{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import boto3\n",
"\n",
"from datetime import timedelta\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"from lifelines.utils import datetimes_to_durations\n",
"from lifelines import CoxPHFitter\n",
"\n",
"from lifetimes import ModifiedBetaGeoFitter, GammaGammaFitter\n",
"from lifetimes.plotting import plot_cumulative_transactions, plot_incremental_transactions, plot_calibration_purchases_vs_holdout_purchases\n",
"from lifetimes.utils import calibration_and_holdout_data\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"from utils import woe\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Parameters"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"obs_date = '2019-04-01'\n",
"spline_censor_date = '2020-03-01'\n",
"censor_date = '2020-08-01'\n",
"app_type = ['basic']"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"s3 = boto3.client('s3')\n",
"\n",
"def get_matching_s3_keys(bucket, prefix='', suffix=''):\n",
" \"\"\"\n",
" Generate the keys in an S3 bucket.\n",
"\n",
" :param bucket: Name of the S3 bucket.\n",
" :param prefix: Only fetch keys that start with this prefix (optional).\n",
" :param suffix: Only fetch keys that end with this suffix (optional).\n",
" \"\"\"\n",
" kwargs = {'Bucket': bucket, 'Prefix': prefix}\n",
" while True:\n",
" resp = s3.list_objects_v2(**kwargs)\n",
" for obj in resp['Contents']:\n",
" key = obj['Key']\n",
" if key.endswith(suffix):\n",
" yield key\n",
"\n",
" try:\n",
" kwargs['ContinuationToken'] = resp['NextContinuationToken']\n",
" except KeyError:\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"key_ls = []\n",
"for key in get_matching_s3_keys(bucket = 'finaccel-ml-model-data-production', \n",
" prefix = 'zhilal/ltv_final/raw_cohorts', \n",
" suffix = '.parquet'):\n",
" key_ls.append(key)\n",
"main_ls = [i for i in key_ls if 'user_set_' in i]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"feat_key_ls = []\n",
"for key in get_matching_s3_keys(bucket = 'finaccel-ml-model-data-production', \n",
" prefix = 'zhilal/ltv_final/features', \n",
" suffix = '.parquet'):\n",
" feat_key_ls.append(key)\n",
"feat_ls = feat_key_ls"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"key_ls = []\n",
"for key in get_matching_s3_keys(bucket = 'finaccel-ml-model-data-production', \n",
" prefix = 'zhilal/ltv_final/trx_level', \n",
" suffix = '.parquet'):\n",
" key_ls.append(key)\n",
"trx_ls = key_ls"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"main_ls = sorted(main_ls)[:17]\n",
"feat_ls = sorted(feat_ls)\n",
"trx_ls = sorted(trx_ls)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['zhilal/ltv_final/raw_cohorts/user_set_2017-07.parquet',\n",
" 'zhilal/ltv_final/raw_cohorts/user_set_2017-08.parquet',\n",
" 'zhilal/ltv_final/raw_cohorts/user_set_2017-09.parquet',\n",
" 'zhilal/ltv_final/raw_cohorts/user_set_2017-10.parquet',\n",
" 'zhilal/ltv_final/raw_cohorts/user_set_2017-11.parquet',\n",
" 'zhilal/ltv_final/raw_cohorts/user_set_2017-12.parquet',\n",
" 'zhilal/ltv_final/raw_cohorts/user_set_2018-01.parquet',\n",
" 'zhilal/ltv_final/raw_cohorts/user_set_2018-02.parquet',\n",
" 'zhilal/ltv_final/raw_cohorts/user_set_2018-03.parquet',\n",
" 'zhilal/ltv_final/raw_cohorts/user_set_2018-04.parquet',\n",
" 'zhilal/ltv_final/raw_cohorts/user_set_2018-05.parquet',\n",
" 'zhilal/ltv_final/raw_cohorts/user_set_2018-06.parquet',\n",
" 'zhilal/ltv_final/raw_cohorts/user_set_2018-07.parquet',\n",
" 'zhilal/ltv_final/raw_cohorts/user_set_2018-08.parquet',\n",
" 'zhilal/ltv_final/raw_cohorts/user_set_2018-09.parquet',\n",
" 'zhilal/ltv_final/raw_cohorts/user_set_2018-10.parquet',\n",
" 'zhilal/ltv_final/raw_cohorts/user_set_2018-11.parquet']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"main_ls"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['zhilal/ltv_final/features/features_2017-07.parquet',\n",
" 'zhilal/ltv_final/features/features_2017-08.parquet',\n",
" 'zhilal/ltv_final/features/features_2017-09.parquet',\n",
" 'zhilal/ltv_final/features/features_2017-10.parquet',\n",
" 'zhilal/ltv_final/features/features_2017-11.parquet',\n",
" 'zhilal/ltv_final/features/features_2017-12.parquet',\n",
" 'zhilal/ltv_final/features/features_2018-01.parquet',\n",
" 'zhilal/ltv_final/features/features_2018-02.parquet',\n",
" 'zhilal/ltv_final/features/features_2018-03.parquet',\n",
" 'zhilal/ltv_final/features/features_2018-04.parquet',\n",
" 'zhilal/ltv_final/features/features_2018-05.parquet',\n",
" 'zhilal/ltv_final/features/features_2018-06.parquet',\n",
" 'zhilal/ltv_final/features/features_2018-07.parquet',\n",
" 'zhilal/ltv_final/features/features_2018-08.parquet',\n",
" 'zhilal/ltv_final/features/features_2018-09.parquet',\n",
" 'zhilal/ltv_final/features/features_2018-10.parquet',\n",
" 'zhilal/ltv_final/features/features_2018-11.parquet']"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feat_ls"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['zhilal/ltv_final/trx_level/trx_level_2018-07.parquet',\n",
" 'zhilal/ltv_final/trx_level/trx_level_2018-08.parquet',\n",
" 'zhilal/ltv_final/trx_level/trx_level_2018-09.parquet',\n",
" 'zhilal/ltv_final/trx_level/trx_level_2018-10.parquet',\n",
" 'zhilal/ltv_final/trx_level/trx_level_2018-11.parquet']"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trx_ls"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0. Get all the data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Users"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"rel_cols = [\n",
" 'user_id'\n",
" , 'set_password_timestamp'\n",
" , 'is_approved_timestamp'\n",
" , 'first_dpd_91_dt'\n",
" , 'first_trx_date'\n",
" , 'application_type'\n",
" , 'user_status'\n",
"]\n",
"\n",
"main_df = pd.DataFrame()\n",
"for key in main_ls:\n",
" temp_df = pd.read_parquet(r's3://finaccel-ml-model-data-production/'+key, columns=rel_cols)\n",
" \n",
" # Make sure there is no user with user_status = 3 (Rejected) or 11 (blacklisted) \n",
" temp_df = temp_df.query(\"user_status in (2,5,9,7)\")\n",
" \n",
" temp_df['application_type_code'] = temp_df['application_type']\n",
" temp_df['application_type'] = np.where(temp_df['application_type'].isin([150, 160, 170, 180]),\n",
" 'upgrade', np.where(temp_df['application_type'].isin([100, 110, 120]), 'fresh_premium', 'basic'))\n",
" temp_df['application_type'] = temp_df['application_type'].astype('str')\n",
" \n",
" # determine app_type before obs_date\n",
" temp_df['app_type_b4_obsdt'] = np.where(temp_df['is_approved_timestamp'] >= pd.to_datetime(obs_date),\n",
" 'basic', temp_df['application_type'])\n",
" # Select specific app_type only\n",
" temp_df = temp_df[temp_df['app_type_b4_obsdt'].isin(app_type)].reset_index(drop=True) \n",
" \n",
" # remove users who NPL before obs_date\n",
" temp_df = temp_df[~(temp_df['first_dpd_91_dt'] < \n",
" pd.to_datetime(obs_date))].reset_index(drop=True)\n",
"\n",
" # remove users who have not made\n",
" # 1st trx before obs_date\n",
" temp_df = temp_df[~(temp_df['first_trx_date'] > pd.to_datetime(obs_date)) & \n",
" temp_df['first_trx_date'].notna()]\n",
" \n",
" main_df = pd.concat([main_df, temp_df]).reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(97949, 9)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"main_df.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Features"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"temp_ls = []\n",
"for feat_key in feat_ls:\n",
" temp_df = pd.read_parquet('s3://finaccel-ml-model-data-production/'+feat_key)\n",
" temp_ls.append(temp_df)\n",
" \n",
"feat_df = pd.concat(temp_ls).reset_index(drop=True)\n",
"feat_df = feat_df.drop_duplicates('user_id', 'first').reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(357579, 415)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feat_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"main_df = main_df.merge(feat_df, how='left', on='user_id')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(97949, 423)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"main_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"del temp_ls, temp_df, feat_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transactions"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"temp_ls = []\n",
"for key in trx_ls:\n",
" temp_df = pd.read_parquet('s3://finaccel-ml-model-data-production/'+key)\n",
" temp_ls.append(temp_df)\n",
" \n",
"trx_df = pd.concat(temp_ls).reset_index(drop=True)\n",
"\n",
"del temp_ls, temp_df"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"trx_df['app_type_b4_obsdt'] = np.where(trx_df['is_approved_timestamp'] > pd.to_datetime(obs_date),\n",
" 'basic', trx_df['application_type'])\n",
"trx_df['app_type'] = np.where(trx_df['app_type_b4_obsdt'] == 'basic', 'basic', 'premium')\n",
"trx_df = trx_df[trx_df['app_type_b4_obsdt'].isin(app_type)].reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4009328, 29)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trx_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"72449"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trx_df['user_id'].nunique()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"trx_df['trx_dt'] = trx_df['transaction_date'].dt.date.astype('datetime64')\n",
"\n",
"trx_df = trx_df.query(f'trx_dt < \"{censor_date}\"')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3808512, 30)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trx_df.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Adjust the LTV components\n",
"- use the LTV component coefficients"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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>app_type</th>\n",
" <th>payment_type</th>\n",
" <th>prop_collections_cost</th>\n",
" <th>prop_latefee</th>\n",
" <th>prop_lateinterest</th>\n",
" <th>prop_lender_interest_fees</th>\n",
" <th>prop_reg_interest</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>basic</td>\n",
" <td>30_days</td>\n",
" <td>0.0058</td>\n",
" <td>0.0300</td>\n",
" <td>0.0295</td>\n",
" <td>0.0163</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>premium</td>\n",
" <td>12_months</td>\n",
" <td>0.0325</td>\n",
" <td>0.0341</td>\n",
" <td>0.0359</td>\n",
" <td>0.0935</td>\n",
" <td>0.2402</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>premium</td>\n",
" <td>30_days</td>\n",
" <td>0.0058</td>\n",
" <td>0.0600</td>\n",
" <td>0.0400</td>\n",
" <td>0.0186</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>premium</td>\n",
" <td>3_months</td>\n",
" <td>0.0104</td>\n",
" <td>0.0321</td>\n",
" <td>0.0305</td>\n",
" <td>0.0290</td>\n",
" <td>0.0704</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>premium</td>\n",
" <td>6_months</td>\n",
" <td>0.0177</td>\n",
" <td>0.0394</td>\n",
" <td>0.0376</td>\n",
" <td>0.0506</td>\n",
" <td>0.1252</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" app_type payment_type prop_collections_cost prop_latefee \\\n",
"0 basic 30_days 0.0058 0.0300 \n",
"1 premium 12_months 0.0325 0.0341 \n",
"2 premium 30_days 0.0058 0.0600 \n",
"3 premium 3_months 0.0104 0.0321 \n",
"4 premium 6_months 0.0177 0.0394 \n",
"\n",
" prop_lateinterest prop_lender_interest_fees prop_reg_interest \n",
"0 0.0295 0.0163 0.0000 \n",
"1 0.0359 0.0935 0.2402 \n",
"2 0.0400 0.0186 0.0000 \n",
"3 0.0305 0.0290 0.0704 \n",
"4 0.0376 0.0506 0.1252 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adj_df = pd.read_parquet('s3://finaccel-ml-model-data-production/zhilal/ltv_final/ltv_components_proportions_20201110.parquet')\n",
"adj_df"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"trx_df = trx_df.merge(adj_df, how='left', on=['app_type', 'payment_type'])"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"payment_type_dict = {'30_days':30,\n",
" '3_months':90,\n",
" '6_months':180,\n",
" '12_months':360}\n",
"trx_df['payment_type_int'] = trx_df['payment_type'].map(payment_type_dict)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"comp_ls = [\n",
" 'reg_interest',\n",
" 'lateinterest',\n",
" 'latefee',\n",
" 'lender_interest_fees',\n",
" 'collections_cost'\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"def recalc_ltv_comp(df, timebound, components):\n",
" df['trx_dob'] = (pd.to_datetime(timebound) - df['transaction_date']).dt.days\n",
" df['flag_mature'] = np.where(df['trx_dob'] >= df['payment_type_int'], 1, 0)\n",
" display(df['flag_mature'].value_counts())\n",
"\n",
" mature_df = df[df['flag_mature'] == 1].reset_index(drop=True)\n",
" print('mature df shape:', mature_df.shape)\n",
"\n",
" immature_df = df[df['flag_mature'] == 0].reset_index(drop=True)\n",
" print('immature df shape:', immature_df.shape)\n",
"\n",
" for comp in components:\n",
" immature_df[comp] = immature_df['orig_principal'] * immature_df['prop_'+comp]\n",
"\n",
" fin = pd.concat([mature_df, immature_df]).reset_index(drop=True)\n",
" print('final dataset size:', fin.shape)\n",
" \n",
" return fin"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3808512, 36)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trx_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"55374"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trx_df['user_id'].nunique()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset size: (1477680, 36)\n",
"\n"
]
},
{
"data": {
"text/plain": [
"1 1268813\n",
"0 208867\n",
"Name: flag_mature, dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mature df shape: (1268813, 38)\n",
"immature df shape: (208867, 38)\n",
"final dataset size: (1477680, 38)\n"
]
}
],
"source": [
"cal_df = trx_df[trx_df['transaction_date'] < pd.to_datetime(obs_date)].reset_index(drop=True)\n",
"print('Dataset size:', cal_df.shape)\n",
"print()\n",
"cal_fin = recalc_ltv_comp(cal_df, obs_date, comp_ls)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset size: (2330832, 36)\n",
"\n"
]
},
{
"data": {
"text/plain": [
"1 2219646\n",
"0 111186\n",
"Name: flag_mature, dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mature df shape: (2219646, 38)\n",
"immature df shape: (111186, 38)\n",
"final dataset size: (2330832, 38)\n"
]
}
],
"source": [
"hld_df = trx_df[(trx_df['transaction_date'] >= pd.to_datetime(obs_date)) &\n",
" (trx_df['transaction_date'] < pd.to_datetime(censor_date))].reset_index(drop=True)\n",
"print('Dataset size:', hld_df.shape)\n",
"print()\n",
"hld_fin = recalc_ltv_comp(hld_df, censor_date, comp_ls)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"trx_df = pd.concat([cal_fin, hld_fin]).reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3808512, 38)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trx_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"55374"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trx_df['user_id'].nunique()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"trx_df['rev'] = trx_df['merchant_fee'] + \\\n",
" trx_df['pl_fee'] + \\\n",
" trx_df['processing_p30_fee'] + \\\n",
" trx_df['travel_fee'] + \\\n",
" trx_df['biller_fee'] + \\\n",
" trx_df['reg_interest'] + \\\n",
" trx_df['latefee'] + \\\n",
" trx_df['lateinterest'] - \\\n",
" trx_df['lender_interest_fees'] - \\\n",
" trx_df['collections_cost'] - \\\n",
" trx_df['discount_cost']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare the Framework Calibration (train) and Holdout (test) Data"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"trx_df['ltv_calib'] = np.where(trx_df['trx_dt'] < obs_date, trx_df['rev'], 0)\n",
"\n",
"trx_df['act_rev_holdout'] = np.where(trx_df['trx_dt'] > obs_date, trx_df['rev'], 0)\n",
"\n",
"trx_df['trx_dpd_91_dt'] = trx_df['trx_dpd_91_dt'].astype('datetime64[s]')\n",
"trx_df['act_npl_holdout'] = np.where((trx_df['trx_dpd_91_dt'] > pd.to_datetime(obs_date)) & \n",
" (trx_df['trx_dpd_91_dt'] < pd.to_datetime(censor_date))\n",
" , trx_df['npl_expenses'], 0)\n",
"\n",
"trx_df['act_ltv_holdout'] = trx_df['act_rev_holdout'] - trx_df['act_npl_holdout']"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"# this is summary of ltv data\n",
"\n",
"main_ltv_df = trx_df.groupby('user_id').agg({'ltv_calib':sum,\n",
" 'act_ltv_holdout':sum, \n",
" 'act_npl_holdout':sum, \n",
" 'act_rev_holdout':sum\n",
" }).reset_index()\n",
"\n",
"main_ltv_df = main_ltv_df.merge(main_df[['user_id','snapshot_os_principal']],\n",
" how = 'right', on ='user_id').fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"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>user_id</th>\n",
" <th>ltv_calib</th>\n",
" <th>act_ltv_holdout</th>\n",
" <th>act_npl_holdout</th>\n",
" <th>act_rev_holdout</th>\n",
" <th>snapshot_os_principal</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1213647</td>\n",
" <td>164541.0770</td>\n",
" <td>1.275909e+05</td>\n",
" <td>0.0</td>\n",
" <td>1.275909e+05</td>\n",
" <td>899910.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1277054</td>\n",
" <td>150372.1500</td>\n",
" <td>6.468507e+05</td>\n",
" <td>0.0</td>\n",
" <td>6.468507e+05</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1408888</td>\n",
" <td>52421.2110</td>\n",
" <td>2.741845e+05</td>\n",
" <td>0.0</td>\n",
" <td>2.741845e+05</td>\n",
" <td>337461.91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1432401</td>\n",
" <td>261203.6816</td>\n",
" <td>1.566007e+06</td>\n",
" <td>0.0</td>\n",
" <td>1.566007e+06</td>\n",
" <td>1000000.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1763204</td>\n",
" <td>66653.0420</td>\n",
" <td>1.029707e+06</td>\n",
" <td>0.0</td>\n",
" <td>1.029707e+06</td>\n",
" <td>203130.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97944</th>\n",
" <td>3808220</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.0</td>\n",
" <td>0.000000e+00</td>\n",
" <td>1482068.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97945</th>\n",
" <td>3806013</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.0</td>\n",
" <td>0.000000e+00</td>\n",
" <td>353000.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97946</th>\n",
" <td>3788410</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.0</td>\n",
" <td>0.000000e+00</td>\n",
" <td>495825.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97947</th>\n",
" <td>5096370</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.0</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97948</th>\n",
" <td>5508986</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.0</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>97949 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" user_id ltv_calib act_ltv_holdout act_npl_holdout \\\n",
"0 1213647 164541.0770 1.275909e+05 0.0 \n",
"1 1277054 150372.1500 6.468507e+05 0.0 \n",
"2 1408888 52421.2110 2.741845e+05 0.0 \n",
"3 1432401 261203.6816 1.566007e+06 0.0 \n",
"4 1763204 66653.0420 1.029707e+06 0.0 \n",
"... ... ... ... ... \n",
"97944 3808220 0.0000 0.000000e+00 0.0 \n",
"97945 3806013 0.0000 0.000000e+00 0.0 \n",
"97946 3788410 0.0000 0.000000e+00 0.0 \n",
"97947 5096370 0.0000 0.000000e+00 0.0 \n",
"97948 5508986 0.0000 0.000000e+00 0.0 \n",
"\n",
" act_rev_holdout snapshot_os_principal \n",
"0 1.275909e+05 899910.00 \n",
"1 6.468507e+05 0.00 \n",
"2 2.741845e+05 337461.91 \n",
"3 1.566007e+06 1000000.00 \n",
"4 1.029707e+06 203130.00 \n",
"... ... ... \n",
"97944 0.000000e+00 1482068.04 \n",
"97945 0.000000e+00 353000.00 \n",
"97946 0.000000e+00 495825.00 \n",
"97947 0.000000e+00 0.00 \n",
"97948 0.000000e+00 0.00 \n",
"\n",
"[97949 rows x 6 columns]"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"main_ltv_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- check data"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(97949, 423) (3808512, 43) (97949, 6)\n"
]
}
],
"source": [
"print(main_df.shape, trx_df.shape, main_ltv_df.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Survival Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare the \"duration\" and \"flag_churn\" variables"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# censor NPL events that happen after censor date\n",
"\n",
"main_df['first_dpd_91_censor'] = np.where(main_df['first_dpd_91_dt'] < pd.to_datetime(spline_censor_date), \n",
" main_df['first_dpd_91_dt'], None)\n",
"main_df['first_dpd_91_censor'] = main_df['first_dpd_91_censor'].astype('datetime64')\n",
"\n",
"# also set the first_dpd_91 to 1st of month\n",
"main_df['first_dpd_91_censor_bom'] = main_df['first_dpd_91_censor'] + pd.offsets.MonthEnd(0) - pd.offsets.MonthBegin(normalize=True)\n",
"\n",
"main_df['t0'] = pd.to_datetime(obs_date)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"main_df['duration_t0'] = np.where(main_df['first_dpd_91_censor'].isna(), 11, \n",
" main_df['first_dpd_91_censor'].dt.year*12 + \\\n",
" main_df['first_dpd_91_censor'].dt.month - \\\n",
" 2019*12 - 3)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0 2488\n",
"2.0 2203\n",
"3.0 1788\n",
"4.0 1534\n",
"5.0 1268\n",
"6.0 1240\n",
"7.0 1054\n",
"8.0 1095\n",
"9.0 915\n",
"10.0 1044\n",
"11.0 83320\n",
"Name: duration_t0, dtype: int64"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"main_df['duration_t0'].value_counts().sort_index()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"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 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=\"2\" halign=\"left\">first_dpd_91_censor_bom</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>max</th>\n",
" <th>min</th>\n",
" </tr>\n",
" <tr>\n",
" <th>duration_t0</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1.0</th>\n",
" <td>2019-04-01</td>\n",
" <td>2019-04-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.0</th>\n",
" <td>2019-05-01</td>\n",
" <td>2019-05-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3.0</th>\n",
" <td>2019-06-01</td>\n",
" <td>2019-06-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4.0</th>\n",
" <td>2019-07-01</td>\n",
" <td>2019-07-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5.0</th>\n",
" <td>2019-08-01</td>\n",
" <td>2019-08-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6.0</th>\n",
" <td>2019-09-01</td>\n",
" <td>2019-09-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7.0</th>\n",
" <td>2019-10-01</td>\n",
" <td>2019-10-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8.0</th>\n",
" <td>2019-11-01</td>\n",
" <td>2019-11-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9.0</th>\n",
" <td>2019-12-01</td>\n",
" <td>2019-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10.0</th>\n",
" <td>2020-01-01</td>\n",
" <td>2020-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11.0</th>\n",
" <td>2020-02-01</td>\n",
" <td>2020-02-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" first_dpd_91_censor_bom \n",
" max min\n",
"duration_t0 \n",
"1.0 2019-04-01 2019-04-01\n",
"2.0 2019-05-01 2019-05-01\n",
"3.0 2019-06-01 2019-06-01\n",
"4.0 2019-07-01 2019-07-01\n",
"5.0 2019-08-01 2019-08-01\n",
"6.0 2019-09-01 2019-09-01\n",
"7.0 2019-10-01 2019-10-01\n",
"8.0 2019-11-01 2019-11-01\n",
"9.0 2019-12-01 2019-12-01\n",
"10.0 2020-01-01 2020-01-01\n",
"11.0 2020-02-01 2020-02-01"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"main_df.groupby('duration_t0').agg({'first_dpd_91_censor_bom':['max', 'min']})"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"main_df['flag_churn'] = main_df['first_dpd_91_censor'].notna()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Minor Feature Transformation"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"main_df['util_latest'] = np.where(main_df['util_latest'] > 1, 1, main_df['util_latest'])\n",
"\n",
"education_mapping = {'pre_high_school': 1, \n",
" 'high_school': 2, \n",
" 'diploma': 3,\n",
" 'undergraduate': 4, \n",
" 'postgraduate': 5}\n",
"main_df['de_education'] = main_df['de_education'].map(education_mapping)\n",
"main_df['snapshot_days_premium'] = main_df['snapshot_days_premium'].fillna(120)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train - Validation - Test Split"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"train_val_temp, test_df = train_test_split(main_df, test_size=0.1, random_state=99)\n",
"train_df, val_df = train_test_split(train_val_temp, test_size=0.1, random_state=99)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('train size:', train_temp.shape)\n",
"print('val size:', val_df.shape)\n",
"print('test size:', test_df.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Selected Features"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"cont_feats = [\n",
" 'ap_co_plo',\n",
" 'delin_dpd_max_90',\n",
" 'rep_full_paid_count_60',\n",
" 'snapshot_current_dpd',\n",
" 'snapshot_dob',\n",
" 'trx_active_count',\n",
" 'trx_denied_count_120',\n",
" 'trx_merch_count_dist_120',\n",
" 'trx_pl_count_60',\n",
" 'trx_suc_amt_sum_120',\n",
" 'util_latest'\n",
"]\n",
"\n",
"disc_feats = [\n",
" 'de_education'\n",
"]\n",
"\n",
"feat_dict = {**{x:'c' for x in cont_feats},\n",
" **{x:'d' for x in disc_feats},\n",
" }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### WoE Feature Transformation"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"def woe_transform(train, test, feature, qnt_num, v_type, target_var):\n",
" print(feature)\n",
" if v_type == 'c':\n",
" try:\n",
" _, bins_man = pd.qcut(train[feature], qnt_num, retbins=True, duplicates='drop')\n",
" except:\n",
" return np.nan\n",
" wo_c = woe.WoE(qnt_num=qnt_num, v_type=v_type, bins=bins_man)\n",
" elif v_type == 'd':\n",
" wo_c = woe.WoE(v_type=v_type)\n",
"\n",
" wo_temp = wo_c.fit(train[feature], train[target_var])\n",
" tr_df = wo_temp.transform(train[feature]).rename(columns={'woe':'std_'+feature})[['std_'+feature]]\n",
" te_df = wo_temp.transform(test[feature]).rename(columns={'woe':'std_'+feature})[['std_'+feature]]\n",
" return wo_temp, tr_df, te_df"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ap_co_plo\n",
"ap_co_plo\n",
"0.26765049578565914\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean</th>\n",
" <th>bad</th>\n",
" <th>obs</th>\n",
" <th>good</th>\n",
" <th>woe</th>\n",
" <th>bins</th>\n",
" <th>labels</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>0.120588</td>\n",
" <td>7557</td>\n",
" <td>62668</td>\n",
" <td>55111</td>\n",
" <td>0.311945</td>\n",
" <td>-inf</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.243688</td>\n",
" <td>1776</td>\n",
" <td>7288</td>\n",
" <td>5512</td>\n",
" <td>-0.542366</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.296319</td>\n",
" <td>966</td>\n",
" <td>3260</td>\n",
" <td>2294</td>\n",
" <td>-0.810041</td>\n",
" <td>1.0</td>\n",
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" <th>3</th>\n",
" <td>0.362300</td>\n",
" <td>2218</td>\n",
" <td>6122</td>\n",
" <td>3904</td>\n",
" <td>-1.109534</td>\n",
" <td>2.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
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" mean bad obs good woe bins labels\n",
"0 0.120588 7557 62668 55111 0.311945 -inf 0\n",
"1 0.243688 1776 7288 5512 -0.542366 0.0 1\n",
"2 0.296319 966 3260 2294 -0.810041 1.0 2\n",
"3 0.362300 2218 6122 3904 -1.109534 2.0 3"
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" <td>6908</td>\n",
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" <td>-1.260892</td>\n",
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" <th>10</th>\n",
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" <td>4411</td>\n",
" <td>6863</td>\n",
" <td>2452</td>\n",
" <td>-2.262127</td>\n",
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" mean bad obs good woe bins labels\n",
"0 0.000000 0 4 4 0.404512 -inf 0\n",
"1 0.044500 426 9573 9147 1.391812 -30.0 1\n",
"2 0.038748 203 5239 5036 1.536232 -2.0 2\n",
"3 0.058338 699 11982 11283 1.106472 -1.0 3\n",
"4 0.081274 500 6152 5652 0.750227 0.0 4\n",
"5 0.085176 312 3663 3351 0.699081 1.0 5\n",
"6 0.101294 728 7187 6459 0.507999 2.0 6\n",
"7 0.145997 1003 6870 5867 0.091418 4.0 7\n",
"8 0.226302 1399 6182 4783 -0.445619 7.0 8\n",
"9 0.397944 2749 6908 4159 -1.260892 14.0 9\n",
"10 0.642722 4411 6863 2452 -2.262127 33.0 10\n",
"0 0.009983 87 8715 8628 2.921930 NaN d_nan"
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"text/plain": [
" mean bad obs good woe bins labels\n",
"0 0.276237 5646 20439 14793 -0.711723 -inf 0\n",
"1 0.162119 1022 6304 5282 -0.032386 0.0 1\n",
"2 0.138030 870 6303 5433 0.156824 1.0 2\n",
"3 0.115135 1367 11873 10506 0.364398 2.0 3\n",
"4 0.108605 597 5497 4900 0.430144 4.0 4\n",
"5 0.107813 523 4851 4328 0.438350 5.0 5\n",
"6 0.092744 703 7580 6877 0.605651 6.0 6\n",
"7 0.091949 780 8483 7703 0.615142 8.0 7\n",
"8 0.083100 593 7136 6543 0.726027 12.0 8\n",
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" <td>1631</td>\n",
" <td>5315</td>\n",
" <td>3684</td>\n",
" <td>-0.860124</td>\n",
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"text/plain": [
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"0 0.040541 3 74 71 1.489138 -inf 0\n",
"1 0.093306 591 6334 5743 0.598991 -30.0 1\n",
"2 0.103261 722 6992 6270 0.486577 -26.0 2\n",
"3 0.102326 585 5717 5132 0.496709 -20.0 3\n",
"4 0.105890 773 7300 6527 0.458494 -15.0 4\n",
"5 0.109306 592 5416 4824 0.422922 -10.0 5\n",
"6 0.107183 767 7156 6389 0.444917 -7.0 6\n",
"7 0.117647 696 5916 5220 0.339973 -4.0 7\n",
"8 0.142703 795 5571 4776 0.118087 -2.0 8\n",
"9 0.306867 1631 5315 3684 -0.860124 0.0 9\n",
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" <td>-0.090942</td>\n",
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" <td>6734</td>\n",
" <td>0.061653</td>\n",
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" <td>999</td>\n",
" <td>8046</td>\n",
" <td>7047</td>\n",
" <td>0.278673</td>\n",
" <td>392.0</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.105571</td>\n",
" <td>830</td>\n",
" <td>7862</td>\n",
" <td>7032</td>\n",
" <td>0.461871</td>\n",
" <td>453.0</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.101222</td>\n",
" <td>828</td>\n",
" <td>8180</td>\n",
" <td>7352</td>\n",
" <td>0.508785</td>\n",
" <td>500.0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.116803</td>\n",
" <td>887</td>\n",
" <td>7594</td>\n",
" <td>6707</td>\n",
" <td>0.348132</td>\n",
" <td>542.0</td>\n",
" <td>10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean bad obs good woe bins labels\n",
"0 0.271605 88 324 236 -0.688435 -inf 0\n",
"1 0.251040 1932 7696 5764 -0.581854 121.0 1\n",
"2 0.232409 1843 7930 6087 -0.480169 145.0 2\n",
"3 0.199109 1564 7855 6291 -0.283056 180.0 3\n",
"4 0.170232 1353 7948 6595 -0.090942 222.0 4\n",
"5 0.149747 1186 7920 6734 0.061653 270.0 5\n",
"6 0.126143 1007 7983 6976 0.260570 329.0 6\n",
"7 0.124161 999 8046 7047 0.278673 392.0 7\n",
"8 0.105571 830 7862 7032 0.461871 453.0 8\n",
"9 0.101222 828 8180 7352 0.508785 500.0 9\n",
"10 0.116803 887 7594 6707 0.348132 542.0 10"
]
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"0.5515443849087518\n"
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" <td>1268</td>\n",
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" <td>1248</td>\n",
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" <td>-0.124915</td>\n",
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" <td>1191</td>\n",
" <td>6216</td>\n",
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" <td>1079</td>\n",
" <td>5326</td>\n",
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" <td>-0.304752</td>\n",
" <td>5.0</td>\n",
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" <th>6</th>\n",
" <td>0.225251</td>\n",
" <td>1686</td>\n",
" <td>7485</td>\n",
" <td>5799</td>\n",
" <td>-0.439603</td>\n",
" <td>6.0</td>\n",
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" <th>7</th>\n",
" <td>0.255578</td>\n",
" <td>1134</td>\n",
" <td>4437</td>\n",
" <td>3303</td>\n",
" <td>-0.605850</td>\n",
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" <th>8</th>\n",
" <td>0.281823</td>\n",
" <td>1676</td>\n",
" <td>5947</td>\n",
" <td>4271</td>\n",
" <td>-0.739492</td>\n",
" <td>10.0</td>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>0.024951</td>\n",
" <td>437</td>\n",
" <td>17514</td>\n",
" <td>17077</td>\n",
" <td>1.990625</td>\n",
" <td>NaN</td>\n",
" <td>d_nan</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean bad obs good woe bins labels\n",
"0 0.170468 1607 9427 7820 -0.092614 -inf 0\n",
"1 0.149473 1191 7968 6777 0.063812 1.0 1\n",
"2 0.160710 1268 7890 6622 -0.021973 2.0 2\n",
"3 0.175084 1248 7128 5880 -0.124915 3.0 3\n",
"4 0.191602 1191 6216 5025 -0.235298 4.0 4\n",
"5 0.202591 1079 5326 4247 -0.304752 5.0 5\n",
"6 0.225251 1686 7485 5799 -0.439603 6.0 6\n",
"7 0.255578 1134 4437 3303 -0.605850 8.0 7\n",
"8 0.281823 1676 5947 4271 -0.739492 10.0 8\n",
"0 0.024951 437 17514 17077 1.990625 NaN d_nan"
]
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" <td>1531</td>\n",
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" <th>3</th>\n",
" <td>0.231576</td>\n",
" <td>883</td>\n",
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" <td>-0.475497</td>\n",
" <td>2.0</td>\n",
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" <td>1731</td>\n",
" <td>5853</td>\n",
" <td>4122</td>\n",
" <td>-0.807290</td>\n",
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" <td>1.320803</td>\n",
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" <td>d_nan</td>\n",
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" </tbody>\n",
"</table>\n",
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"text/plain": [
" mean bad obs good woe bins labels\n",
"0 0.120184 5734 47710 41976 0.315755 -inf 0\n",
"1 0.180154 2638 14643 12005 -0.159628 0.0 1\n",
"2 0.209468 1531 7309 5778 -0.346793 1.0 2\n",
"3 0.231576 883 3813 2930 -0.475497 2.0 3\n",
"4 0.295746 1731 5853 4122 -0.807290 3.0 4\n",
"0 0.000000 0 10 10 1.320803 NaN d_nan"
]
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"\n",
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"0.4124471674133143\n"
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" <td>1722</td>\n",
" <td>11583</td>\n",
" <td>9861</td>\n",
" <td>0.070171</td>\n",
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" <td>0.145408</td>\n",
" <td>904</td>\n",
" <td>6217</td>\n",
" <td>5313</td>\n",
" <td>0.096153</td>\n",
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"text/plain": [
" mean bad obs good woe bins labels\n",
"0 0.009929 91 9165 9074 2.927379 -inf 0\n",
"1 0.201813 2226 11030 8804 -0.299930 0.0 1\n",
"2 0.193004 4072 21098 17026 -0.244322 1.0 2\n",
"3 0.173126 3502 20228 16726 -0.111300 2.0 3\n",
"4 0.148666 1722 11583 9861 0.070171 3.0 4\n",
"5 0.145408 904 6217 5313 0.096153 4.0 5\n",
"0 0.000000 0 17 17 1.851431 NaN d_nan"
]
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" mean bad obs good woe bins labels\n",
"0 0.360086 673 1869 1196 -1.099937 -inf 0\n",
"1 0.215162 1368 6358 4990 -0.380844 0.0 1\n",
"2 0.212574 1082 5090 4008 -0.365448 1.0 2\n",
"3 0.222362 177 796 619 -0.422974 2.0 3\n",
"0 0.141311 9217 65225 56008 0.129515 NaN d_nan"
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" <td>1165</td>\n",
" <td>7016</td>\n",
" <td>5851</td>\n",
" <td>-0.061038</td>\n",
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" <th>6</th>\n",
" <td>0.149537</td>\n",
" <td>1049</td>\n",
" <td>7015</td>\n",
" <td>5966</td>\n",
" <td>0.063310</td>\n",
" <td>1749797.0</td>\n",
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" <tr>\n",
" <th>7</th>\n",
" <td>0.128297</td>\n",
" <td>900</td>\n",
" <td>7015</td>\n",
" <td>6115</td>\n",
" <td>0.241176</td>\n",
" <td>2009112.2</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.131842</td>\n",
" <td>925</td>\n",
" <td>7016</td>\n",
" <td>6091</td>\n",
" <td>0.209844</td>\n",
" <td>2339765.0</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.137277</td>\n",
" <td>963</td>\n",
" <td>7015</td>\n",
" <td>6052</td>\n",
" <td>0.163161</td>\n",
" <td>2793507.4</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.132269</td>\n",
" <td>928</td>\n",
" <td>7016</td>\n",
" <td>6088</td>\n",
" <td>0.206113</td>\n",
" <td>3578467.6</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.009910</td>\n",
" <td>91</td>\n",
" <td>9183</td>\n",
" <td>9092</td>\n",
" <td>2.929361</td>\n",
" <td>NaN</td>\n",
" <td>d_nan</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean bad obs good woe bins labels\n",
"0 0.000000 0 1 1 -0.981783 -inf 0\n",
"1 0.254455 1785 7015 5230 -0.599937 3030.0 1\n",
"2 0.266999 1873 7015 5142 -0.665029 592380.0 2\n",
"3 0.233181 1636 7016 5380 -0.484496 942191.6 3\n",
"4 0.171347 1202 7015 5813 -0.098820 1222550.8 4\n",
"5 0.166049 1165 7016 5851 -0.061038 1496409.8 5\n",
"6 0.149537 1049 7015 5966 0.063310 1749797.0 6\n",
"7 0.128297 900 7015 6115 0.241176 2009112.2 7\n",
"8 0.131842 925 7016 6091 0.209844 2339765.0 8\n",
"9 0.137277 963 7015 6052 0.163161 2793507.4 9\n",
"10 0.132269 928 7016 6088 0.206113 3578467.6 10\n",
"0 0.009910 91 9183 9092 2.929361 NaN d_nan"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"util_latest\n",
"util_latest\n",
"0.7905656380221309\n"
]
},
{
"data": {
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" <th></th>\n",
" <th>mean</th>\n",
" <th>bad</th>\n",
" <th>obs</th>\n",
" <th>good</th>\n",
" <th>woe</th>\n",
" <th>bins</th>\n",
" <th>labels</th>\n",
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" <th>0</th>\n",
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" <td>-inf</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.077807</td>\n",
" <td>481</td>\n",
" <td>6182</td>\n",
" <td>5701</td>\n",
" <td>0.797600</td>\n",
" <td>0.000002</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.105144</td>\n",
" <td>650</td>\n",
" <td>6182</td>\n",
" <td>5532</td>\n",
" <td>0.466403</td>\n",
" <td>0.206168</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.127143</td>\n",
" <td>786</td>\n",
" <td>6182</td>\n",
" <td>5396</td>\n",
" <td>0.251527</td>\n",
" <td>0.362120</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.155750</td>\n",
" <td>963</td>\n",
" <td>6183</td>\n",
" <td>5220</td>\n",
" <td>0.015270</td>\n",
" <td>0.513042</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.186833</td>\n",
" <td>1155</td>\n",
" <td>6182</td>\n",
" <td>5027</td>\n",
" <td>-0.204207</td>\n",
" <td>0.656569</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.220802</td>\n",
" <td>1365</td>\n",
" <td>6182</td>\n",
" <td>4817</td>\n",
" <td>-0.413933</td>\n",
" <td>0.793045</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.305677</td>\n",
" <td>1890</td>\n",
" <td>6183</td>\n",
" <td>4293</td>\n",
" <td>-0.854521</td>\n",
" <td>0.902777</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.412650</td>\n",
" <td>2551</td>\n",
" <td>6182</td>\n",
" <td>3631</td>\n",
" <td>-1.321907</td>\n",
" <td>0.968000</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.181076</td>\n",
" <td>2239</td>\n",
" <td>12365</td>\n",
" <td>10126</td>\n",
" <td>-0.165853</td>\n",
" <td>0.998968</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.024951</td>\n",
" <td>437</td>\n",
" <td>17514</td>\n",
" <td>17077</td>\n",
" <td>1.990625</td>\n",
" <td>NaN</td>\n",
" <td>d_nan</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean bad obs good woe bins labels\n",
"0 0.000000 0 1 1 -0.981783 -inf 0\n",
"1 0.077807 481 6182 5701 0.797600 0.000002 1\n",
"2 0.105144 650 6182 5532 0.466403 0.206168 2\n",
"3 0.127143 786 6182 5396 0.251527 0.362120 3\n",
"4 0.155750 963 6183 5220 0.015270 0.513042 4\n",
"5 0.186833 1155 6182 5027 -0.204207 0.656569 5\n",
"6 0.220802 1365 6182 4817 -0.413933 0.793045 6\n",
"7 0.305677 1890 6183 4293 -0.854521 0.902777 7\n",
"8 0.412650 2551 6182 3631 -1.321907 0.968000 8\n",
"9 0.181076 2239 12365 10126 -0.165853 0.998968 9\n",
"0 0.024951 437 17514 17077 1.990625 NaN d_nan"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"de_education\n",
"de_education\n",
"0.05525516139904528\n"
]
},
{
"data": {
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" <th></th>\n",
" <th>mean</th>\n",
" <th>bad</th>\n",
" <th>obs</th>\n",
" <th>good</th>\n",
" <th>woe</th>\n",
" <th>bins</th>\n",
" <th>labels</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.192060</td>\n",
" <td>387</td>\n",
" <td>2015</td>\n",
" <td>1628</td>\n",
" <td>-0.238247</td>\n",
" <td>1</td>\n",
" <td>d_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.177157</td>\n",
" <td>8573</td>\n",
" <td>48392</td>\n",
" <td>39819</td>\n",
" <td>-0.139203</td>\n",
" <td>2</td>\n",
" <td>d_2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.142669</td>\n",
" <td>1516</td>\n",
" <td>10626</td>\n",
" <td>9110</td>\n",
" <td>0.118368</td>\n",
" <td>3</td>\n",
" <td>d_3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.119194</td>\n",
" <td>1822</td>\n",
" <td>15286</td>\n",
" <td>13464</td>\n",
" <td>0.325155</td>\n",
" <td>4</td>\n",
" <td>d_4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.072541</td>\n",
" <td>219</td>\n",
" <td>3019</td>\n",
" <td>2800</td>\n",
" <td>0.873373</td>\n",
" <td>5</td>\n",
" <td>d_5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean bad obs good woe bins labels\n",
"2 0.192060 387 2015 1628 -0.238247 1 d_1\n",
"1 0.177157 8573 48392 39819 -0.139203 2 d_2\n",
"4 0.142669 1516 10626 9110 0.118368 3 d_3\n",
"3 0.119194 1822 15286 13464 0.325155 4 d_4\n",
"0 0.072541 219 3019 2800 0.873373 5 d_5"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"train_ls = []\n",
"val_ls = []\n",
"test_ls = []\n",
"for feature, d_type in feat_dict.items():\n",
" info, temp_tr, temp_val = woe_transform(train_df, val_df, feature, 10, d_type, 'flag_churn')\n",
" info, temp_tr, temp_te = woe_transform(train_df, test_df, feature, 10, d_type, 'flag_churn')\n",
" train_ls.append(temp_tr)\n",
" val_ls.append(temp_val)\n",
" test_ls.append(temp_te)\n",
" print(info.iv)\n",
" display(info.bins)\n",
" print()\n",
" \n",
"std_train = pd.concat(train_ls, axis=1)\n",
"std_val = pd.concat(val_ls, axis=1)\n",
"std_test = pd.concat(test_ls, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train (79338, 440)\n",
"val (8816, 440)\n",
"test (9795, 440)\n"
]
}
],
"source": [
"train_df = train_df.merge(std_train, how='left', left_index=True, right_index=True)\n",
"val_df = val_df.merge(std_val, how='left', left_index=True, right_index=True)\n",
"test_df = test_df.merge(std_test, how='left', left_index=True, right_index=True)\n",
"\n",
"print('train', train_df.shape)\n",
"print('val', val_df.shape)\n",
"print('test', test_df.shape)\n",
"\n",
"std_cont_feats = ['std_'+feat for feat in cont_feats]\n",
"std_disc_feats = ['std_'+feat for feat in disc_feats]\n",
"std_features = std_cont_feats + std_disc_feats"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"final_feats = sorted(std_features)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Spline Cox Regression Model"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<lifelines.CoxPHFitter: fitted with 79338 total observations, 66821 right-censored observations>"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cph_spline = CoxPHFitter(baseline_estimation_method=\"spline\", n_baseline_knots=2)\n",
"cph_spline.fit(train_df[['duration_t0','flag_churn']+final_feats], 'duration_t0', 'flag_churn')"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>baseline hazard</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1.0</th>\n",
" <td>0.003653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.0</th>\n",
" <td>0.006408</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3.0</th>\n",
" <td>0.007481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4.0</th>\n",
" <td>0.007363</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5.0</th>\n",
" <td>0.006816</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6.0</th>\n",
" <td>0.006326</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7.0</th>\n",
" <td>0.005942</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8.0</th>\n",
" <td>0.005662</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9.0</th>\n",
" <td>0.005470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10.0</th>\n",
" <td>0.005348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11.0</th>\n",
" <td>0.005283</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" baseline hazard\n",
"1.0 0.003653\n",
"2.0 0.006408\n",
"3.0 0.007481\n",
"4.0 0.007363\n",
"5.0 0.006816\n",
"6.0 0.006326\n",
"7.0 0.005942\n",
"8.0 0.005662\n",
"9.0 0.005470\n",
"10.0 0.005348\n",
"11.0 0.005283"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(0.0, 0.009)"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# months level baseline hazard\n",
"display(cph_spline.baseline_hazard_)\n",
"plt.plot(cph_spline.baseline_hazard_)\n",
"plt.ylim([0,0.009])"
]
},
{
"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",
" .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>baseline hazard</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1.0</th>\n",
" <td>0.003653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.0</th>\n",
" <td>0.006408</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3.0</th>\n",
" <td>0.007481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4.0</th>\n",
" <td>0.007363</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5.0</th>\n",
" <td>0.006816</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6.0</th>\n",
" <td>0.006326</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7.0</th>\n",
" <td>0.005942</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8.0</th>\n",
" <td>0.005662</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9.0</th>\n",
" <td>0.005470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10.0</th>\n",
" <td>0.005348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11.0</th>\n",
" <td>0.005283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12.0</th>\n",
" <td>0.005245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13.0</th>\n",
" <td>0.005210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14.0</th>\n",
" <td>0.005177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15.0</th>\n",
" <td>0.005147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16.0</th>\n",
" <td>0.005120</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" baseline hazard\n",
"1.0 0.003653\n",
"2.0 0.006408\n",
"3.0 0.007481\n",
"4.0 0.007363\n",
"5.0 0.006816\n",
"6.0 0.006326\n",
"7.0 0.005942\n",
"8.0 0.005662\n",
"9.0 0.005470\n",
"10.0 0.005348\n",
"11.0 0.005283\n",
"12.0 0.005245\n",
"13.0 0.005210\n",
"14.0 0.005177\n",
"15.0 0.005147\n",
"16.0 0.005120"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f4f324f6430>]"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"display(cph_spline.baseline_hazard_at_times([i for i in range(1,17,1)]))\n",
"plt.plot(cph_spline.baseline_hazard_at_times([i for i in range(1,17,1)]))"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ec2-user/anaconda3/envs/ltv/lib/python3.8/site-packages/lifelines/fitters/__init__.py:2293: ApproximationWarning: Approximating using `predict_survival_function`. To increase accuracy, try using or increasing the resolution of the timeline kwarg in `.fit(..., timeline=timeline)`.\n",
"\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train Concordance: 0.7216929201628738\n",
"Val Concordance: 0.7349597538814092\n",
"Test Concordance: 0.723880277107705\n"
]
}
],
"source": [
"print('Train Concordance:',cph_spline.score(train_df, scoring_method='concordance_index'))\n",
"print('Val Concordance:',cph_spline.score(val_df, scoring_method='concordance_index'))\n",
"print('Test Concordance:',cph_spline.score(test_df, scoring_method='concordance_index'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. MBG NBD Model"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"summary_cal_holdout = calibration_and_holdout_data(trx_df, \n",
" customer_id_col = 'user_id', \n",
" datetime_col = 'trx_dt', \n",
" monetary_value_col = 'rev',\n",
" freq = 'D', \n",
" calibration_period_end = pd.to_datetime(obs_date),\n",
" observation_period_end = pd.to_datetime(censor_date))\n",
"\n",
"summary_cal_holdout = summary_cal_holdout.reset_index()\n",
"\n",
"# summary_cal_holdout.to_parquet(\"summary_cal_holdout.parquet\", compression='gzip')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# summary_cal_holdout = pd.read_parquet(\"summary_cal_holdout.parquet\")"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"uid_tr = tuple(train_df['user_id'])\n",
"uid_tr_good = tuple(train_df.query('first_dpd_91_dt != first_dpd_91_dt')['user_id'])\n",
"\n",
"uid_test = tuple(test_df['user_id'])\n",
"uid_test_good = tuple(test_df.query('first_dpd_91_dt != first_dpd_91_dt')['user_id'])\n",
"uid_test_bad = tuple(test_df.query('first_dpd_91_dt == first_dpd_91_dt')['user_id'])"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"summary_cal_holdout_tr = summary_cal_holdout[summary_cal_holdout.user_id.isin(uid_tr)]\n",
"summary_cal_holdout_tr_good = summary_cal_holdout[summary_cal_holdout.user_id.isin(uid_tr_good)]\n",
"\n",
"summary_cal_holdout_test = summary_cal_holdout[summary_cal_holdout.user_id.isin(uid_test)]\n",
"summary_cal_holdout_test_good = summary_cal_holdout[summary_cal_holdout.user_id.isin(uid_test_good)]\n",
"summary_cal_holdout_test_bad = summary_cal_holdout[summary_cal_holdout.user_id.isin(uid_test_bad)]"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: -67.232997\n",
" Iterations: 26\n",
" Function evaluations: 29\n",
" Gradient evaluations: 29\n"
]
},
{
"data": {
"text/plain": [
"<lifetimes.ModifiedBetaGeoFitter: fitted with 22008 subjects, a: 0.03, alpha: 15.38, b: 0.74, r: 2.26>"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mbgnbd = ModifiedBetaGeoFitter(penalizer_coef=0.01)\n",
"mbgnbd.fit(summary_cal_holdout_tr_good['frequency_cal'], \n",
" summary_cal_holdout_tr_good['recency_cal'], \n",
" summary_cal_holdout_tr_good['T_cal'],\n",
" verbose=True,)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"t = (pd.to_datetime(censor_date) - trx_df['trx_dt'].min()).days\n",
"t_cal = (pd.to_datetime(obs_date) - trx_df['trx_dt'].min()).days"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"488"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary_cal_holdout['duration_holdout'].max().astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"all\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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h0aJFFBcXc/rpp/Pzzz8DMH36dIqLi7nnnnvIzs7m+uuvJzc3l7Zt2/L8888zZMgQLr/8cjp06MDChQvZtWsXDz/8MOeddx4ADz/8MK+++ioJCQmceuqpXH311Zx//vksXrwYgPXr13PhhReyaNGioOf3xBNPMGfOHKqqqnj77bcZMmQIe/fu5corr2TDhg20bduW5557jpEjR9bZbuPGjfzqV7+iurqaKVOm1MxXVf74xz/y8ccfIyL8+c9/ZurUqXz99ddMnz6dDz80HXJvuOEGxo4dS2FhITt27GDSpEl069aNr776qoG/oiVmWT0bktJg8l3w0R9g64/Q78iWlsriQyhLI915jQWuBQ5yXtcAwyLYdwVwgqqOwvTonSIi7q//T1Ud7bxchTEM0/N4ODAF03Q9sRHn1OI8++yz9O7dm6+++opbb72VRYsW8cEHH/DGG29w8803c+utt7JgwQLeffddfvOb3wBw7733MnHiRJYsWVKjbMIxbdo0nnjiCRYtWsT06dO57rrrapbt3LmTefPm8eGHH3LHHXcA8PHHH/Pvf/+bH3/8kWXLlvHHP/6RgQMH0rFjR5YuXQrASy+9xOWXXx7yuN26dWPx4sVce+21TJ8+HYC7776bMWPGsHz5ch588EEuvfTSetvdfPPNXHvttSxYsICePXvWzH/vvfdYunQpy5Yt44svvuC2225j586dQY9/00031Xy/VmG0IlRh9RwYOBlGXQhJbayLKgYJammo6r0AIvIZcLiqFjnT9wBvh9uxmubjxc5ksvMKZWueCbylqhXARhHJAsYD88OfRmDCWQTNxRlnnEGbNm0A+OKLL1i1alXNssLCQoqKipg7dy7vvfceAKeddhqdO3cOuc/i4mK+//57zj///Jp5FRUVNZ/POussEhISGDZsGLt376459hVXXEHbtm0B6NKlCwC/+c1veOmll3jkkUeYOXMmP/30U8hjn3POOQAcccQRNTLPmzePd999F4ATTjiBvLw89u3bV2e77777rmadSy65hNtvv71m24suuojExER69OjBcccdx4IFC+jQoUNIOSytjO2LoXA7nPAXSE2HQ0+Fle/DqX+HRDvCP1aIZER4P6DSZ7oSyIxk546lsAgYBDylqj+KyKnADSJyKbAQ+L2q5mOsmB98Nt/mzPPf5zRgGkC/fv0iEaPFadeuXc1nr9fL/Pnza5SIL4HGGCQlJeH1emum3YFrXq+XTp061VgI/qSmptZ8VscvrKoBj3Huuedy7733csIJJ3DEEUfQtWvXkOfj7jsxMbEmTqMBfM+BjhVoXqBtIfi5W1opq2dDQhIc6rguR06Fle9B1pe18ywtTiSB8FeBn0TkHsfK+BF4OZKdq6pHVUcDfYDxInIY8AwwEOOy2gn8w1k90KisencTVX1OVceq6tiMjIxIxIgpTj75ZJ588smaafemf+yxx9YEzT/++GPy8/MB6NGjBzk5OeTl5VFRUVHj3+/QoQMDBgzg7beN0aeqLFu2LOyxX3zxxZoA8t69ewFTr+mUU07h2muv5YorrmjUefnK//XXX9OtW7d6lsLRRx/NW2+9BVAnQeDYY49l5syZeDwecnNzmTt3LuPHj6d///6sWrWKiooK9u3bx5dfflmzTXp6OkVFRY2S1RKDqBqlMeBYaONY2YMmQ5suJiBuiRnCKg1VfQC4AsgH9gJXqOrfGnIQVS0AvgamqOpuR5l4gecxLigwlkVfn836ADsacpx44PHHH2fhwoWMHDmSYcOG8eyzzwImJjB37lwOP/xwPvvssxorKjk5mbvuuosJEyZw+umnM2TIkJp9vf7667zwwguMGjWK4cOH88EHH4Q89pQpUzjjjDMYO3Yso0eProlHAFx88cWICCeffHKjzuuee+6pOa877riDl1+u/1zx2GOP8dRTTzFu3Lg6rquzzz6bkSNHMmrUKE444QQefvhhevbsSd++fbngggsYOXIkF198MWPGjKnZZtq0aZx66qlMmjSpUfJaYoycVbB3Aww9o3ZeYjIMPxvWfAQV9gEhVpBgroE6K4mMAo7FPPl/q6qhH2nNNhlAlaoWiEgb4DPg78AiVd3prHMrMEFVLxSR4cAbGCXSG/gSGKyqnmDHGDt2rC5cuLDOvNWrVzN06NCw5xTrZGZmsnDhQrp169Ysx5s+fTr79u3jvvvua5bjRZPWcg0cUHz1NzOo7w/roH332vlbfoAXT4GznoXRF7WcfK2PRtfbCRvTEJGbMSmy7zoHek1EnlPVJ8Js2gt42YlrJACzVPVDEXlVREZjFNAm4LcAqrpSRGYBq4Bq4PpQCsPSdJx99tlkZ2fz3//+t6VFsRyorJ4D/f+nrsIA6DsBOvUzLiqrNGKCSALhV2GsgRIAEfk7JqMppNJQ1eXAmADzLwmxzQPAAxHI1OrZtGlTsx3r/fffrzfv7LPPZuPGjXXm/f3vf+eUU05pLrEsBwp52ZCzEqY8VH+ZCIy4AOY9AkW7Ib1H88tnqUMkSkMwg+1cPOyHaWOJDwIpEoslKqyebd6H/jLw8pEXwLfTTSbVkdc2n1yxjqpRqs1MJNlTLwE/+mRP/QC8EFWpLBbLgcOq2dD7cOjYJ/DyjEOh50g70M+lOAfemwbf/iP8ulEgkuypRzDZU3sxGVRXqOqj0RbMYrEcABRshR2LYdgZodcbeYFZb09W88gVi3g98NPz8MRY+Pm9FqvLFVRpiEgH570LJmD9GmbMxmZnnsVisewfa8y4ozqptoE47FxADtwxG9sXwfMnmHpcvUfDdfPhuNtaRJRQMY03gNMxI7p9VZo40wdHUS6LxXIgsHoOdB8OXQeGXq9DbxhwjHFRHX9ni/jyW4SyfPjyPlj4osksO/cFo0Bb8PyDWhqqerqYmg/HqerBPq8BqmoVRjMRSf+Khva42B++/vprTj/9dABmz57NQw8FyHhxKCgo4Omnn66Z3rFjR021XYuF4hzY/H1415TLyKmQv9E8dbd2VGHpm8YVteglmHAN3LAARpzX4gozZEzDKTpo02gOADyehg+JOeOMM2oq6AbCX2n07t2bd96xbTwtDmv+A2jwrCl/hv4SElNbf0A8ZzXMOA3+fQ10GQDTvoFTH4K0ji0tGRBZyu0PIjJOVRdEXZqm5uM7YNeKpt1nzxHmBwzBpk2bmDJlChMnTuSHH35g1KhRXHHFFdx9993k5OTw+uuvM2jQoID9J/z7aviO2H/ttdd4/PHHqaysZMKECTz99NMkJoauHu/KMmHCBJYsWcIhhxzCK6+8Qtu2bcnMzOTKK6/ks88+44YbbqBLly7cfffdVFRUMHDgQF566SXat2/PJ598wi233EK3bt04/PDDa/Y9Y8YMFi5cyJNPPsnu3btr+ogAPPPMMzz++ONkZ2czevRoTjrpJK6//vqa3iDl5eVce+21LFy4kKSkJB555BEmTZrEjBkzmD17NqWlpWRnZ3P22Wfz8MMP4/F4uOqqq1i4cCEiwpVXXsmtt966Hz+kpcVZPRu6DITukXRawNw0D51iUm9PeRASI7l9xREVxWZU/A9Pmyq/v3wcxlwCCbHVKy8SaSZhFEe2iCwXkRUisjzagsU7WVlZ3HzzzSxfvpw1a9bwxhtvMG/ePKZPn86DDz4YtP9EsL4aq1evZubMmXz33XcsXbqUxMTEOkX/QrF27VqmTZvG8uXL6dChQ52n/7S0NObNm8eJJ57I/fffzxdffMHixYsZO3YsjzzyCOXl5Vx99dXMmTOHb7/9ll27dgU8xk033cRxxx3HsmXLWLx4McOHD+ehhx5i4MCBLF26lP/93/+ts/5TTz0FwIoVK3jzzTe57LLLaqrYLl26lJkzZ7JixQpmzpzJ1q1bWbp0Kdu3b+fnn39mxYoVjS6saIkRyvJh41xjPTTE3TLiAtPZb8PXUROt2XH7iDw1Ab5/HEZdBDcsgiMuizmFAZFZGqdGXYpoEcYiiCYDBgxgxIgRAAwfPpzJkycjIowYMYJNmzaxefPmgP0ngvXV+PLLL1m0aBHjxo0DoKysjO7duwc4cn369u3L0UcfDcCvf/1rHn/8cf7whz8AMHXqVAB++OEHVq1aVbNeZWUlRx11FGvWrGHAgAEMHjy4Zvvnnnuu3jH++9//8sorrwCmZHrHjh1rKvUGYt68edx4440ADBkyhP79+7Nu3ToAJk+eTMeOxhQfNmwYmzdvZvjw4WzYsIEbb7yR0047rdGFFS0xwtpPwFsdeTzDZfBJxuJYPhMGnxgd2ZqTvRvh4z/C+s+gx2Fw3ovQb0JLSxWSsEpDVTeLyERM8cCXnEKE7aMvWnzj288iISGhZjohIYHq6mqSkup/9W6viWA9Jy677DL+9rcGFRgOuD/fabfXh6py0kkn8eabb9ZZd+nSpQHl2V9CFcr0/e7cnh2dO3dm2bJlfPrppzz11FPMmjWLF198scnlsjQTq2dDhz5mUF9DSEqFYWfBinegsgRS2oXfJhaproDvHjMD9BKSjLtt/G/jwuUW1vYRkbuB24E7nVnJmDEblv0gWP+JYH01Jk+ezDvvvENOTg5gemFs3rw5omNt2bKF+fNNA8Q333yTiRMn1lvnyCOP5LvvviMrywyeKi0tZd26dQwZMoSNGzeSnZ1ds30gJk+ezDPPPAOYoHphYWHInhe+57lu3Tq2bNnCoYceGvQc9uzZg9fr5dxzz+W+++6r6WluiUMqik1jpYa6plxGToWqElMyPR7J/i88fRR89YDpTnjDAjjq+rhQGBBZTONs4AygBEBVd2B6h1v2g2D9J4L11Rg2bBj3338/J598MiNHjuSkk04K2Ufbl6FDh/Lyyy8zcuRI9u7dy7XX1q/fk5GRwYwZM7jooosYOXIkRx55JGvWrCEtLY3nnnuO0047jYkTJ9K/f/+Ax3jsscf46quvGDFiBEcccQQrV66ka9euHH300Rx22GHcdlvdgUjXXXcdHo+HESNGMHXqVGbMmFHHwvBn+/btHH/88YwePZrLL7+8URaXJUZY/xl4KiLPmvKn31HGSom3gX6FO+Dty+HVswGFX78H588wY1DiiLD9NETkJ1UdLyKLVfVwEWkHzFfVkc0jYnBacz+NpmLTpk01GUsHCvYaiHHevgI2fQu/XwsJobP/gvL53fD9E6b/Rrvm6TnTaDzV8NO/4KsHwVMFx/4B/ucmSE5rSaka7XOOxNKYJSL/AjqJyNXAF5iOexaLxdIwqsqNpTHktMYrDDC1qNQDK2N8GFlFEbx2Dnz6J2MhXf8DHPfHllYY+0UkgfDpInISUAgcCtylqp9HXTJLg8jLy2Py5Mn15n/55ZcHlJVhiXE2fAWVxeFrTYWjx3BTfmT5TBh/ddPI1tSU7oXXz4MdS+GMJ2HMr1t8NHdTEFRpiMhgYDowEFgB/EFVtzeXYPuDqkYl4yeW6dq1K0uXLm1pMVqcSNoXW1qQVbNNymzmMfu/r5Hnwxf3mN7iXWKsslHhThO72LsBpr5qLKtWQij31IvAh8C5mKKF4dq7xgRpaWnk5eXZm8cBiKqSl5dHWlr8mv6tGk8VrP0IDv0FJKXs//5GnG/eV8RYaZq9G+GlKbBvK/z6nValMCC0eypdVd3YxVoRiYscxz59+rBt2zZyc3NbWhRLC5CWlkafPkGa+Vhalk3fQnlB47Om/OnYB/pPNLWojr0tNlw/u1cZC8NTAZfOhj5HtLRETU4opZEmImOojbK38Z1W1ZBKRETSgLlAqnOcd1T1bqcXx0wgE9On4wJVzXe2uRPTk9wD3KSqnzb0hJKTkxkwYEBDN7NYLNFm9RxIbgcDT2i6fY48H+bcDDuXQu8xTbffxrBtIbx2LiS3gSs+hu6tM4MvlNLYCTziM73LZ1qBcL98BXCCqhaLSDIwT0Q+Bs4BvlTVh0TkDuAO4HYRGQZcCAwHegNfiMghqtrw8qsWiyW28Hpg9YemDEhym6bb77Az4aPbYPnbLas0NnwNb/4K2mfApR9A58yWkyXKBFUaqjppf3bslFUvdiaTnZcCZwLHO/NfBr7GjDg/E3hLVSuAjSKSBYwH5u+PHBaLJQbY+hOU5DS81lQ42nSGwSfDz+/AyfftXxpvY1n9IbxzBXQdBJe8DyutKokAACAASURBVOk9m1+GZiSqJRRFJFFElgI5wOeq+iPQQ1V3AjjvbtW9g4CtPptvc+b573OaiCwUkYU2bmGxxAmrZ5teGIOjUGhyxPlQvBs2ftP0+w7H0jdg1iXQcyRc/p9WrzAgykpDVT2qOhroA4wXkcNCrB4oilUvBUpVn1PVsao6NiMjo6lEtVgs0cIt/T3wBNMnoqk5ZAqkdjAuqubkh2fh39fCgGONS6ptl+Y9fgvRLMXaVbUA44aaAuwWkV4AznuOs9o2oK/PZn2AHc0hn8ViiSI7lpj006bKmvInOc24vVbPgaqy6BzDF1X4+iH45HZzTr+aBakHTuHvSKrcfhnJvADrZIhIJ+dzG+BEYA0wG7jMWe0y4APn82zgQhFJFZEBwGDgp0hOwmKxxDCr54Akmoqu0WLEBVBZBGs/jt4xALxe+ORO+PpvMPpiOG+GKdd+ABFqRHga0BboJiKdqXUfdcBkN4WjF/CyiCRilNMsVf1QROZj6lldBWwBzgdQ1ZUiMgtYBVQD19vMKYslzlE18YwBx0TXfZM5EdJ7wX/vh3WfQEKyKTWekAyJKT6fk03/isTkINO+6/lPJ8FPz8OyN+DI6+DkB2Kys160CZVy+1vgFoyC8B2TUQg8FW7HqrocqJcDp6p5QP0iSWbZA8AD4fZtsVjihNw1kJdlbrLRJCERjvk9/PAMbJlvKst6q8wodG+1814F6t3/Yx3/J1N0MBYGE7YAoVJuHwMeE5EbVTUuSohYLJYYY9VsQGDI6dE/1virwxcv9Hp9lEmVUS6eytrP/sv8FU+7btB3fPTPJYaJpFXUPhG51H+mqr4SBXksFktrYvUc6HckpPdoaUkMCQmQkHrAxSGakkiUxjifz2kY19JiwCoNi8USnL0bYPcK0//a0mqIpJ/Gjb7TItIReDVqElksltbB6jnmPVqptpYWoTGh/1JMOqzFYrEEZ9Vs6DUaOvVraUksTUhYS0NE5lA7MjsRGArEWUd3i8XSrOzbDtsXwuS7WloSSxMTSUxjus/namCzqm6LkjwWi6U1sOZD876/bV0tMUdY95SqfoMZyZ0OdAYqoy2UxWKJc1bNhoyh0M16slsbkZQRuQBTzuN84ALgRxE5L9qCWSyWOKU4F7Z8bwPgrZRI3FP/DxinqjlgakoBXwAx1pjXYrHEBGv/Y0ZeN3XvDEtMEEn2VIKrMBzyItzOYrEciKyeYzrX9QjVCcESr0RiaXwiIp8CbzrTU4GPoieSxWKJW8oKYMM3cOS1B2xtptZOJIP7bhORc4GjMZVun1PV96MumcViiT/WfWrqNdmsqVZLJJYGqvou8G6UZbFYLPHOz+9Cem846IiWlsQSJUL10ygiQLtVF1XtEBWJLBZLk7OvrApVpVPblOgdZPN8WP+pKR1+APaZOFAIVRo9HUBE/grswtSbEuBizJgNi8USJ9z+znJKKqt59aoJ0TmA1wuf3AEdDoL/uTH8+pa4JRL31Cmq6nulPSMiPwIPR0kmi8XSxKzYvo/0tIi80Y1j2Zuwcymc83+Q0jZ6x7G0OJHYkB4RuVhEEkUkQUQuBmwbVoslTiitrGZ7QRmV1U3QtS4QFcXw5b3QZxyMsON+WzuRKI1fYUaC73Ze5zvzLBZLHLAhtwSAimgpjXmPQPFumPKQTbM9AIik9tQmVT1TVbupaoaqnqWqm8JtJyJ9ReQrEVktIitF5GZn/j0isl1EljqvX/hsc6eIZInIWhE5Zb/OzGKxAJCdWwxESWnkb4bvn4SRU6HP2KbfvyXmCJU99QShs6duCrPvauD3qrpYRNKBRSLyubPsn6rqWz0XERkGXAgMB3oDX4jIIapqXWEWy36QlWOURmV1FP5KX9wNkgCT7276fVtiklCRsYX7s2NV3QnsdD4Xichq4KAQm5wJvKWqFcBGEckCxgPz90cOi+VAJ2qWxub5sPJ9OP5O6Bjqr21pTYRKuX3Zd9qxFlRVixt6EBHJBMYAP2JGlt8gIpdiFNPvVTUfo1B+8NlsGwGUjIhMA6YB9OtnO4JZLOGosTQ8XlQVaYq4Q50U23BOB0trIpLS6IeJyBLgZ2CViCwSkeGRHkBE2mNGk9+iqoXAM8BAYDTGEvmHu2qAzeu5x1T1OVUdq6pjMzIyIhXDYjkgqfZ42bSnlMQEQRWqPEE9zg3DTbE98V6bYnuAEUn21HPA71S1v6r2A34PPB/JzkUkGaMwXlfV9wBUdbeqelTV6+xnvLP6NqCvz+Z9gB2RnYbFYgnE1vwyKj1eBmW0B4y1sd/YFNsDmkiURjtV/cqdUNWvgXbhNhJjA78ArFbVR3zm9/JZ7WyMBQMwG7hQRFJFZAAwGNP8yWKxNJJsxzU1rLep+lNR1QTBcDfF9pS/2RTbA5BIhohuEJG/YMqIAPwa2BjBdkcDlwArRGSpM+9PwEUiMhrjetoE/BZAVVeKyCxgFSbz6nqbOWWx7B9ZThB8aK903l/SBJaGm2I74gLoO64JJLTEG5EojSuBe4H3MHGHucAV4TZS1XkEjlME7cWhqg8AD0Qgk8ViiYDsnGIy0lPJSE8FoKJqP5WGm2J74j37LZslPomkn0Y+YNMjLJY4JCu3mIEZ7UhJTAT209KwKbYWQg/um0PowX22y4rFEsOoKtk5xZwxujepSSZ82ej6UzbF1uIQytJwR2wLJsvpN9EXx2KxNBW5xRUUllczMKM9KY7SqGjsqPCaKrbP2xTbA5xQg/u+cT+LSLHvtMViiX2yc0yhwkO6JNB31TOcl1hMRXUj+mnUSbE9v4mltMQbkRbYb6IRQRaLpbnIyini9IT5HPmfP5BYvIPrEnuyufoPDd+Rm2I79XWbYmsJGdPo4jOZKCKd8cmGUtW90RTMYrHsB7t+5pjvbuCSlCVo+xEUdR7CQVu+ZX1VdcP2Y1NsLX6EGty3CFMbahHQAVjsfHbnWyyWWKN0L3x0G/zrGLqWZvFUu+uRad9QNmAyqVIFJTkN259NsbX4ESqmMaA5BbFYLBFQVQZl+dChd935Xg8sfhm+vA/KC2DslZy37BiG9cuEhESkoynumVy0HVP2LQJsiq0lAJGUEbFYLLHCvEfhiSMgL7t23pYf4flJ8OGt0H0o/HYuJSf+nbWFyQzMMBV/EjobpZFStDWy49gUW0sQrNKwWOKJwu1QVQpzbobCHfDeNHjxZCjOhXNfgMv/Az1H1LR4HdTdFCpM7GKURlpJhDVAa6rY3mNTbC11iDR7ymKxxAIVhYDApm/h0ZEmm+mY38PE30Fq+5rV3MZLA53qtqltO5Kv7WlTGoHSsCm2lhBEpDREJBHo4bu+qm6JllAWiyUI5YVw0OHQ5WCoLjf9LLoOrLdaVk4xiQlC/67GPZWSlECWdiO9bHv4Y9gUW0sIwioNEbkRuBvYDbg1CBQYGUW5LBZLICoKIa0TnPt/IVfLzi2mf5e2NSPBExOEHWRwRPnO0Psv2GJTbC0hicTSuBk4VFXzoi2MxWIJQ3khdArf5jgrp5iB3dvXmbdLMuhQsQJUg1sQi2aAtwom39UEwlpaI5EEwrcC+6ItiMViiYCKQkjtEHKVao+XTXklNfEMl90J3UnxlpuxHIHwemH523DwJOjUN/A6MUx+SSVb95a2tBitnoiaMAFfi8h/gAp3pm83PovF0kyU74O00Epjy95SqjxakznlkpvY3bQ3K9gM7boG2HA+7NsCk//ShAI3H3/9cBU/bdzLvNsnITYWEzUisTS2AJ8DKUC6z8tisTQn1ZUm+J3aMeRq2U66rTtGw2VPUk/zYV+QsRrL34LkdjDktP0WtSVYuWMf2wvK2JZf1tKitGoiacJ0b3MIYrFYwlBRaN7DWBpZTl9w/5hGfnJPKAcKAiiNqnJY+QEM/SWktKu/PMap9njZuMcoywWb9tK3ix1bEi1CFSx8VFVvCdaMyTZhsrQaNnwDiSnQ/6iWliQ05U5oMUxMIzu3mO7pqXRIS64zvzIxnTJpS5uCANny6z6Bin0wampTSdusuC45gAWb8jnn8D4tLFHrJZSl8arzPj3EOkERkb7AK0BPTKruc6r6mFM9dyaQCWwCLnBayiIidwJXAR7gJlX9tDHHtlgaxOd3QWo6XP5hS0sSmgZYGv5BcIDUlERyk3rQL5B7avlMaN8TBhzXFJI2O+sd6yojPZUFm2wB7mgSNKahqouc928CvSLYdzXwe1UdChwJXC8iw4A7gC9VdTDwpTONs+xCYDgwBXjaGVRosUSXsr1QVtDSUoSn3FUawWMaqkp2bnG9IDhASmICuQndzVgMX0ryYP1nMOI8SIjPv5zrkjvviD5k5RSzt6SyhSVqvUSt9pSq7lTVxc7nImA1cBBwJvCys9rLwFnO5zOBt1S1QlU3AlnA+GjJZ7HUULbPVIaNdVxLI4R7KreogqLy6npBcIDU5ER2SUb9mMbK98BbDaMubEppm5WsnGJ6dUxj0qHdAVi0Ob+FJWq9NEvBQhHJBMYAPwI9VHUnGMUCdHdWOwgzJsRlmzPPf1/TRGShiCzMzc2NptiWAwFPtfHlx5WlEVxpZDk1pwZ1r5/gmJKYwE7pXv98l8+C7sOh54gmFbc5ycox1tXIPh1JSUywLqoo0iClISIJIhLaoVp/m/bAu8AtqloYatUA8wIF4J9T1bGqOjYjI6Mholgs9XGDy5VFRoHEMhEEwrNrMqcCWRoJbNduZsKNa+Rlw7afYOQFTSpqc+L1ak0cJy05kZF9OlqlEUXCKg0ReUNEOohIO2AVsFZEbotk5yKSjFEYr6vqe87s3SLSy1neC3BbiW0DfIeh9gEirONssTQSX7dUeZjCBzuWwGvnmvTUliAC91R2bgntUhLp2SGt3rLUxAS2uUrDdVEtnwVIXFez3bGvjLIqD4N7mDjO2Mwu/Lx9H2WVnhaWrHUSiaUxzLEQzgI+AvoBl4TbSMyQzBeA1X6jx2cDlzmfLwM+8Jl/oYikisgAYDDwU0RnYbE0ljIf33e4uMaGryHrC8hdvf/HzVkDsy41A/YipbzQDL5LDJ706NacCjQiOjU5gS0eV2lsMTWols+EAcfGdWc+Nwg+yMkYGz+gM1UeZdm2OHA5NpLs3GLKq1pGKUaiNJIdi+Es4ANVrSKA2ygAR2OUywkistR5/QJ4CDhJRNYDJznTqOpKYBbGmvkEuF5V7aOCJbr4+vbDxTWKdpt33655jeXnd2HVB1C4LfJtKsKXEMnOLa65efqTkpjAbk97SGpj3FPbFkD+xrgOgEOt0hjcw8RxjujXBYAFG1uvi+rqVxZy3euLW+TYkdSe+hdmPMUyYK6I9AdCxSYAUNV5BI5TAEwOss0DwAMRyGSxNA11LI0wGTfFjtLYs37/j7tjiXlvqKURIt22uKKanfvK640Ed0lJSqCiWqFrX1N/atlbRoEM/WVDJI85snKK6dIuhS7tUgDo2DaZQ3uks6CVZlDtKChjQ24JvxofvtpxNAhraajq46p6kKr+Qg2bgUnNIJvFEn3KG2BpuEojL2v/jqlaqzQ8FaHX9SVMhdsNft36/ElNSqTS44WOfSFvg0m1HXKaGdgYx6zPqT8uZWxmZxZvzsfjjcQpEl/MW78HgImDu7XI8SMJhPcQkRdE5GNnehi1MQmLJb5pSEyjRmlEaGmUFcDnd9cPnO/bBqXmj99wSyNEuq3r2w+QOQXG0vB4FW/HPpCz0px7nLumVLUm3daX8QO6UFxRzeqdYZ0iccfc9blkpKdyaI+WUfaRxDRmAJ8CvZ3pdcAt0RLIYmlWyvJN3SmIwNJwEv3yso21EI71n8N3j8KW7+vO37m09nNDLI3yfWEyp4pJ8mnx6k+q08WvuoOTpNguw/TOiGNyiyvYV1ZVL44zNtPENRa2stRbr1f5LmsPxwzq1mLl3yNRGt1UdRZOq1dVrcbUhrJY4p+yAmjfA5LSQlsalaXGPZTeGyqLoWhX+H3vc8p1+AfOXdcUQHUD3VNhLI1+XduSnBj4b+22fq1q7xTzO+y8kJlY8UBtELyu0jioUxt6d0xrdXGNlTsKyS+t4phDWsY1BZEpjRIR6YqTMSUiR2I7+VlaC2X5pud2WqfQlobrmso82rxHEtdwx0Ls3VB3/o4leMXcrAuKiyOXtTx0TCM7tyRo5hSYmAZAWfcxJq5xxOWRHztGya5xydU/73EDurBw0140EqswTvg2y1TBOHpQbCuN32HGUAwUke8wlWtvjKpUFktzUV4AbTqZVyhLw1Ua/V2lESSu4amuHVnuO+raxQmC53UYCkBFWYQNg6orjCsriKVR5fGyaU9J0MwpqLU0ytr3g1t/hu5DIjt2DLM+p5j2qUkBBzOOzezC7sIKtu5tPU2Z5q3fw5Ce6XRPr3++zUUk2VOLgeOA/wF+CwxX1eXRFsxiaRbK8qFNZ2Np+I4IX/42fHBD7bSrNA463KSp7gliabxzBbx7lflcY2n4KI2CzVCWz/b0UQB4qkK4p4pzat1gNXWnOgVcdcveUqq9GsbSMH/3iurW410ONZhxXGZngFZTUqSs0sPCTfkc00JZUy6RZE+dD7RxBt+dBcwUkcOjLpnF0hyU5RsrI61jXffUuo9h2Zvg9ZppNwie3gu6DoScVYGD4Vt/gs3fmWWupZG/qdb6cOIZW9qZ4oCeQCVJKkvgqwfh0ZHw+BiY/3RtllcQ91R2kG59vqTUKA1v0HXijfU5xQwOcs6HdE+nQ1oSCze3DqXx48Y8Kj1eJg5u2Zp7kbin/qKqRSIyETgFU878meiKZbE0A6pGUbTpXN89VZxjyoWXOMqiaBdIArTtCn0nwIav4KkJsHVB7TYVRVC8C0pyIXctVJVCjxFmP25QfMdSSEhmc+ohAHj9LY3ls+CJI+Cbv8OhUyDzGPj0Tnj9XLM8iHsqq2aMRvBWramtTGnsK6sit6giYDwDICFBGJvZhZ9aycjwb9fvISUpgfFOZlhLEYnScG3Z04BnVPUDICV6IlkszURVmRMncAPhPu6pEqfs/r7t5r14N7TrbpoUnfIgnPm0cWd9/pfabXyD4+s+Me8HO53w3LjGjiXQYzj7vKkAeH2zp7b8AO9dbayZKz+F82fAr2bCuS8Y6wOgTeAbRnZOCT06pJLu1+LVF9fSqGwlSsO/5lQgxmZ2Jju3hLziBmSpxSjz1u9hXGZn2qS0bKOsSJTGdhH5F3AB8JGIpEa4ncUS27iWhWtpVOwDr/OM5Lqj3NpQxTnQ3mn9kpwGYy6GI6+BLfNry4r4BrxrlMak2mWqxtLoPYbiapM95fV1T61836T+XjYH+h1p5omYjnrXLzDKo2/gvmRZQbr1+dLaLI2snCKgfrqtL+Ocp/J4b8qUU1jO2t1FHNPCrimI7OZ/AWZw3xRVLQC6ABGVRrdYYho3TuAGwsFYD55q0wIWfCyNXZDes+72o34FkghLXjPTe9YDYva39Ucz76DDIaW9CYbv3WAUU+8xFFebv566lobXC6tmw6ATITXATbBdV6M8AgR8VZUNQfqC++Km3LYmSyMlKYE+ndsGXWfEQaYp08I4VxrfuqVDWjDV1iWS7KlSpxfGPhHpByQDa6IumcUSDq8Xfnim9sbu9Tas14Ub+HZTbsFYH26JD4BCV2n4WBou6T3gkFNMwNxTbdJwO/WDXqNAvUZZtOkMXQ42loY7qK/3GEqroVoTULeMyPZFULQDhp7RsO8AyCmqoKiiOqylkdLKsqfWO4oyMSH4yOi05ERG9e0Y93GNeVl76NouhWG9GtQDLypEkj11hlPGfCPwjfP+cbQFs1jCkvUFfHJH7ZP+F3fBYyNh96rA66vCG1ONGwgCWxplBbWuKTBKw+txlEaP+vsc82sT71j/mYlpdB0EPQ4zyzr2NZZB10Gwe6UpK5KYCt2HUlpZTSXJtSPCV/0bEpJN8LuB1GROhbU0Wl9MI5yihPhvyqSqfLt+D0cP6kZCCAXZXETinroPOBJYp6oDgBOB76IqlcUSCfOfNO/uQLutC8wNfMZpsHNZ/fWLdppYwybn8nWVRpqfpeFmTCW3NVZM6V5QD7TvWX+fg082AfIlrxprottg6D7MLOvk1HgacZ5xdy1/C3oeBonJlFV5qSSpVmms/RgOPj5k6fNg1PYFj9TSiH+lUVpZzbb8sqDptr6My+xMtVdZujU+mzKt2VXEnuKKFqtq608kSqNKVfOABBFJUNWvgNFRlstiCc3O5bDxG5MGu2edsSJy15iYQEo7eOVM2PVz3W3caXcQn28gvI6l4WRO9RplLI3dK8x014H15UhMNpVi135salJ1HQQ9hptlHR2lMeQ0uGEBHH4pTLjWHLrSYywNj+OeKt4NGYc26qvIdkZFd09PDblea4ppbMg12WSRWBpH9OuCSPwO8nNLobf0oD6XSJRGgYi0B+YCr4vIY0B1dMWyWMLw47Om9enIC00Auni3UQKDTjLZR0ltjOJwR2VD7c3fVRpl+SaQnZoe2NLoNdpYJxu/NcopSOYSYy6hppll10GQMcSkxvYaWbtO50w44wkYaXpxl1V5qCTJBMJVTfpvUuNKQ2TlBh8V7UtrSrnNClFzyp+apkxxqjTmrs9lUPf29OrYpqVFASJTGmcCZcCtmDas2UB8t/qyxD/bFsDASdB3nBlEl/2VmZ9xKHQZYMY3lO6BrM9rt/G3NMqculMi5iYvCVC4w8QvktIg4xAT0F75vrE6gjUryjjEDPgD455KToNbV8KYS4OKX1bloUKTEU8leKqM+yu5cTeF7JySkIP6XFpTGZH1OUUkJgiZQcrA++M2Zar2xJfCLK/y8NPGvTGRNeUSSfZUiap6VLVaVV92OvnlNYdwFksNpXvh7Stq4xCle00/iG5mZDWr55j3DKcIX4/hJrCcv7l2H7v9lYZTdwrMjb7bIbBrhRnY1647dHBKiOdvhH7/E1q+4/5oMp/SnbYzKW0hIfjfy7inkozSqCp1ZGi40igqr2JXYXlET9xJCYJI67E0+ndtW2M9hWNcZhdKKj2s2VUUZcmalkWb86mo9nJsC5ZC9yeS7KlzRGS9iOwTkUIRKRKRsO2wRORFEckRkZ995t0jIttFZKnz+oXPsjtFJEtE1orIKY0/JUurZMt8055020Kn/Ec+tO1SqzSyv4TUjrVjKRISTSC6wFEaVWW1I7Z9lYZvAcCeI02spCQX2mdAx4Nql/UPozQGnQhTXw2pKFxUldIqE9NI8FZAtZMm3Ail4fr2w2VOAYgIqUkJrSIQHqrmVCDcQX7x5qKauz6X5ERhwoCuLS1KDZGo6YeBM1S1o6p2UNV0VY0kWXgGECh/8J+qOtp5fQQ1LWQvBIY72zwtIi07Vt4SW7hlPUr2mJu+eoxLqV2GyTiqLjeuKV+/fqd+UODUfMpZZVxNHfvVDYS7lgYYF1TRDshZ7VgaPkqj31FNdipVHsXjVSpJIsFbVWtpJDVcaTTEtw+Qkhj/SqOy2svmvNKIzxmgd6c2HNSpDQs3xdcgv3nr93B4v860S42dZlmRKI3dqrq6oTtW1blApGr9TOAtVa1Q1Y1AFhAk6mg5IHGVRume2tHabbsaJeFaGxmH1N2mU/9a95Qbz8g8GqpKTBzB1z0FtYHrop3QrptRRintoduhZkR2E1FWZWIKlZpMgqeydkBiIywNt8Vrvy7BR0X7kpqcGPdKY3NeCR6vMrh7w3pkj83szII4asq0p7iClTsKYyZryiWo0nDcUucAC0Vkpohc5M5z5jeWG0RkueO+cv+xBwE+aS5sc+YFkmuaiCwUkYW5ubn7IYYlrijZU/te6jwttnWK99UoDb+mQp36GSVTWWLiGSntjTUBpj+FGwh36emT7dS+u1FIh0wxKbVNSLmrNEgiwVtpXGcQkdLYtKeE/yzfWTOdlVNMZrd2QVu8+pOSmBD3MY31DbSuXMZldiGnqIIte0ujIVaT812WUzokBupN+RLqSvul8+oAlAIn+8w7vZHHewYYiBnnsRP4hzM/UK5gwMcBVX1OVceq6tiMjNj6Mi1RxNfSKHXyMNyKr90Gm3d/pdE507wXbHGqyx5Wa1mU5Rs3la+l0aaTsU7AuKcAznsBjvldk56KOzLZxDQqoTpypfHaD5u56a0lVDlZQNm5xRFlTrmYmEZ8Z0+5LrmDG3De4BvXiA8X1bz1e+jYJpkRBzV8wGc0CeooU9Urmvpgqrrb/SwizwMfOpPbgL4+q/YBdjT18S1xTI3S2OvjnnKUxsHHm/ERvf16g7kKIHeNqS571HW1TYz2bQW0fie8XqNM8Lx99B5IynwsjURvVa2l4RfTyCkqRxV6+LQyLavy4PEqOwrK6N2pDZvzSplyWICR6kFISWodlkafzm1om9IwP//g7u1NU6ZNeznviD5Rkq5pqC0d0jVkba2WIJLsqZdFpJPPdGcRebExBxORXj6TZwNuZtVs4EIRSRWRAcBg4KfGHMMSZ2z9ybRWDUcd95SjNFwrofcYuHFR/bhDp37mfeW/wVtlgtlumY78TXX34eK6r9r5FSdsQkp9LI0kDe6e+tN7K/jdrKV15pVXmRv+lr2lbM4zLV4jyZxyaQ3ZU5HWnPLHbcoUDxlU2bnF7Cosj4lS6P5E4ggd6ZREB0BV84Ex4TYSkTeB+cChIrJNRK4CHhaRFSKyHJiEGTCI00p2FrAKM4DwelWNbxvaEhlz/xfm3GQC06HwD4RLQtB+2TW0724G6bm9LfpOCKA0/PYx+CSjbPxdXU2IG9Oo0CQStSqo0thVWM7OfXWr9pY7rqXNeaUNzpwCU0okni0Nj1fJzm1Yuq0v4zK7xEVTprnrYqcUuj+R2HcJItLZURaISJdItlPViwLMfiHE+g8AD0Qgj6U1kbvGpJzuXA59jgi8jtdTG8coyTOWRlqn8GMiRIwC2LPOZEC17VLbAc8dvxHI0rhlRePPJwLcmIZHUkjyTbn1UxpF5dUUl9et2FPhKJyte0vZV2YUwfGRPwAAIABJREFU7cENsDRSkhIorYzfKkDb8kuprPY2ytIAU7wQYOHmfE4ZHrlbr7mZl7WHzK5t6RthVlxzEoml8Q/gexG5T0T+CnyPGbthsewfFcW14yg2hyicXJZvxli0626aGBXvro1nhMONa7id8MK5p5oBN6ZBUipJVAYd3FdUXk1BWVWdFFHXtbQ5r5TsnGJ6dUyjfQNy+FOTEqiMs1IavtRaVw1Lt3UZ0acjKUkJLIxhF1VltZcfNuTFpGsKwigNEUnAjJk4F9gN5ALnqOqrzSCbpbWzZ13t583fB1/P7W/hVoHdsz5or+x6dPZTGintjWvLHb8RzsUVBVylIckpJGvgwX2qSlF5FR6vUlRRaxm4rq0te0udzKmGPXGnJCVQURW/SqOx6bYuqUmJjO7TiZ9iOINq8ZZ8Sis9MVMK3Z+QSkNVvcA/VHWVqj6pqk+oapAONxZLA8lda977HWXKhHiD3MzceEb3oeZ97wYzsC8SuhzsHMNRGgkJJoPKzcDyj2k0A+6NPzE5jUS8jstMICnVZx0vVR5jYewrraozH1ylUdLgm2drsDQy0lPp2Ca50fsYm9mZldv3xaybbt76PSQmCEcNjJ3SIb5E4p76TETOlXB1ly2WhpK7GhJTYPTFpqRHbpDCA67ScIPT3qrI3VNjLoFfv1urPKDWRZXcts6Nurlws6cSk51jl+8zrimfv1hRea2iKPBRGu4Yi+KKaoorqhs0RgPiP+W2oTWnAjEus0tMN2X6dn0uo/t2okNa4xVjNIlEafwOeBuoaEjBQoslLLlroetgGHCsmQ7monLTbV1LAyKPRaR1MMUE68zr2LB9NDFllbWWhplRUC+eUegTAC8oq6z5XF7lJd0nhjGwwZZG/JYRUVWyG5lu68vh/TsjQkzWoSoorWT59n0xmTXlEklp9HRVTVDVlAYWLLRYQpOzGroPMRlO6b1Mj4xAlOSaZkldB9fOi9TSCEQLK43yKg+pSQlIko+lkeQfBA9saZRXeRjco/amOagRMY14tTR2FZZTXFG935ZGxzax25Tp++w8VImpUuj+RDK479hAr+YQztKKqSwxmVMZQ4xbpsdwU4k2ECW5poBg2y7UVJyJNBAeCFdptEAQHEwgvG1KonHNgXHNhbI0Sn0tDU9Nob70tCQywrR49SdcGRGvV9m4p6RB+2wu3MyphlpXgRiX2SUmmzJ9uz6X9NQkRvVpmWszEiJxT93m8/oLMAe4J4oyWQ4E9qwHtDYjqvswyF0HngDByZI9pgR6QmKthbFflobzh4wgCP7qD5v5Zl3TFsYsq/TQJjmxNp5Svs80gfIheEzDS6e2yXRPT2VgRvgWr/6kJCVQ5VG83sCVXj9fvZtJ07+uKZYXS7hKo6HVbQMxNrNzzDVlUlXmrtvDUQO7khRhAcqWIBL31C99XicBh2HSby2WxrNjsXnvPqz23VNhMqP8cS0NqM2aagpLIwKl8c/P1/HivI2NP1YAyqo8pKX4KI2yAhOU96GoTkzDKA1VpaLaS2pyIpf9Tya/mtCvwceu6RMe5Al75XbTa2T6Z2tjroT4+pxiOrZJplv7lP3e1/gB5vr5aWPsuKg25ZWyvaAs5kqh+9MYdbYNozgslsaz7C0zSrvrIDPtBrkDuajc9qsAbV3lEf2YRnmVh70llWzKa1p3jWtpuDENLS8w5U58cC2NTm2TaywNN4CdlpzA9ZMGccHYvjSU1KTEOvvyxx0HsWRLAV+tzWnw/qOJW3OqKRI5e3V0mjJtjh2lMW+9sWhjrRS6P2GHkorIE9SWKU/AlDVfFk2hLK2Ur/8Oqe1NNtPWH+Hk+2vTTDMONYPuclbD8LPMvB1L4Od3oXA7tHM6A7drSksjtNLY5dR92pZfRpXHW69nhcerJAgNvomVVdVVGuKprGdpFJZVkyDQu2Mb9jnZU+6gvLSkxje1dC0NE9eon9KZlVPMpEMzyM4t4R+freP4Q7qTECNVVrNyijl5WI8m29+4zM58l52HqjaJItpfvl2/hz6d25DZNfZKh/gSiaWxEFjkvOYDt6vq/2/vvMPbKs+HfT9atrxXhuMMZ0+SEJKQRZhhQ6CsUlaAlpbSsjvooPxoS9dXuii0UFbZo6GsMsIIJED23nbiDI947yXber8/3nNk2ZZtxZZtOT73demSdHR0zqPXsp7z7Gt7VCqL4wNvE2R+rGd6e73w5d/gg5/Af28FmwOmX9W8r9OtaykKdjZve/sOWPtPbZFMPE9vC6WlESAQ7vUqduZqF43ZLLDJqzgSYHDPeX/5nF+9e8xDLbXScNmx+deIBIhpxEQ4SIx2UmpYGmazwghn1/3dEaZ7KoCl0dDkJauomkmpcdx51nh25lbwwc6jXT5XKCmp9lBS7el2uq0/s9OTKAyToUyNTV6+2l/MKeNTwkKBdURnbUQuAQYBR5VSzyqlXlBKddAkyMLCjx3/gee/Blmf6ViFp1K38cjZqCfixbRqPz54srY0QDcmzNsGi38At66G0afo7emLYOwZ3SvK68DS+GxfIRf8dTV7j1aSV17r297aRdXY5GVffhVPrs5idcaxBY197imnv9JoG9OIjXSS4Hb5sqfMSvLuWBoRPkujrdI4VFxNo1cxfnAMS2emMXZQNA+v2EdTO0Hz3qQrHX07I5ziGluzy6isbwzbflP+dDTu9VF06/Jk4Jci8vNek8qi/1GUAav/pK0Kk11v6vuDqyHPmAtx5bMw4Tw45Z62xxg8RSuXhlqtaFAw5vSW+5xwOVz3RvdkjTPGusS1nSicXaYVxZ6jFS3akmcVtbwaNTvMAvzw9a1U1HXS2t2POtPS8FcarWIaFXWNxLmdxEc5fecyW4hEOruvNAJZGv4/zHabcPeSiWQUVPH21r6fh5ZRoLOcQqk0xg2KId7tDIsiv8/3FSECC8K0dYg/HVkai4EzlFL3AacBl/SKRBb9k1V/hI8eaM5+8tRo1xTAoa90fMIeAaNPhW+8DGmz2h5j8BTdzbZwLxxYCRHxesBSqBl2Inz7cxgxt81LJVX6qn5/QRV55bUkRDmJi3SQVVRFjaeRj3frxEHTZXTjwnSOVtTxq3eCb8lmxjTs/i6pNnUaDcRGOkg0AuE6c8pwTzm67p5ydWBpZOQbdRBGweB504YyOTWOP3+0zzdetq/ILKgiymVnWHznI3GDxWYTZo9KZH0YBMNXZxYxPS2ehKjuZ4b1NB19+zzmICSlVA2B53hbWOgBSnvf04/NFuf7P9azr4eeADkb9IS+odPA3kE/nbRZOhi+9p9w4FPtkrIf20jPoEmd0aLXk0lJtR7Os7+wmryyOlLj3YxOieZgUQ2PfJLJzc9u4Gh5nc9ldNrEwXzn1LG8uiGbT/YEl4le42ki0mnH7vJ3T7Vtix4X6SDB7aLRq6j2NIXI0tDvDWhpFFaRluAm2mhTYrMJ9yyZwMHiGpZvyu7yOUNBZoHu6BvqoPyc0UkcKKymqA+HMlXUNbDlSFm/cE1Bx0pjkohsM27b/Z6bk/csLDSHvtBVzdDcP2r32zpmsOhuPS8iex2kzuz4OAkjdQxj64u6WnzMaSEX9aH/7WbNgeJ2Xy+uNiyNwiryyutIjY8kPSWaA4VVvLE5B4D8ijqfpZEY5eSOs8YzcUgs9y3f7os7dITpnrI72rc0KusaiI3U7imA0mpPc0yjG4FwVwfuqYz8tn2dzpw8mBkjEvjrx5kdVpL3NF0d8doZvqFMfeii+mp/MU1eFbat0FvT0bdvMnCRcbvQ7/mFxr3FQOXoDsj8qPn57nd076RxS+DgF7pFyL73YeL5kH5K837DOlEaoJVGmjHBb8xpoZSawsp6Hv/8QIc++hJDaRwoqianrFYrjeRocsubR68WV9dTalgaiVEuIhx2fnHRFPIr6nlrS9tjb8su44W1en5HQ5NueR7V2j3VpveUaWlopVFe2+BTGhEhCYS3VADtjVEVEX5w9kRyymp5ed2RLp+3O1TWNZBXXtcjSmNaWt8PZVqdUUSUy86skX3TC+1YaVdpKKUOdXTrTSEtwoimRnjlWnjhCtj/iU6l3fMujDtTz9cuP6xjG3XlMOt6iBnU3GiwM0sDtPvqyufg4kcgpblBYXFVPbf8ewOl1Z4O3twx27K1NZRfUdfuPqbS8DR6Ka9tIDU+ktEpuv24w3CNFFV6fO6pBMMSmD82mUlDY3nqi6w2ldSPfJLJQ0ZqrvnD73bZcUQEdk+ZA5hiI50kRmsfd1lNQ4vivq7SnqWRU1pLfTtjVBeOS+bk0Uk88mmmr0Nvb7K/UGeu9YTSMIcyrT/Ud5bG6swi5o1J9v1twp0ek1JEnhKRAhHZ4bctSURWiEiGcZ/o99p9IpIpIntF5JyeksuiHZSCLx+B4v0d77frv1CapWscXr8J/n0xVObC5Ith1AK9z7rHdVqsOfho9CngjG7Z2rwj4tNg1nUtNm08VMqHu/LZkn1sMxC+2l/ML9/ZRWOTl23Zuv7iaAdKo7ja02JGRWq8m3RDaVw0YxgARdX1lNY04LCJb9SqiHDjwnT2HK3kKz/3l1KKDYdKqWv0opTyTe2LdNpxuAK7p6o9TXiVbkhoWhpltf7uqW4U99kDB8IzC3V2kn8HXRMR4Z6zJ1JYWc9zaw52+dxdpbnnVOiVBsCc0X03lOlISQ1ZRdVh3Qq9NT2p2p4Bzm217cfAx0qp8cDHxnNEZArwdWCq8Z5HRaTr/xkWx05VPnz4U12AB7DrLXhtmdFY0EApWPWwLrb75kf6eVEGnP1rOOEKGDy1uQbitJ80v++Mn8PNH3QcBO8Es5WGmd0UDB/sPMoNT63jydVZrDlQ4mdptAx6vrr+CG9vzUUpRWm1hznpzYWDqfGRTEmN4+q5I7nzrPFEu+w+SyMhytWiEGvpzDSSol28uPawb9v+wipKqj00eRUNTYo6j/6xdjvtONrJnjJbiLSIadQ0UGf80HeruM8ZOBBuZk6NGxS4GeDc0UksnjCIx1bup6q+d39cMwoqcdltjEzqmUrp2eZQpsO9P5RptdEYMpxbobemozqNj43733XlwEqpz4HWjsKlwLPG42dpTuNdCryslKpXSmWh55K3zYe06DlMC8Os4F79MOx8Ax5boC2Klb/VLqmCnXDK3ZA8Fu7YCndugwXf02NUbTaYcbW+jZjTfOyoJJ1F1Q3MGEJJkO6p3LJabnthE5OHxRHlsvPejjyfpVFUVe9LIW3yKh56bzdPrDpARV0jjV7FuMExJBo/1qkJblwOG7/52gmMSo4mOSZCxzSqG3z7mEQ67UxJjSO3rLkocF1Ws9ujtqGJmgb9g+t22Ylw2qlXRnaYw19p6H3i3A7fWNPyGg/1IYhpNFsaLd1MvjGqUe0r9nuWTKC0poGnQ9zAsTP2F1QxOiW6xzq/zhqphzKt74Ng+KqMQobGRR7zrPe+pKO/QqqInApcLCInisgs/1sXzzdEKZUHYNybJcFpgH+ULdvY1gYRuUVENojIhsLC0LasHtAUZ+r78sM63TV3M8z/Hky/Eg6vhZW/gaJ9sOD7MO1yva87oW1l9nm/g0v/EXLxzGylkprglMbGQ6U0ehW/vmQap08czH8351Bc7WFyahxK6aA4wI6ccspqGjhUXONTSEnRLt8/cWp8y6K7lBgXxVUeSms8JAbIqY902qhtaL6K9x/0U9fQ5IsJuJ12PRDJ7P/UjqUR4bAT5bKHLKZhWimt3VMZBVWdDnSaMSKBJVOG8PiqAy3mlvc0GT2UOWUS73YyaWhcrw9lavIqvsjsH61D/OkoCf5+tPtoOPBwq9cUcEYI5Qi0YgF7FyilHgceB5g9e3bf9zc4XijO1DUSygvv/Uhvm32TtihAV2k7Q1dYdayYgedg3VM7cstx2oUJQ2I5d9pQ3t2eB8DZU4awO6+C/Io6hiW4+dyYlVFe20BWkXbRJEW7mJwaR05ZbZv4QXJMBEdKalAKRgVoLBfptPssAtAtKhw2odGrqPU0+WIabpcdp92Gx/wX9FvbilptacRG6tcSo1yU1TbgdtkRabYWukKgmIY5RvXSWQGv01pw95IJnPeXVTyx6gD3njOxy3IES11DE0dKalg6s3PZusOc9ERe35hNY5O312ZZ7Mgpp7y2od+k2pp0lD31ulLqPOD3SqnTW926qjDyRSQVwLg3ey9nA/59nocDfd+7YCBRvF/HKpLGaoti8JRmhQF9qjDAzz0VpKWxM6eCiUNjcTlsnD5pMC6HDaddOHWiLqAyM6hWZRT5avxMn3ZydAT3njORV26Z3+a4KTERFHVgabiddp9iyC2rJaes1pdKWdfY1Jw91YGlYbYkiTOURrxbV4X7xsR246o0UBuR/Ip6Kusbg7qan5wax4XTU3nqiyyKe6EgLquoGq/quSC4yez0JGo8TezO672hTKuMVugL+1EQHIIbwvRLEblYRP6fcbuwG+d7C7jBeHwD8Kbf9q+LSISIjAbGA+u6cR6LY6U4UyuJcWfq55N7thTnUHE1P3hta9ApnD73lOFCen7NId5qp95CKcWO3HKmDdNB+ZgIB+dOHcrc0UmMSNTWQX5FPZV1DWw6XMoZE7WXdPMRrTQSo53Eu52MDGBJpMS4KKmup6ymgYTotv5/t6tZaezOqwBg3hgdWK/1NFFrBsJddlx2Gx5fTKPZDWbGNGIj9fH1TA0P9Y3ebmVOgc6EctltLSyNY20GeOdZE6hraOIfn3WSaRcCMnqgUWEgzCK/3nRRrcooYuqwOFJiutF8sw8IZkb4b4A7gF3G7Q5jW2fvewndSn2iiGSLyM3Ab4ElIpIBLDGeo5TaCbxqHP994DazhYlFL+Bt0j2jksfBlKW6R9TUr/XoKd/ZlsdrG7P5cFdwrbfLWgXCH//8AM9+eTDgvjlltZTVNDA1Ld637Y9XzuCZG+eSHO3CaReOVtTx1f5iGr2Ka+eNAlpaGu2REhOBV+nJd+1aGoYiNLOMhhhxkdoGP/dUG0ujWUH5AuH+SsMo7utOh1uTCIethaVxrM0Axw2O4dITh/Pvrw51WPMSCjILqrAJvlqZniI13s3wRHevKY3q+kY2HS7td64pCGIIE3ABMFMp5QUQkWeBzcB9Hb1JKXV1Oy+d2c7+vwZ+HYQ8FqHg0Few7z1o9MDcb4G3QVsa6YvgviPdaz0eBNuNTKa3t+YG5a/2tzS8XkVeeW27LTt25Ogr/GnD4nzb/AcoDY6NJL+8js/rColy2Vk4LoVBsREUVtbjdtpxu9r/YU72GzXaOnsKdEprfaMXrxHDAEg2CvR0IFwrhEinYWn4YhrNlkZFna4BMQPe8W6X4Z7ydivd1sTlsLXInso0xqgOOoYr3jvOHM+bW3L4+6eZPLi05wZ5ZhZUMjIpqtsWVjDMSU9iVUZRrwxlWptVTEOTYnE/6TflT7DfQP9pNfHt7mXRP8j8CJ4+F774K6x9DNYa2U7m6NUeVhgA241Z1J/tK2yRiZNbVtumolopRVmNBxEdsM4tr6WhSVFQWR9QcezIKcduEyanxrV5DWBIXAT5lXWsyihivlGJO8qoAUiK7rjLqL8VEqgjqdvZPE61xlAapkVS6/G2CIS3b2noDrfmD1dilJNyo7ivZyyNYx+jOjI5iivnjOCldYfJLu25IUa651Tg2pFQMzs9kaKqeg4V9/xQplUZRUQ4bJw0qn+0DvEnGKXxG2CziDxjWBkbgYd6ViyLkJO9EfZ9qB9/9SjEDIUfHoC44bD+X3q7qTR6mNJqDzlltVwwPZWGJsX7O3Vm096jlSz83Sd8uKtlt9iq+kYamhRpCTpYbFoSoEextmZHbjnjB8e0e3U6JC6SbdnlHCqu4RTDPWDGL/wtiUAMivW3NAIpDf0v5e+KMo+pLY3m4j67TfDgQCFgbz6WOYDJJCHKSUOTorTG0610WxNtaTQrjf0FbXtOBcP3zxiHiPC3jzO7LVMgGo1Jgj0dzzCZaxR1rusFF9WqjCLmjk7qFQsq1AQTCH8JmAcsN27zlVIv97RgFl1g5xuw/NttW4E01MIr18BLV8GGp3Xb8jnf1EV3827VabYRcRDdO6ayaWVcM3cko5KjfAHtT/YUoBR81EppmNXgZu3EDuP9AEdaXeXWNTSx6VApJ6S1bxAPiYv0xQ0WT9CfeVSS9pkfm6UROBAORiGfpxG7TXyxCVORuBw27EYfq0Zx0WCLbNGmvbKukTh3s+c4wa1lOlpR56vo7g4RDrvP0iip9lDcxTGqqfFurjl5JK9vyiarqLrzNxwjh0pqaGhSvaY0xg6KISHK2ePNC/PKa8ksqOqXrikI0j2llMpTSr2llHpTKRUeQ4MtWtLUAB/8FLa9DH8/Gb76e/NrG56GyjyISoZ37tSB7tk36tdmXa8VRtKYgPMlegJTaUxNi2fpjGF8tb+Ygso6Xwri5xmFLVxUZrqtqTS2+ymN7Fbznd/akktFXSOXnTS83fMPNQLTaQluX4DVrLnoTGnEu52+xoWBlIZ55VjX0ESNMdY10tW8rc4YwGTSKA4abC3dgRW1DcRGNB/brNLOL6/v1gAmE5fDhseoiO/uGNXvnjYOl93GXz7a1225WtPTPadaYw5l6uk26auM8cD9MQgOPdt7yqI32f0WVOTARX+BCefABz/Rw4yqCnRLkPRT9JhURyTMuAqijS9sZBxc9iQs+b+QivPp3oJ2B9vsyClnVHIU8W4nF88chlfB6xuz2XCwlMGxEeRX1LPP6IUEzUHwsYOjfe+Pi3Tgctg44ueeUkrx7zUHmTAkhpNHJ9EeQ+L0j/TiCYN8fnxTaSR3ojRsNvEpFtMC8MdUGrWeJt/cDLffNnM+uEmjuPBIS6Wh3VPNlobpBvM0dT/lFloGwrs7RnVQbATLFqbz5tZc9uWHtsbBVBpje0lpgK7XOFDUs0OZVmcUkRITwaShvROrCTWW0jheWPOYthZOvB6ueBYmXQjv/RD+33ioLoTTf6r7P92+Bc7/Y8v3Tji727Mr6hqa2HNUxxr2F1Zx49Pr2x2Buj2nnGmG+2jc4FimpMbx908y8TR5uWvJBKC58Ama023HpOgfj+JqD8MToxie6OaIn6Wx+UgZO3IquH5+eodBXbNW49QJze6B9ORobKIzqzojJSaCmAhHwFbW7laWRpRR+e2widF7qqlFdtZe+3iyolr25TLbopv4WzShUBr+gfBQjFH99uIxxLgc/GlFaK2NzIIqhsVH+joJ9wZms8qeclF5vYovMov6XesQf4JSGiKySERuNB4PMgrwLPoapWDLi/Dq9ZC9Hk6+VTcNtDvg8qfgnN/AOQ/Bsv/BKKO6OS4VHF2bQ7wjp5wbnlrXoiGfyR8+2Mv5f1nFnqMVvGR0eX13e16bPP6Sag/ZpbUtYg5LZw6j2tOEy27jkplpjBscw2f7mpWGOUNjjF/L8rRENyMSozjspzQeW7mf2AgHl57YcQrvSaMSefFbJ3PO1CG+bYnRLl781jy+PndEB+/UJMe4ArqmoHVMo9mqcDvt1DV4qTVGvZq8Fnk5zwz9GXe/usUX22kb02g+V6jcU/V+SmPc4O6NUU2IcnHzKaN5b8fRFvGm7pJRUNmrVgbAtLQ4Ihy2HmteuCuvguJqT79qhd6aYIr7fgH8iOa6DCfwfE8KZREkO5fDf2+F7A0w81o48drm1xwRMP+7MP82SF/Y5VNsPFTCgULtJnhvRx6f7Svk+qfWtRiGVF3fyKvrj+BV8Ot3d/OfTdmcODKBRq/i+TUt53V9uV/7c+f6uY/MORWz0xNxu+wsHj+IdVkllNdqt5TpnkqOdvlaa6QluBmR1GxpfLq3gBW78rn19LG+GdftISIsGNv2Sm/emOQWV/jtcdWcESxbkB7wNTMl1lQQUYYSiTQqxesamrcBOO1CTlktyzfl8OmeArxeRZWnZfZUnNvf0ui+0mhtaXTWqDAYblo0moQoJ3/8cG+3jwX6inx/QTXjeynd1iTCYWfGiIQeszTMVuin9NN4BgRnaVwKXAxUAyilcoH+6Yw7nqguhv/9UI9GvXM7XPJ3cIV23kBlXQPXP7mOB97WbqbtORWkxERwuKSG21/e7Ntv+aZsKusbOf+EoazKKKK0poG7l0zgrMlDeGHt4Ra1FKv2FREb6WC6n6UxLMHNT86fxPdO1ym/l52URn2jl6eMFtxlNR7iIh047DaSjQK0tARtaVTUNVJYWc//vbWTMSnRfHPRmJCuQSAunD6Mb54S+DxuV3PKbY2nkSiXVmCRTptOuW0VCHc5bGwx2pcUV3uorG9Eqea+U/q9zXGRUNRpuBy6ANE3RjXA4KVjJS7SybcXj+XTvYVsPNT9H9zc8lpqG5p6LXPKnznpiezIraC6B+aGrMooZOKQWAbHde4GDVeCURoepVNZFICI9Gw9v0X7FOzWLT8a6uDN2/RI1YsfAVvP5Hq/vjGbak8TGw+W0NjkZUdOOWdMGsTdSyawKqOIvUcrUUrx7FeHOCEtnoevnMmw+EhGJUexcGwKyxakU1LtYYWRQquUYnVmEQvGJrfpJHrL4rEsMEz2qcPiOXfqUJ5anUVZjYfSmgbf2FOzCjst0c0IoyDvthc2cbC4hgcuntrnIzN92VOeJmobvD53ldlepLV7ymW30eTVmWIl1fV+bdFbWkvm5w5lTMM3RjVEsxxuWDCKlBgXt/x7I3/8cC955W3dmMHSWz2nAjEnPYkmr/Ip81BR19DE+oOl/drKgOCUxqsi8k8gQUS+BXwEPNGzYlm04cBKeHSeHor09Lm6Bcg5v4YhU3rkdF6v4tkvD+Jy2Kj2NPHR7gJKqj2ckBbPlbNH4LLbeGndYd7elkdmQRXLFqQT6bTzwrfm8fSyOdhswrwxyQyOjeCdbdpXn1VUTU5ZLYuCyE+/c8l4KusbeWLVAUqNKXkASUadxLAEt2+S27qDJdx+5nhfzUVf4suUMlqG+Mc0TPeUfyDcX8kVV3na9J2cFaJZAAAebElEQVQyiTc+f+hiGk1k5JsjXkPjOIhyOXhq2RxmjkjgkU8zWfS7T7n1+Y18tb+4TZV/Z+zv5XRbf2aNMocyhdZFtS6rBE+jt9+m2pp0mpaglPp/IrIEqAAmAvcrpVb0uGQWLdnzrp7uprxQuBeufA6mXNylQ3kavbyy/jCXnzSi3T5Ln2UUcrC4hvvOm8Rv3tvDU19oV9G0tHiSol2cM20oyzdl8/bWXGYMj+cSI/js31jObhPOPyGVF9cdprKuodmfG0QQcNLQOC6aMYx/rcoiKdrlS09MMjrLpiW4iXTaiHTauPTENO46a3yX1iLUuF1ts6dAWwjNtRvNP/xmTyynXSiu9lBR2zyAyR8zGB4qS6O+0UtmYRUuu40RiaFrez99eAJPLpvDkZIanl9ziFc2HOG9HUeZMCSG6+an87UT0zqNOYEeP5sc7fJZmL1JXKQeyhTqeo1VGYW47DZOHp0c0uP2NsEW961QSv1AKXWvpTD6AKUg40MYcyp8dw3cs+eYFUaTV/ly85dvyubnb+7k6S/bH9v57rY8EqKc3LhwNMMT3azLKmnRz+nquSOoqGukvLaB310+3Vfh3JqLZgzD0+jlvR1HeX/HUYYnugMOLwrEfedNwiZCXnmdr1YhPSWalBgXKTEuYiOdrL3vLB669ISwSV80Yw61xpQ+dyul0TqmYVoOC8am4Gn0ctTINmvtnkrwuadClz2VmV/FmEE9M0Z1RFIU950/mTX3ncnvL5+Oy2Hj5//dwbyHPuaBt3b6ajDaI7OwZ6f1dcbc9EQ2HS6lscnb+c5BsiqjyJfs0Z8JJnuqUkQqWt2OiMgbItLzUceBSlEmrH1cWxjFmVB6EMYv0fGLyGPvGXnf8m0sfeQLPI1eXjBSYp/98qAvi6bJq7j7lS2+IOaOnHJmDE/A5bD5evL493OaPyaZxRMG8aNzJzFpaODGgACzRiaQluDmvuXb+XJ/Md84eWTQP/DDEtx8/0wdHDfdU99cNIYP7zrVd4z4KGfYKAzQxX8uh41aj67JiHK1dE/VNjThdjUrBJdD13CcNVnP9DhYpLPB2ioN0z0VujYiGQVVPZ7SGum0c+XsEbz9vUUs/+4CzpoyhBfXHuashz/j2n+t5cOdR30xHROlFBn5lX2qNMyhTLvyKjrfOQgKKuvYc7Sy37umILjW6A+jp+i9iB7L+nVgKLAXeAo4raeEG7CsewL+d69+LPbmgUjjz+7yIVdlFJFXXsddr2xhe045S6YMYcWufN7dnsulJw5nd14Fyzfn4LAL09LiySyo4oxJ+odszugklm/OYfrwZmUlIvz7prmdnldEuHruCP61OotfX3ICF0xPPSa5v7loDDtyyn0T91wOG0ldrDPpLdxOOxV1jTR5lS97yu2yU13fhKfR28LSWDRuEMnREQw3Cg4PFuvgtH+aLTRbGqFojW5aN4dLavhaECNeQ4GIMGtkIrNGJvLTCybzyvojPL/mELc8t5G0BDfXzBvJVbNHkBwTQWFVPRV1jX0SzzCZ7RvKVMr04Qmd7N05Xxiu2f7ab8qfYJTGuUqpk/2ePy4ia5RSD4rIT3pKsAFLZT589H8w+lRdmPfq9bDrvzBoMiSM7NIh8yvqyCuvIybCwbvb84hy2fnjlTO47NEv+deqLC6ZmcaaA8UAbDhYyr6jVTR6FVONyXfzxmgf7Ikju9bG+bbTx/Hd08Z1qYDM5bDx6DUndem8fYXbaffVsfhSZZ12Xw8tMy0X4Bsn67/ptmydqWM2/mtjaYQwpuE/Y7wvruZTYiK47fRxfHvxGD7aXcC/vzrI79/fy59XZHDhjFSmGC7Q3mqJHgjfUKasEm5e1P1a5lUZRSRFu3yfrT8TzGWLV0SuFBGbcbvS77VjS4mw6JyPH4TGOrjgYRg6Da56HpzRMDn4KbutM1U2GxPpfnfZdKJddr42K424SCc3LEhnZ24FO3MrWHNAu6UOFFXzudHCY6oxxGh0SjRvf28Rl3fQBLAjRKRbFcf9jUinzTdh0N89Zc7XcAf44Tf7WR0srsblsLVxQ/ksjRBkT/lbK71dPOePw27j3GlDefFb81hx12K+PncEH+w4yq/e3Q30jULzZ256EhsOlRxz5ldrlFKsztCp5sfD/0EwlsY1wF+AR9FKYg1wrYi4ge/1oGwDj5yNsOV5WHA7pBizLYZM0cV7kcFdoVTWNXDVP9cwbnAMf7pqJnabsOVIGU67cObkwXxy72nEG1etF00fxoNv7+I/m7JZl1XMxCGx7M2v5Pk1h4iNcPhSWgFOGG7N3gqWSKed4mrd8K45EG5r8XprzJbrZTUNpASY6RFvNEcMpaVhE0hPCW1BaFcZPySWB5dO4wfnTOSNzTkUVXl8jSX7itnp2i17sLimW+Nm9+VXUVBZf1y4piC4lNsDwEXtvLw6tOIMIHI3wzt3w+hTYOY1kDIB3v+Jnmmx+Act943uOEVvX34l97+5g2UL0nlray67j1awK6+CIXER/PSCKWw5UsqU1DginfYWPzrxUU5OnzSIF9YextPo5WcXjObnb+4gr7yOuaOTjouror7A7bJTUGwoDb86DZMoV9t/O7fLTpRLWyOtazRA+9jPnTq03WmEx4JpaaQnR4cksB5KYiOdXD8/va/FAHRlOOh6je4ojZV7C4D+2wq9NZ0qDRGJBG4GpgK+2nel1E1dPamIHAQqgSagUSk1W0SSgFeAdOAgcKVSqmcb2/cln/0BCnbB0W169sXE8+HIGrjorwGtilpPE09/mcV180a1yeF/5JNM1hwo8bmY7jtvErlltTyxKothCW62ZZdzRTuupUtPTOODnbpie9H4FGaMSGBdVonPNWVx7Lj94hf+gXDf667ALqakaBc1nto28QzQcYB/XBea2I7LrmXp7WaA/Y1xg2NIjHKyPquEK2d33sgyEJsOl/LnjzKYPSqRYQmhq4fpS4JxTz0H7AHOAR5Eu6t2h+Dcpyulivye/xj4WCn1WxH5sfH8RyE4T/hRvB/2/g8W3wsnfwfevw+2vwpDTmjZdNCPNzbn8Pv395JVWM0frpjB6xuziYmwM2tkIv/bnsf180eRnhxNcXU9tyweQ5NXkVNWy/8ZfaNmjgycAXLaxMHERTpIiHIxLMHNnPREQ2lY7qiu4nbaMd3g/nUaJu25mJJjIsgurQ2qaWJ3MOMifZmd1B8QEU4alcSGQ127ds3Ir+SmZ9YzOC6Cx67tX8kcHRGM0hinlLpCRJYqpZ4VkReBD3pAlqU0p+8+C6zkeFUa6x4HmwNm36yHIV32hFYWSaPb7SP1zrZcROC1jdlU1jXy/s6jiMDJo5No9CpuXDi6hQntsAt/u3oWVz+xhi1HyjhxRODMp0innV9eMg2HTf+QnDFpCE+uzvLVZlgcO5EtXFFtlUagQDg0D4AKZGmEErN1SV8HmvsDc9IT+Wh3PoWV9QyKDT7GklNWy3VPrsNpt/HcTScf03vDnWC+nQ3GfZmITAOOol1I3UEBH4qIAv6plHocGKKUygM9XlZEBgd6o4jcAtwCMHJk11JQ+4zDa2Hri7D1ZZj2NT3bwmTMqe2+raCyjjUHirll8RhW7ink/Z1HuXL2cA4V17DmQAmnTRwU0Ofqdtl59sa5rDtYQnoHPtmlM5tz9U8alcjuB88Nq4K5/kYgpeGvKNqrCO4tpTFpaCyzRiYwf2z/bmfRG8z2G8p03gnB1RgVV9Vz3ZNrqfY08uq35zMyyA4I/YVgvp2Pi0gi8DPgLSAG+Hk3z7tQKZVrKIYVIrIn2DcaCuZxgNmzZ/ePlN+6cj2/e/Nz4IqBCefCmb8I+u3vbT+KV8Fls4ZzzdxRrM0q5vKThlNV38jv39/ry/UPRHyUkyVThrT7eiAshdE9/GMWvi63/tvasTSSjKypQIHwUDI4LpLl3+36jJWBxAlp8b6hTMEojar6Rm58Zj05pbU8/82TQ5K4EG50qDRExAZUGAHpz4GQtA0xZnKglCoQkTeAuUC+iKQaVkYqUBCKc4UF794DO5bDwjvg1B/75l6U1XiIiXD4ev8UVNRxzb/WcrS8jginne+cOoZ5Y5J5dcMRJg6JZYLRjdS8comNdPLLS6b1zWeyaBd3AFdUZBCWRoqRdtvTMQ2L4HE5bMwckcCGIGaE1Dc28e3nNrAzt4LHrzvJNzr2eKPDSiGllJcQ12KISLSIxJqPgbOBHWgr5gZjtxuAN0N53j6jYDdsfx0W3g5LHvQpjL99nMHMB1cw+f73ufX5jTR5FU9+kcX+wiouO2k4k4bG8qt3d3Ph31azM7eCGxem9+3nsAiayADptYEUSWuSesk9ZXFszElPYmcnQ5l077atfJFZzO8vm86Zk4/Nuu9PBPPtXCEi96LTYavNjUqprjabHwK8YbhAHMCLSqn3RWQ9enbHzcBh4IouHj+8WPlbcEXDgtvZnVfBgcJq8ivq+OOKfZw9ZQjJMS5eWneEf3y2nxfXHOaC6cN44OKpAKw5UMzR8jrmj01mSD+e9DXQMJWGy2Hzdf8NJnvKdE9ZSiO8mDM6iUc+zWTz4bKAtRZKKe5/cwfvbs/jZxdM5rIudk7oLwTz7TTrMW7z26booqvKKBacEWB7MXBmV44ZVtSW6oB33hZdwLfvfVj8A77M87LsqS/wGK2WF4xN5pFvzMJpF7JLa/nDB3q28rcXNy+r2fPJon9hWhL+s8DNbU67+GZotMacazE03rpACCdmjUzAZgxlCqQ0/rRiHy+sPcytp41tdwzw8UQwFeHd79Y1ECg7DB89ALvfgaZ6QGDQRDjpRnaPWcYtT29kVHIUv7t8OqXVHuaPTfalPv76khM4+8+fMSc9iWlpVn1Ef8eMWUQFiGN01AZk3OBY3r/zFCaGaJKeRWiINYcyBYhrPPNFFn/9JJOrZo/gh+dM7APpep9gKsKjgLuBkUqpW0RkPDBRKfVOj0vXX2hq0N1oC/fBSctgylJInQERMRwpqeH6x74kLtLBv2+eS2p826rQkclR/O/2U0g5jnK5BzJmnyn/gHdkgHYigehoNolF3zEnPZFXN2TT0OT1WYpvbsnhgbd3cfaUIfz60mkDJuswGPfU08BGYIHxPBt4DbCUhsmqhyF3M/9K/QXzZtxMWoKbNzfkkFdxhA935lPf0MSLty4IqDBMxgyyCq2OF5rdU83/XqYiiernU9sGKnNGJ/HsV4fYlVvBjBEJrNxbwD2vbmXemCT+evWJPTL9MFwJRmmMVUpdJSJXAyilamWgqNRgyN6I+uz3vONdyK+yJiKPrMZpt+Fp9OJy2EiJdvGvG+Yw3nI5DBgCWRUuuw2bhKZLrUXvM3uUTp9df7CEJqW49flNTBwayxPXzx5wf9NglIbHaIOuAERkLFDfo1KFM4314DDcSHUVNL12IwUqgUejvsPH3z+V/27OobKukavnjmTiUEtRDER8nW39rAoRwe209/v50AOVofGRjEhy89bWXB75NJMhcRE8c+PcAVlTE4zSeAB4HxghIi8AC4FlPShT+LL5BXjzuxCVAonp0FiHlB/hds/P+dN3TmPsoBjuOXtgBMMs2scXCG+lINwue6cxDYvwZU56Ess35TA4NoLnbj6++kkdC8FkT30oIhuBeegZ4Xe06k47MKgqhA9+gho6nQ2eUbiLspkQ08hvvTeSesLpVgDTwkdkAEvD3G4pjf7LOVOHsmZ/MU/dOIcRScdXP6ljIZjsqbeAl4C3lFLVne1/3LLifpSnmvvtd/Bcrhu7TYistVHtaeKD08f1tXQWYUSgOg3QQ486ahxpEd6cM3Uo50wd2tdi9DnBuKf+CFwF/FZE1qErw99RStX1qGThxIanYeuLfDVsGc/td/PLpVMZluDm1uc3cc7UIVbswqIFkQGypwCevWkuVgaJRX8nGPfUZ8BnImIHzgC+BTwFDAx/zJYXUe/cRe2oM/lW5llcNms41xnjKD++51SSA8xzthjYuAO0Qwd8LUUsLPozQTW5MbKnLkJbHLPQQ5KOO+qrSomIitODkIr3U/Sfu0nJXckmmcavKr4L9kZ+dG5zoHsg+zUt2ifKaefUCYOYnR548JWFRX8mmJjGK8DJ6AyqvwMrje63xxVZGz9kyNvXUuqII37cfNjzLpHKyT8jb2RV0mVszqrgvvMmMdhqHGjRCTab8OxNc/taDAuLHiHYivBvKKWaAERkoYh8Qyl1Wyfv6zcU7VvLoLdvIF8lcah+MHP2fMjrjWdQeOLtfH/pQr7tsFNQWcegmIGZYmdhYWFhEkxM430RmWlUhF8FZAHLe1yyXmB3TinbXnuIS0qfpogEGq99k315Dm75YB/3nT+Jexc292ocHGtZGBYWFhbtKg0RmQB8HbgaKEZnTYlS6vRekq1HKa9tYO3TP2BZ42vsTliMXPgwk8aPZ/x4WLZgtK8DrYWFhYVFMx1ZGnuAVcBFSqlMABG5q1ek6mGUUjz0ykp+0fAWJWMuZPL1z4NfOy1LYVhYWFgEpqNfx8uAo8CnIvKEiJwJx0ea+erMIiZlPkGkrZGkCx9soTAsLCwsLNqnXaWhlHpDKXUVMAlYCdwFDBGRx0Tk7F6Sr0dYNKiWG1yfIDOvgeSxfS2OhYWFRb+hUz+MUqpaKfWCUupCYDiwBfhxj0vWg0hDHbbRi5FTf9jXolhYWFj0K0Qp1dcytEBEzgX+AtiBfymlftvevrNnz1YbNmzoNdksLCwsjhO67JMPq4iv0ark78B5wBTgahGZ0rdSWVhYWFiYhJXSAOYCmUqpA0opD/AysLSPZbKwsLCwMAg3pZEGHPF7nm1s8yEit4jIBhHZUFhY2KvCWVhYWAx0wk1pBPKztQi6KKUeV0rNVkrNHjRoUC+JZWFhYWEB4ac0soERfs+HA7l9JIuFhYWFRSvCTWmsB8aLyGgRcaHbmLzVxzJZWFhYWBgENU+jt1BKNYrI94AP0Cm3TymldvaxWBYWFhYWBmGlNACUUv8D/tfXclhYWFhYtCXsivuOBREpBA514xApQFGIxOlpLFl7BkvWnqM/yTvQZC1SSp3blTf2a6XRXURkg1Jqdl/LEQyWrD2DJWvP0Z/ktWQNnnALhFtYWFhYhDGW0rCwsLCwCJqBrjQe72sBjgFL1p7BkrXn6E/yWrIGyYCOaVhYWFhYHBsD3dKwsLCwsDgGLKVhYWFhYRE0A1JpiMi5IrJXRDJFJKymEIrICBH5VER2i8hOEbnD2P6AiOSIyBbjdn5fywogIgdFZLsh0wZjW5KIrBCRDOM+sa/lBBCRiX7rt0VEKkTkznBZWxF5SkQKRGSH37Z211JE7jO+w3tF5JwwkPUPIrJHRLaJyBsikmBsTxeRWr/1/UdvytqBvO3+3cNwbV/xk/OgiGwxtvf+2iqlBtQN3Z5kPzAGcAFbgSl9LZeffKnALONxLLAPPZDqAeDevpYvgLwHgZRW234P/Nh4/GPgd30tZzvfg6PAqHBZW2AxMAvY0dlaGt+JrUAEMNr4Ttv7WNazAYfx+Hd+sqb77xdGaxvw7x6Oa9vq9T8C9/fV2g5ESyOsBz0ppfKUUpuMx5XAblrNFOkHLAWeNR4/C1zSh7K0x5nAfqVUdzoKhBSl1OdASavN7a3lUuBlpVS9UioLyER/t3uFQLIqpT5USjUaT9egu1SHBe2sbXuE3dqaiIgAVwIv9ZY8rRmISqPTQU/hgoikAycCa41N3zNM/6fCxeWDnnfyoYhsFJFbjG1DlFJ5oJUgMLjPpGufr9PyHy8c1xbaX8tw/x7fBLzn93y0iGwWkc9E5JS+EioAgf7u4by2pwD5SqkMv229urYDUWl0OugpHBCRGOA/wJ1KqQrgMWAsMBPIQ5uo4cBCpdQs9Fz320RkcV8L1BlG2/2LgdeMTeG6th0Rtt9jEfkp0Ai8YGzKA0YqpU4E7gZeFJG4vpLPj/b+7mG7tsDVtLzY6fW1HYhKI+wHPYmIE60wXlBKLQdQSuUrpZqUUl7gCXrRXO4IpVSucV8AvIGWK19EUgGM+4K+kzAg5wGblFL5EL5ra9DeWobl91hEbgAuBK5RhtPdcPMUG483omMEE/pOSk0Hf/dwXVsH8DXgFXNbX6ztQFQaYT3oyfBZPgnsVko97Lc91W+3S4Edrd/b24hItIjEmo/RgdAd6PW8wdjtBuDNvpGwXVpcrYXj2vrR3lq+BXxdRCJEZDQwHljXB/L5EJFzgR8BFyulavy2DxIRu/F4DFrWA30jZTMd/N3Dbm0NzgL2KKWyzQ19sra9GXUPlxtwPjoraT/w076Wp5Vsi9Cm8DZgi3E7H3gO2G5sfwtIDQNZx6CzTLYCO821BJKBj4EM4z6pr2X1kzkKKAbi/baFxdqiFVke0IC+2r25o7UEfmp8h/cC54WBrJnoWID5vf2Hse9lxvdjK7AJuChM1rbdv3u4ra2x/RngO6327fW1tdqIWFhYWFgEzUB0T1lYWFhYdBFLaVhYWFhYBI2lNCwsLCwsgsZSGhYWFhYWQWMpDQsLCwuLoLGUhkXQiEiT0Ulzh4i8JiJRITjmAyJybyjk6+L5h4nI6z14/HSzW6mIzBaRvxqPu/25ReQnrZ5/2Z3jhYqurKmIrBSR2T0lk0XosJSGxbFQq5SaqZSaBniA7wT7RrMAKdxQSuUqpS7vpXNtUErdHuz+QaxZC6WhlFrQJcFCiIg4enNNLXofS2lYdJVVwDgROU1E3jE3isgjIrLMeHxQRO4XkdXAFaLnmGwSka0i8rHfsaYYV5oHROR2v2P912iEuNNshigidhF5xrB2tovIXcb2sSLyvrH/KhGZZGy/wth3q4h83vpDtLIElonIcuM4GSLy+0AfXETmiMiXxjHXiUiscZxVxufbJCJtfsBbrxUwQ0Q+Mc71Lb99PhWRF9GFZ+2tw28Bt2H5vWBsqzLuRfRsC3ONrvI79koReV303IsXjA4EreVcKSJ/Nj7jDhGZa2yPFt3Yb73oBnlL/dbtNRF5G9280n9NI0XkaUOOzSJyurHdLSIvi24W+ArgDrTWFmFIb1dmWrf+ewOqjHsHup3FrcBpwDt++zwCLDMeHwR+aDwehK4WHm08TzLuHwC+RM8uSEFXaztb7eNGt3hIBk4CVvidL8G4/xgYbzw+GfjEeLwdSPPft9VnSseYRwAsQ7dgiAcigUPAiFb7u4x95hjP44z1iAIijW3jgQ0Bju9bK+NzbzU+W4qxNsOMfarNdWpvHfz/HgH+PpcBK9AzQ4YAh9FzWk4DytG9lGzAV8CiAGuyEnjCeLzYT/6HgGvNtUR3VYg21i3bT07/z3wP8LTxeJIhSyS6ud5Txvbp6AaHs/v6O27dOr9ZlobFseAWPTFsA/qf/8kg3mM2V5sHfK70fAKUUv7zAt5VuvFaEboh3xBj++0ishU9m2EEzX11xojI30T3OqoQ3RF4AfCaId8/0T+SAF8AzxhX8sG4yD5WSpUrpeqAXeghTf5MBPKUUuuNz1Gh9AwJJ/CEiGxHd8+dEsS53lRK1Rqf+1OaG+atM9epg3XoiEXAS0o348sHPgPm+B07W+kmfVvQP/CBeMn4fJ8DcaKn8J0N/NhY45XoH/+Rxv4rWv1N/WV5zjjWHrQinoBWRs8b27ehW3lY9AMcfS2ARb+iVik103+DiDTS0s0Z2eo91eautN9eut7vcRPgEJHT0A3a5iulakRkJfpKvlREZgDnALehB9LcCZS1lg1AKfUdETkZuADYIiIzldEVNFhZWr3e3ue4C8gHZqDXo66Dc/jEa+e5uWa0tw6dHDdQa2+Tzj5fR7IJcJlSam+Lk+n1rSYwHcli9TDqh1iWhkV3OYSOSUSISDx6Il4gvgJOFd01FBFJ6uS48UCp8UM5CW2pICIpgE0p9R/g5+jRuBVAlohcYewjhmJBRMYqpdYqpe4HimjZ8ror7AGGicgc4/ixoltWx6MtEC9wHcFZNUsNn38y2nW0PsA+AdfBoEF0G/3WfA5cZcR/BqGv6o+1S6sZB1kElCulyoEPgO+bcRAROTGI43wOXGPsPwFtmexttX0a2kVl0Q+wLA2LbqGUOiIir6LdCxnA5nb2KzSCuMtFxIZ2Qy3p4NDvA98RkW3oH5k1xvY04GnjGAD3GffXAI+JyM/QrqKX0TGDP4jIePQV78fGti6jlPIYgeW/iYgbqEVbAo8C/zEU16e0f+XtzzrgXfQP6S+VUrnGD6s/7a0DwOPANhHZpJS6xm/7G8B89GdV6LjSUUPpBEup6BTeOPQUPoBfAn82zinomNWFnRznUeAfhtuuER3vqheRx9B/R7Obczi0HrcIAqvLrYWFRQsMF9i9SqkNfS2LRfhhuacsLCwsLILGsjQsLCwsLILGsjQsLCwsLILGUhoWFhYWFkFjKQ0LCwsLi6CxlIaFhYWFRdBYSsPCwsLCImj+P6G3gglsUGc0AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"only good\n"
]
},
{
"data": {
"image/png": 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ffkjTpk2pX78+r7766t/Hevvtt6lRowYdOnTwa4y0oDBkiPMwJpgdXA9fXwvLv4NWT8GDv0Oxypd1SFVlwfYj3P/dErp+Pp8N+0/SsmoJAnHTvbXB+Ou3AXBgbcYe88p6cOOFL1Ml27x5M99++y2tWrXiwQcf/LtmER4ezrx58zhy5Ai33norM2bMIH/+/Lz//vsMGjSI559/nt69ezNz5kyqVq3695AyKT355JNcc801TJo0icTERGJjY3nvvfdYt24dq1atAmD69Ols3bqVJUuWoKp07dqVuXPnkj9/fsaMGcPKlStJSEigUaNGNG6cueMdpUuNGl5HYMyFqTp34//+IoQXhnsnQZXrLuuQCYlJ/LbuAEPn7mDt3hOUKJCbZztW5/5WERQMD8ugwM9nCSYLKF++PK1atQLgnnvuYfDgwQB/J4xFixaxYcOGv7c5e/YsLVq0YNOmTVSqVIlq1ar9vW/yhGW+Zs6cyciRIwEIDQ2lcOHCHDt27Lxtpk+fzvTp07nqqqsAZ9KyrVu3EhMTQ7du3ciXLx/gXHrLEqZOdZZdungbhzEpxUXD5Mdh8y9QtSPc8iUUKJn+w51NYNzSPXwzbydRx+KpXCI/73Srx62NyhIeFpqBgf8vSzD+8qOmESgpJwJLfp08eKWq0rFjR0aPHn3edqtWrcqwScRUlYEDB9K3b9/zyj/99NOsOVHZxx87S0swJphEzoOJveHUYbj+Xbj6kXTf23Ii/hyjFkby7bydHIs7R+OKRXm5c2061rqCkJDM+ZsNWBuMiJQXkVkislFE1ovIU255MRH5Q0S2usuiPvsMFJFtIrJZRK73KW8sImvddYPF/UYTkTwiMtYtXywiET779HLPsVVEegXqfWaG3bt3s3DhQgBGjx5N69atz1vfvHlz5s+fz7Zt2wCIi4tjy5Yt1KxZk507d7J9+/a/901N+/bt+fLLLwGnw8DJkyf/Z+j/66+/nmHDhv3dtrN3714OHTpE27ZtmTRpEvHx8cTExDA1uWZgjPFfYgLMfBuGd4awvPDwDGjxaLqSy9HYM3z4+yZavzeTj6ZvoWH5Ikx4pAUT+7Xk+jpXZlpygcA28icAz6pqLaA58JiI1AYGAH+qajXgT/c17ro7gTrADcAXIpJcf/sS6ANUcx83uOUPAcdUtSrwCfC+e6xiwKvA1UAz4FXfRJbV1KpVixEjRlC/fn2io6Pp16/feetLlizJ8OHD6dmzJ/Xr16d58+Zs2rSJ8PBwhg4dyj/+8Q9at25NxYoVUz3+Z599xqxZs6hXrx6NGzdm/fr1FC9enFatWlG3bl369+9Pp06duOuuu2jRogX16tWje/fuxMTE0KhRI3r06EHDhg257bbbaNOmTWZ8JMZkH8d3w/CbnBsnG97tzNtS5tLHytt/Ip43pm6g9fuz+GL2dtpUL8HPT7Tmuwea0STi0ma9zSiZNly/iEwGPncf7VR1v4iUBmarag0RGQigqu+62/8OvAZEArNUtaZb3tPdv2/yNqq6UERyAQeAkjiJqp2q9nX3GeKeJ/V/4Qne4fojIyPp3Lkz69at8zSOjBAMn+ffbLh+EwzWT4IpT4EmQZdPoV73Sz7ErqOn+GrOdiYsjyJJ4ZaGZenXrjJVS2XODZieD9fvXrq6ClgMXKGq+wHcJJM8MltZYJHPblFu2Tn3ecry5H32uMdKEJETQHHf8lT28Y2rD07NiAoVKqT7/RljzCU5ewqmDYQVI6BsE7jtGyhW6ZIOseVgDF/M2saU1fvIFRpCj6bl6du2CuWL5QtQ0Jcu4AlGRAoAE4GnVfVkGg3Cqa3QNMrTu89/C1SHAkPBqcFcKDAvRUREZIvaS9AZNcrrCExOdWCtM2/Lka3Q+hm49gUI9b+b8NqoE3w+ayu/rz9IvtyhPNS6Er3bVKZUofAABp0+AU0wIhKGk1x+UNXku/wOikhpn0tkh9zyKKC8z+7lgH1ueblUyn33iXIvkRUGot3ydin2mZ2e96CqWbOXVJAJuplTy5e/+DbGZCRVWPK1M+Nk3qJw33+gcju/d1+84yj/nr2duVsOUyg8F09eV5UHWlWiaH5vJxVLS8ASjNvT61tgo6oO8lk1BegFvOcuJ/uU/ygig4AyOI35S1Q1UURiRKQ5ziW2+4B/pTjWQqA7MFNV1W2becenYb8TMPBS30N4eDhHjx6lePHilmQug6py9OhRwsOD6D+ssWOd5QVuPjUmQ506CpMfgy2/QbXr4ZYvIH+Ji+6mqszZcph/z9rG0shjFM+fm+dvqMG9zSsG7ObIjBTIGkwr4F5grYiscstewEks40TkIWA3cDuAqq4XkXHABpweaI+pavIgW/2A4UBe4Df3AU4CGyUi23BqLne6x4oWkTeBpe52b6hq9KW+gXLlyhEVFcXhw4cvdVeTQnh4OOXKlbv4hpnF7ZZtCcYE3M65zgjIcUfhhvfh6r5wkX9YT59LZNLKvQybt5Oth2IpXTic17rUpkfTCuTNHdibIzNSpvUiC3ap9SIz2Zj1IjOBlngOZr8Lfw2C4lWh+zAoXT/NXQ7FnGbUwl38sHg30afOUrt0IR5sXYmuDcqQO1dwDh3peS8yY4zJUY5FwsSHIWopXHUv3Pg+5M5/wc13HjnF0Lk7mLgiinOJSbSveQUPta5E88rFsvTleUswxhiTkdb9BFOfcp53HwZ1b7vgpqv3HOerOduZtv4AYaEhdG9cjt5tKlOpxIWTUVZiCcYYYzLC2VMwbQCsGAnlmsJt30LR1EfPWLXnOB9M28SC7UcpFJ6LR9tVoVfLCEoVDKKOMBnAEozJmSZM8DoCk50cWAcTHnDubWnzLLQbmOq9LXuPx/PBtE1MXrWPEgVy8+JNteh5dQUK5MmeX8XZ810ZczElLt5F1JiL8p23JW+RC97bcjjmDF/N2c73i3YB8Pi1VXmkXZVsm1iSZe93Z8yFDB/uLO+/38soTFYWFw1TnoBNP0O1Ts68LSnubTkae4Yhc3cwcmEk5xKVWxqW5ZlO1SlbJK83MWcySzAmZ7IEYy7HrgXOvC2xB+H6d+DqfucNrX8i7hxD/9rO8PmRxJ9L5JaGZXmifbVs03jvL0swxhjjr6REmPsRzHkPikbAw39Amav+Xh13NoFv/trJ13/tIOZ0Ap3rl+bpDtUybWTjYGMJxhhj/HFir3NH/q55UL8H/ONjyOMkDlXl5zX7eefXjew/cZpOta/gnx2rU6t0IY+D9pYlGGOMuZhNv8LkRyHhLNzyFTTs+feqjftP8uqU9SzZGU3t0oUY3PMqmno0wVewuWiCEZEPgLeAeGAa0ABn6P3vAxybMcZ469xp+OMVWDIErqwP3b+DElUBOHUmgU9nbGHY/EgKhefi7W51ubNpBUIzcUriYOdPDaaTqj4vIt1whsG/HZgFWIIxWdevv3odgQl2h7c487YcXAvNH4UOr0GuPKgqv649wNu/bGDfidPc2bQ8/3dDzaAeNt8r/iSY5LuFbgJGuyMVBzAkYzJBvuCZ9c8EGVVY9QP82h/C8sJd46D69QAs33WMt3/ZwIrdx6l5ZUEG97zKs/nuswJ/EsxUEdmEc4nsUREpCZwObFjGBNgXXzjLRx/1Ng4TXE6fgJ+fgXUTIKIN3Po1FCrN7qNxvP/7Jn5Zs5+SBfPw/m316N64vF0Ouwi/hut3J+466U7+lQ8opKoHAh5dJrLh+nMYG67fpBS13Bnu5UQUXDsQWj/DidNJfD5rKyMW7CI0ROjTtjJ92lYmfza/A/9SZMRw/bWACHda4mQjLzsyY4zxWlISLPwX/PkGFCwND/xGUrlmjF22hw+mbeJ4/Dlub1yOZzrW4MrC2WswykDzpxfZKKAKsApInmFSsQRjjMnqYg/BpEdg+59Qqyt0HcyqI8KrX8xnddQJmkUU47WudahdJmffz5Je/tRgmgC11aa+NMZkJ9v+dJLLmZPQ+ROO1LiLD37ZzLhlUZQsmIdPezTk5oZlsvSEX17zJ8GsA64E9gc4FmOMCbyEszDrLZj/GZSsRcI9kxi5PT+ffDyH+LOJ9G1bmSfaV8v2Ix1nBn8+wRLABhFZApxJLlTVrgGLyphAs8b9nCl6J0x8CPYuh8YPsKj6s7wyZgdbDu6iTbUSvNqlDlVLFfA6ymzDnwTzWqCDMMaYgFs7AX7+J4gQ/Y+veXlrVX75bg3liuZlyL2N6VT7CrsclsEummBUdY6IXAE0dYuWqOqhwIZlTIB99JGzfO45b+MwgXf2FPz2PKz8nqRyzRhV5mXemxJHkh7knx2q0/eayoSHhXodZbbkTy+yO4APgdmAAP8Skf6qanPOmqzr55+dpSWY7G3/Gme4l6Pb2Fm7Hw9FdmDHthhurHslL/6jFuWK2ogOgeTPJbIXgabJtRb3Tv4ZgCUYY0xwUoUlX8P0F0nIU5RBpd7nixXlqFoqjO8fakjrajZldmbwJ8GEpLgkdhQIudDGxhjjqbhomPwYbP6VbUVacffhXpw6VYSX/lGNXi0jCAu1r6/M4k+CmSYivwOj3dc9ABuK1hgTfCLnoRN7o7GH+Sz0AT470IHbGpXn/26sQamCdhd+ZvOnkb+/iNwGtMJpgxmqqpMCHpkxgZQ3r9cRmIyUmABzP0TnfsD+kNL0Pv0aIWUaMvG+OjSuWNTr6HIsv+4kUtWJwMQAx2JM5vntN68jMBnlRBQJ4x8iV9Qifkpsw6BcvXnslqvo0dRGO/baBROMiMxT1dYiEoMz9tjfqwBVVRucxxjjqaSNP3Pup34knDtL/3OPUrDZ3fzSsTpF8tnkX8HggglGVVu7y4KZF44xmeTNN53lyy97G4dJn3OniZ70PMU2jGBzUiW+LvUi/W693galDDIX7U7hjqZ80TJjspQ//3QeJss5FbWeA4NaUWzDCL6XLmzr8hODH+tuySUI+dMGU8f3hTsnTOPAhGOMManTpCRWT/2cGivfIpfmYUTlD7nl9gconC/s4jsbT6TVBjMQeAHIKyInk4uBs8DQTIjNGGMA2BwZxZExj9Lq9BxWhTUgV/ev6VWjhtdhmYtIqw3mXeBdEXlXVQdmYkzGGAPA8bizjPvPJG7Y/CJXy1FWVX+Kene8QmguG0o/K/DnltYlIlI4+YWIFBGRWy62k4gME5FDIrLOp+w1EdkrIqvcx00+6waKyDYR2Swi1/uUNxaRte66weIOdyoieURkrFu+WEQifPbpJSJb3UcvP96jyWmKF3ceJiglJimjF0cy4sOneWBzPwrmCSX+7p9peNcbllyyELnYRJUiskpVG6YoW6mqV11kv7ZALDBSVeu6Za8Bsar6UYpta+OMFNAMKIMz1ll1VU1056F5CliEM4LAYFX9TUQeBeqr6iMicifQTVV7iEgxYBnOTJwKLAcaq+qxtOJt0qSJLlu2LM3PwhgTeOv2nuDDiXN4+PD7tAldx4nKnSl8+78hbxGvQzOpEJHlqtoktXV+jUWWnv1Uda5vreIibgbGqOoZYKeIbAOaiUgkUEhVFwKIyEjgFuA3d5/X3P0nAJ+7tZvrgT9UNdrd5w/gBv471I0xJgipKsPmR/LXr6P5JPeXFMp9Fr3xMwo37gU2T0uW5M8lsmUiMkhEqohIZRH5BKdWkF6Pi8ga9xJa8hgOZYE9PttEuWVl3ecpy8/bR1UTgBNA8TSO9T9EpI+ILBORZYcPH76Mt2SynIEDnYcJCnuPx9Nn+EISpr3I8NzvU7hEWXL1nYM0ud+SSxbmT4J5Aqfn2FhgPHAaeCyd5/sSqAI0BPYDH7vlqf0GaRrl6d3n/ELVoaraRFWblCxZMq24TXazcKHzMJ5SVcYu3c2Dg8byROTj9M31C0mNHyS07ywoVdPr8Mxl8udS1ylgQEacTFUPJj8Xka8Bd9YnooDyPpuWA/a55eVSKffdJ8q9N6cwEO2Wt0uxz+yMiN8Yk3GOx53lxUnrCF0/kUl5hhGeOwxuHkVI7a5eh2YyiD938pcUkQ9F5FcRmZn8SM/JRKS0z8tuQHIPsynAnW7PsEpANZypmfcDMSLS3G1fuQ+Y7LNPcg+x7sBMdXos/A50EpGi7iW4Tm6ZMSZIzN58iK6DptNu8+sMzv05ecvWI6TfPLDkkq3408j/A87lsc7AI75Sq/MAACAASURBVDhf6hdtsBCR0Tg1iRIiEgW8CrQTkYY4l6wigb4AqrpeRMYBG4AE4DFVTXQP1Q8YDuTFadxPHgb3W2CU2yEgGrjTPVa0iLwJLHW3eyO5wd8Y460jsWd4Y+oGNq9ZxKi8/6ZCSBS0eRZp9wKEWvfj7MafbsrLVbWxiKxR1fpu2RxVvSZTIswk1k05h7nnHmf5/ffexpFDqCqTV+3j9Snr6JwwnVfDRhGatzBy61Cocq3X4ZnLcLndlM+5y/0i8g+cNpByaWxvTPCzxJJp9h2P56X/rGPZpp18WXgErXQeVLoOug2BAqW8Ds8EkD8J5i33Tv5ngX8BhYB/BjQqY0yWl5SkjF66m3d/3USdpC3ML/IFBc4egg6vQ8snIcSfTqwmK/OnF1lyT68TgNVlTfbw9NPO8tNPvY0jm4o8cooBP61h8Y4jvF1qFj1jRyB5ysDd06B8U6/DM5nkoglGRD4A3gLigWlAA+BpVbVrDCbrWrXK6wiyrQnLo3hl8jpKykkWlBtO6SPzoVZX6PovG+4lh/GnjtpJVU/i9CKLAqoD/QMalTEmy4k9k8AzY1fx3PjV9Cyxg5n5X6T0sWXwj0Fwx0hLLjmQP20wybP53ASMdrsBBzAkY0xWszbqBE+NWcmeoycZW20WzfZ8h5SoBvdNgivreh2e8Yg/CWaqiGzCuUT2qIiUxBkuxhiTw50+l8hnf25l6Nwd1M1/kuXlhlJoz3K46h648QPInd/rEI2H/GnkHyAi7wMn3eHzT+GMZGxM1lW9utcRZHnLd0XTf8Iadhw+xevVd3LvoQ8JOZEAt34D9W/3OjwTBPy9dbYWEOGO+ZVsZADiMSZzDLVZv9Mr9kwCH/2+mRELI6lYKBfz60+j7JaRULoBdP8OilfxOkQTJPzpRTYKZwTkVUDy8C2KJRhjcpxZmw/x0qR17DsRzz8bCo9Fv0XoljXQ/FHo8BrkyuN1iCaI+FODaQLU1ouNKWNMVtKnj7O0moxfziUm8cG0TXz9106qlirAzA77qbToFciVG3qOgRo3eh2iCUL+JJh1wJU487cYkz1s2eJ1BFnGnug4nhqzkhW7j9O7WUkGMIzQv0ZDhZZw2zdQONX5/IzxK8GUADaIyBLgTHKhqtq42sZkc7+s2c+An9aAwogbw7lmTT84uh2uGQBt+9sIyCZN/vx2vBboIIwxwSXubAJvTN3AmKV7uKp8YYbVXk3Rua9D3qLQaypUauN1iCYL8Keb8pzMCMQYExzWRp3gn+NWsf1wLP9sXZInYj4jZM7PULUjdPsK8pfwOkSTRfjTi6w5zijKtYDcQChwSlULBTg2YwKnYUOvIwg6ZxOSGPznVr6cs53i+XMzuWsu6i96EGIOQKe3nZ5iNgKyuQT+XCL7HGe2yPE4Pcruw5nS2Jisy0ZRPs+WgzE8NWYVG/ef5PZGZXizxAzCp78LRcrDQ79D2cZeh2iyIL9a6FR1m4iEutMYfyciCwIclzEmE6gq382P5L1pmyiYJxcjbq/INetfhLmzoc6t0OVTCC/sdZgmi/InwcSJSG5glTt0/37ABhgyWZtNmczhmDP0n7Ca2ZsPc13NUgxqfJQi026BM7HQZTA0ug9sYFtzGfxJMPfiDOv/OM5MluWB2wIZlDEBFxXldQSe2nTgJA8NX8bh2DO82aU698R9j0z8FErWcnqJlarldYgmG0gzwYhIKPC2qt6DM4Ly65kSlTEmIFSV0Uv28PrU9RQMD2PK3eWpOb8fRC2FxvfD9e9C7nxeh2myiTQTjDt6ckkRya2qZzMrKGNMxos5fY4XJq1j6up9tKlWgs8b7qHw5AdBFboPg7p2YcJkrAsmGBGpoKq7gUhgvohMAU4lr1fVQYEPzxiTEdZEHeeJ0SuJOhbPgA4V6Xt6GDL1WyjTyEkuxSp5HaLJhtKqwfwHaATscx8hQMHMCMqYgGvRwusIMoWqMmx+JO/9tpGSBfIw+Y4S1F3YFw6ug5ZPwHXugJXGBEBaCUYAVNXaXUz28+67XkcQcMdOnaX/hDXM2HiQDjVLMbjWevL9MhDC8sLdE6BaR69DNNlcWgmmrIgMvtBKVX0yAPEYYzLA0shonhy9kiOxZ3jjhorce/Qz5LdxENEGbv0aCpX2OkSTA6SVYOKB5ZkViDGZ6ja3QXviRG/jyGCJScqXs7fxyYytlCual19vL0S1uffCsUi49kVo8yyEhHodpskh0kowR1V1RKZFYkxmOnrU6wgy3KGY0zwzdjXzth2hS/3SfFh+AeFTXoMCpeD+X6BiS69DNDlMWgnGuiUbk0XM23qEp8euJPZMAoM6l6fb7reRP6dBjZvg5n9DvmJeh2hyoAsmGFVtnpmBGGPSZ9TCSF6dsp4qJQvwU+dQKszsCbEH4Yb34OpHbLgX4xmbjs6YLCr2TAIvTlrL5FX76FizBP+O+Ivck99xRkB++A8oc5XXIZoczhKMyZnat/c6gsuy9WAMj/6wgu2HYxnQtgR9j36AzJ4BdbpBl89sBGQTFPxKMCLSGqimqt+JSEmggKruDGxoxgTQyy97HUG6TVgexcv/WUf+PKFM7izUW9QL4qLhH4OgyYN2ScwEDX9mtHwVZ6KxGsB3QBjwPdAqsKEZY3zFnU3glcnrmbA8ipaVijC00hwKzPgQilaCh8dB6fpeh2jMefypwXQDrgJWAKjqPhGxIWNM1nbjjc7yt9+8jcNPyZfEth2OZWCbYvQ58i6yYA7Uux06fwJ57E/SBB9/Jtg+q6oKKICI+DXZmIgME5FDIrLOp6yYiPwhIlvdZVGfdQNFZJuIbBaR633KG4vIWnfdYBGn/i8ieURkrFu+WEQifPbp5Z5jq4j08idek8PExzuPLGDC8ii6fj6fY3FnmXpTAn033IfsWQxd/+XclW/JxQQpfxLMOBEZAhQRkd7ADOBrP/YbDtyQomwA8KeqVgP+dF8jIrWBO4E67j5fuHPRAHwJ9AGquY/kYz4EHFPVqsAnwPvusYoBrwJXA82AV30TmTFZxZmERAb+tJbnxq/mqnIFmdNkIXX/7AV5i0LvWTbjpAl6F00wqvoRMAGYiNMO84qq/suP/eYC0SmKbwaSRwcYAdziUz5GVc+4nQe2Ac1EpDRQSFUXurWokSn2ST7WBKC9W7u5HvhDVaNV9RjwB/+b6IwJavuOx3PHkEWMXrKb51sW4ofcb5N/0cfQoCf0mQVX1PY6RGMuyp9G/n8C41X1jww43xWquh9AVfeLSCm3vCywyGe7KLfsnPs8ZXnyPnvcYyWIyAmguG95KvucR0T64NSOqFChQvrflTEZaN7WIzw1ZiVnEpKY0CGOJiuehHNxcMuX0PAur8Mzxm/+NPIXAn4XkWhgDDBBVQ9mcByp1fM1jfL07nN+oepQYChAkyZNUt3GZFOdO3sdwf9QVb6as4MPft9E9RJ5GVN1NkXnfQ6lasPtw6FkDa9DNOaSXDTBuPPBvC4i9YEewBwRiVLVDuk430ERKe3WXkoDh9zyKKC8z3blcCY5i3Kfpyz33SdKRHIBhXEuyUUB7VLsMzsdsZrs7LnnvI7gPGcSEhkwcS2TVu7l7lqhvHHuPUJXLnbaWW54H3Ln8zpEYy6ZP438yQ4BB4CjQKmLbHshU4DkXl29gMk+5Xe6PcMq4TTmL3Evp8WISHO3feW+FPskH6s7MNNtp/kd6CQiRd3G/U5umTFB6UjsGe7+ejGTVu5lcONDvHXgEUIPrYdbv3F6illyMVmUP20w/XBqLiVxGtN7q+oGP/YbjVOTKCEiUTg9u97D6ZX2ELAbuB1AVdeLyDhgA5AAPKaqie6h+uH0SMsL/OY+AL4FRonINpyay53usaJF5E1gqbvdG6qasrOByenatXOWs2d7GQWbDpzkoeHLOB57ij/rzaTK+mFwRT3nkliJqp7GZszlEuef/jQ2EHkPp4fXqswJyRtNmjTRZcuWeR2GySweJ5ikJGXY/J18+PtmauY9xo9FhpL/8Epo8hBc/w6EhXsSlzGXSkSWq2qT1NZdsAYjIoVU9STwgfv6vAklrFZgTPocjjlD/wmrmb35MP0rbqXf8UGEnFTo/h3UvdXr8IzJMGldIvsR6IwzbXLK3lkKVA5gXMZkS39uPMjzE9Zw+sxpfqv+G7V2/wClG8Lt30Ex+5My2UtaE451dpeVMi8cY7Kn+LOJvP3rBr5ftJt2JU/xZbHPybt7tTMhWMc3IFcer0M0JsP508j/p6q2v1iZMVnKHXdk2qnWRp3gqbEr2XH4FB/X2cmtUe8h50Kgx/dQq0umxWFMZkurDSYcyIfTC6wo/71EVggokwmxGRM4jz4a8FMkJilfzdnOJ39soUx+YWH9Xym95Xso29hpbylaMeAxGOOltGowfYGncZLJcv6bYE4C/w5wXMYEVlycs8wXmHtMDsWc5snRK1m0I5r7aybyUtz75NqyDlo8Du1fhVy5A3JeY4JJWm0wnwGficgT/gxuaUyWctNNzjIA3ZSX7Izm8R9XcPL0Oca0iOLq9W8goWHQcwzUuDHDz2dMsPJnqJh/iUhdoDYQ7lM+MpCBGZPVqCrfL9rF61M3UKVoKNOrTqHIyh+h/NXQfRgULnfxgxiTjfg7ZXI7nATzK3AjMA9n6HxjDM5YYq/8Zz1jl+3hrsqnefPch4Ru3Ait/wnXvgihYV6HaEym82c05e5AA2Clqj4gIlcA3wQ2LGOyjkMxp3lk1HJW7D7OF3U3c+OuD5GwvHD3RKiWnjFhjcke/Ekw8aqaJCIJIlIIZ9BLuyPMGJwuyL1HLuNsfAzzavyHctsmQcVWcNs3UMg6W5qczZ8Es0xEiuBMk7wciAWWBDQqYwLt/vsv+xC/rNnPs+NX0STvQb4p8Tnhu7ZB2+fhmv+DUH/+tIzJ3i462OV5G4tE4ExhvCZQAXnFBrs0/lJVBv+5jU9mbOb5UkvpFzcEyVMQbh0KVa71OjxjMlV6B7tslNY6VV2REcEZ44kjR5xliRKXtNvpc4n0n7CGmau3M7HUGBqf/AMqtXXmbil4RQACNSbrSqse/3Ea6xS4LoNjMSbzdO/uLC/hPpgjsWd4aMQyzu1dzfyiX1E4Zo/TQ6zNsxASGpg4jcnC0rrR0ur6xrh2HjlFr28Xc+2pX3glfCShocXgvilQqY3XoRkTtPy5D+a+1MrtRkuTUyyNjObpEXN5lSF0ClkAla6DbkOhQEmvQzMmqPnT1aWpz/NwoD2wArvR0uQAk1ft5bvxkxmX+zPKcNgZR6zV0xAS4nVoxgQ9f4aKecL3tYgUBkYFLCJjgkD82UTemLqesBXfMD7sR0ILlES6/wIVW3gdmjFZRno668cB1TI6EGMyVb9+F1y17VAs/b+fQ59jn3Bj2FKSqnYipNtXkL94JgZoTNbnTxvMVJxeYwAhOGOSjQtkUMYEXI8eqRb/seEg346dwOfyKaVzRUOHNwlp8bhdEjMmHfypwXzk8zwB2KWqUQGKx5jMsWePsyxfHoCkJOWzGVuInfMZ34eNgYKlCbljGpRvmsZBjDFp8acNZg6AOw5ZLvd5MVWNDnBsxgTOvfc6y9mzOXn6HC//+Bedd75Jx7AVJFa/idBuX0Deot7GaEwW588lsj7Am0A8kIQzs6ViA16abGDboVg+Gz6Kgac+5IpcJ9BO7xLavB+IXHxnY0ya/LlE1h+oo6pHAh2MMZnpeNxZJv/7eT6R0SQULEtoz/FQtrHXYRmTbfiTYLbj9BwzJts4evIUodFbeVbWE1+tM3lv+wLCC3sdljHZij8JZiCwQEQWA2eSC1X1yYBFZUwATft5ItdFryUXiZzu9Dl5W/SxS2LGBIA/CWYIMBNYi9MGY0zWlJTEih9fpuPWf3OydUkKXPcs4S37eh2VMdmWPwkmQVWfCXgkxgRQfPR+9nx7D41OLWNxgWtpNHQEYfnskpgxgeTP3WOzRKSPiJQWkWLJj4BHZkwG2bN8GnH/akGF2NX8Xnkgjf85kbA9B2DzZq9DMyZb86cGc5e7HOhTZt2UTdDTxAQ2jHmJWlu+YreUJvKmUVx/tTu8fl/30tglzAdjjLk0/txoWSkzAjEmI8UdjWLvt3dTJ24Vc/O1p9ZDXxNRwsYSMyYz2XwwJts5sOIX8kztR7mkeKZXf4X2PZ8hNMR6iRmT2Ww+GJN9JCawc8KLVNw4hB2U40SXsXRqYsPrG+MVT+aDEZFIIAZIxOml1sTtODAWiAAigTtU9Zi7/UDgIXf7J1X1d7e8MTAcyAv8CjylqioieXASYGPgKNBDVSMvJ2YT3JKOR7F/2N1UOrmK6bk7Uvuhr6h6RQmvwzImR0vPGOQZNR/MtaraUFWbuK8HAH+qajXgT/c1IlIbuBOoA9wAfCEioe4+XwJ93HiquevBSUbHVLUq8AnwfgbEa4JU3LpfODW4BUVObOSHMi/Stv9Yyl0subz0kvMwxgRMMM0HczPQzn0+ApgN/J9bPkZVzwA7RWQb0MytBRVS1YVunCOBW4Df3H1ec481AfhcRERVk9+HyQ4SzrJr/AAqbv6WDUkV2dR6MHd1vAbx5678Dh0CH58xOZxX88EoMF1EFBiiqkOBK1R1P4Cq7heRUu62ZYFFPvtGuWXn3Ocpy5P32eMeK0FETgDFgfMG7HRHiu4DUKFChct8SyYzndy/jWMj7qXi6Q1MyX0T5e8cxK2VS/t/gFWrnGXDhoEJ0Bhz4QQjIlVxvvTnpChvIyJ5VHX7ZZy3laruc5PIHyKyKY1tU/t3VNMoT2uf8wucxDYUoEmTJla7ySJWTx9JpQUDKKZJTK35Ljfc/gi5c13i1d6nn3aWdh+MMQGT1l/lpzgN8SnFu+vSTVX3uctDwCSgGXBQREoDuMtD7uZRQHmf3csB+9zycqmUn7ePiOQCCgM2QVoWdyImlrmf3k+DBU9wILQM++6cTpeej156cjHGZIq0/jIjVHVNykJVXYbT0ytdRCS/iBRMfg50AtYBU4Be7ma9gMnu8ynAnSKSR0Qq4TTmL3Evp8WISHNxLrrfl2Kf5GN1B2Za+0vWtmXDag4MakPb45NYUbonEc/Po0at+l6HZYxJQ1ptMOFprMt7Gee8ApjkNsTmAn5U1WkishQYJyIPAbuB2wFUdb2IjAM24LQBPaaqie6x+vHfbsq/uQ+Ab4FRboeAaJxeaCYLUlUWTB5Kg5WvkiShbGs/lEZtengdljHGD2klmKUi0ltVv/YtdBPA8vSeUFV3AA1SKT+KcxNnavu8DbydSvkyoG4q5adxE5TJuuJPxbLq60dodXwqW/PUpsT931O1TBWvwzLG+CmtBPM0Tk3jbv6bUJoAuYFugQ7M5GyHd6zh1A/30CJxF0vL9aJRr48IDcudcSd4552MO5YxJlUXTDCqehBoKSLX8t9awi+qOjNTIjM51rbpQym74CVCNA8r235D0/YBqIy2bJnxxzTGnMefoWJmAbMyIRaTw52NO8nmYX2pd+RXVobWpfDdw7mqckYMGpGKBQucpSUaYwLGnxstjQm4PRuXwIQHqJOwlz9KPUDLB98nf948gTvhCy84S7sPxpiAsQRjPKVJSSyZ+AkN1r1LjORn2TXf0fE6a+IzJjuwBGM8E330CFuHPczVp2axNrwRV94/gmalbcgeY7ILSzDGE+uXz6XQ1N401kMsrfo4je96g5DQ0IvvaIzJMizBmEylSUksHPMujTcP4kRIYXZ3GUfTxh29DssYEwCWYEymiT1+hG1f96LlqXmszt+cyr1HUqroFd4E8+llDadnjPGDJRiTKfasmU3YpN7USTrKgqrP0OLul5EQDweptGH6jQk4SzAmoDQpkQ0T3qL6+s84JMXZeON4WjZPdUSgzDVjhrO0iceMCRhLMCZgDu7bxZGRD1Dn9HLmh7emyoPfUv+KK70Oy/HWW87SEowxAWMJxmS4pCRl1q9jaLj0/6hCHH/VeomWtz9LaKjN22JMTmIJxmSoHQeiWTuqPzefmsCesIqcueM/tKneyOuwjDEesARjMoSqMvr3udRZ+E9ulu1sq3AHVe75DMmdz+vQjDEesQRjLltCYhL/+X4wnXe8R2hoKMdv+paqTbp7HZYxxmOWYMxliTp4hM3f9aP76ensLlCP8g//gBSt6HVYFzdkiNcRGJPtWYIx6bZ4wSxKTn+Ma3Ufm2v0pUaPdyA0i/xK1ajhdQTGZHtZ5NvABJPEhAQWjHyFq3d9RUxIYQ7fMo4aDTt5HdalmTrVWXbp4m0cxmRjlmDMJdm/azPHf3iQNmfXsbpwO2o89A3hhUt6Hdal+/hjZ2kJxpiAsQRj/BJ/5hzzxn1M822fUhBYctXbNO36qLfDvRhjgpolGJMmVWXW/PkU/bM/HXUDW/I1pPCdQ2hWsabXoRljgpwlGHNBG3YfZO24N7klZjRnQ8LZ0fJ9qnfsCyJeh2aMyQIswZj/cer0OaaN+5Jm2wfTQw4TWeZGyvf8jMqFPBpa3xiTJVmCMedZ8td08sx8mdt0E/vzViH25iFE1AqC0Y8z2qhRXkdgTLZnCcYAsHHFX8RNf4tmpxcRLUWIbPUeEe37QEg2nca4fHmvIzAm27MEk8NtX7OQE9PepFHcfE6Sn2VVHqPBbf9HsXyFvQ4tsMaOdZY9engbhzHZmCWYHEiTkti2cDKJ8wdTM24FJ8nH4op9qXvrAJoULuZ1eJnjyy+dpSUYYwLGEkwOEncqhnW/D6PU+m+olribg1qUORUfpeEtz3B1sSx4s6QxJqhZgskBdmxYwv4/h1D3yG80k1NsD4lgfr23aHDjQ1yTz4bTN8YEhiWYbCo2ej/rZoyi0JafqJ2wkXKai/VF2pG3+YPUuPpGqtgd+MaYALMEk40kxZ9g+19jSVg9nmqxy2guSewOKc/ias9Qo1MfripZ2usQjTE5iCWYLC563w52L5pE2I4/qBq7jGqcI0pLMq/UXZRufQ816jengt15/78mTPA6AmOyPUswWcy5+Bi2LZ/J8Q1/cuXBOVRKjKQYsIcrWFi0KyH1utOsdSfK5bYfbZpKlPA6AmOyPfsWCmaqJB7bzd4NCzm+dRH5Diym4unN1JJEEjSEzbnrML/CU5Ro3JVqtRpRPtTaVfw2fLizvP9+L6MwJlvL1glGRG4APgNCgW9U9T2PQ7qg0ycOc3DnOk5GbSDh4GbyR2/gyrjNFNKTVABKayibQqsy/4qeFKzelupNO1Inp9yzEgiWYIwJuGybYEQkFPg30BGIApaKyBRV3ZBZMWhSImdOnSD+xBFiTkRz8vhh4o8dJOF4FCEn95E77gAFzhyk5Lm9FCGG5Jnsz2koO6QCS/O2ILZYXYpUbUr1+s2pV7xoZoVujDGXLdsmGKAZsE1VdwCIyBjgZiBDE8zxIwc4/kUHcmmC8+AcuUggTBPIyxnCRQkHUqaGU5qHIyElORFWgo1FriGhaFVyX1GdIuXrUKZSDWrky4vNGm+Mycqyc4IpC+zxeR0FXO27gYj0AfoAVKhQIV0nyZU7D9H5KpEUkhsNyYWGhP33EVaAxPDCSN7C5MpXlPxFSlCo2JUUvTKCAoWKUtHuRTHGZGPZOcGk1jdXz3uhOhQYCtCkSRNNZfuLKlCoKI2em5qeXY0xJlvLzgkmCvAdk70csM+jWEyw+fVXryMwJtvLztdolgLVRKSSiOQG7gSmeByTCRb58jkPY0zAZNsajKomiMjjwO843ZSHqep6j8MyweKLL5zlo496G4cx2Vi2TTAAqvorYNdCzP8aN85ZWoIxJmCy8yUyY4wxHrIEY4wxJiAswRhjjAkISzDGGGMCQlTTdX9htiMih4Fdl3GIEsCRDAonECy+y2PxXR6L7/IEc3wVVbVkaisswWQQEVmmqk28juNCLL7LY/FdHovv8gR7fBdil8iMMcYEhCUYY4wxAWEJJuMM9TqAi7D4Lo/Fd3ksvssT7PGlytpgjDHGBITVYIwxxgSEJRhjjDEBYQnmMonIDSKyWUS2icgAj2IYJiKHRGSdT1kxEflDRLa6y6I+6wa68W4WkeszIb7yIjJLRDaKyHoReSqYYhSRcBFZIiKr3fheD6b4fM4ZKiIrReTnYItPRCJFZK2IrBKRZUEYXxERmSAim9zfwxbBEp+I1HA/t+THSRF5Oljiuyyqao90PnCmAdgOVAZyA6uB2h7E0RZoBKzzKfsAGOA+HwC87z6v7caZB6jkxh8a4PhKA43c5wWBLW4cQREjzuynBdznYcBioHmwxOcT5zPAj8DPQfgzjgRKpCgLpvhGAA+7z3MDRYIpPp84Q4EDQMVgjO+S34/XAWTlB9AC+N3n9UBgoEexRHB+gtkMlHaflwY2pxYjznw5LTI51slAx2CMEcgHrACuDqb4cGZk/RO4zifBBFN8qSWYoIgPKATsxO3UFGzxpYipEzA/WOO71IddIrs8ZYE9Pq+j3LJgcIWq7gdwl6Xcck9jFpEI4CqcWkLQxOhefloFHAL+UNWgig/4FHgeSPIpC6b4FJguIstFpE+QxVcZOAx8515i/EZE8gdRfL7uBEa7z4MxvktiCebySCplwd7v27OYRaQAMBF4WlVPprVpKmUBjVFVE1W1IU5NoZmI1E1j80yNT0Q6A4dUdbm/u6RSFuifcStVbQTcCDwmIm3T2Daz48uFcwn5S1W9CjiFc8npQjz5G3Gndu8KjL/YpqmU/X979/JiYxzHcfz9KZNb45qiqLHQLNwTG0RYMEJkMXJPdpZSmhL/gI0tkYiFW7OWRDbIddySkMmtlFI2Fl+L3+800zQuY3rm+anPq56e5/zOc5rPdM6Z73nOb/r+ivy74wIzON3AtF63pwLva8rS1ydJUwDy/nMeryWzpCZScTkbEZdKzAgQEV+B68DqgvItBtZLegOcB1ZIOlNQPiLifd5/Bi4DiwrK1w1056tSgAukglNKvoY1wL2I+JRvl5ZvwFxgBucOMEPS9Pzpox3orDlTGZ4x7AAAAkVJREFUQyewMx/vJM17NMbbJQ2XNB2YAdyuMogkASeAZxFxtLSMkiZJGpePRwKrgOel5IuIgxExNSJaSK+xaxGxrZR8kkZLam4ck+YRukrJFxEfgXeSWvPQSuBpKfl62ULP12ONHCXlG7i6J4H+9w1oI/1X1Cugo6YM54APwA/Sp5s9wETSpPDLvJ/Q6/yOnPcFsGYI8i0hXcI/Ah7kra2UjMAc4H7O1wUcyuNF5OuTdTk9k/xF5CPNcTzM25PG+6CUfPnnzQPu5uf4CjC+sHyjgC/A2F5jxeT7182tYszMrBL+iszMzCrhAmNmZpVwgTEzs0q4wJiZWSVcYMzMrBIuMGaFkXRY0v66c5gNlguMmZlVwgXGrACSOvLaHleB1jy2V9IdpXVqLkoaJalZ0uvcegdJY/JaLE21/gJm/XCBMauZpAWkFjDzgU3AwnzXpYhYGBFzgWfAnoj4RuqVtjaf0w5cjIgfQ5va7M9cYMzqtxS4HBHfI3WZbvSzmyXppqTHwFZgZh4/DuzOx7uBk0Oa1uwvucCYlaG/nk2ngH0RMRs4AowAiIhbQIukZaSVDLv6eaxZ7VxgzOp3A9goaWTuSrwujzcDH/L8ytY+jzlNanLqqxcrlptdmhVAUgewA3hL6oj9lLQw1oE89hhojohd+fzJpGWAp0Raw8asOC4wZv8hSZuBDRGxve4sZr8yrO4AZjYwko6RVj9sqzuL2e/4CsbMzCrhSX4zM6uEC4yZmVXCBcbMzCrhAmNmZpVwgTEzs0r8BKVZHz0Ak+65AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"only bad\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Mnz+fyspKjjnmGL7//nsAZsyYQWVlJTfccANFRUVcdtllbNmyhczMTB5++GFGjhzJOeecQ25uLvPmzWPjxo3cfvvt/PznPwfg9ttv58knnyQpKYkjjzySCy+8kJNPPpmvvvoKgB9//JHTTjuN+fPnhz2+e++9l9dff536+npefPFFRo4cSUlJCeeddx7Lly8nMzOTmTNnMnbs2GbtVqxYwS9+8QsaGhqYNm1aYLmq8sc//pG33noLEeHaa6/l1FNP5cMPP2TGjBm88YapkHv55ZczYcIEysvLWb9+PYceeii9e/fmgw8+aOWvaLF0Q1RhxUfw5T9gyZugTTDiCJh4L+w2GZKSu3qEQGRNI8d53wNTQ/Y15/uxwMexHFS889BDDzF79mw++OAD7rvvPl5//XXmzJlDjx49+MUvfsFvfvMbDjzwQFavXs3UqVNZvHgxN954IwceeCDXXXcd//73v5k5c2bUfi666CIeeughRowYweeff86ll17K+++/D8CGDRuYM2cOS5YsYfr06fz85z/nrbfe4tVXX+Xzzz8nMzOTkpISevXqRV5eHgsWLGD8+PE89thjnHPOORH77d27N1999RUPPPAAM2bM4B//+AfXX389e++9N6+++irvv/8+Z511FgsWLGjW7le/+hWXXHIJZ511Fvfff39g+csvv8yCBQv45ptv2Lp1KxMnTuSggw4K2/+VV17JnXfeyQcffEDv3l371GWxtJvtpfDNs/DlI1D8I2QWwE+vgAnnQs9hXT26HQgrNFT1RgAReRvYR1UrnO83AC92yujaSTSNoLOYPn06PXr0AODdd99l0aJFgXXl5eVUVFTw8ccf8/LLLwNw9NFH07Nnz4j7rKys5LPPPuPkk08OLKutrQ18Pv7440lKSmL06NFs2rQp0Pe5554bmNLp1asXABdccAGPPfYYd955J88//zxffPFFxL5PPPFEAPbdd9/AmOfMmcM///lPAA477DCKi4spKytr1u7TTz8NbHPmmWdy1VVXBdqefvrpJCcn069fPw4++GC+/PJLcnNzI47DYolr1i8whu1vX4SG7TB4PzhhpjFup2Z09ejC4meCfQhQ5/leBwyL1khEMjAaSbrTz0uqer2I9AKed/axEjhFVbc5ba4BzgcagStV9T9+D6Q7k5WVFfjc1NTE3LlzA0LES6gYg5SUFJqamgLf3cC1pqYm8vPzd3iad0lPTw98VtXAe6g+TjrpJG688UYOO+ww9t13XwoKCiIej7vv5ORkGhoamvUR7XhCLQvVFsIfu8USt9TXGMP2vEdg7ZeQmgljT4GJ58OAcV09Ol/48Z56EvhCRG5wtIzPgcd9tKsFDlPVccB4YJqIHABcDbynqiOA95zviMho4DRgDDANeEBEusckXgdyxBFHcN999wW+uzf9gw46iKeffhqAt956i23btgHQr18/Nm/eTHFxMbW1tYH5/dzcXIYPH86LLxqlT1X55ptvovb96KOPBryOSkpKAJOvaerUqVxyySWce+65bTou7/g//PBDevfuvYOmMGnSJJ577jmAwLZu2+eff57Gxka2bNnCxx9/zH777cfQoUNZtGgRtbW1lJWV8d577wXa5OTkUFFR0aaxWiydTskKePvPcOcoePViqCmDabfBbxfD9HviRmCAD6GhqrcA5wLbgBLgXFX9m492qqqVztdU56XAcQSFzuPA8c7n44DnVLVWVVcAy4D9WnEsccE999zDvHnzGDt2LKNHj+ahhx4C4Prrr+fjjz9mn3324e2332bIkCEApKamct1117H//vtzzDHHMHLkyMC+nn76aR555BHGjRvHmDFj+Ne/Igf7TJs2jenTpzNhwgTGjx/PjBkzAuvOOOMMRIQjjjiiTcd1ww03BI7r6quv5vHHd3yuuPvuu7n//vuZOHFis6mrE044gbFjxzJu3DgOO+wwbr/9dvr3709hYSGnnHIKY8eO5YwzzmDvvfcOtLnooos48sgjOfTQQ9s0Xosl5jQ1wtLZ8NTP4Z69Ye79MPxncPbrcNkXcMDF0CO/q0fZaiTc1ECzjUTGAQdhbvqfqGrkR9pgu2RgPrAbcL+qXiUipaqa79lmm6r2FJH7gP+q6lPO8keAt1T1pRb7vAi4CGDIkCH7rlq1qlmfixcvZtSoUX6G160ZNmwY8+bN6zRD74wZMygrK+Omm27qlP5iSaJcA5Y4pXILfP0kzHsMylZDzgDY9xzY5yzIHdjVo3Npc76dqDYNEfkVcCHwT6ejp0RkpqreG62tqjYC40UkH3hFRPaM1FWoXYTY50xgJsCECROiSzxLVE444QSKiooCnlcWi6WVqMKaL4y77KJXobEOhh8EU2+GPY6C5MRJ1e/HEH4+sL+qVgGIyG3AXCCq0HBR1VIR+RBjq9gkIgNUdYOIDAA2O5utBQo9zQYD6/32kWisXLmy0/p65ZVXdlh2wgknsGLFimbLbrvtNqZOndpZw7JYuj+1lfDdi8ZddtN3kJ4LE84zrz57dPXoYoIfoSEYbyaXRnyoNiLSB6h3BEYP4HDgNky8x9nArc67OxH/GvCMiNwJDARGAJF9Py0xI5QgsVgsDluWGkHxzbNQWw7994Jj74a9Toa0rOjt4xg/QuMx4HMRce8ixwN+kp4MAB537BpJwAuq+oaIzAVeEJHzgdXAyQCqulBEXgAWAQ3AZc70lsVisXQ9jfWw5A0jLFZ+AslpMOYEmHgBDJ4IO0la/qhCQ1XvdKaWDsRoGOeq6tc+2n0L7B1ieTEwOUybW4Bbou3bYrFYOo2QeaBugL3P7PI8UF1BpNxTuapa7gTjrXRe7rpeqloS++FZLBZLFxAyD9QUmHgP7HZ4t8kD1RVE0jSeAY7BuMx6vZTE+b5LDMdlsVgsnU/LPFA9enXrPFBdQdjgPlU9RkzOh4NVdRfPa7iqWoHRSfipX9HaGhft4cMPP+SYY44B4LXXXuPWW28Nu21paSkPPPBA4Pv69esD2XYtlm7Fhm/gtSvgjpEw+2ro0dPkgfrtYphyoxUYHiLaNFRVHQP4vp00HksX0djYSHJy61Tu6dOnM3369LDrXaFx6aWXAjBw4EBeeumlsNtbLJ1KfY2JqfjyH3GbB6or8OM99V8RmaiqX8Z8NB3NW1fDxu86dp/994Ijwz9dg4mxmDZtGgceeCD//e9/GTduHOeeey7XX389mzdv5umnn2a33XYLWX+iZV0Nb8T+U089xT333ENdXR37778/DzzwQNQbvTuW/fffn6+//prdd9+dJ554gszMTIYNG8Z5553H22+/zeWXX06vXr24/vrrqa2tZdddd+Wxxx4jOzub2bNn8+tf/5revXuzzz77BPY9a9Ys5s2bx3333cemTZsCdUQAHnzwQe655x6KiooYP348U6ZM4bLLLgvUBqmpqeGSSy5h3rx5pKSkcOedd3LooYcya9YsXnvtNaqrqykqKuKEE07g9ttvp7GxkfPPP5958+YhIpx33nn85je/accPadlpKVkB8x+Dr56E7SVQMMLkgRp3Wlym9ehs/AiNQ4GLRWQlUIVj01DVsRFb7eQsW7aMF198kZkzZzJx4kSeeeYZ5syZw2uvvcZf//pXCgsLQ9afCFdXY/HixTz//PN8+umnpKamcumll/L0009z1llnRR3L0qVLeeSRR5g0aRLnnXceDzzwAL///e8Bk6xwzpw5bN26lRNPPJF3332XrKwsbrvtNu68807++Mc/cuGFF/L++++z2267ceqpp4bs48orr+Tggw/mlVdeobGxkcrKSm699Va+//77QFJGb8CiW0/ju+++Y8mSJRxxxBH88MMPgEni+PXXX5Oens4ee+zBFVdcwebNm1m3bl2gGFVpaWnbfhjLzklTIyx712gVP74DkmTKpk68wERu7yTush2BH6FxZMxHESuiaASxZPjw4ey1114AjBkzhsmTJyMi7LXXXqxcuZJVq1aFrD8Rrq7Ge++9x/z585k4cSIA27dvp2/fvr7GUlhYyKRJkwD45S9/yT333BMQGq4Q+O9//8uiRYsC29XV1fGTn/yEJUuWMHz4cEaMGBFoH6pA1Pvvv88TTzwBmJTpeXl5gUy9oZgzZw5XXHEFACNHjmTo0KEBoTF58mTy8vIAGD16NKtWrWLMmDEsX76cK664gqOPPrrNiRUtOxlVW+GrJ4J5oLL7w8FXwb5nd6c8UHGFnziNVSJyIDBCVR9zIr2zYz+0+MZbzyIpKSnwPSkpKWytcLfWRLiaE2effTZ/+1vUBMNh9xvqu1vrQ1WZMmUKzz77bLNtFyxYEHI87SVSokzvuXNrdvTs2ZNvvvmG//znP9x///288MILPProox0+LksCECoP1LCfwRE3Ge0igfJAdQVRU6OLyPXAVcA1zqJU4KlYDmpnIFz9iXB1NSZPnsxLL73E5s0mVVdJSQktM/yGY/Xq1cydOxeAZ599lgMPPHCHbQ444AA+/fRTli1bBkB1dTU//PADI0eOZMWKFRQVFQXah2Ly5Mk8+OCDgDGql5eXR6x54T3OH374gdWrV7PHHuFz9WzdupWmpiZOOukkbrrppkBNc4slQG2l0Sge+hk8egT8MBv2PdekIT/nDRhzvBUYHYCfIkwnANMx9gxUdT3B+uGWNhKu/kS4uhqjR4/m5ptv5ogjjmDs2LFMmTKFDRs2+Opr1KhRPP7444wdO5aSkhIuueSSHbbp06cPs2bN4vTTT2fs2LEccMABLFmyhIyMDGbOnMnRRx/NgQceyNChQ0P2cffdd/PBBx+w1157se+++7Jw4UIKCgqYNGkSe+65J3/4wx+abX/ppZfS2NjIXnvtxamnnsqsWbOaaRgtWbduHYcccgjjx4/nnHPOaZPGZUlQtiyFN/9oChy98Wuz7Ni7jbvsUbcnbOLAriJqPQ0R+UJV9xORr1R1HxHJAuZ2B0P4hAkTdN68ec2W2VoKzVm5cmXAY2lnwV4DOwGN9bDk32YKaifOA9UOYldPA5Nc8O9AvohcCJwHPNzWDi0Wi6XNlK83OaDmzzJ5oPJ27jxQXYEfQ/gMEZkClAN7ANep6jsxH5mlVRQXFzN58o55IN97772dSsuwJCCqsOJjJw/Uv20eqC4mUsLCEcAMYFfgO+D3qrquswbWHlQ1Jh4/3ZmCgoJAPMTOjJ/yxZY4IWQeqMuNcbvX8K4e3U5LJE3jUeAJ4GPgWEylvhM7Y1DtISMjg+LiYgoKCnY6wbGzo6oUFxeTkZHR1UOxtIcN3xit4ruXoL7a2ChO+DuMPh5S7W/b1UQSGjmq6toulopIXPg4Dh48mLVr17Jly5auHoqlC8jIyGDw4MFdPQxLW2hsgGdPg2XvQEoPGHsyTDgfBo7v6pFZPEQSGhkisjdBK3sP73dV7ZZCJDU1leHDrepqscQdxT8agbHf/8Chf7J5oLopkYTGBuBOz/eNnu8KHBarQVkslp2QYhNAyrhTrcDoxoQVGqp6aGcOxGKx7OSUOEKj165dOw5LRPxEhFssFkvsKS6CzAKrZXRzrNCwWCzdg5LlVsuIA6zQsFgs3YPiIiiwQqO74yfL7Xt+loXYplBEPhCRxSKyUER+5Sy/QUTWicgC53WUp801IrJMRJaKyNTWHozFYolT6qqhYr3VNOKASBHhGUAm0FtEehJ0vc0F/FQvaQB+p6pfiUgOMF9E3PQj/6eqM1r0Nxo4DRjj7P9dEdldVRtbdUQWiyX+KDFlginYpWvHYYlKJJfb/wF+jbmBe2MyyoH7o+1YVTdg3HZR1QoRWQwMitDkOOA5Va0FVojIMmA/YG60viwWS5xjPafihrDTU6p6t6oOx+ScGu55jVPV+1rTiYgMA/YGPncWXS4i34rIo44WA0agrPE0W0sIISMiF4nIPBGZZ6O+LZYEwY3RsDaNbo8fQ3iZiJzV8uW3AxHJBv4J/FpVy4EHMUkQx2M0kTvcTUM03yH7nKrOVNUJqjqhT58+fodhsVi6MyVFkNUX0m19t+6On3oaEz2fM4DJmOmqJ6I1FJFUjMB4WlVfBlDVTZ71DwNvOF/XAoWe5oOB9T7GZ7FY4p3i5VbLiBP81NO4wvtdRPKAJ6O1E5Ni9hFgsare6Vk+wLF3gCkl6xZ7eEzJopEAACAASURBVA14RkTuxNhRRgBf+DkIi8US55QUwW5TunoUFh/40TRaUo25oUdjEnAm8J2IuIUe/gScLiLjMVNPKzEGd1R1oYi8ACzCeF5dZj2nLJadgNoKqNxkPafihKhCQ0ReJ2hbSAZGAS9Ea6eqcwhtp3gzQptbgFui7dtisSQQrrut9ZyKC/xoGt54igZglaqujdF4LBbLzob1nIoronpPqepHwBIgB+gJ1MV6UBaLZSciEKNhp6fiAT9pRE7BGKRPBk4BPheRn8d6YBaLZSeheDnkDIC0rK4eicUHfqan/heYqKqbAUSkD/Au8FIsB2axWHYSSoqsPSOO8BPcl+QKDIdin+0sFoslOsVF1nMqjvCjacwWkf8AzzrfTyWCB5TFYrH4pqYMqrdaTSOO8BPc9wcROQkTdyHATFV9JeYjs1gsiY/1nIo7fAX3qeo/MelALJadk7pqeOk8mPY36DW8q0eTONgYjbgjUj2NCkIkDHRR1dyYjMhi6Y4UL4Mf3oIxJ1ih0ZG4moY9p3FDWKGhqjkAIvIXYCMm35QAZ2BiNiyWnYf67ea9YXvXjiPRKCmC3MGQ2qOrR2LxiR8vqKmq+oCqVqhquao+CJwU64FZLN2K+mrnvaZrx5FoWM+puMOP0GgUkTNEJFlEkkTkDMAmErTsXFhNIzbYGI24w4/Q+AUmEnyT8zrZWWax7DwkgqZRttZklO0uVJfA9m3WcyrO8ONyuxJTv9ti2XlJBE3j0Wkw8hg48tauHonBek7FJZG8p+4lsvfUlTEZkcXSHXGFRrxqGrWVULYGti7t6pEEsTEacUkkTWNep43CYunuuNNT8applK1x3rtRVYOSIpAk6Dmsq0diaQWRXG4f934XkRyzWCtjPiqLpbsR75pG6WrnfQ2ogoSqj9bJFBdB3mBISe/qkVhagZ/U6HuKyNeYWt6LRGS+iIyJ/dAslm5EvGsartBo2A7VxV07FhfrORWX+PGemgn8VlWHquoQ4HfAw7EdlsXSzUgUTaPl565C1dTRsPaMuMOP0MhS1Q/cL6r6IWCrpVh2LgLeU3EsNMT5u7v2ja6kuhhqy6ymEYf4ERrLReTPIjLMeV0LrIj1wCyWbkUgTiOOp6cGjHM+dwOhYT2n4hY/QuM8oA/wMvCK8/ncWA7KYul2xLumUbYG+u8FadndQ9MI1AW3QiPe8BPctw2wMRmWnZt41jTqqqFqC+QPhbzC7qFplCwHSYaeQ7t6JJZWEim473UiB/dNj7RjESkEngD6A02Y4k13i0gv4HlgGLASOMURTIjINcD5mNxWV6rqf1pzMBZLzAh4T8WhpuFqFvlDIL8QyrqBIby4yIwnObWrR2JpJZE0jRnOu2C8pS5o5b4bgN+p6ldOjMd8EXkHOAd4T1VvFZGrgauBq0RkNHAaMAYYCLwrIrurqk2OaOl64nl6qtQjNPIKYe2XXTseMNNT1p4Rl0QK7vvI/Swild7vflDVDcAG53OFiCwGBmHyWB3ibPY48CFwlbP8OVWtBVaIyDJgP2Bua/q1WGJCPCcsLF1l3l1NY/s2k1YkPbtrxuO62xYe0DX9W9qFH0M4RJim8oOIDAP2Bj4H+jkCxRUsfZ3NBgHeyda1zrKW+7pIROaJyLwtW7a0Z1gWi3+8CQu1XX+Hzqd0NSSlQnZ/o2lA1xrDq7ZAXYXVNOKUsEJDRHq5LyBZRHq2WOYLEcnG1Bf/taqWR9o0xLId/p2qOlNVJ6jqhD59+vgdhsXSPlxNQ5ugsb5rx9JaSlebdB1JSUbbgK41hhdbz6l4JpJNYz7mpu3ezL/yrFMgarktEUnFCIynVfVlZ/EmERmgqhtEZACw2Vm+Fij0NB8MrI9+CBZLJ1C/3TytN9UbbSMlratH5J+yNUFhkTfYWdaFxnDX3dZW7ItLwmoaqjpcVXdx3lu+/AgMAR4BFqvqnZ5VrwFnO5/PBv7lWX6aiKSLyHBgBPBFWw7KYulQGhugsQ4yHQU73uwapauDQiO7vxF+Xa1pJKVA3pCuG4OlzUSN02gHk4Azge9EZIGz7E/ArcALInI+sBpTCRBVXSgiLwCLMJ5Xl1nPKUu3wE1S2KMXVG6Kr6SF9dvNmF2hkZQEeYO61qZRUmTSoSfH8vZjiRUx+9VUdQ6h7RQAk8O0uQW4JVZjsljahGsEj0dNw62fke95qu/qAL/i5daeEcf49Z6yWHZeXCN4j57mPZ40DTejrVdo5A/pOk1D1USDW8+puMWXpiEiyUA/7/aq2g3CSi2WTiCeNY1QQiOvECo2QkNd5xv0KzZCfRX0skbweCWq0BCRK4DrgU2YdCBgvKfGxnBcFkv3IaBpOEIj3jSNpBTIGRBcll8IKJSvg17DO3c8JTa7bbzjR9P4FbCHqnaTcl8WSwfQ1AhJyf62DWgaBc73ONI0ytZA7qDmx+oN8OtsoWFjNOIePzaNNUBZrAdisXQomxfD9/8Mva5iE/xtMKz6zN++Wk5PdZSm8dm98Ok9HbOvcHjdbV3yHaHRFcbwkiJITgvGi1jiDj+axnLgQxH5N1DrLmwRe2GxdC++/Ad8/TSMORGkhRPf5oVmyqlkBQz9afR9tZye6ihN4+unjGF4UgwrD5Suhl0Pa74s18nO0xXG8OIi6Dncv5Zn6Xb4ERqrnVea87JYuj/1241GUFsOGXnN17nGYb8aQyw0DVUzDm2CpiYTP9HRNNRCxYYdNY2UdBPk1yWahvWcinf8FGG6sTMGYrF0KO6NvmLTjkJjm5P1taGWkDQ2mHc3+CwWmkbVluB+KzdB7oDI27eFUDEaLl1RV6OpyQiNlpqPJa6IVITpLlX9dbhiTNGKMFksXYpb96JiA/TZvfk6V9MIV4XvX5cZbeKUJ5pv15GaxraVnvGsio3QcI8zr3DHdXmFsP7rju8zEhXrze9iNY24JpKm8aTzPiPCNhZL98QVGpWbdlzn1pcIV1Bp80IQz3RRnaMRpOea5eE0lNbgajtgBMiQGNSWCBWj4ZJfCEveiN3UWCis51RCEKkI03znvVXFlyyWboE7hVSxccd1AZtGGKFRXWIERGBf1SbWISUNUjI6pk546UrngzTXOjoStw537g5laYym0VgHVZshp39s+t9hPDZGIxGwGcMsiYk7hdRS03AT+EFo24QqVG01bqHeNqmZ5nNKRseUfN22ErL7mYyzHSk0KrfAwpfh2xdg3TzovXvoxIDeuhqdJTSKi8z5yxnYOf1ZYoIVGpbExJ1CcjWNF8+F/nvCyGM924S4+ddVQmNt8ymo+mpI7WE+p/boGEP4tlWQP9QIp/YKjbpqWPomfPs8LHsPtBH67QVT/gJjTwvdJhDgtxoKJ7avf7+ULDfpQzprOswSE1olNEQkCciOUoHPYul6At5TG02lvcWvmyfv/p7sN6GERtVW897oFRrbg0IjJaNjDOGlq0yN7OQ0KHqv9e2bGmHFR0ajWPy6EXa5g+CnV8DYU6DfmMjt3eC6znS7LS6C3iM6rz9LTPCTe+oZ4GKgEVPNL09E7lTV/xfrwVksbcbVFCo3mifcpnpjy1j2rlneo2dooVFd3Lw9OJqGMz3VEZpGY71xhx3raBoVG5oLpnCowoZvjKD4/iUzzZaeB3ueCGNPhSE/9f8Un5FrXJE7K8CvqRG2rYA9pnVOf5aY4UfTGK2q5SJyBvAmcBVGeFihYem+NHjiNLYsCS5f8Ky5UecPDX3zdzUNr0DpaE2jbK0J6ssfagLtwAi0PnuE3n7bKvjuBfj2Rdi61NhBdp9qNIoRUyE1o23jyBvSeZpG2VpjeLeeU3GPH6GR6tT6Ph64T1XrRWSHuA2LpVtRX2PcY+sqYN18QIzxt3SVuXGlZobRNByh0dQQTGroNYR3hKbhuvz2HBYUGttWNRca5eth6Vvw3Yuweq5ZNuSncMxdMPq4YMxIe8gvjJ3nVkus51TC4Edo/B1YCXwDfCwiQwFr07B0X1SNQMhzop6Xf2gExqhjYe590HOo2a62Yse2rqYBZooqLdNMT2X3NctSMoKCpa24N+qeQ83+ALYsNgKq6H1jzN6y2CzvvQdMvg72Ojl0vEV7yCuEFZ+Y89UyP1dHY2M0EgY/aUTuAbypOFeJyKGxG5LF0k4a6wA1N+Wy1bDhWzOdM2KKERr5Q6Bys3FPbYlXIDTUOELDMz2VmtF+TWPbKhP3kTvIaEOpmfDOdWZdcppJojj+dNjtcOg7OnY39PxCo4nVlAarEsaKkuWQmtV57r2WmOHHEN4P+CswUFWPFJHRwE+AR2I9OIulTbjTTj2HwcpPADVTP0N+CgUjzPsPs8N4T3nKxjTWmfdmcRo92mfTqKs2huyBewczvR5yNZRvgN0mw9BJRlB1BgG327WxFxrFRcbdNtYajSXm+JmemgU8Bvyv8/0H4Hms0LDEmsaGthUKqvcIDZc+o0xE9xXzzPcVH0W2aUBwfbM4jXZqGp/eDeVr4aSHg8sm/art+2sP3roa/feKbV8lRdHdgC1xgR//vN6q+gJOqVdVbcC431osseXb5+D+/aGmlTXAXE0gpz8kO4bmlp5JKemR4zQg6HbbUZrGtlXw6V2w50n+6njEmjzHRhJrt9vGBmPHsfaMhMCP0KgSkQKcTLcicgA+KvmJyKMisllEvvcsu0FE1onIAud1lGfdNSKyTESWisjUNhyLJdEoWW6C7KpaaXh2b/YpGSZVB5h0Gl5SwnhBVRebuXd3P6odp2m8fa2xYUz5S9vadzRZvc05Ko1xivSy1cYbzXpOJQR+pqd+C7wG7CoinwJ9gJ/7aDcLuA94osXy/1PVZplzHTvJacAYYCDwrojsrqpWo9mZqdxs3mtb6aznRoOn9jDahgDp2c23SUkPrTFUF0PeINj6gxEaDbWAeuI0ehhB1trssMs/gsWvwaHXdp9SpyJmLLHWNIqXm3eraSQEfrynvhKRg4E9MH+/papa76PdxyIyzOc4jgOeU9VaYIWILAP2A+b6bG9JRKoc76aaVgoNr6Yx8XyTYqMlqT3M029jg6fYUo2TjmOgIzRqgoWSXO3DDaRzPav80NgAs682wXw/vaJ1xxJr8gpjH+BnYzQSiqiPSiJyMtBDVRdiAvyeF5F92tHn5SLyrTN95bpsDAK8V+5aZ1mo8VwkIvNEZN6WLSFcJi2Jg5uNtrWahqtBpGTAuNNg4gU7buMG1XntGq4RPNfRBBprm2stYDSNlu2iMe9R2LwIpv617dHbsSK/sBM0jSJIy4asPrHtx9Ip+NGv/6yqFSJyIDAVeBx4sI39PQjsCowHNgB3OMtD+eGFjDpX1ZmqOkFVJ/TpYy/ChMaNo2itIdy1OUS6QQdu/p4cU67tJG9QcF1A03Ajwp19+q2pUVUMH9wMuxwCI4/216YzyRvilJ7tgCSM4Six7raJhB+h4doVjgYeVNV/AWkRtg+Lqm5S1UZVbQIexkxBgdEsvDUpBwPr29KHJUFQNQWCoA3TU47QSIkgNALTTJ6bZUDTGBjcT0BoeHJPefuIxvs3QW0lTLute9408z2xGrGiuMhOTSUQfoTGOhH5O3AK8KaIpPtstwMi4i2EfALgela9BpwmIukiMhwYAXzRlj4sCUJNWTC4rtXTUz6ERuDm79U0nMA+d3qqoS7E9JTT7t0b4OMZsPAVk3nWK9jqqk1ywadOgvmzYL+LoO/I1h1DZxEI8IvRFFWjk13YGsETBj/eU6cA04AZqlrq3Pj/EK2RiDwLHAL0FpG1wPXAISIyHjP1tBL4HwBVXSgiLwCLgAbgMus5tZPjek5B2zWNSKnGU0JMM1W3nJ6q2XF6avBEGH4QrPnCeEN5yepjjN1blhiDel4h/Oy38LPftW78nYk3wC8WbFtlikJZTSNh8OM9VQ28LCJ9RcTNmLYkUhun3ekhFoeNIlfVW4Bbou3XspNQ5REatT5sGnVV8Nl95iZd3xpNwzPNVLXV5ITKcpITNoQwhOcNgrNfd8ZVaWpElCz3vFbAmBOMAb419S26ipyBJnYkVppGiU1UmGj4yT01HWOwHghsBoZghIbNCWCJHa6mIcn+NI3lH8GHfzVagNd7KhypIYRGTSlk5AfXNfOeCuFem55t0m/EOgVHLElOMYIjVppGsXW3TTT8PAbdBBwA/KCqw4HDgU9jOiqLxY3R6DnUn03DTXNeU9o8TiMcgekpj9BorDOuuG7qkWbeU1Gq6sUzsXS7LSky1QUzC2Kzf0un40do1KtqMZAkIkmq+gHGZdZiiU5TExR9YLyhWkPlZqNl9Bzmz+XWDeCrKTPaQXJa5KmhUNNTjQ1meio5xfTdUBNZ00gUYhngV1wEBdbdNpHwIzRKRSQb+Bh4WkTuxhirLZborJoDTx7vVM9rBZWbTG6kjHx/01NeodFQE4zDCEdqiCC9xjojbMAp67oTaRrl64zQ7GhKiqw9I8HwIzSOA7YDvwFmA0XAsbEclCWBcAPm3Ohu3+22GIN0Rq7P6SlHaGwvdYRGeuTtQ0WENxMa6c0N4ZGmuuKdvELj4VSxoWP321Br4j+sPSOh8OM9VeX5+ngMx2JJRFxbw/ZtrWtXuRmy+0B6bus1jfqa6Ok6XE3E63Lb5MlD5aZOr68223Z3L6j2kO+J1cgvjLxta9i2ErTJahoJhp/cUyeKyI8iUiYi5SJSISK2RrjFH3UeDaA1VG0xac0zco0XkzcIL1I/fqen/GgajXXNS70mKoG6Gh0cFW49pxISP8F9twPHquriWA/GkoC0RdNQdWwafYxNA4y2kR0h15g7PVVTajSGaNNTIW0a9S1sGjXGMJ7IRnAIpmrv6LoagRiNXTp2v5YuxY/OvckKDEub8brC+sVNIZLd10xPucsi0dJ7Kpp2kJRigtrqWwiNJOc5KjktaAhPdE0jLRMye3e8221xkak9ntmrY/dr6VLCahoicqLzcZ6IPA+8CgTmCFT15RiPzZIIuEbs1kxPuTEariEcokeFezWN1KzomoaIU7q1xfRUeo757HpPQcIIjZfmr2VE32zGFebvuDJvcMe73VrPqYQk0vSU10OqGjjC810BKzQs0WnL9JTraZXdJxhoF80YHrCdlBkNIiMvej+pGc2FRlP9jt5T2pgw01M3vLaQn+xawMNnTdhxZX4hbFnasR0WL4dhkzp2n5YuJ6zQUNVzO3MglgTFqwH4ZbVTsLHPqGASwWhut97pqbRMf8WOUjJC2DQ83lPVJdCUbAoIxTk19Y1U1jawYE0pqoq0DLbLGwI/vmvsSR0RiFe/HcrXWk0jAfHjPfW4iOR7vvcUkUdjOyxLwhDQNFohNJa8CYP2hdwBHptGFKHhCqfacpOa3E9cRUrGjmlEvIZw13sqLcv/2LspxVUmzfyWilo2lIWoBZJfaHJ2VRd3TIclK8y79ZxKOPwYwseqauAfr6rbgL1jNyRLQtHa6anyDbD+K9jjKPM9YNOIpmlUOUZsp3iTX6GxQxqRVGedE6dRV5UQNo2tFUGX5W/WhBDgbl2NjvKgsp5TCYsfoZHkqeWNiPTCn6uuxeLxnirzl39q6Zvm3S2N6kfTaGqC+iqTrRWcOA0fQqOlTaOxDpJTWVe6narGlGBEeAIIjeKqoNBYsDaE0Mjv4GJMNkYjYfEjNO4APhORm0TkL8BnmNgNiyU0TY1BAVHnCA1tDAqQSCx9E3oOhz5OpbukZEjLiexy69oz3OJJ4N+mEWJ66s+vfs/cVRUeoRH/hvCtlWZ6qnd2WhRNo4OERkmRceP145BgiSsiCg0RSQKWAScBm4AtwImq+mQnjM0SjzQ1wt3j4Mt/GMFRW2EiuyH6FFVtJaz42GgZXmNstPxTrtDI9QiNaBHhsOP0VFMDJKdSXFlLRUNKQsVpFDtC49A9+vLd2jIam1pofT16GoN/R0WFFy+3WkaCElFoqGoTcIeqLlLV+1T1XlVd1Eljs8Qj21aaKY5NC80Nuakh+BQbzYNq8yLztD/sZ82Xp+fuqGmowovnwLJ3jd0BIHdgcH20OA0wwiDE9FRFbQNVTclGGDXVJ4SmUVxZS4/UZH6yawFVdY0s21zZfAMR8zt11PSUjdFIWPxMT70tIifJDj56FksINi0071VbgtNR7nx5NA+qrT+Y994jmi8PpWnUb4eFrxg3UbefPE+yPT/agWvsdnGmp6pqG6huTDFTan731c0prqqjIDstENgXeopqcMcYwuuqTMbcAmsET0T8CI3fAi8CtTZh4U7I2vkmXsEvoYSGezOPNj219UfjvZQ/tPnyUJluXSFSuTG0TcOPppHSI2jTaGo0GVmTUqmqbaSiITm4XQIIja2VtRRkpzO8IIvcjJTwxvCO0DRKlpt3q2kkJFGFhqrmqGqSqqapaq7zPbczBmfpYlTh8WPgkzsib1dVDOXrzefNjtCo3OzRNJwsqtGmp4qXmXnw5BbOeaE0DXe6qnJzcHoqpz/gKMS+bBrpwXrijfUANCWlUlXXQHWTZwwJMD21tbKOPtlpJCUJ4wrzWbA6jDF8+7ZgzEtbsZ5TCY2f4L6DQr06Y3CWLqauyhiCN34XfpuGOph1NDxxnBEymxyTV9XW0JrG9lJT/rVkhXm697L1ByjYbcc+Qtk0XM2jclPwJpeeG4zr8OM9ldojmF+qyQiNekkx9ntSm28X5xRX1lKQZbSvcYPzWbqpgu11Lc6/K9zbq23YGI2Exk+8xR88nzOA/YD5wGExGZGl++BqBpsjJDn+7G7Y4qxfO89MTaRmGVfbqs1meXZfE2m9vRTevxm+fNgs3+MoOP1Z87mxwQgSNz7DS0buju66tV5Nw1mXlu2Uhy3zGdyXHizC5GgatU3mOaq50IhvTaOpSSlxbBoA4wvzaWxSFq4vY8IwTwZar9tt31Ft77B4ufGYc5M/WhIKP9NTx3peU4A9Me63ERGRR0Vks4h871nWS0TecYo6vdMiaPAaEVkmIktFZGpbD8jSgbhP91Wbg2VbvRQXwUf/D3Y5xCQJ/Og2QINJ6ty57Yw852ZeCqs+g0ETYPdpxr22qclsU7rKPO333n3HftJzjZHaW4jJ1TRqy6HSyYqbnh2MC/AlNHoYY3djg9k/UNtkbBm1mjiaRnlNPQ1NSkG20TTGFppztKClMTwQ4NdOY7j1nEpo2lLDci1GcERjFjCtxbKrgfdUdQTwnvMdERkNnAaMcdo8ICLJWLoWr7fT5kXmRv3eX4I37A//ZjSIE/4OQyfBsnfM8l0OMe9u/qH0HBMHULra7GfEFKNl1FVC6Uqzjes5VdDCcwpCR4V7p6tc4ZSaFRQafm707hRWw/aAprHdERp1CaRpeAP7APrmZDAov8eOQiO7v3FEaG+AX3GR9ZxKYKJOT4nIvZhU6GCEzHjgm2jtVPVjERnWYvFxwCHO58eBD4GrnOXPqWotsEJElmGmweZG68cSQ7w35s2LTersT+4wOZ72uwgW/Qv2PdcYoEdNh5WfmKf3wfuZNu7NPC0beuQbLQOFwRPNdzDeVr12MZ5TAL1D2DS8+afc6n1ew3hJkREYSZ6U6L68p1yhURvQNGoaQ01PxbemUVxpNDTXpgEwrjCPb1p6UCUlGQ+09gT41ZQbzdRqGgmLH5vGPM/nBuBZVf20jf31U9UNAKq6QUT6OssHAf/1bLfWWbYDInIRcBHAkCFD2jgMiy8C3k5iNATXh//zh0zQXmMdTDjPLBt1LLz1B+g7EnKcCPCS5SDJ5qabke/ERAgMnmCeaCUJNn5v2m79wZR37dGz5SiCc+NeQeHVOoqLzNQUBIWR34hwMHaNgKaReEIjoGnkpAWWjS/M583vNhoDebZHwLY3wM99ULCeUwlLtDQixwN9gI2q+riqPt0OgRGxqxDLQma3U9WZqjpBVSf06ROhZrSl/biaRt/RsPpzWPEJ7HKoWf7JHTD0QCMkwKQx3/tM2PPn5uYPxrMpPcdEG7vCoO8oow2kZZqn0U2Oyat4WeipKQhOT3mN4TVlBC6b7SXBmhduTfFWaRo1Ae+p7Y6mUaeJ43LrJitspmkMdoL8WmobeYXtm54KeE5ZoZGohBUaIvIA8BugALhJRP7cAf1tEpEBzv4HAI57DWsBTzgvg4H1HdCfpT24No0h+xsPqaZ6OORq2G2KWT7xvObbH3cf/PRy82Se5mgH7g3f1QAK9wtu339PIzRUjaYRamoKgpqGV7uoLTcRzOJcwq6m0SabRk1geqo6MD2V5tku/jUNEeiZGdSe9hyUR5LAgjUtXJnzC000d0Nd2zordgP7rE0jUYmkaRwEHKaq12DsEMd3QH+vAWc7n88G/uVZfpqIpIvIcGAE8EUH9GdpDzVl5qbfz/F7yOpj7BFH3AwTL4CRx4Zvm9XbvAemjRxNY7BHaPQbY3JVLXzZFP8Z8pPQ+wpMT3k1jXIjiFytxtU0+u1pUqS7GkckAtNTNYHpqSonEjyRXG6LK2vpmZlGSnLw756VnsLu/XJ2TCeSVwgolK9rW2clReb8p8X3ObOEJ5LQqFM1yXdUtZrQU0hhEZFnMYbsPURkrYicD9wKTBGRH4EpzndUdSHwArAImA1c5vZt6UJqSs2Te9/R5vvu00yq8r4j4eg7ICUtfNtsx1zl3vBdITLkgOA2/fYy72/+wdys9vx56H252kPL6an0vGA/rtAYeRT8brH/1OjgaBpGaFQ3CCIeoZGUAsmpYXYQHxRX1lGQteNvNb4wn2/WmvKvAdpbV6O4yNozEpxIhvCRIvKt81mAXZ3vAqiqjo20Y1U9PcyqyWG2vwW4Jcp4LZ1JTZl5Yh8wzrjRTjgvWosgrgbgCo2xp5qcUt4bSn9Hg6kuhqOuCS+EApqGZyqlttxEMKdmAN8FNZrW4BUazjRXZUMS2ekp0OCMJc61DDA2DTewz8u4wnye+3INq4qrGdbbKWnb3roaJUUw8pg2jtQSD0QSGu0ICbUkBDVlQaP1Wf+Kvr2XlkIjPcfEZ3jJHWT2n9LDGNHDpoTkuwAAIABJREFUkZIOyek7Tk9l5BnvLGhbHW+vTcMp81rRIOSkp5Cc1AOaSAyhUVnH6IE7povzGsODQmOweW+LprG91DwAWE0joQkrNFR1VWcOxNIN2V4KvYa3rW1LoREKEZj6N+OiG206KT2nhSHcsbe4wiKtDSkrXLfc+hrc2ajKeiErPQWkB9QQ90ZwgC2VtfTO3tGbbPd+2fRITWbBmlKOG+94uKekmyC/tmga1nNqp8DW+rbsiKq5obuaRlsIGKij3Mz3PsPf/rz5p5qaHE0j15QUhTZOTzk30obtJrANqHCERj0ZjtCIb02jtqGRipqGkDaNlOQk9hqUt6MxvK0p0ottjMbOQFvSiFjihaqtrXtirKuCp0+GF5ypItcQ3hayfWgarSE9JxjcV1cJqBlbS0N4a3C1iIbagCG8vE7IyUghLT2dJiTuNY2SKuM6WxBC0wATGf79+nLqGpqCC9sa4FdSBIip8W5JWCLFabznvN/WecOxdBjFRfDgJPjH4cFCQ5HYXgpPngA/vg0r55gkfnWV/lxXQ+Fneqo1pHs0DVd4pOcG64+3xabhahqeiPDyeshKSyErPZV60uJeaLi1wUMZwsEYw+samli60WMvyhtsUok0NYVsE76zItPWj+eaJW6JpGkMEJGDgekisreI7ON9ddYALW2gdDU8Pt3cZCs3wrfPR2/zyR0mtfmuh5m6F6WOSavNmoZzM8/ooHpdXqHhRqpn5BqbS1JKsBZEa0jxahrm5lpeZ6anMtOSqZOUuJ+e2urkneodRmiMd8q/Nqvklz/EnA83tb1fSopsUN9OQCShcR0mC+1g4E7gDs9rRuyHZmkzb11tbqznvQX9x8Jn9+741Fi2FmYdE6y4t+YLkxNqwvnm+/qvzXuPNmoaBbvBUTNMXqlWoqp8v65FpHKGp+RrjUfTyB0Iv1kEux3e+jEmpxjvq4btJpcWsK0WcjJSyEpPoU4TR9MIZQgHGJTfg97Zac0r+bXV7dbGaOwUhBUaqvqSqh4J3K6qh7Z42QJM3ZWN38PSf5t0HgPGwaRfQfGP8MPs5tst+pfJSrvk32YqasM3MHCfoLeUKzTaqmmIwH4Xhk5AGIW5RcUcc++c5oLDa9Nw392ps5x+pr+24FbvC2gakJWeTGZaMhsoCAa7dSB//6iIcx/rnIQHgbxTYYSGiDBucH7zHFRtqatRXWJsYNZzKuHxU4TpJhGZLiIznJeN3OnOfDLDeCztd5H5Pvp48+T4xd+bb7f8Q/O+cg5sWWKetgftAz2HmeXrF5j3tto02kHRVlPze33p9uDC9BwzPaUa1DQ6YuorJcOxaTip0ZuSyUpPITs9hTMbr4XDrmt/Hy14+at1fFZU3DwSO0ZsrawjPSWJrLTw5WnGFeZTtKWS8hpj12mTpmHrgu80+KkR/jfgV5gUH4uAXznLLN2NrT/Cwldhvwsg0ynjmZxiorFXfByscNdQByudZMWrPoV1883ngfsYg3J2P6N5QNs1jXbgCott1Z6keem5pspefXUwZXt6BwmNhhqjbQH1JJOTnkJmWgrb6tNoTOrYFCJbKmpZuqmC2oYmKmsbOnTfodjqxGhIBE1sfGE+qvD9Wo+tKCOvdR5UNkZjp8GPy+3RwBRVfVRVH8VU1gtRyNnS5Xz3opmmOeDS5sv3PAm0CRa9ar6vmwf1VTDiCKjaYtql5wWNmL12CdbdbqtNox24QqO4yis0PJluaztQ00jNCGS5VUlGSSIrPYWsdPNkXl3XsTf2ucuLA59de0MsKa6sC+s55TJ2sHkw+Nobr5E3pPWahiQFNdU2sqakmo1lNZ2ihVnaht/gvnygxPnc+Y+eFn8sew8G7RuMXXDpNxr6jILvXzZ2huUfmj/4wVc7LrafwPCDAwFu9BwOq52iiV2gaazb5mgaXqHhTVpYU27KzPqpAx6NlAwny20d6mgVWekp1NQbx4HqukZyMjpO25hbFKy1vrWyNpi+I0YUV9XSNyfyecrPTGN476zmQX75hbCtFUkhSorMtFakJJZRWLKxnKPvmUNjk9IjNZmhBZkM753FsN5ZDC8w78MKMumTE1lzssQWP0Ljb8DXIvIBJlnhQcA1MR2VJTqrPzcagRtEV10C67+Cg/4Qevs9T4IPboaydUZoDNzH2DCy+hrXykEeL2rXGJ6U2iUupxE1jdqKYMr2jrhxuNNTTQ00JZm/Q056CjX1JslyZW0D/XzuqqlJOWfWlxw0ojcX/Cy06+mny4oZlN+DdaXbAxX1YklxZR2j+kfXyMYNzmumBZE32Ni7fHfUfs+pGf9ZSmZqMr+fugeriqtZWVzF0o0VvLNoEw1NQc0jKy2ZoQVZjkDJZFjgcxYFWWlWoMSYqEJDVZ8VkQ+BiRihcZWqboz1wCwRaGqEJ46DPY6Ekx8zy1Z8bKagdg3j2LbniUZozDzYRIr/7HfmpjtsEix8BQbuHdzWjejNyOuYG3MraGhsYmO5CUZspmkEqveVmempjor/SAlOTzV5NI2qOiM0qmv9Z+h/47sNfPzDFrZV1YUUGmtKqlldUs3FB+/KQx8VBWIoYoWqOtNT0asYji/M59UF69lYVkP/vAyjNdSWm6DPaFOUqqbM6+CJbR7rvJUlvLt4M3+Yugdn/3RYs3UNjU2sK93Oiq1VrNxaxcrialZsreL79WXMXriRRo9AyUlPMRpJ7yyGF2R6PmfRM0QqFUvr8TU95dT1fi3GY7H4pXy98XZa8obRMDJ7QdF75sY6aELoNgW7wmHXGmN5Sjrs69TC2u1wWPxG8+JIrm2jC+wZmypqce8BJWE1jfKOmzZLzTDBjI11NEpQaGQ5RuoqnzaNhsYm7nrnBwAWbSinuq6BzLTmf6+5ReZJ/thxA3joo6KY2zTKaxqoa2wKG9jnZZwb5LdmG9PyBjSvqxHtOqjaagRMGzUNVeW22UvonZ3OuZOG7bA+JTmJoQVZDC3Igj2ar6tvbGJNidFKVmytdoRKFQvWbOPf367HI0/I65HaTJgM753FMGfaK69HfNdM6UxswsJ4ZNtK895YB9//01TRK/oAhh9kvKXCEWrqatwvTN3v3AHBZb08mkYn405NFWSlUeL1nnI1C9cQ3hGeU+CxaTTw/9s78/ioynPxf5+ZTPZMFkIWkgBhX4JhB8F9QbSudS9ut9pee7Uurb21v7ZWu1ir1161Vi2utG5oxStuKIKICAhhD8iWkJAQsockZM/M+/vjPTOZJJNkEpJMAuf7+cxnZs6cOfPMm8l5zrM7xHBPBQdwvEE/9jUQvmzrEbJLa7hx9nDe2nSYHXmVnD56SKt9vskqJTY8iEmJdqJCbX1uaZQdd9VodK00JibasVmF7XmVLExL1IFw0MHwhCmdv/kEM6fW7Cthc04Ff7hicjtF2xU2q4VRQ8MZNbR977GGZgd55XVuRXLIuN+cU8EHOwrwjLXHhAXqGIorduKOo4T2akzrZMBUGoMRV4uPsDjY9i+dIluZB2f+rPvHslggMqn1tpBonU3lhxoNl9JIS4okI6e85QVPS6PuGAwd1zsfGBCsrTZHI83Gv4Ou09DZUzU+uKcamh08veoA6SlR/HLheN7adJithytaKQ2lFOuzypg3eggiwpCwQHfhXV/higkNCevaPRVsszIp0d4SDO/OBL8TqNFwOrWVMWJIKDfM7kErmE4ICrAyJi6cMXHtFUp9k4PD5bUeLi+tVNZnlbFsW+tRt7HhgW6LpMU60bGUsKBT7xTq0zcWkTOAsUqpV0VkKBCulDrUt6KZdEhFjs5+mn8vfP5r3ZU2bpIu5OsNRGDipT2fpXECHHErDTtf7S+hvslBsM3a0mK9IkdXuE+6vEfHdzoVFotHnMYW7K4IbzYsjVCb1X3F64ul8famPI4cq+Oxq6cQFRrImLhwtuRWtNrnYPFxSqobmGcoktjwIEqr+9Y9VebuO9W10gDtonpvSz4Op8IaNlQr1GM+VIWXZ+l2LD3o//XhzgL2Flbz9A1TsVn7r+l2sM3KuPgIxsW3b6hZ1+ggp6wlfpJTWsOhshrW7i/h31vyW+0bFxHUKrsrNVa7vkbEhBHSSUHlYKZLpSEivwNmor2Jr6LH1bwOzO9b0Uw6pCJHZ7dM/QHseAvGnA/n/rqla2tvcOVzvXesblBwrI6oUBvJ0Tprq6K2kcTIEO12s4Vpd5xy9qinVV2jg/OeXMPPLhzHtTONK2lXRbizmWZlJTwoAItFCDOURleWRm1jM39bfZA5qTGcMUbP9pgxPJrP9hSilHJn8qw34hnzjX1iw4P4rrDK+0F7iVJ33ynfAsDpyVH8c0MuWSXH9ck0Mtl3SyN6RLdnqTc2O3ny8/1MTLRz2WnDuvXeviQk0MrERDsTE9u7QGsamg2FUtvi8iqtYdXeonbZcImRwR4WSqg742t4TKi+EBqk+GJpXAVMA7YCKKUKRKSX+l2b9IiKHF1EFRoDP/nG39L0Kkcq6hgWGUJ0qD7RldcYSgO0i+p4ob6iTeh0RL1XVu8t5mhlPfuLPNqAB7RYGo3o7raA+yqxpouq7SXrcyk93sALN013K4gZI6JZmpFHdmkNow1f+zcHS0mODiElRivD2PBASqv7OqahT2K+Zg21BMOPGUojxbcCv/KsHsUzlm4+zOHyWl79j1mtrb8BTFhQAJOHRTJ5WPt4X1V9E7mltRxyWSmGhbIi8ygVtU3u/URgWGRI63RhQ7kMjwklMGBgjznyRWk0KqWUiCgAEenbaiST1pTs0xXbobE6PTZhii66GneRvyXrEwqO1TN8SKg7eNsqgyrYrpXGxMt7lAr80U7d0beqzkMReMQ0GtE9pwACAywEWi3u1FtvVNU38cJXWZwzfigzR8a4t08foU++W3IrGD00HIdTsTG7jIVpCe59hoQH6eymZmefnSTyK2qJCrX57PYZFRtGRHAAO/KOcd3MFB3X2Le78zcppSf2jeie46G2sZmnVx1kdmoM54wb2q33DlTswTamJEcyJbm9QqmsbeJQWQ25HtbJobJaPtp5lMq6FoViEUiKDmmlTFw1KMnRIf3qwusIX5TGOyLyDyBKRH4E/BB4sW/FOsWoyNVDiwLbFNIpBe/fqYv2ACIS4e7NuhjvBNs1DFQKjtVx+ughrSwNN65geA9cU8cbmlm9V8+HcDfmAx3TUE5oqqNJWVsFNkODrJ3GNF76+hCVdU08sKB1Huio2HAiQ2xsza3gupkp7C6opKq+2e2agpY4Q1lNQ4sl1Yus3FPEv7fmc9XUpK53NrBYdMfb7a5geGSK/q011Xc8WOl4kW5J001L45V1hyg93sA/bp5+ShTjRYbamBoa5Z5f4klFTWMb60THUd7feoRqD0vXahFSokOMyvgw5qTGcPGUxHbH62t8Ke77HxG5EKhCxzUeUkqtPJEPFZEcoBpwAM1KqZkiEgMsBUYCOcB1SqmKjo5x0uB06IK7iZfD5c+0fm33+1phXPF3fWJb/lPdkBD8qjTyK2p5JyOf+84f26tuhar6JqobmhkWFeyead3a0ojSGWOeNSU+suq7IhqanYQGWltd2bkHMTVU06DC3O4p0Cf2T3YVMnNkDJedltjq5FZe08jLX2dzcVoCaUmtrywtFmH68Ch3MNwVzzh9VEs2lcuSKjve2OtKI/NIJfe8tY3TkqP401VdpMu2IT0lkhe+ytYJCK5ut5X5EDvG+xvcmVO+D1+qqGnkH19lc8HEeGaMiOn6DSc50WGBRIcFMn146zECSinKahq1IjEyvHJKdcbXpkPlVNU3DUylAWAoiRNSFF44VylV6vH8QWCVUuoxEXnQeP7LXv7MgUd5ti4uy3wPFv65ZWxpcyOsegTiJkP6jbp1xof3wcbn9et+nMP84tpslmzI5appSaT2Yu+kwkpdCZ4QGUJkiA2LtKkKP+83usutpfsm+kc7j5JgD2ZCYkTr+ghX8kB9FQ3OCLd7CuCp66fy4LKd3PPWNt78NpdHLk9jfIK2dl74KovaJgc/u9B76u+MEdF8ua+EytomvjlYyti4cOLsLVfrLkujpJdrNY5W1nH7ks3EhAXy4i0zup3Bk54chcOp2F1QyQzPuRodKY0e1Gg8/1UWxxub+cVF47ve+RRGRIgNDyI2PKiV+xO0Qmlo7uY43l7Cl9bo1SJS1eaWJyLvi0hvzna8AlhiPF4C9FL+6ACncJe+bzwOezyK7ncu1QHvCx8Bi1UHvUfMg2LDxxw9ot9FBZ2yumK37iLjOsn3FsVV+gQaHxGExSJEhwa27j+VNB1GntHt4zY5nKw7UMqCyfFEhdhaxzRsHpaGM6CVeyotKZIP7jqDP12Vxt7Cai555ms+3nmUoqp6lqzP4aqpSYz1krIJMH2Evmr89lAZm3PK3am2LmI9LI3eoqahmdtfy6C2wcErt83qslGhN9zjX/MqfZurUZale5S59u2Co5V1vLY+h6umJbkVsEn3ERG/ZWD5csn2V+AXQBJ69OsD6JjG28ArPfxcBXwuIltExJgWRLzRrsTVtiTO2xtF5McikiEiGSUlJT38+AFEUWbLjOvtb7Rs3/Y6xI5vPcZ0/CX6PjAcQlufhPqLbXnHKDJO7kcr67rYu3sUV2slNDRCX4VHhwW2nqnRQ3YdqaSuycHcUUOwh9haxzRcnXIbq6lzWtwt0V1YLcKiOSP48ufnMDUligfe3cGD7+3E4VTcd0HHBYbpyVFYLcJL6w5R3+Rknkc8A1osjd6qCnc4Ffe8tY19RdU8u2h6j0/IcfZghkUG67iGfZiuB+os7bY8S7tKO+tE4MHTXxwABfd3snYmAxtflMZCpdQ/lFLVSqkqpdRi4BKl1FKg+7M8NfOVUtOBi4G7ROQsX9+olFqslJqplJo5dOhJkHVRuEsrh2k36xblFTn66i1vI0y9sXWW0PiL9X30yH5vJOhiReZRbFb92Ud729IwUlBdbpyYsMBeuRL/NltXls9OjcEebKOqrgmnqymRR3v1eqfFXZ/RluiwQJ5fNB17SABf7ivhulkpDB/ScQfgsKAAJiZGsOlQORaBuamtlXxooJVgm8VdgHei/OGjPazaW8wjl0/m7BPMRkpPidKV4VYbRAzrwtLI9rkS/GDxcd7JyGPR3OHu1GOTwYcvSsMpIteJiMW4XefxWo8mpSilCoz7YuB9YDZQJCKJAMZ9cU+OPegozISENB23sATAxz/XrUHEoifueRKTqudldNULqI9QSvFpZiHzx8QSFWrrE/dUaKDVHVeICe0dS+PbQ2WMiQsnNjyIyBAbTuXRiNBDadQ5rIR20hYizh7M4ptncsHEOO49f2yXnzvDCGymJUUSGdq68M3lr+6N9uhL1ufw2voc7jgjlZvmnrjbcmpKFIfLa3USQlRKx5aG06ljcj7GM/66ch8hNit3ndtBfMRkUOCL0lgE3Iw+iRcZj28SkRDg7u5+oIiEuYoDjZqPBUAmuouu0XqVW4EPunvsQUdNGVQXQHya/uf83pNw8AtY95TRRNBLlewty+HSp/pfVuC7o9XkV9RxcVoCiZEhveKe+mp/ibvYrri6nriIlqr2mPDA1tlTPaDZ4SQjp4I5qTqQaA/RSqGq3lAaHqmkTbT0nOqI9JQoXrp1FvH2ruMFrrhG28aFLoaEB52we2r13iIe+XA3F06K51eXTDyhY7lwFfntyD/WeYFf9VFd4+JD5tSOvGN8squQO84c5XNbE5OBSZdKQymVrZS6TCkVq5Qaajw+qJSqU0p1Y0qLm3hgnYjsADYBHyulVgCPAReKyAHgQuP5yU2REQR3WQ4zboN5PwUUTLvJ+3uCwjvOme9jth7WKaTzRseSGBl8wu6pf27I4dZXNvH4in2Adk95Bm+1peHhSuoBe45WcbyhmTlGuqvd6Fha5Uq7DWhJd23G2u0uq50xf0wsExIiOmyRkWg/sTXcU1DFT9/cxuRhkTx9w1SsvZT+PCUpEovA9sPH9MVM1RGdGt6WbmROPfHZPmLCArnjTP9l/Zn0Dr70ngoGbgcmA+7/aKXUD3vygUqpbCDdy/Yy4PyeHHPQUthGaQBc8HtIuwYS2y1Rn/LGt7m8vSmP5XfP77DYandBFZEhNpKjQ0iIDG49HrSb/GtDDg99sBurRThUehyAkuoGJg1r6fcTExaIw6morGvq8QAdVzxjrmFpuOYmuGs1PPp1NRJAdC92LY0ND2LFfR2H65KjQ1izv7hVjypfKaqq5/Ylm7GH2Hjp1pm9quzCggIYGxehLY0pyaAc2qqITG69o4/dbdcdKGXdwVJ+e+kks834SYAv7ql/AQnARcBX6Ayq6k7fYdI5hZm63mL/Z7rKO8wjs8ZigWFT+z3Q/fnuInYdqXR3mfXG7oJKJiXaERES7cGU1TS6x6J2h8wjlfz+oz2cPyGO2+aNJK+8DodTUVzV2j0VZz/xWoZvskpJjQ1zB9ftIW0sDVuLpdFEQL+2uk6ODqG+ydk6rdgHahubuX3JZqrqmnjZR1dZd5lqBMNVZ2m35VlgDQJ7cvvXDJRSPP7ZXpKiQlg0p3dbn5v4B1+Uxhil1G+BGqXUEuB7gH8isScDR7bCKxfBigd1tlTSDH9LhFKKnfnaasg84r3zapPDyd7CatKStCWQGKVPtkVV3XOv1DU6uOftbQwJC+LJ69IZExdOo8PJweLj1DQ6WrmnXI+7+xkuSo83uOszXLjdU66Yhoel0YyVsH5sZ+3q5JtXXuvzexxOxb1vb2dPQRXP/mB6K8usN0lPiaKitokCjEwsb8HwsmydnNFJseWnmYXszK/kvgvGDurOriYt+KI0XEntx0QkDYhEt/ow6Q51FbDnA3jjGl2od9cm+M+1cMWz/paM/Io6dxfO3QWVXvfJKjlOY7PT3d0zMVKf0LvyyW/PO8bsP33hDnY/++UBsktqePK6dKJC9XAbgM3GwCVPSyPesDRcdSG+UNfoYEuuPtby7QU0OxXfn9ZyJewOhHuJaTSq/rU0XGmn+RW+JxT8+ZPvWLmniN9dNplzJ3gtZeoV0lP033lbpVHx722uRhfdbZsdTv7ns32MjQvn+9M7tkZMBhe+KI3FIhIN/Aad4bQH+EufSnUyUX5INx18fBS8c4s252/+Pxg6XsctQnpa6tJ77DCsjGCbhcwjrZXGk5/vY9OhcrcF4rI0Egyl0VXa7ZOf76O4uoFXvzlEfZODN789zEWT493N+0bG6hOnW2nYPdxT3bQ0lFLcv3Q7Vz+/gWVb81m2LZ+0JHurQjeXT91bTKMZa7vivr4kKVorLF+Vxusbc3lp3SFumzeSW+eN7EPJYHx8BME2C1uPNulC0raWhtOpf9udZE79e0s+2aU1PHDR+F4L0pv4n04vq0TEAlQZjQPXAr3ZNuTkpmgPrH9GtzW3BMCcO3V31qSZENCzoG5v45qKtzO/kkCrhQsnJbAxu8z9+rHaRv62+iDLdxRw5thYQmxWUmP1fAiXpVHgJe22vslBUICF7XnH+PpAKTFhgfzftgImJNipqG3i5rkj3fvGRwQTbLOw6ZDL0mhxT4UEWrEHB1Dso9JYtvUIK3YXEhseyIPv7aLR4eShSye12sdqESKCAlqqwv0Y0wgPCiA61EZ+Rdfuqa/2l/C75bs5f0Icv23znfqCAKuFKUmRHafdVuWDo6FDS6O+ycFTXxxg2vAoFkyK97qPyeCkU0tDKeWkB7UYpzQNx2HFr+D5edodNesOuGe7bkY4Yt6AURg78o4x5eHP+Gp/CTvyjjFxmJ1pKVGUVDe4T9J7C7VLKbeslqWb85iQGOG+YgwNDCAypH2B3+q9Rcz60xcsfOprHvpgN9GhNl64aQZ1TQ7++PEeUmPDWvVhsliEETFhbjeXp3sKdEFdsQ/Dioqq6nl4+W5mj4zhk3vOJCYskACLcPnU9umuds/+Uxar7p2EVhq9mYXkC8nRoV1aGnsLq7jrja2Mj4/gmRun9dtVe3pyFJlHKnFGeinw6yJz6p8bciisqueXCyecEq3PTyV8cU+tFJEHRCRFRGJctz6XbKBSVwEb/q4zn2rLW79WUwaLz4GNz8Gs2+H+3XDxX8DeP+2L1+4v4czHV5NdcrzLfT/aWUCTQ/HQB5lkHqkkPTnS3eJ7d4F2Re09qu8nJETQ5FCktZlW1rZW49VvDnH7kgySokJwKsWuI5XcceYoZqfGMG14FE0OxaI5w9u1U3e5qGxWIapN5XS8PahD91R9kwOldA3Hp7uOUt3QzKPfn0KcPZi3fzyXV26b5bWQzB5ia9MeXVs3TQT0ayAcdAZVZ5ZGcXU9t7+WQViQlZdvm9mvllB6ShQNzU7KA+K1paE86mU6qdGorGvi719mcfa4ocwd5Z8eaSZ9hy9K44fAXWj31BbjltGXQg1ovngEPvt/8OZ18Pc5UG9kGzU3wNKbdMDwlg90dXdo/+rWpRl55JXXcfeb2zpNhVVKsXJPEUlRIeSW1VLT6OC05Ch3Jo4rrrGvqJroUBuPX3MaVoswc2Tr+EtCZLC7KjyvvJY/ffwd542P4/3/ms9n953Fh3efwZ1n65PK3eeOYfTQMK6Z0T4gOtJorz40PKjdVWl8RLDXQHhNQzPzH1vNS18fAmBDdhkpMSGMiQt3H/OsDnow2YMD2g9iApTFRkA/T0bTSqPOrfw8qWt08KMlGXp2x62z+mRYU2e4Ot4eao7Rld+eF0ll2TqJIKL9BdGLa7OprGsyW5+fpPhSEZ7q5XZqxjZKD8LWf+rK7atf1lPNdi/Tr336Szi8Hq58Dkad06diKKX4yetb+GD7Efe2+iYHa/YWMzHRzp6jVTz6yXcdvj+r5Dg5ZbXcec5oLj1N/9OnJ0cSHhTAqNgwMo0Mqu+OVjMhwc5pyVGsf/C8dpXNiZEhHC6rpbKuiWdWHcBiEf501RRCAq1YLMKU5Ei3K+X8ifGs+vk5RIW2d8+5MqiGeqk3iLMHU1Ld0O6kuiKzkLKaRt45vdcEAAAXeElEQVTbmm+MUy1vNeSoM7R7qr2lYfWD6zA5OpSGZme7HlQOp+Jn72xn55FKnrlxWrtBT/0jWwhDwgLZU2Ok9VZ6ZFCVZ0HMqHbptsXV9by87hCXpQ/zi8wmfY8v8zRCReQ3IrLYeD5WRC7te9EGEM0NegjSqkd04PTc30Da1RA3CbYsgYJtsOVVOP1umHJNn4uTW1bLp5mFPLPqgPtk+s3BUmoaHfxy4Xj+Y/5I/rkhl32FrWsw6xodOJ2KlXt0L8gLJsbxpyun8MyN09xzIaYOj+LbQ+XUNznYV1jNhES9Pd4e3M6tdOXUYdQ3Oblh8UaWbTvCojnD3VlV3cGlNNrGM/TnBtHocHKstqnV9mXb8gEdd/l4l56zPG90bLv3e8MebKO6vs2ccCDA5g+l4cqganFRbcwu4/Jn1/FpZiG/+d4kLvRTIFlESE+JYmO5K+3WI65RluU1c+rZ1Qdpcjj5eQfDqUwGP77Y4q8CjcA843k+8Mc+k2igUbQHnhwPjw2H75ZrxRA+VFdsT79Fj2N970c6LfHs/+4XkTYYGU5ZJTVsPazTZT/bXUhEUADzRsdyz3ljCQu08uyXB93vWZ9VyuxHv+DSv63j31vymJIUSWJkCJGhNi5Pb7EgLjttGMdqm3h9Yy51TQ4mJnRcPDZn1BCeWzSdA0XV2KzCT87p3pxoF66Yhjel4U67rW6JaxQcq2N9Vhk3zNLVyn82rKqOGgO2JbKDmIbF1v+N9FwFfvkVdeSU1vCf/8rghsUbqahp5OkbpvLD+SP7XSZP0pOj2FhutDF3BcMdzbqFf5t4xuGyWt789jDXz0pxuxxNTj58iaqNVkpdLyI3Aiil6uRkSYfI3QAf3Q83L/PeUbbhOLx7q86uWfBHfT/j1pbXT7seVv4Oyg7AxU9AcP+Y4xuyyhgSFkhdk4N3M/JIT45k5Z4izpsYR2CAhcCAQG46fQSL12bz0/PGkJFTwe+WZzI8JpRjtY0UVNZ3OATnjLGxDAkLdCucrob5XDApnjd/NJe6JkePJsWBjltMHmZnxoj2NSueBX4TEvS297cdQSn4r3PGsK+omm2HjzFqaJjP7TTsIQEcb2im2eHUMQyby9Lwh9LQlsZza7I4WFyNzWrhgQXjuOPMUQOignrq8CgqVDiOgFCsLkujMg+cTe0yp/66ch8BVuEeH9rGmwxefFEajUYbdAUgIqOB3h1s7C++fhJKvoP1f9MpsQXb4UgGjDwL6o/B2ieg7KAObKd6aTwXGgPp10N+Bsz8j34RWSnFhuwy5o+JJSjAwoc7CqiobaSitomL0xLc+/3ozFEsWZ/DxU9/jcOpmJMaw+KbZxIYYOHTzKMsmJzg9fg2q4XL0ofx2vocRGBcB+NMPZmdemIBf4tF+PieM72+5lIErgyqZoeTdzLymDUymuFDQrkkLZFth4/5HM+AllYixxuadYzF5Z4K7H/3VFhQALHhQewtrOLaGck8sGB8q1ni/iY9ORIQKgMTiHFZGl4yp/YUVPHBjgLuPHt0n/TCMhk4+KI0HgZWACki8gYwH7itD2XqH8qy4OBKCLJDxqt6CNLr34faluI2rIHawvCmMFxc+rTuAmr1vXvnltxyiqoauGRKIkop/vzpXuakxnD+RO++68raJn72znaq65t56LJJlFQ3cProIYyJC+fdLfl8tb+EX1w0ngWTWhRBbHgQDywYz8bsMhbNGcHZ44a6YxJdtXS4cloSr63PIXVIGCH9nILaFtfoV1ftyDsZ+eSW1fJrY3bE905L5JlVB1iY5l0JeiPS3bSwtdIIDPTPnIeXbp1JsM3ChE5cgf5Ct3oJ5YiKJcbVSqQsW997WBpPfLaXiKAA7jyrZy5Kk8FDl0pDKfW5iGwB5gIC3KuUKu1zyfqazS/pSu0b3oAll8PLC3Sh1y3Ltb/WFgrjFnTtcrJY8C001MIjH+7hu6NVTEmKJKvkOIvXZvPGxlw+vfcs9wjRZoeTDdll5JXXsXhtFkeO1dHkUPznv7YAcPqoIYwYEsoLN01nSnIUSVHt0zHvOHMUd5zZ/US39ORIJiREDIjsl2CblcgQG8XVDdQ1Onjqi/3MGBHtDg4Piwph58MLulVAZm/bHt2P7iloSW0dqKSnRHFgfzRT1Ca9oTxLz6kP13+Db7PL+HJfCQ9ePKHdhEKTkw9f5mksB94CliulavpepH6g4Thsex0mXamtiEmX6+rta16BUWcDZ/f40Es3HyYtKdLd2K8thZX17MzXKa1PrzpAdslxEuzB1DQ2c/8721n647kA3PP2Nj7ZVQjoAPFbP5rL6r3FPLcmi8TIYEYMCUVEWJjW+4WDIsJ7P5k3YPoFuQr8Xvw6m+LqBp79wfRWSqK7ITZ7sGt6n6v/lFYaQUHmRDlvTE2J4sCuaHBW6P+dsizd3VbEaH2+j3h7ELeePtLfopr0A764p54ErgceE5FNwFLgI6VU7w6I7k+KMvX97B/r+0uf0rUXo887ocNuyS3nl+/tIi3Jzod3n+H1ZLZqbxEAZ48byr+36LTRP16ZRkRwAPe+vZ3vPbOOxKhg1uzTLqcrpyURHxFEgNXC1JQocstqGRcf0eetGfqz8rgr4u3BrM8q4/M9RXxvSuIJx1BcloZrjoUKCEYAW6Dpi/dGekoUrykjnbkyX1saCacBsOq7YrbkVvCoUZ9jcvLjS3HfV0qp/0I3K1wMXIeeFz54GT4Xfr4XUmbr56Ex3VIYSilWZBbyvyv3uyuvlVI8+slerBYh80gVXx/w7sH7Yk8Rw2NCefK6dEJsVhLswVw7M5krpibxv9enY7EIa/aV8LMLx3HXuWNIigpxVykHWC38fdF07r3g1MpOiYsIprq+mfmjY3nyuhOfaJgaG0ZUqI3PMrUl57BoC8O0NLwzKdFOkcVQGhWHoCIXhozG4VQ88dk+UmPDuHam2fr8VMGny0kje+oytMUxHVjSl0L1C4HdzyOvrm9i9d5iXt+Yy+YcPS975Z4i/nDlZPYWVrMlt4LfXzGZ577M4u9fHnS3scgrryUjt5z5o2P5JquMm+aMIDY8iH/cPIOwICtBAfoK7appyVw5NYmiqoYeFcmdrFwyJQER+MMVab2Shhpss3LN9GReW59DcVU9EZYgAoDgYHPNvRFssxI6NBUqgJx1OvEjZjQfbD/CvqJqnv3BNGz93H7FxH/4EtNYCsxBZ1D9HVhjdL89JThW28inmYWsyCxkfVYpTQ5Fgj2YR6+awtCIIH7+jp7fADA2LpwfzB5Os0Px+4/28MC7O7BZhfe2HKHR4SQowEJjs5MLJunhOd56I4mIqTDacP7E+A4zy3rKorkjeGndIZZuzuMWCSQECAoy170jUoan0lRuJSB7DQI0Ro3kr+/sJy3JziV9EFczGbj4Ymm8CvxAKeUAEJH5IvIDpdRdfSta//PaN4fILdfpnA6l+NWyXXy4Q3eDHR4Tym3zRrIwLYFpKdHu9NXP7z+bbYcrsFiEOakxBFgt3DA7hXUHS1m9t5iquiaumZHMJVMSdSC3qoFZI0/dJsEDhdTYMM4YE8tbmw5z3RQbkUCoaWl0yGnDh3B0WwzDjXjgspxg8iuO8ehVU9q1lzE5ufEl5XaFiEw1KsKvBw4By/pcsj7kQFE1D7y7gwCrhcgQG1dMHUZW8XGeWa2roKvrm6lpaObTzEJumzeSa2YkM3mY3WvwOSEymIuntL7SCg0M4JXbZgHgdCr3P1VHXVdN/MNNc0dw5+tb2FJm4zxlIzC060LGU5WpKZEcUUMZTgkqKIInvi7j9FFDOHOsb/2+TE4eOlQaIjIOuAG4EShDZ02JUurcfpKtzxARokIDaXY6OVBczb1vbwfg2hnJJEaF8MyqAwA8dOkkfnhG6gl9lnkVNnC5YGIcCfZgfp09mYjGx/nfsHB/izRgGRUbzm6LvugptiVTVtnEfy8cbw5YOgXpzNLYC3wNXKaUOgggIvf3tUAishB4GrACLymlHuvtzxgTF86SH+rMKadT8dX+EvIqarlpzghEIDTQSlhQADfPHdHbH20ygHC5Ep/64gAVxPf71L7BhMUiOOzJUA1bqqNZODmBacP9P9/epP/p7L/karSl8aWIrADeRleE9xkiYkUH2y9Ed9PdLCLLlVJ7+uozLRbh3Alxrba5BgeZnPzcMGs4f1t9EIdTET6AalMGIiFDR0I1HHTE88BFZuvzU5UO8+SUUu8rpa4HJgBrgPuBeBF5XkQW9JE8s4GDSqlspVQjWlFd0UefZWJCQmQwFxqZWaFmcVqnxCaPASBuxCTGxJnxn1MVX4r7apRSbyilLgWSge3Ag30kTxLgOcE+39jmRkR+LCIZIpJRUlLSR2KYnEr8YuF4fnreGGLC+r/L7WAibe5FrI+/kfOvvMXfopj4EfE2m9hfiMi1wEVKqTuM5zcDs5VSP/W2/8yZM1VGxqk7rtzExMSkh/Q41DDQyjjzgRSP58lAgZ9kMTExMTFpw0BTGpuBsSKSKiKB6ED8cj/LZGJiYmJiMKDSRZRSzSJyN/AZOuX2FaXUbj+LZWJiYmJiMKCUBoBS6hPgE3/LYWJiYmLSnoHmnjIxMTExGcCYSsPExMTExGdMpWFiYmJi4jOm0jAxMTEx8ZkBVdzXXUSkBMg9gUPEAt7nsg5sTLn7n8Eq+2CVGwav7INB7lKl1MKevHFQK40TRUQylFIz/S1HdzHl7n8Gq+yDVW4YvLIPVrl9xXRPmZiYmJj4jKk0TExMTEx85lRXGov9LUAPMeXufwar7INVbhi8sg9WuX3ilI5pmJiYmJh0j1Pd0jAxMTEx6Qam0jAxMTEx8ZlTUmmIyEIR2SciB0Wkr6YQnjAikiIiX4rIdyKyW0TuNbY/LCJHRGS7cbvE37J6Q0RyRGSXIWOGsS1GRFaKyAHjPtrfcnoiIuM91nW7iFSJyH0Ddc1F5BURKRaRTI9tHa6xiPzK+N3vE5GL/CN1h3I/ISJ7RWSniLwvIlHG9pEiUuex9i8MMLk7/G0MlPXuVZRSp9QN3XI9CxgFBAI7gEn+lqsDWROB6cbjCGA/MAl4GHjA3/L5IH8OENtm2+PAg8bjB4G/+FvOLn4rhcCIgbrmwFnAdCCzqzU2fjs7gCAg1fg/sA4guRcAAcbjv3jIPdJzvwG43l5/GwNpvXvzdipaGrOBg0qpbKVUI/A2cIWfZfKKUuqoUmqr8bga+I42M9MHIVcAS4zHS4Ar/ShLV5wPZCmlTqTrQJ+ilFoLlLfZ3NEaXwG8rZRqUEodAg6i/x/6HW9yK6U+V0o1G083oid3Dig6WO+OGDDr3ZucikojCcjzeJ7PIDgRi8hIYBrwrbHpbsOMf2WguXg8UMDnIrJFRH5sbItXSh0FrRSBOL9J1zU3AG95PB8Maw4dr/Fg+u3/EPjU43mqiGwTka9E5Ex/CdUJ3n4bg2m9feZUVBreBqoP6LxjEQkH3gPuU0pVAc8Do4GpwFHgST+K1xnzlVLTgYuBu0TkLH8L5CvGuOHLgXeNTYNlzTtjUPz2ReTXQDPwhrHpKDBcKTUN+BnwpojY/SWfFzr6bQyK9e4up6LSyAdSPJ4nAwV+kqVLRMSGVhhvKKWWASilipRSDqWUE3iRAWryKqUKjPti4H20nEUikghg3Bf7T8JOuRjYqpQqgsGz5gYdrfGA/+2LyK3ApcAiZQQGDPdOmfF4Czo2MM5/Uramk9/GgF/vnnAqKo3NwFgRSTWuJm8AlvtZJq+IiAAvA98ppf7qsT3RY7ergMy27/U3IhImIhGux+ggZyZ6rW81drsV+MA/EnbJjXi4pgbDmnvQ0RovB24QkSARSQXGApv8IJ9XRGQh8EvgcqVUrcf2oSJiNR6PQsud7R8p29PJb2NAr3eP8Xck3h834BJ0JlIW8Gt/y9OJnGegzdmdwHbjdgnwL2CXsX05kOhvWb3IPgqdObID2O1aZ2AIsAo4YNzH+FtWL7KHAmVApMe2AbnmaMV2FGhCX9ne3tkaA782fvf7gIsHmNwH0TEA12/9BWPfq43f0A5gK3DZAJO7w9/GQFnv3ryZbURMTExMTHzmVHRPmZiYmJj0EFNpmJiYmJj4jKk0TExMTEx8xlQaJiYmJiY+YyoNExMTExOfMZWGic+IiMPo4pkpIu+KSGgvHPNhEXmgN+Tr4ecPE5F/9+HxR7o6oorITBF5xnh8wt9bRP5fm+frT+R4vUVP1lRE1ojIzL6SyaT3MJWGSXeoU0pNVUqlAY3Anb6+0VWcNdBQShUopa7pp8/KUErd4+v+PqxZK6WhlJrXI8F6EREJ6M81Nel/TKVh0lO+BsaIyDki8pFro4g8KyK3GY9zROQhEVkHXCt6jslWEdkhIqs8jjXJuNLMFpF7PI71f0azw92uhociYhWR1wxrZ5eI3G9sHy0iK4z9vxaRCcb2a419d4jI2rZfoo0lcJuILDOOc0BEHvf2xUVkloisN465SUQijON8bXy/rSLS7gTedq2AdBFZbXzWjzz2+VJE3kQXjHW0Do8BIYbl94ax7bhxL6JnU7jW6HqPY68RkX+LnlvxhtF1oK2ca0TkKeM7ZorIbGN7mOiGfJtFNw+8wmPd3hWRD9ENKj3XNFhEXjXk2CYi5xrbQ0TkbdFN/pYCId7W2mQA4u/qQvM2eG7AceM+AN2a4ifAOcBHHvs8C9xmPM4B/tt4PBRd7ZtqPI8x7h8G1qNnDsSiK7FtbfYJQbdmGALMAFZ6fF6Ucb8KGGs8ngOsNh7vApI8923znUZizEYAbkO3p4gEgoFcIKXN/oHGPrOM53ZjPUKBYGPbWCDDy/Hda2V87x3Gd4s11maYsU+Na506WgfPv4eXv8/VwEr0PJB44DB6Nss5QCW6B5IF2ACc4WVN1gAvGo/P8pD/UeAm11qiuyqEGeuW7yGn53f+OfCq8XiCIUswuvHgK8b209ANCmf6+zdu3rq+mZaGSXcIEZHtQAb6n/9lH96z1LifC6xVeq4ASinPmQQfK92UrhTdXC/e2H6PiOxAz1ZIoaXn0CgR+ZvoXkVVorsAzwPeNeT7B/okCfAN8JpxJe+Li2yVUqpSKVUP7EEPYPJkPHBUKbXZ+B5VSs+AsAEvisgudGfcST581gdKqTrje39JS6O7Ta516mQdOuMM4C2lm+gVAV8BszyOna90c73t6BO8N94yvt9awC56it4C4EFjjdegT/7Djf1XtvmbesryL+NYe9GKeBxaGb1ubN+JbsFhMggI8LcAJoOKOqXUVM8NItJMazdncJv31Lh2peO20A0ejx1AgIicA1wAnK6UqhWRNegr+QoRSQcuAu4CrgPuA461lQ1AKXWniMwBvgdsF5GpyuiY6qssbV7v6HvcDxQB6ej1qO/kM9zidfDctWZ0tA5dHNdbS24XXX2/zmQT4Gql1L5WH6bXtwbvdCaL2cNoEGJaGiYnSi46JhEkIpHoaXfe2ACcLbrbJyIS08VxI4EK40Q5AW2pICKxgEUp9R7wW/Q43CrgkIhca+wjhmJBREYrpb5VSj0ElNK6VXVP2AsME5FZxvEjRCTAkPeocQV/M75ZNVcYPv8haNfRZi/7eF0HgybRrfPbsha43oj/DEVf1Xe3u6orDnIGUKmUqgQ+A37qioOIyDQfjrMWWGTsPw5tmexrsz0N7aIyGQSYlobJCaGUyhORd9DuhQPAtg72KzGCuMtExIJ2Q13YyaFXAHeKyE70SWajsT0JeNU4BsCvjPtFwPMi8hu0q+htdMzgCREZi77iXWVs6zFKqUYjsPw3EQkB6tCWwHPAe4bi+pKOr7w92QR8jD6R/kEpVWCcWD3paB0AFgM7RWSrUmqRx/b3gdPR31Wh40qFhtLxlQrRKbx29BQ9gD8ATxmfKeiY1aVdHOc54AXDbdeMjnc1iMjz6L+jq4Pz4G8Zfopgdrk1MTFpheECe0ApleFvWUwGHqZ7ysTExMTEZ0xLw8TExMTEZ0xLw8TExMTEZ0ylYWJiYmLiM6bSMDExMTHxGVNpmJiYmJj4jKk0TExMTEx85v8DnkAswqwK/O0AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# train using all\n",
"holdout_dur_max = summary_cal_holdout['duration_holdout'].max().astype(int)\n",
"\n",
"print('all')\n",
"plot_cumulative_transactions(mbgnbd, trx_df.query(f'user_id in {uid_test}'), 'trx_dt', 'user_id', t, t_cal, freq='D')\n",
"plt.show()\n",
"\n",
"plot_incremental_transactions(mbgnbd, trx_df.query(f'user_id in {uid_test}'), 'trx_dt', 'user_id', t, t_cal, freq='D')\n",
"plt.show()\n",
"\n",
"plot_calibration_purchases_vs_holdout_purchases(mbgnbd, summary_cal_holdout_test, n=holdout_dur_max)\n",
"sns.despine();\n",
"plt.show()\n",
"\n",
"print('only good')\n",
"plot_cumulative_transactions(mbgnbd, trx_df.query(f'user_id in {uid_test_good}'), 'trx_dt', 'user_id', t, t_cal, freq='D')\n",
"plt.show()\n",
"\n",
"plot_incremental_transactions(mbgnbd, trx_df.query(f'user_id in {uid_test_good}'), 'trx_dt', 'user_id', t, t_cal, freq='D')\n",
"plt.show()\n",
"\n",
"plot_calibration_purchases_vs_holdout_purchases(mbgnbd, summary_cal_holdout_test_good, n=holdout_dur_max)\n",
"sns.despine();\n",
"plt.show()\n",
"\n",
"\n",
"print('only bad')\n",
"plot_cumulative_transactions(mbgnbd, trx_df.query(f'user_id in {uid_test_bad}'), 'trx_dt', 'user_id', t, t_cal, freq='D')\n",
"plt.show()\n",
"\n",
"plot_incremental_transactions(mbgnbd, trx_df.query(f'user_id in {uid_test_bad}'), 'trx_dt', 'user_id', t, t_cal, freq='D')\n",
"plt.show()\n",
"\n",
"plot_calibration_purchases_vs_holdout_purchases(mbgnbd, summary_cal_holdout_test_bad, n=holdout_dur_max)\n",
"sns.despine();\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"# mbgnbd.save_model('mbgnbd_basic.p')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. Gamma Gamma"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"returning_customers_summary = summary_cal_holdout_tr_good.query('frequency_cal >0 and monetary_value_cal > -50000')\n",
"returning_customers_summary['monetary_value_cal'] = \\\n",
" np.where(returning_customers_summary['monetary_value_cal'] <= 0, 1, \n",
" returning_customers_summary['monetary_value_cal'])"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<lifetimes.GammaGammaFitter: fitted with 21289 subjects, p: 2.47, q: 0.17, v: 2.53>"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggf = GammaGammaFitter(penalizer_coef = 0.01)\n",
"ggf.fit(returning_customers_summary['frequency_cal'],\n",
" returning_customers_summary['monetary_value_cal'])"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-11172.869999999999"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary_cal_holdout_test_good.monetary_value_cal.min()"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"gamma_test = summary_cal_holdout_test_good.query('frequency_cal >0 and monetary_value_cal > -50000')\n",
"gamma_test['monetary_value_cal'] = np.where(gamma_test['monetary_value_cal'] <= 0, 1, \n",
" gamma_test['monetary_value_cal'])"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13539.467396332067 5584.167755738024\n",
"5424.4272619047615 3387.1196837833913\n",
"7955.299640594041 1652.3613727451548\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"gamma_test['monetary_pred'] = ggf.conditional_expected_average_profit(\n",
" gamma_test['frequency_cal'],\n",
" gamma_test['monetary_value_cal']\n",
")\n",
"\n",
"gamma_test['error'] = gamma_test['monetary_value_holdout'] - gamma_test['monetary_pred'] \n",
"print(gamma_test['monetary_value_holdout'].mean(), gamma_test['monetary_pred'].mean())\n",
"print(gamma_test['monetary_value_holdout'].median(), gamma_test['monetary_pred'].median())\n",
"print(gamma_test['error'].mean(), gamma_test['error'].median())\n",
"\n",
"_ = plt.hist(gamma_test['error'], bins = 50)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"# ggf.save_model('gammagamma_basic.p')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4. Putting It All Together"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### all numbers are based on the holdout (test) dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Predict Survival Rate and Hazard Rate"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [],
"source": [
"uids = summary_cal_holdout_test['user_id'].unique().tolist()"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3893, 440)"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_df = test_df[test_df['set_password_timestamp'].dt.date.astype(str) >= '2018-07-01']\n",
"test_df = test_df[test_df['user_id'].isin(uids)].reset_index(drop=True)\n",
"test_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [],
"source": [
"orig_features = cont_feats + disc_feats\n",
"std_orig_features = ['std_'+i for i in orig_features]\n",
"rfdm_features = ['frequency_cal', 'recency_cal', 'T_cal', 'monetary_value_cal']\n",
"info_cols = ['user_id', 'snapshot_os_principal']\n",
"\n",
"rename_rfdm = {'frequency_cal':'frequency', 'recency_cal':'recency', \n",
" 'T_cal':'duration', 'monetary_value_cal':'monetary_value'}"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"simulation_dataset = test_df.merge(summary_cal_holdout_test, how='left', on='user_id')\n",
"simulation_dataset = simulation_dataset[info_cols + orig_features + std_orig_features + rfdm_features]\\\n",
" .rename(columns=rename_rfdm)\n",
"simulation_dataset.to_parquet('simulation_dataset_basic.parquet')"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"max_duration = 16"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>user_id</th>\n",
" <th>m</th>\n",
" <th>s_rate</th>\n",
" <th>h_rate</th>\n",
" <th>s_rate_before</th>\n",
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" <th>41480</th>\n",
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" <td>0.008793</td>\n",
" <td>0.973466</td>\n",
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" <td>0.964906</td>\n",
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" <td>0.006490</td>\n",
" <td>0.917983</td>\n",
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" <tr>\n",
" <th>41493</th>\n",
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" <td>14.0</td>\n",
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" <td>0.912025</td>\n",
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" <td>0.006410</td>\n",
" <td>0.906144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41495</th>\n",
" <td>2015505</td>\n",
" <td>16.0</td>\n",
" <td>0.894597</td>\n",
" <td>0.006374</td>\n",
" <td>0.900336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66113</th>\n",
" <td>2321685</td>\n",
" <td>1.0</td>\n",
" <td>0.985956</td>\n",
" <td>0.014044</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66114</th>\n",
" <td>2321685</td>\n",
" <td>2.0</td>\n",
" <td>0.947633</td>\n",
" <td>0.038869</td>\n",
" <td>0.985956</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66115</th>\n",
" <td>2321685</td>\n",
" <td>3.0</td>\n",
" <td>0.897697</td>\n",
" <td>0.052696</td>\n",
" <td>0.947633</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66116</th>\n",
" <td>2321685</td>\n",
" <td>4.0</td>\n",
" <td>0.847605</td>\n",
" <td>0.055800</td>\n",
" <td>0.897697</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user_id m s_rate h_rate s_rate_before\n",
"41480 2015505 1.0 0.997702 0.002298 1.000000\n",
"41481 2015505 2.0 0.991290 0.006427 0.997702\n",
"41482 2015505 3.0 0.982599 0.008766 0.991290\n",
"41483 2015505 4.0 0.973466 0.009295 0.982599\n",
"41484 2015505 5.0 0.964906 0.008793 0.973466\n",
"41485 2015505 6.0 0.957050 0.008142 0.964906\n",
"41486 2015505 7.0 0.949776 0.007601 0.957050\n",
"41487 2015505 8.0 0.942945 0.007192 0.949776\n",
"41488 2015505 9.0 0.936437 0.006901 0.942945\n",
"41489 2015505 10.0 0.930155 0.006709 0.936437\n",
"41490 2015505 11.0 0.924022 0.006594 0.930155\n",
"41491 2015505 12.0 0.917983 0.006535 0.924022\n",
"41492 2015505 13.0 0.912025 0.006490 0.917983\n",
"41493 2015505 14.0 0.906144 0.006448 0.912025\n",
"41494 2015505 15.0 0.900336 0.006410 0.906144\n",
"41495 2015505 16.0 0.894597 0.006374 0.900336\n",
"66113 2321685 1.0 0.985956 0.014044 1.000000\n",
"66114 2321685 2.0 0.947633 0.038869 0.985956\n",
"66115 2321685 3.0 0.897697 0.052696 0.947633\n",
"66116 2321685 4.0 0.847605 0.055800 0.897697"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"surv_df = cph_spline.predict_survival_function(test_df[final_feats], [i for i in range(1,max_duration+1,1)]).T\n",
"\n",
"surv_df[-1.] = 1.0\n",
"surv_df['user_id'] = test_df['user_id']\n",
"\n",
"surv_df = surv_df.set_index('user_id')\n",
"\n",
"surv_df = surv_df.T.unstack().to_frame().reset_index().sort_values(by=['user_id','level_1'])\n",
"surv_df.columns = ['user_id', 'm', 's_rate']\n",
"surv_df['h_rate'] = np.abs(surv_df.groupby('user_id')['s_rate'].pct_change())\n",
"\n",
"surv_df['s_rate_before'] = surv_df['s_rate'].shift(1)\n",
"surv_df = surv_df.query(f'm >=0 and m <={max_duration}')\n",
"surv_df.head(20)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3893"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"surv_df['user_id'].nunique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Predict the Frequency at each month"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [],
"source": [
"for i in range(1,max_duration+1):\n",
" summary_cal_holdout_test[i] = mbgnbd.conditional_expected_number_of_purchases_up_to_time(i * 30, \n",
" summary_cal_holdout_test['frequency_cal'], \n",
" summary_cal_holdout_test['recency_cal'], \n",
" summary_cal_holdout_test['T_cal']).to_frame()"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [],
"source": [
"freq_df = summary_cal_holdout_test.set_index('user_id').copy()"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"freq_df = freq_df[list(range(1,max_duration+1))].copy().unstack().reset_index()\n",
"freq_df.columns = ['m','user_id','pred_freq_holdout']"
]
},
{
"cell_type": "code",
"execution_count": 96,
"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>m</th>\n",
" <th>user_id</th>\n",
" <th>pred_freq_holdout</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2015505</td>\n",
" <td>2.304305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>2321685</td>\n",
" <td>3.819022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>2332009</td>\n",
" <td>2.301316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>2451694</td>\n",
" <td>9.100968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>2518751</td>\n",
" <td>4.748838</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62283</th>\n",
" <td>16</td>\n",
" <td>5792220</td>\n",
" <td>44.896094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62284</th>\n",
" <td>16</td>\n",
" <td>5792975</td>\n",
" <td>79.053676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62285</th>\n",
" <td>16</td>\n",
" <td>5793384</td>\n",
" <td>48.754498</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62286</th>\n",
" <td>16</td>\n",
" <td>5796696</td>\n",
" <td>113.057193</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62287</th>\n",
" <td>16</td>\n",
" <td>5796864</td>\n",
" <td>52.514726</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>62288 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" m user_id pred_freq_holdout\n",
"0 1 2015505 2.304305\n",
"1 1 2321685 3.819022\n",
"2 1 2332009 2.301316\n",
"3 1 2451694 9.100968\n",
"4 1 2518751 4.748838\n",
"... .. ... ...\n",
"62283 16 5792220 44.896094\n",
"62284 16 5792975 79.053676\n",
"62285 16 5793384 48.754498\n",
"62286 16 5796696 113.057193\n",
"62287 16 5796864 52.514726\n",
"\n",
"[62288 rows x 3 columns]"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"freq_df"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3893"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"freq_df['user_id'].nunique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Predict the Monetary Value"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [],
"source": [
"monetary_df = summary_cal_holdout_test.copy()"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [],
"source": [
"monetary_df['pred_avg_amount_holdout'] = ggf.conditional_expected_average_profit(\n",
" summary_cal_holdout_test['frequency_cal'],\n",
" summary_cal_holdout_test['monetary_value_cal']\n",
")\n",
"\n",
"monetary_df = monetary_df[['user_id', 'pred_avg_amount_holdout']].copy()"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3893"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"monetary_df['user_id'].nunique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Predicted LTV\n",
"- combine all 3 models"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [],
"source": [
"test_ltv_df = surv_df.merge(freq_df, how = 'left', on = ['user_id','m']).merge(monetary_df, how = 'left', on ='user_id')"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [],
"source": [
"# adding additional information (actual ltv etc)\n",
"test_ltv_df = test_ltv_df.merge(main_ltv_df, how = 'left', on ='user_id')"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
"test_ltv_df['pred_freq_holdout_inc'] = test_ltv_df.groupby('user_id')['pred_freq_holdout'].diff()\n",
"test_ltv_df['pred_freq_holdout_inc'] = np.where(test_ltv_df['pred_freq_holdout_inc'].isna(), \n",
" test_ltv_df['pred_freq_holdout'], test_ltv_df['pred_freq_holdout_inc'])"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"test_ltv_df['pred_rev_holdout'] = (test_ltv_df['pred_freq_holdout_inc'] * test_ltv_df['pred_avg_amount_holdout'] * test_ltv_df['s_rate']).astype(int)\n",
"test_ltv_df['pred_npl_holdout'] = (test_ltv_df['snapshot_os_principal'] * test_ltv_df['h_rate'] * test_ltv_df['s_rate_before'] * 0.9).astype(int)\n",
"test_ltv_df['pred_ltv_holdout'] = test_ltv_df['pred_rev_holdout'] - test_ltv_df['pred_npl_holdout']"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" }\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>m</th>\n",
" <th>s_rate</th>\n",
" <th>h_rate</th>\n",
" <th>s_rate_before</th>\n",
" <th>pred_freq_holdout</th>\n",
" <th>pred_avg_amount_holdout</th>\n",
" <th>ltv_calib</th>\n",
" <th>act_ltv_holdout</th>\n",
" <th>act_npl_holdout</th>\n",
" <th>act_rev_holdout</th>\n",
" <th>snapshot_os_principal</th>\n",
" <th>pred_freq_holdout_inc</th>\n",
" <th>pred_rev_holdout</th>\n",
" <th>pred_npl_holdout</th>\n",
" <th>pred_ltv_holdout</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2015505</td>\n",
" <td>1.0</td>\n",
" <td>0.997702</td>\n",
" <td>0.002298</td>\n",
" <td>1.000000</td>\n",
" <td>2.304305</td>\n",
" <td>18934.698269</td>\n",
" <td>304156.05</td>\n",
" <td>482218.5922</td>\n",
" <td>0.0</td>\n",
" <td>482218.5922</td>\n",
" <td>1000000.0</td>\n",
" <td>2.304305</td>\n",
" <td>43531</td>\n",
" <td>2068</td>\n",
" <td>41463</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2015505</td>\n",
" <td>2.0</td>\n",
" <td>0.991290</td>\n",
" <td>0.006427</td>\n",
" <td>0.997702</td>\n",
" <td>4.601210</td>\n",
" <td>18934.698269</td>\n",
" <td>304156.05</td>\n",
" <td>482218.5922</td>\n",
" <td>0.0</td>\n",
" <td>482218.5922</td>\n",
" <td>1000000.0</td>\n",
" <td>2.296906</td>\n",
" <td>43112</td>\n",
" <td>5771</td>\n",
" <td>37341</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2015505</td>\n",
" <td>3.0</td>\n",
" <td>0.982599</td>\n",
" <td>0.008766</td>\n",
" <td>0.991290</td>\n",
" <td>6.891542</td>\n",
" <td>18934.698269</td>\n",
" <td>304156.05</td>\n",
" <td>482218.5922</td>\n",
" <td>0.0</td>\n",
" <td>482218.5922</td>\n",
" <td>1000000.0</td>\n",
" <td>2.290332</td>\n",
" <td>42612</td>\n",
" <td>7821</td>\n",
" <td>34791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2015505</td>\n",
" <td>4.0</td>\n",
" <td>0.973466</td>\n",
" <td>0.009295</td>\n",
" <td>0.982599</td>\n",
" <td>9.175962</td>\n",
" <td>18934.698269</td>\n",
" <td>304156.05</td>\n",
" <td>482218.5922</td>\n",
" <td>0.0</td>\n",
" <td>482218.5922</td>\n",
" <td>1000000.0</td>\n",
" <td>2.284419</td>\n",
" <td>42107</td>\n",
" <td>8220</td>\n",
" <td>33887</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015505</td>\n",
" <td>5.0</td>\n",
" <td>0.964906</td>\n",
" <td>0.008793</td>\n",
" <td>0.973466</td>\n",
" <td>11.455010</td>\n",
" <td>18934.698269</td>\n",
" <td>304156.05</td>\n",
" <td>482218.5922</td>\n",
" <td>0.0</td>\n",
" <td>482218.5922</td>\n",
" <td>1000000.0</td>\n",
" <td>2.279048</td>\n",
" <td>41638</td>\n",
" <td>7703</td>\n",
" <td>33935</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user_id m s_rate h_rate s_rate_before pred_freq_holdout \\\n",
"0 2015505 1.0 0.997702 0.002298 1.000000 2.304305 \n",
"1 2015505 2.0 0.991290 0.006427 0.997702 4.601210 \n",
"2 2015505 3.0 0.982599 0.008766 0.991290 6.891542 \n",
"3 2015505 4.0 0.973466 0.009295 0.982599 9.175962 \n",
"4 2015505 5.0 0.964906 0.008793 0.973466 11.455010 \n",
"\n",
" pred_avg_amount_holdout ltv_calib act_ltv_holdout act_npl_holdout \\\n",
"0 18934.698269 304156.05 482218.5922 0.0 \n",
"1 18934.698269 304156.05 482218.5922 0.0 \n",
"2 18934.698269 304156.05 482218.5922 0.0 \n",
"3 18934.698269 304156.05 482218.5922 0.0 \n",
"4 18934.698269 304156.05 482218.5922 0.0 \n",
"\n",
" act_rev_holdout snapshot_os_principal pred_freq_holdout_inc \\\n",
"0 482218.5922 1000000.0 2.304305 \n",
"1 482218.5922 1000000.0 2.296906 \n",
"2 482218.5922 1000000.0 2.290332 \n",
"3 482218.5922 1000000.0 2.284419 \n",
"4 482218.5922 1000000.0 2.279048 \n",
"\n",
" pred_rev_holdout pred_npl_holdout pred_ltv_holdout \n",
"0 43531 2068 41463 \n",
"1 43112 5771 37341 \n",
"2 42612 7821 34791 \n",
"3 42107 8220 33887 \n",
"4 41638 7703 33935 "
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_ltv_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [],
"source": [
"test_agg_ltv_df = test_ltv_df.groupby('user_id').agg({\n",
" 'ltv_calib': max,\n",
" 'act_ltv_holdout': max,\n",
" 'act_npl_holdout': max,\n",
" 'act_rev_holdout': max,\n",
" 'pred_rev_holdout': sum,\n",
" 'pred_npl_holdout': sum,\n",
" 'pred_ltv_holdout': sum}).reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user_id</th>\n",
" <th>ltv_calib</th>\n",
" <th>act_ltv_holdout</th>\n",
" <th>act_npl_holdout</th>\n",
" <th>act_rev_holdout</th>\n",
" <th>pred_rev_holdout</th>\n",
" <th>pred_npl_holdout</th>\n",
" <th>pred_ltv_holdout</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2015505</td>\n",
" <td>304156.0500</td>\n",
" <td>4.822186e+05</td>\n",
" <td>0.00</td>\n",
" <td>4.822186e+05</td>\n",
" <td>647402</td>\n",
" <td>94854</td>\n",
" <td>552548</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2321685</td>\n",
" <td>37886.3126</td>\n",
" <td>-5.907490e+05</td>\n",
" <td>590749.00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>1345315</td>\n",
" <td>263598</td>\n",
" <td>1081717</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2332009</td>\n",
" <td>-33297.6900</td>\n",
" <td>5.324591e+04</td>\n",
" <td>0.00</td>\n",
" <td>5.324591e+04</td>\n",
" <td>30327</td>\n",
" <td>1680</td>\n",
" <td>28647</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2451694</td>\n",
" <td>214989.9650</td>\n",
" <td>1.242435e+06</td>\n",
" <td>0.00</td>\n",
" <td>1.242435e+06</td>\n",
" <td>674305</td>\n",
" <td>163948</td>\n",
" <td>510357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2518751</td>\n",
" <td>89790.5300</td>\n",
" <td>-8.505573e+05</td>\n",
" <td>850557.34</td>\n",
" <td>0.000000e+00</td>\n",
" <td>154537</td>\n",
" <td>419899</td>\n",
" <td>-265362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3888</th>\n",
" <td>5792220</td>\n",
" <td>19462.1600</td>\n",
" <td>1.401095e+05</td>\n",
" <td>0.00</td>\n",
" <td>1.401095e+05</td>\n",
" <td>77050</td>\n",
" <td>0</td>\n",
" <td>77050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3889</th>\n",
" <td>5792975</td>\n",
" <td>102160.6150</td>\n",
" <td>8.855406e+04</td>\n",
" <td>0.00</td>\n",
" <td>8.855406e+04</td>\n",
" <td>359830</td>\n",
" <td>52474</td>\n",
" <td>307356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3890</th>\n",
" <td>5793384</td>\n",
" <td>128187.8244</td>\n",
" <td>-6.163553e+05</td>\n",
" <td>985793.00</td>\n",
" <td>3.694377e+05</td>\n",
" <td>288866</td>\n",
" <td>156992</td>\n",
" <td>131874</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3891</th>\n",
" <td>5796696</td>\n",
" <td>66697.4278</td>\n",
" <td>6.060042e+05</td>\n",
" <td>0.00</td>\n",
" <td>6.060042e+05</td>\n",
" <td>224954</td>\n",
" <td>36108</td>\n",
" <td>188846</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3892</th>\n",
" <td>5796864</td>\n",
" <td>77550.3870</td>\n",
" <td>1.540572e+05</td>\n",
" <td>0.00</td>\n",
" <td>1.540572e+05</td>\n",
" <td>264182</td>\n",
" <td>223412</td>\n",
" <td>40770</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3893 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" user_id ltv_calib act_ltv_holdout act_npl_holdout act_rev_holdout \\\n",
"0 2015505 304156.0500 4.822186e+05 0.00 4.822186e+05 \n",
"1 2321685 37886.3126 -5.907490e+05 590749.00 0.000000e+00 \n",
"2 2332009 -33297.6900 5.324591e+04 0.00 5.324591e+04 \n",
"3 2451694 214989.9650 1.242435e+06 0.00 1.242435e+06 \n",
"4 2518751 89790.5300 -8.505573e+05 850557.34 0.000000e+00 \n",
"... ... ... ... ... ... \n",
"3888 5792220 19462.1600 1.401095e+05 0.00 1.401095e+05 \n",
"3889 5792975 102160.6150 8.855406e+04 0.00 8.855406e+04 \n",
"3890 5793384 128187.8244 -6.163553e+05 985793.00 3.694377e+05 \n",
"3891 5796696 66697.4278 6.060042e+05 0.00 6.060042e+05 \n",
"3892 5796864 77550.3870 1.540572e+05 0.00 1.540572e+05 \n",
"\n",
" pred_rev_holdout pred_npl_holdout pred_ltv_holdout \n",
"0 647402 94854 552548 \n",
"1 1345315 263598 1081717 \n",
"2 30327 1680 28647 \n",
"3 674305 163948 510357 \n",
"4 154537 419899 -265362 \n",
"... ... ... ... \n",
"3888 77050 0 77050 \n",
"3889 359830 52474 307356 \n",
"3890 288866 156992 131874 \n",
"3891 224954 36108 188846 \n",
"3892 264182 223412 40770 \n",
"\n",
"[3893 rows x 8 columns]"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_agg_ltv_df"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"220229.9108983817 80440.25661443616\n",
"211534.8700000001 99879.0\n"
]
}
],
"source": [
"print(test_agg_ltv_df['act_ltv_holdout'].mean(), test_agg_ltv_df['pred_ltv_holdout'].mean())\n",
"print(test_agg_ltv_df['act_ltv_holdout'].median(), test_agg_ltv_df['pred_ltv_holdout'].median()) "
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [],
"source": [
"test_agg_ltv_df['ltv_act'] = test_agg_ltv_df['ltv_calib'] + test_agg_ltv_df['act_ltv_holdout']\n",
"test_agg_ltv_df['ltv_pred'] = test_agg_ltv_df['ltv_calib'] + test_agg_ltv_df['pred_ltv_holdout']"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"318906.37726719753 179116.72298325202\n",
"287387.0101999999 168060.5502\n"
]
}
],
"source": [
"print(test_agg_ltv_df['ltv_act'].mean(), test_agg_ltv_df['ltv_pred'].mean())\n",
"print(test_agg_ltv_df['ltv_act'].median(), test_agg_ltv_df['ltv_pred'].median()) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Evaluation"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [],
"source": [
"# convert the ltv into dollars\n",
"# divide by 14000 (dollar to rupiah exchange rate at the time)\n",
"\n",
"test_agg_ltv_df['bin_act'], _bins = pd.qcut(test_agg_ltv_df['ltv_act']/14000, 5, retbins = True)\n",
"test_agg_ltv_df['bin_pred'] = pd.cut(test_agg_ltv_df['ltv_pred']/14000, bins = _bins)"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-937.92976786, -18.58154853, 10.9956624 , 30.63746843,\n",
" 59.89418657, 525.03283857])"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_bins"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\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>bin_pred</th>\n",
" <th>(-937.93, -18.582]</th>\n",
" <th>(-18.582, 10.996]</th>\n",
" <th>(10.996, 30.637]</th>\n",
" <th>(30.637, 59.894]</th>\n",
" <th>(59.894, 525.033]</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bin_act</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>(-937.9309999999999, -18.582]</th>\n",
" <td>270</td>\n",
" <td>219</td>\n",
" <td>196</td>\n",
" <td>64</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(-18.582, 10.996]</th>\n",
" <td>34</td>\n",
" <td>556</td>\n",
" <td>148</td>\n",
" <td>32</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(10.996, 30.637]</th>\n",
" <td>11</td>\n",
" <td>325</td>\n",
" <td>378</td>\n",
" <td>55</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(30.637, 59.894]</th>\n",
" <td>8</td>\n",
" <td>241</td>\n",
" <td>420</td>\n",
" <td>97</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>(59.894, 525.033]</th>\n",
" <td>8</td>\n",
" <td>159</td>\n",
" <td>319</td>\n",
" <td>232</td>\n",
" <td>61</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"bin_pred (-937.93, -18.582] (-18.582, 10.996] \\\n",
"bin_act \n",
"(-937.9309999999999, -18.582] 270 219 \n",
"(-18.582, 10.996] 34 556 \n",
"(10.996, 30.637] 11 325 \n",
"(30.637, 59.894] 8 241 \n",
"(59.894, 525.033] 8 159 \n",
"\n",
"bin_pred (10.996, 30.637] (30.637, 59.894] \\\n",
"bin_act \n",
"(-937.9309999999999, -18.582] 196 64 \n",
"(-18.582, 10.996] 148 32 \n",
"(10.996, 30.637] 378 55 \n",
"(30.637, 59.894] 420 97 \n",
"(59.894, 525.033] 319 232 \n",
"\n",
"bin_pred (59.894, 525.033] \n",
"bin_act \n",
"(-937.9309999999999, -18.582] 30 \n",
"(-18.582, 10.996] 8 \n",
"(10.996, 30.637] 10 \n",
"(30.637, 59.894] 12 \n",
"(59.894, 525.033] 61 "
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_agg_ltv_df.groupby('bin_act')['bin_pred'].value_counts().unstack()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"mimetype": "text/x-python",
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"nbconvert_exporter": "python",
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