: 0.0088878\ttotal: 2.53s\tremaining: 22.5s\n",
      "101:\tlearn: 0.0087175\ttotal: 2.55s\tremaining: 22.5s\n",
      "102:\tlearn: 0.0085455\ttotal: 2.58s\tremaining: 22.5s\n",
      "103:\tlearn: 0.0083427\ttotal: 2.6s\tremaining: 22.4s\n",
      "104:\tlearn: 0.0081773\ttotal: 2.63s\tremaining: 22.4s\n",
      "105:\tlearn: 0.0079821\ttotal: 2.65s\tremaining: 22.3s\n",
      "106:\tlearn: 0.0077744\ttotal: 2.69s\tremaining: 22.4s\n",
      "107:\tlearn: 0.0076310\ttotal: 2.71s\tremaining: 22.4s\n",
      "108:\tlearn: 0.0075047\ttotal: 2.73s\tremaining: 22.4s\n",
      "109:\tlearn: 0.0073746\ttotal: 2.76s\tremaining: 22.3s\n",
      "110:\tlearn: 0.0071477\ttotal: 2.78s\tremaining: 22.3s\n",
      "111:\tlearn: 0.0070347\ttotal: 2.8s\tremaining: 22.2s\n",
      "112:\tlearn: 0.0069301\ttotal: 2.82s\tremaining: 22.2s\n",
      "113:\tlearn: 0.0067259\ttotal: 2.84s\tremaining: 22.1s\n",
      "114:\tlearn: 0.0066357\ttotal: 2.86s\tremaining: 22s\n",
      "115:\tlearn: 0.0065366\ttotal: 2.89s\tremaining: 22s\n",
      "116:\tlearn: 0.0064424\ttotal: 2.92s\tremaining: 22s\n",
      "117:\tlearn: 0.0063524\ttotal: 2.94s\tremaining: 22s\n",
      "118:\tlearn: 0.0062553\ttotal: 2.97s\tremaining: 22s\n",
      "119:\tlearn: 0.0061003\ttotal: 3s\tremaining: 22s\n",
      "120:\tlearn: 0.0060038\ttotal: 3.04s\tremaining: 22.1s\n",
      "121:\tlearn: 0.0059265\ttotal: 3.06s\tremaining: 22s\n",
      "122:\tlearn: 0.0058529\ttotal: 3.09s\tremaining: 22s\n",
      "123:\tlearn: 0.0057617\ttotal: 3.11s\tremaining: 22s\n",
      "124:\tlearn: 0.0056755\ttotal: 3.14s\tremaining: 22s\n",
      "125:\tlearn: 0.0056047\ttotal: 3.17s\tremaining: 22s\n",
      "126:\tlearn: 0.0055275\ttotal: 3.19s\tremaining: 21.9s\n",
      "127:\tlearn: 0.0054449\ttotal: 3.22s\tremaining: 21.9s\n",
      "128:\tlearn: 0.0053775\ttotal: 3.24s\tremaining: 21.9s\n",
      "129:\tlearn: 0.0052904\ttotal: 3.27s\tremaining: 21.9s\n",
      "130:\tlearn: 0.0052120\ttotal: 3.29s\tremaining: 21.8s\n",
      "131:\tlearn: 0.0051383\ttotal: 3.32s\tremaining: 21.8s\n",
      "132:\tlearn: 0.0050763\ttotal: 3.36s\tremaining: 21.9s\n",
      "133:\tlearn: 0.0049898\ttotal: 3.38s\tremaining: 21.9s\n",
      "134:\tlearn: 0.0049345\ttotal: 3.42s\tremaining: 21.9s\n",
      "135:\tlearn: 0.0048434\ttotal: 3.45s\tremaining: 21.9s\n",
      "136:\tlearn: 0.0047886\ttotal: 3.48s\tremaining: 21.9s\n",
      "137:\tlearn: 0.0047069\ttotal: 3.51s\tremaining: 21.9s\n",
      "138:\tlearn: 0.0046699\ttotal: 3.55s\tremaining: 22s\n",
      "139:\tlearn: 0.0045875\ttotal: 3.57s\tremaining: 21.9s\n",
      "140:\tlearn: 0.0045184\ttotal: 3.6s\tremaining: 21.9s\n",
      "141:\tlearn: 0.0044560\ttotal: 3.63s\tremaining: 22s\n",
      "142:\tlearn: 0.0043957\ttotal: 3.66s\tremaining: 22s\n",
      "143:\tlearn: 0.0043104\ttotal: 3.69s\tremaining: 22s\n",
      "144:\tlearn: 0.0042546\ttotal: 3.73s\tremaining: 22s\n",
      "145:\tlearn: 0.0041568\ttotal: 3.75s\tremaining: 22s\n",
      "146:\tlearn: 0.0041135\ttotal: 3.79s\tremaining: 22s\n",
      "147:\tlearn: 0.0040631\ttotal: 3.82s\tremaining: 22s\n",
      "148:\tlearn: 0.0040196\ttotal: 3.85s\tremaining: 22s\n",
      "149:\tlearn: 0.0039640\ttotal: 3.88s\tremaining: 22s\n",
      "150:\tlearn: 0.0039252\ttotal: 3.9s\tremaining: 22s\n",
      "151:\tlearn: 0.0038857\ttotal: 3.94s\tremaining: 22s\n",
      "152:\tlearn: 0.0038290\ttotal: 3.97s\tremaining: 22s\n",
      "153:\tlearn: 0.0037817\ttotal: 4s\tremaining: 22s\n",
      "154:\tlearn: 0.0037240\ttotal: 4.03s\tremaining: 22s\n",
      "155:\tlearn: 0.0036586\ttotal: 4.06s\tremaining: 22s\n",
      "156:\tlearn: 0.0036144\ttotal: 4.09s\tremaining: 21.9s\n",
      "157:\tlearn: 0.0035859\ttotal: 4.11s\tremaining: 21.9s\n",
      "158:\tlearn: 0.0035537\ttotal: 4.13s\tremaining: 21.9s\n",
      "159:\tlearn: 0.0035152\ttotal: 4.16s\tremaining: 21.8s\n",
      "160:\tlearn: 0.0034719\ttotal: 4.18s\tremaining: 21.8s\n",
      "161:\tlearn: 0.0034432\ttotal: 4.21s\tremaining: 21.8s\n",
      "162:\tlearn: 0.0034063\ttotal: 4.24s\tremaining: 21.8s\n",
      "163:\tlearn: 0.0033606\ttotal: 4.27s\tremaining: 21.8s\n",
      "164:\tlearn: 0.0033385\ttotal: 4.31s\tremaining: 21.8s\n",
      "165:\tlearn: 0.0033108\ttotal: 4.34s\tremaining: 21.8s\n",
      "166:\tlearn: 0.0032655\ttotal: 4.37s\tremaining: 21.8s\n",
      "167:\tlearn: 0.0032335\ttotal: 4.39s\tremaining: 21.8s\n",
      "168:\tlearn: 0.0031983\ttotal: 4.42s\tremaining: 21.7s\n",
      "169:\tlearn: 0.0031463\ttotal: 4.44s\tremaining: 21.7s\n",
      "170:\tlearn: 0.0031210\ttotal: 4.47s\tremaining: 21.7s\n",
      "171:\tlearn: 0.0030835\ttotal: 4.49s\tremaining: 21.6s\n",
      "172:\tlearn: 0.0030405\ttotal: 4.51s\tremaining: 21.6s\n",
      "173:\tlearn: 0.0030171\ttotal: 4.53s\tremaining: 21.5s\n",
      "174:\tlearn: 0.0029908\ttotal: 4.56s\tremaining: 21.5s\n",
      "175:\tlearn: 0.0029507\ttotal: 4.59s\tremaining: 21.5s\n",
      "176:\tlearn: 0.0029232\ttotal: 4.62s\tremaining: 21.5s\n",
      "177:\tlearn: 0.0028840\ttotal: 4.64s\tremaining: 21.5s\n",
      "178:\tlearn: 0.0028632\ttotal: 4.67s\tremaining: 21.4s\n",
      "179:\tlearn: 0.0028427\ttotal: 4.7s\tremaining: 21.4s\n",
      "180:\tlearn: 0.0028111\ttotal: 4.73s\tremaining: 21.4s\n",
      "181:\tlearn: 0.0027748\ttotal: 4.76s\tremaining: 21.4s\n",
      "182:\tlearn: 0.0027521\ttotal: 4.79s\tremaining: 21.4s\n",
      "183:\tlearn: 0.0027315\ttotal: 4.82s\tremaining: 21.4s\n",
      "184:\tlearn: 0.0026962\ttotal: 4.85s\tremaining: 21.4s\n",
      "185:\tlearn: 0.0026752\ttotal: 4.88s\tremaining: 21.3s\n",
      "186:\tlearn: 0.0026480\ttotal: 4.91s\tremaining: 21.3s\n",
      "187:\tlearn: 0.0026289\ttotal: 4.93s\tremaining: 21.3s\n",
      "188:\tlearn: 0.0026058\ttotal: 4.95s\tremaining: 21.3s\n",
      "189:\tlearn: 0.0025785\ttotal: 4.98s\tremaining: 21.2s\n",
      "190:\tlearn: 0.0025508\ttotal: 5.01s\tremaining: 21.2s\n",
      "191:\tlearn: 0.0025322\ttotal: 5.05s\tremaining: 21.3s\n",
      "192:\tlearn: 0.0025090\ttotal: 5.1s\tremaining: 21.3s\n",
      "193:\tlearn: 0.0024914\ttotal: 5.13s\tremaining: 21.3s\n",
      "194:\tlearn: 0.0024708\ttotal: 5.16s\tremaining: 21.3s\n",
      "195:\tlearn: 0.0024452\ttotal: 5.18s\tremaining: 21.3s\n",
      "196:\tlearn: 0.0024229\ttotal: 5.21s\tremaining: 21.2s\n",
      "197:\tlearn: 0.0023982\ttotal: 5.24s\tremaining: 21.2s\n",
      "198:\tlearn: 0.0023821\ttotal: 5.26s\tremaining: 21.2s\n",
      "199:\tlearn: 0.0023589\ttotal: 5.28s\tremaining: 21.1s\n",
      "200:\tlearn: 0.0023483\ttotal: 5.31s\tremaining: 21.1s\n",
      "201:\tlearn: 0.0023236\ttotal: 5.35s\tremaining: 21.1s\n",
      "202:\tlearn: 0.0023098\ttotal: 5.38s\tremaining: 21.1s\n",
      "203:\tlearn: 0.0022840\ttotal: 5.41s\tremaining: 21.1s\n",
      "204:\tlearn: 0.0022587\ttotal: 5.44s\tremaining: 21.1s\n",
      "205:\tlearn: 0.0022481\ttotal: 5.47s\tremaining: 21.1s\n",
      "206:\tlearn: 0.0022277\ttotal: 5.51s\tremaining: 21.1s\n",
      "207:\tlearn: 0.0022065\ttotal: 5.55s\tremaining: 21.1s\n",
      "208:\tlearn: 0.0021894\ttotal: 5.58s\tremaining: 21.1s\n",
      "209:\tlearn: 0.0021755\ttotal: 5.61s\tremaining: 21.1s\n",
      "210:\tlearn: 0.0021586\ttotal: 5.63s\tremaining: 21.1s\n",
      "211:\tlearn: 0.0021294\ttotal: 5.65s\tremaining: 21s\n",
      "212:\tlearn: 0.0021049\ttotal: 5.67s\tremaining: 21s\n",
      "213:\tlearn: 0.0020929\ttotal: 5.71s\tremaining: 21s\n",
      "214:\tlearn: 0.0020763\ttotal: 5.74s\tremaining: 21s\n",
      "215:\tlearn: 0.0020571\ttotal: 5.76s\tremaining: 20.9s\n",
      "216:\tlearn: 0.0020377\ttotal: 5.79s\tremaining: 20.9s\n",
      "217:\tlearn: 0.0020251\ttotal: 5.81s\tremaining: 20.9s\n",
      "218:\tlearn: 0.0020033\ttotal: 5.85s\tremaining: 20.9s\n",
      "219:\tlearn: 0.0019787\ttotal: 5.89s\tremaining: 20.9s\n",
      "220:\tlearn: 0.0019656\ttotal: 5.91s\tremaining: 20.8s\n",
      "221:\tlearn: 0.0019508\ttotal: 5.94s\tremaining: 20.8s\n",
      "222:\tlearn: 0.0019304\ttotal: 5.97s\tremaining: 20.8s\n",
      "223:\tlearn: 0.0019152\ttotal: 6s\tremaining: 20.8s\n",
      "224:\tlearn: 0.0018983\ttotal: 6.05s\tremaining: 20.8s\n",
      "225:\tlearn: 0.0018834\ttotal: 6.08s\tremaining: 20.8s\n",
      "226:\tlearn: 0.0018634\ttotal: 6.1s\tremaining: 20.8s\n",
      "227:\tlearn: 0.0018455\ttotal: 6.12s\tremaining: 20.7s\n",
      "228:\tlearn: 0.0018360\ttotal: 6.15s\tremaining: 20.7s\n",
      "229:\tlearn: 0.0018184\ttotal: 6.18s\tremaining: 20.7s\n",
      "230:\tlearn: 0.0018044\ttotal: 6.21s\tremaining: 20.7s\n",
      "231:\tlearn: 0.0017835\ttotal: 6.25s\tremaining: 20.7s\n",
      "232:\tlearn: 0.0017624\ttotal: 6.28s\tremaining: 20.7s\n",
      "233:\tlearn: 0.0017502\ttotal: 6.3s\tremaining: 20.6s\n",
      "234:\tlearn: 0.0017332\ttotal: 6.33s\tremaining: 20.6s\n",
      "235:\tlearn: 0.0017220\ttotal: 6.36s\tremaining: 20.6s\n",
      "236:\tlearn: 0.0017048\ttotal: 6.38s\tremaining: 20.6s\n",
      "237:\tlearn: 0.0016961\ttotal: 6.41s\tremaining: 20.5s\n",
      "238:\tlearn: 0.0016875\ttotal: 6.43s\tremaining: 20.5s\n",
      "239:\tlearn: 0.0016719\ttotal: 6.46s\tremaining: 20.4s\n",
      "240:\tlearn: 0.0016559\ttotal: 6.48s\tremaining: 20.4s\n",
      "241:\tlearn: 0.0016430\ttotal: 6.51s\tremaining: 20.4s\n",
      "242:\tlearn: 0.0016333\ttotal: 6.53s\tremaining: 20.3s\n",
      "243:\tlearn: 0.0016255\ttotal: 6.56s\tremaining: 20.3s\n",
      "244:\tlearn: 0.0016108\ttotal: 6.59s\tremaining: 20.3s\n",
      "245:\tlearn: 0.0015982\ttotal: 6.62s\tremaining: 20.3s\n",
      "246:\tlearn: 0.0015890\ttotal: 6.64s\tremaining: 20.2s\n",
      "247:\tlearn: 0.0015766\ttotal: 6.67s\tremaining: 20.2s\n",
      "248:\tlearn: 0.0015659\ttotal: 6.7s\tremaining: 20.2s\n",
      "249:\tlearn: 0.0015551\ttotal: 6.72s\tremaining: 20.2s\n",
      "250:\tlearn: 0.0015411\ttotal: 6.74s\tremaining: 20.1s\n",
      "251:\tlearn: 0.0015323\ttotal: 6.77s\tremaining: 20.1s\n",
      "252:\tlearn: 0.0015221\ttotal: 6.8s\tremaining: 20.1s\n",
      "253:\tlearn: 0.0015150\ttotal: 6.82s\tremaining: 20s\n",
      "254:\tlearn: 0.0015074\ttotal: 6.85s\tremaining: 20s\n",
      "255:\tlearn: 0.0015001\ttotal: 6.88s\tremaining: 20s\n",
      "256:\tlearn: 0.0014915\ttotal: 6.91s\tremaining: 20s\n",
      "257:\tlearn: 0.0014845\ttotal: 6.93s\tremaining: 19.9s\n",
      "258:\tlearn: 0.0014775\ttotal: 6.96s\tremaining: 19.9s\n",
      "259:\tlearn: 0.0014706\ttotal: 6.98s\tremaining: 19.9s\n",
      "260:\tlearn: 0.0014636\ttotal: 7.02s\tremaining: 19.9s\n",
      "261:\tlearn: 0.0014551\ttotal: 7.05s\tremaining: 19.9s\n",
      "262:\tlearn: 0.0014443\ttotal: 7.08s\tremaining: 19.8s\n",
      "263:\tlearn: 0.0014343\ttotal: 7.11s\tremaining: 19.8s\n",
      "264:\tlearn: 0.0014223\ttotal: 7.14s\tremaining: 19.8s\n",
      "265:\tlearn: 0.0014150\ttotal: 7.18s\tremaining: 19.8s\n",
      "266:\tlearn: 0.0014073\ttotal: 7.21s\tremaining: 19.8s\n",
      "267:\tlearn: 0.0014021\ttotal: 7.23s\tremaining: 19.8s\n",
      "268:\tlearn: 0.0013928\ttotal: 7.26s\tremaining: 19.7s\n",
      "269:\tlearn: 0.0013839\ttotal: 7.28s\tremaining: 19.7s\n",
      "270:\tlearn: 0.0013756\ttotal: 7.31s\tremaining: 19.7s\n",
      "271:\tlearn: 0.0013654\ttotal: 7.33s\tremaining: 19.6s\n",
      "272:\tlearn: 0.0013593\ttotal: 7.36s\tremaining: 19.6s\n",
      "273:\tlearn: 0.0013518\ttotal: 7.38s\tremaining: 19.6s\n",
      "274:\tlearn: 0.0013423\ttotal: 7.41s\tremaining: 19.5s\n",
      "275:\tlearn: 0.0013333\ttotal: 7.44s\tremaining: 19.5s\n",
      "276:\tlearn: 0.0013238\ttotal: 7.46s\tremaining: 19.5s\n",
      "277:\tlearn: 0.0013126\ttotal: 7.49s\tremaining: 19.4s\n",
      "278:\tlearn: 0.0013069\ttotal: 7.51s\tremaining: 19.4s\n",
      "279:\tlearn: 0.0012981\ttotal: 7.54s\tremaining: 19.4s\n",
      "280:\tlearn: 0.0012921\ttotal: 7.56s\tremaining: 19.4s\n",
      "281:\tlearn: 0.0012844\ttotal: 7.59s\tremaining: 19.3s\n",
      "282:\tlearn: 0.0012804\ttotal: 7.61s\tremaining: 19.3s\n",
      "283:\tlearn: 0.0012772\ttotal: 7.64s\tremaining: 19.3s\n",
      "284:\tlearn: 0.0012704\ttotal: 7.66s\tremaining: 19.2s\n",
      "285:\tlearn: 0.0012611\ttotal: 7.69s\tremaining: 19.2s\n",
      "286:\tlearn: 0.0012550\ttotal: 7.72s\tremaining: 19.2s\n",
      "287:\tlearn: 0.0012525\ttotal: 7.74s\tremaining: 19.1s\n",
      "288:\tlearn: 0.0012470\ttotal: 7.76s\tremaining: 19.1s\n",
      "289:\tlearn: 0.0012412\ttotal: 7.79s\tremaining: 19.1s\n",
      "290:\tlearn: 0.0012371\ttotal: 7.81s\tremaining: 19s\n",
      "291:\tlearn: 0.0012291\ttotal: 7.84s\tremaining: 19s\n",
      "292:\tlearn: 0.0012243\ttotal: 7.86s\tremaining: 19s\n",
      "293:\tlearn: 0.0012175\ttotal: 7.89s\tremaining: 18.9s\n",
      "294:\tlearn: 0.0012116\ttotal: 7.91s\tremaining: 18.9s\n",
      "295:\tlearn: 0.0012037\ttotal: 7.94s\tremaining: 18.9s\n",
      "296:\tlearn: 0.0011981\ttotal: 7.96s\tremaining: 18.8s\n",
      "297:\tlearn: 0.0011931\ttotal: 7.99s\tremaining: 18.8s\n",
      "298:\tlearn: 0.0011873\ttotal: 8.02s\tremaining: 18.8s\n",
      "299:\tlearn: 0.0011848\ttotal: 8.05s\tremaining: 18.8s\n",
      "300:\tlearn: 0.0011786\ttotal: 8.07s\tremaining: 18.7s\n",
      "301:\tlearn: 0.0011723\ttotal: 8.09s\tremaining: 18.7s\n",
      "302:\tlearn: 0.0011676\ttotal: 8.12s\tremaining: 18.7s\n",
      "303:\tlearn: 0.0011620\ttotal: 8.16s\tremaining: 18.7s\n",
      "304:\tlearn: 0.0011541\ttotal: 8.19s\tremaining: 18.7s\n",
      "305:\tlearn: 0.0011498\ttotal: 8.23s\tremaining: 18.7s\n",
      "306:\tlearn: 0.0011451\ttotal: 8.26s\tremaining: 18.6s\n",
      "307:\tlearn: 0.0011389\ttotal: 8.28s\tremaining: 18.6s\n",
      "308:\tlearn: 0.0011326\ttotal: 8.31s\tremaining: 18.6s\n",
      "309:\tlearn: 0.0011270\ttotal: 8.34s\tremaining: 18.6s\n",
      "310:\tlearn: 0.0011201\ttotal: 8.38s\tremaining: 18.6s\n",
      "311:\tlearn: 0.0011150\ttotal: 8.41s\tremaining: 18.5s\n",
      "312:\tlearn: 0.0011071\ttotal: 8.44s\tremaining: 18.5s\n",
      "313:\tlearn: 0.0011032\ttotal: 8.47s\tremaining: 18.5s\n",
      "314:\tlearn: 0.0010967\ttotal: 8.5s\tremaining: 18.5s\n",
      "315:\tlearn: 0.0010898\ttotal: 8.53s\tremaining: 18.5s\n",
      "316:\tlearn: 0.0010825\ttotal: 8.56s\tremaining: 18.4s\n",
      "317:\tlearn: 0.0010740\ttotal: 8.59s\tremaining: 18.4s\n",
      "318:\tlearn: 0.0010680\ttotal: 8.63s\tremaining: 18.4s\n",
      "319:\tlearn: 0.0010617\ttotal: 8.66s\tremaining: 18.4s\n",
      "320:\tlearn: 0.0010567\ttotal: 8.69s\tremaining: 18.4s\n",
      "321:\tlearn: 0.0010523\ttotal: 8.72s\tremaining: 18.4s\n",
      "322:\tlearn: 0.0010483\ttotal: 8.74s\tremaining: 18.3s\n",
      "323:\tlearn: 0.0010408\ttotal: 8.78s\tremaining: 18.3s\n",
      "324:\tlearn: 0.0010372\ttotal: 8.8s\tremaining: 18.3s\n",
      "325:\tlearn: 0.0010325\ttotal: 8.83s\tremaining: 18.2s\n",
      "326:\tlearn: 0.0010287\ttotal: 8.85s\tremaining: 18.2s\n",
      "327:\tlearn: 0.0010247\ttotal: 8.88s\tremaining: 18.2s\n",
      "328:\tlearn: 0.0010191\ttotal: 8.9s\tremaining: 18.2s\n",
      "329:\tlearn: 0.0010149\ttotal: 8.93s\tremaining: 18.1s\n",
      "330:\tlearn: 0.0010114\ttotal: 8.96s\tremaining: 18.1s\n",
      "331:\tlearn: 0.0010071\ttotal: 8.98s\tremaining: 18.1s\n",
      "332:\tlearn: 0.0010023\ttotal: 9.02s\tremaining: 18.1s\n",
      "333:\tlearn: 0.0009972\ttotal: 9.05s\tremaining: 18s\n",
      "334:\tlearn: 0.0009917\ttotal: 9.07s\tremaining: 18s\n",
      "335:\tlearn: 0.0009889\ttotal: 9.11s\tremaining: 18s\n",
      "336:\tlearn: 0.0009846\ttotal: 9.14s\tremaining: 18s\n",
      "337:\tlearn: 0.0009775\ttotal: 9.16s\tremaining: 17.9s\n",
      "338:\tlearn: 0.0009730\ttotal: 9.19s\tremaining: 17.9s\n",
      "339:\tlearn: 0.0009679\ttotal: 9.22s\tremaining: 17.9s\n",
      "340:\tlearn: 0.0009622\ttotal: 9.24s\tremaining: 17.9s\n",
      "341:\tlearn: 0.0009589\ttotal: 9.27s\tremaining: 17.8s\n",
      "342:\tlearn: 0.0009545\ttotal: 9.3s\tremaining: 17.8s\n",
      "343:\tlearn: 0.0009496\ttotal: 9.33s\tremaining: 17.8s\n",
      "344:\tlearn: 0.0009433\ttotal: 9.36s\tremaining: 17.8s\n",
      "345:\tlearn: 0.0009402\ttotal: 9.4s\tremaining: 17.8s\n",
      "346:\tlearn: 0.0009341\ttotal: 9.43s\tremaining: 17.7s\n",
      "347:\tlearn: 0.0009305\ttotal: 9.45s\tremaining: 17.7s\n",
      "348:\tlearn: 0.0009257\ttotal: 9.47s\tremaining: 17.7s\n",
      "349:\tlearn: 0.0009218\ttotal: 9.5s\tremaining: 17.6s\n",
      "350:\tlearn: 0.0009184\ttotal: 9.53s\tremaining: 17.6s\n",
      "351:\tlearn: 0.0009165\ttotal: 9.55s\tremaining: 17.6s\n",
      "352:\tlearn: 0.0009136\ttotal: 9.58s\tremaining: 17.6s\n",
      "353:\tlearn: 0.0009120\ttotal: 9.61s\tremaining: 17.5s\n",
      "354:\tlearn: 0.0009094\ttotal: 9.64s\tremaining: 17.5s\n",
      "355:\tlearn: 0.0009060\ttotal: 9.67s\tremaining: 17.5s\n",
      "356:\tlearn: 0.0009028\ttotal: 9.71s\tremaining: 17.5s\n",
      "357:\tlearn: 0.0008983\ttotal: 9.74s\tremaining: 17.5s\n",
      "358:\tlearn: 0.0008951\ttotal: 9.78s\tremaining: 17.5s\n",
      "359:\tlearn: 0.0008927\ttotal: 9.82s\tremaining: 17.5s\n",
      "360:\tlearn: 0.0008897\ttotal: 9.85s\tremaining: 17.4s\n",
      "361:\tlearn: 0.0008853\ttotal: 9.88s\tremaining: 17.4s\n",
      "362:\tlearn: 0.0008810\ttotal: 9.9s\tremaining: 17.4s\n",
      "363:\tlearn: 0.0008776\ttotal: 9.93s\tremaining: 17.4s\n",
      "364:\tlearn: 0.0008719\ttotal: 9.96s\tremaining: 17.3s\n",
      "365:\tlearn: 0.0008693\ttotal: 9.99s\tremaining: 17.3s\n",
      "366:\tlearn: 0.0008668\ttotal: 10s\tremaining: 17.3s\n",
      "367:\tlearn: 0.0008642\ttotal: 10.1s\tremaining: 17.3s\n",
      "368:\tlearn: 0.0008605\ttotal: 10.1s\tremaining: 17.2s\n",
      "369:\tlearn: 0.0008592\ttotal: 10.1s\tremaining: 17.2s\n",
      "370:\tlearn: 0.0008537\ttotal: 10.1s\tremaining: 17.2s\n",
      "371:\tlearn: 0.0008502\ttotal: 10.2s\tremaining: 17.2s\n",
      "372:\tlearn: 0.0008464\ttotal: 10.2s\tremaining: 17.1s\n",
      "373:\tlearn: 0.0008424\ttotal: 10.2s\tremaining: 17.1s\n",
      "374:\tlearn: 0.0008391\ttotal: 10.2s\tremaining: 17.1s\n",
      "375:\tlearn: 0.0008362\ttotal: 10.3s\tremaining: 17s\n",
      "376:\tlearn: 0.0008326\ttotal: 10.3s\tremaining: 17s\n",
      "377:\tlearn: 0.0008301\ttotal: 10.3s\tremaining: 17s\n",
      "378:\tlearn: 0.0008260\ttotal: 10.4s\tremaining: 17s\n",
      "379:\tlearn: 0.0008227\ttotal: 10.4s\tremaining: 16.9s\n",
      "380:\tlearn: 0.0008187\ttotal: 10.4s\tremaining: 16.9s\n",
      "381:\tlearn: 0.0008146\ttotal: 10.4s\tremaining: 16.9s\n",
      "382:\tlearn: 0.0008099\ttotal: 10.5s\tremaining: 16.9s\n",
      "383:\tlearn: 0.0008055\ttotal: 10.5s\tremaining: 16.8s\n",
      "384:\tlearn: 0.0008028\ttotal: 10.5s\tremaining: 16.8s\n",
      "385:\tlearn: 0.0007998\ttotal: 10.5s\tremaining: 16.8s\n",
      "386:\tlearn: 0.0007972\ttotal: 10.6s\tremaining: 16.8s\n",
      "387:\tlearn: 0.0007950\ttotal: 10.6s\tremaining: 16.7s\n",
      "388:\tlearn: 0.0007918\ttotal: 10.6s\tremaining: 16.7s\n",
      "389:\tlearn: 0.0007888\ttotal: 10.7s\tremaining: 16.7s\n",
      "390:\tlearn: 0.0007865\ttotal: 10.7s\tremaining: 16.7s\n",
      "391:\tlearn: 0.0007829\ttotal: 10.7s\tremaining: 16.6s\n",
      "392:\tlearn: 0.0007805\ttotal: 10.8s\tremaining: 16.6s\n",
      "393:\tlearn: 0.0007777\ttotal: 10.8s\tremaining: 16.6s\n",
      "394:\tlearn: 0.0007735\ttotal: 10.8s\tremaining: 16.6s\n",
      "395:\tlearn: 0.0007714\ttotal: 10.8s\tremaining: 16.5s\n",
      "396:\tlearn: 0.0007689\ttotal: 10.9s\tremaining: 16.5s\n",
      "397:\tlearn: 0.0007667\ttotal: 10.9s\tremaining: 16.5s\n",
      "398:\tlearn: 0.0007639\ttotal: 10.9s\tremaining: 16.4s\n",
      "399:\tlearn: 0.0007616\ttotal: 10.9s\tremaining: 16.4s\n",
      "400:\tlearn: 0.0007595\ttotal: 11s\tremaining: 16.4s\n",
      "401:\tlearn: 0.0007567\ttotal: 11s\tremaining: 16.4s\n",
      "402:\tlearn: 0.0007539\ttotal: 11s\tremaining: 16.4s\n",
      "403:\tlearn: 0.0007518\ttotal: 11.1s\tremaining: 16.3s\n",
      "404:\tlearn: 0.0007494\ttotal: 11.1s\tremaining: 16.3s\n",
      "405:\tlearn: 0.0007467\ttotal: 11.1s\tremaining: 16.3s\n",
      "406:\tlearn: 0.0007428\ttotal: 11.2s\tremaining: 16.3s\n",
      "407:\tlearn: 0.0007393\ttotal: 11.2s\tremaining: 16.2s\n",
      "408:\tlearn: 0.0007360\ttotal: 11.2s\tremaining: 16.2s\n",
      "409:\tlearn: 0.0007325\ttotal: 11.2s\tremaining: 16.2s\n",
      "410:\tlearn: 0.0007311\ttotal: 11.3s\tremaining: 16.1s\n",
      "411:\tlearn: 0.0007302\ttotal: 11.3s\tremaining: 16.1s\n",
      "412:\tlearn: 0.0007276\ttotal: 11.3s\tremaining: 16.1s\n",
      "413:\tlearn: 0.0007252\ttotal: 11.4s\tremaining: 16.1s\n",
      "414:\tlearn: 0.0007228\ttotal: 11.4s\tremaining: 16.1s\n",
      "415:\tlearn: 0.0007207\ttotal: 11.4s\tremaining: 16.1s\n",
      "416:\tlearn: 0.0007169\ttotal: 11.5s\tremaining: 16.1s\n",
      "417:\tlearn: 0.0007134\ttotal: 11.5s\tremaining: 16.1s\n",
      "418:\tlearn: 0.0007108\ttotal: 11.6s\tremaining: 16s\n",
      "419:\tlearn: 0.0007086\ttotal: 11.6s\tremaining: 16s\n",
      "420:\tlearn: 0.0007066\ttotal: 11.6s\tremaining: 16s\n",
      "421:\tlearn: 0.0007042\ttotal: 11.6s\tremaining: 15.9s\n",
      "422:\tlearn: 0.0007018\ttotal: 11.7s\tremaining: 15.9s\n",
      "423:\tlearn: 0.0006999\ttotal: 11.7s\tremaining: 15.9s\n",
      "424:\tlearn: 0.0006980\ttotal: 11.7s\tremaining: 15.9s\n",
      "425:\tlearn: 0.0006964\ttotal: 11.8s\tremaining: 15.9s\n",
      "426:\tlearn: 0.0006945\ttotal: 11.8s\tremaining: 15.8s\n",
      "427:\tlearn: 0.0006930\ttotal: 11.8s\tremaining: 15.8s\n",
      "428:\tlearn: 0.0006911\ttotal: 11.8s\tremaining: 15.8s\n",
      "429:\tlearn: 0.0006876\ttotal: 11.9s\tremaining: 15.7s\n",
      "430:\tlearn: 0.0006851\ttotal: 11.9s\tremaining: 15.7s\n",
      "431:\tlearn: 0.0006828\ttotal: 11.9s\tremaining: 15.7s\n",
      "432:\tlearn: 0.0006805\ttotal: 11.9s\tremaining: 15.6s\n",
      "433:\tlearn: 0.0006787\ttotal: 12s\tremaining: 15.6s\n",
      "434:\tlearn: 0.0006764\ttotal: 12s\tremaining: 15.6s\n",
      "435:\tlearn: 0.0006738\ttotal: 12s\tremaining: 15.6s\n",
      "436:\tlearn: 0.0006719\ttotal: 12.1s\tremaining: 15.6s\n",
      "437:\tlearn: 0.0006695\ttotal: 12.1s\tremaining: 15.5s\n",
      "438:\tlearn: 0.0006669\ttotal: 12.1s\tremaining: 15.5s\n",
      "439:\tlearn: 0.0006649\ttotal: 12.2s\tremaining: 15.5s\n",
      "440:\tlearn: 0.0006626\ttotal: 12.2s\tremaining: 15.5s\n",
      "441:\tlearn: 0.0006609\ttotal: 12.2s\tremaining: 15.4s\n",
      "442:\tlearn: 0.0006573\ttotal: 12.2s\tremaining: 15.4s\n",
      "443:\tlearn: 0.0006541\ttotal: 12.3s\tremaining: 15.4s\n",
      "444:\tlearn: 0.0006526\ttotal: 12.3s\tremaining: 15.3s\n",
      "445:\tlearn: 0.0006500\ttotal: 12.3s\tremaining: 15.3s\n",
      "446:\tlearn: 0.0006466\ttotal: 12.4s\tremaining: 15.3s\n",
      "447:\tlearn: 0.0006433\ttotal: 12.4s\tremaining: 15.3s\n",
      "448:\tlearn: 0.0006405\ttotal: 12.4s\tremaining: 15.2s\n",
      "449:\tlearn: 0.0006373\ttotal: 12.4s\tremaining: 15.2s\n",
      "450:\tlearn: 0.0006358\ttotal: 12.5s\tremaining: 15.2s\n",
      "451:\tlearn: 0.0006334\ttotal: 12.5s\tremaining: 15.2s\n",
      "452:\tlearn: 0.0006307\ttotal: 12.5s\tremaining: 15.1s\n",
      "453:\tlearn: 0.0006280\ttotal: 12.5s\tremaining: 15.1s\n",
      "454:\tlearn: 0.0006257\ttotal: 12.6s\tremaining: 15.1s\n",
      "455:\tlearn: 0.0006249\ttotal: 12.6s\tremaining: 15.1s\n",
      "456:\tlearn: 0.0006224\ttotal: 12.6s\tremaining: 15s\n",
      "457:\tlearn: 0.0006207\ttotal: 12.7s\tremaining: 15s\n",
      "458:\tlearn: 0.0006193\ttotal: 12.7s\tremaining: 15s\n",
      "459:\tlearn: 0.0006175\ttotal: 12.7s\tremaining: 15s\n",
      "460:\tlearn: 0.0006150\ttotal: 12.8s\tremaining: 14.9s\n",
      "461:\tlearn: 0.0006133\ttotal: 12.8s\tremaining: 14.9s\n",
      "462:\tlearn: 0.0006107\ttotal: 12.8s\tremaining: 14.9s\n",
      "463:\tlearn: 0.0006091\ttotal: 12.9s\tremaining: 14.8s\n",
      "464:\tlearn: 0.0006072\ttotal: 12.9s\tremaining: 14.8s\n",
      "465:\tlearn: 0.0006057\ttotal: 12.9s\tremaining: 14.8s\n",
      "466:\tlearn: 0.0006040\ttotal: 13s\tremaining: 14.8s\n",
      "467:\tlearn: 0.0006013\ttotal: 13s\tremaining: 14.8s\n",
      "468:\tlearn: 0.0005983\ttotal: 13s\tremaining: 14.7s\n",
      "469:\tlearn: 0.0005963\ttotal: 13.1s\tremaining: 14.7s\n",
      "470:\tlearn: 0.0005940\ttotal: 13.1s\tremaining: 14.7s\n",
      "471:\tlearn: 0.0005929\ttotal: 13.1s\tremaining: 14.7s\n",
      "472:\tlearn: 0.0005902\ttotal: 13.1s\tremaining: 14.6s\n",
      "473:\tlearn: 0.0005887\ttotal: 13.2s\tremaining: 14.6s\n",
      "474:\tlearn: 0.0005867\ttotal: 13.2s\tremaining: 14.6s\n",
      "475:\tlearn: 0.0005850\ttotal: 13.2s\tremaining: 14.6s\n",
      "476:\tlearn: 0.0005844\ttotal: 13.3s\tremaining: 14.5s\n",
      "477:\tlearn: 0.0005830\ttotal: 13.3s\tremaining: 14.5s\n",
      "478:\tlearn: 0.0005816\ttotal: 13.3s\tremaining: 14.5s\n",
      "479:\tlearn: 0.0005800\ttotal: 13.3s\tremaining: 14.5s\n",
      "480:\tlearn: 0.0005783\ttotal: 13.4s\tremaining: 14.4s\n",
      "481:\tlearn: 0.0005760\ttotal: 13.4s\tremaining: 14.4s\n",
      "482:\tlearn: 0.0005744\ttotal: 13.4s\tremaining: 14.4s\n",
      "483:\tlearn: 0.0005732\ttotal: 13.5s\tremaining: 14.4s\n",
      "484:\tlearn: 0.0005718\ttotal: 13.5s\tremaining: 14.3s\n",
      "485:\tlearn: 0.0005705\ttotal: 13.5s\tremaining: 14.3s\n",
      "486:\tlearn: 0.0005693\ttotal: 13.6s\tremaining: 14.3s\n",
      "487:\tlearn: 0.0005686\ttotal: 13.6s\tremaining: 14.3s\n",
      "488:\tlearn: 0.0005663\ttotal: 13.6s\tremaining: 14.2s\n",
      "489:\tlearn: 0.0005651\ttotal: 13.7s\tremaining: 14.2s\n",
      "490:\tlearn: 0.0005627\ttotal: 13.7s\tremaining: 14.2s\n",
      "491:\tlearn: 0.0005605\ttotal: 13.7s\tremaining: 14.2s\n",
      "492:\tlearn: 0.0005581\ttotal: 13.8s\tremaining: 14.2s\n",
      "493:\tlearn: 0.0005559\ttotal: 13.8s\tremaining: 14.1s\n",
      "494:\tlearn: 0.0005546\ttotal: 13.8s\tremaining: 14.1s\n",
      "495:\tlearn: 0.0005526\ttotal: 13.9s\tremaining: 14.1s\n",
      "496:\tlearn: 0.0005514\ttotal: 13.9s\tremaining: 14.1s\n",
      "497:\tlearn: 0.0005496\ttotal: 14s\tremaining: 14.1s\n",
      "498:\tlearn: 0.0005480\ttotal: 14s\tremaining: 14.1s\n",
      "499:\tlearn: 0.0005464\ttotal: 14s\tremaining: 14s\n",
      "500:\tlearn: 0.0005450\ttotal: 14.1s\tremaining: 14s\n",
      "501:\tlearn: 0.0005433\ttotal: 14.1s\tremaining: 14s\n",
      "502:\tlearn: 0.0005415\ttotal: 14.1s\tremaining: 13.9s\n",
      "503:\tlearn: 0.0005402\ttotal: 14.1s\tremaining: 13.9s\n",
      "504:\tlearn: 0.0005390\ttotal: 14.2s\tremaining: 13.9s\n",
      "505:\tlearn: 0.0005376\ttotal: 14.2s\tremaining: 13.8s\n",
      "506:\tlearn: 0.0005361\ttotal: 14.2s\tremaining: 13.8s\n",
      "507:\tlearn: 0.0005347\ttotal: 14.2s\tremaining: 13.8s\n",
      "508:\tlearn: 0.0005333\ttotal: 14.3s\tremaining: 13.8s\n",
      "509:\tlearn: 0.0005316\ttotal: 14.3s\tremaining: 13.7s\n",
      "510:\tlearn: 0.0005298\ttotal: 14.3s\tremaining: 13.7s\n",
      "511:\tlearn: 0.0005276\ttotal: 14.4s\tremaining: 13.7s\n",
      "512:\tlearn: 0.0005258\ttotal: 14.4s\tremaining: 13.7s\n",
      "513:\tlearn: 0.0005242\ttotal: 14.4s\tremaining: 13.7s\n",
      "514:\tlearn: 0.0005230\ttotal: 14.5s\tremaining: 13.6s\n",
      "515:\tlearn: 0.0005218\ttotal: 14.5s\tremaining: 13.6s\n",
      "516:\tlearn: 0.0005199\ttotal: 14.5s\tremaining: 13.6s\n",
      "517:\tlearn: 0.0005187\ttotal: 14.6s\tremaining: 13.5s\n",
      "518:\tlearn: 0.0005177\ttotal: 14.6s\tremaining: 13.5s\n",
      "519:\tlearn: 0.0005163\ttotal: 14.6s\tremaining: 13.5s\n",
      "520:\tlearn: 0.0005150\ttotal: 14.7s\tremaining: 13.5s\n",
      "521:\tlearn: 0.0005140\ttotal: 14.7s\tremaining: 13.4s\n",
      "522:\tlearn: 0.0005121\ttotal: 14.7s\tremaining: 13.4s\n",
      "523:\tlearn: 0.0005108\ttotal: 14.7s\tremaining: 13.4s\n",
      "524:\tlearn: 0.0005097\ttotal: 14.8s\tremaining: 13.4s\n",
      "525:\tlearn: 0.0005078\ttotal: 14.8s\tremaining: 13.3s\n",
      "526:\tlearn: 0.0005064\ttotal: 14.8s\tremaining: 13.3s\n",
      "527:\tlearn: 0.0005053\ttotal: 14.9s\tremaining: 13.3s\n",
      "528:\tlearn: 0.0005035\ttotal: 14.9s\tremaining: 13.3s\n",
      "529:\tlearn: 0.0005023\ttotal: 14.9s\tremaining: 13.3s\n",
      "530:\tlearn: 0.0005012\ttotal: 15s\tremaining: 13.2s\n",
      "531:\tlearn: 0.0004998\ttotal: 15s\tremaining: 13.2s\n",
      "532:\tlearn: 0.0004989\ttotal: 15s\tremaining: 13.2s\n",
      "533:\tlearn: 0.0004972\ttotal: 15.1s\tremaining: 13.1s\n",
      "534:\tlearn: 0.0004957\ttotal: 15.1s\tremaining: 13.1s\n",
      "535:\tlearn: 0.0004943\ttotal: 15.1s\tremaining: 13.1s\n",
      "536:\tlearn: 0.0004930\ttotal: 15.2s\tremaining: 13.1s\n",
      "537:\tlearn: 0.0004910\ttotal: 15.2s\tremaining: 13s\n",
      "538:\tlearn: 0.0004896\ttotal: 15.2s\tremaining: 13s\n",
      "539:\tlearn: 0.0004885\ttotal: 15.2s\tremaining: 13s\n",
      "540:\tlearn: 0.0004868\ttotal: 15.3s\tremaining: 13s\n",
      "541:\tlearn: 0.0004860\ttotal: 15.3s\tremaining: 12.9s\n",
      "542:\tlearn: 0.0004843\ttotal: 15.3s\tremaining: 12.9s\n",
      "543:\tlearn: 0.0004824\ttotal: 15.4s\tremaining: 12.9s\n",
      "544:\tlearn: 0.0004809\ttotal: 15.4s\tremaining: 12.8s\n",
      "545:\tlearn: 0.0004799\ttotal: 15.4s\tremaining: 12.8s\n",
      "546:\tlearn: 0.0004785\ttotal: 15.4s\tremaining: 12.8s\n",
      "547:\tlearn: 0.0004765\ttotal: 15.5s\tremaining: 12.8s\n",
      "548:\tlearn: 0.0004747\ttotal: 15.5s\tremaining: 12.7s\n",
      "549:\tlearn: 0.0004737\ttotal: 15.5s\tremaining: 12.7s\n",
      "550:\tlearn: 0.0004726\ttotal: 15.6s\tremaining: 12.7s\n",
      "551:\tlearn: 0.0004716\ttotal: 15.6s\tremaining: 12.7s\n",
      "552:\tlearn: 0.0004696\ttotal: 15.6s\tremaining: 12.6s\n",
      "553:\tlearn: 0.0004685\ttotal: 15.7s\tremaining: 12.6s\n",
      "554:\tlearn: 0.0004667\ttotal: 15.7s\tremaining: 12.6s\n",
      "555:\tlearn: 0.0004649\ttotal: 15.7s\tremaining: 12.5s\n",
      "556:\tlearn: 0.0004638\ttotal: 15.7s\tremaining: 12.5s\n",
      "557:\tlearn: 0.0004628\ttotal: 15.8s\tremaining: 12.5s\n",
      "558:\tlearn: 0.0004613\ttotal: 15.8s\tremaining: 12.5s\n",
      "559:\tlearn: 0.0004597\ttotal: 15.8s\tremaining: 12.4s\n",
      "560:\tlearn: 0.0004587\ttotal: 15.9s\tremaining: 12.4s\n",
      "561:\tlearn: 0.0004571\ttotal: 15.9s\tremaining: 12.4s\n",
      "562:\tlearn: 0.0004556\ttotal: 15.9s\tremaining: 12.3s\n",
      "563:\tlearn: 0.0004541\ttotal: 15.9s\tremaining: 12.3s\n",
      "564:\tlearn: 0.0004532\ttotal: 16s\tremaining: 12.3s\n",
      "565:\tlearn: 0.0004517\ttotal: 16s\tremaining: 12.3s\n",
      "566:\tlearn: 0.0004508\ttotal: 16s\tremaining: 12.2s\n",
      "567:\tlearn: 0.0004497\ttotal: 16.1s\tremaining: 12.2s\n",
      "568:\tlearn: 0.0004482\ttotal: 16.1s\tremaining: 12.2s\n",
      "569:\tlearn: 0.0004469\ttotal: 16.2s\tremaining: 12.2s\n",
      "570:\tlearn: 0.0004457\ttotal: 16.2s\tremaining: 12.2s\n",
      "571:\tlearn: 0.0004446\ttotal: 16.2s\tremaining: 12.1s\n",
      "572:\tlearn: 0.0004431\ttotal: 16.3s\tremaining: 12.1s\n",
      "573:\tlearn: 0.0004421\ttotal: 16.3s\tremaining: 12.1s\n",
      "574:\tlearn: 0.0004410\ttotal: 16.3s\tremaining: 12.1s\n",
      "575:\tlearn: 0.0004399\ttotal: 16.4s\tremaining: 12s\n",
      "576:\tlearn: 0.0004390\ttotal: 16.4s\tremaining: 12s\n",
      "577:\tlearn: 0.0004380\ttotal: 16.4s\tremaining: 12s\n",
      "578:\tlearn: 0.0004365\ttotal: 16.4s\tremaining: 12s\n",
      "579:\tlearn: 0.0004355\ttotal: 16.5s\tremaining: 11.9s\n",
      "580:\tlearn: 0.0004347\ttotal: 16.5s\tremaining: 11.9s\n",
      "581:\tlearn: 0.0004338\ttotal: 16.5s\tremaining: 11.9s\n",
      "582:\tlearn: 0.0004323\ttotal: 16.6s\tremaining: 11.8s\n",
      "583:\tlearn: 0.0004309\ttotal: 16.6s\tremaining: 11.8s\n",
      "584:\tlearn: 0.0004296\ttotal: 16.6s\tremaining: 11.8s\n",
      "585:\tlearn: 0.0004287\ttotal: 16.7s\tremaining: 11.8s\n",
      "586:\tlearn: 0.0004277\ttotal: 16.7s\tremaining: 11.7s\n",
      "587:\tlearn: 0.0004268\ttotal: 16.7s\tremaining: 11.7s\n",
      "588:\tlearn: 0.0004254\ttotal: 16.7s\tremaining: 11.7s\n",
      "589:\tlearn: 0.0004240\ttotal: 16.8s\tremaining: 11.7s\n",
      "590:\tlearn: 0.0004230\ttotal: 16.8s\tremaining: 11.6s\n",
      "591:\tlearn: 0.0004221\ttotal: 16.8s\tremaining: 11.6s\n",
      "592:\tlearn: 0.0004208\ttotal: 16.8s\tremaining: 11.6s\n",
      "593:\tlearn: 0.0004196\ttotal: 16.9s\tremaining: 11.5s\n",
      "594:\tlearn: 0.0004183\ttotal: 16.9s\tremaining: 11.5s\n",
      "595:\tlearn: 0.0004170\ttotal: 16.9s\tremaining: 11.5s\n",
      "596:\tlearn: 0.0004154\ttotal: 16.9s\tremaining: 11.4s\n",
      "597:\tlearn: 0.0004135\ttotal: 17s\tremaining: 11.4s\n",
      "598:\tlearn: 0.0004122\ttotal: 17s\tremaining: 11.4s\n",
      "599:\tlearn: 0.0004111\ttotal: 17.1s\tremaining: 11.4s\n",
      "600:\tlearn: 0.0004099\ttotal: 17.1s\tremaining: 11.3s\n",
      "601:\tlearn: 0.0004089\ttotal: 17.1s\tremaining: 11.3s\n",
      "602:\tlearn: 0.0004082\ttotal: 17.1s\tremaining: 11.3s\n",
      "603:\tlearn: 0.0004074\ttotal: 17.2s\tremaining: 11.3s\n",
      "604:\tlearn: 0.0004061\ttotal: 17.2s\tremaining: 11.2s\n",
      "605:\tlearn: 0.0004049\ttotal: 17.2s\tremaining: 11.2s\n",
      "606:\tlearn: 0.0004040\ttotal: 17.3s\tremaining: 11.2s\n",
      "607:\tlearn: 0.0004031\ttotal: 17.3s\tremaining: 11.2s\n",
      "608:\tlearn: 0.0004018\ttotal: 17.3s\tremaining: 11.1s\n",
      "609:\tlearn: 0.0004005\ttotal: 17.4s\tremaining: 11.1s\n",
      "610:\tlearn: 0.0003998\ttotal: 17.4s\tremaining: 11.1s\n",
      "611:\tlearn: 0.0003990\ttotal: 17.4s\tremaining: 11s\n",
      "612:\tlearn: 0.0003978\ttotal: 17.4s\tremaining: 11s\n",
      "613:\tlearn: 0.0003967\ttotal: 17.5s\tremaining: 11s\n",
      "614:\tlearn: 0.0003954\ttotal: 17.5s\tremaining: 11s\n",
      "615:\tlearn: 0.0003943\ttotal: 17.5s\tremaining: 10.9s\n",
      "616:\tlearn: 0.0003935\ttotal: 17.6s\tremaining: 10.9s\n",
      "617:\tlearn: 0.0003924\ttotal: 17.6s\tremaining: 10.9s\n",
      "618:\tlearn: 0.0003917\ttotal: 17.6s\tremaining: 10.9s\n",
      "619:\tlearn: 0.0003904\ttotal: 17.7s\tremaining: 10.8s\n",
      "620:\tlearn: 0.0003892\ttotal: 17.7s\tremaining: 10.8s\n",
      "621:\tlearn: 0.0003879\ttotal: 17.7s\tremaining: 10.8s\n",
      "622:\tlearn: 0.0003868\ttotal: 17.7s\tremaining: 10.7s\n",
      "623:\tlearn: 0.0003856\ttotal: 17.8s\tremaining: 10.7s\n",
      "624:\tlearn: 0.0003849\ttotal: 17.8s\tremaining: 10.7s\n",
      "625:\tlearn: 0.0003842\ttotal: 17.8s\tremaining: 10.6s\n",
      "626:\tlearn: 0.0003830\ttotal: 17.8s\tremaining: 10.6s\n",
      "627:\tlearn: 0.0003819\ttotal: 17.9s\tremaining: 10.6s\n",
      "628:\tlearn: 0.0003811\ttotal: 17.9s\tremaining: 10.6s\n",
      "629:\tlearn: 0.0003802\ttotal: 18s\tremaining: 10.5s\n",
      "630:\tlearn: 0.0003790\ttotal: 18s\tremaining: 10.5s\n",
      "631:\tlearn: 0.0003782\ttotal: 18s\tremaining: 10.5s\n",
      "632:\tlearn: 0.0003770\ttotal: 18s\tremaining: 10.5s\n",
      "633:\tlearn: 0.0003759\ttotal: 18.1s\tremaining: 10.4s\n",
      "634:\tlearn: 0.0003749\ttotal: 18.1s\tremaining: 10.4s\n",
      "635:\tlearn: 0.0003738\ttotal: 18.1s\tremaining: 10.4s\n",
      "636:\tlearn: 0.0003730\ttotal: 18.2s\tremaining: 10.4s\n",
      "637:\tlearn: 0.0003719\ttotal: 18.2s\tremaining: 10.3s\n",
      "638:\tlearn: 0.0003708\ttotal: 18.2s\tremaining: 10.3s\n",
      "639:\tlearn: 0.0003698\ttotal: 18.2s\tremaining: 10.3s\n",
      "640:\tlearn: 0.0003686\ttotal: 18.3s\tremaining: 10.2s\n",
      "641:\tlearn: 0.0003679\ttotal: 18.3s\tremaining: 10.2s\n",
      "642:\tlearn: 0.0003667\ttotal: 18.3s\tremaining: 10.2s\n",
      "643:\tlearn: 0.0003656\ttotal: 18.4s\tremaining: 10.1s\n",
      "644:\tlearn: 0.0003646\ttotal: 18.4s\tremaining: 10.1s\n",
      "645:\tlearn: 0.0003636\ttotal: 18.4s\tremaining: 10.1s\n",
      "646:\tlearn: 0.0003627\ttotal: 18.5s\tremaining: 10.1s\n",
      "647:\tlearn: 0.0003620\ttotal: 18.5s\tremaining: 10s\n",
      "648:\tlearn: 0.0003609\ttotal: 18.5s\tremaining: 10s\n",
      "649:\tlearn: 0.0003599\ttotal: 18.5s\tremaining: 9.98s\n",
      "650:\tlearn: 0.0003590\ttotal: 18.6s\tremaining: 9.96s\n",
      "651:\tlearn: 0.0003580\ttotal: 18.6s\tremaining: 9.93s\n",
      "652:\tlearn: 0.0003569\ttotal: 18.6s\tremaining: 9.9s\n",
      "653:\tlearn: 0.0003559\ttotal: 18.7s\tremaining: 9.87s\n",
      "654:\tlearn: 0.0003553\ttotal: 18.7s\tremaining: 9.84s\n",
      "655:\tlearn: 0.0003543\ttotal: 18.7s\tremaining: 9.81s\n",
      "656:\tlearn: 0.0003533\ttotal: 18.7s\tremaining: 9.78s\n",
      "657:\tlearn: 0.0003523\ttotal: 18.8s\tremaining: 9.75s\n",
      "658:\tlearn: 0.0003512\ttotal: 18.8s\tremaining: 9.72s\n",
      "659:\tlearn: 0.0003502\ttotal: 18.8s\tremaining: 9.7s\n",
      "660:\tlearn: 0.0003492\ttotal: 18.9s\tremaining: 9.67s\n",
      "661:\tlearn: 0.0003483\ttotal: 18.9s\tremaining: 9.64s\n",
      "662:\tlearn: 0.0003473\ttotal: 18.9s\tremaining: 9.62s\n",
      "663:\tlearn: 0.0003465\ttotal: 19s\tremaining: 9.59s\n",
      "664:\tlearn: 0.0003456\ttotal: 19s\tremaining: 9.56s\n",
      "665:\tlearn: 0.0003446\ttotal: 19s\tremaining: 9.54s\n",
      "666:\tlearn: 0.0003436\ttotal: 19s\tremaining: 9.51s\n",
      "667:\tlearn: 0.0003426\ttotal: 19.1s\tremaining: 9.48s\n",
      "668:\tlearn: 0.0003416\ttotal: 19.1s\tremaining: 9.45s\n",
      "669:\tlearn: 0.0003408\ttotal: 19.1s\tremaining: 9.42s\n",
      "670:\tlearn: 0.0003399\ttotal: 19.2s\tremaining: 9.39s\n",
      "671:\tlearn: 0.0003390\ttotal: 19.2s\tremaining: 9.36s\n",
      "672:\tlearn: 0.0003380\ttotal: 19.2s\tremaining: 9.33s\n",
      "673:\tlearn: 0.0003370\ttotal: 19.2s\tremaining: 9.31s\n",
      "674:\tlearn: 0.0003361\ttotal: 19.3s\tremaining: 9.28s\n",
      "675:\tlearn: 0.0003352\ttotal: 19.3s\tremaining: 9.26s\n",
      "676:\tlearn: 0.0003343\ttotal: 19.3s\tremaining: 9.23s\n",
      "677:\tlearn: 0.0003334\ttotal: 19.4s\tremaining: 9.2s\n",
      "678:\tlearn: 0.0003326\ttotal: 19.4s\tremaining: 9.17s\n",
      "679:\tlearn: 0.0003318\ttotal: 19.4s\tremaining: 9.14s\n",
      "680:\tlearn: 0.0003309\ttotal: 19.5s\tremaining: 9.11s\n",
      "681:\tlearn: 0.0003300\ttotal: 19.5s\tremaining: 9.08s\n",
      "682:\tlearn: 0.0003292\ttotal: 19.5s\tremaining: 9.06s\n",
      "683:\tlearn: 0.0003284\ttotal: 19.5s\tremaining: 9.03s\n",
      "684:\tlearn: 0.0003275\ttotal: 19.6s\tremaining: 9s\n",
      "685:\tlearn: 0.0003266\ttotal: 19.6s\tremaining: 8.97s\n",
      "686:\tlearn: 0.0003258\ttotal: 19.6s\tremaining: 8.94s\n",
      "687:\tlearn: 0.0003249\ttotal: 19.6s\tremaining: 8.91s\n",
      "688:\tlearn: 0.0003240\ttotal: 19.7s\tremaining: 8.88s\n",
      "689:\tlearn: 0.0003231\ttotal: 19.7s\tremaining: 8.85s\n",
      "690:\tlearn: 0.0003224\ttotal: 19.7s\tremaining: 8.82s\n",
      "691:\tlearn: 0.0003215\ttotal: 19.8s\tremaining: 8.8s\n",
      "692:\tlearn: 0.0003207\ttotal: 19.8s\tremaining: 8.77s\n",
      "693:\tlearn: 0.0003200\ttotal: 19.8s\tremaining: 8.75s\n",
      "694:\tlearn: 0.0003193\ttotal: 19.9s\tremaining: 8.72s\n",
      "695:\tlearn: 0.0003185\ttotal: 19.9s\tremaining: 8.7s\n",
      "696:\tlearn: 0.0003176\ttotal: 20s\tremaining: 8.68s\n",
      "697:\tlearn: 0.0003167\ttotal: 20s\tremaining: 8.65s\n",
      "698:\tlearn: 0.0003159\ttotal: 20s\tremaining: 8.62s\n",
      "699:\tlearn: 0.0003151\ttotal: 20.1s\tremaining: 8.6s\n",
      "700:\tlearn: 0.0003143\ttotal: 20.1s\tremaining: 8.56s\n",
      "701:\tlearn: 0.0003136\ttotal: 20.1s\tremaining: 8.54s\n",
      "702:\tlearn: 0.0003128\ttotal: 20.1s\tremaining: 8.51s\n",
      "703:\tlearn: 0.0003121\ttotal: 20.2s\tremaining: 8.48s\n",
      "704:\tlearn: 0.0003113\ttotal: 20.2s\tremaining: 8.45s\n",
      "705:\tlearn: 0.0003105\ttotal: 20.2s\tremaining: 8.42s\n",
      "706:\tlearn: 0.0003097\ttotal: 20.3s\tremaining: 8.4s\n",
      "707:\tlearn: 0.0003089\ttotal: 20.3s\tremaining: 8.37s\n",
      "708:\tlearn: 0.0003082\ttotal: 20.3s\tremaining: 8.34s\n",
      "709:\tlearn: 0.0003074\ttotal: 20.3s\tremaining: 8.31s\n",
      "710:\tlearn: 0.0003066\ttotal: 20.4s\tremaining: 8.28s\n",
      "711:\tlearn: 0.0003058\ttotal: 20.4s\tremaining: 8.26s\n",
      "712:\tlearn: 0.0003051\ttotal: 20.5s\tremaining: 8.23s\n",
      "713:\tlearn: 0.0003043\ttotal: 20.5s\tremaining: 8.21s\n",
      "714:\tlearn: 0.0003037\ttotal: 20.5s\tremaining: 8.18s\n",
      "715:\tlearn: 0.0003032\ttotal: 20.5s\tremaining: 8.15s\n",
      "716:\tlearn: 0.0003026\ttotal: 20.6s\tremaining: 8.12s\n",
      "717:\tlearn: 0.0003019\ttotal: 20.6s\tremaining: 8.1s\n",
      "718:\tlearn: 0.0003012\ttotal: 20.7s\tremaining: 8.07s\n",
      "719:\tlearn: 0.0003004\ttotal: 20.7s\tremaining: 8.04s\n",
      "720:\tlearn: 0.0002997\ttotal: 20.7s\tremaining: 8.02s\n",
      "721:\tlearn: 0.0002991\ttotal: 20.7s\tremaining: 7.99s\n",
      "722:\tlearn: 0.0002984\ttotal: 20.8s\tremaining: 7.96s\n",
      "723:\tlearn: 0.0002976\ttotal: 20.8s\tremaining: 7.93s\n",
      "724:\tlearn: 0.0002970\ttotal: 20.8s\tremaining: 7.91s\n",
      "725:\tlearn: 0.0002963\ttotal: 20.9s\tremaining: 7.88s\n",
      "726:\tlearn: 0.0002955\ttotal: 20.9s\tremaining: 7.84s\n",
      "727:\tlearn: 0.0002948\ttotal: 20.9s\tremaining: 7.81s\n",
      "728:\tlearn: 0.0002940\ttotal: 20.9s\tremaining: 7.78s\n",
      "729:\tlearn: 0.0002934\ttotal: 21s\tremaining: 7.75s\n",
      "730:\tlearn: 0.0002928\ttotal: 21s\tremaining: 7.72s\n",
      "731:\tlearn: 0.0002921\ttotal: 21s\tremaining: 7.69s\n",
      "732:\tlearn: 0.0002914\ttotal: 21s\tremaining: 7.66s\n",
      "733:\tlearn: 0.0002907\ttotal: 21.1s\tremaining: 7.63s\n",
      "734:\tlearn: 0.0002901\ttotal: 21.1s\tremaining: 7.6s\n",
      "735:\tlearn: 0.0002894\ttotal: 21.1s\tremaining: 7.57s\n",
      "736:\tlearn: 0.0002887\ttotal: 21.1s\tremaining: 7.54s\n",
      "737:\tlearn: 0.0002880\ttotal: 21.2s\tremaining: 7.51s\n",
      "738:\tlearn: 0.0002873\ttotal: 21.2s\tremaining: 7.49s\n",
      "739:\tlearn: 0.0002867\ttotal: 21.2s\tremaining: 7.46s\n",
      "740:\tlearn: 0.0002860\ttotal: 21.3s\tremaining: 7.43s\n",
      "741:\tlearn: 0.0002854\ttotal: 21.3s\tremaining: 7.41s\n",
      "742:\tlearn: 0.0002847\ttotal: 21.3s\tremaining: 7.38s\n",
      "743:\tlearn: 0.0002840\ttotal: 21.4s\tremaining: 7.35s\n",
      "744:\tlearn: 0.0002834\ttotal: 21.4s\tremaining: 7.32s\n",
      "745:\tlearn: 0.0002827\ttotal: 21.4s\tremaining: 7.29s\n",
      "746:\tlearn: 0.0002821\ttotal: 21.4s\tremaining: 7.26s\n",
      "747:\tlearn: 0.0002815\ttotal: 21.5s\tremaining: 7.23s\n",
      "748:\tlearn: 0.0002808\ttotal: 21.5s\tremaining: 7.2s\n",
      "749:\tlearn: 0.0002802\ttotal: 21.5s\tremaining: 7.17s\n",
      "750:\tlearn: 0.0002796\ttotal: 21.5s\tremaining: 7.14s\n",
      "751:\tlearn: 0.0002790\ttotal: 21.6s\tremaining: 7.11s\n",
      "752:\tlearn: 0.0002783\ttotal: 21.6s\tremaining: 7.08s\n",
      "753:\tlearn: 0.0002777\ttotal: 21.6s\tremaining: 7.05s\n",
      "754:\tlearn: 0.0002771\ttotal: 21.6s\tremaining: 7.02s\n",
      "755:\tlearn: 0.0002765\ttotal: 21.7s\tremaining: 6.99s\n",
      "756:\tlearn: 0.0002759\ttotal: 21.7s\tremaining: 6.96s\n",
      "757:\tlearn: 0.0002752\ttotal: 21.7s\tremaining: 6.93s\n",
      "758:\tlearn: 0.0002746\ttotal: 21.8s\tremaining: 6.91s\n",
      "759:\tlearn: 0.0002741\ttotal: 21.8s\tremaining: 6.88s\n",
      "760:\tlearn: 0.0002734\ttotal: 21.8s\tremaining: 6.86s\n",
      "761:\tlearn: 0.0002728\ttotal: 21.9s\tremaining: 6.83s\n",
      "762:\tlearn: 0.0002722\ttotal: 21.9s\tremaining: 6.8s\n",
      "763:\tlearn: 0.0002717\ttotal: 21.9s\tremaining: 6.78s\n",
      "764:\tlearn: 0.0002711\ttotal: 22s\tremaining: 6.75s\n",
      "765:\tlearn: 0.0002704\ttotal: 22s\tremaining: 6.72s\n",
      "766:\tlearn: 0.0002699\ttotal: 22s\tremaining: 6.69s\n",
      "767:\tlearn: 0.0002693\ttotal: 22s\tremaining: 6.66s\n",
      "768:\tlearn: 0.0002688\ttotal: 22.1s\tremaining: 6.63s\n",
      "769:\tlearn: 0.0002682\ttotal: 22.1s\tremaining: 6.6s\n",
      "770:\tlearn: 0.0002676\ttotal: 22.1s\tremaining: 6.57s\n",
      "771:\tlearn: 0.0002671\ttotal: 22.1s\tremaining: 6.54s\n",
      "772:\tlearn: 0.0002665\ttotal: 22.2s\tremaining: 6.51s\n",
      "773:\tlearn: 0.0002660\ttotal: 22.2s\tremaining: 6.48s\n",
      "774:\tlearn: 0.0002654\ttotal: 22.2s\tremaining: 6.46s\n",
      "775:\tlearn: 0.0002648\ttotal: 22.3s\tremaining: 6.43s\n",
      "776:\tlearn: 0.0002643\ttotal: 22.3s\tremaining: 6.4s\n",
      "777:\tlearn: 0.0002637\ttotal: 22.3s\tremaining: 6.37s\n",
      "778:\tlearn: 0.0002631\ttotal: 22.3s\tremaining: 6.34s\n",
      "779:\tlearn: 0.0002626\ttotal: 22.4s\tremaining: 6.31s\n",
      "780:\tlearn: 0.0002620\ttotal: 22.4s\tremaining: 6.28s\n",
      "781:\tlearn: 0.0002615\ttotal: 22.4s\tremaining: 6.25s\n",
      "782:\tlearn: 0.0002609\ttotal: 22.4s\tremaining: 6.22s\n",
      "783:\tlearn: 0.0002604\ttotal: 22.5s\tremaining: 6.19s\n",
      "784:\tlearn: 0.0002598\ttotal: 22.5s\tremaining: 6.16s\n",
      "785:\tlearn: 0.0002592\ttotal: 22.5s\tremaining: 6.14s\n",
      "786:\tlearn: 0.0002587\ttotal: 22.6s\tremaining: 6.11s\n",
      "787:\tlearn: 0.0002582\ttotal: 22.6s\tremaining: 6.08s\n",
      "788:\tlearn: 0.0002577\ttotal: 22.6s\tremaining: 6.05s\n",
      "789:\tlearn: 0.0002571\ttotal: 22.6s\tremaining: 6.02s\n",
      "790:\tlearn: 0.0002565\ttotal: 22.7s\tremaining: 5.99s\n",
      "791:\tlearn: 0.0002560\ttotal: 22.7s\tremaining: 5.96s\n",
      "792:\tlearn: 0.0002555\ttotal: 22.7s\tremaining: 5.93s\n",
      "793:\tlearn: 0.0002550\ttotal: 22.8s\tremaining: 5.91s\n",
      "794:\tlearn: 0.0002544\ttotal: 22.8s\tremaining: 5.88s\n",
      "795:\tlearn: 0.0002539\ttotal: 22.8s\tremaining: 5.85s\n",
      "796:\tlearn: 0.0002534\ttotal: 22.9s\tremaining: 5.82s\n",
      "797:\tlearn: 0.0002529\ttotal: 22.9s\tremaining: 5.79s\n",
      "798:\tlearn: 0.0002524\ttotal: 22.9s\tremaining: 5.76s\n",
      "799:\tlearn: 0.0002519\ttotal: 22.9s\tremaining: 5.73s\n",
      "800:\tlearn: 0.0002514\ttotal: 23s\tremaining: 5.71s\n",
      "801:\tlearn: 0.0002509\ttotal: 23s\tremaining: 5.68s\n",
      "802:\tlearn: 0.0002504\ttotal: 23s\tremaining: 5.65s\n",
      "803:\tlearn: 0.0002499\ttotal: 23.1s\tremaining: 5.62s\n",
      "804:\tlearn: 0.0002493\ttotal: 23.1s\tremaining: 5.59s\n",
      "805:\tlearn: 0.0002488\ttotal: 23.1s\tremaining: 5.56s\n",
      "806:\tlearn: 0.0002483\ttotal: 23.1s\tremaining: 5.54s\n",
      "807:\tlearn: 0.0002478\ttotal: 23.2s\tremaining: 5.51s\n",
      "808:\tlearn: 0.0002474\ttotal: 23.2s\tremaining: 5.48s\n",
      "809:\tlearn: 0.0002469\ttotal: 23.2s\tremaining: 5.45s\n",
      "810:\tlearn: 0.0002464\ttotal: 23.3s\tremaining: 5.42s\n",
      "811:\tlearn: 0.0002459\ttotal: 23.3s\tremaining: 5.39s\n",
      "812:\tlearn: 0.0002454\ttotal: 23.3s\tremaining: 5.36s\n",
      "813:\tlearn: 0.0002449\ttotal: 23.3s\tremaining: 5.33s\n",
      "814:\tlearn: 0.0002445\ttotal: 23.4s\tremaining: 5.3s\n",
      "815:\tlearn: 0.0002440\ttotal: 23.4s\tremaining: 5.27s\n",
      "816:\tlearn: 0.0002435\ttotal: 23.4s\tremaining: 5.25s\n",
      "817:\tlearn: 0.0002430\ttotal: 23.4s\tremaining: 5.22s\n",
      "818:\tlearn: 0.0002426\ttotal: 23.5s\tremaining: 5.19s\n",
      "819:\tlearn: 0.0002421\ttotal: 23.5s\tremaining: 5.16s\n",
      "820:\tlearn: 0.0002416\ttotal: 23.5s\tremaining: 5.13s\n",
      "821:\tlearn: 0.0002412\ttotal: 23.6s\tremaining: 5.1s\n",
      "822:\tlearn: 0.0002407\ttotal: 23.6s\tremaining: 5.07s\n",
      "823:\tlearn: 0.0002402\ttotal: 23.6s\tremaining: 5.04s\n",
      "824:\tlearn: 0.0002397\ttotal: 23.7s\tremaining: 5.02s\n",
      "825:\tlearn: 0.0002393\ttotal: 23.7s\tremaining: 4.99s\n",
      "826:\tlearn: 0.0002388\ttotal: 23.7s\tremaining: 4.96s\n",
      "827:\tlearn: 0.0002383\ttotal: 23.7s\tremaining: 4.93s\n",
      "828:\tlearn: 0.0002379\ttotal: 23.8s\tremaining: 4.9s\n",
      "829:\tlearn: 0.0002374\ttotal: 23.8s\tremaining: 4.87s\n",
      "830:\tlearn: 0.0002370\ttotal: 23.8s\tremaining: 4.84s\n",
      "831:\tlearn: 0.0002365\ttotal: 23.8s\tremaining: 4.81s\n",
      "832:\tlearn: 0.0002360\ttotal: 23.9s\tremaining: 4.78s\n",
      "833:\tlearn: 0.0002356\ttotal: 23.9s\tremaining: 4.75s\n",
      "834:\tlearn: 0.0002351\ttotal: 23.9s\tremaining: 4.73s\n",
      "835:\tlearn: 0.0002347\ttotal: 23.9s\tremaining: 4.7s\n",
      "836:\tlearn: 0.0002343\ttotal: 24s\tremaining: 4.67s\n",
      "837:\tlearn: 0.0002338\ttotal: 24s\tremaining: 4.64s\n",
      "838:\tlearn: 0.0002334\ttotal: 24s\tremaining: 4.61s\n",
      "839:\tlearn: 0.0002329\ttotal: 24.1s\tremaining: 4.58s\n",
      "840:\tlearn: 0.0002325\ttotal: 24.1s\tremaining: 4.55s\n",
      "841:\tlearn: 0.0002321\ttotal: 24.1s\tremaining: 4.52s\n",
      "842:\tlearn: 0.0002316\ttotal: 24.1s\tremaining: 4.5s\n",
      "843:\tlearn: 0.0002312\ttotal: 24.2s\tremaining: 4.46s\n",
      "844:\tlearn: 0.0002308\ttotal: 24.2s\tremaining: 4.44s\n",
      "845:\tlearn: 0.0002303\ttotal: 24.2s\tremaining: 4.41s\n",
      "846:\tlearn: 0.0002299\ttotal: 24.2s\tremaining: 4.38s\n",
      "847:\tlearn: 0.0002295\ttotal: 24.3s\tremaining: 4.35s\n",
      "848:\tlearn: 0.0002290\ttotal: 24.3s\tremaining: 4.32s\n",
      "849:\tlearn: 0.0002286\ttotal: 24.3s\tremaining: 4.29s\n",
      "850:\tlearn: 0.0002282\ttotal: 24.4s\tremaining: 4.26s\n",
      "851:\tlearn: 0.0002278\ttotal: 24.4s\tremaining: 4.24s\n",
      "852:\tlearn: 0.0002274\ttotal: 24.4s\tremaining: 4.21s\n",
      "853:\tlearn: 0.0002270\ttotal: 24.5s\tremaining: 4.18s\n",
      "854:\tlearn: 0.0002266\ttotal: 24.5s\tremaining: 4.15s\n",
      "855:\tlearn: 0.0002262\ttotal: 24.5s\tremaining: 4.12s\n",
      "856:\tlearn: 0.0002257\ttotal: 24.5s\tremaining: 4.09s\n",
      "857:\tlearn: 0.0002253\ttotal: 24.6s\tremaining: 4.06s\n",
      "858:\tlearn: 0.0002249\ttotal: 24.6s\tremaining: 4.04s\n",
      "859:\tlearn: 0.0002245\ttotal: 24.6s\tremaining: 4.01s\n",
      "860:\tlearn: 0.0002241\ttotal: 24.7s\tremaining: 3.98s\n",
      "861:\tlearn: 0.0002237\ttotal: 24.7s\tremaining: 3.95s\n",
      "862:\tlearn: 0.0002233\ttotal: 24.7s\tremaining: 3.92s\n",
      "863:\tlearn: 0.0002228\ttotal: 24.7s\tremaining: 3.89s\n",
      "864:\tlearn: 0.0002225\ttotal: 24.8s\tremaining: 3.86s\n",
      "865:\tlearn: 0.0002220\ttotal: 24.8s\tremaining: 3.83s\n",
      "866:\tlearn: 0.0002217\ttotal: 24.8s\tremaining: 3.81s\n",
      "867:\tlearn: 0.0002213\ttotal: 24.8s\tremaining: 3.78s\n",
      "868:\tlearn: 0.0002209\ttotal: 24.9s\tremaining: 3.75s\n",
      "869:\tlearn: 0.0002205\ttotal: 24.9s\tremaining: 3.72s\n",
      "870:\tlearn: 0.0002202\ttotal: 24.9s\tremaining: 3.69s\n",
      "871:\tlearn: 0.0002198\ttotal: 24.9s\tremaining: 3.66s\n",
      "872:\tlearn: 0.0002193\ttotal: 25s\tremaining: 3.63s\n",
      "873:\tlearn: 0.0002190\ttotal: 25s\tremaining: 3.6s\n",
      "874:\tlearn: 0.0002186\ttotal: 25s\tremaining: 3.57s\n",
      "875:\tlearn: 0.0002182\ttotal: 25.1s\tremaining: 3.55s\n",
      "876:\tlearn: 0.0002178\ttotal: 25.1s\tremaining: 3.52s\n",
      "877:\tlearn: 0.0002174\ttotal: 25.1s\tremaining: 3.49s\n",
      "878:\tlearn: 0.0002170\ttotal: 25.1s\tremaining: 3.46s\n",
      "879:\tlearn: 0.0002167\ttotal: 25.2s\tremaining: 3.43s\n",
      "880:\tlearn: 0.0002163\ttotal: 25.2s\tremaining: 3.4s\n",
      "881:\tlearn: 0.0002159\ttotal: 25.2s\tremaining: 3.37s\n",
      "882:\tlearn: 0.0002155\ttotal: 25.2s\tremaining: 3.35s\n",
      "883:\tlearn: 0.0002152\ttotal: 25.3s\tremaining: 3.32s\n",
      "884:\tlearn: 0.0002148\ttotal: 25.3s\tremaining: 3.29s\n",
      "885:\tlearn: 0.0002145\ttotal: 25.4s\tremaining: 3.26s\n",
      "886:\tlearn: 0.0002141\ttotal: 25.4s\tremaining: 3.23s\n",
      "887:\tlearn: 0.0002137\ttotal: 25.4s\tremaining: 3.21s\n",
      "888:\tlearn: 0.0002133\ttotal: 25.4s\tremaining: 3.18s\n",
      "889:\tlearn: 0.0002129\ttotal: 25.5s\tremaining: 3.15s\n",
      "890:\tlearn: 0.0002125\ttotal: 25.5s\tremaining: 3.12s\n",
      "891:\tlearn: 0.0002122\ttotal: 25.5s\tremaining: 3.09s\n",
      "892:\tlearn: 0.0002119\ttotal: 25.6s\tremaining: 3.06s\n",
      "893:\tlearn: 0.0002115\ttotal: 25.6s\tremaining: 3.03s\n",
      "894:\tlearn: 0.0002112\ttotal: 25.6s\tremaining: 3.01s\n",
      "895:\tlearn: 0.0002108\ttotal: 25.7s\tremaining: 2.98s\n",
      "896:\tlearn: 0.0002104\ttotal: 25.7s\tremaining: 2.95s\n",
      "897:\tlearn: 0.0002101\ttotal: 25.7s\tremaining: 2.92s\n",
      "898:\tlearn: 0.0002098\ttotal: 25.8s\tremaining: 2.89s\n",
      "899:\tlearn: 0.0002094\ttotal: 25.8s\tremaining: 2.87s\n",
      "900:\tlearn: 0.0002090\ttotal: 25.8s\tremaining: 2.84s\n",
      "901:\tlearn: 0.0002087\ttotal: 25.8s\tremaining: 2.81s\n",
      "902:\tlearn: 0.0002083\ttotal: 25.9s\tremaining: 2.78s\n",
      "903:\tlearn: 0.0002080\ttotal: 25.9s\tremaining: 2.75s\n",
      "904:\tlearn: 0.0002076\ttotal: 25.9s\tremaining: 2.72s\n",
      "905:\tlearn: 0.0002073\ttotal: 26s\tremaining: 2.69s\n",
      "906:\tlearn: 0.0002070\ttotal: 26s\tremaining: 2.67s\n",
      "907:\tlearn: 0.0002066\ttotal: 26s\tremaining: 2.64s\n",
      "908:\tlearn: 0.0002063\ttotal: 26.1s\tremaining: 2.61s\n",
      "909:\tlearn: 0.0002060\ttotal: 26.1s\tremaining: 2.58s\n",
      "910:\tlearn: 0.0002057\ttotal: 26.1s\tremaining: 2.55s\n",
      "911:\tlearn: 0.0002053\ttotal: 26.2s\tremaining: 2.52s\n",
      "912:\tlearn: 0.0002050\ttotal: 26.2s\tremaining: 2.5s\n",
      "913:\tlearn: 0.0002047\ttotal: 26.2s\tremaining: 2.46s\n",
      "914:\tlearn: 0.0002043\ttotal: 26.2s\tremaining: 2.44s\n",
      "915:\tlearn: 0.0002040\ttotal: 26.3s\tremaining: 2.41s\n",
      "916:\tlearn: 0.0002037\ttotal: 26.3s\tremaining: 2.38s\n",
      "917:\tlearn: 0.0002033\ttotal: 26.3s\tremaining: 2.35s\n",
      "918:\tlearn: 0.0002030\ttotal: 26.4s\tremaining: 2.32s\n",
      "919:\tlearn: 0.0002027\ttotal: 26.4s\tremaining: 2.29s\n",
      "920:\tlearn: 0.0002023\ttotal: 26.4s\tremaining: 2.27s\n",
      "921:\tlearn: 0.0002020\ttotal: 26.4s\tremaining: 2.24s\n",
      "922:\tlearn: 0.0002017\ttotal: 26.5s\tremaining: 2.21s\n",
      "923:\tlearn: 0.0002013\ttotal: 26.5s\tremaining: 2.18s\n",
      "924:\tlearn: 0.0002010\ttotal: 26.5s\tremaining: 2.15s\n",
      "925:\tlearn: 0.0002007\ttotal: 26.5s\tremaining: 2.12s\n",
      "926:\tlearn: 0.0002004\ttotal: 26.6s\tremaining: 2.09s\n",
      "927:\tlearn: 0.0002000\ttotal: 26.6s\tremaining: 2.06s\n",
      "928:\tlearn: 0.0001997\ttotal: 26.6s\tremaining: 2.04s\n",
      "929:\tlearn: 0.0001994\ttotal: 26.7s\tremaining: 2.01s\n",
      "930:\tlearn: 0.0001991\ttotal: 26.7s\tremaining: 1.98s\n",
      "931:\tlearn: 0.0001988\ttotal: 26.7s\tremaining: 1.95s\n",
      "932:\tlearn: 0.0001985\ttotal: 26.8s\tremaining: 1.92s\n",
      "933:\tlearn: 0.0001982\ttotal: 26.8s\tremaining: 1.89s\n",
      "934:\tlearn: 0.0001979\ttotal: 26.8s\tremaining: 1.86s\n",
      "935:\tlearn: 0.0001975\ttotal: 26.8s\tremaining: 1.83s\n",
      "936:\tlearn: 0.0001972\ttotal: 26.9s\tremaining: 1.8s\n",
      "937:\tlearn: 0.0001969\ttotal: 26.9s\tremaining: 1.78s\n",
      "938:\tlearn: 0.0001966\ttotal: 26.9s\tremaining: 1.75s\n",
      "939:\tlearn: 0.0001963\ttotal: 26.9s\tremaining: 1.72s\n",
      "940:\tlearn: 0.0001960\ttotal: 27s\tremaining: 1.69s\n",
      "941:\tlearn: 0.0001957\ttotal: 27s\tremaining: 1.66s\n",
      "942:\tlearn: 0.0001954\ttotal: 27s\tremaining: 1.63s\n",
      "943:\tlearn: 0.0001950\ttotal: 27s\tremaining: 1.6s\n",
      "944:\tlearn: 0.0001947\ttotal: 27.1s\tremaining: 1.57s\n",
      "945:\tlearn: 0.0001944\ttotal: 27.1s\tremaining: 1.55s\n",
      "946:\tlearn: 0.0001941\ttotal: 27.1s\tremaining: 1.52s\n",
      "947:\tlearn: 0.0001938\ttotal: 27.2s\tremaining: 1.49s\n",
      "948:\tlearn: 0.0001935\ttotal: 27.2s\tremaining: 1.46s\n",
      "949:\tlearn: 0.0001932\ttotal: 27.2s\tremaining: 1.43s\n",
      "950:\tlearn: 0.0001929\ttotal: 27.3s\tremaining: 1.4s\n",
      "951:\tlearn: 0.0001926\ttotal: 27.3s\tremaining: 1.38s\n",
      "952:\tlearn: 0.0001923\ttotal: 27.3s\tremaining: 1.35s\n",
      "953:\tlearn: 0.0001920\ttotal: 27.4s\tremaining: 1.32s\n",
      "954:\tlearn: 0.0001917\ttotal: 27.4s\tremaining: 1.29s\n",
      "955:\tlearn: 0.0001914\ttotal: 27.4s\tremaining: 1.26s\n",
      "956:\tlearn: 0.0001912\ttotal: 27.5s\tremaining: 1.23s\n",
      "957:\tlearn: 0.0001909\ttotal: 27.5s\tremaining: 1.2s\n",
      "958:\tlearn: 0.0001906\ttotal: 27.5s\tremaining: 1.18s\n",
      "959:\tlearn: 0.0001903\ttotal: 27.5s\tremaining: 1.15s\n",
      "960:\tlearn: 0.0001900\ttotal: 27.6s\tremaining: 1.12s\n",
      "961:\tlearn: 0.0001897\ttotal: 27.6s\tremaining: 1.09s\n",
      "962:\tlearn: 0.0001894\ttotal: 27.6s\tremaining: 1.06s\n",
      "963:\tlearn: 0.0001891\ttotal: 27.7s\tremaining: 1.03s\n",
      "964:\tlearn: 0.0001888\ttotal: 27.7s\tremaining: 1s\n",
      "965:\tlearn: 0.0001886\ttotal: 27.7s\tremaining: 976ms\n",
      "966:\tlearn: 0.0001883\ttotal: 27.8s\tremaining: 947ms\n",
      "967:\tlearn: 0.0001880\ttotal: 27.8s\tremaining: 919ms\n",
      "968:\tlearn: 0.0001877\ttotal: 27.8s\tremaining: 890ms\n",
      "969:\tlearn: 0.0001874\ttotal: 27.9s\tremaining: 861ms\n",
      "970:\tlearn: 0.0001871\ttotal: 27.9s\tremaining: 833ms\n",
      "971:\tlearn: 0.0001869\ttotal: 27.9s\tremaining: 804ms\n",
      "972:\tlearn: 0.0001866\ttotal: 27.9s\tremaining: 775ms\n",
      "973:\tlearn: 0.0001863\ttotal: 28s\tremaining: 747ms\n",
      "974:\tlearn: 0.0001860\ttotal: 28s\tremaining: 718ms\n",
      "975:\tlearn: 0.0001858\ttotal: 28s\tremaining: 689ms\n",
      "976:\tlearn: 0.0001855\ttotal: 28.1s\tremaining: 660ms\n",
      "977:\tlearn: 0.0001853\ttotal: 28.1s\tremaining: 632ms\n",
      "978:\tlearn: 0.0001850\ttotal: 28.1s\tremaining: 603ms\n",
      "979:\tlearn: 0.0001847\ttotal: 28.1s\tremaining: 574ms\n",
      "980:\tlearn: 0.0001844\ttotal: 28.2s\tremaining: 546ms\n",
      "981:\tlearn: 0.0001842\ttotal: 28.2s\tremaining: 517ms\n",
      "982:\tlearn: 0.0001839\ttotal: 28.2s\tremaining: 488ms\n",
      "983:\tlearn: 0.0001836\ttotal: 28.3s\tremaining: 460ms\n",
      "984:\tlearn: 0.0001833\ttotal: 28.3s\tremaining: 431ms\n",
      "985:\tlearn: 0.0001831\ttotal: 28.3s\tremaining: 402ms\n",
      "986:\tlearn: 0.0001828\ttotal: 28.4s\tremaining: 374ms\n",
      "987:\tlearn: 0.0001825\ttotal: 28.4s\tremaining: 345ms\n",
      "988:\tlearn: 0.0001822\ttotal: 28.4s\tremaining: 316ms\n",
      "989:\tlearn: 0.0001820\ttotal: 28.5s\tremaining: 288ms\n",
      "990:\tlearn: 0.0001818\ttotal: 28.5s\tremaining: 259ms\n",
      "991:\tlearn: 0.0001815\ttotal: 28.5s\tremaining: 230ms\n",
      "992:\tlearn: 0.0001812\ttotal: 28.6s\tremaining: 201ms\n",
      "993:\tlearn: 0.0001810\ttotal: 28.6s\tremaining: 173ms\n",
      "994:\tlearn: 0.0001807\ttotal: 28.6s\tremaining: 144ms\n",
      "995:\tlearn: 0.0001805\ttotal: 28.6s\tremaining: 115ms\n",
      "996:\tlearn: 0.0001802\ttotal: 28.7s\tremaining: 86.3ms\n",
      "997:\tlearn: 0.0001800\ttotal: 28.7s\tremaining: 57.5ms\n",
      "998:\tlearn: 0.0001797\ttotal: 28.7s\tremaining: 28.8ms\n",
      "999:\tlearn: 0.0001795\ttotal: 28.8s\tremaining: 0us\n",
      "current noise type: None\n",
      "{'Samples': 5216, 'Features': 64, 'Anomalies': 150, 'Anomalies Ratio(%)': 2.88}\n",
      "best param: None\n",
      "best param: None\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A86AF1E168> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A86AF1E168> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "Model: \"model_14\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_10 (InputLayer)       [(None, 64)]              0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 64)                4096      \n",
      "                                                                 \n",
      " net_output (Dense)          (None, 32)                2048      \n",
      "                                                                 \n",
      " tf.math.subtract_5 (TFOpLam  (None, 32)               0         \n",
      " bda)                                                            \n",
      "                                                                 \n",
      " tf.math.pow_5 (TFOpLambda)  (None, 32)                0         \n",
      "                                                                 \n",
      " tf.math.reduce_sum_5 (TFOpL  (None,)                  0         \n",
      " ambda)                                                          \n",
      "                                                                 \n",
      " tf.math.reduce_mean_5 (TFOp  ()                       0         \n",
      " Lambda)                                                         \n",
      "                                                                 \n",
      " tf.__operators__.add_5 (TFO  ()                       0         \n",
      " pLambda)                                                        \n",
      "                                                                 \n",
      " add_loss_5 (AddLoss)        ()                        0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 6,144\n",
      "Trainable params: 6,144\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/100\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000002A86AF1EDC8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000002A86AF1EDC8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      " 77/103 [=====================>........] - ETA: 0s - loss: 7.1901 WARNING:tensorflow:AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x000002A86B180AF8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x000002A86B180AF8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "103/103 [==============================] - 1s 3ms/step - loss: 6.6618 - val_loss: 6.2838\n",
      "Epoch 2/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 3.9578 - val_loss: 4.6408\n",
      "Epoch 3/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 2.7172 - val_loss: 3.4605\n",
      "Epoch 4/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 2.0354 - val_loss: 2.8097\n",
      "Epoch 5/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 1.6453 - val_loss: 2.2201\n",
      "Epoch 6/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 1.3968 - val_loss: 1.9453\n",
      "Epoch 7/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 1.2235 - val_loss: 1.6787\n",
      "Epoch 8/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 1.0915 - val_loss: 1.5269\n",
      "Epoch 9/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.9925 - val_loss: 1.3620\n",
      "Epoch 10/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.9097 - val_loss: 1.1940\n",
      "Epoch 11/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.8422 - val_loss: 1.1115\n",
      "Epoch 12/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.7851 - val_loss: 1.0448\n",
      "Epoch 13/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.7372 - val_loss: 0.9879\n",
      "Epoch 14/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.7002 - val_loss: 0.8722\n",
      "Epoch 15/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.6659 - val_loss: 0.8237\n",
      "Epoch 16/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.6395 - val_loss: 0.7899\n",
      "Epoch 17/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.6178 - val_loss: 0.7777\n",
      "Epoch 18/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.5967 - val_loss: 0.7385\n",
      "Epoch 19/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.5815 - val_loss: 0.6845\n",
      "Epoch 20/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.5654 - val_loss: 0.6761\n",
      "Epoch 21/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.5501 - val_loss: 0.6804\n",
      "Epoch 22/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.5381 - val_loss: 0.6566\n",
      "Epoch 23/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.5296 - val_loss: 0.6152\n",
      "Epoch 24/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.5235 - val_loss: 0.6039\n",
      "Epoch 25/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.5165 - val_loss: 0.6442\n",
      "Epoch 26/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.5125 - val_loss: 0.5685\n",
      "Epoch 27/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.5071 - val_loss: 0.6032\n",
      "Epoch 28/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.5045 - val_loss: 0.5841\n",
      "Epoch 29/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4988 - val_loss: 0.5594\n",
      "Epoch 30/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4983 - val_loss: 0.5560\n",
      "Epoch 31/100\n",
      "103/103 [==============================] - 0s 3ms/step - loss: 0.4922 - val_loss: 0.5165\n",
      "Epoch 32/100\n",
      "103/103 [==============================] - 0s 3ms/step - loss: 0.4930 - val_loss: 0.5981\n",
      "Epoch 33/100\n",
      "103/103 [==============================] - 0s 3ms/step - loss: 0.4919 - val_loss: 0.5759\n",
      "Epoch 34/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4914 - val_loss: 0.5597\n",
      "Epoch 35/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.5011 - val_loss: 0.5291\n",
      "Epoch 36/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4877 - val_loss: 0.5185\n",
      "Epoch 37/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4795 - val_loss: 0.4970\n",
      "Epoch 38/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4802 - val_loss: 0.4966\n",
      "Epoch 39/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4783 - val_loss: 0.5672\n",
      "Epoch 40/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4780 - val_loss: 0.5264\n",
      "Epoch 41/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4730 - val_loss: 0.5367\n",
      "Epoch 42/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4773 - val_loss: 0.5446\n",
      "Epoch 43/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4790 - val_loss: 0.4969\n",
      "Epoch 44/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4721 - val_loss: 0.4985\n",
      "Epoch 45/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4720 - val_loss: 0.5248\n",
      "Epoch 46/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4760 - val_loss: 0.5206\n",
      "Epoch 47/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4696 - val_loss: 0.5729\n",
      "Epoch 48/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4765 - val_loss: 0.5671\n",
      "Epoch 49/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4705 - val_loss: 0.5197\n",
      "Epoch 50/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4815 - val_loss: 0.5588\n",
      "Epoch 51/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4742 - val_loss: 0.5842\n",
      "Epoch 52/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4765 - val_loss: 0.5105\n",
      "Epoch 53/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4643 - val_loss: 0.4867\n",
      "Epoch 54/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4643 - val_loss: 0.5298\n",
      "Epoch 55/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4701 - val_loss: 0.5058\n",
      "Epoch 56/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4702 - val_loss: 0.5545\n",
      "Epoch 57/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4698 - val_loss: 0.5159\n",
      "Epoch 58/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4693 - val_loss: 0.5159\n",
      "Epoch 59/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4661 - val_loss: 0.4971\n",
      "Epoch 60/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4694 - val_loss: 0.5532\n",
      "Epoch 61/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4693 - val_loss: 0.5487\n",
      "Epoch 62/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4618 - val_loss: 0.4902\n",
      "Epoch 63/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4635 - val_loss: 0.5233\n",
      "Epoch 64/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4649 - val_loss: 0.5477\n",
      "Epoch 65/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4635 - val_loss: 0.4848\n",
      "Epoch 66/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4732 - val_loss: 0.5605\n",
      "Epoch 67/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4648 - val_loss: 0.5067\n",
      "Epoch 68/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4629 - val_loss: 0.4772\n",
      "Epoch 69/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4642 - val_loss: 0.4897\n",
      "Epoch 70/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4632 - val_loss: 0.5023\n",
      "Epoch 71/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4625 - val_loss: 0.5352\n",
      "Epoch 72/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4667 - val_loss: 0.5244\n",
      "Epoch 73/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4630 - val_loss: 0.5303\n",
      "Epoch 74/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4634 - val_loss: 0.5445\n",
      "Epoch 75/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4587 - val_loss: 0.5384\n",
      "Epoch 76/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4697 - val_loss: 0.4930\n",
      "Epoch 77/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4605 - val_loss: 0.5202\n",
      "Epoch 78/100\n",
      "103/103 [==============================] - 0s 3ms/step - loss: 0.4626 - val_loss: 0.4968\n",
      "Epoch 79/100\n",
      "103/103 [==============================] - 0s 3ms/step - loss: 0.4579 - val_loss: 0.5004\n",
      "Epoch 80/100\n",
      "103/103 [==============================] - 0s 4ms/step - loss: 0.4622 - val_loss: 0.5345\n",
      "Epoch 81/100\n",
      "103/103 [==============================] - 0s 3ms/step - loss: 0.4602 - val_loss: 0.5155\n",
      "Epoch 82/100\n",
      "103/103 [==============================] - 0s 3ms/step - loss: 0.4636 - val_loss: 0.4902\n",
      "Epoch 83/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4589 - val_loss: 0.5256\n",
      "Epoch 84/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4661 - val_loss: 0.5003\n",
      "Epoch 85/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4634 - val_loss: 0.5319\n",
      "Epoch 86/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4611 - val_loss: 0.5240\n",
      "Epoch 87/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4618 - val_loss: 0.5045\n",
      "Epoch 88/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4710 - val_loss: 0.6542\n",
      "Epoch 89/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4733 - val_loss: 0.5155\n",
      "Epoch 90/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4595 - val_loss: 0.5621\n",
      "Epoch 91/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4607 - val_loss: 0.5101\n",
      "Epoch 92/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4671 - val_loss: 0.5537\n",
      "Epoch 93/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4583 - val_loss: 0.5231\n",
      "Epoch 94/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4573 - val_loss: 0.4978\n",
      "Epoch 95/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4586 - val_loss: 0.5016\n",
      "Epoch 96/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4590 - val_loss: 0.4919\n",
      "Epoch 97/100\n",
      "103/103 [==============================] - 0s 3ms/step - loss: 0.4614 - val_loss: 0.5165\n",
      "Epoch 98/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4560 - val_loss: 0.4975\n",
      "Epoch 99/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4554 - val_loss: 0.4901\n",
      "Epoch 100/100\n",
      "103/103 [==============================] - 0s 2ms/step - loss: 0.4586 - val_loss: 0.5843\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A86B1801F8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A86B1801F8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "Training size: 3651, No. outliers: 10\n",
      "Epoch 1/50\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000002A859A66048> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000002A859A66048> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "20/20 [==============================] - 2s 25ms/step - loss: 2.6008\n",
      "Epoch 2/50\n",
      "20/20 [==============================] - 0s 23ms/step - loss: 2.2897\n",
      "Epoch 3/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 1.9644\n",
      "Epoch 4/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 1.5778\n",
      "Epoch 5/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 1.1767\n",
      "Epoch 6/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.9690\n",
      "Epoch 7/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.8805\n",
      "Epoch 8/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.7858\n",
      "Epoch 9/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.6960\n",
      "Epoch 10/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.6357\n",
      "Epoch 11/50\n",
      "20/20 [==============================] - 0s 23ms/step - loss: 0.5969\n",
      "Epoch 12/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 0.5560\n",
      "Epoch 13/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.5505\n",
      "Epoch 14/50\n",
      "20/20 [==============================] - 1s 28ms/step - loss: 0.5423\n",
      "Epoch 15/50\n",
      "20/20 [==============================] - 1s 26ms/step - loss: 0.5254\n",
      "Epoch 16/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.5066\n",
      "Epoch 17/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 0.5031\n",
      "Epoch 18/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.4925\n",
      "Epoch 19/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.4754\n",
      "Epoch 20/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.4643\n",
      "Epoch 21/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 0.4396\n",
      "Epoch 22/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.4415\n",
      "Epoch 23/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.4354\n",
      "Epoch 24/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.4150\n",
      "Epoch 25/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.4143\n",
      "Epoch 26/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 0.4077\n",
      "Epoch 27/50\n",
      "20/20 [==============================] - 1s 26ms/step - loss: 0.3924\n",
      "Epoch 28/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.4030\n",
      "Epoch 29/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.3783\n",
      "Epoch 30/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.3744\n",
      "Epoch 31/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.3702\n",
      "Epoch 32/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.3450\n",
      "Epoch 33/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.3633\n",
      "Epoch 34/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.3449\n",
      "Epoch 35/50\n",
      "20/20 [==============================] - 1s 32ms/step - loss: 0.3409\n",
      "Epoch 36/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.3336\n",
      "Epoch 37/50\n",
      "20/20 [==============================] - 0s 24ms/step - loss: 0.3308\n",
      "Epoch 38/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.3202\n",
      "Epoch 39/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.3117\n",
      "Epoch 40/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.3044\n",
      "Epoch 41/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.3094\n",
      "Epoch 42/50\n",
      "20/20 [==============================] - 0s 24ms/step - loss: 0.3066\n",
      "Epoch 43/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.3007\n",
      "Epoch 44/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 0.2862\n",
      "Epoch 45/50\n",
      "20/20 [==============================] - 0s 18ms/step - loss: 0.2930\n",
      "Epoch 46/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.2925\n",
      "Epoch 47/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.2806\n",
      "Epoch 48/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.2818\n",
      "Epoch 49/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.2646\n",
      "Epoch 50/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 0.2661\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A867756168> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A867756168> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "Learning rate set to 0.01791\n",
      "0:\tlearn: 0.6461217\ttotal: 3.65ms\tremaining: 3.65s\n",
      "1:\tlearn: 0.5950066\ttotal: 6.7ms\tremaining: 3.35s\n",
      "2:\tlearn: 0.5548099\ttotal: 9.34ms\tremaining: 3.1s\n",
      "3:\tlearn: 0.5161424\ttotal: 11.9ms\tremaining: 2.97s\n",
      "4:\tlearn: 0.4802768\ttotal: 14.4ms\tremaining: 2.87s\n",
      "5:\tlearn: 0.4444653\ttotal: 17.8ms\tremaining: 2.94s\n",
      "6:\tlearn: 0.4134718\ttotal: 20.5ms\tremaining: 2.91s\n",
      "7:\tlearn: 0.3821667\ttotal: 23.2ms\tremaining: 2.88s\n",
      "8:\tlearn: 0.3554573\ttotal: 26.1ms\tremaining: 2.88s\n",
      "9:\tlearn: 0.3288207\ttotal: 29.1ms\tremaining: 2.88s\n",
      "10:\tlearn: 0.3058822\ttotal: 32.9ms\tremaining: 2.96s\n",
      "11:\tlearn: 0.2847355\ttotal: 36.7ms\tremaining: 3.02s\n",
      "12:\tlearn: 0.2655214\ttotal: 40.5ms\tremaining: 3.08s\n",
      "13:\tlearn: 0.2448478\ttotal: 43.8ms\tremaining: 3.09s\n",
      "14:\tlearn: 0.2296467\ttotal: 48.1ms\tremaining: 3.15s\n",
      "15:\tlearn: 0.2146188\ttotal: 54.4ms\tremaining: 3.35s\n",
      "16:\tlearn: 0.1978419\ttotal: 59.4ms\tremaining: 3.44s\n",
      "17:\tlearn: 0.1857601\ttotal: 64.7ms\tremaining: 3.53s\n",
      "18:\tlearn: 0.1742183\ttotal: 72.9ms\tremaining: 3.77s\n",
      "19:\tlearn: 0.1629233\ttotal: 76.9ms\tremaining: 3.77s\n",
      "20:\tlearn: 0.1527317\ttotal: 80ms\tremaining: 3.73s\n",
      "21:\tlearn: 0.1429812\ttotal: 83.3ms\tremaining: 3.7s\n",
      "22:\tlearn: 0.1346778\ttotal: 86.1ms\tremaining: 3.66s\n",
      "23:\tlearn: 0.1262591\ttotal: 90.4ms\tremaining: 3.68s\n",
      "24:\tlearn: 0.1192918\ttotal: 93.6ms\tremaining: 3.65s\n",
      "25:\tlearn: 0.1121657\ttotal: 97.3ms\tremaining: 3.64s\n",
      "26:\tlearn: 0.1067364\ttotal: 101ms\tremaining: 3.63s\n",
      "27:\tlearn: 0.1013651\ttotal: 104ms\tremaining: 3.62s\n",
      "28:\tlearn: 0.0952671\ttotal: 107ms\tremaining: 3.59s\n",
      "29:\tlearn: 0.0905195\ttotal: 110ms\tremaining: 3.56s\n",
      "30:\tlearn: 0.0858219\ttotal: 114ms\tremaining: 3.56s\n",
      "31:\tlearn: 0.0813908\ttotal: 117ms\tremaining: 3.55s\n",
      "32:\tlearn: 0.0777070\ttotal: 121ms\tremaining: 3.54s\n",
      "33:\tlearn: 0.0738604\ttotal: 124ms\tremaining: 3.53s\n",
      "34:\tlearn: 0.0705827\ttotal: 128ms\tremaining: 3.54s\n",
      "35:\tlearn: 0.0674343\ttotal: 131ms\tremaining: 3.52s\n",
      "36:\tlearn: 0.0640180\ttotal: 135ms\tremaining: 3.5s\n",
      "37:\tlearn: 0.0613030\ttotal: 138ms\tremaining: 3.49s\n",
      "38:\tlearn: 0.0586896\ttotal: 142ms\tremaining: 3.51s\n",
      "39:\tlearn: 0.0560529\ttotal: 146ms\tremaining: 3.5s\n",
      "40:\tlearn: 0.0535653\ttotal: 149ms\tremaining: 3.49s\n",
      "41:\tlearn: 0.0514603\ttotal: 152ms\tremaining: 3.47s\n",
      "42:\tlearn: 0.0496316\ttotal: 155ms\tremaining: 3.45s\n",
      "43:\tlearn: 0.0477651\ttotal: 159ms\tremaining: 3.45s\n",
      "44:\tlearn: 0.0455107\ttotal: 162ms\tremaining: 3.44s\n",
      "45:\tlearn: 0.0439839\ttotal: 165ms\tremaining: 3.42s\n",
      "46:\tlearn: 0.0425360\ttotal: 168ms\tremaining: 3.42s\n",
      "47:\tlearn: 0.0409720\ttotal: 173ms\tremaining: 3.42s\n",
      "48:\tlearn: 0.0396077\ttotal: 176ms\tremaining: 3.42s\n",
      "49:\tlearn: 0.0382172\ttotal: 179ms\tremaining: 3.41s\n",
      "50:\tlearn: 0.0369630\ttotal: 183ms\tremaining: 3.4s\n",
      "51:\tlearn: 0.0356230\ttotal: 186ms\tremaining: 3.39s\n",
      "52:\tlearn: 0.0344986\ttotal: 189ms\tremaining: 3.38s\n",
      "53:\tlearn: 0.0332839\ttotal: 193ms\tremaining: 3.38s\n",
      "54:\tlearn: 0.0320613\ttotal: 196ms\tremaining: 3.36s\n",
      "55:\tlearn: 0.0311116\ttotal: 199ms\tremaining: 3.35s\n",
      "56:\tlearn: 0.0301839\ttotal: 203ms\tremaining: 3.35s\n",
      "57:\tlearn: 0.0293263\ttotal: 205ms\tremaining: 3.34s\n",
      "58:\tlearn: 0.0283885\ttotal: 208ms\tremaining: 3.32s\n",
      "59:\tlearn: 0.0275731\ttotal: 212ms\tremaining: 3.31s\n",
      "60:\tlearn: 0.0266830\ttotal: 214ms\tremaining: 3.29s\n",
      "61:\tlearn: 0.0258509\ttotal: 219ms\tremaining: 3.31s\n",
      "62:\tlearn: 0.0252778\ttotal: 222ms\tremaining: 3.3s\n",
      "63:\tlearn: 0.0244870\ttotal: 225ms\tremaining: 3.3s\n",
      "64:\tlearn: 0.0236597\ttotal: 229ms\tremaining: 3.3s\n",
      "65:\tlearn: 0.0229436\ttotal: 234ms\tremaining: 3.31s\n",
      "66:\tlearn: 0.0224714\ttotal: 237ms\tremaining: 3.31s\n",
      "67:\tlearn: 0.0219143\ttotal: 241ms\tremaining: 3.3s\n",
      "68:\tlearn: 0.0213762\ttotal: 245ms\tremaining: 3.3s\n",
      "69:\tlearn: 0.0207532\ttotal: 249ms\tremaining: 3.31s\n",
      "70:\tlearn: 0.0202671\ttotal: 252ms\tremaining: 3.3s\n",
      "71:\tlearn: 0.0197789\ttotal: 255ms\tremaining: 3.29s\n",
      "72:\tlearn: 0.0192321\ttotal: 258ms\tremaining: 3.28s\n",
      "73:\tlearn: 0.0188381\ttotal: 263ms\tremaining: 3.29s\n",
      "74:\tlearn: 0.0183784\ttotal: 268ms\tremaining: 3.3s\n",
      "75:\tlearn: 0.0180346\ttotal: 271ms\tremaining: 3.29s\n",
      "76:\tlearn: 0.0176048\ttotal: 274ms\tremaining: 3.28s\n",
      "77:\tlearn: 0.0172765\ttotal: 278ms\tremaining: 3.28s\n",
      "78:\tlearn: 0.0169440\ttotal: 281ms\tremaining: 3.27s\n",
      "79:\tlearn: 0.0165940\ttotal: 284ms\tremaining: 3.27s\n",
      "80:\tlearn: 0.0164028\ttotal: 287ms\tremaining: 3.26s\n",
      "81:\tlearn: 0.0161411\ttotal: 291ms\tremaining: 3.26s\n",
      "82:\tlearn: 0.0157707\ttotal: 294ms\tremaining: 3.25s\n",
      "83:\tlearn: 0.0154920\ttotal: 298ms\tremaining: 3.25s\n",
      "84:\tlearn: 0.0152007\ttotal: 301ms\tremaining: 3.24s\n",
      "85:\tlearn: 0.0149866\ttotal: 305ms\tremaining: 3.24s\n",
      "86:\tlearn: 0.0147403\ttotal: 309ms\tremaining: 3.24s\n",
      "87:\tlearn: 0.0144723\ttotal: 312ms\tremaining: 3.23s\n",
      "88:\tlearn: 0.0142819\ttotal: 315ms\tremaining: 3.23s\n",
      "89:\tlearn: 0.0140872\ttotal: 319ms\tremaining: 3.23s\n",
      "90:\tlearn: 0.0139782\ttotal: 322ms\tremaining: 3.22s\n",
      "91:\tlearn: 0.0137820\ttotal: 326ms\tremaining: 3.22s\n",
      "92:\tlearn: 0.0134901\ttotal: 329ms\tremaining: 3.21s\n",
      "93:\tlearn: 0.0132282\ttotal: 333ms\tremaining: 3.21s\n",
      "94:\tlearn: 0.0129784\ttotal: 339ms\tremaining: 3.23s\n",
      "95:\tlearn: 0.0127771\ttotal: 343ms\tremaining: 3.23s\n",
      "96:\tlearn: 0.0125351\ttotal: 348ms\tremaining: 3.24s\n",
      "97:\tlearn: 0.0123391\ttotal: 353ms\tremaining: 3.24s\n",
      "98:\tlearn: 0.0121300\ttotal: 356ms\tremaining: 3.24s\n",
      "99:\tlearn: 0.0119334\ttotal: 360ms\tremaining: 3.24s\n",
      "100:\tlearn: 0.0116888\ttotal: 364ms\tremaining: 3.24s\n",
      "101:\tlearn: 0.0114978\ttotal: 368ms\tremaining: 3.24s\n",
      "102:\tlearn: 0.0113178\ttotal: 372ms\tremaining: 3.24s\n",
      "103:\tlearn: 0.0111442\ttotal: 377ms\tremaining: 3.25s\n",
      "104:\tlearn: 0.0109947\ttotal: 381ms\tremaining: 3.25s\n",
      "105:\tlearn: 0.0108183\ttotal: 385ms\tremaining: 3.25s\n",
      "106:\tlearn: 0.0106409\ttotal: 388ms\tremaining: 3.24s\n",
      "107:\tlearn: 0.0104600\ttotal: 392ms\tremaining: 3.24s\n",
      "108:\tlearn: 0.0103308\ttotal: 395ms\tremaining: 3.23s\n",
      "109:\tlearn: 0.0101684\ttotal: 399ms\tremaining: 3.23s\n",
      "110:\tlearn: 0.0100558\ttotal: 402ms\tremaining: 3.22s\n",
      "111:\tlearn: 0.0099039\ttotal: 405ms\tremaining: 3.21s\n",
      "112:\tlearn: 0.0097588\ttotal: 409ms\tremaining: 3.21s\n",
      "113:\tlearn: 0.0096115\ttotal: 413ms\tremaining: 3.21s\n",
      "114:\tlearn: 0.0094947\ttotal: 416ms\tremaining: 3.2s\n",
      "115:\tlearn: 0.0093706\ttotal: 419ms\tremaining: 3.19s\n",
      "116:\tlearn: 0.0092335\ttotal: 422ms\tremaining: 3.19s\n",
      "117:\tlearn: 0.0090821\ttotal: 426ms\tremaining: 3.18s\n",
      "118:\tlearn: 0.0089640\ttotal: 429ms\tremaining: 3.17s\n",
      "119:\tlearn: 0.0088610\ttotal: 432ms\tremaining: 3.17s\n",
      "120:\tlearn: 0.0087609\ttotal: 435ms\tremaining: 3.16s\n",
      "121:\tlearn: 0.0086640\ttotal: 438ms\tremaining: 3.15s\n",
      "122:\tlearn: 0.0085744\ttotal: 442ms\tremaining: 3.15s\n",
      "123:\tlearn: 0.0084668\ttotal: 444ms\tremaining: 3.14s\n",
      "124:\tlearn: 0.0083500\ttotal: 450ms\tremaining: 3.15s\n",
      "125:\tlearn: 0.0082402\ttotal: 453ms\tremaining: 3.14s\n",
      "126:\tlearn: 0.0081310\ttotal: 456ms\tremaining: 3.14s\n",
      "127:\tlearn: 0.0080414\ttotal: 459ms\tremaining: 3.13s\n",
      "128:\tlearn: 0.0079518\ttotal: 462ms\tremaining: 3.12s\n",
      "129:\tlearn: 0.0078627\ttotal: 466ms\tremaining: 3.12s\n",
      "130:\tlearn: 0.0077817\ttotal: 471ms\tremaining: 3.12s\n",
      "131:\tlearn: 0.0076997\ttotal: 474ms\tremaining: 3.11s\n",
      "132:\tlearn: 0.0076072\ttotal: 477ms\tremaining: 3.11s\n",
      "133:\tlearn: 0.0075382\ttotal: 480ms\tremaining: 3.1s\n",
      "134:\tlearn: 0.0074525\ttotal: 483ms\tremaining: 3.09s\n",
      "135:\tlearn: 0.0073856\ttotal: 486ms\tremaining: 3.09s\n",
      "136:\tlearn: 0.0072849\ttotal: 489ms\tremaining: 3.08s\n",
      "137:\tlearn: 0.0072002\ttotal: 493ms\tremaining: 3.08s\n",
      "138:\tlearn: 0.0071420\ttotal: 496ms\tremaining: 3.07s\n",
      "139:\tlearn: 0.0071111\ttotal: 500ms\tremaining: 3.07s\n",
      "140:\tlearn: 0.0070648\ttotal: 503ms\tremaining: 3.06s\n",
      "141:\tlearn: 0.0069774\ttotal: 506ms\tremaining: 3.06s\n",
      "142:\tlearn: 0.0069591\ttotal: 509ms\tremaining: 3.05s\n",
      "143:\tlearn: 0.0068966\ttotal: 512ms\tremaining: 3.04s\n",
      "144:\tlearn: 0.0068442\ttotal: 516ms\tremaining: 3.04s\n",
      "145:\tlearn: 0.0067649\ttotal: 520ms\tremaining: 3.04s\n",
      "146:\tlearn: 0.0067086\ttotal: 523ms\tremaining: 3.03s\n",
      "147:\tlearn: 0.0066452\ttotal: 526ms\tremaining: 3.03s\n",
      "148:\tlearn: 0.0065878\ttotal: 529ms\tremaining: 3.02s\n",
      "149:\tlearn: 0.0065093\ttotal: 532ms\tremaining: 3.02s\n",
      "150:\tlearn: 0.0064556\ttotal: 535ms\tremaining: 3.01s\n",
      "151:\tlearn: 0.0064118\ttotal: 538ms\tremaining: 3s\n",
      "152:\tlearn: 0.0063419\ttotal: 541ms\tremaining: 2.99s\n",
      "153:\tlearn: 0.0062819\ttotal: 544ms\tremaining: 2.99s\n",
      "154:\tlearn: 0.0062223\ttotal: 547ms\tremaining: 2.98s\n",
      "155:\tlearn: 0.0061812\ttotal: 550ms\tremaining: 2.98s\n",
      "156:\tlearn: 0.0061223\ttotal: 553ms\tremaining: 2.97s\n",
      "157:\tlearn: 0.0060424\ttotal: 556ms\tremaining: 2.96s\n",
      "158:\tlearn: 0.0060045\ttotal: 559ms\tremaining: 2.96s\n",
      "159:\tlearn: 0.0059455\ttotal: 562ms\tremaining: 2.95s\n",
      "160:\tlearn: 0.0058972\ttotal: 565ms\tremaining: 2.94s\n",
      "161:\tlearn: 0.0058565\ttotal: 568ms\tremaining: 2.94s\n",
      "162:\tlearn: 0.0058077\ttotal: 571ms\tremaining: 2.93s\n",
      "163:\tlearn: 0.0057498\ttotal: 574ms\tremaining: 2.93s\n",
      "164:\tlearn: 0.0057000\ttotal: 578ms\tremaining: 2.92s\n",
      "165:\tlearn: 0.0056623\ttotal: 580ms\tremaining: 2.92s\n",
      "166:\tlearn: 0.0056201\ttotal: 583ms\tremaining: 2.91s\n",
      "167:\tlearn: 0.0055742\ttotal: 587ms\tremaining: 2.9s\n",
      "168:\tlearn: 0.0055037\ttotal: 590ms\tremaining: 2.9s\n",
      "169:\tlearn: 0.0054638\ttotal: 593ms\tremaining: 2.9s\n",
      "170:\tlearn: 0.0054105\ttotal: 596ms\tremaining: 2.89s\n",
      "171:\tlearn: 0.0053827\ttotal: 599ms\tremaining: 2.88s\n",
      "172:\tlearn: 0.0053608\ttotal: 603ms\tremaining: 2.88s\n",
      "173:\tlearn: 0.0053169\ttotal: 607ms\tremaining: 2.88s\n",
      "174:\tlearn: 0.0052591\ttotal: 610ms\tremaining: 2.87s\n",
      "175:\tlearn: 0.0051819\ttotal: 613ms\tremaining: 2.87s\n",
      "176:\tlearn: 0.0051586\ttotal: 616ms\tremaining: 2.87s\n",
      "177:\tlearn: 0.0051073\ttotal: 623ms\tremaining: 2.88s\n",
      "178:\tlearn: 0.0050523\ttotal: 628ms\tremaining: 2.88s\n",
      "179:\tlearn: 0.0050050\ttotal: 632ms\tremaining: 2.88s\n",
      "180:\tlearn: 0.0049829\ttotal: 639ms\tremaining: 2.89s\n",
      "181:\tlearn: 0.0049511\ttotal: 642ms\tremaining: 2.89s\n",
      "182:\tlearn: 0.0049023\ttotal: 646ms\tremaining: 2.88s\n",
      "183:\tlearn: 0.0048715\ttotal: 650ms\tremaining: 2.88s\n",
      "184:\tlearn: 0.0048214\ttotal: 654ms\tremaining: 2.88s\n",
      "185:\tlearn: 0.0047963\ttotal: 658ms\tremaining: 2.88s\n",
      "186:\tlearn: 0.0047620\ttotal: 661ms\tremaining: 2.88s\n",
      "187:\tlearn: 0.0047309\ttotal: 665ms\tremaining: 2.87s\n",
      "188:\tlearn: 0.0046852\ttotal: 669ms\tremaining: 2.87s\n",
      "189:\tlearn: 0.0046441\ttotal: 673ms\tremaining: 2.87s\n",
      "190:\tlearn: 0.0046138\ttotal: 678ms\tremaining: 2.87s\n",
      "191:\tlearn: 0.0045810\ttotal: 682ms\tremaining: 2.87s\n",
      "192:\tlearn: 0.0045506\ttotal: 686ms\tremaining: 2.87s\n",
      "193:\tlearn: 0.0045148\ttotal: 690ms\tremaining: 2.87s\n",
      "194:\tlearn: 0.0044906\ttotal: 695ms\tremaining: 2.87s\n",
      "195:\tlearn: 0.0044644\ttotal: 699ms\tremaining: 2.87s\n",
      "196:\tlearn: 0.0044304\ttotal: 703ms\tremaining: 2.87s\n",
      "197:\tlearn: 0.0043970\ttotal: 708ms\tremaining: 2.87s\n",
      "198:\tlearn: 0.0043561\ttotal: 713ms\tremaining: 2.87s\n",
      "199:\tlearn: 0.0043354\ttotal: 716ms\tremaining: 2.86s\n",
      "200:\tlearn: 0.0043123\ttotal: 720ms\tremaining: 2.86s\n",
      "201:\tlearn: 0.0042882\ttotal: 723ms\tremaining: 2.85s\n",
      "202:\tlearn: 0.0042589\ttotal: 727ms\tremaining: 2.85s\n",
      "203:\tlearn: 0.0042307\ttotal: 730ms\tremaining: 2.85s\n",
      "204:\tlearn: 0.0042081\ttotal: 734ms\tremaining: 2.85s\n",
      "205:\tlearn: 0.0041718\ttotal: 737ms\tremaining: 2.84s\n",
      "206:\tlearn: 0.0041465\ttotal: 741ms\tremaining: 2.84s\n",
      "207:\tlearn: 0.0041199\ttotal: 746ms\tremaining: 2.84s\n",
      "208:\tlearn: 0.0040946\ttotal: 750ms\tremaining: 2.84s\n",
      "209:\tlearn: 0.0040683\ttotal: 754ms\tremaining: 2.83s\n",
      "210:\tlearn: 0.0040489\ttotal: 758ms\tremaining: 2.83s\n",
      "211:\tlearn: 0.0040218\ttotal: 762ms\tremaining: 2.83s\n",
      "212:\tlearn: 0.0039992\ttotal: 765ms\tremaining: 2.83s\n",
      "213:\tlearn: 0.0039776\ttotal: 769ms\tremaining: 2.82s\n",
      "214:\tlearn: 0.0039519\ttotal: 773ms\tremaining: 2.82s\n",
      "215:\tlearn: 0.0039305\ttotal: 776ms\tremaining: 2.82s\n",
      "216:\tlearn: 0.0039088\ttotal: 780ms\tremaining: 2.81s\n",
      "217:\tlearn: 0.0038808\ttotal: 783ms\tremaining: 2.81s\n",
      "218:\tlearn: 0.0038639\ttotal: 788ms\tremaining: 2.81s\n",
      "219:\tlearn: 0.0038380\ttotal: 791ms\tremaining: 2.8s\n",
      "220:\tlearn: 0.0038075\ttotal: 795ms\tremaining: 2.8s\n",
      "221:\tlearn: 0.0037827\ttotal: 798ms\tremaining: 2.8s\n",
      "222:\tlearn: 0.0037597\ttotal: 802ms\tremaining: 2.79s\n",
      "223:\tlearn: 0.0037377\ttotal: 806ms\tremaining: 2.79s\n",
      "224:\tlearn: 0.0037103\ttotal: 809ms\tremaining: 2.79s\n",
      "225:\tlearn: 0.0036851\ttotal: 813ms\tremaining: 2.78s\n",
      "226:\tlearn: 0.0036574\ttotal: 817ms\tremaining: 2.78s\n",
      "227:\tlearn: 0.0036320\ttotal: 821ms\tremaining: 2.78s\n",
      "228:\tlearn: 0.0036097\ttotal: 825ms\tremaining: 2.78s\n",
      "229:\tlearn: 0.0035873\ttotal: 828ms\tremaining: 2.77s\n",
      "230:\tlearn: 0.0035691\ttotal: 832ms\tremaining: 2.77s\n",
      "231:\tlearn: 0.0035402\ttotal: 835ms\tremaining: 2.77s\n",
      "232:\tlearn: 0.0035196\ttotal: 839ms\tremaining: 2.76s\n",
      "233:\tlearn: 0.0035068\ttotal: 843ms\tremaining: 2.76s\n",
      "234:\tlearn: 0.0034835\ttotal: 847ms\tremaining: 2.76s\n",
      "235:\tlearn: 0.0034620\ttotal: 850ms\tremaining: 2.75s\n",
      "236:\tlearn: 0.0034389\ttotal: 854ms\tremaining: 2.75s\n",
      "237:\tlearn: 0.0034078\ttotal: 858ms\tremaining: 2.75s\n",
      "238:\tlearn: 0.0033902\ttotal: 861ms\tremaining: 2.74s\n",
      "239:\tlearn: 0.0033719\ttotal: 864ms\tremaining: 2.74s\n",
      "240:\tlearn: 0.0033578\ttotal: 867ms\tremaining: 2.73s\n",
      "241:\tlearn: 0.0033367\ttotal: 870ms\tremaining: 2.73s\n",
      "242:\tlearn: 0.0033172\ttotal: 874ms\tremaining: 2.72s\n",
      "243:\tlearn: 0.0033010\ttotal: 877ms\tremaining: 2.72s\n",
      "244:\tlearn: 0.0032786\ttotal: 880ms\tremaining: 2.71s\n",
      "245:\tlearn: 0.0032534\ttotal: 885ms\tremaining: 2.71s\n",
      "246:\tlearn: 0.0032454\ttotal: 891ms\tremaining: 2.72s\n",
      "247:\tlearn: 0.0032242\ttotal: 896ms\tremaining: 2.71s\n",
      "248:\tlearn: 0.0031981\ttotal: 900ms\tremaining: 2.71s\n",
      "249:\tlearn: 0.0031771\ttotal: 905ms\tremaining: 2.71s\n",
      "250:\tlearn: 0.0031569\ttotal: 909ms\tremaining: 2.71s\n",
      "251:\tlearn: 0.0031367\ttotal: 913ms\tremaining: 2.71s\n",
      "252:\tlearn: 0.0031198\ttotal: 917ms\tremaining: 2.71s\n",
      "253:\tlearn: 0.0031028\ttotal: 921ms\tremaining: 2.7s\n",
      "254:\tlearn: 0.0030886\ttotal: 925ms\tremaining: 2.7s\n",
      "255:\tlearn: 0.0030646\ttotal: 928ms\tremaining: 2.7s\n",
      "256:\tlearn: 0.0030462\ttotal: 932ms\tremaining: 2.69s\n",
      "257:\tlearn: 0.0030357\ttotal: 936ms\tremaining: 2.69s\n",
      "258:\tlearn: 0.0030190\ttotal: 941ms\tremaining: 2.69s\n",
      "259:\tlearn: 0.0030084\ttotal: 944ms\tremaining: 2.69s\n",
      "260:\tlearn: 0.0029954\ttotal: 949ms\tremaining: 2.69s\n",
      "261:\tlearn: 0.0029785\ttotal: 953ms\tremaining: 2.68s\n",
      "262:\tlearn: 0.0029595\ttotal: 956ms\tremaining: 2.68s\n",
      "263:\tlearn: 0.0029475\ttotal: 961ms\tremaining: 2.68s\n",
      "264:\tlearn: 0.0029330\ttotal: 965ms\tremaining: 2.68s\n",
      "265:\tlearn: 0.0029243\ttotal: 969ms\tremaining: 2.67s\n",
      "266:\tlearn: 0.0029052\ttotal: 973ms\tremaining: 2.67s\n",
      "267:\tlearn: 0.0028885\ttotal: 977ms\tremaining: 2.67s\n",
      "268:\tlearn: 0.0028696\ttotal: 982ms\tremaining: 2.67s\n",
      "269:\tlearn: 0.0028561\ttotal: 987ms\tremaining: 2.67s\n",
      "270:\tlearn: 0.0028382\ttotal: 991ms\tremaining: 2.66s\n",
      "271:\tlearn: 0.0028244\ttotal: 996ms\tremaining: 2.67s\n",
      "272:\tlearn: 0.0028106\ttotal: 999ms\tremaining: 2.66s\n",
      "273:\tlearn: 0.0027951\ttotal: 1s\tremaining: 2.66s\n",
      "274:\tlearn: 0.0027853\ttotal: 1.01s\tremaining: 2.65s\n",
      "275:\tlearn: 0.0027693\ttotal: 1.01s\tremaining: 2.65s\n",
      "276:\tlearn: 0.0027575\ttotal: 1.01s\tremaining: 2.65s\n",
      "277:\tlearn: 0.0027435\ttotal: 1.02s\tremaining: 2.64s\n",
      "278:\tlearn: 0.0027303\ttotal: 1.02s\tremaining: 2.64s\n",
      "279:\tlearn: 0.0027197\ttotal: 1.02s\tremaining: 2.63s\n",
      "280:\tlearn: 0.0027077\ttotal: 1.03s\tremaining: 2.63s\n",
      "281:\tlearn: 0.0026974\ttotal: 1.03s\tremaining: 2.63s\n",
      "282:\tlearn: 0.0026870\ttotal: 1.03s\tremaining: 2.62s\n",
      "283:\tlearn: 0.0026729\ttotal: 1.04s\tremaining: 2.62s\n",
      "284:\tlearn: 0.0026587\ttotal: 1.04s\tremaining: 2.62s\n",
      "285:\tlearn: 0.0026432\ttotal: 1.05s\tremaining: 2.62s\n",
      "286:\tlearn: 0.0026345\ttotal: 1.05s\tremaining: 2.61s\n",
      "287:\tlearn: 0.0026241\ttotal: 1.05s\tremaining: 2.61s\n",
      "288:\tlearn: 0.0026145\ttotal: 1.06s\tremaining: 2.61s\n",
      "289:\tlearn: 0.0026061\ttotal: 1.06s\tremaining: 2.6s\n",
      "290:\tlearn: 0.0025907\ttotal: 1.07s\tremaining: 2.6s\n",
      "291:\tlearn: 0.0025790\ttotal: 1.07s\tremaining: 2.59s\n",
      "292:\tlearn: 0.0025689\ttotal: 1.07s\tremaining: 2.59s\n",
      "293:\tlearn: 0.0025510\ttotal: 1.08s\tremaining: 2.58s\n",
      "294:\tlearn: 0.0025429\ttotal: 1.08s\tremaining: 2.58s\n",
      "295:\tlearn: 0.0025259\ttotal: 1.08s\tremaining: 2.58s\n",
      "296:\tlearn: 0.0025154\ttotal: 1.09s\tremaining: 2.57s\n",
      "297:\tlearn: 0.0025000\ttotal: 1.09s\tremaining: 2.57s\n",
      "298:\tlearn: 0.0024812\ttotal: 1.09s\tremaining: 2.56s\n",
      "299:\tlearn: 0.0024708\ttotal: 1.1s\tremaining: 2.56s\n",
      "300:\tlearn: 0.0024651\ttotal: 1.1s\tremaining: 2.56s\n",
      "301:\tlearn: 0.0024497\ttotal: 1.11s\tremaining: 2.56s\n",
      "302:\tlearn: 0.0024383\ttotal: 1.11s\tremaining: 2.56s\n",
      "303:\tlearn: 0.0024283\ttotal: 1.11s\tremaining: 2.55s\n",
      "304:\tlearn: 0.0024164\ttotal: 1.12s\tremaining: 2.54s\n",
      "305:\tlearn: 0.0024031\ttotal: 1.12s\tremaining: 2.54s\n",
      "306:\tlearn: 0.0023870\ttotal: 1.12s\tremaining: 2.54s\n",
      "307:\tlearn: 0.0023828\ttotal: 1.13s\tremaining: 2.53s\n",
      "308:\tlearn: 0.0023771\ttotal: 1.13s\tremaining: 2.53s\n",
      "309:\tlearn: 0.0023665\ttotal: 1.13s\tremaining: 2.52s\n",
      "310:\tlearn: 0.0023593\ttotal: 1.14s\tremaining: 2.52s\n",
      "311:\tlearn: 0.0023514\ttotal: 1.14s\tremaining: 2.51s\n",
      "312:\tlearn: 0.0023331\ttotal: 1.14s\tremaining: 2.51s\n",
      "313:\tlearn: 0.0023235\ttotal: 1.15s\tremaining: 2.5s\n",
      "314:\tlearn: 0.0023163\ttotal: 1.15s\tremaining: 2.5s\n",
      "315:\tlearn: 0.0023078\ttotal: 1.15s\tremaining: 2.5s\n",
      "316:\tlearn: 0.0022945\ttotal: 1.16s\tremaining: 2.49s\n",
      "317:\tlearn: 0.0022870\ttotal: 1.16s\tremaining: 2.49s\n",
      "318:\tlearn: 0.0022798\ttotal: 1.16s\tremaining: 2.48s\n",
      "319:\tlearn: 0.0022683\ttotal: 1.17s\tremaining: 2.49s\n",
      "320:\tlearn: 0.0022623\ttotal: 1.18s\tremaining: 2.49s\n",
      "321:\tlearn: 0.0022523\ttotal: 1.18s\tremaining: 2.49s\n",
      "322:\tlearn: 0.0022432\ttotal: 1.19s\tremaining: 2.48s\n",
      "323:\tlearn: 0.0022312\ttotal: 1.19s\tremaining: 2.48s\n",
      "324:\tlearn: 0.0022228\ttotal: 1.19s\tremaining: 2.48s\n",
      "325:\tlearn: 0.0022145\ttotal: 1.2s\tremaining: 2.48s\n",
      "326:\tlearn: 0.0022060\ttotal: 1.2s\tremaining: 2.47s\n",
      "327:\tlearn: 0.0021946\ttotal: 1.21s\tremaining: 2.47s\n",
      "328:\tlearn: 0.0021865\ttotal: 1.21s\tremaining: 2.47s\n",
      "329:\tlearn: 0.0021771\ttotal: 1.21s\tremaining: 2.46s\n",
      "330:\tlearn: 0.0021695\ttotal: 1.22s\tremaining: 2.46s\n",
      "331:\tlearn: 0.0021610\ttotal: 1.22s\tremaining: 2.46s\n",
      "332:\tlearn: 0.0021522\ttotal: 1.23s\tremaining: 2.46s\n",
      "333:\tlearn: 0.0021454\ttotal: 1.23s\tremaining: 2.46s\n",
      "334:\tlearn: 0.0021380\ttotal: 1.25s\tremaining: 2.49s\n",
      "335:\tlearn: 0.0021236\ttotal: 1.26s\tremaining: 2.5s\n",
      "336:\tlearn: 0.0021155\ttotal: 1.27s\tremaining: 2.5s\n",
      "337:\tlearn: 0.0021060\ttotal: 1.28s\tremaining: 2.5s\n",
      "338:\tlearn: 0.0020968\ttotal: 1.28s\tremaining: 2.5s\n",
      "339:\tlearn: 0.0020896\ttotal: 1.29s\tremaining: 2.5s\n",
      "340:\tlearn: 0.0020813\ttotal: 1.29s\tremaining: 2.49s\n",
      "341:\tlearn: 0.0020726\ttotal: 1.29s\tremaining: 2.49s\n",
      "342:\tlearn: 0.0020634\ttotal: 1.3s\tremaining: 2.49s\n",
      "343:\tlearn: 0.0020559\ttotal: 1.3s\tremaining: 2.49s\n",
      "344:\tlearn: 0.0020530\ttotal: 1.31s\tremaining: 2.49s\n",
      "345:\tlearn: 0.0020397\ttotal: 1.31s\tremaining: 2.49s\n",
      "346:\tlearn: 0.0020314\ttotal: 1.32s\tremaining: 2.48s\n",
      "347:\tlearn: 0.0020254\ttotal: 1.33s\tremaining: 2.48s\n",
      "348:\tlearn: 0.0020175\ttotal: 1.33s\tremaining: 2.48s\n",
      "349:\tlearn: 0.0020125\ttotal: 1.33s\tremaining: 2.48s\n",
      "350:\tlearn: 0.0020070\ttotal: 1.34s\tremaining: 2.47s\n",
      "351:\tlearn: 0.0020048\ttotal: 1.34s\tremaining: 2.47s\n",
      "352:\tlearn: 0.0019967\ttotal: 1.35s\tremaining: 2.47s\n",
      "353:\tlearn: 0.0019937\ttotal: 1.35s\tremaining: 2.46s\n",
      "354:\tlearn: 0.0019870\ttotal: 1.35s\tremaining: 2.46s\n",
      "355:\tlearn: 0.0019824\ttotal: 1.36s\tremaining: 2.46s\n",
      "356:\tlearn: 0.0019743\ttotal: 1.36s\tremaining: 2.46s\n",
      "357:\tlearn: 0.0019689\ttotal: 1.37s\tremaining: 2.46s\n",
      "358:\tlearn: 0.0019616\ttotal: 1.38s\tremaining: 2.46s\n",
      "359:\tlearn: 0.0019538\ttotal: 1.38s\tremaining: 2.45s\n",
      "360:\tlearn: 0.0019498\ttotal: 1.39s\tremaining: 2.45s\n",
      "361:\tlearn: 0.0019454\ttotal: 1.39s\tremaining: 2.45s\n",
      "362:\tlearn: 0.0019363\ttotal: 1.39s\tremaining: 2.45s\n",
      "363:\tlearn: 0.0019326\ttotal: 1.4s\tremaining: 2.45s\n",
      "364:\tlearn: 0.0019251\ttotal: 1.41s\tremaining: 2.45s\n",
      "365:\tlearn: 0.0019170\ttotal: 1.41s\tremaining: 2.45s\n",
      "366:\tlearn: 0.0019112\ttotal: 1.42s\tremaining: 2.44s\n",
      "367:\tlearn: 0.0019045\ttotal: 1.42s\tremaining: 2.44s\n",
      "368:\tlearn: 0.0018981\ttotal: 1.43s\tremaining: 2.44s\n",
      "369:\tlearn: 0.0018894\ttotal: 1.43s\tremaining: 2.43s\n",
      "370:\tlearn: 0.0018855\ttotal: 1.44s\tremaining: 2.44s\n",
      "371:\tlearn: 0.0018744\ttotal: 1.44s\tremaining: 2.43s\n",
      "372:\tlearn: 0.0018675\ttotal: 1.45s\tremaining: 2.43s\n",
      "373:\tlearn: 0.0018634\ttotal: 1.45s\tremaining: 2.43s\n",
      "374:\tlearn: 0.0018591\ttotal: 1.46s\tremaining: 2.43s\n",
      "375:\tlearn: 0.0018496\ttotal: 1.47s\tremaining: 2.43s\n",
      "376:\tlearn: 0.0018444\ttotal: 1.47s\tremaining: 2.43s\n",
      "377:\tlearn: 0.0018415\ttotal: 1.47s\tremaining: 2.42s\n",
      "378:\tlearn: 0.0018359\ttotal: 1.48s\tremaining: 2.42s\n",
      "379:\tlearn: 0.0018296\ttotal: 1.48s\tremaining: 2.42s\n",
      "380:\tlearn: 0.0018236\ttotal: 1.49s\tremaining: 2.41s\n",
      "381:\tlearn: 0.0018194\ttotal: 1.49s\tremaining: 2.41s\n",
      "382:\tlearn: 0.0018137\ttotal: 1.49s\tremaining: 2.41s\n",
      "383:\tlearn: 0.0018098\ttotal: 1.5s\tremaining: 2.4s\n",
      "384:\tlearn: 0.0018039\ttotal: 1.5s\tremaining: 2.4s\n",
      "385:\tlearn: 0.0017945\ttotal: 1.51s\tremaining: 2.4s\n",
      "386:\tlearn: 0.0017864\ttotal: 1.51s\tremaining: 2.39s\n",
      "387:\tlearn: 0.0017816\ttotal: 1.51s\tremaining: 2.39s\n",
      "388:\tlearn: 0.0017761\ttotal: 1.52s\tremaining: 2.39s\n",
      "389:\tlearn: 0.0017695\ttotal: 1.52s\tremaining: 2.38s\n",
      "390:\tlearn: 0.0017654\ttotal: 1.53s\tremaining: 2.38s\n",
      "391:\tlearn: 0.0017604\ttotal: 1.53s\tremaining: 2.38s\n",
      "392:\tlearn: 0.0017550\ttotal: 1.54s\tremaining: 2.37s\n",
      "393:\tlearn: 0.0017510\ttotal: 1.54s\tremaining: 2.37s\n",
      "394:\tlearn: 0.0017442\ttotal: 1.54s\tremaining: 2.36s\n",
      "395:\tlearn: 0.0017388\ttotal: 1.55s\tremaining: 2.36s\n",
      "396:\tlearn: 0.0017355\ttotal: 1.55s\tremaining: 2.36s\n",
      "397:\tlearn: 0.0017299\ttotal: 1.55s\tremaining: 2.35s\n",
      "398:\tlearn: 0.0017259\ttotal: 1.56s\tremaining: 2.35s\n",
      "399:\tlearn: 0.0017227\ttotal: 1.56s\tremaining: 2.34s\n",
      "400:\tlearn: 0.0017195\ttotal: 1.57s\tremaining: 2.34s\n",
      "401:\tlearn: 0.0017163\ttotal: 1.57s\tremaining: 2.34s\n",
      "402:\tlearn: 0.0017103\ttotal: 1.57s\tremaining: 2.33s\n",
      "403:\tlearn: 0.0017061\ttotal: 1.58s\tremaining: 2.33s\n",
      "404:\tlearn: 0.0017004\ttotal: 1.58s\tremaining: 2.33s\n",
      "405:\tlearn: 0.0016948\ttotal: 1.59s\tremaining: 2.33s\n",
      "406:\tlearn: 0.0016900\ttotal: 1.6s\tremaining: 2.33s\n",
      "407:\tlearn: 0.0016845\ttotal: 1.61s\tremaining: 2.33s\n",
      "408:\tlearn: 0.0016784\ttotal: 1.61s\tremaining: 2.33s\n",
      "409:\tlearn: 0.0016745\ttotal: 1.62s\tremaining: 2.33s\n",
      "410:\tlearn: 0.0016696\ttotal: 1.62s\tremaining: 2.33s\n",
      "411:\tlearn: 0.0016647\ttotal: 1.63s\tremaining: 2.33s\n",
      "412:\tlearn: 0.0016601\ttotal: 1.64s\tremaining: 2.32s\n",
      "413:\tlearn: 0.0016562\ttotal: 1.64s\tremaining: 2.33s\n",
      "414:\tlearn: 0.0016507\ttotal: 1.65s\tremaining: 2.32s\n",
      "415:\tlearn: 0.0016488\ttotal: 1.65s\tremaining: 2.32s\n",
      "416:\tlearn: 0.0016457\ttotal: 1.66s\tremaining: 2.31s\n",
      "417:\tlearn: 0.0016390\ttotal: 1.66s\tremaining: 2.31s\n",
      "418:\tlearn: 0.0016348\ttotal: 1.66s\tremaining: 2.31s\n",
      "419:\tlearn: 0.0016322\ttotal: 1.67s\tremaining: 2.3s\n",
      "420:\tlearn: 0.0016281\ttotal: 1.67s\tremaining: 2.3s\n",
      "421:\tlearn: 0.0016247\ttotal: 1.68s\tremaining: 2.3s\n",
      "422:\tlearn: 0.0016203\ttotal: 1.68s\tremaining: 2.29s\n",
      "423:\tlearn: 0.0016181\ttotal: 1.69s\tremaining: 2.29s\n",
      "424:\tlearn: 0.0016119\ttotal: 1.69s\tremaining: 2.29s\n",
      "425:\tlearn: 0.0016099\ttotal: 1.69s\tremaining: 2.28s\n",
      "426:\tlearn: 0.0016028\ttotal: 1.7s\tremaining: 2.28s\n",
      "427:\tlearn: 0.0015981\ttotal: 1.7s\tremaining: 2.27s\n",
      "428:\tlearn: 0.0015950\ttotal: 1.71s\tremaining: 2.27s\n",
      "429:\tlearn: 0.0015902\ttotal: 1.71s\tremaining: 2.27s\n",
      "430:\tlearn: 0.0015868\ttotal: 1.71s\tremaining: 2.26s\n",
      "431:\tlearn: 0.0015840\ttotal: 1.72s\tremaining: 2.26s\n",
      "432:\tlearn: 0.0015815\ttotal: 1.72s\tremaining: 2.25s\n",
      "433:\tlearn: 0.0015795\ttotal: 1.73s\tremaining: 2.25s\n",
      "434:\tlearn: 0.0015732\ttotal: 1.73s\tremaining: 2.25s\n",
      "435:\tlearn: 0.0015680\ttotal: 1.73s\tremaining: 2.24s\n",
      "436:\tlearn: 0.0015636\ttotal: 1.74s\tremaining: 2.24s\n",
      "437:\tlearn: 0.0015597\ttotal: 1.74s\tremaining: 2.23s\n",
      "438:\tlearn: 0.0015574\ttotal: 1.74s\tremaining: 2.23s\n",
      "439:\tlearn: 0.0015543\ttotal: 1.75s\tremaining: 2.23s\n",
      "440:\tlearn: 0.0015508\ttotal: 1.76s\tremaining: 2.23s\n",
      "441:\tlearn: 0.0015463\ttotal: 1.76s\tremaining: 2.23s\n",
      "442:\tlearn: 0.0015422\ttotal: 1.77s\tremaining: 2.22s\n",
      "443:\tlearn: 0.0015387\ttotal: 1.77s\tremaining: 2.22s\n",
      "444:\tlearn: 0.0015359\ttotal: 1.78s\tremaining: 2.21s\n",
      "445:\tlearn: 0.0015321\ttotal: 1.78s\tremaining: 2.21s\n",
      "446:\tlearn: 0.0015309\ttotal: 1.78s\tremaining: 2.21s\n",
      "447:\tlearn: 0.0015279\ttotal: 1.79s\tremaining: 2.2s\n",
      "448:\tlearn: 0.0015249\ttotal: 1.79s\tremaining: 2.2s\n",
      "449:\tlearn: 0.0015223\ttotal: 1.79s\tremaining: 2.19s\n",
      "450:\tlearn: 0.0015188\ttotal: 1.8s\tremaining: 2.19s\n",
      "451:\tlearn: 0.0015133\ttotal: 1.8s\tremaining: 2.19s\n",
      "452:\tlearn: 0.0015100\ttotal: 1.81s\tremaining: 2.18s\n",
      "453:\tlearn: 0.0015073\ttotal: 1.81s\tremaining: 2.18s\n",
      "454:\tlearn: 0.0015031\ttotal: 1.81s\tremaining: 2.17s\n",
      "455:\tlearn: 0.0014999\ttotal: 1.82s\tremaining: 2.17s\n",
      "456:\tlearn: 0.0014971\ttotal: 1.82s\tremaining: 2.17s\n",
      "457:\tlearn: 0.0014950\ttotal: 1.83s\tremaining: 2.16s\n",
      "458:\tlearn: 0.0014919\ttotal: 1.83s\tremaining: 2.16s\n",
      "459:\tlearn: 0.0014890\ttotal: 1.84s\tremaining: 2.16s\n",
      "460:\tlearn: 0.0014871\ttotal: 1.84s\tremaining: 2.15s\n",
      "461:\tlearn: 0.0014815\ttotal: 1.85s\tremaining: 2.15s\n",
      "462:\tlearn: 0.0014777\ttotal: 1.85s\tremaining: 2.15s\n",
      "463:\tlearn: 0.0014751\ttotal: 1.85s\tremaining: 2.14s\n",
      "464:\tlearn: 0.0014702\ttotal: 1.86s\tremaining: 2.14s\n",
      "465:\tlearn: 0.0014685\ttotal: 1.86s\tremaining: 2.13s\n",
      "466:\tlearn: 0.0014665\ttotal: 1.87s\tremaining: 2.13s\n",
      "467:\tlearn: 0.0014617\ttotal: 1.87s\tremaining: 2.13s\n",
      "468:\tlearn: 0.0014589\ttotal: 1.87s\tremaining: 2.12s\n",
      "469:\tlearn: 0.0014565\ttotal: 1.88s\tremaining: 2.12s\n",
      "470:\tlearn: 0.0014529\ttotal: 1.88s\tremaining: 2.11s\n",
      "471:\tlearn: 0.0014491\ttotal: 1.89s\tremaining: 2.11s\n",
      "472:\tlearn: 0.0014460\ttotal: 1.89s\tremaining: 2.11s\n",
      "473:\tlearn: 0.0014421\ttotal: 1.9s\tremaining: 2.1s\n",
      "474:\tlearn: 0.0014388\ttotal: 1.9s\tremaining: 2.1s\n",
      "475:\tlearn: 0.0014356\ttotal: 1.9s\tremaining: 2.09s\n",
      "476:\tlearn: 0.0014348\ttotal: 1.91s\tremaining: 2.09s\n",
      "477:\tlearn: 0.0014317\ttotal: 1.91s\tremaining: 2.08s\n",
      "478:\tlearn: 0.0014284\ttotal: 1.91s\tremaining: 2.08s\n",
      "479:\tlearn: 0.0014253\ttotal: 1.92s\tremaining: 2.08s\n",
      "480:\tlearn: 0.0014234\ttotal: 1.92s\tremaining: 2.07s\n",
      "481:\tlearn: 0.0014208\ttotal: 1.93s\tremaining: 2.07s\n",
      "482:\tlearn: 0.0014171\ttotal: 1.93s\tremaining: 2.06s\n",
      "483:\tlearn: 0.0014141\ttotal: 1.93s\tremaining: 2.06s\n",
      "484:\tlearn: 0.0014114\ttotal: 1.94s\tremaining: 2.06s\n",
      "485:\tlearn: 0.0014078\ttotal: 1.94s\tremaining: 2.05s\n",
      "486:\tlearn: 0.0014059\ttotal: 1.95s\tremaining: 2.05s\n",
      "487:\tlearn: 0.0014027\ttotal: 1.95s\tremaining: 2.04s\n",
      "488:\tlearn: 0.0013991\ttotal: 1.95s\tremaining: 2.04s\n",
      "489:\tlearn: 0.0013957\ttotal: 1.96s\tremaining: 2.04s\n",
      "490:\tlearn: 0.0013913\ttotal: 1.96s\tremaining: 2.04s\n",
      "491:\tlearn: 0.0013890\ttotal: 1.97s\tremaining: 2.03s\n",
      "492:\tlearn: 0.0013863\ttotal: 1.97s\tremaining: 2.03s\n",
      "493:\tlearn: 0.0013845\ttotal: 1.98s\tremaining: 2.02s\n",
      "494:\tlearn: 0.0013809\ttotal: 1.98s\tremaining: 2.02s\n",
      "495:\tlearn: 0.0013778\ttotal: 1.98s\tremaining: 2.02s\n",
      "496:\tlearn: 0.0013759\ttotal: 1.99s\tremaining: 2.01s\n",
      "497:\tlearn: 0.0013732\ttotal: 1.99s\tremaining: 2.01s\n",
      "498:\tlearn: 0.0013702\ttotal: 1.99s\tremaining: 2s\n",
      "499:\tlearn: 0.0013689\ttotal: 2s\tremaining: 2s\n",
      "500:\tlearn: 0.0013667\ttotal: 2s\tremaining: 2s\n",
      "501:\tlearn: 0.0013641\ttotal: 2.01s\tremaining: 1.99s\n",
      "502:\tlearn: 0.0013621\ttotal: 2.01s\tremaining: 1.99s\n",
      "503:\tlearn: 0.0013578\ttotal: 2.02s\tremaining: 1.98s\n",
      "504:\tlearn: 0.0013549\ttotal: 2.02s\tremaining: 1.98s\n",
      "505:\tlearn: 0.0013517\ttotal: 2.02s\tremaining: 1.98s\n",
      "506:\tlearn: 0.0013487\ttotal: 2.03s\tremaining: 1.97s\n",
      "507:\tlearn: 0.0013454\ttotal: 2.03s\tremaining: 1.97s\n",
      "508:\tlearn: 0.0013432\ttotal: 2.04s\tremaining: 1.97s\n",
      "509:\tlearn: 0.0013400\ttotal: 2.04s\tremaining: 1.96s\n",
      "510:\tlearn: 0.0013360\ttotal: 2.05s\tremaining: 1.96s\n",
      "511:\tlearn: 0.0013338\ttotal: 2.06s\tremaining: 1.96s\n",
      "512:\tlearn: 0.0013323\ttotal: 2.06s\tremaining: 1.96s\n",
      "513:\tlearn: 0.0013292\ttotal: 2.07s\tremaining: 1.95s\n",
      "514:\tlearn: 0.0013265\ttotal: 2.07s\tremaining: 1.95s\n",
      "515:\tlearn: 0.0013233\ttotal: 2.08s\tremaining: 1.95s\n",
      "516:\tlearn: 0.0013207\ttotal: 2.08s\tremaining: 1.94s\n",
      "517:\tlearn: 0.0013191\ttotal: 2.08s\tremaining: 1.94s\n",
      "518:\tlearn: 0.0013155\ttotal: 2.09s\tremaining: 1.94s\n",
      "519:\tlearn: 0.0013130\ttotal: 2.1s\tremaining: 1.93s\n",
      "520:\tlearn: 0.0013107\ttotal: 2.1s\tremaining: 1.93s\n",
      "521:\tlearn: 0.0013076\ttotal: 2.1s\tremaining: 1.93s\n",
      "522:\tlearn: 0.0013040\ttotal: 2.11s\tremaining: 1.92s\n",
      "523:\tlearn: 0.0013009\ttotal: 2.11s\tremaining: 1.92s\n",
      "524:\tlearn: 0.0012975\ttotal: 2.11s\tremaining: 1.91s\n",
      "525:\tlearn: 0.0012941\ttotal: 2.12s\tremaining: 1.91s\n",
      "526:\tlearn: 0.0012922\ttotal: 2.12s\tremaining: 1.9s\n",
      "527:\tlearn: 0.0012886\ttotal: 2.12s\tremaining: 1.9s\n",
      "528:\tlearn: 0.0012856\ttotal: 2.13s\tremaining: 1.89s\n",
      "529:\tlearn: 0.0012828\ttotal: 2.13s\tremaining: 1.89s\n",
      "530:\tlearn: 0.0012793\ttotal: 2.14s\tremaining: 1.89s\n",
      "531:\tlearn: 0.0012763\ttotal: 2.14s\tremaining: 1.89s\n",
      "532:\tlearn: 0.0012747\ttotal: 2.15s\tremaining: 1.88s\n",
      "533:\tlearn: 0.0012717\ttotal: 2.15s\tremaining: 1.88s\n",
      "534:\tlearn: 0.0012687\ttotal: 2.15s\tremaining: 1.87s\n",
      "535:\tlearn: 0.0012668\ttotal: 2.16s\tremaining: 1.87s\n",
      "536:\tlearn: 0.0012644\ttotal: 2.16s\tremaining: 1.86s\n",
      "537:\tlearn: 0.0012619\ttotal: 2.17s\tremaining: 1.86s\n",
      "538:\tlearn: 0.0012601\ttotal: 2.17s\tremaining: 1.85s\n",
      "539:\tlearn: 0.0012574\ttotal: 2.17s\tremaining: 1.85s\n",
      "540:\tlearn: 0.0012547\ttotal: 2.18s\tremaining: 1.85s\n",
      "541:\tlearn: 0.0012533\ttotal: 2.18s\tremaining: 1.84s\n",
      "542:\tlearn: 0.0012509\ttotal: 2.19s\tremaining: 1.84s\n",
      "543:\tlearn: 0.0012459\ttotal: 2.19s\tremaining: 1.83s\n",
      "544:\tlearn: 0.0012449\ttotal: 2.19s\tremaining: 1.83s\n",
      "545:\tlearn: 0.0012425\ttotal: 2.2s\tremaining: 1.83s\n",
      "546:\tlearn: 0.0012408\ttotal: 2.2s\tremaining: 1.82s\n",
      "547:\tlearn: 0.0012376\ttotal: 2.2s\tremaining: 1.82s\n",
      "548:\tlearn: 0.0012357\ttotal: 2.21s\tremaining: 1.81s\n",
      "549:\tlearn: 0.0012326\ttotal: 2.21s\tremaining: 1.81s\n",
      "550:\tlearn: 0.0012297\ttotal: 2.21s\tremaining: 1.8s\n",
      "551:\tlearn: 0.0012272\ttotal: 2.22s\tremaining: 1.8s\n",
      "552:\tlearn: 0.0012239\ttotal: 2.23s\tremaining: 1.8s\n",
      "553:\tlearn: 0.0012213\ttotal: 2.23s\tremaining: 1.79s\n",
      "554:\tlearn: 0.0012185\ttotal: 2.23s\tremaining: 1.79s\n",
      "555:\tlearn: 0.0012160\ttotal: 2.24s\tremaining: 1.79s\n",
      "556:\tlearn: 0.0012136\ttotal: 2.24s\tremaining: 1.78s\n",
      "557:\tlearn: 0.0012115\ttotal: 2.24s\tremaining: 1.78s\n",
      "558:\tlearn: 0.0012095\ttotal: 2.25s\tremaining: 1.77s\n",
      "559:\tlearn: 0.0012076\ttotal: 2.25s\tremaining: 1.77s\n",
      "560:\tlearn: 0.0012050\ttotal: 2.26s\tremaining: 1.77s\n",
      "561:\tlearn: 0.0012024\ttotal: 2.26s\tremaining: 1.76s\n",
      "562:\tlearn: 0.0011996\ttotal: 2.27s\tremaining: 1.76s\n",
      "563:\tlearn: 0.0011970\ttotal: 2.27s\tremaining: 1.75s\n",
      "564:\tlearn: 0.0011946\ttotal: 2.27s\tremaining: 1.75s\n",
      "565:\tlearn: 0.0011928\ttotal: 2.28s\tremaining: 1.75s\n",
      "566:\tlearn: 0.0011908\ttotal: 2.28s\tremaining: 1.74s\n",
      "567:\tlearn: 0.0011883\ttotal: 2.29s\tremaining: 1.74s\n",
      "568:\tlearn: 0.0011860\ttotal: 2.29s\tremaining: 1.74s\n",
      "569:\tlearn: 0.0011838\ttotal: 2.29s\tremaining: 1.73s\n",
      "570:\tlearn: 0.0011817\ttotal: 2.3s\tremaining: 1.73s\n",
      "571:\tlearn: 0.0011799\ttotal: 2.3s\tremaining: 1.72s\n",
      "572:\tlearn: 0.0011776\ttotal: 2.3s\tremaining: 1.72s\n",
      "573:\tlearn: 0.0011757\ttotal: 2.31s\tremaining: 1.71s\n",
      "574:\tlearn: 0.0011737\ttotal: 2.31s\tremaining: 1.71s\n",
      "575:\tlearn: 0.0011712\ttotal: 2.31s\tremaining: 1.7s\n",
      "576:\tlearn: 0.0011689\ttotal: 2.32s\tremaining: 1.7s\n",
      "577:\tlearn: 0.0011671\ttotal: 2.32s\tremaining: 1.69s\n",
      "578:\tlearn: 0.0011653\ttotal: 2.32s\tremaining: 1.69s\n",
      "579:\tlearn: 0.0011635\ttotal: 2.33s\tremaining: 1.68s\n",
      "580:\tlearn: 0.0011608\ttotal: 2.33s\tremaining: 1.68s\n",
      "581:\tlearn: 0.0011579\ttotal: 2.33s\tremaining: 1.68s\n",
      "582:\tlearn: 0.0011560\ttotal: 2.34s\tremaining: 1.67s\n",
      "583:\tlearn: 0.0011537\ttotal: 2.34s\tremaining: 1.67s\n",
      "584:\tlearn: 0.0011520\ttotal: 2.35s\tremaining: 1.67s\n",
      "585:\tlearn: 0.0011497\ttotal: 2.35s\tremaining: 1.66s\n",
      "586:\tlearn: 0.0011475\ttotal: 2.35s\tremaining: 1.66s\n",
      "587:\tlearn: 0.0011455\ttotal: 2.36s\tremaining: 1.65s\n",
      "588:\tlearn: 0.0011431\ttotal: 2.36s\tremaining: 1.65s\n",
      "589:\tlearn: 0.0011409\ttotal: 2.37s\tremaining: 1.64s\n",
      "590:\tlearn: 0.0011386\ttotal: 2.37s\tremaining: 1.64s\n",
      "591:\tlearn: 0.0011373\ttotal: 2.37s\tremaining: 1.64s\n",
      "592:\tlearn: 0.0011353\ttotal: 2.38s\tremaining: 1.63s\n",
      "593:\tlearn: 0.0011334\ttotal: 2.38s\tremaining: 1.63s\n",
      "594:\tlearn: 0.0011318\ttotal: 2.38s\tremaining: 1.62s\n",
      "595:\tlearn: 0.0011301\ttotal: 2.39s\tremaining: 1.62s\n",
      "596:\tlearn: 0.0011285\ttotal: 2.39s\tremaining: 1.61s\n",
      "597:\tlearn: 0.0011263\ttotal: 2.39s\tremaining: 1.61s\n",
      "598:\tlearn: 0.0011241\ttotal: 2.4s\tremaining: 1.6s\n",
      "599:\tlearn: 0.0011218\ttotal: 2.4s\tremaining: 1.6s\n",
      "600:\tlearn: 0.0011197\ttotal: 2.41s\tremaining: 1.6s\n",
      "601:\tlearn: 0.0011183\ttotal: 2.41s\tremaining: 1.59s\n",
      "602:\tlearn: 0.0011162\ttotal: 2.42s\tremaining: 1.59s\n",
      "603:\tlearn: 0.0011148\ttotal: 2.42s\tremaining: 1.59s\n",
      "604:\tlearn: 0.0011127\ttotal: 2.42s\tremaining: 1.58s\n",
      "605:\tlearn: 0.0011110\ttotal: 2.43s\tremaining: 1.58s\n",
      "606:\tlearn: 0.0011089\ttotal: 2.43s\tremaining: 1.57s\n",
      "607:\tlearn: 0.0011070\ttotal: 2.43s\tremaining: 1.57s\n",
      "608:\tlearn: 0.0011060\ttotal: 2.44s\tremaining: 1.56s\n",
      "609:\tlearn: 0.0011044\ttotal: 2.44s\tremaining: 1.56s\n",
      "610:\tlearn: 0.0011027\ttotal: 2.44s\tremaining: 1.56s\n",
      "611:\tlearn: 0.0011014\ttotal: 2.45s\tremaining: 1.55s\n",
      "612:\tlearn: 0.0011000\ttotal: 2.45s\tremaining: 1.55s\n",
      "613:\tlearn: 0.0010983\ttotal: 2.46s\tremaining: 1.54s\n",
      "614:\tlearn: 0.0010968\ttotal: 2.46s\tremaining: 1.54s\n",
      "615:\tlearn: 0.0010950\ttotal: 2.47s\tremaining: 1.54s\n",
      "616:\tlearn: 0.0010939\ttotal: 2.47s\tremaining: 1.53s\n",
      "617:\tlearn: 0.0010922\ttotal: 2.47s\tremaining: 1.53s\n",
      "618:\tlearn: 0.0010908\ttotal: 2.48s\tremaining: 1.52s\n",
      "619:\tlearn: 0.0010898\ttotal: 2.48s\tremaining: 1.52s\n",
      "620:\tlearn: 0.0010887\ttotal: 2.48s\tremaining: 1.51s\n",
      "621:\tlearn: 0.0010871\ttotal: 2.49s\tremaining: 1.51s\n",
      "622:\tlearn: 0.0010854\ttotal: 2.49s\tremaining: 1.51s\n",
      "623:\tlearn: 0.0010835\ttotal: 2.49s\tremaining: 1.5s\n",
      "624:\tlearn: 0.0010819\ttotal: 2.5s\tremaining: 1.5s\n",
      "625:\tlearn: 0.0010803\ttotal: 2.5s\tremaining: 1.49s\n",
      "626:\tlearn: 0.0010790\ttotal: 2.5s\tremaining: 1.49s\n",
      "627:\tlearn: 0.0010775\ttotal: 2.51s\tremaining: 1.49s\n",
      "628:\tlearn: 0.0010761\ttotal: 2.52s\tremaining: 1.48s\n",
      "629:\tlearn: 0.0010752\ttotal: 2.52s\tremaining: 1.48s\n",
      "630:\tlearn: 0.0010731\ttotal: 2.52s\tremaining: 1.48s\n",
      "631:\tlearn: 0.0010715\ttotal: 2.53s\tremaining: 1.47s\n",
      "632:\tlearn: 0.0010702\ttotal: 2.53s\tremaining: 1.47s\n",
      "633:\tlearn: 0.0010684\ttotal: 2.53s\tremaining: 1.46s\n",
      "634:\tlearn: 0.0010670\ttotal: 2.54s\tremaining: 1.46s\n",
      "635:\tlearn: 0.0010654\ttotal: 2.54s\tremaining: 1.46s\n",
      "636:\tlearn: 0.0010637\ttotal: 2.55s\tremaining: 1.45s\n",
      "637:\tlearn: 0.0010617\ttotal: 2.55s\tremaining: 1.45s\n",
      "638:\tlearn: 0.0010605\ttotal: 2.55s\tremaining: 1.44s\n",
      "639:\tlearn: 0.0010587\ttotal: 2.56s\tremaining: 1.44s\n",
      "640:\tlearn: 0.0010578\ttotal: 2.56s\tremaining: 1.43s\n",
      "641:\tlearn: 0.0010561\ttotal: 2.56s\tremaining: 1.43s\n",
      "642:\tlearn: 0.0010548\ttotal: 2.57s\tremaining: 1.43s\n",
      "643:\tlearn: 0.0010531\ttotal: 2.57s\tremaining: 1.42s\n",
      "644:\tlearn: 0.0010519\ttotal: 2.58s\tremaining: 1.42s\n",
      "645:\tlearn: 0.0010505\ttotal: 2.58s\tremaining: 1.41s\n",
      "646:\tlearn: 0.0010489\ttotal: 2.58s\tremaining: 1.41s\n",
      "647:\tlearn: 0.0010478\ttotal: 2.59s\tremaining: 1.41s\n",
      "648:\tlearn: 0.0010466\ttotal: 2.59s\tremaining: 1.4s\n",
      "649:\tlearn: 0.0010455\ttotal: 2.59s\tremaining: 1.4s\n",
      "650:\tlearn: 0.0010437\ttotal: 2.6s\tremaining: 1.39s\n",
      "651:\tlearn: 0.0010426\ttotal: 2.6s\tremaining: 1.39s\n",
      "652:\tlearn: 0.0010408\ttotal: 2.6s\tremaining: 1.38s\n",
      "653:\tlearn: 0.0010391\ttotal: 2.61s\tremaining: 1.38s\n",
      "654:\tlearn: 0.0010376\ttotal: 2.61s\tremaining: 1.37s\n",
      "655:\tlearn: 0.0010366\ttotal: 2.61s\tremaining: 1.37s\n",
      "656:\tlearn: 0.0010350\ttotal: 2.62s\tremaining: 1.37s\n",
      "657:\tlearn: 0.0010336\ttotal: 2.63s\tremaining: 1.36s\n",
      "658:\tlearn: 0.0010318\ttotal: 2.63s\tremaining: 1.36s\n",
      "659:\tlearn: 0.0010304\ttotal: 2.63s\tremaining: 1.36s\n",
      "660:\tlearn: 0.0010295\ttotal: 2.64s\tremaining: 1.35s\n",
      "661:\tlearn: 0.0010281\ttotal: 2.64s\tremaining: 1.35s\n",
      "662:\tlearn: 0.0010271\ttotal: 2.65s\tremaining: 1.34s\n",
      "663:\tlearn: 0.0010260\ttotal: 2.65s\tremaining: 1.34s\n",
      "664:\tlearn: 0.0010253\ttotal: 2.65s\tremaining: 1.34s\n",
      "665:\tlearn: 0.0010236\ttotal: 2.66s\tremaining: 1.33s\n",
      "666:\tlearn: 0.0010226\ttotal: 2.66s\tremaining: 1.33s\n",
      "667:\tlearn: 0.0010209\ttotal: 2.67s\tremaining: 1.32s\n",
      "668:\tlearn: 0.0010199\ttotal: 2.67s\tremaining: 1.32s\n",
      "669:\tlearn: 0.0010187\ttotal: 2.67s\tremaining: 1.32s\n",
      "670:\tlearn: 0.0010180\ttotal: 2.68s\tremaining: 1.31s\n",
      "671:\tlearn: 0.0010164\ttotal: 2.68s\tremaining: 1.31s\n",
      "672:\tlearn: 0.0010158\ttotal: 2.69s\tremaining: 1.3s\n",
      "673:\tlearn: 0.0010143\ttotal: 2.69s\tremaining: 1.3s\n",
      "674:\tlearn: 0.0010138\ttotal: 2.7s\tremaining: 1.3s\n",
      "675:\tlearn: 0.0010124\ttotal: 2.7s\tremaining: 1.29s\n",
      "676:\tlearn: 0.0010114\ttotal: 2.71s\tremaining: 1.29s\n",
      "677:\tlearn: 0.0010098\ttotal: 2.71s\tremaining: 1.29s\n",
      "678:\tlearn: 0.0010091\ttotal: 2.71s\tremaining: 1.28s\n",
      "679:\tlearn: 0.0010080\ttotal: 2.72s\tremaining: 1.28s\n",
      "680:\tlearn: 0.0010069\ttotal: 2.72s\tremaining: 1.27s\n",
      "681:\tlearn: 0.0010058\ttotal: 2.73s\tremaining: 1.27s\n",
      "682:\tlearn: 0.0010048\ttotal: 2.73s\tremaining: 1.27s\n",
      "683:\tlearn: 0.0010033\ttotal: 2.74s\tremaining: 1.26s\n",
      "684:\tlearn: 0.0010022\ttotal: 2.74s\tremaining: 1.26s\n",
      "685:\tlearn: 0.0010009\ttotal: 2.75s\tremaining: 1.26s\n",
      "686:\tlearn: 0.0009990\ttotal: 2.75s\tremaining: 1.25s\n",
      "687:\tlearn: 0.0009976\ttotal: 2.75s\tremaining: 1.25s\n",
      "688:\tlearn: 0.0009963\ttotal: 2.76s\tremaining: 1.24s\n",
      "689:\tlearn: 0.0009950\ttotal: 2.76s\tremaining: 1.24s\n",
      "690:\tlearn: 0.0009942\ttotal: 2.77s\tremaining: 1.24s\n",
      "691:\tlearn: 0.0009929\ttotal: 2.77s\tremaining: 1.23s\n",
      "692:\tlearn: 0.0009918\ttotal: 2.77s\tremaining: 1.23s\n",
      "693:\tlearn: 0.0009903\ttotal: 2.78s\tremaining: 1.22s\n",
      "694:\tlearn: 0.0009894\ttotal: 2.78s\tremaining: 1.22s\n",
      "695:\tlearn: 0.0009882\ttotal: 2.79s\tremaining: 1.22s\n",
      "696:\tlearn: 0.0009873\ttotal: 2.79s\tremaining: 1.21s\n",
      "697:\tlearn: 0.0009862\ttotal: 2.8s\tremaining: 1.21s\n",
      "698:\tlearn: 0.0009849\ttotal: 2.8s\tremaining: 1.21s\n",
      "699:\tlearn: 0.0009837\ttotal: 2.81s\tremaining: 1.2s\n",
      "700:\tlearn: 0.0009830\ttotal: 2.81s\tremaining: 1.2s\n",
      "701:\tlearn: 0.0009817\ttotal: 2.81s\tremaining: 1.19s\n",
      "702:\tlearn: 0.0009804\ttotal: 2.82s\tremaining: 1.19s\n",
      "703:\tlearn: 0.0009799\ttotal: 2.82s\tremaining: 1.19s\n",
      "704:\tlearn: 0.0009790\ttotal: 2.83s\tremaining: 1.18s\n",
      "705:\tlearn: 0.0009780\ttotal: 2.83s\tremaining: 1.18s\n",
      "706:\tlearn: 0.0009773\ttotal: 2.83s\tremaining: 1.18s\n",
      "707:\tlearn: 0.0009764\ttotal: 2.84s\tremaining: 1.17s\n",
      "708:\tlearn: 0.0009752\ttotal: 2.85s\tremaining: 1.17s\n",
      "709:\tlearn: 0.0009743\ttotal: 2.85s\tremaining: 1.16s\n",
      "710:\tlearn: 0.0009733\ttotal: 2.85s\tremaining: 1.16s\n",
      "711:\tlearn: 0.0009725\ttotal: 2.86s\tremaining: 1.16s\n",
      "712:\tlearn: 0.0009713\ttotal: 2.86s\tremaining: 1.15s\n",
      "713:\tlearn: 0.0009707\ttotal: 2.87s\tremaining: 1.15s\n",
      "714:\tlearn: 0.0009702\ttotal: 2.87s\tremaining: 1.14s\n",
      "715:\tlearn: 0.0009693\ttotal: 2.88s\tremaining: 1.14s\n",
      "716:\tlearn: 0.0009682\ttotal: 2.88s\tremaining: 1.14s\n",
      "717:\tlearn: 0.0009674\ttotal: 2.88s\tremaining: 1.13s\n",
      "718:\tlearn: 0.0009669\ttotal: 2.89s\tremaining: 1.13s\n",
      "719:\tlearn: 0.0009662\ttotal: 2.89s\tremaining: 1.12s\n",
      "720:\tlearn: 0.0009652\ttotal: 2.89s\tremaining: 1.12s\n",
      "721:\tlearn: 0.0009643\ttotal: 2.9s\tremaining: 1.11s\n",
      "722:\tlearn: 0.0009634\ttotal: 2.9s\tremaining: 1.11s\n",
      "723:\tlearn: 0.0009625\ttotal: 2.9s\tremaining: 1.11s\n",
      "724:\tlearn: 0.0009617\ttotal: 2.91s\tremaining: 1.1s\n",
      "725:\tlearn: 0.0009609\ttotal: 2.92s\tremaining: 1.1s\n",
      "726:\tlearn: 0.0009602\ttotal: 2.92s\tremaining: 1.1s\n",
      "727:\tlearn: 0.0009591\ttotal: 2.93s\tremaining: 1.09s\n",
      "728:\tlearn: 0.0009582\ttotal: 2.93s\tremaining: 1.09s\n",
      "729:\tlearn: 0.0009573\ttotal: 2.94s\tremaining: 1.09s\n",
      "730:\tlearn: 0.0009560\ttotal: 2.95s\tremaining: 1.08s\n",
      "731:\tlearn: 0.0009550\ttotal: 2.95s\tremaining: 1.08s\n",
      "732:\tlearn: 0.0009545\ttotal: 2.96s\tremaining: 1.08s\n",
      "733:\tlearn: 0.0009540\ttotal: 2.96s\tremaining: 1.07s\n",
      "734:\tlearn: 0.0009528\ttotal: 2.96s\tremaining: 1.07s\n",
      "735:\tlearn: 0.0009523\ttotal: 2.97s\tremaining: 1.06s\n",
      "736:\tlearn: 0.0009513\ttotal: 2.97s\tremaining: 1.06s\n",
      "737:\tlearn: 0.0009504\ttotal: 2.98s\tremaining: 1.06s\n",
      "738:\tlearn: 0.0009494\ttotal: 2.98s\tremaining: 1.05s\n",
      "739:\tlearn: 0.0009483\ttotal: 2.98s\tremaining: 1.05s\n",
      "740:\tlearn: 0.0009472\ttotal: 2.99s\tremaining: 1.04s\n",
      "741:\tlearn: 0.0009465\ttotal: 2.99s\tremaining: 1.04s\n",
      "742:\tlearn: 0.0009455\ttotal: 2.99s\tremaining: 1.03s\n",
      "743:\tlearn: 0.0009447\ttotal: 3s\tremaining: 1.03s\n",
      "744:\tlearn: 0.0009438\ttotal: 3s\tremaining: 1.03s\n",
      "745:\tlearn: 0.0009431\ttotal: 3.01s\tremaining: 1.02s\n",
      "746:\tlearn: 0.0009423\ttotal: 3.01s\tremaining: 1.02s\n",
      "747:\tlearn: 0.0009416\ttotal: 3.02s\tremaining: 1.02s\n",
      "748:\tlearn: 0.0009408\ttotal: 3.02s\tremaining: 1.01s\n",
      "749:\tlearn: 0.0009403\ttotal: 3.02s\tremaining: 1.01s\n",
      "750:\tlearn: 0.0009398\ttotal: 3.03s\tremaining: 1s\n",
      "751:\tlearn: 0.0009390\ttotal: 3.03s\tremaining: 1000ms\n",
      "752:\tlearn: 0.0009384\ttotal: 3.03s\tremaining: 996ms\n",
      "753:\tlearn: 0.0009379\ttotal: 3.04s\tremaining: 991ms\n",
      "754:\tlearn: 0.0009374\ttotal: 3.04s\tremaining: 987ms\n",
      "755:\tlearn: 0.0009364\ttotal: 3.04s\tremaining: 983ms\n",
      "756:\tlearn: 0.0009356\ttotal: 3.05s\tremaining: 979ms\n",
      "757:\tlearn: 0.0009349\ttotal: 3.05s\tremaining: 974ms\n",
      "758:\tlearn: 0.0009342\ttotal: 3.06s\tremaining: 970ms\n",
      "759:\tlearn: 0.0009332\ttotal: 3.06s\tremaining: 966ms\n",
      "760:\tlearn: 0.0009328\ttotal: 3.06s\tremaining: 962ms\n",
      "761:\tlearn: 0.0009319\ttotal: 3.06s\tremaining: 958ms\n",
      "762:\tlearn: 0.0009310\ttotal: 3.07s\tremaining: 953ms\n",
      "763:\tlearn: 0.0009302\ttotal: 3.07s\tremaining: 949ms\n",
      "764:\tlearn: 0.0009294\ttotal: 3.08s\tremaining: 945ms\n",
      "765:\tlearn: 0.0009287\ttotal: 3.08s\tremaining: 940ms\n",
      "766:\tlearn: 0.0009279\ttotal: 3.08s\tremaining: 936ms\n",
      "767:\tlearn: 0.0009273\ttotal: 3.08s\tremaining: 932ms\n",
      "768:\tlearn: 0.0009265\ttotal: 3.09s\tremaining: 928ms\n",
      "769:\tlearn: 0.0009256\ttotal: 3.1s\tremaining: 925ms\n",
      "770:\tlearn: 0.0009252\ttotal: 3.1s\tremaining: 921ms\n",
      "771:\tlearn: 0.0009243\ttotal: 3.1s\tremaining: 917ms\n",
      "772:\tlearn: 0.0009237\ttotal: 3.11s\tremaining: 913ms\n",
      "773:\tlearn: 0.0009229\ttotal: 3.11s\tremaining: 909ms\n",
      "774:\tlearn: 0.0009225\ttotal: 3.12s\tremaining: 905ms\n",
      "775:\tlearn: 0.0009220\ttotal: 3.12s\tremaining: 901ms\n",
      "776:\tlearn: 0.0009216\ttotal: 3.12s\tremaining: 896ms\n",
      "777:\tlearn: 0.0009209\ttotal: 3.13s\tremaining: 892ms\n",
      "778:\tlearn: 0.0009205\ttotal: 3.13s\tremaining: 888ms\n",
      "779:\tlearn: 0.0009199\ttotal: 3.13s\tremaining: 884ms\n",
      "780:\tlearn: 0.0009190\ttotal: 3.14s\tremaining: 880ms\n",
      "781:\tlearn: 0.0009182\ttotal: 3.14s\tremaining: 876ms\n",
      "782:\tlearn: 0.0009178\ttotal: 3.14s\tremaining: 871ms\n",
      "783:\tlearn: 0.0009174\ttotal: 3.15s\tremaining: 867ms\n",
      "784:\tlearn: 0.0009165\ttotal: 3.15s\tremaining: 863ms\n",
      "785:\tlearn: 0.0009161\ttotal: 3.15s\tremaining: 859ms\n",
      "786:\tlearn: 0.0009152\ttotal: 3.16s\tremaining: 855ms\n",
      "787:\tlearn: 0.0009143\ttotal: 3.16s\tremaining: 850ms\n",
      "788:\tlearn: 0.0009135\ttotal: 3.16s\tremaining: 846ms\n",
      "789:\tlearn: 0.0009129\ttotal: 3.17s\tremaining: 842ms\n",
      "790:\tlearn: 0.0009120\ttotal: 3.17s\tremaining: 837ms\n",
      "791:\tlearn: 0.0009117\ttotal: 3.17s\tremaining: 833ms\n",
      "792:\tlearn: 0.0009109\ttotal: 3.18s\tremaining: 829ms\n",
      "793:\tlearn: 0.0009101\ttotal: 3.18s\tremaining: 825ms\n",
      "794:\tlearn: 0.0009093\ttotal: 3.18s\tremaining: 821ms\n",
      "795:\tlearn: 0.0009086\ttotal: 3.19s\tremaining: 816ms\n",
      "796:\tlearn: 0.0009079\ttotal: 3.19s\tremaining: 812ms\n",
      "797:\tlearn: 0.0009071\ttotal: 3.19s\tremaining: 808ms\n",
      "798:\tlearn: 0.0009063\ttotal: 3.2s\tremaining: 804ms\n",
      "799:\tlearn: 0.0009056\ttotal: 3.2s\tremaining: 801ms\n",
      "800:\tlearn: 0.0009049\ttotal: 3.21s\tremaining: 796ms\n",
      "801:\tlearn: 0.0009042\ttotal: 3.21s\tremaining: 792ms\n",
      "802:\tlearn: 0.0009034\ttotal: 3.21s\tremaining: 788ms\n",
      "803:\tlearn: 0.0009027\ttotal: 3.22s\tremaining: 784ms\n",
      "804:\tlearn: 0.0009020\ttotal: 3.22s\tremaining: 780ms\n",
      "805:\tlearn: 0.0009013\ttotal: 3.22s\tremaining: 776ms\n",
      "806:\tlearn: 0.0009007\ttotal: 3.23s\tremaining: 772ms\n",
      "807:\tlearn: 0.0009000\ttotal: 3.23s\tremaining: 768ms\n",
      "808:\tlearn: 0.0008995\ttotal: 3.23s\tremaining: 764ms\n",
      "809:\tlearn: 0.0008989\ttotal: 3.24s\tremaining: 760ms\n",
      "810:\tlearn: 0.0008983\ttotal: 3.24s\tremaining: 755ms\n",
      "811:\tlearn: 0.0008977\ttotal: 3.24s\tremaining: 751ms\n",
      "812:\tlearn: 0.0008971\ttotal: 3.25s\tremaining: 747ms\n",
      "813:\tlearn: 0.0008965\ttotal: 3.25s\tremaining: 743ms\n",
      "814:\tlearn: 0.0008959\ttotal: 3.26s\tremaining: 739ms\n",
      "815:\tlearn: 0.0008953\ttotal: 3.26s\tremaining: 735ms\n",
      "816:\tlearn: 0.0008948\ttotal: 3.26s\tremaining: 731ms\n",
      "817:\tlearn: 0.0008941\ttotal: 3.27s\tremaining: 728ms\n",
      "818:\tlearn: 0.0008933\ttotal: 3.27s\tremaining: 723ms\n",
      "819:\tlearn: 0.0008928\ttotal: 3.28s\tremaining: 720ms\n",
      "820:\tlearn: 0.0008922\ttotal: 3.28s\tremaining: 716ms\n",
      "821:\tlearn: 0.0008916\ttotal: 3.29s\tremaining: 712ms\n",
      "822:\tlearn: 0.0008910\ttotal: 3.29s\tremaining: 708ms\n",
      "823:\tlearn: 0.0008905\ttotal: 3.29s\tremaining: 704ms\n",
      "824:\tlearn: 0.0008902\ttotal: 3.3s\tremaining: 700ms\n",
      "825:\tlearn: 0.0008897\ttotal: 3.3s\tremaining: 696ms\n",
      "826:\tlearn: 0.0008892\ttotal: 3.31s\tremaining: 692ms\n",
      "827:\tlearn: 0.0008886\ttotal: 3.31s\tremaining: 688ms\n",
      "828:\tlearn: 0.0008882\ttotal: 3.31s\tremaining: 683ms\n",
      "829:\tlearn: 0.0008876\ttotal: 3.32s\tremaining: 679ms\n",
      "830:\tlearn: 0.0008871\ttotal: 3.32s\tremaining: 675ms\n",
      "831:\tlearn: 0.0008865\ttotal: 3.32s\tremaining: 671ms\n",
      "832:\tlearn: 0.0008861\ttotal: 3.33s\tremaining: 667ms\n",
      "833:\tlearn: 0.0008855\ttotal: 3.33s\tremaining: 663ms\n",
      "834:\tlearn: 0.0008851\ttotal: 3.33s\tremaining: 659ms\n",
      "835:\tlearn: 0.0008845\ttotal: 3.34s\tremaining: 655ms\n",
      "836:\tlearn: 0.0008839\ttotal: 3.34s\tremaining: 651ms\n",
      "837:\tlearn: 0.0008833\ttotal: 3.35s\tremaining: 647ms\n",
      "838:\tlearn: 0.0008829\ttotal: 3.37s\tremaining: 646ms\n",
      "839:\tlearn: 0.0008826\ttotal: 3.37s\tremaining: 642ms\n",
      "840:\tlearn: 0.0008820\ttotal: 3.37s\tremaining: 638ms\n",
      "841:\tlearn: 0.0008816\ttotal: 3.38s\tremaining: 634ms\n",
      "842:\tlearn: 0.0008813\ttotal: 3.38s\tremaining: 630ms\n",
      "843:\tlearn: 0.0008809\ttotal: 3.39s\tremaining: 626ms\n",
      "844:\tlearn: 0.0008804\ttotal: 3.39s\tremaining: 622ms\n",
      "845:\tlearn: 0.0008799\ttotal: 3.39s\tremaining: 618ms\n",
      "846:\tlearn: 0.0008795\ttotal: 3.4s\tremaining: 614ms\n",
      "847:\tlearn: 0.0008791\ttotal: 3.4s\tremaining: 610ms\n",
      "848:\tlearn: 0.0008784\ttotal: 3.4s\tremaining: 606ms\n",
      "849:\tlearn: 0.0008779\ttotal: 3.41s\tremaining: 601ms\n",
      "850:\tlearn: 0.0008773\ttotal: 3.41s\tremaining: 597ms\n",
      "851:\tlearn: 0.0008768\ttotal: 3.42s\tremaining: 593ms\n",
      "852:\tlearn: 0.0008762\ttotal: 3.42s\tremaining: 590ms\n",
      "853:\tlearn: 0.0008755\ttotal: 3.42s\tremaining: 586ms\n",
      "854:\tlearn: 0.0008752\ttotal: 3.43s\tremaining: 581ms\n",
      "855:\tlearn: 0.0008745\ttotal: 3.43s\tremaining: 577ms\n",
      "856:\tlearn: 0.0008740\ttotal: 3.43s\tremaining: 573ms\n",
      "857:\tlearn: 0.0008736\ttotal: 3.44s\tremaining: 569ms\n",
      "858:\tlearn: 0.0008731\ttotal: 3.44s\tremaining: 565ms\n",
      "859:\tlearn: 0.0008725\ttotal: 3.44s\tremaining: 560ms\n",
      "860:\tlearn: 0.0008719\ttotal: 3.45s\tremaining: 556ms\n",
      "861:\tlearn: 0.0008713\ttotal: 3.45s\tremaining: 552ms\n",
      "862:\tlearn: 0.0008706\ttotal: 3.45s\tremaining: 548ms\n",
      "863:\tlearn: 0.0008701\ttotal: 3.46s\tremaining: 544ms\n",
      "864:\tlearn: 0.0008697\ttotal: 3.46s\tremaining: 540ms\n",
      "865:\tlearn: 0.0008694\ttotal: 3.46s\tremaining: 536ms\n",
      "866:\tlearn: 0.0008688\ttotal: 3.46s\tremaining: 531ms\n",
      "867:\tlearn: 0.0008682\ttotal: 3.47s\tremaining: 527ms\n",
      "868:\tlearn: 0.0008677\ttotal: 3.47s\tremaining: 523ms\n",
      "869:\tlearn: 0.0008673\ttotal: 3.47s\tremaining: 519ms\n",
      "870:\tlearn: 0.0008669\ttotal: 3.48s\tremaining: 515ms\n",
      "871:\tlearn: 0.0008663\ttotal: 3.48s\tremaining: 511ms\n",
      "872:\tlearn: 0.0008658\ttotal: 3.48s\tremaining: 507ms\n",
      "873:\tlearn: 0.0008651\ttotal: 3.49s\tremaining: 503ms\n",
      "874:\tlearn: 0.0008646\ttotal: 3.5s\tremaining: 499ms\n",
      "875:\tlearn: 0.0008641\ttotal: 3.5s\tremaining: 495ms\n",
      "876:\tlearn: 0.0008635\ttotal: 3.5s\tremaining: 491ms\n",
      "877:\tlearn: 0.0008630\ttotal: 3.5s\tremaining: 487ms\n",
      "878:\tlearn: 0.0008626\ttotal: 3.51s\tremaining: 483ms\n",
      "879:\tlearn: 0.0008622\ttotal: 3.51s\tremaining: 479ms\n",
      "880:\tlearn: 0.0008617\ttotal: 3.52s\tremaining: 475ms\n",
      "881:\tlearn: 0.0008613\ttotal: 3.52s\tremaining: 471ms\n",
      "882:\tlearn: 0.0008608\ttotal: 3.52s\tremaining: 467ms\n",
      "883:\tlearn: 0.0008605\ttotal: 3.53s\tremaining: 463ms\n",
      "884:\tlearn: 0.0008602\ttotal: 3.53s\tremaining: 459ms\n",
      "885:\tlearn: 0.0008596\ttotal: 3.54s\tremaining: 455ms\n",
      "886:\tlearn: 0.0008590\ttotal: 3.54s\tremaining: 451ms\n",
      "887:\tlearn: 0.0008586\ttotal: 3.55s\tremaining: 448ms\n",
      "888:\tlearn: 0.0008582\ttotal: 3.55s\tremaining: 443ms\n",
      "889:\tlearn: 0.0008577\ttotal: 3.56s\tremaining: 440ms\n",
      "890:\tlearn: 0.0008571\ttotal: 3.56s\tremaining: 436ms\n",
      "891:\tlearn: 0.0008566\ttotal: 3.56s\tremaining: 432ms\n",
      "892:\tlearn: 0.0008562\ttotal: 3.57s\tremaining: 428ms\n",
      "893:\tlearn: 0.0008558\ttotal: 3.57s\tremaining: 424ms\n",
      "894:\tlearn: 0.0008555\ttotal: 3.58s\tremaining: 420ms\n",
      "895:\tlearn: 0.0008552\ttotal: 3.58s\tremaining: 416ms\n",
      "896:\tlearn: 0.0008547\ttotal: 3.58s\tremaining: 411ms\n",
      "897:\tlearn: 0.0008543\ttotal: 3.59s\tremaining: 408ms\n",
      "898:\tlearn: 0.0008539\ttotal: 3.59s\tremaining: 403ms\n",
      "899:\tlearn: 0.0008534\ttotal: 3.59s\tremaining: 399ms\n",
      "900:\tlearn: 0.0008528\ttotal: 3.6s\tremaining: 395ms\n",
      "901:\tlearn: 0.0008524\ttotal: 3.6s\tremaining: 391ms\n",
      "902:\tlearn: 0.0008521\ttotal: 3.6s\tremaining: 387ms\n",
      "903:\tlearn: 0.0008518\ttotal: 3.61s\tremaining: 383ms\n",
      "904:\tlearn: 0.0008515\ttotal: 3.61s\tremaining: 379ms\n",
      "905:\tlearn: 0.0008512\ttotal: 3.62s\tremaining: 375ms\n",
      "906:\tlearn: 0.0008509\ttotal: 3.62s\tremaining: 371ms\n",
      "907:\tlearn: 0.0008505\ttotal: 3.62s\tremaining: 367ms\n",
      "908:\tlearn: 0.0008502\ttotal: 3.63s\tremaining: 363ms\n",
      "909:\tlearn: 0.0008499\ttotal: 3.63s\tremaining: 359ms\n",
      "910:\tlearn: 0.0008496\ttotal: 3.63s\tremaining: 355ms\n",
      "911:\tlearn: 0.0008492\ttotal: 3.64s\tremaining: 351ms\n",
      "912:\tlearn: 0.0008488\ttotal: 3.64s\tremaining: 347ms\n",
      "913:\tlearn: 0.0008482\ttotal: 3.65s\tremaining: 343ms\n",
      "914:\tlearn: 0.0008479\ttotal: 3.65s\tremaining: 339ms\n",
      "915:\tlearn: 0.0008476\ttotal: 3.65s\tremaining: 335ms\n",
      "916:\tlearn: 0.0008471\ttotal: 3.66s\tremaining: 331ms\n",
      "917:\tlearn: 0.0008467\ttotal: 3.66s\tremaining: 327ms\n",
      "918:\tlearn: 0.0008464\ttotal: 3.69s\tremaining: 326ms\n",
      "919:\tlearn: 0.0008459\ttotal: 3.7s\tremaining: 322ms\n",
      "920:\tlearn: 0.0008454\ttotal: 3.7s\tremaining: 318ms\n",
      "921:\tlearn: 0.0008450\ttotal: 3.71s\tremaining: 314ms\n",
      "922:\tlearn: 0.0008446\ttotal: 3.71s\tremaining: 310ms\n",
      "923:\tlearn: 0.0008443\ttotal: 3.71s\tremaining: 305ms\n",
      "924:\tlearn: 0.0008438\ttotal: 3.72s\tremaining: 301ms\n",
      "925:\tlearn: 0.0008433\ttotal: 3.72s\tremaining: 297ms\n",
      "926:\tlearn: 0.0008430\ttotal: 3.72s\tremaining: 293ms\n",
      "927:\tlearn: 0.0008425\ttotal: 3.73s\tremaining: 289ms\n",
      "928:\tlearn: 0.0008420\ttotal: 3.73s\tremaining: 285ms\n",
      "929:\tlearn: 0.0008417\ttotal: 3.73s\tremaining: 281ms\n",
      "930:\tlearn: 0.0008411\ttotal: 3.74s\tremaining: 277ms\n",
      "931:\tlearn: 0.0008408\ttotal: 3.74s\tremaining: 273ms\n",
      "932:\tlearn: 0.0008405\ttotal: 3.75s\tremaining: 269ms\n",
      "933:\tlearn: 0.0008402\ttotal: 3.75s\tremaining: 265ms\n",
      "934:\tlearn: 0.0008399\ttotal: 3.75s\tremaining: 261ms\n",
      "935:\tlearn: 0.0008396\ttotal: 3.76s\tremaining: 257ms\n",
      "936:\tlearn: 0.0008393\ttotal: 3.76s\tremaining: 253ms\n",
      "937:\tlearn: 0.0008391\ttotal: 3.76s\tremaining: 249ms\n",
      "938:\tlearn: 0.0008387\ttotal: 3.77s\tremaining: 245ms\n",
      "939:\tlearn: 0.0008385\ttotal: 3.77s\tremaining: 241ms\n",
      "940:\tlearn: 0.0008383\ttotal: 3.78s\tremaining: 237ms\n",
      "941:\tlearn: 0.0008380\ttotal: 3.78s\tremaining: 233ms\n",
      "942:\tlearn: 0.0008377\ttotal: 3.79s\tremaining: 229ms\n",
      "943:\tlearn: 0.0008372\ttotal: 3.79s\tremaining: 225ms\n",
      "944:\tlearn: 0.0008367\ttotal: 3.79s\tremaining: 221ms\n",
      "945:\tlearn: 0.0008363\ttotal: 3.8s\tremaining: 217ms\n",
      "946:\tlearn: 0.0008358\ttotal: 3.8s\tremaining: 213ms\n",
      "947:\tlearn: 0.0008354\ttotal: 3.81s\tremaining: 209ms\n",
      "948:\tlearn: 0.0008349\ttotal: 3.81s\tremaining: 205ms\n",
      "949:\tlearn: 0.0008346\ttotal: 3.81s\tremaining: 201ms\n",
      "950:\tlearn: 0.0008342\ttotal: 3.82s\tremaining: 197ms\n",
      "951:\tlearn: 0.0008339\ttotal: 3.82s\tremaining: 193ms\n",
      "952:\tlearn: 0.0008331\ttotal: 3.83s\tremaining: 189ms\n",
      "953:\tlearn: 0.0008327\ttotal: 3.83s\tremaining: 185ms\n",
      "954:\tlearn: 0.0008324\ttotal: 3.84s\tremaining: 181ms\n",
      "955:\tlearn: 0.0008321\ttotal: 3.84s\tremaining: 177ms\n",
      "956:\tlearn: 0.0008319\ttotal: 3.85s\tremaining: 173ms\n",
      "957:\tlearn: 0.0008313\ttotal: 3.85s\tremaining: 169ms\n",
      "958:\tlearn: 0.0008307\ttotal: 3.86s\tremaining: 165ms\n",
      "959:\tlearn: 0.0008302\ttotal: 3.86s\tremaining: 161ms\n",
      "960:\tlearn: 0.0008298\ttotal: 3.87s\tremaining: 157ms\n",
      "961:\tlearn: 0.0008293\ttotal: 3.87s\tremaining: 153ms\n",
      "962:\tlearn: 0.0008289\ttotal: 3.88s\tremaining: 149ms\n",
      "963:\tlearn: 0.0008284\ttotal: 3.88s\tremaining: 145ms\n",
      "964:\tlearn: 0.0008280\ttotal: 3.89s\tremaining: 141ms\n",
      "965:\tlearn: 0.0008274\ttotal: 3.89s\tremaining: 137ms\n",
      "966:\tlearn: 0.0008269\ttotal: 3.9s\tremaining: 133ms\n",
      "967:\tlearn: 0.0008265\ttotal: 3.9s\tremaining: 129ms\n",
      "968:\tlearn: 0.0008263\ttotal: 3.9s\tremaining: 125ms\n",
      "969:\tlearn: 0.0008258\ttotal: 3.91s\tremaining: 121ms\n",
      "970:\tlearn: 0.0008256\ttotal: 3.91s\tremaining: 117ms\n",
      "971:\tlearn: 0.0008251\ttotal: 3.91s\tremaining: 113ms\n",
      "972:\tlearn: 0.0008249\ttotal: 3.92s\tremaining: 109ms\n",
      "973:\tlearn: 0.0008244\ttotal: 3.92s\tremaining: 105ms\n",
      "974:\tlearn: 0.0008240\ttotal: 3.93s\tremaining: 101ms\n",
      "975:\tlearn: 0.0008235\ttotal: 3.93s\tremaining: 96.6ms\n",
      "976:\tlearn: 0.0008233\ttotal: 3.93s\tremaining: 92.6ms\n",
      "977:\tlearn: 0.0008230\ttotal: 3.94s\tremaining: 88.6ms\n",
      "978:\tlearn: 0.0008228\ttotal: 3.94s\tremaining: 84.6ms\n",
      "979:\tlearn: 0.0008223\ttotal: 3.95s\tremaining: 80.6ms\n",
      "980:\tlearn: 0.0008219\ttotal: 3.95s\tremaining: 76.5ms\n",
      "981:\tlearn: 0.0008215\ttotal: 3.96s\tremaining: 72.5ms\n",
      "982:\tlearn: 0.0008212\ttotal: 3.96s\tremaining: 68.5ms\n",
      "983:\tlearn: 0.0008207\ttotal: 3.96s\tremaining: 64.5ms\n",
      "984:\tlearn: 0.0008203\ttotal: 3.97s\tremaining: 60.4ms\n",
      "985:\tlearn: 0.0008200\ttotal: 3.97s\tremaining: 56.4ms\n",
      "986:\tlearn: 0.0008197\ttotal: 3.98s\tremaining: 52.4ms\n",
      "987:\tlearn: 0.0008193\ttotal: 3.98s\tremaining: 48.4ms\n",
      "988:\tlearn: 0.0008189\ttotal: 3.98s\tremaining: 44.3ms\n",
      "989:\tlearn: 0.0008185\ttotal: 3.99s\tremaining: 40.3ms\n",
      "990:\tlearn: 0.0008181\ttotal: 3.99s\tremaining: 36.3ms\n",
      "991:\tlearn: 0.0008177\ttotal: 4s\tremaining: 32.2ms\n",
      "992:\tlearn: 0.0008175\ttotal: 4s\tremaining: 28.2ms\n",
      "993:\tlearn: 0.0008172\ttotal: 4s\tremaining: 24.2ms\n",
      "994:\tlearn: 0.0008167\ttotal: 4.01s\tremaining: 20.1ms\n",
      "995:\tlearn: 0.0008165\ttotal: 4.01s\tremaining: 16.1ms\n",
      "996:\tlearn: 0.0008162\ttotal: 4.01s\tremaining: 12.1ms\n",
      "997:\tlearn: 0.0008158\ttotal: 4.01s\tremaining: 8.05ms\n",
      "998:\tlearn: 0.0008154\ttotal: 4.02s\tremaining: 4.02ms\n",
      "999:\tlearn: 0.0008151\ttotal: 4.02s\tremaining: 0us\n",
      "current noise type: None\n",
      "{'Samples': 3686, 'Features': 400, 'Anomalies': 61, 'Anomalies Ratio(%)': 1.65}\n",
      "best param: None\n",
      "best param: None\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A8678735E8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A8678735E8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "Model: \"model_20\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_14 (InputLayer)       [(None, 400)]             0         \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 64)                25600     \n",
      "                                                                 \n",
      " net_output (Dense)          (None, 32)                2048      \n",
      "                                                                 \n",
      " tf.math.subtract_7 (TFOpLam  (None, 32)               0         \n",
      " bda)                                                            \n",
      "                                                                 \n",
      " tf.math.pow_7 (TFOpLambda)  (None, 32)                0         \n",
      "                                                                 \n",
      " tf.math.reduce_sum_7 (TFOpL  (None,)                  0         \n",
      " ambda)                                                          \n",
      "                                                                 \n",
      " tf.math.reduce_mean_7 (TFOp  ()                       0         \n",
      " Lambda)                                                         \n",
      "                                                                 \n",
      " tf.__operators__.add_7 (TFO  ()                       0         \n",
      " pLambda)                                                        \n",
      "                                                                 \n",
      " add_loss_7 (AddLoss)        ()                        0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 27,648\n",
      "Trainable params: 27,648\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/100\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000002A85DC31A68> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000002A85DC31A68> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "62/73 [========================>.....] - ETA: 0s - loss: 14.1699WARNING:tensorflow:AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x000002A85D984F78> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x000002A85D984F78> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "73/73 [==============================] - 1s 5ms/step - loss: 13.7096 - val_loss: 10.7606\n",
      "Epoch 2/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 8.8029 - val_loss: 8.2734\n",
      "Epoch 3/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 6.3712 - val_loss: 6.3979\n",
      "Epoch 4/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 4.6723 - val_loss: 5.0698\n",
      "Epoch 5/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 3.5562 - val_loss: 4.0794\n",
      "Epoch 6/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 2.8334 - val_loss: 3.4363\n",
      "Epoch 7/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 2.3737 - val_loss: 2.9787\n",
      "Epoch 8/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 2.0557 - val_loss: 2.6802\n",
      "Epoch 9/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.8331 - val_loss: 2.4538\n",
      "Epoch 10/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.6703 - val_loss: 2.2506\n",
      "Epoch 11/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.5635 - val_loss: 2.1735\n",
      "Epoch 12/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.5063 - val_loss: 2.0811\n",
      "Epoch 13/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.5180 - val_loss: 2.0496\n",
      "Epoch 14/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.6399 - val_loss: 2.1277\n",
      "Epoch 15/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.7435 - val_loss: 1.9969\n",
      "Epoch 16/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.7084 - val_loss: 1.9978\n",
      "Epoch 17/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.6801 - val_loss: 2.0267\n",
      "Epoch 18/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.6146 - val_loss: 1.8896\n",
      "Epoch 19/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.5570 - val_loss: 1.8790\n",
      "Epoch 20/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.5507 - val_loss: 1.8148\n",
      "Epoch 21/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.5324 - val_loss: 1.8493\n",
      "Epoch 22/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.5418 - val_loss: 1.8900\n",
      "Epoch 23/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.5640 - val_loss: 1.8532\n",
      "Epoch 24/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.5463 - val_loss: 1.8154\n",
      "Epoch 25/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.5441 - val_loss: 1.8160\n",
      "Epoch 26/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.5388 - val_loss: 1.6682\n",
      "Epoch 27/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.5241 - val_loss: 1.8004\n",
      "Epoch 28/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.4994 - val_loss: 1.7583\n",
      "Epoch 29/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.4537 - val_loss: 1.7828\n",
      "Epoch 30/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.4357 - val_loss: 1.7725\n",
      "Epoch 31/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.4487 - val_loss: 1.7560\n",
      "Epoch 32/100\n",
      "73/73 [==============================] - 0s 4ms/step - loss: 1.4586 - val_loss: 1.8040\n",
      "Epoch 33/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.4599 - val_loss: 1.7603\n",
      "Epoch 34/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.4531 - val_loss: 1.7134\n",
      "Epoch 35/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.4352 - val_loss: 1.7624\n",
      "Epoch 36/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.3956 - val_loss: 1.7228\n",
      "Epoch 37/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.3903 - val_loss: 1.6817\n",
      "Epoch 38/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.3735 - val_loss: 1.7670\n",
      "Epoch 39/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.3768 - val_loss: 1.7332\n",
      "Epoch 40/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.3592 - val_loss: 1.6484\n",
      "Epoch 41/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.3614 - val_loss: 1.7747\n",
      "Epoch 42/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.3772 - val_loss: 1.7110\n",
      "Epoch 43/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.3777 - val_loss: 1.6485\n",
      "Epoch 44/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.3511 - val_loss: 1.7285\n",
      "Epoch 45/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.3398 - val_loss: 1.6650\n",
      "Epoch 46/100\n",
      "73/73 [==============================] - 0s 4ms/step - loss: 1.3302 - val_loss: 1.7032\n",
      "Epoch 47/100\n",
      "73/73 [==============================] - 0s 4ms/step - loss: 1.3146 - val_loss: 1.6655\n",
      "Epoch 48/100\n",
      "73/73 [==============================] - 0s 4ms/step - loss: 1.3066 - val_loss: 1.6923\n",
      "Epoch 49/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.3444 - val_loss: 1.7287\n",
      "Epoch 50/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.3346 - val_loss: 1.6857\n",
      "Epoch 51/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.3030 - val_loss: 1.7302\n",
      "Epoch 52/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.2732 - val_loss: 1.7394\n",
      "Epoch 53/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.2596 - val_loss: 1.7327\n",
      "Epoch 54/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.2439 - val_loss: 1.6821\n",
      "Epoch 55/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.2424 - val_loss: 1.7538\n",
      "Epoch 56/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.2706 - val_loss: 1.6875\n",
      "Epoch 57/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.2773 - val_loss: 1.7262\n",
      "Epoch 58/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.2734 - val_loss: 1.8204\n",
      "Epoch 59/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.2769 - val_loss: 1.7243\n",
      "Epoch 60/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.2673 - val_loss: 1.7526\n",
      "Epoch 61/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.2488 - val_loss: 1.7065\n",
      "Epoch 62/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.2272 - val_loss: 1.7067\n",
      "Epoch 63/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.2177 - val_loss: 1.7384\n",
      "Epoch 64/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.2167 - val_loss: 1.7301\n",
      "Epoch 65/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.2173 - val_loss: 1.7222\n",
      "Epoch 66/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.2370 - val_loss: 1.7242\n",
      "Epoch 67/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.2442 - val_loss: 1.7574\n",
      "Epoch 68/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.2402 - val_loss: 1.7249\n",
      "Epoch 69/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.2239 - val_loss: 1.7383\n",
      "Epoch 70/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.2051 - val_loss: 1.7034\n",
      "Epoch 71/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.2046 - val_loss: 1.6765\n",
      "Epoch 72/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.2032 - val_loss: 1.7824\n",
      "Epoch 73/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1966 - val_loss: 1.7929\n",
      "Epoch 74/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.2087 - val_loss: 1.7334\n",
      "Epoch 75/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.2035 - val_loss: 1.7547\n",
      "Epoch 76/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.2192 - val_loss: 1.7397\n",
      "Epoch 77/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.2232 - val_loss: 1.8631\n",
      "Epoch 78/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.2136 - val_loss: 1.7719\n",
      "Epoch 79/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1926 - val_loss: 1.8415\n",
      "Epoch 80/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1773 - val_loss: 1.7112\n",
      "Epoch 81/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1775 - val_loss: 1.8018\n",
      "Epoch 82/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1715 - val_loss: 1.9076\n",
      "Epoch 83/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.1893 - val_loss: 1.8441\n",
      "Epoch 84/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1950 - val_loss: 1.8157\n",
      "Epoch 85/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.2133 - val_loss: 1.7801\n",
      "Epoch 86/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.2015 - val_loss: 1.8572\n",
      "Epoch 87/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1823 - val_loss: 1.8624\n",
      "Epoch 88/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1747 - val_loss: 1.7853\n",
      "Epoch 89/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1930 - val_loss: 1.8346\n",
      "Epoch 90/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1861 - val_loss: 1.8728\n",
      "Epoch 91/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1812 - val_loss: 1.8321\n",
      "Epoch 92/100\n",
      "73/73 [==============================] - 0s 3ms/step - loss: 1.1951 - val_loss: 1.8438\n",
      "Epoch 93/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1806 - val_loss: 1.8027\n",
      "Epoch 94/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1889 - val_loss: 1.8155\n",
      "Epoch 95/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1841 - val_loss: 1.9831\n",
      "Epoch 96/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1738 - val_loss: 1.9128\n",
      "Epoch 97/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1787 - val_loss: 1.9168\n",
      "Epoch 98/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1752 - val_loss: 2.0141\n",
      "Epoch 99/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1698 - val_loss: 1.9576\n",
      "Epoch 100/100\n",
      "73/73 [==============================] - 0s 2ms/step - loss: 1.1648 - val_loss: 1.9099\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A864C9E048> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A864C9E048> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "Training size: 2580, No. outliers: 4\n",
      "Epoch 1/50\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000002A86876DF78> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x000002A86876DF78> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "20/20 [==============================] - 1s 21ms/step - loss: 2.4112\n",
      "Epoch 2/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 1.5985\n",
      "Epoch 3/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 1.0839\n",
      "Epoch 4/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 0.8287\n",
      "Epoch 5/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.7233\n",
      "Epoch 6/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.6716\n",
      "Epoch 7/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 0.6518\n",
      "Epoch 8/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.6228\n",
      "Epoch 9/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.6249\n",
      "Epoch 10/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.5914\n",
      "Epoch 11/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.5713\n",
      "Epoch 12/50\n",
      "20/20 [==============================] - 0s 23ms/step - loss: 0.5510\n",
      "Epoch 13/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.5421\n",
      "Epoch 14/50\n",
      "20/20 [==============================] - 0s 23ms/step - loss: 0.5277\n",
      "Epoch 15/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.5157\n",
      "Epoch 16/50\n",
      "20/20 [==============================] - 0s 19ms/step - loss: 0.5187\n",
      "Epoch 17/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.5126\n",
      "Epoch 18/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.4980\n",
      "Epoch 19/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 0.5011\n",
      "Epoch 20/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.4903\n",
      "Epoch 21/50\n",
      "20/20 [==============================] - 0s 21ms/step - loss: 0.4827\n",
      "Epoch 22/50\n",
      "20/20 [==============================] - 0s 20ms/step - loss: 0.4823\n",
      "Epoch 23/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.4765\n",
      "Epoch 24/50\n",
      "20/20 [==============================] - 0s 24ms/step - loss: 0.4764\n",
      "Epoch 25/50\n",
      "20/20 [==============================] - 0s 23ms/step - loss: 0.4912\n",
      "Epoch 26/50\n",
      "20/20 [==============================] - 0s 24ms/step - loss: 0.4841\n",
      "Epoch 27/50\n",
      "20/20 [==============================] - 0s 22ms/step - loss: 0.4792\n",
      "Epoch 28/50\n",
      "20/20 [==============================] - 1s 31ms/step - loss: 0.4815\n",
      "Epoch 29/50\n",
      "20/20 [==============================] - 1s 30ms/step - loss: 0.4751\n",
      "Epoch 30/50\n",
      "20/20 [==============================] - 1s 27ms/step - loss: 0.4771\n",
      "Epoch 31/50\n",
      "20/20 [==============================] - 1s 35ms/step - loss: 0.4736\n",
      "Epoch 32/50\n",
      "20/20 [==============================] - 1s 28ms/step - loss: 0.4649\n",
      "Epoch 33/50\n",
      "20/20 [==============================] - 1s 40ms/step - loss: 0.4649\n",
      "Epoch 34/50\n",
      "20/20 [==============================] - 1s 33ms/step - loss: 0.4589\n",
      "Epoch 35/50\n",
      "20/20 [==============================] - 1s 31ms/step - loss: 0.4524\n",
      "Epoch 36/50\n",
      "20/20 [==============================] - 1s 29ms/step - loss: 0.4490\n",
      "Epoch 37/50\n",
      "20/20 [==============================] - 1s 31ms/step - loss: 0.4369\n",
      "Epoch 38/50\n",
      "20/20 [==============================] - 1s 29ms/step - loss: 0.4330\n",
      "Epoch 39/50\n",
      "20/20 [==============================] - 1s 32ms/step - loss: 0.4274\n",
      "Epoch 40/50\n",
      "20/20 [==============================] - 1s 28ms/step - loss: 0.4208\n",
      "Epoch 41/50\n",
      "20/20 [==============================] - 0s 23ms/step - loss: 0.4146\n",
      "Epoch 42/50\n",
      "20/20 [==============================] - 0s 24ms/step - loss: 0.3983\n",
      "Epoch 43/50\n",
      "20/20 [==============================] - 1s 26ms/step - loss: 0.3803\n",
      "Epoch 44/50\n",
      "20/20 [==============================] - 0s 24ms/step - loss: 0.3673\n",
      "Epoch 45/50\n",
      "20/20 [==============================] - 1s 26ms/step - loss: 0.3607\n",
      "Epoch 46/50\n",
      "20/20 [==============================] - 0s 24ms/step - loss: 0.3580\n",
      "Epoch 47/50\n",
      "20/20 [==============================] - 0s 23ms/step - loss: 0.3516\n",
      "Epoch 48/50\n",
      "20/20 [==============================] - 1s 30ms/step - loss: 0.3486\n",
      "Epoch 49/50\n",
      "20/20 [==============================] - 1s 26ms/step - loss: 0.3446\n",
      "Epoch 50/50\n",
      "20/20 [==============================] - 0s 23ms/step - loss: 0.3364\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A867F7A708> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x000002A867F7A708> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "Learning rate set to 0.015442\n",
      "0:\tlearn: 0.6519800\ttotal: 65.1ms\tremaining: 1m 4s\n",
      "1:\tlearn: 0.6141763\ttotal: 115ms\tremaining: 57.3s\n",
      "2:\tlearn: 0.5793682\ttotal: 178ms\tremaining: 59.1s\n",
      "3:\tlearn: 0.5469453\ttotal: 234ms\tremaining: 58.3s\n",
      "4:\tlearn: 0.5135033\ttotal: 294ms\tremaining: 58.5s\n",
      "5:\tlearn: 0.4836202\ttotal: 349ms\tremaining: 57.9s\n",
      "6:\tlearn: 0.4572832\ttotal: 411ms\tremaining: 58.3s\n",
      "7:\tlearn: 0.4309952\ttotal: 461ms\tremaining: 57.1s\n",
      "8:\tlearn: 0.4068804\ttotal: 515ms\tremaining: 56.7s\n",
      "9:\tlearn: 0.3838344\ttotal: 575ms\tremaining: 56.9s\n",
      "10:\tlearn: 0.3636639\ttotal: 633ms\tremaining: 56.9s\n",
      "11:\tlearn: 0.3425565\ttotal: 688ms\tremaining: 56.7s\n",
      "12:\tlearn: 0.3228878\ttotal: 751ms\tremaining: 57s\n",
      "13:\tlearn: 0.3047725\ttotal: 827ms\tremaining: 58.3s\n",
      "14:\tlearn: 0.2875810\ttotal: 921ms\tremaining: 1m\n",
      "15:\tlearn: 0.2718535\ttotal: 988ms\tremaining: 1m\n",
      "16:\tlearn: 0.2578197\ttotal: 1.04s\tremaining: 1m\n",
      "17:\tlearn: 0.2458827\ttotal: 1.11s\tremaining: 1m\n",
      "18:\tlearn: 0.2318924\ttotal: 1.17s\tremaining: 1m\n",
      "19:\tlearn: 0.2197141\ttotal: 1.24s\tremaining: 1m\n",
      "20:\tlearn: 0.2076366\ttotal: 1.31s\tremaining: 1m 1s\n",
      "21:\tlearn: 0.1966761\ttotal: 1.38s\tremaining: 1m 1s\n",
      "22:\tlearn: 0.1862458\ttotal: 1.44s\tremaining: 1m 1s\n",
      "23:\tlearn: 0.1764897\ttotal: 1.5s\tremaining: 1m\n",
      "24:\tlearn: 0.1679144\ttotal: 1.58s\tremaining: 1m 1s\n",
      "25:\tlearn: 0.1595817\ttotal: 1.66s\tremaining: 1m 2s\n",
      "26:\tlearn: 0.1513156\ttotal: 1.75s\tremaining: 1m 3s\n",
      "27:\tlearn: 0.1446037\ttotal: 1.81s\tremaining: 1m 2s\n",
      "28:\tlearn: 0.1372089\ttotal: 1.89s\tremaining: 1m 3s\n",
      "29:\tlearn: 0.1316271\ttotal: 1.97s\tremaining: 1m 3s\n",
      "30:\tlearn: 0.1259083\ttotal: 2.03s\tremaining: 1m 3s\n",
      "31:\tlearn: 0.1200037\ttotal: 2.1s\tremaining: 1m 3s\n",
      "32:\tlearn: 0.1146231\ttotal: 2.15s\tremaining: 1m 3s\n",
      "33:\tlearn: 0.1093592\ttotal: 2.21s\tremaining: 1m 2s\n",
      "34:\tlearn: 0.1037133\ttotal: 2.29s\tremaining: 1m 3s\n",
      "35:\tlearn: 0.0992422\ttotal: 2.35s\tremaining: 1m 2s\n",
      "36:\tlearn: 0.0950898\ttotal: 2.42s\tremaining: 1m 2s\n",
      "37:\tlearn: 0.0909492\ttotal: 2.48s\tremaining: 1m 2s\n",
      "38:\tlearn: 0.0861339\ttotal: 2.58s\tremaining: 1m 3s\n",
      "39:\tlearn: 0.0825773\ttotal: 2.66s\tremaining: 1m 3s\n",
      "40:\tlearn: 0.0788873\ttotal: 2.74s\tremaining: 1m 4s\n",
      "41:\tlearn: 0.0756329\ttotal: 2.82s\tremaining: 1m 4s\n",
      "42:\tlearn: 0.0724855\ttotal: 2.89s\tremaining: 1m 4s\n",
      "43:\tlearn: 0.0698313\ttotal: 2.97s\tremaining: 1m 4s\n",
      "44:\tlearn: 0.0671948\ttotal: 3.05s\tremaining: 1m 4s\n",
      "45:\tlearn: 0.0644992\ttotal: 3.12s\tremaining: 1m 4s\n",
      "46:\tlearn: 0.0615031\ttotal: 3.2s\tremaining: 1m 4s\n",
      "47:\tlearn: 0.0587636\ttotal: 3.27s\tremaining: 1m 4s\n",
      "48:\tlearn: 0.0565801\ttotal: 3.33s\tremaining: 1m 4s\n",
      "49:\tlearn: 0.0542690\ttotal: 3.4s\tremaining: 1m 4s\n",
      "50:\tlearn: 0.0523795\ttotal: 3.47s\tremaining: 1m 4s\n",
      "51:\tlearn: 0.0505209\ttotal: 3.55s\tremaining: 1m 4s\n",
      "52:\tlearn: 0.0487983\ttotal: 3.62s\tremaining: 1m 4s\n",
      "53:\tlearn: 0.0468892\ttotal: 3.7s\tremaining: 1m 4s\n",
      "54:\tlearn: 0.0450273\ttotal: 3.77s\tremaining: 1m 4s\n",
      "55:\tlearn: 0.0429567\ttotal: 3.83s\tremaining: 1m 4s\n",
      "56:\tlearn: 0.0413047\ttotal: 3.9s\tremaining: 1m 4s\n",
      "57:\tlearn: 0.0396675\ttotal: 3.97s\tremaining: 1m 4s\n",
      "58:\tlearn: 0.0381561\ttotal: 4.04s\tremaining: 1m 4s\n",
      "59:\tlearn: 0.0366893\ttotal: 4.1s\tremaining: 1m 4s\n",
      "60:\tlearn: 0.0352683\ttotal: 4.18s\tremaining: 1m 4s\n",
      "61:\tlearn: 0.0338490\ttotal: 4.26s\tremaining: 1m 4s\n",
      "62:\tlearn: 0.0325184\ttotal: 4.35s\tremaining: 1m 4s\n",
      "63:\tlearn: 0.0312976\ttotal: 4.45s\tremaining: 1m 5s\n",
      "64:\tlearn: 0.0303047\ttotal: 4.54s\tremaining: 1m 5s\n",
      "65:\tlearn: 0.0291863\ttotal: 4.65s\tremaining: 1m 5s\n",
      "66:\tlearn: 0.0281419\ttotal: 4.75s\tremaining: 1m 6s\n",
      "67:\tlearn: 0.0273320\ttotal: 4.85s\tremaining: 1m 6s\n",
      "68:\tlearn: 0.0264520\ttotal: 4.92s\tremaining: 1m 6s\n",
      "69:\tlearn: 0.0255417\ttotal: 4.99s\tremaining: 1m 6s\n",
      "70:\tlearn: 0.0246701\ttotal: 5.09s\tremaining: 1m 6s\n",
      "71:\tlearn: 0.0238603\ttotal: 5.16s\tremaining: 1m 6s\n",
      "72:\tlearn: 0.0230324\ttotal: 5.24s\tremaining: 1m 6s\n",
      "73:\tlearn: 0.0222120\ttotal: 5.32s\tremaining: 1m 6s\n",
      "74:\tlearn: 0.0214215\ttotal: 5.41s\tremaining: 1m 6s\n",
      "75:\tlearn: 0.0205265\ttotal: 5.51s\tremaining: 1m 6s\n",
      "76:\tlearn: 0.0198773\ttotal: 5.58s\tremaining: 1m 6s\n",
      "77:\tlearn: 0.0189936\ttotal: 5.65s\tremaining: 1m 6s\n",
      "78:\tlearn: 0.0184545\ttotal: 5.73s\tremaining: 1m 6s\n",
      "79:\tlearn: 0.0177718\ttotal: 5.84s\tremaining: 1m 7s\n",
      "80:\tlearn: 0.0173281\ttotal: 5.93s\tremaining: 1m 7s\n",
      "81:\tlearn: 0.0167529\ttotal: 6s\tremaining: 1m 7s\n",
      "82:\tlearn: 0.0162982\ttotal: 6.06s\tremaining: 1m 7s\n",
      "83:\tlearn: 0.0159052\ttotal: 6.13s\tremaining: 1m 6s\n",
      "84:\tlearn: 0.0154756\ttotal: 6.18s\tremaining: 1m 6s\n",
      "85:\tlearn: 0.0151403\ttotal: 6.25s\tremaining: 1m 6s\n",
      "86:\tlearn: 0.0146960\ttotal: 6.31s\tremaining: 1m 6s\n",
      "87:\tlearn: 0.0143105\ttotal: 6.37s\tremaining: 1m 6s\n",
      "88:\tlearn: 0.0139686\ttotal: 6.45s\tremaining: 1m 6s\n",
      "89:\tlearn: 0.0134883\ttotal: 6.51s\tremaining: 1m 5s\n",
      "90:\tlearn: 0.0131200\ttotal: 6.58s\tremaining: 1m 5s\n",
      "91:\tlearn: 0.0128085\ttotal: 6.65s\tremaining: 1m 5s\n",
      "92:\tlearn: 0.0124757\ttotal: 6.74s\tremaining: 1m 5s\n",
      "93:\tlearn: 0.0121864\ttotal: 6.81s\tremaining: 1m 5s\n",
      "94:\tlearn: 0.0118685\ttotal: 6.89s\tremaining: 1m 5s\n",
      "95:\tlearn: 0.0115390\ttotal: 6.98s\tremaining: 1m 5s\n",
      "96:\tlearn: 0.0113098\ttotal: 7.05s\tremaining: 1m 5s\n",
      "97:\tlearn: 0.0110794\ttotal: 7.11s\tremaining: 1m 5s\n",
      "98:\tlearn: 0.0108075\ttotal: 7.17s\tremaining: 1m 5s\n",
      "99:\tlearn: 0.0105582\ttotal: 7.22s\tremaining: 1m 5s\n",
      "100:\tlearn: 0.0103208\ttotal: 7.28s\tremaining: 1m 4s\n",
      "101:\tlearn: 0.0100762\ttotal: 7.35s\tremaining: 1m 4s\n",
      "102:\tlearn: 0.0098305\ttotal: 7.42s\tremaining: 1m 4s\n",
      "103:\tlearn: 0.0095520\ttotal: 7.49s\tremaining: 1m 4s\n",
      "104:\tlearn: 0.0093367\ttotal: 7.55s\tremaining: 1m 4s\n",
      "105:\tlearn: 0.0091264\ttotal: 7.63s\tremaining: 1m 4s\n",
      "106:\tlearn: 0.0088895\ttotal: 7.69s\tremaining: 1m 4s\n",
      "107:\tlearn: 0.0086312\ttotal: 7.78s\tremaining: 1m 4s\n",
      "108:\tlearn: 0.0084326\ttotal: 7.85s\tremaining: 1m 4s\n",
      "109:\tlearn: 0.0082302\ttotal: 7.94s\tremaining: 1m 4s\n",
      "110:\tlearn: 0.0080624\ttotal: 8.02s\tremaining: 1m 4s\n",
      "111:\tlearn: 0.0078822\ttotal: 8.11s\tremaining: 1m 4s\n",
      "112:\tlearn: 0.0077440\ttotal: 8.19s\tremaining: 1m 4s\n",
      "113:\tlearn: 0.0075966\ttotal: 8.27s\tremaining: 1m 4s\n",
      "114:\tlearn: 0.0074539\ttotal: 8.34s\tremaining: 1m 4s\n",
      "115:\tlearn: 0.0073115\ttotal: 8.41s\tremaining: 1m 4s\n",
      "116:\tlearn: 0.0071909\ttotal: 8.47s\tremaining: 1m 3s\n",
      "117:\tlearn: 0.0070748\ttotal: 8.53s\tremaining: 1m 3s\n",
      "118:\tlearn: 0.0069635\ttotal: 8.6s\tremaining: 1m 3s\n",
      "119:\tlearn: 0.0068173\ttotal: 8.67s\tremaining: 1m 3s\n",
      "120:\tlearn: 0.0067320\ttotal: 8.73s\tremaining: 1m 3s\n",
      "121:\tlearn: 0.0066351\ttotal: 8.79s\tremaining: 1m 3s\n",
      "122:\tlearn: 0.0065206\ttotal: 8.87s\tremaining: 1m 3s\n",
      "123:\tlearn: 0.0064628\ttotal: 8.93s\tremaining: 1m 3s\n",
      "124:\tlearn: 0.0063845\ttotal: 9s\tremaining: 1m 3s\n",
      "125:\tlearn: 0.0062677\ttotal: 9.06s\tremaining: 1m 2s\n",
      "126:\tlearn: 0.0061805\ttotal: 9.14s\tremaining: 1m 2s\n",
      "127:\tlearn: 0.0061049\ttotal: 9.22s\tremaining: 1m 2s\n",
      "128:\tlearn: 0.0060188\ttotal: 9.29s\tremaining: 1m 2s\n",
      "129:\tlearn: 0.0059141\ttotal: 9.35s\tremaining: 1m 2s\n",
      "130:\tlearn: 0.0058659\ttotal: 9.41s\tremaining: 1m 2s\n",
      "131:\tlearn: 0.0057534\ttotal: 9.47s\tremaining: 1m 2s\n",
      "132:\tlearn: 0.0056548\ttotal: 9.54s\tremaining: 1m 2s\n",
      "133:\tlearn: 0.0055811\ttotal: 9.6s\tremaining: 1m 2s\n",
      "134:\tlearn: 0.0054966\ttotal: 9.66s\tremaining: 1m 1s\n",
      "135:\tlearn: 0.0054143\ttotal: 9.73s\tremaining: 1m 1s\n",
      "136:\tlearn: 0.0053607\ttotal: 9.81s\tremaining: 1m 1s\n",
      "137:\tlearn: 0.0052962\ttotal: 9.89s\tremaining: 1m 1s\n",
      "138:\tlearn: 0.0052450\ttotal: 9.96s\tremaining: 1m 1s\n",
      "139:\tlearn: 0.0051713\ttotal: 10s\tremaining: 1m 1s\n",
      "140:\tlearn: 0.0051029\ttotal: 10.1s\tremaining: 1m 1s\n",
      "141:\tlearn: 0.0050632\ttotal: 10.2s\tremaining: 1m 1s\n",
      "142:\tlearn: 0.0049848\ttotal: 10.2s\tremaining: 1m 1s\n",
      "143:\tlearn: 0.0049144\ttotal: 10.3s\tremaining: 1m 1s\n",
      "144:\tlearn: 0.0048827\ttotal: 10.4s\tremaining: 1m 1s\n",
      "145:\tlearn: 0.0048237\ttotal: 10.5s\tremaining: 1m 1s\n",
      "146:\tlearn: 0.0047720\ttotal: 10.5s\tremaining: 1m 1s\n",
      "147:\tlearn: 0.0047135\ttotal: 10.6s\tremaining: 1m\n",
      "148:\tlearn: 0.0046538\ttotal: 10.7s\tremaining: 1m\n",
      "149:\tlearn: 0.0046265\ttotal: 10.7s\tremaining: 1m\n",
      "150:\tlearn: 0.0045932\ttotal: 10.8s\tremaining: 1m\n",
      "151:\tlearn: 0.0045343\ttotal: 10.9s\tremaining: 1m\n",
      "152:\tlearn: 0.0044779\ttotal: 10.9s\tremaining: 1m\n",
      "153:\tlearn: 0.0044200\ttotal: 11s\tremaining: 1m\n",
      "154:\tlearn: 0.0043873\ttotal: 11.1s\tremaining: 1m\n",
      "155:\tlearn: 0.0043421\ttotal: 11.2s\tremaining: 1m\n",
      "156:\tlearn: 0.0043176\ttotal: 11.2s\tremaining: 1m\n",
      "157:\tlearn: 0.0042617\ttotal: 11.3s\tremaining: 1m\n",
      "158:\tlearn: 0.0042250\ttotal: 11.4s\tremaining: 1m\n",
      "159:\tlearn: 0.0041698\ttotal: 11.5s\tremaining: 1m\n",
      "160:\tlearn: 0.0041418\ttotal: 11.6s\tremaining: 1m\n",
      "161:\tlearn: 0.0040869\ttotal: 11.6s\tremaining: 1m\n",
      "162:\tlearn: 0.0040650\ttotal: 11.7s\tremaining: 1m\n",
      "163:\tlearn: 0.0040243\ttotal: 11.8s\tremaining: 60s\n",
      "164:\tlearn: 0.0040020\ttotal: 11.8s\tremaining: 59.9s\n",
      "165:\tlearn: 0.0039621\ttotal: 11.9s\tremaining: 59.8s\n",
      "166:\tlearn: 0.0039298\ttotal: 12s\tremaining: 59.8s\n",
      "167:\tlearn: 0.0038923\ttotal: 12.1s\tremaining: 59.9s\n",
      "168:\tlearn: 0.0038518\ttotal: 12.2s\tremaining: 59.9s\n",
      "169:\tlearn: 0.0038143\ttotal: 12.3s\tremaining: 59.8s\n",
      "170:\tlearn: 0.0037884\ttotal: 12.3s\tremaining: 59.7s\n",
      "171:\tlearn: 0.0037711\ttotal: 12.4s\tremaining: 59.6s\n",
      "172:\tlearn: 0.0037389\ttotal: 12.5s\tremaining: 59.5s\n",
      "173:\tlearn: 0.0036837\ttotal: 12.5s\tremaining: 59.5s\n",
      "174:\tlearn: 0.0036387\ttotal: 12.6s\tremaining: 59.4s\n",
      "175:\tlearn: 0.0036022\ttotal: 12.7s\tremaining: 59.4s\n",
      "176:\tlearn: 0.0035786\ttotal: 12.7s\tremaining: 59.3s\n",
      "177:\tlearn: 0.0035578\ttotal: 12.8s\tremaining: 59.2s\n",
      "178:\tlearn: 0.0035165\ttotal: 12.9s\tremaining: 59s\n",
      "179:\tlearn: 0.0034920\ttotal: 12.9s\tremaining: 58.9s\n",
      "180:\tlearn: 0.0034535\ttotal: 13s\tremaining: 59s\n",
      "181:\tlearn: 0.0034256\ttotal: 13.1s\tremaining: 59.1s\n",
      "182:\tlearn: 0.0034065\ttotal: 13.2s\tremaining: 59.1s\n",
      "183:\tlearn: 0.0033807\ttotal: 13.3s\tremaining: 59.1s\n",
      "184:\tlearn: 0.0033459\ttotal: 13.4s\tremaining: 59.1s\n",
      "185:\tlearn: 0.0033239\ttotal: 13.5s\tremaining: 59.1s\n",
      "186:\tlearn: 0.0033031\ttotal: 13.6s\tremaining: 59.1s\n",
      "187:\tlearn: 0.0032712\ttotal: 13.7s\tremaining: 59s\n",
      "188:\tlearn: 0.0032419\ttotal: 13.8s\tremaining: 59s\n",
      "189:\tlearn: 0.0032227\ttotal: 13.8s\tremaining: 59s\n",
      "190:\tlearn: 0.0031950\ttotal: 13.9s\tremaining: 58.9s\n",
      "191:\tlearn: 0.0031742\ttotal: 14s\tremaining: 58.8s\n",
      "192:\tlearn: 0.0031545\ttotal: 14s\tremaining: 58.7s\n",
      "193:\tlearn: 0.0031328\ttotal: 14.1s\tremaining: 58.6s\n",
      "194:\tlearn: 0.0031098\ttotal: 14.2s\tremaining: 58.6s\n",
      "195:\tlearn: 0.0030784\ttotal: 14.2s\tremaining: 58.4s\n",
      "196:\tlearn: 0.0030558\ttotal: 14.3s\tremaining: 58.3s\n",
      "197:\tlearn: 0.0030391\ttotal: 14.4s\tremaining: 58.2s\n",
      "198:\tlearn: 0.0030170\ttotal: 14.4s\tremaining: 58.1s\n",
      "199:\tlearn: 0.0029999\ttotal: 14.5s\tremaining: 58s\n",
      "200:\tlearn: 0.0029726\ttotal: 14.6s\tremaining: 57.9s\n",
      "201:\tlearn: 0.0029465\ttotal: 14.6s\tremaining: 57.7s\n",
      "202:\tlearn: 0.0029157\ttotal: 14.7s\tremaining: 57.7s\n",
      "203:\tlearn: 0.0028913\ttotal: 14.8s\tremaining: 57.6s\n",
      "204:\tlearn: 0.0028470\ttotal: 14.8s\tremaining: 57.5s\n",
      "205:\tlearn: 0.0028214\ttotal: 14.9s\tremaining: 57.5s\n",
      "206:\tlearn: 0.0027935\ttotal: 15s\tremaining: 57.5s\n",
      "207:\tlearn: 0.0027827\ttotal: 15.1s\tremaining: 57.4s\n",
      "208:\tlearn: 0.0027542\ttotal: 15.2s\tremaining: 57.4s\n",
      "209:\tlearn: 0.0027282\ttotal: 15.2s\tremaining: 57.3s\n",
      "210:\tlearn: 0.0027057\ttotal: 15.3s\tremaining: 57.3s\n",
      "211:\tlearn: 0.0026749\ttotal: 15.4s\tremaining: 57.2s\n",
      "212:\tlearn: 0.0026659\ttotal: 15.5s\tremaining: 57.1s\n",
      "213:\tlearn: 0.0026480\ttotal: 15.6s\tremaining: 57.1s\n",
      "214:\tlearn: 0.0026220\ttotal: 15.7s\tremaining: 57.2s\n",
      "215:\tlearn: 0.0026091\ttotal: 15.7s\tremaining: 57.1s\n",
      "216:\tlearn: 0.0025887\ttotal: 15.8s\tremaining: 57s\n",
      "217:\tlearn: 0.0025743\ttotal: 15.9s\tremaining: 56.9s\n",
      "218:\tlearn: 0.0025605\ttotal: 15.9s\tremaining: 56.9s\n",
      "219:\tlearn: 0.0025318\ttotal: 16s\tremaining: 56.8s\n",
      "220:\tlearn: 0.0025066\ttotal: 16.1s\tremaining: 56.7s\n",
      "221:\tlearn: 0.0024888\ttotal: 16.2s\tremaining: 56.6s\n",
      "222:\tlearn: 0.0024753\ttotal: 16.2s\tremaining: 56.6s\n",
      "223:\tlearn: 0.0024519\ttotal: 16.3s\tremaining: 56.5s\n",
      "224:\tlearn: 0.0024237\ttotal: 16.4s\tremaining: 56.5s\n",
      "225:\tlearn: 0.0024139\ttotal: 16.5s\tremaining: 56.4s\n",
      "226:\tlearn: 0.0023947\ttotal: 16.6s\tremaining: 56.4s\n",
      "227:\tlearn: 0.0023736\ttotal: 16.6s\tremaining: 56.4s\n",
      "228:\tlearn: 0.0023648\ttotal: 16.7s\tremaining: 56.4s\n",
      "229:\tlearn: 0.0023514\ttotal: 16.8s\tremaining: 56.3s\n",
      "230:\tlearn: 0.0023328\ttotal: 16.9s\tremaining: 56.2s\n",
      "231:\tlearn: 0.0023123\ttotal: 17s\tremaining: 56.1s\n",
      "232:\tlearn: 0.0022932\ttotal: 17s\tremaining: 56.1s\n",
      "233:\tlearn: 0.0022664\ttotal: 17.1s\tremaining: 56s\n",
      "234:\tlearn: 0.0022542\ttotal: 17.2s\tremaining: 55.9s\n",
      "235:\tlearn: 0.0022326\ttotal: 17.3s\tremaining: 55.8s\n",
      "236:\tlearn: 0.0022128\ttotal: 17.3s\tremaining: 55.8s\n",
      "237:\tlearn: 0.0021976\ttotal: 17.4s\tremaining: 55.8s\n",
      "238:\tlearn: 0.0021829\ttotal: 17.5s\tremaining: 55.7s\n",
      "239:\tlearn: 0.0021634\ttotal: 17.6s\tremaining: 55.7s\n",
      "240:\tlearn: 0.0021456\ttotal: 17.7s\tremaining: 55.7s\n",
      "241:\tlearn: 0.0021350\ttotal: 17.7s\tremaining: 55.6s\n",
      "242:\tlearn: 0.0021240\ttotal: 17.8s\tremaining: 55.6s\n",
      "243:\tlearn: 0.0021076\ttotal: 17.9s\tremaining: 55.5s\n",
      "244:\tlearn: 0.0020954\ttotal: 18s\tremaining: 55.4s\n",
      "245:\tlearn: 0.0020714\ttotal: 18.1s\tremaining: 55.4s\n",
      "246:\tlearn: 0.0020626\ttotal: 18.1s\tremaining: 55.3s\n",
      "247:\tlearn: 0.0020532\ttotal: 18.2s\tremaining: 55.2s\n",
      "248:\tlearn: 0.0020383\ttotal: 18.3s\tremaining: 55.1s\n",
      "249:\tlearn: 0.0020200\ttotal: 18.3s\tremaining: 55s\n",
      "250:\tlearn: 0.0020011\ttotal: 18.4s\tremaining: 55s\n",
      "251:\tlearn: 0.0019914\ttotal: 18.5s\tremaining: 54.9s\n",
      "252:\tlearn: 0.0019818\ttotal: 18.6s\tremaining: 54.9s\n",
      "253:\tlearn: 0.0019751\ttotal: 18.7s\tremaining: 54.8s\n",
      "254:\tlearn: 0.0019662\ttotal: 18.7s\tremaining: 54.8s\n",
      "255:\tlearn: 0.0019525\ttotal: 18.8s\tremaining: 54.7s\n",
      "256:\tlearn: 0.0019392\ttotal: 18.9s\tremaining: 54.7s\n",
      "257:\tlearn: 0.0019291\ttotal: 19s\tremaining: 54.6s\n",
      "258:\tlearn: 0.0019158\ttotal: 19s\tremaining: 54.5s\n",
      "259:\tlearn: 0.0018984\ttotal: 19.1s\tremaining: 54.5s\n",
      "260:\tlearn: 0.0018680\ttotal: 19.2s\tremaining: 54.4s\n",
      "261:\tlearn: 0.0018521\ttotal: 19.3s\tremaining: 54.4s\n",
      "262:\tlearn: 0.0018402\ttotal: 19.4s\tremaining: 54.4s\n",
      "263:\tlearn: 0.0018237\ttotal: 19.5s\tremaining: 54.3s\n",
      "264:\tlearn: 0.0018132\ttotal: 19.5s\tremaining: 54.2s\n",
      "265:\tlearn: 0.0018062\ttotal: 19.6s\tremaining: 54.1s\n",
      "266:\tlearn: 0.0017945\ttotal: 19.7s\tremaining: 54s\n",
      "267:\tlearn: 0.0017856\ttotal: 19.8s\tremaining: 53.9s\n",
      "268:\tlearn: 0.0017735\ttotal: 19.8s\tremaining: 53.9s\n",
      "269:\tlearn: 0.0017626\ttotal: 19.9s\tremaining: 53.8s\n",
      "270:\tlearn: 0.0017557\ttotal: 20s\tremaining: 53.7s\n",
      "271:\tlearn: 0.0017495\ttotal: 20s\tremaining: 53.7s\n",
      "272:\tlearn: 0.0017333\ttotal: 20.1s\tremaining: 53.6s\n",
      "273:\tlearn: 0.0017205\ttotal: 20.2s\tremaining: 53.5s\n",
      "274:\tlearn: 0.0017128\ttotal: 20.2s\tremaining: 53.4s\n",
      "275:\tlearn: 0.0017014\ttotal: 20.3s\tremaining: 53.3s\n",
      "276:\tlearn: 0.0016951\ttotal: 20.4s\tremaining: 53.3s\n",
      "277:\tlearn: 0.0016886\ttotal: 20.5s\tremaining: 53.2s\n",
      "278:\tlearn: 0.0016817\ttotal: 20.5s\tremaining: 53.1s\n",
      "279:\tlearn: 0.0016703\ttotal: 20.6s\tremaining: 53s\n",
      "280:\tlearn: 0.0016635\ttotal: 20.7s\tremaining: 53s\n",
      "281:\tlearn: 0.0016510\ttotal: 20.8s\tremaining: 52.9s\n",
      "282:\tlearn: 0.0016335\ttotal: 20.9s\tremaining: 52.8s\n",
      "283:\tlearn: 0.0016220\ttotal: 20.9s\tremaining: 52.8s\n",
      "284:\tlearn: 0.0016169\ttotal: 21s\tremaining: 52.7s\n",
      "285:\tlearn: 0.0016097\ttotal: 21.1s\tremaining: 52.7s\n",
      "286:\tlearn: 0.0016042\ttotal: 21.2s\tremaining: 52.6s\n",
      "287:\tlearn: 0.0015976\ttotal: 21.2s\tremaining: 52.5s\n",
      "288:\tlearn: 0.0015919\ttotal: 21.3s\tremaining: 52.4s\n",
      "289:\tlearn: 0.0015854\ttotal: 21.4s\tremaining: 52.4s\n",
      "290:\tlearn: 0.0015771\ttotal: 21.5s\tremaining: 52.3s\n",
      "291:\tlearn: 0.0015723\ttotal: 21.5s\tremaining: 52.2s\n",
      "292:\tlearn: 0.0015658\ttotal: 21.6s\tremaining: 52.1s\n",
      "293:\tlearn: 0.0015617\ttotal: 21.7s\tremaining: 52.1s\n",
      "294:\tlearn: 0.0015527\ttotal: 21.8s\tremaining: 52s\n",
      "295:\tlearn: 0.0015415\ttotal: 21.8s\tremaining: 51.9s\n",
      "296:\tlearn: 0.0015357\ttotal: 21.9s\tremaining: 51.8s\n",
      "297:\tlearn: 0.0015288\ttotal: 22s\tremaining: 51.7s\n",
      "298:\tlearn: 0.0015246\ttotal: 22s\tremaining: 51.6s\n",
      "299:\tlearn: 0.0015144\ttotal: 22.1s\tremaining: 51.5s\n",
      "300:\tlearn: 0.0015060\ttotal: 22.2s\tremaining: 51.5s\n",
      "301:\tlearn: 0.0014951\ttotal: 22.2s\tremaining: 51.4s\n",
      "302:\tlearn: 0.0014833\ttotal: 22.3s\tremaining: 51.4s\n",
      "303:\tlearn: 0.0014777\ttotal: 22.4s\tremaining: 51.3s\n",
      "304:\tlearn: 0.0014690\ttotal: 22.5s\tremaining: 51.3s\n",
      "305:\tlearn: 0.0014586\ttotal: 22.6s\tremaining: 51.2s\n",
      "306:\tlearn: 0.0014495\ttotal: 22.7s\tremaining: 51.1s\n",
      "307:\tlearn: 0.0014393\ttotal: 22.7s\tremaining: 51s\n",
      "308:\tlearn: 0.0014312\ttotal: 22.8s\tremaining: 51s\n",
      "309:\tlearn: 0.0014226\ttotal: 22.8s\tremaining: 50.8s\n",
      "310:\tlearn: 0.0014190\ttotal: 22.9s\tremaining: 50.8s\n",
      "311:\tlearn: 0.0014117\ttotal: 23s\tremaining: 50.7s\n",
      "312:\tlearn: 0.0014030\ttotal: 23.1s\tremaining: 50.6s\n",
      "313:\tlearn: 0.0013971\ttotal: 23.1s\tremaining: 50.6s\n",
      "314:\tlearn: 0.0013918\ttotal: 23.2s\tremaining: 50.5s\n",
      "315:\tlearn: 0.0013832\ttotal: 23.3s\tremaining: 50.4s\n",
      "316:\tlearn: 0.0013745\ttotal: 23.3s\tremaining: 50.3s\n",
      "317:\tlearn: 0.0013681\ttotal: 23.4s\tremaining: 50.2s\n",
      "318:\tlearn: 0.0013581\ttotal: 23.5s\tremaining: 50.1s\n",
      "319:\tlearn: 0.0013420\ttotal: 23.6s\tremaining: 50.1s\n",
      "320:\tlearn: 0.0013353\ttotal: 23.6s\tremaining: 50s\n",
      "321:\tlearn: 0.0013311\ttotal: 23.7s\tremaining: 50s\n",
      "322:\tlearn: 0.0013253\ttotal: 23.8s\tremaining: 49.9s\n",
      "323:\tlearn: 0.0013169\ttotal: 23.9s\tremaining: 49.8s\n",
      "324:\tlearn: 0.0013102\ttotal: 24s\tremaining: 49.8s\n",
      "325:\tlearn: 0.0013009\ttotal: 24.1s\tremaining: 49.8s\n",
      "326:\tlearn: 0.0012916\ttotal: 24.1s\tremaining: 49.7s\n",
      "327:\tlearn: 0.0012828\ttotal: 24.2s\tremaining: 49.6s\n",
      "328:\tlearn: 0.0012780\ttotal: 24.3s\tremaining: 49.5s\n",
      "329:\tlearn: 0.0012697\ttotal: 24.4s\tremaining: 49.4s\n",
      "330:\tlearn: 0.0012617\ttotal: 24.4s\tremaining: 49.4s\n",
      "331:\tlearn: 0.0012532\ttotal: 24.5s\tremaining: 49.3s\n",
      "332:\tlearn: 0.0012472\ttotal: 24.6s\tremaining: 49.2s\n",
      "333:\tlearn: 0.0012358\ttotal: 24.6s\tremaining: 49.1s\n",
      "334:\tlearn: 0.0012286\ttotal: 24.7s\tremaining: 49s\n",
      "335:\tlearn: 0.0012255\ttotal: 24.8s\tremaining: 49s\n",
      "336:\tlearn: 0.0012180\ttotal: 24.9s\tremaining: 48.9s\n",
      "337:\tlearn: 0.0012101\ttotal: 24.9s\tremaining: 48.9s\n",
      "338:\tlearn: 0.0012070\ttotal: 25s\tremaining: 48.8s\n",
      "339:\tlearn: 0.0011985\ttotal: 25.1s\tremaining: 48.7s\n",
      "340:\tlearn: 0.0011953\ttotal: 25.2s\tremaining: 48.6s\n",
      "341:\tlearn: 0.0011879\ttotal: 25.2s\tremaining: 48.6s\n",
      "342:\tlearn: 0.0011854\ttotal: 25.3s\tremaining: 48.5s\n",
      "343:\tlearn: 0.0011782\ttotal: 25.4s\tremaining: 48.4s\n",
      "344:\tlearn: 0.0011714\ttotal: 25.4s\tremaining: 48.3s\n",
      "345:\tlearn: 0.0011649\ttotal: 25.5s\tremaining: 48.2s\n",
      "346:\tlearn: 0.0011571\ttotal: 25.6s\tremaining: 48.1s\n",
      "347:\tlearn: 0.0011514\ttotal: 25.6s\tremaining: 48.1s\n",
      "348:\tlearn: 0.0011463\ttotal: 25.7s\tremaining: 48s\n",
      "349:\tlearn: 0.0011433\ttotal: 25.8s\tremaining: 47.9s\n",
      "350:\tlearn: 0.0011374\ttotal: 25.9s\tremaining: 47.8s\n",
      "351:\tlearn: 0.0011309\ttotal: 25.9s\tremaining: 47.7s\n",
      "352:\tlearn: 0.0011281\ttotal: 26s\tremaining: 47.7s\n",
      "353:\tlearn: 0.0011220\ttotal: 26.1s\tremaining: 47.6s\n",
      "354:\tlearn: 0.0011198\ttotal: 26.1s\tremaining: 47.5s\n",
      "355:\tlearn: 0.0011124\ttotal: 26.2s\tremaining: 47.4s\n",
      "356:\tlearn: 0.0011093\ttotal: 26.3s\tremaining: 47.4s\n",
      "357:\tlearn: 0.0011029\ttotal: 26.4s\tremaining: 47.3s\n",
      "358:\tlearn: 0.0010947\ttotal: 26.4s\tremaining: 47.2s\n",
      "359:\tlearn: 0.0010917\ttotal: 26.5s\tremaining: 47.1s\n",
      "360:\tlearn: 0.0010858\ttotal: 26.6s\tremaining: 47s\n",
      "361:\tlearn: 0.0010800\ttotal: 26.6s\tremaining: 47s\n",
      "362:\tlearn: 0.0010742\ttotal: 26.7s\tremaining: 46.9s\n",
      "363:\tlearn: 0.0010709\ttotal: 26.8s\tremaining: 46.9s\n",
      "364:\tlearn: 0.0010641\ttotal: 26.9s\tremaining: 46.8s\n",
      "365:\tlearn: 0.0010553\ttotal: 27s\tremaining: 46.8s\n",
      "366:\tlearn: 0.0010498\ttotal: 27.1s\tremaining: 46.7s\n",
      "367:\tlearn: 0.0010444\ttotal: 27.1s\tremaining: 46.6s\n",
      "368:\tlearn: 0.0010414\ttotal: 27.2s\tremaining: 46.5s\n",
      "369:\tlearn: 0.0010382\ttotal: 27.3s\tremaining: 46.5s\n",
      "370:\tlearn: 0.0010318\ttotal: 27.4s\tremaining: 46.4s\n",
      "371:\tlearn: 0.0010256\ttotal: 27.4s\tremaining: 46.3s\n",
      "372:\tlearn: 0.0010176\ttotal: 27.5s\tremaining: 46.2s\n",
      "373:\tlearn: 0.0010118\ttotal: 27.6s\tremaining: 46.1s\n",
      "374:\tlearn: 0.0010098\ttotal: 27.6s\tremaining: 46s\n",
      "375:\tlearn: 0.0010058\ttotal: 27.7s\tremaining: 45.9s\n",
      "376:\tlearn: 0.0009948\ttotal: 27.8s\tremaining: 45.9s\n",
      "377:\tlearn: 0.0009894\ttotal: 27.8s\tremaining: 45.8s\n",
      "378:\tlearn: 0.0009837\ttotal: 27.9s\tremaining: 45.7s\n",
      "379:\tlearn: 0.0009784\ttotal: 28s\tremaining: 45.7s\n",
      "380:\tlearn: 0.0009735\ttotal: 28.1s\tremaining: 45.6s\n",
      "381:\tlearn: 0.0009683\ttotal: 28.2s\tremaining: 45.6s\n",
      "382:\tlearn: 0.0009647\ttotal: 28.3s\tremaining: 45.5s\n",
      "383:\tlearn: 0.0009594\ttotal: 28.3s\tremaining: 45.5s\n",
      "384:\tlearn: 0.0009575\ttotal: 28.4s\tremaining: 45.4s\n",
      "385:\tlearn: 0.0009526\ttotal: 28.5s\tremaining: 45.3s\n",
      "386:\tlearn: 0.0009447\ttotal: 28.5s\tremaining: 45.2s\n",
      "387:\tlearn: 0.0009428\ttotal: 28.6s\tremaining: 45.1s\n",
      "388:\tlearn: 0.0009386\ttotal: 28.7s\tremaining: 45s\n",
      "389:\tlearn: 0.0009320\ttotal: 28.7s\tremaining: 45s\n",
      "390:\tlearn: 0.0009293\ttotal: 28.8s\tremaining: 44.9s\n",
      "391:\tlearn: 0.0009270\ttotal: 28.9s\tremaining: 44.8s\n",
      "392:\tlearn: 0.0009242\ttotal: 29s\tremaining: 44.8s\n",
      "393:\tlearn: 0.0009164\ttotal: 29.1s\tremaining: 44.7s\n",
      "394:\tlearn: 0.0009139\ttotal: 29.1s\tremaining: 44.6s\n",
      "395:\tlearn: 0.0009113\ttotal: 29.2s\tremaining: 44.5s\n",
      "396:\tlearn: 0.0009059\ttotal: 29.3s\tremaining: 44.4s\n",
      "397:\tlearn: 0.0009028\ttotal: 29.3s\tremaining: 44.3s\n",
      "398:\tlearn: 0.0008981\ttotal: 29.4s\tremaining: 44.3s\n",
      "399:\tlearn: 0.0008947\ttotal: 29.5s\tremaining: 44.2s\n",
      "400:\tlearn: 0.0008913\ttotal: 29.5s\tremaining: 44.1s\n",
      "401:\tlearn: 0.0008869\ttotal: 29.6s\tremaining: 44s\n",
      "402:\tlearn: 0.0008814\ttotal: 29.7s\tremaining: 43.9s\n",
      "403:\tlearn: 0.0008736\ttotal: 29.7s\tremaining: 43.9s\n",
      "404:\tlearn: 0.0008710\ttotal: 29.8s\tremaining: 43.8s\n",
      "405:\tlearn: 0.0008691\ttotal: 29.9s\tremaining: 43.7s\n",
      "406:\tlearn: 0.0008651\ttotal: 29.9s\tremaining: 43.6s\n",
      "407:\tlearn: 0.0008605\ttotal: 30s\tremaining: 43.5s\n",
      "408:\tlearn: 0.0008559\ttotal: 30.1s\tremaining: 43.5s\n",
      "409:\tlearn: 0.0008520\ttotal: 30.2s\tremaining: 43.4s\n",
      "410:\tlearn: 0.0008479\ttotal: 30.2s\tremaining: 43.3s\n",
      "411:\tlearn: 0.0008440\ttotal: 30.3s\tremaining: 43.2s\n",
      "412:\tlearn: 0.0008387\ttotal: 30.4s\tremaining: 43.2s\n",
      "413:\tlearn: 0.0008351\ttotal: 30.4s\tremaining: 43.1s\n",
      "414:\tlearn: 0.0008331\ttotal: 30.5s\tremaining: 43s\n",
      "415:\tlearn: 0.0008290\ttotal: 30.6s\tremaining: 43s\n",
      "416:\tlearn: 0.0008257\ttotal: 30.7s\tremaining: 42.9s\n",
      "417:\tlearn: 0.0008208\ttotal: 30.7s\tremaining: 42.8s\n",
      "418:\tlearn: 0.0008178\ttotal: 30.8s\tremaining: 42.7s\n",
      "419:\tlearn: 0.0008141\ttotal: 30.8s\tremaining: 42.6s\n",
      "420:\tlearn: 0.0008103\ttotal: 30.9s\tremaining: 42.5s\n",
      "421:\tlearn: 0.0008069\ttotal: 31s\tremaining: 42.4s\n",
      "422:\tlearn: 0.0008053\ttotal: 31.1s\tremaining: 42.4s\n",
      "423:\tlearn: 0.0007990\ttotal: 31.2s\tremaining: 42.3s\n",
      "424:\tlearn: 0.0007928\ttotal: 31.2s\tremaining: 42.2s\n",
      "425:\tlearn: 0.0007908\ttotal: 31.3s\tremaining: 42.2s\n",
      "426:\tlearn: 0.0007872\ttotal: 31.4s\tremaining: 42.1s\n",
      "427:\tlearn: 0.0007839\ttotal: 31.4s\tremaining: 42s\n",
      "428:\tlearn: 0.0007812\ttotal: 31.5s\tremaining: 41.9s\n",
      "429:\tlearn: 0.0007776\ttotal: 31.6s\tremaining: 41.8s\n",
      "430:\tlearn: 0.0007743\ttotal: 31.6s\tremaining: 41.7s\n",
      "431:\tlearn: 0.0007706\ttotal: 31.7s\tremaining: 41.7s\n",
      "432:\tlearn: 0.0007672\ttotal: 31.7s\tremaining: 41.6s\n",
      "433:\tlearn: 0.0007643\ttotal: 31.8s\tremaining: 41.5s\n",
      "434:\tlearn: 0.0007625\ttotal: 31.9s\tremaining: 41.4s\n",
      "435:\tlearn: 0.0007600\ttotal: 31.9s\tremaining: 41.3s\n",
      "436:\tlearn: 0.0007566\ttotal: 32s\tremaining: 41.2s\n",
      "437:\tlearn: 0.0007534\ttotal: 32.1s\tremaining: 41.2s\n",
      "438:\tlearn: 0.0007499\ttotal: 32.1s\tremaining: 41.1s\n",
      "439:\tlearn: 0.0007483\ttotal: 32.2s\tremaining: 41s\n",
      "440:\tlearn: 0.0007450\ttotal: 32.3s\tremaining: 40.9s\n",
      "441:\tlearn: 0.0007429\ttotal: 32.3s\tremaining: 40.8s\n",
      "442:\tlearn: 0.0007398\ttotal: 32.4s\tremaining: 40.7s\n",
      "443:\tlearn: 0.0007362\ttotal: 32.5s\tremaining: 40.7s\n",
      "444:\tlearn: 0.0007300\ttotal: 32.5s\tremaining: 40.6s\n",
      "445:\tlearn: 0.0007259\ttotal: 32.6s\tremaining: 40.5s\n",
      "446:\tlearn: 0.0007226\ttotal: 32.7s\tremaining: 40.4s\n",
      "447:\tlearn: 0.0007210\ttotal: 32.8s\tremaining: 40.4s\n",
      "448:\tlearn: 0.0007193\ttotal: 32.8s\tremaining: 40.3s\n",
      "449:\tlearn: 0.0007161\ttotal: 32.9s\tremaining: 40.2s\n",
      "450:\tlearn: 0.0007129\ttotal: 33s\tremaining: 40.2s\n",
      "451:\tlearn: 0.0007112\ttotal: 33.1s\tremaining: 40.1s\n",
      "452:\tlearn: 0.0007078\ttotal: 33.1s\tremaining: 40s\n",
      "453:\tlearn: 0.0007050\ttotal: 33.2s\tremaining: 40s\n",
      "454:\tlearn: 0.0007011\ttotal: 33.3s\tremaining: 39.9s\n",
      "455:\tlearn: 0.0006981\ttotal: 33.4s\tremaining: 39.8s\n",
      "456:\tlearn: 0.0006967\ttotal: 33.5s\tremaining: 39.8s\n",
      "457:\tlearn: 0.0006952\ttotal: 33.6s\tremaining: 39.7s\n",
      "458:\tlearn: 0.0006919\ttotal: 33.6s\tremaining: 39.6s\n",
      "459:\tlearn: 0.0006904\ttotal: 33.7s\tremaining: 39.6s\n",
      "460:\tlearn: 0.0006874\ttotal: 33.8s\tremaining: 39.5s\n",
      "461:\tlearn: 0.0006861\ttotal: 33.8s\tremaining: 39.4s\n",
      "462:\tlearn: 0.0006832\ttotal: 33.9s\tremaining: 39.3s\n",
      "463:\tlearn: 0.0006806\ttotal: 34s\tremaining: 39.2s\n",
      "464:\tlearn: 0.0006772\ttotal: 34s\tremaining: 39.2s\n",
      "465:\tlearn: 0.0006753\ttotal: 34.1s\tremaining: 39.1s\n",
      "466:\tlearn: 0.0006726\ttotal: 34.2s\tremaining: 39s\n",
      "467:\tlearn: 0.0006708\ttotal: 34.3s\tremaining: 39s\n",
      "468:\tlearn: 0.0006685\ttotal: 34.3s\tremaining: 38.9s\n",
      "469:\tlearn: 0.0006657\ttotal: 34.4s\tremaining: 38.8s\n",
      "470:\tlearn: 0.0006630\ttotal: 34.5s\tremaining: 38.7s\n",
      "471:\tlearn: 0.0006601\ttotal: 34.5s\tremaining: 38.6s\n",
      "472:\tlearn: 0.0006572\ttotal: 34.6s\tremaining: 38.6s\n",
      "473:\tlearn: 0.0006551\ttotal: 34.7s\tremaining: 38.5s\n",
      "474:\tlearn: 0.0006526\ttotal: 34.7s\tremaining: 38.4s\n",
      "475:\tlearn: 0.0006497\ttotal: 34.8s\tremaining: 38.3s\n",
      "476:\tlearn: 0.0006485\ttotal: 34.9s\tremaining: 38.2s\n",
      "477:\tlearn: 0.0006467\ttotal: 34.9s\tremaining: 38.1s\n",
      "478:\tlearn: 0.0006440\ttotal: 35s\tremaining: 38.1s\n"
     ]
    }
   ],
   "source": [
    "# seed for reproducible results\n",
    "seed = 42\n",
    "\n",
    "for dataset in dataset_list:\n",
    "    '''\n",
    "    la: ratio of labeled anomalies, from 0.0 to 1.0\n",
    "    realistic_synthetic_mode: types of synthetic anomalies, can be local, global, dependency or cluster\n",
    "    noise_type: inject data noises for testing model robustness, can be duplicated_anomalies, irrelevant_features or label_contamination\n",
    "    '''\n",
    "    \n",
    "    # import the dataset\n",
    "    datagenerator.dataset = dataset # specify the dataset name\n",
    "    data = datagenerator.generator(la=0.1, realistic_synthetic_mode=None, noise_type=None) # only 10% labeled anomalies are available\n",
    "    \n",
    "    for name, clf in model_dict.items():\n",
    "        # model initialization\n",
    "        clf = clf(seed=seed, model_name=name)\n",
    "        \n",
    "        # training, for unsupervised models the y label will be discarded\n",
    "        clf = clf.fit(X_train=data['X_train'], y_train=data['y_train'])\n",
    "        \n",
    "        # output predicted anomaly score on testing set\n",
    "        score = clf.predict_score(data['X_test'])\n",
    "\n",
    "        # evaluation\n",
    "        result = utils.metric(y_true=data['y_test'], y_score=score)\n",
    "        \n",
    "        # save results\n",
    "        df_AUCROC.loc[dataset, name] = result['aucroc']\n",
    "        df_AUCPR.loc[dataset, name] = result['aucpr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T07:50:14.555117Z",
     "start_time": "2022-07-08T07:50:14.511234Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df_AUCROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-08T07:50:14.587032Z",
     "start_time": "2022-07-08T07:50:14.560106Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df_AUCPR"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}