{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7c5d059b-ed8a-4e2e-9420-25890f648895",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import psycopg2 as pg\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import confusion_matrix,matthews_corrcoef,accuracy_score\n",
    "import optuna\n",
    "import pickle\n",
    "from sklearn.feature_selection import SequentialFeatureSelector\n",
    "reload = False\n",
    "def norm(x):\n",
    "    if len(x)==1 and x[0]=='':\n",
    "        return []\n",
    "    else:\n",
    "        return x\n",
    "if reload:\n",
    "    engine = pg.connect(\"dbname='safeidx' user='fbk_mpba' host='172.104.247.67' port='5432' password='fbk2024$'\")\n",
    "    df = pd.read_sql('select * from data_safeidx', con=engine)\n",
    "    with open('data.pkl','wb') as f:\n",
    "        pickle.dump(df,f)\n",
    "else:\n",
    "    with open('data.pkl','rb') as f:\n",
    "        df = pickle.load(f)\n",
    "    #df = pd.read_csv('pid.csv').drop(columns='Unnamed: 0')\n",
    "    #df.evacuation_vehicles =  df.evacuation_vehicles.apply(lambda x:norm(x.replace(' ','').replace('[','').replace(']','').split(',')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "25ebaf4f-f7bd-4119-930f-410bb78f0b27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>difficulty</th>\n",
       "      <th>cause</th>\n",
       "      <th>town</th>\n",
       "      <th>province</th>\n",
       "      <th>gender</th>\n",
       "      <th>equipment</th>\n",
       "      <th>helmet</th>\n",
       "      <th>destination</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>india</th>\n",
       "      <th>age</th>\n",
       "      <th>country</th>\n",
       "      <th>injury_side</th>\n",
       "      <th>injury_general_location</th>\n",
       "      <th>evacuation_vehicles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>novice</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>SIKLOS</td>\n",
       "      <td></td>\n",
       "      <td>F</td>\n",
       "      <td>ski</td>\n",
       "      <td>None</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>distortion</td>\n",
       "      <td>None</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Ungheria</td>\n",
       "      <td>L</td>\n",
       "      <td>lower_limbs</td>\n",
       "      <td>[akja]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>advanced</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>MALMO</td>\n",
       "      <td></td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>None</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>bruise</td>\n",
       "      <td>None</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Svezia</td>\n",
       "      <td>R</td>\n",
       "      <td>skull_or_face</td>\n",
       "      <td>[akja]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>advanced</td>\n",
       "      <td>fall_alone</td>\n",
       "      <td>CALDARO</td>\n",
       "      <td>BZ</td>\n",
       "      <td>F</td>\n",
       "      <td>ski</td>\n",
       "      <td>None</td>\n",
       "      <td>domicile</td>\n",
       "      <td>other</td>\n",
       "      <td>None</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Italia</td>\n",
       "      <td>R</td>\n",
       "      <td>None</td>\n",
       "      <td>[snowmobile]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>advanced</td>\n",
       "      <td>collision_person</td>\n",
       "      <td>LINZ</td>\n",
       "      <td></td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>None</td>\n",
       "      <td>hospital_emergency_room</td>\n",
       "      <td>bruise</td>\n",
       "      <td>None</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Austria</td>\n",
       "      <td>R</td>\n",
       "      <td>lower_limbs</td>\n",
       "      <td>[snowmobile]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>advanced</td>\n",
       "      <td>collision_person</td>\n",
       "      <td>RUSAVA</td>\n",
       "      <td></td>\n",
       "      <td>M</td>\n",
       "      <td>ski</td>\n",
       "      <td>None</td>\n",
       "      <td>other</td>\n",
       "      <td>bruise</td>\n",
       "      <td>None</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Repubblica Ceca</td>\n",
       "      <td>L</td>\n",
       "      <td>lower_limbs</td>\n",
       "      <td>[other]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  difficulty             cause     town province gender equipment helmet  \\\n",
       "0     novice        fall_alone   SIKLOS               F       ski   None   \n",
       "1   advanced        fall_alone    MALMO               M       ski   None   \n",
       "2   advanced        fall_alone  CALDARO       BZ      F       ski   None   \n",
       "3   advanced  collision_person     LINZ               M       ski   None   \n",
       "4   advanced  collision_person   RUSAVA               M       ski   None   \n",
       "\n",
       "               destination   diagnosis india   age          country  \\\n",
       "0  hospital_emergency_room  distortion  None  32.0         Ungheria   \n",
       "1  hospital_emergency_room      bruise  None  32.0           Svezia   \n",
       "2                 domicile       other  None  12.0           Italia   \n",
       "3  hospital_emergency_room      bruise  None  58.0          Austria   \n",
       "4                    other      bruise  None  25.0  Repubblica Ceca   \n",
       "\n",
       "  injury_side injury_general_location evacuation_vehicles  \n",
       "0           L             lower_limbs              [akja]  \n",
       "1           R           skull_or_face              [akja]  \n",
       "2           R                    None        [snowmobile]  \n",
       "3           R             lower_limbs        [snowmobile]  \n",
       "4           L             lower_limbs             [other]  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03aa2a04-93fa-469e-a678-685cacdebd6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ev = set({})\n",
    "for i,row in df.iterrows():\n",
    "    ev = ev.union(set(row.evacuation_vehicles))\n",
    "for c in ev:\n",
    "    df[c] = False\n",
    "for i,row in df.iterrows():\n",
    "    for c in row.evacuation_vehicles:\n",
    "        df.loc[i,c] = True\n",
    "df.drop(columns=['town','province','evacuation_vehicles'],inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8831be31-0ba5-459f-916a-e81f18e9e1b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['difficulty', 'cause', 'gender', 'equipment', 'helmet', 'destination',\n",
       "       'diagnosis', 'india', 'age', 'country', 'injury_side',\n",
       "       'injury_general_location', 'akja', 'snowmobile_sled', 'indipendently',\n",
       "       'quad', 'car', 'ambulance', 'offroad_vehicle', 'ski_lift', 'other',\n",
       "       'helicopter', 'skiarea_ambulance', 'snowmobile', 'privat_helicopter'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "33617e77-7c2b-41a3-96c0-8930aa5ac869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labeled = df[~pd.isna(df.india)].reset_index().drop(columns='index')\n",
    "labeled['age'] =  labeled['age'].astype(np.float32).fillna(np.nan)\n",
    "plt.hist(labeled.india);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b5342102-3219-4362-a6ef-0095a8144e37",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_7203/4107850114.py:8: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n",
      "  plt.figure()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tmwYNGqSRI0dqwYIFqqurU0ZGhlJSUqyvm995552aMWOG0tLSNHnyZO3cuVNz5szRc889Z6378MMP65prrtEzzzyjpKQkvfHGG9qyZYvH18wBAEDLddpncrZs2aLLLrtMl112mSRp3LhxuuyyyzR9+vRTXmPx4sXq2rWrrr/+eg0ePFhXX321RzhxOBxavXq1SkpK1KtXL40fP17Tp0/3uJfOlVdeqSVLluill15S9+7d9c9//lM5OTm69NJLT/eQAACAgX7TfXKaO+6TAwDm4D453sF9cgAAAJo4Qg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEinHXLy8/N14403KioqSjabTTk5OdZcXV2dJk+erLi4OLVu3VpRUVG6++67dejQIY81ysvLlZqaKrvdrtDQUKWlpamqqsqjZvv27erXr5+Cg4MVHR2t7OzsE3pZtmyZunbtquDgYMXFxendd9893cMBAACGOu2Qc/ToUXXv3l3z5s07Ye6HH37Q1q1bNW3aNG3dulVvvfWWiouL9cc//tGjLjU1Vbt27VJeXp5Wrlyp/Px8jRo1ypp3uVwaOHCgOnbsqMLCQj399NPKysrSSy+9ZNVs3LhRd9xxh9LS0vTpp58qOTlZycnJ2rlz5+keEgAAMJDN7Xa7z/jJNpuWL1+u5OTkn63ZvHmzrrjiCn399dfq0KGDdu/erdjYWG3evFm9e/eWJOXm5mrw4ME6ePCgoqKiNH/+fE2dOlVOp1OBgYGSpMzMTOXk5GjPnj2SpNtvv11Hjx7VypUrrX317dtXPXr00IIFC06pf5fLJYfDocrKStnt9jN8FQAATUGnzFW+bsEI+2Yn+bqFX3Wqf78b/ZqcyspK2Ww2hYaGSpIKCgoUGhpqBRxJSkhIkJ+fnzZt2mTV9O/f3wo4kpSYmKji4mIdOXLEqklISPDYV2JiogoKChr5iAAAQHMQ0JiLV1dXa/LkybrjjjuspOV0OhUeHu7ZRECAwsLC5HQ6rZqYmBiPmoiICGuubdu2cjqd1thPaxrWOJmamhrV1NRYj10u15kfHAAAaNIa7UxOXV2dbrvtNrndbs2fP7+xdnNaZs2aJYfDYW3R0dG+bgkAADSSRgk5DQHn66+/Vl5ensfnZZGRkSorK/OoP3bsmMrLyxUZGWnVlJaWetQ0PP61mob5k5kyZYoqKyut7cCBA2d+kAAAoEnzeshpCDh79+7Vv/71L7Vr185jPj4+XhUVFSosLLTG1q5dq/r6evXp08eqyc/PV11dnVWTl5enLl26qG3btlbNmjVrPNbOy8tTfHz8z/YWFBQku93usQEAADOddsipqqpSUVGRioqKJEklJSUqKirS/v37VVdXp1tuuUVbtmzR4sWLdfz4cTmdTjmdTtXW1kqSunXrpkGDBmnkyJH65JNPtGHDBmVkZCglJUVRUVGSpDvvvFOBgYFKS0vTrl27tHTpUs2ZM0fjxo2z+nj44YeVm5urZ555Rnv27FFWVpa2bNmijIwML7wsAACguTvtr5CvW7dOAwYMOGF8+PDhysrKOuGC4QYffPCBrr32Wkk/3gwwIyNDK1askJ+fn4YOHaq5c+fq3HPPteq3b9+u9PR0bd68We3bt9fo0aM1efJkjzWXLVumRx55RPv27dNFF12k7OxsDR48+JSPha+QA4A5+Aq5d5j0FfLfdJ+c5o6QAwDmIOR4h0khh9+uAgAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMdNohJz8/XzfeeKOioqJks9mUk5PjMe92uzV9+nSdf/75CgkJUUJCgvbu3etRU15ertTUVNntdoWGhiotLU1VVVUeNdu3b1e/fv0UHBys6OhoZWdnn9DLsmXL1LVrVwUHBysuLk7vvvvu6R4OAAAw1GmHnKNHj6p79+6aN2/eSeezs7M1d+5cLViwQJs2bVLr1q2VmJio6upqqyY1NVW7du1SXl6eVq5cqfz8fI0aNcqad7lcGjhwoDp27KjCwkI9/fTTysrK0ksvvWTVbNy4UXfccYfS0tL06aefKjk5WcnJydq5c+fpHhIAADCQze12u8/4yTabli9fruTkZEk/nsWJiorS+PHjNWHCBElSZWWlIiIitHDhQqWkpGj37t2KjY3V5s2b1bt3b0lSbm6uBg8erIMHDyoqKkrz58/X1KlT5XQ6FRgYKEnKzMxUTk6O9uzZI0m6/fbbdfToUa1cudLqp2/fvurRo4cWLFhwSv27XC45HA5VVlbKbref6csAAGgCOmWu8nULRtg3O8nXLfyqU/377dVrckpKSuR0OpWQkGCNORwO9enTRwUFBZKkgoIChYaGWgFHkhISEuTn56dNmzZZNf3797cCjiQlJiaquLhYR44csWp+up+Gmob9AACAli3Am4s5nU5JUkREhMd4RESENed0OhUeHu7ZRECAwsLCPGpiYmJOWKNhrm3btnI6nb+4n5OpqalRTU2N9djlcp3O4QEAgGakRX27atasWXI4HNYWHR3t65YAAEAj8WrIiYyMlCSVlpZ6jJeWllpzkZGRKisr85g/duyYysvLPWpOtsZP9/FzNQ3zJzNlyhRVVlZa24EDB073EAEAQDPh1ZATExOjyMhIrVmzxhpzuVzatGmT4uPjJUnx8fGqqKhQYWGhVbN27VrV19erT58+Vk1+fr7q6uqsmry8PHXp0kVt27a1an66n4aahv2cTFBQkOx2u8cGAADMdNohp6qqSkVFRSoqKpL048XGRUVF2r9/v2w2m8aMGaOZM2fqnXfe0Y4dO3T33XcrKirK+gZWt27dNGjQII0cOVKffPKJNmzYoIyMDKWkpCgqKkqSdOeddyowMFBpaWnatWuXli5dqjlz5mjcuHFWHw8//LByc3P1zDPPaM+ePcrKytKWLVuUkZHx218VAADQ7J32hcdbtmzRgAEDrMcNwWP48OFauHChJk2apKNHj2rUqFGqqKjQ1VdfrdzcXAUHB1vPWbx4sTIyMnT99dfLz89PQ4cO1dy5c615h8Oh1atXKz09Xb169VL79u01ffp0j3vpXHnllVqyZIkeeeQR/c///I8uuugi5eTk6NJLLz2jFwIAAJjlN90np7njPjkAYA7uk+Md3CcHAACgiSPkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACM5PWQc/z4cU2bNk0xMTEKCQnR7373Oz3++ONyu91Wjdvt1vTp03X++ecrJCRECQkJ2rt3r8c65eXlSk1Nld1uV2hoqNLS0lRVVeVRs337dvXr10/BwcGKjo5Wdna2tw8HAAA0U14POU899ZTmz5+vF198Ubt379ZTTz2l7OxsvfDCC1ZNdna25s6dqwULFmjTpk1q3bq1EhMTVV1dbdWkpqZq165dysvL08qVK5Wfn69Ro0ZZ8y6XSwMHDlTHjh1VWFiop59+WllZWXrppZe8fUgAAKAZsrl/eorFC2644QZFRETor3/9qzU2dOhQhYSE6PXXX5fb7VZUVJTGjx+vCRMmSJIqKysVERGhhQsXKiUlRbt371ZsbKw2b96s3r17S5Jyc3M1ePBgHTx4UFFRUZo/f76mTp0qp9OpwMBASVJmZqZycnK0Z8+eU+rV5XLJ4XCosrJSdrvdmy8DAOAs65S5ytctGGHf7CRft/CrTvXvt9fP5Fx55ZVas2aNPv/8c0nStm3b9NFHH+kPf/iDJKmkpEROp1MJCQnWcxwOh/r06aOCggJJUkFBgUJDQ62AI0kJCQny8/PTpk2brJr+/ftbAUeSEhMTVVxcrCNHjnj7sAAAQDMT4O0FMzMz5XK51LVrV/n7++v48eN64oknlJqaKklyOp2SpIiICI/nRUREWHNOp1Ph4eGejQYEKCwszKMmJibmhDUa5tq2bXtCbzU1NaqpqbEeu1yu33KoAACgCfP6mZw333xTixcv1pIlS7R161YtWrRIf/rTn7Ro0SJv7+q0zZo1Sw6Hw9qio6N93RIAAGgkXg85EydOVGZmplJSUhQXF6dhw4Zp7NixmjVrliQpMjJSklRaWurxvNLSUmsuMjJSZWVlHvPHjh1TeXm5R83J1vjpPv7TlClTVFlZaW0HDhz4jUcLAACaKq+HnB9++EF+fp7L+vv7q76+XpIUExOjyMhIrVmzxpp3uVzatGmT4uPjJUnx8fGqqKhQYWGhVbN27VrV19erT58+Vk1+fr7q6uqsmry8PHXp0uWkH1VJUlBQkOx2u8cGAADM5PWQc+ONN+qJJ57QqlWrtG/fPi1fvlzPPvusbr75ZkmSzWbTmDFjNHPmTL3zzjvasWOH7r77bkVFRSk5OVmS1K1bNw0aNEgjR47UJ598og0bNigjI0MpKSmKioqSJN15550KDAxUWlqadu3apaVLl2rOnDkaN26ctw8JAAA0Q16/8PiFF17QtGnT9N///d8qKytTVFSU7r//fk2fPt2qmTRpko4ePapRo0apoqJCV199tXJzcxUcHGzVLF68WBkZGbr++uvl5+enoUOHau7cuda8w+HQ6tWrlZ6erl69eql9+/aaPn26x710AABAy+X1++Q0J9wnBwDMwX1yvIP75AAAADRxhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEaJeR88803uuuuu9SuXTuFhIQoLi5OW7ZssebdbremT5+u888/XyEhIUpISNDevXs91igvL1dqaqrsdrtCQ0OVlpamqqoqj5rt27erX79+Cg4OVnR0tLKzsxvjcAAAQDPk9ZBz5MgRXXXVVWrVqpXee+89ffbZZ3rmmWfUtm1bqyY7O1tz587VggULtGnTJrVu3VqJiYmqrq62alJTU7Vr1y7l5eVp5cqVys/P16hRo6x5l8ulgQMHqmPHjiosLNTTTz+trKwsvfTSS94+JAAA0AzZ3G6325sLZmZmasOGDfrwww9POu92uxUVFaXx48drwoQJkqTKykpFRERo4cKFSklJ0e7duxUbG6vNmzerd+/ekqTc3FwNHjxYBw8eVFRUlObPn6+pU6fK6XQqMDDQ2ndOTo727NlzSr26XC45HA5VVlbKbrd74egBAL7SKXOVr1swwr7ZSb5u4Ved6t9vr5/Jeeedd9S7d2/deuutCg8P12WXXaaXX37Zmi8pKZHT6VRCQoI15nA41KdPHxUUFEiSCgoKFBoaagUcSUpISJCfn582bdpk1fTv398KOJKUmJio4uJiHTlyxNuHBQAAmhmvh5yvvvpK8+fP10UXXaT3339fDz74oB566CEtWrRIkuR0OiVJERERHs+LiIiw5pxOp8LDwz3mAwICFBYW5lFzsjV+uo//VFNTI5fL5bEBAAAzBXh7wfr6evXu3VtPPvmkJOmyyy7Tzp07tWDBAg0fPtzbuzsts2bN0owZM3zaAwAAODu8fibn/PPPV2xsrMdYt27dtH//fklSZGSkJKm0tNSjprS01JqLjIxUWVmZx/yxY8dUXl7uUXOyNX66j/80ZcoUVVZWWtuBAwfO5BABAEAz4PWQc9VVV6m4uNhj7PPPP1fHjh0lSTExMYqMjNSaNWuseZfLpU2bNik+Pl6SFB8fr4qKChUWFlo1a9euVX19vfr06WPV5Ofnq66uzqrJy8tTly5dPL7J9VNBQUGy2+0eGwAAMJPXQ87YsWP18ccf68knn9QXX3yhJUuW6KWXXlJ6erokyWazacyYMZo5c6beeecd7dixQ3fffbeioqKUnJws6cczP4MGDdLIkSP1ySefaMOGDcrIyFBKSoqioqIkSXfeeacCAwOVlpamXbt2aenSpZozZ47GjRvn7UMCAADNkNevybn88su1fPlyTZkyRY899phiYmL0/PPPKzU11aqZNGmSjh49qlGjRqmiokJXX321cnNzFRwcbNUsXrxYGRkZuv766+Xn56ehQ4dq7ty51rzD4dDq1auVnp6uXr16qX379po+fbrHvXQAAEDL5fX75DQn3CcHAMzBfXK8g/vkAAAANHGEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkRo95MyePVs2m01jxoyxxqqrq5Wenq527drp3HPP1dChQ1VaWurxvP379yspKUnnnHOOwsPDNXHiRB07dsyjZt26derZs6eCgoLUuXNnLVy4sLEPBwAANBONGnI2b96sv/zlL/r973/vMT527FitWLFCy5Yt0/r163Xo0CENGTLEmj9+/LiSkpJUW1urjRs3atGiRVq4cKGmT59u1ZSUlCgpKUkDBgxQUVGRxowZo/vuu0/vv/9+Yx4SAABoJhot5FRVVSk1NVUvv/yy2rZta41XVlbqr3/9q5599lldd9116tWrl/72t79p48aN+vjjjyVJq1ev1meffabXX39dPXr00B/+8Ac9/vjjmjdvnmprayVJCxYsUExMjJ555hl169ZNGRkZuuWWW/Tcc8811iEBAIBmpNFCTnp6upKSkpSQkOAxXlhYqLq6Oo/xrl27qkOHDiooKJAkFRQUKC4uThEREVZNYmKiXC6Xdu3aZdX859qJiYnWGgAAoGULaIxF33jjDW3dulWbN28+Yc7pdCowMFChoaEe4xEREXI6nVbNTwNOw3zD3C/VuFwu/fvf/1ZISMgJ+66pqVFNTY312OVynf7BAQCAZsHrZ3IOHDighx9+WIsXL1ZwcLC3l/9NZs2aJYfDYW3R0dG+bgkAADQSr4ecwsJClZWVqWfPngoICFBAQIDWr1+vuXPnKiAgQBEREaqtrVVFRYXH80pLSxUZGSlJioyMPOHbVg2Pf63Gbref9CyOJE2ZMkWVlZXWduDAAW8cMgAAaIK8HnKuv/567dixQ0VFRdbWu3dvpaamWv+7VatWWrNmjfWc4uJi7d+/X/Hx8ZKk+Ph47dixQ2VlZVZNXl6e7Ha7YmNjrZqfrtFQ07DGyQQFBclut3tsAADATF6/JqdNmza69NJLPcZat26tdu3aWeNpaWkaN26cwsLCZLfbNXr0aMXHx6tv376SpIEDByo2NlbDhg1Tdna2nE6nHnnkEaWnpysoKEiS9MADD+jFF1/UpEmTdO+992rt2rV68803tWrVKm8fEgAAaIYa5cLjX/Pcc8/Jz89PQ4cOVU1NjRITE/XnP//Zmvf399fKlSv14IMPKj4+Xq1bt9bw4cP12GOPWTUxMTFatWqVxo4dqzlz5uiCCy7QK6+8osTERF8cEgAAaGJsbrfb7esmfMXlcsnhcKiyspKPrgCgmeuUyZl8b9g3O8nXLfyqU/37zW9XAQAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABG8nrImTVrli6//HK1adNG4eHhSk5OVnFxsUdNdXW10tPT1a5dO5177rkaOnSoSktLPWr279+vpKQknXPOOQoPD9fEiRN17Ngxj5p169apZ8+eCgoKUufOnbVw4UJvHw4AAGimvB5y1q9fr/T0dH388cfKy8tTXV2dBg4cqKNHj1o1Y8eO1YoVK7Rs2TKtX79ehw4d0pAhQ6z548ePKykpSbW1tdq4caMWLVqkhQsXavr06VZNSUmJkpKSNGDAABUVFWnMmDG677779P7773v7kAAAQDNkc7vd7sbcweHDhxUeHq7169erf//+qqys1HnnnaclS5bolltukSTt2bNH3bp1U0FBgfr27av33ntPN9xwgw4dOqSIiAhJ0oIFCzR58mQdPnxYgYGBmjx5slatWqWdO3da+0pJSVFFRYVyc3NPqTeXyyWHw6HKykrZ7XbvHzwA4KzplLnK1y0YYd/sJF+38KtO9e93o1+TU1lZKUkKCwuTJBUWFqqurk4JCQlWTdeuXdWhQwcVFBRIkgoKChQXF2cFHElKTEyUy+XSrl27rJqfrtFQ07AGAABo2QIac/H6+nqNGTNGV111lS699FJJktPpVGBgoEJDQz1qIyIi5HQ6rZqfBpyG+Ya5X6pxuVz697//rZCQkBP6qampUU1NjfXY5XL9tgMEAABNVqOeyUlPT9fOnTv1xhtvNOZuTtmsWbPkcDisLTo62tctAQCARtJoIScjI0MrV67UBx98oAsuuMAaj4yMVG1trSoqKjzqS0tLFRkZadX857etGh7/Wo3dbj/pWRxJmjJliiorK63twIEDv+kYAQBA0+X1kON2u5WRkaHly5dr7dq1iomJ8Zjv1auXWrVqpTVr1lhjxcXF2r9/v+Lj4yVJ8fHx2rFjh8rKyqyavLw82e12xcbGWjU/XaOhpmGNkwkKCpLdbvfYAACAmbx+TU56erqWLFmit99+W23atLGuoXE4HAoJCZHD4VBaWprGjRunsLAw2e12jR49WvHx8erbt68kaeDAgYqNjdWwYcOUnZ0tp9OpRx55ROnp6QoKCpIkPfDAA3rxxRc1adIk3XvvvVq7dq3efPNNrVrF1fUAAKARzuTMnz9flZWVuvbaa3X++edb29KlS62a5557TjfccIOGDh2q/v37KzIyUm+99ZY17+/vr5UrV8rf31/x8fG66667dPfdd+uxxx6zamJiYrRq1Srl5eWpe/fueuaZZ/TKK68oMTHR24cEAACaoUa/T05Txn1yAMAc3CfHO7hPDgAAQBNHyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYKcDXDQBofjplrvJ1C8bYNzvJ1y0AxuJMDgAAMBJncpoB/qvZe/ivZgBoOTiTAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIzT7kzJs3T506dVJwcLD69OmjTz75xNctAQCAJqBZh5ylS5dq3LhxevTRR7V161Z1795diYmJKisr83VrAADAx5p1yHn22Wc1cuRIjRgxQrGxsVqwYIHOOeccvfrqq75uDQAA+FizDTm1tbUqLCxUQkKCNebn56eEhAQVFBT4sDMAANAUBPi6gTP13Xff6fjx44qIiPAYj4iI0J49e076nJqaGtXU1FiPKysrJUkul6vxGvWC+poffN2CMZr6/9fNBe9J7+E96T28L72jObwnG3p0u92/WNdsQ86ZmDVrlmbMmHHCeHR0tA+6gS84nvd1B4An3pNoaprTe/L777+Xw+H42flmG3Lat28vf39/lZaWeoyXlpYqMjLypM+ZMmWKxo0bZz2ur69XeXm52rVrJ5vN1qj9mszlcik6OloHDhyQ3W73dTuAJN6XaHp4T3qP2+3W999/r6ioqF+sa7YhJzAwUL169dKaNWuUnJws6cfQsmbNGmVkZJz0OUFBQQoKCvIYCw0NbeROWw673c4/uGhyeF+iqeE96R2/dAanQbMNOZI0btw4DR8+XL1799YVV1yh559/XkePHtWIESN83RoAAPCxZh1ybr/9dh0+fFjTp0+X0+lUjx49lJube8LFyAAAoOVp1iFHkjIyMn724ymcHUFBQXr00UdP+CgQ8CXel2hqeE+efTb3r33/CgAAoBlqtjcDBAAA+CWEHAAAYCRCDgAAMBIhBwAAGImQAwBAI/nwww911113KT4+Xt98840k6bXXXtNHH33k485aBkIOAACN4H//93+VmJiokJAQffrpp9YPRFdWVurJJ5/0cXctAyEHv1ltba2Ki4t17NgxX7cC6NixY/rXv/6lv/zlL/r+++8lSYcOHVJVVZWPO0NLM3PmTC1YsEAvv/yyWrVqZY1fddVV2rp1qw87azkIOThjP/zwg9LS0nTOOefokksu0f79+yVJo0eP1uzZs33cHVqir7/+WnFxcbrpppuUnp6uw4cPS5KeeuopTZgwwcfdoaUpLi5W//79Txh3OByqqKg4+w21QIQcnLEpU6Zo27ZtWrdunYKDg63xhIQELV261IedoaV6+OGH1bt3bx05ckQhISHW+M0336w1a9b4sDO0RJGRkfriiy9OGP/oo4904YUX+qCjlqfZ/6wDfCcnJ0dLly5V3759ZbPZrPFLLrlEX375pQ87Q0v14YcfauPGjQoMDPQY79Spk3XRJ3C2jBw5Ug8//LBeffVV2Ww2HTp0SAUFBZowYYKmTZvm6/ZaBEIOztjhw4cVHh5+wvjRo0c9Qg9wttTX1+v48eMnjB88eFBt2rTxQUdoyTIzM1VfX6/rr79eP/zwg/r376+goCBNmDBBo0eP9nV7LQIfV+GM9e7dW6tWrbIeNwSbV155RfHx8b5qCy3YwIED9fzzz1uPbTabqqqq9Oijj2rw4MG+awwtks1m09SpU1VeXq6dO3fq448/1uHDh/X444/7urUWgx/oxBn76KOP9Ic//EF33XWXFi5cqPvvv1+fffaZNm7cqPXr16tXr16+bhEtzMGDB5WYmCi32629e/eqd+/e2rt3r9q3b6/8/PyTnnkEYC5CDn6TL7/8UrNnz9a2bdtUVVWlnj17avLkyYqLi/N1a2ihjh07pjfeeEPbt2+33pOpqakeFyIDZ8OAAQN+8aP7tWvXnsVuWiZCDgAAjWDs2LEej+vq6lRUVKSdO3dq+PDhmjNnjo86azm48BhnbOvWrWrVqpV11ubtt9/W3/72N8XGxiorK+uEb7gAZ8PevXv1wQcfqKysTPX19R5z06dP91FXaImee+65k45nZWVxc8qzhDM5OGOXX365MjMzNXToUH311VeKjY3VkCFDtHnzZiUlJXlcAAqcDS+//LIefPBBtW/fXpGRkR4fFdhsNu4yiybhiy++0BVXXKHy8nJft2I8zuTgjH3++efq0aOHJGnZsmW65pprtGTJEm3YsEEpKSmEHJx1M2fO1BNPPKHJkyf7uhXgZxUUFHjcQBWNh5CDM+Z2u62PA/71r3/phhtukCRFR0fru+++82VraKGOHDmiW2+91ddtAJKkIUOGeDx2u9369ttvtWXLFm4GeJZwnxycsd69e2vmzJl67bXXtH79eiUlJUmSSkpKFBER4ePu0BLdeuutWr16ta/bACT9+BtVP93CwsJ07bXX6t1339Wjjz7q6/ZaBM7k4Iw9//zzSk1NVU5OjqZOnarOnTtLkv75z3/qyiuv9HF3aIk6d+6sadOm6eOPP1ZcXJzHLz9L0kMPPeSjztDSHD9+XCNGjFBcXJzatm3r63ZaLC48htdVV1fL39//hD8wQGOLiYn52TmbzaavvvrqLHaDli44OFi7d+/+xfclGhdncuB1XFAHXykpKfF1C4Dl0ksv1VdffUXI8SHO5OC0tG3b9pR/fJOvR8KXGv7Vxo/Fwldyc3M1ZcoUPf744+rVq5dat27tMW+3233UWctByMFpWbRo0SnXDh8+vBE7AU7u73//u55++mnt3btXknTxxRdr4sSJGjZsmI87Q0vx2GOPafz48R6/fP/TsO12u2Wz2XT8+HFftNeiEHIAGOPZZ5/VtGnTlJGRoauuukrSjz8kO2/ePM2cOfOE2+wDjcHf31/ffvutdu/e/Yt111xzzVnqqOUi5MArqqurVVtb6zHGqVicbTExMZoxY4buvvtuj/FFixYpKyuLa3ZwVvj5+cnpdPKr900A98nBGTt69KgyMjIUHh6u1q1bq23bth4bcLZ9++23J719wZVXXqlvv/3WBx2hpeJasKaBkIMzNmnSJK1du1bz589XUFCQXnnlFc2YMUNRUVH6+9//7uv20AJ17txZb7755gnjS5cu1UUXXeSDjtBSXXzxxQoLC/vFDY2Pr5DjjK1YsUJ///vfde2112rEiBHq16+fOnfurI4dO2rx4sVKTU31dYtoYWbMmKHbb79d+fn51jU5GzZs0Jo1a04afoDGMmPGDDkcDl+30eJxTQ7O2LnnnqvPPvtMHTp00AUXXKC33npLV1xxhUpKShQXF6eqqipft4gWqLCwUM8++6z27NkjSerWrZvGjx+vyy67zMedoaXgmpymgzM5OGMXXnihSkpK1KFDB3Xt2lVvvvmmrrjiCq1YsUKhoaG+bg8tVK9evbR48WJft4EWjOtxmg6uycFp++qrr1RfX68RI0Zo27ZtkqTMzEzNmzdPwcHBGjt2rCZOnOjjLtGS+Pn5yd/f/xe3gAD+mw5nBx+QNB18XIXT1nAPiIZTsbfffrvmzp2r6upqFRYWqnPnzvr973/v4y7Rkrz99ts/O1dQUKC5c+eqvr5e1dXVZ7ErAL5GyMFp+8/Pm9u0aaNt27bpwgsv9HFnwP9XXFyszMxMrVixQqmpqXrsscfUsWNHX7cF4Czi4yoARjl06JBGjhypuLg4HTt2TEVFRVq0aBEBB2iBCDk4bTab7YQL67jQDr5WWVmpyZMnq3Pnztq1a5fWrFmjFStW6NJLL/V1awB8hCvxcNrcbrfuueceBQUFSfrxJx0eeOCBE35h96233vJFe2iBsrOz9dRTTykyMlL/+Mc/dNNNN/m6JQBNANfk4LSNGDHilOr+9re/NXInwI/8/PwUEhKihIQE+fv7/2wdwRtoWTiTg9NGeEFTc/fdd/ORKYATcCYHAAAYiQuPAQCAkQg5AADASIQcAABgJEIOAJ+69tprNWbMmDN+flZWlnr06OG1fgCYg5ADAKfgnnvuUXJysq/bAHAaCDkAAMBIhBwAPldfX69JkyYpLCxMkZGRysrKsuYqKip033336bzzzpPdbtd1112nbdu2/exaDWdcnnzySUVERCg0NFSPPfaYjh07pokTJyosLEwXXHDBCfd7OnDggG677TaFhoYqLCxMN910k/bt2yfpx4/EFi1apLffftv6WZN169Y1wisBwJsIOQB8btGiRWrdurU2bdqk7OxsPfbYY8rLy5Mk3XrrrSorK9N7772nwsJC9ezZU9dff73Ky8t/dr21a9fq0KFDys/P17PPPqtHH31UN9xwg9q2batNmzbpgQce0P3336+DBw9Kkurq6pSYmKg2bdroww8/1IYNG3Tuuedq0KBBqq2t1YQJE3Tbbbdp0KBB+vbbb/Xtt9/qyiuvPCuvDYAzx80AAfjUtddeq+PHj+vDDz+0xq644gpdd911uuGGG5SUlKSysjLrt9IkqXPnzpo0aZJGjRqlrKws5eTkqKioSNKPZ3LWrVunr776Sn5+P/53XNeuXRUeHq78/HxJ0vHjx+VwOPTKK68oJSVFr7/+umbOnKndu3dbd06ura1VaGiocnJyNHDgQN1zzz2qqKhQTk7O2XlhAPxm/KwDAJ/7/e9/7/H4/PPPV1lZmbZt26aqqiq1a9fOY/7f//63vvzyy59d75JLLrECjiRFRER4/Bq5v7+/2rVrp7KyMknStm3b9MUXX6hNmzYe61RXV//ifgA0bYQcAD7XqlUrj8c2m0319fWqqqrS+eeff9LrX0JDQ09rvZ/bhyRVVVWpV69eWrx48QlrnXfeead4FACaGkIOgCarZ8+ecjqdCggIUKdOnRp1P0uXLlV4eLjsdvtJawIDA3X8+PFG6wGA93HhMYAmKyEhQfHx8UpOTtbq1au1b98+bdy4UVOnTtWWLVu8tp/U1FS1b99eN910kz788EOVlJRo3bp1euihh6yLkzt16qTt27eruLhY3333nerq6ry2fwCNg5ADoMmy2Wx699131b9/f40YMUIXX3yxUlJS9PXXXysiIsJr+znnnHOUn5+vDh06aMiQIerWrZvS0tJUXV1tndkZOXKkunTpot69e+u8887Thg0bvLZ/AI2Db1cBAAAjcSYHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACP9Px8r9rtk0z+0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot of categorical variables, there are some under-represented categories, \n",
    "## maybe it is possible to obtain a more stable model removing such classes\n",
    "to_remove = {}\n",
    "TH = 0.5\n",
    "for c in labeled.columns:\n",
    "    if c not in ['india','age']:\n",
    "        labeled[c] = labeled[c].astype('str')  \n",
    "        plt.figure()\n",
    "        labeled.groupby(c)[c].count().plot(kind='bar')\n",
    "        plt.title(c)\n",
    "        tmp = labeled.groupby(c)[c].count()\n",
    "        tmp = 100*tmp/tmp.max()\n",
    "        tmp = tmp[tmp<TH]\n",
    "        if len(tmp)>0:\n",
    "            for k in tmp.index:\n",
    "                if c not in to_remove.keys():\n",
    "                    to_remove[c]= []\n",
    "                to_remove[c].append(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "456c45af-c0e7-4c32-aafe-7e5f4473ed1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "labeled_small = labeled.copy()\n",
    "for c in to_remove.keys():\n",
    "    for k in to_remove[c]:\n",
    "        labeled_small.loc[labeled_small[c]==k,c] = 'other'\n",
    "for c in labeled_small.columns:\n",
    "    if c!='age':\n",
    "        labeled_small[c] =  labeled_small[c].fillna('None').astype('category')  \n",
    "        labeled[c] =  labeled[c].fillna('None').astype('category')\n",
    "labeled.dropna(inplace=True)\n",
    "labeled_small.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4b2cfcf0-7fb7-459d-8829-c35f5575fe1b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:3: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n",
      "  plt.figure()\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
      "/tmp/ipykernel_7203/1258876718.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  labeled_small.groupby(c)[c].count().plot(kind='bar')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iIlIcBjBERESkOAxgiIiISHEYwBAREZHi5CiAWbp0KTw9PaFWq6FWq+Hj44Pdu3dL97948QIDBgxA0aJFYWFhgbZt2+Lu3bsabcTHx6NZs2YwNzeHnZ0dRo0ahVevXmmcExoaiqpVq8LExASurq4ICAjI/TMkIiKiAidHAUypUqUwY8YMRERE4NSpU6hfvz5atWqFs2fPAgCGDRuGf/75B3/++SfCwsJw+/ZttGnTRnp8eno6mjVrhpcvX+Lo0aMIDAxEQEAAJk6cKJ1z9epVNGvWDPXq1UNkZCSGDh2K3r17Izg4WKanTEREREqnEkIIbRqwsbHB7Nmz8fXXX8PW1hbr16/H119/DQC4cOECKlasiPDwcNSqVQu7d+9G8+bNcfv2bdjb2wMAli1bhjFjxuD+/fswNjbGmDFjsHPnTvz7779SHx06dMCTJ08QFBT0weNKTEyElZUVnj59CrVarc1T/GiVHrszx4+5NqOZDkZC9OFy+nvL31mi/OVDP79znQOTnp6ODRs2IDk5GT4+PoiIiEBaWhoaNmwonVOhQgU4OTkhPDwcABAeHg4PDw8peAEAX19fJCYmSrM44eHhGm1knpPZxrukpqYiMTFR40ZEREQFU44DmJiYGFhYWMDExAR9+/bF33//DXd3dyQkJMDY2BjW1tYa59vb2yMhIQEAkJCQoBG8ZN6fed/7zklMTERKSso7xzV9+nRYWVlJN0dHx5w+NSIiIlKIHAcw5cuXR2RkJI4fP45+/frBz88P586d08XYcmTcuHF4+vSpdLtx40ZeD4mIiIh0pFBOH2BsbAxXV1cAQLVq1XDy5EksWLAA7du3x8uXL/HkyRONWZi7d+/CwcEBAODg4IATJ05otJe5SunNc95euXT37l2o1WqYmZm9c1wmJiYwMTHJ6dMhIiIiBdK6DkxGRgZSU1NRrVo1GBkZISQkRLrv4sWLiI+Ph4+PDwDAx8cHMTExuHfvnnTO3r17oVar4e7uLp3zZhuZ52S2QURERJSjGZhx48ahSZMmcHJywrNnz7B+/XqEhoYiODgYVlZW6NWrF4YPHw4bGxuo1WoMGjQIPj4+qFWrFgCgUaNGcHd3R9euXTFr1iwkJCRg/PjxGDBggDR70rdvXyxevBijR49Gz549sX//fmzatAk7d+Z8RQwREREVTDkKYO7du4du3brhzp07sLKygqenJ4KDg/Hll18CAObNmwcDAwO0bdsWqamp8PX1xS+//CI93tDQEDt27EC/fv3g4+ODwoULw8/PD/7+/tI5Li4u2LlzJ4YNG4YFCxagVKlSWLlyJXx9fWV6ykRERKR0WteBya9YB0Z7rANDSsQ6METKpvM6MERERER5hQEMERERKQ4DGCIiIlIcBjBERESkOAxgiIiISHEYwBAREZHiMIAhIiIixWEAQ0RERIrDAIaIiIgUhwEMERERKQ4DGCIiIlIcBjBERESkOAxgiIiISHEYwBAREZHiMIAhIiIixWEAQ0RERIrDAIaIiIgUhwEMERERKQ4DGCIiIlIcBjBERESkOAxgiIiISHEYwBAREZHiMIAhIiIixWEAQ0RERIrDAIaIiIgUhwEMERERKQ4DGCIiIlIcBjBERESkOAxgiIiISHEYwBAREZHiMIAhIiIixWEAQ0RERIrDAIaIiIgUhwEMERERKQ4DGCIiIlIcBjBERESkOAxgiIiISHEYwBAREZHi5CiAmT59Oj755BNYWlrCzs4OrVu3xsWLFzXOqVu3LlQqlcatb9++GufEx8ejWbNmMDc3h52dHUaNGoVXr15pnBMaGoqqVavCxMQErq6uCAgIyN0zJCIiogInRwFMWFgYBgwYgGPHjmHv3r1IS0tDo0aNkJycrHFenz59cOfOHek2a9Ys6b709HQ0a9YML1++xNGjRxEYGIiAgABMnDhROufq1ato1qwZ6tWrh8jISAwdOhS9e/dGcHCwlk+XiIiICoJCOTk5KChI4+eAgADY2dkhIiICtWvXlo6bm5vDwcEh2zb27NmDc+fOYd++fbC3t4e3tzd+/PFHjBkzBpMnT4axsTGWLVsGFxcXzJkzBwBQsWJFHD58GPPmzYOvr29OnyMREREVMFrlwDx9+hQAYGNjo3F83bp1KFasGCpXroxx48bh+fPn0n3h4eHw8PCAvb29dMzX1xeJiYk4e/asdE7Dhg012vT19UV4ePg7x5KamorExESNGxERERVMOZqBeVNGRgaGDh2Kzz77DJUrV5aOd+rUCc7OzihRogSio6MxZswYXLx4EVu2bAEAJCQkaAQvAKSfExIS3ntOYmIiUlJSYGZmlmU806dPx5QpU3L7dIiIiEhBch3ADBgwAP/++y8OHz6scfzbb7+V/t/DwwPFixdHgwYNEBcXh7Jly+Z+pP9h3LhxGD58uPRzYmIiHB0dddYfERER5Z1cXUIaOHAgduzYgQMHDqBUqVLvPbdmzZoAgNjYWACAg4MD7t69q3FO5s+ZeTPvOketVmc7+wIAJiYmUKvVGjciIiIqmHIUwAghMHDgQPz999/Yv38/XFxc/vMxkZGRAIDixYsDAHx8fBATE4N79+5J5+zduxdqtRru7u7SOSEhIRrt7N27Fz4+PjkZLhERERVQOQpgBgwYgN9//x3r16+HpaUlEhISkJCQgJSUFABAXFwcfvzxR0RERODatWvYvn07unXrhtq1a8PT0xMA0KhRI7i7u6Nr166IiopCcHAwxo8fjwEDBsDExAQA0LdvX1y5cgWjR4/GhQsX8Msvv2DTpk0YNmyYzE+fiIiIlChHAczSpUvx9OlT1K1bF8WLF5duGzduBAAYGxtj3759aNSoESpUqIARI0agbdu2+Oeff6Q2DA0NsWPHDhgaGsLHxwddunRBt27d4O/vL53j4uKCnTt3Yu/evfDy8sKcOXOwcuVKLqEmIiIiADlM4hVCvPd+R0dHhIWF/Wc7zs7O2LVr13vPqVu3Ls6cOZOT4REREdFHgnshERERkeIwgCEiIiLFYQBDREREisMAhoiIiBSHAQwREREpDgMYIiIiUhwGMERERKQ4DGCIiIhIcRjAEBERkeIwgCEiIiLFYQBDREREisMAhoiIiBSHAQwREREpDgMYIiIiUhwGMERERKQ4DGCIiIhIcQrl9QCIiEj/So/dmePHXJvRTAcjIcodzsAQERGR4jCAISIiIsVhAENERESKwwCGiIiIFIcBDBERESkOAxgiIiJSHAYwREREpDgMYIiIiEhxGMAQERGR4jCAISIiIsVhAENERESKwwCGiIiIFIcBDBERESkOAxgiIiJSHAYwREREpDgMYIiIiEhxGMAQERGR4jCAISIiIsVhAENERESKwwCGiIiIFCdHAcz06dPxySefwNLSEnZ2dmjdujUuXryocc6LFy8wYMAAFC1aFBYWFmjbti3u3r2rcU58fDyaNWsGc3Nz2NnZYdSoUXj16pXGOaGhoahatSpMTEzg6uqKgICA3D1DIiIiKnByFMCEhYVhwIABOHbsGPbu3Yu0tDQ0atQIycnJ0jnDhg3DP//8gz///BNhYWG4ffs22rRpI92fnp6OZs2a4eXLlzh69CgCAwMREBCAiRMnSudcvXoVzZo1Q7169RAZGYmhQ4eid+/eCA4OluEpExERkdIVysnJQUFBGj8HBATAzs4OERERqF27Np4+fYpVq1Zh/fr1qF+/PgDgt99+Q8WKFXHs2DHUqlULe/bswblz57Bv3z7Y29vD29sbP/74I8aMGYPJkyfD2NgYy5Ytg4uLC+bMmQMAqFixIg4fPox58+bB19dXpqdORERESqVVDszTp08BADY2NgCAiIgIpKWloWHDhtI5FSpUgJOTE8LDwwEA4eHh8PDwgL29vXSOr68vEhMTcfbsWemcN9vIPCezDSIiIvq45WgG5k0ZGRkYOnQoPvvsM1SuXBkAkJCQAGNjY1hbW2uca29vj4SEBOmcN4OXzPsz73vfOYmJiUhJSYGZmVmW8aSmpiI1NVX6OTExMbdPjYiIiPK5XM/ADBgwAP/++y82bNgg53hybfr06bCyspJujo6OeT0kIiIi0pFcBTADBw7Ejh07cODAAZQqVUo67uDggJcvX+LJkyca59+9excODg7SOW+vSsr8+b/OUavV2c6+AMC4cePw9OlT6Xbjxo3cPDUiIiJSgBwFMEIIDBw4EH///Tf2798PFxcXjfurVasGIyMjhISESMcuXryI+Ph4+Pj4AAB8fHwQExODe/fuSefs3bsXarUa7u7u0jlvtpF5TmYb2TExMYFarda4ERERUcGUoxyYAQMGYP369di2bRssLS2lnBUrKyuYmZnBysoKvXr1wvDhw2FjYwO1Wo1BgwbBx8cHtWrVAgA0atQI7u7u6Nq1K2bNmoWEhASMHz8eAwYMgImJCQCgb9++WLx4MUaPHo2ePXti//792LRpE3bu3Cnz0yciIiIlytEMzNKlS/H06VPUrVsXxYsXl24bN26Uzpk3bx6aN2+Otm3bonbt2nBwcMCWLVuk+w0NDbFjxw4YGhrCx8cHXbp0Qbdu3eDv7y+d4+Ligp07d2Lv3r3w8vLCnDlzsHLlSi6hJiIiIgA5nIERQvznOaampliyZAmWLFnyznOcnZ2xa9eu97ZTt25dnDlzJifDIyIioo8E90IiIiIixWEAQ0RERIrDAIaIiIgUhwEMERERKQ4DGCIiIlIcBjBERESkOAxgiIiISHEYwBAREZHiMIAhIiIixWEAQ0RERIrDAIaIiIgUhwEMERERKQ4DGCIiIlKcHO1GTUSkjdJjd+b4MddmNNPBSIhI6TgDQ0RERIrDAIaIiIgUhwEMERERKQ4DGCIiIlIcBjBERESkOAxgiIiISHEYwBAREZHiMIAhIiIixWEAQ0RERIrDAIaIiIgUhwEMERERKQ4DGCIiIlIcBjBERESkOAxgiIiISHEYwBAREZHiMIAhIiIixWEAQ0RERIrDAIaIiIgUhwEMERERKQ4DGCIiIlIcBjBERESkOAxgiIiISHEYwBAREZHiMIAhIiIixclxAHPw4EG0aNECJUqUgEqlwtatWzXu7969O1QqlcatcePGGuc8evQInTt3hlqthrW1NXr16oWkpCSNc6Kjo/HFF1/A1NQUjo6OmDVrVs6fHRERERVIOQ5gkpOT4eXlhSVLlrzznMaNG+POnTvS7Y8//tC4v3Pnzjh79iz27t2LHTt24ODBg/j222+l+xMTE9GoUSM4OzsjIiICs2fPxuTJk7FixYqcDpeIiIgKoEI5fUCTJk3QpEmT955jYmICBweHbO87f/48goKCcPLkSVSvXh0AsGjRIjRt2hQ///wzSpQogXXr1uHly5dYvXo1jI2NUalSJURGRmLu3LkagQ4RERF9nHSSAxMaGgo7OzuUL18e/fr1w8OHD6X7wsPDYW1tLQUvANCwYUMYGBjg+PHj0jm1a9eGsbGxdI6vry8uXryIx48fZ9tnamoqEhMTNW5ERERUMMkewDRu3Bhr1qxBSEgIZs6cibCwMDRp0gTp6ekAgISEBNjZ2Wk8plChQrCxsUFCQoJ0jr29vcY5mT9nnvO26dOnw8rKSro5OjrK/dSIiIgon8jxJaT/0qFDB+n/PTw84OnpibJlyyI0NBQNGjSQuzvJuHHjMHz4cOnnxMREBjFEREQFlM6XUZcpUwbFihVDbGwsAMDBwQH37t3TOOfVq1d49OiRlDfj4OCAu3fvapyT+fO7cmtMTEygVqs1bkRERFQwyT4D87abN2/i4cOHKF68OADAx8cHT548QUREBKpVqwYA2L9/PzIyMlCzZk3pnB9++AFpaWkwMjICAOzduxfly5dHkSJFdD1kIiJSiNJjd+bo/GszmuloJKRvOZ6BSUpKQmRkJCIjIwEAV69eRWRkJOLj45GUlIRRo0bh2LFjuHbtGkJCQtCqVSu4urrC19cXAFCxYkU0btwYffr0wYkTJ3DkyBEMHDgQHTp0QIkSJQAAnTp1grGxMXr16oWzZ89i48aNWLBggcYlIiIiIvp45TiAOXXqFKpUqYIqVaoAAIYPH44qVapg4sSJMDQ0RHR0NFq2bIly5cqhV69eqFatGg4dOgQTExOpjXXr1qFChQpo0KABmjZtis8//1yjxouVlRX27NmDq1evolq1ahgxYgQmTpzIJdREREQEIBeXkOrWrQshxDvvDw4O/s82bGxssH79+vee4+npiUOHDuV0eERERPQR4F5IREREpDgMYIiIiEhxGMAQERGR4jCAISIiIsVhAENERESKwwCGiIiIFIcBDBERESkOAxgiIiJSHAYwREREpDgMYIiIiEhxGMAQERGR4jCAISIiIsVhAENERESKwwCGiIiIFIcBDBERESkOAxgiIiJSHAYwREREpDgMYIiIiEhxGMAQERGR4jCAISIiIsVhAENERESKwwCGiIiIFIcBDBERESkOAxgiIiJSHAYwREREpDgMYIiIiEhxGMAQERGR4jCAISIiIsVhAENERESKwwCGiIiIFIcBDBERESkOAxgiIiJSHAYwREREpDgMYIiIiEhxGMAQERGR4jCAISIiIsVhAENERESKk+MA5uDBg2jRogVKlCgBlUqFrVu3atwvhMDEiRNRvHhxmJmZoWHDhrh8+bLGOY8ePULnzp2hVqthbW2NXr16ISkpSeOc6OhofPHFFzA1NYWjoyNmzZqV82dHREREBVKOA5jk5GR4eXlhyZIl2d4/a9YsLFy4EMuWLcPx48dRuHBh+Pr64sWLF9I5nTt3xtmzZ7F3717s2LEDBw8exLfffivdn5iYiEaNGsHZ2RkRERGYPXs2Jk+ejBUrVuTiKRIREVFBUyinD2jSpAmaNGmS7X1CCMyfPx/jx49Hq1atAABr1qyBvb09tm7dig4dOuD8+fMICgrCyZMnUb16dQDAokWL0LRpU/z8888oUaIE1q1bh5cvX2L16tUwNjZGpUqVEBkZiblz52oEOkRERPRxynEA8z5Xr15FQkICGjZsKB2zsrJCzZo1ER4ejg4dOiA8PBzW1tZS8AIADRs2hIGBAY4fP46vvvoK4eHhqF27NoyNjaVzfH19MXPmTDx+/BhFihSRc9hERER5qvTYnTk6/9qMZjoaiXLIGsAkJCQAAOzt7TWO29vbS/clJCTAzs5OcxCFCsHGxkbjHBcXlyxtZN6XXQCTmpqK1NRU6efExEQtnw3RxyWnb6AA30SJKO8UmFVI06dPh5WVlXRzdHTM6yERERGRjsgawDg4OAAA7t69q3H87t270n0ODg64d++exv2vXr3Co0ePNM7Jro03+3jbuHHj8PTpU+l248YN7Z8QERER5UuyBjAuLi5wcHBASEiIdCwxMRHHjx+Hj48PAMDHxwdPnjxBRESEdM7+/fuRkZGBmjVrSuccPHgQaWlp0jl79+5F+fLl35n/YmJiArVarXEjIiKiginHAUxSUhIiIyMRGRkJ4HXibmRkJOLj46FSqTB06FBMnToV27dvR0xMDLp164YSJUqgdevWAICKFSuicePG6NOnD06cOIEjR45g4MCB6NChA0qUKAEA6NSpE4yNjdGrVy+cPXsWGzduxIIFCzB8+HDZnjgREREpV46TeE+dOoV69epJP2cGFX5+fggICMDo0aORnJyMb7/9Fk+ePMHnn3+OoKAgmJqaSo9Zt24dBg4ciAYNGsDAwABt27bFwoULpfutrKywZ88eDBgwANWqVUOxYsUwceJELqEmIiIiALkIYOrWrQshxDvvV6lU8Pf3h7+//zvPsbGxwfr169/bj6enJw4dOpTT4REREdFHoMCsQiIiIqKPBwMYIiIiUhxZC9kREREVNKySmz9xBoaIiIgUhwEMERERKQ4DGCIiIlIcBjBERESkOAxgiIiISHEYwBAREZHiMIAhIiIixWEAQ0RERIrDAIaIiIgUhwEMERERKQ4DGCIiIlIc7oVEREREWsvpnlGAdvtGcQaGiIiIFIcBDBERESkOAxgiIiJSHAYwREREpDgMYIiIiEhxGMAQERGR4jCAISIiIsVhAENERESKwwCGiIiIFIcBDBERESkOAxgiIiJSHAYwREREpDgMYIiIiEhxGMAQERGR4jCAISIiIsUplNcDyAulx+7M0fnXZjTT0UiIiIgoNzgDQ0RERIrDAIaIiIgUhwEMERERKQ4DGCIiIlIcBjBERESkOAxgiIiISHEYwBAREZHiyB7ATJ48GSqVSuNWoUIF6f4XL15gwIABKFq0KCwsLNC2bVvcvXtXo434+Hg0a9YM5ubmsLOzw6hRo/Dq1Su5h0pEREQKpZNCdpUqVcK+ffv+10mh/3UzbNgw7Ny5E3/++SesrKwwcOBAtGnTBkeOHAEApKeno1mzZnBwcMDRo0dx584ddOvWDUZGRpg2bZouhktEREQKo5MAplChQnBwcMhy/OnTp1i1ahXWr1+P+vXrAwB+++03VKxYEceOHUOtWrWwZ88enDt3Dvv27YO9vT28vb3x448/YsyYMZg8eTKMjY11MWQiIiJSEJ3kwFy+fBklSpRAmTJl0LlzZ8THxwMAIiIikJaWhoYNG0rnVqhQAU5OTggPDwcAhIeHw8PDA/b29tI5vr6+SExMxNmzZ9/ZZ2pqKhITEzVuREREVDDJHsDUrFkTAQEBCAoKwtKlS3H16lV88cUXePbsGRISEmBsbAxra2uNx9jb2yMhIQEAkJCQoBG8ZN6fed+7TJ8+HVZWVtLN0dFR3idGRERE+Ybsl5CaNGki/b+npydq1qwJZ2dnbNq0CWZmZnJ3Jxk3bhyGDx8u/ZyYmMgghoiIqIDS+TJqa2trlCtXDrGxsXBwcMDLly/x5MkTjXPu3r0r5cw4ODhkWZWU+XN2eTWZTExMoFarNW5ERERUMOk8gElKSkJcXByKFy+OatWqwcjICCEhIdL9Fy9eRHx8PHx8fAAAPj4+iImJwb1796Rz9u7dC7VaDXd3d10Pl4iIiBRA9ktII0eORIsWLeDs7Izbt29j0qRJMDQ0RMeOHWFlZYVevXph+PDhsLGxgVqtxqBBg+Dj44NatWoBABo1agR3d3d07doVs2bNQkJCAsaPH48BAwbAxMRE7uESERGRAskewNy8eRMdO3bEw4cPYWtri88//xzHjh2Dra0tAGDevHkwMDBA27ZtkZqaCl9fX/zyyy/S4w0NDbFjxw7069cPPj4+KFy4MPz8/ODv7y/3UImIiEihZA9gNmzY8N77TU1NsWTJEixZsuSd5zg7O2PXrl1yD42IiIgKCO6FRERERIrDAIaIiIgUhwEMERERKQ4DGCIiIlIcnWzmSERERPlH6bE7c/yYazOa6WAk8uEMDBERESkOAxgiIiJSHAYwREREpDgMYIiIiEhxGMAQERGR4jCAISIiIsVhAENERESKwwCGiIiIFIcBDBERESkOAxgiIiJSHAYwREREpDgMYIiIiEhxGMAQERGR4jCAISIiIsVhAENERESKwwCGiIiIFIcBDBERESlOobweABF9mNJjd+bo/GszmuloJEREeY8zMERERKQ4DGCIiIhIcRjAEBERkeIwB4aIKB9izhPR+3EGhoiIiBSHMzAKxm9oRET0seIMDBERESkOAxgiIiJSHAYwREREpDgMYIiIiEhxGMAQERGR4nAVEhFRDnEFIFHeYwBDBR4/bIiICh5eQiIiIiLFydcBzJIlS1C6dGmYmpqiZs2aOHHiRF4PiYiIiPKBfHsJaePGjRg+fDiWLVuGmjVrYv78+fD19cXFixdhZ2eX18Mj0sDLVERE+pVvA5i5c+eiT58+6NGjBwBg2bJl2LlzJ1avXo2xY8fm8ej+Gz/QiOhjl9P3QYDvhfTh8mUA8/LlS0RERGDcuHHSMQMDAzRs2BDh4eF5ODKSG9/giIgoN/JlAPPgwQOkp6fD3t5e47i9vT0uXLiQ7WNSU1ORmpoq/fz06VMAQGJiYpZzM1Kf52g82bXxXwpCHzltv6D0kR9fC330kR9fC330kR9fC330kR9fC330kR9fC330kR9fi3f1kXlMCPH+B4t86NatWwKAOHr0qMbxUaNGiRo1amT7mEmTJgkAvPHGG2+88cZbAbjduHHjvbFCvpyBKVasGAwNDXH37l2N43fv3oWDg0O2jxk3bhyGDx8u/ZyRkYFHjx6haNGiUKlU/9lnYmIiHB0dcePGDajVau2eAPvI1+2zj/zVR0F4Duwj/7TPPvJXH7lpXwiBZ8+eoUSJEu89L18GMMbGxqhWrRpCQkLQunVrAK8DkpCQEAwcODDbx5iYmMDExETjmLW1dY77VqvVOvtFYR/5q332kb/6KAjPgX3kn/bZR/7qI6ftW1lZ/ec5+TKAAYDhw4fDz88P1atXR40aNTB//nwkJydLq5KIiIjo45VvA5j27dvj/v37mDhxIhISEuDt7Y2goKAsib1ERET08cm3AQwADBw48J2XjORmYmKCSZMmZbkMxT7030dBeA7sI/+0zz7yVx8F4Tmwj/zRvkqI/1qnRERERJS/5Ou9kIiIiIiywwCGiIiIFIcBDBERESkOAxgiIiJSHAYwREREpDj5ehl1QXH58mUcOHAA9+7dQ0ZGhsZ9EydO1Lr9nj17YsGCBbC0tNQ4npycjEGDBmH16tVa91HQJCUlZXkt5KpCqevXW1996MPLly+zfQ5OTk6ytP/kyRNs3rwZcXFxGDVqFGxsbHD69GnY29ujZMmSsvRBeS8tLQ1mZmaIjIxE5cqVddZPcnIywsLCEB8fj5cvX2rcN3jwYNn6iY2NRVxcHGrXrg0zMzMIIT5oS5yPDZdRA3j16hVCQ0MRFxeHTp06wdLSErdv34ZarYaFhYVWbf/666/o168fihUrBgcHB41fQpVKhdOnT2s7fBgaGuLOnTuws7PTOP7gwQM4ODjg1atXWveR6eXLl7h69SrKli2LQoWUFf9evXoVAwcORGhoKF68eCEdz3xzSE9P17oPfbzeuu7j7t27GDlyJEJCQnDv3r0sO8LK8e90+fJl9OzZE0ePHtU4LudrER0djYYNG8LKygrXrl3DxYsXUaZMGYwfPx7x8fFYs2aN1n0Aun3/oA9XpkwZ/P333/Dy8tJJ+2fOnEHTpk3x/PlzJCcnw8bGBg8ePIC5uTns7Oxw5coVrft4+PAh2rdvj/3790OlUuHy5csoU6YMevbsiSJFimDOnDkyPBPdKFKkyAcHWY8ePZKlT2V9AunA9evX0bhxY8THxyM1NRVffvklLC0tMXPmTKSmpmLZsmVatT916lT89NNPGDNmjEwj/p/ExEQIIaSNr0xNTaX70tPTsWvXrixBTW49f/4cgwYNQmBgIADg0qVLKFOmDAYNGoSSJUti7NixuWq3TZs2H3zuli1bctVHpi5dukAIgdWrV8Pe3l4n32h0+Xrrq4/u3bsjPj4eEyZMQPHixXXy79S9e3cUKlQIO3bs0Fkfw4cPR/fu3TFr1iyN2cmmTZuiU6dOsvSh6/cP4PW3/hkzZkgB5duzVXJ8cOqqD31+qP3www/4/vvvsXbtWtjY2GjVVnaGDRuGFi1aYNmyZbCyssKxY8dgZGSELl26YMiQIbL1UahQIcTHx6NixYrS8fbt22P48OFaBTC6fi3mz58v/f/Dhw8xdepU+Pr6wsfHBwAQHh6O4OBgTJgwIcdtv8tHH8AMGTIE1atXR1RUFIoWLSod/+qrr9CnTx+t23/8+DHatWundTvZsba2hkqlgkqlQrly5bLcr1KpMGXKFFn6GjduHKKiohAaGorGjRtLxxs2bIjJkyfnOoB5c8MuIQT+/vtvWFlZoXr16gCAiIgIPHnyJEeBzrtERUUhIiIC5cuX17qtd9Hl662vPg4fPoxDhw7B29tbZ31ERkYiIiICFSpU0FkfJ0+exPLly7McL1myJBISEmTpQ9fvHwDQu3dvhIWFoWvXrjoL9nTVx5sfarq2ePFixMbGokSJEnB2dkbhwoU17td2ZjIyMhLLly+HgYEBDA0NkZqaijJlymDWrFnw8/OT5T1qz549CA4ORqlSpTSOu7m54fr161q1resAw8/PT/r/tm3bwt/fX6OS/uDBg7F48WLs27cPw4YNy92TeMtHH8AcOnQIR48ehbGxscbx0qVL49atW1q3365dO+zZswd9+/bVuq23HThwAEII1K9fH3/99ZfGtw5jY2M4Ozv/53bkH2rr1q3YuHEjatWqpfHmVqlSJcTFxeW63d9++036/zFjxuCbb77BsmXLYGhoCOD1TFL//v1lyU/55JNPcOPGDZ0GMLp8vfXVh6OjY5bLRnJzd3fHgwcPdNqHiYkJEhMTsxy/dOkSbG1tZelD1+8fALB7927s3LkTn332mSzt6bOPNz/UdK1169Y6bd/IyAgGBq/XvdjZ2UmzJFZWVrhx44YsfSQnJ8Pc3DzL8UePHmldil+fAUZwcDBmzpyZ5Xjjxo1z/WU3Ox99AJORkZHt9fabN29mSYrNDVdXV0yYMAHHjh2Dh4cHjIyMNO7XJvGrTp06AF7ndjg6Okp/XLpw//79bC9HJScny/ZtbfXq1Th8+LAUvACv83uGDx+OTz/9FLNnz9aq/ZUrV6Jv3764desWKleunOW18PT01Kp9QLevt776mD9/PsaOHYvly5ejdOnSWrX1LjNnzsTo0aMxbdq0bJ+DHAFry5Yt4e/vj02bNgF4PSMZHx+PMWPGoG3btlq3D+j+/QN4PfWvi0si+u7jTS9evMiSBKvtaz5p0iStHv9fqlSpgpMnT8LNzQ116tTBxIkT8eDBA6xdu1a2xOEvvvgCa9aswY8//gjg9e9sRkYGZs2ahXr16snSB6D7AKNo0aLYtm0bRowYoXF827ZtGjOVWhMfuW+++Ub06dNHCCGEhYWFuHLlinj27JmoX7++6N69u9btly5d+p03FxcXrdvP9PjxYxEcHCzWrl0rAgMDNW5y+OKLL8TChQuFEP/7dxJCiIEDBwpfX19Z+rC2thZbt27Ncnzr1q3C2tpa6/bDw8OFi4uLUKlU0s3AwED6rxz08Xrrug9ra2thbGwsDAwMhIWFhShSpIjGTQ5v/vu/eZPztXjy5Ilo2LChsLa2FoaGhsLR0VEYGRmJ2rVri6SkJFn60PX7hxBCrF27Vnz99dciOTlZlvbyqo+kpCQxYMAAYWtrm+V1l+s1f/z4sfj111/F2LFjxcOHD4UQQkRERIibN29q3fbJkyfF/v37hRBC3L17V/j6+gpLS0tRtWpVERkZqXX7QggRExMj7OzsROPGjYWxsbH4+uuvRcWKFYW9vb2IjY2VpQ8hhHBychI///xzluM///yzcHJy0rr93377TRgaGormzZuLH3/8Ufz444+iefPmolChQuK3337Tuv1MH/0qpJs3b8LX1xdCCFy+fBnVq1fH5cuXUaxYMRw8eFC2JFhd+ueff9C5c2ckJSVBrVZnWZUiR8b34cOH0aRJE3Tp0gUBAQH47rvvcO7cORw9ehRhYWGoVq2a1n0MHz4ca9aswffff48aNWoAAI4fP44ZM2aga9eumDt3rlbtu7u7o2LFihg9enS2SbzOzs5atV9QZCZqv4sclwXCwsLee3/m7KIcjhw5gqioKCQlJaFq1apo2LChbG3r4/2jSpUqiIuLgxACpUuXzjJbJcfKNn30MWDAABw4cAA//vgjunbtiiVLluDWrVtYvnw5ZsyYgc6dO2vVvr5Wnena06dPsXjxYo3f2QEDBqB48eKy9REQEIDevXujSZMmqFmzJoDX77VBQUH49ddf0b17d637OH78OBYuXIjz588DACpWrIjBgwdL/cnhow9ggNfLIDds2IDo6GjpF6Zz584wMzPL66F9kHLlyqFp06aYNm1attdP5RIXF4cZM2Zo/GGNGTMGHh4esrSfkZGBn3/+GQsWLMCdO3cAAMWLF8eQIUMwYsQIjUtLuVG4cGFERUXB1dVVjuHmC5l/vqwRoUlfdUGA1+8fGzdu1Pi7kPP9478S8eW4dKKPPpycnLBmzRrUrVsXarUap0+fhqurK9auXYs//vgDu3bt0qr9hg0bomrVqtKqs6ioKJQpUwZHjx5Fp06dcO3aNa2fQ0GijwBD1xjA6MDw4cPx448/onDhwhg+fPh7z9V2VgF4/cEcExODMmXKaN1WfpGZfClXcTkAaNGiBbp37y5b/kMmfb/eALBmzRrMnj0bly9fBvA6iB01ahS6du0qS/vp6enYunWr9OZWqVIltGzZUusg8k1PnjzBqlWrNPro2bOnxso0bei6LgjljIWFBc6dOwcnJyeUKlUKW7ZsQY0aNXD16lV4eHggKSlJq/atrKxw+vRplC1bViOAuX79OsqXL69R+yk3Hj58iIkTJ76zgKRctU2ePHmCEydOZNtHt27dZOlDHzIyMhAbG5vt86hdu7YsfXz0SbyA/FVNz5w5g7S0NOn/30Wub82+vr44deqUTgOY06dPw8jISJpt2bZtG3777Te4u7tj8uTJWVZhaEvOwCVTixYtMGzYMMTExGSbONqyZctctavv13vu3LmYMGECBg4cKK0aOXz4MPr27YsHDx5ovYIgNjYWTZs2xa1bt6QVW9OnT4ejoyN27tyJsmXLav0cTp06BV9fX5iZmUmXC+fOnYuffvoJe/bsQdWqVbXuQ9d1QYDXl9uKFSuGZs2aAQBGjx6NFStWwN3dHX/88YeslyUjIiI0gr0qVarI1rY+lClTBlevXoWTkxMqVKiATZs2oUaNGvjnn39gbW2tdfu6XnXWtWtXxMbGolevXjqrI/Vf6QByBjBxcXH47bffcOXKFcyfPx92dnbYvXs3nJycUKlSJa3aPnbsGDp16oTr169nWdEoV6FKAEziXbFihTA0NBT29vbCy8tLeHt7S7cqVark9fA+yMqVK4WTk5OYNGmS2Lx5s9i2bZvGTQ7Vq1cXmzdvFkIIERcXJ0xMTETHjh2Fq6urGDJkiCx9JCQkiC5duojixYsLQ0ND2ZP83kzeffsmVxKhPpQuXTrb5OyAgABRunRprdtv0qSJaNy4sZQEKYQQDx48EI0bNxZNmzbVun0hhPj8889F9+7dRVpamnQsLS1N+Pn5iS+++EKWPry9vYWFhYUwMTER5cqVE1WqVNG4yaFcuXIiJCRECCHE0aNHhZmZmVi+fLlo0aKF+Oqrr2Tp4+7du6JevXpCpVJJidQqlUrUr19f3Lt3L9ftFilSRNy/f18I8Tpx++1kbbkTt+fOnSsWLFgghBBi7969wtTUVJiYmAgDAwMxf/58rdvv1auXaN26tXj58qWUUH39+nVRpUoVWd6jLCwsZEvWfRc3NzcxZMgQnSZTCyFEaGioMDMzEw0bNhTGxsYiLi5OCCHE9OnTRdu2bbVu38vLS7Rr106cO3dOPH78WDx58kTjJpeP/hKSs7Mz+vfvr9PKqbr2vuXTckW7b07Pzpw5E/v370dwcDCOHDmCDh06yFIHoUmTJoiPj8fAgQOzLabVqlUrrfsoCExNTfHvv/9myeW5fPkyPDw8tJ4qL1y4sLRE+01RUVH47LPPtJ7qBwAzMzOcOXMmSyG7c+fOoXr16nj+/LnWfegjr8Pc3BwXLlyAk5MTxowZgzt37mDNmjU4e/Ys6tati/v372vdR/v27XHlyhWsWbNGqs567tw5+Pn5wdXVFX/88Ueu2g0MDESHDh1gYmKil8Ttt12/fh0RERFwdXWVpYTB06dP8fXXX+PUqVN49uwZSpQogYSEBPj4+GDXrl1ZCtvl1CeffIJFixahVq1aWo/1XfSVDuDj44N27dph+PDhGpfbTpw4gTZt2uDmzZtata+vfMOP/hKSPiqnnjp1Cps2bcp2AzBty+MDyHLZSxeEEFI/+/btQ/PmzQG8LnomV0EyfVSAzfTixQuNrRfkpOvX29XVFZs2bcL333+vcXzjxo1wc3PTun0TExM8e/Ysy/GkpCTZLhWq1WrEx8dnCWBu3LghW/0UXdcFAV7ndTx8+BBOTk7Ys2ePlANlamqKlJQUWfoICgrCvn37NErLu7u7Y8mSJWjUqFGu230zKNFnwblMzs7Osl5is7Kywt69e3H48GGNBRlyrTr75ZdfMHbsWEycODHbOlJyXPbWRzoAAMTExGD9+vVZjtvZ2cnyfl6zZk3ExsYygNE1XVc13bBhA7p16wZfX1/s2bMHjRo1wqVLl3D37l189dVXsvenqw/m6tWrY+rUqWjYsCHCwsKwdOlSAK+L6Nnb28vSh64rwKanp2PatGlYtmwZ7t69K+3nNGHCBJQuXRq9evXSug99vN5TpkxB+/btcfDgQSkH5siRIwgJCZGKtmmjefPm+Pbbb7Fq1SqN5ex9+/bNdZ7Q29q3b49evXrh559/xqeffgrg9XMYNWoUOnbsKEsf+vDll1+id+/eqFKlCi5duoSmTZsCAM6ePStbEcCMjIwsH5bA68qwuvjyoosic8DrAouurq5ZCi1mbgEg17YDn3/+OT7//HNZ2nqTtbU1EhMTUb9+fY3jQsYNSJs1a4ZRo0bh3Llzsubpvc3a2hp37tyBi4uLxvEzZ87Iskv7oEGDMGLECCQkJGT7POSYcQPAHJhp06aJYsWKCT8/P/Hzzz+LBQsWaNy05eHhIRYvXiyEeH0NNS4uTmRkZIg+ffqIiRMnat2+EEK8evVK+Pv7ixIlSghDQ0Ppeub48ePFypUrZekjKipKVK5cWajVajF58mTp+MCBA0XHjh1l6SM4OFg0atRIXL16VZb23jZlyhRRpkwZ8fvvvwszMzPp32nDhg2iVq1asvShj9dbCCFOnTolOnfuLKpWrSqqVq0qOnfuLE6fPi1L248fPxYtW7YUKpVKGBsbS0XtWrduLdv169TUVDF48GCpbQMDA2FiYiKGDh0qXrx4IUsf2RXK00XhtAEDBoiWLVuK3bt3S8cnTpwopk6dKksfLVu2FLVr1xa3bt2Sjt28eVPUqVNHtG7dWpY+9FFkrkSJEuLUqVNZjkdERIiSJUvK0se+ffvEuHHjRK9evUSPHj00btr65JNPhI+Pj9iwYYM4cOCACA0N1bjJQV95eiNGjBCff/65uHPnjrC0tBSXL18Whw8fFmXKlNF4f8+td41f7ufx0Qcwuq5qam5uLn0g29jYiOjoaCGEEOfOnRMODg5aty+Efj6Y3yUlJUW8fPlSlrZ0XQG2bNmyYt++fUKI/wUXQghx/vx5WSr9CqGf11tfLl26JLZv3y62b98uLl++rJM+kpOTRXR0tIiOjpY9cXHr1q0atz///FN8//33omTJkrIF9voQHx8vvL29hZGRkShTpowoU6aMMDIyElWqVBE3btyQpY/+/fuLihUris2bNwszMzOxevVq8eOPP4pSpUqJ33//XZY+TExMsv09unz5sjAxMdG6/cmTJwsDAwNRo0YN0apVK9G6dWuNm7bMzMzEhQsXtG4nP0hNTRW9e/cWhQoVEiqVShgZGQkDAwPRpUsX8erVK63bv3bt2ntvcvnoLyFdvXpVp+0XKVJEyicoWbIk/v33X3h4eODJkyeyJCoCr2uCrFixAg0aNNC4FObl5YULFy7I0se7yHm5Stc71966dSvba7IZGRnSMmht6er1TkxMlKbxs1sq+ia5lqC7ubnJklPzPubm5rIVQnxbdknfX3/9NSpVqoSNGzfm+pJhdHQ0KleuDAMDA0RHR7/3XDmmyh0dHXH69Gns27dP+nuuWLGirBWF//nnH6nIXI8ePfDFF1/A1dUVzs7OWLdundZVcoHXuVtBQUEaGwgCrzeSlCPnY9myZQgICJCtFtLbqlevrvPNYPXF2NgYv/76KyZMmIB///0XSUlJqFKlimx/7/qqav7RBzBvEjqoalq7dm3s3bsXHh4eaNeuHYYMGYL9+/dj7969aNCggSx96OqD2cbGBpcuXUKxYsVQpEiR9/67yFHESdeJhO7u7jh06FCWP67NmzfLVlNDV693kSJFcOfOHdjZ2cHa2jrb10JocS1eH8X42rRpg4CAAKjVarRp0+a958qR7PwutWrVwrfffpvrx3t7eyMhIQF2dnbw9vaGSqXSyN3K/FnOehcqlQpffvklvvzyS1nae9ujR4+kIEKtVkt/z59//jn69esnSx/Dhw/HwIEDcf/+fSmPJCQkBHPmzJHly8vLly+lfCpdGDRoEIYMGYJRo0bpNK8jOTkZYWFh2S4CkGMz2Dc5OTnByclJ1jYB/Oe2DXLVs2EAA91WNV28eLG0rPWHH36AkZERjh49irZt22L8+PFatw/o7oN53rx50ooQXc+OAEB8fPx779f2D23ixInw8/PDrVu3kJGRgS1btuDixYtYs2YNduzYoVXbmXT1eu/fv18qxnbgwAFZxvqmDy3Gpw0rKysp8Hq7SJe+pKSkYOHChVolKl69elUqjKarGdyFCxfi22+/hampKRYuXPjec+X4UNN1kTkA6NmzJ1JTU/HTTz9Juy2XLl0aS5culeUDrXfv3li/fj0mTJigdVvZad++PYDXzyOT3MHqmTNn0LRpUzx//hzJycmwsbHBgwcPYG5uDjs7O9kCGCEENm/e/M4Crtp+gRgyZIjGz2lpaXj+/DmMjY1hbm4uWwDz0deBeVdV0yVLlmDq1KlaVzXVh23btsHPzw/jxo2Dv78/pkyZovHBrKtvbXIzMDB474eaHG8Qhw4dgr+/v8a+NRMnTtRqOSrlT2/PGgoh8OzZM5ibm+P333+XbUWHLri4uODUqVMoWrRolpUib1KpVLhy5YrW/c2bNw+GhoYYPHgw9u3bhxYtWkAIgbS0NMydOzfLB5K27t+/DzMzM1hYWGjVzpuzhRkZGQgMDISnpyc8PT2zzJBou43H9evX33u/HJdN6tati3LlymHZsmWwsrJCVFQUjIyM0KVLFwwZMuQ/Zy4/1JAhQ7B8+XLUq1cv26rCv/32myz9vOny5cvo168fRo0aBV9fX1na/OgDGBcXF0yZMiVLRBgYGIjJkyfL8g0rPT0df//9t1QG3N3dHa1atUKhQvJNgOn6g1nXsyPA60Jpb0pLS8OZM2ekEvNy/fHqmq5f76CgIFhYWEhLRZcsWYJff/1Vqg1SpEgRrdrv2bMnFixYkKUeS3JyMgYNGoTVq1dr1T4A1K9fH1u2bMny7T4xMRGtW7fG/v37te4jICBA443ZwMAAtra2qFmzptb/Rm87d+5ctlP++TlIeh+5i8zpSr169T74XF3MXMrN2toax48fR/ny5WFtbY3w8HBUrFgRx48fh5+fn2w5jTY2Nvj999+lZf/6curUKXTp0kW25/HRBzC6rmp69uxZtGzZEgkJCVLyV+beHP/884/Od8qViz5mR95l586dmD17NkJDQ3XWh1z08Xp7eHhg5syZaNq0KWJiYlC9enWMGDECBw4cQIUKFbT+9mRoaCjl27zpwYMHcHBwwKtXr7RqH3j9+5SZR/Kme/fuoWTJkrIkVcfHx8PR0THb39v4+HhZgu4rV67gq6++QkxMjEYuTGafuvi7SE9PR0xMDJydnWUJxNLS0tC4cWMsW7ZM9qTtqlWrIiQkBEWKFEGVKlXe+x5y+vRpWfuWmz7yOmxtbXH06FG4ubmhXLlyWLRoEXx9fXHhwgVUq1YNycnJWvcBvP7ivnv37iyFJHUtMjIStWvX/s+FCB/qo8+B0XVV0969e6NSpUo4deqU9Gbz+PFjdO/eHd9++y2OHj2qdR9vSkpKynI9U45VKW/nRbw9O6JL5cuXx8mTJ3P12P9KPn6THInI+ni9r169Cnd3dwDAX3/9hRYtWmDatGk4ffq0Vt+oEhMTIV6XVsCzZ880Vpilp6dj165dWQKOnHpz1c65c+eQkJCg0UdQUJAshbSA12/S2QViDx8+hIuLiyzBxZAhQ+Di4oKQkBC4uLjgxIkTePjwIUaMGIGff/5Z6/YBYOjQofDw8ECvXr2Qnp6O2rVrIzw8HObm5tixYwfq1q2rVftGRkb/uZoqt1q1agUTExMAQOvWrXXSRyZdzxzqI6+jSpUqOHnyJNzc3FCnTh1MnDgRDx48wNq1a2X9sjt58mRMmTIFq1evhpmZmWztZtq+fbvGz0II3LlzB4sXL5ZSNWQh24Jshdq8ebMwNDQUvr6+wt/fX/j7+wtfX19RqFAhsWXLFq3bNzU1Ff/++2+W4zExMcLU1FTr9oUQ4sqVK6Jp06bC3Nxco/iUPjYp3LFjh6hTp44sbT19+lTj9uTJE3H+/HnRvn174eXllas2AwICPvgmB3283kWKFBFnz54VQgjx2WefieXLlwshhLh69aowMzPLdbv/VfjN0NBQ6+Jsb/aRXbErc3NzsWrVKq36eLOvu3fvZjl+7do1YW5uLksfRYsWFVFRUUIIIdRqtVQnJCQkRHh7e8vSR8mSJcXJkyeFEEL8/fffokSJEuLixYti/Pjx4tNPP5Wlj6FDh4oxY8bI0lZeMTAwyPb1vn//vjA0NNRJn5cuXRINGjQQQUFBsrR38uRJsX//fiHE6008fX19haWlpahataqsG0k+f/5c+Pr6CgsLC1G5cmXZNzrNroidvb296Nixo7h9+7YMz+C1j34Gpm3btjh+/Djmzp2LrVu3AnhdY+HEiROyLK0tV64c7t69m2V78nv37sm2T0SXLl0ghMDq1at1ts37u2gzO/K27JYHCyHg6OiIDRs25KpNfe/xoo/X+/PPP8fw4cPx2Wef4cSJE9i4cSOA15eqSpUqlet2Dxw4ACEE6tevj7/++kta9QS8rhvh7OyMEiVKaDX2q1evQgghbRyXuZonsw87OzsYGhpq1UdmYqdKpcLEiRNhbm4u3Zeeno7jx4/Ltt9Wenq69I2/WLFiuH37NsqXLw9nZ2dcvHhRlj4yL90BwK5du9CuXTuUK1dOmnGQw6tXr7B69Wrs27cP1apVy7LxobYJsLqkj5nDd3Fzc8OMGTNkyesQQsDOzk6aabGzs0NQUJAcw8zCz88PERER6NKli04+M/SxPx/AS0gAgGrVqmHdunU6aXv69OkYPHgwJk+eLO1ieuzYMfj7+2PmzJka1wJze6knKioKEREROi2w9PY1S/H/U4KTJ0+W7br520l2mUmXrq6usiXA6jrBVh+v9+LFi9G/f39s3rwZS5culS657N69G40bN8712OvUqQPgdZDh6Oj43l3Oc8vZ2RlpaWnw8/ND0aJFdVLwKvNypxACMTExGhtQGhsbw8vLCyNHjpSlr8qVKyMqKgouLi6oWbMmZs2aBWNjY6xYsUK2Dfns7e1x7tw5FC9eHEFBQdI+ZM+fP9c62Mv077//omrVqgBeB8JvkuvD7V2Xc1UqFUxNTeHq6oru3bujR48eOWo384uPSqVCuXLlsm3/v3Ym10ahQoVw+/ZtrdsRQsDV1RVnz57VeQHJnTt3Ijg4WCd7Rr1N6KC+WqaPNon3v5JSgdf/4NomLL75IZDZ39svqNCyjkC9evXwww8/yFqZ823Z/Xu9OTvi4+Ojs77loo8EW3283vry/PnzbFfWyLEqxdraGmfOnHnvEmFt9ejRAwsWLJCtMnF2goODkZycjDZt2iA2NhbNmzfHpUuXULRoUWzcuDHLxn+5MXnyZMyfPx/FixfH8+fPcenSJZiYmGD16tX49ddfER4eLsMz0b158+bhp59+QpMmTaRNQk+cOIGgoCAMGzYMV69exdq1a7Fo0SL06dPng9sNCwvT+cwh8P68DkdHR+zevVvrPipVqoRVq1ZJX350JbPWjy5XmOmyvlqmjzaA2bZt2zvvCw8Px8KFC5GRkaH1KqSwsLAPPjfzG3BOxcXFoW/fvujSpUu227zL8Uv69vPQxewI8Pq5zJ8/X2OGZMiQIShbtqzWbfv4+MDW1haBgYFZEmzv378vS4KtPl7v06dPw8jISCrBv23bNvz2229wd3fH5MmTNWYccuP+/fvo0aPHO9+Q5Qi8/Pz84O3trdM6S0+fPkV6errGBxrwOlm7UKFCOgtsHj16lKPk8Q+xefNm3LhxA+3atZMuEwYGBsLa2jrbLRO0cePGDQCvtzCQU9u2bfHll19qbHcCAMuXL8eePXvw119/YdGiRVixYgViYmJy3P7169ehVquxevVq6f2jUqVK6NmzJ6ysrLQe/9szkiqVCra2tqhfvz7mzJmD4sWLa93HP//8g1mzZmHp0qU6XaG6c+dOLFq0CMuWLZNt1/Q36a2+mmzZNAXAhQsXROvWrYWhoaHo1q2brJtO/Zd+/fqJ+/fv5+qx4eHhwsXFRWc7f758+VL06NFDXLlyReu23icoKEgYGxuLGjVqiGHDholhw4aJGjVqCBMTE7Fnzx6t29dHgu2H0ub1rl69uti8ebMQQoi4uDhhamoqOnbsKFxdXcWQIUO0HlunTp3EZ599Jk6ePCkKFy4s9uzZI9auXSvKly8vduzYoXX7Qgjx448/Cmtra9G2bVsxbdo02XeBF0KIxo0biyVLlmQ5vnTpUtGkSRNZ+tC3lJQUnbSblpYmxo8fL9RqtZRkrVarxQ8//CDbZq2FCxd+52aOhQsXFkIIERsbm+sE65MnT4qiRYuKkiVLiq+++kp89dVXolSpUqJo0aIiIiJCq7Hry5sb2pqamsq+oW12/ehi49zSpUuLwMDALMcDAgJE6dKltW4/00c7A/Om27dvY9KkSQgMDISvry+mT5+u9/osarUakZGRubpu7u7ujooVK2L06NHZJmTJkWdgZWWFyMhInU75V6lSBb6+vpgxY4bG8bFjx2LPnj1a14nw8vLCvHnzskzr79+/H0OGDMnVt77c0ub1trKywunTp1G2bFnMnDkT+/fvR3BwMI4cOYIOHTpI36Bzq3jx4ti2bRtq1KgBtVqNU6dOoVy5cti+fTtmzZqFw4cPa9U+AL1Ul7WxscGRI0dQsWJFjeMXLlzAZ599hocPH+aq3ZwUVJRjT6f09HRMmzYNy5Ytw927d3Hp0iWUKVMGEyZMQOnSpXO9KeWb+vXrhy1btsDf31+6HBweHo7JkyejdevWUt6NNpycnDBs2LAs377nzZuHefPmIT4+HtHR0WjUqJHG8voPlbkB5a+//irNCr969Qq9e/fGlStXcPDgQa2fg64FBga+9365FiXouh9d11fL9FEn8T59+hTTpk3DokWL4O3tjZCQEHzxxRd5MhZt4sjr169j+/btsq1yyU7r1q2xdetWnU75nz9/Hps2bcpyvGfPnrnei+nNpNn/SrDVJ21ebyGElOW/b98+NG/eHMDrKf8HDx5oPbbk5GRp1UaRIkVw//59lCtXDh4eHrIVG9P1LvAAkJqamm0OW1paGlJSUnLdrhyXI3Lip59+QmBgIGbNmqWRG1K5cmXMnz9flgBm/fr12LBhA5o0aSId8/T0hKOjIzp27ChLADNhwgT069cPBw4ckHJgTp48iV27dmHZsmUAgL179+b60uqpU6c0ghfgdYLt6NGjUb16da3HDwA3b97E9u3bs80Nk2Ollj5WTaalpSEsLAwTJkzQ2RdSXddXy/TRBjCzZs3CzJkz4eDggD/++EP268j6VL9+fURFRek0gHFzc4O/vz+OHDmS7TJLOTYZs7W1RWRkZJZf8MjIyFwvg3x7abYQAt98802WBNsWLVrk+6TaTNWrV8fUqVPRsGFDhIWFSR8uV69ehb29vdbtly9fHhcvXkTp0qXh5eWF5cuXo3Tp0li2bJks1/nfJnS0SqFGjRpYsWIFFi1apHF82bJlqFatWq7b1cU+Me+zZs0arFixAg0aNNDIH/Hy8pKtJLuJiUm2uRAuLi5a51Rl6tOnD9zd3bF48WJpZqp8+fIICwuTdpEeMWJErttXq9WIj4/PUl32xo0bWYrb5UZISAhatmyJMmXK4MKFC6hcuTKuXbsGIYS0gktbu3btgqGhYZa9gvbs2YP09HSNADO3jIyM8Ndff+ls00sAmDJlCtq3b4+DBw9KOTBHjhxBSEhItl9Sc+ujDWDGjh0LMzMzuLq6IjAw8J1TanJMAetaixYtMGzYMMTExGS7zbsc+7GsWrUK1tbWiIiIQEREhMZ9KpVKlgCmT58++Pbbb3HlyhXpDe3IkSOYOXOmxqZtOaGE/U9yav78+ejcuTO2bt2KH374QQpcN2/eLP27aWPIkCG4c+cOAGDSpElo3Lgx1q1bB2NjYwQEBGjdfiZdr1LIDPKioqLQoEEDAK8/hE6ePIk9e/bI0oc+3Lp1K9svJxkZGbJsuQAAAwcOxI8//ojffvtNqpybuXP0wIEDZekDAD777DN5K7G+oX379ujVqxd+/vlnjfePUaNGoWPHjlq3P27cOIwcORJTpkyBpaUl/vrrL9jZ2aFz585alS9409ixY7NcQgdev9Zjx46VJYABdD+j3rZtW5w4cUJn9dUyfbQ5MN27d/+gb3z6+rZlaWmJqKioXOVEvK9ehy6W6+rqG7MQAvPnz8ecOXOkugolSpTAqFGjMHjwYL0W6NM1bV7vd3nx4gUMDQ2zBLDaev78OS5cuAAnJycUK1ZMljb1tUohMjISs2fPRmRkJMzMzODp6Ylx48bJOo29efNmbNq0KdvLCnJccqtWrRqGDRuGLl26aPze+Pv7Y+/evTh06JDWfXz11VcICQmBiYkJvLy8ALyuL/Xy5Usp+MukzZe69PR0bN26VWOVUMuWLWWpZ/Py5UuMGjUKy5Ytky4dGhkZoV+/fpgxY4YUmOWWpaUlIiMjUbZsWRQpUgSHDx9GpUqVEBUVhVatWuHatWtaPwczMzOcP38+y2zYtWvXUKlSJdn2Qpo6dSrmzJmDBg0a6GRGvVu3bqhXrx5q164tywrSd/loA5j8RhcfaHJbtWoV5s2bJ31jdnNzw9ChQ9G7d2/Z+3r27BkAyDL1+6YnT55g1apVOllmmRNKeL11SR+7wOvDwoUL8cMPP6B79+5YsWIFevTogbi4OJw8eRIDBgyQZZ+wbdu2wc/PD+PGjYO/vz+mTJmCixcvYs2aNdixYwe+/PJLrfvISfG43H6pi42NRdOmTXHr1i2pDtPFixfh6OiInTt3yvZB9/z5c8TFxQEAypYtq1GJWRsODg44cOAAKlasCHd3d8yYMQMtW7ZEVFQUPvvsMyQlJcnSx/r167MsNNi3bx86deqEe/fuad0HoPsk+t69e+PgwYOIi4tDiRIlUKdOHdStWxd16tSRt0ifbOuZSCt9+/bN9bJafZgwYYIoXLiwGDt2rNi2bZvYtm2bGDt2rLCwsBATJkyQpY9Vq1bpdKn2yZMnhY2NjU6XWV6/fl1kZGRkOZ6RkSGuX78u/azN6/1fexZpq02bNmLGjBlZjs+cOVN8/fXXWrcvhBAmJibZLqm9dOmSMDExkaWPN6WkpGTZa0sO5cuXF+vXrxdCCGFhYSHi4uKEEK//XgYMGCBLH0IIcfDgQdGwYUNha2srzMzMxGeffSaCg4Nla18fmjRpIho3biwePnwoHXvw4IFo3LixaNq0aR6O7MO0atVKrFixQgghxIgRI4Srq6uYOnWqqFq1qmjQoIEsfXz77bfCw8NDxMbGSscuX74sPD09Ra9evWTpQ59u3rwp1q9fL7777jtRoUIFYWBgIEqWLClb+5yB0YGc7OwqVyXE5ORkhIWFZTuNLVeC7cKFC7NcS/7jjz8waNAgWVa/uLm54cqVKyhZsiTq1KkjRe1yJSfrY5mloaHhO3dAtrOzk+Vy3ttFGDN3Bg8MDMSUKVO0XpVia2uL/fv3S4XyMsXExKBhw4a4e/euVu0Dr1fQdOrUKcsqhalTp2Ljxo2yLGl//vw5Ro8ejU2bNmW7ZFqO18Lc3Bznz5+Hs7Mz7OzssHfvXnh5eeHy5cuoVatWrpdq61tKSgqEENJsxfXr1/H333/D3d0djRo1kqWPwoUL49ixY1l+r+ScwdClK1euICkpCZ6enkhOTsaIESNw9OhRuLm5Ye7cubKUq3j69CkaN26MU6dOSQULb968iS+++AJbtmyBtbW11n3o0/Pnz3H48GEcOHAAoaGhOH36NNzd3aXtPrT10Sbx6pK3tzdUKtU7l8pm3idXfsqZM2fQtGlTPH/+HMnJybCxscGDBw9gbm4OOzs7WQKYtLS0bJciVqtWTevtFjJdvnwZt27dQmhoKA4ePIiff/4Z3333HYoXL466devi999/16p9fSyzzHxd35aUlKSxyZw2slsx9/XXX6NSpUrYuHGj1gFMUlJStitPjIyMsuyJlVv6WKUwatQoHDhwAEuXLkXXrl2xZMkS3Lp1C8uXL882UTI3HBwc8OjRIzg7O8PJyQnHjh2Dl5eXtGmlUrRq1Qpt2rRB37598eTJE9SoUQPGxsZ48OAB5s6di379+mndh4mJiXRp+E3v+n3LT9LT03Hz5k3pC2fhwoWlpd9ysrKywtGjR7F3715ERUVJeVu1a9eWvS9dLgn//vvvERoaijNnzqBixYqoU6cOxo4di9q1a0tV0GUh21wOSa5du/bBNznUqVNH9OnTR6Snp0vT2PHx8aJ27drir7/+kqWPgQMHimHDhmU5PmLECNG/f39Z+nhTcnKyCAoKEn5+fqJQoULC0NBQ6zbt7OyynXYPCgoSdnZ2WrWdWTnYwMBAfPfdd9LPw4YNE4MHDxY1a9YUn376qVZ9/Je4uDipoqk2PvnkEzFlypQsxydNmiSqVq2qdfuZTp06JTp37iyqVq0qqlatKjp37ixOnz4tW/uOjo7iwIEDQgghLC0tpUtWa9aska0Sb69evcTkyZOFEEIsXrxYmJmZiYYNGwpra2vRs2fPXLdrbW2dpTrqu25yKFq0qFSl+tdffxWenp4iPT1dbNq0SVSoUEGWPrp27SoqVaokjh07JjIyMkRGRoYIDw8XlStXFn5+frL0oUsmJiY6r0b+oSpXrizi4+Nz/fh9+/YJc3NzUblyZVGoUCHh7e0trK2thZWVlahXr57W41OpVMLOzk5Mnz5dXLx4Uev23oUzMDqgix123ycyMhLLly+HgYEBDA0NkZqaijJlymDWrFnw8/PLUeXQN725dFmlUmHlypXYs2ePVATu+PHjiI+Pz5KImVt79uxBaGholsh98+bNsnwD0eUyS33ugJydlJQULFy4UNqZWhsTJkxAmzZtEBcXJyUThoSE4I8//sCff/6pdfuZqlWrpvWs2vs8evRISpJWq9V49OgRAODzzz+XZUYBAFasWCEVFRwwYACKFSuGI0eOoGXLlln2/MmJ3BZuzK3nz59LCfN79uxBmzZtYGBggFq1auH69euy9LFw4UL4+fnBx8dHWin36tUrtGzZEgsWLJClD12qXLkyrly5otNq5B/q2rVrWi2h1/WS8DNnziAsLAyhoaGYM2cOjI2NpZSAunXrZrtreK7oLDQiDWfPnhW7d++WEmAzb3IoVqyYuHTpkhBCCDc3NxEUFCSEEOL8+fO53ldECCHq1q37QTc5InYh/he1z5w5Uzx+/FiWNt+UmpoqBg8eLO0BYmBgIExMTMTQoUPFixcvZOmje/fusiWIvsvb386tra2FoaGhsLS0lO13aseOHeLTTz8V5ubmomjRoqJevXoiNDRUlraFEMLAwEDcvXs3y/EHDx7IkogshBAeHh7SmBs0aCBGjBghhBBiwYIFsiYSpqSkiOPHj4t//vlH4297+/btsvXxIaZPn57rvxsPDw+xYMECER8fL9RqtTh69KgQ4vUsmb29vYyjfJ2ovX37drF9+/ZsE7nzq927dwtvb2/xzz//iNu3b+skKfxDvZkwntvHZyYKW1tbS7NvkZGRwtnZWY4haoiMjJRm0+X6+xZCCAYwOhYXFyc8PT01Nlh8cxWJHL788kuxbt06IYQQvXv3FjVq1BC///678PX1FTVq1JClD32YN2+e+Oqrr0TRokVFiRIlRMeOHcXy5ctlmYJ89eqVCAsLE48ePRLJyckiOjpaREdHi+TkZBlG/m5Pnz4Vf//9tzh//rxsbf72228iICBAuq1Zs0bs3r1bPHr0SLY+dE2lUmUbwNy6dUu2jTXnzp0rbQy5d+9eYWpqKkxMTISBgYGYP3++LH3s3r1bFCtWTGMj1Tf/xvXJ0tIy1x9qf/75pzAyMhIGBgbiyy+/lI5PmzZNNG7cWK4hKtrbr23mLS9ea20DGHt7e3Hu3DkhhBAVK1aUvvhERkbKchk6IyNDREREiDlz5ogWLVqIIkWKCENDQ1GlShUxdOhQrdvPxFVIOtaiRQsYGhpi5cqVcHFxwYkTJ/Dw4UOMGDECP//8syx7L506dQrPnj1DvXr1cO/ePXTr1k3Kjl+9erVUmEpJYmJiEBYWhv3792PHjh2ws7PDzZs3tWrT1NQU58+f1+kU8DfffIPatWtj4MCBSElJgZeXl1RufMOGDWjbtq3O+pZbRESERr0cOSpoLly4EAAwbNgw/Pjjj7CwsJDuS09Px8GDB3Ht2jXZVim86fr164iIiICrq6tsq//c3NzQqFEjTJw4UZZtHLShbW2hhIQE3LlzB15eXlJxzBMnTkCtVkvl+W/evIkSJUq8t3jmm3JSQVuOvYR0KTAwEI6OjlmK7mVkZCA+Pl4v+xhl0va1bt26NZo1a4Y+ffpg5MiR2LZtG7p3744tW7agSJEi2Ldvn1bjK1KkCJKSkuDl5SVdOvriiy/kX0UlWyhE2SpatKiIiooSQgihVqvFhQsXhBBChISECG9v77wcWr70ZuTevHlz6fKIHP9W1apVE/v27ZNhlO9mb28vIiMjhRBCrFu3Tri6uork5GTxyy+/yPZ6r169WmzatCnL8U2bNomAgACt2797966oV6+eUKlU0mUqlUol6tevL+7du6dV26VLlxalS5cWKpVKODo6Sj+XLl1alCtXTjRq1EgcO3ZM6+fw8uVLUb9+fenSqq5YWlpq1OzIS9p+K/8QOZ3l0fdlaF3Sx2XPD6Xtax0XFyd9LiUlJYnvvvtOeHh4iDZt2siyuGTHjh16uazGAEbHrK2tpcz1MmXKiP379wshhIiNjRVmZmZ5ObR8p3nz5tJUY9WqVcXw4cPFtm3bZMuH0cc1bFNTU2l1QNeuXcWYMWOEEK8L3MkxNSvE6zynzN+jN4WGhopy5cpp3f4333wjqlevLk0xC/E6h6t69eqiQ4cOWrcvxOsPNl1f8nozN0xXevToIVauXKnTPj6UPgIYffSRX6lUqmwD+GvXrmmVa5gbH/Pr8CauQtKxypUrIyoqCi4uLqhZsyZmzZoFY2NjrFixQrYy8lWqVMm29ohKpYKpqSlcXV3RvXt31KtXT5b+dKVChQr47rvv8MUXX+iktH/Tpk0BvN7c8u0dquWqyePo6Ijw8HDY2NggKCgIGzZsAAA8fvxYtjow8fHx2V4Gc3Z2Rnx8vNbtBwUFYd++fahYsaJ0zN3dHUuWLJGtqNnbm2ymp6cjJiYGzs7OstWJ6NKlC1atWiVbzZfsLF68GO3atcOhQ4ey3UhVjhpMBVHm5eDMYm35WeZlMJVKhQkTJmhsTZCeno7jx4/D29tbr2Navny5LJcsX758iXv37kkr6TI5OTlp3bY+MIDRsfHjx0sbcPn7+6N58+b44osvULRoUWzcuFGWPho3boylS5fCw8MDNWrUAACcPHkS0dHR6N69O86dO4eGDRtiy5Yt2RZByy9mz579Qed5eHhg165dcHR0zFH7+tiZeujQoejcuTMsLCzg5OSEunXrAgAOHjyYpQJpbtnZ2SE6OjrLhm9RUVEoWrSo1u1nZGRkuyGkkZFRlje63Bo6dCg8PDzQq1cvpKeno3bt2ggPD4e5uTl27Ngh/btp49WrV1i9ejX27duX7YZ1cuRc/PHHH9izZw9MTU0RGhqqERjLtUt7QZGRkSFtIphZddfS0hIjRozADz/88MF5Nfqm7xIJH1JVvVOnTlr1cenSJfTq1QtHjx7VOC7nlzm9yOMZoI/Sw4cPs90vJ7d69+4t/P39sxz/8ccfRe/evYUQQkycOFFUq1ZNtj7zUn6fPj158qTYsmWLePbsmXRsx44d4vDhw7K0P3r0aOHs7Cz2798vXr16JV69eiVCQkKEs7OztFRYGy1bthS1a9cWt27dko7dvHlT1KlTR7Ru3Vrr9oUQokSJEuLkyZNCCCH+/vtvUaJECXHx4kUxfvx4rQr+RUVFifT0dCHE+/Mv5Mq5sLe3Fz/99JPUZ15q0qSJuH37tk770OZvb+zYscLW1lb88ssvIioqSkRFRYklS5YIW1tb8f3338s8Uvnpo0TC6dOnhYODg1Cr1cLQ0FDY2toKlUolChcuLFxcXGTr59NPPxW1a9cWu3btEmfOnBGRkZEaN6VgAFMAqNXqbOspXL58WajVaiHE65owFhYW+h6aTmjzJvro0SMxe/Zs0bNnT9GzZ0/x888/a2wuJ5fU1FRx4cIFkZaWppO2v/nmG6FSqYSRkZEwMjIShoaGokePHiI1NVXr9uPj44W3t7cwMjISZcqUEWXKlBFGRkaiSpUq4saNGzI8g9dVTTPb6tOnjxgyZIgQQogrV64IS0vLXLf7ZqKli4uLePDggdZjfZ8iRYroJYk3NjZW/PDDD6JDhw7S89u1a5dUv0NftFmqXbx48WzrFG3dulWUKFFC26EVCPqoqi6EEObm5rKWdsgr+XPOrgBJTk7GhAkT8Omnn8LV1RVlypTRuMnB1NQ0y1QgABw9elTKu8jIyJAtB0OpDh48iNKlS2PhwoV4/PgxHj9+jIULF8LFxUWWjRyB1xVNe/XqBXNzc1SqVEnKSRk0aJBsuRjGxsbYuHEjLly4gHXr1mHLli2Ii4vD6tWrZdlTxtHREadPn8bOnTsxdOhQDB06FLt27cLp06dly1mwt7fHuXPnkJ6ejqCgIHz55ZcAXv/7vb1MNSesra1x9epVAK+rlcp1yetd/Pz8ZLsU/C5hYWHw8PDA8ePHsWXLFunyS1RUFCZNmqTTvt8mtKi68ejRI2k59psqVKggVUn+2EVGRmLEiBEaVdUdHR0xa9asLBufasPd3V2WDXjzGnNgdKx3794ICwtD165dUbx48WyTbbU1aNAg9O3bFxEREfjkk08AvM6BWblypfRLHxwcrPdEs/xmwIABaN++PZYuXSp9SKanp6N///4YMGCALDsgjxs3DlFRUQgNDdUoyd2wYUNMnjwZY8eO1bqPTOXKlZOvJPdbVCoVvvzySymwkFuPHj3wzTffSH8TDRs2BPB6e4rsPuQ+VNu2bVGnTh2p3erVq78zILpy5Uqu+8mUnp6OWbNmITg4GJ6enllyh+TIsxk7diymTp2K4cOHS+X+AaB+/fpYvHix1u2npaXBzMwMkZGRqFy58nvPPXfuHEqUKJGrfry8vLB48WKpFlCmxYsXK7JWlS4YGRlJuUB2dnaIj49HxYoVYWVlhRs3bmjV9psbsc6cOROjR4/GtGnTsk0+V6vVWvWlLyxkp2PW1tbYuXOntOOurqxbtw6LFy/GxYsXAQDly5fHoEGDpGSvlJQUaVWS0uW2iFPmm3T58uU1jl+8eBHe3t5ISUnRemzOzs7YuHEjatWqpTHO2NhYVK1aVZbdnNPT0xEQEICQkJBsVxDs378/x22+/aHyPnIlpm7evBk3btxAu3btpJmdwMBAWFtba5VsHhQUhNjYWAwePBj+/v4aH/pvGjJkSK77yPS+lX0qlSpXr8XbLCwsEBMTAxcXF43fqWvXrqFChQp48eKF1n2UKVMGf//9t04DibCwMDRr1gxOTk7w8fEBAISHh+PGjRvYtWuXLEU9la5Ro0bo3r07OnXqhD59+iA6OhqDBw/G2rVr8fjxYxw/fjzXbRsYGGS7+vJNQmFJvJyB0bEiRYrAxsZG5/107twZnTt3fuf9ZmZmOh9Dfle1alWcP38+SwBz/vx52d6479+/Dzs7uyzHk5OTZZt9GzJkCAICAtCsWTNUrlxZlnbnzZv3QefJubLm66+/znJMjmqmmTNfERERGDJkyDsDGDnoY2WbtbU17ty5k2Xp/JkzZ2TZvBMAfvjhB3z//fdYu3atzt6v6tSpg0uXLmHJkiW4cOECAKBNmzbo379/rmd1Cppp06bh2bNnAICffvoJ3bp1Q79+/aSq6trQx++qvnEGRsd+//13bNu2DYGBgRr1Ayj31q9fj1atWmVZFvtfNm7ciNGjR2PQoEHSjtrHjh3DkiVLMGPGDI26J7ktNV+7dm20a9cOgwYNgqWlJaKjo+Hi4oJBgwbh8uXLCAoKylW7bypWrBjWrFkj1bVRioULF+Lbb7+Fqanpf874cPnx/4wcORLHjx/Hn3/+iXLlyuH06dO4e/cuunXrhm7dusmSB1OlShXExsYiLS0Nzs7OWf62Tp8+rVX7aWlpaNy4MZYtWwY3Nzet2iLKxABGx6pUqYK4uDgIIVC6dOks1xpz+8ZgY2ODS5cuoVixYihSpMh7v4Xn5wQ5fV66+K86EyqVSusp1MOHD6NJkybo0qULAgIC8N133+HcuXM4evQowsLCUK1atVy1+6YSJUogNDRUZ/kvb5OryJyLiwtOnTqFokWLvnc/KpVKJUt+SkHx8uVLDBgwAAEBAUhPT0ehQoWQnp6OTp06ISAgQKuk50xTpkx57/1yBEm2trbSHm2U9548eYITJ05kexm6W7dueTSqnGEAo2O6emMIDAxEhw4dYGJigsDAwPeeq89NxnLq7Q+y+/fv4/nz59KmX0+ePIG5uTns7Oy0/lC7fv36B5/r7Oyc637i4uIwY8YMREVFISkpCVWrVsWYMWNkK2Q3Z84cXLlyBYsXL9ZJUrg+isxRzsXHx+Pff/9FUlISqlSporhAYNiwYTAxMdFpZWSle/jwISZOnIgDBw5kG1jI9WX0n3/+QefOnZGUlAS1Wp2lAGN+/tL7JgYwlG+sX78ev/zyC1atWiXlqVy8eBF9+vTBd999994cHzk1a9YMK1euRPHixfXSX0599dVXOHDgAGxsbFCpUqUss3pbtmzRqv1SpUph69atqF69OrZu3YoBAwbgwIEDWLt2Lfbv348jR47kqt0P3ZlYpVJhzpw5ueqD8q9BgwZhzZo1cHNz01llZKVr2rQpYmNj0atXL9jb22f5giLXl9Fy5cqhadOmmDZtmqJTGxjAKFROVrMoZUlc2bJlsXnzZlSpUkXjeEREBL7++mupvoeuabtVfUZGBmJjY7P9BlW7dm2tx9ejR4/33v/bb79p1b6pqSliY2NRqlQpfPvttzA3N8f8+fNx9epVeHl55Xol1dsrdk6fPo1Xr15JweqlS5dgaGiIatWqybJ6R8k+NNgD5PngT09Px7x587Bp06ZsS9jL8Y1cHyu2lM7S0hKHDx/W+bLywoULIyYmRrZaZHmFq5B0QB/5KdbW1v95+UBpS+Lu3LmDV69eZTmenp6Ou3fv5sGIcu7YsWPo1KkTrl+/nqXol1yvhbYByn/JLDJXvHhxBAUFYenSpQC0LzL35iqIuXPnwtLSEoGBgVJezePHj9GjRw8up8X/9t/5L3JdQpwyZQpWrlyJESNGYPz48fjhhx9w7do1bN26FRMnTpSlj4K4CkZuFSpUkKWcw3/x9fXFqVOnGMBQVvPmzZOWbs6fP18nfRTEN4MGDRrgu+++w8qVK1G1alUAr2df+vXrJxU6y+/69u2L6tWrY+fOnTorXJjp/v37GnV/bG1tZWlXV0Xm3jRnzhzs2bNHIym4SJEimDp1Kho1aoQRI0bI0o9S6fvve926dfj111/RrFkzTJ48GR07dkTZsmXh6emJY8eOcVWYnvzyyy8YO3YsJk6ciMqVK+uswFyzZs0watQonDt3LttCdi1btpSlH13jJaSPSP/+/eHv749ixYrl9VCydf/+ffj5+SEoKEj6g3r16hV8fX0REBCQbX0VXdDmElLhwoURFRUFV1dXHYzsteTkZCmfIPMSlaGhIbp164ZFixbJck1bV0XmMllaWuKff/7JkhB84MABtGzZUqqFQcDTp0+Rnp6epT7Lo0ePUKhQIVk+1AoXLozz58/DyckJxYsXx86dO1G1alVcuXIFVapUwdOnT7Xuo169eu8N6HkJCbh8+TI6deqUZXWq3LPp71uRqaRZe87A6Mm9e/eyzYnIbb2R3Pj9998xcuTIfBvA2NraYteuXbh06ZJU6KpChQp6Wy4sh5o1ayI2NlanAczw4cMRFhaGf/75R6rwfPjwYQwePBgjRoyQLvloQ1dF5jJ99dVX6NGjB+bMmYMaNWoAeD3DM2rUKLRp00a2fgqCDh06oEWLFujfv7/G8U2bNmH79u3YtWuX1n2UKlUKd+7cgZOTE8qWLYs9e/agatWqOHnyJExMTLRuH0CWrUzS0tIQGRmJf//9N1+vlNSnzp07w8jICOvXr882iVcuut4jTF84A6NjERER8PPzw/nz53WWE/GhtE1O1ZeXL1/i6tWrKFu2LAoV0n+MndN/p+joaOn/4+LiMH78eIwaNSrbqVk5AtZixYph8+bN2c5efPPNN7h//36O29R3kbnnz59j5MiRWL16NdLS0gAAhQoVQq9evTB79uwcFyksyGxsbHDkyBGNQosAcOHCBXz22Wd4+PCh1n2MHTsWarUa33//PTZu3IguXbqgdOnSiI+Px7Bhw3S69Hny5MlISkrCzz//rLM+lMLc3BxnzpzJUi1cbv7+/u+8T6VSYcKECTrtXy4MYHTMy8sLZcuWxZgxY7KNqLWpN5JT+T2Aef78OQYNGiTVtbl06RLKlCmDQYMGoWTJklpthJiWlobvvvsOEyZMeG8RNQCYPn06+vXrJ9Wi+S+Ze4y8609JjgJ5bzI3N0dERESWD7SzZ8+iRo0aSE5OznGbeVVkLjk5GXFxcQBer0Jj4JJV4cKFcezYsSx1hGJiYlCzZk08f/5c9j7Dw8MRHh4ONzc3tGjRQvb23xQbG4saNWoopvaILtWuXRsTJ07Uec7f2ys909LScPXqVRQqVAhly5bVuvKyvjCA0TFLS0ucOXNGp5cUcjKW/BzADBkyBEeOHMH8+fPRuHFjREdHo0yZMti2bRsmT578wSsz3sXKygqRkZH/GcDklL4K5GVq0KABihYtijVr1kibc6akpMDPzw+PHj3Cvn37tO6D8o969eqhcuXKWLRokcbxAQMGIDo6GocOHcqjkclj7dq1GDNmDG7fvp3XQ8lzf/75JyZPnqzTGdx3SUxMRPfu3fHVV1+ha9euOutHTgxgdKx169bo2rUr2rZtm9dDyfcBjK53cvbz84O3tzeGDRsm04izmj59Ouzt7dGzZ0+N46tXr8b9+/cxZswYrfuIiYlB48aNkZqaKtWLiIqKgomJCfbs2YNKlSrluE0Wmcu/jhw5goYNG+KTTz5BgwYNAAAhISE4efIk9uzZk+tl59u3b//gc+VYlfJ2bpMQAnfu3MGpU6cwYcIEWbYrULrskmvlnsF9n5iYGLRo0QLXrl3TaT9yYRKvjq1cuRJ+fn74999/s10Wp5Tlavqg652c3dzc4O/vjyNHjmRbCVSO3I7ly5dj/fr1WY5XqlQJHTp0kCWA8fDwwOXLl7Fu3Top2bljx47o3Llzrncdf3t2631F5ki/PvvsM4SHh2P27NnYtGkTzMzM4OnpiVWrVmm1nUDr1q0/6Dy5PjitrKw0fjYwMED58uXh7++PRo0aad1+QaCvYp3v8vTpU1lWnOkLAxgdCw8Px5EjR7B79+4s9+k7ibdLly75uipvZv2UQYMGAfhfka6VK1fCx8dH6/ZXrVoFa2trREREICIiQuM+lUolSwCTkJCQ7RYEtra2uHPnjtbtA/+b5enTp4/GcW1meVhkLn/z9vbGunXrZG1T3ytRdF2AsSDQV07k24n6mbNha9euRZMmTfQyBjnwEpKOlS5dGs2bN8eECRNgb2+vs34OHTqE5cuXIy4uDps3b0bJkiWxdu1auLi44PPPP9dZv3LSx07Ouubm5oZJkyahS5cuGsfXrl2LSZMmyZIAW7p0aaxfvx6ffvqpxvHjx4+jQ4cOWn+LK1myZLaXov799180atSIuQp6dvr0aRgZGUlJvNu2bcNvv/0Gd3d3TJ48GcbGxnk8wg/35MkTbN68GXFxcRg1ahRsbGxw+vRp2Nvbo2TJknk9vDy3Zs2a994v1y7Rb+cBGhgYwNbWFvXr18e4ceOkQqz5HWdgdOzhw4cYNmyYToOXv/76C127dkXnzp1x5swZpKamAng9HTht2jRZ6kTow+eff47IyEjMmDEDHh4eUi2K8PBw2XZy1rU+ffpg6NChSEtLQ/369QG8zlcYPXq0bNVldT3Lk5iYmO1S7Pv377PAXB747rvvMHbsWHh4eODKlSto37492rRpgz///BPPnz+Xpdr3+5bVApBlO4Ho6Gg0aNAA1tbWuHbtGvr06QMbGxts2bIF8fHx//nh/TEYMmSIxs9paWl4/vw5jI2NYW5uLlsAk9eXqmQjSKe6desmfv31V5324e3tLQIDA4UQQlhYWIi4uDghhBCnT58W9vb2Ou1baW7cuCGWLFkixowZI4YNG6Zxk0NGRoYYPXq0MDU1FQYGBsLAwECYm5uLKVOmyNK+EEK4urqKtWvXZjm+Zs0a4eLionX7Xbt2FaVLlxZ//fWXuHHjhrhx44bYvHmzcHFxEd26ddO6fcoZtVotYmNjhRBCzJgxQzRq1EgIIcThw4dFqVKlZOnD29tb41apUiVhbm4u1Gq1qFKliix9NGjQQIwaNUoIofk+deTIEeHs7CxLHwXRpUuXRIMGDURQUFBeDyXf4QyMjpUrVw7jxo3D4cOHs10WJ0fexcWLF7Pd5djKygpPnjzRun190fVUeUhICFq2bIkyZcrgwoULqFy5Mq5duwYhhLT3krZUKhVmzpyJCRMm4Pz58zAzM4Obm5ts1UwB3c/yLFu2DCNHjkSnTp2yLTJH+iWEkPJV9u3bh+bNmwMAHB0d8eDBA1n6yK5EwZvLauVw8uRJLF++PMvxkiVLIiEhQZY+CiI3NzfMmDEDXbp0kZL26f/ldQRV0JUuXfqdNzm+LQshhIuLi9i7d68QQvObTWBgoKhYsaIsfehD9erVxebNm4UQQsTFxQkTExPRsWNH4erqKoYMGaJ1+5988omYOHGiEOJ//07Pnj0TLVu2FL/88ovW7euLPmZ5hBAiKSlJREVFiaioKJGUlCRr2/Th6tWrJ7p16ybWrFkjjIyMxOXLl4UQQoSGhup85iI6Olq2PmxtbcXp06eFEJrvU3v27JFtJqmgOnPmjLC0tMzrYeQ7DGAKgGnTpgl3d3dx7NgxYWlpKQ4dOiR+//13YWtrKxYuXJjXw/tgup4qt7CwkNq3trYW//77rxBCiMjISEVOYT979kycOHFCxMTEiBcvXuT1cEhHoqKiROXKlYVarRaTJ0+Wjg8cOFB07NhRp30fOnRIWFtby9JWr169ROvWrcXLly+FhYWFuHLlirh+/bqoUqWKLF9QCoJt27Zp3LZu3SqWLl0qKlWqJBo3bpzXw8t3eAlJj8T/L/iSe4OusWPHIiMjAw0aNMDz589Ru3ZtmJiYYOTIkdKSZCUQOp4qL1y4MF6+fAkAKF68OOLi4qSVNnJNxeuThYUFPvnkk7weBumYp6cnYmJishyfPXs2DA0NZelDH8tq58yZg6+//hp2dnZISUlBnTp1kJCQgFq1auGnn36SpQ+le7s2j0qlklYHsYBkVlxGrQdr1qzB7NmzcfnyZQCv82JGjRole7nmly9fIjY2FklJSXB3d4eFhYWs7eta/fr14ejoiIYNG6JXr144d+4cXF1dERYWBj8/P62rQ7Zu3RrNmjVDnz59MHLkSGzbtg3du3fHli1bUKRIEZbgp4+WPpfVHjlyBFFRUUhKSkLVqlV1vu8PFVwMYHRs7ty5mDBhAgYOHIjPPvsMwOt6J0uWLMHUqVNlKWvfs2dPLFiwIMubTHJyMgYNGoTVq1dr3Yc+REdHo3PnzoiPj8fw4cOl0uKDBg3Cw4cPs61wmxNXrlxBUlISPD09kZycjBEjRuDo0aNwc3PD3Llz9bqxJtGHytws9F30WQxTWyEhIQgJCcG9e/eyFNJTyvsU5R8MYHTMxcUFU6ZMybJ+PzAwEJMnT5ZlPb6hoSHu3LmTpQz/gwcP4ODggFevXmndR1568eIFDA0Ns6zgIvoYbNu2TePntLQ0nDlzBoGBgZgyZQp69eqldR9Pnz5Feno6bGxsNI4/evQIhQoVkqWC95QpU+Dv74/q1aujePHiWYKyv//+W+s+CoKbN29i+/btiI+Ply55Z5o7d24ejSp/Yg6Mjt25cydLxVQA+PTTT7UuOpaYmAjxOhEbz549k3YmBl5/K9u1a1e2ewspzZvPi+hj06pVqyzHvv76a1SqVAkbN26UJYDp0KEDWrRogf79+2sc37RpE7Zv3y5LMcxly5YhICBAMTsd5wV9lHooSBjA6Jirqys2bdqE77//XuP4xo0btdqIDQCsra2hUqmgUqlQrly5LPerVCpMmTJFqz50zcbGBpcuXUKxYsVQpEiR906VP3r0KMft/1eb2rZPlFdq1aqFb7/9Vpa2jh8/nu23+7p16+KHH36QpY+XL19m+2WO/mfcuHEYOXIkpkyZAktLS/z111+ws7ND586d0bhx47weXr7DAEbHpkyZgvbt2+PgwYNSDsyRI0cQEhKCTZs2adX2gQMHIIRA/fr18ddff2lM/xobG8PZ2RklSpTQqg9dmzdvnpS7I0dJ9Lfpok2ivJaSkoKFCxfKtn9Qampqtpea09LSkJKSIksfvXv3xvr16zFhwgRZ2iuIzp8/jz/++APA6+KRKSkpsLCwgL+/P1q1aoV+/frl8QjzFwYwOta2bVscP34c8+bNw9atWwEAFStWxIkTJ1ClShWt2q5Tpw6A1/taODo6wsDAQNvh6p2fn1+2/6+L9omU6O1ZxMxLxmZmZrLtUF2jRg2sWLECixYt0ji+bNky2TZRffHiBVasWIF9+/bB09MzS04b8zsKXqkHXWMSbwHy/PnzbBO/PD0982hEORMfH//e+52cnPJ1+0S6EBAQoBHAZC5xrlmzJooUKSJLH0eOHEHDhg3xySefoEGDBgBe52OcPHkSe/bswRdffKF1H/Xq1XvnfSqVCvv379e6D6VjqYecYQCjA4mJiR98rhzZ/ffv30ePHj2we/fubO9XyjJLXS8XLUjLUenj8uLFC0RHR2e7/Lhly5ay9BEZGYnZs2cjMjISZmZm8PT0xLhx47TO1aMPx1IPOcNLSDqQmVz7IeT40Bw6dCiePHmC48ePo27duvj7779x9+5dTJ06VVHVG9/eUC5zuejcuXNlqdSp6/aJdCEoKAjdunXDw4cP8fb3TZVKJVvg7e3tLdslKcq59PR03Lx5U5oxL1y4MJYtW5bHo8rfOAOjA2FhYdL/X7t2DWPHjkX37t3h4+MDAAgPD0dgYCCmT58uS45G8eLFsW3bNtSoUQNqtRqnTp1CuXLlsH37dsyaNQuHDx/Wuo+8tHPnTsyePRuhoaGKbJ9IG25ubmjUqBEmTpwIe3t7nfSh653g6cOYmpri/PnzWSoj0zvodeelj1D9+vXF+vXrsxxft26dqFOnjix9WFpaiqtXrwohhHBychKHDx8WQghx5coVYWZmJksfeeny5cvC3Nxcse0TacPS0lLahFRXdL0TPH2YatWqiX379uX1MBRDectWFCY8PBzVq1fPcrx69eo4ceKELH2UL18eFy9eBAB4eXlh+fLluHXrFpYtW4bixYvL0oc+JCYmatyePn2KCxcuYPz48bJch9d1+0S68PXXX+t8dvDSpUvw9vYGAPz555+oU6cO1q9fj4CAAPz111867Zv+Z+rUqRg5ciR27NiBO3fuZHnPIk3MgdExR0dH/Prrr5g1a5bG8ZUrV8LR0VGWPoYMGSJV9Z00aRIaN26MdevWwdjYGAEBAbL0oQ/Z5Q4JIeDo6IgNGzbk+/aJdGHx4sVo164dDh06BA8PjyzLjwcPHqx1H0LHO8HTh2natCmA14nZby+dlzPfqaBgDoyO7dq1C23btoWrqytq1qwJADhx4gQuX76Mv/76S/qFldPz589x4cIFODk5oVixYrK3rytv5g4B/1su6urqikKFtI+1Q0NDs12OKlf7RLqwatUq9O3bF6ampihatKjG77BKpcKVK1e07kPXO8HThwkMDISjoyMMDQ01jmdkZCA+Pp51rd7CAEYPbt68iV9++QUXLlwA8LqQXd++fWWbgSkI0tLS8N1332HChAlMYCN6g4ODAwYPHoyxY8fqrFilrneCpw/zro15Hz58CDs7O87AvIUBjEINHz4cP/74IwoXLozhw4e/91ylVLi0srJCZGSkzgKY6dOnw97eHj179tQ4vnr1aty/fx9jxozRSb9E2rCxscHJkydRtmxZvffNneD1y8DAAHfv3oWtra3G8evXr8Pd3R3Jycl5NLL8ifPmevD48WOsWrUK58+fBwC4u7ujR48eWbauz4kzZ84gLS1N+v93+dB6NPlB69atsXXrVgwbNkwn7S9fvjzbb5KVKlVChw4dGMBQvuTn54eNGzdm2RBWH7gTvH5kfglVqVSYMGECzM3NpfvS09Nx/PhxKcma/ocBjI4dPHgQLVq0gJWVlbQaaeHChfD398c///yD2rVr56rdAwcOZPv/Subm5gZ/f38cOXIE1apVQ+HChTXu1zZZMSEhIdtVWba2tlISNFF+k56ejlmzZiE4OFhnewixSnXeyvwSKoRATEyMRt0dY2NjeHl5YeTIkXk1vHyLl5B0zMPDAz4+Pli6dKmUmJWeno7+/fvj6NGjiImJ0bqPp0+fIj09PcuMzqNHj1CoUCFZtivQh/ddOpIjWdHNzQ2TJk1Cly5dNI6vXbsWkyZNkiUZkkhu+thDaNu2bRo/Z1apDgwMxJQpU9CrVy+t+6D/1qNHDyxYsEAx79l5jQGMjpmZmSEyMhLly5fXOH7x4kV4e3vLslV9kyZN0KJFC/Tv31/j+LJly7B9+3bs2rVL6z70LfPXUs5LYLNmzcKsWbMwe/Zs1K9fH8DrDetGjx6NESNGYNy4cbL1RVQQrF+/Hhs3bswS4BDlByxkp2NVq1aVcl/edP78eXh5ecnSx/Hjx7P9lla3bl0cP35clj70ZdWqVahcuTJMTU1hamqKypUrY+XKlbK0PWrUKPTq1Qv9+/dHmTJlUKZMGQwaNAiDBw9m8EKUjVq1aiEkJCSvh0GULebA6EB0dLT0/4MHD8aQIUMQGxuLWrVqAQCOHTuGJUuWYMaMGbL0l5qailevXmU5npaWJssMj75MnDgRc+fOxaBBgzT2jRo2bBji4+Ph7++vVfsqlQozZ87EhAkTcP78eZiZmcHNzQ0mJiZyDJ+oQElJScHChQtRsmTJvB4KUbZ4CUkHMhPi/uufVq7KivXq1UPlypWxaNEijeMDBgxAdHQ0Dh06pHUf+mBra4uFCxeiY8eOGsf/+OMPDBo0SPaKoImJidi/fz/Kly+PihUryto2kZIUKVIkS+XXZ8+ewczMDOvWrUPLli3zcHRE2eMMjA5cvXpVr/1NnToVDRs2RFRUFBo0aADgdW7HyZMnsWfPHr2ORRtpaWnZ7htVrVq1bGeYcuqbb75B7dq1MXDgQKSkpKB69eq4du0ahBDYsGED2rZtq3UfREo0b968bKtU16xZE0WKFMnDkRG9G2dg9OTcuXOIj4/Hy5cvpWMqlQotWrSQpf3IyEjMnj0bkZGRMDMzg6enJ8aNG6eoTQoHDRoEIyOjLMtCR44ciZSUFCxZskSr9h0cHBAcHAwvLy+sX78ekyZNQlRUFAIDA7FixYr31tMhKuhevHiB6Oho3Lt3T9oXKRNnYCg/YgCjY1euXMFXX32FmJgYjctKmd92Pvb6Cm9WEX716hUCAgLg5OQk5QsdP34c8fHx6NatW5ZLZDllZmaGS5cuwdHREd26dUOJEiUwY8YMxMfHw93dHUlJSVq1T6RUQUFB6NatGx4+fJjl0jc3EaT8ipeQdGzIkCFwcXFBSEgIXFxccPz4cTx69AgjRozAzz//LEsf8fHx773fyclJln504e1Zj2rVqgEA4uLiAADFihVDsWLFcPbsWa37cnR0RHh4OGxsbBAUFCTtQP348WNWHKWP2qBBg9CuXTtMnDgR9vb2eT0cog/CGRgdK1asGPbv3w9PT09YWVnhxIkTKF++PPbv348RI0bIctmCVTQ/zC+//IIhQ4bAwsICzs7OOH36NAwMDLBo0SJs2bKlwFQ0JsoptVqNM2fO5Ml+S0S5xRkYHUtPT4elpSWA18HM7du3Ub58eTg7O+PixYuy9PF2EJRZRXPu3Ln46aefZOmjIOjfvz9q1qyJ+Ph4fPnll9LOvmXKlMHUqVPzeHREeefrr79GaGgoAxhSFM7A6NgXX3yBESNGoHXr1ujUqRMeP36M8ePHY8WKFYiIiMC///6rs7537tyJ2bNnIzQ0VGd9EJHyPX/+HO3atYOtrS08PDyy7Lek7T5kRLrAAEbHgoODkZycjDZt2iA2NhbNmzfHpUuXULRoUWzcuFEqaa8LsbGx8PLy4hbsb7h58ya2b9+eZUUYIM+meERKtGrVKvTt2xempqYoWrSoxiVpOfYhI9IFBjB54NGjR1kKR2kjMTFR42chBO7cuYPJkyfjwoULiIyMlKUfpQsJCUHLli1RpkwZXLhwAZUrV5bqwFStWlWWTfGIlMjBwQGDBw/G2LFjpUurRPkdA5gCILskXiEEHB0dsWHDBqks/8euRo0aaNKkCaZMmQJLS0tERUXBzs4OnTt3RuPGjdGvX7+8HiJRnrCxscHJkyeZA0OKwgCmAAgLC9P4ObOKpqurKwoVYp52JktLS0RGRqJs2bIoUqQIDh8+jEqVKiEqKgqtWrXCtWvX8nqIRHli2LBhsLW1xffff5/XQyH6YPx0U7i0tDQEBgZiwoQJcHFxyevh5GuFCxeW8l6KFy+OuLg4VKpUCQBk32eJSEnS09Mxa9YsBAcHw9PTM0sSL/PDKD9iAKNwRkZG+OuvvzBhwoS8Hkq+V6tWLRw+fBgVK1ZE06ZNMWLECMTExGDLli1S5V+ij1FMTAyqVKkCAFlWRsqVq0ckN15CKgD8/Pzg7e2NYcOG5fVQ8rUrV64gKSkJnp6eSE5OxogRI3D06FG4ublh7ty5cHZ2zushEhHRB2IAUwBMnToVc+bMQYMGDVCtWjUULlxY437WcCAiooKGAUwB8L7cF9Zw+J/evXujS5cuqFu3bl4PhYiItMQcmALg6tWreT0ERbh//z4aN24MW1tbdOjQAV26dIGXl1deD4uIiHKBMzD0UXn8+DH+/PNPrF+/HocOHUKFChXQuXNndOrUCaVLl87r4RER0QdiAFMAtG3bFjVq1MCYMWM0js+aNQsnT/5fe/cfVfP9xwH8efPtrlsqRREq+uFnv8QpRHbCcfzIWe1sfh3Z/Niw0/xI4RDZhoR0zLbGydYxw4zNThMhyq5loR9+hBUSSlOYhOr2+f7h7J7lYm7ofe+n5+Mcf9z3533veR7/eHm/X5/3Oxs7d+4UlMywXbt2Ddu2bcPmzZvx559/oq6uTnQkIiJ6QTwzWgYyMzMxYsQInfHhw4cjMzNTQCLDV1tbixMnTuD48eO4cuUK2rZtKzoSERHpgQWMDFRVVUGpVOqMm5qa6tyT1NwdPnwY06ZNQ9u2bfHee+/BysoKKSkpuHbtmuhoRESkBxYwMuDp6YkdO3bojG/fvh09evQQkMgwdejQASNGjMCtW7ewceNG3Lx5E5s3b8bgwYN5WBcRkZHhW0gyEB0djdDQUBQVFSEoKAjA45uXt23bxv6Xf4mJicE777yDVq1aiY5CREQviU28MvHrr79ixYoVyM3NhUqlgpeXF5YuXYpBgwaJjmaQ/tky6tixo+AkRETUGCxgmpFt27Zh9OjROif1Nhf19fXaU4urqqoAPL6hOiIiAosWLYKJCXdUiYiMBbeQmpEPP/wQ/v7+cHFxER1FiEWLFiEpKQmxsbEICAgAAPz222+IiYnBw4cPsXz5csEJiYjoRXEFphmxtLREXl5esy1g2rdvj8TERIwePbrB+J49ezBz5kxcv35dUDIiItIX18yp2aisrES3bt10xrt164bKykoBiYiIqLFYwFCz4e3tjQ0bNuiMb9iwgXciEREZGfbAULMRFxeHkSNH4uDBg+jXrx8A4Pfff0dJSQn27t0rOB0REemDKzDUbAwaNAgXL15ESEgI7ty5gzt37iA0NBQXLlzAwIEDRccjIiI9sInXyGk0GqjVanh5ef3nAW0eHh5ITU2Fo6Nj04QjIiJ6TVjAyICZmRkKCgrQuXNn0VEMTn5+Pjw8PGBiYoL8/PznzvXy8mqiVERE9LLYAyMDHh4euHTpEguYp/Dx8UFZWRns7e3h4+MDhUKBp9XsCoUCGo1GQEIiImoMrsDIwL59+7Bw4UJ8+umn6N27t85Ju1ZWVoKSiVdcXAwnJycoFAoUFxc/d66zs3MTpSIiopfFAkYG/n0E/r9vVZYkiSsLREQkS9xCkoHDhw+LjmCwfvnllxee++QJvUREZLi4AkOy9uQFjU/2wPx7xYorVURExoPnwMjEnTt3sHbtWkydOhVTp07FunXrcPfuXdGxhKuvr9f+SUtLg4+PD1JTU7XnwOzduxe+vr7Yt2+f6KhERKQHrsDIwIkTJzBs2DCoVCr4+fkBALKzs/HgwQOkpaXB19dXcELD4OHhgcTERAwYMKDB+NGjR/HBBx+goKBAUDIiItIXCxgZGDhwINzc3LBp0yb873+P25rq6uowdepUXLp0CZmZmYITGgaVSoXs7Gx4eHg0GM/Pz4e/vz8ePHggKBkREemLBYwMqFQq5OTk6Ny0fO7cOfTp0wfV1dWCkhmWwMBAmJmZYcuWLWjbti0A4ObNmwgLC8PDhw+RkZEhOCEREb0o9sDIgJWVFa5evaozXlJSAktLSwGJDFNSUhJKS0vh5OQENzc3uLm5wcnJCdevX0dSUpLoeEREpAe+Ri0DY8aMwZQpU7BmzRr0798fAKBWqxEZGYlx48YJTmc43N3dkZ+fjwMHDuD8+fMAgO7du2PIkCEN3kYiIiLDxy0kGaipqUFkZCQSExNRV1cHADA1NcWMGTMQGxuLN954Q3BC8Wpra6FSqZCbm6vTA0NERMaHBYyMVFdXo6ioCADg6uoKc3NzwYkMi4uLC3766Sd4e3uLjkJERC+JBQw1G0lJSdi9eze2bNkCW1tb0XGIiOglsIAxUqGhofj2229hZWWF0NDQ587dvXt3E6UybL169UJhYSFqa2vh7Oysc+nlqVOnBCUjIiJ9sYnXSFlbW2sbT62trQWnMQ5vvfWW6AhERPSKcAXGyEmShJKSEtjZ2UGlUomOQ0RE1CR4DoyRkyQJbm5uuHbtmugoRERETYYFjJEzMTGBu7s7KioqREcxeBqNBmvWrIGfnx/atWsHW1vbBn+IiMh4sICRgdjYWERGRuLMmTOioxi0ZcuWIT4+HmPGjMHdu3cxd+5chIaGwsTEBDExMaLjERGRHtgDIwM2Njaorq5GXV0dlEqlTi9MZWWloGSGxdXVFevXr8fIkSNhaWmJ3Nxc7VhWVha+//570RGJiOgF8S0kGUhISBAdwSiUlZXB09MTANCyZUvcvXsXADBq1ChER0eLjEZERHpiASMDkyZNEh3BKHTs2FF7maOrqyvS0tLg6+uL7OxsXrdARGRk2AMjE0VFRVi8eDHGjRuH8vJyAEBqairOnj0rOJnhCAkJwaFDhwAA4eHhiI6Ohru7O8LCwjB58mTB6YiISB/sgZGBjIwMDB8+HAEBAcjMzERBQQFcXFwQGxuLEydO4McffxQd0SBlZWXh2LFjcHd3R3BwsOg4RESkB67AyMCCBQvw2Wef4cCBA1AqldrxoKAgZGVlCUxmWFauXInNmzdrP/ft2xdz587FX3/9hVWrVglMRkRE+mIBIwOnT59GSEiIzri9vT1u3bolIJFh+vrrr9GtWzed8Z49eyIxMVFAIiIiaiwWMDLQqlUrlJaW6ozn5OSgQ4cOAhIZprKyMjg4OOiM29nZPfXvj4iIDBcLGBkYO3Ys5s+fj7KyMigUCtTX10OtVmPevHkICwsTHc9gODo6Qq1W64yr1Wq0b99eQCIiImosvkYtAytWrMBHH30ER0dHaDQa9OjRAxqNBuPHj8fixYtFxzMY06ZNw+zZs1FbW4ugoCAAwKFDhxAVFYWIiAjB6YiISB98C0lGSkpKcPr0aVRVVaFXr15wd3cXHcmgSJKEBQsWYP369aipqQEAmJmZYf78+ViyZIngdEREpA8WMDLwySefYN68eTA3N28w/uDBA6xevZr/OD+hqqoKBQUFUKlUcHd35yF2RERGiAWMDLRo0QKlpaWwt7dvMF5RUQF7e3toNBpByYiIiF4PNvHKgCRJUCgUOuN5eXmwtbUVkIiIiOj1YhOvEbOxsYFCoYBCoUCXLl0aFDEajQZVVVWYPn26wIRERESvB7eQjFhycjIkScLkyZORkJAAa2tr7TOlUolOnTqhX79+AhMSERG9HixgZCAjIwP9+/eHqamp6ChERERNggWMTNTX16OwsBDl5eWor69v8CwwMFBQKiIioteDPTAykJWVhfHjx6O4uBhP1qMKhYJvIRERkexwBUYGfHx80KVLFyxbtgwODg46byT9uzeGiIhIDljAyICFhQXy8vLg5uYmOgoREVGT4DkwMuDv74/CwkLRMYiIiJoMe2BkIDw8HBERESgrK4Onp6fO20heXl6CkhEREb0e3EKSAROTZy+ksYmXiIjkiCswMnD58mXREYiIiJoUCxgZcHZ2BgCcO3cOV69eRU1NjfaZQqHQPiciIpILFjAycOnSJYSEhOD06dNQKBTas2D+eZ2aW0hERCQ3fAtJBmbNmoXOnTujvLwc5ubmOHPmDDIzM9GnTx8cOXJEdDwiIqJXjk28MtCmTRukp6fDy8sL1tbW+OOPP9C1a1ekp6cjIiICOTk5oiMSERG9UlyBkQGNRgNLS0sAj4uZGzduAHjcG3PhwgWR0YiIiF4L9sDIgIeHB/Ly8tC5c2f4+/sjLi4OSqUSGzduhIuLi+h4RERErxy3kGRg//79uH//PkJDQ1FYWIhRo0bh4sWLaN26NXbs2IGgoCDREYmIiF4pFjAyVVlZCRsbG52LHYmIiOSABQwREREZHTbxEhERkdFhAUNERERGhwUMERERGR0WMET0Sr355puYPXs2AKBTp05ISEgQmkdfV65cgUKhQG5urugoRPQcPAeGiF6b7OxsWFhYiI6hF0dHR5SWlqJNmzaioxDRc7CAIaLXxs7OTnQEvbVo0QLt2rUTHYOI/gO3kIio0e7fv4+wsDC0bNkSDg4OWLt2bYPnT24hxcfHw9PTExYWFnB0dMTMmTNRVVXV4DubNm2Co6MjzM3NERISgvj4eLRq1Ur7PCYmBj4+PtiyZQs6deoEa2trjB07Fvfu3dPOefToET7++GPY29vDzMwMAwYMQHZ2tvb57du3MWHCBNjZ2UGlUsHd3R3ffPMNAN0tpOfNJSJxWMAQUaNFRkYiIyMDe/bsQVpaGo4cOYJTp049c76JiQnWr1+Ps2fPIjk5Genp6YiKitI+V6vVmD59OmbNmoXc3FwMHToUy5cv1/mdoqIi/Pzzz0hJSUFKSgoyMjIQGxurfR4VFYVdu3YhOTkZp06dgpubG4YNG4bKykoAQHR0NM6dO4fU1FQUFBTgq6++euaWkT5ziagJSUREjXDv3j1JqVRKP/zwg3asoqJCUqlU0qxZsyRJkiRnZ2dp3bp1z/yNnTt3Sq1bt9Z+HjNmjDRy5MgGcyZMmCBZW1trPy9dulQyNzeX/v77b+1YZGSk5O/vL0mSJFVVVUmmpqbS1q1btc9ramqk9u3bS3FxcZIkSVJwcLD0/vvvPzXT5cuXJQBSTk7Of84lInG4AkNEjVJUVISamhr4+/trx2xtbdG1a9dnfufgwYMYPHgwOnToAEtLS0ycOBEVFRWorq4GAFy4cAF+fn4NvvPkZ+Dx1tQ/N7ADgIODA8rLy7W5amtrERAQoH1uamoKPz8/FBQUAABmzJiB7du3w8fHB1FRUTh27NgzM+szl4iaDgsYImoSV65cwahRo+Dl5YVdu3bh5MmT+OKLLwAANTU1ev2Wqalpg88KhQL19fUv/P3hw4ejuLgYc+bMwY0bNzB48GDMmzfvpecSUdNhAUNEjeLq6gpTU1McP35cO3b79m1cvHjxqfNPnjyJ+vp6rF27Fn379kWXLl1w48aNBnO6du3aoNkWgM7nF8mlVCqhVqu1Y7W1tcjOzkaPHj20Y3Z2dpg0aRK+++47JCQkYOPGjc/8TX3mElHT4GvURNQoLVu2xJQpUxAZGYnWrVvD3t4eixYtgonJ0/9f5ObmhtraWnz++ecIDg6GWq1GYmJigznh4eEIDAxEfHw8goODkZ6ejtTUVL1uVbewsMCMGTMQGRkJW1tbODk5IS4uDtXV1ZgyZQoAYMmSJejduzd69uyJR48eISUlBd27d3/q7+kzl4iaDldgiKjRVq9ejYEDByI4OBhDhgzBgAED0Lt376fO9fb2Rnx8PFatWgUPDw9s3boVK1eubDAnICAAiYmJiI+Ph7e3N/bt24c5c+bAzMxMr1yxsbF4++23MXHiRPj6+qKwsBD79++HjY0NAECpVGLhwoXw8vJCYGAgWrRoge3btz/1t/SZS0RNRyFJkiQ6BBHRs0ybNg3nz5/H0aNHRUchIgPCLSQiMihr1qzB0KFDYWFhgdTUVCQnJ+PLL78UHYuIDAxXYIjIoLz77rs4cuQI7t27BxcXF4SHh2P69OmiYxGRgWEBQ0REREaHTbxERERkdFjAEBERkdFhAUNERERGhwUMERERGR0WMERERGR0WMAQERGR0WEBQ0REREaHBQwREREZHRYwREREZHT+D+tiFMLUrALiAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zZWRkaNKkSXr44Ye1cuVKSVJRUZEkKSQkxOO4kJAQ91hRUZGCg4M9xps3b662bdt61JxrjjM/44fmzp2rwMBA9xYWFubNqQEAAIN4FWBqamrUq1cvPfXUU7rxxhs1ceJETZgwQcuXL79c/dVZSkqKysrK3NvRo0cbuiUAAHCZeBVgOnbsqKioKI99kZGRKiwslCSFhoZKkoqLiz1qiouL3WOhoaEqKSnxGK+qqtI333zjUXOuOc78jB+yWq2y2WweGwAAaJq8CjD9+vVTQUGBx75//etf6tq1q6T/3NAbGhqqrKws97jT6dTevXtlt9slSXa7XaWlpcrJyXHXvP3226qpqVGfPn3cNTt37lRlZaW7JjMzU9dcc43HE08AAODK5FWASU5O1p49e/TUU0/p008/1erVq/Xiiy8qMTFRkmSxWJSUlKQnn3xSGzZsUF5enuLj49WpUyfdddddkv6zYjNo0CBNmDBB7733nt59911NmTJF9913nzp16iRJ+u1vfys/Pz+NGzdO+fn5eu2117R48WJNnTq1fs8eAAAYyavHqH/xi1/ojTfeUEpKiubMmaPw8HA9++yzGjlypLtm2rRpOnnypCZOnKjS0lLdfPPN2rJli/z9/d01q1at0pQpU3T77bfLx8dHw4cP15IlS9zjgYGB2rp1qxITE9W7d2+1b99eM2fO5BFqAAAgSbK4XC5XQzdxOTidTgUGBqqsrKxR3w/T7bH0hm6hyfjs6biGbgHAZcL/V9afxv7/lXX9+5t3IQEAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxvAows2bNksVi8dh69OjhHj99+rQSExPVrl07tWrVSsOHD1dxcbHHHIWFhYqLi1OLFi0UHBysRx99VFVVVR4127dvV69evWS1WhUREaG0tLSLP0MAANDkeL0Cc+211+r48ePubdeuXe6x5ORkbdy4UevWrdOOHTt07NgxDRs2zD1eXV2tuLg4VVRUaPfu3Vq5cqXS0tI0c+ZMd83hw4cVFxen2267Tbm5uUpKStL48eOVkZFxiacKAACaiuZeH9C8uUJDQ8/aX1ZWphUrVmj16tUaMGCAJCk1NVWRkZHas2eP+vbtq61bt+rjjz/WW2+9pZCQEN1www164oknNH36dM2aNUt+fn5avny5wsPDtWDBAklSZGSkdu3apUWLFsnhcFzi6QIAgKbA6wBz8OBBderUSf7+/rLb7Zo7d666dOminJwcVVZWKjY21l3bo0cPdenSRdnZ2erbt6+ys7MVHR2tkJAQd43D4dCkSZOUn5+vG2+8UdnZ2R5z1NYkJSVdsK/y8nKVl5e7f3Y6nd6eGoD/0+2x9IZuoUn47Om4hm4BaLK8uoTUp08fpaWlacuWLXr++ed1+PBh3XLLLfruu+9UVFQkPz8/BQUFeRwTEhKioqIiSVJRUZFHeKkdrx27UI3T6dSpU6fO29vcuXMVGBjo3sLCwrw5NQAAYBCvVmAGDx7s/nPPnj3Vp08fde3aVWvXrlVAQEC9N+eNlJQUTZ061f2z0+kkxAAA0ERd0mPUQUFBuvrqq/Xpp58qNDRUFRUVKi0t9agpLi523zMTGhp61lNJtT//WI3NZrtgSLJarbLZbB4bAABomi4pwJw4cUL//ve/1bFjR/Xu3Vu+vr7KyspyjxcUFKiwsFB2u12SZLfblZeXp5KSEndNZmambDaboqKi3DVnzlFbUzsHAACAVwHm97//vXbs2KHPPvtMu3fv1m9+8xs1a9ZM999/vwIDAzVu3DhNnTpV27ZtU05OjsaMGSO73a6+fftKkgYOHKioqCiNGjVK+/fvV0ZGhmbMmKHExERZrVZJUkJCgg4dOqRp06bpwIEDWrZsmdauXavk5OT6P3sAAGAkr+6B+fzzz3X//ffr66+/VocOHXTzzTdrz5496tChgyRp0aJF8vHx0fDhw1VeXi6Hw6Fly5a5j2/WrJk2bdqkSZMmyW63q2XLlho9erTmzJnjrgkPD1d6erqSk5O1ePFide7cWS+//DKPUAMAADevAsyaNWsuOO7v76+lS5dq6dKl563p2rWr3nzzzQvO079/f33wwQfetAYAAK4gvAsJAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGOeSAszTTz8ti8WipKQk977Tp08rMTFR7dq1U6tWrTR8+HAVFxd7HFdYWKi4uDi1aNFCwcHBevTRR1VVVeVRs337dvXq1UtWq1URERFKS0u7lFYBAEATctEBZt++fXrhhRfUs2dPj/3JycnauHGj1q1bpx07dujYsWMaNmyYe7y6ulpxcXGqqKjQ7t27tXLlSqWlpWnmzJnumsOHDysuLk633XabcnNzlZSUpPHjxysjI+Ni2wUAAE3IRQWYEydOaOTIkXrppZfUpk0b9/6ysjKtWLFCCxcu1IABA9S7d2+lpqZq9+7d2rNnjyRp69at+vjjj/W3v/1NN9xwgwYPHqwnnnhCS5cuVUVFhSRp+fLlCg8P14IFCxQZGakpU6bo7rvv1qJFi+rhlAEAgOkuKsAkJiYqLi5OsbGxHvtzcnJUWVnpsb9Hjx7q0qWLsrOzJUnZ2dmKjo5WSEiIu8bhcMjpdCo/P99d88O5HQ6He45zKS8vl9Pp9NgAAEDT1NzbA9asWaN//vOf2rdv31ljRUVF8vPzU1BQkMf+kJAQFRUVuWvODC+147VjF6pxOp06deqUAgICzvrsuXPnavbs2d6eDgAAMJBXKzBHjx7VI488olWrVsnf3/9y9XRRUlJSVFZW5t6OHj3a0C0BAIDLxKsAk5OTo5KSEvXq1UvNmzdX8+bNtWPHDi1ZskTNmzdXSEiIKioqVFpa6nFccXGxQkNDJUmhoaFnPZVU+/OP1dhstnOuvkiS1WqVzWbz2AAAQNPkVYC5/fbblZeXp9zcXPcWExOjkSNHuv/s6+urrKws9zEFBQUqLCyU3W6XJNntduXl5amkpMRdk5mZKZvNpqioKHfNmXPU1tTOAQAArmxe3QPTunVrXXfddR77WrZsqXbt2rn3jxs3TlOnTlXbtm1ls9n00EMPyW63q2/fvpKkgQMHKioqSqNGjdL8+fNVVFSkGTNmKDExUVarVZKUkJCgP//5z5o2bZrGjh2rt99+W2vXrlV6enp9nDMAADCc1zfx/phFixbJx8dHw4cPV3l5uRwOh5YtW+Yeb9asmTZt2qRJkybJbrerZcuWGj16tObMmeOuCQ8PV3p6upKTk7V48WJ17txZL7/8shwOR323CwAADHTJAWb79u0eP/v7+2vp0qVaunTpeY/p2rWr3nzzzQvO279/f33wwQeX2h4AAGiCeBcSAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGMerAPP888+rZ8+estlsstlsstvt2rx5s3v89OnTSkxMVLt27dSqVSsNHz5cxcXFHnMUFhYqLi5OLVq0UHBwsB599FFVVVV51Gzfvl29evWS1WpVRESE0tLSLv4MAQBAk+NVgOncubOefvpp5eTk6P3339eAAQM0dOhQ5efnS5KSk5O1ceNGrVu3Tjt27NCxY8c0bNgw9/HV1dWKi4tTRUWFdu/erZUrVyotLU0zZ8501xw+fFhxcXG67bbblJubq6SkJI0fP14ZGRn1dMoAAMB0FpfL5bqUCdq2bas//elPuvvuu9WhQwetXr1ad999tyTpwIEDioyMVHZ2tvr27avNmzfrzjvv1LFjxxQSEiJJWr58uaZPn64vv/xSfn5+mj59utLT0/XRRx+5P+O+++5TaWmptmzZUue+nE6nAgMDVVZWJpvNdimneFl1eyy9oVtoMj57Oq6hW2gy+F7WD76T9YfvZP1p7N/Luv79fdH3wFRXV2vNmjU6efKk7Ha7cnJyVFlZqdjYWHdNjx491KVLF2VnZ0uSsrOzFR0d7Q4vkuRwOOR0Ot2rONnZ2R5z1NbUznE+5eXlcjqdHhsAAGiavA4weXl5atWqlaxWqxISEvTGG28oKipKRUVF8vPzU1BQkEd9SEiIioqKJElFRUUe4aV2vHbsQjVOp1OnTp06b19z585VYGCgewsLC/P21AAAgCG8DjDXXHONcnNztXfvXk2aNEmjR4/Wxx9/fDl680pKSorKysrc29GjRxu6JQAAcJk09/YAPz8/RURESJJ69+6tffv2afHixRoxYoQqKipUWlrqsQpTXFys0NBQSVJoaKjee+89j/lqn1I6s+aHTy4VFxfLZrMpICDgvH1ZrVZZrVZvTwcAABjokn8PTE1NjcrLy9W7d2/5+voqKyvLPVZQUKDCwkLZ7XZJkt1uV15enkpKStw1mZmZstlsioqKctecOUdtTe0cAAAAXq3ApKSkaPDgwerSpYu+++47rV69Wtu3b1dGRoYCAwM1btw4TZ06VW3btpXNZtNDDz0ku92uvn37SpIGDhyoqKgojRo1SvPnz1dRUZFmzJihxMRE9+pJQkKC/vznP2vatGkaO3as3n77ba1du1bp6dyBDgAA/sOrAFNSUqL4+HgdP35cgYGB6tmzpzIyMnTHHXdIkhYtWiQfHx8NHz5c5eXlcjgcWrZsmfv4Zs2aadOmTZo0aZLsdrtatmyp0aNHa86cOe6a8PBwpaenKzk5WYsXL1bnzp318ssvy+Fw1NMpAwAA03kVYFasWHHBcX9/fy1dulRLly49b03Xrl315ptvXnCe/v3764MPPvCmNQAAcAXhXUgAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOF4FmLlz5+oXv/iFWrdureDgYN11110qKCjwqDl9+rQSExPVrl07tWrVSsOHD1dxcbFHTWFhoeLi4tSiRQsFBwfr0UcfVVVVlUfN9u3b1atXL1mtVkVERCgtLe3izhAAADQ5XgWYHTt2KDExUXv27FFmZqYqKys1cOBAnTx50l2TnJysjRs3at26ddqxY4eOHTumYcOGucerq6sVFxeniooK7d69WytXrlRaWppmzpzprjl8+LDi4uJ02223KTc3V0lJSRo/frwyMjLq4ZQBAIDpmntTvGXLFo+f09LSFBwcrJycHN16660qKyvTihUrtHr1ag0YMECSlJqaqsjISO3Zs0d9+/bV1q1b9fHHH+utt95SSEiIbrjhBj3xxBOaPn26Zs2aJT8/Py1fvlzh4eFasGCBJCkyMlK7du3SokWL5HA46unUAQCAqS7pHpiysjJJUtu2bSVJOTk5qqysVGxsrLumR48e6tKli7KzsyVJ2dnZio6OVkhIiLvG4XDI6XQqPz/fXXPmHLU1tXOcS3l5uZxOp8cGAACaposOMDU1NUpKSlK/fv103XXXSZKKiork5+enoKAgj9qQkBAVFRW5a84ML7XjtWMXqnE6nTp16tQ5+5k7d64CAwPdW1hY2MWeGgAAaOQuOsAkJibqo48+0po1a+qzn4uWkpKisrIy93b06NGGbgkAAFwmXt0DU2vKlCnatGmTdu7cqc6dO7v3h4aGqqKiQqWlpR6rMMXFxQoNDXXXvPfeex7z1T6ldGbND59cKi4uls1mU0BAwDl7slqtslqtF3M6AADAMF6twLhcLk2ZMkVvvPGG3n77bYWHh3uM9+7dW76+vsrKynLvKygoUGFhoex2uyTJbrcrLy9PJSUl7prMzEzZbDZFRUW5a86co7amdg4AAHBl82oFJjExUatXr9Y//vEPtW7d2n3PSmBgoAICAhQYGKhx48Zp6tSpatu2rWw2mx566CHZ7Xb17dtXkjRw4EBFRUVp1KhRmj9/voqKijRjxgwlJia6V1ASEhL05z//WdOmTdPYsWP19ttva+3atUpPT6/n0wcAACbyagXm+eefV1lZmfr376+OHTu6t9dee81ds2jRIt15550aPny4br31VoWGhur11193jzdr1kybNm1Ss2bNZLfb9cADDyg+Pl5z5sxx14SHhys9PV2ZmZm6/vrrtWDBAr388ss8Qg0AACR5uQLjcrl+tMbf319Lly7V0qVLz1vTtWtXvfnmmxecp3///vrggw+8aQ8AAFwheBcSAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGMfrALNz504NGTJEnTp1ksVi0fr16z3GXS6XZs6cqY4dOyogIECxsbE6ePCgR80333yjkSNHymazKSgoSOPGjdOJEyc8aj788EPdcsst8vf3V1hYmObPn+/92QEAgCbJ6wBz8uRJXX/99Vq6dOk5x+fPn68lS5Zo+fLl2rt3r1q2bCmHw6HTp0+7a0aOHKn8/HxlZmZq06ZN2rlzpyZOnOgedzqdGjhwoLp27aqcnBz96U9/0qxZs/Tiiy9exCkCAICmprm3BwwePFiDBw8+55jL5dKzzz6rGTNmaOjQoZKkV155RSEhIVq/fr3uu+8+ffLJJ9qyZYv27dunmJgYSdJzzz2nX/3qV3rmmWfUqVMnrVq1ShUVFfrLX/4iPz8/XXvttcrNzdXChQs9gg4AALgy1es9MIcPH1ZRUZFiY2Pd+wIDA9WnTx9lZ2dLkrKzsxUUFOQOL5IUGxsrHx8f7d27111z6623ys/Pz13jcDhUUFCgb7/99pyfXV5eLqfT6bEBAICmqV4DTFFRkSQpJCTEY39ISIh7rKioSMHBwR7jzZs3V9u2bT1qzjXHmZ/xQ3PnzlVgYKB7CwsLu/QTAgAAjVKTeQopJSVFZWVl7u3o0aMN3RIAALhM6jXAhIaGSpKKi4s99hcXF7vHQkNDVVJS4jFeVVWlb775xqPmXHOc+Rk/ZLVaZbPZPDYAANA01WuACQ8PV2hoqLKystz7nE6n9u7dK7vdLkmy2+0qLS1VTk6Ou+btt99WTU2N+vTp467ZuXOnKisr3TWZmZm65ppr1KZNm/psGQAAGMjrAHPixAnl5uYqNzdX0n9u3M3NzVVhYaEsFouSkpL05JNPasOGDcrLy1N8fLw6deqku+66S5IUGRmpQYMGacKECXrvvff07rvvasqUKbrvvvvUqVMnSdJvf/tb+fn5ady4ccrPz9drr72mxYsXa+rUqfV24gAAwFxeP0b9/vvv67bbbnP/XBsqRo8erbS0NE2bNk0nT57UxIkTVVpaqptvvllbtmyRv7+/+5hVq1ZpypQpuv322+Xj46Phw4dryZIl7vHAwEBt3bpViYmJ6t27t9q3b6+ZM2fyCDUAAJB0EQGmf//+crlc5x23WCyaM2eO5syZc96atm3bavXq1Rf8nJ49e+qdd97xtj0AAHAFaDJPIQEAgCsHAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwTqMOMEuXLlW3bt3k7++vPn366L333mvolgAAQCPQaAPMa6+9pqlTp+rxxx/XP//5T11//fVyOBwqKSlp6NYAAEADa7QBZuHChZowYYLGjBmjqKgoLV++XC1atNBf/vKXhm4NAAA0sOYN3cC5VFRUKCcnRykpKe59Pj4+io2NVXZ29jmPKS8vV3l5ufvnsrIySZLT6by8zV6imvLvG7qFJqOx/3dtEr6X9YPvZP3hO1l/Gvv3srY/l8t1wbpGGWC++uorVVdXKyQkxGN/SEiIDhw4cM5j5s6dq9mzZ5+1Pyws7LL0iMYn8NmG7gDwxHcSjZEp38vvvvtOgYGB5x1vlAHmYqSkpGjq1Knun2tqavTNN9+oXbt2slgsDdiZ+ZxOp8LCwnT06FHZbLaGbgfgO4lGh+9k/XG5XPruu+/UqVOnC9Y1ygDTvn17NWvWTMXFxR77i4uLFRoaes5jrFarrFarx76goKDL1eIVyWaz8T9MNCp8J9HY8J2sHxdaeanVKG/i9fPzU+/evZWVleXeV1NTo6ysLNnt9gbsDAAANAaNcgVGkqZOnarRo0crJiZGv/zlL/Xss8/q5MmTGjNmTEO3BgAAGlijDTAjRozQl19+qZkzZ6qoqEg33HCDtmzZctaNvbj8rFarHn/88bMu0QENhe8kGhu+kz89i+vHnlMCAABoZBrlPTAAAAAXQoABAADGIcAAAADjEGAAAIBxCDAAAMA4BBh45fPPP9fEiRMbug0AaPROnTrV0C00aQQYeOXrr7/WihUrGroNAGi0ysvLtWDBAoWHhzd0K00aAQaAMaqqqvTWW2/phRde0HfffSdJOnbsmE6cONHAneFKU15erpSUFMXExOimm27S+vXrJUmpqakKDw/Xs88+q+Tk5IZtsolrtL+JFwDOdOTIEQ0aNEiFhYUqLy/XHXfcodatW2vevHkqLy/X8uXLG7pFXEFmzpypF154QbGxsdq9e7fuuecejRkzRnv27NHChQt1zz33qFmzZg3dZpNGgAFghEceeUQxMTHav3+/2rVr597/m9/8RhMmTGjAznAlWrdunV555RX9+te/1kcffaSePXuqqqpK+/fvl8Viaej2rggEGHgYNmzYBcdLS0t/mkaAH3jnnXe0e/du+fn5eezv1q2bvvjiiwbqCleqzz//XL1795YkXXfddbJarUpOTia8/IQIMPAQGBj4o+Px8fE/UTfA/6upqVF1dfVZ+z///HO1bt26ATrClay6utojTDdv3lytWrVqwI6uPLzMEYARRowYocDAQL344otq3bq1PvzwQ3Xo0EFDhw5Vly5dlJqa2tAt4gri4+OjwYMHu98+vXHjRg0YMEAtW7b0qHv99dcbor0rAgEGgBE+//xzORwOuVwuHTx4UDExMTp48KDat2+vnTt3Kjg4uKFbxBVkzJgxdaojWF8+BBgAxqiqqtKaNWv04Ycf6sSJE+rVq5dGjhypgICAhm4NwE+MAAMAAIzDTbwAjHHw4EFt27ZNJSUlqqmp8RibOXNmA3UFoCGwAgPACC+99JImTZqk9u3bKzQ01ONxVYvFon/+858N2B2AnxoBBoARunbtqsmTJ2v69OkN3QqARoAAA8AINptNubm56t69e0O3AqAR4GWOAIxwzz33aOvWrQ3dBoBGgpt4ARghIiJCf/jDH7Rnzx5FR0fL19fXY/zhhx9uoM4ANAQuIQEwQnh4+HnHLBaLDh069BN2A6ChEWAAAIBxuAcGgHFcLpf4txdwZSPAADDGK6+8oujoaAUEBCggIEA9e/bUX//614ZuC0AD4CZeAEZYuHCh/vCHP2jKlCnq16+fJGnXrl1KSEjQV199peTk5AbuEMBPiXtgABghPDxcs2fPVnx8vMf+lStXatasWTp8+HADdQagIXAJCYARjh8/rptuuums/TfddJOOHz/eAB0BaEgEGABGiIiI0Nq1a8/a/9prr+nnP/95A3QEoCFxDwwAI8yePVsjRozQzp073ffAvPvuu8rKyjpnsAHQtHEPDABj5OTkaOHChTpw4IAkKTIyUv/1X/+lG2+8sYE7A/BTI8AAAADjcAkJQKPm4+Mji8VywRqLxaKqqqqfqCMAjQEBBkCj9sYbb5x3LDs7W0uWLFFNTc1P2BGAxoBLSACMU1BQoMcee0wbN27UyJEjNWfOHHXt2rWh2wLwE+IxagDGOHbsmCZMmKDo6GhVVVUpNzdXK1euJLwAVyACDIBGr6ysTNOnT1dERITy8/OVlZWljRs36rrrrmvo1gA0EO6BAdCozZ8/X/PmzVNoaKheffVVDR06tKFbAtAIcA8MgEbNx8dHAQEBio2NVbNmzc5b9/rrr/+EXQFoaKzAAGjU4uPjf/QxagBXHlZgAACAcbiJFwAAGIcAAwAAjEOAAQAAxiHAAKiT/v37Kykpqc71FotF69evv2z9XKrt27fLYrGotLT0vDVpaWkKCgr6yXoCUHc8hQSgTl5//XX5+vrWuf748eNq06bNZezo0tx00006fvy4AgMDG7oVABeBAAOgTtq2betVfWho6CV9XnV1tSwWi3x8Ls9CsZ+f3yX3CKDhcAkJQJ2ceQmpW7dueuqppzR27Fi1bt1aXbp00YsvvuhRf+YlpHNdrsnNzZXFYtFnn30m6f8v12zYsEFRUVGyWq3atWuXfH19VVRU5DF3UlKSbrnllh/t+ciRIxoyZIjatGmjli1b6tprr9Wbb7553p7S0tLUpUsXtWjRQr/5zW/09ddfnzXnP/7xD/Xq1Uv+/v7q3r27Zs+eraqqqh/tBUD9IsAAuCgLFixQTEyMPvjgA02ePFmTJk1SQUHBJc35/fffa968eXr55ZeVn5+vmJgYde/eXX/961/dNZWVlVq1apXGjh37o/MlJiaqvLxcO3fuVF5enubNm6dWrVqds3bv3r0aN26cpkyZotzcXN1222168sknPWreeecdxcfH65FHHtHHH3+sF154QWlpafrjH/94SecNwHsEGAAX5Ve/+pUmT56siIgITZ8+Xe3bt9e2bdsuac7KykotW7ZMN910k6655hq1aNFC48aNU2pqqrtm48aNOn36tO69994fna+wsFD9+vVTdHS0unfvrjvvvFO33nrrOWsXL16sQYMGadq0abr66qv18MMPy+FweNTMnj1bjz32mEaPHq3u3bvrjjvu0BNPPKEXXnjhks4bgPcIMAAuSs+ePd1/tlgsCg0NVUlJySXN6efn5zGvJD344IP69NNPtWfPHkn/ucxz7733qmXLlj8638MPP6wnn3xS/fr10+OPP64PP/zwvLWffPKJ+vTp47HPbrd7/Lx//37NmTNHrVq1cm8TJkzQ8ePH9f3339f1NAHUAwIMgIvywyeSLBaLampqzllbeyPumW8uqaysPKsuICDgrPceBQcHa8iQIUpNTVVxcbE2b95cp8tHkjR+/HgdOnRIo0aNUl5enmJiYvTcc8/V6dhzOXHihGbPnq3c3Fz3lpeXp4MHD8rf3/+i5wXgPZ5CAnDZdejQQZLno9W5ubl1Pn78+PG6//771blzZ1111VXq169fnY8NCwtTQkKCEhISlJKSopdeekkPPfTQWXWRkZHau3evx77aVZ9avXr1UkFBgSIiIur8+QAuDwIMgMsuIiJCYWFhmjVrlv74xz/qX//6lxYsWFDn4x0Oh2w2m5588knNmTOnzsclJSVp8ODBuvrqq/Xtt99q27ZtioyMPGftww8/rH79+umZZ57R0KFDlZGRoS1btnjUzJw5U3feeae6dOmiu+++Wz4+Ptq/f78++uijs274BXB5cQkJwGXn6+urV199VQcOHFDPnj01b948r/7C9/Hx0YMPPqjq6mrFx8fX+bjq6molJiYqMjJSgwYN0tVXX61ly5ads7Zv37566aWXtHjxYl1//fXaunWrZsyY4VHjcDi0adMmbd26Vb/4xS/Ut29fLVq0SF27dq1zTwDqh8V15kVpAKgH5eXl8vf3V2ZmpmJjY+tlznHjxunLL7/Uhg0b6mU+AGbjEhKAeuV0OvX666/Lx8dHPXr0uOT5ysrKlJeXp9WrVxNeALhxCQlAvXr88cc1ffp0zZs3T507d77k+YYOHaqBAwcqISFBd9xxh8fY4MGDPR5pPnN76qmnLvmzATReXEICYKwvvvhCp06dOudY27ZtvX5/EwBzEGAAAIBxuIQEAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABjnfwH0VEh/k+twvwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IMAAAwHIIMAAAwHLymF0AcL+UGLzM7BLu2bH3GptdAgBYAiMwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcggwAADAcjIVYJKSkjR06FCVLFlSPj4+Kl26tN5++20ZhuHoYxiGhg0bpkKFCsnHx0cRERGKiYlx2s7FixfVtm1b2e12BQUFqUuXLrp06ZJTnz179qhevXry9vZWaGioxo0bdw+7CQAAcpNMBZixY8dq6tSp+uijj3TgwAGNHTtW48aN0+TJkx19xo0bp0mTJmnatGnaunWr/Pz8FBkZqWvXrjn6tG3bVvv379eqVau0dOlSbdiwQS+//LJjeUJCgho2bKjixYtr586d+u9//6sRI0bo448/vg+7DAAArM5m3Dp8cgfPPPOMgoOD9emnnzraWrRoIR8fH3355ZcyDEOFCxfWgAEDNHDgQElSfHy8goODNWvWLLVq1UoHDhxQhQoVtH37dtWoUUOStGLFCjVq1EinTp1S4cKFNXXqVP3nP/9RbGysPD09JUmDBw/W4sWLdfDgwQzVmpCQoMDAQMXHx8tut2f4DYF1lRi8zOwS7tmx9xqbXQIAmCqjv9+ZGoGpXbu2Vq9erd9++02StHv3bm3cuFFPP/20JOno0aOKjY1VRESEY53AwEDVrFlTmzdvliRt3rxZQUFBjvAiSREREXJzc9PWrVsdfR599FFHeJGkyMhIHTp0SH/99VeatV2/fl0JCQlODwAAkDvlyUznwYMHKyEhQeXKlZO7u7uSkpL07rvvqm3btpKk2NhYSVJwcLDTesHBwY5lsbGxKliwoHMRefIoX758Tn1KliyZahspy/LmzZuqtjFjxmjkyJGZ2R0AAGBRmRqBmT9/vubMmaO5c+fql19+0ezZs/X+++9r9uzZWVVfhg0ZMkTx8fGOx8mTJ80uCQAAZJFMjcC8/vrrGjx4sFq1aiVJCgsL0/HjxzVmzBh17NhRISEhkqRz586pUKFCjvXOnTunKlWqSJJCQkJ0/vx5p+3evHlTFy9edKwfEhKic+fOOfVJeZ7S53ZeXl7y8vLKzO4AAACLytQIzJUrV+Tm5ryKu7u7kpOTJUklS5ZUSEiIVq9e7ViekJCgrVu3Kjw8XJIUHh6uuLg47dy509FnzZo1Sk5OVs2aNR19NmzYoMTEREefVatW6cEHH0zz8BEAAHAtmQowTZo00bvvvqtly5bp2LFj+vbbbzVhwgQ1a9ZMkmSz2dS3b1+98847WrJkifbu3asOHTqocOHCatq0qSSpfPnyeuqpp9StWzdt27ZNUVFR6tWrl1q1aqXChQtLktq0aSNPT0916dJF+/fv19dff60PP/xQ/fv3v797DwAALClTh5AmT56soUOH6tVXX9X58+dVuHBhvfLKKxo2bJijzxtvvKHLly/r5ZdfVlxcnOrWrasVK1bI29vb0WfOnDnq1auXnnjiCbm5ualFixaaNGmSY3lgYKBWrlypnj17qnr16sqfP7+GDRvmdK0YAADgujJ1HRgr4TowrofrwACA9WXJdWAAAAByAgIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwHAIMAACwnEwHmNOnT6tdu3Z64IEH5OPjo7CwMO3YscOx3DAMDRs2TIUKFZKPj48iIiIUExPjtI2LFy+qbdu2stvtCgoKUpcuXXTp0iWnPnv27FG9evXk7e2t0NBQjRs37i53EQAA5DaZCjB//fWX6tSpIw8PDy1fvly//vqrxo8fr7x58zr6jBs3TpMmTdK0adO0detW+fn5KTIyUteuXXP0adu2rfbv369Vq1Zp6dKl2rBhg15++WXH8oSEBDVs2FDFixfXzp079d///lcjRozQxx9/fB92GQAAWJ3NMAwjo50HDx6sqKgo/fzzz2kuNwxDhQsX1oABAzRw4EBJUnx8vIKDgzVr1iy1atVKBw4cUIUKFbR9+3bVqFFDkrRixQo1atRIp06dUuHChTV16lT95z//UWxsrDw9PR2vvXjxYh08eDBDtSYkJCgwMFDx8fGy2+0Z3UVYWInBy8wu4Z4de6+x2SUAgKky+vudqRGYJUuWqEaNGnrhhRdUsGBBVa1aVZ988olj+dGjRxUbG6uIiAhHW2BgoGrWrKnNmzdLkjZv3qygoCBHeJGkiIgIubm5aevWrY4+jz76qCO8SFJkZKQOHTqkv/76K83arl+/roSEBKcHAADInTIVYH7//XdNnTpVZcuW1Y8//qgePXrotdde0+zZsyVJsbGxkqTg4GCn9YKDgx3LYmNjVbBgQaflefLkUb58+Zz6pLWNW1/jdmPGjFFgYKDjERoampldAwAAFpKpAJOcnKxq1app9OjRqlq1ql5++WV169ZN06ZNy6r6MmzIkCGKj493PE6ePGl2SQAAIItkKsAUKlRIFSpUcGorX768Tpw4IUkKCQmRJJ07d86pz7lz5xzLQkJCdP78eaflN2/e1MWLF536pLWNW1/jdl5eXrLb7U4PAACQO2UqwNSpU0eHDh1yavvtt99UvHhxSVLJkiUVEhKi1atXO5YnJCRo69atCg8PlySFh4crLi5OO3fudPRZs2aNkpOTVbNmTUefDRs2KDEx0dFn1apVevDBB53OeAIAAK4pUwGmX79+2rJli0aPHq3Dhw9r7ty5+vjjj9WzZ09Jks1mU9++ffXOO+9oyZIl2rt3rzp06KDChQuradOmkv4ZsXnqqafUrVs3bdu2TVFRUerVq5datWqlwoULS5LatGkjT09PdenSRfv379fXX3+tDz/8UP3797+/ew8AACwpT2Y6P/zww/r22281ZMgQjRo1SiVLltQHH3ygtm3bOvq88cYbunz5sl5++WXFxcWpbt26WrFihby9vR195syZo169eumJJ56Qm5ubWrRooUmTJjmWBwYGauXKlerZs6eqV6+u/Pnza9iwYU7XigEAAK4rU9eBsRKuA+N6uA4MAFhfllwHBgAAICcgwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMvJcy8rv/feexoyZIj69OmjDz74QJJ07do1DRgwQPPmzdP169cVGRmp//3vfwoODnasd+LECfXo0UNr166Vv7+/OnbsqDFjxihPnv8rZ926derfv7/279+v0NBQvfXWW+rUqdO9lAsAgGlKDF5mdgn3xbH3GptdgqR7GIHZvn27pk+frkqVKjm19+vXT99//70WLFig9evX68yZM2revLljeVJSkho3bqwbN25o06ZNmj17tmbNmqVhw4Y5+hw9elSNGzfWY489pujoaPXt21ddu3bVjz/+eLflAgCAXOSuAsylS5fUtm1bffLJJ8qbN6+jPT4+Xp9++qkmTJigxx9/XNWrV9fMmTO1adMmbdmyRZK0cuVK/frrr/ryyy9VpUoVPf3003r77bc1ZcoU3bhxQ5I0bdo0lSxZUuPHj1f58uXVq1cvPf/885o4ceJ92GUAAGB1dxVgevbsqcaNGysiIsKpfefOnUpMTHRqL1eunIoVK6bNmzdLkjZv3qywsDCnQ0qRkZFKSEjQ/v37HX1u33ZkZKRjG2m5fv26EhISnB4AACB3yvQcmHnz5umXX37R9u3bUy2LjY2Vp6engoKCnNqDg4MVGxvr6HNreElZnrIsvT4JCQm6evWqfHx8Ur32mDFjNHLkyMzuDgAAsKBMjcCcPHlSffr00Zw5c+Tt7Z1VNd2VIUOGKD4+3vE4efKk2SUBAIAskqkAs3PnTp0/f17VqlVTnjx5lCdPHq1fv16TJk1Snjx5FBwcrBs3biguLs5pvXPnzikkJESSFBISonPnzqVanrIsvT52uz3N0RdJ8vLykt1ud3oAAIDcKVMB5oknntDevXsVHR3teNSoUUNt27Z1/NvDw0OrV692rHPo0CGdOHFC4eHhkqTw8HDt3btX58+fd/RZtWqV7Ha7KlSo4Ohz6zZS+qRsAwAAuLZMzYEJCAhQxYoVndr8/Pz0wAMPONq7dOmi/v37K1++fLLb7erdu7fCw8NVq1YtSVLDhg1VoUIFtW/fXuPGjVNsbKzeeust9ezZU15eXpKk7t2766OPPtIbb7yhl156SWvWrNH8+fO1bFnuOIceAADcm3u6kF1aJk6cKDc3N7Vo0cLpQnYp3N3dtXTpUvXo0UPh4eHy8/NTx44dNWrUKEefkiVLatmyZerXr58+/PBDFS1aVDNmzFBkZOT9LhcAAFiQzTAMw+wiskJCQoICAwMVHx/PfBgXkRuucplTrnAJ4P7LDd9RUtZ/T2X095t7IQEAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMshwAAAAMvJVIAZM2aMHn74YQUEBKhgwYJq2rSpDh065NTn2rVr6tmzpx544AH5+/urRYsWOnfunFOfEydOqHHjxvL19VXBggX1+uuv6+bNm0591q1bp2rVqsnLy0tlypTRrFmz7m4PAQBArpOpALN+/Xr17NlTW7Zs0apVq5SYmKiGDRvq8uXLjj79+vXT999/rwULFmj9+vU6c+aMmjdv7lielJSkxo0b68aNG9q0aZNmz56tWbNmadiwYY4+R48eVePGjfXYY48pOjpaffv2VdeuXfXjjz/eh10GAABWZzMMw7jblf/44w8VLFhQ69ev16OPPqr4+HgVKFBAc+fO1fPPPy9JOnjwoMqXL6/NmzerVq1aWr58uZ555hmdOXNGwcHBkqRp06Zp0KBB+uOPP+Tp6alBgwZp2bJl2rdvn+O1WrVqpbi4OK1YsSJDtSUkJCgwMFDx8fGy2+13u4uwkBKDl5ldwj079l5js0sAkEVyw3eUlPXfUxn9/b6nOTDx8fGSpHz58kmSdu7cqcTEREVERDj6lCtXTsWKFdPmzZslSZs3b1ZYWJgjvEhSZGSkEhIStH//fkefW7eR0idlG2m5fv26EhISnB4AACB3uusAk5ycrL59+6pOnTqqWLGiJCk2Nlaenp4KCgpy6hscHKzY2FhHn1vDS8rylGXp9UlISNDVq1fTrGfMmDEKDAx0PEJDQ+921wAAQA531wGmZ8+e2rdvn+bNm3c/67lrQ4YMUXx8vONx8uRJs0sCAABZJM/drNSrVy8tXbpUGzZsUNGiRR3tISEhunHjhuLi4pxGYc6dO6eQkBBHn23btjltL+UspVv73H7m0rlz52S32+Xj45NmTV5eXvLy8rqb3QEAABaTqQBjGIZ69+6tb7/9VuvWrVPJkiWdllevXl0eHh5avXq1WrRoIUk6dOiQTpw4ofDwcElSeHi43n33XZ0/f14FCxaUJK1atUp2u10VKlRw9Pnhhx+ctr1q1SrHNgDkbExWBJDVMhVgevbsqblz5+q7775TQECAY85KYGCgfHx8FBgYqC5duqh///7Kly+f7Ha7evfurfDwcNWqVUuS1LBhQ1WoUEHt27fXuHHjFBsbq7feeks9e/Z0jKB0795dH330kd544w299NJLWrNmjebPn69ly3LHlyIAALg3mZoDM3XqVMXHx6tBgwYqVKiQ4/H11187+kycOFHPPPOMWrRooUcffVQhISFatGiRY7m7u7uWLl0qd3d3hYeHq127durQoYNGjRrl6FOyZEktW7ZMq1atUuXKlTV+/HjNmDFDkZGR92GXAQCA1WX6ENKdeHt7a8qUKZoyZcq/9ilevHiqQ0S3a9CggXbt2pWZ8gAAgIvgXkgAAMByCDAAAMByCDAAAMBy7uo6MAAAa+CUduRWjMAAAADLIcAAAADL4RDSPcoNw7MMzQIArIYRGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDk5OsBMmTJFJUqUkLe3t2rWrKlt27aZXRIAAMgBcmyA+frrr9W/f38NHz5cv/zyiypXrqzIyEidP3/e7NIAAIDJcmyAmTBhgrp166bOnTurQoUKmjZtmnx9ffXZZ5+ZXRoAADBZHrMLSMuNGze0c+dODRkyxNHm5uamiIgIbd68Oc11rl+/ruvXrzuex8fHS5ISEhKytNbk61eydPvZIavfo+zCZ5Fz5IbPQsodnwefRc7BZ5G57RuGkW6/HBlgLly4oKSkJAUHBzu1BwcH6+DBg2muM2bMGI0cOTJVe2hoaJbUmJsEfmB2BUjBZ5Gz8HnkHHwWOUd2fRZ///23AgMD/3V5jgwwd2PIkCHq37+/43lycrIuXryoBx54QDabzcTK7l5CQoJCQ0N18uRJ2e12s8txeXweOQefRc7BZ5Fz5JbPwjAM/f333ypcuHC6/XJkgMmfP7/c3d117tw5p/Zz584pJCQkzXW8vLzk5eXl1BYUFJRVJWYru91u6f8Ycxs+j5yDzyLn4LPIOXLDZ5HeyEuKHDmJ19PTU9WrV9fq1asdbcnJyVq9erXCw8NNrAwAAOQEOXIERpL69++vjh07qkaNGnrkkUf0wQcf6PLly+rcubPZpQEAAJPl2ADTsmVL/fHHHxo2bJhiY2NVpUoVrVixItXE3tzMy8tLw4cPT3VoDObg88g5+CxyDj6LnMPVPgubcafzlAAAAHKYHDkHBgAAID0EGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGAAAYDkEGACWceTIEb311ltq3bq1zp8/L0lavny59u/fb3JlrmPfvn3/umzx4sXZVwhcHgEmh7p586Z++uknTZ8+XX///bck6cyZM7p06ZLJlbmWkydP6tSpU47n27ZtU9++ffXxxx+bWJVrWr9+vcLCwrR161YtWrTI8f/C7t27NXz4cJOrcx2RkZE6evRoqvaFCxeqbdu2JlSEWyUkJGjx4sU6cOCA2aVkPQM5zrFjx4xy5coZvr6+hru7u3HkyBHDMAzjtddeM1555RWTq3MtdevWNT7//HPDMAzj7Nmzht1uN8LDw438+fMbI0eONLk611KrVi1j/PjxhmEYhr+/v+P/i61btxpFihQxszSXMmzYMKNUqVLG2bNnHW3z5s0zfH19jfnz55tYmWt64YUXjMmTJxuGYRhXrlwxypYta3h4eBh58uQxvvnmG5Ory1qMwORAffr0UY0aNfTXX3/Jx8fH0d6sWTOnG1wi6+3bt0+PPPKIJGn+/PmqWLGiNm3apDlz5mjWrFnmFudi9u7dq2bNmqVqL1iwoC5cuGBCRa5p5MiRatSokSIiInTx4kXNnTtXnTt31ueff64XXnjB7PJczoYNG1SvXj1J0rfffivDMBQXF6dJkybpnXfeMbm6rEWAyYF+/vlnvfXWW/L09HRqL1GihE6fPm1SVa4pMTHRcV+Rn376Sc8++6wkqVy5cjp79qyZpbmcoKCgNN/zXbt2qUiRIiZU5LomT56sypUrq1atWurWrZu++uortWjRwuyyXFJ8fLzy5csnSVqxYoVatGghX19fNW7cWDExMSZXl7Vy7M0cXVlycrKSkpJStZ86dUoBAQEmVOS6HnroIU2bNk2NGzfWqlWr9Pbbb0v6Zz7SAw88YHJ1rqVVq1YaNGiQFixYIJvNpuTkZEVFRWngwIHq0KGD2eXlakuWLEnV1rx5c/38889q3bq1bDabo09KyEf2CA0N1ebNm5UvXz6tWLFC8+bNkyT99ddf8vb2Nrm6rMXNHHOgli1bKjAwUB9//LECAgK0Z88eFShQQM8995yKFSummTNnml2iy1i3bp2aNWumhIQEdezYUZ999pkk6c0339TBgwe1aNEikyt0HTdu3FDPnj01a9YsJSUlKU+ePEpKSlKbNm00a9Ysubu7m11iruXmlrHBepvNluYfX8g6//vf/9SnTx/5+/urePHi+uWXX+Tm5qbJkydr0aJFWrt2rdklZhkCTA506tQpRUZGyjAMxcTEqEaNGoqJiVH+/Pm1YcMGFSxY0OwSXUpSUpISEhKUN29eR9uxY8fk6+vLZ2GCkydPau/evbp06ZKqVq2qsmXLml0SYKodO3bo5MmTevLJJ+Xv7y9JWrZsmYKCglSnTh2Tq8s6BJgc6ubNm5o3b5727NmjS5cuqVq1amrbtq3TpF5kn/Pnz+vQoUOSpAcffJDgAiDHSfk5t9lsJleSPQgwQDr+/vtvvfrqq5o3b55jaNzd3V0tW7bUlClTFBgYaHKFrqNFixZ65JFHNGjQIKf2cePGafv27VqwYIFJlbme1atXa/Xq1Tp//rySk5OdlqUcZkX2+fTTTzVx4kTHpN2yZcuqb9++6tq1q8mVZS0m8eZQMTExWrt2bZpfEMOGDTOpKtfTtWtX7dq1S0uXLlV4eLgkafPmzerTp49eeeUVx4Q5ZL0NGzZoxIgRqdqffvppjR8/PvsLclEjR47UqFGjVKNGDRUqVMhl/trPqYYNG6YJEyaod+/eTt9R/fr104kTJzRq1CiTK8w6jMDkQJ988ol69Oih/PnzKyQkxOkLwmaz6ZdffjGxOtfi5+enH3/8UXXr1nVq//nnn/XUU0/p8uXLJlXmenx8fBQdHa0HH3zQqf3gwYOqWrWqrl69alJlrqVQoUIaN26c2rdvb3YpkFSgQAFNmjRJrVu3dmr/6quv1Lt371x9jSRGYHKgd955R++++26qoXJkvwceeCDNw0SBgYFOk3qR9cLCwvT111+nGoGcN2+eKlSoYFJVrufGjRuqXbu22WXg/5eYmKgaNWqkaq9evbpu3rxpQkXZhxGYHMhutys6OlqlSpUyuxSX9/HHH2vBggX64osvFBISIkmKjY1Vx44d1bx5c73yyismV+g6vv/+ezVv3lxt2rTR448/LumfuRhfffWVFixYoKZNm5pboIsYNGiQ/P39NXToULNLgaTevXvLw8NDEyZMcGofOHCgrl69qilTpphUWdZjBCYHeuGFF7Ry5Up1797d7FJcUtWqVZ0O28XExKhYsWIqVqyYJOnEiRPy8vLSH3/8QYDJRk2aNNHixYs1evRoffPNN/Lx8VGlSpX0008/qX79+maX5zKuXbumjz/+WD/99JMqVaokDw8Pp+W3/5Di/uvfv7/j3zabTTNmzNDKlStVq1YtSdLWrVt14sSJXH+BRwJMDlSmTBkNHTpUW7ZsUVhYWKoviNdee82kylwDf8nnXI0bN1bjxo3NLsOl7dmzR1WqVJH0z73CbsWE3uyxa9cup+fVq1eXJB05ckSSlD9/fuXPn1/79+/P9tqyE4eQcqCSJUv+6zKbzabff/89G6sBACDnIcAAGXTp0qVUp7Tb7XaTqnE9SUlJmjhxoubPn68TJ07oxo0bTssvXrxoUmUAzMAhpBzO1a6smNMcPXpUvXr10rp163Tt2jVHu2EY3Pclm40cOVIzZszQgAED9NZbb+k///mPjh07psWLF3NtpGz02GOPpft9tGbNmmysBteuXdPkyZP/9bphufmyGwSYHOrzzz/Xf//7X8eVFf/f//t/ev3117n2QjZr166dDMPQZ599puDgYIKkiebMmaNPPvlEjRs31ogRI9S6dWuVLl1alSpV0pYtW5gblk1S5r+kSExMVHR0tPbt26eOHTuaU5QL69Kli1auXKnnn39ejzzyiEt9RxFgcqAJEyZo6NCh6tWrl+NGXBs3blT37t114cIF9evXz+QKXcfu3bu1c+fOVBdPQ/aLjY1VWFiYJMnf31/x8fGSpGeeeYZTerPRxIkT02wfMWKELl26lM3VYOnSpfrhhx9y9U0b/03G7pGObDV58mRNnTpVY8eO1bPPPqtnn31W48aN0//+9z9NmjTJ7PJcysMPP6yTJ0+aXQYkFS1aVGfPnpUklS5dWitXrpQkbd++XV5eXmaWBv0zWsl9kLJfkSJFFBAQYHYZpmAEJgc6e/Zsmle6rF27tuMLHNljxowZ6t69u06fPq2KFSumOqW9UqVKJlXmepo1a6bVq1erZs2a6t27t9q1a6dPP/1UJ06cYFQyB9i8ebO8vb3NLsPljB8/XoMGDdK0adNUvHhxs8vJVgSYHKhMmTKaP3++3nzzTaf2r7/+WmXLljWpKtf0xx9/6MiRI+rcubOjzWazMYnXBO+9957j3y1btlTx4sW1adMmlS1bVk2aNDGxMtfSvHlzp+eGYejs2bPasWMHh/JMUKNGDV27dk2lSpWSr69vqj+ycvPZeZxGnQMtXLhQLVu2VEREhOO4ZlRUlFavXq358+erWbNmJlfoOipUqKDy5cvrjTfeSHMSr6v9xZPdqlWrptWrVytv3rwaNWqUBg4cKF9fX7PLcmm3hnlJcnNzU4ECBfT444+rYcOGJlXluiIiInTixAl16dIlze+o3DyxmgCTQ+3cuVMTJkzQwYMHJUnly5fXgAEDVLVqVZMrcy1+fn7avXu3ypQpY3YpLsnHx0cxMTEqWrSo3N3ddfbsWRUsWNDsslxWUlKSoqKiFBYWxs1McwhfX19t3rxZlStXNruUbMchpByqevXqmjNnjtlluLzHH3+cAGOiKlWqqHPnzqpbt64Mw9D7778vf3//NPtyLZis5+7uroYNG+rAgQMEmByiXLlyunr1qtllmIIAk4O4ubnd8Rx+m82W62+RnpM0adJE/fr10969e9O8L9Wzzz5rUmWuYdasWRo+fLiWLl0qm82m5cuXK0+e1F9bNpuNAJNNKlasqN9//z3dW54g+7z33nsaMGCA3n333TS/o3Lz1cI5hJSDfPfdd/+6bPPmzZo0aZKSk5OdrgiLrOXm9u9XGmASb/Zyc3NTbGwsh5BMtmLFCg0ZMkRvv/22qlevLj8/P6flufkHMydK+Y66/Y9fVzjRgACTwx06dEiDBw/W999/r7Zt22rUqFFMHAXS0bhxY82YMUOFChUyu5Rc6dZQf+uPpiv8YOZE69evT3d5/fr1s6mS7MchpBzqzJkzGj58uGbPnq3IyEhFR0erYsWKZpcF5HgbNmxw2TkB2WHmzJkKDQ2Vu7u7U3tycrJOnDhhUlWuKzcHlDthBCaHiY+P1+jRozV58mRVqVJFY8eOVb169cwuy6VMmjRJL7/8sry9ve945WPuv5PzBAQEaPfu3SpVqpTZpeRK/3Y22J9//qmCBQsyApMN9uzZo4oVK8rNzU179uxJt29uvtgmASYHGTdunMaOHauQkBCNHj1azz33nNkluaSSJUtqx44deuCBB9KdqGiz2fT7779nY2XICAJM1nJzc9O5c+dUoEABp/bjx4+rQoUKunz5skmVuY5b54OlnPyR1k95bj+kR4DJQdzc3OTj46OIiIhUw7O3WrRoUTZWBVgLASZr9O/fX5L04Ycfqlu3bk4XFExKStLWrVvl7u6uqKgos0p0GcePH1exYsVks9l0/PjxdPvm5jmTzIHJQTp06OBSt0IHYB27du2S9M9k3b1798rT09OxzNPTU5UrV9bAgQPNKs+l3BpKcnNAuRNGYIDbpPylmRETJkzIwkpwNxiByVqdO3fWhx9+yOnSJlqyZEmG++bma1UxAgPcJuUvzTthtCz7JCYm6pVXXtHQoUPveAG1N998U/ny5cumylzPzJkzzS7B5TVt2jRD/ZgDA+COTp06pcKFC6d74Tvcm8DAQEVHR3MFWACSJL5tgfugQoUKOnbsmNll5GpNmzbV4sWLzS4DsKSwsDCdPHnS7DLuKw4hAfcBA5lZr2zZsho1apSioqLSvIQ91+QB/t2xY8eUmJhodhn3FYeQgPuAiaNZj2vyAHcvN35HMQIDwBKOHj1qdgkAchDmwACwHMMwOGwHuDgCDHAfcEp19vj8888VFhYmHx8f+fj4qFKlSvriiy/MLguACTiEBNwHjAZkvQkTJmjo0KHq1auX6tSpI0nauHGjunfvrgsXLqhfv34mVwggOzGJF/gXiYmJ8vHxUXR0tCpWrJhu35MnT6pw4cLp3sMK96ZkyZIaOXKkOnTo4NQ+e/ZsjRgxgjkycDmJiYl66qmnNG3aNJUtWzbdvnPnztVzzz2X6uw9K2MEBvgXHh4eKlasWIauZBkaGpoNFbm2s2fPqnbt2qnaa9eurbNnz5pQEWAuDw8P7dmzJ0N927Rpk8XVZD/mwADp+M9//qM333xTFy9eNLsUl1emTBnNnz8/VfvXX399x78+gdyqXbt2+vTTT80uwxSMwADp+Oijj3T48GEVLlxYxYsXTzX8+ssvv5hUmesZOXKkWrZsqQ0bNjjmwERFRWn16tVpBhvAFdy8eVOfffaZfvrppzQv8JibbzhLgAHSkdGbpiHrtWjRQlu3btXEiRMdtxQoX768tm3bpqpVq5pbHGCSffv2qVq1apKk3377zWlZbj87kkm8AHKV9957T927d1dQUJDZpQDIQsyBAe4gLi5OM2bM0JAhQxxzYX755RedPn3a5MqQltGjRzNnCS7n8OHD+vHHH3X16lVJrnFpBw4hAenYs2ePIiIiFBgYqGPHjqlbt27Kly+fFi1apBMnTujzzz83u0TcxhW+uIEUf/75p1588UWtXbtWNptNMTExKlWqlLp06aK8efNq/PjxZpeYZRiBAdLRv39/derUSTExMfL29na0N2rUSBs2bDCxMgCQ+vXrJw8PD504cUK+vr6O9pYtW2rFihUmVpb1GIEB0rF9+3ZNnz49VXuRIkUUGxtrQkUA8H9WrlypH3/8UUWLFnVqL1u2rI4fP25SVdmDERggHV5eXkpISEjV/ttvv6lAgQImVAQA/+fy5ctOIy8pLl68KC8vLxMqyj4EGCAdzz77rEaNGqXExERJ/5yWeOLECQ0aNEgtWrQwuToArq5evXpOc/FsNpuSk5M1btw4PfbYYyZWlvU4jRpIR3x8vJ5//nnt2LFDf//9twoXLqzY2FiFh4frhx9+yFX3FcktGjVqpE8//VSFChUyuxQgy+3bt09PPPGEqlWrpjVr1ujZZ5/V/v37dfHiRUVFRal06dJml5hlCDBABmzcuFF79uzRpUuXVK1aNUVERJhdkktI6/Ddv7Hb7VlYCZBzxcfH66OPPtLu3bsd31E9e/bM9SGeAAOk49q1a05nHyF7ubm53fFqooZhyGazZeimmwByD85CAtIRFBSkRx55RPXr19djjz2m8PBw+fj4mF2Wy1i7dq3ZJQA53l9//aVPP/1UBw4ckCRVqFBBnTt3Vr58+UyuLGsxAgOkY+PGjdqwYYPWrVunTZs26ebNm6pRo4bq16+vBg0a6MknnzS7RAAubMOGDWrSpIkCAwNVo0YNSdLOnTsVFxen77//Xo8++qjJFWYdAgyQQTdv3nRcF2bOnDlKTk7msEUW27NnT4b7VqpUKQsrAXKmsLAwhYeHa+rUqXJ3d5ckJSUl6dVXX9WmTZu0d+9ekyvMOgQY4A5+++03rVu3zvG4fv26Hn30UTVo0EB9+vQxu7xcLWUOzJ2+ppgDA1fl4+Oj6OhoPfjgg07thw4dUpUqVRz3RsqNmAMDpKNIkSK6evWqGjRooAYNGmjQoEGqVKlSrr9NfU5x9OhRs0sAcrRq1arpwIEDqQLMgQMHVLlyZZOqyh4EGCAdBQoU0MGDBxUbG6vY2FidO3dOV69eTfPKl7j/ihcvbnYJQI722muvqU+fPjp8+LBq1aolSdqyZYumTJmi9957z+kwbG47zMohJOAO4uLitGHDBq1fv17r16/Xr7/+qipVquixxx7Tu+++a3Z5LuNOd/7u0KFDNlUC5BxubulfUD/lEGxuPMxKgAEy6M8//9S6dev03Xff6auvvmISbzbLmzev0/PExERduXJFnp6e8vX11cWLF02qDDBPZm7YmNtGNDmEBKRj0aJFjsm7v/76q/Lly6e6detq/Pjxql+/vtnluZS//vorVVtMTIx69Oih119/3YSKAPPltlCSGYzAAOkoWLCg44yj+vXrKywszOyScJsdO3aoXbt2OnjwoNmlAKY4dOiQJk+e7LiQXfny5dW7d+9UE3tzG0ZggHScP3/e7BJwB3ny5NGZM2fMLgMwxcKFC9WqVSvVqFFD4eHhkv6ZxFuxYkXNmzdPLVq0MLnCrMMIDHAHSUlJWrx4sdNlup977jnHRaOQPZYsWeL03DAMnT17Vh999JFCQ0O1fPlykyoDzFO6dGm1bdtWo0aNcmofPny4vvzySx05csSkyrIeAQZIx+HDh9WoUSOdPn3aMRx76NAhhYaGatmyZbn6VvU5ze1nW9hsNhUoUECPP/64xo8fn+vvvAukxdfXV3v27FGZMmWc2mNiYlS5cmVduXLFpMqyHoeQgHS89tprKl26tLZs2eK4Mdqff/6pdu3a6bXXXtOyZctMrtB1JCcn/+sy/g6Dq2rQoIF+/vnnVAFm48aNqlevnklVZQ9GYIB0+Pn5acuWLakm7+7evVt16tTRpUuXTKrM9fz3v/9N82yjpKQktWvXTl999ZUJVQHmmjZtmoYNG6YXX3zR6UJ2CxYs0MiRI1W4cGFH32effdasMrMEAQZIR758+bR06VLVrl3bqT0qKkpNmjTh2iPZqGDBghozZoy6dOniaLt586Zat26tffv2OeYoAa7kTheyS5EbL2SXsT0HXNQzzzyjl19+WVu3bpVhGDIMQ1u2bFH37t1z3V8zOd2yZcs0cOBAffPNN5L+CS8vvvii9u/fr7Vr15pcHWCO5OTkDD1yW3iRGIEB0hUXF6eOHTvq+++/l4eHh6R/rgD73HPPaebMmQoKCjK3QBezZs0aNW3aVF9++aU+/fRTHT58WGvWrFFwcLDZpQGmuP3so1vZbDYNHTo0G6vJXgQYIAMOHz7sdJGo2yfMIfssXrxYL7zwgsqXL681a9Yof/78ZpcEmKZq1apOzxMTE3X06FHlyZNHpUuX1i+//GJSZVmPAAPcpn///hnuO2HChCysBM2bN0+zfcuWLSpTpoxTeFm0aFF2lQXkaAkJCerUqZOaNWum9u3bm11OluE0auA2u3btylA/m82WxZUgMDAwzfbIyMhsrgSwDrvdrpEjR6pJkya5OsAwAgPAEq5evark5GT5+flJko4dO6bFixerfPnyBBrgNhs3blSTJk3SvAlqbsEIDABLeO6559S8eXN1795dcXFxqlWrljw8PHThwgVNmDBBPXr0MLtEINtNmjTJ6XnKLTa++OILPf300yZVlT0YgQFgCfnz59f69ev10EMPacaMGZo8ebJ27dqlhQsXatiwYVwHBi6pZMmSTs/d3Nwct9gYMmSIAgICTKos6zECA8ASrly54vgyXrlypZo3by43NzfVqlVLx48fN7k6wBxHjx41uwTTcCE7AJZQpkwZLV68WCdPntSPP/6ohg0bSpLOnz8vu91ucnUAshsBBoAlDBs2TAMHDlSJEiVUs2ZNhYeHS/pnNOb2a2EAyP2YAwPAMmJjY3X27FlVrlzZcQ+Ybdu2yW63q1y5ciZXByA7EWAAAIDlcAgJAABYDgEGAABYDgEGAABYDgEGAABYDgEGMEmDBg3Ut2/fDPe32WxavHhxltXjKmbNmqWgoKAM9R0xYoSqVKmSpfVkRGZqBlwFV+IFTLJo0SJ5eHhkuP/Zs2eVN2/eLKwIOUGJEiXUt29fp3DbsmVLNWrUyLyigByIAAOYJF++fJnqHxISck+vl5SUJJvN5rh+Sm5z48YNeXp6ml1GlvDx8ZGPj4/ZZQA5Su78JgMs4NZDSCVKlNDo0aP10ksvKSAgQMWKFdPHH3/s1P/WQ0jr1q2TzWZTXFycY3l0dLRsNpuOHTsm6f8OOyxZskQVKlSQl5eXNm7cKA8PD8XGxjptu2/fvqpXr16G6v7kk08UGhoqX19fNWvWTBMmTEh1eOO7775TtWrV5O3trVKlSmnkyJG6efOm077MmDFDzZo1k6+vr8qWLaslS5Y4bWPfvn16+umn5e/vr+DgYLVv314XLlxwev969eqlvn37Kn/+/IqMjJQkTZgwQWFhYfLz81NoaKheffVVXbp0KUP7difJyckaNWqUihYtKi8vL1WpUkUrVqxw6nPq1Cm1bt1a+fLlk5+fn2rUqKGtW7dKko4cOaLnnntOwcHB8vf318MPP6yffvrJaZ+OHz+ufv36yWazyWazSUr7ENLUqVNVunRpeXp66sEHH9QXX3zhtDwj7zFgZQQYIIcYP368atSooV27dunVV19Vjx49dOjQoXva5pUrVzR27FjNmDFD+/fvV40aNVSqVCmnH7vExETNmTNHL7300h23FxUVpe7du6tPnz6Kjo7Wk08+qXfffdepz88//6wOHTqoT58++vXXXzV9+nTNmjUrVb+RI0fqxRdf1J49e9SoUSO1bdtWFy9elCTFxcXp8ccfV9WqVbVjxw6tWLFC586d04svvui0jdmzZ8vT01NRUVGaNm2apH/uxjtp0iTt379fs2fP1po1a/TGG2/c1ft3uw8//FDjx4/X+++/rz179igyMlLPPvusYmJiJEmXLl1S/fr1dfr0aS1ZskS7d+/WG2+8oeTkZMfyRo0aafXq1dq1a5eeeuopNWnSRCdOnJD0z2HFokWLatSoUTp79qzOnj2bZh3ffvut+vTpowEDBmjfvn165ZVX1LlzZ61duzbD7zFgeQYAU9SvX9/o06ePYRiGUbx4caNdu3aOZcnJyUbBggWNqVOnOtokGd9++61hGIaxdu1aQ5Lx119/OZbv2rXLkGQcPXrUMAzDmDlzpiHJiI6OdnrdsWPHGuXLl3c8X7hwoeHv729cunTpjjW3bNnSaNy4sVNb27ZtjcDAQMfzJ554whg9erRTny+++MIoVKiQ07689dZbjueXLl0yJBnLly83DMMw3n77baNhw4ZO2zh58qQhyTh06JBhGP+8f1WrVr1jzQsWLDAeeOABx/OZM2c61Zue4cOHG5UrV3Y8L1y4sPHuu+869Xn44YeNV1991TAMw5g+fboREBBg/PnnnxnavmEYxkMPPWRMnjzZ8bx48eLGxIkTnfrcXnPt2rWNbt26OfV54YUXjEaNGjme3+k9BqyOERggh6hUqZLj3zabTSEhITp//vw9bdPT09Npu5LUqVMnHT58WFu2bJH0z+GJF198UX5+fnfc3qFDh/TII484td3+fPfu3Ro1apT8/f0dj27duuns2bO6cuWKo9+tdfn5+clutzv2d/fu3Vq7dq3TNlLudXTkyBHHetWrV09V408//aQnnnhCRYoUUUBAgNq3b68///zT6bXvRkJCgs6cOaM6deo4tdepU0cHDhyQ9M9hvKpVq/7r/KZLly5p4MCBKl++vIKCguTv768DBw44RmAy6sCBA+nWkSK99xiwOibxAjnE7Wck2Ww2x6GH26VMxDVuuZVZYmJiqn4+Pj6OeRQpChYsqCZNmmjmzJkqWbKkli9frnXr1t1j9f/n0qVLGjlypJo3b55qmbe3t+Pf6e3vpUuX1KRJE40dOzbVNgoVKuT49+2h69ixY3rmmWfUo0cPvfvuu8qXL582btyoLl266MaNG/L19b2nfbuTO020HThwoFatWqX3339fZcqUkY+Pj55//nnduHEjS+rJzH9TgNUQYAALKlCggCTnU6ujo6MzvH7Xrl3VunVrFS1aVKVLl0711/y/efDBB7V9+3anttufV6tWTYcOHVKZMmUyXM/tqlWrpoULF6pEiRLKkyfjX1M7d+5UcnKyxo8f7wh58+fPv+s6bmW321W4cGFFRUWpfv36jvaoqCjHKFSlSpU0Y8YMXbx4Mc1RmKioKHXq1EnNmjWT9E9QS5l0ncLT01NJSUnp1lK+fHlFRUWpY8eOTtuuUKHC3e4eYDkcQgIsqEyZMgoNDdWIESMUExOjZcuWafz48RlePzIyUna7Xe+88446d+6c4fV69+6tH374QRMmTFBMTIymT5+u5cuXO43yDBs2TJ9//rlGjhyp/fv368CBA5o3b57eeuutDL9Oz549dfHiRbVu3Vrbt2/XkSNH9OOPP6pz587p/riXKVNGiYmJmjx5sn7//Xd98cUXjsm998Prr7+usWPH6uuvv9ahQ4c0ePBgRUdHq0+fPpKk1q1bKyQkRE2bNlVUVJR+//13LVy4UJs3b5YklS1bVosWLVJ0dLR2796tNm3apBoRKVGihDZs2KDTp087nXV1ex2zZs3S1KlTFRMTowkTJmjRokUaOHDgfdtXIKcjwAAW5OHhoa+++koHDx5UpUqVNHbsWL3zzjsZXt/NzU2dOnVSUlKSOnTokOH16tSpo2nTpmnChAmqXLmyVqxYoX79+jkdGoqMjNTSpUu1cuVKPfzww6pVq5YmTpyo4sWLZ/h1UkY6kpKS1LBhQ4WFhalv374KCgpK9zo2lStX1oQJEzR27FhVrFhRc+bM0ZgxYzL8unfy2muvqX///howYIDCwsK0YsUKLVmyRGXLlpX0z+jJypUrVbBgQTVq1EhhYWF677335O7uLumfU7zz5s2r2rVrq0mTJoqMjFS1atWcXmPUqFE6duyYSpcu7Rhpu13Tpk314Ycf6v3339dDDz2k6dOna+bMmWrQoMF921cgp7MZtx5EB5AjXb9+Xd7e3lq1apUiIiLuyza7dOmiP/74456vDdKtWzcdPHhQP//8832pCwAygjkwQA6XkJCgRYsWyc3NzXEmzr2Ij4/X3r17NXfu3LsKL++//76efPJJ+fn5afny5Zo9e7b+97//3XNdAJAZHEICcrjhw4dr0KBBGjt2rIoWLXrP23vuuefUsGFDde/eXU8++aTTspQr36b1GD16tCRp27ZtevLJJxUWFqZp06Zp0qRJ6tq16z3XZYaHHnroX/d3zpw5ZpcHIB0cQgLgcPr0aV29ejXNZfny5cv0/ZtyuuPHj6d5+rkkBQcHKyAgIJsrApBRBBgAAGA5HEICAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACW8/8BcCnNfK8PPXEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fEDkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAj1XrkHDt2TA8//LAiIyMVFBSkiy66SI8//rg8Ho+1xuPxaPr06Wrfvr2CgoIUGxurL774wut5SktLlZiYKLvdruDgYCUlJenQoUNeaz799FNdddVVCgwMVEREhNLS0mr7cAAAQANV65Hz9NNPa8GCBXr++ee1Y8cOPf3000pLS9O8efOsNWlpaZo7d67S09OVl5enFi1aKC4uTkeOHLHWJCYmqrCwUFlZWcrIyFBOTo7Gjx9v7Xe73RoyZIg6duyo/Px8PfPMM0pNTdXChQtr+5AAAEADZPMcf4qlFlx//fUKDQ3V3//+d2vb8OHDFRQUpH/84x/yeDwKDw/Xvffeq/vuu0+S5HK5FBoaqsWLFyshIUE7duxQVFSUNm3apL59+0qSMjMzdd111+mbb75ReHi4FixYoAcffFBOp1P+/v6SpKlTp2r16tXauXPnKc3qdrvlcDjkcrlkt9tr85+h3us0dY2vR8A59PXMob4eAQBqzan+/q71MzlXXHGF1q5dq88//1yStHXrVn344Ye69tprJUnFxcVyOp2KjY21HuNwONSvXz/l5uZKknJzcxUcHGwFjiTFxsbKz89PeXl51pqBAwdagSNJcXFxKioq0oEDB2r7sAAAQAPTtLafcOrUqXK73eratauaNGmiY8eO6cknn1RiYqIkyel0SpJCQ0O9HhcaGmrtczqdCgkJ8R60aVO1adPGa01kZOQJz1Gzr3Xr1ifMVlFRoYqKCuu+2+0+m0MFAAD1WK2fyVmxYoWWLl2qZcuWacuWLVqyZIn++te/asmSJbX9UqdtxowZcjgc1i0iIsLXIwEAgDpS65Fz//33a+rUqUpISFB0dLRuueUWTZo0STNmzJAkhYWFSZJKSkq8HldSUmLtCwsL0759+7z2Hz16VKWlpV5rTvYcx7/Gz02bNk0ul8u67dmz5yyPFgAA1Fe1HjmHDx+Wn5/30zZp0kTV1dWSpMjISIWFhWnt2rXWfrfbrby8PMXExEiSYmJiVFZWpvz8fGtNdna2qqur1a9fP2tNTk6OqqqqrDVZWVnq0qXLSd+qkqSAgADZ7XavGwAAMFOtR86f/vQnPfnkk1qzZo2+/vprrVq1Ss8++6xuuOEGSZLNZtPEiRP1xBNP6I033tC2bdt06623Kjw8XPHx8ZKkbt266ZprrtG4ceO0ceNGffTRR0pJSVFCQoLCw8MlSTfffLP8/f2VlJSkwsJCLV++XHPmzNHkyZNr+5AAAEADVOsXHs+bN08PP/yw7rrrLu3bt0/h4eG64447NH36dGvNlClTVF5ervHjx6usrEwDBgxQZmamAgMDrTVLly5VSkqKBg8eLD8/Pw0fPlxz58619jscDr377rtKTk5Wnz591K5dO02fPt3ru3QAAEDjVevfk9OQ8D05aCz4nhwAJvHZ9+QAAADUB0QOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACM1NTXAwAAalenqWt8PQLOoa9nDvX1CPUWZ3IAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgpDqJnG+//VajR49W27ZtFRQUpOjoaG3evNna7/F4NH36dLVv315BQUGKjY3VF1984fUcpaWlSkxMlN1uV3BwsJKSknTo0CGvNZ9++qmuuuoqBQYGKiIiQmlpaXVxOAAAoAGq9cg5cOCArrzySjVr1kxvv/22PvvsM82aNUutW7e21qSlpWnu3LlKT09XXl6eWrRoobi4OB05csRak5iYqMLCQmVlZSkjI0M5OTkaP368td/tdmvIkCHq2LGj8vPz9cwzzyg1NVULFy6s7UMCAAANUK3/gc6nn35aERERevnll61tkZGR1n/2eDyaPXu2HnroIf35z3+WJL3yyisKDQ3V6tWrlZCQoB07digzM1ObNm1S3759JUnz5s3Tddddp7/+9a8KDw/X0qVLVVlZqZdeekn+/v7q3r27CgoK9Oyzz3rFEAAAaJxq/UzOG2+8ob59++rGG29USEiILrvsMi1atMjaX1xcLKfTqdjYWGubw+FQv379lJubK0nKzc1VcHCwFTiSFBsbKz8/P+Xl5VlrBg4cKH9/f2tNXFycioqKdODAgZPOVlFRIbfb7XUDAABmqvXI2bVrlxYsWKCLL75Y77zzjiZMmKD//d//1ZIlSyRJTqdTkhQaGur1uNDQUGuf0+lUSEiI1/6mTZuqTZs2XmtO9hzHv8bPzZgxQw6Hw7pFRESc5dECAID6qtYjp7q6Wr1799ZTTz2lyy67TOPHj9e4ceOUnp5e2y912qZNmyaXy2Xd9uzZ4+uRAABAHan1yGnfvr2ioqK8tnXr1k27d++WJIWFhUmSSkpKvNaUlJRY+8LCwrRv3z6v/UePHlVpaanXmpM9x/Gv8XMBAQGy2+1eNwAAYKZaj5wrr7xSRUVFXts+//xzdezYUdJPFyGHhYVp7dq11n632628vDzFxMRIkmJiYlRWVqb8/HxrTXZ2tqqrq9WvXz9rTU5Ojqqqqqw1WVlZ6tKli9cnuQAAQONU65EzadIkffzxx3rqqaf05ZdfatmyZVq4cKGSk5MlSTabTRMnTtQTTzyhN954Q9u2bdOtt96q8PBwxcfHS/rpzM8111yjcePGaePGjfroo4+UkpKihIQEhYeHS5Juvvlm+fv7KykpSYWFhVq+fLnmzJmjyZMn1/YhAQCABqjWP0L+u9/9TqtWrdK0adP02GOPKTIyUrNnz1ZiYqK1ZsqUKSovL9f48eNVVlamAQMGKDMzU4GBgdaapUuXKiUlRYMHD5afn5+GDx+uuXPnWvsdDofeffddJScnq0+fPmrXrp2mT5/Ox8cBAIAkyebxeDy+HsJX3G63HA6HXC5Xo7s+p9PUNb4eAefQ1zOH+noEnEP8fDcujfHn+1R/f/O3qwAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJHqPHJmzpwpm82miRMnWtuOHDmi5ORktW3bVi1bttTw4cNVUlLi9bjdu3dr6NChat68uUJCQnT//ffr6NGjXmvWrVun3r17KyAgQJ07d9bixYvr+nAAAEADUaeRs2nTJr3wwgu69NJLvbZPmjRJb775pl577TWtX79ee/fu1bBhw6z9x44d09ChQ1VZWakNGzZoyZIlWrx4saZPn26tKS4u1tChQ3X11VeroKBAEydO1F/+8he98847dXlIAACggaizyDl06JASExO1aNEitW7d2trucrn097//Xc8++6z+8Ic/qE+fPnr55Ze1YcMGffzxx5Kkd999V5999pn+8Y9/qFevXrr22mv1+OOPa/78+aqsrJQkpaenKzIyUrNmzVK3bt2UkpKiESNG6LnnnqurQwIAAA1InUVOcnKyhg4dqtjYWK/t+fn5qqqq8tretWtXXXDBBcrNzZUk5ebmKjo6WqGhodaauLg4ud1uFRYWWmt+/txxcXHWcwAAgMataV086b/+9S9t2bJFmzZtOmGf0+mUv7+/goODvbaHhobK6XRaa44PnJr9Nft+bY3b7daPP/6ooKCgE167oqJCFRUV1n232336BwcAABqEWj+Ts2fPHt1zzz1aunSpAgMDa/vpz8qMGTPkcDisW0REhK9HAgAAdaTWIyc/P1/79u1T79691bRpUzVt2lTr16/X3Llz1bRpU4WGhqqyslJlZWVejyspKVFYWJgkKSws7IRPW9Xc/601drv9pGdxJGnatGlyuVzWbc+ePbVxyAAAoB6q9cgZPHiwtm3bpoKCAuvWt29fJSYmWv+5WbNmWrt2rfWYoqIi7d69WzExMZKkmJgYbdu2Tfv27bPWZGVlyW63Kyoqylpz/HPUrKl5jpMJCAiQ3W73ugEAADPV+jU5rVq1Uo8ePby2tWjRQm3btrW2JyUlafLkyWrTpo3sdrvuvvtuxcTEqH///pKkIUOGKCoqSrfccovS0tLkdDr10EMPKTk5WQEBAZKkO++8U88//7ymTJmi22+/XdnZ2VqxYoXWrFlT24cEAAAaoDq58Pi3PPfcc/Lz89Pw4cNVUVGhuLg4/e1vf7P2N2nSRBkZGZowYYJiYmLUokULjRkzRo899pi1JjIyUmvWrNGkSZM0Z84cnX/++XrxxRcVFxfni0MCAAD1jM3j8Xh8PYSvuN1uORwOuVyuRvfWVaepnPFqTL6eOdTXI+Ac4ue7cWmMP9+n+vubv10FAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAj1XrkzJgxQ7/73e/UqlUrhYSEKD4+XkVFRV5rjhw5ouTkZLVt21YtW7bU8OHDVVJS4rVm9+7dGjp0qJo3b66QkBDdf//9Onr0qNeadevWqXfv3goICFDnzp21ePHi2j4cAADQQNV65Kxfv17Jycn6+OOPlZWVpaqqKg0ZMkTl5eXWmkmTJunNN9/Ua6+9pvXr12vv3r0aNmyYtf/YsWMaOnSoKisrtWHDBi1ZskSLFy/W9OnTrTXFxcUaOnSorr76ahUUFGjixIn6y1/+onfeeae2DwkAADRANo/H46nLF9i/f79CQkK0fv16DRw4UC6XS+edd56WLVumESNGSJJ27typbt26KTc3V/3799fbb7+t66+/Xnv37lVoaKgkKT09XQ888ID2798vf39/PfDAA1qzZo22b99uvVZCQoLKysqUmZl5SrO53W45HA65XC7Z7fbaP/h6rNPUNb4eAefQ1zOH+noEnEP8fDcujfHn+1R/f9f5NTkul0uS1KZNG0lSfn6+qqqqFBsba63p2rWrLrjgAuXm5kqScnNzFR0dbQWOJMXFxcntdquwsNBac/xz1KypeY6TqaiokNvt9roBAAAz1WnkVFdXa+LEibryyivVo0cPSZLT6ZS/v7+Cg4O91oaGhsrpdFprjg+cmv01+35tjdvt1o8//njSeWbMmCGHw2HdIiIizvoYAQBA/VSnkZOcnKzt27frX//6V12+zCmbNm2aXC6XdduzZ4+vRwIAAHWkaV09cUpKijIyMpSTk6Pzzz/f2h4WFqbKykqVlZV5nc0pKSlRWFiYtWbjxo1ez1fz6avj1/z8E1klJSWy2+0KCgo66UwBAQEKCAg462MDAAD1X62fyfF4PEpJSdGqVauUnZ2tyMhIr/19+vRRs2bNtHbtWmtbUVGRdu/erZiYGElSTEyMtm3bpn379llrsrKyZLfbFRUVZa05/jlq1tQ8BwAAaNxq/UxOcnKyli1bpv/85z9q1aqVdQ2Nw+FQUFCQHA6HkpKSNHnyZLVp00Z2u1133323YmJi1L9/f0nSkCFDFBUVpVtuuUVpaWlyOp166KGHlJycbJ2JufPOO/X8889rypQpuv3225Wdna0VK1ZozRo+VQAAAOrgTM6CBQvkcrk0aNAgtW/f3rotX77cWvPcc8/p+uuv1/DhwzVw4ECFhYVp5cqV1v4mTZooIyNDTZo0UUxMjEaPHq1bb71Vjz32mLUmMjJSa9asUVZWlnr27KlZs2bpxRdfVFxcXG0fEgAAaIDq/Hty6jO+JweNRWP8Ho3GjJ/vxqUx/nzXm+/JAQAA8AUiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYKQGHznz589Xp06dFBgYqH79+mnjxo2+HgkAANQDDTpyli9frsmTJ+uRRx7Rli1b1LNnT8XFxWnfvn2+Hg0AAPhYg46cZ599VuPGjdNtt92mqKgopaenq3nz5nrppZd8PRoAAPCxBhs5lZWVys/PV2xsrLXNz89PsbGxys3N9eFkAACgPmjq6wHO1Pfff69jx44pNDTUa3toaKh27tx50sdUVFSooqLCuu9yuSRJbre77gatp6orDvt6BJxDjfG/440ZP9+NS2P8+a45Zo/H86vrGmzknIkZM2bo0UcfPWF7RESED6YBzh3HbF9PAKCuNOaf74MHD8rhcPzi/gYbOe3atVOTJk1UUlLitb2kpERhYWEnfcy0adM0efJk6351dbVKS0vVtm1b2Wy2Op0Xvud2uxUREaE9e/bIbrf7ehwAtYif78bF4/Ho4MGDCg8P/9V1DTZy/P391adPH61du1bx8fGSfoqWtWvXKiUl5aSPCQgIUEBAgNe24ODgOp4U9Y3dbud/BAFD8fPdePzaGZwaDTZyJGny5MkaM2aM+vbtq8svv1yzZ89WeXm5brvtNl+PBgAAfKxBR87IkSO1f/9+TZ8+XU6nU7169VJmZuYJFyMDAIDGp0FHjiSlpKT84ttTwPECAgL0yCOPnPCWJYCGj59vnIzN81ufvwIAAGiAGuyXAQIAAPwaIgcAABiJyAEAAEYicgAAgJGIHABAg/TBBx9o9OjRiomJ0bfffitJevXVV/Xhhx/6eDLUF0QOAKDB+fe//624uDgFBQXpk08+sf74ssvl0lNPPeXj6VBfEDloFCorK1VUVKSjR4/6ehQAteCJJ55Qenq6Fi1apGbNmlnbr7zySm3ZssWHk6E+IXJgtMOHDyspKUnNmzdX9+7dtXv3bknS3XffrZkzZ/p4OgBnqqioSAMHDjxhu8PhUFlZ2bkfCPUSkQOjTZs2TVu3btW6desUGBhobY+NjdXy5ct9OBmAsxEWFqYvv/zyhO0ffvihLrzwQh9MhPqIyIHRVq9ereeff14DBgyQzWaztnfv3l1fffWVDycDcDbGjRune+65R3l5ebLZbNq7d6+WLl2q++67TxMmTPD1eKgnGvzfrgJ+zf79+xUSEnLC9vLycq/oAdCwTJ06VdXV1Ro8eLAOHz6sgQMHKiAgQPfdd5/uvvtuX4+HeoIzOTBa3759tWbNGut+Tdi8+OKLiomJ8dVYAM6SzWbTgw8+qNLSUm3fvl0ff/yx9u/fr8cff9zXo6Ee4UwOjPbUU0/p2muv1WeffaajR49qzpw5+uyzz7RhwwatX7/e1+MBOEv+/v6Kiory9Riop/gr5DDeV199pZkzZ2rr1q06dOiQevfurQceeEDR0dG+Hg3AGbr66qt/9S3n7OzsczgN6ivO5MB4F110kRYtWuTrMQDUol69enndr6qqUkFBgbZv364xY8b4ZijUO0QOjLZlyxY1a9bMOmvzn//8Ry+//LKioqKUmpoqf39/H08I4Ew899xzJ92empqqQ4cOneNpUF9x4TGMdscdd+jzzz+XJO3atUsjR45U8+bN9dprr2nKlCk+ng5AbRs9erReeuklX4+BeoLIgdE+//xz67T2a6+9pt///vdatmyZFi9erH//+9++HQ5ArcvNzfX64k80brxdBaN5PB5VV1dLkt577z1df/31kqSIiAh9//33vhwNwFkYNmyY132Px6PvvvtOmzdv1sMPP+yjqVDfEDkwWt++ffXEE08oNjZW69ev14IFCyRJxcXFCg0N9fF0AM6Uw+Hwuu/n56cuXbroscce05AhQ3w0FeobIgdGmz17thITE7V69Wo9+OCD6ty5syTp9ddf1xVXXOHj6QCciWPHjum2225TdHS0Wrdu7etxUI/xPTlolI4cOaImTZqoWbNmvh4FwBkIDAzUjh07FBkZ6etRUI9x4TEapcDAQAIHaMB69OihXbt2+XoM1HOcyYFxWrdufcp/fLO0tLSOpwFQFzIzMzVt2jQ9/vjj6tOnj1q0aOG13263+2gy1CdEDoyzZMmSU17LN6MCDctjjz2me++9V61atbK2Hf9/ajwej2w2m44dO+aL8VDPEDkAgAajSZMm+u6777Rjx45fXff73//+HE2E+ozIQaNx5MgRVVZWem3jlDbQsPj5+cnpdCokJMTXo6AB4MJjGK28vFwpKSkKCQlRixYt1Lp1a68bgIbnVK+5A/ieHBhtypQpev/997VgwQLdcsstmj9/vr799lu98MILmjlzpq/HA3AGLrnkkt8MHT5UAIm3q2C4Cy64QK+88ooGDRoku92uLVu2qHPnznr11Vf1z3/+U2+99ZavRwRwGvz8/DR79uwTvvH45/hQASTO5MBwpaWluvDCCyX9dP1Nzf+7GzBggCZMmODL0QCcoYSEBK7JwSnhmhwY7cILL1RxcbEkqWvXrlqxYoUk6c0331RwcLAPJwNwJrgeB6eDyIGRdu3aperqat12223aunWrJGnq1KmaP3++AgMDNWnSJN1///0+nhLA6eIKC5wOrsmBkWq+S6PmlPbIkSM1d+5cHTlyRPn5+ercubMuvfRSH08JAKhLRA6M9PPv0mjVqpW2bt1qXZ8DADAfb1cBAAAjETkwks1mO+ECRS5YBIDGhY+Qw0gej0djx45VQECApJ/+pMOdd955wl8qXrlypS/GAwCcA0QOjPTzLwIbPXq0jyYBAPgKFx4DAAAjcU0OAAAwEpEDAACMROQAAAAjETkA6rWxY8cqPj7+F/enpqaqV69eZ/RcgwYN0sSJE637hw8f1vDhw2W322Wz2VRWVnZGMwOoH/h0FYAG7b777tPdd999Ro9duXKlmjVrZt1fsmSJPvjgA23YsEHt2rXTgQMH1Lp1a33yySenHFIA6g8iB0CD1rJlS7Vs2fKMHtumTRuv+1999ZW6deumHj16SJK+/vrrsx0PgA/xdhWAeuH1119XdHS0goKC1LZtW8XGxqq8vPyEdZs2bdJ5552np59+WtLpvV31c8e/XTVo0CDNmjVLOTk5stlsGjRokCIjIyVJl112mbUNQMPBmRwAPvfdd99p1KhRSktL0w033KCDBw/qgw8+0M+/xis7O1vDhg1TWlqaxo8fX6szrFy5UlOnTtX27du1cuVK+fv766uvvtLll1+u9957T927d5e/v3+tviaAukXkAPC57777TkePHtWwYcPUsWNHSVJ0dLTXmlWrVunWW2/Viy++qJEjR9b6DG3atFHz5s3l7++vsLAwSZLb7ZYktW3b1toGoOHg7SoAPtezZ08NHjxY0dHRuvHGG7Vo0SIdOHDA2p+Xl6cbb7xRr776ap0EDgAzETkAfK5JkybKysrS22+/raioKM2bN09dunRRcXGxJOmiiy5S165d9dJLL6mqqsrH0wJoKIgcAPWCzWbTlVdeqUcffVSffPKJ/P39tWrVKklSu3btlJ2drS+//FI33XTTOQudmmtwjh07dk5eD0DtInIA+FxeXp6eeuopbd68Wbt379bKlSu1f/9+devWzVoTEhKi7Oxs7dy5U6NGjdLRo0frfK6QkBAFBQUpMzNTJSUlcrlcdf6aAGoPkQPA5+x2u3JycnTdddfpkksu0UMPPaRZs2bp2muv9VoXFham7Oxsbdu2TYmJiXV+hqVp06aaO3euXnjhBYWHh+vPf/5znb4egNpl8/z8M5oAAAAG4EwOAAAwEpEDwBg1f+LhZLcPPvjA1+MBOMd4uwqAMb788stf3NehQwcFBQWdw2kA+BqRAwAAjMTbVQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAj/T9r5HzSRIa7QQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2VPfu3XX33XerU6dOHvfACQgI0Jo1a3T48GF17NhRTzzxhKZMmeJxL53bb79dS5Ys0csvv6x27drp3Xff1bJly9SmTZs/8vsAAACG+EP3yanuuE8OrhXcJweASbx6nxwAAABvI3IAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGCkckfOpk2bdO+99yosLEw2m03Lli2z9p0/f14TJ05UVFSU6tatq7CwMA0aNEjHjh3zOEbjxo1ls9k8ltTUVI8xu3btUufOneXn56fw8HDNmDHjgrksXbpULVu2lJ+fn6KiorRy5cryng4AADBUuSOnsLBQ7dq107x58y7Yd+bMGe3YsUN/+9vftGPHDr3//vvKycnRX/7ylwvGTps2TcePH7eWRx991NrncrnUo0cPNWrUSFlZWZo5c6aSk5P18ssvW2MyMjLUt29fJSYm6osvvlBCQoISEhK0Z8+e8p4SAAAwUM3yPqBXr17q1avXRfcFBAQoPT3dY9sLL7ygW2+9VUeOHNENN9xgbff391dISMhFj7N48WKdO3dOCxculK+vr1q3bq3s7Gw999xzGjFihCRp7ty56tmzp8aPHy9Jmj59utLT0/XCCy9owYIF5T0tAABgmEq/Jic/P182m0316tXz2J6amqrrrrtON910k2bOnKni4mJrX2Zmprp06SJfX19rW1xcnHJycnTq1ClrTGxsrMcx4+LilJmZecm5FBUVyeVyeSwAAMBM5X4lpzzOnj2riRMnqm/fvnI4HNb2xx57TB06dFD9+vWVkZGhpKQkHT9+XM8995wkyel0KiIiwuNYwcHB1r7AwEA5nU5r26/HOJ3OS84nJSVFU6dOrajTAwAAVVilRc758+f117/+VW63W/Pnz/fYN27cOOvntm3bytfXVw899JBSUlJkt9sra0pKSkryeG6Xy6Xw8PBKez4AAOA9lRI5ZYHzzTff6JNPPvF4FedioqOjVVxcrNzcXLVo0UIhISHKy8vzGFO2XnYdz6XGXOo6H0my2+2VGlEAAKDqqPBrcsoC5+uvv9batWt13XXX/e5jsrOzVaNGDQUFBUmSYmJitGnTJp0/f94ak56erhYtWigwMNAas27dOo/jpKenKyYmpgLPBgAAVFflfiWnoKBABw4csNYPHz6s7Oxs1a9fX6Ghofrf//1f7dixQ8uXL1dJSYl1jUz9+vXl6+urzMxMbd26Vd26dZO/v78yMzM1duxYDRgwwAqYfv36aerUqUpMTNTEiRO1Z88ezZ07V7Nnz7ae9/HHH9cdd9yhWbNmKT4+Xm+99Za2b9/u8TFzAABw7bK53W53eR6wYcMGdevW7YLtgwcPVnJy8gUXDJdZv369unbtqh07duiRRx7R/v37VVRUpIiICA0cOFDjxo3zeCtp165dGjVqlLZt26YGDRro0Ucf1cSJEz2OuXTpUk2ePFm5ubm68cYbNWPGDN19992XfS4ul0sBAQHKz8//3bfUTNN40gpvTwFXUW5qvLenAAAV5nL/fpc7ckxC5OBaQeQAMMnl/v3mu6sAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABip3JGzadMm3XvvvQoLC5PNZtOyZcs89rvdbk2ZMkWhoaGqXbu2YmNj9fXXX3uMOXnypPr37y+Hw6F69eopMTFRBQUFHmN27dqlzp07y8/PT+Hh4ZoxY8YFc1m6dKlatmwpPz8/RUVFaeXKleU9HQAAYKhyR05hYaHatWunefPmXXT/jBkz9Pzzz2vBggXaunWr6tatq7i4OJ09e9Ya079/f+3du1fp6elavny5Nm3apBEjRlj7XS6XevTooUaNGikrK0szZ85UcnKyXn75ZWtMRkaG+vbtq8TERH3xxRdKSEhQQkKC9uzZU95TAgAABrK53W73FT/YZtMHH3yghIQESb+8ihMWFqYnnnhCTz75pCQpPz9fwcHBSktLU58+ffTll18qMjJS27Zt08033yxJWrVqle6++259++23CgsL0/z58/X000/L6XTK19dXkjRp0iQtW7ZM+/fvlyQ98MADKiws1PLly6353HbbbWrfvr0WLFhwWfN3uVwKCAhQfn6+HA7Hlf4aqqXGk1Z4ewq4inJT4709BQCoMJf797tCr8k5fPiwnE6nYmNjrW0BAQGKjo5WZmamJCkzM1P16tWzAkeSYmNjVaNGDW3dutUa06VLFytwJCkuLk45OTk6deqUNebXz1M2pux5LqaoqEgul8tjAQAAZqrQyHE6nZKk4OBgj+3BwcHWPqfTqaCgII/9NWvWVP369T3GXOwYv36OS40p238xKSkpCggIsJbw8PDyniIAAKgmrqlPVyUlJSk/P99ajh496u0pAQCASlKhkRMSEiJJysvL89iel5dn7QsJCdGJEyc89hcXF+vkyZMeYy52jF8/x6XGlO2/GLvdLofD4bEAAAAzVWjkREREKCQkROvWrbO2uVwubd26VTExMZKkmJgYnT59WllZWdaYTz75RKWlpYqOjrbGbNq0SefPn7fGpKenq0WLFgoMDLTG/Pp5ysaUPQ8AALi2lTtyCgoKlJ2drezsbEm/XGycnZ2tI0eOyGazacyYMXrmmWf00Ucfaffu3Ro0aJDCwsKsT2C1atVKPXv21PDhw/X555/rs88+0+jRo9WnTx+FhYVJkvr16ydfX18lJiZq7969evvttzV37lyNGzfOmsfjjz+uVatWadasWdq/f7+Sk5O1fft2jR49+o//VgAAQLVXs7wP2L59u7p162atl4XH4MGDlZaWpgkTJqiwsFAjRozQ6dOn1alTJ61atUp+fn7WYxYvXqzRo0ere/fuqlGjhnr37q3nn3/e2h8QEKA1a9Zo1KhR6tixoxo0aKApU6Z43Evn9ttv15IlSzR58mQ99dRTuvHGG7Vs2TK1adPmin4RAADALH/oPjnVHffJwbWC++QAMIlX7pMDAABQVRA5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASBUeOY0bN5bNZrtgGTVqlCSpa9euF+wbOXKkxzGOHDmi+Ph41alTR0FBQRo/fryKi4s9xmzYsEEdOnSQ3W5Xs2bNlJaWVtGnAgAAqrGaFX3Abdu2qaSkxFrfs2eP7rrrLt1///3WtuHDh2vatGnWep06dayfS0pKFB8fr5CQEGVkZOj48eMaNGiQatWqpWeffVaSdPjwYcXHx2vkyJFavHix1q1bp2HDhik0NFRxcXEVfUoAAKAaqvDIadiwocd6amqqmjZtqjvuuMPaVqdOHYWEhFz08WvWrNG+ffu0du1aBQcHq3379po+fbomTpyo5ORk+fr6asGCBYqIiNCsWbMkSa1atdLmzZs1e/ZsIgcAAEiq5Gtyzp07pzfffFMPPvigbDabtX3x4sVq0KCB2rRpo6SkJJ05c8bal5mZqaioKAUHB1vb4uLi5HK5tHfvXmtMbGysx3PFxcUpMzOzMk8HAABUIxX+Ss6vLVu2TKdPn9aQIUOsbf369VOjRo0UFhamXbt2aeLEicrJydH7778vSXI6nR6BI8ladzqdvznG5XLp559/Vu3atS86n6KiIhUVFVnrLpfrD58jAAComio1cl599VX16tVLYWFh1rYRI0ZYP0dFRSk0NFTdu3fXwYMH1bRp08qcjlJSUjR16tRKfQ4AAFA1VNrbVd98843Wrl2rYcOG/ea46OhoSdKBAwckSSEhIcrLy/MYU7Zedh3PpcY4HI5LvoojSUlJScrPz7eWo0ePlu+kAABAtVFpkfPaa68pKChI8fHxvzkuOztbkhQaGipJiomJ0e7du3XixAlrTHp6uhwOhyIjI60x69at8zhOenq6YmJifvO57Ha7HA6HxwIAAMxUKZFTWlqq1157TYMHD1bNmv//HbGDBw9q+vTpysrKUm5urj766CMNGjRIXbp0Udu2bSVJPXr0UGRkpAYOHKidO3dq9erVmjx5skaNGiW73S5JGjlypA4dOqQJEyZo//79evHFF/XOO+9o7NixlXE6AACgGqqUyFm7dq2OHDmiBx980GO7r6+v1q5dqx49eqhly5Z64okn1Lt3b3388cfWGB8fHy1fvlw+Pj6KiYnRgAEDNGjQII/76kRERGjFihVKT09Xu3btNGvWLL3yyit8fBwAAFhsbrfb7e1JeIvL5VJAQIDy8/OvubeuGk9a4e0p4CrKTf3tt40BoDq53L/ffHcVAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjVXjkJCcny2azeSwtW7a09p89e1ajRo3Sddddpz/96U/q3bu38vLyPI5x5MgRxcfHq06dOgoKCtL48eNVXFzsMWbDhg3q0KGD7Ha7mjVrprS0tIo+FQAAUI1Vyis5rVu31vHjx61l8+bN1r6xY8fq448/1tKlS7Vx40YdO3ZM9913n7W/pKRE8fHxOnfunDIyMrRo0SKlpaVpypQp1pjDhw8rPj5e3bp1U3Z2tsaMGaNhw4Zp9erVlXE6AACgGqpZKQetWVMhISEXbM/Pz9err76qJUuW6M4775Qkvfbaa2rVqpW2bNmi2267TWvWrNG+ffu0du1aBQcHq3379po+fbomTpyo5ORk+fr6asGCBYqIiNCsWbMkSa1atdLmzZs1e/ZsxcXFVcYpAQCAaqZSXsn5+uuvFRYWpiZNmqh///46cuSIJCkrK0vnz59XbGysNbZly5a64YYblJmZKUnKzMxUVFSUgoODrTFxcXFyuVzau3evNebXxygbU3aMSykqKpLL5fJYAACAmSo8cqKjo5WWlqZVq1Zp/vz5Onz4sDp37qyffvpJTqdTvr6+qlevnsdjgoOD5XQ6JUlOp9MjcMr2l+37rTEul0s///zzJeeWkpKigIAAawkPD/+jpwsAAKqoCn+7qlevXtbPbdu2VXR0tBo1aqR33nlHtWvXruinK5ekpCSNGzfOWne5XIQOAACGqvSPkNerV0/NmzfXgQMHFBISonPnzun06dMeY/Ly8qxreEJCQi74tFXZ+u+NcTgcvxlSdrtdDofDYwEAAGaq9MgpKCjQwYMHFRoaqo4dO6pWrVpat26dtT8nJ0dHjhxRTEyMJCkmJka7d+/WiRMnrDHp6elyOByKjIy0xvz6GGVjyo4BAABQ4ZHz5JNPauPGjcrNzVVGRob+53/+Rz4+Purbt68CAgKUmJiocePGaf369crKytLQoUMVExOj2267TZLUo0cPRUZGauDAgdq5c6dWr16tyZMna9SoUbLb7ZKkkSNH6tChQ5owYYL279+vF198Ue+8847Gjh1b0acDAACqqQq/Jufbb79V37599eOPP6phw4bq1KmTtmzZooYNG0qSZs+erRo1aqh3794qKipSXFycXnzxRevxPj4+Wr58uR5++GHFxMSobt26Gjx4sKZNm2aNiYiI0IoVKzR27FjNnTtX119/vV555RU+Pg4AACw2t9vt9vYkvMXlcikgIED5+fnX3PU5jSet8PYUcBXlpsZ7ewoAUGEu9+83310FAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIFR45KSkpuuWWW+Tv76+goCAlJCQoJyfHY0zXrl1ls9k8lpEjR3qMOXLkiOLj41WnTh0FBQVp/PjxKi4u9hizYcMGdejQQXa7Xc2aNVNaWlpFnw4AAKimKjxyNm7cqFGjRmnLli1KT0/X+fPn1aNHDxUWFnqMGz58uI4fP24tM2bMsPaVlJQoPj5e586dU0ZGhhYtWqS0tDRNmTLFGnP48GHFx8erW7duys7O1pgxYzRs2DCtXr26ok8JAABUQzUr+oCrVq3yWE9LS1NQUJCysrLUpUsXa3udOnUUEhJy0WOsWbNG+/bt09q1axUcHKz27dtr+vTpmjhxopKTk+Xr66sFCxYoIiJCs2bNkiS1atVKmzdv1uzZsxUXF1fRpwUAAKqZSr8mJz8/X5JUv359j+2LFy9WgwYN1KZNGyUlJenMmTPWvszMTEVFRSk4ONjaFhcXJ5fLpb1791pjYmNjPY4ZFxenzMzMS86lqKhILpfLYwEAAGaq8Fdyfq20tFRjxozRn//8Z7Vp08ba3q9fPzVq1EhhYWHatWuXJk6cqJycHL3//vuSJKfT6RE4kqx1p9P5m2NcLpd+/vln1a5d+4L5pKSkaOrUqRV6jgAAoGqq1MgZNWqU9uzZo82bN3tsHzFihPVzVFSUQkND1b17dx08eFBNmzattPkkJSVp3Lhx1rrL5VJ4eHilPR8AAPCeSnu7avTo0Vq+fLnWr1+v66+//jfHRkdHS5IOHDggSQoJCVFeXp7HmLL1sut4LjXG4XBc9FUcSbLb7XI4HB4LAAAwU4VHjtvt1ujRo/XBBx/ok08+UURExO8+Jjs7W5IUGhoqSYqJidHu3bt14sQJa0x6erocDociIyOtMevWrfM4Tnp6umJiYiroTAAAQHVW4ZEzatQovfnmm1qyZIn8/f3ldDrldDr1888/S5IOHjyo6dOnKysrS7m5ufroo480aNAgdenSRW3btpUk9ejRQ5GRkRo4cKB27typ1atXa/LkyRo1apTsdrskaeTIkTp06JAmTJig/fv368UXX9Q777yjsWPHVvQpAQCAaqjCI2f+/PnKz89X165dFRoaai1vv/22JMnX11dr165Vjx491LJlSz3xxBPq3bu3Pv74Y+sYPj4+Wr58uXx8fBQTE6MBAwZo0KBBmjZtmjUmIiJCK1asUHp6utq1a6dZs2bplVde4ePjAABAkmRzu91ub0/CW1wulwICApSfn3/NXZ/TeNIKb08BV1Fuary3pwAAFeZy/37z3VUAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwUk1vTwAAULEaT1rh7SngKspNjff2FKosXskBAABGInIAAICRiBwAAGCkah858+bNU+PGjeXn56fo6Gh9/vnn3p4SAACoAqp15Lz99tsaN26c/v73v2vHjh1q166d4uLidOLECW9PDQAAeFm1jpznnntOw4cP19ChQxUZGakFCxaoTp06WrhwobenBgAAvKzaRs65c+eUlZWl2NhYa1uNGjUUGxurzMxML84MAABUBdX2Pjk//PCDSkpKFBwc7LE9ODhY+/fvv+hjioqKVFRUZK3n5+dLklwuV+VNtIoqLTrj7SngKroW/zd+LePf97XlWvz3XXbObrf7N8dV28i5EikpKZo6deoF28PDw70wG+DqCZjj7RkAqCzX8r/vn376SQEBAZfcX20jp0GDBvLx8VFeXp7H9ry8PIWEhFz0MUlJSRo3bpy1XlpaqpMnT+q6666TzWar1PnC+1wul8LDw3X06FE5HA5vTwdABeLf97XF7Xbrp59+UlhY2G+Oq7aR4+vrq44dO2rdunVKSEiQ9Eu0rFu3TqNHj77oY+x2u+x2u8e2evXqVfJMUdU4HA7+TxAwFP++rx2/9QpOmWobOZI0btw4DR48WDfffLNuvfVWzZkzR4WFhRo6dKi3pwYAALysWkfOAw88oO+//15TpkyR0+lU+/bttWrVqgsuRgYAANeeah05kjR69OhLvj0F/Jrdbtff//73C96yBFD98e8bF2Nz/97nrwAAAKqhanszQAAAgN9C5AAAACMROQAAwEhEDgAAMBKRAwColj799FMNGDBAMTEx+u677yRJb7zxhjZv3uzlmaGqIHIAANXOe++9p7i4ONWuXVtffPGF9eXL+fn5evbZZ708O1QVRA6uCefOnVNOTo6Ki4u9PRUAFeCZZ57RggUL9M9//lO1atWytv/5z3/Wjh07vDgzVCVEDox25swZJSYmqk6dOmrdurWOHDkiSXr00UeVmprq5dkBuFI5OTnq0qXLBdsDAgJ0+vTpqz8hVElEDoyWlJSknTt3asOGDfLz87O2x8bG6u233/bizAD8ESEhITpw4MAF2zdv3qwmTZp4YUaoiogcGG3ZsmV64YUX1KlTJ9lsNmt769atdfDgQS/ODMAfMXz4cD3++OPaunWrbDabjh07psWLF+vJJ5/Uww8/7O3poYqo9t9dBfyW77//XkFBQRdsLyws9IgeANXLpEmTVFpaqu7du+vMmTPq0qWL7Ha7nnzyST366KPenh6qCF7JgdFuvvlmrVixwlovC5tXXnlFMTEx3poWgD/IZrPp6aef1smTJ7Vnzx5t2bJF33//vaZPn+7tqaEK4ZUcGO3ZZ59Vr169tG/fPhUXF2vu3Lnat2+fMjIytHHjRm9PD8Af5Ovrq8jISG9PA1UU30IO4x08eFCpqanauXOnCgoK1KFDB02cOFFRUVHenhqAK9StW7fffMv5k08+uYqzQVXFKzkwXtOmTfXPf/7T29MAUIHat2/vsX7+/HllZ2drz549Gjx4sHcmhSqHyIHRduzYoVq1almv2nz44Yd67bXXFBkZqeTkZPn6+np5hgCuxOzZsy+6PTk5WQUFBVd5NqiquPAYRnvooYf01VdfSZIOHTqkBx54QHXq1NHSpUs1YcIEL88OQEUbMGCAFi5c6O1poIogcmC0r776ynpZe+nSpbrjjju0ZMkSpaWl6b333vPu5ABUuMzMTI8bf+LaxttVMJrb7VZpaakkae3atbrnnnskSeHh4frhhx+8OTUAf8B9993nse52u3X8+HFt375df/vb37w0K1Q1RA6MdvPNN+uZZ55RbGysNm7cqPnz50uSDh8+rODgYC/PDsCVCggI8FivUaOGWrRooWnTpqlHjx5emhWqGiIHRpszZ4769++vZcuW6emnn1azZs0kSe+++65uv/12L88OwJUoKSnR0KFDFRUVpcDAQG9PB1UY98nBNens2bPy8fFRrVq1vD0VAFfAz89PX375pSIiIrw9FVRhXHiMa5Kfnx+BA1Rjbdq00aFDh7w9DVRxvJID4wQGBl72l2+ePHmykmcDoDKsWrVKSUlJmj59ujp27Ki6det67Hc4HF6aGaoSIgfGWbRo0WWP5c6oQPUybdo0PfHEE/L397e2/fo/atxut2w2m0pKSrwxPVQxRA4AoNrw8fHR8ePH9eWXX/7muDvuuOMqzQhVGZGDa8bZs2d17tw5j228pA1ULzVq1JDT6VRQUJC3p4JqgAuPYbTCwkKNHj1aQUFBqlu3rgIDAz0WANXP5V5zB3CfHBhtwoQJWr9+vebPn6+BAwdq3rx5+u677/TSSy8pNTXV29MDcAWaN2/+u6HDhwog8XYVDHfDDTfo9ddfV9euXeVwOLRjxw41a9ZMb7zxhv71r39p5cqV3p4igHKoUaOG5syZc8Edj/8THyqAxCs5MNzJkyfVpEkTSb9cf1P2X3edOnXSww8/7M2pAbhCffr04ZocXBauyYHRmjRposOHD0uSWrZsqXfeeUeS9PHHH6tevXpenBmAK8H1OCgPIgdGOnTokEpLSzV06FDt3LlTkjRp0iTNmzdPfn5+Gjt2rMaPH+/lWQIoL66wQHlwTQ6MVHYvjbKXtB944AE9//zzOnv2rLKystSsWTO1bdvWy7MEAFQmIgdG+s97afj7+2vnzp3W9TkAAPPxdhUAADASkQMj2Wy2Cy5Q5IJFALi28BFyGMntdmvIkCGy2+2SfvlKh5EjR17wTcXvv/++N6YHALgKiBwY6T9vBDZgwAAvzQQA4C1ceAwAAIzENTkAAMBIRA4AADASkQMAAIxE5AAwRteuXTVmzBhvTwNAFUHkAKh2NmzYIJvNptOnT3t7KgCqMCIHAH7DuXPnvD0FAFeIyAFQJRUVFemxxx5TUFCQ/Pz81KlTJ23btk25ubnq1q2bJCkwMFA2m01DhgyxHldaWqoJEyaofv36CgkJUXJyssdxT58+rWHDhqlhw4ZyOBy68847rW+ql6Tk5GS1b99er7zyiiIiIuTn53c1ThdAJSByAFRJEyZM0HvvvadFixZpx44datasmeLi4uTv76/33ntPkpSTk6Pjx49r7ty51uMWLVqkunXrauvWrZoxY4amTZum9PR0a//999+vEydO6N///reysrLUoUMHde/eXSdPnrTGHDhwQO+9957ef/99ZWdnX7VzBlCxuBkggCqnsLBQgYGBSktLU79+/SRJ58+fV+PGjTVmzBjdcsst6tatm06dOqV69epZj+vatatKSkr06aefWttuvfVW3XnnnUpNTdXmzZsVHx+vEydOWF/5IUnNmjXThAkTNGLECCUnJ+vZZ5/Vd999p4YNG161cwZQ8fhaBwBVzsGDB3X+/Hn9+c9/trbVqlVLt956q7788kvdcsstl3xs27ZtPdZDQ0N14sQJSdLOnTtVUFCg6667zmPMzz//rIMHD1rrjRo1InAAAxA5AIxSq1Ytj3WbzabS0lJJUkFBgUJDQ7Vhw4YLHvfrV4T+84tcAVRPRA6AKqdp06by9fXVZ599pkaNGkn65e2qbdu2acyYMfL19ZUklZSUlOu4HTp0kNPpVM2aNdW4ceOKnjaAKoYLjwFUOXXr1tXDDz+s8ePHa9WqVdq3b5+GDx+uM2fOKDExUY0aNZLNZtPy5cv1/fffq6Cg4LKOGxsbq5iYGCUkJGjNmjXKzc1VRkaGnn76aW3fvr2SzwrA1UbkAKiSUlNT1bt3bw0cOFAdOnTQgQMHtHr1agUGBuq//uu/NHXqVE2aNEnBwcEaPXr0ZR3TZrNp5cqV6tKli4YOHarmzZurT58++uabbxQcHFzJZwTgauPTVQAAwEi8kgMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADDS/wMZlDZR0Zo9qQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in labeled_small.columns:\n",
    "    if c not in ['india','age']:\n",
    "        plt.figure()\n",
    "        labeled_small.groupby(c)[c].count().plot(kind='bar')\n",
    "        plt.title(c)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fabf354e-f39e-4cde-af84-c65a277d309a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( labeled.drop(columns=['india']),\n",
    "                                                    labeled.india, test_size=0.33, random_state=0,stratify=labeled.india)\n",
    "X_train_small, X_test_small, y_train_small, y_test_small = train_test_split( labeled_small.drop(columns=['india']),\n",
    "                                                    labeled_small.india, test_size=0.33, random_state=0,stratify=labeled_small.india)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f3772e4-8142-4f0f-934a-40406253cde2",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert (y_test_small!=y_test).sum()==0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cf6cf5d8-d43e-499e-98a5-65ecd0b8ccda",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(X_train,y_train, test_size=0.33, random_state=0,stratify=y_train)\n",
    "X_train_small, X_valid_small, y_train_small, y_valid_small = train_test_split(X_train_small,y_train_small, test_size=0.33, random_state=0,stratify=y_train_small)\n",
    "assert (y_train!=y_train_small).sum()==0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8a26ce2b-d197-4356-abce-af6001fbe64a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  class          p\n",
      "0    i0   0.238038\n",
      "1    i1   0.014605\n",
      "2    i2   0.059746\n",
      "3    i3   1.326712\n",
      "4    i4  18.043281\n"
     ]
    }
   ],
   "source": [
    "w = pd.DataFrame(np.unique(y_train,return_counts=True)).T\n",
    "w.columns = ['class','p']\n",
    "w.p = np.sqrt(w.p.sum())/w.p\n",
    "print(w)\n",
    "weight_train = pd.merge(pd.DataFrame({'class':y_train}),w).p.values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ee98e56a-1527-4d78-b80a-185527c7c5e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  class          p\n",
      "0    i0   4.294987\n",
      "1    i1   0.263526\n",
      "2    i2   1.078013\n",
      "3    i3  23.938235\n",
      "4    i4     325.56\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.26352598348712963, 0.26352598348712963, 0.26352598348712963, ...,\n",
       "       0.26352598348712963, 0.26352598348712963, 0.26352598348712963],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##THIS IS BETTER\n",
    "w = pd.DataFrame(np.unique(y_train,return_counts=True)).T\n",
    "w.columns = ['class','p']\n",
    "w.p = w.p.sum()/w.p/w.shape[0]\n",
    "print(w)\n",
    "weight_train = pd.merge(pd.DataFrame({'class':y_train}),w).p.values\n",
    "weight_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d1453965-f927-4edc-ad3b-421997d62268",
   "metadata": {},
   "outputs": [],
   "source": [
    "#w = pd.DataFrame(np.unique(y_train,return_counts=True)).T\n",
    "#w.columns = ['class','p']\n",
    "#w.p = np.sqrt(np.array((w.p.sum()/w.p).values).astype(float))\n",
    "#print(w)\n",
    "#weight_train = pd.merge(pd.DataFrame({'class':y_train}),w).p.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bd45f7a3-75ed-4494-bb21-38285da6ce61",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/agobbi/miniconda3/envs/pid/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9d687e5e-43d6-41a2-8c66-e92e743ad10b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-02-06 14:37:36,968] A new study created in memory with name: no-name-8b1bdfdd-e3cb-45db-9c0e-6eb1ae9b8446\n",
      "[I 2024-02-06 14:37:37,530] Trial 0 finished with value: 0.41412437398662677 and parameters: {'learning_rate': 0.15853226702714224, 'max_depth': 7, 'min_child_weight': 7, 'gamma': 4.729626369017826, 'subsample': 0.5426209375713748, 'colsample_bytree': 0.9703389471149142, 'alpha': 5.586954983474277, 'lambda': 9.3082424924606}. Best is trial 0 with value: 0.41412437398662677.\n",
      "[I 2024-02-06 14:37:37,796] Trial 1 finished with value: 0.3960132364280116 and parameters: {'learning_rate': 0.16613146689992717, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 6.900506860723924, 'subsample': 0.5100114314446524, 'colsample_bytree': 0.950468769462696, 'alpha': 7.878260265301471, 'lambda': 5.5206531074325795}. Best is trial 0 with value: 0.41412437398662677.\n",
      "[I 2024-02-06 14:37:38,314] Trial 2 finished with value: 0.39687281254752094 and parameters: {'learning_rate': 0.08479412076572246, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 7.662023329998096, 'subsample': 0.8488069814115731, 'colsample_bytree': 0.8045425657918114, 'alpha': 8.894955994182647, 'lambda': 9.63781685259056}. Best is trial 0 with value: 0.41412437398662677.\n",
      "[I 2024-02-06 14:37:38,642] Trial 3 finished with value: 0.3759768203803619 and parameters: {'learning_rate': 0.13302551254540243, 'max_depth': 14, 'min_child_weight': 7, 'gamma': 9.73085724459636, 'subsample': 0.2047778927045657, 'colsample_bytree': 0.8081952084888382, 'alpha': 9.324352611245374, 'lambda': 9.098379868425022}. Best is trial 0 with value: 0.41412437398662677.\n",
      "[I 2024-02-06 14:37:39,056] Trial 4 finished with value: 0.35555999731897037 and parameters: {'learning_rate': 0.06570835226512539, 'max_depth': 14, 'min_child_weight': 6, 'gamma': 7.215729375362661, 'subsample': 0.04281772446372059, 'colsample_bytree': 0.41023784943298297, 'alpha': 9.167816866468687, 'lambda': 4.219106389674052}. Best is trial 0 with value: 0.41412437398662677.\n",
      "[I 2024-02-06 14:37:39,698] Trial 5 finished with value: 0.426288200845195 and parameters: {'learning_rate': 0.04039887783441406, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 4.387934197104326, 'subsample': 0.9847226751793198, 'colsample_bytree': 0.5612035013666518, 'alpha': 1.2884350364112596, 'lambda': 2.2355345846186636}. Best is trial 5 with value: 0.426288200845195.\n",
      "[I 2024-02-06 14:37:40,245] Trial 6 finished with value: 0.40538997935734 and parameters: {'learning_rate': 0.12253605053006308, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 6.324157165610997, 'subsample': 0.37147441091264133, 'colsample_bytree': 0.5926808265349612, 'alpha': 2.885900435604558, 'lambda': 2.809446613304952}. Best is trial 5 with value: 0.426288200845195.\n",
      "[I 2024-02-06 14:37:40,856] Trial 7 finished with value: 0.4075176783705835 and parameters: {'learning_rate': 0.04067915856490809, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 4.79136538435559, 'subsample': 0.7822735351887224, 'colsample_bytree': 0.8041623575524781, 'alpha': 7.215848281324257, 'lambda': 4.956340012049848}. Best is trial 5 with value: 0.426288200845195.\n",
      "[I 2024-02-06 14:37:41,177] Trial 8 finished with value: 0.4094139735408071 and parameters: {'learning_rate': 0.183847164196302, 'max_depth': 5, 'min_child_weight': 8, 'gamma': 5.531811895559764, 'subsample': 0.6051361312417848, 'colsample_bytree': 0.9497185598903474, 'alpha': 4.661793655755828, 'lambda': 4.866880952255706}. Best is trial 5 with value: 0.426288200845195.\n",
      "[I 2024-02-06 14:37:41,534] Trial 9 finished with value: 0.3632644325004713 and parameters: {'learning_rate': 0.18434662490382112, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 5.469184250087285, 'subsample': 0.08848895380964826, 'colsample_bytree': 0.2874584441575179, 'alpha': 7.118230864626296, 'lambda': 8.93510722413118}. Best is trial 5 with value: 0.426288200845195.\n",
      "[I 2024-02-06 14:37:42,241] Trial 10 finished with value: 0.3630168806856452 and parameters: {'learning_rate': 0.014944293885624627, 'max_depth': 8, 'min_child_weight': 4, 'gamma': 1.2589951767684249, 'subsample': 0.970126711802375, 'colsample_bytree': 0.03992205193433129, 'alpha': 0.12776612978548396, 'lambda': 0.11173343041388772}. Best is trial 5 with value: 0.426288200845195.\n",
      "[I 2024-02-06 14:37:42,706] Trial 11 finished with value: 0.428343578636755 and parameters: {'learning_rate': 0.14826381737932542, 'max_depth': 8, 'min_child_weight': 5, 'gamma': 3.1345200388175916, 'subsample': 0.6894417379863257, 'colsample_bytree': 0.5793075180000802, 'alpha': 2.7038740205201064, 'lambda': 1.474056508891266}. Best is trial 11 with value: 0.428343578636755.\n",
      "[I 2024-02-06 14:37:43,262] Trial 12 finished with value: 0.44718574096980196 and parameters: {'learning_rate': 0.10595088482297615, 'max_depth': 8, 'min_child_weight': 5, 'gamma': 2.4750590826512986, 'subsample': 0.7366463159576073, 'colsample_bytree': 0.5690839235428518, 'alpha': 1.4203309793829706, 'lambda': 0.7354667875828078}. Best is trial 12 with value: 0.44718574096980196.\n",
      "[I 2024-02-06 14:37:43,855] Trial 13 finished with value: 0.4281147192581671 and parameters: {'learning_rate': 0.09894735149428843, 'max_depth': 12, 'min_child_weight': 5, 'gamma': 2.149725008369449, 'subsample': 0.6985902130164663, 'colsample_bytree': 0.3971689376248564, 'alpha': 2.8910707809317726, 'lambda': 0.47882825949423413}. Best is trial 12 with value: 0.44718574096980196.\n",
      "[I 2024-02-06 14:37:44,372] Trial 14 finished with value: 0.43418653988661726 and parameters: {'learning_rate': 0.1340423465188037, 'max_depth': 9, 'min_child_weight': 5, 'gamma': 2.5333889234609233, 'subsample': 0.6968380664457667, 'colsample_bytree': 0.6693470906037085, 'alpha': 2.288570619681711, 'lambda': 1.6868291704983969}. Best is trial 12 with value: 0.44718574096980196.\n",
      "[I 2024-02-06 14:37:45,537] Trial 15 finished with value: 0.4658857286642129 and parameters: {'learning_rate': 0.11554737617786369, 'max_depth': 12, 'min_child_weight': 4, 'gamma': 0.16271602225864346, 'subsample': 0.38125127873430004, 'colsample_bytree': 0.6860602485839223, 'alpha': 1.2177594396905203, 'lambda': 3.315547091785045}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:46,427] Trial 16 finished with value: 0.4458393700897827 and parameters: {'learning_rate': 0.10763810534357598, 'max_depth': 12, 'min_child_weight': 4, 'gamma': 0.3350110943827662, 'subsample': 0.3620984642487738, 'colsample_bytree': 0.24735830805163272, 'alpha': 0.6132785193761345, 'lambda': 3.410425540351563}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:47,468] Trial 17 finished with value: 0.43986562511797245 and parameters: {'learning_rate': 0.07151434929064948, 'max_depth': 12, 'min_child_weight': 3, 'gamma': 0.15936192928168014, 'subsample': 0.3513948381920854, 'colsample_bytree': 0.6849621388691487, 'alpha': 4.484177685001926, 'lambda': 6.845294510602313}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:48,002] Trial 18 finished with value: 0.43876660401163725 and parameters: {'learning_rate': 0.10706967676878548, 'max_depth': 12, 'min_child_weight': 6, 'gamma': 1.5299687671384876, 'subsample': 0.19839748527383758, 'colsample_bytree': 0.6876061296308189, 'alpha': 1.4289336134675639, 'lambda': 0.8110780600836689}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:48,608] Trial 19 finished with value: 0.41305235196026574 and parameters: {'learning_rate': 0.08665010476682465, 'max_depth': 11, 'min_child_weight': 3, 'gamma': 3.456595003176961, 'subsample': 0.43539686695366575, 'colsample_bytree': 0.45053833190012726, 'alpha': 4.3178374754045805, 'lambda': 6.506350859361994}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:49,183] Trial 20 finished with value: 0.43224761425877567 and parameters: {'learning_rate': 0.11943805979398354, 'max_depth': 9, 'min_child_weight': 6, 'gamma': 1.2693555849865374, 'subsample': 0.8627528540752838, 'colsample_bytree': 0.25213737157119614, 'alpha': 3.5522438316866793, 'lambda': 3.4109132555314807}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:49,917] Trial 21 finished with value: 0.41896853642910353 and parameters: {'learning_rate': 0.10392011039227927, 'max_depth': 11, 'min_child_weight': 4, 'gamma': 0.05787471358894791, 'subsample': 0.2814737069936835, 'colsample_bytree': 0.08880292465827955, 'alpha': 0.03345838010271174, 'lambda': 3.578343992654701}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:50,697] Trial 22 finished with value: 0.44778741658908267 and parameters: {'learning_rate': 0.1162081433371139, 'max_depth': 13, 'min_child_weight': 4, 'gamma': 0.7001788563399416, 'subsample': 0.40910947164567113, 'colsample_bytree': 0.2761168562824251, 'alpha': 1.1943023281190634, 'lambda': 2.3661905820346147}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:51,377] Trial 23 finished with value: 0.4450702558261694 and parameters: {'learning_rate': 0.14169938760944925, 'max_depth': 13, 'min_child_weight': 4, 'gamma': 0.9396276936984513, 'subsample': 0.44966612429030284, 'colsample_bytree': 0.34473287247010675, 'alpha': 1.6233881827804175, 'lambda': 2.347493119300866}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:52,150] Trial 24 finished with value: 0.44491489782159876 and parameters: {'learning_rate': 0.08516618376910176, 'max_depth': 14, 'min_child_weight': 5, 'gamma': 2.2072022717310027, 'subsample': 0.5948053890251113, 'colsample_bytree': 0.476387330087755, 'alpha': 1.8983949119426222, 'lambda': 1.366417449361343}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:52,895] Trial 25 finished with value: 0.4303536984892455 and parameters: {'learning_rate': 0.11953235381349948, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 0.7986830054140268, 'subsample': 0.2852637050199765, 'colsample_bytree': 0.17377134860284693, 'alpha': 0.8907797597848682, 'lambda': 2.5982633904061054}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:53,475] Trial 26 finished with value: 0.4172698297090147 and parameters: {'learning_rate': 0.06338763457234486, 'max_depth': 11, 'min_child_weight': 2, 'gamma': 3.9046403372764718, 'subsample': 0.5920604379560516, 'colsample_bytree': 0.5135753223064197, 'alpha': 5.913783623063998, 'lambda': 1.503081938932016}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:54,077] Trial 27 finished with value: 0.4309414183110265 and parameters: {'learning_rate': 0.1642740190357448, 'max_depth': 15, 'min_child_weight': 4, 'gamma': 1.9322067311522015, 'subsample': 0.4493746063280518, 'colsample_bytree': 0.7355557182423111, 'alpha': 3.3403495953737385, 'lambda': 0.8230911593013204}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:54,772] Trial 28 finished with value: 0.42576019955845834 and parameters: {'learning_rate': 0.12792023556596205, 'max_depth': 9, 'min_child_weight': 6, 'gamma': 2.885079316784847, 'subsample': 0.18972830320256823, 'colsample_bytree': 0.6221079466677001, 'alpha': 0.8990562025001587, 'lambda': 4.157574642863015}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:55,375] Trial 29 finished with value: 0.40988738978928607 and parameters: {'learning_rate': 0.15091738348413553, 'max_depth': 7, 'min_child_weight': 7, 'gamma': 0.7791755009196515, 'subsample': 0.530686511273632, 'colsample_bytree': 0.14166870114971641, 'alpha': 5.68078008099233, 'lambda': 2.034761176043805}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:56,113] Trial 30 finished with value: 0.43106829265079427 and parameters: {'learning_rate': 0.09563222984866317, 'max_depth': 13, 'min_child_weight': 5, 'gamma': 1.7096905468466692, 'subsample': 0.2790058090617548, 'colsample_bytree': 0.502544766446729, 'alpha': 3.479205865551649, 'lambda': 2.909740073327499}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:57,052] Trial 31 finished with value: 0.45248079857706214 and parameters: {'learning_rate': 0.11246208075453015, 'max_depth': 12, 'min_child_weight': 4, 'gamma': 0.2058974916791118, 'subsample': 0.36653556625825906, 'colsample_bytree': 0.23796185476080944, 'alpha': 0.6563006232715923, 'lambda': 3.3640222485684173}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:58,173] Trial 32 finished with value: 0.454133339749885 and parameters: {'learning_rate': 0.11196290275555312, 'max_depth': 11, 'min_child_weight': 3, 'gamma': 0.042644708343605314, 'subsample': 0.4294220516851407, 'colsample_bytree': 0.3499126155600013, 'alpha': 2.005560696168964, 'lambda': 5.610635080370502}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:37:59,394] Trial 33 finished with value: 0.44770040770860153 and parameters: {'learning_rate': 0.11744686881197013, 'max_depth': 11, 'min_child_weight': 3, 'gamma': 0.06537881804816223, 'subsample': 0.4081304903067513, 'colsample_bytree': 0.32853454247700475, 'alpha': 2.1268893389184407, 'lambda': 6.380905470217758}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:00,190] Trial 34 finished with value: 0.45560948205748353 and parameters: {'learning_rate': 0.13878603528922565, 'max_depth': 13, 'min_child_weight': 2, 'gamma': 0.6012227436627824, 'subsample': 0.49691697558132497, 'colsample_bytree': 0.21423212825967233, 'alpha': 0.5422842951954188, 'lambda': 5.76998891234815}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:00,709] Trial 35 finished with value: 0.40090562784836437 and parameters: {'learning_rate': 0.158564410337237, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 8.161107047753276, 'subsample': 0.510272459802705, 'colsample_bytree': 0.17136138816827398, 'alpha': 0.4765444472349181, 'lambda': 5.658178678426394}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:01,491] Trial 36 finished with value: 0.4344904956300448 and parameters: {'learning_rate': 0.17217093159733632, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.5011483527781968, 'subsample': 0.3061932951634753, 'colsample_bytree': 0.21962204985461442, 'alpha': 0.4596361139767752, 'lambda': 7.275000942742279}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:02,122] Trial 37 finished with value: 0.40188607352186007 and parameters: {'learning_rate': 0.14590889576004448, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 9.488056098571136, 'subsample': 0.49137314857456693, 'colsample_bytree': 0.3463480203468421, 'alpha': 2.111750963319208, 'lambda': 5.633322390952045}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:02,789] Trial 38 finished with value: 0.3944058162811504 and parameters: {'learning_rate': 0.13704885021104665, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 1.2101832952994638, 'subsample': 0.14255007078108214, 'colsample_bytree': 0.11207595440222767, 'alpha': 0.9462986436355001, 'lambda': 7.702080404674589}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:03,597] Trial 39 finished with value: 0.3690363059331729 and parameters: {'learning_rate': 0.07415909192399361, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 1.6852521426000784, 'subsample': 0.5603899200636498, 'colsample_bytree': 0.010840585765077237, 'alpha': 8.465473150799706, 'lambda': 4.418234929116575}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:04,801] Trial 40 finished with value: 0.45350909078986434 and parameters: {'learning_rate': 0.12864657743634894, 'max_depth': 13, 'min_child_weight': 2, 'gamma': 0.5194346574695949, 'subsample': 0.23785838282080687, 'colsample_bytree': 0.8993491463395583, 'alpha': 2.463420441790274, 'lambda': 5.251907990049247}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:06,025] Trial 41 finished with value: 0.44467840383951085 and parameters: {'learning_rate': 0.12813463623013718, 'max_depth': 13, 'min_child_weight': 2, 'gamma': 0.48578255850071383, 'subsample': 0.2335708459876764, 'colsample_bytree': 0.9707976294966152, 'alpha': 2.485724132722211, 'lambda': 5.344634727447018}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:06,970] Trial 42 finished with value: 0.45721138067144274 and parameters: {'learning_rate': 0.09216406021457454, 'max_depth': 11, 'min_child_weight': 2, 'gamma': 1.0292735071286818, 'subsample': 0.352094781422846, 'colsample_bytree': 0.8647107049071965, 'alpha': 1.5854063980004744, 'lambda': 5.99915465889943}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:07,561] Trial 43 finished with value: 0.4388927627455014 and parameters: {'learning_rate': 0.09109682119373652, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 1.0832532765630019, 'subsample': 0.03500149034738492, 'colsample_bytree': 0.9053871775805131, 'alpha': 1.741653844402122, 'lambda': 6.007457767578966}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:08,229] Trial 44 finished with value: 0.4418289154609987 and parameters: {'learning_rate': 0.1281622924086638, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 1.438938539107239, 'subsample': 0.31356293515058364, 'colsample_bytree': 0.873213404193854, 'alpha': 3.0624930013353255, 'lambda': 4.553554505519396}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:09,249] Trial 45 finished with value: 0.44709988013358426 and parameters: {'learning_rate': 0.05769546585575677, 'max_depth': 13, 'min_child_weight': 2, 'gamma': 0.629692903844382, 'subsample': 0.48352787357781724, 'colsample_bytree': 0.8288443340650928, 'alpha': 3.9385961857767837, 'lambda': 4.91556037777239}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:09,779] Trial 46 finished with value: 0.38776531714653606 and parameters: {'learning_rate': 0.07687103704764559, 'max_depth': 11, 'min_child_weight': 2, 'gamma': 6.294086438631032, 'subsample': 0.122183021175024, 'colsample_bytree': 0.9965844038137314, 'alpha': 5.293397840398358, 'lambda': 8.001814335838311}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:10,487] Trial 47 finished with value: 0.4292768577187501 and parameters: {'learning_rate': 0.09557685811684165, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 2.756267665864211, 'subsample': 0.23184163180846037, 'colsample_bytree': 0.7615063362895591, 'alpha': 2.6176139039425528, 'lambda': 3.979874655099163}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:11,334] Trial 48 finished with value: 0.4539812649533698 and parameters: {'learning_rate': 0.1408087981686343, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 1.0096597755685162, 'subsample': 0.3402671262573771, 'colsample_bytree': 0.8524692805900649, 'alpha': 1.3553057867591063, 'lambda': 5.227841033561904}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:11,869] Trial 49 finished with value: 0.44279587316121766 and parameters: {'learning_rate': 0.17592796944731287, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 4.2060696414572085, 'subsample': 0.64513920988593, 'colsample_bytree': 0.8410541465575788, 'alpha': 0.052262391818181264, 'lambda': 6.054070576359573}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:12,471] Trial 50 finished with value: 0.43199567442709497 and parameters: {'learning_rate': 0.19266346858854366, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 2.2064593912114177, 'subsample': 0.32858399050480486, 'colsample_bytree': 0.40407391558363165, 'alpha': 1.4149009872313234, 'lambda': 6.937055048765011}. Best is trial 15 with value: 0.4658857286642129.\n",
      "[I 2024-02-06 14:38:14,284] Trial 51 finished with value: 0.46756871512573905 and parameters: {'learning_rate': 0.13861504665386581, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 0.009023303652492015, 'subsample': 0.4077602312208479, 'colsample_bytree': 0.9143294884131938, 'alpha': 1.9624509128089658, 'lambda': 5.128043153960862}. Best is trial 51 with value: 0.46756871512573905.\n",
      "[I 2024-02-06 14:38:15,665] Trial 52 finished with value: 0.47562442557716783 and parameters: {'learning_rate': 0.15347634575913977, 'max_depth': 12, 'min_child_weight': 3, 'gamma': 0.00037145056683333955, 'subsample': 0.3938410905352111, 'colsample_bytree': 0.7713010475597937, 'alpha': 1.8411457515489364, 'lambda': 6.038710339106673}. Best is trial 52 with value: 0.47562442557716783.\n",
      "[I 2024-02-06 14:38:17,067] Trial 53 finished with value: 0.46882894882945203 and parameters: {'learning_rate': 0.15073283914916197, 'max_depth': 12, 'min_child_weight': 3, 'gamma': 0.0009185064450308866, 'subsample': 0.4033005102780394, 'colsample_bytree': 0.7775812751614595, 'alpha': 1.8843758061993643, 'lambda': 6.096750835634255}. Best is trial 52 with value: 0.47562442557716783.\n",
      "[I 2024-02-06 14:38:18,458] Trial 54 finished with value: 0.4793740964462298 and parameters: {'learning_rate': 0.1534536955531825, 'max_depth': 12, 'min_child_weight': 3, 'gamma': 0.020644284454291117, 'subsample': 0.38880755260038286, 'colsample_bytree': 0.7749031183124936, 'alpha': 1.0954380940227206, 'lambda': 6.254985092901284}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:19,786] Trial 55 finished with value: 0.45672567006285514 and parameters: {'learning_rate': 0.1532714796573306, 'max_depth': 12, 'min_child_weight': 3, 'gamma': 0.037808171642983376, 'subsample': 0.3922497062239361, 'colsample_bytree': 0.7571654162543379, 'alpha': 2.94792249500091, 'lambda': 6.49143357854534}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:20,617] Trial 56 finished with value: 0.4368059505819242 and parameters: {'learning_rate': 0.16214077062867924, 'max_depth': 12, 'min_child_weight': 3, 'gamma': 1.0721269720856041, 'subsample': 0.3815907026402089, 'colsample_bytree': 0.7883820189082522, 'alpha': 6.554611734011546, 'lambda': 8.296116106467284}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:21,274] Trial 57 finished with value: 0.45298075528343806 and parameters: {'learning_rate': 0.1725291091053559, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 1.452968049824023, 'subsample': 0.45345001405629115, 'colsample_bytree': 0.6380559714007277, 'alpha': 1.765664244851816, 'lambda': 7.197748589402634}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:22,257] Trial 58 finished with value: 0.46430347906615166 and parameters: {'learning_rate': 0.1531324218279993, 'max_depth': 11, 'min_child_weight': 4, 'gamma': 0.3604604231975935, 'subsample': 0.5430028873987529, 'colsample_bytree': 0.7262988734095832, 'alpha': 0.9710804276007836, 'lambda': 6.10684609814626}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:23,211] Trial 59 finished with value: 0.4691136092878393 and parameters: {'learning_rate': 0.15574861487796351, 'max_depth': 14, 'min_child_weight': 4, 'gamma': 0.39285603433355076, 'subsample': 0.6366505580386781, 'colsample_bytree': 0.7325933360385737, 'alpha': 1.1087929267676, 'lambda': 4.686579086268219}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:23,988] Trial 60 finished with value: 0.4629064964940783 and parameters: {'learning_rate': 0.18049890832721754, 'max_depth': 14, 'min_child_weight': 4, 'gamma': 0.3635438041126377, 'subsample': 0.8019910148153486, 'colsample_bytree': 0.7111712382901756, 'alpha': 1.1387482787040357, 'lambda': 4.686659455977615}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:24,980] Trial 61 finished with value: 0.47534139948100873 and parameters: {'learning_rate': 0.15453990493016387, 'max_depth': 12, 'min_child_weight': 4, 'gamma': 0.46117680520973564, 'subsample': 0.547203056089946, 'colsample_bytree': 0.7946624972663555, 'alpha': 0.9332147502674533, 'lambda': 6.260585373198202}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:26,043] Trial 62 finished with value: 0.4661085209288253 and parameters: {'learning_rate': 0.15739113619806555, 'max_depth': 13, 'min_child_weight': 4, 'gamma': 0.013407437884990547, 'subsample': 0.4143110081219317, 'colsample_bytree': 0.7819018448875865, 'alpha': 0.27797794898798656, 'lambda': 3.842593788716065}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:27,171] Trial 63 finished with value: 0.4378446129128034 and parameters: {'learning_rate': 0.16899885670719003, 'max_depth': 15, 'min_child_weight': 4, 'gamma': 0.0026331934280063966, 'subsample': 0.6700309881218277, 'colsample_bytree': 0.7997459460302926, 'alpha': 9.970287704399361, 'lambda': 5.072059985924027}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:27,916] Trial 64 finished with value: 0.4652444320875261 and parameters: {'learning_rate': 0.15926034538817901, 'max_depth': 14, 'min_child_weight': 5, 'gamma': 0.8209769961862596, 'subsample': 0.6341952606981959, 'colsample_bytree': 0.7756610597074249, 'alpha': 0.6891319521104884, 'lambda': 3.912568666689535}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:29,112] Trial 65 finished with value: 0.46595885012983196 and parameters: {'learning_rate': 0.14560014784063763, 'max_depth': 13, 'min_child_weight': 4, 'gamma': 0.4026174760724099, 'subsample': 0.5610658181753463, 'colsample_bytree': 0.9454890458211522, 'alpha': 0.29720179580030703, 'lambda': 6.742805611802006}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:29,953] Trial 66 finished with value: 0.4434457518740023 and parameters: {'learning_rate': 0.16416118294532275, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 1.946830814420567, 'subsample': 0.4638678765772726, 'colsample_bytree': 0.6496748297662723, 'alpha': 0.21788316820048423, 'lambda': 4.827637248296088}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:30,973] Trial 67 finished with value: 0.4500289255492259 and parameters: {'learning_rate': 0.19279783345619164, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.6889574882307871, 'subsample': 0.5249604919601499, 'colsample_bytree': 0.9053089051296236, 'alpha': 2.24259608544281, 'lambda': 7.245135031909756}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:32,053] Trial 68 finished with value: 0.43912636683170875 and parameters: {'learning_rate': 0.15508018903376758, 'max_depth': 14, 'min_child_weight': 4, 'gamma': 1.384134109435886, 'subsample': 0.4254760279316848, 'colsample_bytree': 0.8254877347470826, 'alpha': 1.859493647056887, 'lambda': 3.8780991712029316}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:33,789] Trial 69 finished with value: 0.4664643735785775 and parameters: {'learning_rate': 0.1859575273136181, 'max_depth': 13, 'min_child_weight': 5, 'gamma': 0.2876028897602333, 'subsample': 0.7510302493680336, 'colsample_bytree': 0.608001888900177, 'alpha': 1.1234084472153114, 'lambda': 6.3137141773994}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:35,045] Trial 70 finished with value: 0.44675616014198966 and parameters: {'learning_rate': 0.1985796471977942, 'max_depth': 12, 'min_child_weight': 5, 'gamma': 1.7604320662146185, 'subsample': 0.7695682652923792, 'colsample_bytree': 0.6047722405665734, 'alpha': 1.1065451930649468, 'lambda': 6.367253978456284}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:43,487] Trial 71 finished with value: 0.47233319263908274 and parameters: {'learning_rate': 0.18318587774802414, 'max_depth': 13, 'min_child_weight': 5, 'gamma': 0.004190162008190931, 'subsample': 0.7125846089877039, 'colsample_bytree': 0.7011289176884117, 'alpha': 0.795912708759768, 'lambda': 6.759908073770229}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:51,690] Trial 72 finished with value: 0.4638724578675043 and parameters: {'learning_rate': 0.1773257707101761, 'max_depth': 14, 'min_child_weight': 6, 'gamma': 0.32394369873035256, 'subsample': 0.8131430218919785, 'colsample_bytree': 0.5457438739040843, 'alpha': 0.7514739112098627, 'lambda': 6.735314854398396}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:54,998] Trial 73 finished with value: 0.44485590793819757 and parameters: {'learning_rate': 0.1823468842967176, 'max_depth': 13, 'min_child_weight': 5, 'gamma': 0.8611239640578949, 'subsample': 0.7260050095911641, 'colsample_bytree': 0.7088194480889161, 'alpha': 1.410791884395386, 'lambda': 6.205305743721738}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:55,926] Trial 74 finished with value: 0.4574684183199778 and parameters: {'learning_rate': 0.18911328160308166, 'max_depth': 12, 'min_child_weight': 5, 'gamma': 0.3244115859053424, 'subsample': 0.8533208235641049, 'colsample_bytree': 0.6689094099519223, 'alpha': 1.6482498247959265, 'lambda': 7.631499020555536}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:56,706] Trial 75 finished with value: 0.45869143234008786 and parameters: {'learning_rate': 0.1472028955570263, 'max_depth': 6, 'min_child_weight': 6, 'gamma': 0.6320333416876149, 'subsample': 0.6363099297540588, 'colsample_bytree': 0.7446839612714714, 'alpha': 1.1880810067184966, 'lambda': 5.85372791387533}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:57,098] Trial 76 finished with value: 0.41369985918951907 and parameters: {'learning_rate': 0.1866324678059612, 'max_depth': 14, 'min_child_weight': 5, 'gamma': 5.260899786096795, 'subsample': 0.742184302757579, 'colsample_bytree': 0.6874749851462774, 'alpha': 2.3298140768950573, 'lambda': 5.516663340991636}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:57,534] Trial 77 finished with value: 0.43365066205280267 and parameters: {'learning_rate': 0.17097849723536596, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 3.4235993333177124, 'subsample': 0.6118450069433261, 'colsample_bytree': 0.8123799951705026, 'alpha': 2.0526191168936654, 'lambda': 6.566175156366117}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:58,317] Trial 78 finished with value: 0.4476832521381637 and parameters: {'learning_rate': 0.13403452704735347, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 1.1964313046875816, 'subsample': 0.897287679299936, 'colsample_bytree': 0.7154666354221912, 'alpha': 0.9204425362623251, 'lambda': 6.930340475970517}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:59,337] Trial 79 finished with value: 0.46120814481110445 and parameters: {'learning_rate': 0.16742838606081772, 'max_depth': 13, 'min_child_weight': 4, 'gamma': 0.2576337687633174, 'subsample': 0.7093380090321584, 'colsample_bytree': 0.5804244818749462, 'alpha': 3.1797188111235895, 'lambda': 5.419874992272966}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:38:59,868] Trial 80 finished with value: 0.41208273892724034 and parameters: {'learning_rate': 0.14853822379556889, 'max_depth': 12, 'min_child_weight': 6, 'gamma': 7.811645260541498, 'subsample': 0.7613277415624221, 'colsample_bytree': 0.6510530935444185, 'alpha': 0.47202038714413297, 'lambda': 7.53801819072978}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:00,774] Trial 81 finished with value: 0.4719911974293244 and parameters: {'learning_rate': 0.15891419600648865, 'max_depth': 13, 'min_child_weight': 4, 'gamma': 0.7795266089524695, 'subsample': 0.6751602436166722, 'colsample_bytree': 0.779414444256287, 'alpha': 0.2763343632286034, 'lambda': 4.348334817289939}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:01,544] Trial 82 finished with value: 0.4651648678304659 and parameters: {'learning_rate': 0.16242700215535574, 'max_depth': 12, 'min_child_weight': 5, 'gamma': 0.6103055344740703, 'subsample': 0.6860948270420352, 'colsample_bytree': 0.7455237184216763, 'alpha': 0.7731249154235496, 'lambda': 9.840274895553293}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:02,972] Trial 83 finished with value: 0.46812716265897764 and parameters: {'learning_rate': 0.14353256599028014, 'max_depth': 13, 'min_child_weight': 4, 'gamma': 0.27235466455832413, 'subsample': 0.6718705713147738, 'colsample_bytree': 0.8823600073929382, 'alpha': 1.4754653029497693, 'lambda': 4.34526772337347}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:03,747] Trial 84 finished with value: 0.4563767257177557 and parameters: {'learning_rate': 0.14255655410641127, 'max_depth': 14, 'min_child_weight': 4, 'gamma': 0.7868403059400341, 'subsample': 0.5765277329872467, 'colsample_bytree': 0.8802452300288818, 'alpha': 1.605657773622064, 'lambda': 4.292531551865773}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:04,659] Trial 85 finished with value: 0.4621516663230219 and parameters: {'learning_rate': 0.1509215610865819, 'max_depth': 13, 'min_child_weight': 4, 'gamma': 0.9081692514983748, 'subsample': 0.6605063458443601, 'colsample_bytree': 0.9350409472501204, 'alpha': 1.4702376900953071, 'lambda': 5.070600497653765}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:05,765] Trial 86 finished with value: 0.4632623004125858 and parameters: {'learning_rate': 0.12425971611769747, 'max_depth': 12, 'min_child_weight': 3, 'gamma': 0.1889729363142152, 'subsample': 0.4686796905202738, 'colsample_bytree': 0.9262443190043183, 'alpha': 2.7386722461407045, 'lambda': 3.102742033945885}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:06,543] Trial 87 finished with value: 0.4560491964127947 and parameters: {'learning_rate': 0.1344936408752104, 'max_depth': 11, 'min_child_weight': 3, 'gamma': 0.5082291707667274, 'subsample': 0.7167572299503016, 'colsample_bytree': 0.8040071503306329, 'alpha': 1.9772116681341116, 'lambda': 4.693241607402056}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:07,275] Trial 88 finished with value: 0.46911543171664816 and parameters: {'learning_rate': 0.1416285050367182, 'max_depth': 13, 'min_child_weight': 4, 'gamma': 1.138605376125232, 'subsample': 0.6145057483080305, 'colsample_bytree': 0.8459884949107137, 'alpha': 0.032289877417258195, 'lambda': 3.625332405858154}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:07,940] Trial 89 finished with value: 0.4584160660154688 and parameters: {'learning_rate': 0.15825683370114715, 'max_depth': 14, 'min_child_weight': 4, 'gamma': 1.2495095948166641, 'subsample': 0.5968737900185774, 'colsample_bytree': 0.836410041856754, 'alpha': 0.016318800793006405, 'lambda': 4.2328242491098855}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:08,543] Trial 90 finished with value: 0.4568841976398463 and parameters: {'learning_rate': 0.1669920853143716, 'max_depth': 13, 'min_child_weight': 4, 'gamma': 1.682048102571795, 'subsample': 0.6176833847189898, 'colsample_bytree': 0.8795125765547, 'alpha': 0.4530710165706767, 'lambda': 4.539844141869607}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:09,618] Trial 91 finished with value: 0.4726615705200225 and parameters: {'learning_rate': 0.1429362623010612, 'max_depth': 12, 'min_child_weight': 4, 'gamma': 0.18116584960193313, 'subsample': 0.6471629397595402, 'colsample_bytree': 0.7677949521111737, 'alpha': 0.726255383777514, 'lambda': 5.170013861597177}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:10,409] Trial 92 finished with value: 0.46323721533446427 and parameters: {'learning_rate': 0.14396431674823448, 'max_depth': 13, 'min_child_weight': 4, 'gamma': 0.9203243856265432, 'subsample': 0.6780810202954562, 'colsample_bytree': 0.7642307442260675, 'alpha': 0.6513949829442993, 'lambda': 3.671524702111723}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:11,326] Trial 93 finished with value: 0.47129241901200686 and parameters: {'learning_rate': 0.14940310312209512, 'max_depth': 12, 'min_child_weight': 4, 'gamma': 0.4724490420057655, 'subsample': 0.6601443244685219, 'colsample_bytree': 0.8529357456025839, 'alpha': 0.2674667851169832, 'lambda': 5.777894736463889}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:12,195] Trial 94 finished with value: 0.4572121592280316 and parameters: {'learning_rate': 0.15075764285526555, 'max_depth': 12, 'min_child_weight': 4, 'gamma': 0.5500160768405522, 'subsample': 0.5645283712913538, 'colsample_bytree': 0.8160767998228089, 'alpha': 0.18807276291532019, 'lambda': 5.7585559053208355}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:12,841] Trial 95 finished with value: 0.4608501990536885 and parameters: {'learning_rate': 0.1548424727992551, 'max_depth': 11, 'min_child_weight': 3, 'gamma': 1.0754927729509909, 'subsample': 0.6471163538151004, 'colsample_bytree': 0.8601796257197442, 'alpha': 0.38572885879928837, 'lambda': 5.9562239770456}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:13,608] Trial 96 finished with value: 0.4690935625416184 and parameters: {'learning_rate': 0.13808217141567367, 'max_depth': 12, 'min_child_weight': 4, 'gamma': 0.7305429275709303, 'subsample': 0.6971144245008455, 'colsample_bytree': 0.7843500744921935, 'alpha': 0.0015277318094815834, 'lambda': 5.437085252290537}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:14,283] Trial 97 finished with value: 0.4612272519730492 and parameters: {'learning_rate': 0.13050312100761616, 'max_depth': 12, 'min_child_weight': 4, 'gamma': 1.4459523704297097, 'subsample': 0.6989525867388319, 'colsample_bytree': 0.736690981442952, 'alpha': 0.0673460576385391, 'lambda': 5.483135747305546}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:14,811] Trial 98 finished with value: 0.423002001719934 and parameters: {'learning_rate': 0.13766704153080733, 'max_depth': 12, 'min_child_weight': 5, 'gamma': 6.688299772460397, 'subsample': 0.6220724873661232, 'colsample_bytree': 0.7943523825192084, 'alpha': 0.8536427560597386, 'lambda': 5.256268692016101}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:15,385] Trial 99 finished with value: 0.4612258734212125 and parameters: {'learning_rate': 0.17654685699590886, 'max_depth': 11, 'min_child_weight': 4, 'gamma': 1.9519104652847075, 'subsample': 0.5849603248811328, 'colsample_bytree': 0.8445033274821967, 'alpha': 0.6187638219802245, 'lambda': 4.8380996085680845}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:16,183] Trial 100 finished with value: 0.4740328478993543 and parameters: {'learning_rate': 0.12256489011017105, 'max_depth': 13, 'min_child_weight': 4, 'gamma': 0.7451093396344769, 'subsample': 0.6575097714347757, 'colsample_bytree': 0.6928220739959691, 'alpha': 0.21194716108201445, 'lambda': 6.6237952571684735}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:17,014] Trial 101 finished with value: 0.4637765804144881 and parameters: {'learning_rate': 0.1246750049178043, 'max_depth': 13, 'min_child_weight': 4, 'gamma': 0.6687884235766661, 'subsample': 0.6491460577281137, 'colsample_bytree': 0.7002115364176891, 'alpha': 0.3205998096992122, 'lambda': 6.609572975693297}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:18,484] Trial 102 finished with value: 0.4569394421293944 and parameters: {'learning_rate': 0.021496799524095417, 'max_depth': 14, 'min_child_weight': 4, 'gamma': 0.5015806979347283, 'subsample': 0.539795964145303, 'colsample_bytree': 0.7616068954697146, 'alpha': 0.024146773495897333, 'lambda': 6.956254232516329}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:19,241] Trial 103 finished with value: 0.44866735690101556 and parameters: {'learning_rate': 0.13443033292516446, 'max_depth': 12, 'min_child_weight': 4, 'gamma': 1.1950016435172037, 'subsample': 0.7963231187258211, 'colsample_bytree': 0.6750406965984246, 'alpha': 1.0190135020881832, 'lambda': 5.7939377053076315}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:20,001] Trial 104 finished with value: 0.46803281385100065 and parameters: {'learning_rate': 0.1394579348673517, 'max_depth': 13, 'min_child_weight': 4, 'gamma': 0.7838722301849314, 'subsample': 0.7259527661771241, 'colsample_bytree': 0.7306290862796343, 'alpha': 0.5418866175739322, 'lambda': 6.249600248173737}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:20,708] Trial 105 finished with value: 0.4429126575381851 and parameters: {'learning_rate': 0.14760499124613977, 'max_depth': 13, 'min_child_weight': 5, 'gamma': 2.4780289440605734, 'subsample': 0.6586755135673014, 'colsample_bytree': 0.7738908200560687, 'alpha': 0.21502381368596157, 'lambda': 7.108618512468581}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:21,892] Trial 106 finished with value: 0.4698652915722266 and parameters: {'learning_rate': 0.1621267189117212, 'max_depth': 15, 'min_child_weight': 4, 'gamma': 0.22380114319311903, 'subsample': 0.7038131876314732, 'colsample_bytree': 0.8137313381392134, 'alpha': 1.2416339724978775, 'lambda': 5.65560341496735}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:22,836] Trial 107 finished with value: 0.4712333433475454 and parameters: {'learning_rate': 0.16032306949656436, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.22789310897456105, 'subsample': 0.6059644646609108, 'colsample_bytree': 0.8188652310973935, 'alpha': 1.262489579145098, 'lambda': 4.093425012288416}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:23,903] Trial 108 finished with value: 0.4718792682056816 and parameters: {'learning_rate': 0.16073393980820086, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.19085343742921196, 'subsample': 0.5177351414201173, 'colsample_bytree': 0.8258679363767616, 'alpha': 1.269847078319235, 'lambda': 3.593031247437814}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:24,971] Trial 109 finished with value: 0.4765455877947259 and parameters: {'learning_rate': 0.16298850642204463, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.19512166647927875, 'subsample': 0.5093841327736296, 'colsample_bytree': 0.8141396873178834, 'alpha': 1.2164470063196782, 'lambda': 4.098974399537648}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:26,040] Trial 110 finished with value: 0.4754394925394126 and parameters: {'learning_rate': 0.15950504945122487, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.11199455903772698, 'subsample': 0.5156144972287197, 'colsample_bytree': 0.8277644010192046, 'alpha': 0.8007379014982727, 'lambda': 2.739908256433309}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:26,905] Trial 111 finished with value: 0.4693646595720862 and parameters: {'learning_rate': 0.16127297844463556, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.1893038799628443, 'subsample': 0.5054216942576956, 'colsample_bytree': 0.8015937208070594, 'alpha': 0.8167033022317044, 'lambda': 2.603765088363849}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:27,934] Trial 112 finished with value: 0.46481633216539286 and parameters: {'learning_rate': 0.16599072989072505, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.14836527116975867, 'subsample': 0.4890147941884162, 'colsample_bytree': 0.8285913850293962, 'alpha': 0.9325792701158311, 'lambda': 2.1135955967840854}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:29,056] Trial 113 finished with value: 0.4676133579441276 and parameters: {'learning_rate': 0.15744226881062717, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.48735462011921843, 'subsample': 0.522684544356981, 'colsample_bytree': 0.8684550000942124, 'alpha': 1.2970579392730963, 'lambda': 2.9479476759294236}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:31,896] Trial 114 finished with value: 0.4772981835833788 and parameters: {'learning_rate': 0.15292958266020623, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.0086099661156811, 'subsample': 0.5443899163252831, 'colsample_bytree': 0.7512214911290158, 'alpha': 0.7161325958837728, 'lambda': 3.296391705339865}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:43,278] Trial 115 finished with value: 0.4767086056215072 and parameters: {'learning_rate': 0.15023871573872002, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.021353686691243767, 'subsample': 0.5463276866416932, 'colsample_bytree': 0.7038453076012887, 'alpha': 0.5298905249463185, 'lambda': 3.2136428856398878}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:51,584] Trial 116 finished with value: 0.4723586024397299 and parameters: {'learning_rate': 0.1726650992883773, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.019318573204860134, 'subsample': 0.553463227209586, 'colsample_bytree': 0.7516299588366182, 'alpha': 0.5720090525920603, 'lambda': 3.328297085594687}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:52,588] Trial 117 finished with value: 0.46166459551243333 and parameters: {'learning_rate': 0.17990036602499904, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.03647949906152213, 'subsample': 0.5488356183402112, 'colsample_bytree': 0.7021033223713462, 'alpha': 0.6658394578994984, 'lambda': 3.1342426566363133}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:52,864] Trial 118 finished with value: 0.41097407990246665 and parameters: {'learning_rate': 0.1737354802808406, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 5.963107509248827, 'subsample': 0.4398744793122394, 'colsample_bytree': 0.7473888898294568, 'alpha': 4.9211776789835655, 'lambda': 2.7164092915944424}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:53,374] Trial 119 finished with value: 0.42342481711882957 and parameters: {'learning_rate': 0.15289883021935535, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 4.68368451367439, 'subsample': 0.48055086499437605, 'colsample_bytree': 0.6550562736026149, 'alpha': 0.7568656565125778, 'lambda': 1.7735655929072243}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:54,155] Trial 120 finished with value: 0.4641942629322999 and parameters: {'learning_rate': 0.10151821907377284, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.9321345604163416, 'subsample': 0.5787919685467348, 'colsample_bytree': 0.7217343957843044, 'alpha': 0.48307597240050226, 'lambda': 3.171686759721651}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:55,083] Trial 121 finished with value: 0.46131447857377994 and parameters: {'learning_rate': 0.1666026006167989, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.009488743263138755, 'subsample': 0.5122557577475485, 'colsample_bytree': 0.7626370708966949, 'alpha': 0.897555058484371, 'lambda': 3.2977277362439477}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:55,833] Trial 122 finished with value: 0.43991573602511275 and parameters: {'learning_rate': 0.17134076633946282, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.3449230051824163, 'subsample': 0.5344340899310265, 'colsample_bytree': 0.7869127966053575, 'alpha': 7.718154107988392, 'lambda': 2.384390263979543}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:56,696] Trial 123 finished with value: 0.46527542161644725 and parameters: {'learning_rate': 0.15578412003926256, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.41924551049142, 'subsample': 0.5610195160094369, 'colsample_bytree': 0.6247069311350891, 'alpha': 0.9985730027108394, 'lambda': 3.544966416325881}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:57,624] Trial 124 finished with value: 0.4640765655657242 and parameters: {'learning_rate': 0.16907385956557938, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.0031667651515241446, 'subsample': 0.5007339575626113, 'colsample_bytree': 0.6902209668034454, 'alpha': 0.5454608598197939, 'lambda': 2.925476922790232}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:57,887] Trial 125 finished with value: 0.4095042704135628 and parameters: {'learning_rate': 0.16149657957352181, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 9.905555367962243, 'subsample': 0.45914264767258295, 'colsample_bytree': 0.7189987089745354, 'alpha': 1.5595525427423333, 'lambda': 3.5132064233325617}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:58,655] Trial 126 finished with value: 0.46174392405635045 and parameters: {'learning_rate': 0.17745805931479064, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.6546909488531438, 'subsample': 0.5233231690433985, 'colsample_bytree': 0.7487571884096393, 'alpha': 0.3455830616995862, 'lambda': 3.724020558229568}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:39:59,678] Trial 127 finished with value: 0.4754243931190686 and parameters: {'learning_rate': 0.16467667640027286, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.2201944555087591, 'subsample': 0.4794107053966573, 'colsample_bytree': 0.7921493132605444, 'alpha': 1.1031457058803473, 'lambda': 4.1082704952627305}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:00,689] Trial 128 finished with value: 0.4719094637215763 and parameters: {'learning_rate': 0.16456986060567974, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.3862251581313511, 'subsample': 0.37903119563567067, 'colsample_bytree': 0.7725383899026133, 'alpha': 0.7765566311815773, 'lambda': 4.4919734892356935}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:01,554] Trial 129 finished with value: 0.45743853320400796 and parameters: {'learning_rate': 0.173343468517912, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.653846606356248, 'subsample': 0.4381693975446239, 'colsample_bytree': 0.7952184402274096, 'alpha': 1.0891991792020712, 'lambda': 4.092087762165015}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:02,359] Trial 130 finished with value: 0.4583489909049612 and parameters: {'learning_rate': 0.14625450330144865, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.9482489842818953, 'subsample': 0.4686862303296606, 'colsample_bytree': 0.7227589763258673, 'alpha': 0.5065129625419249, 'lambda': 2.5268140485442596}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:02,856] Trial 131 finished with value: 0.4120192400284746 and parameters: {'learning_rate': 0.16569685923849367, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 8.854334354441473, 'subsample': 0.3567298299472014, 'colsample_bytree': 0.7693631081392178, 'alpha': 0.7206461769447474, 'lambda': 4.421294411286881}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:03,869] Trial 132 finished with value: 0.4623204979875095 and parameters: {'learning_rate': 0.15224343633665335, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.36322061081178625, 'subsample': 0.371971328350973, 'colsample_bytree': 0.667549215694845, 'alpha': 0.8196275946001504, 'lambda': 4.010316507258193}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:05,021] Trial 133 finished with value: 0.4647946962404512 and parameters: {'learning_rate': 0.15689355133941718, 'max_depth': 15, 'min_child_weight': 2, 'gamma': 0.17432905250093922, 'subsample': 0.31660178883923595, 'colsample_bytree': 0.7490373780891011, 'alpha': 1.7620537566827967, 'lambda': 6.555807889424715}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:05,956] Trial 134 finished with value: 0.4578956279099569 and parameters: {'learning_rate': 0.1639715746379856, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.4725727800761816, 'subsample': 0.38508861028135755, 'colsample_bytree': 0.7760864090852003, 'alpha': 1.124349573855912, 'lambda': 3.416729690473642}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:06,795] Trial 135 finished with value: 0.47731805590592224 and parameters: {'learning_rate': 0.17025702137139195, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.24653517972658417, 'subsample': 0.4170433430623971, 'colsample_bytree': 0.8010319379921091, 'alpha': 0.3593460892203975, 'lambda': 3.7830789610373716}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:07,867] Trial 136 finished with value: 0.456456233497218 and parameters: {'learning_rate': 0.17076552694869435, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.01509226533027988, 'subsample': 0.5462768034570157, 'colsample_bytree': 0.44536949147031524, 'alpha': 3.845816484956936, 'lambda': 3.8353499324422224}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:08,778] Trial 137 finished with value: 0.4671158149025002 and parameters: {'learning_rate': 0.17879973149766168, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.7692477994185083, 'subsample': 0.4132661828134283, 'colsample_bytree': 0.8038177467751295, 'alpha': 0.36344161229581695, 'lambda': 3.282262685648238}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:09,649] Trial 138 finished with value: 0.4543700990602313 and parameters: {'learning_rate': 0.19049357808310208, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.26470265544116955, 'subsample': 0.4332292843164613, 'colsample_bytree': 0.702362682323122, 'alpha': 0.2509795183939578, 'lambda': 2.992284839282743}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:10,605] Trial 139 finished with value: 0.462934694938471 and parameters: {'learning_rate': 0.15417650885781534, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.536183877688139, 'subsample': 0.4813847055263243, 'colsample_bytree': 0.7461706686331696, 'alpha': 1.4060279956672033, 'lambda': 6.764773851912392}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:11,540] Trial 140 finished with value: 0.4527462799042286 and parameters: {'learning_rate': 0.18385603936119407, 'max_depth': 9, 'min_child_weight': 5, 'gamma': 0.14353580234668273, 'subsample': 0.5689697846266846, 'colsample_bytree': 0.891975128358964, 'alpha': 0.5754662964284378, 'lambda': 2.810316339576329}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:12,577] Trial 141 finished with value: 0.45865653346348323 and parameters: {'learning_rate': 0.1644907835349673, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.42758193283482576, 'subsample': 0.33939442882992343, 'colsample_bytree': 0.7869459834202853, 'alpha': 0.9434538345719572, 'lambda': 4.51472633637256}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:13,659] Trial 142 finished with value: 0.44251458364099316 and parameters: {'learning_rate': 0.16856457429815136, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.2849591004187114, 'subsample': 0.3892562861698309, 'colsample_bytree': 0.7622286424131444, 'alpha': 6.2632178670768335, 'lambda': 4.138090220459505}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:14,849] Trial 143 finished with value: 0.46517227786585524 and parameters: {'learning_rate': 0.1587168158811931, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.6935423896467965, 'subsample': 0.2935418087767726, 'colsample_bytree': 0.8438321613379643, 'alpha': 0.67863652636731, 'lambda': 3.8222829208720177}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:15,940] Trial 144 finished with value: 0.47217742060094137 and parameters: {'learning_rate': 0.17463981182342417, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.3624934986256071, 'subsample': 0.5003013803068415, 'colsample_bytree': 0.8131647944904903, 'alpha': 0.7906305433490091, 'lambda': 6.39249395950257}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:17,099] Trial 145 finished with value: 0.46972549808353614 and parameters: {'learning_rate': 0.18246624406048786, 'max_depth': 13, 'min_child_weight': 2, 'gamma': 0.025767316050408485, 'subsample': 0.4531306075845224, 'colsample_bytree': 0.8152233755392837, 'alpha': 0.3522369418905654, 'lambda': 6.329620852121745}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:17,917] Trial 146 finished with value: 0.4578592005575426 and parameters: {'learning_rate': 0.17452023117331783, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 0.8621236884088702, 'subsample': 0.5008441680796466, 'colsample_bytree': 0.7313876819856142, 'alpha': 1.1866874343364826, 'lambda': 6.5275123802990755}. Best is trial 54 with value: 0.4793740964462298.\n",
      "[I 2024-02-06 14:40:19,194] Trial 147 finished with value: 0.4800767945469113 and parameters: {'learning_rate': 0.14792870839198485, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.19375894093017226, 'subsample': 0.5867092481996331, 'colsample_bytree': 0.7867880227677111, 'alpha': 0.9906091280172452, 'lambda': 7.4486985556185825}. Best is trial 147 with value: 0.4800767945469113.\n",
      "[I 2024-02-06 14:40:20,234] Trial 148 finished with value: 0.4801207968304306 and parameters: {'learning_rate': 0.1967450571781139, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.20498467573662624, 'subsample': 0.5451488531944823, 'colsample_bytree': 0.8240010801275418, 'alpha': 0.9998785410758824, 'lambda': 6.989941839166176}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:40:21,742] Trial 149 finished with value: 0.4790269513785199 and parameters: {'learning_rate': 0.1977347685465329, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.0004827516729649928, 'subsample': 0.5872744080061413, 'colsample_bytree': 0.8334629588238561, 'alpha': 1.6198978383189355, 'lambda': 7.47157746002171}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:40:22,797] Trial 150 finished with value: 0.47745895823128565 and parameters: {'learning_rate': 0.19203175881607812, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.254192265020121, 'subsample': 0.5842233529595388, 'colsample_bytree': 0.8540673196866483, 'alpha': 1.5779270515661026, 'lambda': 7.96299283430585}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:40:24,251] Trial 151 finished with value: 0.4687664670810846 and parameters: {'learning_rate': 0.18681637243707738, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.1911146206300815, 'subsample': 0.5866188154887468, 'colsample_bytree': 0.8319012465791803, 'alpha': 1.6522574194741204, 'lambda': 8.359482202262315}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:40:26,275] Trial 152 finished with value: 0.4679452423467855 and parameters: {'learning_rate': 0.19814563084748155, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.5625842472660607, 'subsample': 0.5532035062908223, 'colsample_bytree': 0.8568892811138256, 'alpha': 1.3223051768458074, 'lambda': 7.994975224909595}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:40:37,731] Trial 153 finished with value: 0.46512914410685097 and parameters: {'learning_rate': 0.1914428126661526, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.2269505363926298, 'subsample': 0.5415453843667786, 'colsample_bytree': 0.7951822481692818, 'alpha': 1.5136305939085632, 'lambda': 7.431709958750389}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:40:46,355] Trial 154 finished with value: 0.46380298257528146 and parameters: {'learning_rate': 0.19671376650619768, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.5661047377348604, 'subsample': 0.6099613123672144, 'colsample_bytree': 0.8632481047226626, 'alpha': 1.7832936604637544, 'lambda': 8.030149184791224}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:40:51,013] Trial 155 finished with value: 0.46731297798953997 and parameters: {'learning_rate': 0.19598470170677731, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.1841667073273007, 'subsample': 0.5917279398583117, 'colsample_bytree': 0.8349747987467464, 'alpha': 1.0656212801171177, 'lambda': 7.805511888808922}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:40:51,995] Trial 156 finished with value: 0.45885614496541044 and parameters: {'learning_rate': 0.19384675254505396, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.3653493304396553, 'subsample': 0.5734977954880565, 'colsample_bytree': 0.7981472574927063, 'alpha': 2.2122441170860294, 'lambda': 8.525330881905239}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:40:53,823] Trial 157 finished with value: 0.4725668431418835 and parameters: {'learning_rate': 0.11127622064868811, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.011504546168803975, 'subsample': 0.6304031342526922, 'colsample_bytree': 0.9160576161176285, 'alpha': 1.0608673583123194, 'lambda': 7.052829855977715}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:40:54,747] Trial 158 finished with value: 0.46678564694616287 and parameters: {'learning_rate': 0.1993376055279568, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.5519653411441064, 'subsample': 0.6293388749415625, 'colsample_bytree': 0.9666761490854187, 'alpha': 1.3922394132975666, 'lambda': 7.302817093272548}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:40:56,410] Trial 159 finished with value: 0.47571803429609555 and parameters: {'learning_rate': 0.11245065468473431, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.006172752283281083, 'subsample': 0.5913843324905812, 'colsample_bytree': 0.9191084923213128, 'alpha': 1.0320608737464523, 'lambda': 7.423305558235986}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:40:57,957] Trial 160 finished with value: 0.4756284614342568 and parameters: {'learning_rate': 0.11549438308401642, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.23702314515425382, 'subsample': 0.6037412338609649, 'colsample_bytree': 0.8951485037634694, 'alpha': 1.5290418946093283, 'lambda': 7.398402414397747}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:40:59,223] Trial 161 finished with value: 0.46471364033084533 and parameters: {'learning_rate': 0.11565415082255422, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.23141844140232104, 'subsample': 0.6006322665515178, 'colsample_bytree': 0.9065654284895485, 'alpha': 1.6067309268148762, 'lambda': 7.8086183953566906}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:00,336] Trial 162 finished with value: 0.4604201381575386 and parameters: {'learning_rate': 0.10675609338321433, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.37432423947043925, 'subsample': 0.5290899269530606, 'colsample_bytree': 0.8699563321857886, 'alpha': 1.885347738165735, 'lambda': 7.425686590784139}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:01,675] Trial 163 finished with value: 0.4717940312492162 and parameters: {'learning_rate': 0.11072391764754323, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.20889661273679, 'subsample': 0.5770742382979078, 'colsample_bytree': 0.8393749271129429, 'alpha': 1.3174926942239482, 'lambda': 7.186973506515799}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:02,516] Trial 164 finished with value: 0.47008206239271577 and parameters: {'learning_rate': 0.15012415941115095, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.6618475383911359, 'subsample': 0.5980156084736216, 'colsample_bytree': 0.8833425190447921, 'alpha': 0.9749356676584473, 'lambda': 7.513863534905073}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:04,280] Trial 165 finished with value: 0.47072332079687207 and parameters: {'learning_rate': 0.11812911558601637, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.001967243176954153, 'subsample': 0.5632509789327682, 'colsample_bytree': 0.93811950260456, 'alpha': 1.1731306549954488, 'lambda': 7.779993056918764}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:05,297] Trial 166 finished with value: 0.45942846705929863 and parameters: {'learning_rate': 0.14389896661423282, 'max_depth': 15, 'min_child_weight': 7, 'gamma': 0.4353186189028604, 'subsample': 0.5287861214497099, 'colsample_bytree': 0.8892492098475686, 'alpha': 1.4765848279146954, 'lambda': 6.915242551384819}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:06,750] Trial 167 finished with value: 0.4725157605068022 and parameters: {'learning_rate': 0.12348146278331107, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.20943993172854972, 'subsample': 0.5839973993570751, 'colsample_bytree': 0.9887363768486755, 'alpha': 2.069909099364146, 'lambda': 8.103580301624296}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:07,515] Trial 168 finished with value: 0.4576451030006469 and parameters: {'learning_rate': 0.1493244179508872, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.9368168810116878, 'subsample': 0.6220155107427614, 'colsample_bytree': 0.8548839241846078, 'alpha': 0.9587059488121146, 'lambda': 7.231537908043368}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:08,518] Trial 169 finished with value: 0.46363882403586437 and parameters: {'learning_rate': 0.15375731285924096, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.5041586495989896, 'subsample': 0.5560886643299668, 'colsample_bytree': 0.8105398126735277, 'alpha': 1.6546271010358287, 'lambda': 7.057792849547076}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:09,357] Trial 170 finished with value: 0.45214199944973477 and parameters: {'learning_rate': 0.12128355028735502, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.695777555111855, 'subsample': 0.6060412813058725, 'colsample_bytree': 0.783804120353565, 'alpha': 1.233646207026199, 'lambda': 9.177930858923826}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:11,347] Trial 171 finished with value: 0.4793302257238432 and parameters: {'learning_rate': 0.1092133551228638, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.036988449591462746, 'subsample': 0.6359487687740878, 'colsample_bytree': 0.9549739409903438, 'alpha': 1.0653127790667476, 'lambda': 7.648082824323274}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:12,706] Trial 172 finished with value: 0.4746141810450945 and parameters: {'learning_rate': 0.10304100720324175, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.20643980519978572, 'subsample': 0.5143739235042859, 'colsample_bytree': 0.9705210360068279, 'alpha': 0.9360083477909862, 'lambda': 7.4068463201334325}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:14,371] Trial 173 finished with value: 0.4767082944381168 and parameters: {'learning_rate': 0.10246687626097492, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.008603684841910417, 'subsample': 0.508616628534105, 'colsample_bytree': 0.9689548163144258, 'alpha': 0.9024438360319096, 'lambda': 7.503557526057613}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:15,763] Trial 174 finished with value: 0.4650690784534555 and parameters: {'learning_rate': 0.10079730187707091, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.17262379955428508, 'subsample': 0.4772460883363289, 'colsample_bytree': 0.9644289442052545, 'alpha': 1.356159680645964, 'lambda': 7.583456400153572}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:17,026] Trial 175 finished with value: 0.466420081033913 and parameters: {'learning_rate': 0.09502240762879387, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.34646533594629086, 'subsample': 0.5166215908605043, 'colsample_bytree': 0.9489889111020481, 'alpha': 1.029867051745151, 'lambda': 7.373739176991462}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:18,709] Trial 176 finished with value: 0.4692376617410255 and parameters: {'learning_rate': 0.1057316474888209, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.0012278925533907858, 'subsample': 0.535880655770206, 'colsample_bytree': 0.9931644080036336, 'alpha': 0.9133500001985227, 'lambda': 8.640195466583611}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:20,434] Trial 177 finished with value: 0.47683634347035597 and parameters: {'learning_rate': 0.09649412865727691, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.002883213639710164, 'subsample': 0.4952975911591902, 'colsample_bytree': 0.9306613855807425, 'alpha': 1.4916412848586105, 'lambda': 7.623757726293721}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:22,295] Trial 178 finished with value: 0.46483487260316536 and parameters: {'learning_rate': 0.09073231937239441, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.011147959469934066, 'subsample': 0.48461198145906126, 'colsample_bytree': 0.929325102427196, 'alpha': 1.7536379368671149, 'lambda': 7.762913891055193}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:28,824] Trial 179 finished with value: 0.46114303172902077 and parameters: {'learning_rate': 0.07955961387788904, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.3867213845777476, 'subsample': 0.5428861761623955, 'colsample_bytree': 0.9869856222551946, 'alpha': 2.4488701489896885, 'lambda': 7.672953641906168}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:36,065] Trial 180 finished with value: 0.43287288214420605 and parameters: {'learning_rate': 0.09457258616044721, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 3.7730227292373657, 'subsample': 0.4098918183492905, 'colsample_bytree': 0.9156530470117125, 'alpha': 1.513099327200813, 'lambda': 8.231872882676923}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:49,417] Trial 181 finished with value: 0.4753995605187072 and parameters: {'learning_rate': 0.10006864046359665, 'max_depth': 8, 'min_child_weight': 3, 'gamma': 0.19292914736076267, 'subsample': 0.5018553319137397, 'colsample_bytree': 0.9651947272487044, 'alpha': 1.1899362238831912, 'lambda': 7.906525074819389}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:50,860] Trial 182 finished with value: 0.4722574974058313 and parameters: {'learning_rate': 0.09927539931437954, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.3139733774944129, 'subsample': 0.44930872603086836, 'colsample_bytree': 0.952679351686113, 'alpha': 1.1690372674497036, 'lambda': 7.942127222731303}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:52,350] Trial 183 finished with value: 0.46822111906554714 and parameters: {'learning_rate': 0.0850517286678774, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.01413312795459095, 'subsample': 0.49553306789948154, 'colsample_bytree': 0.9337141191260274, 'alpha': 1.5191063060720724, 'lambda': 7.7322008181630375}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:53,399] Trial 184 finished with value: 0.4631133223856985 and parameters: {'learning_rate': 0.10892192024686519, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.5147388576985245, 'subsample': 0.5099978958501246, 'colsample_bytree': 0.8993348516593878, 'alpha': 1.3299534086724587, 'lambda': 7.537026287107292}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:54,648] Trial 185 finished with value: 0.4716614985744058 and parameters: {'learning_rate': 0.10530476276575412, 'max_depth': 8, 'min_child_weight': 3, 'gamma': 0.19743875393124344, 'subsample': 0.5541064769199116, 'colsample_bytree': 0.9656760873935236, 'alpha': 1.9182179844540843, 'lambda': 7.92777835052995}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:55,982] Trial 186 finished with value: 0.46802694589134675 and parameters: {'learning_rate': 0.051914617273270416, 'max_depth': 6, 'min_child_weight': 3, 'gamma': 0.0011588043763730068, 'subsample': 0.46708205033616146, 'colsample_bytree': 0.9440169280258541, 'alpha': 0.6635076541145285, 'lambda': 7.2254395064900665}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:57,173] Trial 187 finished with value: 0.4742642125449543 and parameters: {'learning_rate': 0.08853175044993301, 'max_depth': 8, 'min_child_weight': 3, 'gamma': 0.3612286703624996, 'subsample': 0.5286764826750034, 'colsample_bytree': 0.9081492639576845, 'alpha': 1.2215545411064277, 'lambda': 7.60688682103195}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:58,260] Trial 188 finished with value: 0.47232084636120303 and parameters: {'learning_rate': 0.11263987468369825, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 0.20655441843000355, 'subsample': 0.5811244778816653, 'colsample_bytree': 0.8230399964919173, 'alpha': 0.8287702194255001, 'lambda': 8.277300120033173}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:41:59,360] Trial 189 finished with value: 0.4629490294530248 and parameters: {'learning_rate': 0.09811686552401608, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.5506129702300252, 'subsample': 0.42077169024022676, 'colsample_bytree': 0.8783413117756687, 'alpha': 1.0831952141825973, 'lambda': 8.596891709421222}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:00,534] Trial 190 finished with value: 0.4643548663201855 and parameters: {'learning_rate': 0.1146033270903291, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.3698306577480114, 'subsample': 0.4856081789638035, 'colsample_bytree': 0.8630658167869839, 'alpha': 0.545970200787201, 'lambda': 6.958986402029671}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:01,817] Trial 191 finished with value: 0.4708084636049603 and parameters: {'learning_rate': 0.09793555687370936, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.16833362187697617, 'subsample': 0.508333571577835, 'colsample_bytree': 0.9711904170462988, 'alpha': 0.9089707663606146, 'lambda': 7.4350916187176885}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:03,135] Trial 192 finished with value: 0.46715651228856764 and parameters: {'learning_rate': 0.10486687138664827, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.21767675942238812, 'subsample': 0.517019788368798, 'colsample_bytree': 0.9996470329903374, 'alpha': 1.37454045394137, 'lambda': 7.365716999077509}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:04,716] Trial 193 finished with value: 0.47086795022181666 and parameters: {'learning_rate': 0.10253340441247638, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.21360117304937018, 'subsample': 0.5614340491268628, 'colsample_bytree': 0.9734997538991356, 'alpha': 0.996894275051797, 'lambda': 7.9061443710087485}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:05,807] Trial 194 finished with value: 0.4636169723271166 and parameters: {'learning_rate': 0.10193229109631714, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.4731783982106629, 'subsample': 0.5390585629918259, 'colsample_bytree': 0.9443167634852607, 'alpha': 1.706130497222966, 'lambda': 7.129147357203896}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:07,585] Trial 195 finished with value: 0.4721132228018213 and parameters: {'learning_rate': 0.09297373289088004, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.00299735724046071, 'subsample': 0.575659036012462, 'colsample_bytree': 0.9287000570146299, 'alpha': 0.7310295304091028, 'lambda': 7.650396708495257}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:08,877] Trial 196 finished with value: 0.46850848196362643 and parameters: {'learning_rate': 0.15701458193240708, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.1720510350329948, 'subsample': 0.5019714322196679, 'colsample_bytree': 0.9586732769983909, 'alpha': 1.183913468117878, 'lambda': 8.13139243513499}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:09,867] Trial 197 finished with value: 0.46096374114595895 and parameters: {'learning_rate': 0.10774934576878384, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.5702900458436067, 'subsample': 0.591698570113912, 'colsample_bytree': 0.916814936935483, 'alpha': 1.5051164259486778, 'lambda': 7.4284747001981515}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:10,847] Trial 198 finished with value: 0.47302776409453495 and parameters: {'learning_rate': 0.19314155029417102, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.3467085851931945, 'subsample': 0.5216959223320734, 'colsample_bytree': 0.8380224134059951, 'alpha': 0.5245865500069049, 'lambda': 6.801123983125102}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:11,908] Trial 199 finished with value: 0.45816866040281984 and parameters: {'learning_rate': 0.0967800065189541, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.7413815371766874, 'subsample': 0.4522425244391191, 'colsample_bytree': 0.9779701517420838, 'alpha': 0.8453548292018104, 'lambda': 7.747906829486024}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:12,620] Trial 200 finished with value: 0.4446299522500779 and parameters: {'learning_rate': 0.1878032268624662, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.008201073827344807, 'subsample': 0.07282343505767991, 'colsample_bytree': 0.8151016480568805, 'alpha': 1.0899417087079881, 'lambda': 7.501454748117073}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:13,806] Trial 201 finished with value: 0.46750023439070976 and parameters: {'learning_rate': 0.0886393141278385, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.3498126076889163, 'subsample': 0.5377206936208601, 'colsample_bytree': 0.8869903723591732, 'alpha': 1.1783614132497753, 'lambda': 7.590509292439015}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:15,047] Trial 202 finished with value: 0.46913833282608297 and parameters: {'learning_rate': 0.08092521925409246, 'max_depth': 8, 'min_child_weight': 3, 'gamma': 0.3168941084048953, 'subsample': 0.5231928823629233, 'colsample_bytree': 0.905083221008296, 'alpha': 1.2428221112116793, 'lambda': 7.248686765293782}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:16,514] Trial 203 finished with value: 0.4709079193680662 and parameters: {'learning_rate': 0.08867467890281627, 'max_depth': 8, 'min_child_weight': 3, 'gamma': 0.14497853319121626, 'subsample': 0.5630806311198094, 'colsample_bytree': 0.9205756090379852, 'alpha': 1.376785943850933, 'lambda': 7.911941666634356}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:18,053] Trial 204 finished with value: 0.45529256405529717 and parameters: {'learning_rate': 0.10282956831887563, 'max_depth': 8, 'min_child_weight': 3, 'gamma': 0.4097808653117689, 'subsample': 0.4901025520701163, 'colsample_bytree': 0.7972762793438026, 'alpha': 0.9667871825596118, 'lambda': 7.633810197715821}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:19,070] Trial 205 finished with value: 0.4387305065585166 and parameters: {'learning_rate': 0.1595656225970539, 'max_depth': 8, 'min_child_weight': 3, 'gamma': 0.2211198067287017, 'subsample': 0.5473263512267692, 'colsample_bytree': 0.9541795738104368, 'alpha': 8.851502798082837, 'lambda': 7.346936447134233}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:20,408] Trial 206 finished with value: 0.46926714705494443 and parameters: {'learning_rate': 0.15411233816369235, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.5745295969110658, 'subsample': 0.4695309998962339, 'colsample_bytree': 0.8430324897005073, 'alpha': 0.7106961221915598, 'lambda': 8.07130911295898}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:30,953] Trial 207 finished with value: 0.4749044019288392 and parameters: {'learning_rate': 0.10935492465209648, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.008539312356022205, 'subsample': 0.6086055615965501, 'colsample_bytree': 0.906507383841005, 'alpha': 1.6057239061678656, 'lambda': 7.1802267771795645}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:47,204] Trial 208 finished with value: 0.4739080660470792 and parameters: {'learning_rate': 0.1077434786249336, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.015266886919560024, 'subsample': 0.5992213344669245, 'colsample_bytree': 0.7806696428430521, 'alpha': 1.7850994817941952, 'lambda': 7.051963795870411}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:48,884] Trial 209 finished with value: 0.4786329264627773 and parameters: {'learning_rate': 0.11072541205077223, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.0043352502692153546, 'subsample': 0.6158894379549296, 'colsample_bytree': 0.8251144744279671, 'alpha': 1.6077331863339492, 'lambda': 6.02306767013708}. Best is trial 148 with value: 0.4801207968304306.\n",
      "[I 2024-02-06 14:42:50,512] Trial 210 finished with value: 0.4817046193244972 and parameters: {'learning_rate': 0.11677426053332168, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.016772116519990873, 'subsample': 0.6187663699654486, 'colsample_bytree': 0.8211651245799981, 'alpha': 1.9403647594631037, 'lambda': 6.13012295669279}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:42:51,706] Trial 211 finished with value: 0.4713099957055446 and parameters: {'learning_rate': 0.11702488001946713, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.15801760094018108, 'subsample': 0.6217418705408678, 'colsample_bytree': 0.822797268552529, 'alpha': 1.989742791104802, 'lambda': 6.1190981094987045}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:42:52,947] Trial 212 finished with value: 0.47108390278978285 and parameters: {'learning_rate': 0.11368397980833049, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.13434758456239315, 'subsample': 0.6310944554542115, 'colsample_bytree': 0.8027474032875116, 'alpha': 1.6521530605447183, 'lambda': 6.283020388391826}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:42:54,403] Trial 213 finished with value: 0.47623265643711776 and parameters: {'learning_rate': 0.10931997472472117, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.03203744243739359, 'subsample': 0.614376621440814, 'colsample_bytree': 0.8608569327784281, 'alpha': 2.192192496586901, 'lambda': 5.989507684705128}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:42:55,508] Trial 214 finished with value: 0.4591821519090187 and parameters: {'learning_rate': 0.11210012070656418, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.3599161308754728, 'subsample': 0.6440574185694058, 'colsample_bytree': 0.8589519675051582, 'alpha': 2.127077165859332, 'lambda': 5.955201784888516}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:42:57,000] Trial 215 finished with value: 0.4799335441510012 and parameters: {'learning_rate': 0.19994738162532807, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.010475400384511134, 'subsample': 0.580619079086539, 'colsample_bytree': 0.8329464917326086, 'alpha': 1.925076081108633, 'lambda': 6.086806527893842}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:42:58,433] Trial 216 finished with value: 0.47434928768214774 and parameters: {'learning_rate': 0.19745824135332407, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.006426012811655194, 'subsample': 0.6089932358984108, 'colsample_bytree': 0.8517893887543685, 'alpha': 1.9417983498064837, 'lambda': 5.907848453460898}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:42:59,012] Trial 217 finished with value: 0.4165912007835827 and parameters: {'learning_rate': 0.19299103543333557, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 7.147684716557843, 'subsample': 0.5882535191418625, 'colsample_bytree': 0.834175354196032, 'alpha': 1.8507728853586727, 'lambda': 6.0799223106995495}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:00,141] Trial 218 finished with value: 0.4746766020186305 and parameters: {'learning_rate': 0.11851592289792763, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.20170959227838187, 'subsample': 0.5809067108653674, 'colsample_bytree': 0.8199447239228066, 'alpha': 2.2311401290610107, 'lambda': 5.7905993896741945}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:01,441] Trial 219 finished with value: 0.47315952142856965 and parameters: {'learning_rate': 0.1953435713107907, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.0022108232016633464, 'subsample': 0.6215654155264562, 'colsample_bytree': 0.8816147504219651, 'alpha': 2.2911487592750337, 'lambda': 5.64669899186822}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:02,578] Trial 220 finished with value: 0.46215082145035336 and parameters: {'learning_rate': 0.19991761378343023, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.32687400161684943, 'subsample': 0.3991292369450776, 'colsample_bytree': 0.8075717263207627, 'alpha': 2.0856163149516918, 'lambda': 6.080396822987966}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:03,829] Trial 221 finished with value: 0.47022328230835425 and parameters: {'learning_rate': 0.14925830420685973, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.2138207117130615, 'subsample': 0.56771026996909, 'colsample_bytree': 0.7905011303829137, 'alpha': 1.6714764804855222, 'lambda': 6.426251648430013}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:04,787] Trial 222 finished with value: 0.4547776192079331 and parameters: {'learning_rate': 0.10943235919535656, 'max_depth': 11, 'min_child_weight': 3, 'gamma': 0.4709543873704327, 'subsample': 0.6027892082109928, 'colsample_bytree': 0.8299902854879239, 'alpha': 1.4380630202238176, 'lambda': 6.224435171836962}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:06,072] Trial 223 finished with value: 0.4705628286326106 and parameters: {'learning_rate': 0.15594784334927247, 'max_depth': 7, 'min_child_weight': 2, 'gamma': 0.011852401955697756, 'subsample': 0.6398985632755284, 'colsample_bytree': 0.7710288803851826, 'alpha': 2.69638679237394, 'lambda': 5.895220097792573}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:07,435] Trial 224 finished with value: 0.46058704415441476 and parameters: {'learning_rate': 0.18968803179832527, 'max_depth': 8, 'min_child_weight': 3, 'gamma': 0.24432273327558054, 'subsample': 0.5701362247313201, 'colsample_bytree': 0.8545803748130487, 'alpha': 1.8258517241663328, 'lambda': 3.1218969380358375}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:08,473] Trial 225 finished with value: 0.452699231615236 and parameters: {'learning_rate': 0.1468593954416471, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.474372440556994, 'subsample': 0.360567553244461, 'colsample_bytree': 0.8048574071355117, 'alpha': 1.5641982986194458, 'lambda': 6.647404075010604}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:09,544] Trial 226 finished with value: 0.46942535748725933 and parameters: {'learning_rate': 0.16166069431943145, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.170641227192373, 'subsample': 0.5923377420891216, 'colsample_bytree': 0.8282809953764233, 'alpha': 1.3634166606695324, 'lambda': 6.2368886083388055}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:10,494] Trial 227 finished with value: 0.44084063334557627 and parameters: {'learning_rate': 0.11345629466983743, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.3689027261010275, 'subsample': 0.5557170556929711, 'colsample_bytree': 0.7847148991845377, 'alpha': 4.21249034602821, 'lambda': 5.559905273264078}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:11,471] Trial 228 finished with value: 0.4527296111678184 and parameters: {'learning_rate': 0.15227747862439361, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.5985913172613349, 'subsample': 0.5824493933351103, 'colsample_bytree': 0.7613195365448789, 'alpha': 1.059925040513003, 'lambda': 2.6968065833453405}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:13,195] Trial 229 finished with value: 0.4688561848433967 and parameters: {'learning_rate': 0.1213217456117741, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.0018665080552791626, 'subsample': 0.6209496960370972, 'colsample_bytree': 0.8507658177963796, 'alpha': 2.44217813118509, 'lambda': 3.417900793567915}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:13,975] Trial 230 finished with value: 0.4748340118119439 and parameters: {'learning_rate': 0.19527476696913906, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.2155007618532969, 'subsample': 0.5507025362915245, 'colsample_bytree': 0.814438963979257, 'alpha': 1.2588351081383404, 'lambda': 0.17683352136638764}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:15,533] Trial 231 finished with value: 0.4743721041519329 and parameters: {'learning_rate': 0.10800249105717112, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.01582007342689375, 'subsample': 0.6140229687016315, 'colsample_bytree': 0.8772666208587768, 'alpha': 1.618082525115154, 'lambda': 6.393758124584033}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:17,035] Trial 232 finished with value: 0.4674029075683792 and parameters: {'learning_rate': 0.11172855206023584, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.1692086518227259, 'subsample': 0.6125418454380762, 'colsample_bytree': 0.8951616290285583, 'alpha': 1.5431637247157561, 'lambda': 3.7185759676794277}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:23,510] Trial 233 finished with value: 0.4740388748844496 and parameters: {'learning_rate': 0.11659651241080266, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.007794178528875786, 'subsample': 0.6645561380590067, 'colsample_bytree': 0.8681664910434479, 'alpha': 1.8668644131682437, 'lambda': 6.896267406680582}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:35,305] Trial 234 finished with value: 0.4689869770025988 and parameters: {'learning_rate': 0.10458546178150983, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.36169743510971164, 'subsample': 0.640734969107984, 'colsample_bytree': 0.7935975774156654, 'alpha': 1.415549516725326, 'lambda': 7.141321045704842}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:44,729] Trial 235 finished with value: 0.4699189302869087 and parameters: {'learning_rate': 0.10005675535057684, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.18177491908737872, 'subsample': 0.591522612087613, 'colsample_bytree': 0.8413421038988411, 'alpha': 1.7488149994879265, 'lambda': 6.087649610731986}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:46,418] Trial 236 finished with value: 0.4754860856149396 and parameters: {'learning_rate': 0.10721437227958239, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.006420683621913871, 'subsample': 0.5704350238155103, 'colsample_bytree': 0.9320921666407271, 'alpha': 0.7949466722686693, 'lambda': 7.806484546024213}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:47,425] Trial 237 finished with value: 0.4682011532656095 and parameters: {'learning_rate': 0.15228074752343543, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.4151090531442701, 'subsample': 0.5637985419915617, 'colsample_bytree': 0.9353166265826474, 'alpha': 0.5670984978822148, 'lambda': 7.774449477097877}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:48,203] Trial 238 finished with value: 0.468930951618013 and parameters: {'learning_rate': 0.1995139046013, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.26570688317036, 'subsample': 0.936886803311213, 'colsample_bytree': 0.8159528026681798, 'alpha': 0.7424292959464462, 'lambda': 7.9274112759816}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:50,068] Trial 239 finished with value: 0.4722282844924528 and parameters: {'learning_rate': 0.10493312567194474, 'max_depth': 11, 'min_child_weight': 2, 'gamma': 0.0025076561146349097, 'subsample': 0.5387843287306581, 'colsample_bytree': 0.7773770928922294, 'alpha': 0.8582833664729375, 'lambda': 8.138515834945375}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:51,214] Trial 240 finished with value: 0.46660590685893544 and parameters: {'learning_rate': 0.159705758906792, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 0.4652722283570505, 'subsample': 0.5771104003581179, 'colsample_bytree': 0.945432499163091, 'alpha': 0.4066801325334361, 'lambda': 7.530120246554443}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:52,697] Trial 241 finished with value: 0.47506416487001196 and parameters: {'learning_rate': 0.10897756424618667, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.008476733186696311, 'subsample': 0.6012951397251083, 'colsample_bytree': 0.922555115292518, 'alpha': 1.097475430876723, 'lambda': 2.463022000492486}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:53,867] Trial 242 finished with value: 0.47874161025288103 and parameters: {'learning_rate': 0.111052697726477, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.2009894479095122, 'subsample': 0.5989405161790081, 'colsample_bytree': 0.937965133901142, 'alpha': 1.1013956386509582, 'lambda': 2.402549232124993}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:55,124] Trial 243 finished with value: 0.4739793068227224 and parameters: {'learning_rate': 0.11567090925736224, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.21891765052337092, 'subsample': 0.5030380111050996, 'colsample_bytree': 0.9347632411131059, 'alpha': 0.8874378641599567, 'lambda': 2.204735292238565}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:56,361] Trial 244 finished with value: 0.46737703267001507 and parameters: {'learning_rate': 0.11190738487427927, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.27908879678021337, 'subsample': 0.5374399326389158, 'colsample_bytree': 0.957437517594087, 'alpha': 1.208719532257661, 'lambda': 8.364509020693939}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:57,363] Trial 245 finished with value: 0.47357778140139933 and parameters: {'learning_rate': 0.15588293864328917, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.17812533764812252, 'subsample': 0.5721357653719907, 'colsample_bytree': 0.8340390042770118, 'alpha': 1.0426462218887815, 'lambda': 2.5868477097654776}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:58,355] Trial 246 finished with value: 0.4626092997953687 and parameters: {'learning_rate': 0.09765792246627293, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.6271151412013098, 'subsample': 0.5919053911770896, 'colsample_bytree': 0.7996971843144166, 'alpha': 0.7511250027366964, 'lambda': 2.2553294494587868}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:58,959] Trial 247 finished with value: 0.44192399068193655 and parameters: {'learning_rate': 0.118940015154954, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 2.9484967813099603, 'subsample': 0.631002672357839, 'colsample_bytree': 0.8967363044999298, 'alpha': 1.355979685861062, 'lambda': 2.911467969962759}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:43:59,989] Trial 248 finished with value: 0.4664326822084493 and parameters: {'learning_rate': 0.16491980492912064, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.3919689802336634, 'subsample': 0.5510584277116206, 'colsample_bytree': 0.7660657811486767, 'alpha': 0.561583289674463, 'lambda': 7.719098076050496}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:01,487] Trial 249 finished with value: 0.4715726745607556 and parameters: {'learning_rate': 0.07002851555897058, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.13967046535365404, 'subsample': 0.5210136334679131, 'colsample_bytree': 0.868701084525405, 'alpha': 1.0603499111410692, 'lambda': 2.001802179896839}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:03,164] Trial 250 finished with value: 0.48071467912631616 and parameters: {'learning_rate': 0.10481936444041742, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.00406079394488975, 'subsample': 0.38508248298464137, 'colsample_bytree': 0.824826084768906, 'alpha': 1.2468628404605757, 'lambda': 5.98036135145491}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:04,644] Trial 251 finished with value: 0.47646709143706273 and parameters: {'learning_rate': 0.10841659958520393, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.00012822209915999694, 'subsample': 0.3922227075166263, 'colsample_bytree': 0.8469999826570178, 'alpha': 1.2680220806068283, 'lambda': 5.858545433862662}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:06,019] Trial 252 finished with value: 0.46726663307923855 and parameters: {'learning_rate': 0.10616041389792964, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.16114379319254146, 'subsample': 0.3810016592620351, 'colsample_bytree': 0.845410212307493, 'alpha': 1.5055943368519764, 'lambda': 6.019671318881109}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:07,509] Trial 253 finished with value: 0.4687888284329035 and parameters: {'learning_rate': 0.1086741068882686, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.0047662113598972745, 'subsample': 0.3695963836321744, 'colsample_bytree': 0.829834210830124, 'alpha': 1.993452920898109, 'lambda': 5.760258898217525}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:09,196] Trial 254 finished with value: 0.4651936293487073 and parameters: {'learning_rate': 0.11279920929325171, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.3437971651519242, 'subsample': 0.33869082335599476, 'colsample_bytree': 0.8683635463826521, 'alpha': 1.2489130885491129, 'lambda': 3.966066032761024}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:10,496] Trial 255 finished with value: 0.4454545284759927 and parameters: {'learning_rate': 0.1052245021055402, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.001275078231852864, 'subsample': 0.39511751970390296, 'colsample_bytree': 0.807193856295255, 'alpha': 6.765465851481106, 'lambda': 5.875177763273362}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:11,861] Trial 256 finished with value: 0.46419999136504164 and parameters: {'learning_rate': 0.11548146199872825, 'max_depth': 10, 'min_child_weight': 8, 'gamma': 0.0009599265825829093, 'subsample': 0.4236604789503265, 'colsample_bytree': 0.8497910922885024, 'alpha': 1.376131486796024, 'lambda': 5.621807137638966}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:12,628] Trial 257 finished with value: 0.4297250973273786 and parameters: {'learning_rate': 0.10976238669615017, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 5.6121582094515094, 'subsample': 0.39955987979752744, 'colsample_bytree': 0.8207915409891597, 'alpha': 0.8921124269880794, 'lambda': 5.9907388707206035}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:14,967] Trial 258 finished with value: 0.4577415690344551 and parameters: {'learning_rate': 0.1257745927263016, 'max_depth': 11, 'min_child_weight': 3, 'gamma': 0.3150368983108465, 'subsample': 0.3680068003602594, 'colsample_bytree': 0.8310825038688926, 'alpha': 1.7594820163152791, 'lambda': 3.213832637093804}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:25,959] Trial 259 finished with value: 0.471334367615879 and parameters: {'learning_rate': 0.10169246880606256, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.5400247539234174, 'subsample': 0.43184553429477235, 'colsample_bytree': 0.5174380326905647, 'alpha': 0.6252145026449313, 'lambda': 5.336086379569311}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:40,031] Trial 260 finished with value: 0.46507427509322863 and parameters: {'learning_rate': 0.1041639849410095, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.24029173672996335, 'subsample': 0.3482067205771, 'colsample_bytree': 0.887111305091591, 'alpha': 1.0754200628594925, 'lambda': 4.2570837777255}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:41,425] Trial 261 finished with value: 0.46803623819665036 and parameters: {'learning_rate': 0.1923680541939155, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.19005589000427447, 'subsample': 0.4097203310132777, 'colsample_bytree': 0.785052287216052, 'alpha': 1.485200113129188, 'lambda': 6.225269447209827}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:42,571] Trial 262 finished with value: 0.4466356256069825 and parameters: {'learning_rate': 0.11356464851186364, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.46495946053744996, 'subsample': 0.6098078801514762, 'colsample_bytree': 0.8559586371702991, 'alpha': 5.653666448164011, 'lambda': 7.3433296833249075}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:43,526] Trial 263 finished with value: 0.4722111041512996 and parameters: {'learning_rate': 0.195717041364414, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.1807465060242393, 'subsample': 0.6452621841616513, 'colsample_bytree': 0.8058895972670309, 'alpha': 1.299365390494684, 'lambda': 3.539613514581384}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:45,048] Trial 264 finished with value: 0.47775326706914856 and parameters: {'learning_rate': 0.12034441348146759, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.007312097591802409, 'subsample': 0.3844718651233225, 'colsample_bytree': 0.907281622087765, 'alpha': 0.7707053040498497, 'lambda': 2.852145336172775}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:46,231] Trial 265 finished with value: 0.46976729227219954 and parameters: {'learning_rate': 0.12061729485296147, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.3631177074341754, 'subsample': 0.39286270648406457, 'colsample_bytree': 0.9175103624442913, 'alpha': 0.7189661412219239, 'lambda': 3.178857588602787}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:47,355] Trial 266 finished with value: 0.46329027921019683 and parameters: {'learning_rate': 0.1307804372130744, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.14510500099624532, 'subsample': 0.31887774668718805, 'colsample_bytree': 0.892636701546273, 'alpha': 0.480772062829524, 'lambda': 2.773164821127637}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:47,649] Trial 267 finished with value: 0.4108765394519904 and parameters: {'learning_rate': 0.19997737343126964, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 9.317621450244367, 'subsample': 0.370461465321738, 'colsample_bytree': 0.9103940435502493, 'alpha': 0.8282656075834038, 'lambda': 2.8241409152528734}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:49,072] Trial 268 finished with value: 0.4761909793172375 and parameters: {'learning_rate': 0.1174885590449062, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.01586816222074849, 'subsample': 0.5922804311880797, 'colsample_bytree': 0.9330836667407599, 'alpha': 2.0514217709057636, 'lambda': 2.9490561438152536}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:50,878] Trial 269 finished with value: 0.4742543294968664 and parameters: {'learning_rate': 0.11604700000624353, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.0006050929449457551, 'subsample': 0.5939659698708958, 'colsample_bytree': 0.9375275170131125, 'alpha': 1.9630534177023757, 'lambda': 5.791392804622014}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:52,227] Trial 270 finished with value: 0.46003406680858183 and parameters: {'learning_rate': 0.1238227685313557, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.01111384843576707, 'subsample': 0.6241635202148786, 'colsample_bytree': 0.36900622940816435, 'alpha': 2.3574511732828918, 'lambda': 6.49701179973317}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:53,037] Trial 271 finished with value: 0.4575514803181995 and parameters: {'learning_rate': 0.12030082400548152, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.6883887721588501, 'subsample': 0.5720719502908388, 'colsample_bytree': 0.9240090351101917, 'alpha': 2.138027680312051, 'lambda': 2.964687033601638}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:54,165] Trial 272 finished with value: 0.4486336827350027 and parameters: {'learning_rate': 0.10889238005446394, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.37208945077016176, 'subsample': 0.3864526455764864, 'colsample_bytree': 0.9478981820890161, 'alpha': 1.7351328562932589, 'lambda': 7.546103938436426}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:55,731] Trial 273 finished with value: 0.47718822540640066 and parameters: {'learning_rate': 0.11863198356713608, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 0.00024774941357535475, 'subsample': 0.6053563170076461, 'colsample_bytree': 0.8756790968114809, 'alpha': 2.076381924009613, 'lambda': 3.0696510836238593}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:56,686] Trial 274 finished with value: 0.465038968790816 and parameters: {'learning_rate': 0.12694312045067496, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 0.31640009764139054, 'subsample': 0.6205992489117841, 'colsample_bytree': 0.9033746133354852, 'alpha': 2.1631397117078968, 'lambda': 3.1162008200536495}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:57,748] Trial 275 finished with value: 0.45847488221796756 and parameters: {'learning_rate': 0.11993059688929607, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 0.4900723220468145, 'subsample': 0.3512635095018286, 'colsample_bytree': 0.8767154212445419, 'alpha': 1.867518047711291, 'lambda': 2.9265248131359582}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:44:58,947] Trial 276 finished with value: 0.4714795749001694 and parameters: {'learning_rate': 0.11657339910622225, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.19636310268400153, 'subsample': 0.6576743951246666, 'colsample_bytree': 0.8678389097342583, 'alpha': 2.0200983346992456, 'lambda': 3.3850856690292703}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:00,175] Trial 277 finished with value: 0.47187031706853977 and parameters: {'learning_rate': 0.11170364831221119, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 0.1770953189048274, 'subsample': 0.6000920232220729, 'colsample_bytree': 0.891604348297347, 'alpha': 1.595364838667143, 'lambda': 3.0523227979956093}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:01,140] Trial 278 finished with value: 0.4608967350702278 and parameters: {'learning_rate': 0.11736493524339663, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 0.5459614681770898, 'subsample': 0.6306521480412747, 'colsample_bytree': 0.8479688175853171, 'alpha': 2.578610036778943, 'lambda': 2.4680973142034257}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:02,285] Trial 279 finished with value: 0.4491441906662835 and parameters: {'learning_rate': 0.11291846086075397, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.3463685576585802, 'subsample': 0.26160344593974694, 'colsample_bytree': 0.8747363185964903, 'alpha': 2.2715032766208845, 'lambda': 5.488862096413157}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:03,018] Trial 280 finished with value: 0.40900723137595313 and parameters: {'learning_rate': 0.18685444997922268, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 4.611393040164287, 'subsample': 0.41461232993931707, 'colsample_bytree': 0.30425441839242207, 'alpha': 5.120130391104171, 'lambda': 6.119649173828108}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:04,264] Trial 281 finished with value: 0.4630954815324847 and parameters: {'learning_rate': 0.12264134266054738, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.17415881241378323, 'subsample': 0.5892003110381892, 'colsample_bytree': 0.7409976429308147, 'alpha': 1.760302257974704, 'lambda': 6.299572978213727}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:06,173] Trial 282 finished with value: 0.461028298794272 and parameters: {'learning_rate': 0.01777482804535005, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.016736710815572157, 'subsample': 0.681792529245598, 'colsample_bytree': 0.8421694226924292, 'alpha': 1.9750851063646861, 'lambda': 3.3104012675938668}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:07,062] Trial 283 finished with value: 0.433896838297841 and parameters: {'learning_rate': 0.14436288888241786, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.010234472555519718, 'subsample': 0.604173207725596, 'colsample_bytree': 0.08968469639516857, 'alpha': 1.5797445746596308, 'lambda': 1.7914899500282044}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:08,421] Trial 284 finished with value: 0.4605310570417441 and parameters: {'learning_rate': 0.11793633306389562, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.6885076332783786, 'subsample': 0.6487759529937686, 'colsample_bytree': 0.9141250551245178, 'alpha': 1.4590518409144533, 'lambda': 2.694786407152855}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:14,958] Trial 285 finished with value: 0.46564824063908145 and parameters: {'learning_rate': 0.11119821816933934, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.32976349977111014, 'subsample': 0.5840881456213803, 'colsample_bytree': 0.8611004529498589, 'alpha': 1.227220286738624, 'lambda': 5.902660453495526}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:26,347] Trial 286 finished with value: 0.46753575744689785 and parameters: {'learning_rate': 0.18930947012330468, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.19101608235963163, 'subsample': 0.6096119760406571, 'colsample_bytree': 0.8164745792306352, 'alpha': 2.153634696406715, 'lambda': 6.721372911611819}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:35,386] Trial 287 finished with value: 0.4741209225182406 and parameters: {'learning_rate': 0.11490527751570159, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 0.4867449664444777, 'subsample': 0.6323599406604596, 'colsample_bytree': 0.9772697507081548, 'alpha': 1.7570182741697309, 'lambda': 5.677481333731604}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:36,525] Trial 288 finished with value: 0.4411636451853885 and parameters: {'learning_rate': 0.14750216496801957, 'max_depth': 11, 'min_child_weight': 3, 'gamma': 0.012081740366538442, 'subsample': 0.3815300669604214, 'colsample_bytree': 0.7527838046358779, 'alpha': 7.817884972137409, 'lambda': 7.2825110647170215}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:38,108] Trial 289 finished with value: 0.46519664957779683 and parameters: {'learning_rate': 0.02974061718401065, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.3090482865651118, 'subsample': 0.5618606662446176, 'colsample_bytree': 0.8913591387626956, 'alpha': 1.3788002703223607, 'lambda': 1.2698099303355335}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:39,401] Trial 290 finished with value: 0.47176373021671897 and parameters: {'learning_rate': 0.0946754743824904, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.1905656253390206, 'subsample': 0.587631757845916, 'colsample_bytree': 0.8257904864180016, 'alpha': 1.0186610697024399, 'lambda': 7.488392433308547}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:40,154] Trial 291 finished with value: 0.47472777406488664 and parameters: {'learning_rate': 0.1957628707691276, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.5081746433473321, 'subsample': 0.4287397353855901, 'colsample_bytree': 0.8473435650754332, 'alpha': 0.25040782010885565, 'lambda': 3.48633857443811}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:41,412] Trial 292 finished with value: 0.4712494678219895 and parameters: {'learning_rate': 0.10070647146221019, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.18995912925175512, 'subsample': 0.6108437831294282, 'colsample_bytree': 0.9485312420648648, 'alpha': 1.616760950271782, 'lambda': 6.142498789593559}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:42,677] Trial 293 finished with value: 0.4619900092229518 and parameters: {'learning_rate': 0.10651001397608903, 'max_depth': 11, 'min_child_weight': 3, 'gamma': 0.8370763648177553, 'subsample': 0.36579488651779757, 'colsample_bytree': 0.7850554194575788, 'alpha': 1.9209108527198793, 'lambda': 4.967891085759565}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:43,682] Trial 294 finished with value: 0.44489620483714104 and parameters: {'learning_rate': 0.11038528203978197, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.3421703028553785, 'subsample': 0.4024529989123152, 'colsample_bytree': 0.8755327339888617, 'alpha': 4.610823625016601, 'lambda': 6.468025710843667}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:44,037] Trial 295 finished with value: 0.40923555026364805 and parameters: {'learning_rate': 0.1401954574899672, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 8.350514042889955, 'subsample': 0.5702584828267248, 'colsample_bytree': 0.806922608560519, 'alpha': 1.221611007168666, 'lambda': 3.0472385024269557}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:45,417] Trial 296 finished with value: 0.4611900867719134 and parameters: {'learning_rate': 0.12734017963447558, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.16083360435014735, 'subsample': 0.5531301888231057, 'colsample_bytree': 0.920920051569563, 'alpha': 1.001281556742632, 'lambda': 5.976339647131747}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:47,144] Trial 297 finished with value: 0.46669901925145574 and parameters: {'learning_rate': 0.11437474983791748, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.004402683399942313, 'subsample': 0.3330881884926269, 'colsample_bytree': 0.8357337681948594, 'alpha': 2.365047848298677, 'lambda': 3.642687476614071}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:49,005] Trial 298 finished with value: 0.4756621174051163 and parameters: {'learning_rate': 0.10407509737167858, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 0.005759305827159086, 'subsample': 0.6031004016629231, 'colsample_bytree': 0.7667354406839352, 'alpha': 1.4022737719293583, 'lambda': 6.956517890149935}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:50,088] Trial 299 finished with value: 0.45486065218630756 and parameters: {'learning_rate': 0.10174154190220178, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 0.5893112821056748, 'subsample': 0.6277371270771253, 'colsample_bytree': 0.896298881845763, 'alpha': 1.4006451715760249, 'lambda': 6.943594902144446}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:51,156] Trial 300 finished with value: 0.46558939113567505 and parameters: {'learning_rate': 0.10402521539976622, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.37943928471806315, 'subsample': 0.6017100680581637, 'colsample_bytree': 0.7659567920211767, 'alpha': 1.2327585423580765, 'lambda': 7.076542768563777}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:52,432] Trial 301 finished with value: 0.4732906718897538 and parameters: {'learning_rate': 0.10901428668484496, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.19224870295009613, 'subsample': 0.5827331681613851, 'colsample_bytree': 0.9569217705642789, 'alpha': 1.5249760459594992, 'lambda': 7.228410999286106}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:53,645] Trial 302 finished with value: 0.4694591150535557 and parameters: {'learning_rate': 0.09541298731119666, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 0.35450551486519394, 'subsample': 0.6540759805959276, 'colsample_bytree': 0.8555430161806749, 'alpha': 1.0206273844543925, 'lambda': 6.720004215911259}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:55,472] Trial 303 finished with value: 0.47079373686137826 and parameters: {'learning_rate': 0.09872499592195573, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 0.0005517429245287966, 'subsample': 0.6175050127692194, 'colsample_bytree': 0.5625143498345664, 'alpha': 0.36900560554656003, 'lambda': 7.627813553685656}. Best is trial 210 with value: 0.4817046193244972.\n",
      "[I 2024-02-06 14:45:56,761] Trial 304 finished with value: 0.4837039806721491 and parameters: {'learning_rate': 0.12099957618036579, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.18607036345503994, 'subsample': 0.5903199889263333, 'colsample_bytree': 0.8206008650916811, 'alpha': 0.6321300750854764, 'lambda': 7.386664943136651}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:45:58,224] Trial 305 finished with value: 0.48014705400784724 and parameters: {'learning_rate': 0.12449978336665511, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.010080774861437346, 'subsample': 0.5742919670397973, 'colsample_bytree': 0.8005812940454545, 'alpha': 0.45764014534714803, 'lambda': 3.2742840105965674}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:45:59,222] Trial 306 finished with value: 0.47369594478721794 and parameters: {'learning_rate': 0.12342257889528614, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.6618659135114594, 'subsample': 0.5483390654193236, 'colsample_bytree': 0.8079088154806557, 'alpha': 0.34420812628307623, 'lambda': 3.2414464381233654}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:45:59,804] Trial 307 finished with value: 0.4361523140769142 and parameters: {'learning_rate': 0.13623188102466563, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 4.9896024466944295, 'subsample': 0.5721078445029273, 'colsample_bytree': 0.8223376122915114, 'alpha': 0.686060803732059, 'lambda': 2.8049487336397214}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:01,080] Trial 308 finished with value: 0.472098455306372 and parameters: {'learning_rate': 0.12077625082247265, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.1926289019734467, 'subsample': 0.5644440361161497, 'colsample_bytree': 0.7941971213653152, 'alpha': 0.5097387212774493, 'lambda': 2.6723834018972545}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:02,139] Trial 309 finished with value: 0.45036490680137337 and parameters: {'learning_rate': 0.12819523575708447, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.000234767263881476, 'subsample': 0.1579888549147982, 'colsample_bytree': 0.8341660327082371, 'alpha': 0.07596239490002366, 'lambda': 3.3260793439683}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:03,225] Trial 310 finished with value: 0.4610001048992134 and parameters: {'learning_rate': 0.1317603613294819, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.4499577334272901, 'subsample': 0.5848121046532234, 'colsample_bytree': 0.814409879580255, 'alpha': 0.6087228800066669, 'lambda': 3.0459298011071163}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:03,894] Trial 311 finished with value: 0.4428012639871373 and parameters: {'learning_rate': 0.1209715027481314, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 4.211418411036133, 'subsample': 0.5373761294227748, 'colsample_bytree': 0.7879435239364566, 'alpha': 0.41029149060009895, 'lambda': 2.3641773529919234}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:06,070] Trial 312 finished with value: 0.47716553418568275 and parameters: {'learning_rate': 0.11838109189540229, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.30167530028348755, 'subsample': 0.554455092037099, 'colsample_bytree': 0.9837591117451615, 'alpha': 0.18325644673416525, 'lambda': 3.3320770894709426}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:17,201] Trial 313 finished with value: 0.4747808629866411 and parameters: {'learning_rate': 0.12491421706451256, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.49534038955850207, 'subsample': 0.5336323396143517, 'colsample_bytree': 0.9984779220329641, 'alpha': 0.08705829434424295, 'lambda': 3.555696952633939}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:30,605] Trial 314 finished with value: 0.46021320657751263 and parameters: {'learning_rate': 0.1190820904209, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.3222153150693755, 'subsample': 0.5593035721759222, 'colsample_bytree': 0.975342724404389, 'alpha': 3.671142676008242, 'lambda': 3.1826788020650727}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:33,464] Trial 315 finished with value: 0.4643490531140811 and parameters: {'learning_rate': 0.1919815985671029, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.2044690954807299, 'subsample': 0.5474264632203212, 'colsample_bytree': 0.9876652570091399, 'alpha': 0.22337578499333688, 'lambda': 3.8024904054383306}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:35,147] Trial 316 finished with value: 0.45866933857206654 and parameters: {'learning_rate': 0.11794438405849074, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.5739829571241156, 'subsample': 0.5301284624861761, 'colsample_bytree': 0.825244023936459, 'alpha': 0.2431576232728559, 'lambda': 2.9029230601000857}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:36,368] Trial 317 finished with value: 0.44926235983803564 and parameters: {'learning_rate': 0.12437086492531493, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.8475159288168663, 'subsample': 0.5757745591953781, 'colsample_bytree': 0.85424733960397, 'alpha': 3.2081938468157576, 'lambda': 3.403986101837201}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:37,767] Trial 318 finished with value: 0.4555939285517395 and parameters: {'learning_rate': 0.19990850329023707, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.33058955704798165, 'subsample': 0.6277449622397819, 'colsample_bytree': 0.7999063977408296, 'alpha': 2.9256474863681583, 'lambda': 3.6143246368902937}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:39,239] Trial 319 finished with value: 0.464944021124258 and parameters: {'learning_rate': 0.12078626657535152, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.16994072232021581, 'subsample': 0.5523374029124789, 'colsample_bytree': 0.9615495273545214, 'alpha': 0.46582448787828656, 'lambda': 3.274405920340498}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:40,679] Trial 320 finished with value: 0.47203978053057627 and parameters: {'learning_rate': 0.19606261993071522, 'max_depth': 15, 'min_child_weight': 4, 'gamma': 0.37515481400961614, 'subsample': 0.6016139177131093, 'colsample_bytree': 0.8392646968625497, 'alpha': 0.6932541351568802, 'lambda': 3.094627783811717}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:42,615] Trial 321 finished with value: 0.4717395854797804 and parameters: {'learning_rate': 0.11557453376669322, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 0.007577756843372457, 'subsample': 0.5171652530064265, 'colsample_bytree': 0.8168673871502369, 'alpha': 0.5353411650581753, 'lambda': 3.4080021559262916}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:43,552] Trial 322 finished with value: 0.4717751571160684 and parameters: {'learning_rate': 0.18240283909843968, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.6907641203475456, 'subsample': 0.5809037695387267, 'colsample_bytree': 0.9773695834596494, 'alpha': 0.870740684540537, 'lambda': 2.549131453952018}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:45,158] Trial 323 finished with value: 0.4756955570688851 and parameters: {'learning_rate': 0.11271348211849894, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.2062923259115475, 'subsample': 0.44610933196298397, 'colsample_bytree': 0.8653173213800023, 'alpha': 0.20861618487055092, 'lambda': 3.6925650207000564}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:46,385] Trial 324 finished with value: 0.47809341613108747 and parameters: {'learning_rate': 0.1269052418672207, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.3926787354134925, 'subsample': 0.6447444091057161, 'colsample_bytree': 0.7779401883825621, 'alpha': 0.8516421579154634, 'lambda': 2.857827369237591}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:47,688] Trial 325 finished with value: 0.470713528532627 and parameters: {'learning_rate': 0.1296010422078065, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.5564309982006473, 'subsample': 0.6581644868105735, 'colsample_bytree': 0.7790435961598511, 'alpha': 0.8173394393224148, 'lambda': 6.385275974521848}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:48,927] Trial 326 finished with value: 0.47671880006276834 and parameters: {'learning_rate': 0.12585123732034612, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.41437428956935707, 'subsample': 0.6392604797627914, 'colsample_bytree': 0.7400077377104737, 'alpha': 0.6304784246293408, 'lambda': 5.832340801329369}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:49,807] Trial 327 finished with value: 0.4624367035687042 and parameters: {'learning_rate': 0.12254141249718292, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 1.0143018021051298, 'subsample': 0.6688493873339058, 'colsample_bytree': 0.712626653800279, 'alpha': 0.39011539456642164, 'lambda': 5.5902186156115}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:50,889] Trial 328 finished with value: 0.4615477706528149 and parameters: {'learning_rate': 0.1282229246622838, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.7628699624839915, 'subsample': 0.6918022092168713, 'colsample_bytree': 0.7359955954836725, 'alpha': 0.6761702217551877, 'lambda': 5.788370768802468}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:52,068] Trial 329 finished with value: 0.47372595141320045 and parameters: {'learning_rate': 0.13535148310101286, 'max_depth': 9, 'min_child_weight': 2, 'gamma': 0.4299875593741137, 'subsample': 0.646205204601227, 'colsample_bytree': 0.7307749320588589, 'alpha': 0.48473271611621294, 'lambda': 7.754760539049469}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:53,299] Trial 330 finished with value: 0.47026087918063986 and parameters: {'learning_rate': 0.12609262329370832, 'max_depth': 9, 'min_child_weight': 6, 'gamma': 0.5195271703631864, 'subsample': 0.6408537403997766, 'colsample_bytree': 0.7413544770308605, 'alpha': 0.8262370490606653, 'lambda': 2.089642378893279}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:54,639] Trial 331 finished with value: 0.46154711966641926 and parameters: {'learning_rate': 0.13181629867838043, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.30928113902714616, 'subsample': 0.6698477267879924, 'colsample_bytree': 0.45468677598441903, 'alpha': 0.6175632348221658, 'lambda': 3.229210428849714}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:55,860] Trial 332 finished with value: 0.456268686112438 and parameters: {'learning_rate': 0.12369623360080108, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.6797529703835156, 'subsample': 0.629517762753203, 'colsample_bytree': 0.7477770187958683, 'alpha': 0.9052050199825512, 'lambda': 2.766637104810921}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:57,083] Trial 333 finished with value: 0.4663685717388243 and parameters: {'learning_rate': 0.1910270494552557, 'max_depth': 15, 'min_child_weight': 3, 'gamma': 0.2991374034435196, 'subsample': 0.4991925775942227, 'colsample_bytree': 0.7667391868102466, 'alpha': 0.023462957069721013, 'lambda': 8.050713659845481}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:58,860] Trial 334 finished with value: 0.478210402640385 and parameters: {'learning_rate': 0.13210970456811538, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 0.4866095813593939, 'subsample': 0.3547191126991338, 'colsample_bytree': 0.7598523100210329, 'alpha': 1.1063174422711504, 'lambda': 5.1815584424264385}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:46:59,742] Trial 335 finished with value: 0.43724043518621475 and parameters: {'learning_rate': 0.13982525209418936, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 3.4111317490168283, 'subsample': 0.35379414736496895, 'colsample_bytree': 0.7577634917875151, 'alpha': 1.0470241285215387, 'lambda': 5.064980541247883}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:47:00,637] Trial 336 finished with value: 0.4388795023774373 and parameters: {'learning_rate': 0.1314163910335655, 'max_depth': 9, 'min_child_weight': 2, 'gamma': 0.7154956750962241, 'subsample': 0.5441468331820618, 'colsample_bytree': 0.7798490447134833, 'alpha': 8.094668236804047, 'lambda': 3.0362789788150266}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:47:01,735] Trial 337 finished with value: 0.467240116335803 and parameters: {'learning_rate': 0.12699750594930648, 'max_depth': 8, 'min_child_weight': 2, 'gamma': 0.466778092082376, 'subsample': 0.526272487873629, 'colsample_bytree': 0.7273708378777257, 'alpha': 0.6412239426209394, 'lambda': 5.28452928334408}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:47:03,072] Trial 338 finished with value: 0.4596773616903 and parameters: {'learning_rate': 0.1285843108868912, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 0.4757625199912541, 'subsample': 0.3218843689915505, 'colsample_bytree': 0.7576034218691173, 'alpha': 0.8919286040550563, 'lambda': 5.473228056528126}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:47:04,525] Trial 339 finished with value: 0.46020705545516394 and parameters: {'learning_rate': 0.14601598880906824, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.8316369952297233, 'subsample': 0.5637882090040154, 'colsample_bytree': 0.7920634368115815, 'alpha': 0.36096567222902165, 'lambda': 3.4737177835741373}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:47:06,973] Trial 340 finished with value: 0.4784055249750136 and parameters: {'learning_rate': 0.12417090346612542, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 0.23822159708338095, 'subsample': 0.6443566307573121, 'colsample_bytree': 0.7751165643166827, 'alpha': 1.0744901577145396, 'lambda': 3.810124328823907}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:47:11,834] Trial 341 finished with value: 0.4274384851206466 and parameters: {'learning_rate': 0.1337003796766827, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 6.358463618917414, 'subsample': 0.6771748659197357, 'colsample_bytree': 0.7716077592752718, 'alpha': 0.7481748245580058, 'lambda': 4.815900532028535}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:47:24,797] Trial 342 finished with value: 0.4688006502711265 and parameters: {'learning_rate': 0.12496740808092902, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0.24647548283446985, 'subsample': 0.6414171369007389, 'colsample_bytree': 0.746746998400561, 'alpha': 1.04947757221511, 'lambda': 3.895473564967836}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:47:33,118] Trial 343 finished with value: 0.46484694129838156 and parameters: {'learning_rate': 0.12045315052273144, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 0.542120381711893, 'subsample': 0.3009597402877075, 'colsample_bytree': 0.7889014187738584, 'alpha': 0.5809754895803723, 'lambda': 2.6425041360494466}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:47:34,531] Trial 344 finished with value: 0.46005641606977643 and parameters: {'learning_rate': 0.12887700567710234, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 0.21405674131110533, 'subsample': 0.6553892195750107, 'colsample_bytree': 0.7254100259942956, 'alpha': 0.25297598483107453, 'lambda': 7.628964887532748}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:47:35,651] Trial 345 finished with value: 0.45451577278496347 and parameters: {'learning_rate': 0.13457075047192574, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 0.38855942699928664, 'subsample': 0.6180486545733745, 'colsample_bytree': 0.17525008782072032, 'alpha': 0.9038271589762431, 'lambda': 7.36939137069566}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:47:36,495] Trial 346 finished with value: 0.44974178762553296 and parameters: {'learning_rate': 0.12296941780578272, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 2.2215731009185724, 'subsample': 0.6302421404195423, 'colsample_bytree': 0.7667129309784225, 'alpha': 1.1424576469588388, 'lambda': 6.213880286680738}. Best is trial 304 with value: 0.4837039806721491.\n",
      "[I 2024-02-06 14:47:37,707] Trial 347 finished with value: 0.48560756020354895 and parameters: {'learning_rate': 0.12613520721635582, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0.18193392458368993, 'subsample': 0.6896965840578013, 'colsample_bytree': 0.7989777341562381, 'alpha': 0.3776553494077578, 'lambda': 2.8831246814282134}. Best is trial 347 with value: 0.48560756020354895.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def objective(trial):#,X_train,y_train,X_valid,y_valid,weight_train):\n",
    "    params = dict(\n",
    "                learning_rate = trial.suggest_float(\"learning_rate\", 0.01, 0.2),\n",
    "                max_depth= trial.suggest_int(\"max_depth\",5, 15),\n",
    "                min_child_weight = trial.suggest_int(\"min_child_weight\", 1, 8),\n",
    "                gamma = trial.suggest_float(\"gamma\", 0, 10),\n",
    "                subsample = trial.suggest_float(\"subsample\", 0.01,1),\n",
    "                colsample_bytree = trial.suggest_float(\"colsample_bytree\", 0.01,1),\n",
    "                alpha = trial.suggest_float(\"alpha\", 0, 10),\n",
    "                objective= 'multi:softprob', \n",
    "                nthread=4, \n",
    "                 num_class= 5,\n",
    "                seed=27)\n",
    "    params['lambda'] = trial.suggest_float(\"lambda\", 0, 10)\n",
    "\n",
    "    \n",
    "    dtrain = xgb.DMatrix(X_train,y_train.apply(lambda x:x.replace('i','')).astype(int), \n",
    "                         enable_categorical=True,\n",
    "                         weight=weight_train)\n",
    "    dvalid = xgb.DMatrix(X_valid,y_valid.apply(lambda x:x.replace('i','')).astype(int),\n",
    "                         enable_categorical=True,\n",
    "                         )\n",
    "\n",
    "\n",
    "    bst = xgb.train(params, dtrain,verbose_eval=False, num_boost_round=400,\n",
    "                    evals = [(dtrain, \"train\"), (dvalid, \"valid\")],\n",
    "                    early_stopping_rounds=100)\n",
    "\n",
    "    preds = bst.predict(dvalid)\n",
    "    ##MCC is more solid\n",
    "    mcc = matthews_corrcoef(y_valid.apply(lambda x:x.replace('i','')).astype(int),preds.argmax(1))    \n",
    "    \n",
    "    return mcc\n",
    "\n",
    "\n",
    "\n",
    "study = optuna.create_study(direction=\"maximize\")\n",
    "study.optimize(objective, n_trials=400, timeout=600)\n",
    "params_final = dict(\n",
    "            objective= 'multi:softprob', \n",
    "            nthread=4, \n",
    "             num_class= 5,\n",
    "            seed=27)\n",
    "params_final.update(study.best_params)\n",
    "dtrain = xgb.DMatrix(X_train,y_train.apply(lambda x:x.replace('i','')).astype(int), \n",
    "                     enable_categorical=True,\n",
    "                     weight=weight_train)\n",
    "dvalid = xgb.DMatrix(X_valid,y_valid.apply(lambda x:x.replace('i','')).astype(int),\n",
    "                     enable_categorical=True,\n",
    "                     )   \n",
    "bst = xgb.train(params_final, dtrain,verbose_eval=False, num_boost_round=400,\n",
    "                evals = [(dtrain, \"train\"), (dvalid, \"valid\")],\n",
    "                early_stopping_rounds=100,)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d47e795b-7f13-473c-bf1c-90e5964b67ed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-02-08 08:49:27,816] A new study created in memory with name: no-name-2f227395-b778-4aaa-b296-1d0640474238\n",
      "[I 2024-02-08 08:49:28,661] Trial 0 finished with value: 0.375796038397225 and parameters: {'learning_rate': 0.030801545039845647, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 7.746823493461809, 'subsample': 0.8234749751126704, 'colsample_bytree': 0.11185335931744318, 'alpha': 3.6100663490935494, 'lambda': 6.3115859791272655}. Best is trial 0 with value: 0.375796038397225.\n",
      "[I 2024-02-08 08:49:29,278] Trial 1 finished with value: 0.40996246582648016 and parameters: {'learning_rate': 0.035067820664654865, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 4.267923278149038, 'subsample': 0.08083659979118042, 'colsample_bytree': 0.39374409585882136, 'alpha': 0.37017563988801294, 'lambda': 4.645896006641118}. Best is trial 1 with value: 0.40996246582648016.\n",
      "[I 2024-02-08 08:49:30,002] Trial 2 finished with value: 0.38866482460937335 and parameters: {'learning_rate': 0.022240447636450424, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 6.493004549731072, 'subsample': 0.6324294596621204, 'colsample_bytree': 0.34984366683306756, 'alpha': 7.875336096377895, 'lambda': 9.260709071552501}. Best is trial 1 with value: 0.40996246582648016.\n",
      "[I 2024-02-08 08:49:30,576] Trial 3 finished with value: 0.39550766507988894 and parameters: {'learning_rate': 0.02384634881762539, 'max_depth': 12, 'min_child_weight': 4, 'gamma': 6.5331964284959545, 'subsample': 0.05771952081567027, 'colsample_bytree': 0.4820301883133483, 'alpha': 1.7000325293218077, 'lambda': 5.360988796313464}. Best is trial 1 with value: 0.40996246582648016.\n",
      "[I 2024-02-08 08:49:31,038] Trial 4 finished with value: 0.3684587319590019 and parameters: {'learning_rate': 0.18891857993955946, 'max_depth': 11, 'min_child_weight': 4, 'gamma': 5.700353138998615, 'subsample': 0.1332460875968357, 'colsample_bytree': 0.010677516763627019, 'alpha': 2.8715687569190695, 'lambda': 8.635475940432705}. Best is trial 1 with value: 0.40996246582648016.\n",
      "[I 2024-02-08 08:49:31,776] Trial 5 finished with value: 0.42585592640139264 and parameters: {'learning_rate': 0.07177116187004699, 'max_depth': 14, 'min_child_weight': 3, 'gamma': 1.101664436199079, 'subsample': 0.230212202199773, 'colsample_bytree': 0.48961940340714766, 'alpha': 4.77978246541257, 'lambda': 1.4429561704735094}. Best is trial 5 with value: 0.42585592640139264.\n",
      "[I 2024-02-08 08:49:32,337] Trial 6 finished with value: 0.402596738168488 and parameters: {'learning_rate': 0.06012289155006209, 'max_depth': 8, 'min_child_weight': 2, 'gamma': 6.131259068842983, 'subsample': 0.7640870323553677, 'colsample_bytree': 0.6482093692967019, 'alpha': 7.045435811270018, 'lambda': 6.112683263733349}. Best is trial 5 with value: 0.42585592640139264.\n",
      "[I 2024-02-08 08:49:32,876] Trial 7 finished with value: 0.39818846552865766 and parameters: {'learning_rate': 0.1172313411604026, 'max_depth': 11, 'min_child_weight': 5, 'gamma': 6.528255575648558, 'subsample': 0.2743545156050805, 'colsample_bytree': 0.7515025963550045, 'alpha': 7.181398122952199, 'lambda': 5.746573886140746}. Best is trial 5 with value: 0.42585592640139264.\n",
      "[I 2024-02-08 08:49:33,765] Trial 8 finished with value: 0.4274269000807006 and parameters: {'learning_rate': 0.026423848614678523, 'max_depth': 6, 'min_child_weight': 8, 'gamma': 2.7340943581847634, 'subsample': 0.6708469250597618, 'colsample_bytree': 0.7035667808881418, 'alpha': 0.30371234628723043, 'lambda': 7.7344573792918005}. Best is trial 8 with value: 0.4274269000807006.\n",
      "[I 2024-02-08 08:49:34,399] Trial 9 finished with value: 0.40262838741295326 and parameters: {'learning_rate': 0.027196609263835922, 'max_depth': 12, 'min_child_weight': 3, 'gamma': 3.887666400749797, 'subsample': 0.10694279638873776, 'colsample_bytree': 0.32703244416559857, 'alpha': 0.4789177219711116, 'lambda': 2.368920851580775}. Best is trial 8 with value: 0.4274269000807006.\n",
      "[I 2024-02-08 08:49:35,082] Trial 10 finished with value: 0.4252138940565952 and parameters: {'learning_rate': 0.12008598407998802, 'max_depth': 5, 'min_child_weight': 8, 'gamma': 0.2572806326816508, 'subsample': 0.9824186184549092, 'colsample_bytree': 0.9301634663548002, 'alpha': 9.754960474859864, 'lambda': 7.700714097062654}. Best is trial 8 with value: 0.4274269000807006.\n",
      "[I 2024-02-08 08:49:35,748] Trial 11 finished with value: 0.4235309874768229 and parameters: {'learning_rate': 0.08082830679822478, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 1.8636503861154674, 'subsample': 0.3860311821658946, 'colsample_bytree': 0.7008191412838679, 'alpha': 5.098184282075545, 'lambda': 0.4200868970495364}. Best is trial 8 with value: 0.4274269000807006.\n",
      "[I 2024-02-08 08:49:36,374] Trial 12 finished with value: 0.42772128157773903 and parameters: {'learning_rate': 0.07769553833944139, 'max_depth': 9, 'min_child_weight': 6, 'gamma': 2.3216327209714045, 'subsample': 0.5250989840618554, 'colsample_bytree': 0.8995861234681938, 'alpha': 4.942358102827328, 'lambda': 3.089149141396544}. Best is trial 12 with value: 0.42772128157773903.\n",
      "[I 2024-02-08 08:49:36,959] Trial 13 finished with value: 0.42500562682099213 and parameters: {'learning_rate': 0.1542485702054961, 'max_depth': 8, 'min_child_weight': 6, 'gamma': 2.812136204281694, 'subsample': 0.5313360211118465, 'colsample_bytree': 0.9768442000351865, 'alpha': 5.484115156048238, 'lambda': 3.6537545965333007}. Best is trial 12 with value: 0.42772128157773903.\n",
      "[I 2024-02-08 08:49:37,567] Trial 14 finished with value: 0.42972015118169743 and parameters: {'learning_rate': 0.09752148796224633, 'max_depth': 9, 'min_child_weight': 7, 'gamma': 2.740676672528621, 'subsample': 0.6201116068761641, 'colsample_bytree': 0.8171649576581687, 'alpha': 2.296698133227551, 'lambda': 3.3538370366603503}. Best is trial 14 with value: 0.42972015118169743.\n",
      "[I 2024-02-08 08:49:37,944] Trial 15 finished with value: 0.40149140281184376 and parameters: {'learning_rate': 0.09757813849582471, 'max_depth': 9, 'min_child_weight': 6, 'gamma': 9.37279425606313, 'subsample': 0.4736737278511867, 'colsample_bytree': 0.8504735685826945, 'alpha': 2.77436616005511, 'lambda': 2.829677348113227}. Best is trial 14 with value: 0.42972015118169743.\n",
      "[I 2024-02-08 08:49:38,491] Trial 16 finished with value: 0.4253974975302651 and parameters: {'learning_rate': 0.14601406764295344, 'max_depth': 10, 'min_child_weight': 6, 'gamma': 3.044093810878806, 'subsample': 0.5341385465717939, 'colsample_bytree': 0.8672076232233182, 'alpha': 4.089353747490774, 'lambda': 4.041981368355122}. Best is trial 14 with value: 0.42972015118169743.\n",
      "[I 2024-02-08 08:49:39,353] Trial 17 finished with value: 0.438847816424844 and parameters: {'learning_rate': 0.057908356208467834, 'max_depth': 9, 'min_child_weight': 7, 'gamma': 1.3257926771311774, 'subsample': 0.3972487404446644, 'colsample_bytree': 0.8101709649468332, 'alpha': 1.838794967076652, 'lambda': 0.019500385013305088}. Best is trial 17 with value: 0.438847816424844.\n",
      "[I 2024-02-08 08:49:40,288] Trial 18 finished with value: 0.45100471333462727 and parameters: {'learning_rate': 0.09957639343414079, 'max_depth': 7, 'min_child_weight': 7, 'gamma': 0.5769428564661473, 'subsample': 0.37663228646204705, 'colsample_bytree': 0.5832093610118532, 'alpha': 1.5257671844557308, 'lambda': 0.04013569912523417}. Best is trial 18 with value: 0.45100471333462727.\n",
      "[I 2024-02-08 08:49:41,381] Trial 19 finished with value: 0.44632301652450374 and parameters: {'learning_rate': 0.0531495192832089, 'max_depth': 6, 'min_child_weight': 7, 'gamma': 0.07194672801931024, 'subsample': 0.34915141029710084, 'colsample_bytree': 0.5958056174413942, 'alpha': 1.8492846768259341, 'lambda': 0.1881862253615923}. Best is trial 18 with value: 0.45100471333462727.\n",
      "[I 2024-02-08 08:49:42,485] Trial 20 finished with value: 0.4468052129731475 and parameters: {'learning_rate': 0.05102707870920449, 'max_depth': 6, 'min_child_weight': 7, 'gamma': 0.004595364370797772, 'subsample': 0.2834992571966727, 'colsample_bytree': 0.5844829674154246, 'alpha': 1.3324824225976868, 'lambda': 1.4737316058210224}. Best is trial 18 with value: 0.45100471333462727.\n",
      "[I 2024-02-08 08:49:43,499] Trial 21 finished with value: 0.44323034894727975 and parameters: {'learning_rate': 0.04937200135997596, 'max_depth': 6, 'min_child_weight': 7, 'gamma': 0.4209009161509014, 'subsample': 0.2783089549113562, 'colsample_bytree': 0.5887033371882247, 'alpha': 0.974740224985541, 'lambda': 1.2596688199303674}. Best is trial 18 with value: 0.45100471333462727.\n",
      "[I 2024-02-08 08:49:44,631] Trial 22 finished with value: 0.4439884146360427 and parameters: {'learning_rate': 0.05167005196198366, 'max_depth': 7, 'min_child_weight': 7, 'gamma': 0.3911202210598027, 'subsample': 0.373090921384366, 'colsample_bytree': 0.5807150787920548, 'alpha': 1.390237818505157, 'lambda': 1.754328682638793}. Best is trial 18 with value: 0.45100471333462727.\n",
      "[I 2024-02-08 08:49:45,241] Trial 23 finished with value: 0.4257758220804541 and parameters: {'learning_rate': 0.13486350875483985, 'max_depth': 6, 'min_child_weight': 5, 'gamma': 1.2943980327579503, 'subsample': 0.2017159325694333, 'colsample_bytree': 0.2506544346144736, 'alpha': 3.3696040691411087, 'lambda': 0.8811788496308564}. Best is trial 18 with value: 0.45100471333462727.\n",
      "[I 2024-02-08 08:49:46,302] Trial 24 finished with value: 0.44736318148270593 and parameters: {'learning_rate': 0.09030244019524701, 'max_depth': 7, 'min_child_weight': 8, 'gamma': 0.18851748272197683, 'subsample': 0.3345476315549612, 'colsample_bytree': 0.5571947342259302, 'alpha': 2.191707419390408, 'lambda': 2.076719383291112}. Best is trial 18 with value: 0.45100471333462727.\n",
      "[I 2024-02-08 08:49:47,240] Trial 25 finished with value: 0.42867307999047644 and parameters: {'learning_rate': 0.08906577361197268, 'max_depth': 7, 'min_child_weight': 8, 'gamma': 0.986950604968703, 'subsample': 0.19983681485701127, 'colsample_bytree': 0.4458752755752383, 'alpha': 1.0330685542380056, 'lambda': 1.9844892810268866}. Best is trial 18 with value: 0.45100471333462727.\n",
      "[I 2024-02-08 08:49:47,910] Trial 26 finished with value: 0.4364657750134023 and parameters: {'learning_rate': 0.1115692197170313, 'max_depth': 8, 'min_child_weight': 8, 'gamma': 1.927166177822759, 'subsample': 0.445454871776007, 'colsample_bytree': 0.547982238549154, 'alpha': 2.5323351492562414, 'lambda': 2.464509421271925}. Best is trial 18 with value: 0.45100471333462727.\n",
      "[I 2024-02-08 08:49:48,513] Trial 27 finished with value: 0.4020639126786458 and parameters: {'learning_rate': 0.0716697295393728, 'max_depth': 5, 'min_child_weight': 5, 'gamma': 4.04261058750548, 'subsample': 0.3108818341199394, 'colsample_bytree': 0.27121196364537564, 'alpha': 4.0100491865025, 'lambda': 1.0066045903844618}. Best is trial 18 with value: 0.45100471333462727.\n",
      "[I 2024-02-08 08:49:49,037] Trial 28 finished with value: 0.42774634795409594 and parameters: {'learning_rate': 0.12994509095324813, 'max_depth': 7, 'min_child_weight': 7, 'gamma': 1.855630532932281, 'subsample': 0.15337450550797632, 'colsample_bytree': 0.6545137486093779, 'alpha': 0.028136186321107814, 'lambda': 4.167365053496991}. Best is trial 18 with value: 0.45100471333462727.\n",
      "[I 2024-02-08 08:49:50,004] Trial 29 finished with value: 0.3960754735005596 and parameters: {'learning_rate': 0.011836873857141744, 'max_depth': 8, 'min_child_weight': 6, 'gamma': 8.94490447506017, 'subsample': 0.4437936144180264, 'colsample_bytree': 0.5256672780930629, 'alpha': 3.6612480470303517, 'lambda': 0.7864148902710504}. Best is trial 18 with value: 0.45100471333462727.\n",
      "[I 2024-02-08 08:49:50,796] Trial 30 finished with value: 0.39274111452873756 and parameters: {'learning_rate': 0.041482957710100216, 'max_depth': 7, 'min_child_weight': 8, 'gamma': 7.898681778901064, 'subsample': 0.32441961136817227, 'colsample_bytree': 0.4139749032312582, 'alpha': 6.05594870158132, 'lambda': 1.9304577955760682}. Best is trial 18 with value: 0.45100471333462727.\n",
      "[I 2024-02-08 08:49:52,117] Trial 31 finished with value: 0.44500267751242134 and parameters: {'learning_rate': 0.06605148170800286, 'max_depth': 6, 'min_child_weight': 7, 'gamma': 0.13390603380097355, 'subsample': 0.3449481151007805, 'colsample_bytree': 0.59691330461465, 'alpha': 2.1642750245178743, 'lambda': 0.061379142668946285}. Best is trial 18 with value: 0.45100471333462727.\n",
      "[I 2024-02-08 08:49:53,056] Trial 32 finished with value: 0.4512367910256753 and parameters: {'learning_rate': 0.0886456731524347, 'max_depth': 5, 'min_child_weight': 7, 'gamma': 0.8011719981069342, 'subsample': 0.24324941463087896, 'colsample_bytree': 0.6189402173385813, 'alpha': 1.0605548539960374, 'lambda': 0.5426600461957561}. Best is trial 32 with value: 0.4512367910256753.\n",
      "[I 2024-02-08 08:49:53,726] Trial 33 finished with value: 0.37341645477235386 and parameters: {'learning_rate': 0.08873745310985423, 'max_depth': 5, 'min_child_weight': 8, 'gamma': 0.7629745916050865, 'subsample': 0.016609493490836957, 'colsample_bytree': 0.7556328256687157, 'alpha': 0.9868363723025333, 'lambda': 0.7687077067964199}. Best is trial 32 with value: 0.4512367910256753.\n",
      "[I 2024-02-08 08:49:54,498] Trial 34 finished with value: 0.42205201061773534 and parameters: {'learning_rate': 0.1039897401402333, 'max_depth': 5, 'min_child_weight': 6, 'gamma': 3.520029525758673, 'subsample': 0.24455247380322118, 'colsample_bytree': 0.506972794992627, 'alpha': 3.117863926149366, 'lambda': 1.4527443082150608}. Best is trial 32 with value: 0.4512367910256753.\n",
      "[I 2024-02-08 08:49:55,476] Trial 35 finished with value: 0.4302294247530394 and parameters: {'learning_rate': 0.08712006632232988, 'max_depth': 6, 'min_child_weight': 7, 'gamma': 1.5020009444481355, 'subsample': 0.1670892526317289, 'colsample_bytree': 0.6808023504317389, 'alpha': 0.7205890903424861, 'lambda': 2.3927607918551312}. Best is trial 32 with value: 0.4512367910256753.\n",
      "[I 2024-02-08 08:49:55,965] Trial 36 finished with value: 0.411271465202978 and parameters: {'learning_rate': 0.17238825796134807, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 5.347031019728787, 'subsample': 0.41947303806253555, 'colsample_bytree': 0.35916849144607577, 'alpha': 1.6572387344636517, 'lambda': 0.6567249057984167}. Best is trial 32 with value: 0.4512367910256753.\n",
      "[I 2024-02-08 08:49:56,932] Trial 37 finished with value: 0.4415032199269408 and parameters: {'learning_rate': 0.03709525457729346, 'max_depth': 5, 'min_child_weight': 4, 'gamma': 0.7182540330569105, 'subsample': 0.2844717149621158, 'colsample_bytree': 0.453087376349287, 'alpha': 1.35943518551335, 'lambda': 9.827809074953887}. Best is trial 32 with value: 0.4512367910256753.\n",
      "[I 2024-02-08 08:49:57,805] Trial 38 finished with value: 0.4414742489614325 and parameters: {'learning_rate': 0.10612395435850439, 'max_depth': 10, 'min_child_weight': 5, 'gamma': 0.8418590549149004, 'subsample': 0.23307645785014458, 'colsample_bytree': 0.6323750820891345, 'alpha': 2.2308425406532324, 'lambda': 4.8700567747997106}. Best is trial 32 with value: 0.4512367910256753.\n",
      "[I 2024-02-08 08:49:58,231] Trial 39 finished with value: 0.43101078758224415 and parameters: {'learning_rate': 0.12331072967985929, 'max_depth': 8, 'min_child_weight': 8, 'gamma': 4.716060876652886, 'subsample': 0.596315191230435, 'colsample_bytree': 0.7466035298711411, 'alpha': 0.4400123274903205, 'lambda': 1.5144382268910705}. Best is trial 32 with value: 0.4512367910256753.\n",
      "[I 2024-02-08 08:49:58,659] Trial 40 finished with value: 0.37245293662891776 and parameters: {'learning_rate': 0.0661967092504059, 'max_depth': 6, 'min_child_weight': 7, 'gamma': 7.397487030972352, 'subsample': 0.06980165779481093, 'colsample_bytree': 0.5418152526872342, 'alpha': 9.033338182976653, 'lambda': 6.676717652676771}. Best is trial 32 with value: 0.4512367910256753.\n",
      "[I 2024-02-08 08:49:59,778] Trial 41 finished with value: 0.4430497776404199 and parameters: {'learning_rate': 0.04217442310140254, 'max_depth': 6, 'min_child_weight': 7, 'gamma': 0.029164879509052888, 'subsample': 0.32054965179229394, 'colsample_bytree': 0.6165135955009339, 'alpha': 1.78796950836455, 'lambda': 0.3064994374350199}. Best is trial 32 with value: 0.4512367910256753.\n",
      "[I 2024-02-08 08:50:00,828] Trial 42 finished with value: 0.44979286339461827 and parameters: {'learning_rate': 0.07988798024354538, 'max_depth': 5, 'min_child_weight': 6, 'gamma': 0.05394288983392412, 'subsample': 0.36058267118413584, 'colsample_bytree': 0.49417693770213406, 'alpha': 1.3161145859172865, 'lambda': 0.02021961651326254}. Best is trial 32 with value: 0.4512367910256753.\n",
      "[I 2024-02-08 08:50:01,995] Trial 43 finished with value: 0.4405161021398137 and parameters: {'learning_rate': 0.0788644484624099, 'max_depth': 5, 'min_child_weight': 6, 'gamma': 0.7050737784954394, 'subsample': 0.27150064385965444, 'colsample_bytree': 0.4743897541479751, 'alpha': 1.303954657110797, 'lambda': 1.2695184995030036}. Best is trial 32 with value: 0.4512367910256753.\n",
      "[I 2024-02-08 08:50:02,852] Trial 44 finished with value: 0.42695350754642575 and parameters: {'learning_rate': 0.09647631716945217, 'max_depth': 5, 'min_child_weight': 8, 'gamma': 1.618983457349866, 'subsample': 0.12367460175091327, 'colsample_bytree': 0.3969156728463783, 'alpha': 0.12371787908127807, 'lambda': 0.5764203467828066}. Best is trial 32 with value: 0.4512367910256753.\n",
      "[I 2024-02-08 08:50:03,676] Trial 45 finished with value: 0.44164655446736345 and parameters: {'learning_rate': 0.07335673708122886, 'max_depth': 7, 'min_child_weight': 6, 'gamma': 1.278017135162316, 'subsample': 0.4765305318485803, 'colsample_bytree': 0.5502536569813344, 'alpha': 2.7434773400875025, 'lambda': 2.0134518766609424}. Best is trial 32 with value: 0.4512367910256753.\n",
      "[I 2024-02-08 08:50:04,448] Trial 46 finished with value: 0.4276307263833235 and parameters: {'learning_rate': 0.10855838978273834, 'max_depth': 5, 'min_child_weight': 7, 'gamma': 2.361927216582718, 'subsample': 0.9514807785568026, 'colsample_bytree': 0.7255900244402227, 'alpha': 0.6951774873655707, 'lambda': 1.146059599758427}. Best is trial 32 with value: 0.4512367910256753.\n",
      "[I 2024-02-08 08:50:05,583] Trial 47 finished with value: 0.45487056834981116 and parameters: {'learning_rate': 0.08339358698448276, 'max_depth': 6, 'min_child_weight': 8, 'gamma': 0.3788183711436398, 'subsample': 0.37172524153283004, 'colsample_bytree': 0.6821453856841537, 'alpha': 1.440762092271295, 'lambda': 2.730839635009598}. Best is trial 47 with value: 0.45487056834981116.\n",
      "[I 2024-02-08 08:50:06,345] Trial 48 finished with value: 0.4283887725306216 and parameters: {'learning_rate': 0.08455786455645516, 'max_depth': 7, 'min_child_weight': 8, 'gamma': 2.0754255376383104, 'subsample': 0.37767193631317053, 'colsample_bytree': 0.6693476758256167, 'alpha': 2.195725238559408, 'lambda': 3.7094963222997324}. Best is trial 47 with value: 0.45487056834981116.\n",
      "[I 2024-02-08 08:50:07,512] Trial 49 finished with value: 0.44365030853582366 and parameters: {'learning_rate': 0.09716785058299521, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 0.5824343328402093, 'subsample': 0.5677912430317806, 'colsample_bytree': 0.7779762535693052, 'alpha': 4.395568879317773, 'lambda': 2.6957795442275523}. Best is trial 47 with value: 0.45487056834981116.\n",
      "[I 2024-02-08 08:50:08,106] Trial 50 finished with value: 0.42330540482733714 and parameters: {'learning_rate': 0.11454974806572857, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 3.263345889697471, 'subsample': 0.6802949269225328, 'colsample_bytree': 0.4919376422951194, 'alpha': 3.0679355686856704, 'lambda': 3.1455965397324097}. Best is trial 47 with value: 0.45487056834981116.\n",
      "[I 2024-02-08 08:50:09,215] Trial 51 finished with value: 0.44726996188572327 and parameters: {'learning_rate': 0.06048274609296708, 'max_depth': 6, 'min_child_weight': 7, 'gamma': 0.03354424130691728, 'subsample': 0.1967806057526675, 'colsample_bytree': 0.6464090610152967, 'alpha': 1.4718043139849355, 'lambda': 0.03271028849761848}. Best is trial 47 with value: 0.45487056834981116.\n",
      "[I 2024-02-08 08:50:10,216] Trial 52 finished with value: 0.4400048851386312 and parameters: {'learning_rate': 0.06993845360173218, 'max_depth': 6, 'min_child_weight': 7, 'gamma': 1.0758474141738494, 'subsample': 0.1961265433064897, 'colsample_bytree': 0.6863057474988237, 'alpha': 0.6404078725596098, 'lambda': 0.423570037089943}. Best is trial 47 with value: 0.45487056834981116.\n",
      "[I 2024-02-08 08:50:11,195] Trial 53 finished with value: 0.44686670451907484 and parameters: {'learning_rate': 0.061244401014976496, 'max_depth': 5, 'min_child_weight': 6, 'gamma': 0.44612372466335637, 'subsample': 0.4094366929160044, 'colsample_bytree': 0.6380839223508906, 'alpha': 1.6738084789466354, 'lambda': 0.008550585683276768}. Best is trial 47 with value: 0.45487056834981116.\n",
      "[I 2024-02-08 08:50:11,802] Trial 54 finished with value: 0.4062900900030287 and parameters: {'learning_rate': 0.09341205696640346, 'max_depth': 8, 'min_child_weight': 8, 'gamma': 0.9860821158548788, 'subsample': 0.49909012847809986, 'colsample_bytree': 0.09657990873138994, 'alpha': 2.527112048551042, 'lambda': 0.582864382754368}. Best is trial 47 with value: 0.45487056834981116.\n",
      "[I 2024-02-08 08:50:12,850] Trial 55 finished with value: 0.4536586040663105 and parameters: {'learning_rate': 0.07959605919157764, 'max_depth': 7, 'min_child_weight': 7, 'gamma': 0.5076597582372274, 'subsample': 0.34554358858464784, 'colsample_bytree': 0.7101352695788521, 'alpha': 1.0399419313242153, 'lambda': 1.0125813392022986}. Best is trial 47 with value: 0.45487056834981116.\n",
      "[I 2024-02-08 08:50:13,735] Trial 56 finished with value: 0.4526269873402834 and parameters: {'learning_rate': 0.0770958614405022, 'max_depth': 7, 'min_child_weight': 2, 'gamma': 1.5317451569534475, 'subsample': 0.4263401876337861, 'colsample_bytree': 0.800314761622971, 'alpha': 0.9383391928656888, 'lambda': 1.7114613685120188}. Best is trial 47 with value: 0.45487056834981116.\n",
      "[I 2024-02-08 08:50:14,791] Trial 57 finished with value: 0.4557081954163981 and parameters: {'learning_rate': 0.081643369239302, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 1.3021333965457518, 'subsample': 0.4380408557756317, 'colsample_bytree': 0.8452269726587639, 'alpha': 1.0986652838458895, 'lambda': 1.4732299721373554}. Best is trial 57 with value: 0.4557081954163981.\n",
      "[I 2024-02-08 08:50:15,491] Trial 58 finished with value: 0.4457657178679824 and parameters: {'learning_rate': 0.10253406480058626, 'max_depth': 9, 'min_child_weight': 2, 'gamma': 2.4021119052103344, 'subsample': 0.44106908084824425, 'colsample_bytree': 0.9422694469203943, 'alpha': 0.38566920583920195, 'lambda': 1.7239225471800055}. Best is trial 57 with value: 0.4557081954163981.\n",
      "[I 2024-02-08 08:50:16,286] Trial 59 finished with value: 0.4532815392867324 and parameters: {'learning_rate': 0.07720786712835341, 'max_depth': 8, 'min_child_weight': 2, 'gamma': 1.7351836179407778, 'subsample': 0.5507858950305293, 'colsample_bytree': 0.8094231271664113, 'alpha': 0.9520183551387904, 'lambda': 1.0947065497172153}. Best is trial 57 with value: 0.4557081954163981.\n",
      "[I 2024-02-08 08:50:17,218] Trial 60 finished with value: 0.45498320675560994 and parameters: {'learning_rate': 0.08247945461468106, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 1.6410338193251486, 'subsample': 0.7333181727198224, 'colsample_bytree': 0.8563292958874276, 'alpha': 0.9595263269697141, 'lambda': 2.7184056376839716}. Best is trial 57 with value: 0.4557081954163981.\n",
      "[I 2024-02-08 08:50:18,126] Trial 61 finished with value: 0.4550972018716765 and parameters: {'learning_rate': 0.08133216186754436, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 1.5438570731535788, 'subsample': 0.7890640986452028, 'colsample_bytree': 0.8413746805222089, 'alpha': 0.9282630520967513, 'lambda': 2.738523612726331}. Best is trial 57 with value: 0.4557081954163981.\n",
      "[I 2024-02-08 08:50:19,080] Trial 62 finished with value: 0.455861692763111 and parameters: {'learning_rate': 0.07689613738791451, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 1.6363629832881066, 'subsample': 0.8113094898122293, 'colsample_bytree': 0.8548103255607845, 'alpha': 0.8230249335304325, 'lambda': 2.71368681935422}. Best is trial 62 with value: 0.455861692763111.\n",
      "[I 2024-02-08 08:50:19,926] Trial 63 finished with value: 0.4479427771170631 and parameters: {'learning_rate': 0.08179506641607433, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 2.5968056443056926, 'subsample': 0.8117232244727574, 'colsample_bytree': 0.8516169526590653, 'alpha': 0.21228429846004904, 'lambda': 2.929486220986084}. Best is trial 62 with value: 0.455861692763111.\n",
      "[I 2024-02-08 08:50:20,868] Trial 64 finished with value: 0.4447797404609335 and parameters: {'learning_rate': 0.07339640794294938, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 2.073487381172345, 'subsample': 0.8799640896893062, 'colsample_bytree': 0.8767755132584101, 'alpha': 0.6284755849601683, 'lambda': 3.3902926534898987}. Best is trial 62 with value: 0.455861692763111.\n",
      "[I 2024-02-08 08:50:22,551] Trial 65 finished with value: 0.4505361387561934 and parameters: {'learning_rate': 0.06626525358133825, 'max_depth': 11, 'min_child_weight': 2, 'gamma': 1.6190709030451453, 'subsample': 0.6854993678819483, 'colsample_bytree': 0.9195059045207371, 'alpha': 1.9162861803958058, 'lambda': 4.14113824943893}. Best is trial 62 with value: 0.455861692763111.\n",
      "[I 2024-02-08 08:50:23,832] Trial 66 finished with value: 0.44182600427935276 and parameters: {'learning_rate': 0.08283605947736547, 'max_depth': 9, 'min_child_weight': 2, 'gamma': 3.0384173169262563, 'subsample': 0.727669850034657, 'colsample_bytree': 0.9973491682761816, 'alpha': 0.00954458924202184, 'lambda': 5.359269941960923}. Best is trial 62 with value: 0.455861692763111.\n",
      "[I 2024-02-08 08:50:25,322] Trial 67 finished with value: 0.45101275527291923 and parameters: {'learning_rate': 0.05634922784224575, 'max_depth': 12, 'min_child_weight': 3, 'gamma': 1.8069682947312042, 'subsample': 0.8040859143863242, 'colsample_bytree': 0.8878276399345648, 'alpha': 1.0141503466668298, 'lambda': 2.6474177039245324}. Best is trial 62 with value: 0.455861692763111.\n",
      "[I 2024-02-08 08:50:26,464] Trial 68 finished with value: 0.46284420754108585 and parameters: {'learning_rate': 0.1930614346274348, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 1.1711246989171649, 'subsample': 0.8720644704624354, 'colsample_bytree': 0.8312463724767666, 'alpha': 0.44719168534469556, 'lambda': 2.3290824625446027}. Best is trial 68 with value: 0.46284420754108585.\n",
      "[I 2024-02-08 08:50:27,493] Trial 69 finished with value: 0.4338208606823808 and parameters: {'learning_rate': 0.1959702957377528, 'max_depth': 11, 'min_child_weight': 3, 'gamma': 1.248913103170588, 'subsample': 0.885188367836714, 'colsample_bytree': 0.8348801158707568, 'alpha': 6.509215074173568, 'lambda': 2.21590747953364}. Best is trial 68 with value: 0.46284420754108585.\n",
      "[I 2024-02-08 08:50:28,757] Trial 70 finished with value: 0.4430168293485733 and parameters: {'learning_rate': 0.1836874525430404, 'max_depth': 10, 'min_child_weight': 4, 'gamma': 2.089148882541363, 'subsample': 0.8664387743993992, 'colsample_bytree': 0.9511219441325168, 'alpha': 0.3231772583975474, 'lambda': 3.696797637917964}. Best is trial 68 with value: 0.46284420754108585.\n",
      "[I 2024-02-08 08:50:30,008] Trial 71 finished with value: 0.4572079443643501 and parameters: {'learning_rate': 0.09221602780612677, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 1.0950743971187331, 'subsample': 0.9294263926808791, 'colsample_bytree': 0.8012673843505109, 'alpha': 0.8660503567426715, 'lambda': 2.4062805175796367}. Best is trial 68 with value: 0.46284420754108585.\n",
      "[I 2024-02-08 08:50:31,512] Trial 72 finished with value: 0.4517609227559218 and parameters: {'learning_rate': 0.09263489616452002, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 1.1762796568346725, 'subsample': 0.9820343439174007, 'colsample_bytree': 0.7790018296732127, 'alpha': 0.6739227231653672, 'lambda': 2.505398540749633}. Best is trial 68 with value: 0.46284420754108585.\n",
      "[I 2024-02-08 08:50:34,262] Trial 73 finished with value: 0.4550264082053916 and parameters: {'learning_rate': 0.14112643982446263, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.3894393501725806, 'subsample': 0.841843197029941, 'colsample_bytree': 0.9125996741214213, 'alpha': 2.010863774882265, 'lambda': 4.4588473174947705}. Best is trial 68 with value: 0.46284420754108585.\n",
      "[I 2024-02-08 08:50:40,331] Trial 74 finished with value: 0.42783688332463543 and parameters: {'learning_rate': 0.15735742540290004, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 1.389775623419954, 'subsample': 0.7694167898989053, 'colsample_bytree': 0.909047135974569, 'alpha': 7.919546072615248, 'lambda': 4.404519210254146}. Best is trial 68 with value: 0.46284420754108585.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def objective_small(trial):#,X_train,y_train,X_valid,y_valid,weight_train):\n",
    "    params = dict(\n",
    "                learning_rate = trial.suggest_float(\"learning_rate\", 0.01, 0.2),\n",
    "                max_depth= trial.suggest_int(\"max_depth\",5, 15),\n",
    "                min_child_weight = trial.suggest_int(\"min_child_weight\", 1, 8),\n",
    "                gamma = trial.suggest_float(\"gamma\", 0, 10),\n",
    "                subsample = trial.suggest_float(\"subsample\", 0.01,1),\n",
    "                colsample_bytree = trial.suggest_float(\"colsample_bytree\", 0.01,1),\n",
    "                alpha = trial.suggest_float(\"alpha\", 0, 10),\n",
    "                objective= 'multi:softprob', \n",
    "                nthread=4, \n",
    "                 num_class= 5,\n",
    "                seed=27)\n",
    "    params['lambda'] = trial.suggest_float(\"lambda\", 0, 10)\n",
    "\n",
    "    \n",
    "    dtrain = xgb.DMatrix(X_train_small,y_train_small.apply(lambda x:x.replace('i','')).astype(int), \n",
    "                         enable_categorical=True,\n",
    "                         weight=weight_train)\n",
    "    dvalid = xgb.DMatrix(X_valid_small,y_valid_small.apply(lambda x:x.replace('i','')).astype(int),\n",
    "                         enable_categorical=True,\n",
    "                         )\n",
    "\n",
    "\n",
    "    bst = xgb.train(params, dtrain,verbose_eval=False, num_boost_round=400,\n",
    "                    evals = [(dtrain, \"train\"), (dvalid, \"valid\")],\n",
    "                    early_stopping_rounds=100)\n",
    "\n",
    "    preds = bst.predict(dvalid)\n",
    "    ##MCC is more solid\n",
    "    mcc = matthews_corrcoef(y_valid_small.apply(lambda x:x.replace('i','')).astype(int),preds.argmax(1))    \n",
    "    \n",
    "    return mcc\n",
    "\n",
    "\n",
    "\n",
    "study_small = optuna.create_study(direction=\"maximize\")\n",
    "study_small.optimize(objective_small, n_trials=400, timeout=600)\n",
    "params_final = dict(\n",
    "            objective= 'multi:softprob', \n",
    "            nthread=4, \n",
    "             num_class= 5,\n",
    "            seed=27)\n",
    "params_final.update(study_small.best_params)\n",
    "dtrain_small = xgb.DMatrix(X_train_small,y_train_small.apply(lambda x:x.replace('i','')).astype(int), \n",
    "                     enable_categorical=True,\n",
    "                     weight=weight_train)\n",
    "dvalid_small = xgb.DMatrix(X_valid_small,y_valid_small.apply(lambda x:x.replace('i','')).astype(int),\n",
    "                     enable_categorical=True,\n",
    "                     )   \n",
    "bst_small = xgb.train(params_final, dtrain_small,verbose_eval=False, num_boost_round=400,\n",
    "                evals = [(dtrain_small, \"train\"), (dvalid_small, \"valid\")],\n",
    "                early_stopping_rounds=100,)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b794f14-4b51-40ad-9d18-8ad72138922f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2eafaf84-04d1-429a-b630-a785fa70e7f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature Importance Score')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_imp = pd.Series(bst_small.get_fscore()).sort_values(ascending=False)\n",
    "feat_imp.plot(kind='bar', title='Feature Importances')\n",
    "plt.ylabel('Feature Importance Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "06b5f27d-2f28-48f0-b584-5a1729631cdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 142   43    2    0    0]\n",
      " [  58 2529  455    1    0]\n",
      " [   0  266  461   17    0]\n",
      " [   0    3   18   13    0]\n",
      " [   0    2    0    0    0]]\n",
      "0.48560756020354895\n",
      "0.78428927680798\n",
      "########################################\n",
      "[[ 379    0    0    0    0]\n",
      " [  74 5564  535    4    0]\n",
      " [   1   32 1476    1    0]\n",
      " [   0    0    0   68    0]\n",
      " [   0    0    0    0    5]]\n",
      "0.8268686048760869\n",
      "0.9205062046934512\n"
     ]
    }
   ],
   "source": [
    "preds_class_valid = bst.predict(dvalid)\n",
    "preds_class_train= bst.predict(dtrain)\n",
    "print(confusion_matrix(y_valid.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid.argmax(1)))\n",
    "print(matthews_corrcoef(y_valid.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid.argmax(1)))\n",
    "print(accuracy_score(y_valid.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid.argmax(1)))\n",
    "print('########################################')\n",
    "print(confusion_matrix(y_train.apply(lambda x:x.replace('i','')).astype(int),preds_class_train.argmax(1)))\n",
    "print(matthews_corrcoef(y_train.apply(lambda x:x.replace('i','')).astype(int),preds_class_train.argmax(1)))\n",
    "print(accuracy_score(y_train.apply(lambda x:x.replace('i','')).astype(int),preds_class_train.argmax(1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b2f861da-5fa1-4e1b-8e6d-b1080dab1c24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 142   43    2    0    0]\n",
      " [  65 2482  494    2    0]\n",
      " [   2  231  484   27    0]\n",
      " [   0    3   18   13    0]\n",
      " [   0    2    0    0    0]]\n",
      "0.4899696708847772\n",
      "0.7783042394014963\n",
      "########################################\n",
      "[[ 379    0    0    0    0]\n",
      " [  96 5392  683    6    0]\n",
      " [   1   76 1432    1    0]\n",
      " [   0    0    0   68    0]\n",
      " [   0    0    0    0    5]]\n",
      "0.7743830583167292\n",
      "0.893967317852316\n"
     ]
    }
   ],
   "source": [
    "preds_class_valid = bst_small.predict(dvalid_small)\n",
    "preds_class_train= bst_small.predict(dtrain_small)\n",
    "print(confusion_matrix(y_valid_small.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid.argmax(1)))\n",
    "print(matthews_corrcoef(y_valid_small.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid.argmax(1)))\n",
    "print(accuracy_score(y_valid_small.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid.argmax(1)))\n",
    "print('########################################')\n",
    "print(confusion_matrix(y_train_small.apply(lambda x:x.replace('i','')).astype(int),preds_class_train.argmax(1)))\n",
    "print(matthews_corrcoef(y_train_small.apply(lambda x:x.replace('i','')).astype(int),preds_class_train.argmax(1)))\n",
    "print(accuracy_score(y_train_small.apply(lambda x:x.replace('i','')).astype(int),preds_class_train.argmax(1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d7c1f2f6-e484-4b57-adc8-7307bbb2022f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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        "plotlyServerURL": "https://plot.ly"
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          "min_child_weight (IntDistribution): 0.007485986800238328<extra></extra>",
          "learning_rate (FloatDistribution): 0.01762004148511711<extra></extra>",
          "lambda (FloatDistribution): 0.04816496533298376<extra></extra>",
          "subsample (FloatDistribution): 0.05859905392748021<extra></extra>",
          "colsample_bytree (FloatDistribution): 0.09995389887317006<extra></extra>",
          "alpha (FloatDistribution): 0.2006415457880566<extra></extra>",
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         "name": "Objective Value",
         "orientation": "h",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from optuna.visualization import plot_param_importances,plot_edf,plot_optimization_history\n",
    "#plot_edf(study)\n",
    "#plot_optimization_history(study)\n",
    "\n",
    "plot_param_importances(study_small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "eea04db3-8653-4be6-8ad8-107133e7236f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'objective': 'multi:softprob',\n",
       " 'nthread': 4,\n",
       " 'num_class': 5,\n",
       " 'seed': 27,\n",
       " 'learning_rate': 0.05512860195007843,\n",
       " 'max_depth': 7,\n",
       " 'min_child_weight': 1,\n",
       " 'gamma': 0.18858850578168745,\n",
       " 'subsample': 0.6635246134540167,\n",
       " 'colsample_bytree': 0.9300947822236749,\n",
       " 'alpha': 0.2006663031138782,\n",
       " 'lambda': 4.858042373367558}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d6cb7bd5-35fa-4f80-b04d-b82f4049ef22",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-02-06 15:45:34,987] A new study created in memory with name: no-name-13274e7d-fb35-4b87-8b4f-04cb66fe397e\n",
      "[I 2024-02-06 15:45:36,050] Trial 0 finished with value: 0.4057089921074421 and parameters: {'gamma': 8.53351713182836, 'subsample': 0.9733024325557763, 'colsample_bytree': 0.7796967537918551, 'alpha': 0.23966291939500506, 'lambda': 7.588089377597536}. Best is trial 0 with value: 0.4057089921074421.\n",
      "[I 2024-02-06 15:45:37,604] Trial 1 finished with value: 0.4611715187336915 and parameters: {'gamma': 0.02477032024294057, 'subsample': 0.6324878172866865, 'colsample_bytree': 0.6506407517812984, 'alpha': 2.8121200570991753, 'lambda': 4.209373703072621}. Best is trial 1 with value: 0.4611715187336915.\n",
      "[I 2024-02-06 15:45:38,241] Trial 2 finished with value: 0.403681140412304 and parameters: {'gamma': 5.426488257981369, 'subsample': 0.596709754009448, 'colsample_bytree': 0.9033378255865846, 'alpha': 8.301806776056935, 'lambda': 8.01244058347282}. Best is trial 1 with value: 0.4611715187336915.\n",
      "[I 2024-02-06 15:45:38,938] Trial 3 finished with value: 0.36746297315269866 and parameters: {'gamma': 8.470918353335442, 'subsample': 0.5403952906862229, 'colsample_bytree': 0.04555973531379143, 'alpha': 5.024941200477031, 'lambda': 6.515621233278594}. Best is trial 1 with value: 0.4611715187336915.\n",
      "[I 2024-02-06 15:45:39,616] Trial 4 finished with value: 0.376112272738501 and parameters: {'gamma': 0.3765609431232597, 'subsample': 0.8943207599950969, 'colsample_bytree': 0.11157551044616418, 'alpha': 9.23973953305498, 'lambda': 9.953194187763376}. Best is trial 1 with value: 0.4611715187336915.\n",
      "[I 2024-02-06 15:45:40,164] Trial 5 finished with value: 0.37101902350199123 and parameters: {'gamma': 3.7255891364234373, 'subsample': 0.4029775537830337, 'colsample_bytree': 0.06912550636329041, 'alpha': 9.034200012910883, 'lambda': 5.680411876385641}. Best is trial 1 with value: 0.4611715187336915.\n",
      "[I 2024-02-06 15:45:40,906] Trial 6 finished with value: 0.41235921364173245 and parameters: {'gamma': 3.3437250214677094, 'subsample': 0.17114770117261238, 'colsample_bytree': 0.6489379474317031, 'alpha': 3.203953296029015, 'lambda': 3.351954173460887}. Best is trial 1 with value: 0.4611715187336915.\n",
      "[I 2024-02-06 15:45:41,578] Trial 7 finished with value: 0.3927324528425983 and parameters: {'gamma': 9.40303473300666, 'subsample': 0.4812878561745889, 'colsample_bytree': 0.39632376271214675, 'alpha': 7.214433902321744, 'lambda': 2.5638441912043533}. Best is trial 1 with value: 0.4611715187336915.\n",
      "[I 2024-02-06 15:45:42,620] Trial 8 finished with value: 0.4403177568947459 and parameters: {'gamma': 0.6526143331590606, 'subsample': 0.4095378012362146, 'colsample_bytree': 0.7884612906631643, 'alpha': 4.417918116966877, 'lambda': 4.0453353825752}. Best is trial 1 with value: 0.4611715187336915.\n",
      "[I 2024-02-06 15:45:43,610] Trial 9 finished with value: 0.43690219393942176 and parameters: {'gamma': 0.3701443189307063, 'subsample': 0.6782845434023048, 'colsample_bytree': 0.6142353235814887, 'alpha': 7.240004250639245, 'lambda': 1.0189640435957836}. Best is trial 1 with value: 0.4611715187336915.\n",
      "[I 2024-02-06 15:45:44,429] Trial 10 finished with value: 0.3869327397946241 and parameters: {'gamma': 5.794779273177621, 'subsample': 0.07926817322936308, 'colsample_bytree': 0.3630744181023996, 'alpha': 1.8895164259431718, 'lambda': 0.02444871255119807}. Best is trial 1 with value: 0.4611715187336915.\n",
      "[I 2024-02-06 15:45:45,293] Trial 11 finished with value: 0.43487401574862145 and parameters: {'gamma': 1.8872113217643705, 'subsample': 0.3048081773810226, 'colsample_bytree': 0.9490295615085181, 'alpha': 4.754521576089703, 'lambda': 4.3990984887954365}. Best is trial 1 with value: 0.4611715187336915.\n",
      "[I 2024-02-06 15:45:46,065] Trial 12 finished with value: 0.4342169882433265 and parameters: {'gamma': 1.8819689342238968, 'subsample': 0.772689842220825, 'colsample_bytree': 0.751265061736337, 'alpha': 4.300883664214762, 'lambda': 4.1894414397005}. Best is trial 1 with value: 0.4611715187336915.\n",
      "[I 2024-02-06 15:45:47,159] Trial 13 finished with value: 0.4374731670257182 and parameters: {'gamma': 1.7966593945925957, 'subsample': 0.31674376222217493, 'colsample_bytree': 0.4922327683456361, 'alpha': 2.3653551463507863, 'lambda': 2.407085876302244}. Best is trial 1 with value: 0.4611715187336915.\n",
      "[I 2024-02-06 15:45:48,587] Trial 14 finished with value: 0.47768767443722493 and parameters: {'gamma': 0.24162708053596066, 'subsample': 0.7936946135040182, 'colsample_bytree': 0.8086822561814027, 'alpha': 0.6302113045591522, 'lambda': 5.077265745186624}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:45:49,262] Trial 15 finished with value: 0.42153224471514217 and parameters: {'gamma': 3.961875376006926, 'subsample': 0.7772057343777553, 'colsample_bytree': 0.587429667928409, 'alpha': 0.44483801632276826, 'lambda': 5.717852480415367}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:45:49,942] Trial 16 finished with value: 0.41289946527881616 and parameters: {'gamma': 6.8128719973939145, 'subsample': 0.7151612963219814, 'colsample_bytree': 0.8518078142414052, 'alpha': 1.492288637449084, 'lambda': 7.021164477955692}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:45:50,645] Trial 17 finished with value: 0.42719143383209424 and parameters: {'gamma': 2.6894906425737872, 'subsample': 0.8493872737158275, 'colsample_bytree': 0.9963646344460635, 'alpha': 2.8813746903013313, 'lambda': 8.994467464319168}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:45:51,783] Trial 18 finished with value: 0.44664694055086257 and parameters: {'gamma': 0.09975083621222974, 'subsample': 0.9981856079539952, 'colsample_bytree': 0.24444625586298768, 'alpha': 1.229255191324233, 'lambda': 5.650568255437403}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:45:52,810] Trial 19 finished with value: 0.4423265138953932 and parameters: {'gamma': 1.3702381509287154, 'subsample': 0.6417092308961592, 'colsample_bytree': 0.6931641917428899, 'alpha': 3.3679649688377937, 'lambda': 2.220017039852874}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:45:53,456] Trial 20 finished with value: 0.40812771508846385 and parameters: {'gamma': 4.574312931636753, 'subsample': 0.8245693636275424, 'colsample_bytree': 0.5138428486854739, 'alpha': 5.555290998718218, 'lambda': 4.824784962287322}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:45:54,616] Trial 21 finished with value: 0.4307228858458474 and parameters: {'gamma': 0.010174249246507977, 'subsample': 0.9822414828420064, 'colsample_bytree': 0.20385846622357073, 'alpha': 1.1721148707889204, 'lambda': 5.736832507437121}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:45:55,453] Trial 22 finished with value: 0.43789514635383325 and parameters: {'gamma': 1.2712067676898193, 'subsample': 0.9166977346002614, 'colsample_bytree': 0.23681455097637022, 'alpha': 1.09670017953716, 'lambda': 3.148198436034801}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:45:56,605] Trial 23 finished with value: 0.43504944833526943 and parameters: {'gamma': 2.763719412976592, 'subsample': 0.7263228243089771, 'colsample_bytree': 0.3431709524758616, 'alpha': 0.13296994893607783, 'lambda': 5.324608842635747}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:45:57,505] Trial 24 finished with value: 0.44348301435093324 and parameters: {'gamma': 1.0335376319519123, 'subsample': 0.9968939675986355, 'colsample_bytree': 0.5031009801201334, 'alpha': 2.3439502677357082, 'lambda': 6.549253857163535}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:45:59,170] Trial 25 finished with value: 0.47008513716285827 and parameters: {'gamma': 0.042612348726500215, 'subsample': 0.8764730891831611, 'colsample_bytree': 0.7020339823323646, 'alpha': 0.9484689278103888, 'lambda': 3.549392693966113}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:45:59,904] Trial 26 finished with value: 0.43315625258073454 and parameters: {'gamma': 2.365189571412615, 'subsample': 0.6087566281232062, 'colsample_bytree': 0.7095775952865297, 'alpha': 3.59778136815412, 'lambda': 1.319138084725231}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:00,877] Trial 27 finished with value: 0.45684360780863853 and parameters: {'gamma': 1.048449505301412, 'subsample': 0.8108745396268869, 'colsample_bytree': 0.8353926496875976, 'alpha': 2.1064560297325543, 'lambda': 3.8362287483690825}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:01,881] Trial 28 finished with value: 0.4142249209720259 and parameters: {'gamma': 6.439835063465354, 'subsample': 0.8697748666478017, 'colsample_bytree': 0.5768000946265736, 'alpha': 0.6204980207721184, 'lambda': 4.666873330788709}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:02,594] Trial 29 finished with value: 0.41286374448277113 and parameters: {'gamma': 7.293411827434225, 'subsample': 0.9254968426537394, 'colsample_bytree': 0.7700360448180701, 'alpha': 0.1014321354391996, 'lambda': 3.241167432974229}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:04,039] Trial 30 finished with value: 0.45006332763969437 and parameters: {'gamma': 0.7456496908440104, 'subsample': 0.5496545077193133, 'colsample_bytree': 0.8684745619499542, 'alpha': 2.7077973590680786, 'lambda': 3.6009170458709225}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:04,876] Trial 31 finished with value: 0.45332303234052007 and parameters: {'gamma': 1.1441986461979143, 'subsample': 0.782948281308735, 'colsample_bytree': 0.8159789541930826, 'alpha': 2.250232181490585, 'lambda': 1.6888915714846804}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:06,580] Trial 32 finished with value: 0.46517943646670157 and parameters: {'gamma': 0.020563798925695487, 'subsample': 0.7259928577657181, 'colsample_bytree': 0.688064295539132, 'alpha': 1.6508681987661682, 'lambda': 3.9098030110728925}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:08,051] Trial 33 finished with value: 0.47505454518503804 and parameters: {'gamma': 0.16973996189817908, 'subsample': 0.6773287755930613, 'colsample_bytree': 0.6902489215996384, 'alpha': 0.7105098383286117, 'lambda': 4.960613221938276}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:09,571] Trial 34 finished with value: 0.47663263981131554 and parameters: {'gamma': 0.08486878732567611, 'subsample': 0.6668368134594849, 'colsample_bytree': 0.6948949255102344, 'alpha': 0.7605173672477663, 'lambda': 6.482263702358043}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:10,473] Trial 35 finished with value: 0.450874738718109 and parameters: {'gamma': 1.71569514544242, 'subsample': 0.6601002090464034, 'colsample_bytree': 0.9070977488514564, 'alpha': 0.7897143557579994, 'lambda': 7.993097287413335}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:11,717] Trial 36 finished with value: 0.4638847748768045 and parameters: {'gamma': 0.6423670444843349, 'subsample': 0.5603093529315799, 'colsample_bytree': 0.7428760437694489, 'alpha': 0.0007337594456446883, 'lambda': 6.451126929233225}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:12,477] Trial 37 finished with value: 0.439456820959186 and parameters: {'gamma': 2.578825939599241, 'subsample': 0.6973382866277598, 'colsample_bytree': 0.6326887297184982, 'alpha': 0.7548435358445854, 'lambda': 5.003816178334107}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:13,179] Trial 38 finished with value: 0.40717269731928707 and parameters: {'gamma': 3.406486976235291, 'subsample': 0.47496058648853867, 'colsample_bytree': 0.5730107013214151, 'alpha': 5.9345881892153125, 'lambda': 7.069490845925341}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:14,047] Trial 39 finished with value: 0.45411396601737447 and parameters: {'gamma': 0.6102003740745753, 'subsample': 0.9247624228957996, 'colsample_bytree': 0.6795384509265769, 'alpha': 1.5086453809950626, 'lambda': 6.575752973963671}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:14,667] Trial 40 finished with value: 0.4057522710508818 and parameters: {'gamma': 8.73078319243628, 'subsample': 0.6024866815123687, 'colsample_bytree': 0.44935847734806544, 'alpha': 0.9033559323360129, 'lambda': 6.1678402766530445}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:16,077] Trial 41 finished with value: 0.46907232596982273 and parameters: {'gamma': 0.04581775105365455, 'subsample': 0.7569320425322238, 'colsample_bytree': 0.7201269115772561, 'alpha': 1.76022014767626, 'lambda': 2.9679047075023073}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:17,215] Trial 42 finished with value: 0.47350304133635707 and parameters: {'gamma': 0.5229739317794712, 'subsample': 0.7431738706638691, 'colsample_bytree': 0.7816270954465419, 'alpha': 0.47901697652520225, 'lambda': 3.0222374824649125}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:18,340] Trial 43 finished with value: 0.4685521087328895 and parameters: {'gamma': 0.6031409869270108, 'subsample': 0.8426413656424151, 'colsample_bytree': 0.7931414701831713, 'alpha': 0.46607953506686484, 'lambda': 5.258597479688788}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:19,228] Trial 44 finished with value: 0.4574529493745363 and parameters: {'gamma': 1.4415491456298009, 'subsample': 0.6644442216929298, 'colsample_bytree': 0.9352347477446334, 'alpha': 1.2046723392201515, 'lambda': 4.587061013755385}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:20,006] Trial 45 finished with value: 0.4401150372305811 and parameters: {'gamma': 2.2400673686591266, 'subsample': 0.8710790636838792, 'colsample_bytree': 0.6551751829945411, 'alpha': 0.5339422392507269, 'lambda': 2.6520368270829366}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:21,310] Trial 46 finished with value: 0.44946848458671657 and parameters: {'gamma': 0.548134809637314, 'subsample': 0.5143215170187667, 'colsample_bytree': 0.7838819839527232, 'alpha': 3.6988767317114295, 'lambda': 7.601761508959891}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:22,238] Trial 47 finished with value: 0.4486911579590794 and parameters: {'gamma': 0.8287019007891993, 'subsample': 0.8051532189731265, 'colsample_bytree': 0.8732236143536831, 'alpha': 1.8110410585909038, 'lambda': 4.2330042489612225}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:22,991] Trial 48 finished with value: 0.4274074448471313 and parameters: {'gamma': 1.5564655464261647, 'subsample': 0.7402735883050463, 'colsample_bytree': 0.6107169192380966, 'alpha': 8.350254900905261, 'lambda': 1.9923597278060372}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:24,033] Trial 49 finished with value: 0.4544648106947113 and parameters: {'gamma': 0.4268373638123144, 'subsample': 0.9489661026346696, 'colsample_bytree': 0.5450451754983672, 'alpha': 2.866620132480178, 'lambda': 3.551176272023362}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:24,846] Trial 50 finished with value: 0.4207516423440865 and parameters: {'gamma': 2.1686221771113625, 'subsample': 0.6861197868669829, 'colsample_bytree': 0.7372704059547796, 'alpha': 6.943060066854463, 'lambda': 9.018094352636151}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:26,439] Trial 51 finished with value: 0.4756526024171505 and parameters: {'gamma': 0.04751724602696455, 'subsample': 0.7694430666371312, 'colsample_bytree': 0.7425359211976469, 'alpha': 1.3634544829688504, 'lambda': 2.767255713317357}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:27,505] Trial 52 finished with value: 0.4729924454872296 and parameters: {'gamma': 0.37550742376735097, 'subsample': 0.8909685848384815, 'colsample_bytree': 0.8077569719801674, 'alpha': 0.3999089730615365, 'lambda': 3.0454805251800465}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:28,387] Trial 53 finished with value: 0.4735312372260193 and parameters: {'gamma': 1.0776657601418709, 'subsample': 0.777763790977481, 'colsample_bytree': 0.8228031343978036, 'alpha': 0.012420370921250479, 'lambda': 0.16385955808658625}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:29,633] Trial 54 finished with value: 0.4621652227041969 and parameters: {'gamma': 1.0443697994989052, 'subsample': 0.7719301415607767, 'colsample_bytree': 0.8859970846791857, 'alpha': 0.0077774238470436075, 'lambda': 0.9954849474787177}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:30,350] Trial 55 finished with value: 0.42997280544999034 and parameters: {'gamma': 5.020430719646456, 'subsample': 0.6211511406146587, 'colsample_bytree': 0.9717984635317386, 'alpha': 1.2409806878380287, 'lambda': 0.16144606132813652}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:31,428] Trial 56 finished with value: 0.4324661574204878 and parameters: {'gamma': 0.33269057196261537, 'subsample': 0.5743900740902226, 'colsample_bytree': 0.8402258877433506, 'alpha': 9.925409340268693, 'lambda': 0.7039666940341243}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:32,306] Trial 57 finished with value: 0.45105402111667714 and parameters: {'gamma': 1.3861401290296254, 'subsample': 0.810384553052394, 'colsample_bytree': 0.7479158653488099, 'alpha': 0.40365564852955693, 'lambda': 1.8097602494652558}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:32,973] Trial 58 finished with value: 0.43552286036689525 and parameters: {'gamma': 1.8963273396730094, 'subsample': 0.0333964213354595, 'colsample_bytree': 0.9151794376399667, 'alpha': 1.49375865688774, 'lambda': 6.086465483546543}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:33,894] Trial 59 finished with value: 0.44378395239836915 and parameters: {'gamma': 1.0615159776697785, 'subsample': 0.705425165125502, 'colsample_bytree': 0.6488138151076606, 'alpha': 2.5564455380582274, 'lambda': 2.6138392511801505}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:34,651] Trial 60 finished with value: 0.43442398047971365 and parameters: {'gamma': 3.110194510392754, 'subsample': 0.6351382310835325, 'colsample_bytree': 0.7613469210949306, 'alpha': 1.8971324166817005, 'lambda': 0.3644880928526648}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:35,942] Trial 61 finished with value: 0.4743193318717686 and parameters: {'gamma': 0.31328218021924115, 'subsample': 0.8410856737533755, 'colsample_bytree': 0.8241556844823902, 'alpha': 0.3781041286376625, 'lambda': 2.854133861872326}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:37,192] Trial 62 finished with value: 0.4728852451939498 and parameters: {'gamma': 0.2764049667986953, 'subsample': 0.8345236335601598, 'colsample_bytree': 0.8251341559290258, 'alpha': 0.8610363637446009, 'lambda': 1.2744342527704788}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:38,263] Trial 63 finished with value: 0.45545249133540067 and parameters: {'gamma': 0.9264636782393637, 'subsample': 0.7465109980881796, 'colsample_bytree': 0.7844989407048854, 'alpha': 0.33599843205179464, 'lambda': 6.941564202837635}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:39,473] Trial 64 finished with value: 0.45790554585233945 and parameters: {'gamma': 0.35353790552164294, 'subsample': 0.7922140869252602, 'colsample_bytree': 0.8497700356981297, 'alpha': 1.293583193506317, 'lambda': 2.29997142781714}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:40,871] Trial 65 finished with value: 0.46218230958900747 and parameters: {'gamma': 0.8029413682813252, 'subsample': 0.43172269648357114, 'colsample_bytree': 0.6679164600767128, 'alpha': 0.7442563563289557, 'lambda': 4.3198410726361445}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:41,974] Trial 66 finished with value: 0.45426874966882863 and parameters: {'gamma': 1.4107408050682109, 'subsample': 0.1814156937260321, 'colsample_bytree': 0.7262387944714249, 'alpha': 0.18683495452702292, 'lambda': 4.978119000915093}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:42,757] Trial 67 finished with value: 0.43503261081734007 and parameters: {'gamma': 4.2277611301679645, 'subsample': 0.6745560271549331, 'colsample_bytree': 0.885187874660315, 'alpha': 0.9909040735009819, 'lambda': 5.4697765721368725}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:43,616] Trial 68 finished with value: 0.4426737029591143 and parameters: {'gamma': 1.926507523975415, 'subsample': 0.8516376957706584, 'colsample_bytree': 0.8104504020042439, 'alpha': 0.6302326467632674, 'lambda': 5.913961328797049}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:45,349] Trial 69 finished with value: 0.4677711256562607 and parameters: {'gamma': 0.22066923079840023, 'subsample': 0.7242510621194524, 'colsample_bytree': 0.9477409196960324, 'alpha': 1.9608828031362067, 'lambda': 8.731086177190484}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:46,790] Trial 70 finished with value: 0.45318387606043997 and parameters: {'gamma': 0.8185465265653185, 'subsample': 0.7641148399034589, 'colsample_bytree': 0.6197196280046297, 'alpha': 1.4819882531771658, 'lambda': 3.87443309939486}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:49,179] Trial 71 finished with value: 0.4692830344715579 and parameters: {'gamma': 0.3499833112354867, 'subsample': 0.9061209877611196, 'colsample_bytree': 0.8004545274650967, 'alpha': 0.33477512424899597, 'lambda': 2.912712401094305}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:46:51,779] Trial 72 finished with value: 0.4614879764631534 and parameters: {'gamma': 0.5169680962603922, 'subsample': 0.8269642745779995, 'colsample_bytree': 0.8549660538722141, 'alpha': 0.38183801650074434, 'lambda': 3.3803364756258674}. Best is trial 14 with value: 0.47768767443722493.\n",
      "[I 2024-02-06 15:47:08,413] Trial 73 finished with value: 0.4814658398467143 and parameters: {'gamma': 0.03847405773427859, 'subsample': 0.8847637462074133, 'colsample_bytree': 0.7646342963827821, 'alpha': 0.9898167556049068, 'lambda': 3.028780113951234}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:11,824] Trial 74 finished with value: 0.47572511430122405 and parameters: {'gamma': 0.03255354611949045, 'subsample': 0.9552851857746044, 'colsample_bytree': 0.6959995712037957, 'alpha': 1.0465858719644774, 'lambda': 1.9895812692289456}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:13,045] Trial 75 finished with value: 0.4637770306398382 and parameters: {'gamma': 0.1557876107174731, 'subsample': 0.9503490977425693, 'colsample_bytree': 0.7053320426120469, 'alpha': 0.9553664743150143, 'lambda': 1.421200858614363}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:13,833] Trial 76 finished with value: 0.44670557053225185 and parameters: {'gamma': 1.1661892289314086, 'subsample': 0.9600354620576945, 'colsample_bytree': 0.7613130265534307, 'alpha': 1.3679909372258883, 'lambda': 2.0992106236040646}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:15,423] Trial 77 finished with value: 0.47703544761710615 and parameters: {'gamma': 0.021546354467167336, 'subsample': 0.863362708946746, 'colsample_bytree': 0.6820668398418767, 'alpha': 1.0859645141345227, 'lambda': 1.6346216680461212}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:17,045] Trial 78 finished with value: 0.4799778132861681 and parameters: {'gamma': 0.00019905520743368488, 'subsample': 0.8997535803974239, 'colsample_bytree': 0.6836895025056199, 'alpha': 1.1137950028989405, 'lambda': 1.678688670855283}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:18,238] Trial 79 finished with value: 0.4639853261736007 and parameters: {'gamma': 0.15288223519779365, 'subsample': 0.8933420481914531, 'colsample_bytree': 0.5982905912341419, 'alpha': 2.268032078635385, 'lambda': 1.860875591501836}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:19,620] Trial 80 finished with value: 0.46841192426012307 and parameters: {'gamma': 0.0441593243894982, 'subsample': 0.9335443940336855, 'colsample_bytree': 0.6757554180396347, 'alpha': 1.60610340013711, 'lambda': 1.5265671402340533}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:20,646] Trial 81 finished with value: 0.45464271065558987 and parameters: {'gamma': 0.6992890949504934, 'subsample': 0.8632916304388304, 'colsample_bytree': 0.6405365115303031, 'alpha': 1.1142334872688555, 'lambda': 2.3772093826282834}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:21,780] Trial 82 finished with value: 0.3704409302806548 and parameters: {'gamma': 0.7824080498694082, 'subsample': 0.9774556471596523, 'colsample_bytree': 0.017757147947763174, 'alpha': 0.7007354819214351, 'lambda': 2.736994717460901}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:23,540] Trial 83 finished with value: 0.4730101263154304 and parameters: {'gamma': 0.025034326112981297, 'subsample': 0.8929245960800821, 'colsample_bytree': 0.703039725317937, 'alpha': 1.1521070597619463, 'lambda': 1.6447677503170992}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:24,564] Trial 84 finished with value: 0.45844598386970997 and parameters: {'gamma': 0.4708554521777203, 'subsample': 0.9156682264395531, 'colsample_bytree': 0.5630111148061188, 'alpha': 2.1453686661972626, 'lambda': 0.7728381403423162}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:25,151] Trial 85 finished with value: 0.4074018478684123 and parameters: {'gamma': 7.849043978560465, 'subsample': 0.8467587533845312, 'colsample_bytree': 0.7285240457599066, 'alpha': 3.1778336526922586, 'lambda': 2.207541629920955}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:26,636] Trial 86 finished with value: 0.47216156793243713 and parameters: {'gamma': 0.02549149380636337, 'subsample': 0.8119376363221338, 'colsample_bytree': 0.6872684331987049, 'alpha': 1.6906678307717948, 'lambda': 1.2423661337719079}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:27,504] Trial 87 finished with value: 0.4435550190020793 and parameters: {'gamma': 0.6317534105357563, 'subsample': 0.8718027223640286, 'colsample_bytree': 0.7523364436754046, 'alpha': 5.469124911273128, 'lambda': 1.9680340579455722}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:28,241] Trial 88 finished with value: 0.43916614679960325 and parameters: {'gamma': 1.5636670107982091, 'subsample': 0.9760341965505012, 'colsample_bytree': 0.7180174553390603, 'alpha': 0.9889943657040424, 'lambda': 2.438498333437109}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:28,964] Trial 89 finished with value: 0.4155653972603823 and parameters: {'gamma': 6.235792844577146, 'subsample': 0.9444817932800599, 'colsample_bytree': 0.5316027148048592, 'alpha': 0.5956027722587136, 'lambda': 1.0496827718262622}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:29,802] Trial 90 finished with value: 0.4359211941585963 and parameters: {'gamma': 1.2263326211447247, 'subsample': 0.7940990486086337, 'colsample_bytree': 0.66622056577534, 'alpha': 4.13415230814835, 'lambda': 2.751965659214081}. Best is trial 73 with value: 0.4814658398467143.\n",
      "[I 2024-02-06 15:47:31,190] Trial 91 finished with value: 0.4894052449165375 and parameters: {'gamma': 0.3578576528105515, 'subsample': 0.7768063041307463, 'colsample_bytree': 0.7730544010816968, 'alpha': 0.20734897672839675, 'lambda': 0.44335404961041913}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:47:31,791] Trial 92 finished with value: 0.40784336220953776 and parameters: {'gamma': 9.515158485956082, 'subsample': 0.830309337911762, 'colsample_bytree': 0.7438631476618486, 'alpha': 0.18451904593416102, 'lambda': 3.3065084311135475}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:47:33,115] Trial 93 finished with value: 0.48317715376822323 and parameters: {'gamma': 0.29085781910960506, 'subsample': 0.654299812393865, 'colsample_bytree': 0.7692799975062447, 'alpha': 0.7915097383538593, 'lambda': 0.812177033075746}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:47:34,123] Trial 94 finished with value: 0.4532529918974916 and parameters: {'gamma': 0.8976940541611421, 'subsample': 0.6552241804043482, 'colsample_bytree': 0.4745091489968426, 'alpha': 0.7977084949309506, 'lambda': 0.4999358409361552}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:47:35,695] Trial 95 finished with value: 0.4682947356452511 and parameters: {'gamma': 0.2649197178179937, 'subsample': 0.7006805403807359, 'colsample_bytree': 0.7740008195053174, 'alpha': 1.3121998651214208, 'lambda': 1.0301649517548983}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:47:36,548] Trial 96 finished with value: 0.41733987075778073 and parameters: {'gamma': 0.5109175970231187, 'subsample': 0.7289571574822881, 'colsample_bytree': 0.13315016298383592, 'alpha': 1.94455526687814, 'lambda': 0.7706897313214567}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:47:38,138] Trial 97 finished with value: 0.46861990049860497 and parameters: {'gamma': 0.23658662113746526, 'subsample': 0.5811878764558025, 'colsample_bytree': 0.7002846360812175, 'alpha': 1.0013254617827947, 'lambda': 7.398871474492401}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:47:39,802] Trial 98 finished with value: 0.4689959962970093 and parameters: {'gamma': 0.6586290847269908, 'subsample': 0.34459330707015656, 'colsample_bytree': 0.6270176304828626, 'alpha': 0.5852881198755676, 'lambda': 0.49717139099377267}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:47:40,376] Trial 99 finished with value: 0.3994330948656762 and parameters: {'gamma': 9.87119388319747, 'subsample': 0.7589053864432501, 'colsample_bytree': 0.6577275936911837, 'alpha': 1.486094847645512, 'lambda': 1.7006512123086361}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:47:41,458] Trial 100 finished with value: 0.4684221097812261 and parameters: {'gamma': 0.9871252792021441, 'subsample': 0.6472624714930504, 'colsample_bytree': 0.7708714931811216, 'alpha': 0.20022654811058904, 'lambda': 1.1703221912234638}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:47:42,951] Trial 101 finished with value: 0.47374797144611575 and parameters: {'gamma': 0.31824869628801333, 'subsample': 0.5306011756034, 'colsample_bytree': 0.7961142077433148, 'alpha': 0.7807349193693347, 'lambda': 1.495923971848107}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:47:44,497] Trial 102 finished with value: 0.4887412480416781 and parameters: {'gamma': 0.004057032212150757, 'subsample': 0.8782798729094761, 'colsample_bytree': 0.732151654961912, 'alpha': 0.575662442962185, 'lambda': 0.051271849865529795}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:47:45,523] Trial 103 finished with value: 0.45680599958591145 and parameters: {'gamma': 0.4708727675119059, 'subsample': 0.8763366231634924, 'colsample_bytree': 0.7366219683324949, 'alpha': 1.0785656277364206, 'lambda': 0.10922787176425068}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:47:47,161] Trial 104 finished with value: 0.4843613543047365 and parameters: {'gamma': 0.18048194940907988, 'subsample': 0.6188404595526915, 'colsample_bytree': 0.6844940968858618, 'alpha': 1.3215252813814224, 'lambda': 0.39275606282338016}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:47:48,909] Trial 105 finished with value: 0.4761414748626403 and parameters: {'gamma': 0.00840963470202933, 'subsample': 0.9138901517642802, 'colsample_bytree': 0.7222483257941131, 'alpha': 1.2609798612705516, 'lambda': 0.283023173549418}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:47:53,710] Trial 106 finished with value: 0.4720565725857816 and parameters: {'gamma': 0.017248457311180174, 'subsample': 0.9956283403109683, 'colsample_bytree': 0.599177331518879, 'alpha': 1.793349309809519, 'lambda': 0.015909886562237907}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:02,915] Trial 107 finished with value: 0.45751762121539596 and parameters: {'gamma': 0.6460519603475137, 'subsample': 0.9131508920436484, 'colsample_bytree': 0.7186222048367127, 'alpha': 1.2699731051702707, 'lambda': 0.28276780828696424}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:14,074] Trial 108 finished with value: 0.4511055208559198 and parameters: {'gamma': 0.9190011686852043, 'subsample': 0.6206248670177474, 'colsample_bytree': 0.6862264002156798, 'alpha': 2.525536684748065, 'lambda': 0.5127479105054994}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:15,509] Trial 109 finished with value: 0.43019698893638036 and parameters: {'gamma': 0.22942789554057685, 'subsample': 0.8976511367800196, 'colsample_bytree': 0.32231661809565404, 'alpha': 6.532839827815929, 'lambda': 0.3281399703402277}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:16,598] Trial 110 finished with value: 0.4780297275845198 and parameters: {'gamma': 0.4326493386658289, 'subsample': 0.8604279301672917, 'colsample_bytree': 0.767672467287027, 'alpha': 0.5817680618095608, 'lambda': 0.8751593698451365}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:17,626] Trial 111 finished with value: 0.4644800428688967 and parameters: {'gamma': 0.470857673665427, 'subsample': 0.930711505357672, 'colsample_bytree': 0.7663108411595706, 'alpha': 0.5439375172287314, 'lambda': 0.8921929165069316}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:18,543] Trial 112 finished with value: 0.4532747036357893 and parameters: {'gamma': 0.7514508830950022, 'subsample': 0.8620313299510699, 'colsample_bytree': 0.6447769637927183, 'alpha': 0.9078157157674391, 'lambda': 0.6370238974792666}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:20,188] Trial 113 finished with value: 0.48648004336507134 and parameters: {'gamma': 0.19833245930986998, 'subsample': 0.48227188582165403, 'colsample_bytree': 0.7156548852509873, 'alpha': 0.31158959773270717, 'lambda': 0.9089436276552169}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:21,793] Trial 114 finished with value: 0.48100029420910373 and parameters: {'gamma': 0.4157314541007839, 'subsample': 0.5103030763751077, 'colsample_bytree': 0.7963498672158098, 'alpha': 0.3119892444636311, 'lambda': 0.8246029189491948}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:23,270] Trial 115 finished with value: 0.47584039029521613 and parameters: {'gamma': 0.44369659828622365, 'subsample': 0.47990844112646514, 'colsample_bytree': 0.7958300751690031, 'alpha': 0.25049789478896844, 'lambda': 0.8036843367883639}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:24,903] Trial 116 finished with value: 0.4831533494451513 and parameters: {'gamma': 0.2501793133486302, 'subsample': 0.522134903672654, 'colsample_bytree': 0.8337045192373782, 'alpha': 0.1676018457482127, 'lambda': 0.48356668088827104}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:26,297] Trial 117 finished with value: 0.47116679891134733 and parameters: {'gamma': 1.1394169840470005, 'subsample': 0.40877543325350674, 'colsample_bytree': 0.8318152936810219, 'alpha': 0.02045433353428458, 'lambda': 0.5291840449172875}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:27,046] Trial 118 finished with value: 0.4335362566111501 and parameters: {'gamma': 5.371450413315905, 'subsample': 0.45805355461791875, 'colsample_bytree': 0.8688426274200036, 'alpha': 0.4745430013424803, 'lambda': 1.1243189606181316}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:28,577] Trial 119 finished with value: 0.4758578439751555 and parameters: {'gamma': 0.26293226404708225, 'subsample': 0.5062467584095816, 'colsample_bytree': 0.777612911089365, 'alpha': 0.19003486818859022, 'lambda': 0.926563415737744}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:29,831] Trial 120 finished with value: 0.4485369288406186 and parameters: {'gamma': 1.6389422790800379, 'subsample': 0.4524822301635384, 'colsample_bytree': 0.8473429404565985, 'alpha': 0.713065405938847, 'lambda': 1.4155610193693093}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:31,115] Trial 121 finished with value: 0.4764456912055704 and parameters: {'gamma': 0.6082105872751826, 'subsample': 0.5305116285668985, 'colsample_bytree': 0.7530192985733966, 'alpha': 0.30937056039360966, 'lambda': 0.6729377934898659}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:32,612] Trial 122 finished with value: 0.4846551063743505 and parameters: {'gamma': 0.22080315263179007, 'subsample': 0.5613896065336637, 'colsample_bytree': 0.8162939989800739, 'alpha': 0.5690539803459975, 'lambda': 0.23025066753438006}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:33,767] Trial 123 finished with value: 0.471199519035642 and parameters: {'gamma': 0.8555291788522971, 'subsample': 0.5567094014939376, 'colsample_bytree': 0.8006969226313698, 'alpha': 0.4185055504289816, 'lambda': 0.30041374236817797}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:35,196] Trial 124 finished with value: 0.4788397450071628 and parameters: {'gamma': 0.3631420055548262, 'subsample': 0.5926854236697133, 'colsample_bytree': 0.8950524625769491, 'alpha': 0.019849750373138297, 'lambda': 0.10778570725947667}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:36,198] Trial 125 finished with value: 0.47373638381078503 and parameters: {'gamma': 1.261412389147405, 'subsample': 0.5930823343900664, 'colsample_bytree': 0.9157801100711749, 'alpha': 0.007431508072361592, 'lambda': 0.02443923994367514}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:37,643] Trial 126 finished with value: 0.48459897791506684 and parameters: {'gamma': 0.28660875607247943, 'subsample': 0.5122880654311942, 'colsample_bytree': 0.8915018903748554, 'alpha': 0.5770368309103171, 'lambda': 0.6172637015853955}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:39,160] Trial 127 finished with value: 0.48292097758254837 and parameters: {'gamma': 0.37280448794611215, 'subsample': 0.5122590501701979, 'colsample_bytree': 0.8957339257554772, 'alpha': 0.5480362781040424, 'lambda': 0.44567852522921786}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:40,627] Trial 128 finished with value: 0.47648459605404725 and parameters: {'gamma': 0.23240127429538743, 'subsample': 0.4980040661276559, 'colsample_bytree': 0.9096601863080745, 'alpha': 0.24739796651988835, 'lambda': 0.38096203299162035}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:41,865] Trial 129 finished with value: 0.4649567301246776 and parameters: {'gamma': 0.7988838946028953, 'subsample': 0.3777838662426268, 'colsample_bytree': 0.8944845512959319, 'alpha': 0.8644637976316943, 'lambda': 0.6036366043067911}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:43,213] Trial 130 finished with value: 0.47959775773970276 and parameters: {'gamma': 0.5983335514190189, 'subsample': 0.5702000414532433, 'colsample_bytree': 0.88128112748442, 'alpha': 0.5197966425327257, 'lambda': 0.18690908977892376}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:44,470] Trial 131 finished with value: 0.4843013905719495 and parameters: {'gamma': 0.6003495810893058, 'subsample': 0.5419297441364063, 'colsample_bytree': 0.9322033665053794, 'alpha': 0.5557600118076247, 'lambda': 0.16861922525398726}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:45,815] Trial 132 finished with value: 0.47774198460975553 and parameters: {'gamma': 0.554589939194557, 'subsample': 0.5351578599801196, 'colsample_bytree': 0.9609808427562783, 'alpha': 0.5809279829024363, 'lambda': 0.44007447197092353}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:47,133] Trial 133 finished with value: 0.4842916404511695 and parameters: {'gamma': 0.6779164883560029, 'subsample': 0.5658419829406798, 'colsample_bytree': 0.8617051188646815, 'alpha': 0.42458078237452934, 'lambda': 0.20023045340113715}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:48:51,926] Trial 134 finished with value: 0.4810697443243824 and parameters: {'gamma': 0.22998292996623676, 'subsample': 0.5168808255922361, 'colsample_bytree': 0.9747974503135122, 'alpha': 0.8334091704837361, 'lambda': 0.006108307440233385}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:09,261] Trial 135 finished with value: 0.48290692672103774 and parameters: {'gamma': 0.23707865096301112, 'subsample': 0.49589925467530516, 'colsample_bytree': 0.9858120611654351, 'alpha': 0.2855920165192391, 'lambda': 0.7014381122738778}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:16,274] Trial 136 finished with value: 0.46360823584437516 and parameters: {'gamma': 0.992192093900161, 'subsample': 0.5459134720229003, 'colsample_bytree': 0.9987003772954881, 'alpha': 0.7929863219839058, 'lambda': 0.6369445701540093}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:17,764] Trial 137 finished with value: 0.43988780591507987 and parameters: {'gamma': 0.1985646929441369, 'subsample': 0.48803774121139926, 'colsample_bytree': 0.9444482437714244, 'alpha': 7.942167519819982, 'lambda': 0.22213382459029243}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:19,208] Trial 138 finished with value: 0.4709809788609653 and parameters: {'gamma': 0.7647891234133458, 'subsample': 0.46172604835430375, 'colsample_bytree': 0.9838922515357442, 'alpha': 0.185231386584371, 'lambda': 0.40914378069001966}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:21,224] Trial 139 finished with value: 0.47907756192121853 and parameters: {'gamma': 0.21624470409231195, 'subsample': 0.5162066560567851, 'colsample_bytree': 0.9300957583801951, 'alpha': 0.4560940500757118, 'lambda': 1.0529375110372197}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:22,599] Trial 140 finished with value: 0.46138387822005456 and parameters: {'gamma': 1.340450499352142, 'subsample': 0.43104799254593096, 'colsample_bytree': 0.9736588531728525, 'alpha': 0.7231090476235784, 'lambda': 0.012068493638186947}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:24,203] Trial 141 finished with value: 0.4803583222481512 and parameters: {'gamma': 0.42281310417374085, 'subsample': 0.5205169264933915, 'colsample_bytree': 0.9217895615828775, 'alpha': 0.3045706813189661, 'lambda': 0.7198014438912368}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:25,674] Trial 142 finished with value: 0.47410931087378494 and parameters: {'gamma': 0.39803902907988914, 'subsample': 0.547271032604144, 'colsample_bytree': 0.8551132111736492, 'alpha': 0.8781024085839312, 'lambda': 0.4662860249149161}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:27,077] Trial 143 finished with value: 0.47389379171331086 and parameters: {'gamma': 0.62532197168117, 'subsample': 0.49502319044999304, 'colsample_bytree': 0.9528124302033186, 'alpha': 0.39879308696674454, 'lambda': 0.9277224716298771}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:28,683] Trial 144 finished with value: 0.48545725594424494 and parameters: {'gamma': 0.23932453815021099, 'subsample': 0.5674560201831456, 'colsample_bytree': 0.8165475616250941, 'alpha': 0.6579340192671836, 'lambda': 0.005221746083444112}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:30,761] Trial 145 finished with value: 0.47996043373430824 and parameters: {'gamma': 0.22536315439418816, 'subsample': 0.5691415004047327, 'colsample_bytree': 0.8687185436067301, 'alpha': 0.6538798994316377, 'lambda': 0.00862957826629418}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:32,142] Trial 146 finished with value: 0.4717489903796559 and parameters: {'gamma': 0.8295194466653789, 'subsample': 0.6210186008104744, 'colsample_bytree': 0.8296964601507841, 'alpha': 0.9289828555586402, 'lambda': 0.27993267826028523}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:33,626] Trial 147 finished with value: 0.46938223557346254 and parameters: {'gamma': 0.6464447789882596, 'subsample': 0.6086084508405408, 'colsample_bytree': 0.9346342975617726, 'alpha': 0.14689633312979233, 'lambda': 0.22146176174195895}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:35,488] Trial 148 finished with value: 0.48241732329177933 and parameters: {'gamma': 0.18255906462262467, 'subsample': 0.5851598218602111, 'colsample_bytree': 0.8130050948447054, 'alpha': 0.637816333104484, 'lambda': 0.5437906780712761}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:36,870] Trial 149 finished with value: 0.4619706585268909 and parameters: {'gamma': 1.029702719197894, 'subsample': 0.5835119345810166, 'colsample_bytree': 0.8123441243065035, 'alpha': 0.5640274518879475, 'lambda': 1.2926871945977814}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:38,877] Trial 150 finished with value: 0.48167521600351076 and parameters: {'gamma': 0.017062434290142647, 'subsample': 0.5593613509515618, 'colsample_bytree': 0.8431453151102346, 'alpha': 0.008070357490591706, 'lambda': 0.484560762699364}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:40,563] Trial 151 finished with value: 0.47873200268963245 and parameters: {'gamma': 0.16533207169497235, 'subsample': 0.5496908704856217, 'colsample_bytree': 0.8418148877567229, 'alpha': 0.02018100561755992, 'lambda': 0.6706365902036269}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:42,305] Trial 152 finished with value: 0.48756475064202925 and parameters: {'gamma': 0.017625837704452085, 'subsample': 0.5612862377285519, 'colsample_bytree': 0.8747387025612059, 'alpha': 0.42796120564604956, 'lambda': 0.4838760780275464}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:44,145] Trial 153 finished with value: 0.48002000022616503 and parameters: {'gamma': 0.4342874337574591, 'subsample': 0.6009672647757323, 'colsample_bytree': 0.8599564960108497, 'alpha': 0.33303885861090143, 'lambda': 0.4866114995638938}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:45,938] Trial 154 finished with value: 0.48919723470109866 and parameters: {'gamma': 0.01680586081300403, 'subsample': 0.6363465922601232, 'colsample_bytree': 0.8973771799837257, 'alpha': 0.6144326994907476, 'lambda': 0.3607630581998096}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:48,026] Trial 155 finished with value: 0.4840234741470768 and parameters: {'gamma': 0.3036670241804884, 'subsample': 0.6362747802919833, 'colsample_bytree': 0.8874638622422104, 'alpha': 0.619524340453893, 'lambda': 0.27542017892123083}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:49,552] Trial 156 finished with value: 0.47791399533631485 and parameters: {'gamma': 0.5515167650536392, 'subsample': 0.6375562324260713, 'colsample_bytree': 0.88680803808993, 'alpha': 0.4797763419904546, 'lambda': 0.2689174644628576}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:51,426] Trial 157 finished with value: 0.4770812466014712 and parameters: {'gamma': 0.366047913698596, 'subsample': 0.4705279682282675, 'colsample_bytree': 0.8923430142078218, 'alpha': 0.18678626525182498, 'lambda': 0.7581147341227181}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:53,877] Trial 158 finished with value: 0.4738812119042265 and parameters: {'gamma': 0.6749558179579533, 'subsample': 0.43019972614786484, 'colsample_bytree': 0.9117774991670434, 'alpha': 0.6244339329494611, 'lambda': 0.2145606534199752}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:49:57,240] Trial 159 finished with value: 0.4827329776113537 and parameters: {'gamma': 0.3003352245745672, 'subsample': 0.6797603006793803, 'colsample_bytree': 0.8668914811191334, 'alpha': 0.40006948831996236, 'lambda': 1.1538482722766243}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:02,184] Trial 160 finished with value: 0.46143962060198573 and parameters: {'gamma': 0.8609187013272273, 'subsample': 0.6280553209766571, 'colsample_bytree': 0.904667934492356, 'alpha': 1.125463579155867, 'lambda': 0.31808730707638544}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:18,259] Trial 161 finished with value: 0.48135050026925613 and parameters: {'gamma': 0.32067892865657077, 'subsample': 0.6549719359729864, 'colsample_bytree': 0.8708872051064249, 'alpha': 0.3324231343415589, 'lambda': 1.152692660768653}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:20,052] Trial 162 finished with value: 0.48662425683762134 and parameters: {'gamma': 0.0039757834565859185, 'subsample': 0.6697634026936445, 'colsample_bytree': 0.9355567987650412, 'alpha': 0.7238539750460314, 'lambda': 0.9677281863152799}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:21,674] Trial 163 finished with value: 0.48920534810135763 and parameters: {'gamma': 0.009790209781818715, 'subsample': 0.5406524869320977, 'colsample_bytree': 0.9236678642281685, 'alpha': 0.7744734662828029, 'lambda': 0.888056271095151}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:23,547] Trial 164 finished with value: 0.48823132988451046 and parameters: {'gamma': 0.000970631560993581, 'subsample': 0.6083772904734839, 'colsample_bytree': 0.9262654835729698, 'alpha': 0.8129333245232587, 'lambda': 0.8776066984354081}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:25,613] Trial 165 finished with value: 0.4770271442423703 and parameters: {'gamma': 0.021653866695982515, 'subsample': 0.6409489341303127, 'colsample_bytree': 0.9282603410550792, 'alpha': 0.8887944708278208, 'lambda': 0.9211156544960757}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:27,229] Trial 166 finished with value: 0.4882622502939723 and parameters: {'gamma': 0.002037242128003128, 'subsample': 0.6185358980871648, 'colsample_bytree': 0.951097614049222, 'alpha': 1.3423516474173494, 'lambda': 0.9235845653304832}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:28,822] Trial 167 finished with value: 0.4714319117371983 and parameters: {'gamma': 0.029598248918401744, 'subsample': 0.5962634105593949, 'colsample_bytree': 0.9549196665635455, 'alpha': 1.4647759891012302, 'lambda': 0.9600389414796302}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:30,358] Trial 168 finished with value: 0.45842487783501085 and parameters: {'gamma': 0.005219836627944448, 'subsample': 0.6079051164506484, 'colsample_bytree': 0.9360081074297445, 'alpha': 4.773586128599625, 'lambda': 1.2964162463332896}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:31,120] Trial 169 finished with value: 0.43601763340723304 and parameters: {'gamma': 3.569436511004796, 'subsample': 0.6954375258194272, 'colsample_bytree': 0.9578269566355292, 'alpha': 1.2597814787963495, 'lambda': 0.6625853157666974}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:32,321] Trial 170 finished with value: 0.4738896373270712 and parameters: {'gamma': 0.5336716046097219, 'subsample': 0.6637296446954938, 'colsample_bytree': 0.9115986992535807, 'alpha': 1.0677975513707358, 'lambda': 0.16870156530755276}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:34,119] Trial 171 finished with value: 0.48134145311345444 and parameters: {'gamma': 0.15554209165865412, 'subsample': 0.5614703007831839, 'colsample_bytree': 0.8800851509485996, 'alpha': 0.7521963727616845, 'lambda': 0.8267009295182467}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:35,754] Trial 172 finished with value: 0.4813576422924323 and parameters: {'gamma': 0.19745288164599992, 'subsample': 0.6218766014742095, 'colsample_bytree': 0.8341098852491791, 'alpha': 0.7458537787345816, 'lambda': 0.41496540765517587}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:37,170] Trial 173 finished with value: 0.4688151518748772 and parameters: {'gamma': 0.46938205473684075, 'subsample': 0.5711535900551411, 'colsample_bytree': 0.9044838847914094, 'alpha': 1.0026900215836767, 'lambda': 0.5908920240555158}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:38,959] Trial 174 finished with value: 0.4799539849754938 and parameters: {'gamma': 0.01013704226444213, 'subsample': 0.5312989148150534, 'colsample_bytree': 0.9500638935489321, 'alpha': 0.6831602630392223, 'lambda': 0.16187250057366734}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:40,586] Trial 175 finished with value: 0.48252365722446516 and parameters: {'gamma': 0.3547974783967746, 'subsample': 0.6107284647122825, 'colsample_bytree': 0.9245879926570274, 'alpha': 0.46017569412477266, 'lambda': 1.062697043477417}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:42,240] Trial 176 finished with value: 0.46300886337517105 and parameters: {'gamma': 0.6271725244535106, 'subsample': 0.5428580644477485, 'colsample_bytree': 0.8604452857730442, 'alpha': 1.2317040667159798, 'lambda': 0.013257666488476816}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:43,840] Trial 177 finished with value: 0.4842967106539679 and parameters: {'gamma': 0.18911267477998478, 'subsample': 0.5818671252700082, 'colsample_bytree': 0.8836654933146444, 'alpha': 0.8630273589214618, 'lambda': 0.3513078985342732}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:44,578] Trial 178 finished with value: 0.40586993969999063 and parameters: {'gamma': 7.341068588426206, 'subsample': 0.641662287768854, 'colsample_bytree': 0.41346717169142705, 'alpha': 1.6458512361668762, 'lambda': 0.8528815497501043}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:46,091] Trial 179 finished with value: 0.4783190352008484 and parameters: {'gamma': 0.18638461649141164, 'subsample': 0.5780594859756863, 'colsample_bytree': 0.8831299267123903, 'alpha': 0.8825922771146664, 'lambda': 0.31208261527685066}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:47,380] Trial 180 finished with value: 0.4717957370180187 and parameters: {'gamma': 0.49373605429689754, 'subsample': 0.6696948933886856, 'colsample_bytree': 0.9364611242431322, 'alpha': 1.3614275912123337, 'lambda': 0.6317704059957905}. Best is trial 91 with value: 0.4894052449165375.\n",
      "[I 2024-02-06 15:50:49,554] Trial 181 finished with value: 0.4918889505448801 and parameters: {'gamma': 0.0010325372141307978, 'subsample': 0.5900129604496058, 'colsample_bytree': 0.8250497521138986, 'alpha': 0.5450019039944709, 'lambda': 0.41346895795681216}. Best is trial 181 with value: 0.4918889505448801.\n",
      "[I 2024-02-06 15:50:51,036] Trial 182 finished with value: 0.4370390117720318 and parameters: {'gamma': 4.066121556181202, 'subsample': 0.5927155794730128, 'colsample_bytree': 0.8555226147972679, 'alpha': 0.7270403162434541, 'lambda': 0.3869708838083724}. Best is trial 181 with value: 0.4918889505448801.\n",
      "[I 2024-02-06 15:51:03,979] Trial 183 finished with value: 0.4758141563462328 and parameters: {'gamma': 0.0014880504673496085, 'subsample': 0.6235962526933381, 'colsample_bytree': 0.811357070232808, 'alpha': 1.0337720350701871, 'lambda': 0.18256265380869277}. Best is trial 181 with value: 0.4918889505448801.\n",
      "[I 2024-02-06 15:51:18,670] Trial 184 finished with value: 0.4864836824661347 and parameters: {'gamma': 0.2118078654489532, 'subsample': 0.561520015499208, 'colsample_bytree': 0.8978332342594388, 'alpha': 0.5214990772139632, 'lambda': 0.7233566469391195}. Best is trial 181 with value: 0.4918889505448801.\n",
      "[I 2024-02-06 15:51:20,243] Trial 185 finished with value: 0.4837087922314823 and parameters: {'gamma': 0.18830144428882006, 'subsample': 0.5743870339616002, 'colsample_bytree': 0.8992900562584178, 'alpha': 0.5214768816768361, 'lambda': 0.5870233233590041}. Best is trial 181 with value: 0.4918889505448801.\n",
      "[I 2024-02-06 15:51:21,887] Trial 186 finished with value: 0.48383688060040725 and parameters: {'gamma': 0.45991072058129034, 'subsample': 0.5467475208033243, 'colsample_bytree': 0.9221747626951505, 'alpha': 0.5540173908976922, 'lambda': 0.35811385045149285}. Best is trial 181 with value: 0.4918889505448801.\n",
      "[I 2024-02-06 15:51:23,996] Trial 187 finished with value: 0.48436313412806525 and parameters: {'gamma': 0.18286911140842127, 'subsample': 0.5646455226911531, 'colsample_bytree': 0.8742940354084789, 'alpha': 0.9012879570673993, 'lambda': 0.013235658677706819}. Best is trial 181 with value: 0.4918889505448801.\n",
      "[I 2024-02-06 15:51:25,752] Trial 188 finished with value: 0.49247618359105655 and parameters: {'gamma': 0.01816462815301706, 'subsample': 0.5664042399476649, 'colsample_bytree': 0.8741574900972948, 'alpha': 0.917730343271103, 'lambda': 0.04730917854355529}. Best is trial 188 with value: 0.49247618359105655.\n",
      "[I 2024-02-06 15:51:26,537] Trial 189 finished with value: 0.4429993171649639 and parameters: {'gamma': 2.907821497249204, 'subsample': 0.5946076948642296, 'colsample_bytree': 0.91617371171929, 'alpha': 1.0258043647473771, 'lambda': 0.09920901143204175}. Best is trial 188 with value: 0.49247618359105655.\n",
      "[I 2024-02-06 15:51:28,123] Trial 190 finished with value: 0.48033980612262844 and parameters: {'gamma': 0.145823780486632, 'subsample': 0.5378345569162569, 'colsample_bytree': 0.969877411997115, 'alpha': 1.2512476046570196, 'lambda': 0.719075637277796}. Best is trial 188 with value: 0.49247618359105655.\n",
      "[I 2024-02-06 15:51:29,937] Trial 191 finished with value: 0.4904644079615548 and parameters: {'gamma': 0.011308159221358582, 'subsample': 0.5607084873984424, 'colsample_bytree': 0.8464779526964316, 'alpha': 0.8287511536162483, 'lambda': 0.045089758909551796}. Best is trial 188 with value: 0.49247618359105655.\n",
      "[I 2024-02-06 15:51:31,473] Trial 192 finished with value: 0.4913300556641524 and parameters: {'gamma': 0.017378667269097425, 'subsample': 0.5517884430207026, 'colsample_bytree': 0.875729533928722, 'alpha': 0.8633888367156677, 'lambda': 0.08284669155116861}. Best is trial 188 with value: 0.49247618359105655.\n",
      "[I 2024-02-06 15:51:33,191] Trial 193 finished with value: 0.4769142592289417 and parameters: {'gamma': 0.02734966820473579, 'subsample': 0.5560872670611503, 'colsample_bytree': 0.8222761329320464, 'alpha': 1.1108009002741324, 'lambda': 0.014859216745796933}. Best is trial 188 with value: 0.49247618359105655.\n",
      "[I 2024-02-06 15:51:34,551] Trial 194 finished with value: 0.4800329546668469 and parameters: {'gamma': 0.3467153961963431, 'subsample': 0.5617146670560267, 'colsample_bytree': 0.8407872385915247, 'alpha': 0.8523226059766831, 'lambda': 0.5058139881595528}. Best is trial 188 with value: 0.49247618359105655.\n",
      "[I 2024-02-06 15:51:35,270] Trial 195 finished with value: 0.44273330287867063 and parameters: {'gamma': 4.650687974558814, 'subsample': 0.5212536667318085, 'colsample_bytree': 0.8722033769635884, 'alpha': 0.6941082796324214, 'lambda': 0.09164678750089811}. Best is trial 188 with value: 0.49247618359105655.\n",
      "[I 2024-02-06 15:51:36,843] Trial 196 finished with value: 0.49292639297872975 and parameters: {'gamma': 0.023976468801481056, 'subsample': 0.6060795915678104, 'colsample_bytree': 0.9033045357066087, 'alpha': 0.3338215796869078, 'lambda': 0.03644283287948819}. Best is trial 196 with value: 0.49292639297872975.\n",
      "[I 2024-02-06 15:51:38,485] Trial 197 finished with value: 0.48422228004136975 and parameters: {'gamma': 0.03089865168046, 'subsample': 0.6104926043281396, 'colsample_bytree': 0.9016662416279857, 'alpha': 0.28246012665205344, 'lambda': 0.4821942433719949}. Best is trial 196 with value: 0.49292639297872975.\n",
      "[I 2024-02-06 15:51:39,799] Trial 198 finished with value: 0.44598052879787636 and parameters: {'gamma': 0.023846980890365202, 'subsample': 0.25589528782832094, 'colsample_bytree': 0.7885471792024272, 'alpha': 4.017189552962937, 'lambda': 0.0348115558806939}. Best is trial 196 with value: 0.49292639297872975.\n",
      "[I 2024-02-06 15:51:41,449] Trial 199 finished with value: 0.4821916953155308 and parameters: {'gamma': 0.006539558561642622, 'subsample': 0.5986780785188677, 'colsample_bytree': 0.8533182254565177, 'alpha': 1.4049920971790915, 'lambda': 1.0017657118718795}. Best is trial 196 with value: 0.49292639297872975.\n",
      "[I 2024-02-06 15:51:42,786] Trial 200 finished with value: 0.4791733206282499 and parameters: {'gamma': 0.3425704759039796, 'subsample': 0.5674032384550277, 'colsample_bytree': 0.8189214488552801, 'alpha': 0.8650580437611756, 'lambda': 0.36367774931080743}. Best is trial 196 with value: 0.49292639297872975.\n",
      "[I 2024-02-06 15:51:44,268] Trial 201 finished with value: 0.4796277547340821 and parameters: {'gamma': 0.21033915337707965, 'subsample': 0.5483988825455022, 'colsample_bytree': 0.940619063277291, 'alpha': 0.5842273133406152, 'lambda': 0.009348627767712392}. Best is trial 196 with value: 0.49292639297872975.\n",
      "[I 2024-02-06 15:51:45,622] Trial 202 finished with value: 0.4824244569597587 and parameters: {'gamma': 0.4266399518821202, 'subsample': 0.5375298557476155, 'colsample_bytree': 0.9065701028348758, 'alpha': 0.3634208231230427, 'lambda': 0.6389879591664209}. Best is trial 196 with value: 0.49292639297872975.\n",
      "[I 2024-02-06 15:51:47,152] Trial 203 finished with value: 0.48936769411156694 and parameters: {'gamma': 0.16115426873519118, 'subsample': 0.5816632387400942, 'colsample_bytree': 0.8755969018861471, 'alpha': 0.45985221407536814, 'lambda': 0.2948128457108026}. Best is trial 196 with value: 0.49292639297872975.\n",
      "[I 2024-02-06 15:51:48,817] Trial 204 finished with value: 0.4914919425385498 and parameters: {'gamma': 0.20984534438831598, 'subsample': 0.5848733708521046, 'colsample_bytree': 0.846284377388393, 'alpha': 0.2481007003779831, 'lambda': 0.28812654427398243}. Best is trial 196 with value: 0.49292639297872975.\n",
      "[I 2024-02-06 15:52:00,960] Trial 205 finished with value: 0.48471433333023667 and parameters: {'gamma': 0.206205592470752, 'subsample': 0.5849373997690519, 'colsample_bytree': 0.8777012734105059, 'alpha': 0.26440362310451765, 'lambda': 0.27212924538994854}. Best is trial 196 with value: 0.49292639297872975.\n",
      "[I 2024-02-06 15:52:15,920] Trial 206 finished with value: 0.4848168030671075 and parameters: {'gamma': 0.003986801859837158, 'subsample': 0.585534011524507, 'colsample_bytree': 0.8475922016863526, 'alpha': 0.23592043628817005, 'lambda': 0.8417112812345361}. Best is trial 196 with value: 0.49292639297872975.\n",
      "[I 2024-02-06 15:52:17,729] Trial 207 finished with value: 0.4993161143601864 and parameters: {'gamma': 0.022248396655920568, 'subsample': 0.5858388593269531, 'colsample_bytree': 0.839008217365743, 'alpha': 0.20183746095172217, 'lambda': 0.835483783385556}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:19,408] Trial 208 finished with value: 0.47977311740720646 and parameters: {'gamma': 0.032695481185537854, 'subsample': 0.5830011027614015, 'colsample_bytree': 0.8538363641749269, 'alpha': 0.12301804581426144, 'lambda': 1.4233981048143454}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:20,939] Trial 209 finished with value: 0.4821334546813169 and parameters: {'gamma': 0.4426939426785996, 'subsample': 0.6063658761988358, 'colsample_bytree': 0.8373612140573761, 'alpha': 0.16764840068928483, 'lambda': 0.8696954971488257}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:22,683] Trial 210 finished with value: 0.48744211440806456 and parameters: {'gamma': 0.012525492479307895, 'subsample': 0.5854226962029077, 'colsample_bytree': 0.868071461763313, 'alpha': 0.19956583926575433, 'lambda': 1.0679720898447718}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:24,340] Trial 211 finished with value: 0.4907820629738538 and parameters: {'gamma': 0.009505967054503711, 'subsample': 0.5855926412868363, 'colsample_bytree': 0.8677299842085306, 'alpha': 0.24294484201166328, 'lambda': 1.1539197334648754}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:26,194] Trial 212 finished with value: 0.4824080251133241 and parameters: {'gamma': 0.006502836515363755, 'subsample': 0.6039878361010375, 'colsample_bytree': 0.8466296660873732, 'alpha': 0.035534886837256585, 'lambda': 9.870264829362771}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:28,048] Trial 213 finished with value: 0.48916661978763387 and parameters: {'gamma': 0.002269337946644103, 'subsample': 0.628742676732443, 'colsample_bytree': 0.8634113132893375, 'alpha': 0.35708053168279597, 'lambda': 1.1782732073851268}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:29,643] Trial 214 finished with value: 0.47923612288888623 and parameters: {'gamma': 0.26111738662821193, 'subsample': 0.6371629576216375, 'colsample_bytree': 0.8734404182409665, 'alpha': 0.41893921567776826, 'lambda': 1.164746643652152}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:31,641] Trial 215 finished with value: 0.48821363510325555 and parameters: {'gamma': 0.003101379685941248, 'subsample': 0.6216716754311528, 'colsample_bytree': 0.8949296105172921, 'alpha': 0.3388597605773785, 'lambda': 1.3778544717342684}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:33,242] Trial 216 finished with value: 0.4847990592385861 and parameters: {'gamma': 0.17095917383814285, 'subsample': 0.6239152942802367, 'colsample_bytree': 0.9064147837393272, 'alpha': 0.3299559208878125, 'lambda': 1.275081414077605}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:34,833] Trial 217 finished with value: 0.48213641895422593 and parameters: {'gamma': 0.004109344013524186, 'subsample': 0.614858223209284, 'colsample_bytree': 0.8937387485771211, 'alpha': 0.18358663810765327, 'lambda': 1.0232348605624473}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:36,146] Trial 218 finished with value: 0.48415983718515315 and parameters: {'gamma': 0.418619652213459, 'subsample': 0.6515475032411453, 'colsample_bytree': 0.868032401516818, 'alpha': 0.3799434685149832, 'lambda': 1.5641701357320503}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:37,780] Trial 219 finished with value: 0.48401378623329755 and parameters: {'gamma': 0.36920651365558327, 'subsample': 0.6068493819346136, 'colsample_bytree': 0.9162225847029511, 'alpha': 0.03072967967109938, 'lambda': 1.3675626220881123}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:39,757] Trial 220 finished with value: 0.490127441024481 and parameters: {'gamma': 0.001305281752721387, 'subsample': 0.6321783271249483, 'colsample_bytree': 0.8923445750627144, 'alpha': 0.4382350334554339, 'lambda': 1.1546978860001471}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:41,517] Trial 221 finished with value: 0.48863940120101573 and parameters: {'gamma': 0.006071726171028956, 'subsample': 0.6233898036304916, 'colsample_bytree': 0.8844922425291238, 'alpha': 0.43963375359991314, 'lambda': 1.1048844205344046}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:43,498] Trial 222 finished with value: 0.4921807932410637 and parameters: {'gamma': 0.027422574106899557, 'subsample': 0.6324323831752551, 'colsample_bytree': 0.8883405498465607, 'alpha': 0.45650403732490885, 'lambda': 1.1320667119789862}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:45,128] Trial 223 finished with value: 0.48761065823018596 and parameters: {'gamma': 0.02248857893155366, 'subsample': 0.6334416684295979, 'colsample_bytree': 0.879531513704536, 'alpha': 0.40984388911111086, 'lambda': 1.1927615558160038}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:47,154] Trial 224 finished with value: 0.4850108334292731 and parameters: {'gamma': 0.01174457086578922, 'subsample': 0.6319613980417141, 'colsample_bytree': 0.8682487251603364, 'alpha': 0.41716100685885915, 'lambda': 1.1887320783894746}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:52:49,670] Trial 225 finished with value: 0.48626132569825303 and parameters: {'gamma': 0.002518464429207525, 'subsample': 0.6446923237978115, 'colsample_bytree': 0.8873500426039765, 'alpha': 0.21213742049626352, 'lambda': 1.497448180235352}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:04,646] Trial 226 finished with value: 0.4843649743443408 and parameters: {'gamma': 0.19028740495809976, 'subsample': 0.6214655735243187, 'colsample_bytree': 0.857131902416279, 'alpha': 0.4156220980971004, 'lambda': 1.3836803249273366}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:15,855] Trial 227 finished with value: 0.4864238174700436 and parameters: {'gamma': 0.36044976488865316, 'subsample': 0.5955914437090102, 'colsample_bytree': 0.8819134575128281, 'alpha': 0.013679682430894236, 'lambda': 1.1883502613107568}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:17,212] Trial 228 finished with value: 0.4904512686415743 and parameters: {'gamma': 0.3021716198245939, 'subsample': 0.6600874211592828, 'colsample_bytree': 0.83270938495624, 'alpha': 0.2310359346393637, 'lambda': 1.0916543208161444}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:18,403] Trial 229 finished with value: 0.4823959873404173 and parameters: {'gamma': 0.516205683496452, 'subsample': 0.6480219655609518, 'colsample_bytree': 0.8308173435073559, 'alpha': 0.489761237949479, 'lambda': 1.2803793343231729}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:20,156] Trial 230 finished with value: 0.48885735600961844 and parameters: {'gamma': 0.2280976554779977, 'subsample': 0.6771003763747752, 'colsample_bytree': 0.8390746160138461, 'alpha': 0.7278353069045604, 'lambda': 1.1575795794740895}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:21,786] Trial 231 finished with value: 0.4852087829565095 and parameters: {'gamma': 0.20789220643109418, 'subsample': 0.6829423798381443, 'colsample_bytree': 0.8447962165625437, 'alpha': 0.6749644076080901, 'lambda': 1.6115998954247985}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:23,338] Trial 232 finished with value: 0.4915178364951838 and parameters: {'gamma': 0.20265761674596353, 'subsample': 0.712314319138274, 'colsample_bytree': 0.8553128480508864, 'alpha': 0.4337797306323794, 'lambda': 1.0904683942911475}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:24,609] Trial 233 finished with value: 0.4816107173995578 and parameters: {'gamma': 0.355019734380659, 'subsample': 0.7113565911391303, 'colsample_bytree': 0.8316700429332536, 'alpha': 0.2696366935651072, 'lambda': 1.1011472218319427}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:26,030] Trial 234 finished with value: 0.48453947961641036 and parameters: {'gamma': 0.20389103319636875, 'subsample': 0.6851447793446721, 'colsample_bytree': 0.8496582604189048, 'alpha': 0.6952427097166423, 'lambda': 1.3857991338215148}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:27,327] Trial 235 finished with value: 0.47799126651134993 and parameters: {'gamma': 0.5264752457021586, 'subsample': 0.6674614025114846, 'colsample_bytree': 0.8969781611514086, 'alpha': 0.5610792666935612, 'lambda': 1.778070696954866}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:28,868] Trial 236 finished with value: 0.48695471646693184 and parameters: {'gamma': 0.19419441179255117, 'subsample': 0.6548593951393452, 'colsample_bytree': 0.8566250890489472, 'alpha': 0.40103091444313976, 'lambda': 1.0250364774381457}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:30,322] Trial 237 finished with value: 0.49014810891135474 and parameters: {'gamma': 0.35414988789101587, 'subsample': 0.6301402497778361, 'colsample_bytree': 0.832768520646061, 'alpha': 0.18831877631823227, 'lambda': 1.251931420859576}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:31,845] Trial 238 finished with value: 0.4844405167762613 and parameters: {'gamma': 0.3763390721274527, 'subsample': 0.6236425880601821, 'colsample_bytree': 0.8181106815391848, 'alpha': 0.11935772576991505, 'lambda': 1.4694225994829497}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:33,066] Trial 239 finished with value: 0.47306018088749086 and parameters: {'gamma': 0.6245030199379452, 'subsample': 0.7115561438697658, 'colsample_bytree': 0.8273700219836121, 'alpha': 0.7664981875780053, 'lambda': 0.8977610870210526}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:34,453] Trial 240 finished with value: 0.48299875528393604 and parameters: {'gamma': 0.35267835654185725, 'subsample': 0.6550445276097816, 'colsample_bytree': 0.7947890735082603, 'alpha': 0.1519607509065422, 'lambda': 1.2979409171662737}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:36,092] Trial 241 finished with value: 0.48778666803499926 and parameters: {'gamma': 0.172048014877288, 'subsample': 0.6318741360750427, 'colsample_bytree': 0.8875213458642839, 'alpha': 0.39169136227534296, 'lambda': 1.1710951092496065}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:36,792] Trial 242 finished with value: 0.4194254759428402 and parameters: {'gamma': 8.418291253266796, 'subsample': 0.6333164722888017, 'colsample_bytree': 0.9201119224682808, 'alpha': 0.0015219602010510536, 'lambda': 1.0968036591096373}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:38,263] Trial 243 finished with value: 0.48899356843477976 and parameters: {'gamma': 0.1922524238705128, 'subsample': 0.6115474148256423, 'colsample_bytree': 0.8400364802265031, 'alpha': 0.5300543058209232, 'lambda': 0.8934238042321596}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:39,765] Trial 244 finished with value: 0.49174526654041417 and parameters: {'gamma': 0.20006955045697733, 'subsample': 0.605840995745661, 'colsample_bytree': 0.838414409880102, 'alpha': 0.6366231132135514, 'lambda': 0.8114280508265005}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:41,186] Trial 245 finished with value: 0.4824992335042139 and parameters: {'gamma': 0.3396851902018725, 'subsample': 0.6070965660979719, 'colsample_bytree': 0.8347732513096265, 'alpha': 0.6557895266184857, 'lambda': 0.7997207823028691}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:42,903] Trial 246 finished with value: 0.4933979614427449 and parameters: {'gamma': 0.1881257487634154, 'subsample': 0.599301058271842, 'colsample_bytree': 0.8498070015746068, 'alpha': 0.8737853821285547, 'lambda': 0.765736749163903}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:44,594] Trial 247 finished with value: 0.47064031704652004 and parameters: {'gamma': 0.5282271528889235, 'subsample': 0.5927142812837495, 'colsample_bytree': 0.8132818510934828, 'alpha': 0.5781998693909054, 'lambda': 0.6599293169740098}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:53:57,593] Trial 248 finished with value: 0.48345457534744013 and parameters: {'gamma': 0.23520890617921134, 'subsample': 0.681295532757513, 'colsample_bytree': 0.841612010183968, 'alpha': 0.8933615620416953, 'lambda': 0.7159335699370382}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:05,378] Trial 249 finished with value: 0.42995921297100603 and parameters: {'gamma': 6.031093450811236, 'subsample': 0.6620994432843184, 'colsample_bytree': 0.8010401270538952, 'alpha': 0.6005953601172899, 'lambda': 0.9442149077940055}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:13,353] Trial 250 finished with value: 0.47493611261500485 and parameters: {'gamma': 0.4122226557004999, 'subsample': 0.600906881990088, 'colsample_bytree': 0.8606395932884159, 'alpha': 0.9382063452068251, 'lambda': 0.5537275391703607}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:14,778] Trial 251 finished with value: 0.4406889403815985 and parameters: {'gamma': 0.24572493596368877, 'subsample': 0.6441994164301951, 'colsample_bytree': 0.8348844613478118, 'alpha': 8.764834098465172, 'lambda': 0.7809441033530188}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:16,333] Trial 252 finished with value: 0.4689239009025482 and parameters: {'gamma': 0.6652688825886008, 'subsample': 0.5760184954128238, 'colsample_bytree': 0.8547252830947816, 'alpha': 0.25621398496261855, 'lambda': 1.0121334636094994}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:18,051] Trial 253 finished with value: 0.48821211736401027 and parameters: {'gamma': 0.19983835258975213, 'subsample': 0.6953124367205606, 'colsample_bytree': 0.8205745918526687, 'alpha': 0.540732871092061, 'lambda': 0.33986257792648483}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:19,278] Trial 254 finished with value: 0.4779202624886512 and parameters: {'gamma': 0.4803199185913215, 'subsample': 0.7331553893216545, 'colsample_bytree': 0.8599008865206886, 'alpha': 0.8014531894818895, 'lambda': 0.5455192118416194}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:20,744] Trial 255 finished with value: 0.46190473054730413 and parameters: {'gamma': 0.18426241364173335, 'subsample': 0.6119242152640041, 'colsample_bytree': 0.8009485561920063, 'alpha': 3.2005532674839356, 'lambda': 0.8133884876414368}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:22,433] Trial 256 finished with value: 0.4841517633411133 and parameters: {'gamma': 0.39373652660606906, 'subsample': 0.5879834929654331, 'colsample_bytree': 0.8379414214032121, 'alpha': 0.4762689243815626, 'lambda': 1.0632178663766458}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:24,034] Trial 257 finished with value: 0.4915624924698822 and parameters: {'gamma': 0.17311993582588053, 'subsample': 0.6278995845864865, 'colsample_bytree': 0.8670734034285311, 'alpha': 0.2694483430582526, 'lambda': 0.22969321659783826}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:25,350] Trial 258 finished with value: 0.47995257683303943 and parameters: {'gamma': 0.6731972852773762, 'subsample': 0.6679009498829603, 'colsample_bytree': 0.8684182502779869, 'alpha': 0.2378689501706864, 'lambda': 0.21652781086816905}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:26,972] Trial 259 finished with value: 0.49322879836527284 and parameters: {'gamma': 0.19475022480826162, 'subsample': 0.6392152382106362, 'colsample_bytree': 0.8471384301293644, 'alpha': 0.27171950891663277, 'lambda': 0.3691713150041129}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:28,510] Trial 260 finished with value: 0.4891441258353445 and parameters: {'gamma': 0.33363929634112865, 'subsample': 0.6413401317380464, 'colsample_bytree': 0.8485534161933288, 'alpha': 0.010748825297522746, 'lambda': 0.2594144856513133}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:29,705] Trial 261 finished with value: 0.48951478546509475 and parameters: {'gamma': 0.5622000485604065, 'subsample': 0.6544822467385272, 'colsample_bytree': 0.8466296957089262, 'alpha': 0.004463421418336111, 'lambda': 0.35832729690596477}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:30,960] Trial 262 finished with value: 0.4739442485282399 and parameters: {'gamma': 0.7496295938814139, 'subsample': 0.6460664889235017, 'colsample_bytree': 0.8543034800530386, 'alpha': 0.025785620701798323, 'lambda': 0.37949581745047134}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:32,014] Trial 263 finished with value: 0.4418005897144078 and parameters: {'gamma': 0.515268197770042, 'subsample': 0.602137862408196, 'colsample_bytree': 0.8281096252292519, 'alpha': 5.379433140095469, 'lambda': 0.4611107801970217}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:33,250] Trial 264 finished with value: 0.48139073541521776 and parameters: {'gamma': 0.5071924974491189, 'subsample': 0.6467165819638389, 'colsample_bytree': 0.8626018414156408, 'alpha': 0.1447643094286553, 'lambda': 0.2622278315439683}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:34,676] Trial 265 finished with value: 0.47693313337522736 and parameters: {'gamma': 0.37279671284962834, 'subsample': 0.5800030435400805, 'colsample_bytree': 0.8121904464473532, 'alpha': 0.008454712487260457, 'lambda': 0.5720521274707796}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:36,094] Trial 266 finished with value: 0.48286805303291774 and parameters: {'gamma': 0.29268321282388976, 'subsample': 0.6263947520284209, 'colsample_bytree': 0.8481222904704314, 'alpha': 0.22690799969832026, 'lambda': 0.28816359492945154}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:37,416] Trial 267 finished with value: 0.45818138074498455 and parameters: {'gamma': 0.6124638723519581, 'subsample': 0.5990084828521695, 'colsample_bytree': 0.2653986927276622, 'alpha': 0.20168095115578005, 'lambda': 0.49843874992010423}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:39,206] Trial 268 finished with value: 0.4940425400616019 and parameters: {'gamma': 0.19317344338735598, 'subsample': 0.6559848615913954, 'colsample_bytree': 0.8733990048035811, 'alpha': 0.011577879532280844, 'lambda': 0.23520035096051747}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:40,589] Trial 269 finished with value: 0.4792313409783991 and parameters: {'gamma': 0.33561387815516175, 'subsample': 0.6630328051857666, 'colsample_bytree': 0.8753793476914585, 'alpha': 0.027161893604000065, 'lambda': 0.2132694890522815}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:41,366] Trial 270 finished with value: 0.4088590456390235 and parameters: {'gamma': 0.7956410687602629, 'subsample': 0.6402599889409527, 'colsample_bytree': 0.08557563290614234, 'alpha': 0.004792598872636639, 'lambda': 0.139883212397503}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:42,885] Trial 271 finished with value: 0.49188856640653655 and parameters: {'gamma': 0.16372771287604582, 'subsample': 0.6574079094055325, 'colsample_bytree': 0.8705395902170594, 'alpha': 0.2575433137958579, 'lambda': 0.3578599624320482}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:44,523] Trial 272 finished with value: 0.487215700950105 and parameters: {'gamma': 0.16124572476973947, 'subsample': 0.7010136174487233, 'colsample_bytree': 0.8723137086785405, 'alpha': 0.23799348248808946, 'lambda': 0.4339607791400245}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:46,548] Trial 273 finished with value: 0.49085662783261824 and parameters: {'gamma': 0.17905896734836674, 'subsample': 0.6640050711853286, 'colsample_bytree': 0.8990800759862446, 'alpha': 0.3146460165701749, 'lambda': 0.0029220692588053376}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:54:55,847] Trial 274 finished with value: 0.4704431094658017 and parameters: {'gamma': 0.5190798002528225, 'subsample': 0.11804119659822027, 'colsample_bytree': 0.9030869794682789, 'alpha': 0.2898247412359416, 'lambda': 0.170300023227859}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:55:12,335] Trial 275 finished with value: 0.4879354736803014 and parameters: {'gamma': 0.18930024563947986, 'subsample': 0.6886821157111126, 'colsample_bytree': 0.8870410069835001, 'alpha': 0.2765556063275666, 'lambda': 0.00770950554055852}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:55:17,349] Trial 276 finished with value: 0.4443943810523485 and parameters: {'gamma': 0.4960883013290348, 'subsample': 0.664587446561425, 'colsample_bytree': 0.9112238757091127, 'alpha': 6.217142385099469, 'lambda': 0.3401139290637488}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:55:18,730] Trial 277 finished with value: 0.48628366242794874 and parameters: {'gamma': 0.19357523944576302, 'subsample': 0.7547248739732472, 'colsample_bytree': 0.8881754107787867, 'alpha': 0.3795242704618076, 'lambda': 0.6210276506349218}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:55:19,909] Trial 278 finished with value: 0.49102937255046436 and parameters: {'gamma': 0.3905003692094421, 'subsample': 0.7203593771208302, 'colsample_bytree': 0.8747442806519852, 'alpha': 0.18926003130684926, 'lambda': 0.0022428464649322244}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:55:20,494] Trial 279 finished with value: 0.40183910953532576 and parameters: {'gamma': 6.65601188182558, 'subsample': 0.7758571417655418, 'colsample_bytree': 0.8695258054562898, 'alpha': 7.547142917349286, 'lambda': 0.16179614873775836}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:55:21,497] Trial 280 finished with value: 0.47488876643028854 and parameters: {'gamma': 0.7499923358314509, 'subsample': 0.7345665797767782, 'colsample_bytree': 0.8234672569206472, 'alpha': 0.15855899935505824, 'lambda': 0.09450866118802445}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:55:23,198] Trial 281 finished with value: 0.48894384059192336 and parameters: {'gamma': 0.41828985809496005, 'subsample': 0.7147255142828386, 'colsample_bytree': 0.785346902234366, 'alpha': 0.20906885743674686, 'lambda': 0.4170563893985597}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:55:24,384] Trial 282 finished with value: 0.48096393917395774 and parameters: {'gamma': 0.597875345845519, 'subsample': 0.6904759641773798, 'colsample_bytree': 0.8761930259390774, 'alpha': 0.4154765243453099, 'lambda': 0.20881676461046472}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:55:25,559] Trial 283 finished with value: 0.47853461355660865 and parameters: {'gamma': 0.35454883075548776, 'subsample': 0.8002829548908662, 'colsample_bytree': 0.8530256086978912, 'alpha': 0.18976077230215718, 'lambda': 0.6750054408962032}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:55:26,715] Trial 284 finished with value: 0.4539129838058091 and parameters: {'gamma': 2.5360792796192735, 'subsample': 0.5713124317241, 'colsample_bytree': 0.9020382003422127, 'alpha': 0.41751582527577774, 'lambda': 0.46057290854465355}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:55:28,466] Trial 285 finished with value: 0.48147270646089946 and parameters: {'gamma': 0.19658767457392914, 'subsample': 0.6687012778809471, 'colsample_bytree': 0.8260501410917361, 'alpha': 0.011037500743851902, 'lambda': 0.02967999819048034}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:55:30,346] Trial 286 finished with value: 0.4787458141211951 and parameters: {'gamma': 0.37979622821828807, 'subsample': 0.5436764096977367, 'colsample_bytree': 0.8635459100482159, 'alpha': 0.29132508718348016, 'lambda': 0.2442009252059932}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:55:32,089] Trial 287 finished with value: 0.49600733625699567 and parameters: {'gamma': 0.17406986143905814, 'subsample': 0.5886608032815485, 'colsample_bytree': 0.84417477044019, 'alpha': 0.4624321736461887, 'lambda': 0.5905548910569065}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:55:33,766] Trial 288 finished with value: 0.47343670015908623 and parameters: {'gamma': 0.7263363841883266, 'subsample': 0.5882292719795817, 'colsample_bytree': 0.8073332273601955, 'alpha': 0.4736093944924084, 'lambda': 0.019413035321748873}. Best is trial 207 with value: 0.4993161143601864.\n",
      "[I 2024-02-06 15:55:35,035] Trial 289 finished with value: 0.48296526446035826 and parameters: {'gamma': 0.5641015120881893, 'subsample': 0.7140938454307142, 'colsample_bytree': 0.8421344140115169, 'alpha': 0.1876502377284216, 'lambda': 0.5503166133579208}. Best is trial 207 with value: 0.4993161143601864.\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b839d8e-32a4-4417-ba87-3e1f98bf6717",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d732cf71-6ab1-4632-99b2-a1a671de78d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32117aa9-4bfb-4ace-803d-f467e0c2f297",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7348aa9-9172-48cc-80f6-3bea7242b054",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54f915f6-4096-4a29-a3ca-9a5faac065ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "feb69790-bbcf-4965-92f8-b20e15667edc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e42eccb-71d5-4d4d-a842-f4c311c92048",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "417325cd-17cd-4c21-acca-a3bb719e14a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "### if you want to use shap you need to one-hot encoding all the categorical featues :-( "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "0b5a2a7a-c01a-4523-ad5d-6e656e2ddc4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform(x):\n",
    "    labels = ['age']\n",
    "    tmp = [np.array(x.age.values).reshape(-1,1)]\n",
    "    for c in x.columns:\n",
    "        if c!='age':\n",
    "            values = x[c].cat.categories.values\n",
    "            m = np.zeros((x.shape[0],len(values)))\n",
    "            for i in range(x.shape[0]):\n",
    "                idx = np.where(x[c].values[i]==x[c].cat.categories.values)[0][0]\n",
    "                m[i,idx] = 1\n",
    "            labels+=[f'{c}_{v}' for v in values]\n",
    "            tmp.append( m )\n",
    "    return pd.DataFrame(np.hstack(tmp),columns=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "bbdb6fcb-865e-4fbf-9b31-273c642646fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_oh = transform(X_train)\n",
    "X_valid_oh = transform(X_valid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "ba44dcbf-0e2d-4af0-b760-a62cf4297315",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-02-05 16:14:27,578] A new study created in memory with name: no-name-1915742c-6759-4e33-ab8e-13965e7fcb3d\n",
      "[I 2024-02-05 16:14:28,230] Trial 0 finished with value: 0.0 and parameters: {'learning_rate': 0.0658738232703949, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 5.448972648984092, 'subsample': 0.04217710019453476, 'colsample_bytree': 0.8030593829858329, 'alpha': 4.734211939434967, 'lambda': 2.1883972022445164}. Best is trial 0 with value: 0.0.\n",
      "[I 2024-02-05 16:14:29,107] Trial 1 finished with value: 0.31912965831163864 and parameters: {'learning_rate': 0.04746325622117008, 'max_depth': 9, 'min_child_weight': 5, 'gamma': 4.647214261820661, 'subsample': 0.7833462910828093, 'colsample_bytree': 0.22586498890152337, 'alpha': 6.791756742408147, 'lambda': 0.7955658916862274}. Best is trial 1 with value: 0.31912965831163864.\n",
      "[I 2024-02-05 16:14:30,188] Trial 2 finished with value: 0.3462484882869544 and parameters: {'learning_rate': 0.05001069509508385, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 5.493380249851477, 'subsample': 0.7597352298567936, 'colsample_bytree': 0.9552738461763262, 'alpha': 1.9777522732857888, 'lambda': 1.3472852644641886}. Best is trial 2 with value: 0.3462484882869544.\n",
      "[I 2024-02-05 16:14:31,274] Trial 3 finished with value: 0.28940550382066244 and parameters: {'learning_rate': 0.01947590340907985, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 9.957913926508548, 'subsample': 0.9401694993920422, 'colsample_bytree': 0.9122588597164862, 'alpha': 5.569299836650669, 'lambda': 3.3947221076980973}. Best is trial 2 with value: 0.3462484882869544.\n",
      "[I 2024-02-05 16:14:31,499] Trial 4 finished with value: 0.0 and parameters: {'learning_rate': 0.1856585818436585, 'max_depth': 5, 'min_child_weight': 4, 'gamma': 8.458271181893744, 'subsample': 0.024756950287881732, 'colsample_bytree': 0.7984638158329372, 'alpha': 5.113831844359722, 'lambda': 0.8536272086992625}. Best is trial 2 with value: 0.3462484882869544.\n",
      "[I 2024-02-05 16:14:32,274] Trial 5 finished with value: 0.19340998711344637 and parameters: {'learning_rate': 0.06640939028605501, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 6.564069754521461, 'subsample': 0.9133224412408644, 'colsample_bytree': 0.3749142299185776, 'alpha': 9.957739199704317, 'lambda': 8.722097428209869}. Best is trial 2 with value: 0.3462484882869544.\n",
      "[I 2024-02-05 16:14:32,950] Trial 6 finished with value: 0.32829994815929014 and parameters: {'learning_rate': 0.12419264095226622, 'max_depth': 13, 'min_child_weight': 5, 'gamma': 1.4232934862759283, 'subsample': 0.3509510526691761, 'colsample_bytree': 0.6887363184180352, 'alpha': 4.7341195809608685, 'lambda': 5.615059190038575}. Best is trial 2 with value: 0.3462484882869544.\n",
      "[I 2024-02-05 16:14:33,337] Trial 7 finished with value: -0.0028513001845430685 and parameters: {'learning_rate': 0.071689777617089, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 7.708698617546687, 'subsample': 0.10508624108328332, 'colsample_bytree': 0.7853788478198429, 'alpha': 7.4601737765234875, 'lambda': 5.090831461984278}. Best is trial 2 with value: 0.3462484882869544.\n",
      "[I 2024-02-05 16:14:34,143] Trial 8 finished with value: 0.283494591059955 and parameters: {'learning_rate': 0.050863992237419525, 'max_depth': 8, 'min_child_weight': 7, 'gamma': 7.578470844109102, 'subsample': 0.8453112645700375, 'colsample_bytree': 0.5762370474839383, 'alpha': 7.223059266811212, 'lambda': 8.568308829111546}. Best is trial 2 with value: 0.3462484882869544.\n",
      "[I 2024-02-05 16:14:34,956] Trial 9 finished with value: 0.3175964153964839 and parameters: {'learning_rate': 0.15865863947882128, 'max_depth': 13, 'min_child_weight': 8, 'gamma': 5.20298545134247, 'subsample': 0.6654921757456297, 'colsample_bytree': 0.09533189973670159, 'alpha': 1.6326290846076408, 'lambda': 8.955310061204575}. Best is trial 2 with value: 0.3462484882869544.\n",
      "[I 2024-02-05 16:14:35,607] Trial 10 finished with value: 0.35880954157483264 and parameters: {'learning_rate': 0.11097036064153917, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 2.528586422997807, 'subsample': 0.5583670091132883, 'colsample_bytree': 0.444014702829117, 'alpha': 0.04029593653832375, 'lambda': 3.3311958187272905}. Best is trial 10 with value: 0.35880954157483264.\n",
      "[I 2024-02-05 16:14:36,184] Trial 11 finished with value: 0.35073639261874767 and parameters: {'learning_rate': 0.1088825726778681, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 2.3811040546985316, 'subsample': 0.47993288321921806, 'colsample_bytree': 0.4378715522489728, 'alpha': 0.16888421437743384, 'lambda': 3.1852000913751284}. Best is trial 10 with value: 0.35880954157483264.\n",
      "[I 2024-02-05 16:14:36,908] Trial 12 finished with value: 0.3474138212847221 and parameters: {'learning_rate': 0.114314994549357, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 1.9345216323376395, 'subsample': 0.46308677371262225, 'colsample_bytree': 0.40644767718021424, 'alpha': 0.7128457981535193, 'lambda': 3.6904941149351767}. Best is trial 10 with value: 0.35880954157483264.\n",
      "[I 2024-02-05 16:14:37,490] Trial 13 finished with value: 0.35684193075347964 and parameters: {'learning_rate': 0.1382509159023335, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 3.125974688497713, 'subsample': 0.5520058999439668, 'colsample_bytree': 0.49534938505993764, 'alpha': 0.1994981044309574, 'lambda': 6.778615118824462}. Best is trial 10 with value: 0.35880954157483264.\n",
      "[I 2024-02-05 16:14:37,992] Trial 14 finished with value: 0.33822571896724263 and parameters: {'learning_rate': 0.15452901396916452, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 3.2191275713204397, 'subsample': 0.6241474932143283, 'colsample_bytree': 0.5688791532100307, 'alpha': 2.896788082685632, 'lambda': 6.817286317028833}. Best is trial 10 with value: 0.35880954157483264.\n",
      "[I 2024-02-05 16:14:38,819] Trial 15 finished with value: 0.3452432516593209 and parameters: {'learning_rate': 0.14126994453550468, 'max_depth': 15, 'min_child_weight': 2, 'gamma': 0.5535582868575677, 'subsample': 0.33551446472903557, 'colsample_bytree': 0.2666874624107072, 'alpha': 3.330090002522477, 'lambda': 6.627990028828398}. Best is trial 10 with value: 0.35880954157483264.\n",
      "[I 2024-02-05 16:14:39,677] Trial 16 finished with value: 0.2860044821760441 and parameters: {'learning_rate': 0.19626080765045764, 'max_depth': 11, 'min_child_weight': 2, 'gamma': 3.6386066272256756, 'subsample': 0.6099231488118575, 'colsample_bytree': 0.012143786147400248, 'alpha': 0.013262296574282517, 'lambda': 7.520001661484208}. Best is trial 10 with value: 0.35880954157483264.\n",
      "[I 2024-02-05 16:14:40,706] Trial 17 finished with value: 0.35111063242872204 and parameters: {'learning_rate': 0.09206699461415271, 'max_depth': 13, 'min_child_weight': 6, 'gamma': 0.03984226243721878, 'subsample': 0.32202938600079345, 'colsample_bytree': 0.520819346043843, 'alpha': 1.5207169426627147, 'lambda': 9.877796647656815}. Best is trial 10 with value: 0.35880954157483264.\n",
      "[I 2024-02-05 16:14:41,545] Trial 18 finished with value: 0.08806829027861623 and parameters: {'learning_rate': 0.1335032826022735, 'max_depth': 10, 'min_child_weight': 4, 'gamma': 3.6778872449336735, 'subsample': 0.21921810421473148, 'colsample_bytree': 0.2952903840248904, 'alpha': 3.595008610631435, 'lambda': 4.463018701330036}. Best is trial 10 with value: 0.35880954157483264.\n",
      "[I 2024-02-05 16:14:42,218] Trial 19 finished with value: 0.35500866506672046 and parameters: {'learning_rate': 0.09258184643177642, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 2.8843836732104378, 'subsample': 0.5624965540066477, 'colsample_bytree': 0.6416796615365371, 'alpha': 2.465596026103616, 'lambda': 5.937181283183363}. Best is trial 10 with value: 0.35880954157483264.\n",
      "[I 2024-02-05 16:14:42,675] Trial 20 finished with value: 0.36223118482760985 and parameters: {'learning_rate': 0.16738090309592793, 'max_depth': 11, 'min_child_weight': 3, 'gamma': 1.1460592569600465, 'subsample': 0.7411967455923516, 'colsample_bytree': 0.3324013872369898, 'alpha': 0.8136780164403529, 'lambda': 2.1567213530889227}. Best is trial 20 with value: 0.36223118482760985.\n",
      "[I 2024-02-05 16:14:43,105] Trial 21 finished with value: 0.36759291726815707 and parameters: {'learning_rate': 0.171322611148649, 'max_depth': 11, 'min_child_weight': 3, 'gamma': 1.6189937661456721, 'subsample': 0.6764465918378344, 'colsample_bytree': 0.46715735487970056, 'alpha': 0.918934079894087, 'lambda': 1.8821218930785437}. Best is trial 21 with value: 0.36759291726815707.\n",
      "[I 2024-02-05 16:14:43,977] Trial 22 finished with value: 0.357990193698394 and parameters: {'learning_rate': 0.16920337016218706, 'max_depth': 10, 'min_child_weight': 4, 'gamma': 1.3592841832987574, 'subsample': 0.7222731560292026, 'colsample_bytree': 0.18291646380257703, 'alpha': 0.9422827001435317, 'lambda': 2.165786280302518}. Best is trial 21 with value: 0.36759291726815707.\n",
      "[I 2024-02-05 16:14:44,435] Trial 23 finished with value: 0.3694640452499067 and parameters: {'learning_rate': 0.1793838447597677, 'max_depth': 11, 'min_child_weight': 3, 'gamma': 0.8893095836564888, 'subsample': 0.8510491175747608, 'colsample_bytree': 0.3378418181066608, 'alpha': 1.1994510560927139, 'lambda': 0.012108769466085079}. Best is trial 23 with value: 0.3694640452499067.\n",
      "[I 2024-02-05 16:14:45,288] Trial 24 finished with value: 0.3685208256793768 and parameters: {'learning_rate': 0.17539618567841156, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.9873525315962485, 'subsample': 0.9994025022691349, 'colsample_bytree': 0.3367206096224239, 'alpha': 1.2950133398610968, 'lambda': 0.03160226685317499}. Best is trial 23 with value: 0.3694640452499067.\n",
      "[I 2024-02-05 16:14:46,027] Trial 25 finished with value: 0.36609983314448336 and parameters: {'learning_rate': 0.1844096074519265, 'max_depth': 9, 'min_child_weight': 4, 'gamma': 0.09411436367668502, 'subsample': 0.9981650213568565, 'colsample_bytree': 0.16696828504788988, 'alpha': 4.0114928969690355, 'lambda': 0.6754119925948054}. Best is trial 23 with value: 0.3694640452499067.\n",
      "[I 2024-02-05 16:14:46,706] Trial 26 finished with value: 0.3589887181667087 and parameters: {'learning_rate': 0.1997204580797514, 'max_depth': 9, 'min_child_weight': 5, 'gamma': 1.004061238761722, 'subsample': 0.8913773095111649, 'colsample_bytree': 0.3393620042953043, 'alpha': 2.356742761270872, 'lambda': 0.35546714034049287}. Best is trial 23 with value: 0.3694640452499067.\n",
      "[I 2024-02-05 16:14:47,553] Trial 27 finished with value: 0.3586301524905977 and parameters: {'learning_rate': 0.17541278380164627, 'max_depth': 8, 'min_child_weight': 3, 'gamma': 1.9712569654394896, 'subsample': 0.9970719376749815, 'colsample_bytree': 0.1078776813201473, 'alpha': 1.360678279783638, 'lambda': 0.10804202313131306}. Best is trial 23 with value: 0.3694640452499067.\n",
      "[I 2024-02-05 16:14:47,926] Trial 28 finished with value: 0.33713148544189997 and parameters: {'learning_rate': 0.1558157556962681, 'max_depth': 12, 'min_child_weight': 6, 'gamma': 4.538437718369824, 'subsample': 0.8260588287066675, 'colsample_bytree': 0.4870155561924544, 'alpha': 2.8595932577347116, 'lambda': 1.5940710683223318}. Best is trial 23 with value: 0.3694640452499067.\n",
      "[I 2024-02-05 16:14:48,391] Trial 29 finished with value: 0.3729805300135051 and parameters: {'learning_rate': 0.1806919105460859, 'max_depth': 8, 'min_child_weight': 3, 'gamma': 0.8363140969567027, 'subsample': 0.6860592813116058, 'colsample_bytree': 0.6955912166526383, 'alpha': 4.284987964881241, 'lambda': 1.5768442408609107}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:14:49,177] Trial 30 finished with value: 0.3635029428709179 and parameters: {'learning_rate': 0.18393483055452697, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.8419472807361975, 'subsample': 0.8272349244725349, 'colsample_bytree': 0.680364290314781, 'alpha': 6.098380056207788, 'lambda': 2.7213116539929407}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:14:50,047] Trial 31 finished with value: 0.36825770895864707 and parameters: {'learning_rate': 0.14870495873047393, 'max_depth': 8, 'min_child_weight': 3, 'gamma': 0.44849848745829457, 'subsample': 0.6746616183425653, 'colsample_bytree': 0.5776847060680944, 'alpha': 8.583303991145726, 'lambda': 1.3754868536620188}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:14:50,722] Trial 32 finished with value: 0.3626389683584765 and parameters: {'learning_rate': 0.14875676805822466, 'max_depth': 8, 'min_child_weight': 4, 'gamma': 0.5611784959769879, 'subsample': 0.8814369861073245, 'colsample_bytree': 0.6951927734621293, 'alpha': 8.080065020355939, 'lambda': 1.267638275906708}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:14:51,434] Trial 33 finished with value: 0.36684814741256105 and parameters: {'learning_rate': 0.19074024577668608, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.060404306764023974, 'subsample': 0.7746157338084197, 'colsample_bytree': 0.5927915816720878, 'alpha': 9.30151787120337, 'lambda': 0.07799964599294773}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:14:52,604] Trial 34 finished with value: 0.365322442701407 and parameters: {'learning_rate': 0.17531056812886323, 'max_depth': 8, 'min_child_weight': 2, 'gamma': 2.354278869400154, 'subsample': 0.9439291893650514, 'colsample_bytree': 0.8661696052479316, 'alpha': 4.264914020065078, 'lambda': 1.059324779025718}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:14:53,321] Trial 35 finished with value: 0.3644376244783045 and parameters: {'learning_rate': 0.1629013705647848, 'max_depth': 6, 'min_child_weight': 4, 'gamma': 0.5984097803385131, 'subsample': 0.7163719762643761, 'colsample_bytree': 0.7417872431900973, 'alpha': 6.01530413011585, 'lambda': 2.76307399340995}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:14:54,501] Trial 36 finished with value: 0.3399967902709498 and parameters: {'learning_rate': 0.1252417515125521, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 4.152628619375129, 'subsample': 0.8096794619422876, 'colsample_bytree': 0.8771310376134351, 'alpha': 8.881169819692001, 'lambda': 0.7046389136146521}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:14:55,425] Trial 37 finished with value: 0.31878248465474635 and parameters: {'learning_rate': 0.18018161432675153, 'max_depth': 10, 'min_child_weight': 5, 'gamma': 5.909097484279759, 'subsample': 0.9515589021805809, 'colsample_bytree': 0.2530673980722987, 'alpha': 6.579161798952931, 'lambda': 1.4639082552224871}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:14:56,145] Trial 38 finished with value: 0.3088614499495012 and parameters: {'learning_rate': 0.14264407424697143, 'max_depth': 6, 'min_child_weight': 3, 'gamma': 9.775236590388314, 'subsample': 0.8766106951537671, 'colsample_bytree': 0.3721762635505651, 'alpha': 2.075324600276495, 'lambda': 0.8146233309505512}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:14:56,860] Trial 39 finished with value: 0.3602357993856773 and parameters: {'learning_rate': 0.1920507972141153, 'max_depth': 9, 'min_child_weight': 4, 'gamma': 1.5391525326314102, 'subsample': 0.6505113917343505, 'colsample_bytree': 0.9842250994056989, 'alpha': 4.885960643540564, 'lambda': 3.96251109473387}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:14:57,917] Trial 40 finished with value: 0.34051551954086706 and parameters: {'learning_rate': 0.02372993879087662, 'max_depth': 8, 'min_child_weight': 2, 'gamma': 1.913958924844453, 'subsample': 0.7520622598229164, 'colsample_bytree': 0.552318080723251, 'alpha': 8.237760092193863, 'lambda': 2.557451113443131}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:14:58,533] Trial 41 finished with value: 0.36750898524289594 and parameters: {'learning_rate': 0.17008477490469945, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 1.6347417000075841, 'subsample': 0.6940163015404852, 'colsample_bytree': 0.6293504836680539, 'alpha': 1.0901515585899764, 'lambda': 1.9000900346623}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:14:59,216] Trial 42 finished with value: 0.3648535186771593 and parameters: {'learning_rate': 0.1525150439177865, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0.9882152372635757, 'subsample': 0.7825279120180927, 'colsample_bytree': 0.4408649576333687, 'alpha': 1.765229152578328, 'lambda': 1.7464121486335733}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:14:59,967] Trial 43 finished with value: 0.3514774015151322 and parameters: {'learning_rate': 0.16430718579880002, 'max_depth': 7, 'min_child_weight': 3, 'gamma': 0.5795157434066145, 'subsample': 0.4353398259746766, 'colsample_bytree': 0.7460609378519446, 'alpha': 5.523740992073462, 'lambda': 0.460215425392398}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:15:00,572] Trial 44 finished with value: 0.36208637592299026 and parameters: {'learning_rate': 0.17786896613184144, 'max_depth': 10, 'min_child_weight': 4, 'gamma': 1.39234931445164, 'subsample': 0.6701837144430819, 'colsample_bytree': 0.38095457645806374, 'alpha': 0.5924605751138606, 'lambda': 1.1661605196481855}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:15:01,619] Trial 45 finished with value: 0.3570342640331798 and parameters: {'learning_rate': 0.1920312218314917, 'max_depth': 11, 'min_child_weight': 2, 'gamma': 2.5544606611547893, 'subsample': 0.5767597034634488, 'colsample_bytree': 0.46476330581129244, 'alpha': 2.146945516494907, 'lambda': 0.02813722020874152}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:15:02,591] Trial 46 finished with value: 0.24491695226803462 and parameters: {'learning_rate': 0.14853772774429616, 'max_depth': 12, 'min_child_weight': 5, 'gamma': 1.9935833261772946, 'subsample': 0.5087632770608904, 'colsample_bytree': 0.2982262797617681, 'alpha': 9.889338035136896, 'lambda': 0.9128669562947845}. Best is trial 29 with value: 0.3729805300135051.\n",
      "[I 2024-02-05 16:15:03,703] Trial 47 finished with value: 0.37506739696664587 and parameters: {'learning_rate': 0.1304489490192495, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.39098815332195735, 'subsample': 0.9405794048160583, 'colsample_bytree': 0.5317947506077858, 'alpha': 2.8141723581396056, 'lambda': 2.3559101388694565}. Best is trial 47 with value: 0.37506739696664587.\n",
      "[I 2024-02-05 16:15:04,532] Trial 48 finished with value: 0.37384251334748825 and parameters: {'learning_rate': 0.1261908250889089, 'max_depth': 8, 'min_child_weight': 2, 'gamma': 0.27259090747072123, 'subsample': 0.9251729138193919, 'colsample_bytree': 0.529447644129501, 'alpha': 4.420892572734718, 'lambda': 2.9532807796436176}. Best is trial 47 with value: 0.37506739696664587.\n",
      "[I 2024-02-05 16:15:05,500] Trial 49 finished with value: 0.3753346677236099 and parameters: {'learning_rate': 0.12035541192203376, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 0.2799262216160343, 'subsample': 0.9452392961728767, 'colsample_bytree': 0.51335130409435, 'alpha': 4.288369159018559, 'lambda': 3.1021465043654355}. Best is trial 49 with value: 0.3753346677236099.\n",
      "[I 2024-02-05 16:15:06,408] Trial 50 finished with value: 0.37537819345092255 and parameters: {'learning_rate': 0.09783054622978109, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.2136911241020506, 'subsample': 0.9242063853350891, 'colsample_bytree': 0.5306439876706301, 'alpha': 4.367385637665442, 'lambda': 4.292674091722524}. Best is trial 50 with value: 0.37537819345092255.\n",
      "[I 2024-02-05 16:15:07,429] Trial 51 finished with value: 0.3751681423094029 and parameters: {'learning_rate': 0.08932436101008687, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.27497197144985347, 'subsample': 0.9259606142981651, 'colsample_bytree': 0.5143063736587151, 'alpha': 4.256243249383909, 'lambda': 4.2676050606949865}. Best is trial 50 with value: 0.37537819345092255.\n",
      "[I 2024-02-05 16:15:08,444] Trial 52 finished with value: 0.37960202515905095 and parameters: {'learning_rate': 0.09721383120618866, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.27499473112313644, 'subsample': 0.9147538436030836, 'colsample_bytree': 0.5303210017726595, 'alpha': 4.180444849903479, 'lambda': 4.856801858487902}. Best is trial 52 with value: 0.37960202515905095.\n",
      "[I 2024-02-05 16:15:09,307] Trial 53 finished with value: 0.37467843545223306 and parameters: {'learning_rate': 0.09773930965390804, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.19315998158960895, 'subsample': 0.9237205165727892, 'colsample_bytree': 0.5258900024221876, 'alpha': 3.5018637254256344, 'lambda': 4.689654133687835}. Best is trial 52 with value: 0.37960202515905095.\n",
      "[I 2024-02-05 16:15:10,636] Trial 54 finished with value: 0.38223343289413164 and parameters: {'learning_rate': 0.09589614915559752, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.09152278539930048, 'subsample': 0.9539280467211881, 'colsample_bytree': 0.6166085210207131, 'alpha': 3.6361617559273007, 'lambda': 4.831696360976032}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:11,749] Trial 55 finished with value: 0.3422301172433051 and parameters: {'learning_rate': 0.06891601893247597, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 6.8618666896816665, 'subsample': 0.9592618425518384, 'colsample_bytree': 0.6241764904242922, 'alpha': 3.116899698839391, 'lambda': 5.510118732082694}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:12,823] Trial 56 finished with value: 0.3765505007662123 and parameters: {'learning_rate': 0.08269547576024157, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.06331010688643984, 'subsample': 0.8646821367815983, 'colsample_bytree': 0.4182508468837915, 'alpha': 3.846541403455457, 'lambda': 4.049134862517165}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:13,868] Trial 57 finished with value: 0.3624665010742788 and parameters: {'learning_rate': 0.0835862751378238, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 1.297590672270045, 'subsample': 0.8969575864131284, 'colsample_bytree': 0.43007532787491776, 'alpha': 3.908699333119511, 'lambda': 3.7554841085248913}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:14,912] Trial 58 finished with value: 0.38160540907503393 and parameters: {'learning_rate': 0.07876707488093934, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.019933301961577304, 'subsample': 0.8532913628045913, 'colsample_bytree': 0.5960173161970469, 'alpha': 5.087364656441366, 'lambda': 4.285143379175866}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:15,683] Trial 59 finished with value: 0.38124068997867566 and parameters: {'learning_rate': 0.11681965273018322, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.04482907590881751, 'subsample': 0.8011646692083744, 'colsample_bytree': 0.6031882325386688, 'alpha': 5.320211393280869, 'lambda': 5.072920966452286}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:16,807] Trial 60 finished with value: 0.36542677745261937 and parameters: {'learning_rate': 0.058148136636769675, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.803645617508102, 'subsample': 0.8461560296438262, 'colsample_bytree': 0.6040389183973558, 'alpha': 5.4505632658625895, 'lambda': 4.936321464257539}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:17,854] Trial 61 finished with value: 0.3786552280110109 and parameters: {'learning_rate': 0.11689373895926204, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.11108682817996424, 'subsample': 0.8667705747805681, 'colsample_bytree': 0.5545536823375423, 'alpha': 4.66727306996233, 'lambda': 5.2875182743473275}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:19,131] Trial 62 finished with value: 0.38025542306581644 and parameters: {'learning_rate': 0.08050385271454213, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.009538466310439572, 'subsample': 0.8024687061549659, 'colsample_bytree': 0.6597195477468368, 'alpha': 5.134210383616038, 'lambda': 5.487225808822191}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:20,126] Trial 63 finished with value: 0.36931305250283825 and parameters: {'learning_rate': 0.07578063956254118, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.6545099898305955, 'subsample': 0.8068569126517624, 'colsample_bytree': 0.6614270083501661, 'alpha': 5.132126759680351, 'lambda': 6.122400744995182}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:21,206] Trial 64 finished with value: 0.3789148903527978 and parameters: {'learning_rate': 0.07766562401869666, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.07367052754333692, 'subsample': 0.8637821882782288, 'colsample_bytree': 0.7387366306964895, 'alpha': 4.65403676489642, 'lambda': 5.464606297150816}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:22,196] Trial 65 finished with value: 0.36607044563861907 and parameters: {'learning_rate': 0.10533916312500309, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 1.2220388252725536, 'subsample': 0.7904823557517892, 'colsample_bytree': 0.7160701427781883, 'alpha': 5.194306511602386, 'lambda': 5.222834524381956}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:23,213] Trial 66 finished with value: 0.1762403075962776 and parameters: {'learning_rate': 0.11551063238451123, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.6026283825654032, 'subsample': 0.11310705021361311, 'colsample_bytree': 0.7802892090358297, 'alpha': 5.954018804032774, 'lambda': 6.061930200995615}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:24,335] Trial 67 finished with value: 0.36404386202226074 and parameters: {'learning_rate': 0.061293025938080795, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 1.1269497046658903, 'subsample': 0.9742477663865122, 'colsample_bytree': 0.6623486750196129, 'alpha': 4.6969923938944715, 'lambda': 5.443832202263916}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:25,511] Trial 68 finished with value: 0.36434295382257215 and parameters: {'learning_rate': 0.07613782184400852, 'max_depth': 5, 'min_child_weight': 8, 'gamma': 0.07003759090750696, 'subsample': 0.864339046491934, 'colsample_bytree': 0.8432548710052508, 'alpha': 6.8717947854922405, 'lambda': 6.478517860122375}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:26,544] Trial 69 finished with value: 0.3807330248991345 and parameters: {'learning_rate': 0.10001238121022606, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.010707665712004627, 'subsample': 0.8964136620786028, 'colsample_bytree': 0.6069648093968373, 'alpha': 4.668004111502885, 'lambda': 7.211143848468353}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:27,559] Trial 70 finished with value: 0.3670907513453835 and parameters: {'learning_rate': 0.1000626054352584, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.47701062970674235, 'subsample': 0.8271685064230992, 'colsample_bytree': 0.6075060413058412, 'alpha': 5.270528896727885, 'lambda': 7.243567883116812}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:28,544] Trial 71 finished with value: 0.36868852839056165 and parameters: {'learning_rate': 0.08359502978566379, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.7829419410418098, 'subsample': 0.9027412371520553, 'colsample_bytree': 0.5685551022826056, 'alpha': 4.722074856510295, 'lambda': 4.699952985208202}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:29,540] Trial 72 finished with value: 0.3752813059195307 and parameters: {'learning_rate': 0.10884107710573471, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.10767135144821277, 'subsample': 0.845724435130988, 'colsample_bytree': 0.66277947470288, 'alpha': 5.825982232169358, 'lambda': 8.101996526233773}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:30,645] Trial 73 finished with value: 0.3728545173458392 and parameters: {'learning_rate': 0.04364896600300391, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.025814286703977805, 'subsample': 0.7557732829433376, 'colsample_bytree': 0.7287097221646047, 'alpha': 6.4970056378922685, 'lambda': 5.117687688812765}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:31,607] Trial 74 finished with value: 0.3759129720427929 and parameters: {'learning_rate': 0.0893379793732517, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.4466020548864441, 'subsample': 0.9693196846567993, 'colsample_bytree': 0.7729253045760135, 'alpha': 4.908136011793192, 'lambda': 5.928062079034395}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:32,595] Trial 75 finished with value: 0.3716555433678917 and parameters: {'learning_rate': 0.10553676131595911, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 1.103551839402366, 'subsample': 0.8929459774679047, 'colsample_bytree': 0.814695001252948, 'alpha': 3.789794007502439, 'lambda': 5.757299578781662}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:33,365] Trial 76 finished with value: 0.3644771811010638 and parameters: {'learning_rate': 0.11600947480328411, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 1.6688689361120668, 'subsample': 0.7922680503341316, 'colsample_bytree': 0.561400979366944, 'alpha': 4.6225357413174315, 'lambda': 3.461267346267448}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:34,327] Trial 77 finished with value: 0.2825011900459881 and parameters: {'learning_rate': 0.0749198512215743, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 8.718082281393709, 'subsample': 0.7209599949445278, 'colsample_bytree': 0.6437462582514346, 'alpha': 5.635571997629735, 'lambda': 4.908603776603619}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:35,583] Trial 78 finished with value: 0.36702842747103376 and parameters: {'learning_rate': 0.09477826836816086, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0.7581012030288075, 'subsample': 0.8203843199487961, 'colsample_bytree': 0.585683424933806, 'alpha': 6.307014999544804, 'lambda': 5.292497244325534}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:36,642] Trial 79 finished with value: 0.37048616044048305 and parameters: {'learning_rate': 0.10100106025956215, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.41416913296569147, 'subsample': 0.9863691279043041, 'colsample_bytree': 0.4717562680893591, 'alpha': 3.4712716506512455, 'lambda': 4.552844693050409}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:37,895] Trial 80 finished with value: 0.36778186006718583 and parameters: {'learning_rate': 0.07949028360943759, 'max_depth': 5, 'min_child_weight': 7, 'gamma': 0.9317094264296968, 'subsample': 0.8760103914627919, 'colsample_bytree': 0.6955444178009644, 'alpha': 3.2242412462278756, 'lambda': 6.4509944218462225}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:39,083] Trial 81 finished with value: 0.37993129810465914 and parameters: {'learning_rate': 0.08631198781500822, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.032473888697405466, 'subsample': 0.867841408589769, 'colsample_bytree': 0.6117363898438709, 'alpha': 4.09083087514847, 'lambda': 4.066444680515318}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:40,264] Trial 82 finished with value: 0.3817741345998006 and parameters: {'learning_rate': 0.09067436168763827, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.01679653090344082, 'subsample': 0.850490990067708, 'colsample_bytree': 0.6278338117449723, 'alpha': 4.978760967391447, 'lambda': 4.227360833296766}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:41,375] Trial 83 finished with value: 0.3704901691495067 and parameters: {'learning_rate': 0.09013819105644227, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.4704007624056724, 'subsample': 0.7694973714295564, 'colsample_bytree': 0.6250401618070969, 'alpha': 4.051050745514929, 'lambda': 3.6224442349250663}. Best is trial 54 with value: 0.38223343289413164.\n",
      "[I 2024-02-05 16:15:42,816] Trial 84 finished with value: 0.38447286482835163 and parameters: {'learning_rate': 0.08728059847356269, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.005539025357508678, 'subsample': 0.8432669727337364, 'colsample_bytree': 0.5963061154111076, 'alpha': 5.691573279197829, 'lambda': 4.1601701727822356}. Best is trial 84 with value: 0.38447286482835163.\n",
      "[I 2024-02-05 16:15:44,545] Trial 85 finished with value: 0.3660809676050876 and parameters: {'learning_rate': 0.08594814887599221, 'max_depth': 7, 'min_child_weight': 2, 'gamma': 0.7223101536556419, 'subsample': 0.9043949061350206, 'colsample_bytree': 0.611025287692323, 'alpha': 5.710867658883889, 'lambda': 4.008083651083821}. Best is trial 84 with value: 0.38447286482835163.\n",
      "[I 2024-02-05 16:15:46,664] Trial 86 finished with value: 0.3644383822475014 and parameters: {'learning_rate': 0.06939535957370523, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.005546852346134493, 'subsample': 0.3849503136205032, 'colsample_bytree': 0.5872197141268038, 'alpha': 5.393814779092649, 'lambda': 9.554134717686402}. Best is trial 84 with value: 0.38447286482835163.\n",
      "[I 2024-02-05 16:15:48,610] Trial 87 finished with value: 0.3740421856771891 and parameters: {'learning_rate': 0.11068946616572296, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.40597701523243557, 'subsample': 0.8390772472651373, 'colsample_bytree': 0.6755368449126795, 'alpha': 5.1275772904307715, 'lambda': 4.380703796873652}. Best is trial 84 with value: 0.38447286482835163.\n",
      "[I 2024-02-05 16:15:50,345] Trial 88 finished with value: 0.3662152905649268 and parameters: {'learning_rate': 0.06205178906833676, 'max_depth': 7, 'min_child_weight': 2, 'gamma': 0.971404054695991, 'subsample': 0.7366336670252096, 'colsample_bytree': 0.6435090250462275, 'alpha': 4.952007666572199, 'lambda': 3.79839222057748}. Best is trial 84 with value: 0.38447286482835163.\n",
      "[I 2024-02-05 16:15:52,119] Trial 89 finished with value: 0.3393988148979778 and parameters: {'learning_rate': 0.09433753011506472, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 5.207856360984749, 'subsample': 0.7936814706057774, 'colsample_bytree': 0.5491659820745229, 'alpha': 4.1283473419109535, 'lambda': 4.754890216310482}. Best is trial 84 with value: 0.38447286482835163.\n",
      "[I 2024-02-05 16:15:53,726] Trial 90 finished with value: 0.2006939273180902 and parameters: {'learning_rate': 0.10089995239781527, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 1.3382434006850137, 'subsample': 0.2882554896375785, 'colsample_bytree': 0.5004750890496455, 'alpha': 6.9808206555406676, 'lambda': 4.232465944233081}. Best is trial 84 with value: 0.38447286482835163.\n",
      "[I 2024-02-05 16:15:55,711] Trial 91 finished with value: 0.3744410920995563 and parameters: {'learning_rate': 0.0803789724661109, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.2651341192496304, 'subsample': 0.9063430205722566, 'colsample_bytree': 0.5967788988639523, 'alpha': 4.494405037446006, 'lambda': 4.922582948704067}. Best is trial 84 with value: 0.38447286482835163.\n",
      "[I 2024-02-05 16:15:59,084] Trial 92 finished with value: 0.37328357888885055 and parameters: {'learning_rate': 0.08822651938680545, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.32974546025546436, 'subsample': 0.8160394017402665, 'colsample_bytree': 0.7189638747453949, 'alpha': 5.005908532968909, 'lambda': 5.710813150829663}. Best is trial 84 with value: 0.38447286482835163.\n",
      "[I 2024-02-05 16:16:08,670] Trial 93 finished with value: 0.3748897700582131 and parameters: {'learning_rate': 0.09416979306295377, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.562622829209472, 'subsample': 0.8529792830449331, 'colsample_bytree': 0.6361500856599773, 'alpha': 3.6954933645417896, 'lambda': 7.136754519532755}. Best is trial 84 with value: 0.38447286482835163.\n",
      "[I 2024-02-05 16:16:11,914] Trial 94 finished with value: 0.36778366702537457 and parameters: {'learning_rate': 0.07219243278346976, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 0.7031861706943242, 'subsample': 0.9524991273356171, 'colsample_bytree': 0.7021461831716567, 'alpha': 6.208144166183638, 'lambda': 4.525722175344181}. Best is trial 84 with value: 0.38447286482835163.\n",
      "[I 2024-02-05 16:16:13,136] Trial 95 finished with value: 0.3747295258664962 and parameters: {'learning_rate': 0.08075560778652868, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0.26159224977400586, 'subsample': 0.921898411234046, 'colsample_bytree': 0.7583371876659534, 'alpha': 5.3439314969333305, 'lambda': 3.4077780641389444}. Best is trial 84 with value: 0.38447286482835163.\n",
      "[I 2024-02-05 16:16:14,111] Trial 96 finished with value: 0.38224083371474715 and parameters: {'learning_rate': 0.06500479266522916, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.020447160976446468, 'subsample': 0.8773638293916227, 'colsample_bytree': 0.6612377132580695, 'alpha': 5.7783077727346495, 'lambda': 4.012617661161584}. Best is trial 84 with value: 0.38447286482835163.\n",
      "[I 2024-02-05 16:16:14,947] Trial 97 finished with value: 0.3394709864642458 and parameters: {'learning_rate': 0.05375663781812392, 'max_depth': 13, 'min_child_weight': 2, 'gamma': 5.88374369608122, 'subsample': 0.8301140300324382, 'colsample_bytree': 0.6505634551932811, 'alpha': 5.827400553022916, 'lambda': 4.0633504494168315}. Best is trial 84 with value: 0.38447286482835163.\n",
      "[I 2024-02-05 16:16:15,996] Trial 98 finished with value: 0.36515802546773013 and parameters: {'learning_rate': 0.04651746227988955, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.9516213517741551, 'subsample': 0.8840269769197511, 'colsample_bytree': 0.6165555404061006, 'alpha': 5.556020035333692, 'lambda': 8.229603694455992}. Best is trial 84 with value: 0.38447286482835163.\n",
      "[I 2024-02-05 16:16:16,919] Trial 99 finished with value: 0.3831134373916465 and parameters: {'learning_rate': 0.06504676289708615, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.25698619258552163, 'subsample': 0.9762122247110407, 'colsample_bytree': 0.6757970611931983, 'alpha': 2.671511850024477, 'lambda': 4.1820057444625975}. Best is trial 84 with value: 0.38447286482835163.\n",
      "[I 2024-02-05 16:16:18,023] Trial 100 finished with value: 0.39419907896623013 and parameters: {'learning_rate': 0.06616370163695198, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.0030343343448390875, 'subsample': 0.974050507518603, 'colsample_bytree': 0.6918500476463855, 'alpha': 2.9941024923866717, 'lambda': 3.6009133307561783}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:19,168] Trial 101 finished with value: 0.38970714376700377 and parameters: {'learning_rate': 0.03919689899883331, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.022716055070008255, 'subsample': 0.9787516816676803, 'colsample_bytree': 0.6764938617714695, 'alpha': 2.6205880401484896, 'lambda': 3.1461573371147535}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:20,107] Trial 102 finished with value: 0.37602916698470856 and parameters: {'learning_rate': 0.039647117702194146, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.615253599423174, 'subsample': 0.9990652577087393, 'colsample_bytree': 0.6808872502442317, 'alpha': 2.4140202521241205, 'lambda': 3.308028762637989}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:21,184] Trial 103 finished with value: 0.3787884162235965 and parameters: {'learning_rate': 0.03692546232775505, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.34043095016658387, 'subsample': 0.9787657097359164, 'colsample_bytree': 0.7087199759240803, 'alpha': 2.8689893257563646, 'lambda': 3.5688189644450388}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:22,171] Trial 104 finished with value: 0.37601084387547734 and parameters: {'learning_rate': 0.032946533458703825, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.4771004914440379, 'subsample': 0.9626209920201071, 'colsample_bytree': 0.6751322148480089, 'alpha': 2.6645810823102134, 'lambda': 3.0794740212114458}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:23,318] Trial 105 finished with value: 0.3834239941698453 and parameters: {'learning_rate': 0.06259392300341177, 'max_depth': 15, 'min_child_weight': 2, 'gamma': 0.00411612722864451, 'subsample': 0.9635067339243919, 'colsample_bytree': 0.5772203054316192, 'alpha': 2.6284022187842777, 'lambda': 3.8039211885872373}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:24,617] Trial 106 finished with value: 0.38201880615958467 and parameters: {'learning_rate': 0.06467323924374557, 'max_depth': 15, 'min_child_weight': 2, 'gamma': 0.22873812633122484, 'subsample': 0.9382142607845827, 'colsample_bytree': 0.5738229796953559, 'alpha': 3.05838040844482, 'lambda': 2.8105333886665993}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:26,022] Trial 107 finished with value: 0.37361228122643 and parameters: {'learning_rate': 0.0655417263606616, 'max_depth': 15, 'min_child_weight': 2, 'gamma': 0.7849240923823548, 'subsample': 0.9409018333182451, 'colsample_bytree': 0.5798923289674048, 'alpha': 1.8848434843940893, 'lambda': 2.7147069650718745}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:27,113] Trial 108 finished with value: 0.3701513376069351 and parameters: {'learning_rate': 0.06133458080137027, 'max_depth': 15, 'min_child_weight': 2, 'gamma': 1.1072135940616423, 'subsample': 0.97470507421744, 'colsample_bytree': 0.5510675931208836, 'alpha': 2.9823986311018094, 'lambda': 3.843449734545053}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:28,295] Trial 109 finished with value: 0.377425421567266 and parameters: {'learning_rate': 0.054432972207432075, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.22548349975700702, 'subsample': 0.9346096386712933, 'colsample_bytree': 0.633058404951939, 'alpha': 2.2389843955032056, 'lambda': 2.457546405910575}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:29,244] Trial 110 finished with value: 0.36932226254692735 and parameters: {'learning_rate': 0.055832257518681636, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 1.7880488963255794, 'subsample': 0.9602293023607266, 'colsample_bytree': 0.48969174916311636, 'alpha': 2.5665520649563893, 'lambda': 3.3132716165131475}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:30,806] Trial 111 finished with value: 0.3740209728464492 and parameters: {'learning_rate': 0.014200506766012921, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.23901815933899898, 'subsample': 0.8894348719072939, 'colsample_bytree': 0.5951367690971926, 'alpha': 3.234546583417517, 'lambda': 4.293585371895981}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:31,839] Trial 112 finished with value: 0.3751557301594564 and parameters: {'learning_rate': 0.06503794632391109, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.6124297349285815, 'subsample': 0.9809286341553983, 'colsample_bytree': 0.5689081305510213, 'alpha': 1.5501088135145145, 'lambda': 3.027684678049525}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:33,196] Trial 113 finished with value: 0.3848824359256179 and parameters: {'learning_rate': 0.048293321835524074, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.013127064979082585, 'subsample': 0.9159272019044737, 'colsample_bytree': 0.6891676599850979, 'alpha': 3.3946640063131506, 'lambda': 3.614426869884935}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:34,209] Trial 114 finished with value: 0.3683542963397362 and parameters: {'learning_rate': 0.04852827455388224, 'max_depth': 15, 'min_child_weight': 6, 'gamma': 0.41222510283098196, 'subsample': 0.9414401484422277, 'colsample_bytree': 0.7999405762086256, 'alpha': 3.4607799677492768, 'lambda': 3.667032745802083}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:35,216] Trial 115 finished with value: 0.37846493149125926 and parameters: {'learning_rate': 0.06994980081406049, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.21389562783828134, 'subsample': 0.9166577179837841, 'colsample_bytree': 0.6860058561841599, 'alpha': 2.749692035179159, 'lambda': 2.9029309087064465}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:36,448] Trial 116 finished with value: 0.384076742622432 and parameters: {'learning_rate': 0.051300856512595035, 'max_depth': 13, 'min_child_weight': 2, 'gamma': 0.0031665070145890425, 'subsample': 0.9931873978987559, 'colsample_bytree': 0.6560200998150137, 'alpha': 2.5238236472403313, 'lambda': 3.8993320990475366}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:37,438] Trial 117 finished with value: 0.37166615296146327 and parameters: {'learning_rate': 0.02831094463452423, 'max_depth': 13, 'min_child_weight': 2, 'gamma': 3.8753426239747624, 'subsample': 0.9540252407605833, 'colsample_bytree': 0.729557607113427, 'alpha': 3.09801779403818, 'lambda': 3.8699998433755884}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:38,590] Trial 118 finished with value: 0.37635265062530376 and parameters: {'learning_rate': 0.052597753686666054, 'max_depth': 15, 'min_child_weight': 2, 'gamma': 0.6392825057837344, 'subsample': 0.995504692063518, 'colsample_bytree': 0.6549012861664515, 'alpha': 2.39432263490697, 'lambda': 4.570944923680097}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:39,604] Trial 119 finished with value: 0.3742132585570355 and parameters: {'learning_rate': 0.04429345575082454, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.8575620196976066, 'subsample': 0.931266574697573, 'colsample_bytree': 0.7044188867766842, 'alpha': 1.97318143111718, 'lambda': 3.2030648583386556}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:40,789] Trial 120 finished with value: 0.3742118091800199 and parameters: {'learning_rate': 0.05751852773774349, 'max_depth': 15, 'min_child_weight': 2, 'gamma': 1.4532084259464235, 'subsample': 0.9665646221780357, 'colsample_bytree': 0.6699535045801002, 'alpha': 2.2630816683163952, 'lambda': 4.154420016176944}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:41,872] Trial 121 finished with value: 0.3795299116774004 and parameters: {'learning_rate': 0.07363730484353083, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.24844826957757232, 'subsample': 0.9065997728881472, 'colsample_bytree': 0.6274866352487689, 'alpha': 2.5446308823272403, 'lambda': 3.474537012946886}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:43,251] Trial 122 finished with value: 0.38778053138829266 and parameters: {'learning_rate': 0.06125384489382522, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.004335836601530807, 'subsample': 0.9838435446105525, 'colsample_bytree': 0.6425430767781201, 'alpha': 2.9417817261987165, 'lambda': 4.43390655262186}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:16:51,345] Trial 123 finished with value: 0.3791531791891406 and parameters: {'learning_rate': 0.050909222521233345, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.45334348196934804, 'subsample': 0.9829858276635889, 'colsample_bytree': 0.7622534363021451, 'alpha': 3.0478207386218465, 'lambda': 4.4028052951960515}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:17:02,679] Trial 124 finished with value: 0.3866154383476178 and parameters: {'learning_rate': 0.06332518953372716, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.03723544682377706, 'subsample': 0.9515482980296228, 'colsample_bytree': 0.6534378454265948, 'alpha': 2.7271190345075915, 'lambda': 3.734540733954036}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:17:12,467] Trial 125 finished with value: 0.3887614372575411 and parameters: {'learning_rate': 0.05926903101886127, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.0038516460374433603, 'subsample': 0.9958245123795402, 'colsample_bytree': 0.6912895310478384, 'alpha': 3.2754603156103803, 'lambda': 3.6869424162149427}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:17:13,512] Trial 126 finished with value: 0.36429878570515384 and parameters: {'learning_rate': 0.06003045499818634, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 3.1557486011765516, 'subsample': 0.9544710219523171, 'colsample_bytree': 0.6875750854761478, 'alpha': 3.3358481234297064, 'lambda': 3.7913333206194157}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:17:14,697] Trial 127 finished with value: 0.37802424445587507 and parameters: {'learning_rate': 0.06520211701654385, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.3757280679197215, 'subsample': 0.9932789253249785, 'colsample_bytree': 0.7414181065171322, 'alpha': 2.7069792691511507, 'lambda': 2.7947363177429247}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:17:15,640] Trial 128 finished with value: 0.37710073213066003 and parameters: {'learning_rate': 0.043098584322647376, 'max_depth': 15, 'min_child_weight': 2, 'gamma': 0.2205124852594066, 'subsample': 0.9980319606488068, 'colsample_bytree': 0.6470381622544346, 'alpha': 2.9002377879029657, 'lambda': 3.565730923059308}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:17:16,451] Trial 129 finished with value: 0.348506068893356 and parameters: {'learning_rate': 0.04938882635893843, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 4.746092497938951, 'subsample': 0.9675375809110341, 'colsample_bytree': 0.7020098671209973, 'alpha': 3.6731445087096932, 'lambda': 2.1597087617687016}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:17:17,405] Trial 130 finished with value: 0.37584034252199267 and parameters: {'learning_rate': 0.06739291803044536, 'max_depth': 14, 'min_child_weight': 2, 'gamma': 0.5623405672542519, 'subsample': 0.9422787901205125, 'colsample_bytree': 0.6663218332945816, 'alpha': 2.1332680760283482, 'lambda': 3.2220735938505}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:17:18,566] Trial 131 finished with value: 0.38359379682989264 and parameters: {'learning_rate': 0.06382848329435079, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.05605481785451954, 'subsample': 0.9225418125585187, 'colsample_bytree': 0.6279308557057652, 'alpha': 2.5406772420443744, 'lambda': 4.042865037536879}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:17:19,538] Trial 132 finished with value: 0.38136009478430183 and parameters: {'learning_rate': 0.06264383046804298, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.17799149330969674, 'subsample': 0.9233220907874605, 'colsample_bytree': 0.6497116692513372, 'alpha': 2.58684464853941, 'lambda': 3.9816126763154767}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:17:20,850] Trial 133 finished with value: 0.3876826448134014 and parameters: {'learning_rate': 0.05534437386922702, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.01711220969653257, 'subsample': 0.9683330928789117, 'colsample_bytree': 0.7184798074422494, 'alpha': 1.7219206434855467, 'lambda': 3.765286253964428}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:17:22,223] Trial 134 finished with value: 0.3922472898643475 and parameters: {'learning_rate': 0.058014999808319326, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.008630814256971462, 'subsample': 0.9720430561903968, 'colsample_bytree': 0.7211854322203733, 'alpha': 1.7431720613944144, 'lambda': 3.6524528403995897}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:17:23,256] Trial 135 finished with value: 0.3795373450625689 and parameters: {'learning_rate': 0.05919711677383895, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.42199945737625366, 'subsample': 0.9990050007044308, 'colsample_bytree': 0.7482771916246829, 'alpha': 1.7007207657692318, 'lambda': 3.5996658342000862}. Best is trial 100 with value: 0.39419907896623013.\n",
      "[I 2024-02-05 16:17:24,966] Trial 136 finished with value: 0.40115779437866844 and parameters: {'learning_rate': 0.03891429499186114, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.0020055206752192176, 'subsample': 0.9771419690248313, 'colsample_bytree': 0.7251575579179709, 'alpha': 1.2283339158605653, 'lambda': 3.72889573434574}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:17:25,985] Trial 137 finished with value: 0.37607008424128396 and parameters: {'learning_rate': 0.03888623042394131, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.7451465772879369, 'subsample': 0.9753275833022795, 'colsample_bytree': 0.7159644821666711, 'alpha': 0.5794491714610306, 'lambda': 3.7825828824790433}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:17:28,008] Trial 138 finished with value: 0.39757032106506457 and parameters: {'learning_rate': 0.05624137165543023, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.015058828568378382, 'subsample': 0.9727117528097537, 'colsample_bytree': 0.7919215028585406, 'alpha': 1.3630789723245957, 'lambda': 3.509658032312326}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:17:29,097] Trial 139 finished with value: 0.3823102431548974 and parameters: {'learning_rate': 0.03489929207802399, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.5190334830588776, 'subsample': 0.9544628124526793, 'colsample_bytree': 0.840605378136728, 'alpha': 1.0381482095948158, 'lambda': 3.3499452580430473}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:17:30,294] Trial 140 finished with value: 0.3889432500329843 and parameters: {'learning_rate': 0.04639865168791841, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.05228998861360186, 'subsample': 0.9116107456659773, 'colsample_bytree': 0.7672086783220109, 'alpha': 1.3506237155861205, 'lambda': 3.115672632416503}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:17:31,645] Trial 141 finished with value: 0.39797650113916305 and parameters: {'learning_rate': 0.04671563248663975, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.013128966716811389, 'subsample': 0.9119794101352726, 'colsample_bytree': 0.7897700806753728, 'alpha': 1.3440134519290683, 'lambda': 3.1187359835505664}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:17:32,818] Trial 142 finished with value: 0.3821285931276686 and parameters: {'learning_rate': 0.04717169569196231, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.366558754290732, 'subsample': 0.9162165320891459, 'colsample_bytree': 0.7899834102207686, 'alpha': 1.2687265704478978, 'lambda': 3.039629314026555}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:17:34,411] Trial 143 finished with value: 0.3963390170163804 and parameters: {'learning_rate': 0.04160099944520811, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.011870762713911222, 'subsample': 0.9360871915733182, 'colsample_bytree': 0.8278426667929482, 'alpha': 1.456206628089392, 'lambda': 3.4478465664573767}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:17:35,643] Trial 144 finished with value: 0.3828631704528272 and parameters: {'learning_rate': 0.02924798626124474, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.1887197288090344, 'subsample': 0.9823565279450501, 'colsample_bytree': 0.8298252050174011, 'alpha': 1.4763710504219139, 'lambda': 3.4880944051342926}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:17:36,622] Trial 145 finished with value: 0.3397754457786187 and parameters: {'learning_rate': 0.040817473832829514, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 7.363035739030234, 'subsample': 0.9430486859234873, 'colsample_bytree': 0.7756683203833228, 'alpha': 0.6516154753290099, 'lambda': 2.59303804280375}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:17:37,714] Trial 146 finished with value: 0.38348713707209736 and parameters: {'learning_rate': 0.0524604496289249, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.5871983803593529, 'subsample': 0.8977343712573361, 'colsample_bytree': 0.8890590237588869, 'alpha': 0.8989286159665233, 'lambda': 3.180969391815739}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:17:38,855] Trial 147 finished with value: 0.379930080377174 and parameters: {'learning_rate': 0.031479882905843026, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.4058675378998009, 'subsample': 0.9671839899390198, 'colsample_bytree': 0.805071175119419, 'alpha': 1.295627055556936, 'lambda': 3.640052545045855}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:17:40,540] Trial 148 finished with value: 0.3866535694759919 and parameters: {'learning_rate': 0.045699835032341186, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.036841413217075125, 'subsample': 0.9296739285049598, 'colsample_bytree': 0.762390860342462, 'alpha': 1.796789232408107, 'lambda': 3.3869207124272287}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:17:48,711] Trial 149 finished with value: 0.37336689975757303 and parameters: {'learning_rate': 0.04278687233254707, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.9094362038358991, 'subsample': 0.9342953711992726, 'colsample_bytree': 0.8204365950273176, 'alpha': 1.8033885482820193, 'lambda': 3.3624366824111176}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:01,920] Trial 150 finished with value: 0.38326125982856535 and parameters: {'learning_rate': 0.02141937145139755, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.23810809717346085, 'subsample': 0.9023976807205021, 'colsample_bytree': 0.7623036519465964, 'alpha': 1.466865172122315, 'lambda': 2.9579786726506088}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:12,525] Trial 151 finished with value: 0.3857878726344196 and parameters: {'learning_rate': 0.045649443777925305, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.069936497745832, 'subsample': 0.9803512595951412, 'colsample_bytree': 0.7862213907675175, 'alpha': 1.6529237708339646, 'lambda': 3.169801641763135}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:14,099] Trial 152 finished with value: 0.3973281645576476 and parameters: {'learning_rate': 0.038459098488873085, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.010878717215679226, 'subsample': 0.9472097692210499, 'colsample_bytree': 0.8544769223227967, 'alpha': 1.1308427043251488, 'lambda': 2.344827541496204}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:15,076] Trial 153 finished with value: 0.3864494394662702 and parameters: {'learning_rate': 0.0461577677268612, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.2959660526728708, 'subsample': 0.9516762584969847, 'colsample_bytree': 0.8635513049116588, 'alpha': 1.1732214264523044, 'lambda': 2.345117248356931}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:16,060] Trial 154 finished with value: 0.3835394036247798 and parameters: {'learning_rate': 0.039183181761529816, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.38376019833399727, 'subsample': 0.9485084619875666, 'colsample_bytree': 0.9144817044194133, 'alpha': 1.197215255546682, 'lambda': 1.8310091995763744}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:17,285] Trial 155 finished with value: 0.3785658695774641 and parameters: {'learning_rate': 0.03544920224431178, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.6509038584412727, 'subsample': 0.972898399949578, 'colsample_bytree': 0.8546015780248833, 'alpha': 1.6983384610821999, 'lambda': 2.3264077512222014}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:19,444] Trial 156 finished with value: 0.381568258451896 and parameters: {'learning_rate': 0.026020534000904524, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.25370666832875194, 'subsample': 0.9561212260899061, 'colsample_bytree': 0.8800780629856915, 'alpha': 2.0347653995077866, 'lambda': 2.369518604050366}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:20,533] Trial 157 finished with value: 0.3771967490064541 and parameters: {'learning_rate': 0.0459943314077044, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.4908020557774649, 'subsample': 0.9338967909885264, 'colsample_bytree': 0.784657147981028, 'alpha': 1.4403950364338327, 'lambda': 2.579169900290163}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:21,664] Trial 158 finished with value: 0.3879620749073933 and parameters: {'learning_rate': 0.05481856319412842, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.22740939210161376, 'subsample': 0.9811274428818767, 'colsample_bytree': 0.9211291188893787, 'alpha': 0.44684844980244365, 'lambda': 3.1209331129661995}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:22,579] Trial 159 finished with value: 0.38724153017110924 and parameters: {'learning_rate': 0.05563596056445158, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.8073051724341999, 'subsample': 0.956018023643264, 'colsample_bytree': 0.9749352138789737, 'alpha': 0.348339965946039, 'lambda': 1.9693527499158217}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:23,676] Trial 160 finished with value: 0.3894015455743552 and parameters: {'learning_rate': 0.055717416755751994, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.6526970805524321, 'subsample': 0.9983532274949608, 'colsample_bytree': 0.9558491457080022, 'alpha': 0.48368659602776265, 'lambda': 2.931790028095251}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:24,601] Trial 161 finished with value: 0.386883733322142 and parameters: {'learning_rate': 0.056081303265402734, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.7069523461754001, 'subsample': 0.9958963804092923, 'colsample_bytree': 0.9456774728504671, 'alpha': 0.39644157001336755, 'lambda': 2.9073445544806877}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:25,784] Trial 162 finished with value: 0.3884849541391972 and parameters: {'learning_rate': 0.056483589512329804, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.7994638269608073, 'subsample': 0.9834829710405243, 'colsample_bytree': 0.9844056558523298, 'alpha': 0.3251115321401793, 'lambda': 2.6774618042153}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:26,748] Trial 163 finished with value: 0.38650656144317197 and parameters: {'learning_rate': 0.05646908931373588, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 1.209218867732491, 'subsample': 0.982700419567161, 'colsample_bytree': 0.9991795974765854, 'alpha': 0.15517813088472665, 'lambda': 2.7307320155535453}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:27,711] Trial 164 finished with value: 0.38608182875094343 and parameters: {'learning_rate': 0.05558706070131285, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.9651296637896751, 'subsample': 0.9992604310140742, 'colsample_bytree': 0.9364160897522522, 'alpha': 0.5226300115031046, 'lambda': 2.872830730779035}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:29,045] Trial 165 finished with value: 0.38867864499628524 and parameters: {'learning_rate': 0.057859190599161676, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.7875014428317421, 'subsample': 0.998861658229128, 'colsample_bytree': 0.948787211594068, 'alpha': 0.3730224181253207, 'lambda': 2.082996144216756}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:30,224] Trial 166 finished with value: 0.38491139166793936 and parameters: {'learning_rate': 0.05030443025314628, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.7822181186559375, 'subsample': 0.9722104587065006, 'colsample_bytree': 0.9724564769875037, 'alpha': 0.787194393393188, 'lambda': 2.0218529037376127}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:31,369] Trial 167 finished with value: 0.3855866561186833 and parameters: {'learning_rate': 0.05922749907272977, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.5238971356732391, 'subsample': 0.9681100606884643, 'colsample_bytree': 0.9158590707001393, 'alpha': 0.34562101991958516, 'lambda': 2.0702604290843225}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:32,655] Trial 168 finished with value: 0.35386964668517207 and parameters: {'learning_rate': 0.05276133471542749, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 1.1539845843824756, 'subsample': 0.14044550121532556, 'colsample_bytree': 0.9744515014232783, 'alpha': 0.2663994790012387, 'lambda': 1.7080461881571782}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:33,678] Trial 169 finished with value: 0.38116615824384636 and parameters: {'learning_rate': 0.057820783095959186, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.37565208207237055, 'subsample': 0.9849360555532792, 'colsample_bytree': 0.955924698492387, 'alpha': 0.9582478670503056, 'lambda': 2.5424897182644948}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:34,670] Trial 170 finished with value: 0.3686997450376375 and parameters: {'learning_rate': 0.04180497287386644, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 2.7377042292201526, 'subsample': 0.9982392306873211, 'colsample_bytree': 0.9989231062182399, 'alpha': 0.7206274379217873, 'lambda': 1.4955600808015468}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:35,649] Trial 171 finished with value: 0.38508018411843037 and parameters: {'learning_rate': 0.05619548506383775, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.7359186464564991, 'subsample': 0.9664867329366985, 'colsample_bytree': 0.9277580215192044, 'alpha': 0.4314507269076563, 'lambda': 2.6812306530777437}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:36,713] Trial 172 finished with value: 0.3891816159723161 and parameters: {'learning_rate': 0.05060294346435787, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.6737770049822067, 'subsample': 0.9968598780113561, 'colsample_bytree': 0.9563176482966619, 'alpha': 0.22131665691543362, 'lambda': 2.958935643453725}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:37,945] Trial 173 finished with value: 0.39721329027929736 and parameters: {'learning_rate': 0.05061091186616899, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.2211525462529026, 'subsample': 0.9594699163777959, 'colsample_bytree': 0.8884441207628693, 'alpha': 0.13654761850988995, 'lambda': 3.1688800198126885}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:39,677] Trial 174 finished with value: 0.398277499482501 and parameters: {'learning_rate': 0.04978104138933991, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.2165812126523207, 'subsample': 0.5280843671464583, 'colsample_bytree': 0.8920735668688221, 'alpha': 0.035792499720056825, 'lambda': 3.18101987154748}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:18:50,730] Trial 175 finished with value: 0.39441405600769297 and parameters: {'learning_rate': 0.03714660471922583, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.27097821140639217, 'subsample': 0.6220595398667668, 'colsample_bytree': 0.8826087136268392, 'alpha': 0.0408976412918855, 'lambda': 3.0368268255820725}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:07,525] Trial 176 finished with value: 0.39438903054788316 and parameters: {'learning_rate': 0.03741270573977018, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.2534162432457822, 'subsample': 0.5149972258628958, 'colsample_bytree': 0.9023277912795384, 'alpha': 0.07992395436055184, 'lambda': 3.0713921382708}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:14,111] Trial 177 finished with value: 0.38794884905290267 and parameters: {'learning_rate': 0.032282948422502615, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.5418595505251981, 'subsample': 0.4794783954180636, 'colsample_bytree': 0.8929991559134904, 'alpha': 0.014889277247382149, 'lambda': 3.0712350773746566}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:15,352] Trial 178 finished with value: 0.3851024905259036 and parameters: {'learning_rate': 0.036175802638806556, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.35006422629697787, 'subsample': 0.5277517079056283, 'colsample_bytree': 0.8889826528080874, 'alpha': 0.1351835927472598, 'lambda': 2.835791350339844}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:15,998] Trial 179 finished with value: 0.33798453012260765 and parameters: {'learning_rate': 0.03976983773993441, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 9.25292888616785, 'subsample': 0.43828420348501257, 'colsample_bytree': 0.951302048659777, 'alpha': 0.05619912473471006, 'lambda': 3.3180410662980524}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:16,964] Trial 180 finished with value: 0.37357549455342964 and parameters: {'learning_rate': 0.05014282276443083, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.9897829115244138, 'subsample': 0.51071477281336, 'colsample_bytree': 0.8988666377831036, 'alpha': 0.22443272762294308, 'lambda': 2.9703519451825815}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:18,256] Trial 181 finished with value: 0.3866089909999743 and parameters: {'learning_rate': 0.03623801953228989, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.21818075244899707, 'subsample': 0.5907457177396791, 'colsample_bytree': 0.9248169460804417, 'alpha': 0.5521282840215276, 'lambda': 3.2438558290797466}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:19,603] Trial 182 finished with value: 0.3824794367866454 and parameters: {'learning_rate': 0.04062513074021653, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.2403414429843857, 'subsample': 0.5344607980524165, 'colsample_bytree': 0.8593508033376265, 'alpha': 0.805145638738254, 'lambda': 3.086271829077609}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:20,656] Trial 183 finished with value: 0.38787002969955914 and parameters: {'learning_rate': 0.04941997966461502, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.5485331167115185, 'subsample': 0.5451142820455729, 'colsample_bytree': 0.957830135387704, 'alpha': 0.32424347717575663, 'lambda': 2.531868751986141}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:21,959] Trial 184 finished with value: 0.3714552843122959 and parameters: {'learning_rate': 0.04439122939649611, 'max_depth': 14, 'min_child_weight': 7, 'gamma': 0.23623187746930555, 'subsample': 0.610536167034493, 'colsample_bytree': 0.9087260389496173, 'alpha': 0.6505942037723357, 'lambda': 3.1685025662128874}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:22,891] Trial 185 finished with value: 0.3677543723754252 and parameters: {'learning_rate': 0.03192951566577677, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 2.195319810251006, 'subsample': 0.5733412822943321, 'colsample_bytree': 0.8712161277495085, 'alpha': 0.002285207281458687, 'lambda': 3.500757090053293}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:23,801] Trial 186 finished with value: 0.3785730786830694 and parameters: {'learning_rate': 0.0518859574232843, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.47279205547954684, 'subsample': 0.7007490373254208, 'colsample_bytree': 0.8280886607073076, 'alpha': 1.0246178877291052, 'lambda': 2.2677553063517917}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:25,226] Trial 187 finished with value: 0.3924654349748583 and parameters: {'learning_rate': 0.042675131008215794, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.18433282061916112, 'subsample': 0.49435257765403956, 'colsample_bytree': 0.8456500192690052, 'alpha': 0.4476945339986113, 'lambda': 2.727554405123837}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:26,429] Trial 188 finished with value: 0.38568175935728094 and parameters: {'learning_rate': 0.03817796374791249, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.4093725379238559, 'subsample': 0.4932472274956657, 'colsample_bytree': 0.8432257371624439, 'alpha': 0.2054876901299568, 'lambda': 2.755426431675028}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:27,430] Trial 189 finished with value: 0.3734342948957842 and parameters: {'learning_rate': 0.04784008433203483, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.6405127310589358, 'subsample': 0.518675607394301, 'colsample_bytree': 0.8143078736951097, 'alpha': 0.8798984826653669, 'lambda': 2.6350037079239685}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:28,623] Trial 190 finished with value: 0.3858601307804389 and parameters: {'learning_rate': 0.042559110179667334, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.1643772775860704, 'subsample': 0.9447258522220114, 'colsample_bytree': 0.8707262524022327, 'alpha': 1.0664506945447345, 'lambda': 2.8794886209564043}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:30,015] Trial 191 finished with value: 0.3842911294507536 and parameters: {'learning_rate': 0.04358145802089424, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.2132950718127472, 'subsample': 0.6442729746228226, 'colsample_bytree': 0.9341081037820009, 'alpha': 0.48533480615062535, 'lambda': 3.0679400327084383}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:31,657] Trial 192 finished with value: 0.38586867738779096 and parameters: {'learning_rate': 0.0280790333445998, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.1891278437180859, 'subsample': 0.4561052564283347, 'colsample_bytree': 0.913649716041666, 'alpha': 0.26120973714316675, 'lambda': 3.3790669376668263}. Best is trial 136 with value: 0.40115779437866844.\n",
      "[I 2024-02-05 16:19:33,349] Trial 193 finished with value: 0.40812156803124205 and parameters: {'learning_rate': 0.05161843963902832, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.0016828152939352692, 'subsample': 0.9990105371694347, 'colsample_bytree': 0.8477066767186282, 'alpha': 0.7218214442490498, 'lambda': 3.1865876560531996}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:19:35,127] Trial 194 finished with value: 0.3939576658210761 and parameters: {'learning_rate': 0.04931068724564766, 'max_depth': 14, 'min_child_weight': 1, 'gamma': 0.007352414159297755, 'subsample': 0.43572411337543765, 'colsample_bytree': 0.8463351124145262, 'alpha': 1.3319478745817424, 'lambda': 3.4073120296622768}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:19:36,666] Trial 195 finished with value: 0.3965854281481789 and parameters: {'learning_rate': 0.0494515242434827, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.005452675497206915, 'subsample': 0.37600758275416196, 'colsample_bytree': 0.8521123029943217, 'alpha': 1.2164342330021172, 'lambda': 3.4848795689188976}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:19:38,303] Trial 196 finished with value: 0.3935842764947453 and parameters: {'learning_rate': 0.04834584490638945, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.01612639309499586, 'subsample': 0.3853530593689461, 'colsample_bytree': 0.8516702741293808, 'alpha': 1.329475923132206, 'lambda': 3.463476657024049}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:19:45,808] Trial 197 finished with value: 0.3915613484271947 and parameters: {'learning_rate': 0.04832249578865239, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.01117520099829909, 'subsample': 0.35052978872675483, 'colsample_bytree': 0.8460003180667519, 'alpha': 1.3098275168903506, 'lambda': 3.487754650587849}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:01,261] Trial 198 finished with value: 0.396255922759877 and parameters: {'learning_rate': 0.03738748177428662, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.0062196917564612784, 'subsample': 0.3981494886691512, 'colsample_bytree': 0.8522276256073099, 'alpha': 1.2791582611712884, 'lambda': 3.470327490834981}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:13,595] Trial 199 finished with value: 0.390653323770901 and parameters: {'learning_rate': 0.03439403509452326, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.0007962488714490317, 'subsample': 0.36615899460225915, 'colsample_bytree': 0.8413506619622149, 'alpha': 1.3171409830845557, 'lambda': 3.4739800325854238}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:14,935] Trial 200 finished with value: 0.3780013290865176 and parameters: {'learning_rate': 0.034495571724568255, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.20421766467068614, 'subsample': 0.35027506864428254, 'colsample_bytree': 0.8461247198744175, 'alpha': 1.315120771640901, 'lambda': 3.480474451305262}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:16,050] Trial 201 finished with value: 0.377323749726134 and parameters: {'learning_rate': 0.03769182663907111, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.368568767005816, 'subsample': 0.4127644079856631, 'colsample_bytree': 0.8323442691128313, 'alpha': 1.1822576670713147, 'lambda': 3.3308400574067}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:17,335] Trial 202 finished with value: 0.39307875975947926 and parameters: {'learning_rate': 0.041977581494729375, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 0.021427346090784394, 'subsample': 0.3829985886919126, 'colsample_bytree': 0.8573474770088643, 'alpha': 1.3929775326324296, 'lambda': 3.4918962779957408}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:18,946] Trial 203 finished with value: 0.3815015094888452 and parameters: {'learning_rate': 0.024506374739987534, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.006401876790128547, 'subsample': 0.3813009696675308, 'colsample_bytree': 0.8586104633801677, 'alpha': 1.5594240927258576, 'lambda': 3.576697439535423}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:20,380] Trial 204 finished with value: 0.386131197204554 and parameters: {'learning_rate': 0.04092875488385257, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 0.005260117887867042, 'subsample': 0.31086757191521497, 'colsample_bytree': 0.8073182420651839, 'alpha': 1.4386658996369157, 'lambda': 3.5359115933203267}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:21,564] Trial 205 finished with value: 0.3804919566820569 and parameters: {'learning_rate': 0.03104733645583463, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.18660949364676208, 'subsample': 0.39519352350712367, 'colsample_bytree': 0.8822323700425171, 'alpha': 1.0695547185078842, 'lambda': 3.2940536435534615}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:22,957] Trial 206 finished with value: 0.38879831767402123 and parameters: {'learning_rate': 0.0353061627735412, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 0.0042052889385366995, 'subsample': 0.3607026787116154, 'colsample_bytree': 0.8392224252274607, 'alpha': 1.2310060717336235, 'lambda': 3.4525601197919644}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:23,976] Trial 207 finished with value: 0.37586821634201334 and parameters: {'learning_rate': 0.04283242861551699, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.3591124955247743, 'subsample': 0.40661943344531465, 'colsample_bytree': 0.822596042667304, 'alpha': 1.4992644264253716, 'lambda': 3.694074401729905}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:25,466] Trial 208 finished with value: 0.3952312896407275 and parameters: {'learning_rate': 0.038671253323230605, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 0.0010329561062322851, 'subsample': 0.32623873749612115, 'colsample_bytree': 0.8629218621423653, 'alpha': 0.7556996339041527, 'lambda': 3.2387791217893294}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:26,524] Trial 209 finished with value: 0.3620684701690263 and parameters: {'learning_rate': 0.047333859950773285, 'max_depth': 11, 'min_child_weight': 6, 'gamma': 0.22457802468238, 'subsample': 0.36207638894845695, 'colsample_bytree': 0.8606385375554855, 'alpha': 0.9081719965982333, 'lambda': 3.908412922796015}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:27,798] Trial 210 finished with value: 0.3776752493036738 and parameters: {'learning_rate': 0.03813195178975996, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.3533969190764664, 'subsample': 0.3150758430066966, 'colsample_bytree': 0.8831429282850879, 'alpha': 0.791233199190455, 'lambda': 3.293094475364098}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:29,305] Trial 211 finished with value: 0.3917677473425607 and parameters: {'learning_rate': 0.042334749198949, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 0.007858223465871058, 'subsample': 0.3356667292069171, 'colsample_bytree': 0.860703729127546, 'alpha': 1.0935461126563544, 'lambda': 3.2295945290784394}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:30,503] Trial 212 finished with value: 0.3820613644028909 and parameters: {'learning_rate': 0.042823087800701445, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 0.2114584034706306, 'subsample': 0.3375234046509272, 'colsample_bytree': 0.8480517847368507, 'alpha': 1.1609496669400803, 'lambda': 3.555293663951243}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:32,056] Trial 213 finished with value: 0.38844776604050874 and parameters: {'learning_rate': 0.034047423315107885, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 0.020265677236428158, 'subsample': 0.3734940327022293, 'colsample_bytree': 0.870308597035758, 'alpha': 1.40621144619557, 'lambda': 3.2701198733759393}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:33,401] Trial 214 finished with value: 0.38662632939712543 and parameters: {'learning_rate': 0.04709360895530311, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 0.18354555304967762, 'subsample': 0.43586830313877417, 'colsample_bytree': 0.805891518774232, 'alpha': 1.0556598248700153, 'lambda': 3.4344060904744653}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:34,864] Trial 215 finished with value: 0.36711681147549735 and parameters: {'learning_rate': 0.0410467361722458, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.3869520404357088, 'subsample': 0.24899014139147813, 'colsample_bytree': 0.8281392760881763, 'alpha': 1.5988266237111357, 'lambda': 3.735167369819467}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:36,562] Trial 216 finished with value: 0.4021068625269315 and parameters: {'learning_rate': 0.04523947898463647, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.003837370844832583, 'subsample': 0.4189753703663591, 'colsample_bytree': 0.8980621028142899, 'alpha': 1.3089708423259279, 'lambda': 3.1511122282275594}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:37,870] Trial 217 finished with value: 0.38089642065100676 and parameters: {'learning_rate': 0.04933115100163857, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 0.20646486616807616, 'subsample': 0.414377323960942, 'colsample_bytree': 0.8892159321646296, 'alpha': 1.9141561918848116, 'lambda': 3.170800773048087}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:20:43,787] Trial 218 finished with value: 0.37676157582852055 and parameters: {'learning_rate': 0.04548370049160511, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.4323027670460816, 'subsample': 0.29358691670734555, 'colsample_bytree': 0.8606615734581626, 'alpha': 0.6764996932348595, 'lambda': 3.0218391551400345}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:00,014] Trial 219 finished with value: 0.4064850965773898 and parameters: {'learning_rate': 0.052090788649297726, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.009168921106653983, 'subsample': 0.4742603336185528, 'colsample_bytree': 0.9050885072816501, 'alpha': 0.9215508818547825, 'lambda': 3.245831104204676}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:13,491] Trial 220 finished with value: 0.38258386060476096 and parameters: {'learning_rate': 0.05190210296948494, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 0.3195714815684185, 'subsample': 0.45868110939025386, 'colsample_bytree': 0.899813355423576, 'alpha': 0.8614348349611279, 'lambda': 3.2658617808389354}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:14,969] Trial 221 finished with value: 0.3926601467821407 and parameters: {'learning_rate': 0.04442378896196575, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.018715343352033897, 'subsample': 0.3326004335742244, 'colsample_bytree': 0.8726849997582836, 'alpha': 1.0134015535707765, 'lambda': 3.401962579340257}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:16,190] Trial 222 finished with value: 0.3826831762018884 and parameters: {'learning_rate': 0.043036228265574274, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.19425318081933202, 'subsample': 0.4768268160277486, 'colsample_bytree': 0.8725891556654136, 'alpha': 0.9967032250661678, 'lambda': 3.1935155447722705}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:17,275] Trial 223 finished with value: 0.3411757094877361 and parameters: {'learning_rate': 0.039988538338395156, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 3.41813336812142, 'subsample': 0.41811707238211465, 'colsample_bytree': 0.9014955517865051, 'alpha': 1.1372140235773507, 'lambda': 3.6660071858495398}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:18,567] Trial 224 finished with value: 0.3886823261359304 and parameters: {'learning_rate': 0.04434539308499065, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 0.16747765621396477, 'subsample': 0.38700401618211216, 'colsample_bytree': 0.8756145897703675, 'alpha': 0.7653837664462215, 'lambda': 2.945741087403873}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:19,878] Trial 225 finished with value: 0.37061150187218667 and parameters: {'learning_rate': 0.037944366704404944, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.43081340876835406, 'subsample': 0.33323165283171485, 'colsample_bytree': 0.8165104732893286, 'alpha': 1.6522187381056836, 'lambda': 3.402475666815008}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:21,225] Trial 226 finished with value: 0.38757620499191264 and parameters: {'learning_rate': 0.05123832456080362, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 0.15222893557197492, 'subsample': 0.4407559428515452, 'colsample_bytree': 0.9000679468123173, 'alpha': 0.9495738283486155, 'lambda': 3.9053931474434926}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:22,729] Trial 227 finished with value: 0.3986265294344069 and parameters: {'learning_rate': 0.046794806775903884, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.021010941545343765, 'subsample': 0.4901705056925455, 'colsample_bytree': 0.8583503555186379, 'alpha': 1.3564495448736458, 'lambda': 3.1410447056262965}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:24,382] Trial 228 finished with value: 0.393458280004937 and parameters: {'learning_rate': 0.047035501701296674, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.00019035038833881135, 'subsample': 0.39112220420494115, 'colsample_bytree': 0.8512017760406417, 'alpha': 1.400858261980775, 'lambda': 3.0589458650884396}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:25,457] Trial 229 finished with value: 0.37714414152649556 and parameters: {'learning_rate': 0.048388841249156586, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.3992928748854487, 'subsample': 0.4970315384795182, 'colsample_bytree': 0.7964019091869948, 'alpha': 1.5367180313502553, 'lambda': 2.859677792732009}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:26,699] Trial 230 finished with value: 0.3876657462887334 and parameters: {'learning_rate': 0.04546918168918393, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.21614084556180602, 'subsample': 0.4608039909137872, 'colsample_bytree': 0.8813068417051138, 'alpha': 0.6598776178178256, 'lambda': 3.072561571309094}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:28,418] Trial 231 finished with value: 0.4001864431583213 and parameters: {'learning_rate': 0.05107118945438712, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.01991832375855726, 'subsample': 0.3923565316914715, 'colsample_bytree': 0.8517840054194221, 'alpha': 1.3266536612250648, 'lambda': 3.338740205467522}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:29,798] Trial 232 finished with value: 0.4017543778405347 and parameters: {'learning_rate': 0.05272522189976425, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.015587552701660174, 'subsample': 0.3918748479028047, 'colsample_bytree': 0.8510377255056866, 'alpha': 1.2581801881876693, 'lambda': 3.056374712543273}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:30,948] Trial 233 finished with value: 0.3881259256788306 and parameters: {'learning_rate': 0.052277109218418306, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.1718143814687677, 'subsample': 0.39719935359214315, 'colsample_bytree': 0.8296983217529056, 'alpha': 1.2746518072534367, 'lambda': 3.287044986451602}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:32,371] Trial 234 finished with value: 0.3970283292831114 and parameters: {'learning_rate': 0.04902121544199167, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.030503717557482712, 'subsample': 0.4254133313857122, 'colsample_bytree': 0.8585057675134146, 'alpha': 1.3331638348254327, 'lambda': 3.046058707828609}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:33,681] Trial 235 finished with value: 0.37324482946065074 and parameters: {'learning_rate': 0.052597954844536096, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.4277863874369911, 'subsample': 0.4260374039248992, 'colsample_bytree': 0.8535802408748876, 'alpha': 1.4737388607516733, 'lambda': 3.007242967620362}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:35,688] Trial 236 finished with value: 0.39842069396233365 and parameters: {'learning_rate': 0.04883803527712017, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.0023462013059563427, 'subsample': 0.39966291847972746, 'colsample_bytree': 0.8985404281519506, 'alpha': 1.3444927418433408, 'lambda': 3.134771106900104}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:36,837] Trial 237 finished with value: 0.37813986061214716 and parameters: {'learning_rate': 0.04854957608132536, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.28887453832897936, 'subsample': 0.40098324739549374, 'colsample_bytree': 0.9024317604922067, 'alpha': 1.2402645788726965, 'lambda': 3.0989675119132096}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:38,277] Trial 238 finished with value: 0.3932641935408957 and parameters: {'learning_rate': 0.052924445611569434, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.014471740081862913, 'subsample': 0.4395613458911096, 'colsample_bytree': 0.8870539012151719, 'alpha': 1.5892367868413686, 'lambda': 2.834079053330756}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:40,579] Trial 239 finished with value: 0.3786370654725228 and parameters: {'learning_rate': 0.05156226410836302, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.32944226821493244, 'subsample': 0.42093785813579615, 'colsample_bytree': 0.8250946135330299, 'alpha': 1.3348157757150076, 'lambda': 3.1130601128248303}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:21:52,209] Trial 240 finished with value: 0.38817662920064067 and parameters: {'learning_rate': 0.048875218519533685, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.4988929318960859, 'subsample': 0.3920473368416727, 'colsample_bytree': 0.9185288372959504, 'alpha': 0.0026087902928383366, 'lambda': 3.179475217162076}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:09,186] Trial 241 finished with value: 0.39583908079389063 and parameters: {'learning_rate': 0.05316106715692073, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.00715787277930475, 'subsample': 0.44951246015587243, 'colsample_bytree': 0.8840918971352901, 'alpha': 1.6329807318919656, 'lambda': 2.8425682862596746}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:13,410] Trial 242 finished with value: 0.3978801578360037 and parameters: {'learning_rate': 0.04763195910737098, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.008450961502064796, 'subsample': 0.46256734415155304, 'colsample_bytree': 0.8741591158768219, 'alpha': 1.8581384429884582, 'lambda': 2.9188204679769645}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:14,978] Trial 243 finished with value: 0.3958654797630425 and parameters: {'learning_rate': 0.053368749771718474, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.00018280466832356257, 'subsample': 0.47169236337359666, 'colsample_bytree': 0.8913605969784472, 'alpha': 1.731392326244917, 'lambda': 2.670378398625101}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:16,149] Trial 244 finished with value: 0.3777578062773984 and parameters: {'learning_rate': 0.05339702201843834, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.22793430030103248, 'subsample': 0.4488461170932303, 'colsample_bytree': 0.8900777334745453, 'alpha': 1.902746950302359, 'lambda': 2.4609536129821477}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:18,006] Trial 245 finished with value: 0.382483016740897 and parameters: {'learning_rate': 0.054553156306279514, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.21233756246159205, 'subsample': 0.46745181516756157, 'colsample_bytree': 0.9054897500285474, 'alpha': 1.764692761749456, 'lambda': 2.663006278803708}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:19,069] Trial 246 finished with value: 0.38186768571880325 and parameters: {'learning_rate': 0.0495263727162844, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.20336749697095718, 'subsample': 0.4863153062012605, 'colsample_bytree': 0.8738006798684862, 'alpha': 1.6455361128830848, 'lambda': 2.8224573024118813}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:20,187] Trial 247 finished with value: 0.3715860734195704 and parameters: {'learning_rate': 0.03734233753648818, 'max_depth': 13, 'min_child_weight': 5, 'gamma': 0.35562025050948326, 'subsample': 0.4613021575768384, 'colsample_bytree': 0.9102837561847693, 'alpha': 1.1640379429518801, 'lambda': 2.924307604046376}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:21,085] Trial 248 finished with value: 0.34072434400760876 and parameters: {'learning_rate': 0.04588633569729122, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 6.419500192084979, 'subsample': 0.5083275999366814, 'colsample_bytree': 0.8745543481835261, 'alpha': 2.031343235980772, 'lambda': 2.608657758871586}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:22,449] Trial 249 finished with value: 0.396145057242742 and parameters: {'learning_rate': 0.05294595331419327, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.009137067303965499, 'subsample': 0.42708031640003613, 'colsample_bytree': 0.834495774375165, 'alpha': 1.4732154847943009, 'lambda': 2.814323355449447}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:23,545] Trial 250 finished with value: 0.38704292376067817 and parameters: {'learning_rate': 0.060219179697895385, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.18520526696107806, 'subsample': 0.47366040241302215, 'colsample_bytree': 0.8080450729539372, 'alpha': 1.5482620943297007, 'lambda': 2.795224016230987}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:24,619] Trial 251 finished with value: 0.371547727319127 and parameters: {'learning_rate': 0.05407137974483316, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.5024248123223549, 'subsample': 0.4282489525218753, 'colsample_bytree': 0.9307683462564051, 'alpha': 1.917784614306254, 'lambda': 2.9546019933199266}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:26,050] Trial 252 finished with value: 0.40349068155720624 and parameters: {'learning_rate': 0.05274031491413772, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.00634818165639094, 'subsample': 0.4561403655997885, 'colsample_bytree': 0.8911899653054635, 'alpha': 1.150903973597722, 'lambda': 2.4605328620183817}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:27,528] Trial 253 finished with value: 0.38387928836801544 and parameters: {'learning_rate': 0.05279310680043001, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.20836145017741273, 'subsample': 0.4779139738302723, 'colsample_bytree': 0.8945205929494343, 'alpha': 1.1048953033888913, 'lambda': 2.3611653639333032}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:28,667] Trial 254 finished with value: 0.3860606996556206 and parameters: {'learning_rate': 0.045786741046996454, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.3627644211296055, 'subsample': 0.4544393456318535, 'colsample_bytree': 0.8697479109598945, 'alpha': 0.8202856713533104, 'lambda': 2.489309760279977}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:30,122] Trial 255 finished with value: 0.38862731858512833 and parameters: {'learning_rate': 0.03847316810858318, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.011035942080339105, 'subsample': 0.5458366068839898, 'colsample_bytree': 0.8855385928308322, 'alpha': 1.5519818887872046, 'lambda': 2.696919110507409}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:31,288] Trial 256 finished with value: 0.3893572055011687 and parameters: {'learning_rate': 0.05755366942476575, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.2060796017524611, 'subsample': 0.40603002718361886, 'colsample_bytree': 0.8278015995575906, 'alpha': 1.1663634576392217, 'lambda': 2.550086396023721}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:32,008] Trial 257 finished with value: 0.3476172202902557 and parameters: {'learning_rate': 0.050870223365195616, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 4.174241136134245, 'subsample': 0.5011849616064472, 'colsample_bytree': 0.9171864324864019, 'alpha': 0.9105590792007936, 'lambda': 2.9232769677985955}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:33,363] Trial 258 finished with value: 0.3738962814026169 and parameters: {'learning_rate': 0.04043995382781928, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.3668792520611969, 'subsample': 0.42810648952474933, 'colsample_bytree': 0.8620672183098318, 'alpha': 1.763600649221193, 'lambda': 2.768655189687379}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:35,264] Trial 259 finished with value: 0.391896812126883 and parameters: {'learning_rate': 0.029088275679533687, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.01596622476774277, 'subsample': 0.4488059880680802, 'colsample_bytree': 0.8976936128572107, 'alpha': 1.4742750577588484, 'lambda': 3.186622813810412}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:38,585] Trial 260 finished with value: 0.37817236372286694 and parameters: {'learning_rate': 0.04533455146448452, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.5007105544303644, 'subsample': 0.411521418284662, 'colsample_bytree': 0.83712707912667, 'alpha': 1.191267910284207, 'lambda': 2.2705387828415855}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:22:50,431] Trial 261 finished with value: 0.38236255918613415 and parameters: {'learning_rate': 0.05446465119719966, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.20412767415490443, 'subsample': 0.49000075392850206, 'colsample_bytree': 0.8738112580003675, 'alpha': 0.9323911707309602, 'lambda': 3.0149128422211304}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:03,883] Trial 262 finished with value: 0.38323636518390636 and parameters: {'learning_rate': 0.033977859901666005, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.18435819393664624, 'subsample': 0.4728646897874457, 'colsample_bytree': 0.9297689007715277, 'alpha': 1.3801844101019953, 'lambda': 3.223001617678923}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:10,823] Trial 263 finished with value: 0.3641711481008996 and parameters: {'learning_rate': 0.050384684653102606, 'max_depth': 12, 'min_child_weight': 8, 'gamma': 0.579119505173227, 'subsample': 0.5251688621026047, 'colsample_bytree': 0.9045722720051105, 'alpha': 1.679152875209346, 'lambda': 2.5164823512110885}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:12,376] Trial 264 finished with value: 0.3979603726875986 and parameters: {'learning_rate': 0.058939144006552854, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.01737035019651651, 'subsample': 0.4212185687418425, 'colsample_bytree': 0.7967955046447724, 'alpha': 1.136999676275675, 'lambda': 2.81208164849992}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:13,382] Trial 265 finished with value: 0.3619771891113709 and parameters: {'learning_rate': 0.060634280134408955, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.017741771005361347, 'subsample': 0.420519548676274, 'colsample_bytree': 0.7933267570576427, 'alpha': 7.946360033943752, 'lambda': 2.9098533866526344}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:15,129] Trial 266 finished with value: 0.3966441599151634 and parameters: {'learning_rate': 0.05832745603564561, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.003319358426871681, 'subsample': 0.37299743661908485, 'colsample_bytree': 0.8058179740320968, 'alpha': 1.1408230554729035, 'lambda': 2.6560395872736664}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:16,552] Trial 267 finished with value: 0.3955962688580453 and parameters: {'learning_rate': 0.057924400489445804, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.006594738916057984, 'subsample': 0.3625137653381445, 'colsample_bytree': 0.8161775903861144, 'alpha': 1.0832237346600513, 'lambda': 2.3619844301101516}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:17,869] Trial 268 finished with value: 0.38559746384253274 and parameters: {'learning_rate': 0.0585234323181303, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.15537160026902785, 'subsample': 0.36938312594934114, 'colsample_bytree': 0.779642170106147, 'alpha': 1.1006681950259716, 'lambda': 2.2167877213883753}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:18,904] Trial 269 finished with value: 0.366178467292221 and parameters: {'learning_rate': 0.05521666787980465, 'max_depth': 12, 'min_child_weight': 4, 'gamma': 0.3613313539476949, 'subsample': 0.36680204719453935, 'colsample_bytree': 0.7911819792446682, 'alpha': 1.273253950362085, 'lambda': 2.397655432483184}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:19,812] Trial 270 finished with value: 0.3367513974082115 and parameters: {'learning_rate': 0.057808982744780336, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 7.950878239326084, 'subsample': 0.4043407172917474, 'colsample_bytree': 0.7959075442830503, 'alpha': 1.4633677575072528, 'lambda': 2.647859084845665}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:21,109] Trial 271 finished with value: 0.39165057982811513 and parameters: {'learning_rate': 0.06154411112799586, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 0.16785370739229516, 'subsample': 0.4493448470883851, 'colsample_bytree': 0.830898563529145, 'alpha': 1.1050656272033175, 'lambda': 2.3653585436601956}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:22,970] Trial 272 finished with value: 0.39674758120048215 and parameters: {'learning_rate': 0.053944241790714276, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.007601913798349305, 'subsample': 0.37482890078447983, 'colsample_bytree': 0.8165820555959987, 'alpha': 0.9880572372358574, 'lambda': 2.1791562732235294}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:24,598] Trial 273 finished with value: 0.4019029894875032 and parameters: {'learning_rate': 0.05327202306037321, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.006302977257038998, 'subsample': 0.4263043111738008, 'colsample_bytree': 0.812267010271467, 'alpha': 1.310149431248825, 'lambda': 2.78782386851286}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:25,785] Trial 274 finished with value: 0.3282713148877702 and parameters: {'learning_rate': 0.05019206387613516, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.39265477982166586, 'subsample': 0.05101647553544397, 'colsample_bytree': 0.8062761865855539, 'alpha': 1.3042377281377309, 'lambda': 2.6756467549896046}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:27,398] Trial 275 finished with value: 0.4008613911678887 and parameters: {'learning_rate': 0.0540860769460474, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.008501735383310125, 'subsample': 0.4194006228214008, 'colsample_bytree': 0.8272192292791405, 'alpha': 1.2835489331710759, 'lambda': 1.9661882682869216}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:28,707] Trial 276 finished with value: 0.37557654644830896 and parameters: {'learning_rate': 0.04712201416899608, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.5337165782614327, 'subsample': 0.40774908725305015, 'colsample_bytree': 0.812879553482745, 'alpha': 0.9292147510221966, 'lambda': 2.060031007320383}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:30,014] Trial 277 finished with value: 0.3650197818203136 and parameters: {'learning_rate': 0.05529555665235869, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.25530072846977636, 'subsample': 0.3805578905393938, 'colsample_bytree': 0.09069540290262129, 'alpha': 1.2515017269493973, 'lambda': 1.7416477016154348}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:32,012] Trial 278 finished with value: 0.40208354693496395 and parameters: {'learning_rate': 0.04988299312306213, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.0012339205585778189, 'subsample': 0.42511263051061254, 'colsample_bytree': 0.778690057333182, 'alpha': 0.9816758082420514, 'lambda': 1.901519843349501}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:33,427] Trial 279 finished with value: 0.38579581600511237 and parameters: {'learning_rate': 0.04873098705453163, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.3324506056405434, 'subsample': 0.4162759997553967, 'colsample_bytree': 0.771287677796712, 'alpha': 0.9552721266578794, 'lambda': 1.5577442661456338}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:35,948] Trial 280 finished with value: 0.3908873494962796 and parameters: {'learning_rate': 0.044725778364398054, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.19609113796159242, 'subsample': 0.3918148280773127, 'colsample_bytree': 0.7497455444967288, 'alpha': 0.698746092213505, 'lambda': 2.2007567830458297}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:23:45,049] Trial 281 finished with value: 0.37588312145211483 and parameters: {'learning_rate': 0.0495800702904616, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.5346396674951267, 'subsample': 0.37671565543802166, 'colsample_bytree': 0.7824876274985452, 'alpha': 0.9855813976082901, 'lambda': 1.8073390615860987}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:24:03,170] Trial 282 finished with value: 0.363452213971577 and parameters: {'learning_rate': 0.05879835352584102, 'max_depth': 13, 'min_child_weight': 7, 'gamma': 0.00045009617287018397, 'subsample': 0.4292966323143938, 'colsample_bytree': 0.8114409171469293, 'alpha': 1.1984908760832944, 'lambda': 2.103286954579487}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:24:14,080] Trial 283 finished with value: 0.38620577782341076 and parameters: {'learning_rate': 0.04593476820575975, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.2138954974113916, 'subsample': 0.41291791975214664, 'colsample_bytree': 0.798011174086322, 'alpha': 0.8229204168083176, 'lambda': 1.9455589821536314}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:24:15,359] Trial 284 finished with value: 0.3718018642564369 and parameters: {'learning_rate': 0.011004155257217381, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.3777677650557092, 'subsample': 0.39766647100367397, 'colsample_bytree': 0.8268276291515618, 'alpha': 1.3802918829994568, 'lambda': 1.9268074305357201}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:24:16,518] Trial 285 finished with value: 0.3888930446048216 and parameters: {'learning_rate': 0.05201831172780701, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.1647835860541475, 'subsample': 0.43891430942527043, 'colsample_bytree': 0.8462625632701949, 'alpha': 1.1330474921488392, 'lambda': 3.3481237320708592}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:24:17,999] Trial 286 finished with value: 0.39656351210058993 and parameters: {'learning_rate': 0.05565577115516582, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.0006024723413493253, 'subsample': 0.3558935909190282, 'colsample_bytree': 0.7795527378234667, 'alpha': 1.0045776104937423, 'lambda': 1.6464179249068942}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:24:19,282] Trial 287 finished with value: 0.3819527043762378 and parameters: {'learning_rate': 0.06321680895360972, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.3679236542867375, 'subsample': 0.3472659407091002, 'colsample_bytree': 0.770993793421596, 'alpha': 0.6953427242541828, 'lambda': 2.1655633906382334}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:24:20,402] Trial 288 finished with value: 0.38455243132103273 and parameters: {'learning_rate': 0.05591667006979695, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.19108988518309056, 'subsample': 0.4361146698567853, 'colsample_bytree': 0.751870078908827, 'alpha': 0.9478908533356906, 'lambda': 1.7493834097923042}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:24:21,399] Trial 289 finished with value: 0.377629143352308 and parameters: {'learning_rate': 0.06136328747057419, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.5866413662253674, 'subsample': 0.3716646983186714, 'colsample_bytree': 0.7866473320190807, 'alpha': 0.5865899754979774, 'lambda': 1.3200575416549578}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:24:22,531] Trial 290 finished with value: 0.3850720702350455 and parameters: {'learning_rate': 0.05547644913917085, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.19300505785741792, 'subsample': 0.3542679100506874, 'colsample_bytree': 0.8219115490455009, 'alpha': 1.0153604067154527, 'lambda': 1.1771935855653621}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:24:23,833] Trial 291 finished with value: 0.38081188002417726 and parameters: {'learning_rate': 0.05016530885626691, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.36042850503189183, 'subsample': 0.3819208620149656, 'colsample_bytree': 0.8017260552900821, 'alpha': 0.8201135663561342, 'lambda': 1.7691796087918268}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:24:25,125] Trial 292 finished with value: 0.3837198658023129 and parameters: {'learning_rate': 0.0476990251161941, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.1522650197646253, 'subsample': 0.41511051922502523, 'colsample_bytree': 0.781214726565928, 'alpha': 1.210377745330004, 'lambda': 1.5817309166202138}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:24:26,267] Trial 293 finished with value: 0.36878511704284395 and parameters: {'learning_rate': 0.052787562127941584, 'max_depth': 15, 'min_child_weight': 1, 'gamma': 0.5111823514469207, 'subsample': 0.396830375978304, 'colsample_bytree': 0.8174297616244462, 'alpha': 1.3948313262364214, 'lambda': 2.0476737444683666}. Best is trial 193 with value: 0.40812156803124205.\n",
      "[I 2024-02-05 16:24:27,972] Trial 294 finished with value: 0.397205428265094 and parameters: {'learning_rate': 0.05869461878890147, 'max_depth': 13, 'min_child_weight': 1, 'gamma': 0.0019290884458997687, 'subsample': 0.3528466435693111, 'colsample_bytree': 0.8399483938208397, 'alpha': 1.0725716516957604, 'lambda': 1.9420830055391785}. Best is trial 193 with value: 0.40812156803124205.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def objective(trial):#,X_train,y_train,X_valid,y_valid,weight_train):\n",
    "    params = dict(\n",
    "                learning_rate = trial.suggest_float(\"learning_rate\", 0.01, 0.2),\n",
    "                max_depth= trial.suggest_int(\"max_depth\",5, 15),\n",
    "                min_child_weight = trial.suggest_int(\"min_child_weight\", 1, 8),\n",
    "                gamma = trial.suggest_float(\"gamma\", 0, 10),\n",
    "                subsample = trial.suggest_float(\"subsample\", 0.01,1),\n",
    "                colsample_bytree = trial.suggest_float(\"colsample_bytree\", 0.01,1),\n",
    "                alpha = trial.suggest_float(\"alpha\", 0, 10),\n",
    "                objective= 'multi:softprob', \n",
    "                nthread=4, \n",
    "                 num_class= 5,\n",
    "                seed=27)\n",
    "    params['lambda'] = trial.suggest_float(\"lambda\", 0, 10)\n",
    "\n",
    "    \n",
    "    dtrain = xgb.DMatrix(X_train_oh,y_train.apply(lambda x:x.replace('i','')).astype(int), \n",
    "                         weight=weight_train)\n",
    "    dvalid = xgb.DMatrix(X_valid_oh,y_valid.apply(lambda x:x.replace('i','')).astype(int),\n",
    "                         )\n",
    "\n",
    "\n",
    "    bst = xgb.train(params, dtrain,verbose_eval=False, num_boost_round=400,\n",
    "                    evals = [(dtrain, \"train\"), (dvalid, \"valid\")],\n",
    "                    early_stopping_rounds=100)\n",
    "\n",
    "    preds = bst.predict(dvalid)\n",
    "    ##MCC is more solid\n",
    "    mcc = matthews_corrcoef(y_valid.apply(lambda x:x.replace('i','')).astype(int),preds.argmax(1))    \n",
    "    \n",
    "    return mcc\n",
    "\n",
    "\n",
    "\n",
    "study = optuna.create_study(direction=\"maximize\")\n",
    "study.optimize(objective, n_trials=400, timeout=600)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "eedc457f-587c-46c0-a712-addb35719191",
   "metadata": {},
   "outputs": [],
   "source": [
    "params_final = dict(\n",
    "            objective= 'multi:softprob', \n",
    "            nthread=4, \n",
    "             num_class= 5,\n",
    "            seed=27)\n",
    "params_final.update(study_small.best_params)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "dtrain = xgb.DMatrix(X_train_small,y_train_small.apply(lambda x:x.replace('i','')).astype(int), \n",
    "                    \n",
    "                     weight=weight_train)\n",
    "dvalid = xgb.DMatrix(X_valid_small,y_valid_small.apply(lambda x:x.replace('i','')).astype(int),\n",
    "                   \n",
    "                     )\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "bst = xgb.train(params_final, dtrain,verbose_eval=False, num_boost_round=1000,\n",
    "                evals = [(dtrain, \"train\"), (dvalid, \"valid\")],\n",
    "                early_stopping_rounds=100,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "a25880ed-fef2-48cd-bb01-fb5d7bfb2616",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 158   26    2    1    0]\n",
      " [ 236 2056  731   19    1]\n",
      " [   4  159  494   85    2]\n",
      " [   1    1   13   18    1]\n",
      " [   1    0    0    0    1]]\n",
      "0.4078378215401631\n",
      "0.6800498753117207\n",
      "########################################\n",
      "[[ 350   23    4    2    0]\n",
      " [ 477 4252 1416   32    0]\n",
      " [  13  268 1090  134    5]\n",
      " [   0    0    0   68    0]\n",
      " [   0    0    0    0    5]]\n",
      "0.46853573039294066\n",
      "0.7083179751812262\n"
     ]
    }
   ],
   "source": [
    "preds_class_valid = bst.predict(dvalid)\n",
    "preds_class_train= bst.predict(dtrain)\n",
    "print(confusion_matrix(y_valid.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid.argmax(1)))\n",
    "print(matthews_corrcoef(y_valid.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid.argmax(1)))\n",
    "print(accuracy_score(y_valid.apply(lambda x:x.replace('i','')).astype(int),preds_class_valid.argmax(1)))\n",
    "print('########################################')\n",
    "print(confusion_matrix(y_train.apply(lambda x:x.replace('i','')).astype(int),preds_class_train.argmax(1)))\n",
    "print(matthews_corrcoef(y_train.apply(lambda x:x.replace('i','')).astype(int),preds_class_train.argmax(1)))\n",
    "print(accuracy_score(y_train.apply(lambda x:x.replace('i','')).astype(int),preds_class_train.argmax(1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "c4b06899-07fd-4258-b5e6-9d69f0b4f816",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4010, 184, 5)\n"
     ]
    }
   ],
   "source": [
    "import shap\n",
    "explainer = shap.TreeExplainer(bst,feature_names=X_valid_oh.columns)\n",
    "\n",
    "explanation = explainer(dvalid)\n",
    "\n",
    "shap_values = explanation\n",
    "print(shap_values.values.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "c5a80cdb-36a8-4f65-95b7-e9c84c4a1243",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diagnosis_muscular_sprain               0.0\n",
       "'ski_lift'_False                        0.0\n",
       "age                                    15.0\n",
       "injury_general_location_lower_limbs     1.0\n",
       "'ski_lift'_True                         1.0\n",
       "gender_M                                0.0\n",
       "diagnosis_fracture                      0.0\n",
       "injury_side_None                        0.0\n",
       "'helicopter'_True                       0.0\n",
       "difficulty_easy                         1.0\n",
       "'ambulance'_False                       1.0\n",
       "'helicopter'_False                      1.0\n",
       "gender_F                                1.0\n",
       "destination_hospital_emergency_room     0.0\n",
       "injury_side_L                           1.0\n",
       "injury_general_location_None            0.0\n",
       "'ambulance'_True                        0.0\n",
       "country_Germania                        1.0\n",
       "injury_general_location_trunk           0.0\n",
       "diagnosis_None                          0.0\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_oh.loc[0][X_train_oh.columns[np.argsort(-shap_values.values[0,:,1],)][0:20]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "d75295b4-7bc9-4590-9cb5-1e43379952af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['diagnosis_muscular_sprain', ''ski_lift'_False', 'age',\n",
       "       'injury_general_location_lower_limbs', ''ski_lift'_True', 'gender_M',\n",
       "       'diagnosis_fracture', 'injury_side_None', ''helicopter'_True',\n",
       "       'difficulty_easy'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_oh.columns[np.argsort(-shap_values.values[0,:,1],)][0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "1f350558-3eb1-43ee-98ca-246ddb3cd037",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Glyph 9 (\t) missing from current font.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x650 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.plots.waterfall(shap_values[0,:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec9234a7-7d0f-4d13-8bf4-cfd20f916376",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}