Optimize TF-IDF XGB Hyperopt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import sys\n",
    "# !{sys.executable} -m pip install imbalanced-learn --user\n",
    "# !{sys.executable} -m pip install hyperopt --user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/newsgac/nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy\n",
    "from time import time\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from hyperopt import tpe, fmin, hp, STATUS_OK, Trials\n",
    "from sklearn.metrics import accuracy_score, log_loss, roc_auc_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from copy import deepcopy\n",
    "\n",
    "from newsgac import config\n",
    "from newsgac.genres import genre_codes\n",
    "from newsgac.learners import learners, LearnerSVC, LearnerNB, LearnerXGB, LearnerGB, LearnerMLP, LearnerRF, LearnerLGBM\n",
    "from newsgac.pipelines.get_sk_pipeline import get_sk_pipeline\n",
    "from newsgac.pipelines.utils import report\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "\n",
    "from newsgac import database\n",
    "from newsgac.data_sources import DataSource\n",
    "from newsgac.pipelines import Pipeline\n",
    "\n",
    "# from imblearn.under_sampling import RandomUnderSampler\n",
    "# from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN\n",
    "# from imblearn.combine import SMOTEENN, SMOTETomek\n",
    "import numpy as np\n",
    "import nltk\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'N2BGS Training',\n",
       " u'N2BGS Testing',\n",
       " u'Linked NRC (2930)',\n",
       " u'BOW + 5 features (N2BGS train)',\n",
       " u'BOW + 5 features (N2BGS test)',\n",
       " u'nrc-1950-1994-a.txt',\n",
       " u'nrc-1950-1994-b.txt',\n",
       " u'nrc-1950-1994-c.txt',\n",
       " u'nrc-1950-1994-d.txt',\n",
       " u'nrc-1950-1994-e.txt',\n",
       " u'telegraaf-1950-1994-a.txt',\n",
       " u'telegraaf-1950-1994-b.txt',\n",
       " u'telegraaf-1950-1994-c.txt',\n",
       " u'telegraaf-1950-1994-d.txt',\n",
       " u'telegraaf-1950-1994-e.txt',\n",
       " u'volkskrant-1950-1995-a.txt',\n",
       " u'volkskrant-1950-1995-b.txt',\n",
       " u'volkskrant-1950-1995-c.txt',\n",
       " u'volkskrant-1950-1995-d.txt',\n",
       " u'volkskrant-1950-1995-e.txt',\n",
       " u'nrc-1965.txt',\n",
       " u'nrc-1985.txt',\n",
       " u'BOW + 9 features (N2BGS train)',\n",
       " u'BOW + 9 features (N2BGS test)',\n",
       " u'Linked NRC (2930/9 features)',\n",
       " u'BOW Train unique (9 features)',\n",
       " u'BOW Test unique (9 features)',\n",
       " u'Linked NRC (unique/9 features)',\n",
       " u'BOW Train unique (N3BGS/9 features)',\n",
       " u'N3BGS FROG Test',\n",
       " u'N3BGS FROG Train',\n",
       " u'BOW Test unique (N3BGS/9 features)']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[d.display_title for d in DataSource.objects.all()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'dummy frog',\n",
       " u'dummy bow',\n",
       " u'RF FROG',\n",
       " u'SVC FROG',\n",
       " u'XGB FROG',\n",
       " u'NB FROG',\n",
       " u'MLP FROG',\n",
       " u'RF BOW',\n",
       " u'SVC BOW',\n",
       " u'XGB BOW',\n",
       " u'NB BOW',\n",
       " u'MLP BOW',\n",
       " u'SVC LIN BOW',\n",
       " u'Erik MLP BOW (with stop-words)',\n",
       " u'Erik MLP BOW (5 features) ',\n",
       " u'Erik MLP BOW (9 features) ',\n",
       " u'Erik SVC BOW (9 features)',\n",
       " u'Erik RF BOW (9 features)',\n",
       " u'Erik XGB BOW (9 features)',\n",
       " u'Erik NB BOW (9 features)',\n",
       " u'Erik MLP unique 9 features',\n",
       " u'Erik NB unique (9 features)',\n",
       " u'Erik XGB unique (9 features)',\n",
       " u'Erik SVC unique (9 features)',\n",
       " u'Erik RF unique (9 features)',\n",
       " u'N3BGS MLP unique 9 features',\n",
       " u'N3BGS NB unique 9 features ',\n",
       " u'N3BGS SVC unique 9 features ',\n",
       " u'N3BGS RF unique 9 features',\n",
       " u'N3BGS XGB unique 9 features ',\n",
       " u'dummy n3bgs bow',\n",
       " u'dummy n3bgs frog',\n",
       " u'RF FROG N3BGS',\n",
       " u'SVC FROG N3BGS',\n",
       " u'XGB FROG N3BGS']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[d.display_title for d in Pipeline.objects.all()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataSource: BOW Train unique (N3BGS/9 features)\n",
      "NLP Tool: TF-IDF\n",
      "Classifier: Support Vector\n",
      "Task status: Status.SUCCESS\n"
     ]
    }
   ],
   "source": [
    "p = Pipeline.objects.all()[30]\n",
    "print 'DataSource: ' + p.data_source.display_title\n",
    "print 'NLP Tool: ' + p.nlp_tool.name\n",
    "print 'Classifier: ' + p.learner.name\n",
    "print 'Task status: ' + str(p.task.status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "857"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(p.data_source.articles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing data source: BOW Test unique (N3BGS/9 features)\n"
     ]
    }
   ],
   "source": [
    "test_data_source = DataSource.objects.all()[31]\n",
    "print 'Testing data source: ' + test_data_source.display_title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('CleanOCR', <newsgac.nlp_tools.transformers.CleanOCR at 0x7f8550114310>),\n",
       " ('StopWordRemoval',\n",
       "  <newsgac.nlp_tools.transformers.StopWordRemoval at 0x7f8550114350>),\n",
       " ('FeatureExtraction', FeatureUnion(n_jobs=None,\n",
       "         transformer_list=[('TFIDF', Pipeline(memory=None,\n",
       "       steps=[('RemoveQuotes', <newsgac.nlp_tools.transformers.RemoveQuotes object at 0x7f8550114390>), ('TF-IDF', TfidfVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',\n",
       "          dtype=<type 'numpy.float64'>, encoding=u'utf-8', input=u'content',\n",
       "          lowercase=True, max_df=1.0, max_featu...        token_pattern=u'(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
       "          vocabulary=None))]))],\n",
       "         transformer_weights=None))]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skp = p.get_sk_pipeline()\n",
    "skp_opt = deepcopy(skp)\n",
    "skp_opt.steps.pop()\n",
    "skp_opt.steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "texts = numpy.array([article.raw_text for article in p.data_source.articles])\n",
    "labels = numpy.array([article.label for article in p.data_source.articles])\n",
    "\n",
    "X = skp_opt.fit_transform(texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the test dataset for optimization accuracy\n",
    "texts_test = numpy.array([article.raw_text for article in test_data_source.articles])\n",
    "labels_test = numpy.array([article.label for article in test_data_source.articles])\n",
    "\n",
    "X_test = skp_opt.transform(texts_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X\n",
    "y_train = labels\n",
    "y_test = labels_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(685, 8528)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(172,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "importance_type_list = ['gain', 'weight', 'cover', 'total_gain', 'total_cover']\n",
    "booster_list = ['gbtree', 'gblinear', 'dart']\n",
    "max_depth_list = np.arange(1, 10, dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize(evals, trials, optimizer=tpe.suggest):\n",
    "    space = {\n",
    "#         'n_estimators': hp.quniform('n_estimators', 100, 1000, 1),\n",
    "        'n_estimators': hp.choice('n_estimators', np.arange(10, 500, dtype=int)),\n",
    "        'learning_rate': hp.quniform('learning_rate', 0.025, 0.5, 0.025),\n",
    "        # A problem with max_depth casted to float instead of int with\n",
    "        # the hp.quniform method.\n",
    "        'max_depth':  hp.choice('max_depth', max_depth_list),\n",
    "#         'max_depth': hp.quniform('max_depth', 1, 30, 1),\n",
    "        'min_child_weight': hp.quniform('min_child_weight', 1, 6, 1),\n",
    "        'subsample': hp.quniform('subsample', 0.5, 1, 0.05),\n",
    "        'gamma': hp.quniform('gamma', 0.5, 1, 0.05),\n",
    "        'colsample_bytree': hp.quniform('colsample_bytree', 0.5, 1, 0.05),\n",
    "        'importance_type': hp.choice('importance_type', importance_type_list),\n",
    "#         'eval_metric': 'auc',\n",
    "#         'objective': 'binary:logistic',\n",
    "        # Increase this number if you have more cores. Otherwise, remove it and it will default \n",
    "        # to the maxium number. \n",
    "#         'nthread': 48,\n",
    "        'booster': hp.choice('booster', booster_list),\n",
    "#         'tree_method': 'exact',\n",
    "#         'silent': 1,\n",
    "#         'seed': 42\n",
    "    }\n",
    "    best = fmin(score, space, algo=optimizer, max_evals=evals, trials=trials)\n",
    "    pbar.close()\n",
    "    return best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_acc_list = []\n",
    "train_loss_list = []\n",
    "test_acc_list = []\n",
    "test_loss_list = []\n",
    "\n",
    "def score(params):\n",
    "    eval_set=[(X_train, y_train), (X_val, y_val)]\n",
    "    \n",
    "    model = XGBClassifier(n_estimators = params['n_estimators'],\n",
    "                          learning_rate = params['learning_rate'],\n",
    "                            max_depth = params['max_depth'],\n",
    "                            min_child_weight = params['min_child_weight'],\n",
    "                            subsample = params['subsample'], \n",
    "                            gamma = params['gamma'],\n",
    "                            colsample_bytree = params['colsample_bytree'],\n",
    "                            importance_type = params['importance_type'],\n",
    "                            booster=params['booster'],\n",
    "                            objective = \"multi:softprob\",\n",
    "                            n_jobs=24,\n",
    "                            random_state=42,\n",
    "                            silent=False,)\n",
    "\n",
    "    model.fit(X_train, y_train, eval_set=eval_set, verbose=False)   \n",
    "\n",
    "    train_acc = accuracy_score(y_train, model.predict(X_train))\n",
    "    train_loss = log_loss(y_train, model.predict_proba(X_train))\n",
    "    test_acc = accuracy_score(y_test, model.predict(X_test))\n",
    "    test_loss = log_loss(y_test, model.predict_proba(X_test))\n",
    "    \n",
    "    train_acc_list.append(train_acc)\n",
    "    train_loss_list.append(train_loss)\n",
    "    test_acc_list.append(test_acc)\n",
    "    test_loss_list.append(test_loss)\n",
    "    \n",
    "    print('Parameters with this training accuracy {} and loss {} :'.format(train_acc, train_loss))\n",
    "    print('Parameters with this testing accuracy {} and loss {} :'.format(test_acc, test_loss))\n",
    "    print(params)\n",
    "    pbar.update()\n",
    "#     return {'loss': test_loss, 'status': STATUS_OK}\n",
    "    return {'loss': -test_acc, 'status': STATUS_OK}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   0%|          | 1/1000 [00:19<5:26:34, 19.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.211387486613 :\n",
      "Parameters with this testing accuracy 0.210526315789 and loss 2.7098758204 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.45, 'min_child_weight': 4.0, 'n_estimators': 486, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   0%|          | 2/1000 [00:23<4:09:55, 15.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.217478549114 :\n",
      "Parameters with this testing accuracy 0.210526315789 and loss 2.44236802807 :\n",
      "{'colsample_bytree': 1.0, 'learning_rate': 0.5, 'min_child_weight': 5.0, 'n_estimators': 84, 'subsample': 0.9500000000000001, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.75, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   0%|          | 3/1000 [00:41<4:21:03, 15.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.169447634639 :\n",
      "Parameters with this testing accuracy 0.221052631579 and loss 2.56279872754 :\n",
      "{'colsample_bytree': 1.0, 'learning_rate': 0.225, 'min_child_weight': 5.0, 'n_estimators': 492, 'subsample': 0.75, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   0%|          | 4/1000 [00:54<4:07:35, 14.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.208908137479 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.24955617223 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.325, 'min_child_weight': 3.0, 'n_estimators': 299, 'subsample': 0.75, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   0%|          | 5/1000 [00:59<3:19:40, 12.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883507 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876593 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.35000000000000003, 'min_child_weight': 3.0, 'n_estimators': 277, 'subsample': 0.9500000000000001, 'importance_type': 'cover', 'max_depth': 3, 'gamma': 0.9, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   1%|          | 6/1000 [01:18<3:51:42, 13.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.167015870959 :\n",
      "Parameters with this testing accuracy 0.221052631579 and loss 2.78948235612 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.35000000000000003, 'min_child_weight': 4.0, 'n_estimators': 474, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 5, 'gamma': 0.65, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   1%|          | 7/1000 [01:31<3:47:42, 13.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.158922613551 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.24856121746 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.225, 'min_child_weight': 3.0, 'n_estimators': 229, 'subsample': 0.8500000000000001, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.65, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   1%|          | 8/1000 [01:34<2:52:11, 10.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883646 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876342 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.47500000000000003, 'min_child_weight': 4.0, 'n_estimators': 128, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   1%|          | 9/1000 [01:47<3:05:32, 11.23s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.211031745732 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.26471344822 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.5, 'min_child_weight': 4.0, 'n_estimators': 293, 'subsample': 0.9, 'importance_type': 'total_cover', 'max_depth': 7, 'gamma': 0.9, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   1%|          | 10/1000 [01:49<2:19:02,  8.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.978102189781 and loss 0.322006796212 :\n",
      "Parameters with this testing accuracy 0.252631578947 and loss 2.35757681371 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.375, 'min_child_weight': 4.0, 'n_estimators': 35, 'subsample': 0.65, 'importance_type': 'cover', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   1%|          | 11/1000 [01:56<2:15:26,  8.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883472 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876593 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.225, 'min_child_weight': 4.0, 'n_estimators': 411, 'subsample': 0.8500000000000001, 'importance_type': 'total_cover', 'max_depth': 8, 'gamma': 0.7000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   1%|          | 12/1000 [02:02<2:02:10,  7.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883194 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876091 :\n",
      "{'colsample_bytree': 1.0, 'learning_rate': 0.15000000000000002, 'min_child_weight': 3.0, 'n_estimators': 288, 'subsample': 0.9, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 0.8, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   1%|▏         | 13/1000 [02:13<2:21:17,  8.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.141337818424 :\n",
      "Parameters with this testing accuracy 0.221052631579 and loss 2.50974609558 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.30000000000000004, 'min_child_weight': 3.0, 'n_estimators': 240, 'subsample': 0.6000000000000001, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 0.55, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   1%|▏         | 14/1000 [02:16<1:54:05,  6.94s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76773244865 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77285711138 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.025, 'min_child_weight': 6.0, 'n_estimators': 154, 'subsample': 1.0, 'importance_type': 'total_cover', 'max_depth': 9, 'gamma': 0.6000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   2%|▏         | 15/1000 [02:20<1:38:43,  6.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.186718391882 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.35920727697 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.42500000000000004, 'min_child_weight': 3.0, 'n_estimators': 100, 'subsample': 0.65, 'importance_type': 'gain', 'max_depth': 4, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   2%|▏         | 16/1000 [02:23<1:22:32,  5.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7676432359 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77318180235 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 4.0, 'n_estimators': 135, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 9, 'gamma': 0.6000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   2%|▏         | 17/1000 [02:28<1:23:21,  5.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.978102189781 and loss 0.390210430088 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.20536002411 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.07500000000000001, 'min_child_weight': 4.0, 'n_estimators': 84, 'subsample': 0.8, 'importance_type': 'total_gain', 'max_depth': 9, 'gamma': 0.8, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   2%|▏         | 18/1000 [02:29<1:02:19,  3.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882985 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327807075 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.5, 'min_child_weight': 3.0, 'n_estimators': 28, 'subsample': 0.65, 'importance_type': 'cover', 'max_depth': 2, 'gamma': 0.7000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   2%|▏         | 19/1000 [02:42<1:46:57,  6.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.123710466571 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.26131251158 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.35000000000000003, 'min_child_weight': 2.0, 'n_estimators': 246, 'subsample': 0.75, 'importance_type': 'cover', 'max_depth': 6, 'gamma': 0.55, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   2%|▏         | 20/1000 [02:46<1:35:07,  5.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883298 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876342 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.375, 'min_child_weight': 1.0, 'n_estimators': 213, 'subsample': 1.0, 'importance_type': 'total_gain', 'max_depth': 3, 'gamma': 0.6000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   2%|▏         | 21/1000 [03:07<2:48:50, 10.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.187383606708 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.20700750457 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 409, 'subsample': 0.8, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   2%|▏         | 22/1000 [03:22<3:11:21, 11.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.205166229695 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.22230160427 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 284, 'subsample': 0.7000000000000001, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   2%|▏         | 23/1000 [03:37<3:27:39, 12.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.172747466772 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.19342314646 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.275, 'min_child_weight': 2.0, 'n_estimators': 299, 'subsample': 0.8, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   2%|▏         | 24/1000 [03:43<2:53:03, 10.64s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.201986847769 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.19107314587 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.15000000000000002, 'min_child_weight': 2.0, 'n_estimators': 108, 'subsample': 0.8, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   2%|▎         | 25/1000 [03:50<2:38:52,  9.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.197872756197 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.17159735812 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.30000000000000004, 'min_child_weight': 2.0, 'n_estimators': 300, 'subsample': 0.7000000000000001, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   3%|▎         | 26/1000 [03:56<2:17:28,  8.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.202359286467 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.14654101063 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.275, 'min_child_weight': 1.0, 'n_estimators': 201, 'subsample': 0.7000000000000001, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   3%|▎         | 27/1000 [03:59<1:52:02,  6.91s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.230132980526 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.13337725993 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.2, 'min_child_weight': 2.0, 'n_estimators': 117, 'subsample': 0.7000000000000001, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   3%|▎         | 28/1000 [04:02<1:34:14,  5.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.237060208673 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.149308691 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.2, 'min_child_weight': 2.0, 'n_estimators': 117, 'subsample': 0.7000000000000001, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   3%|▎         | 29/1000 [04:10<1:41:57,  6.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.201252486692 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.13940015514 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 300, 'subsample': 0.6000000000000001, 'importance_type': 'gain', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   3%|▎         | 30/1000 [04:22<2:09:09,  7.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.192860315871 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.20135504655 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 488, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   3%|▎         | 31/1000 [04:31<2:13:33,  8.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.193405761456 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.15881899138 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 355, 'subsample': 0.6000000000000001, 'importance_type': 'gain', 'max_depth': 2, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   3%|▎         | 32/1000 [04:34<1:50:21,  6.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.940145985401 and loss 0.428443653087 :\n",
      "Parameters with this testing accuracy 0.263157894737 and loss 2.43536016768 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 5.0, 'n_estimators': 224, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 1, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   3%|▎         | 33/1000 [04:53<2:48:41, 10.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.171449821557 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.17911702418 :\n",
      "{'colsample_bytree': 1.0, 'learning_rate': 0.25, 'min_child_weight': 1.0, 'n_estimators': 300, 'subsample': 0.6000000000000001, 'importance_type': 'gain', 'max_depth': 8, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   3%|▎         | 34/1000 [05:02<2:40:30,  9.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.191837560167 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.15657139524 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.2, 'min_child_weight': 2.0, 'n_estimators': 157, 'subsample': 0.65, 'importance_type': 'gain', 'max_depth': 7, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   4%|▎         | 35/1000 [05:10<2:32:37,  9.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.970802919708 and loss 0.46502863608 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.14761904266 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 326, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 2, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   4%|▎         | 36/1000 [05:22<2:41:13, 10.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.985401459854 and loss 0.26138808968 :\n",
      "Parameters with this testing accuracy 0.263157894737 and loss 2.45480499026 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.125, 'min_child_weight': 6.0, 'n_estimators': 340, 'subsample': 0.75, 'importance_type': 'total_gain', 'max_depth': 3, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   4%|▎         | 37/1000 [05:24<2:05:54,  7.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.260491361396 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.14449621588 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.2, 'min_child_weight': 2.0, 'n_estimators': 113, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   4%|▍         | 38/1000 [05:31<1:59:39,  7.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.28374718062 :\n",
      "Parameters with this testing accuracy 0.210526315789 and loss 2.47778834058 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.25, 'min_child_weight': 5.0, 'n_estimators': 163, 'subsample': 0.65, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   4%|▍         | 39/1000 [05:50<2:53:44, 10.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.169509743439 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.15406477406 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 315, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 7, 'gamma': 0.7000000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   4%|▍         | 40/1000 [05:52<2:13:20,  8.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.211549584954 :\n",
      "Parameters with this testing accuracy 0.242105263158 and loss 2.2759020453 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.42500000000000004, 'min_child_weight': 2.0, 'n_estimators': 50, 'subsample': 0.75, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   4%|▍         | 41/1000 [05:58<1:59:48,  7.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.247996258482 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.28177428536 :\n",
      "{'colsample_bytree': 1.0, 'learning_rate': 0.17500000000000002, 'min_child_weight': 3.0, 'n_estimators': 379, 'subsample': 0.6000000000000001, 'importance_type': 'gain', 'max_depth': 1, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   4%|▍         | 42/1000 [06:09<2:17:17,  8.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.187411261784 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.27752821939 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.325, 'min_child_weight': 1.0, 'n_estimators': 365, 'subsample': 0.8500000000000001, 'importance_type': 'total_cover', 'max_depth': 2, 'gamma': 0.8500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   4%|▍         | 43/1000 [06:29<3:11:13, 11.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.194891230136 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.18293731983 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.225, 'min_child_weight': 2.0, 'n_estimators': 499, 'subsample': 0.7000000000000001, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   4%|▍         | 44/1000 [06:35<2:45:34, 10.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883055 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876593 :\n",
      "{'colsample_bytree': 1.0, 'learning_rate': 0.15000000000000002, 'min_child_weight': 3.0, 'n_estimators': 341, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 3, 'gamma': 0.5, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   4%|▍         | 45/1000 [06:36<1:59:52,  7.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.944525547445 and loss 0.557755124466 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.30405551348 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.30000000000000004, 'min_child_weight': 1.0, 'n_estimators': 21, 'subsample': 0.9, 'importance_type': 'cover', 'max_depth': 2, 'gamma': 0.75, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   5%|▍         | 46/1000 [06:50<2:29:25,  9.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.226921488044 :\n",
      "Parameters with this testing accuracy 0.210526315789 and loss 2.57971957318 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.225, 'min_child_weight': 5.0, 'n_estimators': 468, 'subsample': 0.65, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   5%|▍         | 47/1000 [06:57<2:18:32,  8.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883612 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327848485 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 354, 'subsample': 0.6000000000000001, 'importance_type': 'total_gain', 'max_depth': 4, 'gamma': 0.7000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   5%|▍         | 48/1000 [07:22<3:37:11, 13.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.182685928939 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.30549858307 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.25, 'min_child_weight': 1.0, 'n_estimators': 406, 'subsample': 0.9500000000000001, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   5%|▍         | 49/1000 [07:46<4:24:34, 16.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.150429165665 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.19809688583 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 352, 'subsample': 0.7000000000000001, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.65, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   5%|▌         | 50/1000 [07:48<3:15:49, 12.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763891895 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77326570812 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 3.0, 'n_estimators': 104, 'subsample': 0.8500000000000001, 'importance_type': 'gain', 'max_depth': 1, 'gamma': 0.9, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   5%|▌         | 51/1000 [07:58<3:01:17, 11.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.161004008525 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.32286761763 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.375, 'min_child_weight': 1.0, 'n_estimators': 345, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   5%|▌         | 52/1000 [08:06<2:45:31, 10.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.365525144439 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.14404336614 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 118, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 9, 'gamma': 0.8, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   5%|▌         | 53/1000 [08:14<2:31:59,  9.63s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.237241932294 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.48087823155 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.325, 'min_child_weight': 4.0, 'n_estimators': 321, 'subsample': 0.75, 'importance_type': 'total_gain', 'max_depth': 2, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   5%|▌         | 54/1000 [08:20<2:17:02,  8.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883055 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876844 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.4, 'min_child_weight': 3.0, 'n_estimators': 318, 'subsample': 0.65, 'importance_type': 'cover', 'max_depth': 3, 'gamma': 0.9500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   6%|▌         | 55/1000 [08:40<3:12:04, 12.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.17604858363 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.18999002843 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 382, 'subsample': 0.8, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   6%|▌         | 56/1000 [08:59<3:42:43, 14.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.116921550295 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.27912241333 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.47500000000000003, 'min_child_weight': 1.0, 'n_estimators': 432, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 4, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   6%|▌         | 57/1000 [09:01<2:45:44, 10.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.98102189781 and loss 0.276635389788 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.24289551544 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.275, 'min_child_weight': 3.0, 'n_estimators': 64, 'subsample': 0.7000000000000001, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   6%|▌         | 58/1000 [09:10<2:35:49,  9.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7676388302 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876593 :\n",
      "{'colsample_bytree': 1.0, 'learning_rate': 0.15000000000000002, 'min_child_weight': 4.0, 'n_estimators': 436, 'subsample': 0.65, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.8500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   6%|▌         | 59/1000 [09:16<2:18:29,  8.83s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.979562043796 and loss 0.285856407253 :\n",
      "Parameters with this testing accuracy 0.242105263158 and loss 2.43998160691 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.2, 'min_child_weight': 6.0, 'n_estimators': 263, 'subsample': 0.9, 'importance_type': 'gain', 'max_depth': 2, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   6%|▌         | 60/1000 [09:43<3:41:31, 14.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.179644193541 :\n",
      "Parameters with this testing accuracy 0.263157894737 and loss 2.28334607413 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.325, 'min_child_weight': 2.0, 'n_estimators': 428, 'subsample': 0.8, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   6%|▌         | 61/1000 [09:50<3:08:13, 12.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.220444933524 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.2783460472 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 111, 'subsample': 0.75, 'importance_type': 'total_gain', 'max_depth': 8, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   6%|▌         | 62/1000 [09:54<2:32:10,  9.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883089 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876593 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 208, 'subsample': 0.6000000000000001, 'importance_type': 'cover', 'max_depth': 1, 'gamma': 0.9, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   6%|▋         | 63/1000 [10:09<2:57:58, 11.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.174764749923 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.26569441273 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.30000000000000004, 'min_child_weight': 2.0, 'n_estimators': 378, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 7, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   6%|▋         | 64/1000 [10:12<2:15:14,  8.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.278788931349 :\n",
      "Parameters with this testing accuracy 0.252631578947 and loss 2.33432810645 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.225, 'min_child_weight': 4.0, 'n_estimators': 72, 'subsample': 0.9500000000000001, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.65, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   6%|▋         | 65/1000 [10:21<2:20:06,  8.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.179762210153 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.1884268307 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 174, 'subsample': 0.7000000000000001, 'importance_type': 'gain', 'max_depth': 5, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   7%|▋         | 66/1000 [10:22<1:41:32,  6.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.83795620438 and loss 0.935435977045 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.27164606389 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.25, 'min_child_weight': 2.0, 'n_estimators': 16, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   7%|▋         | 67/1000 [10:31<1:53:36,  7.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.187786054946 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.18337798871 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.35000000000000003, 'min_child_weight': 2.0, 'n_estimators': 344, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   7%|▋         | 68/1000 [10:40<2:01:45,  7.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.199951802398 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.44502393931 :\n",
      "{'colsample_bytree': 1.0, 'learning_rate': 0.4, 'min_child_weight': 3.0, 'n_estimators': 342, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   7%|▋         | 69/1000 [10:54<2:27:36,  9.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.180213978513 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.21881912342 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.275, 'min_child_weight': 1.0, 'n_estimators': 497, 'subsample': 0.7000000000000001, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   7%|▋         | 70/1000 [11:01<2:17:49,  8.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.190700474826 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.18160539483 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.35000000000000003, 'min_child_weight': 2.0, 'n_estimators': 272, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   7%|▋         | 71/1000 [11:05<1:52:32,  7.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.20572193991 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.304425942 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.45, 'min_child_weight': 2.0, 'n_estimators': 116, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   7%|▋         | 72/1000 [11:20<2:31:17,  9.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.157377599683 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.18620628848 :\n",
      "{'colsample_bytree': 1.0, 'learning_rate': 0.35000000000000003, 'min_child_weight': 1.0, 'n_estimators': 408, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   7%|▋         | 73/1000 [11:41<3:22:42, 13.12s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.198782333355 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.22966216038 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.2, 'min_child_weight': 3.0, 'n_estimators': 435, 'subsample': 0.75, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   7%|▋         | 74/1000 [11:50<3:04:32, 11.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.166179592779 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.2248773054 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.30000000000000004, 'min_child_weight': 2.0, 'n_estimators': 348, 'subsample': 0.6000000000000001, 'importance_type': 'total_gain', 'max_depth': 2, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   8%|▊         | 75/1000 [12:14<3:59:22, 15.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.139488745097 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.14206591976 :\n",
      "{'colsample_bytree': 1.0, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 498, 'subsample': 0.7000000000000001, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   8%|▊         | 76/1000 [12:23<3:26:18, 13.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.199450803042 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.2465393556 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.4, 'min_child_weight': 2.0, 'n_estimators': 150, 'subsample': 0.65, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   8%|▊         | 77/1000 [12:26<2:41:24, 10.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.21029882646 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.30124865196 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.42500000000000004, 'min_child_weight': 1.0, 'n_estimators': 126, 'subsample': 0.8, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   8%|▊         | 78/1000 [12:33<2:22:08,  9.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.985401459854 and loss 0.265931549303 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.18669523547 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 3.0, 'n_estimators': 127, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   8%|▊         | 79/1000 [12:38<2:03:42,  8.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883194 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327875338 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.225, 'min_child_weight': 1.0, 'n_estimators': 272, 'subsample': 0.8500000000000001, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.8500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   8%|▊         | 80/1000 [12:43<1:48:03,  7.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.963503649635 and loss 0.347387867245 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.50815821943 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.25, 'min_child_weight': 5.0, 'n_estimators': 219, 'subsample': 0.65, 'importance_type': 'gain', 'max_depth': 1, 'gamma': 0.9500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   8%|▊         | 81/1000 [12:46<1:31:09,  5.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.199787134466 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.15442739908 :\n",
      "{'colsample_bytree': 1.0, 'learning_rate': 0.375, 'min_child_weight': 2.0, 'n_estimators': 117, 'subsample': 0.75, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   8%|▊         | 82/1000 [13:01<2:13:00,  8.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.200948704326 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.19203453995 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.275, 'min_child_weight': 2.0, 'n_estimators': 357, 'subsample': 0.6000000000000001, 'importance_type': 'total_gain', 'max_depth': 5, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   8%|▊         | 83/1000 [13:15<2:34:56, 10.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.211346285486 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.33979845339 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.325, 'min_child_weight': 3.0, 'n_estimators': 331, 'subsample': 0.7000000000000001, 'importance_type': 'total_gain', 'max_depth': 5, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   8%|▊         | 84/1000 [13:22<2:21:11,  9.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.211337984422 :\n",
      "Parameters with this testing accuracy 0.263157894737 and loss 2.29639931158 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.275, 'min_child_weight': 1.0, 'n_estimators': 138, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 5, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   8%|▊         | 85/1000 [13:25<1:54:17,  7.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.223235238959 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.42739556926 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.45, 'min_child_weight': 3.0, 'n_estimators': 80, 'subsample': 0.6000000000000001, 'importance_type': 'total_gain', 'max_depth': 5, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   9%|▊         | 86/1000 [13:31<1:47:01,  7.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883577 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876091 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.2, 'min_child_weight': 4.0, 'n_estimators': 282, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 5, 'gamma': 0.8, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   9%|▊         | 87/1000 [13:47<2:27:53,  9.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.191417458781 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.20572607435 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.375, 'min_child_weight': 2.0, 'n_estimators': 356, 'subsample': 0.65, 'importance_type': 'total_gain', 'max_depth': 5, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   9%|▉         | 88/1000 [13:54<2:15:41,  8.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.202716800351 :\n",
      "Parameters with this testing accuracy 0.252631578947 and loss 2.50298995997 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.47500000000000003, 'min_child_weight': 3.0, 'n_estimators': 232, 'subsample': 0.6000000000000001, 'importance_type': 'total_gain', 'max_depth': 3, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   9%|▉         | 89/1000 [14:08<2:37:09, 10.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.195990064268 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.12687184693 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 259, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   9%|▉         | 90/1000 [14:25<3:06:14, 12.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.194559428538 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.17740818895 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 262, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.8500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   9%|▉         | 91/1000 [14:25<2:13:05,  8.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76829434381 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77234844158 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 15, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.75, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   9%|▉         | 92/1000 [14:31<1:59:14,  7.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.884671532847 and loss 0.724601771136 :\n",
      "Parameters with this testing accuracy 0.263157894737 and loss 2.21133895214 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 5.0, 'n_estimators': 140, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   9%|▉         | 93/1000 [14:49<2:42:04, 10.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.2749473778 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.0953116282 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 323, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   9%|▉         | 94/1000 [14:58<2:34:05, 10.20s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.229819584244 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.13435296985 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 198, 'subsample': 0.6000000000000001, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  10%|▉         | 95/1000 [15:05<2:22:53,  9.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.250696073563 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.33880451531 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.15000000000000002, 'min_child_weight': 4.0, 'n_estimators': 259, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 4, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  10%|▉         | 96/1000 [15:10<2:01:59,  8.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882602 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327865299 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 258, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 5, 'gamma': 0.7000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  10%|▉         | 97/1000 [15:22<2:16:20,  9.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.982481751825 and loss 0.28874247139 :\n",
      "Parameters with this testing accuracy 0.231578947368 and loss 2.50487501158 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.225, 'min_child_weight': 6.0, 'n_estimators': 297, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 9, 'gamma': 0.8500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  10%|▉         | 98/1000 [15:35<2:34:41, 10.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.193720393088 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.2104712703 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.17500000000000002, 'min_child_weight': 2.0, 'n_estimators': 254, 'subsample': 0.7000000000000001, 'importance_type': 'cover', 'max_depth': 6, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  10%|▉         | 99/1000 [15:37<1:59:01,  7.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.204124187674 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.18968578769 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.30000000000000004, 'min_child_weight': 1.0, 'n_estimators': 33, 'subsample': 0.7000000000000001, 'importance_type': 'total_gain', 'max_depth': 8, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  10%|█         | 100/1000 [15:39<1:30:14,  6.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.948905109489 and loss 0.483281962215 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.29436090898 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.275, 'min_child_weight': 2.0, 'n_estimators': 19, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 7, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  10%|█         | 101/1000 [15:45<1:31:47,  6.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883472 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.7732787584 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.25, 'min_child_weight': 3.0, 'n_estimators': 328, 'subsample': 0.8, 'importance_type': 'total_cover', 'max_depth': 1, 'gamma': 0.6000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  10%|█         | 102/1000 [15:57<1:59:51,  8.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.160688101836 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.16699016709 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 257, 'subsample': 0.65, 'importance_type': 'cover', 'max_depth': 5, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  10%|█         | 103/1000 [16:05<1:57:12,  7.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.985401459854 and loss 0.314478528205 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.17299067888 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 175, 'subsample': 0.75, 'importance_type': 'total_gain', 'max_depth': 3, 'gamma': 0.8, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  10%|█         | 104/1000 [16:22<2:39:51, 10.70s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.298627526025 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.10958376684 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 313, 'subsample': 0.6000000000000001, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  10%|█         | 105/1000 [16:43<3:22:30, 13.58s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.260789072416 :\n",
      "Parameters with this testing accuracy 0.252631578947 and loss 2.37874650516 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 308, 'subsample': 1.0, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  11%|█         | 106/1000 [16:57<3:25:19, 13.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.218340219633 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.08213621107 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 302, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  11%|█         | 107/1000 [17:18<3:58:39, 16.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.207800308444 :\n",
      "Parameters with this testing accuracy 0.431578947368 and loss 2.10013418951 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 399, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  11%|█         | 108/1000 [17:24<3:13:45, 13.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882707 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.7732777721 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 307, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.75, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  11%|█         | 109/1000 [17:44<3:42:58, 15.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.180928419203 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.07766225191 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 362, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  11%|█         | 110/1000 [18:04<4:03:56, 16.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.158507470736 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.09105706933 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 302, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.7000000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  11%|█         | 111/1000 [18:15<3:40:01, 14.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.174430878368 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.11435858721 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 197, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  11%|█         | 112/1000 [18:28<3:31:27, 14.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.147582693533 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.07555947541 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 231, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  11%|█▏        | 113/1000 [18:32<2:48:13, 11.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.342885923668 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.1735710075 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 68, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  11%|█▏        | 114/1000 [18:45<2:52:56, 11.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.369524619484 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.19462061313 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 197, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  12%|█▏        | 115/1000 [18:47<2:10:27,  8.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883229 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327754121 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 97, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.6000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  12%|█▏        | 116/1000 [19:13<3:28:22, 14.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.206170928242 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.30286052737 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.05, 'min_child_weight': 5.0, 'n_estimators': 441, 'subsample': 0.9500000000000001, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.55, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  12%|█▏        | 117/1000 [19:24<3:12:58, 13.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.147117678403 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.14947938027 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 187, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 9, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  12%|█▏        | 118/1000 [19:37<3:10:24, 12.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.406007713213 :\n",
      "Parameters with this testing accuracy 0.431578947368 and loss 2.14473578099 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 231, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  12%|█▏        | 119/1000 [19:44<2:46:49, 11.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.865693430657 and loss 0.94469905153 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.25795576133 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.025, 'min_child_weight': 4.0, 'n_estimators': 159, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 7, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  12%|█▏        | 120/1000 [20:00<3:07:20, 12.77s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.158276007953 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.22439820192 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 311, 'subsample': 0.8500000000000001, 'importance_type': 'total_cover', 'max_depth': 8, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  12%|█▏        | 121/1000 [20:06<2:36:34, 10.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76764116287 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77320980273 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 298, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 1, 'gamma': 0.6000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  12%|█▏        | 122/1000 [20:19<2:47:40, 11.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.217155383758 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.23327609904 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.1, 'min_child_weight': 3.0, 'n_estimators': 242, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.7000000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  12%|█▏        | 123/1000 [20:35<3:03:47, 12.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.174369440233 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.11264638779 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 325, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  12%|█▏        | 124/1000 [20:50<3:17:19, 13.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.178730604133 :\n",
      "Parameters with this testing accuracy 0.242105263158 and loss 2.33023663793 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.125, 'min_child_weight': 3.0, 'n_estimators': 487, 'subsample': 0.9, 'importance_type': 'total_cover', 'max_depth': 3, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  12%|█▎        | 125/1000 [21:11<3:47:27, 15.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.192394351781 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.11825607005 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 399, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  13%|█▎        | 126/1000 [21:24<3:35:32, 14.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.18215969989 :\n",
      "Parameters with this testing accuracy 0.210526315789 and loss 2.5622321005 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 4.0, 'n_estimators': 451, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  13%|█▎        | 127/1000 [21:24<2:33:22, 10.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7694084519 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77216306235 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 13, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 9, 'gamma': 0.8500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  13%|█▎        | 128/1000 [21:48<3:28:32, 14.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.260035153121 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.16465114131 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 391, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.8, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  13%|█▎        | 129/1000 [21:53<2:48:33, 11.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.216275604273 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.34714146456 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 71, 'subsample': 1.0, 'importance_type': 'total_cover', 'max_depth': 8, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  13%|█▎        | 130/1000 [21:55<2:07:40,  8.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.738686131387 and loss 1.83415317944 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.51761219251 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.025, 'min_child_weight': 3.0, 'n_estimators': 42, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  13%|█▎        | 131/1000 [22:06<2:16:37,  9.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.198649005285 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.17212822835 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 255, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  13%|█▎        | 132/1000 [22:11<1:55:17,  7.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.973722627737 and loss 0.301341086108 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.37783709633 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.17500000000000002, 'min_child_weight': 4.0, 'n_estimators': 279, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 1, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  13%|█▎        | 133/1000 [22:13<1:30:43,  6.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763885909 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.7732728406 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.15000000000000002, 'min_child_weight': 2.0, 'n_estimators': 78, 'subsample': 0.65, 'importance_type': 'total_cover', 'max_depth': 3, 'gamma': 0.9, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  13%|█▎        | 134/1000 [22:16<1:18:55,  5.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.794160583942 and loss 1.56649815714 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.4273992802 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.025, 'min_child_weight': 3.0, 'n_estimators': 61, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 6, 'gamma': 0.6000000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  14%|█▎        | 135/1000 [22:28<1:43:45,  7.20s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.177668362005 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.12926551437 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 269, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  14%|█▎        | 136/1000 [22:53<3:03:40, 12.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.17360982485 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.16457309776 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 485, 'subsample': 0.65, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  14%|█▎        | 137/1000 [22:59<2:34:11, 10.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.820437956204 and loss 0.880786229757 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.3234383565 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.05, 'min_child_weight': 6.0, 'n_estimators': 148, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  14%|█▍        | 138/1000 [23:08<2:26:32, 10.20s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.213910681096 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.22834946666 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 142, 'subsample': 0.9500000000000001, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  14%|█▍        | 139/1000 [23:18<2:25:47, 10.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883124 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327875338 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.1, 'min_child_weight': 3.0, 'n_estimators': 399, 'subsample': 0.8, 'importance_type': 'total_cover', 'max_depth': 8, 'gamma': 0.65, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  14%|█▍        | 140/1000 [23:23<2:02:42,  8.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.816058394161 and loss 1.05815659119 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.23763518879 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 302, 'subsample': 0.6000000000000001, 'importance_type': 'gain', 'max_depth': 1, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  14%|█▍        | 141/1000 [23:37<2:24:09, 10.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.262570962474 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.50809337325 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 5.0, 'n_estimators': 322, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.8, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  14%|█▍        | 142/1000 [23:49<2:32:47, 10.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.206367457674 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.12578298838 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 234, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  14%|█▍        | 143/1000 [23:58<2:25:07, 10.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.170078331016 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.1570863197 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 267, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 3, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  14%|█▍        | 144/1000 [24:09<2:28:34, 10.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.282690550527 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.24020333126 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.07500000000000001, 'min_child_weight': 4.0, 'n_estimators': 248, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 6, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  14%|█▍        | 145/1000 [24:15<2:08:53,  9.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883438 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.7732787584 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 275, 'subsample': 0.8500000000000001, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.8500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  15%|█▍        | 146/1000 [24:37<3:05:09, 13.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.135944572362 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.08827371122 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 410, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  15%|█▍        | 147/1000 [24:48<2:56:44, 12.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.154608090218 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.11262942811 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 197, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  15%|█▍        | 148/1000 [24:56<2:37:03, 11.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.305982574084 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.1220250312 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 122, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  15%|█▍        | 149/1000 [25:23<3:45:47, 15.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.170683869689 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.0887448094 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 443, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  15%|█▌        | 150/1000 [25:43<4:01:19, 17.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.136291561834 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.18721078063 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 453, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  15%|█▌        | 151/1000 [25:57<3:47:56, 16.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.148358477863 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.12625474753 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 231, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  15%|█▌        | 152/1000 [26:23<4:31:01, 19.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.163255029429 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.08992295593 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 433, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  15%|█▌        | 153/1000 [26:33<3:51:28, 16.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.187052267096 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.12043052657 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 182, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  15%|█▌        | 154/1000 [26:42<3:18:31, 14.08s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.259585377003 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.1069347938 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 147, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  16%|█▌        | 155/1000 [26:58<3:27:15, 14.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.163084941227 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.15657417144 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 360, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  16%|█▌        | 156/1000 [27:23<4:11:09, 17.86s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.174582499812 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.08663363233 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 434, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 7, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  16%|█▌        | 157/1000 [27:50<4:48:16, 20.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.152938913558 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.21105025346 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 471, 'subsample': 0.9, 'importance_type': 'total_cover', 'max_depth': 8, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  16%|█▌        | 158/1000 [28:14<5:04:31, 21.70s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.149641942981 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.13220294839 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 439, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  16%|█▌        | 159/1000 [28:20<3:58:55, 17.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.273858507735 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.12956655028 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 419, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 1, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  16%|█▌        | 160/1000 [28:37<3:58:26, 17.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.143637868922 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.19009784189 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.15000000000000002, 'min_child_weight': 2.0, 'n_estimators': 395, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 6, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  16%|█▌        | 161/1000 [28:55<3:58:07, 17.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.176665045001 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.33182321992 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.1, 'min_child_weight': 3.0, 'n_estimators': 303, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 6, 'gamma': 0.55, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  16%|█▌        | 162/1000 [28:56<2:53:09, 12.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.972262773723 and loss 0.436103102225 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.19590151232 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 2.0, 'n_estimators': 36, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 3, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  16%|█▋        | 163/1000 [29:00<2:16:35,  9.79s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.973722627737 and loss 0.707791266563 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.28722317282 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 57, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  16%|█▋        | 164/1000 [29:09<2:13:23,  9.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.172026565916 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.16917087146 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 202, 'subsample': 0.65, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  16%|█▋        | 165/1000 [29:31<3:06:34, 13.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.259469155979 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.15428020653 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.025, 'min_child_weight': 3.0, 'n_estimators': 477, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  17%|█▋        | 166/1000 [29:36<2:29:29, 10.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882915 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.7732787584 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 231, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 9, 'gamma': 0.5, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  17%|█▋        | 167/1000 [29:45<2:24:49, 10.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.114112815555 :\n",
      "Parameters with this testing accuracy 0.263157894737 and loss 2.38234607141 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.5, 'min_child_weight': 1.0, 'n_estimators': 189, 'subsample': 0.6000000000000001, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  17%|█▋        | 168/1000 [29:59<2:37:46, 11.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.294187794063 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.09554943748 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 211, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 8, 'gamma': 0.9500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  17%|█▋        | 169/1000 [30:17<3:06:30, 13.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.245019785536 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.21380740494 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.1, 'min_child_weight': 3.0, 'n_estimators': 304, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 8, 'gamma': 0.9500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  17%|█▋        | 170/1000 [30:19<2:16:28,  9.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.929927007299 and loss 0.6286141094 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.19524665179 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.225, 'min_child_weight': 1.0, 'n_estimators': 67, 'subsample': 0.65, 'importance_type': 'total_cover', 'max_depth': 1, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  17%|█▋        | 171/1000 [30:39<2:56:50, 12.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.265739860698 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.10699978859 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 359, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  17%|█▋        | 172/1000 [30:59<3:28:30, 15.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.166111143861 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.10295411039 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 396, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  17%|█▋        | 173/1000 [31:05<2:52:02, 12.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.196065546661 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.14063249191 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 131, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  17%|█▋        | 174/1000 [31:13<2:31:47, 11.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883089 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.7732787584 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 396, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.75, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  18%|█▊        | 175/1000 [31:23<2:27:46, 10.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.216655606031 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.28400113466 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.17500000000000002, 'min_child_weight': 4.0, 'n_estimators': 260, 'subsample': 0.6000000000000001, 'importance_type': 'cover', 'max_depth': 5, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  18%|█▊        | 176/1000 [31:37<2:41:09, 11.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.174458324793 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.07014839145 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 285, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  18%|█▊        | 177/1000 [31:40<2:04:32,  9.08s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.96496350365 and loss 0.604181929389 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.28338145629 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 3.0, 'n_estimators': 52, 'subsample': 0.75, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  18%|█▊        | 178/1000 [32:00<2:48:54, 12.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.1762797191 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.16415150182 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 459, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  18%|█▊        | 179/1000 [32:01<2:00:34,  8.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76797802918 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77251770873 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 12, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.8, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  18%|█▊        | 180/1000 [32:14<2:18:26, 10.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.336950607312 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.12234574662 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 250, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  18%|█▊        | 181/1000 [32:30<2:43:16, 11.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.149990255181 :\n",
      "Parameters with this testing accuracy 0.231578947368 and loss 2.64454868148 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.42500000000000004, 'min_child_weight': 3.0, 'n_estimators': 390, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 3, 'gamma': 0.7000000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  18%|█▊        | 182/1000 [32:37<2:24:42, 10.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.976642335766 and loss 0.299422242124 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.45807450424 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.17500000000000002, 'min_child_weight': 6.0, 'n_estimators': 190, 'subsample': 0.7000000000000001, 'importance_type': 'cover', 'max_depth': 5, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  18%|█▊        | 183/1000 [32:41<1:54:24,  8.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.298501411784 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.16327840023 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 48, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  18%|█▊        | 184/1000 [32:51<2:03:17,  9.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.197559500925 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.16020207144 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 266, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  18%|█▊        | 185/1000 [32:56<1:44:22,  7.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.969343065693 and loss 0.391678832662 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.25010196571 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.125, 'min_child_weight': 4.0, 'n_estimators': 94, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 7, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  19%|█▊        | 186/1000 [32:59<1:27:57,  6.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7676392691 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77324913175 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 180, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  19%|█▊        | 187/1000 [33:15<2:05:22,  9.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.230713053691 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.10096184354 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 481, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 3, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  19%|█▉        | 188/1000 [33:17<1:34:44,  7.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.953284671533 and loss 0.501543496149 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.19535926322 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.225, 'min_child_weight': 1.0, 'n_estimators': 83, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 1, 'gamma': 0.75, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  19%|█▉        | 189/1000 [33:38<2:31:58, 11.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.214059990213 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.13422530256 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 482, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  19%|█▉        | 190/1000 [33:48<2:27:29, 10.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.236315579276 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.25340730118 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 3.0, 'n_estimators': 253, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 5, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  19%|█▉        | 191/1000 [33:54<2:07:25,  9.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.483106969139 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.25758722409 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 82, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  19%|█▉        | 192/1000 [34:17<3:02:22, 13.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.14928340232 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.18084704351 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.325, 'min_child_weight': 1.0, 'n_estimators': 467, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 7, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  19%|█▉        | 193/1000 [34:21<2:23:43, 10.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882672 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327868311 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 196, 'subsample': 0.65, 'importance_type': 'total_cover', 'max_depth': 8, 'gamma': 0.6000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  19%|█▉        | 194/1000 [34:28<2:05:56,  9.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.985401459854 and loss 0.316298760986 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.20059653444 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 462, 'subsample': 0.9500000000000001, 'importance_type': 'total_cover', 'max_depth': 1, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  20%|█▉        | 195/1000 [34:30<1:39:09,  7.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.976642335766 and loss 0.331382219081 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.28481234053 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.2, 'min_child_weight': 3.0, 'n_estimators': 49, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.7000000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  20%|█▉        | 196/1000 [34:41<1:53:49,  8.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.324759078706 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.24809847184 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 195, 'subsample': 0.7000000000000001, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  20%|█▉        | 197/1000 [34:51<1:56:34,  8.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.174721842647 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.20778369571 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.25, 'min_child_weight': 2.0, 'n_estimators': 364, 'subsample': 0.8, 'importance_type': 'total_cover', 'max_depth': 2, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  20%|█▉        | 198/1000 [35:02<2:05:11,  9.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.972262773723 and loss 0.321390221176 :\n",
      "Parameters with this testing accuracy 0.263157894737 and loss 2.34880390542 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.125, 'min_child_weight': 5.0, 'n_estimators': 327, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  20%|█▉        | 199/1000 [35:16<2:24:32, 10.83s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.173861199413 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.10771165071 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 423, 'subsample': 0.6000000000000001, 'importance_type': 'cover', 'max_depth': 3, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  20%|██        | 200/1000 [35:28<2:28:25, 11.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.982481751825 and loss 0.455711648504 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.2566625232 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.025, 'min_child_weight': 4.0, 'n_estimators': 191, 'subsample': 0.9, 'importance_type': 'gain', 'max_depth': 6, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  20%|██        | 201/1000 [35:40<2:32:04, 11.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.238300987903 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.106498155 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 221, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  20%|██        | 202/1000 [35:48<2:21:03, 10.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883194 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327877095 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.4, 'min_child_weight': 3.0, 'n_estimators': 464, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 9, 'gamma': 0.8500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  20%|██        | 203/1000 [36:06<2:48:03, 12.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.129882142916 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.24210991063 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 320, 'subsample': 0.75, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  20%|██        | 204/1000 [36:14<2:31:07, 11.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.156048786819 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.48854262051 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.45, 'min_child_weight': 2.0, 'n_estimators': 228, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  20%|██        | 205/1000 [36:24<2:24:57, 10.94s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.198495660918 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.19238414678 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 136, 'subsample': 0.8500000000000001, 'importance_type': 'total_cover', 'max_depth': 8, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  21%|██        | 206/1000 [36:42<2:51:27, 12.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.195065128978 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.21401804221 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 361, 'subsample': 0.65, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  21%|██        | 207/1000 [36:58<3:05:22, 14.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.181564020055 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.10349766537 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 314, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  21%|██        | 208/1000 [37:01<2:19:16, 10.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883194 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876593 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.35000000000000003, 'min_child_weight': 2.0, 'n_estimators': 112, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  21%|██        | 209/1000 [37:13<2:23:44, 10.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.209716118459 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.15091185881 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 424, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 1, 'gamma': 0.65, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  21%|██        | 210/1000 [37:27<2:37:59, 12.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.184156125127 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.28537766865 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.30000000000000004, 'min_child_weight': 1.0, 'n_estimators': 286, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  21%|██        | 211/1000 [37:36<2:26:43, 11.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.196571521035 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.23051589419 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.17500000000000002, 'min_child_weight': 2.0, 'n_estimators': 252, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 3, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  21%|██        | 212/1000 [37:41<1:59:31,  9.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.960583941606 and loss 0.491086220119 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.13871111642 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 4.0, 'n_estimators': 85, 'subsample': 0.7000000000000001, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  21%|██▏       | 213/1000 [37:56<2:22:10, 10.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.123071242036 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.46630275136 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.375, 'min_child_weight': 2.0, 'n_estimators': 396, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  21%|██▏       | 214/1000 [38:02<2:04:09,  9.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.973722627737 and loss 0.574095869924 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.23290763246 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 155, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  22%|██▏       | 215/1000 [38:03<1:31:41,  7.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883333 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327877095 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.47500000000000003, 'min_child_weight': 3.0, 'n_estimators': 46, 'subsample': 1.0, 'importance_type': 'cover', 'max_depth': 6, 'gamma': 0.75, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  22%|██▏       | 216/1000 [38:13<1:43:19,  7.91s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.241597436542 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.26587237159 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 114, 'subsample': 0.8, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.8500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  22%|██▏       | 217/1000 [38:19<1:35:39,  7.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.940145985401 and loss 0.46112350125 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.24164597424 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.1, 'min_child_weight': 5.0, 'n_estimators': 137, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 8, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  22%|██▏       | 218/1000 [38:28<1:41:40,  7.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.173856191596 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.2257338467 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.125, 'min_child_weight': 3.0, 'n_estimators': 222, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  22%|██▏       | 219/1000 [38:47<2:25:50, 11.20s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.219757460487 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.16993398508 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 371, 'subsample': 0.65, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  22%|██▏       | 220/1000 [39:02<2:40:00, 12.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.180235217796 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.15843132824 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.225, 'min_child_weight': 1.0, 'n_estimators': 243, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 7, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  22%|██▏       | 221/1000 [39:09<2:17:22, 10.58s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.801459854015 and loss 1.00162431853 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.29306831123 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.025, 'min_child_weight': 5.0, 'n_estimators': 417, 'subsample': 0.75, 'importance_type': 'total_cover', 'max_depth': 1, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  22%|██▏       | 222/1000 [39:16<2:05:57,  9.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883264 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327875589 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 2.0, 'n_estimators': 392, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.65, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  22%|██▏       | 223/1000 [39:18<1:33:03,  7.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.951824817518 and loss 0.509191828001 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.16238155769 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 38, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 2, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  22%|██▏       | 224/1000 [39:22<1:21:21,  6.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.185252760319 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.20683143619 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.17500000000000002, 'min_child_weight': 2.0, 'n_estimators': 79, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 5, 'gamma': 0.5, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  22%|██▎       | 225/1000 [39:24<1:06:25,  5.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.719708029197 and loss 1.38879501641 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.41447966977 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 6.0, 'n_estimators': 53, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  23%|██▎       | 226/1000 [39:31<1:11:09,  5.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.196970891275 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.12715103556 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 171, 'subsample': 0.6000000000000001, 'importance_type': 'gain', 'max_depth': 3, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  23%|██▎       | 227/1000 [39:43<1:38:20,  7.63s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.167030621767 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.24376312404 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.25, 'min_child_weight': 3.0, 'n_estimators': 289, 'subsample': 0.8500000000000001, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  23%|██▎       | 228/1000 [39:45<1:16:14,  5.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.931386861314 and loss 1.78887635252 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.51965355873 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 25, 'subsample': 0.7000000000000001, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  23%|██▎       | 229/1000 [39:58<1:41:59,  7.94s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.200839009114 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.16909647575 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 245, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 9, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  23%|██▎       | 230/1000 [40:07<1:45:43,  8.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883472 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876342 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 475, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.55, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  23%|██▎       | 231/1000 [40:29<2:41:36, 12.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.236452579869 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.15898174381 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 370, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 8, 'gamma': 0.75, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  23%|██▎       | 232/1000 [40:43<2:46:37, 13.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.162708974149 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.11920219466 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 270, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  23%|██▎       | 233/1000 [40:46<2:06:14,  9.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.896350364964 and loss 0.697767941178 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.22880079229 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 4.0, 'n_estimators': 45, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  23%|██▎       | 234/1000 [40:53<1:53:35,  8.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.202822901763 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.13378701685 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 438, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 1, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  24%|██▎       | 235/1000 [40:56<1:33:36,  7.34s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.960583941606 and loss 0.555357828671 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.17223779151 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 70, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  24%|██▎       | 236/1000 [41:03<1:29:55,  7.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882881 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876091 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.275, 'min_child_weight': 1.0, 'n_estimators': 332, 'subsample': 0.65, 'importance_type': 'total_cover', 'max_depth': 2, 'gamma': 0.8500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  24%|██▎       | 237/1000 [41:06<1:14:38,  5.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.789781021898 and loss 1.57593901436 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.44077380707 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.025, 'min_child_weight': 3.0, 'n_estimators': 56, 'subsample': 0.6000000000000001, 'importance_type': 'total_gain', 'max_depth': 5, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  24%|██▍       | 238/1000 [41:10<1:07:34,  5.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.932846715328 and loss 0.996432963101 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.34148260543 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 99, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 3, 'gamma': 0.6000000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  24%|██▍       | 239/1000 [41:26<1:50:23,  8.70s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.233610028747 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.11795688279 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 329, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  24%|██▍       | 240/1000 [41:33<1:41:39,  8.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.12785905476 :\n",
      "Parameters with this testing accuracy 0.263157894737 and loss 2.33412289337 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 106, 'subsample': 0.9500000000000001, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  24%|██▍       | 241/1000 [41:45<1:55:45,  9.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.196823178629 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.13499919802 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 273, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  24%|██▍       | 242/1000 [41:55<2:00:35,  9.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.982481751825 and loss 0.308371143991 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.16612671363 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 230, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 9, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  24%|██▍       | 243/1000 [42:04<1:58:39,  9.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882846 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876342 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 484, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 0.9500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  24%|██▍       | 244/1000 [42:14<2:00:48,  9.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.169441810593 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.21613971784 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 2.0, 'n_estimators': 212, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 8, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  24%|██▍       | 245/1000 [42:19<1:43:37,  8.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.915328467153 and loss 0.616576383722 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.19058287497 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.07500000000000001, 'min_child_weight': 4.0, 'n_estimators': 103, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 6, 'gamma': 0.8, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  25%|██▍       | 246/1000 [42:39<2:26:02, 11.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.166895427565 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.23970720846 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.325, 'min_child_weight': 1.0, 'n_estimators': 351, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 7, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  25%|██▍       | 247/1000 [42:41<1:50:30,  8.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.900729927007 and loss 0.768477192687 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.18757556596 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 125, 'subsample': 0.65, 'importance_type': 'total_gain', 'max_depth': 1, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  25%|██▍       | 248/1000 [42:45<1:32:32,  7.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.260722111035 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.22818111075 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 63, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  25%|██▍       | 249/1000 [42:51<1:25:38,  6.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.916788321168 and loss 0.7495562825 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.20132164296 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 220, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 2, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  25%|██▌       | 250/1000 [42:56<1:21:22,  6.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883194 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876342 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.225, 'min_child_weight': 3.0, 'n_estimators': 292, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.6000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  25%|██▌       | 251/1000 [42:59<1:06:28,  5.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.158781316634 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.19047827274 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.30000000000000004, 'min_child_weight': 1.0, 'n_estimators': 41, 'subsample': 0.7000000000000001, 'importance_type': 'cover', 'max_depth': 6, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  25%|██▌       | 252/1000 [43:05<1:08:05,  5.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.982481751825 and loss 0.336103351953 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.15672970116 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 145, 'subsample': 0.75, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.8500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  25%|██▌       | 253/1000 [43:18<1:36:57,  7.79s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.203987048571 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.19603086616 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.1, 'min_child_weight': 3.0, 'n_estimators': 310, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  25%|██▌       | 254/1000 [43:35<2:12:41, 10.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.165745850233 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.15088155138 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 375, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 4, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  26%|██▌       | 255/1000 [43:48<2:19:15, 11.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.210757806321 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.31290758942 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 156, 'subsample': 0.9, 'importance_type': 'total_cover', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  26%|██▌       | 256/1000 [43:55<2:03:59, 10.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.985401459854 and loss 0.586975042315 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.28322763255 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 129, 'subsample': 0.8, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  26%|██▌       | 257/1000 [44:03<1:55:12,  9.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882881 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876091 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 5.0, 'n_estimators': 402, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.55, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  26%|██▌       | 258/1000 [44:13<1:58:21,  9.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.302145758417 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.05446247576 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 215, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  26%|██▌       | 259/1000 [44:19<1:45:20,  8.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.972262773723 and loss 0.473286434336 :\n",
      "Parameters with this testing accuracy 0.442105263158 and loss 2.09717679157 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 123, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  26%|██▌       | 260/1000 [44:37<2:19:04, 11.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.265353660676 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.09327289627 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 422, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  26%|██▌       | 261/1000 [44:51<2:28:28, 12.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.985401459854 and loss 0.3642832736 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.11111204479 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 291, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  26%|██▌       | 262/1000 [45:08<2:47:25, 13.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.288431751247 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.1780365425 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 425, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  26%|██▋       | 263/1000 [45:14<2:18:32, 11.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.913868613139 and loss 0.948420833562 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.26766656229 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 123, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  26%|██▋       | 264/1000 [45:22<2:05:46, 10.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.261519234096 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.12627850228 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 152, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  26%|██▋       | 265/1000 [45:30<2:00:26,  9.83s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.967883211679 and loss 0.392967901315 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.13825193734 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 215, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  27%|██▋       | 266/1000 [45:38<1:50:53,  9.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.263649639142 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.14187934381 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 149, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  27%|██▋       | 267/1000 [45:42<1:32:22,  7.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.890510948905 and loss 1.24816804607 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.40043907762 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 77, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  27%|██▋       | 268/1000 [45:58<2:03:26, 10.12s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.292702766477 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.18446401422 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 429, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  27%|██▋       | 269/1000 [46:20<2:48:36, 13.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.203040533872 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.11643540695 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 401, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  27%|██▋       | 270/1000 [46:33<2:44:48, 13.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.211726885366 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.14640943876 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 295, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  27%|██▋       | 271/1000 [46:34<1:57:56,  9.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.604379562044 and loss 1.87019063273 :\n",
      "Parameters with this testing accuracy 0.263157894737 and loss 2.44694321407 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 24, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  27%|██▋       | 272/1000 [46:38<1:37:15,  8.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.868613138686 and loss 1.27702029556 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.37440511678 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 81, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  27%|██▋       | 273/1000 [46:50<1:51:00,  9.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.199974856792 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.10375993164 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 200, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  27%|██▋       | 274/1000 [47:01<1:58:52,  9.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.969343065693 and loss 0.470132197364 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.12906509034 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 249, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  28%|██▊       | 275/1000 [47:02<1:26:36,  7.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.759124087591 and loss 1.69746253647 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.47046700026 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 27, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  28%|██▊       | 276/1000 [47:12<1:35:45,  7.94s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.973722627737 and loss 0.375925663413 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.15404436182 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 227, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  28%|██▊       | 277/1000 [47:13<1:10:05,  5.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.76204379562 and loss 1.86384556737 :\n",
      "Parameters with this testing accuracy 0.263157894737 and loss 2.56753308397 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 11, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  28%|██▊       | 278/1000 [47:14<52:24,  4.36s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.597080291971 and loss 2.41054988151 :\n",
      "Parameters with this testing accuracy 0.221052631579 and loss 2.68795887546 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.025, 'min_child_weight': 3.0, 'n_estimators': 14, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  28%|██▊       | 279/1000 [47:38<2:05:20, 10.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.230619511042 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.11619108502 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 394, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  28%|██▊       | 280/1000 [47:41<1:36:32,  8.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.345833402709 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.18040332779 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 59, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 3, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  28%|██▊       | 281/1000 [47:52<1:47:42,  8.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.221546902203 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.1474330886 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 199, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  28%|██▊       | 282/1000 [48:04<1:57:23,  9.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.259757839979 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.14567580278 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 301, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  28%|██▊       | 283/1000 [48:24<2:33:45, 12.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.250176045709 :\n",
      "Parameters with this testing accuracy 0.431578947368 and loss 2.10369265225 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 377, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  28%|██▊       | 284/1000 [48:49<3:18:41, 16.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.217969224104 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.13228289252 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 476, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  28%|██▊       | 285/1000 [48:53<2:34:18, 12.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.767664097 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77305151789 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 205, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  29%|██▊       | 286/1000 [49:15<3:05:41, 15.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.273505486477 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.14145990347 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 400, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  29%|██▊       | 287/1000 [49:35<3:18:23, 16.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.286490044826 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.19635519315 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 4.0, 'n_estimators': 302, 'subsample': 0.8500000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  29%|██▉       | 288/1000 [49:41<2:41:10, 13.58s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.292407502202 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.13043499314 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 3.0, 'n_estimators': 133, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  29%|██▉       | 289/1000 [49:45<2:05:42, 10.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.217268056183 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.41914877812 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.42500000000000004, 'min_child_weight': 2.0, 'n_estimators': 76, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  29%|██▉       | 290/1000 [50:08<2:51:20, 14.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.144155932585 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.09263086199 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 478, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  29%|██▉       | 291/1000 [50:15<2:25:18, 12.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.169378685826 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.10727716077 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 123, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  29%|██▉       | 292/1000 [50:20<1:59:33, 10.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.957664233577 and loss 0.637450341913 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.15913275653 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 93, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  29%|██▉       | 293/1000 [50:22<1:31:01,  7.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7676415969 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77320101638 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 95, 'subsample': 0.65, 'importance_type': 'cover', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  29%|██▉       | 294/1000 [50:43<2:14:52, 11.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.187222309272 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.07292112712 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 377, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  30%|██▉       | 295/1000 [51:07<3:01:49, 15.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.170228615602 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.15410217553 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 367, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 8, 'gamma': 0.7000000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  30%|██▉       | 296/1000 [51:16<2:36:50, 13.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.180712953758 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.16927452146 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 173, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  30%|██▉       | 297/1000 [51:19<2:00:58, 10.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.846715328467 and loss 0.939937331234 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.1737784188 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 194, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  30%|██▉       | 298/1000 [51:21<1:30:39,  7.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.718248175182 and loss 1.99972453918 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.57282247794 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 3.0, 'n_estimators': 31, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  30%|██▉       | 299/1000 [51:39<2:07:17, 10.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.265547391188 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.08096296889 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 399, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  30%|███       | 300/1000 [51:52<2:13:47, 11.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.253705675041 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.25050412526 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 3.0, 'n_estimators': 233, 'subsample': 1.0, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  30%|███       | 301/1000 [52:00<2:00:39, 10.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882811 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876844 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 412, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  30%|███       | 302/1000 [52:22<2:41:40, 13.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.185105519959 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.06546559889 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 496, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 5, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  30%|███       | 303/1000 [52:24<2:02:12, 10.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.875912408759 and loss 1.40723163625 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.42597945991 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 65, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 3, 'gamma': 0.55, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  30%|███       | 304/1000 [52:32<1:52:42,  9.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.56924144803 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.28260537355 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 130, 'subsample': 0.6000000000000001, 'importance_type': 'cover', 'max_depth': 9, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  30%|███       | 305/1000 [52:49<2:15:47, 11.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.161095954603 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.10991345448 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 373, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 5, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  31%|███       | 306/1000 [53:03<2:24:54, 12.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.208884714462 :\n",
      "Parameters with this testing accuracy 0.431578947368 and loss 2.04896846808 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 386, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  31%|███       | 307/1000 [53:20<2:38:51, 13.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.105680690277 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.46539407234 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.5, 'min_child_weight': 1.0, 'n_estimators': 445, 'subsample': 0.7000000000000001, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  31%|███       | 308/1000 [53:38<2:53:31, 15.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.101729252985 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.18953198145 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.4, 'min_child_weight': 1.0, 'n_estimators': 489, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  31%|███       | 309/1000 [53:39<2:04:38, 10.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7681006985 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77245690195 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 30, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  31%|███       | 310/1000 [53:50<2:07:15, 11.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.229024354106 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.15176761641 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 296, 'subsample': 0.6000000000000001, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  31%|███       | 311/1000 [54:07<2:25:33, 12.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.133998206772 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.12200917311 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 316, 'subsample': 0.65, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.55, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  31%|███       | 312/1000 [54:16<2:11:38, 11.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.159679390699 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.09004509823 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 225, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  31%|███▏      | 313/1000 [54:26<2:09:03, 11.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.110142136306 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.27490463377 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.45, 'min_child_weight': 1.0, 'n_estimators': 305, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  31%|███▏      | 314/1000 [54:29<1:38:13,  8.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.643795620438 and loss 1.58492422234 :\n",
      "Parameters with this testing accuracy 0.252631578947 and loss 2.50617387295 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 6.0, 'n_estimators': 55, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  32%|███▏      | 315/1000 [54:33<1:22:41,  7.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.116606722722 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.23976910071 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.375, 'min_child_weight': 1.0, 'n_estimators': 73, 'subsample': 0.6000000000000001, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  32%|███▏      | 316/1000 [54:49<1:52:31,  9.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.185095089097 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.05355940316 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 418, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  32%|███▏      | 317/1000 [54:51<1:27:25,  7.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883229 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327765917 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 4.0, 'n_estimators': 119, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  32%|███▏      | 318/1000 [54:58<1:24:38,  7.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.139472198632 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.08487780785 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 164, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  32%|███▏      | 319/1000 [55:03<1:15:32,  6.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.201535434646 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.1913364079 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 101, 'subsample': 0.75, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  32%|███▏      | 320/1000 [55:06<1:03:54,  5.64s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.877372262774 and loss 0.896338124486 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.19697791358 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 203, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 1, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  32%|███▏      | 321/1000 [55:18<1:23:12,  7.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.148958371074 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.1605493131 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 193, 'subsample': 0.65, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  32%|███▏      | 322/1000 [55:28<1:34:39,  8.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.213327623427 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.12151686015 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 463, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 2, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  32%|███▏      | 323/1000 [55:45<2:01:25, 10.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.104734365786 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.23486221901 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.35000000000000003, 'min_child_weight': 1.0, 'n_estimators': 377, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 9, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  32%|███▏      | 324/1000 [56:01<2:21:16, 12.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.163424846892 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.14901536082 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 407, 'subsample': 0.6000000000000001, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  32%|███▎      | 325/1000 [56:02<1:41:44,  9.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76794868316 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77256360054 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 26, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.6000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  33%|███▎      | 326/1000 [56:13<1:47:42,  9.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.976642335766 and loss 0.302719890009 :\n",
      "Parameters with this testing accuracy 0.242105263158 and loss 2.42601728485 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 5.0, 'n_estimators': 386, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 9, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  33%|███▎      | 327/1000 [56:42<2:50:55, 15.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.238925066348 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.20112442123 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 4.0, 'n_estimators': 430, 'subsample': 0.8, 'importance_type': 'gain', 'max_depth': 8, 'gamma': 0.8, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  33%|███▎      | 328/1000 [56:54<2:42:18, 14.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.203001110938 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.22842703046 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.15000000000000002, 'min_child_weight': 2.0, 'n_estimators': 343, 'subsample': 0.9, 'importance_type': 'total_gain', 'max_depth': 4, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  33%|███▎      | 329/1000 [56:58<2:04:28, 11.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.759124087591 and loss 1.31169270763 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.30555091751 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 206, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  33%|███▎      | 330/1000 [57:07<1:58:53, 10.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.169195381409 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.18057334725 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 414, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  33%|███▎      | 331/1000 [57:10<1:31:19,  8.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.213947973262 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.28783491726 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.47500000000000003, 'min_child_weight': 1.0, 'n_estimators': 37, 'subsample': 0.6000000000000001, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  33%|███▎      | 332/1000 [57:28<2:05:38, 11.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.219123369083 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.10941092203 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 397, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  33%|███▎      | 333/1000 [57:35<1:49:22,  9.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883229 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.7732787584 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.25, 'min_child_weight': 1.0, 'n_estimators': 335, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 3, 'gamma': 0.55, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  33%|███▎      | 334/1000 [57:51<2:10:44, 11.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.218496614049 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.16917062528 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 457, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  34%|███▎      | 335/1000 [58:15<2:50:08, 15.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.169801212639 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.17151182114 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 386, 'subsample': 0.65, 'importance_type': 'cover', 'max_depth': 9, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  34%|███▎      | 336/1000 [58:45<3:40:09, 19.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.156087354155 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.1074130333 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 444, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 8, 'gamma': 0.65, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  34%|███▎      | 337/1000 [58:51<2:54:53, 15.83s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.217844496685 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.13896419692 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 146, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  34%|███▍      | 338/1000 [59:00<2:31:10, 13.70s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.300919637065 :\n",
      "Parameters with this testing accuracy 0.431578947368 and loss 2.10930084665 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 167, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  34%|███▍      | 339/1000 [59:20<2:52:39, 15.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.200939481427 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.10380833606 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 447, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  34%|███▍      | 340/1000 [59:22<2:05:29, 11.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.956204379562 and loss 0.455314181132 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.16669296622 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.325, 'min_child_weight': 3.0, 'n_estimators': 74, 'subsample': 0.8500000000000001, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  34%|███▍      | 341/1000 [59:28<1:47:51,  9.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883368 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876593 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.275, 'min_child_weight': 2.0, 'n_estimators': 324, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  34%|███▍      | 342/1000 [59:29<1:19:10,  7.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.729927007299 and loss 1.26247910331 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.315899816 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 4.0, 'n_estimators': 18, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  34%|███▍      | 343/1000 [59:50<2:03:41, 11.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.257942286669 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.12123939603 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 347, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  34%|███▍      | 344/1000 [1:00:16<2:51:02, 15.64s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.204785352811 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.18298256167 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 440, 'subsample': 0.6000000000000001, 'importance_type': 'total_gain', 'max_depth': 7, 'gamma': 0.9, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  34%|███▍      | 345/1000 [1:00:25<2:29:30, 13.70s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.220208378985 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.22428206735 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 139, 'subsample': 0.75, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  35%|███▍      | 346/1000 [1:00:30<2:02:46, 11.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.23880222164 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.11809105473 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 102, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  35%|███▍      | 347/1000 [1:00:39<1:54:00, 10.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.251153390509 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.14795911759 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.07500000000000001, 'min_child_weight': 3.0, 'n_estimators': 160, 'subsample': 0.7000000000000001, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  35%|███▍      | 348/1000 [1:00:54<2:09:18, 11.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.246482460401 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.13356777288 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 306, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  35%|███▍      | 349/1000 [1:01:02<1:54:58, 10.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763910621 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77325616385 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 383, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 2, 'gamma': 0.55, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  35%|███▌      | 350/1000 [1:01:22<2:25:01, 13.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.216680626747 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.20789039707 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 2.0, 'n_estimators': 426, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  35%|███▌      | 351/1000 [1:01:30<2:08:42, 11.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.957664233577 and loss 0.420775780765 :\n",
      "Parameters with this testing accuracy 0.252631578947 and loss 2.39028714786 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.07500000000000001, 'min_child_weight': 6.0, 'n_estimators': 239, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 9, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  35%|███▌      | 352/1000 [1:01:46<2:20:28, 13.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.180268214725 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.29108315561 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 238, 'subsample': 0.9500000000000001, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.8, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  35%|███▌      | 353/1000 [1:01:56<2:10:33, 12.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.973722627737 and loss 0.417115501272 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.12192547384 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 287, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  35%|███▌      | 354/1000 [1:02:14<2:29:55, 13.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.137469921878 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.07896551717 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 333, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 6, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  36%|███▌      | 355/1000 [1:02:26<2:21:58, 13.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.985401459854 and loss 0.279594418835 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.20172888314 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 3.0, 'n_estimators': 280, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  36%|███▌      | 356/1000 [1:02:35<2:08:58, 12.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.215885971219 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.18683059474 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.225, 'min_child_weight': 1.0, 'n_estimators': 167, 'subsample': 0.6000000000000001, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  36%|███▌      | 357/1000 [1:02:46<2:07:16, 11.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.26994815313 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.1275911721 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 214, 'subsample': 0.7000000000000001, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  36%|███▌      | 358/1000 [1:02:51<1:44:17,  9.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882602 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876342 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 241, 'subsample': 1.0, 'importance_type': 'total_gain', 'max_depth': 7, 'gamma': 0.9, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  36%|███▌      | 359/1000 [1:02:56<1:28:55,  8.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.249757060585 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.16239151594 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 121, 'subsample': 0.8, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  36%|███▌      | 360/1000 [1:03:04<1:26:55,  8.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.829197080292 and loss 1.10245415175 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.29115466052 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.025, 'min_child_weight': 4.0, 'n_estimators': 144, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.55, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  36%|███▌      | 361/1000 [1:03:09<1:16:55,  7.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.918248175182 and loss 0.683020916336 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.19065017826 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 110, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  36%|███▌      | 362/1000 [1:03:30<2:00:14, 11.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.201982678606 :\n",
      "Parameters with this testing accuracy 0.431578947368 and loss 2.08172876684 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 405, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  36%|███▋      | 363/1000 [1:03:41<1:58:46, 11.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.393641251956 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.18667743888 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 192, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  36%|███▋      | 364/1000 [1:03:49<1:49:55, 10.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.238340659653 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.10603058244 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 167, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  36%|███▋      | 365/1000 [1:04:09<2:21:16, 13.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.239417016033 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.10383650857 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 385, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  37%|███▋      | 366/1000 [1:04:15<1:56:37, 11.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7676388295 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876593 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 281, 'subsample': 0.6000000000000001, 'importance_type': 'gain', 'max_depth': 6, 'gamma': 0.8, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  37%|███▋      | 367/1000 [1:04:28<2:03:46, 11.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.202544459787 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.18861942113 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 363, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  37%|███▋      | 368/1000 [1:04:30<1:30:40,  8.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.617518248175 and loss 1.64629477128 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.42895158341 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 4.0, 'n_estimators': 62, 'subsample': 0.9, 'importance_type': 'cover', 'max_depth': 1, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  37%|███▋      | 369/1000 [1:04:41<1:37:32,  9.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.182050847595 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.14370470496 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 210, 'subsample': 0.65, 'importance_type': 'total_cover', 'max_depth': 8, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  37%|███▋      | 370/1000 [1:05:00<2:08:53, 12.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.190786796292 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.19785581935 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 405, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  37%|███▋      | 371/1000 [1:05:18<2:27:57, 14.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.98102189781 and loss 0.296766126942 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.373350717 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.05, 'min_child_weight': 5.0, 'n_estimators': 473, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  37%|███▋      | 372/1000 [1:05:39<2:47:08, 15.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.18839017214 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.13036491254 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 415, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  37%|███▋      | 373/1000 [1:05:42<2:07:26, 12.20s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.953284671533 and loss 0.541675775678 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.21833481675 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.1, 'min_child_weight': 3.0, 'n_estimators': 60, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  37%|███▋      | 374/1000 [1:05:47<1:45:56, 10.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882881 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876844 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 268, 'subsample': 0.6000000000000001, 'importance_type': 'total_gain', 'max_depth': 2, 'gamma': 0.8, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  38%|███▊      | 375/1000 [1:05:56<1:40:35,  9.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.129773191418 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.2685380638 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.30000000000000004, 'min_child_weight': 1.0, 'n_estimators': 134, 'subsample': 0.9500000000000001, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  38%|███▊      | 376/1000 [1:06:06<1:41:03,  9.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.203803338742 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.20902484967 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.2, 'min_child_weight': 2.0, 'n_estimators': 166, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 9, 'gamma': 0.9, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  38%|███▊      | 377/1000 [1:06:28<2:19:28, 13.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.238130391937 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.17111503318 :\n",
      "{'colsample_bytree': 1.0, 'learning_rate': 0.07500000000000001, 'min_child_weight': 3.0, 'n_estimators': 450, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  38%|███▊      | 378/1000 [1:06:40<2:14:38, 12.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.37261620903 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.14738524909 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 217, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  38%|███▊      | 379/1000 [1:06:41<1:38:57,  9.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.919708029197 and loss 1.25128956233 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.4236665412 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 22, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  38%|███▊      | 380/1000 [1:06:44<1:19:08,  7.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.188175555575 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.29566298152 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.4, 'min_child_weight': 2.0, 'n_estimators': 54, 'subsample': 0.7000000000000001, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  38%|███▊      | 381/1000 [1:06:58<1:38:30,  9.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.217672166574 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.18429822685 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 431, 'subsample': 0.65, 'importance_type': 'total_cover', 'max_depth': 3, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  38%|███▊      | 382/1000 [1:07:07<1:35:33,  9.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763889041 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77326932204 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 449, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.6000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  38%|███▊      | 383/1000 [1:07:08<1:10:03,  6.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.85401459854 and loss 1.3520992859 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.41993900412 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 10, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  38%|███▊      | 384/1000 [1:07:40<2:28:25, 14.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.193589409514 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.15774416904 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 460, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.75, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  38%|███▊      | 385/1000 [1:07:47<2:03:10, 12.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.16394861453 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.32902979699 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 3.0, 'n_estimators': 169, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  39%|███▊      | 386/1000 [1:08:02<2:14:21, 13.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.89197080292 and loss 0.644834661913 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.30869777495 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 6.0, 'n_estimators': 480, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  39%|███▊      | 387/1000 [1:08:17<2:16:58, 13.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.133413198963 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.27628399577 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 177, 'subsample': 0.8500000000000001, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  39%|███▉      | 388/1000 [1:08:22<1:53:33, 11.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.922627737226 and loss 1.02139626489 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.32622315193 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 98, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  39%|███▉      | 389/1000 [1:08:25<1:28:47,  8.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.975182481752 and loss 0.863792142838 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.31886182992 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 43, 'subsample': 0.6000000000000001, 'importance_type': 'cover', 'max_depth': 6, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  39%|███▉      | 390/1000 [1:08:34<1:28:04,  8.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883472 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876342 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.45, 'min_child_weight': 1.0, 'n_estimators': 446, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  39%|███▉      | 391/1000 [1:08:41<1:23:42,  8.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.257200144594 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.18661572141 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 3.0, 'n_estimators': 170, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  39%|███▉      | 392/1000 [1:08:55<1:40:35,  9.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.235577695425 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.242181359 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 218, 'subsample': 0.75, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  39%|███▉      | 393/1000 [1:09:12<2:00:53, 11.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.121910602372 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.33642515793 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.35000000000000003, 'min_child_weight': 2.0, 'n_estimators': 358, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 4, 'gamma': 0.55, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  39%|███▉      | 394/1000 [1:09:15<1:32:58,  9.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.27235946147 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.1631253182 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.25, 'min_child_weight': 1.0, 'n_estimators': 176, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 1, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  40%|███▉      | 395/1000 [1:09:23<1:30:04,  8.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.203323492884 :\n",
      "Parameters with this testing accuracy 0.242105263158 and loss 2.89931730924 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.42500000000000004, 'min_child_weight': 5.0, 'n_estimators': 389, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 2, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  40%|███▉      | 396/1000 [1:09:41<1:56:37, 11.58s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.131817258491 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.11062643522 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 380, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 9, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  40%|███▉      | 397/1000 [1:10:01<2:21:51, 14.12s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.194285778724 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.11087342418 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 405, 'subsample': 0.65, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  40%|███▉      | 398/1000 [1:10:06<1:54:55, 11.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882811 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327877597 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 4.0, 'n_estimators': 265, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  40%|███▉      | 399/1000 [1:10:25<2:19:07, 13.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.219553939461 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.12552437412 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 384, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  40%|████      | 400/1000 [1:10:45<2:35:40, 15.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.131984134361 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.26461178915 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.275, 'min_child_weight': 1.0, 'n_estimators': 461, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  40%|████      | 401/1000 [1:10:54<2:16:29, 13.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.24965918947 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.15153034027 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 3.0, 'n_estimators': 172, 'subsample': 0.6000000000000001, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 0.7000000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  40%|████      | 402/1000 [1:10:58<1:46:19, 10.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.975182481752 and loss 0.682095623027 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.28494683128 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 66, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  40%|████      | 403/1000 [1:11:10<1:50:13, 11.08s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.263171476262 :\n",
      "Parameters with this testing accuracy 0.431578947368 and loss 2.07320120033 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 317, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  40%|████      | 404/1000 [1:11:24<1:58:40, 11.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.227041915081 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.06383537576 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 368, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  40%|████      | 405/1000 [1:11:37<2:01:02, 12.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.259923281818 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.10257599087 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 251, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  41%|████      | 406/1000 [1:11:49<2:02:29, 12.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.158301491662 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.07063385725 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 336, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  41%|████      | 407/1000 [1:12:07<2:16:23, 13.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.196939549814 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.05790393503 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 458, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  41%|████      | 408/1000 [1:12:19<2:12:15, 13.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.146641841003 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.0902934399 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 339, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  41%|████      | 409/1000 [1:12:38<2:28:34, 15.08s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.133520970667 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.11841995112 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 442, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  41%|████      | 410/1000 [1:12:51<2:21:51, 14.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.246149428435 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.07452741963 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 330, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 4, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  41%|████      | 411/1000 [1:12:53<1:45:20, 10.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.894890510949 and loss 1.6217325757 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.49017738543 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 44, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  41%|████      | 412/1000 [1:13:08<1:57:18, 11.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.224625913755 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.10785607358 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 381, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  41%|████▏     | 413/1000 [1:13:18<1:51:48, 11.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.204030393858 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.07817250655 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 183, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  41%|████▏     | 414/1000 [1:13:44<2:33:25, 15.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.162632425102 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.10949136932 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 456, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  42%|████▏     | 415/1000 [1:13:49<2:03:18, 12.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.957664233577 and loss 0.505568891335 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.11807248836 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 393, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 1, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  42%|████▏     | 416/1000 [1:14:08<2:19:49, 14.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.164710854177 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.10920668438 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 374, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 8, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  42%|████▏     | 417/1000 [1:14:21<2:17:17, 14.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.240072036136 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.06893081006 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 346, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  42%|████▏     | 418/1000 [1:14:42<2:36:36, 16.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.146718020423 :\n",
      "Parameters with this testing accuracy 0.442105263158 and loss 2.1001167268 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 388, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  42%|████▏     | 419/1000 [1:14:51<2:15:17, 13.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.159313589347 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.10837614491 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 158, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  42%|████▏     | 420/1000 [1:15:09<2:26:54, 15.20s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.134413955555 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.10260538353 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 337, 'subsample': 0.6000000000000001, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  42%|████▏     | 421/1000 [1:15:25<2:29:31, 15.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.165543256444 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.10349034891 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 312, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  42%|████▏     | 422/1000 [1:15:46<2:44:47, 17.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.160274579443 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.19206408927 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 495, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  42%|████▏     | 423/1000 [1:15:52<2:12:24, 13.77s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.972262773723 and loss 0.630982034659 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.25016303768 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 141, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  42%|████▏     | 424/1000 [1:16:11<2:28:11, 15.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.172795423124 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.10547088569 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 388, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  42%|████▎     | 425/1000 [1:16:23<2:15:39, 14.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.177612093931 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.19570066539 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 470, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 2, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  43%|████▎     | 426/1000 [1:16:41<2:26:24, 15.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.176528268502 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.15768972988 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 448, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  43%|████▎     | 427/1000 [1:16:46<1:58:27, 12.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.255539567407 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.11220172142 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 105, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 7, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  43%|████▎     | 428/1000 [1:16:55<1:47:30, 11.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.208832762205 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.10671521027 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 271, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 3, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  43%|████▎     | 429/1000 [1:16:57<1:19:48,  8.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.915328467153 and loss 0.946491995584 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.37550698205 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 20, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  43%|████▎     | 430/1000 [1:17:09<1:32:45,  9.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.163321109109 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.08564571634 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 236, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  43%|████▎     | 431/1000 [1:17:30<2:03:18, 13.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.157313683121 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.13665259226 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 490, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  43%|████▎     | 432/1000 [1:17:45<2:07:41, 13.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.214589967076 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.06489643982 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 388, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  43%|████▎     | 433/1000 [1:17:53<1:52:25, 11.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883264 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876342 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 420, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 9, 'gamma': 0.5, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  43%|████▎     | 434/1000 [1:18:01<1:41:28, 10.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.28867090842 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.09329259501 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 165, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  44%|████▎     | 435/1000 [1:18:09<1:32:19,  9.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.187528448787 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.09278449794 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 132, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.55, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  44%|████▎     | 436/1000 [1:18:16<1:25:42,  9.12s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.261572499212 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.10720154839 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 143, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  44%|████▎     | 437/1000 [1:18:29<1:36:43, 10.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.170985099452 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.1946144841 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 294, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 9, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  44%|████▍     | 438/1000 [1:18:51<2:09:28, 13.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.167273627622 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.18027968769 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.30000000000000004, 'min_child_weight': 1.0, 'n_estimators': 479, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  44%|████▍     | 439/1000 [1:18:58<1:50:33, 11.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.139732403201 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.13014302348 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 115, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  44%|████▍     | 440/1000 [1:19:02<1:27:38,  9.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.972262773723 and loss 0.553517320192 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.18917761147 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 86, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  44%|████▍     | 441/1000 [1:19:05<1:10:54,  7.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.748905109489 and loss 1.29900115493 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.30036279826 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 216, 'subsample': 0.6000000000000001, 'importance_type': 'cover', 'max_depth': 1, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  44%|████▍     | 442/1000 [1:19:09<58:54,  6.33s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76771702488 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77289034442 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 161, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.6000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  44%|████▍     | 443/1000 [1:19:28<1:34:53, 10.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.144828789398 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.11527028276 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 349, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  44%|████▍     | 444/1000 [1:19:45<1:54:32, 12.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.184852330257 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.10428811504 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 237, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.65, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  44%|████▍     | 445/1000 [1:19:56<1:48:21, 11.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.202296928809 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.2312112429 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 261, 'subsample': 0.8, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  45%|████▍     | 446/1000 [1:20:05<1:42:48, 11.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.19545961291 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.2350635202 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.15000000000000002, 'min_child_weight': 3.0, 'n_estimators': 247, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  45%|████▍     | 447/1000 [1:20:07<1:14:56,  8.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.870072992701 and loss 1.49675382289 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.45289892899 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 17, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  45%|████▍     | 448/1000 [1:20:14<1:11:31,  7.77s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.244904752915 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.12510046222 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 120, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  45%|████▍     | 449/1000 [1:20:25<1:22:33,  8.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.194525173555 :\n",
      "Parameters with this testing accuracy 0.242105263158 and loss 2.57230462122 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.325, 'min_child_weight': 4.0, 'n_estimators': 334, 'subsample': 0.65, 'importance_type': 'gain', 'max_depth': 6, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  45%|████▌     | 450/1000 [1:20:43<1:47:04, 11.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.183249539934 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.04509761159 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 469, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  45%|████▌     | 451/1000 [1:20:53<1:42:01, 11.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.219936598043 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.14278003209 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 207, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  45%|████▌     | 452/1000 [1:20:55<1:15:20,  8.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76775954762 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77279800365 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 47, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.7000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  45%|████▌     | 453/1000 [1:21:05<1:22:08,  9.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.96204379562 and loss 0.522538975254 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.14915243921 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 317, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 2, 'gamma': 0.55, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  45%|████▌     | 454/1000 [1:21:09<1:06:22,  7.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.976642335766 and loss 0.444398060857 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.15536119797 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 58, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  46%|████▌     | 455/1000 [1:21:20<1:18:00,  8.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.22404666208 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.08366106031 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 209, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  46%|████▌     | 456/1000 [1:21:22<59:00,  6.51s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.807299270073 and loss 1.40281286853 :\n",
      "Parameters with this testing accuracy 0.252631578947 and loss 2.42932198016 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.07500000000000001, 'min_child_weight': 3.0, 'n_estimators': 23, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  46%|████▌     | 457/1000 [1:21:40<1:30:04,  9.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.200801309538 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.13102324268 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 369, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  46%|████▌     | 458/1000 [1:21:51<1:32:27, 10.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.982481751825 and loss 0.329307157068 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.10507219755 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 350, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 3, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  46%|████▌     | 459/1000 [1:21:53<1:10:40,  7.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.919708029197 and loss 1.24533536895 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.40720593553 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 34, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  46%|████▌     | 460/1000 [1:22:02<1:14:26,  8.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763885735 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327335006 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.025, 'min_child_weight': 3.0, 'n_estimators': 491, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.55, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  46%|████▌     | 461/1000 [1:22:14<1:23:58,  9.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.196049556189 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.14238278383 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 231, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  46%|████▌     | 462/1000 [1:22:41<2:11:44, 14.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.216225569656 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.06944955215 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 494, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  46%|████▋     | 463/1000 [1:22:44<1:37:39, 10.91s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.639416058394 and loss 1.7458749571 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.42952018035 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 96, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 0.65, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  46%|████▋     | 464/1000 [1:23:01<1:55:09, 12.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.150034492468 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.09861333057 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 465, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  46%|████▋     | 465/1000 [1:23:09<1:42:36, 11.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.163151786623 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.14172617346 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 168, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  47%|████▋     | 466/1000 [1:23:25<1:52:36, 12.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.114581577906 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.36559987966 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.47500000000000003, 'min_child_weight': 1.0, 'n_estimators': 427, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  47%|████▋     | 467/1000 [1:23:27<1:26:03,  9.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.922627737226 and loss 0.760710294822 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.23662235941 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 109, 'subsample': 0.7000000000000001, 'importance_type': 'total_cover', 'max_depth': 2, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  47%|████▋     | 468/1000 [1:23:40<1:32:12, 10.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.212286932876 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.10049855239 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 317, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 4, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  47%|████▋     | 469/1000 [1:23:45<1:18:38,  8.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.265568400222 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.1452852624 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 91, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  47%|████▋     | 470/1000 [1:23:47<1:00:26,  6.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763932339 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77324614023 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.1, 'min_child_weight': 4.0, 'n_estimators': 87, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.75, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  47%|████▋     | 471/1000 [1:24:02<1:22:44,  9.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.151584177208 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.24530818504 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.2, 'min_child_weight': 2.0, 'n_estimators': 290, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.65, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  47%|████▋     | 472/1000 [1:24:16<1:32:45, 10.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.130051025846 :\n",
      "Parameters with this testing accuracy 0.263157894737 and loss 2.58394745921 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.375, 'min_child_weight': 3.0, 'n_estimators': 437, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  47%|████▋     | 473/1000 [1:24:37<2:01:04, 13.79s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.199541646922 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.20643257558 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 366, 'subsample': 0.75, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  47%|████▋     | 474/1000 [1:24:42<1:37:44, 11.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.165474939138 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.22852455217 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.225, 'min_child_weight': 2.0, 'n_estimators': 162, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 3, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  48%|████▊     | 475/1000 [1:24:51<1:31:59, 10.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.277427109852 :\n",
      "Parameters with this testing accuracy 0.442105263158 and loss 2.07692007826 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 235, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  48%|████▊     | 476/1000 [1:25:04<1:37:40, 11.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.225658991051 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.14715834701 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 244, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  48%|████▊     | 477/1000 [1:25:05<1:12:47,  8.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.154868507886 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.28867655491 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.5, 'min_child_weight': 2.0, 'n_estimators': 35, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  48%|████▊     | 478/1000 [1:25:14<1:13:59,  8.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883472 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876342 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 472, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  48%|████▊     | 479/1000 [1:25:18<1:01:43,  7.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.260787294537 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.08899070626 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 69, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  48%|████▊     | 480/1000 [1:25:37<1:33:12, 10.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.182459071404 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.15545011343 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 398, 'subsample': 0.65, 'importance_type': 'total_gain', 'max_depth': 7, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  48%|████▊     | 481/1000 [1:26:01<2:05:32, 14.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.2193537554 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.23103083733 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 411, 'subsample': 0.9, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  48%|████▊     | 482/1000 [1:26:02<1:30:04, 10.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.710948905109 and loss 1.45821783521 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.33143261577 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 32, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  48%|████▊     | 483/1000 [1:26:10<1:24:12,  9.77s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.978102189781 and loss 0.387850220357 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.11208940478 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 184, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 8, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  48%|████▊     | 484/1000 [1:26:19<1:21:56,  9.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.229597059465 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.1048448085 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 235, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  48%|████▊     | 485/1000 [1:26:26<1:17:12,  9.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.137844500714 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.10788909671 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 204, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  49%|████▊     | 486/1000 [1:26:33<1:10:58,  8.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.215361206436 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.1352406169 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 274, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  49%|████▊     | 487/1000 [1:26:35<54:51,  6.42s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7681503731 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77243720356 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 5.0, 'n_estimators': 88, 'subsample': 0.7000000000000001, 'importance_type': 'cover', 'max_depth': 3, 'gamma': 0.6000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  49%|████▉     | 488/1000 [1:26:38<44:28,  5.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.912408759124 and loss 1.68365543785 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.51498635192 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 39, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  49%|████▉     | 489/1000 [1:26:49<1:01:14,  7.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.252441579939 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.15717657244 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 278, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  49%|████▉     | 490/1000 [1:27:01<1:12:44,  8.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.16614951282 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.09059023816 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 235, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  49%|████▉     | 491/1000 [1:27:36<2:18:54, 16.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.244872410608 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.13617037539 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 455, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  49%|████▉     | 492/1000 [1:27:45<2:00:43, 14.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.273432161906 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.25158159309 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 226, 'subsample': 0.8, 'importance_type': 'gain', 'max_depth': 4, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  49%|████▉     | 493/1000 [1:27:47<1:30:17, 10.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.690510948905 and loss 1.5765255578 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.38542428456 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 135, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 1, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  49%|████▉     | 494/1000 [1:27:59<1:31:28, 10.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.248252602094 :\n",
      "Parameters with this testing accuracy 0.252631578947 and loss 2.35136365495 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.25, 'min_child_weight': 4.0, 'n_estimators': 319, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  50%|████▉     | 495/1000 [1:28:07<1:25:49, 10.20s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.140251117385 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.15101851919 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 179, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  50%|████▉     | 496/1000 [1:28:16<1:21:50,  9.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882846 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876342 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 452, 'subsample': 1.0, 'importance_type': 'cover', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  50%|████▉     | 497/1000 [1:28:23<1:15:10,  8.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.274977860728 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.12292085274 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 124, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  50%|████▉     | 498/1000 [1:28:37<1:27:38, 10.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.181281738832 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.11355755723 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 276, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  50%|████▉     | 499/1000 [1:28:39<1:05:12,  7.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.849635036496 and loss 1.91295085528 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.55892882975 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 29, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 4, 'gamma': 0.55, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  50%|█████     | 500/1000 [1:28:57<1:31:22, 10.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.164567259539 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.21132656452 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 454, 'subsample': 0.65, 'importance_type': 'cover', 'max_depth': 5, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  50%|█████     | 501/1000 [1:29:08<1:30:08, 10.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.191547365652 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.11377142162 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 256, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  50%|█████     | 502/1000 [1:29:18<1:28:55, 10.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.32584433201 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.10923163173 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 345, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 3, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  50%|█████     | 503/1000 [1:29:33<1:38:56, 11.94s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.267460259427 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.31725735259 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 5.0, 'n_estimators': 403, 'subsample': 0.6000000000000001, 'importance_type': 'gain', 'max_depth': 7, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  50%|█████     | 504/1000 [1:29:36<1:15:46,  9.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.970802919708 and loss 0.591977063768 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.21185960315 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 40, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  50%|█████     | 505/1000 [1:29:39<1:00:52,  7.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76764075391 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77321500778 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 151, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.65, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  51%|█████     | 506/1000 [1:29:42<51:12,  6.22s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.960583941606 and loss 0.409349789128 :\n",
      "Parameters with this testing accuracy 0.263157894737 and loss 2.4656431649 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.225, 'min_child_weight': 6.0, 'n_estimators': 92, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  51%|█████     | 507/1000 [1:29:53<1:01:46,  7.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.149130426438 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.22949296573 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 181, 'subsample': 0.8500000000000001, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  51%|█████     | 508/1000 [1:30:01<1:04:07,  7.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.147147645295 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.0654672669 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 178, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 4, 'gamma': 0.5, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  51%|█████     | 509/1000 [1:30:22<1:35:40, 11.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.205619604019 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.14001505426 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 404, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  51%|█████     | 510/1000 [1:30:29<1:24:21, 10.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.94598540146 and loss 0.655057250801 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.20214113123 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 3.0, 'n_estimators': 186, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  51%|█████     | 511/1000 [1:30:46<1:40:25, 12.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.207285086056 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.16897311642 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 338, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  51%|█████     | 512/1000 [1:30:49<1:17:11,  9.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.951824817518 and loss 0.534451288723 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.12766741336 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 185, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  51%|█████▏    | 513/1000 [1:30:58<1:15:43,  9.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.213799508025 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.09788183221 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 154, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 7, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  51%|█████▏    | 514/1000 [1:31:04<1:06:48,  8.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883089 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327872577 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 283, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  52%|█████▏    | 515/1000 [1:31:26<1:40:28, 12.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.178209079396 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.12311493617 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 413, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  52%|█████▏    | 516/1000 [1:31:43<1:50:29, 13.70s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.214105171495 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.17292255638 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 309, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  52%|█████▏    | 517/1000 [1:31:55<1:46:44, 13.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.186114571801 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.24505001126 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.325, 'min_child_weight': 2.0, 'n_estimators': 213, 'subsample': 0.65, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.9, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  52%|█████▏    | 518/1000 [1:31:57<1:19:39,  9.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.813138686131 and loss 1.49699265191 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.41068467906 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 75, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  52%|█████▏    | 519/1000 [1:32:16<1:41:28, 12.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.181566437202 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.08036899916 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 492, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  52%|█████▏    | 520/1000 [1:32:41<2:10:53, 16.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.228010361076 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.10627192124 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 493, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  52%|█████▏    | 521/1000 [1:32:56<2:06:12, 15.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.12072524589 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.24822390487 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 372, 'subsample': 0.75, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  52%|█████▏    | 522/1000 [1:33:07<1:56:11, 14.58s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.171208629397 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.11619504125 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 297, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  52%|█████▏    | 523/1000 [1:33:09<1:26:10, 10.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763922838 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77324948311 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 89, 'subsample': 0.9500000000000001, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.8500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  52%|█████▏    | 524/1000 [1:33:22<1:31:01, 11.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.228044944976 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.13352831935 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 399, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  52%|█████▎    | 525/1000 [1:33:25<1:10:21,  8.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.175904210764 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.25891652644 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.35000000000000003, 'min_child_weight': 2.0, 'n_estimators': 51, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 8, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  53%|█████▎    | 526/1000 [1:33:30<1:01:10,  7.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.94598540146 and loss 0.658623600436 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.22563527462 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 90, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 7, 'gamma': 0.7000000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  53%|█████▎    | 527/1000 [1:33:41<1:08:07,  8.64s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.204706992211 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.20436134677 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.17500000000000002, 'min_child_weight': 2.0, 'n_estimators': 208, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  53%|█████▎    | 528/1000 [1:33:47<1:01:39,  7.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.160777081083 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.21146045055 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.275, 'min_child_weight': 1.0, 'n_estimators': 416, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 1, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  53%|█████▎    | 529/1000 [1:34:03<1:21:53, 10.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.212598239558 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.34089814457 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 4.0, 'n_estimators': 483, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  53%|█████▎    | 530/1000 [1:34:17<1:29:58, 11.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.215206513478 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.13285162847 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 264, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  53%|█████▎    | 531/1000 [1:34:36<1:45:53, 13.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.201912153554 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.19457042472 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 376, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  53%|█████▎    | 532/1000 [1:34:44<1:33:42, 12.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763894575 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77326505059 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 421, 'subsample': 0.6000000000000001, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.55, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  53%|█████▎    | 533/1000 [1:34:59<1:39:12, 12.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.153185773455 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.08565221699 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 284, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  53%|█████▎    | 534/1000 [1:35:12<1:41:32, 13.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.198559479104 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.11001662522 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 432, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 3, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  54%|█████▎    | 535/1000 [1:35:20<1:28:56, 11.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.215283620766 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.22643675192 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.4, 'min_child_weight': 1.0, 'n_estimators': 263, 'subsample': 0.8, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  54%|█████▎    | 536/1000 [1:35:42<1:52:16, 14.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.148856052675 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.16803947271 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 435, 'subsample': 0.7000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  54%|█████▎    | 537/1000 [1:35:59<1:57:49, 15.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.219135125104 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.13455161735 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 340, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  54%|█████▍    | 538/1000 [1:36:14<1:56:29, 15.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.200839550569 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.20879664374 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 288, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  54%|█████▍    | 539/1000 [1:36:30<1:59:51, 15.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.226409541159 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.0795633869 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 387, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  54%|█████▍    | 540/1000 [1:36:33<1:29:02, 11.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.214078225353 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.21192501352 :\n",
      "{'colsample_bytree': 1.0, 'learning_rate': 0.30000000000000004, 'min_child_weight': 2.0, 'n_estimators': 28, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  54%|█████▍    | 541/1000 [1:36:35<1:07:31,  8.83s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76801441777 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77252463291 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 100, 'subsample': 0.6000000000000001, 'importance_type': 'cover', 'max_depth': 1, 'gamma': 0.6000000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  54%|█████▍    | 542/1000 [1:36:40<57:34,  7.54s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.957664233577 and loss 0.898636605835 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.30214597959 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 107, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  54%|█████▍    | 543/1000 [1:36:44<50:19,  6.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.96204379562 and loss 0.496115849964 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.14369473402 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 3.0, 'n_estimators': 84, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  54%|█████▍    | 544/1000 [1:37:15<1:46:08, 13.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.221404116754 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.16200090376 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 474, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  55%|█████▍    | 545/1000 [1:37:31<1:49:55, 14.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.171735789269 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.08202154062 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 326, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  55%|█████▍    | 546/1000 [1:37:40<1:37:37, 12.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.401241723935 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.13170864543 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 224, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  55%|█████▍    | 547/1000 [1:37:59<1:51:14, 14.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.134934228094 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.10838452997 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 486, 'subsample': 0.55, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  55%|█████▍    | 548/1000 [1:38:17<1:57:25, 15.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.181383224301 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.11453383302 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 353, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  55%|█████▍    | 549/1000 [1:38:31<1:54:07, 15.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.24288041402 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.28280802538 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.07500000000000001, 'min_child_weight': 4.0, 'n_estimators': 321, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  55%|█████▌    | 550/1000 [1:38:42<1:44:17, 13.91s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.235454752088 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.13828250143 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 466, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  55%|█████▌    | 551/1000 [1:38:46<1:21:39, 10.91s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7676388302 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327690626 :\n",
      "{'colsample_bytree': 0.8500000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 3.0, 'n_estimators': 188, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.8, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  55%|█████▌    | 552/1000 [1:39:04<1:38:00, 13.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.202588840329 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.17795599022 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 355, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  55%|█████▌    | 553/1000 [1:39:15<1:32:36, 12.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.222867823789 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.11836811253 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 201, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 4, 'gamma': 0.75, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  55%|█████▌    | 554/1000 [1:39:26<1:30:26, 12.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.169724422565 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.18292867095 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 342, 'subsample': 0.65, 'importance_type': 'cover', 'max_depth': 3, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  56%|█████▌    | 555/1000 [1:39:34<1:19:24, 10.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.252554717816 :\n",
      "Parameters with this testing accuracy 0.442105263158 and loss 2.15040628687 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 128, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  56%|█████▌    | 556/1000 [1:39:41<1:11:40,  9.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.236088110621 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.19617035399 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 128, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  56%|█████▌    | 557/1000 [1:39:44<57:35,  7.80s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.253976024177 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.17666356301 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 50, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  56%|█████▌    | 558/1000 [1:39:48<48:08,  6.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.2186785382 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.30811999215 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.225, 'min_child_weight': 3.0, 'n_estimators': 223, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  56%|█████▌    | 559/1000 [1:39:55<49:08,  6.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.15642558577 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.25502095383 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.25, 'min_child_weight': 2.0, 'n_estimators': 159, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  56%|█████▌    | 560/1000 [1:40:02<50:35,  6.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883298 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327875589 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.15000000000000002, 'min_child_weight': 2.0, 'n_estimators': 379, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  56%|█████▌    | 561/1000 [1:40:06<43:43,  5.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.23022943709 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.14665925859 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 140, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  56%|█████▌    | 562/1000 [1:40:16<51:01,  6.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.957664233577 and loss 0.400465104709 :\n",
      "Parameters with this testing accuracy 0.210526315789 and loss 2.49726962432 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 6.0, 'n_estimators': 331, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  56%|█████▋    | 563/1000 [1:40:25<55:19,  7.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.23131035866 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.16066073448 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 153, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  56%|█████▋    | 564/1000 [1:40:35<1:02:05,  8.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.193465080437 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.32265376978 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 4.0, 'n_estimators': 344, 'subsample': 0.7000000000000001, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  56%|█████▋    | 565/1000 [1:40:52<1:18:51, 10.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.197155485 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.17291798984 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 293, 'subsample': 0.75, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  57%|█████▋    | 566/1000 [1:40:58<1:09:12,  9.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.276413629082 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.09037083756 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 113, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  57%|█████▋    | 567/1000 [1:41:08<1:09:28,  9.63s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.121999585151 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.2732572594 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 240, 'subsample': 0.9, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  57%|█████▋    | 568/1000 [1:41:15<1:02:52,  8.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.203894840529 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.14557482126 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 157, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  57%|█████▋    | 569/1000 [1:41:17<49:35,  6.90s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882881 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876593 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 118, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  57%|█████▋    | 570/1000 [1:41:24<49:43,  6.94s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.226859659283 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.30933284838 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.275, 'min_child_weight': 3.0, 'n_estimators': 174, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  57%|█████▋    | 571/1000 [1:41:29<45:56,  6.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.183031523757 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.21358373004 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 108, 'subsample': 0.8500000000000001, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.8, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  57%|█████▋    | 572/1000 [1:41:47<1:08:45,  9.64s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.229457219806 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.0905826122 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 341, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  57%|█████▋    | 573/1000 [1:41:54<1:04:03,  9.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.237803719752 :\n",
      "Parameters with this testing accuracy 0.442105263158 and loss 2.09016520793 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 123, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  57%|█████▋    | 574/1000 [1:42:02<1:01:01,  8.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.204210897739 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.19706522477 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.225, 'min_child_weight': 1.0, 'n_estimators': 128, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  57%|█████▊    | 575/1000 [1:42:07<53:19,  7.53s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.259263809523 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.10061718618 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 80, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  58%|█████▊    | 576/1000 [1:42:31<1:28:19, 12.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.180161742728 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.14694062277 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 488, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  58%|█████▊    | 577/1000 [1:42:48<1:37:01, 13.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.193345943761 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.18211205647 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 365, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  58%|█████▊    | 578/1000 [1:43:02<1:38:33, 14.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.213459793779 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.15665900348 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 277, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  58%|█████▊    | 579/1000 [1:43:17<1:39:08, 14.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.203356765979 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.15998551922 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 267, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  58%|█████▊    | 580/1000 [1:43:29<1:35:17, 13.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.210568886304 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.18412805292 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 246, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  58%|█████▊    | 581/1000 [1:43:42<1:34:28, 13.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.215555664639 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.13952136279 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 262, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  58%|█████▊    | 582/1000 [1:44:00<1:42:48, 14.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.219596219566 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.13632011121 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 348, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  58%|█████▊    | 583/1000 [1:44:20<1:54:01, 16.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.215904556266 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.15754086238 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 391, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  58%|█████▊    | 584/1000 [1:44:35<1:50:51, 15.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.230934136781 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.10518343115 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 382, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  58%|█████▊    | 585/1000 [1:44:58<2:05:35, 18.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.222121357488 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.09113541058 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 436, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  59%|█████▊    | 586/1000 [1:45:18<2:07:48, 18.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.208449237845 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.15867685507 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.15000000000000002, 'min_child_weight': 2.0, 'n_estimators': 428, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  59%|█████▊    | 587/1000 [1:45:27<1:47:56, 15.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.25931358883 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.12148470937 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 167, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  59%|█████▉    | 588/1000 [1:45:51<2:05:33, 18.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.210932544748 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.12709766258 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 497, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  59%|█████▉    | 589/1000 [1:46:13<2:11:53, 19.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.181973919636 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.15776675183 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 408, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  59%|█████▉    | 590/1000 [1:46:23<1:53:25, 16.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.233115165542 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.12230549235 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 175, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  59%|█████▉    | 591/1000 [1:46:40<1:53:45, 16.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.199305726663 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.1514765552 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 318, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  59%|█████▉    | 592/1000 [1:46:59<1:57:23, 17.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.222663958013 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.08366148205 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 352, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  59%|█████▉    | 593/1000 [1:47:05<1:35:19, 14.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.248685721383 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.20167373376 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 2.0, 'n_estimators': 123, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  59%|█████▉    | 594/1000 [1:47:06<1:09:14, 10.23s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.886131386861 and loss 1.30032573856 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.41927751867 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 16, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  60%|█████▉    | 595/1000 [1:47:09<53:13,  7.88s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.880291970803 and loss 0.877313871616 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.18109032763 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 138, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  60%|█████▉    | 596/1000 [1:47:16<51:36,  7.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.321811329894 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.13860671167 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 123, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  60%|█████▉    | 597/1000 [1:47:18<39:04,  5.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.90802919708 and loss 1.29079021994 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.3958296757 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 21, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 5, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  60%|█████▉    | 598/1000 [1:47:22<35:38,  5.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.262857002441 :\n",
      "Parameters with this testing accuracy 0.431578947368 and loss 2.05193019035 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 163, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  60%|█████▉    | 599/1000 [1:47:29<39:18,  5.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.220020802616 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.11655121676 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 299, 'subsample': 0.6000000000000001, 'importance_type': 'gain', 'max_depth': 2, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  60%|██████    | 600/1000 [1:47:32<33:43,  5.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.259952983725 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.12603598234 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 116, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  60%|██████    | 601/1000 [1:47:45<48:38,  7.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.20179225943 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.15667147593 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 219, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  60%|██████    | 602/1000 [1:48:01<1:06:13,  9.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.222488659819 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.15252126757 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 328, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  60%|██████    | 603/1000 [1:48:05<54:41,  8.27s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.985401459854 and loss 0.337788587779 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.09967981275 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 72, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  60%|██████    | 604/1000 [1:48:24<1:16:35, 11.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.241230284047 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.0950998442 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 409, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  60%|██████    | 605/1000 [1:48:40<1:24:51, 12.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.172205930956 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.12345373034 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 325, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  61%|██████    | 606/1000 [1:48:54<1:26:27, 13.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.230307156043 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.10375480283 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 354, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  61%|██████    | 607/1000 [1:49:00<1:12:41, 11.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.323590159617 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.11278162724 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 104, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  61%|██████    | 608/1000 [1:49:22<1:32:47, 14.20s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.205296922152 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.13907413079 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 441, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  61%|██████    | 609/1000 [1:49:35<1:31:04, 13.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.224897615047 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.14939482977 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 255, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  61%|██████    | 610/1000 [1:49:37<1:06:02, 10.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.848175182482 and loss 1.86847811624 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.55538304856 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 15, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  61%|██████    | 611/1000 [1:49:44<1:00:33,  9.34s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.238937438219 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.13481043386 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 307, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  61%|██████    | 612/1000 [1:49:47<47:42,  7.38s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7678262815 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.772706993 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 127, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  61%|██████▏   | 613/1000 [1:49:48<36:27,  5.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.902189781022 and loss 1.15728481882 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.37396925813 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 19, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  61%|██████▏   | 614/1000 [1:50:24<1:34:18, 14.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.228145413649 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.17927106466 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 498, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  62%|██████▏   | 615/1000 [1:50:28<1:13:58, 11.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.237011065375 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.1183549115 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 163, 'subsample': 0.6000000000000001, 'importance_type': 'total_gain', 'max_depth': 2, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  62%|██████▏   | 616/1000 [1:50:37<1:07:53, 10.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.267721945904 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.12257274844 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 254, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  62%|██████▏   | 617/1000 [1:50:42<56:29,  8.85s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.258335716083 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.16366553875 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 313, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 1, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  62%|██████▏   | 618/1000 [1:50:58<1:10:52, 11.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.212351821 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.17479190334 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.2, 'min_child_weight': 2.0, 'n_estimators': 357, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  62%|██████▏   | 619/1000 [1:51:05<1:02:31,  9.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.957664233577 and loss 0.817748711753 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.27177977891 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 117, 'subsample': 0.6000000000000001, 'importance_type': 'total_gain', 'max_depth': 8, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  62%|██████▏   | 620/1000 [1:51:26<1:24:12, 13.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.220950439121 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.09255008282 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 487, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 5, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  62%|██████▏   | 621/1000 [1:51:30<1:05:32, 10.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763884725 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327479312 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 126, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.8500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  62%|██████▏   | 622/1000 [1:51:51<1:26:29, 13.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.193241425222 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.15572253854 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 378, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  62%|██████▏   | 623/1000 [1:52:02<1:19:41, 12.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.127832374524 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.17455525565 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.225, 'min_child_weight': 1.0, 'n_estimators': 257, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  62%|██████▏   | 624/1000 [1:52:12<1:15:31, 12.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.268350401905 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.18619901938 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 242, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 3, 'gamma': 0.6000000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  62%|██████▎   | 625/1000 [1:52:18<1:03:24, 10.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.218696394159 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.14714720007 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 229, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  63%|██████▎   | 626/1000 [1:52:34<1:14:40, 11.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.241419194182 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.10661066829 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 311, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  63%|██████▎   | 627/1000 [1:52:40<1:03:50, 10.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.214506268506 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.16258417396 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 250, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  63%|██████▎   | 628/1000 [1:52:45<54:00,  8.71s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.165290878444 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.17976893146 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.25, 'min_child_weight': 2.0, 'n_estimators': 97, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  63%|██████▎   | 629/1000 [1:52:59<1:02:54, 10.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.975182481752 and loss 0.369868742768 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.15088879168 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 4.0, 'n_estimators': 356, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  63%|██████▎   | 630/1000 [1:53:02<49:01,  7.95s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.724087591241 and loss 1.41901534672 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.37574559199 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 5.0, 'n_estimators': 52, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 8, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  63%|██████▎   | 631/1000 [1:53:07<44:27,  7.23s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883194 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327873581 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 232, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  63%|██████▎   | 632/1000 [1:53:12<40:21,  6.58s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.247922738261 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.15264637062 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 198, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 2, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  63%|██████▎   | 633/1000 [1:53:20<41:30,  6.79s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.205272379799 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.16344746568 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 298, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  63%|██████▎   | 634/1000 [1:53:48<1:21:08, 13.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.246909675231 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.15270093832 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 406, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  64%|██████▎   | 635/1000 [1:53:58<1:15:19, 12.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.221256197929 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.15035943595 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 439, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  64%|██████▎   | 636/1000 [1:54:11<1:15:04, 12.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.96204379562 and loss 0.473449755697 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.14531836372 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 3.0, 'n_estimators': 269, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  64%|██████▎   | 637/1000 [1:54:14<59:01,  9.76s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.960583941606 and loss 0.580668065594 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.21389910728 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 61, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  64%|██████▍   | 638/1000 [1:54:21<53:05,  8.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.232356634571 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.13400700596 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 272, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 2, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  64%|██████▍   | 639/1000 [1:54:33<59:07,  9.83s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.224183175546 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.13493565639 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 322, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  64%|██████▍   | 640/1000 [1:54:48<1:08:33, 11.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.286670259201 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.1054059838 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 279, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 8, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  64%|██████▍   | 641/1000 [1:54:58<1:04:50, 10.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882741 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.7732787057 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 405, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 1, 'gamma': 0.9500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  64%|██████▍   | 642/1000 [1:55:12<1:10:50, 11.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.242235474452 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.15542911664 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 266, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  64%|██████▍   | 643/1000 [1:55:15<54:52,  9.22s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.256175256515 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.12543672621 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 111, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  64%|██████▍   | 644/1000 [1:55:42<1:25:14, 14.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.193548623045 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.15400967057 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 410, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.8500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  64%|██████▍   | 645/1000 [1:55:55<1:23:04, 14.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.174077170987 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.28065356565 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.42500000000000004, 'min_child_weight': 1.0, 'n_estimators': 282, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  65%|██████▍   | 646/1000 [1:56:13<1:29:55, 15.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.106160432878 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.1917962419 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.30000000000000004, 'min_child_weight': 1.0, 'n_estimators': 485, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  65%|██████▍   | 647/1000 [1:56:33<1:37:26, 16.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.234130067966 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.10952631025 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 377, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  65%|██████▍   | 648/1000 [1:56:39<1:19:18, 13.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.244597630119 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.12871041759 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 258, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  65%|██████▍   | 649/1000 [1:56:55<1:23:23, 14.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.212488517447 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.10390240138 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 323, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  65%|██████▌   | 650/1000 [1:57:04<1:13:53, 12.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.206464658255 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.09953381384 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 187, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 5, 'gamma': 0.55, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  65%|██████▌   | 651/1000 [1:57:12<1:06:37, 11.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7676388302 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876091 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 451, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  65%|██████▌   | 652/1000 [1:57:29<1:15:22, 12.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.196163159954 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.16333853677 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 499, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  65%|██████▌   | 653/1000 [1:57:59<1:44:58, 18.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.305002369375 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.15050155967 :\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.025, 'min_child_weight': 3.0, 'n_estimators': 388, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.8500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  65%|██████▌   | 654/1000 [1:58:09<1:30:50, 15.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.194052035995 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.19849727716 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 434, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  66%|██████▌   | 655/1000 [1:58:14<1:11:30, 12.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.98102189781 and loss 0.448718099606 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.2144913402 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 64, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  66%|██████▌   | 656/1000 [1:58:18<56:37,  9.88s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.27394396451 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.11607964729 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 148, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  66%|██████▌   | 657/1000 [1:58:25<51:29,  9.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.272054823415 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.08750379038 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 123, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  66%|██████▌   | 658/1000 [1:58:34<51:03,  8.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.213978635467 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.13384359273 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 362, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  66%|██████▌   | 659/1000 [1:58:47<58:28, 10.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.312161010133 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.11555761087 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 303, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  66%|██████▌   | 660/1000 [1:58:58<58:30, 10.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.19392263883 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.16145548482 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 433, 'subsample': 0.6000000000000001, 'importance_type': 'total_cover', 'max_depth': 2, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  66%|██████▌   | 661/1000 [1:59:04<51:38,  9.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76764064322 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77321826031 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 308, 'subsample': 0.8, 'importance_type': 'gain', 'max_depth': 6, 'gamma': 0.55, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  66%|██████▌   | 662/1000 [1:59:08<42:35,  7.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.250417991335 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.09122541826 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 147, 'subsample': 0.7000000000000001, 'importance_type': 'total_gain', 'max_depth': 2, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  66%|██████▋   | 663/1000 [1:59:21<52:19,  9.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.365626113381 :\n",
      "Parameters with this testing accuracy 0.431578947368 and loss 2.13595175194 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 260, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  66%|██████▋   | 664/1000 [1:59:40<1:08:03, 12.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.331858665537 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.09347058017 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 275, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.5, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  66%|██████▋   | 665/1000 [1:59:52<1:07:29, 12.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.21498937605 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.17148254396 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 202, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  67%|██████▋   | 666/1000 [1:59:54<51:13,  9.20s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.820437956204 and loss 1.0957261128 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.24610025608 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 150, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 1, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  67%|██████▋   | 667/1000 [2:00:13<1:05:54, 11.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.223124776709 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.10461062407 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 468, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  67%|██████▋   | 668/1000 [2:00:30<1:15:05, 13.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.204296606389 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.19630754184 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 443, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  67%|██████▋   | 669/1000 [2:00:42<1:12:17, 13.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.177217016118 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.06420043287 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 300, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 4, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  67%|██████▋   | 670/1000 [2:00:48<59:59, 10.91s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.985401459854 and loss 0.271654122297 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.25663287134 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.15000000000000002, 'min_child_weight': 4.0, 'n_estimators': 260, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  67%|██████▋   | 671/1000 [2:00:50<45:20,  8.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763906653 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77325637968 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 94, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  67%|██████▋   | 672/1000 [2:00:51<33:37,  6.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.902189781022 and loss 1.19991092055 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.44605462865 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 13, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  67%|██████▋   | 673/1000 [2:01:15<1:01:33, 11.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.22399412131 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.10039095516 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 462, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  67%|██████▋   | 674/1000 [2:01:18<47:54,  8.82s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.916788321168 and loss 0.848606301279 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.23973662038 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 71, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.55, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  68%|██████▊   | 675/1000 [2:01:23<41:40,  7.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.950364963504 and loss 0.409650609317 :\n",
      "Parameters with this testing accuracy 0.242105263158 and loss 2.33933284581 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 5.0, 'n_estimators': 211, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 2, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  68%|██████▊   | 676/1000 [2:01:25<32:29,  6.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.8 and loss 1.89878466956 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.56126197514 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 33, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.65, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  68%|██████▊   | 677/1000 [2:01:43<52:07,  9.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.261425972107 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.16049184329 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 3.0, 'n_estimators': 481, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  68%|██████▊   | 678/1000 [2:01:47<42:14,  7.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.970802919708 and loss 0.528507760222 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.26236791924 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.07500000000000001, 'min_child_weight': 3.0, 'n_estimators': 57, 'subsample': 0.75, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  68%|██████▊   | 679/1000 [2:01:54<41:49,  7.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.934306569343 and loss 0.464478520168 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.4929505777 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 6.0, 'n_estimators': 253, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  68%|██████▊   | 680/1000 [2:02:02<41:06,  7.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.195797422145 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.13055208723 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.225, 'min_child_weight': 1.0, 'n_estimators': 317, 'subsample': 0.55, 'importance_type': 'total_cover', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  68%|██████▊   | 681/1000 [2:02:03<30:54,  5.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76775039965 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.7728144219 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 48, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 4, 'gamma': 0.8500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  68%|██████▊   | 682/1000 [2:02:13<37:58,  7.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.233060737295 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.0903408514 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 189, 'subsample': 0.5, 'importance_type': 'total_cover', 'max_depth': 6, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  68%|██████▊   | 683/1000 [2:02:25<44:48,  8.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.276102384142 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.30426825222 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 180, 'subsample': 1.0, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.6000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  68%|██████▊   | 684/1000 [2:02:38<51:54,  9.86s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.889051094891 and loss 0.806290287492 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.17967773792 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 482, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 0.6000000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  68%|██████▊   | 685/1000 [2:02:39<37:58,  7.23s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.74598540146 and loss 1.82554035065 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.52509188966 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 12, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  69%|██████▊   | 686/1000 [2:02:52<45:48,  8.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.272688156283 :\n",
      "Parameters with this testing accuracy 0.452631578947 and loss 2.10185280191 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 234, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  69%|██████▊   | 687/1000 [2:03:04<51:03,  9.79s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.239436507127 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.0869397055 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 234, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  69%|██████▉   | 688/1000 [2:03:25<1:08:22, 13.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.215893240365 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.16071549293 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 399, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  69%|██████▉   | 689/1000 [2:03:37<1:06:39, 12.86s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.239325857123 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.08610034969 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 235, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  69%|██████▉   | 690/1000 [2:03:47<1:02:52, 12.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.224768116171 :\n",
      "Parameters with this testing accuracy 0.442105263158 and loss 2.09271603907 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 182, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  69%|██████▉   | 691/1000 [2:04:09<1:17:48, 15.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.188471069778 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.18327268846 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 395, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  69%|██████▉   | 692/1000 [2:04:14<1:01:37, 12.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883716 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876342 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 234, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  69%|██████▉   | 693/1000 [2:04:22<55:01, 10.75s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.226024410216 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.16546952021 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 122, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  69%|██████▉   | 694/1000 [2:04:47<1:16:39, 15.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.191617869438 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.14324699397 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 477, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  70%|██████▉   | 695/1000 [2:05:21<1:45:34, 20.77s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.19420755596 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.19270699348 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 459, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  70%|██████▉   | 696/1000 [2:05:34<1:33:44, 18.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.212045813977 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.25803480125 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 182, 'subsample': 0.8500000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  70%|██████▉   | 697/1000 [2:05:58<1:41:53, 20.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.187897884685 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.17780651583 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 419, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  70%|██████▉   | 698/1000 [2:06:13<1:33:13, 18.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.210160838476 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.13244127504 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 259, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  70%|██████▉   | 699/1000 [2:06:16<1:09:21, 13.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.385691068066 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.15620528318 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 36, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  70%|███████   | 700/1000 [2:06:27<1:04:35, 12.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.19755856805 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.19388509087 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.225, 'min_child_weight': 1.0, 'n_estimators': 182, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  70%|███████   | 701/1000 [2:06:31<50:40, 10.17s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.313913762845 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.13155548682 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 56, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  70%|███████   | 702/1000 [2:06:39<48:35,  9.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882672 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876091 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 471, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  70%|███████   | 703/1000 [2:06:52<52:44, 10.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.191819817738 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.20169150978 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.25, 'min_child_weight': 1.0, 'n_estimators': 252, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  70%|███████   | 704/1000 [2:07:18<1:14:55, 15.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.199126905494 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.27136868372 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 360, 'subsample': 0.9, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  70%|███████   | 705/1000 [2:07:33<1:14:20, 15.12s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.223270672929 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.15679544979 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 286, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  71%|███████   | 706/1000 [2:07:52<1:19:18, 16.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.201001598788 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.19454327407 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 304, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  71%|███████   | 707/1000 [2:08:11<1:23:13, 17.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.203933545403 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.16011481287 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 315, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  71%|███████   | 708/1000 [2:08:23<1:16:45, 15.77s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.196296857312 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.18576953976 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 228, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  71%|███████   | 709/1000 [2:08:27<58:12, 12.00s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.307402331972 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.21191962032 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 42, 'subsample': 0.7000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  71%|███████   | 710/1000 [2:08:48<1:11:54, 14.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.209526899854 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.28250817178 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 292, 'subsample': 0.9500000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  71%|███████   | 711/1000 [2:08:57<1:02:22, 12.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.205107541121 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.15975603781 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 137, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  71%|███████   | 712/1000 [2:08:58<46:03,  9.60s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763890712 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77326530155 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 68, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  71%|███████▏  | 713/1000 [2:09:09<47:02,  9.83s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.244785762257 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.09212184344 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 191, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  71%|███████▏  | 714/1000 [2:09:29<1:01:38, 12.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.214495499404 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.12966337502 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 359, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  72%|███████▏  | 715/1000 [2:10:01<1:28:26, 18.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.191336589311 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.18180937973 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 475, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  72%|███████▏  | 716/1000 [2:10:08<1:11:17, 15.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.219160986368 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.1599530642 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 114, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  72%|███████▏  | 717/1000 [2:10:28<1:18:26, 16.63s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.217152841074 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.11892008844 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 361, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  72%|███████▏  | 718/1000 [2:10:32<59:50, 12.73s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.321023501178 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.26128222433 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 46, 'subsample': 0.75, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  72%|███████▏  | 719/1000 [2:10:36<48:38, 10.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.235049092597 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.15353541206 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 78, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  72%|███████▏  | 720/1000 [2:10:39<37:38,  8.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.975182481752 and loss 0.556178007574 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.21449043076 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 38, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  72%|███████▏  | 721/1000 [2:10:52<44:56,  9.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.227024289233 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.14995868011 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 327, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  72%|███████▏  | 722/1000 [2:10:54<33:57,  7.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763892243 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77326413707 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 67, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  72%|███████▏  | 723/1000 [2:11:10<45:27,  9.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.23157941142 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.08878633256 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 285, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  72%|███████▏  | 724/1000 [2:11:24<50:32, 10.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.969343065693 and loss 0.345021967597 :\n",
      "Parameters with this testing accuracy 0.263157894737 and loss 2.39742839711 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 5.0, 'n_estimators': 464, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  72%|███████▎  | 725/1000 [2:11:38<55:17, 12.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.187955239079 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.25704568931 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.375, 'min_child_weight': 2.0, 'n_estimators': 248, 'subsample': 0.8, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  73%|███████▎  | 726/1000 [2:11:56<1:02:17, 13.64s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.206455523944 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.13690100116 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 314, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  73%|███████▎  | 727/1000 [2:12:01<50:28, 11.09s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.289299852635 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.11899865207 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 82, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  73%|███████▎  | 728/1000 [2:12:16<55:30, 12.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.169575499252 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.38487343514 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.47500000000000003, 'min_child_weight': 1.0, 'n_estimators': 320, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  73%|███████▎  | 729/1000 [2:12:18<42:10,  9.34s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.859854014599 and loss 0.88477693527 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.14105581724 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 142, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  73%|███████▎  | 730/1000 [2:12:38<55:24, 12.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.17969660703 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.20610616313 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.275, 'min_child_weight': 1.0, 'n_estimators': 390, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  73%|███████▎  | 731/1000 [2:12:55<1:02:37, 13.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.181760435309 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.23932059593 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 453, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  73%|███████▎  | 732/1000 [2:12:57<45:38, 10.22s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76764277785 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77318224656 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 53, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  73%|███████▎  | 733/1000 [2:13:01<37:07,  8.34s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.29326186466 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.16790619551 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 112, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  73%|███████▎  | 734/1000 [2:13:06<33:20,  7.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.253680163535 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.17730345279 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 99, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  74%|███████▎  | 735/1000 [2:13:15<34:00,  7.70s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.236163063807 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.11199481964 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 128, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  74%|███████▎  | 736/1000 [2:13:23<34:25,  7.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.236018850158 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.12906467128 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 156, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  74%|███████▎  | 737/1000 [2:13:28<30:56,  7.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.339706114068 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.10934702299 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 85, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  74%|███████▍  | 738/1000 [2:13:29<23:34,  5.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.902189781022 and loss 0.686725932183 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.18119295408 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 70, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  74%|███████▍  | 739/1000 [2:13:34<21:55,  5.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.33071199371 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.12987664628 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 83, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  74%|███████▍  | 740/1000 [2:13:42<26:09,  6.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.22477220417 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.1717531542 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 155, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  74%|███████▍  | 741/1000 [2:14:04<46:09, 10.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.183298213282 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.22417395558 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.225, 'min_child_weight': 1.0, 'n_estimators': 424, 'subsample': 0.6000000000000001, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  74%|███████▍  | 742/1000 [2:14:08<38:08,  8.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882602 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327829411 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 222, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  74%|███████▍  | 743/1000 [2:14:24<47:01, 10.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.217118263256 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.14681778736 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 371, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  74%|███████▍  | 744/1000 [2:14:45<59:09, 13.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.18450016573 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.17708962481 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 364, 'subsample': 0.7000000000000001, 'importance_type': 'total_gain', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  74%|███████▍  | 745/1000 [2:14:50<48:08, 11.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.290293021913 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.14498943345 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 79, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  75%|███████▍  | 746/1000 [2:14:59<44:58, 10.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.985401459854 and loss 0.336213097282 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.0751866383 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 190, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  75%|███████▍  | 747/1000 [2:15:12<48:16, 11.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.196304681806 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.18565078713 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.25, 'min_child_weight': 1.0, 'n_estimators': 243, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  75%|███████▍  | 748/1000 [2:15:17<39:33,  9.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.916788321168 and loss 0.501304723647 :\n",
      "Parameters with this testing accuracy 0.221052631579 and loss 2.48953669527 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 6.0, 'n_estimators': 301, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  75%|███████▍  | 749/1000 [2:15:24<35:47,  8.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.266524123834 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.08615583479 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 131, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  75%|███████▌  | 750/1000 [2:15:43<49:26, 11.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.214020632833 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.28294233986 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 289, 'subsample': 1.0, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  75%|███████▌  | 751/1000 [2:15:56<50:07, 12.08s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.200605634541 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.27328019413 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.225, 'min_child_weight': 1.0, 'n_estimators': 423, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  75%|███████▌  | 752/1000 [2:15:59<38:38,  9.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.973722627737 and loss 0.492977627454 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.19306340806 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 49, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  75%|███████▌  | 753/1000 [2:16:05<34:52,  8.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882707 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876844 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 329, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  75%|███████▌  | 754/1000 [2:16:30<54:12, 13.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.20494145483 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.20497517429 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 467, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  76%|███████▌  | 755/1000 [2:16:57<1:11:15, 17.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.191662374783 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.21737838813 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 407, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  76%|███████▌  | 756/1000 [2:17:02<55:50, 13.73s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.979562043796 and loss 0.320029024501 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.13053595173 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 351, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  76%|███████▌  | 757/1000 [2:17:24<1:05:55, 16.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.227196836114 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.09281691521 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 402, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  76%|███████▌  | 758/1000 [2:17:42<1:07:07, 16.64s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.225191962262 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.12362778202 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 417, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  76%|███████▌  | 759/1000 [2:17:43<48:18, 12.03s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.849635036496 and loss 1.85763618441 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.56437869323 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 14, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  76%|███████▌  | 760/1000 [2:17:49<41:36, 10.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.244477528105 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.12807264395 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 195, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 3, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  76%|███████▌  | 761/1000 [2:18:00<41:57, 10.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.225409855666 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.13897856109 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 230, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  76%|███████▌  | 762/1000 [2:18:10<40:22, 10.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.213705073103 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.13235306971 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 171, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  76%|███████▋  | 763/1000 [2:18:27<49:08, 12.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.247239282257 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.12819421025 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 332, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  76%|███████▋  | 764/1000 [2:18:30<37:03,  9.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76764031048 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77322220551 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 4.0, 'n_estimators': 103, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  76%|███████▋  | 765/1000 [2:18:46<44:38, 11.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.16918870241 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.23892659668 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.35000000000000003, 'min_child_weight': 1.0, 'n_estimators': 291, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 0.9, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  77%|███████▋  | 766/1000 [2:18:49<35:07,  9.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.899270072993 and loss 0.626983221575 :\n",
      "Parameters with this testing accuracy 0.242105263158 and loss 2.26608110494 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 5.0, 'n_estimators': 221, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  77%|███████▋  | 767/1000 [2:19:05<43:11, 11.12s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.221783208904 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.16337352554 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 310, 'subsample': 0.7000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  77%|███████▋  | 768/1000 [2:19:14<40:02, 10.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.227457641904 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.16958671769 :\n",
      "{'colsample_bytree': 1.0, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 129, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  77%|███████▋  | 769/1000 [2:19:20<35:27,  9.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.266686017523 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.07585689888 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 196, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  77%|███████▋  | 770/1000 [2:19:39<46:15, 12.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.219071487743 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.11589911513 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 438, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  77%|███████▋  | 771/1000 [2:19:41<34:20,  9.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.937226277372 and loss 0.931558993785 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.34833093035 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 25, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  77%|███████▋  | 772/1000 [2:19:44<27:33,  7.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.264083630093 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.17755005501 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 45, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  77%|███████▋  | 773/1000 [2:19:48<24:02,  6.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7676388295 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876342 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 205, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  77%|███████▋  | 774/1000 [2:19:51<19:38,  5.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.792700729927 and loss 1.12431497429 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.24740414557 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 145, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  78%|███████▊  | 775/1000 [2:20:04<28:25,  7.58s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.245979977365 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.11265831152 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 295, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  78%|███████▊  | 776/1000 [2:20:13<30:16,  8.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.959124087591 and loss 0.377846230345 :\n",
      "Parameters with this testing accuracy 0.210526315789 and loss 2.57276198112 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 6.0, 'n_estimators': 225, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  78%|███████▊  | 777/1000 [2:20:32<41:30, 11.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.169440916778 :\n",
      "Parameters with this testing accuracy 0.263157894737 and loss 2.28337993753 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.45, 'min_child_weight': 1.0, 'n_estimators': 370, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  78%|███████▊  | 778/1000 [2:20:44<42:13, 11.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.239015339318 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.2628907566 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 400, 'subsample': 0.9500000000000001, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  78%|███████▊  | 779/1000 [2:20:57<44:01, 11.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.225326834845 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.12151198464 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 296, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  78%|███████▊  | 780/1000 [2:21:23<59:58, 16.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.208792659466 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.12350112757 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 496, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  78%|███████▊  | 781/1000 [2:21:31<50:26, 13.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.199903239337 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.1737623022 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 136, 'subsample': 0.65, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  78%|███████▊  | 782/1000 [2:21:43<48:16, 13.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.251866090349 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.24803300363 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.15000000000000002, 'min_child_weight': 4.0, 'n_estimators': 273, 'subsample': 0.9, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  78%|███████▊  | 783/1000 [2:21:46<36:39, 10.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763884551 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327410547 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 123, 'subsample': 0.8500000000000001, 'importance_type': 'total_gain', 'max_depth': 1, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  78%|███████▊  | 784/1000 [2:21:47<26:41,  7.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.808759124088 and loss 2.04375131339 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.57251523796 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 11, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  78%|███████▊  | 785/1000 [2:21:54<25:19,  7.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.266734351743 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.09612693947 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 125, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  79%|███████▊  | 786/1000 [2:21:58<22:37,  6.34s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.972262773723 and loss 0.579524194803 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.21238101548 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 81, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  79%|███████▊  | 787/1000 [2:22:02<19:54,  5.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.944525547445 and loss 1.27226649884 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.40231308937 :\n",
      "{'colsample_bytree': 0.7000000000000001, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 63, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  79%|███████▉  | 788/1000 [2:22:12<23:56,  6.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.342716051499 :\n",
      "Parameters with this testing accuracy 0.431578947368 and loss 2.10497163879 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 249, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 4, 'gamma': 0.5, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  79%|███████▉  | 789/1000 [2:22:26<31:51,  9.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.298605610786 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.08929712808 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 392, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  79%|███████▉  | 790/1000 [2:22:28<24:28,  6.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.944525547445 and loss 0.98208592571 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.34636666053 :\n",
      "{'colsample_bytree': 0.75, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 30, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  79%|███████▉  | 791/1000 [2:22:31<19:46,  5.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.94598540146 and loss 1.09322048504 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.37962436864 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 41, 'subsample': 0.5, 'importance_type': 'cover', 'max_depth': 8, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  79%|███████▉  | 792/1000 [2:22:45<29:00,  8.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.264291793921 :\n",
      "Parameters with this testing accuracy 0.452631578947 and loss 2.10308238111 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 280, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  79%|███████▉  | 793/1000 [2:22:57<32:04,  9.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.27977891154 :\n",
      "Parameters with this testing accuracy 0.442105263158 and loss 2.10851570638 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 212, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  79%|███████▉  | 794/1000 [2:23:08<33:46,  9.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.464950138635 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.15686220723 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 199, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  80%|███████▉  | 795/1000 [2:23:20<35:54, 10.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.421632802277 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.14508113445 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 220, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  80%|███████▉  | 796/1000 [2:23:43<48:25, 14.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.243664609528 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.10458124633 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 463, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  80%|███████▉  | 797/1000 [2:23:45<35:39, 10.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.921167883212 and loss 1.45511546065 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.45251445958 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 24, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  80%|███████▉  | 798/1000 [2:23:50<30:09,  8.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.957664233577 and loss 0.987814559423 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.33355065835 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 93, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  80%|███████▉  | 799/1000 [2:24:09<39:44, 11.86s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.234220619789 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.10932142125 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 375, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  80%|████████  | 800/1000 [2:24:13<32:15,  9.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.973722627737 and loss 0.626036147936 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.23588827441 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 76, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  80%|████████  | 801/1000 [2:24:29<37:39, 11.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.244468858606 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.13611623296 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 280, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  80%|████████  | 802/1000 [2:24:55<52:01, 15.77s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.221637861722 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.07814809523 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 476, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  80%|████████  | 803/1000 [2:25:03<44:39, 13.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.323989015141 :\n",
      "Parameters with this testing accuracy 0.431578947368 and loss 2.10802511032 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 152, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  80%|████████  | 804/1000 [2:25:16<43:55, 13.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.197010551263 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.22977949949 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.325, 'min_child_weight': 1.0, 'n_estimators': 280, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  80%|████████  | 805/1000 [2:25:29<42:43, 13.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.985401459854 and loss 0.409596890641 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.1405894761 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 227, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  81%|████████  | 806/1000 [2:25:42<42:45, 13.23s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.231193587797 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.08512035597 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 233, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  81%|████████  | 807/1000 [2:26:01<48:04, 14.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.194979527084 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.20096224276 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.275, 'min_child_weight': 1.0, 'n_estimators': 401, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  81%|████████  | 808/1000 [2:26:03<35:05, 10.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.916788321168 and loss 1.35203677281 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.41644060047 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 18, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  81%|████████  | 809/1000 [2:26:07<28:52,  9.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.40573756679 :\n",
      "Parameters with this testing accuracy 0.442105263158 and loss 2.14692218735 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 77, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  81%|████████  | 810/1000 [2:26:12<24:26,  7.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.973722627737 and loss 0.616730266842 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.23213900359 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 77, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  81%|████████  | 811/1000 [2:26:33<36:52, 11.70s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.233482085593 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.12972509618 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 429, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  81%|████████  | 812/1000 [2:26:40<31:58, 10.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.96496350365 and loss 0.794937376189 :\n",
      "Parameters with this testing accuracy 0.431578947368 and loss 2.26757890864 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 119, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  81%|████████▏ | 813/1000 [2:26:50<32:03, 10.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.291675543211 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.11010055526 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 194, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  81%|████████▏ | 814/1000 [2:27:01<32:43, 10.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.463014591202 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.1556201796 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 200, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  82%|████████▏ | 815/1000 [2:27:16<36:07, 11.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.232553996487 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.12737991931 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 270, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  82%|████████▏ | 816/1000 [2:27:19<28:23,  9.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.932846715328 and loss 1.35867385903 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.42971601612 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 59, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  82%|████████▏ | 817/1000 [2:27:39<37:28, 12.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.223451746947 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.12702533775 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 367, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  82%|████████▏ | 818/1000 [2:27:48<34:55, 11.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.231806648453 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.09479475794 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 306, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  82%|████████▏ | 819/1000 [2:27:53<28:19,  9.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.970802919708 and loss 0.649876196911 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.23712914806 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 73, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  82%|████████▏ | 820/1000 [2:27:57<23:40,  7.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.322240176699 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.11723380261 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 65, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  82%|████████▏ | 821/1000 [2:28:21<38:04, 12.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.212829994189 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.09452495332 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 445, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  82%|████████▏ | 822/1000 [2:28:47<49:03, 16.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.201915953447 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.09487693306 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 478, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  82%|████████▏ | 823/1000 [2:28:51<38:12, 12.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.973722627737 and loss 0.616730266842 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.23213900359 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 77, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  82%|████████▏ | 824/1000 [2:29:05<38:44, 13.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.237296812516 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.13653004803 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 268, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  82%|████████▎ | 825/1000 [2:29:07<28:38,  9.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.674452554745 and loss 1.72418924061 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.42674330787 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 101, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 1, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  83%|████████▎ | 826/1000 [2:29:38<46:39, 16.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.281763158964 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.13262009072 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 444, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  83%|████████▎ | 827/1000 [2:29:45<38:57, 13.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.208907813844 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.18179650589 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.25, 'min_child_weight': 1.0, 'n_estimators': 149, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  83%|████████▎ | 828/1000 [2:30:07<45:30, 15.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.216515807153 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.14354099323 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 412, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  83%|████████▎ | 829/1000 [2:30:26<48:21, 16.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.223267669319 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.12688501724 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 373, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  83%|████████▎ | 830/1000 [2:30:40<45:48, 16.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.245711304042 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.0911187866 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 245, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  83%|████████▎ | 831/1000 [2:31:02<49:52, 17.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.233943632183 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.13031667538 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 422, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  83%|████████▎ | 832/1000 [2:31:12<43:02, 15.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.305923441063 :\n",
      "Parameters with this testing accuracy 0.431578947368 and loss 2.10885954891 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 173, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  83%|████████▎ | 833/1000 [2:31:25<41:03, 14.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.174239157368 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.14340055306 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 251, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  83%|████████▎ | 834/1000 [2:31:30<32:39, 11.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7676388302 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327849739 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 238, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  84%|████████▎ | 835/1000 [2:31:53<41:27, 15.08s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.21440382086 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.19044324203 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 425, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  84%|████████▎ | 836/1000 [2:31:56<31:43, 11.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.766423357664 and loss 1.30032655521 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.30874792996 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 212, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 1, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  84%|████████▎ | 837/1000 [2:32:26<46:00, 16.94s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.218075073529 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.14282592215 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 426, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  84%|████████▍ | 838/1000 [2:32:30<35:39, 13.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.938686131387 and loss 0.848000897967 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.25735283274 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 133, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  84%|████████▍ | 839/1000 [2:32:39<32:21, 12.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.325549650599 :\n",
      "Parameters with this testing accuracy 0.431578947368 and loss 2.08176854135 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 212, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  84%|████████▍ | 840/1000 [2:32:45<27:00, 10.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.277526767304 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.10684778679 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 139, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  84%|████████▍ | 841/1000 [2:33:01<31:06, 11.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.313281098419 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.09570625789 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 316, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  84%|████████▍ | 842/1000 [2:33:15<33:00, 12.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.235250796582 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.0999834573 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 495, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  84%|████████▍ | 843/1000 [2:33:37<40:32, 15.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.20948037786 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.11066015706 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 418, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  84%|████████▍ | 844/1000 [2:33:41<30:50, 11.86s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882707 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327877346 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 164, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  84%|████████▍ | 845/1000 [2:33:45<24:50,  9.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.328685904776 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.10180269558 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 106, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 4, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  85%|████████▍ | 846/1000 [2:33:47<19:02,  7.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.797080291971 and loss 1.17851246695 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.26297333821 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 130, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  85%|████████▍ | 847/1000 [2:33:57<20:24,  8.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.251683194835 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.09609745159 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 217, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 5, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  85%|████████▍ | 848/1000 [2:34:08<22:35,  8.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.300390642093 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.13212320177 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 161, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  85%|████████▍ | 849/1000 [2:34:22<26:18, 10.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.216241515662 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.18773582546 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 214, 'subsample': 0.75, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  85%|████████▌ | 850/1000 [2:34:29<23:20,  9.34s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.292419404197 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.09885894507 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 110, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  85%|████████▌ | 851/1000 [2:34:32<19:05,  7.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.362021463182 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.21368029023 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 55, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  85%|████████▌ | 852/1000 [2:34:34<14:41,  5.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.89197080292 and loss 1.46598001475 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.46681986733 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 26, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  85%|████████▌ | 853/1000 [2:34:39<13:27,  5.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.944525547445 and loss 1.07577130629 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.40562736988 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 69, 'subsample': 0.8, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  85%|████████▌ | 854/1000 [2:34:47<15:03,  6.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.275210550255 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.08245986635 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 193, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  86%|████████▌ | 855/1000 [2:34:53<15:21,  6.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.969343065693 and loss 0.550033725396 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.16110032311 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 206, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  86%|████████▌ | 856/1000 [2:34:54<11:26,  4.77s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76776915397 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77277282665 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 27, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  86%|████████▌ | 857/1000 [2:35:02<13:07,  5.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.353014501307 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.15075491439 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 121, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  86%|████████▌ | 858/1000 [2:35:21<23:02,  9.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.985401459854 and loss 0.32274409764 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.08284986364 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 431, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  86%|████████▌ | 859/1000 [2:35:39<28:49, 12.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.245136083898 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.13265305656 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 334, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 4, 'gamma': 0.9500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  86%|████████▌ | 860/1000 [2:35:55<30:52, 13.23s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.242057986284 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.10766947748 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 305, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  86%|████████▌ | 861/1000 [2:35:58<23:52, 10.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.909489051095 and loss 0.9488222745 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.25788528606 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 62, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  86%|████████▌ | 862/1000 [2:36:15<28:24, 12.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.240848993926 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.12954887034 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 330, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  86%|████████▋ | 863/1000 [2:36:20<22:50, 10.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.821897810219 and loss 1.10829009351 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.25688318979 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 287, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  86%|████████▋ | 864/1000 [2:36:43<31:22, 13.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.160953947866 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.1589351196 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 473, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  86%|████████▋ | 865/1000 [2:36:52<27:52, 12.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.281069389532 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.10952678409 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 192, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  87%|████████▋ | 866/1000 [2:37:16<35:24, 15.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.20578831464 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.06183882485 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 489, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  87%|████████▋ | 867/1000 [2:37:25<30:42, 13.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763886118 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327282303 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 484, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 6, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  87%|████████▋ | 868/1000 [2:37:40<31:07, 14.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.251525164473 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.09028141491 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 394, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  87%|████████▋ | 869/1000 [2:37:42<23:04, 10.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.94598540146 and loss 0.788922674878 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.29799649292 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 31, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  87%|████████▋ | 870/1000 [2:38:03<29:44, 13.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.207682368613 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.14643900755 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 430, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.9, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  87%|████████▋ | 871/1000 [2:38:22<32:56, 15.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.215679181079 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.1269866343 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 397, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  87%|████████▋ | 872/1000 [2:38:27<25:46, 12.08s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.414477039447 :\n",
      "Parameters with this testing accuracy 0.431578947368 and loss 2.16530138756 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 74, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  87%|████████▋ | 873/1000 [2:38:42<27:32, 13.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.187221396396 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.21424288571 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.4, 'min_child_weight': 1.0, 'n_estimators': 333, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  87%|████████▋ | 874/1000 [2:38:54<26:54, 12.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.208098203043 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.20566648272 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 2.0, 'n_estimators': 234, 'subsample': 0.8500000000000001, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  88%|████████▊ | 875/1000 [2:38:57<20:32,  9.86s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.916788321168 and loss 1.30781081027 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.40605080253 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 66, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  88%|████████▊ | 876/1000 [2:39:02<17:18,  8.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.940145985401 and loss 0.58997211936 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.13869024545 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 335, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  88%|████████▊ | 877/1000 [2:39:22<24:01, 11.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.208957060397 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.12682571717 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 343, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  88%|████████▊ | 878/1000 [2:39:33<23:44, 11.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.947445255474 and loss 0.473300044149 :\n",
      "Parameters with this testing accuracy 0.242105263158 and loss 2.3272642041 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 5.0, 'n_estimators': 324, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  88%|████████▊ | 879/1000 [2:39:41<21:16, 10.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7676389635 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77326374556 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 414, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  88%|████████▊ | 880/1000 [2:39:52<21:10, 10.58s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.254956710897 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.14208362344 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 210, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  88%|████████▊ | 881/1000 [2:39:59<18:50,  9.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.947445255474 and loss 0.491187788663 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.20171565127 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 4.0, 'n_estimators': 146, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  88%|████████▊ | 882/1000 [2:40:23<27:34, 14.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.226304129005 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.29906727946 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 440, 'subsample': 0.9500000000000001, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  88%|████████▊ | 883/1000 [2:40:36<26:42, 13.70s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.311753515812 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.09797714866 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 385, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  88%|████████▊ | 884/1000 [2:40:48<25:19, 13.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.216925157861 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.17319480981 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 241, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  88%|████████▊ | 885/1000 [2:41:06<28:07, 14.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.163779444396 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.28260790329 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.5, 'min_child_weight': 1.0, 'n_estimators': 447, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  89%|████████▊ | 886/1000 [2:41:30<33:08, 17.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.208791651785 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.12009955594 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 415, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  89%|████████▊ | 887/1000 [2:41:51<34:36, 18.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.250241807518 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.10519051152 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 388, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  89%|████████▉ | 888/1000 [2:41:52<24:30, 13.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.572262773723 and loss 2.37417742318 :\n",
      "Parameters with this testing accuracy 0.263157894737 and loss 2.62753705351 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 22, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  89%|████████▉ | 889/1000 [2:41:55<18:59, 10.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7676388295 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327814604 :\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 160, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  89%|████████▉ | 890/1000 [2:42:11<22:06, 12.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.270363295424 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.08361159073 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 374, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  89%|████████▉ | 891/1000 [2:42:22<21:02, 11.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.220231551844 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.11638398206 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 182, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  89%|████████▉ | 892/1000 [2:42:44<26:44, 14.86s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.241792136331 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.11424625414 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 457, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  89%|████████▉ | 893/1000 [2:43:16<35:32, 19.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.215095322506 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.13758893244 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 461, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  89%|████████▉ | 894/1000 [2:43:26<29:56, 16.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.311512854056 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.05438969006 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 203, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  90%|████████▉ | 895/1000 [2:43:49<32:32, 18.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.163716926643 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.14888977889 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 456, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  90%|████████▉ | 896/1000 [2:44:16<36:35, 21.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.198587582833 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.17336142402 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 480, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  90%|████████▉ | 897/1000 [2:44:20<27:38, 16.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.947445255474 and loss 0.957427170851 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.31174982159 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 102, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  90%|████████▉ | 898/1000 [2:44:24<21:27, 12.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.89197080292 and loss 0.974259037378 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.25359735144 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 134, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  90%|████████▉ | 899/1000 [2:44:34<19:39, 11.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882741 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327877346 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 490, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  90%|█████████ | 900/1000 [2:44:58<25:48, 15.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.197526311889 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.1377619801 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 446, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  90%|█████████ | 901/1000 [2:45:08<22:42, 13.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.246142764577 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.09792070063 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 172, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  90%|█████████ | 902/1000 [2:45:26<24:27, 14.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.164667323777 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.13148663591 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 383, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  90%|█████████ | 903/1000 [2:45:36<21:40, 13.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.245480657043 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.14256520671 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 197, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 4, 'gamma': 0.9500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  90%|█████████ | 904/1000 [2:45:48<21:08, 13.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.195472262724 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.18814478079 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 239, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  90%|█████████ | 905/1000 [2:45:51<15:52, 10.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.937226277372 and loss 0.636123409351 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.11780315904 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 98, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  91%|█████████ | 906/1000 [2:45:57<14:01,  8.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.906569343066 and loss 0.609621806698 :\n",
      "Parameters with this testing accuracy 0.242105263158 and loss 2.22623447288 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 4.0, 'n_estimators': 449, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  91%|█████████ | 907/1000 [2:46:18<19:20, 12.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.189325297462 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.18263863935 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.30000000000000004, 'min_child_weight': 1.0, 'n_estimators': 458, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  91%|█████████ | 908/1000 [2:46:19<13:52,  9.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.808759124088 and loss 1.87349538211 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.56280779588 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 10, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  91%|█████████ | 909/1000 [2:46:38<18:10, 11.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.182478711634 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.20648412267 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 368, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  91%|█████████ | 910/1000 [2:46:41<14:09,  9.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.978102189781 and loss 0.587926792707 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.23225802716 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 54, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  91%|█████████ | 911/1000 [2:46:49<12:57,  8.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883264 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327875589 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.15000000000000002, 'min_child_weight': 2.0, 'n_estimators': 363, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  91%|█████████ | 912/1000 [2:46:58<13:05,  8.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.983941605839 and loss 0.462260986718 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.2038205135 :\n",
      "{'colsample_bytree': 0.9500000000000001, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 183, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  91%|█████████▏| 913/1000 [2:47:00<09:54,  6.83s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.272785237958 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.1790477548 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.225, 'min_child_weight': 1.0, 'n_estimators': 44, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  91%|█████████▏| 914/1000 [2:47:02<07:51,  5.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.932846715328 and loss 1.1833414066 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.4033000946 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 37, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  92%|█████████▏| 915/1000 [2:47:09<08:31,  6.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.204904980155 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.23311296898 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.35000000000000003, 'min_child_weight': 2.0, 'n_estimators': 166, 'subsample': 0.75, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.9500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  92%|█████████▏| 916/1000 [2:47:26<12:46,  9.12s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.222618056049 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.15228765736 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 281, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  92%|█████████▏| 917/1000 [2:47:29<10:20,  7.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.966423357664 and loss 0.784804153203 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.28759102664 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 60, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  92%|█████████▏| 918/1000 [2:47:36<10:01,  7.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.969343065693 and loss 0.355531388637 :\n",
      "Parameters with this testing accuracy 0.252631578947 and loss 2.27366361091 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.125, 'min_child_weight': 6.0, 'n_estimators': 177, 'subsample': 0.8, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  92%|█████████▏| 919/1000 [2:47:45<10:14,  7.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.219290917227 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.1423937215 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 347, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  92%|█████████▏| 920/1000 [2:48:07<15:51, 11.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.216010860823 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.09398529839 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 393, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  92%|█████████▏| 921/1000 [2:48:10<12:13,  9.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7677598915 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77280556277 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 144, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 0.75, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  92%|█████████▏| 922/1000 [2:48:21<12:53,  9.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.306886977327 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.21585953243 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 235, 'subsample': 0.7000000000000001, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  92%|█████████▏| 923/1000 [2:48:45<17:56, 13.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.189997309955 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.06878159103 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 450, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  92%|█████████▏| 924/1000 [2:48:46<12:53, 10.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.665693430657 and loss 1.6228462643 :\n",
      "Parameters with this testing accuracy 0.242105263158 and loss 2.49377827268 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 5.0, 'n_estimators': 20, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  92%|█████████▎| 925/1000 [2:49:20<21:29, 17.20s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.2296006727 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.13568331533 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 479, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  93%|█████████▎| 926/1000 [2:49:37<21:19, 17.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.237046407631 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.11175846024 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 346, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  93%|█████████▎| 927/1000 [2:49:52<20:02, 16.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.270762379389 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.21975641556 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 358, 'subsample': 0.9, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  93%|█████████▎| 928/1000 [2:50:03<18:01, 15.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.248086387619 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.08586505212 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 380, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  93%|█████████▎| 929/1000 [2:50:13<15:52, 13.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.221803660215 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.15784814377 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 158, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  93%|█████████▎| 930/1000 [2:50:20<13:24, 11.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.323161644918 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.08236461984 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 143, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  93%|█████████▎| 931/1000 [2:50:32<13:26, 11.68s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.360524321861 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.14828388895 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 218, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  93%|█████████▎| 932/1000 [2:50:55<16:59, 14.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.150774344071 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.09907371566 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 465, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  93%|█████████▎| 933/1000 [2:51:03<14:37, 13.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7676388302 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.7732787584 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 448, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  93%|█████████▎| 934/1000 [2:51:26<17:34, 15.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.221578080219 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.12677350056 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 469, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  94%|█████████▎| 935/1000 [2:51:44<17:47, 16.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.184695210069 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.24856562754 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.375, 'min_child_weight': 1.0, 'n_estimators': 470, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  94%|█████████▎| 936/1000 [2:51:56<16:05, 15.08s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.944525547445 and loss 0.532600623505 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.16866518948 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 427, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 1, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  94%|█████████▎| 937/1000 [2:52:13<16:37, 15.83s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.159854597857 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.26265386476 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.42500000000000004, 'min_child_weight': 1.0, 'n_estimators': 336, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  94%|█████████▍| 938/1000 [2:52:23<14:37, 14.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.231005115658 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.10667271818 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 176, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  94%|█████████▍| 939/1000 [2:52:37<14:14, 14.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.224985896693 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.1479038818 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 2.0, 'n_estimators': 339, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  94%|█████████▍| 940/1000 [2:52:52<14:09, 14.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.191166545199 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.11679455424 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 261, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  94%|█████████▍| 941/1000 [2:52:58<11:37, 11.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.947445255474 and loss 0.639763624876 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.16588368235 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 265, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  94%|█████████▍| 942/1000 [2:53:07<10:39, 11.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.204154783565 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.18999600459 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.2, 'min_child_weight': 2.0, 'n_estimators': 204, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  94%|█████████▍| 943/1000 [2:53:24<12:04, 12.70s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.202378364129 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.16365804186 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 384, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  94%|█████████▍| 944/1000 [2:53:27<09:10,  9.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882985 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327850743 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 141, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  94%|█████████▍| 945/1000 [2:53:38<09:15, 10.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.284416736507 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.09846365295 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 209, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  95%|█████████▍| 946/1000 [2:53:51<09:54, 11.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.978102189781 and loss 0.29485779363 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.36292811717 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 4.0, 'n_estimators': 389, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  95%|█████████▍| 947/1000 [2:54:05<10:43, 12.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.224254104706 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.19380888841 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 276, 'subsample': 0.55, 'importance_type': 'total_gain', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  95%|█████████▍| 948/1000 [2:54:13<09:14, 10.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.323772481376 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.14129178206 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.07500000000000001, 'min_child_weight': 3.0, 'n_estimators': 123, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.8500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  95%|█████████▍| 949/1000 [2:54:26<09:37, 11.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.240382143854 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.10585693817 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 312, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  95%|█████████▌| 950/1000 [2:54:27<06:59,  8.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.750364963504 and loss 1.99629381225 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.56986491931 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 32, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  95%|█████████▌| 951/1000 [2:54:51<10:32, 12.91s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.197697764817 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.13769679295 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 442, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  95%|█████████▌| 952/1000 [2:54:54<08:00, 10.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.970802919708 and loss 0.89163758709 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.34104342837 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 47, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.75, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  95%|█████████▌| 953/1000 [2:55:01<07:07,  9.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.255924653107 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.09953451157 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 115, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  95%|█████████▌| 954/1000 [2:55:07<06:24,  8.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.925547445255 and loss 0.894682683442 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.26285027987 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 128, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  96%|█████████▌| 955/1000 [2:55:14<05:53,  7.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763882985 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327830415 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 337, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 4, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  96%|█████████▌| 956/1000 [2:55:20<05:14,  7.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.935766423358 and loss 0.708730912807 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.18081064146 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 237, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  96%|█████████▌| 957/1000 [2:55:24<04:30,  6.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.244061066436 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.16541406367 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.2, 'min_child_weight': 2.0, 'n_estimators': 86, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  96%|█████████▌| 958/1000 [2:55:27<03:41,  5.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.96496350365 and loss 0.794167726601 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.31169127289 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 39, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 9, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  96%|█████████▌| 959/1000 [2:55:49<07:02, 10.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.247306822689 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.09574695618 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 437, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  96%|█████████▌| 960/1000 [2:55:56<06:14,  9.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.913868613139 and loss 0.778733011868 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.18061528092 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 494, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  96%|█████████▌| 961/1000 [2:56:20<08:54, 13.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.214158096058 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.10189132675 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 460, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  96%|█████████▌| 962/1000 [2:56:26<07:10, 11.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.220461736259 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.1637101973 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.17500000000000002, 'min_child_weight': 2.0, 'n_estimators': 105, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  96%|█████████▋| 963/1000 [2:56:37<06:56, 11.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.228198781251 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.10536338134 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 207, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  96%|█████████▋| 964/1000 [2:56:40<05:19,  8.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.309864534797 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.17905173459 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 43, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  96%|█████████▋| 965/1000 [2:56:46<04:38,  7.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.986861313869 and loss 0.459091688943 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.17152620852 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 91, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  97%|█████████▋| 966/1000 [2:56:48<03:32,  6.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.7681373899 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77244449415 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 2.0, 'n_estimators': 89, 'subsample': 0.5, 'importance_type': 'gain', 'max_depth': 5, 'gamma': 1.0, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  97%|█████████▋| 967/1000 [2:56:49<02:36,  4.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.883211678832 and loss 1.44481085017 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.44181675785 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 17, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  97%|█████████▋| 968/1000 [2:56:51<02:02,  3.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.932846715328 and loss 0.999008186781 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.36391814571 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 23, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  97%|█████████▋| 969/1000 [2:57:20<05:50, 11.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.240032973678 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.2109656421 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 3.0, 'n_estimators': 472, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 8, 'gamma': 0.75, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  97%|█████████▋| 970/1000 [2:57:44<07:34, 15.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.203370231175 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.22857077212 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 369, 'subsample': 0.8500000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  97%|█████████▋| 971/1000 [2:57:56<06:53, 14.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.978102189781 and loss 0.338677732874 :\n",
      "Parameters with this testing accuracy 0.210526315789 and loss 2.27896571589 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.15000000000000002, 'min_child_weight': 6.0, 'n_estimators': 290, 'subsample': 1.0, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  97%|█████████▋| 972/1000 [2:58:01<05:25, 11.63s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.403107562808 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.2100049371 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 96, 'subsample': 0.75, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  97%|█████████▋| 973/1000 [2:58:12<05:07, 11.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.210500872474 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.1689527669 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 185, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  97%|█████████▋| 974/1000 [2:58:16<03:57,  9.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.36712855031 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.13732202696 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 1.0, 'n_estimators': 58, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  98%|█████████▊| 975/1000 [2:58:31<04:30, 10.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.246428494709 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.11154511908 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 274, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  98%|█████████▊| 976/1000 [2:58:51<05:24, 13.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.191934985362 :\n",
      "Parameters with this testing accuracy 0.347368421053 and loss 2.23383856706 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.325, 'min_child_weight': 1.0, 'n_estimators': 420, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  98%|█████████▊| 977/1000 [2:59:00<04:43, 12.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883403 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.7732787584 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 492, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 0.9500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  98%|█████████▊| 978/1000 [2:59:13<04:36, 12.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 1.0 and loss 0.185026736765 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.22388038137 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.225, 'min_child_weight': 1.0, 'n_estimators': 349, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 4, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  98%|█████████▊| 979/1000 [2:59:18<03:33, 10.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.931386861314 and loss 0.643226882734 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.14906574725 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 309, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 1, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  98%|█████████▊| 980/1000 [2:59:41<04:38, 13.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.240881743381 :\n",
      "Parameters with this testing accuracy 0.378947368421 and loss 2.08804488188 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.07500000000000001, 'min_child_weight': 2.0, 'n_estimators': 366, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  98%|█████████▊| 981/1000 [2:59:45<03:31, 11.12s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.985401459854 and loss 0.296701862195 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.14940220998 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.07500000000000001, 'min_child_weight': 1.0, 'n_estimators': 170, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 2, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  98%|█████████▊| 982/1000 [2:59:58<03:27, 11.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.356947541868 :\n",
      "Parameters with this testing accuracy 0.368421052632 and loss 2.17987208649 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 212, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.7000000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  98%|█████████▊| 983/1000 [3:00:19<04:04, 14.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.183032366357 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.20902733556 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 350, 'subsample': 0.65, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  98%|█████████▊| 984/1000 [3:00:37<04:09, 15.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.994160583942 and loss 0.224066725183 :\n",
      "Parameters with this testing accuracy 0.389473684211 and loss 2.0994913551 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 381, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  98%|█████████▊| 985/1000 [3:00:43<03:09, 12.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.997080291971 and loss 0.211941957982 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.1560310372 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 87, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.8, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  99%|█████████▊| 986/1000 [3:00:56<02:57, 12.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.985401459854 and loss 0.272165654207 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.27967214214 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.1, 'min_child_weight': 4.0, 'n_estimators': 338, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  99%|█████████▊| 987/1000 [3:01:02<02:21, 10.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.96496350365 and loss 0.722521331069 :\n",
      "Parameters with this testing accuracy 0.410526315789 and loss 2.24499681623 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 132, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 5, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  99%|█████████▉| 988/1000 [3:01:07<01:49,  9.08s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883542 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327875589 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.25, 'min_child_weight': 1.0, 'n_estimators': 236, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 9, 'gamma': 0.9, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  99%|█████████▉| 989/1000 [3:01:13<01:27,  7.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.992700729927 and loss 0.280539790745 :\n",
      "Parameters with this testing accuracy 0.357894736842 and loss 2.1069496734 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 88, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  99%|█████████▉| 990/1000 [3:01:23<01:28,  8.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.2432542229 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.12376392457 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.1, 'min_child_weight': 2.0, 'n_estimators': 247, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  99%|█████████▉| 991/1000 [3:01:25<01:00,  6.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.851094890511 and loss 1.79263791255 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.53198648628 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 35, 'subsample': 0.5, 'importance_type': 'total_gain', 'max_depth': 4, 'gamma': 1.0, 'booster': 'dart'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  99%|█████████▉| 992/1000 [3:01:37<01:06,  8.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.242766220911 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.20827522066 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 178, 'subsample': 0.7000000000000001, 'importance_type': 'weight', 'max_depth': 7, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  99%|█████████▉| 993/1000 [3:01:41<00:49,  7.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.954744525547 and loss 0.562245115137 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.13127949889 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.05, 'min_child_weight': 2.0, 'n_estimators': 124, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 3, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  99%|█████████▉| 994/1000 [3:01:50<00:45,  7.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.991240875912 and loss 0.233305826443 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.06803557663 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.15000000000000002, 'min_child_weight': 1.0, 'n_estimators': 179, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 1.0, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt: 100%|█████████▉| 995/1000 [3:02:15<01:02, 12.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.203740591552 :\n",
      "Parameters with this testing accuracy 0.421052631579 and loss 2.09284138113 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.125, 'min_child_weight': 1.0, 'n_estimators': 432, 'subsample': 0.55, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt: 100%|█████████▉| 996/1000 [3:02:19<00:41, 10.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.988321167883 and loss 0.249030515537 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.27030112881 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.30000000000000004, 'min_child_weight': 3.0, 'n_estimators': 120, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt: 100%|█████████▉| 997/1000 [3:02:22<00:23,  7.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.995620437956 and loss 0.295391246274 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.21259431184 :\n",
      "{'colsample_bytree': 0.65, 'learning_rate': 0.17500000000000002, 'min_child_weight': 1.0, 'n_estimators': 34, 'subsample': 0.6000000000000001, 'importance_type': 'weight', 'max_depth': 9, 'gamma': 0.8500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt: 100%|█████████▉| 998/1000 [3:02:32<00:16,  8.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.998540145985 and loss 0.207096947233 :\n",
      "Parameters with this testing accuracy 0.336842105263 and loss 2.21227160648 :\n",
      "{'colsample_bytree': 0.6000000000000001, 'learning_rate': 0.25, 'min_child_weight': 2.0, 'n_estimators': 165, 'subsample': 0.8, 'importance_type': 'weight', 'max_depth': 8, 'gamma': 0.9500000000000001, 'booster': 'gbtree'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt: 100%|█████████▉| 999/1000 [3:02:35<00:07,  7.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.0686131386861 and loss 2.76763883368 :\n",
      "Parameters with this testing accuracy 0.0631578947368 and loss 2.77327876091 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.2, 'min_child_weight': 1.0, 'n_estimators': 169, 'subsample': 0.55, 'importance_type': 'gain', 'max_depth': 6, 'gamma': 0.9500000000000001, 'booster': 'gblinear'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt: 100%|██████████| 1000/1000 [3:02:39<00:00,  5.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.925547445255 and loss 1.18228318191 :\n",
      "Parameters with this testing accuracy 0.4 and loss 2.36940498352 :\n",
      "{'colsample_bytree': 0.5, 'learning_rate': 0.025, 'min_child_weight': 1.0, 'n_estimators': 77, 'subsample': 0.5, 'importance_type': 'weight', 'max_depth': 4, 'gamma': 1.0, 'booster': 'gbtree'}\n",
      "------------------------------------\n",
      "('The best hyperparameters are: ', '\\n')\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 224, 'subsample': 0.5, 'gamma': 1.0, 'max_depth': 8, 'importance_type': 1, 'booster': 0}\n",
      "Time elapsed to optimize 1000 executions: 10959.3922791\n",
      "\n",
      " Best score:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.989781021898 and loss 0.276279670597 :\n",
      "Parameters with this testing accuracy 0.452631578947 and loss 2.10525133445 :\n",
      "{'colsample_bytree': 0.55, 'learning_rate': 0.05, 'min_child_weight': 1.0, 'n_estimators': 224, 'subsample': 0.5, 'gamma': 1.0, 'max_depth': 9, 'importance_type': 'weight', 'booster': 'gbtree'}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'loss': -0.45263157894736844, 'status': 'ok'}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "\n",
    "trials = Trials()\n",
    "cores = 48\n",
    "start = time.time()\n",
    "evaluations = 1000\n",
    "pbar = tqdm(total=evaluations, desc=\"Hyperopt\")\n",
    "best_param = optimize(evals=evaluations,\n",
    "                      optimizer=tpe.suggest,\n",
    "                      trials=trials)\n",
    "print(\"------------------------------------\")\n",
    "print(\"The best hyperparameters are: \", \"\\n\")\n",
    "print(best_param)\n",
    "end = time.time()\n",
    "print('Time elapsed to optimize {0} executions: {1}'.format(evaluations, end - start))\n",
    "best_param['importance_type'] = importance_type_list[best_param['importance_type']]\n",
    "best_param['booster'] = booster_list[best_param['booster']]\n",
    "best_param['max_depth'] = max_depth_list[best_param['max_depth']]\n",
    "print('\\n Best score:')\n",
    "score(best_param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the best params and train the model\n",
    "xgb_opt = XGBClassifier(n_estimators = best_param['n_estimators'],\n",
    "                          learning_rate = best_param['learning_rate'],\n",
    "                            max_depth = best_param['max_depth'],\n",
    "                            min_child_weight = best_param['min_child_weight'],\n",
    "                            subsample = best_param['subsample'], \n",
    "                            gamma = best_param['gamma'],\n",
    "                            colsample_bytree = best_param['colsample_bytree'],\n",
    "                            importance_type = best_param['importance_type'],\n",
    "                            booster=best_param['booster'],\n",
    "                            objective = \"multi:softprob\",\n",
    "                            n_jobs=24,\n",
    "                            random_state=42,\n",
    "                            silent=True,)\n",
    "\n",
    "eval_set=[(X_train, y_train), (X_val, y_val)]\n",
    "\n",
    "fitted_model = xgb_opt.fit(X=X_train,y=y_train, eval_set=eval_set, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.03806866, 0.05204341, 0.03794817, ..., 0.02485807, 0.05698483,\n",
       "        0.04739683],\n",
       "       [0.03019504, 0.02760956, 0.08758957, ..., 0.08181053, 0.0211191 ,\n",
       "        0.06418205],\n",
       "       [0.0349534 , 0.05254348, 0.11281843, ..., 0.10257877, 0.0315582 ,\n",
       "        0.11758216],\n",
       "       ...,\n",
       "       [0.02006801, 0.04081858, 0.4133624 , ..., 0.03623043, 0.02095573,\n",
       "        0.03738834],\n",
       "       [0.11751704, 0.05704644, 0.05229732, ..., 0.03299164, 0.08070482,\n",
       "        0.09475508],\n",
       "       [0.01481836, 0.02847671, 0.01521092, ..., 0.03842077, 0.01346349,\n",
       "        0.03118242]], dtype=float32)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_score = fitted_model.predict_proba(X_test)\n",
    "y_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45263157894736844"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_acc = accuracy_score(y_test, fitted_model.predict(X_test))\n",
    "test_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt \n",
    "%matplotlib inline \n",
    "x_list =range(len(train_acc_list))\n",
    "\n",
    "plt.plot(x_list, train_acc_list, label='Training accuracy')\n",
    "plt.plot(x_list, test_acc_list, label='Testing accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_list, train_loss_list, label='Training loss')\n",
    "plt.plot(x_list, test_loss_list, label='Testing loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}