{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"import pickle\n",
"import pymysql\n",
"\n",
"from sklearn.metrics import roc_auc_score\n",
"\n",
"from lightgbm import LGBMClassifier, plot_importance\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"RANDOM_STATE = 99\n",
"TARGET_VAR = 'flag_bad_usr'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import configparser\n",
"config = configparser.ConfigParser()\n",
"config.read('/home/ec2-user/SageMaker/zhilal/config.ini')\n",
"\n",
"host = config['MYSQL-ROOT']['HOST']\n",
"user = config['MYSQL-ROOT']['USER']\n",
"password = config['MYSQL-ROOT']['PASSWORD']\n",
"\n",
"def connect_sql():\n",
" cnx = pymysql.connect(host=host,\n",
" user=user,\n",
" password=password,\n",
" cursorclass=pymysql.cursors.DictCursor)\n",
" return cnx"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load Data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"raw_df = pd.read_parquet('data/feats_proba_user_data.parquet')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"raw_df = raw_df.rename(columns={'raw_pd':'raw_pd_v2',\n",
" 'flag_reject_model':'flag_reject_v2'})"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(71017, 41)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 35,
"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>flag_bad_usr</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>len</th>\n",
" <td>71017.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sum</th>\n",
" <td>2958.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.041652</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" flag_bad_usr\n",
"len 71017.000000\n",
"sum 2958.000000\n",
"mean 0.041652"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_df.agg({'flag_bad_usr':[len,sum,'mean']})"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"raw_df['mo'] = raw_df['transaction_date'].dt.month"
]
},
{
"cell_type": "code",
"execution_count": 37,
"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>obs</th>\n",
" <th>bad</th>\n",
" <th>ODR</th>\n",
" </tr>\n",
" <tr>\n",
" <th>mo</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>27729</td>\n",
" <td>1083.0</td>\n",
" <td>0.039057</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>37002</td>\n",
" <td>1656.0</td>\n",
" <td>0.044754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>6286</td>\n",
" <td>219.0</td>\n",
" <td>0.034839</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" obs bad ODR\n",
"mo \n",
"6 27729 1083.0 0.039057\n",
"7 37002 1656.0 0.044754\n",
"8 6286 219.0 0.034839"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"monthly_odr = raw_df.groupby('mo').agg({'flag_bad_usr':[len,sum,'mean']})\n",
"monthly_odr.columns = ['obs', 'bad', 'ODR']\n",
"monthly_odr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Quickly add new feature\n",
"- count of previous PL transactions"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"uid = tuple(raw_df['user_id'])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"query = f\"\"\"select user_id,id,transaction_date \n",
" from l2alpha.transaction \n",
" where user_id in {uid} and status = 4 and transaction_type = 2\"\"\"\n",
"\n",
"df_add = pd.read_sql(query, connect_sql())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"df_add['series'] = df_add.groupby('user_id')['transaction_date'].rank()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"raw_df = raw_df.merge(df_add[['id','series']], how = 'left', left_on = 'trx_id', right_on = 'id')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Convert payment_type to integer"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"raw_df['payment_type'] = np.where(raw_df['payment_type'] == '3_months', 0, 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"features = ['ap_co_plo',\n",
" 'calibrated_final',\n",
" 'current_dpd',\n",
" 'delin_max_dpd_3mo',\n",
" 'delin_max_dpd_amt_3mo',\n",
" 'flag_bad_pefindo',\n",
" 'flag_good_pefindo',\n",
" 'os_amount',\n",
" 'oth_first_trx_amount',\n",
" 'oth_last_rep_channel',\n",
" 'oth_last_rep_days',\n",
" 'oth_last_rep_dpd',\n",
" 'oth_last_trx_amount',\n",
" 'pf_con_open_count_12mo',\n",
" 'pf_delin_dist_contractcode_30_dpd_3mo',\n",
" 'pf_delin_max_dpd_12mo',\n",
" 'pf_util_creditcard_avg_12mo',\n",
" 'time_approve_to_pl_hour',\n",
" 'util_non_pl',\n",
" 'util_pl',\n",
" 'series',\n",
" 'payment_type']"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"categorical_feats = ['flag_bad_pefindo',\n",
" 'flag_good_pefindo',\n",
" 'oth_last_rep_channel',\n",
" 'payment_type'\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">flag_bad_usr</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>len</th>\n",
" <th>mean</th>\n",
" <th>sum</th>\n",
" </tr>\n",
" <tr>\n",
" <th>payment_type</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>11325</td>\n",
" <td>0.028521</td>\n",
" <td>323.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>59692</td>\n",
" <td>0.044143</td>\n",
" <td>2635.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" flag_bad_usr \n",
" len mean sum\n",
"payment_type \n",
"0 11325 0.028521 323.0\n",
"1 59692 0.044143 2635.0"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_df.groupby('payment_type').agg({'flag_bad_usr':[len, 'mean', sum]})"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" 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>user_id</th>\n",
" <th>perc_obs</th>\n",
" </tr>\n",
" <tr>\n",
" <th>flag_bad_usr</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>False</th>\n",
" <td>68059</td>\n",
" <td>0.958348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>True</th>\n",
" <td>2958</td>\n",
" <td>0.041652</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user_id perc_obs\n",
"flag_bad_usr \n",
"False 68059 0.958348\n",
"True 2958 0.041652"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flag_mean = raw_df.groupby(TARGET_VAR).agg({'user_id':'count'})\n",
"flag_mean['perc_obs'] = flag_mean['user_id']/flag_mean['user_id'].sum()\n",
"flag_mean"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" 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>count</th>\n",
" <th>perc_obs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>27729</td>\n",
" <td>0.390456</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>37002</td>\n",
" <td>0.521030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>6286</td>\n",
" <td>0.088514</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count perc_obs\n",
"6 27729 0.390456\n",
"7 37002 0.521030\n",
"8 6286 0.088514"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"month_split = raw_df['mo'].value_counts().to_frame()\n",
"month_split['perc_obs'] = month_split['mo']/month_split['mo'].sum()\n",
"month_split.columns = ['count', 'perc_obs']\n",
"month_split.sort_index()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"train_month = [6]\n",
"val_month = [7]\n",
"test_month = [8]"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"train = raw_df[raw_df['mo'].isin(train_month)]\n",
"val = raw_df[raw_df['mo'].isin(val_month)]\n",
"test = raw_df[raw_df['mo'].isin(test_month)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Model"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"cat_feat_indexes = tuple([raw_df.columns.get_loc(col) for col in categorical_feats])"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3 µs, sys: 0 ns, total: 3 µs\n",
"Wall time: 6.2 µs\n",
"[1]\tvalid_0's auc: 0.674874\tvalid_0's binary_logloss: 0.164919\tvalid_1's auc: 0.668376\tvalid_1's binary_logloss: 0.183167\n",
"Training until validation scores don't improve for 250 rounds\n",
"[2]\tvalid_0's auc: 0.728075\tvalid_0's binary_logloss: 0.164903\tvalid_1's auc: 0.716991\tvalid_1's binary_logloss: 0.183151\n",
"[3]\tvalid_0's auc: 0.729714\tvalid_0's binary_logloss: 0.164887\tvalid_1's auc: 0.712928\tvalid_1's binary_logloss: 0.183134\n",
"[4]\tvalid_0's auc: 0.736355\tvalid_0's binary_logloss: 0.164874\tvalid_1's auc: 0.716273\tvalid_1's binary_logloss: 0.183122\n",
"[5]\tvalid_0's auc: 0.736084\tvalid_0's binary_logloss: 0.164859\tvalid_1's auc: 0.715558\tvalid_1's binary_logloss: 0.183106\n",
"[6]\tvalid_0's auc: 0.743285\tvalid_0's binary_logloss: 0.16485\tvalid_1's auc: 0.721438\tvalid_1's binary_logloss: 0.183098\n",
"[7]\tvalid_0's auc: 0.747406\tvalid_0's binary_logloss: 0.164832\tvalid_1's auc: 0.7253\tvalid_1's binary_logloss: 0.18308\n",
"[8]\tvalid_0's auc: 0.7445\tvalid_0's binary_logloss: 0.164817\tvalid_1's auc: 0.722139\tvalid_1's binary_logloss: 0.183065\n",
"[9]\tvalid_0's auc: 0.747229\tvalid_0's binary_logloss: 0.164804\tvalid_1's auc: 0.723852\tvalid_1's binary_logloss: 0.183052\n",
"[10]\tvalid_0's auc: 0.751719\tvalid_0's binary_logloss: 0.164791\tvalid_1's auc: 0.729783\tvalid_1's binary_logloss: 0.183038\n",
"[11]\tvalid_0's auc: 0.751759\tvalid_0's binary_logloss: 0.164775\tvalid_1's auc: 0.728933\tvalid_1's binary_logloss: 0.183023\n",
"[12]\tvalid_0's auc: 0.752828\tvalid_0's binary_logloss: 0.16476\tvalid_1's auc: 0.729439\tvalid_1's binary_logloss: 0.18301\n",
"[13]\tvalid_0's auc: 0.75459\tvalid_0's binary_logloss: 0.164744\tvalid_1's auc: 0.731646\tvalid_1's binary_logloss: 0.182992\n",
"[14]\tvalid_0's auc: 0.756466\tvalid_0's binary_logloss: 0.16473\tvalid_1's auc: 0.733913\tvalid_1's binary_logloss: 0.182977\n",
"[15]\tvalid_0's auc: 0.754574\tvalid_0's binary_logloss: 0.164716\tvalid_1's auc: 0.732517\tvalid_1's binary_logloss: 0.182963\n",
"[16]\tvalid_0's auc: 0.755129\tvalid_0's binary_logloss: 0.164703\tvalid_1's auc: 0.733586\tvalid_1's binary_logloss: 0.182949\n",
"[17]\tvalid_0's auc: 0.754325\tvalid_0's binary_logloss: 0.164688\tvalid_1's auc: 0.733364\tvalid_1's binary_logloss: 0.182933\n",
"[18]\tvalid_0's auc: 0.757084\tvalid_0's binary_logloss: 0.164672\tvalid_1's auc: 0.734313\tvalid_1's binary_logloss: 0.182919\n",
"[19]\tvalid_0's auc: 0.758703\tvalid_0's binary_logloss: 0.164651\tvalid_1's auc: 0.734764\tvalid_1's binary_logloss: 0.182899\n",
"[20]\tvalid_0's auc: 0.758183\tvalid_0's binary_logloss: 0.164637\tvalid_1's auc: 0.733976\tvalid_1's binary_logloss: 0.182886\n",
"[21]\tvalid_0's auc: 0.75864\tvalid_0's binary_logloss: 0.16463\tvalid_1's auc: 0.734338\tvalid_1's binary_logloss: 0.182878\n",
"[22]\tvalid_0's auc: 0.759021\tvalid_0's binary_logloss: 0.164624\tvalid_1's auc: 0.73452\tvalid_1's binary_logloss: 0.182873\n",
"[23]\tvalid_0's auc: 0.759141\tvalid_0's binary_logloss: 0.164609\tvalid_1's auc: 0.734738\tvalid_1's binary_logloss: 0.182858\n",
"[24]\tvalid_0's auc: 0.76071\tvalid_0's binary_logloss: 0.164592\tvalid_1's auc: 0.735777\tvalid_1's binary_logloss: 0.182841\n",
"[25]\tvalid_0's auc: 0.759505\tvalid_0's binary_logloss: 0.164584\tvalid_1's auc: 0.734693\tvalid_1's binary_logloss: 0.182834\n",
"[26]\tvalid_0's auc: 0.759123\tvalid_0's binary_logloss: 0.164569\tvalid_1's auc: 0.734958\tvalid_1's binary_logloss: 0.182819\n",
"[27]\tvalid_0's auc: 0.758492\tvalid_0's binary_logloss: 0.16456\tvalid_1's auc: 0.735149\tvalid_1's binary_logloss: 0.182808\n",
"[28]\tvalid_0's auc: 0.759171\tvalid_0's binary_logloss: 0.164544\tvalid_1's auc: 0.735861\tvalid_1's binary_logloss: 0.182793\n",
"[29]\tvalid_0's auc: 0.759651\tvalid_0's binary_logloss: 0.164525\tvalid_1's auc: 0.735882\tvalid_1's binary_logloss: 0.182774\n",
"[30]\tvalid_0's auc: 0.759806\tvalid_0's binary_logloss: 0.164514\tvalid_1's auc: 0.736766\tvalid_1's binary_logloss: 0.182762\n",
"[31]\tvalid_0's auc: 0.760132\tvalid_0's binary_logloss: 0.164507\tvalid_1's auc: 0.737635\tvalid_1's binary_logloss: 0.182754\n",
"[32]\tvalid_0's auc: 0.759571\tvalid_0's binary_logloss: 0.164496\tvalid_1's auc: 0.737738\tvalid_1's binary_logloss: 0.182743\n",
"[33]\tvalid_0's auc: 0.760299\tvalid_0's binary_logloss: 0.164484\tvalid_1's auc: 0.737832\tvalid_1's binary_logloss: 0.182731\n",
"[34]\tvalid_0's auc: 0.760876\tvalid_0's binary_logloss: 0.16447\tvalid_1's auc: 0.738375\tvalid_1's binary_logloss: 0.182717\n",
"[35]\tvalid_0's auc: 0.760459\tvalid_0's binary_logloss: 0.164457\tvalid_1's auc: 0.738232\tvalid_1's binary_logloss: 0.182703\n",
"[36]\tvalid_0's auc: 0.761189\tvalid_0's binary_logloss: 0.164439\tvalid_1's auc: 0.738384\tvalid_1's binary_logloss: 0.182686\n",
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"[1969]\tvalid_0's auc: 0.773548\tvalid_0's binary_logloss: 0.151438\tvalid_1's auc: 0.753238\tvalid_1's binary_logloss: 0.169627\n",
"[1970]\tvalid_0's auc: 0.773553\tvalid_0's binary_logloss: 0.151437\tvalid_1's auc: 0.753245\tvalid_1's binary_logloss: 0.169626\n",
"[1971]\tvalid_0's auc: 0.77357\tvalid_0's binary_logloss: 0.151435\tvalid_1's auc: 0.753258\tvalid_1's binary_logloss: 0.169624\n",
"[1972]\tvalid_0's auc: 0.773602\tvalid_0's binary_logloss: 0.151429\tvalid_1's auc: 0.753286\tvalid_1's binary_logloss: 0.169619\n",
"[1973]\tvalid_0's auc: 0.773611\tvalid_0's binary_logloss: 0.151426\tvalid_1's auc: 0.753292\tvalid_1's binary_logloss: 0.169617\n",
"[1974]\tvalid_0's auc: 0.773607\tvalid_0's binary_logloss: 0.151422\tvalid_1's auc: 0.753291\tvalid_1's binary_logloss: 0.169613\n",
"[1975]\tvalid_0's auc: 0.77361\tvalid_0's binary_logloss: 0.15142\tvalid_1's auc: 0.753305\tvalid_1's binary_logloss: 0.16961\n",
"[1976]\tvalid_0's auc: 0.773624\tvalid_0's binary_logloss: 0.151417\tvalid_1's auc: 0.753311\tvalid_1's binary_logloss: 0.169607\n",
"[1977]\tvalid_0's auc: 0.773651\tvalid_0's binary_logloss: 0.151416\tvalid_1's auc: 0.753333\tvalid_1's binary_logloss: 0.169606\n",
"[1978]\tvalid_0's auc: 0.773646\tvalid_0's binary_logloss: 0.151412\tvalid_1's auc: 0.753333\tvalid_1's binary_logloss: 0.169602\n",
"[1979]\tvalid_0's auc: 0.773637\tvalid_0's binary_logloss: 0.15141\tvalid_1's auc: 0.753336\tvalid_1's binary_logloss: 0.169599\n",
"[1980]\tvalid_0's auc: 0.773637\tvalid_0's binary_logloss: 0.151408\tvalid_1's auc: 0.753339\tvalid_1's binary_logloss: 0.169597\n",
"[1981]\tvalid_0's auc: 0.773644\tvalid_0's binary_logloss: 0.151406\tvalid_1's auc: 0.75334\tvalid_1's binary_logloss: 0.169595\n",
"[1982]\tvalid_0's auc: 0.773659\tvalid_0's binary_logloss: 0.151402\tvalid_1's auc: 0.75334\tvalid_1's binary_logloss: 0.169593\n",
"[1983]\tvalid_0's auc: 0.77368\tvalid_0's binary_logloss: 0.151398\tvalid_1's auc: 0.753356\tvalid_1's binary_logloss: 0.169589\n",
"[1984]\tvalid_0's auc: 0.773684\tvalid_0's binary_logloss: 0.151394\tvalid_1's auc: 0.753371\tvalid_1's binary_logloss: 0.169584\n",
"[1985]\tvalid_0's auc: 0.773695\tvalid_0's binary_logloss: 0.151391\tvalid_1's auc: 0.753382\tvalid_1's binary_logloss: 0.169581\n",
"[1986]\tvalid_0's auc: 0.77371\tvalid_0's binary_logloss: 0.151387\tvalid_1's auc: 0.753406\tvalid_1's binary_logloss: 0.169577\n",
"[1987]\tvalid_0's auc: 0.773712\tvalid_0's binary_logloss: 0.151383\tvalid_1's auc: 0.753402\tvalid_1's binary_logloss: 0.169574\n",
"[1988]\tvalid_0's auc: 0.773711\tvalid_0's binary_logloss: 0.15138\tvalid_1's auc: 0.753403\tvalid_1's binary_logloss: 0.169571\n",
"[1989]\tvalid_0's auc: 0.773725\tvalid_0's binary_logloss: 0.151377\tvalid_1's auc: 0.753417\tvalid_1's binary_logloss: 0.169567\n",
"[1990]\tvalid_0's auc: 0.773712\tvalid_0's binary_logloss: 0.151373\tvalid_1's auc: 0.753404\tvalid_1's binary_logloss: 0.169564\n",
"[1991]\tvalid_0's auc: 0.773709\tvalid_0's binary_logloss: 0.15137\tvalid_1's auc: 0.753404\tvalid_1's binary_logloss: 0.16956\n",
"[1992]\tvalid_0's auc: 0.773705\tvalid_0's binary_logloss: 0.151368\tvalid_1's auc: 0.753405\tvalid_1's binary_logloss: 0.169558\n",
"[1993]\tvalid_0's auc: 0.773706\tvalid_0's binary_logloss: 0.151364\tvalid_1's auc: 0.753417\tvalid_1's binary_logloss: 0.169553\n",
"[1994]\tvalid_0's auc: 0.773724\tvalid_0's binary_logloss: 0.15136\tvalid_1's auc: 0.753438\tvalid_1's binary_logloss: 0.169549\n",
"[1995]\tvalid_0's auc: 0.773736\tvalid_0's binary_logloss: 0.151356\tvalid_1's auc: 0.753452\tvalid_1's binary_logloss: 0.169546\n",
"[1996]\tvalid_0's auc: 0.773733\tvalid_0's binary_logloss: 0.151354\tvalid_1's auc: 0.753455\tvalid_1's binary_logloss: 0.169543\n",
"[1997]\tvalid_0's auc: 0.773734\tvalid_0's binary_logloss: 0.151351\tvalid_1's auc: 0.753449\tvalid_1's binary_logloss: 0.169541\n",
"[1998]\tvalid_0's auc: 0.77373\tvalid_0's binary_logloss: 0.151349\tvalid_1's auc: 0.753452\tvalid_1's binary_logloss: 0.169539\n",
"[1999]\tvalid_0's auc: 0.773717\tvalid_0's binary_logloss: 0.151346\tvalid_1's auc: 0.753441\tvalid_1's binary_logloss: 0.169535\n",
"[2000]\tvalid_0's auc: 0.773725\tvalid_0's binary_logloss: 0.151344\tvalid_1's auc: 0.753456\tvalid_1's binary_logloss: 0.169533\n",
"[2001]\tvalid_0's auc: 0.773733\tvalid_0's binary_logloss: 0.15134\tvalid_1's auc: 0.753467\tvalid_1's binary_logloss: 0.169529\n",
"[2002]\tvalid_0's auc: 0.773734\tvalid_0's binary_logloss: 0.151337\tvalid_1's auc: 0.753465\tvalid_1's binary_logloss: 0.169527\n",
"[2003]\tvalid_0's auc: 0.77374\tvalid_0's binary_logloss: 0.151336\tvalid_1's auc: 0.753478\tvalid_1's binary_logloss: 0.169524\n",
"[2004]\tvalid_0's auc: 0.773753\tvalid_0's binary_logloss: 0.15133\tvalid_1's auc: 0.753495\tvalid_1's binary_logloss: 0.169519\n",
"[2005]\tvalid_0's auc: 0.77377\tvalid_0's binary_logloss: 0.151327\tvalid_1's auc: 0.753508\tvalid_1's binary_logloss: 0.169516\n",
"[2006]\tvalid_0's auc: 0.773777\tvalid_0's binary_logloss: 0.151322\tvalid_1's auc: 0.753508\tvalid_1's binary_logloss: 0.169512\n",
"[2007]\tvalid_0's auc: 0.773774\tvalid_0's binary_logloss: 0.15132\tvalid_1's auc: 0.753511\tvalid_1's binary_logloss: 0.169509\n",
"[2008]\tvalid_0's auc: 0.773803\tvalid_0's binary_logloss: 0.151316\tvalid_1's auc: 0.753533\tvalid_1's binary_logloss: 0.169506\n",
"[2009]\tvalid_0's auc: 0.773803\tvalid_0's binary_logloss: 0.151311\tvalid_1's auc: 0.753521\tvalid_1's binary_logloss: 0.169503\n",
"[2010]\tvalid_0's auc: 0.7738\tvalid_0's binary_logloss: 0.151309\tvalid_1's auc: 0.753527\tvalid_1's binary_logloss: 0.1695\n",
"[2011]\tvalid_0's auc: 0.773811\tvalid_0's binary_logloss: 0.151304\tvalid_1's auc: 0.753537\tvalid_1's binary_logloss: 0.169495\n",
"[2012]\tvalid_0's auc: 0.773816\tvalid_0's binary_logloss: 0.151303\tvalid_1's auc: 0.753542\tvalid_1's binary_logloss: 0.169494\n",
"[2013]\tvalid_0's auc: 0.77382\tvalid_0's binary_logloss: 0.151301\tvalid_1's auc: 0.75355\tvalid_1's binary_logloss: 0.169491\n",
"[2014]\tvalid_0's auc: 0.773823\tvalid_0's binary_logloss: 0.151298\tvalid_1's auc: 0.753557\tvalid_1's binary_logloss: 0.169489\n",
"[2015]\tvalid_0's auc: 0.773826\tvalid_0's binary_logloss: 0.151293\tvalid_1's auc: 0.75356\tvalid_1's binary_logloss: 0.169484\n",
"[2016]\tvalid_0's auc: 0.773827\tvalid_0's binary_logloss: 0.15129\tvalid_1's auc: 0.753567\tvalid_1's binary_logloss: 0.169481\n",
"[2017]\tvalid_0's auc: 0.773834\tvalid_0's binary_logloss: 0.151286\tvalid_1's auc: 0.753565\tvalid_1's binary_logloss: 0.169478\n",
"[2018]\tvalid_0's auc: 0.77383\tvalid_0's binary_logloss: 0.151284\tvalid_1's auc: 0.75356\tvalid_1's binary_logloss: 0.169476\n",
"[2019]\tvalid_0's auc: 0.773825\tvalid_0's binary_logloss: 0.151282\tvalid_1's auc: 0.753556\tvalid_1's binary_logloss: 0.169474\n",
"[2020]\tvalid_0's auc: 0.773825\tvalid_0's binary_logloss: 0.15128\tvalid_1's auc: 0.753553\tvalid_1's binary_logloss: 0.169471\n",
"[2021]\tvalid_0's auc: 0.773834\tvalid_0's binary_logloss: 0.151276\tvalid_1's auc: 0.753548\tvalid_1's binary_logloss: 0.169468\n",
"[2022]\tvalid_0's auc: 0.773839\tvalid_0's binary_logloss: 0.151275\tvalid_1's auc: 0.753557\tvalid_1's binary_logloss: 0.169467\n",
"[2023]\tvalid_0's auc: 0.773853\tvalid_0's binary_logloss: 0.151272\tvalid_1's auc: 0.753573\tvalid_1's binary_logloss: 0.169465\n",
"[2024]\tvalid_0's auc: 0.773854\tvalid_0's binary_logloss: 0.151269\tvalid_1's auc: 0.753574\tvalid_1's binary_logloss: 0.169462\n",
"[2025]\tvalid_0's auc: 0.773851\tvalid_0's binary_logloss: 0.151265\tvalid_1's auc: 0.753571\tvalid_1's binary_logloss: 0.169458\n",
"[2026]\tvalid_0's auc: 0.773865\tvalid_0's binary_logloss: 0.15126\tvalid_1's auc: 0.753596\tvalid_1's binary_logloss: 0.169453\n",
"[2027]\tvalid_0's auc: 0.773864\tvalid_0's binary_logloss: 0.151256\tvalid_1's auc: 0.753588\tvalid_1's binary_logloss: 0.16945\n",
"[2028]\tvalid_0's auc: 0.773864\tvalid_0's binary_logloss: 0.151252\tvalid_1's auc: 0.753587\tvalid_1's binary_logloss: 0.169447\n",
"[2029]\tvalid_0's auc: 0.773868\tvalid_0's binary_logloss: 0.151249\tvalid_1's auc: 0.753598\tvalid_1's binary_logloss: 0.169443\n",
"[2030]\tvalid_0's auc: 0.773891\tvalid_0's binary_logloss: 0.151246\tvalid_1's auc: 0.753618\tvalid_1's binary_logloss: 0.16944\n",
"[2031]\tvalid_0's auc: 0.773884\tvalid_0's binary_logloss: 0.151243\tvalid_1's auc: 0.753614\tvalid_1's binary_logloss: 0.169437\n",
"[2032]\tvalid_0's auc: 0.773884\tvalid_0's binary_logloss: 0.151239\tvalid_1's auc: 0.753609\tvalid_1's binary_logloss: 0.169434\n",
"[2033]\tvalid_0's auc: 0.773902\tvalid_0's binary_logloss: 0.151233\tvalid_1's auc: 0.753627\tvalid_1's binary_logloss: 0.169428\n",
"[2034]\tvalid_0's auc: 0.773908\tvalid_0's binary_logloss: 0.151228\tvalid_1's auc: 0.753626\tvalid_1's binary_logloss: 0.169424\n"
]
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"[2035]\tvalid_0's auc: 0.773902\tvalid_0's binary_logloss: 0.151224\tvalid_1's auc: 0.753626\tvalid_1's binary_logloss: 0.16942\n",
"[2036]\tvalid_0's auc: 0.77391\tvalid_0's binary_logloss: 0.151221\tvalid_1's auc: 0.753644\tvalid_1's binary_logloss: 0.169416\n",
"[2037]\tvalid_0's auc: 0.77392\tvalid_0's binary_logloss: 0.151219\tvalid_1's auc: 0.753658\tvalid_1's binary_logloss: 0.169413\n",
"[2038]\tvalid_0's auc: 0.773918\tvalid_0's binary_logloss: 0.151217\tvalid_1's auc: 0.753655\tvalid_1's binary_logloss: 0.169412\n",
"[2039]\tvalid_0's auc: 0.773932\tvalid_0's binary_logloss: 0.151213\tvalid_1's auc: 0.753666\tvalid_1's binary_logloss: 0.169409\n",
"[2040]\tvalid_0's auc: 0.773928\tvalid_0's binary_logloss: 0.151209\tvalid_1's auc: 0.753668\tvalid_1's binary_logloss: 0.169405\n",
"[2041]\tvalid_0's auc: 0.773955\tvalid_0's binary_logloss: 0.151205\tvalid_1's auc: 0.75369\tvalid_1's binary_logloss: 0.1694\n",
"[2042]\tvalid_0's auc: 0.773962\tvalid_0's binary_logloss: 0.151202\tvalid_1's auc: 0.7537\tvalid_1's binary_logloss: 0.169397\n",
"[2043]\tvalid_0's auc: 0.773969\tvalid_0's binary_logloss: 0.151199\tvalid_1's auc: 0.75372\tvalid_1's binary_logloss: 0.169393\n",
"[2044]\tvalid_0's auc: 0.773968\tvalid_0's binary_logloss: 0.151194\tvalid_1's auc: 0.753719\tvalid_1's binary_logloss: 0.169389\n",
"[2045]\tvalid_0's auc: 0.773968\tvalid_0's binary_logloss: 0.151191\tvalid_1's auc: 0.753719\tvalid_1's binary_logloss: 0.169386\n",
"[2046]\tvalid_0's auc: 0.773965\tvalid_0's binary_logloss: 0.151187\tvalid_1's auc: 0.753716\tvalid_1's binary_logloss: 0.169383\n",
"[2047]\tvalid_0's auc: 0.773958\tvalid_0's binary_logloss: 0.151184\tvalid_1's auc: 0.753713\tvalid_1's binary_logloss: 0.16938\n",
"[2048]\tvalid_0's auc: 0.773964\tvalid_0's binary_logloss: 0.151182\tvalid_1's auc: 0.753716\tvalid_1's binary_logloss: 0.169378\n",
"[2049]\tvalid_0's auc: 0.773952\tvalid_0's binary_logloss: 0.151181\tvalid_1's auc: 0.753711\tvalid_1's binary_logloss: 0.169376\n",
"[2050]\tvalid_0's auc: 0.773962\tvalid_0's binary_logloss: 0.151177\tvalid_1's auc: 0.753712\tvalid_1's binary_logloss: 0.169374\n",
"[2051]\tvalid_0's auc: 0.773972\tvalid_0's binary_logloss: 0.151173\tvalid_1's auc: 0.753722\tvalid_1's binary_logloss: 0.169369\n",
"[2052]\tvalid_0's auc: 0.773968\tvalid_0's binary_logloss: 0.151171\tvalid_1's auc: 0.753719\tvalid_1's binary_logloss: 0.169367\n",
"[2053]\tvalid_0's auc: 0.773965\tvalid_0's binary_logloss: 0.151168\tvalid_1's auc: 0.753714\tvalid_1's binary_logloss: 0.169365\n",
"[2054]\tvalid_0's auc: 0.773978\tvalid_0's binary_logloss: 0.151163\tvalid_1's auc: 0.753724\tvalid_1's binary_logloss: 0.16936\n",
"[2055]\tvalid_0's auc: 0.773974\tvalid_0's binary_logloss: 0.151159\tvalid_1's auc: 0.753726\tvalid_1's binary_logloss: 0.169357\n",
"[2056]\tvalid_0's auc: 0.77397\tvalid_0's binary_logloss: 0.151157\tvalid_1's auc: 0.753727\tvalid_1's binary_logloss: 0.169354\n",
"[2057]\tvalid_0's auc: 0.773977\tvalid_0's binary_logloss: 0.151154\tvalid_1's auc: 0.753742\tvalid_1's binary_logloss: 0.169351\n",
"[2058]\tvalid_0's auc: 0.773982\tvalid_0's binary_logloss: 0.151152\tvalid_1's auc: 0.75375\tvalid_1's binary_logloss: 0.169348\n",
"[2059]\tvalid_0's auc: 0.773987\tvalid_0's binary_logloss: 0.15115\tvalid_1's auc: 0.753762\tvalid_1's binary_logloss: 0.169346\n",
"[2060]\tvalid_0's auc: 0.773981\tvalid_0's binary_logloss: 0.151146\tvalid_1's auc: 0.753769\tvalid_1's binary_logloss: 0.169342\n",
"[2061]\tvalid_0's auc: 0.773984\tvalid_0's binary_logloss: 0.151141\tvalid_1's auc: 0.753767\tvalid_1's binary_logloss: 0.169337\n",
"[2062]\tvalid_0's auc: 0.774016\tvalid_0's binary_logloss: 0.151138\tvalid_1's auc: 0.753788\tvalid_1's binary_logloss: 0.169335\n",
"[2063]\tvalid_0's auc: 0.774008\tvalid_0's binary_logloss: 0.151135\tvalid_1's auc: 0.753774\tvalid_1's binary_logloss: 0.169333\n",
"[2064]\tvalid_0's auc: 0.774006\tvalid_0's binary_logloss: 0.151134\tvalid_1's auc: 0.753767\tvalid_1's binary_logloss: 0.169332\n",
"[2065]\tvalid_0's auc: 0.774\tvalid_0's binary_logloss: 0.151131\tvalid_1's auc: 0.753767\tvalid_1's binary_logloss: 0.169329\n",
"[2066]\tvalid_0's auc: 0.77401\tvalid_0's binary_logloss: 0.151127\tvalid_1's auc: 0.753786\tvalid_1's binary_logloss: 0.169325\n",
"[2067]\tvalid_0's auc: 0.774006\tvalid_0's binary_logloss: 0.151124\tvalid_1's auc: 0.753783\tvalid_1's binary_logloss: 0.169322\n",
"[2068]\tvalid_0's auc: 0.774011\tvalid_0's binary_logloss: 0.15112\tvalid_1's auc: 0.753791\tvalid_1's binary_logloss: 0.169318\n",
"[2069]\tvalid_0's auc: 0.774007\tvalid_0's binary_logloss: 0.151118\tvalid_1's auc: 0.753784\tvalid_1's binary_logloss: 0.169316\n",
"[2070]\tvalid_0's auc: 0.774017\tvalid_0's binary_logloss: 0.151114\tvalid_1's auc: 0.753785\tvalid_1's binary_logloss: 0.169312\n",
"[2071]\tvalid_0's auc: 0.774016\tvalid_0's binary_logloss: 0.151112\tvalid_1's auc: 0.753782\tvalid_1's binary_logloss: 0.169311\n",
"[2072]\tvalid_0's auc: 0.774039\tvalid_0's binary_logloss: 0.151107\tvalid_1's auc: 0.753808\tvalid_1's binary_logloss: 0.169306\n",
"[2073]\tvalid_0's auc: 0.774035\tvalid_0's binary_logloss: 0.151105\tvalid_1's auc: 0.753809\tvalid_1's binary_logloss: 0.169304\n",
"[2074]\tvalid_0's auc: 0.774033\tvalid_0's binary_logloss: 0.151101\tvalid_1's auc: 0.753817\tvalid_1's binary_logloss: 0.1693\n",
"[2075]\tvalid_0's auc: 0.774027\tvalid_0's binary_logloss: 0.151099\tvalid_1's auc: 0.753812\tvalid_1's binary_logloss: 0.169298\n",
"[2076]\tvalid_0's auc: 0.774018\tvalid_0's binary_logloss: 0.151097\tvalid_1's auc: 0.753802\tvalid_1's binary_logloss: 0.169295\n",
"[2077]\tvalid_0's auc: 0.774015\tvalid_0's binary_logloss: 0.151093\tvalid_1's auc: 0.753791\tvalid_1's binary_logloss: 0.169292\n",
"[2078]\tvalid_0's auc: 0.774016\tvalid_0's binary_logloss: 0.15109\tvalid_1's auc: 0.753792\tvalid_1's binary_logloss: 0.169288\n",
"[2079]\tvalid_0's auc: 0.774014\tvalid_0's binary_logloss: 0.151088\tvalid_1's auc: 0.753793\tvalid_1's binary_logloss: 0.169286\n",
"[2080]\tvalid_0's auc: 0.774016\tvalid_0's binary_logloss: 0.151085\tvalid_1's auc: 0.753796\tvalid_1's binary_logloss: 0.169283\n",
"[2081]\tvalid_0's auc: 0.774025\tvalid_0's binary_logloss: 0.151082\tvalid_1's auc: 0.753807\tvalid_1's binary_logloss: 0.16928\n",
"[2082]\tvalid_0's auc: 0.774022\tvalid_0's binary_logloss: 0.151078\tvalid_1's auc: 0.753809\tvalid_1's binary_logloss: 0.169276\n",
"[2083]\tvalid_0's auc: 0.774025\tvalid_0's binary_logloss: 0.151078\tvalid_1's auc: 0.753815\tvalid_1's binary_logloss: 0.169274\n",
"[2084]\tvalid_0's auc: 0.774021\tvalid_0's binary_logloss: 0.151072\tvalid_1's auc: 0.753814\tvalid_1's binary_logloss: 0.169269\n",
"[2085]\tvalid_0's auc: 0.774034\tvalid_0's binary_logloss: 0.151069\tvalid_1's auc: 0.753827\tvalid_1's binary_logloss: 0.169267\n",
"[2086]\tvalid_0's auc: 0.774027\tvalid_0's binary_logloss: 0.151067\tvalid_1's auc: 0.753828\tvalid_1's binary_logloss: 0.169264\n",
"[2087]\tvalid_0's auc: 0.774021\tvalid_0's binary_logloss: 0.151066\tvalid_1's auc: 0.753821\tvalid_1's binary_logloss: 0.169263\n",
"[2088]\tvalid_0's auc: 0.774019\tvalid_0's binary_logloss: 0.151061\tvalid_1's auc: 0.753824\tvalid_1's binary_logloss: 0.169259\n",
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"[4144]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4145]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4146]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4147]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4148]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4149]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4150]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4151]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4152]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4153]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4154]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4155]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4156]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4157]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4158]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4159]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4160]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4161]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4162]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4163]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4164]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4165]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4166]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4167]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4168]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4169]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4170]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4171]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4172]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4173]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4174]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4175]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4176]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4177]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4178]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4179]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4180]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4181]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4182]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4183]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4184]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4185]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[4186]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4187]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4188]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4189]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4190]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4191]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4192]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4193]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4194]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4195]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4196]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4197]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4198]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4199]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4200]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4201]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4202]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4203]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4204]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4205]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4206]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4207]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4208]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4209]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4210]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4211]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4212]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4213]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4214]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4215]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4216]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4217]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4218]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4219]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"[4220]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"Early stopping, best iteration is:\n",
"[3970]\tvalid_0's auc: 0.781201\tvalid_0's binary_logloss: 0.147792\tvalid_1's auc: 0.761066\tvalid_1's binary_logloss: 0.166105\n",
"\n",
"defaultdict(<class 'collections.OrderedDict'>, {'valid_0': OrderedDict([('auc', 0.7812010159674302), ('binary_logloss', 0.14779245941415162)]), 'valid_1': OrderedDict([('auc', 0.7610660356650925), ('binary_logloss', 0.16610510648322166)])})\n",
"3970\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"params_ = {\n",
" # highest impact to control overfitting\n",
" 'num_iterations': 10000,\n",
" 'num_leaves': 100, #should be < 2^(max_depth)\n",
" 'max_depth': 10,\n",
" 'min_data_in_leaf':100,\n",
" 'min_gain_to_split': 10,\n",
" # optional parameters\n",
" 'categorical_feature':cat_feat_indexes,\n",
" 'learning_rate': 0.001,\n",
" 'subsample': 0.5,\n",
" 'colsample_bytree': 0.5,\n",
" 'subsample_freq':1,\n",
" # for regularization\n",
" 'lambda_l2':20,\n",
"}\n",
"\n",
"mod_lgb = LGBMClassifier(n_jobs=-1, random_state=RANDOM_STATE,\n",
" num_iterations=params_['num_iterations'],\n",
" num_leaves=params_['num_leaves'],\n",
" max_depth=params_['max_depth'],\n",
" min_data_in_leaf=params_['min_data_in_leaf'],\n",
" min_gain_to_split=params_['min_gain_to_split'],\n",
" categorical_feature=params_['categorical_feature'],\n",
" learning_rate=params_['learning_rate'],\n",
" subsample=params_['subsample'],\n",
" colsample_bytree=params_['colsample_bytree'],\n",
" subsample_freq=params_['subsample_freq'],\n",
" lambda_l2=params_['lambda_l2']\n",
" )\n",
"\n",
"eval_set = [(train[features], train[TARGET_VAR]),(val[features], val[TARGET_VAR])]\n",
"%time\n",
"mod_lgb.fit(train[features], train[TARGET_VAR], eval_set=eval_set, eval_metric='auc',\n",
" early_stopping_rounds=250, verbose=True)\n",
"\n",
"train_result = mod_lgb.evals_result_['valid_0']['auc']\n",
"val_result = mod_lgb.evals_result_['valid_1']['auc']\n",
"\n",
"plt.plot(train_result)\n",
"plt.plot(val_result)\n",
"\n",
"print()\n",
"print(mod_lgb.best_score_)\n",
"print(mod_lgb.best_iteration_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Evaluate the Model"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train Gini: 0.5624020319348602\n",
"Val Gini: 0.5221320713301849\n",
"Test Gini: 0.547643400595933\n"
]
}
],
"source": [
"train['pred'] = mod_lgb.predict_proba(train[features], num_iteration=mod_lgb.best_iteration_)[:,1]\n",
"val['pred'] = mod_lgb.predict_proba(val[features], num_iteration=mod_lgb.best_iteration_)[:,1]\n",
"test['pred'] = mod_lgb.predict_proba(test[features], num_iteration=mod_lgb.best_iteration_)[:,1]\n",
"\n",
"print('Train Gini:', roc_auc_score(train[TARGET_VAR], train['pred'])*2-1)\n",
"print('Val Gini:',roc_auc_score(val[TARGET_VAR], val['pred'])*2-1)\n",
"print('Test Gini:',roc_auc_score(test[TARGET_VAR], test['pred'])*2-1)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5754485866725674\n"
]
}
],
"source": [
"train_val = pd.concat([train, val])\n",
"mod_lgb.fit(train_val[features], train_val[TARGET_VAR])\n",
"\n",
"test['pred_2'] = mod_lgb.predict_proba(test[features], num_iteration=mod_lgb.best_iteration_)[:,1]\n",
"print(roc_auc_score(test[TARGET_VAR], test['pred_2'])*2-1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check Feature Importances"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"feat_imp = pd.DataFrame(features, columns=['feature_name'])\n",
"feat_imp['feature_importance'] = mod_lgb.feature_importances_"
]
},
{
"cell_type": "code",
"execution_count": 73,
"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>feature_name</th>\n",
" <th>feature_importance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>delin_max_dpd_3mo</td>\n",
" <td>5141</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>time_approve_to_pl_hour</td>\n",
" <td>4260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>util_pl</td>\n",
" <td>3487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>pf_delin_max_dpd_12mo</td>\n",
" <td>2061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>oth_last_rep_days</td>\n",
" <td>1935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ap_co_plo</td>\n",
" <td>1668</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>delin_max_dpd_amt_3mo</td>\n",
" <td>1385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>os_amount</td>\n",
" <td>1362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>oth_last_trx_amount</td>\n",
" <td>1322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>oth_last_rep_dpd</td>\n",
" <td>1244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>util_non_pl</td>\n",
" <td>1238</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>flag_good_pefindo</td>\n",
" <td>585</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>oth_first_trx_amount</td>\n",
" <td>373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>current_dpd</td>\n",
" <td>323</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>pf_util_creditcard_avg_12mo</td>\n",
" <td>307</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>calibrated_final</td>\n",
" <td>210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>pf_delin_dist_contractcode_30_dpd_3mo</td>\n",
" <td>171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>flag_bad_pefindo</td>\n",
" <td>124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>payment_type</td>\n",
" <td>86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>pf_con_open_count_12mo</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>oth_last_rep_channel</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>series</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature_name feature_importance\n",
"3 delin_max_dpd_3mo 5141\n",
"17 time_approve_to_pl_hour 4260\n",
"19 util_pl 3487\n",
"15 pf_delin_max_dpd_12mo 2061\n",
"10 oth_last_rep_days 1935\n",
"0 ap_co_plo 1668\n",
"4 delin_max_dpd_amt_3mo 1385\n",
"7 os_amount 1362\n",
"12 oth_last_trx_amount 1322\n",
"11 oth_last_rep_dpd 1244\n",
"18 util_non_pl 1238\n",
"6 flag_good_pefindo 585\n",
"8 oth_first_trx_amount 373\n",
"2 current_dpd 323\n",
"16 pf_util_creditcard_avg_12mo 307\n",
"1 calibrated_final 210\n",
"14 pf_delin_dist_contractcode_30_dpd_3mo 171\n",
"5 flag_bad_pefindo 124\n",
"21 payment_type 86\n",
"13 pf_con_open_count_12mo 24\n",
"9 oth_last_rep_channel 0\n",
"20 series 0"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feat_imp.sort_values('feature_importance', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f9068a0c350>"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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SJEmrCpeeSpIkSZIqGBQlSZIkSRUMipIkSZKkCgZFSZIkSVIFg6IkSZIkqYJBUZIkSZJUwaAoSZIkSapgUJQkSZIkVTAoSpIkSZIqGBQlSZIkSRUMipIkSZKkCgZFSZIkSVIFg6IkSZIkqYJBUZIkSZJUwaAoSZIkSapgUJQkSZIkVTAoSpIkSZIqGBQlSZIkSRUMipIkSZKkCgZFSZIkSVIFg6IkSZIkqYJBUZIkSZJUwaAoSZIkSapgUJQkSZJWsgULFrDFFluw++67AzB8+HD69u3LgAEDGDlyJPPmzas9tlQqUVVVRf/+/fnSl75U237HHXfQt29fNttsM8aNG7fS70FtW2RmS9cgqZ5efTbLdvv/tqXLUDM7buB8zpzaoaXL0Arg2LZNjmvb1JLjOn3cbrXbv/71r5k0aRLvvvsut912G3/84x/ZZZddADjooIMYNmwY3/72t5k1axaf//znueOOO+jVqxdvvPEG3bt3Z8GCBXzmM5/hrrvuomfPnmy99dZMmDCBfv36tci9tQalUonq6uqWLmOVEhGTM3OrhvY5o9hKRMR6EXF0sd0jIq5v6Zpam4j4fkSs3cx9jo+I/Zawf3pEdGvOa0qSpNXbyy+/zO23386oUaNq23bddVcigohgm2224eWXXwbgqquuYp999qFXr14AdO/eHYCHHnqIzTbbjD59+rDGGmtwwAEHcPPNN6/8m1GbZVBsPdYDjgbIzFczs9Hw0lpFRPsVfInvA80aFFtClPlnT5Kk1dT3v/99Tj/9dNq1W/yvA/PmzePyyy9n5513BuDZZ5/l7bffprq6miFDhnDZZZcB8Morr7DRRhvVntezZ09eeeWVlXMDWi24nqL1GAdsGhGPAc8Bm2fmgIgYAewFdAY+DZwBrAEcAnwA7JqZ/4mITYFzgfWB94BvZubTDV0oIvYAflz08xYwPDNfj4ixwKbAZkA34PTM/H1EVAM/BWYX+yYCR2fmwoioAf4X+CrwnYhYs6ixA/Aw8G1ge+CIzPx6cf1q4PjM3D0idgROBdYE/gUcnpk1DdR8DNADmBgRMzNz+4g4EDgJCOD2zPxhY19uUefvgR2BGcABmflmY8fX893iO+sIfD0zn46IrsDFQB/K3/eRmfl48R3WZOYZxXWfAHYv+rkTeBAYAuwKvFivxiOBIwG6dVufkwfOb2J5WlVs0Km85Eltj2PbNjmubVNLjmupVOL+++9n3rx5zJ49m8cee4y33nqLUqlUe8wZZ5xBnz59WLBgAaVSiRdffJFnnnmGM888kw8//JDvfOc7RATPP/88r732Wu25Tz31FK+88kpFX6ubmpqa1fr+m5tBsfU4ERiQmVUR0Ru4rc6+AcAWwFrAP4EfZuYWEfEb4FDgLOBC4KjMfC4ihgLnAV9u5Fr3Ap/LzIyIUcAJwHHFvkHA5ygH00cj4vaifRugH+VwcwewD3B9cdyDmXlcRKxFOeR+JTOfjYjLKAfFc4ALI6JzZs4BvgFcXSzp/DHw1cycExE/BI6lHEorZObZEXEssH1mzoyIHsAvKYeut4E/R8RemXlTI/fcGZiUmT+IiJOBU4DRjRxb38zM3LJYGnw8MIpyuH00M/eKiC8DlwFVS+nn08BhmflAQzsz80LK40ivPpulz8W0PT7v1HY5tm2T49o2tegzisOrufPOO5k8eTIjRoxg7ty5vPvuu1x00UVcccUVnHrqqXTo0IFrr722drbxgQceYNCgQbXPL95yyy2stdZa7LjjjvzjH/+ofSbv/vvvZ5tttlmtn9HzGcXm5fK3VcPEzJxdzIC9A9xatE8FekdEF+DzwHXFjOT/Ahsuob+ewJ0RMRUYA/Svs+/mzHw/M2dSnjncpmh/KDOfz8wFwARgu6J9AXBDsd0XeCEzny0+XwoMy8z5lMPlHhHRAdgNuJlyIO0H3FfUfRiwcRO/k62BUma+WfR/JTBsCccvBK4ptq+oU39T/F/x+2Sgd7G9HXA5QGbeDXwiItZdSj8vNhYSJUnS6uEXv/gFL7/8MtOnT+fqq6/my1/+MldccQUXXXQRd955JxMmTKhYkvq1r32Ne++9l/nz5/Pee+/x4IMPsvnmm7P11lvz3HPP8cILL/Dhhx9y9dVXs+eee7bgnamt8cdkq4YP6mwvrPN5IeUxbAfMysylzWgt8jvg15l5S7EMdGydffVfg5tLaZ9bhMeluZryDN5/KM/szY6IAO7KzAObWHdzWpbX/S76vhew9D8z86n8AcxadbbnLMM1JUnSauSoo45i4403ZttttwVgn3324eSTT2bzzTdn5513ZtCgQbRr145Ro0YxYMAAAM455xx22mknFixYwMiRI+nfv/+SLiEtE4Ni6zEbWGd5TszMdyPihYj4emZeVwSwQZk5pZFTPgYsetr5sHr7vhYRv6C8VLOa8pLYzwDbRMQmlJeefoNiiWQ9z1Ce4dwsM/9J+TnKvxX7/kb5mb5vUg6NAA8A5y46PiI6A5+qMyNZ36LvaCbwEHB2sXz1beBAygG4Me2A/YprH0R5+e1H8XdgOHBaEbZnFuMwneKZxIjYEthkeTrv1LE9z9R5hbbahlKpxPTh1S1dhlYAx7ZtclzbptY0rtXV1bVLJefPb/y5yTFjxjBmzJjF2nfddVd23XXXFVWeVnMuPW0lMvMtykswnwB+tRxdDAeOiIgpwJPA15Zw7FjKy1QnUw5ddT1OecnpA8Bpmflq0f4w5WcNnwJeAG5s4B7mAocXfU+lPON5QbFvAeXnLncpfqdYSjsCmBARjwP3A59dQt0XAndExMTMfI1yiJ0ITAEmZ+aS3gk9h3LYfYLys5uLPQe5jMYCQ4q6x/HfwH0D0DUinqQ8g9pY6JUkSZJaLWcUW5HMPKiBtvHA+Dqfeze0LzNfAHZu4nVupvyMYEMez8xDG2h/NzN3r9+YmV3qff4r5RfvNHTd0dR7gUzxfN/WTaz7d9SZNczMCZSfl2ySzDy2gbYRSzmnd53tSZRnWcnM/1B+G23949+n/GbVhgxoaq2SJElSS3JGUZIkSZJUwRnFNiwifgR8vV7zdZn584aOz8yxjbSXgFJz1rYkEXEjiz/b98PMvLMJ5z5I+d9krOuQ+jOfzXlNSZIkqa0xKLZhRSBsMBS2Zpm590c4d+jKvqYkSZLU1rj0VJIkSZJUwaAoSZIkSapgUJQkSZIkVTAoSpIkSZIqGBQlSZIkSRUMipIkSZKkCgZFSZIkSVIFg6IkSZIkqYJBUZIkSZJUwaAoSZIkSapgUJQkSZIkVTAoSpIkSZIqGBQlSZIkSRUMipIkSZKkCgZFSZIkSVIFg6IkSZIkqYJBUZIkSZJUwaAoSZIkSapgUJQkSZIkVTAoSpIkSZIqGBQlSZIkSRUMipIkSZKkCgZFSZKk1dzcuXPZZpttGDx4MP379+eUU06p2H/MMcfQpUuX2s///ve/2X777dliiy0YNGgQf/zjHwG48sorqaqqqv3Vrl07HnvssZV6L5KaR4eWLkDS4t6ft4DeJ97e0mWomR03cD4jHNc2ybFtm1aXcZ0+bjfWXHNN7r77brp06cK8efPYbrvt2GWXXfjc5z7HpEmTePvttyvO+dnPfsb+++/Pt7/9baZNm8auu+7K9OnTGT58OMOHDwdg6tSp7LXXXlRVVbXEbUn6iJxRlJYgIkZERI86ny+KiH7F9vSI6LYcfZYiYqvmrFOSpI8iImpnDOfNm8e8efOICBYsWMCYMWM4/fTTFzv+3XffBeCdd96hR48ei/U5YcIEDjjggBVfvKQVwqAoLdkIoPb/fpk5KjOntVw5kiStGAsWLKCqqoru3buzww47MHToUM455xz23HNPNtxww4pjx44dyxVXXEHPnj3Zdddd+d3vfrdYf9dccw0HHnjgyipfUjNz6akERERv4LbMHFB8Pp5ySOwNXBkR7wPbAn8Cjs/MSU3o7w5gMrAl8CRwaGa+t4RzjgSOBOjWbX1OHjj/I9yRWqMNOpWXsqntcWzbptVlXEulUu32WWedRU1NDT/5yU/o0aMHF110EWeddRalUokFCxbUHnvttdfyxS9+kf33358nn3ySfffdl4svvph27cpzENOmTSMzmTlzZkX/rUFNTU2rq0nNw7FtXgZFqXHXA9XUCYYRsSzn9wWOyMz7IuJi4GjgjMYOzswLgQsBevXZLM+c6h/Ptua4gfNxXNsmx7ZtWl3Gdfrw6sXaHnnkEWbNmsWbb77JEUccAcAHH3zAqFGj+Oc//8l3vvMd7rjjDjbaaCOqq6s588wzGTBgAN27dwfg5ptvZtSoUVRXL953SyuVSq2yLn10jm3zcumptOK8lJn3FdtXANu1ZDGSJDXmzTffZNasWQC8//773HXXXQwZMoQZM2Ywffp0pk+fztprr80///lPAHr16sVf//pXAJ566inmzp3L+uuvD8DChQu59tprfT5RWsW1/R+TSU0zn8ofnKzVDH3mUj5LktQqvPbaaxx22GEsWLCAhQsXsv/++7P77rs3evyZZ57JN7/5TX7zm98QEYwfP7521c0999zDRhttRJ8+fVZW+ZJWAIOiVPY60D0iPgHUALtTfsZwNrDOcvbZKyK2zcz7gYOAe5t6YqeO7Xlm3G7LeVm1VqVSqcElXlr1ObZt0+o0roMGDeLRRx9d4jE1NTW12/369eO+++5r8Ljq6moeeOCBZq1P0srn0lMJyMx5wE+Bh4C7gKeLXeOBCyLisYjotIzdPgN8JyKeAj4OnN9M5UqSJEkrlDOKUiEzzwbObmDXDXW2q+sc33spXc7PzIMbuE51A8dKkiRJrYYzipIkSZKkCs4oSh9B8UzjXxvY9ZVF/yajJEmStKoxKEofQWa+BVS1dB2SJElSc3LpqSRJkiSpgkFRkiRJklTBoChJkiRJqmBQlCRJkiRVMChKkiRJkioYFCVJkiRJFQyKkiRJkqQKBkVJkiRJUgWDoiRJkiSpgkFRkiRJklTBoChJkiRJqmBQlCRJkiRVMChKkiRJkioYFCVJkiRJFQyKkiRJkqQKBkVJkiRJUgWDoiRJkiSpgkFRkiRJklTBoChJkiRJqmBQlCRJkiRVMChKkiRJkioYFCVJkiRJFQyKkiStYC+99BLbb789/fr1o3///vz2t78F4D//+Q877LADn/70p9lhhx14++23a88plUpUVVXRv39/vvSlL9W2jxw5ku7duzNgwICVfh+SpNVHh5YuQNLi3p+3gN4n3t7SZaiZHTdwPiMc1zZpSWM7fdxudOjQgTPPPJMtt9yS2bNnM2TIEHbYYQfGjx/PV77yFU488UTGjRvHuHHj+OUvf8msWbM4+uijueOOO+jVqxdvvPFGbX8jRoxg9OjRHHrooSvr9iRJqyFnFFcREfHFiHgyIh6LiE5NOH58ROzX1GMi4qKI6Ndc9a5oETEiIs5ZxnNKEbHVEvb/PCJeioiaeu3HRsS0iHg8Iv4aERsvb92SVk8bbrghW265JQDrrLMOm2++Oa+88go333wzhx12GACHHXYYN910EwBXXXUV++yzD7169QKge/futX0NGzaMrl27ruQ7kCStbgyKq47hwC8ysyoz32/uzjNzVGZOa+5+VzG3Ats00P4osFVmDgKuB05fqVVJalOmT5/Oo48+ytChQ3n99dfZcMMNAfjkJz/J66+/DsCzzz7L22+/TXV1NUOGDOGyyy5ryZIlSashg2IrExG9I+LpiLgyIp6KiOsj4hhgf+C0iLiykfMiIs6JiGci4i9A9zr7hkTE3yJickTcGREbNnB+7WxbRNQUs2tTIuKBiNhgCfWOj4jzi+Oej4jqiLi4qH18nePOj4hJxazoqUXbx4p6+xafJ0TEN5dwrcMj4tmIeAj4Qr0aLij6fzYidi/aO0XE1UUtNwJLnInNzAcy87UG2idm5nvFxweAnkX/1cX3enNx7+MiYnhEPBQRUyNi0+K43hFxd50ZyV5LqkNS21VTU8O+++7LWWedxbrrrluxLyKICADmz5/P5MmTuf3227nzzjs57bTTePbZZ1uiZEnSaspnFFunvsARmXlfRFwMrAHcAtyWmdc3cs7exXn9gA2AacDFEdER+B3wtcx8MyK+AfwcGLmE63cGHsjMH3013tUAACAASURBVEXE6cA3gZ8t4fiPA9sCexZ1fgEYBTwcEVWZ+Rjwo8z8T0S0B/4aEYMy8/GIGA2Mj4jfAh/PzN83dIEi3J4KDAHeASZSnulbpDfl2cBNgYkRsRnwbeC9zNw8IgYBjyzhHprqCOBPdT4PBjYH/gM8D1yUmdtExPeA7wLfp/z9X5qZl0bESOBsYK8G7vFI4EiAbt3W5+SB85uhXLUmG3QqP8umtmdJY1sqlYBy+Pt//+//MXToULp27UqpVGLdddflhhtu4BOf+ARvvfUW66yzDqVSiQ8//JC+ffvy8MMPA/DpT3+aq666iurqagBmzJjBnDlzavvWilFTU+N33AY5rm2XY9u8DIqt00uZeV+xfQVwDDBrKecMAyZk5gLg1Yi4u2jvCwwA7ip+Ut0eWGzWrJ4PgduK7cnADks5/tbMzIiYCryemVMBIuJJygHuMWD/Igh1ADakHGgfz8y7IuLrwLmUQ1djhgKlzHyz6Psa4DN19l+bmQuB5yLieeCzlL+TswGKUPr4Uu5jiSLiYGAr4Et1mh9eNAsZEf8C/ly0TwW2L7a3BfYpti+nkaWrmXkhcCFArz6b5ZlT/ePZ1hw3cD6Oa9u0pLGdPryazOSwww7jC1/4AmeddVbtvm984xs899xz7LvvvowbN44DDjiA6upqNthgA0aPHs12223Hhx9+yL///W9OP/302jedTp8+nc6dO9cGR60YpVLJ77gNclzbLse2efk3ltYpl/J5WQTwZGZuuwznzMvMRddcwNL/O/mg+H1hne1FnztExCbA8cDWmfl2sSR1LYCIaEd5Ru49yjOTLy9DnXU153e2mIj4KvAj4EuZWfce699v3e/CP1+SALjvvvu4/PLLGThwIFVVVQD8z//8DyeeeCL7778/f/jDH9h444259tprAdh8883ZeeedGTRoEO3atWPUqFG1IfHAAw+kVCoxc+ZMevbsyamnnsoRRxzRYvcmSWqb/Its69QrIrbNzPuBg4B7gYFLOece4FsRcSnl5xO3B64CngHWX9RfsRT1M5n55Aqsv751gTnAO8XzjrsApWLfD4CngJOAS4o65zXQx4PAbyPiE8C7wNeBKXX2f724902APpTv+x7K39/dETEAGLQ8xUfEFsD/Ajtn5htLO74B/wAOoDybOBz4+9JO6NSxPc+M2205LqXWrFQqMX14dUuXoRVgaWO73Xbb8d+fv1X661//2mD7mDFjGDNmzGLtEyZMWK4aJUlaFr7MpnV6BvhORDxFeZbt/CaccyPwHOVnEy8D7gfIzA+B/YBfRsQUystAP78iim5MZk6h/Dzh05TD630AxUtsRgHHZebfKQe7HzfSx2vAWMr3dR/lcFnXv4GHKD8/eFRmzqX8vXUpvsefUl5G26iIOD0iXgbWjoiXI2JssetXQBfguij/8yS3NP3ugfKziocXS18PAb63jOdLkiRJK5Uziq3T/Mw8uF7biCWdUCwVHd3IvscoP69Xv31Ene3qOttd6mxfT/mfhGjsunX7mE75eciG9jVW/+Z1jjm2sesU+y8BLmlk918y86h6x79PeSavSTLzBOCEBtq/2sjxJf47M1r/O6zdl5kvAl9uah2SJElSS3NGUZIkSZJUwRnFVqb+rFx9ETGQ8rNudX2QmUNXZF0R8SPKzwXWdV1m/nwFXOtBYM16zYcseptqfUuYrWyW/iVJkqTVjUFxFVOEmaoWuO7PKf/7iyvjWis09K7o/iVJkqRVnUtPJUmSJEkVDIqSJEmSpAoGRUmSJElSBYOiJEmSJKmCQVGSJEmSVMGgKEmSJEmqYFCUJEmSJFUwKEqSJEmSKhgUJUmSJEkVDIqSJEmSpAoGRUmSJElSBYOiJEmSJKmCQVGSJEmSVMGgKEmSJEmqYFCUJEmSJFUwKEqSJEmSKhgUJUmSJEkVDIqSJEmSpAoGRUmSJElSBYOiJEmSJKmCQVGSJEmSVMGgKEmSJEmqYFCUJKkJRo4cSffu3RkwYEBt25QpU9h2220ZOXIke+yxB++++y4ADz30EFVVVVRVVTF48GBuvPHG2nN69+7NwIEDqaqqYquttlrp9yFJUlN0aOkCJC3u/XkL6H3i7S1dhprZcQPnM8JxXSVNH7cbI0aMYPTo0Rx66KG17aNGjeKMM84gM3n++ef51a9+xWmnncaAAQOYNGkSHTp04LXXXmPw4MHssccedOhQ/t/uxIkT6datW0vdjiRJS+WMoposItaLiKPrfK6OiNuW4fxSRCzzj88jYq+I6Les5zW3iBgfEfu1dB2SWsawYcPo2rVrRduzzz7LsGHDANhhhx244YYbAFh77bVrQ+HcuXOJiJVbrCRJH5FBUctiPeDopR7V/PYCmhwUI8KZckkrRf/+/bn55psBuO6663jppZdq9z344IP079+fgQMHcsEFF9QGx4hgxx13ZMiQIVx44YUtUrckSUtjUFSjIuLYiHii+PV9YBywaUQ8FhG/Kg7rEhHXR8TTEXFlNPHH5hFxfkRMiognI+LUOu3jImJaRDweEWdExOeBPYFfFdfdtJH+ShFxVkRMAr4XEetHxA0R8XDx6wvFcWMj4vKIuD8inouIby6hxoiIcyLimYj4C9C9zr6Ti36fiIgLi2M3jYhH6hzz6UWf699XU74jSa3fxRdfzHnnnceRRx7J7NmzWWONNWr3DR06lCeffJKHH36YX/ziF8ydOxeAe++9l0ceeYQ//elPnHvuudxzzz0tVb4kSY1y5kUNioghwOHAUCCAB4GDgQGZWVUcUw1sAfQHXgXuA74A3NuES/woM/8TEe2Bv0bEIOAVYG/gs5mZEbFeZs6KiFuA2zLz+qX0uUZmblXUdhXwm8y8NyJ6AXcCmxfHDQI+B3QGHo2I2zPz1Qb62xvoS3k2cwNgGnBxse+czPxpca3Lgd0z89aIeCciqjLzseL7uyQiPlH/vhoqPiKOBI4E6NZtfU4eOH8pt6tVzQadys8patVTKpUAmDFjBnPmzKn9DHDSSSdRU1PD22+/Tffu3Sv2LTJ//nwuvfRS+vbtC8Bzzz0HwBZbbMGECRNYuHDhir4FLYeampoGx1OrNse17XJsm5dBUY3ZDrgxM+cARMT/AV9s4LiHMvPl4pjHgN40LSjuXwSjDsCGlMPYNGAu8Ifi2ccmP/9YuKbO9leBfnUmONeNiC7F9s2Z+T7wfkRMBLYBbmqgv2HAhMxcALwaEXfX2bd9RJwArA10BZ4EbgUuAg6PiGOBbxR9v9OU+8rMC4ELAXr12SzPnOofz7bmuIHzcVxXTdOHV5d/nz6dzp07U11d/vzGG2/QvXt37r77bq699lrGjBlDdXU1L7zwAhtttBEdOnTgxRdfZMaMGey777506tSJhQsXss466zBnzhxOOukkTj755Nr+1LqUSiXHpg1yXNsux7Z5+TcWfVQf1NleQBP+m4qITYDjga0z8+2IGA+slZnzI2Ib4CvAfsBo4MvLUMucOtvtgM9l5tx61wbIeufV/7y0+tcCzgO2ysyXImIssFax+wbgFOBuYHJmvlWc81HuS1IrcOCBB1IqlZg5cyY9e/bk1FNPpaamhnPPPZf333+f4cOHc/jhhwPl5aXjxo2jY8eOtGvXjvPOO49u3brx/PPPs/feewPlWcaDDjqInXfeuSVvS5KkBhkU1Zi/A+MjYhzlpad7A4cBxzVD3+tSDnXvRMQGwC5AqZjxWzsz/xgR9wHPF8fPBtZZxmv8Gfgu8CuAOstBAb4WEb+gvPS0GjixkT7uAb4VEZdSfj5xe+Aq/hsKZxY17wdcD5CZcyPiTuB84Iji2o3dl6RVyIQJExps/973vrfYT7EPOeQQDjnkkMWO7dOnD1OmTFlRJUqS1GwMimpQZj5SzPQ9VDRdlJmTI+K+iHgC+BOwXP8gXGZOiYhHgaeBlyg/2wjlMHhzMWMXwLFF+9XA7yPiGGC/zPxXEy5zDHBuRDxO+b/ze4Cjin2PAxOBbsBpjTyfCHAj5Zm/acC/gfuL+mdFxO+BJ4AZwMP1zruScrD+81Luq1GdOrbnmXG7NeE2tSoplUq1SxglSZJaM4OiGpWZvwZ+Xa/toHqHlersG72U/qrrbI9o5LBtGjjvPpbyz2PU7bv4PJPyM4INeTwzD21kX90+kvIy0Yb2/Rj4cSOnbgdcUjzbSGa+RgP3JUmSJLVWBkWpGUXEjcCm+AyiJEmSVmEGRTW7IixtUq/5h5l5ZzP0fS7lf4Kjrt9m5iVNOT8zxzbQ50Dg8nrNH2Tm0GWtLzP3XtZzJEmSpNbGoKhmtyLDUmZ+ZwX0ORWoau5+JUmSpFVVu5YuQJIkSZLUuhgUJUmSJEkVDIqSJEmSpAoGRUmSJElSBYOiJEmSJKmCQVGSJEmSVMGgKEmSJEmqYFCUJEmSJFUwKEqSJEmSKhgUJUmSJEkVDIqSJEmSpAoGRUmSJElSBYOiJEmSJKmCQVGSJEmSVMGgKEmSJEmqYFCUJEmSJFUwKEqSJEmSKhgUJUmSJEkVDIqSJEmSpAoGRUmSJElSBYOiJEmSJKmCQVGSJEmSVMGgKEmSJEmqYFCUJK0yRo4cSffu3RkwYEBF++9+9zs++9nP0r9/f0444YTa9scff5xtt92W/v37M3DgQObOnQvAhAkTGDhwIIMGDWLnnXdm5syZK/U+JElq7Tq0dAGSFvf+vAX0PvH2li5Dzey4gfMZ4bgul+njdgNgxIgRjB49mkMPPbR238SJE7n55puZMmUKa665Jm+88QYA8+fP5+CDD+byyy9n8ODBvPXWW3Ts2JH58+fzve99j2nTptGtWzdOOOEEzjnnHMaOHdsStyZJUqvkjKK0AkTE9Ijo1tJ1SG3NsGHD6Nq1a0Xb+eefz4knnsiaa64JQPfu3QH485//zKBBgxg8eDAAn/jEJ2jfvj2ZSWYyZ84cMpN3332XHj16rNwbkSSplTMoSpJWac8++yx///vfGTp0KF/60pd4+OGHa9sjgp122oktt9yS008/HYCOHTty/vnnM3DgQHr06MG0adM44ogjWvIWJElqdVx6qlVWRNwEbASsBfw2My+MiBrg98COwAzggMx8s5HzNwMuANYHFgBfB54HTgd2ARL4WWZe08j51cBPgdnAZsBE4OjMXFjvuGOBkcXHizLzrEb6OxI4EqBbt/U5eeD8JnwLWpVs0Km8/FTLrlQq1W7PmDGDOXPm1La98847TJ06lXHjxvH000+z5557ctVVV/HMM8/wl7/8hQsuuIA111yT4447jvbt2zN48GD+53/+h/PPP58ePXpw9tlnc+SRR3LIIYcsd301NTUVNaptcFzbJse17XJsm5dBUauykZn5n4joBDwcETcAnYFJmfmDiDgZOAUY3cj5VwLjMvPGiFiL8gz7PkAVMBjoVvR7T2a+1kgf2wD9gBeBO4rzr1+0MyKGAIcDQ4EAHoyIv2Xmo/U7yswLgQsBevXZLM+c6h/Ptua4gfNxXJfP9OHV/92ePp3OnTtTXV1u69u3L9/97nfZfvvt2X777TnjjDMYMGAAr7/+Ou+99x5f+9rXAHj44YdZuHAhH/vYx/j4xz/O8OHDAWjfvj3jxo2r7W95lEqlj3S+WifHtW1yXNsux7Z5ufRUq7JjImIK8ADlmcVPAwuBRTOAVwDbNXRiRKwDfCozbwTIzLmZ+V5x/ITMXJCZrwN/A7ZeQg0PZebzmbkAmNDA9bYDbszMOZlZA/wf8MXluFdJjdhrr72YOHEiUF5u+uGHH9KtWzd22mknpk6dynvvvcf8+fP529/+Rr9+/fjUpz7FtGnTePPN8mKDu+66i80337wlb0GSpFbHH21rlVQs+/wqsG1mvhcRJcpLUOvLFVxK/f5X9PWk1dqBBx5IqVRi5syZ9OzZk1NPPZWRI0cycuRIBgwYwBprrMGll15KRPDxj3+cY489lq233pqIYNddd2W33cpvTz3llFMYNmwYHTt2ZOONN2b8+PEte2OSJLUyBkWtqj4GvF2ExM8Cnyva2wH7AVcDBwH3NnRyZs6OiJcjYq/MvCki1gTaA38HvhURlwJdgWHAmCXUsU1EbEJ56ek3KJaO1vF3YHxEjKO89HRvYKkPQnXq2J5nin8OQG1HqVSqWEKpZTdhwoQG26+44ooG2w8++GAOPvjgxdqPOuoojjrqqGatTZKktsSlp1pV3QF0iIingHGUl58CzKEc3p4Avkz5ZTONOYTy8tXHgX8AnwRuBB4HpgB3Aydk5owl9PEwcA7wFPBCcX6tzHwEGA88BDxI+WU2iz2fKEmSJLUmzihqlZSZH1B+M2mFiCAzj21iH89RDpP1jWHJs4h1vZuZuzfQd+86278Gft3E/iRJkqQW54yiJEmSJKmCM4pqUzKzS/22iDgX+EK95t9m5iVN6TMiBgKX12v+IDOHAqXlqVOSJElqzQyKavMy8zsf8fyplP9tRUmSJGm14NJTSZIkSVIFg6IkSZIkqYJBUZIkSZJUwaAoSZIkSapgUJQkSZIkVTAoSpIkSZIqGBQlSZIkSRUMipIkSZKkCgZFSZIkSVIFg6IkSZIkqYJBUZIkSZJUwaAoSZIkSapgUJQkSZIkVTAoSpIkSZIqGBQlSZIkSRUMipIkSZKkCgZFSZIkSVIFg6IkSZIkqYJBUZIkSZJUwaAoSZIkSapgUJQkSZIkVTAoSpIkSZIqGBQlSUs1cuRIunfvzoABA2rbfvKTnzBo0CCqqqrYcccdefXVVwF455132GOPPRg8eDD9+/fnkksuqT2nffv2VFVVUVVVxZ577rnS70OSJDVNh5YuQNLi3p+3gN4n3t7SZaiZHTdwPiNWwXGdPm43RowYwejRozn00ENr28eMGcNpp50GwNlnn81Pf/pTLrjgAs4991z69evHrbfeyptvvknfvn0ZPnw4a6yxBp06deKxxx5rqVuRJElN5IxiKxQRYyPi+Kbsj4ifRsRXV151H01EVEfEbct4zviI2G8F1bNeRBy9lGM2johHIuKxiHgyIo5aEbVIrdmwYcPo2rVrRdu6665buz1nzhwiAoCIYPbs2WQmNTU1dO3alQ4d/LmkJEmrEv/PvYrLzJNbuoZV3HrA0cB5SzjmNWDbzPwgIroAT0TELZn56kqpUGrFfvSjH3HZZZfxsY99jIkTJwIwevRo9txzT3r06MHs2bO55ppraNeu/HPJuXPnstVWW9GhQwdOPPFE9tprr5YsX5IkNcKg2EpExI+Aw4A3gJeAyRGxKXAusD7wHvDNzHy63nnjgdsy8/qImA5cCuwBdAS+Xv/4OueNBTYB+gC9gB8AnwN2AV4B9sjMeRFxctFfJ+AfwLeA9sD9wJjMLEXEL4CFmfmjRq61M3BWcQ/31qthU2AzoBtwemb+PsrTEr8Ddii+iw+X8t0tVmNmZkSUgEeBLwKdgUOB/wcMBK7JzB8D44BNI+Ix4K7MHFO//8yse/01qTMTHxE1wPnArpQD5UnA6cV3+v3MvCUi1iqO2QqYDxybmRMbuI8jgSMBunVbn5MHzl/SbWsVtEGn8vLTVU2pVAJgxowZzJkzp/YzwA477MAOO+zAlVdeyfHHH8/hhx/O3/72N7p168ZVV13Fq6++yqhRo7jooovo3LkzEyZMYP311+fVV1/lqKOOYs6cOXzqU59qmRtrRjU1NRXfi9oGx7VtclzbLse2eRkUW4GIGAIcAFRRHpNHgMnAhcBRmflcRAylPOv15aV0NzMztyyWUx4PjFrCsZsC2wP9KAe/fTPzhIi4EdgNuIn/z969x2lVlov//1w4JAgeIsSfhwjJEuXgKARq6B62wc60knRbxk5J3dY2OqpBuT1hbcBDHks8bjxFpnlKPPVVx5RSFARRt2gl5rk0USFEBq7fH89ieh4Y5mAMMwyf9+s1r1nrXve617XWzbyYa+77Xg9cmJkTizivBg7MzF9HxFjghoj4JvBpYNha7q0LcGkR9x+A61arMohSgtoNeCwiZhT7OxdxbQM8BVzRyH2sESPw6+LYe5k5JCK+DdwCDAb+BvwxIs4BJgADMrO6kfaJiA8DMygltSeUjSZ2A+7NzBOK5/YjSgnurpSS9luBbwCZmQMjoh9wd0R8PDPfLb9GZl5Cqc/p3XenPHu+P54dzXED69gQ+3XhmJrS94UL6datGzU1NWvU6du3L5/5zGe48sorOfPMM5kwYQL77LMPAJdffjlbb701Q4cOrTjn7rvvZtNNN22wvQ1NbW1th7gPVbJfOyb7teOyb9ct1yi2D/sAN2Xm3zPzbUrJRRdgb+D6YrTrYmDbZrR1Y/F9NtCnibp3ZOZyYD6lUcI7i/L5ZeeOiIiHI2I+pWSvP0BmPglcDdwGHLnaqFu5fsBzmflsZiZwzWrHb8nMpZn5OnAfMBTYF5iemSuKhOzeJu6jwRgLt5bd05OZ+UpmLgP+BHy4iXbrZeYLmTmIUqJ4RERsUxx6j8rndn/ZM+1TlA9fdd/FCO/zwMebe22pvXr22Wfrt2+55Rb69esHQO/evbnnnnsAeO2111iwYAF9+/blzTffZNmyZQC8/vrrzJw5k1133XX9By5Jkpq04f1pe+PRCVjU1EhXA5YV31fQdP8uA8jMlRGxvEjkAFYCVcVo4M+AIZn5QjFVtEvZ+QOBRUCvFsZYLpvYb1QzYlz1PFaWba/ab/G//8x8OSKeoJTc3wCs/tzKn6k/X+owDjvsMGpra3n99dfZYYcdOO2007j99ttZsGABnTp14iMf+QhTp04FSh+bMXbsWAYOHEhmMmXKFHr27Mnvfvc7vva1r9GpUydWrlzJhAkTTBQlSWqn/EW2ffgtMK1Y61dFab3dxcBzEfHvmXl9sW5vUGbOW49xrUq4Xi9e4nIIpeSIiPgC0IPS6N9tETE0Mxc10MbTQJ+I+Ghm/hE4bLXjny/uuxtQQ2kq6CbA1yLiSkpJ6Ajg5y2NsZneATZvrEJE7AC8kZlLI+KDlEYIz2nBNR4AxgD3RsTHKa1fXNDYCV07b8KCyQe04BLaENTW1tZP49zQTJ8+fY2yo446qsG62223HXffffca5XvvvTfz589f57FJkqR1z6mn7UBmzqG0dm8ecAfwSHFoDHBURMwDngQ+v57jWkRpfeETwF2r4oqInpReAnN0Zj4DXAict5Y23qX0gpYZETGH0st6yj1OacrpQ8DpxVTTm4BnKa1NvIrS+skWxdiCe3wDmBkRT0TEmWuptgvwcNEP9wNnZWZLftv9GdCpmBp7HTC2mP4qSZIktUvxj1lz0vpVTBNdnJlntXUs7c3OO++cCxY0OuioDZCL7Dsu+7Zjsl87Jvu147JvWy4iZmfmkIaOOaIoSZIkSargGsUOLiK+Cnx7teKZmfmNVrjWTZQ+m7Hc+My8q6H6mXlqa7bfUhExkNKbXMsty8wGP/pDkiRJ6qhMFDu4zPxf4H/X07VGb+Dtz6f0WZaSJEnSRs2pp5IkSZKkCiaKkiRJkqQKJoqSJEmSpAomipIkSZKkCiaKkiRJkqQKJoqSJEmSpAomipIkSZKkCiaKkiRJkqQKJoqSJEmSpAomipIkSZKkCiaKkiRJkqQKJoqSJEmSpAomipIkSZKkCiaKkiRJkqQKJoqSJEmSpAomipIkSZKkCiaKkiRJkqQKJoqSJEmSpAomipIkSZKkCiaKkiRJkqQKJoqSJEmSpAomipIkSZKkCiaKkqQGHXnkkfTq1YsBAwbUl5100kkMGjSI6upqRo0axcsvv1x/rLa2lurqavr378+//Mu/APDCCy8wYsQIdt11V/r3789555233u9DkiS1XFVbByBtLCKiGtguM29vqu7S5SvoM2HGeohK69NxA+sYu4H068LJBzB27FjGjRvH4YcfXl9+wgkncPrppwNw/vnnM3HiRKZOncqiRYs49thjufPOO+nduzd/+ctfAKiqquLss89mjz324J133mHw4MGMHDmSXXfdtU3uS5IkNY8jitL6Uw18pq2DkJpr3333pUePHhVlW2yxRf32kiVLiAgAfv7zn/OFL3yB3r17A9CrVy8Att12W/bYYw8ANt98c3bZZRdeeuml9RG+JEn6J5goaoMQEd+LiCeKr+9ERLeImBER84qyLzZy7skR8UhR75IofrONiNqIOCciHo2I/4uIT0TEjRHxbET8aG3XLsr6RMQTZXWOj4hTy9qdEhGzIuKZiNgnIj4ATAS+GBFzG4tXau9OPPFEPvzhD3PttdcyceJEAJ555hnefPNNampqGDx4MFddddUa5y1cuJDHHnuMYcOGre+QJUlSC5koqt2LiMHAV4FhwJ7AfwJfB17OzN0ycwBwZyNNXJiZnyjqdQUOLDv2XmYOAaYCtwDfAAYAYyPiQw1dOyJ2b0bYVZk5FPgOcEpmvgecDFyXmdWZeV2zH4DUzvz4xz/mhRdeYMyYMVx44YUA1NXVMXv2bGbMmMFdd93F6aefzjPPPFN/zuLFizn44IM599xzK0YlJUlS++QaRW0IhgM3ZeYSgIi4EVgOjIyIKcBtmflAI+ePiIjvA5sBPYAngV8Xx24tvs8HnszMV4pr/An48FquvU/ZeWtzY/F9NtCnOTcZEccAxwD07Lk1Jw+sa85p2oBs07W0TnFDUFtbC8Crr77KkiVL6vfL9e3blwkTJjBixAjee+89dt55Zx555BEAPvaxj/Hzn/+cmpoa6urq+MEPfsCwYcPo0aNHg21t6BYvXtwh72tjZ792TPZrx2XfrlsmitqQ7UFpzd+PIuKezJy4eoWI6AL8DBiSmS8U00O7lFVZVnxfWba9ar+xn486Kkfku6x2fFVbK5pop15mXgJcAtC770559nx/PDua4wbWsaH068IxNaXvCxfSrVs3ampK+88++ywf+9jHALjgggsYPHgwNTU1bLPNNowbN47hw4fz3nvv8ec//5kzzjiD/v37c8QRR/DJT36Sc889t43uKe6jCgAAIABJREFUpvXV1tbWPyN1HPZrx2S/dlz27brl1FNtCB4ADoqIzSKiGzCa0kjd3zPzGuBMSkljQ1YlcK9HRHfgkHVw7QeA14BexfTUTamczro27wCbt/D6Ups57LDD2GuvvViwYAE77LADl19+ORMmTGDAgAEMGjSIu+++u/7jLnbZZRc+/elPM2jQIIYOHcrRRx/NgAEDmDlzJldffTX33nsv1dXVVFdXc/vtTb74V5IktbEN40/b2qhl5pyImAbMKoouA7oDsyJiJaVpqP+1lnMXRcSlwBPAq8Aj/+y1M/MxgIiYWJS/BDzdjObuAyZExFxgUmPrFLt23oQFkw9oSajaANTW1taP1G0Ipk+fvkbZUUcdtdb6J5xwAieccEJF2fDhw8nMdR6bJElqXSaK2iBk5k+An6xWfFczz/1v4L8bKK8p264FatdyrKFrk5nnA+c30e7rFGsUM/NvwCeaE7MkSZLUlpx6KkmSJEmq4IiiOoyIuAnYcbXi8ZnZrJFHSZIkSSUmiuowMnN0W8cgSZIkdQROPZUkSZIkVTBRlCRJkiRVMFGUJEmSJFUwUZQkSZIkVTBRlCRJkiRVMFGUJEmSJFUwUZQkSZIkVTBRlCRJkiRVMFGUJEmSJFUwUZQkSZIkVTBRlCRJkiRVMFGUJEmSJFUwUZQkSZIkVTBRlCRJkiRVMFGUJEmSJFUwUZQkSZIkVTBRlCRJkiRVMFGUJEmSJFUwUZQkSZIkVTBRlCRJkiRVMFGUJEmSJFUwUZQkSZIkVTBRlCRJkiRVMFGUJAFw5JFH0qtXLwYMGFBfdtJJJzFo0CCqq6sZNWoUL7/8MgDXXnstgwYNYuDAgey9997MmzcPgBdeeIERI0aw66670r9/f84777w2uRdJkvTPqWrrACStaenyFfSZMKOtw9A6dtzAOsa2w35dOPkAAMaOHcu4ceM4/PDD64+dcMIJnH766QCcf/75TJw4kalTp7Ljjjty//3388EPfpA77riDY445hocffpiqqirOPvts9thjD9555x0GDx7MyJEj2XXXXdvk3iRJ0vvjiOJGLCK2iohjy/ZrIuK2FpxfGxFD3sd1D4qIRn9rjIixEbFdS9tuzyKiOiI+09ZxSGuz77770qNHj4qyLbbYon57yZIlRAQAe++9Nx/84AcB2HPPPXnxxRcB2Hbbbdljjz0A2Hzzzdlll1146aWX1kf4kiRpHTJR3LhtBRzbZK117yCgqeGFsUCDiWJEbLKuA1pPqgETRW1wTjzxRD784Q9z7bXXMnHixDWOX3755ey///5rlC9cuJDHHnuMYcOGrY8wJUnSOmSiuBGJiO9FxBPF13eAycBHI2JuRJxZVOseETdExNMRcW2sGj5ouu2LIuLRiHgyIk4rK58cEU9FxOMRcVZE7A18DjizuO5HG2jrEGAIcG1Rp2tELIyIKRExBzgsIh6JiJqi/qSI+HEjsZ1c1H8iIi5ZdU/FiOg5Rdz/FxGfiIgbI+LZiPhRI8+NiOgTEU+U1Tk+Ik4ta3dKRMyKiGciYp+I+AAwEfhicU9fbM5zldqDH//4x7zwwguMGTOGCy+8sOLYfffdx+WXX86UKVMqyhcvXszBBx/MueeeWzEqKUmSNgyuUdxIRMRg4KvAMCCAh4H/AAZkZnVRpwbYHegPvAzMBD4JPNiMS5yYmX8rRvvuiYhBwEvAaKBfZmZEbJWZiyLiVuC2zLyhoYYy84aIGAccn5mPFrEBvJGZexT7jwE3RMQ3gU8X97U2F2bmxOK8q4EDgV8Xx97LzCER8W3gFmAw8DfgjxFxDtBn9ecWEfcDbzbxPKoyc2gx1fSUzPxURJwMDMnMcQ2dEBHHAMcA9Oy5NScPrGviEtrQbNO1tE6xvamtra3ffvXVV1myZElF2Sp9+/ZlwoQJjBgxAoA//vGPnHzyyUyePJn58+fX16urq+MHP/gBw4YNo0ePHg221dEsXrx4o7jPjY392jHZrx2XfbtumShuPIYDN2XmEoCIuBHYp4F6szLzxaLOXEqJUnMSxUOLRKcK2JbS1NKngHeBy4u1j81e/7gW163ayMwni6TvNmCvzHyvkfNGRMT3gc2AHsCT/CNRvLX4Ph94MjNfAYiIPwEfZu3P7VYad2PxfTalZ9ikzLwEuASgd9+d8uz5/nh2NMcNrKM99uvCMTX/2F64kG7dulFTUyp79tln+djHPgbABRdcwODBg6mpqeHPf/4zRx99NNdffz177713/fmZyRFHHMEnP/lJzj333PV5G22qtra2/pmp47BfOyb7teOyb9et9vcbi9rasrLtFTTj30hE7AgcD3wiM9+MiGlAl8ysi4ihwH7AIcA44F//idiWrLY/EFgE9Gokti7AzyiN5L1QTA/tUlZl1f2upPLeV9L4vddROXW7y2rHV7XVrGcotQeHHXYYtbW1vP766+ywww6cdtpp3H777SxYsIBOnTrxkY98hKlTpwIwceJE3njjDY49trTMuaqqikcffZSZM2dy9dVXM3DgQKqrqwH4n//5Hz7zGZfnSpK0IfEX2I3HA8C0iJhMaQrlaOAI4Lh10PYWlJK4tyJiG2B/oDYiugObZebtETET+FNR/x1g8ybabLRORHyB0ujgvsBtETE0Mxc1UHVVAvd6Ec8hQINTXteioef2FeA1oFdEfAhYTGk6651NtNWc+5bazPTp09coO+qooxqse9lll3HZZZetUT58+HAyc53HJkmS1i8TxY1EZs4pRvpmFUWXZebsiJhZvJTlDuB9fcBbZs4r1gw+DbxAaW0jlJKiW4pRvQC+V5T/Arg0Ir4FHJKZf2yg2WnA1IhYCuxVfiAielJ6Ec9+xSjhhcB5lBLf1WNbFBGXAk8ArwKPtPDeGnpujxVxTCzKXyruvSn3AROKKb2TMvO6tVXs2nkTFhSfbaeOo7a2tmKapyRJUnsV/uVXan923nnnXLBgQVuHoXXMtRMdl33bMdmvHZP92nHZty0XEbMzs8HPRffjMSRJkiRJFZx6qiZFxE3AjqsVj8/Mu9ZB2z+l9BEc5c7LzP99H221WpySJEnSxsREUU3KzNGt2PY31mFbrRanJEmStDFx6qkkSZIkqYKJoiRJkiSpgomiJEmSJKmCiaIkSZIkqYKJoiRJkiSpgomiJEmSJKmCiaIkSZIkqYKJoiRJkiSpgomiJEmSJKmCiaIkSZIkqYKJoiRJkiSpgomiJEmSJKmCiaIkSZIkqYKJoiRJkiSpgomiJEmSJKmCiaIkSZIkqYKJoiRJkiSpgomiJEmSJKmCiaIkSZIkqYKJoiRJkiSpgomiJEmSJKmCiaIkSZIkqYKJoiRJkiSpgomiJHVwRx55JL169WLAgAH1ZSeccAL9+vVj0KBBjB49mkWLFlWc8+c//5nu3btz1llnVZSvWLGC3XffnQMPPHC9xC5JktpGVVsHIGlNS5evoM+EGW0dhtax4wbWMXY99+vCyQcwduxYxo0bx+GHH15fPnLkSCZNmkRVVRXjx49n0qRJTJkypf749773Pfbff/812jvvvPPYZZddePvtt9dL/JIkqW04oqj3LSK2iohjy/ZrIuK2FpxfGxFD3sd1D4qIXVt63roWEdMi4pAWnrO4teKR1mbfffelR48eFWWjRo2iqqr0t8I999yTF198sf7YzTffzI477kj//v0rznnxxReZMWMGRx99dOsHLUmS2lSLE8WI+GBEDGqNYLTB2Qo4tsla695BQLMTxYhw5FxqxBVXXFE/erh48WKmTJnCKaecska973znO5xxxhl06uTfGCVJ6uia9Qt0RNQCnyvqzwb+EhEzM/N7rRib2pmI+B5wZLF7GbAn8NGImAv8BpgBdI+IG4ABlP6t/EdmZjPavgj4BNAVuCEzTynKJ1P6t1cH3A3cWOz/S0T8N3BwZv6xgfZqgbnAcGB6RFwFTAV6F1W+k5kzI+JU4KPATkBP4IzMvHQtMQZwATASeAF4r+zYQuCXwP7AUuDLmfmHiNgR+DnQHbiliWdwDHAMQM+eW3PywLrGqmsDtE3X0vTT9am2thaAV199lSVLltTvr3LNNdewaNEitt9+e2pra7nooosYNWoUjz76KAsXLqRr167U1tby+9//nuXLl/POO+8wd+5c3njjjTXa2pgtXrzY59EB2a8dk/3acdm361ZzR1q2zMy3I+Jo4KrMPCUiHm/NwNS+RMRg4KvAMCCAh4H/AAZkZnVRpwbYHegPvAzMBD4JPNiMS5yYmX+LiE2Ae4pR65eA0UC/zMyI2CozF0XErcBtmXlDE21+IDOHFLH9HDgnMx+MiN7AXcAuRb1BlJLebsBjETEjM19uoL3RwM6URjO3AZ4Crig7/lZmDoyIw4FzgQOB84CLMvOqiPhGY8Fm5iXAJQC9++6UZ893ILSjOW5gHeu7XxeOqSl9X7iQbt26UVNTU39s2rRpPPnkk9xzzz1sttlmAJx00kk8/PDDXHnllSxatIhOnTrRv39/3n77bWbPns3YsWN59913efvtt7nsssu45ppr1uv9tFe1tbUVz1Ydg/3aMdmvHZd9u2419zeWqojYFjgUOLEV41H7NRy4KTOXAETEjcA+DdSblZkvFnXmAn1oXqJ4aDGiVgVsSykZewp4F7i8WPvY7PWPhevKtj8F7FoaFARgi4joXmzfkplLgaURcR8wFLi5gfb2BaZn5grg5Yi4d7Xj08u+n1NsfxI4uNi+GpiC1A7ceeednHHGGdx///31SSLAAw88UL996qmn0r17d8aNGwfApEmTgNJ/xGeddZZJoiRJHVhzF5pMpDQC88fMfCQi+gLPtl5Y2oAtK9teQTP+GFFMzzwe2C8zB1GawtolM+soJW03UBqdu7OFsSwp2+4E7JmZ1cXX9pm56sUyq0+NbXKq7FpkM7al9e6www5jr732YsGCBeywww5cfvnljBs3jnfeeYeRI0dSXV3N17/+9bYOU5IktSPNGlHMzOuB68v2/8Q/Rkm0cXgAmFasGQxK0zCPAI5bB21vQSmpeysitqG0zq+2GPHbLDNvj4iZwJ+K+u8Am7fwGncD3wTOBIiI6sycWxz7fERMojT1tAaYsJY2fgt8LSKuBHoBIyitP1zli8Dk4vvvi7KZwJeAa4AxzQ22a+dNWDD5gOZW1waitra2firo+jR9+vQ1yo466qgmzzv11FMbLK+pqXFqjyRJHVyzRhQj4uMRcU9EPFHsDypeJKKNRGbOAaYBsyitT7wsM2cDMyPiiYg4859oex7wGPA0pcRrZnFoc+C2Yj3sg8Cqlyf9AjghIh6LiI828zLfAoZExOMR8RRQPnzyOHAf8BBw+lrWJwLcRGkk/SngKv6RDK7ywSLWbwPfLcq+DXwjIuYD2zczVkmSJKlNNXeN4qXACcDFAJn5ePFykB+1VmBqfzLzJ8BPViv78mrVasuOjWuivZqy7bFrqTa0gfNm0sTHY5S3Xey/TmmkryGPZ+bhazlW3kYCjd3TmZk5frVzngP2KivyDyySJElq95q7RnGzzJy1Wpnv7pckSZKkDqi5I4qvF1P8EiAiDgFeabWo1KFExE3AjqsVj8/Mu9ZB2z+l9GbRcudl5v825/zMPLWBNgdSekNpuWWZOayRdvo053qSJEnShqC5ieI3KH2+W7+IeAl4jha8mEMbt8wc3YptN/rZhO+zzflA9bpuV5IkSdpQNOejCzoBQzLzUxHRDeiUme+0fmiSJEmSpLbQ5BrFzFwJfL/YXmKSKEmSJEkdW3NfZvP/IuL4iPhwRPRY9dWqkUmSJEmS2kRz1yiu+liB8vVgCfRdt+FIkiRJktpasxLFzFz9jZWSJEmSpA6qWYliRDT4YeSZedW6DUeSJEmS1NaaO/X0E2XbXYD9gDmAiaIkSZIkdTDNnXr6zfL9iNgK+EWrRCRJkiRJalPNfevp6pYArluUJEmSpA6ouWsUf03pLadQSi53Ba5vraAkSZIkSW2nuWsUzyrbrgOez8wXWyEeSZIkSVIba+7U089k5v3F18zMfDEiprRqZJIkSZKkNtHcRHFkA2X7r8tAJEmSJEntQ6NTTyPiv4Bjgb4R8XjZoc2Bma0ZmCRJkiSpbTS1RvHnwB3AJGBCWfk7mfm3VotKkiRJktRmGk0UM/Mt4C3gMICI6AV0AbpHRPfM/HPrhyhJkiRJWp+atUYxIj4bEc8CzwH3AwspjTRKkiRJkjqY5r7M5kfAnsAzmbkjsB/wUKtFJUmSJElqM81NFJdn5htAp4jolJn3AUNaMS5JkiRJUhtp6mU2qyyKiO7AA8C1EfEXYEnrhSVJkiRJaivNHVH8PPB34DvAncAfgc+2VlCSJEmSpLbTrBHFzFwSER8BPpaZV0bEZsAmrRuaJEmSJKktNPetp/8J3ABcXBRtD9zcWkFJktadI488kl69ejFgwID6shNOOIF+/foxaNAgRo8ezaJFiwCYNWsW1dXVVFdXs9tuu3HTTTfVn3POOefQv39/BgwYwGGHHca777673u9FkiStH81do/gNYCjwMEBmPlt8pqKkVrB0+Qr6TJjR1mFoHTtuYB1j12O/Lpx8AABjx45l3LhxHH744fXHRo4cyaRJk6iqqmL8+PFMmjSJKVOmMGDAAB599FGqqqp45ZVX2G233fjsZz/La6+9xvnnn89TTz1F165dOfTQQ/nFL37B2LFj19v9SJKk9ae5axSXZeZ7q3YiogrI1glJar6IGBsR25XtXxYRuxbbCyOiZ9tF17CIWNzWMWjjsu+++9KjR4+KslGjRlFVVfpb4Z577smLL74IwGabbVZf/u677xIR9efU1dWxdOlS6urq+Pvf/852222HJEnqmJqbKN4fET8EukbESOB64NetF5bUbGOB+t9WM/PozHyq7cKRNjxXXHEF+++/f/3+ww8/TP/+/Rk4cCBTp06lqqqK7bffnuOPP57evXuz7bbbsuWWWzJq1Kg2jFqSJLWm5k49nQAcBcwHvgbcDlzWWkFJq4uIPsBtmTmg2D+eUpLYh9JHtiwF9gLuAI7PzEeb0d4dwIPA3sBLwOczc2lEVANTgc0oveH3yMx8MyJqKU2/HgFsBRyVmQ+spf2xwGhgS0preq/JzNOaiOkY4BiAnj235uSBdY1V1wZom66l6afrS21tbf32q6++ypIlSyrKAK655hoWLVrE9ttvX3Hspz/9Kc8//zw//OEP6datG8uWLePKK6/kmmuuoXv37px66qmceOKJjBw5cv3cTDu3ePHiNZ6tNnz2a8dkv3Zc9u261WiiGBG9M/PPmbkSuLT4ktqLG4AayhLD8mlyzfAx4LDM/M+I+CVwMHANcBXwzcy8PyImAqdQ+mgYgKrMHBoRnynKP9VI+0OBAZQ+WuaRiJjRWAKbmZcAlwD07rtTnj2/uX/H0YbiuIF1rM9+XTim5h/bCxfSrVs3amr+UTZt2jSefPJJ7rnnHjbbbLMG27jyyivp0aMHzz33HLvvvjsHHXQQAC+//DIPPfRQRXsbs9raWp9FB2S/dkz2a8dl365bTU09rX+zaUT8qpVjkda35zJzbrE9G+gTEVsCW2Xm/UX5lcC+ZefcWF6/ifZ/k5lvZObS4rzh6yZs6Z935513csYZZ3DrrbdWJInPPfccdXWlUc/nn3+ep59+mj59+tC7d28eeugh/v73v5OZ3HPPPeyyyy5tFb4kSWplTf1pu3x4pm9rBiI1oY7KP2x0WQdtLivbXgF0bcE5K2j652f1Fz75Aii1icMOO4za2lpef/11dthhB0477TQmTZrEsmXL6qeO7rnnnkydOpUHH3yQyZMn07lzZzp16sTPfvYzevbsSc+ePTnkkEPYY489qKqqYvfdd+eYY45p4zuTJEmtpSW/6PpLrtrSa0CviPgQsBg4ELgTeAfYfF1dJDPfiog3I2KfYv3hV4D7mzpvLUZGRA9gKXAQcGRzT+zaeRMWFB9toI6jtra2Yjro+jJ9+vQ1yo466qgG637lK1/hK1/5SoPHTjvtNE47rdGltpIkqYNoKlHcLSLepjSy2LXYptjPzNyiVaOTCpm5vFgvOIvSi2eeLg5NA6aWvcxmXTiiaHMz4E/AV99nO7OAXwE7UHqZTaMv2JEkSZLai0YTxczcZH0FIjUlM88Hzm/gUPn62Zqy+n0aaWshpRfNrNo/q2x7LrBnA+eUt/06Ta9RfDEzD2qgne5NnCdJkiS1qeZ+jqIkSZIkaSPh+/fVoRVrGu9p4NB+mfnGOmj/34ApqxU/l5mjKU2LlSRJkjY4Jorq0IpksLoV278LuKu12pckSZLaglNPJUmSJEkVTBQlSZIkSRVMFCVJkiRJFUwUJUmSJEkVTBQlSZIkSRVMFCVJkiRJFUwUJUmSJEkVTBQlSZIkSRVMFCVJkiRJFUwUJUmSJEkVTBQlSZIkSRVMFCVJkiRJFUwUJUmSJEkVTBQlSZIkSRVMFCVJkiRJFUwUJUmSJEkVTBQlSZIkSRVMFCVJkiRJFUwUJUmSJEkVTBQlSZIkSRVMFCVJkiRJFUwUJUmSJEkVTBQlbVD69OnDwIEDqa6uZsiQIQDMnTuXPffcs75s1qxZANTW1rLllltSXV1NdXU1EydObMvQJUmSNhhVbR2ApDUtXb6CPhNmtHUY7crCyQfUb99333307Nmzfv/73/8+p5xyCvvvvz+333473//+96mtrQVgn3324bbbblvf4UqSJG3QHFHUGiLiWxHxfxHxUkRc2NbxNCUi+kTEE+u4zX8vnsF9ETEkIs5v4fmnRsTx6zImrV1E8PbbbwPw1ltvsd1227VxRJIkSRs2RxTVkGOBTxVfQ9o4lrZyFPCfmflgsf9oWwajf4gIRo0aRUTwta99jWOOOYZzzz2Xf/u3f+P4449n5cqV/O53v6uv//vf/57ddtuN7bbbjrPOOov+/fu3YfSSJEkbBkcUVSEipgJ9gTuAD5aVfzYiHo6IxyLi/0XENkX51hHxm4h4MiIui4jnI6LnWponIk6KiAUR8WBETF816hYR1RHxUEQ8HhE3RcQHmygfHBHzImIe8I0m7mlsRNwSEbUR8WxEnFJ27D8iYlZEzI2IiyNik4g4GRgOXB4RZ0ZETUTcVtQ/NSKuKNr6U0R8q6ytEyPimYh4ENi5rLzBe9D78+CDDzJnzhzuuOMOfvrTn/Lb3/6Wiy66iHPOOYcXXniBc845h6OOOgqAPfbYg+eff5558+bxzW9+k4MOOqiNo5ckSdowRGa2dQxqZyJiIaWRxAOBIZk5rkhuFmVmRsTRwC6ZeVwxNfWlzJwUEZ+mlGBunZmvN9DuJ4BLgT2BzsAc4OLMPCsiHge+mZn3R8REYIvM/E4T5eMy87cRcSawf2YOWMv9jAUmAQOAvwOPAGOBJcAZwBcyc3lE/Ax4KDOvioha4PjMfDQiaortAyPiVGAUMALYHFgA/H/AIGAaMIzSSP0cYGpj99ZAnMcAxwD07Ln14JPPvbSJntq4DNx+yzXKpk2bRteuXbn66qv59a9/TUSQmRx44IHMmLHmGs8vfelLXHzxxWy55ZptrQ+LFy+me/fubXJttS77tmOyXzsm+7Xjsm9bbsSIEbMzs8EZhE49VXPtAFwXEdsCHwCeK8qHA6MBMvPOiHizkTY+CdySme8C70bErwEiYktgq8y8v6h3JXB9I+VbFeW/LcqvBvZvIv7fZOYbxfVuLOKuAwYDj0QEQFfgL020AzAjM5cByyLiL8A2wD7ATZn59+IatzZ2bw01mpmXAJcA9O67U5493x/PcgvH1LBkyRJWrlzJ5ptvzpIlS/jhD3/IySefTG1tLRFBTU0N99xzD/369aOmpoZXX32VbbbZhohg1qxZfOADH+Bzn/scRX+vd7W1tdTU1LTJtdW67NuOyX7tmOzXjsu+Xbf8TVTNdQHwk8y8tRhhO7Vtw2mx1YfOEwjgysz8QQvbWla2vQJ/jtab1157jdGjRwNQV1fHl7/8ZT796U/TvXt3vv3tb1NXV0eXLl245JJLALjhhhu46KKLqKqqomvXrvziF79osyRRkiRpQ+IvuGquLYGXiu0jyspnAocCUyJiFGXrGhswE7g4IiZR+rd3IHBJZr4VEW9GxD6Z+QDwFeD+RsoXRcSiiBhevGxmTDPiHxkRPYClwEHAkZSmod4SEedk5l+K45tn5vPNfCblfgtMK7u3z1KaVtvgPTTVWNfOm7Cg7OMgVNK3b1/mzZu3Rvnw4cOZPXv2GuXjxo1j3Lhx6yM0SZKkDsVEUc11KqVpn28C9wI7FuWnAdMj4ivA74FXgXcaaiAzHymmZD4OvAbMB94qDh8BTI2IzYA/AV9tovyrwBURkcDdzYh/FvArSlNor8nMRwEi4r+BuyOiE7Cc0otxWpwoZuaciLgOmEdp+uojZYfXdg+SJElSu2SiqDVkZp9ic1rxRWbeAtzSQPW3gH/LzLqI2Av4RLF+b23OysxTi6Tpt8Dsov25lF5ys3osayufDexWVvT9xu+KFzNzjVdeZuZ1wHUNlNeUbdcCtcX2qavVG1C2/WPgx829B0mSJKm9MlHUP6s38MtiRO494D+bqH9JROwKdKG0PnBOawcoSZIkqWVMFPVPycxngd3LyyLiQ8A9DVTfLzO/3FqxRMS/AVNWK34uM0dTjIxKkiRJapqJota54mMoqtvguncBd63v60qSJEkdTae2DkCSJEmS1L6YKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEpaJ959912GDh3KbrvtRv/+/TmtLLb4AAAgAElEQVTllFMA2Geffaiurqa6uprtttuOgw46CIBbbrmFQYMGUV1dzZAhQ3jwwQfbMnxJkiSVqWrrACStaenyFfSZMKOtw2i2hZMPYNNNN+Xee++le/fuLF++nOHDh7P//vvzwAMP1Nc7+OCD+fznPw/Afvvtx+c+9zkigscff5xDDz2Up59+uq1uQZIkSWUcUdwIRcRWEXFs2X5NRNzWgvP3iYgnI2JuRGwfETe08Po/bEl8HUVEjI2I7do6jtYSEXTv3h2A5cuXs3z5ciKi/vjbb7/NvffeWz+i2L179/rjS5YsqagrSZKktmWiuHHaCvhnErExwKTMrM7MlzLzkNUrRERjo9WNJoqNxddEu+3dWKDDJooAK1asoLq6ml69ejFy5EiGDRtWf+zmm29mv/32Y4sttqgvu+mmm+jXrx8HHHAAV1xxRVuELEmSpAaYKG4EIuJ7EfFE8fUdYDLw0WJE8MyiWveIuCEino6Ia2MtwzsRcTRwKHB6Ua9PRDxRHBsbEbdGxL3APRGxbUT8trjOE8VI5GSga1F27VpCroivGPF8ICJuBZ6KiE9ExOMR0SUiuhWjmwPWEm/3iLgnIuZExPyI+HxR3qe412kR8UxxL5+KiJkR8WxEDC3q9YiIm4vrPRQRg4ryUyPi+LLrPFG02Sci/i8iLi3iujsiukbEIcAQ4Nrivrq2pA83FJtssglz587lxRdfZNasWTzxxBP1x6ZPn85hhx1WUX/06NE8/fTT3HzzzZx00knrO1xJkiStRWRmW8egVhQRg4FpwJ5AAA8D/wFcnZkDijo1wC1Af+BlYCZwQmY2+HaRiJgG3JaZN0REn2J7QESMBX4EDMrMv0XEcUCXzPxxRGwCbJaZ70TE4szs3kjM9W2WxTcDGJCZzxVlPwK6AF2BFzNz0lraqiqu+3ZE9AQeAj4GfAT4A7A78CTwCDAPOAr4HPDVzDwoIi4AXs/M0yLiX4GfZGZ1RJwKLM7Ms4rrPAEcWFz2D8CQzJwbEb8Ebs3MayKiFjg+Mx9dS6zHAMcA9Oy59eCTz710bY+o3Rm4/ZZrlF155ZV06dKFL37xi7z11lscfvjhXH/99XzgAx9osI0vf/nLXHTRRWy55ZptdRSLFy+un56rjsW+7Zjs147Jfu247NuWGzFixOzMHNLQsQ15Gp+aZzhwU2YuAYiIG4F9Gqg3KzNfLOrMBfoA7+c1lL/JzL8V248AV0REZ+DmzJz7Ptorj++5sv2JRfvvAt9q5LwA/ici9gVWAtsD2xTHnsvM+QAR8SRwT2ZmRMyndP9Qen4HA2TmvRHxoYjYgsY9V3avs8vaalRmXgJcAtC770559vwN58dz4Zga/vrXv9K5c2e22morli5dykknncT48eOpqalh6tSpHHTQQYwaNar+nD/84Q989KMfJSKYM2cOEVH/cpuOqra2lpqamrYOQ63Avu2Y7NeOyX7tuOzbdWvD+U1UrW1Z2fYK3v+/jSWrNjLzt0WCdgAwLSJ+kplX/bPtFj4EdAc6UxpZXP34KmOArYHBmbk8IhYW9aHynleW7a+k6fuvo3Lqdpey7dWfZYecZrq6V155hSOOOIIVK1awcuVKDj30UA48sDTI+otf/IIJEyZU1P/Vr37FVVddRefOnenatSvXXXddh04SJUmSNiQmih3fA5SStMmURtdGA0cAx7X2hSPiI5SmhV4aEZsCewBXAcsjonNmLl/Lqe8AmzfR/MXAScCOwBRg3FrqbQn8pUgSR1CactoSD1BKNk8vpsC+XkxjXUgx1TQi9ijiaEpz7muDNWjQIB577LEGj9XW1q5RNn78eMaPH9/KUUmSJOn9MFHs4DJzTrGmcFZRdFlmzi5e2vIEcAel9X+toQY4ISKWA4uBw4vyS4DHI2JOZo5pIOY3GosvIg4Hlmfmz4u1j7+LiH/NzHsbiOFa4NfFdNJHgZZ+UN+plKbPPg78nVKSDfAr4PBiyurDwDPNaGsaMDUilgJ7ZebStVXs2nkTFkw+oIWhSpIkSeuGL7OR2qGdd945FyxY0NZhaB1z7UTHZd92TPZrx2S/dlz2bctFxFpfZuPHY0iSJEmSKjj1VGsVETex5tq78Zl51zpq/0PAPQ0c2i8z32hhWwOBq1crXpaZwxqqL0mSJGntTBS1Vpk5upXbfwOoXkdtzV9XbUmSJEkbO6eeSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkipUtXUAkta0dPkK+kyY0dZhrGHh5AN499132XfffVm2bBl1dXUccsghnHbaaYwZM4ZHH32Uzp07M3ToUC6++GI6d+7MLbfcwkknnUSnTp2oqqri3HPPZfjw4W19K5IkSWqEI4pSISJqImLvFp6zuIX1x0bEhS2LrH3ZdNNNuffee5k3bx5z587lzjvv5KGHHmLMmDE8/fTTzJ8/n6VLl3LZZZcBsN9++9XXveKKKzj66KPb+A4kSZLUFBNFbfAiomq1/YiI9/NvuwZoUaK4MYoIunfvDsDy5ctZvnw5EcFnPvMZIoKIYOjQobz44osAdO/enYgAYMmSJfXbkiRJar9MFNWuRMThEfF4RMyLiKsjYlpEHFJ2fHHxvSYiHoiIW4GnIqJPRCyIiKuAJ4APR8SoiPh9RMyJiOsjontx7sKIOK0onx8R/SKiD/B14LsRMTci9llLfDsWbc6PiB+VlddExG8jYkYRx9RVyWpEfDUinomIWcAnW+fJrV8rVqygurqaXr16MXLkSIYNG1Z/bPny5Vx99dV8+tOfri+76aab6NevHwcccABXXHFFW4QsSZKkFojMbOsYJAAioj9wE7B3Zr4eET2AnwC3ZeYNRZ3Fmdk9ImqAGcCAzHyuSPT+VJz7UET0BG4E9s/MJRExHtg0MydGxELg7My8ICKOBfbIzKMj4lRgcWae1UiMtwI3ZOZVEfENYEpZPHcCuwLPF9sXAzOBh4HBwFvAfcBjmTmugbaPAY4B6Nlz68Enn3vp+32UrWbg9ltW7C9evJiTTjqJb33rW+y4444AnHXWWXTp0oVx49a4RebNm8dVV13F2WefvV7ibW8WL15cPxqrjsW+7Zjs147Jfu247NuWGzFixOzMHNLQMV9mo/bkX4HrM/N1gMz8WxPTFGdl5nNl+89n5kPF9p6UkraZRRsfAH5fVvfG4vts4AstiPGTwMHF9tXAlNXi+RNAREwHhgN1QG1m/rUovw74eEMNZ+YlwCUAvfvulGfPb38/ngvH1KxRNmfOHN544w2++tWvctppp1FVVcUvf/lLOnVac8JCTU0N5513HgMGDKBnz57rIeL2pba2lpqamrYOQ63Avu2Y7NeOyX7tuOzbdcupp2rv6ij+nRZTOT9QdmzJanXL9wP4TWZWF1+7ZuZRZceXFd9X0PI/mKxtGH718g45XP/Xv/6VRYsWAbB06VJ+85vf0K9fPy677DLuuusupk+fXpEk/uEPf2DVzIU5c+awbNkyPvShD7VJ7JIkSWqe9jdkoY3ZvcBNEfGTzHyjmHq6kNK0zV8CnwM6N7Oth4CfRsROmfmHiOgGbJ+ZzzRyzjvAFk20OxP4EnANMGa1Y0MjYkdKU0+/SGl08GHgvIj4EPA28O/AvGbeQ7v0yiuvcMQRR7BixQpWrlzJoYceyoEHHkhVVRUf+chH2GuvvQD4whe+wMknn8yvfvUrrrrqKjp37kzXrl257rrrfKGNJElSO2eiqHYjM5+MiB8D90fECuAxYDxwS0TMo7Tub/VRxLW19deIGAtMj4hNi+L/BhpLFH8N3BARnwe+mZkPNFDn28DPizWPt6x27BHgQmAnSmsRb8rMlcXax98Di4C5zYm/a+dNWDD5gOZUXe8GDRrEY489tkZ5XV1dg/XHjx/P+PHjWzssSZIkrUMmimpXMvNK4MrVivcs2x5f1KsFasvOWwgMWK2te4FPNHCNPmXbj1L6WAyK0cZBTcT3HLBXWdF/l22/nZkHNnDO/wL/21i7kiRJUnviGkVJkiRJUgVHFKUGRMSJlNYTlrs+M3/cUP3VRzglSZKkDZmJotSAIiFsMCmUJEmSOjqnnkqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKkiRJkiqYKEqSJEmSKpgoSpIkSZIqmChKapZ3332XoUOHsttuu9G/f39OOeUUAJ577jmGDRvGTjvtxBe/+EXee+89AL773e9SXV1NdXU1H//4x9lqq63aMnxJkiS1QFVbByBpTUuXr6DPhBltHUa9hZMPYNNNN+Xee++le/fuLF++nOHDh7P//vvzk5/8hO9+97t86Utf4utf/zqXX345//Vf/8U555xTf/4FF1zAY4891oZ3IEmSpJZwRLGVRMQ+EfFkRMyNiK4tPPc7EfH/t3fvcXZV9f3/X28IyCUIIpcvEjHIJZKAjoAiEmlCAYvYCj+RaxWEFm3FO1YsChFvqEUQUSpFLgIFBEEQWy4FApjKJUDIBYhoicagRRCQQEhJ+Pz+ODvxnMlMJgkzmTC+no/HPM4+a6+19mfvNTPJZ9ba+6zT9v4/kmzQbM/t71iXEsesJBs12//dvI5McugAHnOlnV8Pxz4myS+S1KLzbsoPSzI1ybQk/53kDYMV42BKwvDhwwF4/vnnef7550nCTTfdxAEHHADA4Ycfzo9+9KMl2l588cUccsghKzVeSZIkrTgTxYFzGPCVquqqqnnL2fZjwOJEsareUVVP9kdQSVZoFrmq3tpsjgT6JVFc0VgG0CRgT+BX3cofBv6iqnYAvgCctbIDW1UsXLiQrq4uNtlkE/baay+22morNthgA4YNaw3liBEjmDNnTkebX/3qVzz88MPssccegxGyJEmSVsCq9h/1l5wkI4FrgbuBHYEZwK3AgcDbk+xTVYf10G4ccGxVvbN5fwYwGXg58Crg5iSPVdX4JLOAnavqsWWI59PA3wIvAP9ZVcclmQhMAcYCFyf5PvCvwBZNs49V1aQkrwQuBjYHfgakrd+5VTUcOBnYLskU4HzgdOCrwF81x/y3qvpWkhOAvwbWBv4b+EBVVQ+xXAH8OzAcuKqPc1tU5xXAGsBnq+qqJCcDs6vq2029CcBc4BvAGcAewGzgeeCcqrq8p/6r6t6mfffy/257ezswoqk3ktbY3w68FbgLOBf4PLAJcFhV3ZlkQ+Ac4LXAs8DRVTW1h/M7GjgaYKONNuaEHRYs7XKsVBMnTly8fdpppzF37lw+97nPMWLECObNm7d4/6OPPsozzzzTUf/iiy9m11135bbbblu5Qa+C5s6d23FtNHQ4tkOT4zo0Oa5Dl2Pbv0wU+8co4Kgm2ToHWBO4Grimt6SkN1V1epJPAOOXJTFsl2Qf4F3ALlX1bJOgLLJmVe3c1Pt34NSq+mmSLYDrgO2AE4GfVtVJSfYFjurhMMfRmeD+A61Zxq6qWtB2zDOq6qSmzgXAO4Ef9xDL1cCZVfX9JB/q4xSfA/avqj82S0Nvb9pfCpwGfLupdyDwduD/a2IbTStxe4BWwvZiHAX8Z9v7rYH3AEfSShQPpZUE/w3wz8B+tBLHe6tqvyR7AN8Hurp3XFVn0cxWbvHareuUaavOj+esw8YtUXbPPffw3HPPMX/+fMaOHcuwYcP42c9+xrbbbsu4cX+q//GPf5xvf/vbvPWtb12ijz83EydO7Lg2Gjoc26HJcR2aHNehy7HtXy497R+zq2pSs30hrURhMOwJnFtVzwJU1R/a9l3ard4Zzazg1cDLm9m63WnFT1X9BHhiGY/53apa0O2Y45PckWQarRm9Mb3EshutWUyAC/o4VoAvJ5kK/Betmc9Nm5nATZK8qrl/8Imqmk1rHC6rqheq6nfAzctwPr0fPBlPK1H8dFvxw1U1rapeoDWbfGNVFTCNVpJKE8cFAFV1E/DKJC9/MbEMht///vc8+WRrBfS8efO44YYb2G677Rg/fjyXX976e8j555/Pu971rsVtHnzwQZ544gl23XXXQYlZkiRJK2bVmbJ4aas+3vdkAZ2J+lr9F06PnmnbXg14S1U9116h+5LLFZVkLeA7tJbLzm6Wgraf3zPdmizL9YLWfZ8bAztV1fPNktxF/V4GHAD8PzoT0X6R5PXA2cA+VfV42675bdsvtL1/gSH28/Xb3/6Www8/nIULF/LCCy9w4IEH8s53vpPRo0dz8MEH89nPfpY3vvGNHHXUnyaiL7nkEg4++OB++96SJEnSyjGk/iM7iLZIsmtV/YzW0sOfAjv00eZXwOgkL6N1H99fNu0AngbWA5Zr6SlwA3BCkosWLT3tNqu4yPXAh4GvAyTpqqoptO6tPBT4YrOM9RU9tF0UW/sxP5Dk5ralpy80+x5rZioPAHpbgjsJOJjWTOYS93J2sz7waJMkjgde07bvUuDfgI2Av2jr+/Ak59NKMMfRuh9yuTTLc68A3ltVP1/e9sBttM7tC829qY9V1R+X1mDtNVZn5sn7rsChBs7rX//6Hj/i4rWvfS133nlnj20mTJgwwFFJkiRpILj0tH/MBD6U5AFaydWZfTVolkb+AJjevLb/D/ws4Noky7VUsqqupbWUdHKzrPTYXqp+BNi5+ciH+4EPNuWfB3ZPMoPW/X2/7qHtVGBhkvuSfJzWLNuvgalJ7gMObZ7Q+m/NuV1H69693nyU1rWbRmsp6dJc1MQ9DXgf8GDbuc+glcDOqarfNsU/BH4D3E8rEb0HeKq3zpN8JMlvaD2sZmqSs5tdJwCvBL7TfNzJ5D7i7G4CsFOzZPZk4PDlbC9JkiStVGndTqUV1Tz58pqq2n6QQ1EPkgyvqrnNE13vBHZr7ldcpY0aNapmzpw52GGon3mT/dDl2A5NjuvQ5LgOXY7t8kty96KHTHbn0lMNddck2YDWk2i/8FJIEiVJkqTBZqL4IlXVLKDX2cQkO7Dk0zznV9UuK3rMgehzVdKf51dV43ro/0pgy27Fn66q65a3f0mSJGkoMlEcYFU1jR4+M29V63NVMtDnV1X7D1TfkiRJ0lDgw2wkSZIkSR1MFCVJkiRJHUwUJUmSJEkdTBQlSZIkSR1MFCVJkiRJHUwUJUmSJEkdTBQlSZIkSR1MFCVJkiRJHUwUJUmSJEkdTBQlSZIkSR1MFCVJkiRJHUwUJUmSJEkdTBQlSZIkSR1MFCVJkiRJHUwUJUmSJEkdTBQlSZIkSR1MFCVJkiRJHUwUJUmSJEkdTBQlSZIkSR1MFCVJkiRJHUwUJUmSJEkdTBQlSZIkSR1MFKU/Y7Nnz2b8+PGMHj2aMWPG8M1vfhOAyy67jDFjxrDaaqsxefLkjjZf+cpX2HrrrRk1ahTXXXfdYIQtSZKkATZssAOQtKR5zy9k5HE/GdBjzDp5X4YNG8Ypp5zCjjvuyNNPP81OO+3EXnvtxfbbb88VV1zBBz7wgY42999/P5dccgkzZszgkUceYc899+TnP/85q6+++oDGKkmSpJXLGUWtdEmOSHJGs/3BJO9rticm2flF9DsyyaEr0O68JAcsZf/bksxIMiXJ5kkufxExvqhz7G+bbbYZO+64IwDrrbce2223HXPmzGG77bZj1KhRS9S/6qqrOPjgg3nZy17GlltuydZbb82dd965ssOWJEnSADNR1KCqqn+tqu8va/0kS5sFHwksd6K4DA4DvlJVXVU1p6p6TSpfymbNmsW9997LLrvs0mudOXPm8OpXv3rx+xEjRjBnzpyVEZ4kSZJWIhNF9Zsk70syNcl9SS5I8tdJ7khyb5L/SrJpD20mJDm2rei9zczd9CRvbqtzQZJJwAXNzOFtSe5pvt7atD0ZeFvT/uNJVk/y9SR3NXF9oOkvSc5IMjPJfwGbLOWc/g44EPhCkouaY09v9h2R5Iok1yZ5KMnX2tqdmWRyMxP5+Rd5aQfc3Llzefe7381pp53Gy1/+8sEOR5IkSYPMexTVL5KMAT4LvLWqHkuyIVDAW6qqmoTrn4BP9tHVOlXVlWR34Bxg+6Z8NDC2quYlWQfYq6qeS7INcDGwM3AccGxVvbOJ6Wjgqap6U5KXAZOSXA+8ERjV9LkpcH9zrCVU1dlJxgLXVNXlSUZ2q9LV9DcfmJnkW1U1Gzi+qv6QZHXgxiSvr6qpfVzDo4GjATbaaGNO2GFBH5fqxZk4cSIACxYs4DOf+Qy77LILG2644eJygCeffJK7776buXPnAjB//nxuueUWRowYAcDUqVPZcccdO9qod3PnzvVaDVGO7dDkuA5NjuvQ5dj2LxNF9Zc9gMuq6jGAJknaAbg0yWbAmsDDy9DPxU37W5O8PMkGTfnVVTWv2V4DOCNJF7AQ2LaXvvYGXt92/+H6wDbA7sDFVbUQeCTJTct1pp1urKqnAJLcD7wGmA0c2CR+w4DNaCWlS00Uq+os4CyALV67dZ0ybWB/PGcdNo6q4vDDD2e33XbjtNNOW6LOBhtswE477cTOO7duq9x444059NBDOeOMM3jkkUd4/PHH+eAHP+jDbJbRxIkTGTdu3GCHoQHg2A5NjuvQ5LgOXY5t/zJR1ED6FvCNqro6yThgwjK0qV7eP9NW9nHgf4E30Fo+/VwvfQX4cFV1fIZDkncsQxzLan7b9kJgWJItgWOBN1XVE0nOA9bqx2P2m0mTJnHBBRewww470NXVBcCXv/xl5s+fz4c//GF+//vfs++++9LV1cV1113HmDFjOPDAAxk9ejTDhg3j29/+tkmiJEnSEGSiqP5yE3Blkm9U1ePN0tP1gUVPOjl8Gfs5CLi5We75VFU9laR7nfWB31TVC0kOBxZlKk8D67XVuw74hyQ3VdXzSbZt4rkV+ECS82ndnzge+PflOtulezmtxPap5r7MfYCJy9PB2muszsyT9+3HkHo2duxYqrrn5i37779/j+XHH388xx9//ECGJUmSpEFmoqh+UVUzknwJuCXJQuBeWjOIlyV5glYiueUydPVckntpLS89spc63wF+2HysxrX8abZxKrAwyX3AecA3aT0J9Z60ss3fA/sBV9JaKns/8GvgZ8t1sn2oqvuac3iQ1jLUSf3ZvyRJkjTQTBTVb6rqfOD8bsVX9VDvPFqJHFU1oa18XC/9Tuj2/iHg9W1Fn27Kn6eVALb75+aru2N6OlYvxz+ibXsWzQN22s+jef/Ontp062vcsh5XkiRJGix+PIYkSZIkqYMzilIjyZUsuTz2090fhiNJkiQNdSaKUqOqen56iyRJkvRnxqWnkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJorSn4kjjzySTTbZhO23335x2UEHHURXVxddXV2MHDmSrq4uAB5//HHGjx/P8OHDOeaYYwYrZEmSJA2SYYMdgKQlzXt+ISOP+0m/9Tfr5H054ogjOOaYY3jf+963uPzSSy9dvP3JT36S9ddfH4C11lqLL3zhC0yfPp3p06f3WxySJEl6aVjpM4pJ3pZkRpIpSdZehvrnJTlgWeskOTvJ6H6Ic27z+qokly+l3gZJ/vHFHm8ZY+pK8o5+7O+IJK/qp74mJtl5Bdp9L8l9SaYmuTzJ8Kb8ZUkuTfKLJHckGTlQsSQZmaTXbCjJm5vv1ylNrPsva9+rkt13350NN9ywx31VxQ9+8AMOOeQQANZdd13Gjh3LWmuttTJDlCRJ0ipiMJaeHgZ8paq6qmpef3deVX9XVff3Y3+PVNXSEtUNgJWSKAJdQI+JYpIVmR0+AuiXRPFF+HhVvaGqXg/8Gli0zvEo4Imq2ho4FfjqYAUITAd2rqou4K+A767g9V5l3XbbbWy66aZss802gx2KJEmSVgEDlig2szQPJrkoyQPNbNFHgAOBLyS5qJd2SXJGkplJ/gvYpG3fTkluSXJ3kuuSbNZD+8WzSUnmJvlSMwt0e5JNlxLvlkl+lmRaki92O4/pzfaYJHc2M0tTk2wDnAxs1ZR9fSn9f7rp+74kJzdlXU1cU5NcmeQVbefw1eZYP29mYdcETgIOao51UJIJSS5IMgm4oIn1tiT3NF9v7e34zQzszsBFi2Z3k7wpyX83de5Msl6StZKc27S9N8n4pr+1k1zSjO2VwNptx9q7uZb3JLls0SxhT6rqj4vGvemjml3vAs5vti8H/rKp09O1XVosc5OcmtYs9o1JNm7Kd2rO8z7gQ73F18T4bFUtaN6utSjGtu/x85pxuijJnkkmJXkoyZubehsm+VEzzrcnef3SjjcYLr744sWziZIkSdJAz4qMAo6qqklJzgHWBK4Grqmq3pZz7t+0Gw1sCtwPnJNkDeBbwLuq6vdJDgK+BBy5lOOvC9xeVccn+Rrw98AXe6n7TeDMqvp+kt4Shw8C36yqi5rEbXXgOGD7ZrapR0n2oZX47FJVzyZZtP7v+8CHq+qWJCcBJwIfa/YNq6o3p0nEGHcAACAASURBVLXU9MSq2jPJCbRmto5p+p3QXKexVTUvyTrAXlX1XJPEXgzs3NPxq+oPSY4Bjq2qyc35XAocVFV3JXk5MA/4KFBVtUOS1wHXJ9kW+Afg2ararkl87mli2gj4LLBnVT2T5NPAJ2glub1dn3NpzZTeD3yyKd4cmE3r4AuSPAW8Enishy56jKWxLjC5qj7eXL8Tac1angscU1W3Li3Bb4txF+Ac4DXAe5uYALYG3kPr+/Au4FBgLPA3wD8D+wGfB+6tqv2S7EFr3Jf4fklyNHA0wEYbbcwJOyzoXmWFTZw4EYDf/e53PPPMM4vfAyxcuJBLL72U7373ux3lAA8++CBz5sxZolwrZu7cuV7LIcqxHZoc16HJcR26HNv+NdCJ4uyqmtRsXwh8BHiyjza7AxdX1ULgkSQ3NeWjgO2BG5r/oK8O/LaPvv4PuKbZvhvYayl1dwPe3WxfQM9LHX8GHJ9kBHBFVT3UyyRXd3sC51bVswBNkrY+sEFV3dLUOR+4rK3NFW1xj1xK31e3LeFdAzgjSRewENi2t+P30M8o4LdVdVdTZ9FM31haCTpV9WCSXzX97g6c3pRPTTK16ecttJLXSc21WZPWdetVVb0/yerNcQ6ilcQtj95iAXiBVgIMre/BK5JsQOva39qUXwDs00eMdwBjkmwHnJ/kP5tdD1fVNIAkM4Abq6qSTONP4zaW5nurqm5K8sokL190jduOcRZwFsAWr926TpnWfz+esw4b13qdNYt1112XcePGLd537bXXssMOO/Ce97xnyXazZjF37tyO+lpxEydO9FoOUY7t0OS4Dk2O69Dl2Pavgb5Hsfp4vzwCzGjubeyqqh2qau8+2jxfVYuOuZC+E+OlxldV/05rpmge8B/N7NBAmd+89hX3M23bHwf+F3gDrWWlaw5MaEsV4Ia2cRpdVUf11aj5w8Al/ClZnwO8Ghbff7k+8Hg/xPdivgepqgeAubT+aAF/GidoJaXz27ZXqfsYDznkEHbddVdmzpzJiBEj+N73vgfAJZdc0uOy05EjR/KJT3yC8847jxEjRnD//f12668kSZJWcQP9H9ktkuxaVT+jtSTvp8AOfbS5FfhAkvNp3Z84Hvh3YCaw8aL+mqWo21bVjH6KdRJwMK1Zp8N6qpDktcD/VNXpSbYAXg/cB6zXR983ACckuajb0s8nkrytqm4D3gvc0kc/T/dxrPWB31TVC0kOpzXr2uvxu/U3E9gsyZuapafr0UqIb2uux03NktMtmrq30hrTm5Js31wLgNuBbyfZuqp+kWRdYPOq+nn3YJt7Drdq6oVWEv5gs/tq4HBas5EHADe1Jf3d9RYLtP4YcgCtJPRQ4KdV9WSSJ5OMraqf0st4t8W5Ja3Z8QVJXgO8DpgF9HrvZTeLruEXkowDHus+m9jd2muszsyT913G7pfNxRdf3GP5eeed12P5rFmz+vX4kiRJeukY6BnFmcCHkjwAvAI4cxnaXAk8ROt+te/TLFusqv+j9R/+rzYPIJkCvLW3TlbAR5tYp9G6P64nBwLTk0yhNaP0/ap6nNYyy+m93etWVdfSSnwmN22PbXYdDny9WSrZxVLu42vcDIxO8zCbHvZ/Bzi8uT6vo5ltXMrxzwP+tSlbndayz2817W+g9eCW7wCrNdflUuCIqppPayyHN2N7Eq0lslTV72k9TfXi5rx+1sTSk9BaxjkNmAZs1nYNvge8MskvaN3jeNxSrkuPsTSeAd6c1gOJ9mjr//20EtopTRxLMxa4r6l7JfCPVdXTvZK9mQDs1FyPk2mNuyRJkrTKSu+TNC+y49bn3l1TVdv3UVUaMEnmVtWyzvytMkaNGlUzZ84c7DDUz7x3YuhybIcmx3VoclyHLsd2+SW5u6p6/PzxwfgcRUmSJEnSKmzA7lGsqln86YEfS0iyA62nTbabX1W7DFRMzXGPp/VxBu0uq6ov9UPfg3JOLwVpfb7hlt2KP11V1y1HH29nyafRPlxV+/fWZnlmE1ekf0mSJGkoGrSnMjYfKdDrZw8O4HG/ROvzFwei70E5p5eC/ki2mqRymRPLVa1/SZIk6aXCpaeSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDsMGOwBJS5r3/EJGHveTFWo76+R9OfLII7nmmmvYZJNNmD59OgCf+tSn+PGPf8yaa67JVlttxbnnnssGG2ywuN2vf/1rRo8ezYQJEzj22GP75TwkSZL00uSMopZZko8keSDJnCRn9GO/I5NMH6z2vfT5nuZcb06yc5LTl7P9hCSDlm0dccQRXHvttR1le+21F9OnT2fq1Klsu+22fOUrX+nY/4lPfIJ99tlnZYYpSZKkVZQziloe/wjs2XztPMixDLSjgL+vqp827ycPZjDLa/fdd2fWrFkdZXvvvffi7be85S1cfvnli9//6Ec/Ysstt2TdddddWSFKkiRpFeaMopZJkn8FXgv8J/CKtvK/TnJHknuT/FeSTZvyjZPckGRGkrOT/CrJRks5xLAkFzWzeJcnWafp54QkdyWZnuSsJGnKd0pyX5L7gA/1EfsRSa5KMjHJQ0lObNv3t0nuTDIlyXeTrJ7kBGAs8L0kX08yLsk1Tf0JSc5p+vqfJB9p6+v4JD9P8lNgVFt5V5Lbk0xNcmWSVzDIzjnnnMWzh3PnzuWrX/0qJ554Yh+tJEmS9OfCGUUtk6r6YJK/AsYD72zb9VPgLVVVSf4O+Cfgk8CJwE1V9ZWm3VF9HGIUcFRVTUpyDq3Zy38BzqiqkwCSXNAc+8fAucAxVXVrkq8vwym8GdgeeBa4K8lPgGeAg4Ddqur5JN8BDquqk5LsARxbVZOTjOvW1+ua67AeMDPJmcDrgYOBLlo/V/cAdzf1vw98uKpuSXJSc20+1j3AJEcDRwNstNHGnLDDgmU4rSVNnDgRgN/97nc888wzi98vcuGFF/Lkk0+y+eabM3HiRM4880z23ntvJk+ezKxZs1h77bWXaKP+MXfuXK/tEOXYDk2O69DkuA5djm3/MlHUizUCuDTJZsCawMNN+Vhgf4CqujbJE330M7uqJjXbFwIfoZUojk/yT8A6wIbAjCS3ARtU1a1N/QuAvm6uu6GqHgdIckUT3wJgJ1qJI8DawKN9nzI/qar5wPwkjwKbAm8DrqyqZ5tjXN28rt/EekvT9nzgsp46raqzgLMAtnjt1nXKtBX78Zx12LjW66xZrLvuuowbN27xvvPOO48ZM2Zw4403ss466wDwuc99jjvuuIPzzz+fJ598ktVWW40xY8ZwzDHHrNDx1buJEyd2jIeGDsd2aHJchybHdehybPuXiaJerG8B36iqq5uZtwkr2E91f59kLeA7wM5VNTvJBGCt/uofCHB+VX1mOfua37a9kJfIz9G1117L1772NW655ZbFSSLAbbfdtnh7woQJDB8+3CRRkiTpz5z3KOrFWh+Y02wf3lY+CTgQIMnetN3X2IstkuzabB9Ka0nroqTwsSTDgQMAqupJ4MkkY5v9hy1DnHsl2TDJ2sB+TXw3Agck2aSJc8Mkr1mGvnpyK7BfkrWTrAf8dRPrU8ATSd7W1HsvcEsvffSbQw45hF133ZWZM2cyYsQIvve973HMMcfw9NNPs9dee9HV1cUHP/jBgQ5DkiRJL1EviZkQrdImAJc1S0tvArZsyj8PXJzkvcDPgN8BTy+ln5nAh5r7E+8HzqyqZ5P8GzC9aX9XW/33A+ckKeD6ZYjzTuCHtJbKXlhVkwGSfBa4PslqwPO0Hozzq2Xor0NV3ZPkUuA+WstX22M9HPjX5gE9/9PEvlRrr7E6M0/ed3nDWOziiy9eouyoo/q6TbQ1oyhJkiSZKGqZVdXIZvO85ouqugq4qofqTwFvr6oFzUzhm5r7+nrqdxatB8T0tO+zwGd7KL8beENb0T/1Ef5vqmq/Hvq5FLi0h/JxbdsTgYnN9oRu9bZv2/4S8KUe+poCvKWP+CRJkqRVhomiBsoWwA+ambr/A/5+kOORJEmStIxMFDUgquoh4I3tZUleSeu+wO7+ctETSV+MJG8Hvtqt+OGq2p9mBlSSJElS30wUtdI0yWDXAPZ/HXDdQPUvSZIk/bnwqaeSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaKkiRJkqQOJoqSJEmSpA4mipIkSZKkDiaK+rOW5Igkr+qjzseSrLOyYloRp556KmPGjGH77bfnkEMO4bnnnqOqOP7449l2223ZbrvtOP300wc7TEmSJL1EDBvsAKRBdgQwHXhkKXU+BlwIPLsyAgKY9/xCRh73kz7rzTp5X+bMmcPpp5/O/fffz9prr82BBx7IJZdcQlUxe/ZsHnzwQVZbbTUeffTRlRC5JEmShgJnFDWgkoxM8mCSi5I8kOTyJOskOSHJXUmmJzkrLVsluaet7TaL3ieZleQrSaYkmZxkxyTXJfllkg+2tflU0+/UJJ9vi+GBJP+WZEaS65OsneQAYGfgoqbftXuI/yPAq4Cbk9yc5Mgkp7Xt//skp/Z2nk2dnZLckuTuJubN+vs6L1iwgHnz5rFgwQKeffZZXvWqV3HmmWdywgknsNpqrR/zTTbZpL8PK0mSpCHKRFErwyjgO1W1HfBH4B+BM6rqTVW1PbA28M6q+iXwVJKupt37gXPb+vl1VXUBtwHnAQcAbwEWJYR7A9sAbwa6gJ2S7N603Qb4dlWNAZ4E3l1VlwOTgcOqqquq5nUPvKpOpzXbOL6qxgM/AP46yRptMZ7T23k29b4FHFBVOzV1v7T8l7B3m2++OcceeyxbbLEFm222Geuvvz577703v/zlL7n00kvZeeed2WeffXjooYf687CSJEkawlx6qpVhdlVNarYvBD4CPJzkn4B1gA2BGcCPgbOB9yf5BHAQraRvkaub12nA8Kp6Gng6yfwkGwB7N1/3NvWG00oQfw08XFVTmvK7gZErciJVNTfJTcA7kzwArFFV05KM7OU8rwW2B25IArA68Nue+k5yNHA0wEYbbcwJOyzoM56JEyfy9NNPc/7553PhhRcyfPhwJkyYwPHHH8+zzz7LnDlz+Jd/+RduvfVW3v3ud3uf4iCbO3cuEydOHOwwNAAc26HJcR2aHNehy7HtXyaKWhmqh/ffAXauqtlJJgBrNft+CJwI3ATcXVWPt7Wb37y+0La96P0wIMBXquq77Qdrkrj2+gtpzWKuqLOBfwYepHPGs6fzDDCjqnbtq9OqOgs4C2CL125dp0zr+8dz1mHjuOyyy3jjG9/IfvvtB8AjjzzC7bffzmte8xo+9alPseWWW/IXf/EXnHLKKYwbN24ZTk8DZeLEiY7BEOXYDk2O69DkuA5djm3/cumpVoYtkixKlA4FftpsP5ZkOK0lpABU1XPAdcCZdCZhy+I64MimT5JsnqSvG/OeBtZbnjpVdQfwalrncnFbvZ7Ocyaw8aLyJGskGbOM57NMtthiC26//XaeffZZqoobb7yR7bbbjv3224+bb74ZgFtuuYVtt922Pw8rSZKkIcwZRa0MM4EPJTkHuJ9WEvgKWk8b/R1wV7f6FwH7A9cvz0Gq6vok2wE/a5Z5zgX+ltYMYm/OA/41yTxg157uU6Q1y3dtkkea+xShda9iV1U90VZvifOsqv9rHppzepL1af3MnUZrqW2/2GWXXTjggAPYcccdGTZsGG984xs5+uijmTdvHocddhinnnoqw4cP5+yzz+6vQ0qSJGmIS1X31XJS/2mWfV7TPLRmWdscC6xfVZ8bqLherCTXAKdW1Y3N+5Es53kuzahRo2rmzJn90ZVWIS6JGboc26HJcR2aHNehy7Fdfknurqqde9rnjKJWKUmuBLYC9hjsWHrSPDTnTuC+RUmiJEmSNNSYKGpAVdUsWk/9XNb6+w9cNEvXJKlbdiv+dFVdt+hNVT0JLHGz3/KepyRJkrQqM1GUGoOZpEqSJEmrEp96KkmSJEnqYKIoSZIkSepgoihJkiRJ6mCiKEmSJEnqYKIoSZIkSepgoihJkiRJ6mCiKEmSJEnqYKIoSZIkSepgoihJkiRJ6mCiKEmSJEnqYKIoSZIkSepgoihJkiRJ6mCiKEmSJEnqYKIoSZIkSepgoihJkiRJ6mCiKEmSJEnqYKIoSZIkSepgoihJkiRJ6mCiKEmSJEnqYKIoSZIkSepgoihJkiRJ6mCiKEmSJEnqYKIoSZIkSepgoihJkiRJ6mCiKL0EzJ49m/HjxzN69GjGjBnDN7/5zY79p5xyCkl47LHHBilCSZIkDSXDBjsASUua9/xCRh73EwBmnbwvw4YN45RTTmHHHXfk6aefZqeddmKvvfZi9OjRzJ49m+uvv54ttthikKOWJEnSUOGM4iooyduSzEgyJcnagx3PUJCkK8k7+qjzuiQ/SzI/ybFt5a9OcnOS+5tx+ejAR9xps802Y8cddwRgvfXWY7vttmPOnDkAfPzjH+drX/saSVZ2WJIkSRqiTBRXTYcBX6mqrqqaN9jBDBFdwFITReAPwEeAf+lWvgD4ZFWNBt4CfCjJ6P4PcdnMmjWLe++9l1122YWrrrqKzTffnDe84Q2DFY4kSZKGIBPFQZRkZJIHk1yU5IEklyf5CHAg8IUkFy2l7aeTTEtyX5KTm7KuJLcnmZrkyiSvaMonJvlqkjuT/DzJ25bS71pJzm36vjfJ+Kb8iCRXNX09lOTEtjZ/2/Q9Jcl3k6zelM9N8qUmxtuTbLqU427axHxf8/XWpvwTSaY3Xx9ru27T29oem2RCb+eaZE3gJOCgJsaDeoqhqh6tqruA57uV/7aq7mm2nwYeADZvO96pSSY3Y/imJFc01+iLbTEucR4rYu7cubz73e/mtNNOY9iwYXz5y1/mpJNOWtHuJEmSpB55j+LgGwUcVVWTkpwDrAlcDVxTVZf31CDJPsC7gF2q6tkkGza7vg98uKpuSXIScCKwKCkZVlVvbpZfngjs2Us8HwKqqnZI8jrg+iTbNvveDGwPPAvcleQnwDPAQcBuVfV8ku/QmhH9PrAucHtVHZ/ka8DfA1+kZ6cDt1TV/k2iOTzJTsD7gV2AAHckuQV4opc+Fuk416raM8kJwM5VdUwfbZcqyUjgjcAdbcX/V1U7N0tSrwJ2ojU7+cskpwIjezqPqrq3W99HA0cDbLTRxpywwwIAJk6cCMCCBQv4zGc+wy677MKGG27IJZdcws9//nNGjRoFwO9//3vGjBnDmWeeyYYbbohWPXPnzl08nhpaHNuhyXEdmhzXocux7V8mioNvdlVNarYvpLX08ck+2uwJnFtVzwJU1R+SrA9sUFW3NHXOBy5ra3NF83o3rcSlN2OBbzX9PpjkV8CiRPGGqnocIMkVTd0FtBKju5p75NYGHm3q/x9wTdtx91rKcfcA3tccdyHwVJKxwJVV9UzbMd9GK5FemmU91+WSZDjwQ+BjVfXHtl2L4pkGzKiq3zb1/wd4Na3r1NN5dCSKVXUWcBbAFq/duk6Z1vrxnHXYOKqKww8/nN12243TTjsNgHHjxnHkkUcubj9y5EgmT57MRhtt1F+nrH42ceJExo0bN9hhaAA4tkOT4zo0Oa5Dl2Pbv1x6Oviqj/f9ZX7zupAV/wNBT7EGOL+5n7KrqkZV1YRm//NVtajNizludwvo/N5dq9v+/jjXDknWoJUkXlRVV3Tbveh4L7RtL3rfL8efNGkSF1xwATfddBNdXV10dXXxH//xH/3RtSRJkrQEE8XBt0WSXZvtQ4GfLkObG4D3J1kHIMmGVfUU8ETb/YfvBW7prYOluI3W0lGaJadbADObfXsl2bB5Eut+wCTgRuCAJJssiiXJa1bguDcC/9D0sXozQ3obsF+SdZKsC+zflP0vsEmSVyZ5GfDOZej/aWC9FYiLtKZKvwc8UFXfWIEuejuPXq29xurMOnlfZp28LwBjx46lqpg6dSpTpkxhypQpvOMdnc/mmTVrlrOJkiRJ6hcmioNvJq2naD4AvAI4s68GVXUtreWOk5NMARZ9lMPhwNeTTKX1lM8VecrJd4DVkkwDLgWOqKpFs2R30ppVmwr8sKomV9X9wGdp3cs4lVYSu9kKHPejwPjmuHcDo5sHyJzXHPcO4Oyqureqnm/O7c7meA8uQ/83A6OX9jCbJP8vyW+ATwCfTfKbJC8HdqOVeO/RtJ+SPj5qo11v57Gs7SVJkqSVzXsUB9+CqvrbbmVH9NWoqk4GTu5WNoXWxzd0rzuubfsxlnLfXlU9R+vBKz35TVXt10ObS2klld3Lh7dtXw70+HCeZv//0npAT/fybwBLzOJV1em0HoDTvXxc2/bic62qPwBv6u34TZ3fASN62PVTWktse2rTfryJwMRe9vV4HpIkSdKqyBlFSZIkSVIHZxQHUVXNovVxEz1KsgNwQbfi+VW1y4s9dpK3A1/tVvxwVe3fU/2qOo/W8skXe9zjgfd0K76sqr70YvtejhjeT2upa7tJVfWhlRWDJEmStCozUVyFVdU0WvcaDkTf1wHXDUTffRz3S8BKSwp7ieFc4NzBjEGSJElalbn0VJIkSZLUwURRkiRJktTBRFGSJEmS1MFEUZIkSZLUwURRkiRJktTBRFGSJEmS1MFEUZIkSZLUwURRkiRJktTBRFGSJEmS1MFEUZIkSZLUwURRkiRJktTBRFGSJEmS1MFEUZIkSZLUwURRkiRJktTBRFGSJEmS1MFEUZIkSZLUwURRkiRJktTBRFGSJEmS1MFEUZIkSZLUwURRkiRJktTBRFGSJEmS1MFEUZIkSZLUwURRkiRJktTBRFGSJEmS1MFEUZIkSZLUwURRkiRJktTBRFGSJEmS1CFVNdgxSOomydPAzMGOQ/1uI+CxwQ5CA8KxHZoc16HJcR26HNvl95qq2rinHcNWdiSSlsnMqtp5sINQ/0oy2XEdmhzboclxHZoc16HLse1fLj2VJEmSJHUwUZQkSZIkdTBRlFZNZw12ABoQjuvQ5dgOTY7r0OS4Dl2ObT/yYTaSJEmSpA7OKEqSJEmSOpgoSquQJH+VZGaSXyQ5brDjUd+SnJPk0STT28o2THJDkoea11c05UlyejO+U5Ps2Nbm8Kb+Q0kOH4xz0Z8keXWSm5Pcn2RGko825Y7tS1iStZLcmeS+Zlw/35RvmeSOZvwuTbJmU/6y5v0vmv0j2/r6TFM+M8nbB+eM1C7J6knuTXJN895xHQKSzEoyLcmUJJObMn8XrwQmitIqIsnqwLeBfYDRwCFJRg9uVFoG5wF/1a3sOODGqtoGuLF5D62x3ab5Oho4E1r/4AEnArsAbwZOXPSPngbNAuCTVTUaeAvwoebn0bF9aZsP7FFVbwC6gL9K8hbgq8CpVbU18ARwVFP/KOCJpvzUph7N98LBwBhaP//faX6Ha3B9FHig7b3jOnSMr6quto++8HfxSmCiKK063gz8oqr+p6r+D7gEeNcgx6Q+VNWtwB+6Fb8LOL/ZPh/Yr638+9VyO7BBks2AtwM3VNUfquoJ4AaWTD61ElXVb6vqnmb7aVr/+dwcx/YlrRmfuc3bNZqvAvYALm/Ku4/rovG+HPjLJGnKL6mq+VX1MPALWr/DNUiSjAD2Bc5u3gfHdSjzd/FKYKIorTo2B2a3vf9NU6aXnk2r6rfN9u+ATZvt3sbYsV+FNcvS3gjcgWP7ktcsT5wCPErrP4u/BJ6sqgVNlfYxWjx+zf6ngFfiuK6KTgP+CXihef9KHNehooDrk9yd5OimzN/FK8GwwQ5AkoayqqokPl76JSrJcOCHwMeq6o+tSYcWx/alqaoWAl1JNgCuBF43yCHpRUryTuDRqro7ybjBjkf9bmxVzUmyCXBDkgfbd/q7eOA4oyitOuYAr257P6Ip00vP/zZLXWheH23Kextjx34VlGQNWkniRVV1RVPs2A4RVfUkcDOwK63laYv+eN4+RovHr9m/PvA4juuqZjfgb5LMonXbxh7AN3Fch4SqmtO8Pkrrjztvxt/FK4WJorTquAvYpnlK25q0bqi/epBj0oq5Glj0RLXDgavayt/XPJXtLcBTzdKZ64C9k7yiubl+76ZMg6S5X+l7wANV9Y22XY7tS1iSjZuZRJKsDexF6/7Tm4EDmmrdx3XReB8A3FStD6C+Gji4eXrmlrQenHHnyjkLdVdVn6mqEVU1kta/nTdV1WE4ri95SdZNst6ibVq/Q6fj7+KVwqWn0iqiqhYkOYbWL67VgXOqasYgh6U+JLkYGAdslOQ3tJ6qdjLwgyRHAb8CDmyq/wfwDloPSHgWeD9AVf0hyRdo/bEA4KSq6v6AHK1cuwHvBaY197MB/DOO7UvdZsD5zZMsVwN+UFXXJLkfuCTJF4F7af2RgOb1giS/oPXQqoMBqmpGkh8A99N6Qu6HmiWtWrV8Gsf1pW5T4Mpm2f8w4N+r6tokd+Hv4gGX1h9QJEmSJElqcempJEmSJKmDiaIkSZIkqYOJoiRJkiSpg4miJEmSJKmDiaIkSZIkqYOJoiRJIsnCJFPavkauQB8bJPnH/o9ucf9/k+S4geq/l2Pul2T0yjymJK0K/HgMSZJEkrlVNfxF9jESuKaqtl/Odquvip9Xl2QY6XVj6gAAA4pJREFUcDatc7p8sOORpJXJGUVJktSjJKsn+XqSu5JMTfKBpnx4khuT3JNkWpJ3NU1OBrZqZiS/nmRckmva+jsjyRHN9qwkX01yD/CeJFsluTbJ3UluS/K6HuI5IskZzfZ5Sc5McnuS/2mOdU6SB5Kc19ZmbpJTk8xoYt64Ke9q2k5NcmWSVzTlE5OclmQyrQ9s/xvg6805bZXk75vrcV+SHyZZpy2e05P8dxPPAW0xfLq5TvclObkp6/N8JWkwDRvsACRJ0iph7SRTmu2Hq2p/4Cjgqap6U5KXAZOSXA/MBvavqj8m2Qi4PcnVwHHA9lXVBZBkXB/HfLyqdmzq3gh8sKoeSrIL8B1gjz7avwLYlVYydzWwG/B3wF1JuqpqCrAuMLmqPp7kBOBE4Bjg+8CHq+qWJCc15R9r+l2zqnZu4tqGthnFJE9W1b81219srtG3mnabAWOB1zXxXJ5kH+BdwC5V9WySDZu6Z63A+UrSSmOiKEmSAOYtSvDa7A28vm12bH1gG+A3wJeT7A68AGwObLoCx7wUWjOUwFuBy5Is2veyZWj/46qqJNOA/62qaU1/M4CRwJQmvkub+hcCVyRZH9igqm5pys8HLuseVy+2bxLEDYDhwHVt+35UVS8A9ydZdD32BM6tqmcBquoPL+J8JWmlMVGUJEm9Ca1Zt+s6ClvLRzcGdqqq55PMAtbqof0COm9z6V7nmeZ1NeDJHhLVvsxvXl9o2170vrf/4yzLwxmeWcq+84D9quq+5jqM6yEeaF273qzo+UrSSuM9ipIkqTfXAf+QZA2AJNsmWZfWzOKjTZI4HnhNU/9pYL229r8CRid5WZINgL/s6SBV9Ufg4STvaY6TJG/op3NYDVg0I3oo8NOqegp4IsnbmvL3Arf01Jglz2k94LfNNTlsGY5/A/D+tnsZNxzg85WkfmGiKEmSenM2cD9wT5LpwHdpzdRdBOzcLPl8H/AgQFU9Tus+xulJvl5Vs4EfANOb13uXcqzDgKOS3AfMoHVfX394BnhzE/8ewElN+eG0HlIzFehqK+/uEuBTSe5NshXwOeAOYBLNeS9NVV1L637Fyc09oMc2uwbqfCWpX/jxGJIkachKP3zshyT9OXJGUZIkSZLUwRlFSZIkSVIHZxQlSZIkSR1MFCVJkiRJHUwUJUmSJEkdTBQlSZIkSR1MFCVJkiRJHUwUJUmSJEkd/n/cUvVPnwYMmgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 864x1008 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_importance(mod_lgb, figsize=(12,14))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}