From 3646e18b078ec1fe35f77bf304701b36d641ef06 Mon Sep 17 00:00:00 2001 From: Rijul Sherathia Date: Thu, 18 May 2023 12:26:34 -0700 Subject: [PATCH] Add files via upload --- card transactions data explore.ipynb | 3624 ++++++++ transaction_models.ipynb | 7797 +++++++++++++++++ transactions feature selection.ipynb | 1533 ++++ transactions variable creation.ipynb | 11355 +++++++++++++++++++++++++ 4 files changed, 24309 insertions(+) create mode 100644 card transactions data explore.ipynb create mode 100644 transaction_models.ipynb create mode 100644 transactions feature selection.ipynb create mode 100644 transactions variable creation.ipynb diff --git a/card transactions data explore.ipynb b/card transactions data explore.ipynb new file mode 100644 index 0000000..9b213e9 --- /dev/null +++ b/card transactions data explore.ipynb @@ -0,0 +1,3624 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import datetime as dt\n", + "import matplotlib.pyplot as plt\n", + "from datetime import datetime\n", + "from datetime import timedelta\n", + "%matplotlib inline\n", + "start_time = datetime.now()" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# data = pd.read_csv('card transactions.csv', converters={'Merchnum': lambda x: str(x)})" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('card transactions.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Field Name# Records With Values% Populated# ZerosMinMaxMeanMost CommonStdev
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" + ], + "text/plain": [ + " Field Name # Records With Values % Populated # Zeros Min \\\n", + "0 Date 96753 100.0% 0 2010-01-01 00:00:00 \n", + "1 Amount 96753 100.0% 0 0.01 \n", + "\n", + " Max Mean Most Common \\\n", + "0 2010-12-31 00:00:00 2010-06-25 22:21:52.480232960 2010-02-28 00:00:00 \n", + "1 3102045.53 427.885677 3.62 \n", + "\n", + " Stdev \n", + "0 98 days 21:38:57.704372484 \n", + "1 10006.140302 " + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "statistics_of_data1 = []\n", + "for col in numerics:\n", + " statistics_of_data1.append((col,\n", + " data[col].notnull().sum(),\n", + " f'{data[col].notnull().sum()/data.shape[0]*100}%',\n", + " count_zeros(data[col]),\n", + " data[col].min(),\n", + " data[col].max(),\n", + " data[col].mean(),\n", + " data[col].mode()[0],\n", + " data[col].std()\n", + " ))\n", + "\n", + "stats_df = pd.DataFrame(statistics_of_data1, columns=['Field Name','# Records With Values', '% Populated', '# Zeros','Min', 'Max','Mean',\n", + " 'Most Common','Stdev'])\n", + "#stats_df.to_csv('numerics.csv')\n", + "stats_df" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Field Name # Records With Values % Populated # Zeros \\\n", + "0 Recnum 96753 100.0% 0 \n", + "1 Cardnum 96753 100.0% 0 \n", + "2 Merchnum 93378 96.51173607019938% 0 \n", + "3 Merch description 96753 100.0% 0 \n", + "4 Merch state 95558 98.76489617892985% 0 \n", + "5 Merch zip 92097 95.1877461163995% 0 \n", + "6 Transtype 96753 100.0% 0 \n", + "7 Fraud 96753 100.0% 95694 \n", + "\n", + " # Unique Values Most Common \n", + "0 96753 1 \n", + "1 1645 5142148452 \n", + "2 13091 930090121224 \n", + "3 13126 GSA-FSS-ADV \n", + "4 227 TN \n", + "5 4567 38118.0 \n", + "6 4 P \n", + "7 2 0 " + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "statistics_of_data = []\n", + "for col in categoricals:\n", + " statistics_of_data.append((col,\n", + " data[col].notnull().sum(),\n", + " f'{data[col].notnull().sum()/data.shape[0]*100}%',\n", + " count_zeros(data[col]),\n", + " data[col].nunique(),\n", + " data[col].mode()[0],\n", + "\n", + " ))\n", + "stats_df = pd.DataFrame(statistics_of_data, columns=['Field Name', '# Records With Values', '% Populated', '# Zeros','# Unique Values', 'Most Common',])\n", + "#stats_df.to_csv('categoricals.csv')\n", + "stats_df" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 96753 entries, 0 to 96752\n", + "Data columns (total 10 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Recnum 96753 non-null int64 \n", + " 1 Cardnum 96753 non-null int64 \n", + " 2 Date 96753 non-null datetime64[ns]\n", + " 3 Merchnum 93378 non-null object \n", + " 4 Merch description 96753 non-null object \n", + " 5 Merch state 95558 non-null object \n", + " 6 Merch zip 92097 non-null float64 \n", + " 7 Transtype 96753 non-null object \n", + " 8 Amount 96753 non-null float64 \n", + " 9 Fraud 96753 non-null int64 \n", + "dtypes: datetime64[ns](1), float64(2), int64(3), object(4)\n", + "memory usage: 7.4+ MB\n" + ] + } + ], + "source": [ + "# data[categoricals] = data[categoricals].astype(str)\n", + "data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\rijul\\AppData\\Local\\Temp\\ipykernel_49732\\1336221646.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n", + " data.describe(include='all')\n" + ] + }, + { + "data": { + "text/html": [ + "
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RecnumCardnumDateMerchnumMerch descriptionMerch stateMerch zipTranstypeAmountFraud
0151421904392010-01-015509006296254FEDEX SHP 12/23/09 AB#TN38118.0P3.620
1251421839732010-01-0161003026333SERVICE MERCHANDISE #81MA1803.0P31.420
2351421317212010-01-014503082993600OFFICE DEPOT #191MD20706.0P178.490
3451421484522010-01-015509006296254FEDEX SHP 12/28/09 AB#TN38118.0P3.620
4551421904392010-01-015509006296254FEDEX SHP 12/23/09 AB#TN38118.0P3.620
5651421498742010-01-015509006296254FEDEX SHP 12/22/09 AB#TN38118.0P3.670
6751421892772010-01-015509006296254FEDEX SHP 12/28/09 AB#TN38118.0P3.620
7851421911822010-01-016098208200062MIAMI COMPUTER SUPPLYOH45429.0P230.320
8951422586292010-01-01602608969534FISHER SCI ATLGA30091.0P62.110
91051421904392010-01-015509006296254FEDEX SHP 12/23/09 AB#TN38118.0P3.620
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" + ], + "text/plain": [ + " Recnum Cardnum Date Merchnum Merch description \\\n", + "0 1 5142190439 2010-01-01 5509006296254 FEDEX SHP 12/23/09 AB# \n", + "1 2 5142183973 2010-01-01 61003026333 SERVICE MERCHANDISE #81 \n", + "2 3 5142131721 2010-01-01 4503082993600 OFFICE DEPOT #191 \n", + "3 4 5142148452 2010-01-01 5509006296254 FEDEX SHP 12/28/09 AB# \n", + "4 5 5142190439 2010-01-01 5509006296254 FEDEX SHP 12/23/09 AB# \n", + "5 6 5142149874 2010-01-01 5509006296254 FEDEX SHP 12/22/09 AB# \n", + "6 7 5142189277 2010-01-01 5509006296254 FEDEX SHP 12/28/09 AB# \n", + "7 8 5142191182 2010-01-01 6098208200062 MIAMI COMPUTER SUPPLY \n", + "8 9 5142258629 2010-01-01 602608969534 FISHER SCI ATL \n", + "9 10 5142190439 2010-01-01 5509006296254 FEDEX SHP 12/23/09 AB# \n", + "\n", + " Merch state Merch zip Transtype Amount Fraud \n", + "0 TN 38118.0 P 3.62 0 \n", + "1 MA 1803.0 P 31.42 0 \n", + "2 MD 20706.0 P 178.49 0 \n", + "3 TN 38118.0 P 3.62 0 \n", + "4 TN 38118.0 P 3.62 0 \n", + "5 TN 38118.0 P 3.67 0 \n", + "6 TN 38118.0 P 3.62 0 \n", + "7 OH 45429.0 P 230.32 0 \n", + "8 GA 30091.0 P 62.11 0 \n", + "9 TN 38118.0 P 3.62 0 " + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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RecnumCardnumDateMerchnumMerch descriptionMerch stateMerch zipTranstypeAmountFraud
count96753.0000009.675300e+049675393378967539555892097.000000967539.675300e+0496753.000000
uniqueNaNNaNNaN1309113126227NaN4NaNNaN
topNaNNaNNaN930090121224GSA-FSS-ADVTNNaNPNaNNaN
freqNaNNaNNaN9310168812035NaN96398NaNNaN
mean48377.0000005.142202e+092010-06-25 22:21:52.480232960NaNNaNNaN44706.596740NaN4.278857e+020.010945
min1.0000005.142110e+092010-01-01 00:00:00NaNNaNNaN1.000000NaN1.000000e-020.000000
25%24189.0000005.142152e+092010-04-03 00:00:00NaNNaNNaN20855.000000NaN3.348000e+010.000000
50%48377.0000005.142196e+092010-06-27 00:00:00NaNNaNNaN38118.000000NaN1.379800e+020.000000
75%72565.0000005.142246e+092010-09-12 00:00:00NaNNaNNaN63103.000000NaN4.282000e+020.000000
max96753.0000005.142847e+092010-12-31 00:00:00NaNNaNNaN99999.000000NaN3.102046e+061.000000
std27930.3296355.567084e+04NaNNaNNaNNaN28369.537945NaN1.000614e+040.104047
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" + ], + "text/plain": [ + " Recnum Cardnum Date \\\n", + "count 96753.000000 9.675300e+04 96753 \n", + "unique NaN NaN NaN \n", + "top NaN NaN NaN \n", + "freq NaN NaN NaN \n", + "mean 48377.000000 5.142202e+09 2010-06-25 22:21:52.480232960 \n", + "min 1.000000 5.142110e+09 2010-01-01 00:00:00 \n", + "25% 24189.000000 5.142152e+09 2010-04-03 00:00:00 \n", + "50% 48377.000000 5.142196e+09 2010-06-27 00:00:00 \n", + "75% 72565.000000 5.142246e+09 2010-09-12 00:00:00 \n", + "max 96753.000000 5.142847e+09 2010-12-31 00:00:00 \n", + "std 27930.329635 5.567084e+04 NaN \n", + "\n", + " Merchnum Merch description Merch state Merch zip Transtype \\\n", + "count 93378 96753 95558 92097.000000 96753 \n", + "unique 13091 13126 227 NaN 4 \n", + "top 930090121224 GSA-FSS-ADV TN NaN P \n", + "freq 9310 1688 12035 NaN 96398 \n", + "mean NaN NaN NaN 44706.596740 NaN \n", + "min NaN NaN NaN 1.000000 NaN \n", + "25% NaN NaN NaN 20855.000000 NaN \n", + "50% NaN NaN NaN 38118.000000 NaN \n", + "75% NaN NaN NaN 63103.000000 NaN \n", + "max NaN NaN NaN 99999.000000 NaN \n", + "std NaN NaN NaN 28369.537945 NaN \n", + "\n", + " Amount Fraud \n", + "count 9.675300e+04 96753.000000 \n", + "unique NaN NaN \n", + "top NaN NaN \n", + "freq NaN NaN \n", + "mean 4.278857e+02 0.010945 \n", + "min 1.000000e-02 0.000000 \n", + "25% 3.348000e+01 0.000000 \n", + "50% 1.379800e+02 0.000000 \n", + "75% 4.282000e+02 0.000000 \n", + "max 3.102046e+06 1.000000 \n", + "std 1.000614e+04 0.104047 " + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe(include = 'all',datetime_is_numeric=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Recnum 100.000000\n", + "Cardnum 100.000000\n", + "Date 100.000000\n", + "Merchnum 96.511736\n", + "Merch description 100.000000\n", + "Merch state 98.764896\n", + "Merch zip 95.187746\n", + "Transtype 100.000000\n", + "Amount 100.000000\n", + "Fraud 100.000000\n", + "dtype: float64" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.count() * 100 / len(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 1)", + "output_type": "error", + "traceback": [ + "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n", + " File \u001b[0;32m\"C:\\Users\\rijul\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\"\u001b[0m, line \u001b[0;32m3457\u001b[0m, in \u001b[0;35mrun_code\u001b[0m\n exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \u001b[0;32m\"C:\\Users\\rijul\\AppData\\Local\\Temp\\ipykernel_49732\\43749893.py\"\u001b[0m, line \u001b[0;32m1\u001b[0m, in \u001b[0;35m\u001b[0m\n get_ipython().run_cell_magic('time', '', \"pip install pandas_profiling\\nimport pandas_profiling\\nprofile = pandas_profiling.ProfileReport(data)\\nprofile.to_file('Data Summary.html')\\n\")\n", + " File \u001b[0;32m\"C:\\Users\\rijul\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\"\u001b[0m, line \u001b[0;32m2419\u001b[0m, in \u001b[0;35mrun_cell_magic\u001b[0m\n result = fn(*args, **kwargs)\n", + " File \u001b[0;32m\"C:\\Users\\rijul\\anaconda3\\lib\\site-packages\\decorator.py\"\u001b[0m, line \u001b[0;32m232\u001b[0m, in \u001b[0;35mfun\u001b[0m\n return caller(func, *(extras + args), **kw)\n", + " File \u001b[0;32m\"C:\\Users\\rijul\\anaconda3\\lib\\site-packages\\IPython\\core\\magic.py\"\u001b[0m, line \u001b[0;32m187\u001b[0m, in \u001b[0;35m\u001b[0m\n call = lambda f, *a, **k: f(*a, **k)\n", + " File \u001b[0;32m\"C:\\Users\\rijul\\anaconda3\\lib\\site-packages\\IPython\\core\\magics\\execution.py\"\u001b[0m, line \u001b[0;32m1291\u001b[0m, in \u001b[0;35mtime\u001b[0m\n expr_ast = self.shell.compile.ast_parse(expr)\n", + "\u001b[1;36m File \u001b[1;32m\"C:\\Users\\rijul\\anaconda3\\lib\\site-packages\\IPython\\core\\compilerop.py\"\u001b[1;36m, line \u001b[1;32m101\u001b[1;36m, in \u001b[1;35mast_parse\u001b[1;36m\u001b[0m\n\u001b[1;33m return compile(source, filename, symbol, self.flags | PyCF_ONLY_AST, 1)\u001b[0m\n", + "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m pip install pandas_profiling\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "%%time\n", + "pip install pandas_profiling\n", + "import pandas_profiling\n", + "profile = pandas_profiling.ProfileReport(data)\n", + "profile.to_file('Data Summary.html')" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#goods: 95694 #bads: 1059\n" + ] + } + ], + "source": [ + "goods = data[data['Fraud'] == 0]\n", + "bads = data[data['Fraud'] == 1]\n", + "print(\"#goods:\", len(goods), \" #bads:\", len(bads))" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RecnumCardnumDateMerchnumMerch descriptionMerch stateMerch zipTranstypeAmountFraud
0151421904392010-01-015509006296254FEDEX SHP 12/23/09 AB#TN38118.0P3.620
1251421839732010-01-0161003026333SERVICE MERCHANDISE #81MA1803.0P31.420
2351421317212010-01-014503082993600OFFICE DEPOT #191MD20706.0P178.490
3451421484522010-01-015509006296254FEDEX SHP 12/28/09 AB#TN38118.0P3.620
4551421904392010-01-015509006296254FEDEX SHP 12/23/09 AB#TN38118.0P3.620
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" + ], + "text/plain": [ + " Recnum Cardnum Date Merchnum Merch description \\\n", + "0 1 5142190439 2010-01-01 5509006296254 FEDEX SHP 12/23/09 AB# \n", + "1 2 5142183973 2010-01-01 61003026333 SERVICE MERCHANDISE #81 \n", + "2 3 5142131721 2010-01-01 4503082993600 OFFICE DEPOT #191 \n", + "3 4 5142148452 2010-01-01 5509006296254 FEDEX SHP 12/28/09 AB# \n", + "4 5 5142190439 2010-01-01 5509006296254 FEDEX SHP 12/23/09 AB# \n", + "\n", + " Merch state Merch zip Transtype Amount Fraud \n", + "0 TN 38118.0 P 3.62 0 \n", + "1 MA 1803.0 P 31.42 0 \n", + "2 MD 20706.0 P 178.49 0 \n", + "3 TN 38118.0 P 3.62 0 \n", + "4 TN 38118.0 P 3.62 0 " + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "goods.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [], + "source": [ + "def three_commas(x):\n", + " b, a = divmod(len(x), 3)\n", + " return \",\".join(([x[:a]] if a else []) + [x[a+3*i:a+3*i+3] for i in range(b)])" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.010945397041952187\n" + ] + } + ], + "source": [ + "overall_fraud_rate = len(bads) / len(data)\n", + "print(overall_fraud_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "96753" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(data['Recnum'].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1645" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(data['Cardnum'].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(200.0, 1300.0)" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.rcParams['font.size'] = 13\n", + "plt.rcParams[\"figure.figsize\"] = (15,6)\n", + "data['Cardnum'].value_counts().head(15).plot(kind = 'bar',)\n", + "for i,j in enumerate(data['Cardnum'].value_counts().head(15)):\n", + " plt.text(i,j*1.02,j,ha = 'center', fontsize = 13)\n", + "#plt.yscale('log')\n", + "plt.xticks(rotation=34)\n", + "plt.title('Credit Card Transaction Count')\n", + "plt.ylabel('Number of Transactions')\n", + "plt.xlabel('Credit Card Number')\n", + "plt.ylim(bottom = 200)\n", + "plt.ylim(top = 1300)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "365" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(data['Date'].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data['Date'].value_counts().head(15).plot(kind = 'bar')\n", + "for i,j in enumerate(data['Date'].value_counts().head(15)):\n", + " plt.text(i,j*1.01,j,ha = 'center', fontsize = 14)" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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RecnumCardnumMerchnumMerch descriptionMerch stateMerch zipTranstypeAmountFraud
Date
2010-01-01515151515151515151
2010-01-02292929292910292929
2010-01-03159159153159157153159159159
2010-01-04229229221229226221229229229
2010-01-05309309286309299297309309309
2010-01-06330330317330328321330330330
2010-01-07307307292307305294307307307
2010-01-08104104104104104104104104104
2010-01-09191919191919191919
2010-01-10323323307323314309323323323
2010-01-11318318302318316310318318318
2010-01-12442442426442433422442442442
2010-01-13374374365374371353374374374
2010-01-14350350339350347332350350350
2010-01-15139139136139139136139139139
2010-01-16999999999
2010-01-17177177172177172173177177177
2010-01-18235235230235234223235235235
2010-01-19302302296302301265302302302
2010-01-20359359322359334310359359359
\n", + "
" + ], + "text/plain": [ + " Recnum Cardnum Merchnum Merch description Merch state \\\n", + "Date \n", + "2010-01-01 51 51 51 51 51 \n", + "2010-01-02 29 29 29 29 29 \n", + "2010-01-03 159 159 153 159 157 \n", + "2010-01-04 229 229 221 229 226 \n", + "2010-01-05 309 309 286 309 299 \n", + "2010-01-06 330 330 317 330 328 \n", + "2010-01-07 307 307 292 307 305 \n", + "2010-01-08 104 104 104 104 104 \n", + "2010-01-09 19 19 19 19 19 \n", + "2010-01-10 323 323 307 323 314 \n", + "2010-01-11 318 318 302 318 316 \n", + "2010-01-12 442 442 426 442 433 \n", + "2010-01-13 374 374 365 374 371 \n", + "2010-01-14 350 350 339 350 347 \n", + "2010-01-15 139 139 136 139 139 \n", + "2010-01-16 9 9 9 9 9 \n", + "2010-01-17 177 177 172 177 172 \n", + "2010-01-18 235 235 230 235 234 \n", + "2010-01-19 302 302 296 302 301 \n", + "2010-01-20 359 359 322 359 334 \n", + "\n", + " Merch zip Transtype Amount Fraud \n", + "Date \n", + "2010-01-01 51 51 51 51 \n", + "2010-01-02 10 29 29 29 \n", + "2010-01-03 153 159 159 159 \n", + "2010-01-04 221 229 229 229 \n", + "2010-01-05 297 309 309 309 \n", + "2010-01-06 321 330 330 330 \n", + "2010-01-07 294 307 307 307 \n", + "2010-01-08 104 104 104 104 \n", + "2010-01-09 19 19 19 19 \n", + "2010-01-10 309 323 323 323 \n", + "2010-01-11 310 318 318 318 \n", + "2010-01-12 422 442 442 442 \n", + "2010-01-13 353 374 374 374 \n", + "2010-01-14 332 350 350 350 \n", + "2010-01-15 136 139 139 139 \n", + "2010-01-16 9 9 9 9 \n", + "2010-01-17 173 177 177 177 \n", + "2010-01-18 223 235 235 235 \n", + "2010-01-19 265 302 302 302 \n", + "2010-01-20 310 359 359 359 " + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "count_day = data.groupby('Date').count()\n", + "count_day.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Number of Transactions')" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data.assign(trx = np.ones(len(data))).set_index(data['Date']).resample(timedelta(days = 1))\\\n", + " .count()[:-1].trx.plot(title = 'Daily Transactions')\n", + "plt.ylabel('Number of Transactions', labelpad = 15)" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Weekly Transactions')" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "week_plot = data['Date'].value_counts().groupby(pd.Grouper(freq = '7D')).sum()\n", + "\n", + "week_plot.head(51).plot()\n", + "plt.xlabel('Date', labelpad = 15)\n", + "plt.ylabel('Number of Transactions', labelpad = 15)\n", + "# plt.rcParams.update({'figure.figsize':(8,6)})\n", + "plt.title('Weekly Transactions', pad = 15)" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Date\n", + "2010-01-03 73943.29\n", + "2010-01-10 612470.46\n", + "2010-01-17 621412.40\n", + "2010-01-24 572035.25\n", + "2010-01-31 532496.82\n", + "2010-02-07 700952.73\n", + "2010-02-14 690981.26\n", + "2010-02-21 618247.23\n", + "2010-02-28 743780.07\n", + "2010-03-07 711272.65\n", + "2010-03-14 697672.54\n", + "2010-03-21 886950.79\n", + "2010-03-28 701898.69\n", + "2010-04-04 741220.36\n", + "2010-04-11 640829.84\n", + "2010-04-18 677806.47\n", + "2010-04-25 593620.67\n", + "2010-05-02 863988.95\n", + "2010-05-09 721180.71\n", + "2010-05-16 776357.36\n", + "2010-05-23 755204.06\n", + "2010-05-30 651522.35\n", + "2010-06-06 878174.11\n", + "2010-06-13 757829.85\n", + "2010-06-20 755333.04\n", + "2010-06-27 899811.33\n", + "2010-07-04 666189.46\n", + "2010-07-11 784056.79\n", + "2010-07-18 3929628.71\n", + "2010-07-25 773759.24\n", + "2010-08-01 890240.27\n", + "2010-08-08 1265458.24\n", + "2010-08-15 1064378.68\n", + "2010-08-22 852384.34\n", + "2010-08-29 957550.70\n", + "2010-09-05 944387.97\n", + "2010-09-12 1004740.83\n", + "2010-09-19 1247424.64\n", + "2010-09-26 1034408.16\n", + "2010-10-03 1050403.48\n", + "2010-10-10 660283.83\n", + "2010-10-17 563761.54\n", + "2010-10-24 505211.73\n", + "2010-10-31 595523.46\n", + "2010-11-07 511654.73\n", + "2010-11-14 481331.92\n", + "2010-11-21 575646.23\n", + "2010-11-28 492776.24\n", + "2010-12-05 622327.21\n", + "2010-12-12 626964.77\n", + "2010-12-19 614012.78\n", + "2010-12-26 405138.51\n", + "2011-01-02 402585.21\n", + "Freq: W-SUN, Name: Amount, dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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2X3/9NQEBAeeEReVhMpno2LEjP//8M126dGHdunX88ssvRaFTadg75s5+v9jffiNGjMDHx6fMdZ3dheZsM2bMICsrCx8fH8aNG3fO4ydOnADg66+/5r///S++vr7nnHOhesvyupyv+7Cs5/yd/f1xPhX59i6PgICAc45d6udoeV/n8oTeIiIink4BnIiISCXWp0+fogCuSZMm2Gy2osDNrlu3bvj4+LB48WK6dOlCfn4+jRs3pnr16kXn2Lt7Bg4cyL///e9S3dv+nMDAQKZOnVrm2n19ffnll18YNmwYc+bMYfDgwfz222+EhoaW6TqxsbEMHTqU77//ntmzZ5OQkEBmZiZjxowhIiKizHWdj9lspnfv3qxbt47ExMQSzzl8+DCtW7cu8ThAjRo1io7VqlWLvXv38tJLLxV1bLkr+/s3Pz+/qIOyJKdOnWLOnDnceOONTqvF3kl34MCB855z8OBBoPjb2x4KZmRklPicI0eOOKpEt3Gpn6NlVaNGDQ4cOMChQ4eIjY0953F7R56IiEhl5N6/qhMREZFLYu9iW7p0KQsXLgQ4J4ALDAykffv2rFixggULFgCcsx+UfT+xH3/88aKdQHaxsbG0bt2auLi4ouWtZeXn58esWbMYOnQoy5cvZ+DAgaSmppb5OuPHjwfg448/ZtKkSQDcf//9ZbpGabqm9u7dC5S8nBIKO8D+Ljs7m59//hko/r6xv83tSzudzd5lV1BQUKbn7d+/n6VLl+Lj40NSUhJG4ZCvc/785z//AXB60NOhQweCgoI4cOAAy5cvP+fxAwcOsHTpUsxmM7169So6bg/jdu/efc5z8vLyWLRokUPrtAd+ZX17O5IjPkfLwv7/UUmfBxaLhZkzZzq9BhEREVdRACciIlKJtW3blvDwcNLS0pg6dSohISG0a9funPN69+5NdnZ20T5pZy8/hcI9z4YOHcr27du5/fbbi5YUnu3gwYNMnDix2LGXXnoJgFtvvbXEzqjs7Gy++eYbdu7ced7XwcfHhxkzZjB8+HBWr17NgAEDSE5Ovvgrf5Y+ffrQsmVL5s+fz5YtW2jfvn2Z95SbPXs2N910E4sWLTonjLNarXz44YfMmTMH4LzLTydOnFhsM3ur1cqTTz5JYmIi7dq1KxYIPfHEE4SGhvL8888zefLkokEadjabjb/++ov58+eX6fU4H3sAdaH3RUnsgdpVV11F1apVz3veyJEjMZvNzJ07t8SPH0cJCAgoWgb74IMPFutGzMjIKNoTcNiwYcX2Revbty8mk4m5c+cWC+7y8vJ46KGHiroUHaW8b29Hc8TnaGmNHz8es9nM5MmTiwWaNpuNp59+2qF78omIiLgbBXAiIiKVmNlspmfPngCkpKTQvXv3Ejeztwc/KSkpwLkdcFAYtPTp04dvvvmmaMLkrbfeysCBA2nSpAn169c/Z3nqkCFDeOuttzh8+DB9+/alRYsWDB06lKFDh9KxY0eio6MZMWLERcMNb29vvv76a26//XY2bNhAv379zrvM83zsXXAADzzwQJmeC4UhwaxZs+jXrx9RUVEMHDiQkSNHcvXVV1O3bl3Gjx+PYRg88cQTDBo0qMRr3HXXXfTs2ZMrrriCESNG0LhxYz788EMiIiL44osvip1bq1YtfvrpJwICAhg7dix169Zl8ODB3HLLLfTo0YOYmBgGDBhQ1Nl4qexTMgcMGMCtt97K2LFjGTt27AWfYxhGUd233377Bc+tWbMmvXv3pqCggC+//NIhNZ/Pyy+/TK9evdi0aRMNGzbkhhtu4Oabb6Z+/fr8+eeftGzZ8pywuE6dOowbNw6bzUa/fv244oorGDJkCPXr12f27NnceeedDq1xyJAhmM1m3n33XQYNGsTdd9/N2LFjWbFihUPvU5o6HPE5WhodOnTgpZdeIjc3lwEDBtCvXz9GjBhB06ZN+fDDD0vcP1BERKSyUAAnIiJSyZ3dzfb35ad2PXv2LNpQvWHDhiXuzxQaGsqff/7J1KlT6datG7t37+aHH35gy5YthISEMGHCBGbNmnXO8yZMmMC6desYM2YM2dnZzJ07l7/++ovMzEyuvvpqvvzyy2KdX+fj5eXFtGnTuOuuu9iyZQv9+vUrUyfVlVdeCUBYWBi33XZbqZ9nN3jwYObOncuECRNo3Lgxu3btYubMmSxatAh/f39uv/12Fi1axJtvvnnea7z77ru8++67nDhxgh9//JG0tDRuvfVW1q5dS8uWLc85v1+/fmzfvp2nnnqKiIgIli1bxuzZszl27Bht27bl3Xff5eGHHy7z61KSV155hQkTJhAcHMysWbOYPHkykydPvuBz/vzzT44cOUJoaOhFN/EHGDVqFOD8ZagBAQEsWLCAt99+m4YNG7JgwQLmzJlDtWrVePHFF1m5ciWRkZHnPO+DDz7glVdeoU6dOixZsoTVq1czcOBA1q9fX+IU0UvRtm1bvvvuOzp16sSKFSv4/PPPmTx5Mnv27HHofUrDUZ+jpfF///d/fPvtt3To0IHVq1czd+5cGjZsyLJly+jatatD7iEiIuKOTEZ5xkCJiIiIeJhXXnmFZ599locffpj33nuvQu9tn/qob7tERERELk8K4ERERKTSS05OplmzZiQlJbFz504aN25cofdXACciIiJyefN2dQEiIiIizvLmm2+ydetWFi1axMmTJxk7dmyFh28iIiIiIuqAExERkUqrb9++LF68mGrVqjFs2DDefPNNAgICKrwOdcCJiIiIXN4UwImIiIiIiIiIiDiRpqCKiIiIiIiIiIg4kfaAKwObzUZCQgIhISFFS0lEREREREREROTyYxgGGRkZxMbGYjZfuMdNAVwZJCQkUKtWLVeXISIiIiIiIiIibuLo0aPUrFnzgucogCuDkJAQoPANGxoa6uJqRERERERERETEVdLT06lVq1ZRXnQhCuDKwL7sNDQ0VAGciIiIiIiIiIiUapsyDWEQERERERERERFxIgVwIiIiIiIiIiIiTqQATkRERERERERExIkUwImIiIiIiIiIiDiRAjgREREREREREREnUgAnIiIiIiIiIiLiRArgREREREREREREnEgBnIiIiIiIiIiIiBMpgBMREREREREREXEiBXAiIiIiIiIiIiJOpABORERERERERETEiRTAiYiIiIiIiIiIOJECOBERERERERERESdSACciIiIiIiIiIuJECuBEREREREREREScSAGciIiIiIiIiIiIEymAK4WJEyfSvHlzOnXq5OpSRERERERERETEw5gMwzBcXYSnSE9PJywsjLS0NEJDQ11djoiIiIiUID41h08W72dMj3rUiwxydTkiIiJSSZUlJ1IHnIiIiIhUKl+uOsy0lYeZsvygq0sRERERARTAiYiIiEglcyQ5G4Cjp1+KiIiIuJoCOBERERGpVBJSc4DCpagiIiIi7kABnIiIiIhUKvEpOUUvtd2xiIiIuAMFcCIiIiJSaeQVWDmZkQdAlsVKem6BiysSERERUQAnIiIiIpXI8bTcYv+2d8OJiIiIuJICOBERERGpNP4euCVoHzgRERFxAwrgRERERKTS+PvghYQ0BXAiIiLiegrgRERERKTS+HsAp0moIiIi4g4UwImIiIhIpWFfchoe6ANoDzgRERFxDwrgRERERKTSsHe8daxTBdAecCIiIuIeFMCJiIiISKWRkFo4BbVT3Yhi/xYRERFxJQVwIiIiIlIp2GxGUQdcp3qFHXAnMnKxFNhcWZaIiIiIAjgRERERqRxOZVmwFNgwmaBFbCi+3mYMA06kqwtOREREXEsBnIiIiIhUCvbut2oh/vh5e1EjPKDYcRERERFXUQAnIiIiIpWCfeBCjYjC4C023L/YcRERERFXUQAnIiIiIpVCfEph0BZ7uvMtNiyg2HERERERV1EAJyIiIiKVgn2pqX3pqb0TLiFNAZyIiIi4lgI4EREREakUzgRwhUtPY4v2gNMQBhEREXEtBXAiIiIiUinYl5raO9+KhjCkZLusJhERERFQACciIiIilYR9qWnRHnCnXyak5mIYhsvqEhEREVEAJyIiIiIeLyuvgNTsfOBM51v1sMKlqDn51qLHRERERFxBAZyIiIiIeLyE0/u/hfh7E+LvA4C/jxeRwX7Amf3hRERERFxBAZyIiIiIeLy4v01AtbMPZFAAJyIiIq6kAE5EREREPF7C+QK4iIBij4uIiIi4ggI4EREREfF4f5+AahcbpgBOREREXE8BnIiIiIh4PHvAFvu3Djj7v7UEVURERFxJAZyIiIiIeLz48yxBPRPA5VZ4TSIiIiJ2CuBERERExOMlnA7Y/t4BV1N7wImIiIgbUABXChMnTqR58+Z06tTJ1aWIiIiIyN8UWG0cTy8M4Gr+fQ+404FcYkYeeQXWCq9NREREBBTAlcr48ePZsWMHa9eudXUpIiIiIvI3JzLysNoMfLxMRAX7FXssItAHf5/Cb3mPaRmqiIiIuIgCOBERERHxaPYJqNXDAjCbTcUeM5lMRfvCaRmqiIiIuIoCOBERERHxaAnnGcBgp0moIiIi4moK4ERERETEo9mDtb8PYLCroQBOREREXEwBnIiIiIh4NHuwViPiwgGclqCKiIiIqyiAExERERGPZt8Drka4f4mPxxYFcBrCICIiIq6hAE5EREREPFpRB1x4YImPx6oDTkRERFxMAZyIiIiIeCzDMIqCtdjzdMCdvQecYRgVVpuIiIiInQI4EREREfFYqdn5ZFuswPmHMMSE+WMyQV6BjVNZloosT0RERARQACciIiIiHsy+/DQy2Bd/H68Sz/H1NhMd4gdoGaqIiIi4hgI4EREREfFYZ/Z/K7n7zc7eHWcf2CAiIiJSkRTAiYiIiIjHOrP/24UDuLP3gRMRERGpaArgRERERMRj2TvaLtYBV6NoEmqu02sSERER+TsFcCIiIiLisRLSStcBF1sUwKkDTkRERCqeAjgRERER8VhFHXARpdwDTgGciIiIuIACOBERERHxWPGnl5SWfgmqAjgRERGpeArgRERERMQj5eZbScrMA0ofwJ3KspCbb3V6bSIiIiJnUwAnIiIiIh7pWFph91ugrxfhgT4XPDc0wJsgXy9Ay1BFRESk4imAExERERGPZN//LTY8AJPJdMFzTSZT0T5xWoYqIiIiFU0BnIiIiIh4JHuQdrHlp3aahCoiIiKuogBORERERDxSXOqZDrjSODMJNddpNYmIiIiURAGciIiIiHgk+xLUmhGlC+DsnXL254mIiIhUFAVwIiIiIuKREoo64PxLdX4NLUEVERERF1EAJyIiIiIeKb5oD7jAUp1ftAdcmgI4ERERqVgK4ERERETE49hsBsfSytYBZz/vWGouNpvhtNpERERE/k4BnIiIiIh4nMTMPPKtBmYTxISWLoCLCfXHbAKL1UZSZp6TKxQRERE5QwGciIiIiHgc+/LTmFB/vL1K9y2tt5e5KKyL1z5wIiIiUoEUwImIiIiIx7FPMq1RygmodkX7wKXmOrwmERERkfNRACciIiIiHiehaABD+QK4+NRsh9ckIiIicj4K4ERERETE49iXkMaWMYCzd8ypA05EREQqkgI4EREREfE4RR1w5VyCqj3gREREpCIpgBMRERERjxOXUs4OuPDTQxhSFMCJiIhIxVEAJyIiIiIex94BV7PMAVxg4fPTFMCJiIhIxVEAJyIiIiIeJSM3n/TcAqDsHXCxpzvgUrPzycorcHhtIiIiIiVRACciIiIiHsU+QCE80IcgP+8yPTfE34cQ/8LnHFMXnIiIiFQQBXAiIiIi4lHiU7MBiA0rW/ebXY3TXXNx2gdOREREKogCOBERERHxKPYBCmWdgGpnD+DsnXQiIiIizqYATkREREQ8Svzp4KxGGfd/s4stCuDUASciIiIVQwGciIiIiHiU+NPB2aUGcPEK4ERERKSCKIATEREREY9i71wr6wRUO/vSVQVwIiIiUlEUwImIiIiIR7n0PeD8AS1BFRERkYqjAE5EREREPEa+1caJjMI94GJPB2llZe+cO56Wi9VmOKw2ERERkfNRAFcKEydOpHnz5nTq1MnVpYiIiIhc1o6n5WIY4OttJjLIr1zXiA7xx9tsosBmcDJDk1BFRETE+RTAlcL48ePZsWMHa9eudXUpIiIiIpe1swcwmM2mcl3Dy2wiJkzLUEVERKTiKIATEREREY9h3/+tvMtP7c5MQlUHnIiIiDifAjgRERER8RgJZ3XAXQr78+2BnoiIiIgzKYATEREREY9hX4Ia66AATktQRUREpCIogBMRERERjxHvoA64WAVwIiIiUoEUwImIiIiIx3BcAOdf7HoiIiIizqQATkREREQ8gmEYZ/aAi3DQHnAK4ERERKQCKIATEREREY+QnGUhN98GQEyYY6agZuQWkJ6bf8m1iYiIiFyIAjgRERER8Qj2brXoED/8vL0u6VpBft6EB/oAcCw195JrExEREbkQBXAiIiIi4hESHDQB1S42zL4MNdsh1xMRERE5HwVwIiIiIuIR4lIcs/+bnf068eqAExERESdTACciIiIiHiHhdFB2qRNQ7ezXSdAgBhEREXEyBXAiIiIi4hHsS0UdFcDFhhcOclAAJyIiIs6mAE5EREREPIK9A85he8Cdvk58igI4ERERcS4FcCIiIiLiEexTULUEVURERDyNAjgRERERcXs5FivJWRbAgUMYTgdwx9NzKbDaHHJNERERkZIogBMRERERt2fvfgv28ybU39sh14wM9sPXy4zNKAzhRERERJxFAZyIiIiIuL2Es5afmkwmh1zTbDZRvWgQgwI4ERERcR4FcCIiIiLi9uwdcPbJpY4SG6Z94ERERMT5FMCJiIiIiNsr6oBz0P5vdkWTUBXAiYiIiBMpgBMRERERtxefYu+Ac2wAV+N0R50COBEREXEmBXAiIiIi4vbiz9oDzpHsHXVagioiIiLOpABORERERNyeswI4e0edAjgRERFxJgVwIiIiIuLWrDaD42mFU0qdtgdcSg6GYTj02iIiIiJ2CuBERERExK2dzMilwGbgbTYRHeLYKaj2jrosi5X0nAKHXltERETETgGciIiIiLg1+wCGmDB/vMwmh17b38eLqkG+hffRMlQRERFxEgVwIiIiIuLW7MGYoyeg2mkfOBEREXE2BXAiIiIi4tbsAVxNpwVw/sXuIyIiIuJoCuBERERExK3ZO9McPYDBrkZ4YLH7iIiIiDiaAjgRERERcWv2PeCctwRVHXAiIiLiXArgRERERMStJaTmAmcmljqa/boK4ERERMRZFMCJiIiIiNsyDMPpQxjsS1u1BFVEREScRQGciIiIiLit9NwCMvMKAOd1wNmDvZMZeVgKbE65h4iIiFzeFMCJiIiIiNuy7/9WJciXAF8vp9yjapAvvt5mDANOpOc65R4iIiJyeVMAJyIiIiJuq2gCqpO63wBMJlPR9eNStAxVREREHE8BnIiIiIi4rTP7v/k79T72AE77wImIiIgzKIATEREREbd1pgMu0Kn3sQd8CuBERETEGRTAiYiIiIjbiqugDjj7IIZ4BXAiIiLiBArgRERERMRt2TvSakY4bw84OLMEVQGciIiIOIMCOBERERFxW/YpqLFOHMIA2gNOREREnEsBnIiIiIi4pbwCKycz8gDnTkGFMwFfQmouhmE49V4iIiJy+VEAJyIiIiJu6XhaLgD+PmaqBPk69V4xYYV7zOXkW0nJznfqvUREROTyowBORERERNzS2ctPTSaTU+/l7+NFVIgfoGWoIiIi4ngK4ERERETELdkHIjh7+amdJqGKiIiIsyiAExERERG3VNEBXI3wwmWo9s47EREREUdRACciIiIibimhwgM4TUIVERER51AAJyIiIiJuyd4BF1vBS1AT0jwvgFt94BQ3fbSC2ZsTXF2KiIiIlMDb1QWIiIiIiJQkIbVwCmqNiIreAy63Qu7nKJYCG0/O3MKR5GzWH07hRHouY3vVd3VZ4gS5+VbWH06hc70q+Hipl0JExJPof20RERERcTs2m+GCPeBOB3AetgfcV6sPcyQ5G9/TgczLv+7k1d92YrMZLq5MHCk338rIz1Yz8rPV3D1tHbn5VleXJCIiZaAATkRERETczqksC5YCGyYTxIT5V8g97QFcUmaex4Qb6bn5/O+vfQA8f31znhrcFIBPlhxgwoxNWApsrixPHMRqM3j4m42sP5wCwJI9idw1dS3ZlgIXVyYiIqWlAE5ERERE3I69+61aiH+FLbULD/QhwMcLgONpnrEMddLi/SRnWWgQFcQtHWtxf98G/PfmNniZTfy0KYG7p60lK08hjSczDIMXftnO7ztO4Ott5tlrmhHk68WK/acY8/laMvX+FRHxCArgRERERMTtFE1AraD93wBMJhOx4YXddvEeMAn1WFoOny09CMDTVzXD+3RQOaxDTT4b3ZEAHy+W7k3itk9XkZSZ58pS5RJ8tHg/01cdxmSCd29py9he9Zk+tgsh/t6sOZTMHZNXk56b7+oyRUTkIhTAiYiIiIjbse/DVlETUO1qRAQW3t8DArh3/thDXoGNTnUjuKJZdLHH+jWJ5pt7u1IlyJctcWnc9NEKDp/KclGlUl6zNsTxxrzdADx3bXOublUdgPa1I/h6bFfCAnzYcCSVUZ+tJi1bIZyIiDtTACciIiIibqeiBzDY1TjdAZfg5gHcruPpzFwfB8AzVzfDZDKdc07bWuHMvK8bNSMCOHwqm5s+WsG2+LSKLlXKaeneRP4xcwsA9/auz5096hV7vFXNML65pzBk3RyXxm2friI5y+KKUkVEpBQUwImIiIiI2zkTwFXMAAa72LDCwM/dA7jX5+7CZsDVrWJoXzvivOfVjwpm1v3daV49lKRMC7dMWsmyvUkVWKmUx7b4NO6bvp4Cm8H1bWJ5+vRwjb9rHhvKt/d2JTLYjx3H0rntk1UkZmi5sYiIO1IAJyIiIiJux74EtSL3gIMzS17deQnqin1JLNydiLfZxJODSg5mzhYd6s9347rSvUFVsixW7py6hp83xVdApVIeR5OzuXPqWrIsVrrVr8qbN7fGbD63w9GucbUQvr23K9Ehfuw+kcGtn6zkRLpnDBEREbmcKIATEREREbeTkOaqPeDsHXDuGWDYbAavzd0FwMgutakXGVSq54X4+zDlzk5c07o6+VaDR77dxGdLDzizVCmHlCwLo6esITEjj6YxIUy6owN+3l4XfV7D6GBmjOtGbJg/+xOzuGXSSrfv4hQRudwogBMRERERt5KVV0Dq6Q3lK34PuDMdcIZhVOi9S2P2lgS2xqcR7OfNQwMalem5ft5e/O/WdozpXheAl3/dyau/7cRmc7/X83KUm29l7BfrOJCYRWyYP1Pv7Eyov0+pn183MojvxhXu+XfoVDa3fLKSo8nZTqxYRETKQgGciIiIiLgVe+dOqL83IWUIIByhWqg/JhNYCmwkZbrXhvZ5BVbenF84EfO+PvWJDPYr8zXMZhPPX9ecp07vKfbJkgM8/v1m8q02h9YqZWO1GTzy7UbWH04h1N+bqXd1Jias7Psf1qoSyIxx3ahbNZCjyTnc+skqTb8VEXETCuBERERExK3Epbpm+SmAr7eZaiHuOQl1+srDxKXkUC3Uj7t71i/3dUwmE/f3bcB/b26Dl9nEjxvjuXvaOrLyChxYrZSWYRi8OHs787efwNfLzKd3dKRxtZByXy82PIDvxnWjQVQQ8ak5DJ+0kv2JmQ6sWEREykMBnIiIiIi4FXvwVbOCBzDYxYa7XwCXlp3P//7aB8CEKxsT4HvxfcEuZliHmnw2uiMBPl4s2ZPIbZ+uIilTEzQr2seLD/DFysOYTPDOLW3pUr/qJV+zWqg/397bjcbVgjmRnsctk1ax50SGA6oVEZHyUgAnIiIiIm7FPgHVFR1wZ9/XnSahfrh4H2k5+TSuFsxN7Ws67Lr9mkTzzb1dqRLky5a4NIZ9tIIjp7RvWEWZtSGO1+cVDtX41zXNuaZ1dYddOyrEj2/u6Uqz6qEkZeZx6yer2JGQ7rDri4hI2SiAExERERG3Yu88q+gBDHY13CyAi0/NYcryQwA8fVVTvL0c+y1821rhzLzvzOb9N360nG3xaQ69h5xr6d5E/jFzCwD39KrHXT3rOfweVYP9+OaeLrSuGUZyloXbPl3F1ji9b0VEXEEBnIiIiIi4lXgX7gEHUOP00ld3WYL69u97sBTY6Fq/Cv2aRDvlHvWjgpl1f3eaVw8lKdPCLZNWsmxvklPuJbAtPo37pq+nwGZwXZtYnrmqmdPuFR7oy5dju9CudjhpOfmM+GwVG4+kOO1+IiJSsssugJs9ezbt27cnODiY6tWr8+abb7q6JBERERE5S0JqLnAmCKtosWH2AC7XJfc/246EdGZtjAPgmauaYTKZnHav6FB/vhvXle4NqpJlsXLn1DXM3pzgtPtdro4mZ3Pn1LVkWax0q1+V/97cGrPZee9XgFB/H6bf3YXOdauQkVvAqMlrWHso2an3FBGR4i6rAG7evHncd999vPnmm6SmprJ7926uuuoqV5clIiIiIqcVWG0cTz8dwGkPOP4zbxeGAde1iaVNrXCn3y/E34cpd3bimtbVybcaPPbdJnXCOVBKloXRU9aQmJFH05gQPh7VAT/vSx+oURrBft5MvasT3RtUJTOvgNGfr2Hl/lMOu77NZpCSZWF/YibJWRaHXVdEpLLwdnUBFelf//oXzz77LAMGDAAgNDSUli1burgqEREREbE7kZGH1Wbg42UiKtjPJTXYO++SsyzkWKwOmThaHkv3JrJkTyI+XiaeHNikwu7r5+3F/25th5fJxC+bE7j/y/XMvL87TWJCKqyGyig338rYL9ZxIDGL6mH+TLmzE2EBPhVaQ6CvN5+P6cQ9X6xj6d4k7py6hk/v6EivRlEl1nsqy0JKlqXoZbL9T7aF5MzTL08/lpJtwWYUPjfE35u/Hu9LVIhrPodFRNyRWwZwr732Ghs2bGD9+vUcPHiQOnXqcOjQofOe/8033/Df//6XHTt2EBQUxJVXXsl//vMf6tSpU3ROVlYW69evZ/jw4TRt2pSUlBS6dOnCe++9R716jt/wVERERETKzj4BtXpYgNOX5Z1PqL83wX7eZOYVkJCWQ4Oo4AqvwWYzeO23wumYo7rWpXbVwAq9v9ls4s2bW3M8LZc1h5K5a+pafnygO9Gh/hVaR2VhtRk88u1G1h9OIdTfm2l3daZ6mGs6PP19vPj0jo488NUG/tp1krunrWNwixjScvLPBGxZFnLyreW6vpfZREZuAd+tPcKD/Rs5uHoREc/llgHcP//5T6pUqUL79u1JTU294LkffPABDz30ED169OCdd94hKSmJd999lyVLlrB27VpiY2MBSElJwTAMvvjiC+bNm0d0dDSPPvooN954Ixs2bHDqfhoiIiIiUjqunoAKYDKZiA33Z8+JTBJSXRPA/bw5nh3H0gnx8+bB/g0r/P5Q2Ak3aVQHbvxoBQeTsrh72jq+G9eVQF+3/BHCbRmGwYuztzN/+wl8vcx8ckdHGldzbTehv48XH9/egYe+2cD87Sf45Tx7/fl4magS5EtEoC9Vg0+/DPIlIujMyyqBvlQJLnwZHujLnC0JTJixma9XH+G+Pg0cPrVXRMRTueVXz/3791O/fn0AWrZsSWZmZonnnTp1imeeeYb27duzaNEivL0LX53BgwfTuXNnnnvuOT777DMAQkIKv8g98sgj1K1bF4BXX32VqKgojh49Su3atZ38WomIiIjIxbh6AqpdbHgAe05kFnXkVaTcfCv/nb8HgPv7NaBKkG+F12AXEeTLlDGduPGjFWyNT+PhbzYyaVRHvFzUneiJPl58gC9WHgbg7Vva0LV+VRdXVMjX28wHI9rz48Z4UrMtfwvZ/IgI8iHYz7vMjQpXt6rOv+fsICEtl792nWRgixgnvQYiIp7FLX8dYQ/fLubnn38mMzOThx9+uCh8A+jYsSO9e/dmxowZWCyFG4CGhYVRp06dYl9A1PUmIiIi4l7iTgderpqAamfvwEtwwSCGL1YeIj41h+ph/tzVw/VbpdSNDOLTOzrg621mwc6T/HvODleX5BHSc/P5bOkBXp9XuJT4X9c259rWsS6uqjgfLzPDO9bi3t4NuLljLfo3rUa72hHUrhpIiL9PuX5e8vfxYninWgBMX3XY0SWLiHgstwzgSmvNmjUAdO/e/ZzHunfvTkZGBrt27So6dt999/Hee+9x9OhRcnNzefbZZ+nQocN5u9/y8vJIT08v9kdEREREnMceeNV0gw44gPjU3Aq9b2q2hQ/+2gfAhCsb4+/jmgEQf9ehThXeGd4WgKkrDvH5soOuLchNncrM49s1RxgzZQ0d/v0HL/+6E4CxPetxd0/Xh6kVZWTnOphMsHRvEgeTslxdjoiIW3DLJailFR8fD0DNmjXPecx+LC4ujtatWwPwj3/8g5SUFNq3b4/NZqNnz57MmjXrvNd/7bXXePHFF51QuYiIiIiUxF2WoNYoCuCyK/S+ExfuIz23gKYxIdzY/tzvcV3pmtbViUtpymtzd/HvX3dQIyKAQVpeyPG0XOZvP87cbcdYczC5aBIoQIOoIIZ1qMW43qVb4VNZ1K4aSN/GUSzcnchXqw7z7LXNXV2SiIjLeXQAl51d+A2Rn9+54639/f2LnQNgNpt5/fXXef3110t1/WeeeYYJEyYU/Ts9PZ1atWpdSskiIiIich6GYZwZwuDqJagR9iWoFdcBdzQ5m2krCpfsPX1VU7fcZ+3e3vU5nJzN16uP8Mi3G/nu3m60qRXu6rIq3JFT2czddox524+z8UhqscdaxIZyVcsYBreMoWG0a4ctuNKobnVYuDuR79fH8fjAJgT4ukc3p4iIq3h0ABcYWDiOPS8vj4CA4t+k5eTkFDunPPz8/EoM90RERETE8VKz88m2WAGoHubv0lrsHXjH0nKw2QzMFRCGvfX7bixWGz0aVqVP4yin3688TCYTL13fgviUHBbvSeTuaWv58YEe1KpS/u+5PYFhGOw9mcm8bceZu+04O48V35qmQ50IBrcoDN0q+9uitPo0jqZmRABxKTnM3pLA8I5qZBCRy5tHB3A1atQACpeZNmrUqNhjF1qeKiIiIiLux778NDLYz+V7n1UL8cNsgnyrQVJmHtGhzg0Et8Wn8dOmBACeuaqZWw8L8/YyM3Fke27+eCU7j6Vz59S1/HB/d8ICfCq0juQsCwt3ncTX20x4oA8Rgb6EBfgQEeRLkK/XJb8NDcNgW3x6UafbgcQze5l5mU10qVeFq1rGMLBFDNWc/PHhibzMJkZ2qcPr83bx5arDCuBE5LLn0QFcp06dmDRpEitWrDgngFuxYgXBwcE0bdrURdWJiIiISFnYA7ga4a4PM7y9zMSE+pOQlktcao5TAzjDMHhtbuFm/Te0jaVljTCn3ctRgv28+XxMR4ZOXMG+k5ncN3090+7qjK+382e82WwG36w9whvzdpOWk1/iOd5mE+GBPoQH+hIe4FPs7xFBhUHd30O78AAf/H282HAkhXnbjjNv2/Gij0kAXy8zPRtFMrhFDFc0r0aVIF+nv66ebnjHmrzzxx62xKWx+WjqZblcWUTEzqMDuCFDhvDwww/z/vvvM3LkSLy9C1+ddevWsWTJEu688058ffWFUURERMQTuMv+b3Y1IgJISMslITWH9rUjnHafJXuTWL7vFL5eZh4f2MRp93G06mEBfD6mEzd/vIKVB07x9KwtvHVzG6d2722NS+PZn7ayOS4NgPpRQUQF+5GWk09KtoWU7HwsBTYKbAZJmRaSMi1lur7ZRLEhCgE+XvRtEsXgljH0bxpNiH/Fdvl5uqrBflzTujo/boxn+qrDCuBE5LLmlgHc9OnTOXy4cAPaxMRELBYLL7/8MgDh4eE8+OCDAERGRvLqq6/y6KOP0rdvX0aNGkVSUhLvvPMO1apV46WXXnLZ6yAiIiIiZROfcnoCaph7BHCF+8ClFAWDzmC1Gbz2W2H32+judTxu/7DmsaF8MLI9Y6etY9aGeOpUCeKRKxpd/IlllJadz5u/7+Kr1UcwjMIOvAlXNuaObnXw9iredZdjsZKaYyElK5/UHAtp2fmkZBf+PTU7n9Rs+8vCYymnj+VbDWwGhPh7c0WzagxqEUOfxlEaHnCJbu9ahx83xjN7cwL/d3UzItQ5KCKXKbcM4CZPnszixYuLHfvXv/4FQJ06dYoCOIBHHnmEyMhI3nrrLR599FECAwO58soree2114r2iBMRERER95eQ5l4dcPZBDM6chPrjxnh2Hc8g1N+b8f0aOu0+ztSvSTT/HtKSf/64lXcW7KFWlQBubO+YfZhtNoOZG+L4z9xdJGcVdrMNaRvL/13d7LzLggN8vQjwDaB6GYJcwzDIybeSlpNP1SC/CllKe7loXzuc5tVD2XEsnZnr47ind31XlyQi4hJuGcAtWrSoTOePHDmSkSNHOqcYEREREakQRR1w4e4RwNU4XUdcinM64HLzrbz1+24AHuzfkPBAz+0MGtGlNoeTs5i0+ABP/bCF6mEBdGtQ9ZKuuSMhned+3sa6wykANIwO5qUhLejeINIRJRdjMpkI9PUm0NctfzzyaCaTiTu61eHpWVv5cvVh7u5Zr0KmCouIuBv9akdERERE3EL86U6zGm4WwDlrCeqU5Yc4lpZLjfAA7uhW1yn3qEhPDWrKNa2qk281GDd9HftOZpTrOhm5+bw4ezvXfbCMdYdTCPT14pmrmvLbw72cEr6J813fNpYQf28On8pm6b4kV5cjIuISCuBEREREKrHl+5IYNXk1K9z8h97cfCtJmXmA+wRwRUtQ0xwfwCVnWfhw4T4AnhjUGH8fz99nzGw28dbwNrSvHU56bgFjpqwlMSOv1M83DIOfN8XT/63FTFl+CKvN4OpWMSyY0IdxfRpoWagHC/T1ZliHwmXJ01cednE1IiKuoa9ipTBx4kSaN29Op06dXF2KiFQSJ9Nznbqpt4gIwC+bExgzZQ1L9ybx8LcbSc0u20TIinQsrbD7LdDXi/BA95g0GRteuMdYanY+WXkFDruuzWbw+txdZOQV0Lx6KEPaVJ59i/19vPj0jo7UqRpIXEoOY79YR47FetHn7T2RwYhPV/PIt5tIzMijXmQQ0+7qzIcjO7jNkmS5NLd3rQPAX7tOEJeS7eJqREQqngK4Uhg/fjw7duxg7dq1ri5FRCqBXcfT6fffRfT97yI2HElxdTkil539iZnkW22uLsPppi4/yCPfbiTfauDrbSYp08Irv+50dVnndfb+byaTe+wPFeLvQ6h/4Z5gjvqlycmMXO74fA3frTsKwD+vblbp9sOqGuzHlDGdCA/0YfPRVB79biNWm1HiuVl5Bbw2dydXvbeUlQdO4edt5vErGzPv0V70aRxVwZWLMzWICqZHw6rYDPhmzRFXlyMiUuEUwImIVKDUbAv3frGeLIsVS4GNcdPXc8wJS5tEpGSTFu9nwFuLuWHico6cqpwdGIZh8Nbvu3lh9g4MA0Z3q8PXY7tgMsH36+NYttc9l6LaAy53WX5qZ+++indAALdo90muencpy/Yl4e9j5o2bWtOzUeXc06x+VDCfjOqIr5eZ+dtP8NpvxcNfwzCYu/UYV7y9mEmLD1BgM7iiWTQLJvThoQGN8PP2/CW5cq5Rp7vgvlt7lLyCi3dGiohUJgrgREQqiNVm8NA3GzmSnE3NiACaVAshMSOPe79YT26+vgmtjP7adYKFu0+6ugw5bX9iJm/9sQeA7QnpXPu/pfyx44SLq3KsAquNf/64lf/9Vbi32ONXNuaF61vQsW6Voh98//nj1lItCaxocfYALsK9Argzgxhyy30NS4GNV37dwZgpazmVZaFpTAhzHurJ8E61HFWmW+pcrwpv3twagM+WHWTaikMAHEzKYvSUtdz/1QaOpeVSMyKAz+7oyGejO1GrSqALKxZnu6JZNaqF+pGUaWHetuOuLkdEpEIpgBMRqSBvzt/N0r2FXQ+fjOrIZ6M7UiXIl63xaTw5cwuGUfLyHPFMm4+mctfUddw5ZS3vLdir96+L2WwGT/+wBUuBjW71qxZtEn/PF+v4z9xdFFSCJam5+VYe+GoD36w5itkErw5txUMDGhUt53xyUBOqh/lzJDmbdxfscXG157IvQXW3Djh7IBifWr6OyUNJWdz00Qo+XXoQgDu61eGn8T1oGB3isBrd2ZC2NXhyUBMAXpy9nSe+38ygd5awZE8ivl5mHurfkD8e68MVzau5uFKpCN5eZkZ0LvxlwJerNIxBRC4vCuBERCrAnC0JfLx4PwBvDGtD89hQalUJ5MOR7fE2m5i9OYEPF+13cZXiSG//cSbgeGfBHl75dadCOBf6es0R1h5KIdDXizdvbs1347pxV496AHy8eD8jPlvNyfTydzi5WnpuPqM/X8PvO07g623mw5HtGdGldrFzQvx9ePmGlkBhN9K2+DRXlHpe7r4EtTwdcD9ujOOa95eyNT6NsAAfJo3qwEtDWlaKiadl8UDfBtzSsRY2A2auj8NitdGrUSTzH+vN4wObEOB7eb09Lne3dq6Ft9nE2kMp7DyW7upyREQqjAI4EREn23ksnSe/3wLAvb3rc32b2KLHutavyotDWgDw3993V7rlcJer9YeTWbwnES+zifv6NAAKA4+nf9h63o3IxXmOpeXwn7m7gMIusJoRgfh4mXnuuuZ8OLI9wX7erDmYzNXvL2Pl/lMurrbsTqbncsukVaw+mEyInzfT7uzM4JbVSzx3QLNqXNu6OlabwVM/bHGrzj/7HmvuNvGyPHvAZeYVMGHGJh77bjNZFiud61Vh7iO9GNQixlllujWTycTLQ1tybevqNIwO5qOR7fnirs7UiwxydWniAtVC/Ys+F9QFJyKXEwVwIiJOlJpt4d7p68jJt9KzYST/OL0M52wju9RhVNc6GAY8+u1Gdh/PcEGl4khv/V7Y/Ta8Y02evqopbw5rjdkE3607ysPfbsRS4D6hR2VnGAb/+mkbmXkFtKsdzh3d6hZ7/OpW1fnlwR40jQkhKTOPkZ+t4qNF+7F5SFB6KCmLmz5ewc5j6UQG+/HtuK50a1D1gs95/roWhAX4sD0hncnLDlZQpRdmsxlFA2ncbw84f+DMEtmL2RqXxnX/W8asDfGYTfDoFY345p6ubhcsVjQfLzMfjGjPggl9uKpVdbeZdCuucfvpPSl/3BhPRm6+i6sREakYCuBERJzEPnThaHIOtaoE8L/b2uHtVfJ/u89d15xu9auSZbEy9ou1pGRZKrhacZQV+5NYsf8Uvl5mHuzfCICbO9Zi4oj2+HiZ+HXLscJQ1g03wa+M5mw5xoKdJ/HxMvH6Ta3xMp/7Q3/9qGB+fKAHN7avgc2A1+ft4t7p60jLdu8fCrfFp3HTRys4mpxDnaqB/HB/N1rEhl30eVEhfvzfNc2AwqXSh09lObvUi0rMzCPfauBlNlEtxM/V5RRTI7xwKMDx9NwLdrAahsFnSw9w40fLOZiURfUwf769txuPXtG4xI87kctZ1/pVaBgdTLbFyo8b411djohIhVAAJyLiJG/M31U0dGHS7R2JCPI977k+XoV7NtWqEsDR5Bwe+GoD+W60NExKxzAM3j7d/XZr51rF9rK6qlV1PhvdCX8fM4t2JzL68zX6rb+TpWRZeOGX7QCM79eQxtXOv+l9gK8Xb93chv/c2ApfbzMLdp7kmv8tZWuce+2TZrdiXxK3frKKU1kWWsSGMvO+7tSpWvrlfDd3qEn3BlXJKyicmurq/QntyztjQv3P+4sKV4kK8cPbbMJqMziZUfI+cEmZedw1dS0v/7qTfKvBwObVmPtILzrXq1LB1Yp4BpPJVDSZefrKwy7/P0hEpCK413c4IiKVxOzNCUxafAA4M3ThYiKCfJk8uhNBvl6sPHCKl2bvcHaZ4mBL9iax7nAKft5mxvdreM7jfRpH8eXdXQjx82bNoWRGfLqaZHU7Os2/f93BqSwLjaKDub9vg4uebzKZuLVzbWbd353aVQKJS8nhpo9W8NVq9/rh8LetxxgzZS2ZeQV0q1+Vb+/tSlQZu8ZMJhOvDm2Fn7eZ5ftOMXN9nJOqLR378s7Y08s93YmX2URMWGFdCSXsA7d8XxJXvbeUhbsT8fU28+8hLZg0qgPhgef/pYuIwND2NQj09WLvyUxWH0x2dTkiIk6nAE5ExMF2JKTzj5mFQxfG/W3owsU0rhbCe7e2w2SC6asOa3NiD1LY/bYbKNzbplpoyUFCx7pV+OberlQJ8mVrfBrDJ63keJrnTt90V4v3JDJrQzwmE/znptb4eZd+ymLLGmHMfqgnVzavhsVq4/9+3MbjMzaTbSlwYsWlM33VYcZ/vQGL1cZVLWOYcmcnQvx9ynWtupFBPHZlYwBe/nUniRl5jiy1TNx1Aqqdva64s/aBy7faeGPeLm6fvJrEjDwaRgfzy4M9GNWtrvY3EymFUH8fbmhXAyj8v01EpLJTACci4kApWRbGfVk4dKFXo0j+Mbhpma9xRfNqPDGwcFjDC79sZ9UBz5vKeDn6c+dJNselEeDjddFuq5Y1wpgxrhvVw/zZdzKTmyet4Mip7AqqtPLLyivgn7O2AjC6W1061Iko8zXCAnz4ZFQHnrmqKV5mE7M2xnPDxOXsT8x0dLmlYhgG7/yxh3/9tA3DgJFdavPBiPb4+5Q+WCzJ2J71aBEbSlpOPi/O3u6gasvOXSeg2tkDuITUwrD8aHI2wyet5MNF+zEMuK1zLWY/2JOmMRfvdhaRM27vUrgMdf6245xM1y+jRKRyUwBXChMnTqR58+Z06tTJ1aWIiBsrsNp4+NviQxfKu/H2A30bcH2bWApsBvd/uZ6jyQpn3JnNZvD2H4V7v43uXpfI4IsvB2wYHcz393WjbtVAjibnMOzjFZqA6yBv/b6H+NQcaoQH8GQJk4dLy2QyMa5PA74e24WoED/2nMjk+v8tY86WBAdWe3FWm8GzP23jvT/3AvDIgEa8fENLh2zs7+1lLhpOMWfLMf7ceeKSr1keRR1wbjYB1S62KIDLYc6WBK5+bykbj6QS4u/NxBHtee3G1gT4XloYKnI5ah4bSsc6ERTYDL5de9TV5YiIOJUCuFIYP348O3bsYO3ata4uRUTc2Jvzd7N0bxIBPl58MqrjJe3/YzKZeGNYa1rVCCMlO5+x09aRmef65W9Ssnnbj7PjWDrBft6M612/1M+rGRHIjPu60aRaCCcz8rjlk5VsPprqvEL/xmYz2JGQztHk7AtOd/QkG4+kMGXFQQBevbEVQX7el3zNLvWr8uvDPelavwpZFisPfr2RF37ZjqXA+YNS8gqsPPTNBr5afQSTCf49pAWPXdnYoUscW9YI4+6e9QB49qdtFf5/zZa4VNYdTgHctwPOXtesDXE8+PVGMvIKaF87nN8e7sU1rau7uDoRzzaqW2EX3Nerj1CgAVQiUokpgBMRcYBfNicwaUnh0IU3b25Ns+qXvgzJ38eLT+7oQFSIH7tPZDDhu03YKklIUplYbYVLAwHu6lnvgtNuSxId4s9347rStlY4qdn5jPh0FSv3O3fZ8dHkbN7+Yw+93ljI1e8vpdcbC2n2r3lc8fZixk5bx8tzdvDlqsMs35dEfGqOx3zcWQpsPP3DVgwDbmxXgz6Noxx27egQf768u0vR8uKpKw5xyycrS9yU31EycvMZ8/laftt6HF8vMx/c1p5R3eo65V6PXdGY2lUCOZaWy5vzdjnlHiWZt+0YwyetJDU7n6YxIXSrX7XC7l0W9s68LIsVkwnG92vAd+O6UatKoIsrE/F8g1vGUDXIl+PpuSzYedLV5YiIOI3JcKexXm4uPT2dsLAw0tLSCA3VHh8iUmhHQjo3frSc3Hwb9/VpwNNXlX3ftwvZcCSFWz9ZhaXAxkP9G/L4wPIvqRPH+3lTPI98u4lQf2+WPtWfsIDybYifmVfAvV+sY8X+U/h6m/loZHsGNKvmsDpz863M336cGeuOsnzfmYAv0NeLfKuNfOv5vx3w8zZTp2ogdaoGUS8yiLpVg6gbGUi9yCCqhfhjdsBSSEd4b8Fe3lmwh6pBviyY0KfMYWhpLdhxggkzNpGeW0BEoA/v3trOoWEfQGJGHmOmrGF7QjpBvl58ekdHujeMdOg9/m7Z3iRun7wakwlm3te9XHvnlZZhGHy0eD9vzCscXNK3SRT/u61duQdKONvJjFx6vb6QsAAf3r2lrdPfFyKXmzfm7eLDRfvp2TCSL8d2cXU5IiKlVpacSAFcGSiAE5G/S8mycN0Hy4hLyaF34yimjOnkkH2Z/u6H9XE8/v1mAP53WzuuK8NkVXGeAquNK99ZwsGkLJ4Y2JgH+ze6pOvl5lt58OsNLNh5Em+zibdvaVumKbp/ZxgG2+LT+W7dEX7elEBG7pmlhT0bRnJzx5oMahGDj5eZhNQcDiZlcehUFoeSsk+/zOJIcjYFF+iA8/cxU6dKYSBXNzKIelWD6N4gktpVK7YzaO+JDK5+fyn5VoP3b2t3SW+30jianM39X61nW3w6JhP0axJNoK8X3mYT3l5mvM0mvMwmfLzMeJlNp4+b8DKb8TGb8PI6fcxsxtvr9MvTzzGb4d0Fezl8KpuqQb5MvbMzrWqGOfX1sXvi+83MXB9Ho+hgfn24F77ejl8sYSmw8X8/buX79XEAjOlel2evaYa3l3svzEjKzCPYz/uSB1+IyLniUrLp9cZCDAP+fLwPDaKCXV2SiEipKIBzEgVwInK2AquNMVPWsmxfErWrBPLLgz0uad+3i3n1t518suQA/j5mZt7XnZY1KuYHcjm/79cd5cmZW4gI9GHpU/0JdsB+Y/lWG09+v5mfNiVgMsErN7RiRJfaZbpGcpaFnzbGM2PdUXadNdihRngAN3esyU3ta5Z66VyB1UZ8ag6HTmVzKCnrrJAui6MpOSXuHefrbebfQ1pwS6ey1V1eVpvBzR+vYMORVAY0jeaz0R0dukfa+eTmW/n3nB18tfqIU65fMyKA6Xd3oV5kkFOuX5KULAtXvL2YU1kWHruiMY9ccWmhcknXv+/L9aw+mIzZBC9c34I7nLSsVkQ8y9hpa1mw8yR39ajHc9c1d3U5IiKlogDOSRTAiTiezVa4DOlgUhbta0fQqW4EDaKC3WZJ24XYA7FAXy9mPdCdpjHO/X/BajO4e9paFu1OpHqYPz8/2IPoEH+n3CstO5+dx9NpVj203EsqK7t8q43+by3iaHIOz1zVlHF9Gjjs2jabwb9+3lYU7JTm+labwdK9icxYd5Q/dpwoWlLq621mcIsYhnesRfcGVR36uZVvtRGfksPB04Hc4VPZbDyaWjRI4uYONXlpSEunT4ectuIQz/+ynWA/b35/rHeFb+S/cv8p9iVmUmC1YbUZFNgMCqy20y8L/221FS7zLXzcRsHpv+effsx+nv25MaH+PH1VU6JDnfM5fiG/bE7g4W824uNl4reHe9GoWohDrnsgMZO7p63jYFIWwX7efDCiHX2bRDvk2iLi+RbtPsmYKWsJ8fdm9T8HEOh76b/UEhFxNgVwTqIATsSxrDaDf87aynfrio+dDwvwoWOdCDrUjaBT3Sq0qhHmdkt+7Pt+AUwc0b7CpuCl5+Zzw8TlHEjMon3tcL65tyt+3pf+trHZDLYlpLF4dyKL9iSy8UgKNgOiQ/z4323t6OKmG6O70terj/DPH7cSGezH0n/0c3jIZBgGb8zfzUeL9gPwYL+GPD7w3OmXh09l8f26OGauj+N4em7R8ZY1QrmlYy2ub1ODsMCKC1Htofpbv+/GZkDTmBA+ur2D07q44lNzGPj2YrIsVv49pIXThhRcTgzD4O5p6/hr10k61Ing+3HdLjm4XbE/ifu/3EBaTj41wgP4fEwnmsQ4JtgTkcrBZjPo+99FHEnO5vWbWlVYF7WIyKVQAOckCuBEHKfAauPx7zfz86YEzCa4tXNtDiZmseloKjn51mLn+nqZaVkjlE51q9ChTgQd61ahipM2Vy+N7Qlp3PTRCnLzbdzftwFPDXbs0IWLOZCYyQ0Tl5OeW8CwDjV5c1jrci23S86ysHRvIot3J7JkbyJJmZZijwf7eZOZV4CX2cQTA5swrnd9j+hMrAi5+Vb6/XcRx9Jyee7a5tzVs57T7vXhon1FG9WP7laH569rQV6BjbnbjjFj3VFWHUguOjc80Icb2tbg5o41aRHr2iXKK/Yn8fA3G0nKtBDi582bN7dmcEvHBtWGYXDn1MKu0E51I/ju3ksPiqRQsWDzhpaM6lqn3NeasfYo//xxKwU2g3a1w/lkVEeiQvwcWK2IVBafLNnPq7/tokVsKHMe6lkh2wmIiFwKpwdwX3zxBb1796Zu3bolPn7o0CGWLFnCHXfcUdZLuzUFcCKOYSmw8fA3G5m3/TjeZhPv3dquqIMs32pjR0I6aw8ls/5wCmsPpZCUmXfONepHBdGpTpWiLrm6VQMr5Ju0s4cu9GkcxedOGrpwMUv2JDJmyhpsBjx7TTPG9qp/0edYbQZb4lJZtDuRxXsS2RyXytlfAYJ8vejeMJK+TaLo0ziKiEBfnv1pGz9ujAegX5Mo3h7e1mmTJT3J1OUHeWH2DmJC/Vn0ZF+nd2hOX3WY537ehmFAu9rh7DuRSUZe4UAFkwl6N4pieMdaXNE82iEdkY5yIj2XB7/ewNpDKQDc06se/xjcFB8Hbbb/08Z4Hv1uE75eZn57pBcNo7VptyPZP86D/bz5Y0JvqoeVbWmvzWbw+vxdTFp8AIDr28TyxrDWbtfRLCLuIyXLQpfX/sRSYOPHB7rTrrbzpjGLiDiC0wM4Ly8vpk+fzogRI0p8/LvvvmPEiBFYrdYSH/dUCuBELl1uvpX7v1zPwt2J+HqZ+XBke65oXu285xuGwZHkbNYeSmH94WTWHkph38nMc86LDPYt7I6rU4WOdSNoERvm8Ol9BVYbo6esYfm+U9SpGsgv43tW6NK+v/t82UFemrMDswmm3NmZPo2jzjknMSOPpXsTWbQ7kaV7E0nJzi/2eNOYEPqcDtw61qlyztvMMAy+W3uU537ZjqXARmyYPx+MbE/7y/gb4hyLld5vLiQxI4+Xb2jJ7ZfQGVQWP26M44nvtxQNPahVJYDhHWpxU4eaFb7nWVnkW228MW8Xny49CECnuhF8MKI91S5xb7NTmXlc8fZiUrLzHTKBVs5ltRkM+3gFG4+kckWzaD69o/TDLbItBTz23Sbmbz8BwCMDGvHoFY3UzSIiF/X4jM38sCGOG9vX4O3hbV1djojIBTk9gDObzXz55ZfnDeC++OILxo4di8ViKfFxT6UATuTSZFsKGDttHSv2n8Lfx8ynd3SkV6NzQ6OLScmysP5wCusOp7DuUDJb4tKwWG3FzvHxMhEd4k+1UL8zL0P9qRbqT3SIH9VCC4+FBfiU+gfCV37dwadLDxLo68WPD/Rw+f5FhmHw1A9bmLEujhB/b34e34PaVQLZdPRMl9vW+LRizwnx86ZnI3uXWzQxYaULQbYnpDH+qw0cOpWNt9nE01c15e6e9S7LH6Y/XXKAV37bSc2IAP56vK/Dg94LWbo3kT93nmRQixi61KviUcst5207xpPfbyEjr4DIYF/ev60d3RtElvt6j3y7kZ83JdA0JoTZD/V0WFedFLfnRAbXvL+UfKtR6v0uT6Tncve0tWyLT8fXy8ybN7dmSNsaFVCtiFQGm46mcsPE5fh6m1n1zACXbjsiInIxTgng0tPTSU1NBaBu3bq89957DBky5JzzUlJSePrpp9m1axcHDx4se/VuTAGcVIStcWlsjkslPNCHKoG+hAf6EhHkQ0Sgr0cv28nIzefOKWtZdziFIF8vPh/TyWEb++fmW9kWn1bUJbfucAqpf+v0Oh9fb3OxQC46xJ/oUD+qhZwO607/fdGek0VDFz4c2Z6rW1XM0IWLySuwMvLT1aw7nEJksB+WAivpuQXFzmkRG0qfxlH0bRJNu9rh5Q4qMnLzefqHrfy69RgAg1pU441hbS6rKalZeQX0emMhyVkW3ripNcM71XJ1SR7lYFIW93+5nl3HMzCb4PGBTbi/T4MyB4l/7TrBXVPXYTbBT+N70LpmuHMKFgDe/mMP7/+5l8hgP/6c0OeCnb/b4tMYO20dx9NzqRrkyyd3dKBDnSoVWK2IeDrDMLj+g+VsjU9z+JRxERFHc0oA9+KLL/LSSy+VqgDDMHj11Vd5+umnS3W+p1AAJ862PzGTa95fSm6+rcTH/X3MRNhDuUCf038v/jIiyOf044XnhPr7uLxLJjXbwujP17A5Lo1Qf2+m3dXZqXt62GwGx9JzOZGey8n0XE6k53Eyo/DlifRcEjMKX/59OWZpPNC3Af+o4KELF5OYkceQD5aRkFY4ATMswIdejSLp2ySa3o0iib7EpX5nMwyD6asO8+85O8i3GtSqEsCHIzrQqqZrN/yvKBMX7uPN+bupWzWQBRP64K2uqzLLzbfyr5+28f36OAAGNI3m7eFtS72cOzOvgIFvLyYhLZd7etXj/65p7sxyhcKg/+r3lrI/MYtbOtbi9WGtSzzv9+3HeeTbTeTkW2kUHcznYzpRq0pgBVcrIpXBjLVH+ccPW6hVJYDFT/Rz+feyIiLn45QAbvHixSxatAjDMHjppZcYOnQorVsX/wbMZDIRHBxMly5d6NGjR/lfAzczceJEJk6ciNVqZc+ePQrgxCnyrTZu+mgFW+LSqBcZRFSwHynZFlKy80nNtlBgK9/AYrMJokP8ubNHXe7sUa9Cl8sBJGXmcftnq9l1PIOIQB+m392FljXcI6zJzbeSmJHHyYy800FdLicy8jhZFNgVhnZpOYVB3YCm0XxyR0eXDF24mCOnsvl9x3Ha1Y6gba1wp9e4+Wgq47/eQFxKDr5eZv51XXNu71K7Ui9JTc/Np9frC0nLyeedW9owtF1NV5fk0WasPcq/ft5GXoGNmhEBfDiyfak62Z7/eRvTVh6mdpVA5j/amwBfz+0M9iRrDyVz88crAfh6bBe6NzyzfNgwDD5deoDX5u7CMKBXo0gmjmxPqP/l0x0rIo6VY7HS5dUFpOcWMOXOTvRrEu3qkkRESuT0PeD69evHs88+y4ABA8pdpCdSB5w401u/7+Z/f+0jLMCH+Y/2LrY3l2EYZOYVkJKVfzqUs5CanV8soDvz0kJKVuHfsyzFB6HUiwzi2Wua0b9pdIUEJSfScxnx6Sr2J2YRGezH1/d0oXE11+6bVh65+VZSsi1Eh/i7ZfjmKmnZ+TwxczN/7CjcZP26NrG8dmMrgv28XVyZc7zzxx7e+3MvDaODmf9ob30sOMD2hDTu/3IDR5Kz8fUy8/z1zRnR+fxB7vrDyQz7eCWGAV/e3YWejcq/h5yU3bM/beXLVUeoU7Uw/PT38SLfauNfP23j27VHAbi9a21euK6FukNF5JL9e84OJi87yICm0Uwe08nV5YiIlMjpAdzlSgGcOMv6w4WdBTYDPhjRjmtbxzrkunkFVlKz81m8J5E35u0mKTMPgN6No3ju2mY0jHZeGBaXks3Iz1Zz+FQ21cP8+WpsF+pHBTvtfuIahmEwedlB/jN3FwU2g/qRQUwc2Z5m1Z33f2RCag4Ld59k4a5EUrMt3N+3AQOanX+SriOkZlvo9fpCMvIKHPo5KpCWk88T358Jcm9sV4OXh7Yk0Ld4kJtXYOWa95ex72QmN3eoyZs3t3FFuZe19Nx8rnx7MSfS87i/bwPu692A+79az4r9pzCb4NlrmnNnj7qVuhNWRCrOgcRM+r+1GJMJljzZT0vaRcQtVVgAl52dzaFDhzh16hQlXaZ3797lvbRbUgAnzpCZV8DV7y3lSHI2Q9vV4J1b2jrlPhm5+UxcuJ/Plx3EYrXhZTZxR7c6PDqgcan3XiqtQ0lZjPxsNfGpOdSqEsDXY7vqm6ZKbv3hZB78eiPH0nLx8zbz7yEtubljTYf8IF5gtbHxaCp/7TrJwl0n2XU845xzbu1Ui2evbe607rs35u3iw0X7aRoTwm8P99JeNA5mGAafLDnAG/N3Y7UZNKkWwoe3t6fBWaH927/v5v2/9pVqEIA4z+/bj3Pv9PV4mU3UjAjg8Klsgny9+N+IdvRv6twgXEQuP6Mmr2bp3iTu79uAp9xsD14REaiAAC4rK4vHH3+cKVOmUFBQcM7jhmFgMpmwWq0lPNtzKYATZ3hq5ha+W3eUGuEBzH20l9P3zDmUlMXLv+5kwc7CbpMqQb48PrAxt3aq7ZAldftOZjDi09WczMijflQQX4/tWmw5rVReyVkWHvtuE4v3JAJwU/ua/PuGFud0MpX2Wov3FHa5Ld6TWLQPH4DJBO1qhdO/aTTJWflMWXEQw4BaVQJ46+a2dK7n2ImLpzLz6PXGQrItVj4Z1YGBLWIcen05Y/WBUzz4zUYSM/II8vXijWFtuKZ1dXYdT+fa95dRYDPcagrx5eqBr9bz29bjAMSG+TN5TCendr2KyOVr/vbjjJu+nipBvqx8pj9+3tr3U0Tci9MDuHvuuYfJkydz9dVX079/f6pWrVrieaNHjy7rpd2aAjhxNPs3FSYTfHNPV7rWL/lzyRmW7Enk33N2sPdkJgBNY0J4/roWdGtQ/hp2JKQzavJqTmVZaBoTwvS7uxAV4ueoksUD2GwGHy3ez1u/78ZmQONqwXw4sv1FlzsbhsH2hHQW7T7JX7tOsvFoKmd/dQoL8KFP4yj6N42md+MoqgT5Fj22cv8pnvh+M/GpOZhMcG/v+ky4srHDvkl/5dcdfLr0IK1rhvHz+B5aXudkJzNyeejrjaw+mAzAnT3qsuFIKpuPpjKweTUmjeqg94GLnczIZcSnq4kO8ePdW9sSHaJfsoiIcxRYbfR6YyHH0nK5q0c9nr2mmbrQRcStOD2Ai4qKYuDAgXz11VflLtITKYATRzqZkcvgd5eSnGVhXO/6PHN1swqvId9q46tVh3n7jz2k5xZ2s17dKoZnrmpW5iWjm46mMvrzNaTl5NOqRhhf3NWZiLNCErm8rNx/ioe/LexkCvT14tWhrbihXY1i52TmFbBsbxKLdp9k4e6TnEjPK/Z405gQ+jeNpn/TaNrWCr/gpu4Zufm8NHsH36+PK3ru28Pb0jz20v6vPpGeS+83FpJXYNMUtgpUYLXx1h97+GjR/qJjIf7eLJjQh2qhCnvcgX21g4iIs32z5gjPzNoKwBXNonnnlraEaMqyiLgJpwdwQUFBvPvuu9xzzz3lLtITKYATRzEMg7umrmXh7kSaVQ/lp/HdXdpSn5xl4Z0/9vDV6sPYDPD1NjOud33u79ugVMsH1x5K5s4pa8nMK6BDnQim3NnJ6Utpxf0lZuTx6HcbWb7vFAC3da7NnT3qsnRvEgt3nWT1wVPkW898CQrw8aJHw0j6N42mX9MoqocFlPmev28/zjOztnIqy4KPl4nHrmzMuN4Nyr28+vmftzFt5WHa1w7nh/u7K3CoYH/sOMGEGZvIyC3gPze24tbOtV1dkoiIuMBPG+P5xw9bsBTYaFwtmE/v6EidqkGuLktExPkBXO/evenWrRuvv/56uYv0RArgxFG+XHWYZ3/ahq+3mdkP9qRJjPOmkZbFruPpvPjLDlYeKAxMYkL9eebqplzfJva8wcPyfUmMnbaOnHwrXetXYfLoTgQ5aSN88TxWm8H7f+7l/b/2UtJXmzpVA+nXpLDLrUv9Kg4JopMy83hm1taiqZod6kTw9vA2Zf5GPT41h35vLsJitfHV2C70aBh5ybVJ2Z1Mz+XQqWyH7+0nIiKeZfPRVO75Yh0nM/IID/ThwxHt6a6vzSLiYk4P4JYvX84NN9zAb7/9RqdOncpdqKdRACeOcCAxk2veX0ZOvpVnr2nG2F71XV1SMYZhMH/7cV7+dSdxKTlAYYDx/HXNaV0zvNi5C3edZNyX67EU2OjTOIpJozrg76PNceVcS/cmMmHGZlKzLXSpV5W+TQr3c6t/1pRLRzIMg5nr43hx9g4y8woI9PXi2Wuac1vnWqXuYntm1la+WXOELvWq8O29XdX9JiIi4mIn0nO594t1bI5Lw8ts4oXrmnN71zr6Gi0iLuP0AO6uu+5i48aNbN26lW7dulG/fn28vIr/0G0ymZg8eXJZL+3WFMDJpcq32hj20Qo2x6XRo2FVpt/VxW03ks3NtzJ52UEmLtxHtqVwovHNHWry5OAmRIf4M3frMR7+diP5VoOBzavxvxHtNJlKLsgwDPIKbBUa0h5NzuaJ7zcXbejfr0kUr9/UmuiL7CN25FQ2/d9aRIHNYMa4buq+EhERcRO5+Vae/mELP21KAGBEl9q8cF0LfL3Pv1esiDPYv7fNzbeSk28lx1L4MjffSo7FVngs30quxUpuwZnH7cdy8q3YDIgO8aN6mD8xYQFUD/OnWqg/VYN83fbnRCnO6QGc2Xzx/9xMJhNWq7Wsl3ZrCuAcyzAMMvIKSM3KJznbQkq2hdRsC8lZ+aSe/ndKdj6h/j7c1rnWOd1XnujtP/bw/p97CfX3Zv5jvcu1x1VFO56WyxvzdjFrYzwAwX7eXNemOjPWxWG1GVzXJpa3h7fB5wIb5Iu4ks1m8Pnyg7wxfzeWAhsRgT68MrQVV7eqft7nPPH9Zmauj6NXo0im392lAqsVERGRizEMg0lLDvD6vF0YBnSpV4WPbu9QbEq6iCPlFVh59sdtrNh/qihsyy2wlrjFiiP4eJmoFupfFMzFhPoVBXQxYYXHo4L9LjikTCqG0wO4y5UCuItLyszjUFIWKdn5pGSdCdEKgzULqdn5p48V/r3AVvoPvw51IhjTvS6DW8Z4ZNiz/nAKwyetxGoz+N9t7biuTayrSyqTDUdSeHH2DjYfTS06NqxDTV6/qXW5N7gXqUh7TmTw2Heb2J6QDsANbWN5cUhLwgKKDww5kJjJFW8vxmbAjw90p13tCFeUKyIiIhfx164TPPzNJjLzCqgZEcCnd3SkWXX9nCaOZRgG/5i5he/Xx533HF8vM34+ZgJ8vAjw9SLAxwt/H6+if/v7mM/8u+hY4aqQk+m5HEvL5fjpl0mZeaUK9swmiAo5HcyFFgZzNcIDGNI29qKrPcRxFMA5iQK4C4tLyab/fxdjsdrK9LwAHy8iAn0ID/SlSpAv4YE+RAT6Fh3bGp/GnC0JRdMSq4X6MaprHW7rXJuqwX7OeFUcLiuvgKvfX8rhU9nc0DaWd29t5+qSysVmM/hxYzyTluynX9NonhrUVK3R4lEsBTbe/3MvHy7ah82A6mH+vDmsDT0bndnE+eFvNvLL5gQGNI1m8pjLZ59TERERT7T3RAZjv1jH4VPZBPp68c4tbRnUIsbVZUkl8tnSA7z8607MJnh7eFuax4aeCdh8vfD3Nju0Ey3fauNkRh7H03IKg7nTf46ln/n7ifTc8zaz9GhYla/GdnVYPXJhCuCcRAHchc3ffpxx09fj72OmSUwoEaeDtPBAH6oE+hIeVBiqVQn0JTzQl4igwsdLsx/UyfRcvlp9hK9WHyEpMw8AX28zQ9rEMqZHXVrEhjn71bskT/+whW/XHiU2zJ+5j/Y+p+NGRCrWhiMpTPhuE4dOZQMwpntdnhrclCPJ2Qx+bwmGAXMe6knLGu79f4uIiIhAaraF8V9vYPm+UwA8fmVjHuzfUMMZ5JIt3pPInVPWYDPgX9c25+6e9VxdElDYGJGUlVcYzJ0O5RLScpi89CAFNoNfHuxRKbZw8gROD+D69+9/0XNMJhN//vlnWS/t1hTAXdhXqw/zfz9u44pm1fhsdEen3COvwMpvW48xZfkhtsSlFR3vXK8Kd3avy5XNq7ndOvg/dpzgni/WYTLB12O70q1BVVeXJCJAtqWAV3/byZerjgBQPyqIyGA/1hxMZnCLGD4e1cHFFYqIiEhp5VttvPLrTqauOATAta2r8+awNgT4et6QMJvN4Hh6LrHh7r9fdGW2PzGTGyYuJyO3gOEdC7fecfdQd8J3m5i1MZ5rWlVn4sj2ri7nslCWnMi7PDc4cODAOR94BQUFHDt2DJvNRmRkJEFBQeW5tFuaOHEiEydOrHRDJRwtKcMCFK5DdxY/by+GtqvJDW1rsOFIKlNXHGLu1mOsOZjMmoPJxIb5M6pbXW7rXIvwQNdvwpqYkcfTP2wB4J5e9RW+ibiRQF9vXr6hFVc2j+HJ7zdzIDGLA4lZmEzw2JWNXV2eiIiIlIGPl5kXrm9Bk5gQnvt5G3O2HOPQqSw+GdXRo4Ks5CwL901fz5pDydzaqRYvXN+iQifIS6G07HzumbaOjNwCOtSJ4N83tHT78A3g3j71mbUxnrnbjnEoKYu6kZUnl6kMHLoENS8vj7fffpspU6awePFiqlc//4Q5T6QOuAt79qetfLnqCA/3b8iEgU0q7L7H03L5ctVhvl5zhOSswhDQ38fM0HY1GN29Lk1jXPO+MgyDu6et469dJ2kaE8LPD/bAz1tfPEXcUWq2hWd/Kvxm/ZaOtXh9WGtXlyQiIiLltOZgMvd9uZ7kLAuRwX5MGtWBDnXcf6jSgcRM7py6lsOnt8gAaFMzjI9u7+BRIaKnK7DauHPqWpbuTSI2zJ+fH+zp1CYTR7tzyhoW7k7k9q61efmGVq4up9Jz+R5wo0aNoqCggG+++cbRl3YpBXAXdt/09czbfpyXhrTgjm51K/z+uflWZm9OYMryQ+w4ll50vHuDqozpXpcBzapV6LRO+5JcXy8zvzzUw2VBoIiUXkJqDjGh/houIiIi4uGOJmdzzxfr2HU8A18vM68MbcnNHWu5uqzzWnXgFOOmryctJ58a4QGM79eQN+bvIjU7n6pBvvxvRDu6N4i8+IXkkr00ewefLz9IgI8XM+/v5vb7jf/d6gOnuOWTVfh5m1n+dH8iPWRwoacqS07klM2yevbsyfz5851xaXFj9uEIUS76BPf38eLmjrX49eGefH9fN65pVR0vs4kV+09x7/T19HlzIZ8uOUBadr7TazmQmMnLc3YC8I/BTRS+iXiI2PAAhW8iIiKVQK0qgfxwf3cGtaiGxWrjyZlbeHnODqznmRzpSj+sj2PU5NWk5eTTtlY4P43vwYgutZn9YE+aVw/lVJaFUZPX8NnSA1TWGYqbjqby5vxdnMzIdWkdM9Ye5fPlBwF4e3gbjwvfoHB/9La1wskrsDHt9J6I4h6cEsAdPHgQi8XijEuLG7MHcJEubs81mUx0qluFiSPbs+Qf/bi/bwPCA32IS8nhld920vW1P5kwYxMr9idhc8IX4HyrjcdmbCYn30r3BlW5q4d7TMoREREREbmcBPl589HIDjw8oBEAny07yF1T15KW4/xfyJeGYRi8/ftuHv9+M/lWg6tbxfDtvV2LljvaQ8Qb29XAajN4+dedPPTNRrItBS6u3LEW7DjB8EkrmbhwP9f/bzmbj6a6pI61h5L5v5+2AvDoFY24qpVnbqllMpm4r099AL5YeZisvMr18eLJyrUE9ciRIyUeT05OZsGCBTz33HP07duX33777ZILdCdagnphLZ6bR5bFysIn+lLPzTZ7zM238vOmeKYsP8Su4xlFx2uEB3BT+xrc2L6mwzaofOePPbz3515C/b2Z92hv7dcgIiIiIuJiv245xuPfbyI330b9yCAmjepAo2ohLqsnN9/KkzO3MHtzAgD3923AkwOblNiJbxgGX6w8zL/n7KDAZtCkWggfj+rgdj9zlcePG+N44vstWG0GAT5e5ORb8fU289rQVtzUoWaF1RGXks2QD5ZzKsvC1a1i+OC29h69KsJqM7jy7cUcSMriX9c25+6eagpxFqfvAWc2m887AcQwDJo2bcovv/xCw4YNy3ppt6YA7vxyLFaaPTcPgK0vDCTE38fFFZXMMAw2HElh5vp45mxJICP3zG8DOtaJYFiHmlzdujqh5ax/45EUhn28EqvN4L1b2zKkbQ1HlS4iIiIiIpdgW3wa936xjoS0XLzNJkZ3r8vD/RsRFlixP7ucyszj3unrWX84BW+ziVeHtmJ4p4vvT7f2UDIPfLWBxIw8Qvy9efeWtgxoVq0CKnaOaSsO8fwv2wG4sV0NnruuOU98v5kFO08CcFePevzz6qZ4ezll4V6RrLwChn28kp3H0mlePZSZ93cj0NfbqfesCN+sOcIzs7YSG+bP4n/0w8fJb8fLldMDuBdeeOGcAM5kMlGlShUaN27MFVdcgdlc+d65CuDO72hyNr3eWIi/j5mdLw32iBHNuflWft9xgh/Wx7F0byL21ah+3mYGtYhhWIea9GgYWerBDVl5BVzz/lIOncrm+jaxvH9bOydWLyIiIiIiZZWYkcfTP2zhz12FIU9EoA+PXdmYEZ1rOz3oAdh3MpO7pq7lSHI2If7efHx7B3o0LP1whRPpuTzw1QbWH04B4JEBjXhkQCOP6tYyDIP//bWPt//YA8CY7nV57trmmM0mbDaDdxfs4f2/9gHQo2FVPritPRFBvk6pxWYzeOCrDczbfpzIYF9+frAnNSrJCqbcfCs9X19IUmYebw9vw43tK66j8HLi8imolZUCuPPbcCSFGz9cQc2IAJY91d/V5ZTZifRcftwYzw/r49h7MrPoeEyoP0Pb1+Cm9jVpGB18wWs8M2sr36w5QvUwf+Y90rvCf5MmIiIiIiKls3hPIi/P2VH0vX+j6GCevbY5fRpHOe2eK/Ylcd+X60nPLaBWlQCmjOlEw+iyL4O1FNh4+dcdfLHyMAD9m0bzzi1tCQtw/58/bKf3srMPOnj0isIA8e8NHPO2HWPCjM1kW6zUqhLAJ6M60qy6438Gf/uPPbz/5158vEx8e29XOtSp4vB7uNKHi/bxxrzdNKkWwrxHe3lEo4ynUQDnJArgzm/+9uOMm76+aGqPpzIMgy1xafywIY6fNyUU26C1Ta1whnWoyfWtY88J1/7YcYJ7vlgHwNdju9C9DL/FEhERERGRildgtfHNmiO8/cceUrILv+/v1ySK/7um+UV/+V5WM9Yd5Z+ztlJgM2hfO5xP7+hI1eBLG143c30c//fjVvIKbNSpGsikUR1oGuO+P6cWWG08PWsrM9fHAfDctc256wJ7k+06ns69X6znSHI2AT5evDW8DVc7cDDCr1uOMf7rDQC8Maw1wztefBmwp0nLyaf7a3+SZbEy5c5O9GsS7eqSKp2y5ETl7rHNy8vjP//5D507dyYyMpLIyEi6dOnCG2+8QW6ua0cHS8UrmoB6iV9EXM1kMtGmVjgvDWnJmv8bwEcj23NFs2i8zCY2H03lXz9to9MrCxj/1Qb+2nWCAqutqI0d4J5e9RS+iYiIiIh4AG8vM6O61WXRE/0Y27Me3mYTC3cnMvjdJbzwy3ZSsy2XfA+bzeCNebv4x8wtFNgMrm1dna/v6XrJ4RvAsA41+eH+7tQID+DwqWyGTlzBL6eHOrib3HwrD3y1gZnr4/Aym3jr5jYXDN8AmsaE8suDPejZMJKc08//7/zd2GyX3kO0LT6Nx7/fBMDdPetVyvANICzAhxFdagPw8aL9Lq5GytUBl56eTt++fdm0aRPBwcE0aNAAwzA4cOAAmZmZtGvXjkWLFhES4rqpMs6gDrjze2/BXt5ZsIfbOtfmtRtbuboch0vMyOPnTfHMXB9XbIpqZLAfkcG+7DqeQdOYEH5+sAd+3l4urFRERERERMrjQGImr/62iwU7TwCF4cVjVzRiZNc65drAPjffyuPfb+bXLccAeKh/Qx67orHD92tLzrLw8DcbWbYvCYCxPevx9FXOH15QWpl5Bdz7xTpW7D+Fr7eZD25rx8AWMaV+foHVxn/m7uKzZYXLVgc0jeadW9uWe3BeYkYeQz5YRkJaLr0bR/H56I5u87ZyhmNpOfR+YyH5VoMfH+hOu9oRri6pUnF6B9yLL77Ipk2bePnllzl58iQbN25k06ZNJCYm8uqrr7Jx40ZeeumlchUvnikxs7DrMSrYOZtjulpUiB9je9Vn3qO9+fXhntzVox5Vg3xJysxj1/EMfL3MvHtrW4VvIiIiIiIeqn5UMJ+N7siXd3ehSbUQ0nLyeWH2Dq56bymLdp8s07WSMvO47dNV/LrlGD5eJv57cxseH9jEKcMSqgT5Mu2uztzftwEAny07yO2TVxetUnKllCwLIz9dxYr9pwjy9WLqnZ3KFL5BYafis9c25+3hbfD1NvPnrpPcMHE5+xMzL/7kv8krsDJueuEk3PpRQfzvtnaVOnwDqB4WwJC2NQCYtPiAi6u5vJWrA65evXr07t2badOmlfj46NGjWbJkCQcPHrzkAt2JOuDO777p65m3/TgvDWnBHd3qurqcCpFvtbFodyK/bz9O/6bRXOXA/QhERERERMR1Cqw2vlt3lLd+30NyVuFS1L5Nonj2mmYXHZyw90QGd01by9HkHMICfPj49g50a1C1Ispm7tZjPPH9ZrIsVqqH+fPR7R1oWyu8Qu79d8fTchk1eTV7T2YSEejD1Ds70+YSa9kSl8q46es5lpZLiJ8379/Wjn5NS7evmWEYPDlzCzPXxxHq781P43tQP8qxe/25q70nMrjynSWYTPDnhD6XzetdEZzeAZeQkEC3bt3O+3i3bt04duxYeS4tHsr+25UoD98Drix8vMxc2bwab97cRuGbiIiIiEgl4u1lZmSXOix8oi/39q6Pj5eJRbsTGfTuUl74ZTspWSXvD7dsbxI3frSCo8k51KkayKwHuldY+AZwVavq/PxgD+pHBXEsLZfhH6/kmzVHKuz+doeSshj28Qr2nswkJtSfGeO6XXL4BtC6Zji/PNiTjnUiyMgr4K5pa5m4cB+l6SuavOwgM9fHYTbBByPaX1YhVKNqIVzRLBrDgE+XVq5GKU9SrgCuatWq7N69+7yP79q1iypVKtf4XrmwoiEMIZdPACciIiIiIpVbWIAP/7y6GX881oeBzathtRlMXXGIvv9dxOfLDpJvtRWd++2aI4yZsoaM3AI61Y3gxwd60MAFIU/D6BB+Ht+DK5tXw2K18cysrTz9wxZy860Vcv+dx9IZ9vFK4lJyqFs1kO/v60ajao7bHz4qxI+v7+nKiC61MQx4c/5uHvxmI9mWgvM+Z+Huk7z6204Anr2mOb0bRzmsHk8xrk/hEuUfNsRxMkODM12hXAHcwIED+eijj5gzZ845j/32229MmjSJQYMGXXJx4jkSMyrHFFQREREREZG/qxsZxCd3dOTrsV1oGlO4P9xLc3Yw6N0l/LnzBP+Zu4unZ22lwGYwpG0sX47tQpUg1+2PHeLvw6TbO/DkoCaYTPDt2qN0emUBj367kXnbjl0wrLoU6w+ncMuklSRl5tE0JoQZ93WjVpVAh9/H19vMq0Nb8crQlnibTfy65Rg3fbSSo8nZ55y772QmD3+9EZsBt3SsxZ096jq8Hk/QqW4VOtSJwFJgY+ryQ64u57JUrj3gjh49SqdOnUhMTKRVq1a0aNECgO3bt7N161aio6NZs2YNtWpVrlG+2gOuZDkWK82emwfA1hcGElLOaTQiIiIiIiLuzmozmLHuKP+dv5tTf1uK+siARjx6RSNMJscPWyivxXsSeWrmFo6nn+l68vM207txFINbxDCgWTThgZceFi7Zk8i46evJybfSoU4En4/pRFiA8382XHsomfu/XE9SpoWIQB8mjmhP94aRAKRl53PDh8s5mJRFxzoRfHVPl8t6cN7v249z7/T1hPh7s/KZAQT7ebu6JI9XlpyoXAEcQFxcHE8//TSzZ88mIyMDgJCQEK6//npee+01atasWZ7LujUFcCU7mpxNrzcW4u9jZudLg93qi42IiIiIiIgzpOfmM3HhPqYsOwTA68NaMbSde/4cbLUZbDiSwvxtx5m/4zhHk3OKHvMym+hWvyqDWlRjYIsYqoX6l/n6v209xiPfbiTfatCncRQf3d6eQN+KC3cSUnMYN309W+PT8DKbePaaZozqWoc7p65l6d4kaoQH8PODPS77FVs2m8GV7yxmf2IW/3d1M+7pXd/VJXm8Cgng7AzDIDExEcMwiI6OrpThy8SJE5k4cSJWq5U9e/YogPub9YdTuOmjFdSMCGDZU/1dXY6IiIiIiEiFOZmei8Vqo2aE45daOoNhGOw8lsG87cf5fftxdh3PKPZ4u9rhDGoRw6AWMdSLDLro9b5be4RnZm3FZsA1ravzzvC2+HqXa7erS5Kbb+WZWVv5cWM8AA2jg9l3MpMAHy9m3t+NFrFhFV6TO5qx9ij/+GELMaH+LPlHP5e8ryqTCg3gLifqgCvZ/O3HGTd9PW1rhfPT+B6uLkdERERERERK6VBSFvO3H2f+9uNsOJJa7LEm1UIY1KIag1rG0Lx66DkNN58s2c+rv+0C4LbOtXn5hpZ4mV3XlGMYBpOXHeTV33ZiO510fHx7ewa3rO6ymtxNXoGVXq8v5GRGHm8Oa83NHSvX1mEVrSw50SX1hO7Zs4d9+/Zx6tSpEsf+3nHHHZdyefEQRRNQL/N2XhEREREREU9TNzKIcX0aMK5PA06k5/L7jhP8vv04K/efYveJDHafyOD9v/ZRMyKAQS1iGNwyhva1I3jr9918uGg/APf1acBTg5u4fEWcyWRibK/6NI0J5Y35u7ipfU2Fb3/j5+3FXT3r8Z+5u/hkyQFual8TswtD08tJuTrg4uPjGT16NAsXLgQoMXwzmUxYrRUz5riiqAOuZO8t2Ms7C/ZwW+favHZjK1eXIyIiIiIiIpcoLTufP3edYP724yzek0huvq3osRA/bzLyCiepPjW4Kff3beCqMqUc0nPz6fHaX2TkFTB5dEcGNKvm6pI8ltM74MaNG8fixYt59NFH6dWrFxEREeUqVCqHxMzCaTpRwa4bsy0iIiIiIiKOExbow43ta3Jj+5rkWKws3pPI79uPs2DnCdJzCzCZ4NWhrbitc21XlyplFOrvw4iutZm0+AAfL96vAK6ClCuA++uvv3jkkUf473//6+h6xAMlZRSO3o4M0RJUERERERGRyibA14vBLQuXn+Zbbaw9mEygnzdta4W7ujQpp7t61GPKskOsPZTC+sPJdKhTxdUlVXrlGncREhJCw4YNHV2LeCj7HnBR2gNORERERESkUvPxMtO9YaTCNw9XLdSfoe1qADBp8QEXV3N5KFcAd+211/LXX385uhbxUEVDGNQBJyIiIiIiIuIR7uldH5MJ/th5gn0nM11dTqVXrgDurbfeYv/+/TzxxBMcPHiwxCEMcvlIzNAUVBERERERERFP0jA6mCubVcMw4NMl6oJztnIFcOHh4dxxxx288847NGzYEG9vb7y8vIr98fYu1/Zy4mFyLFayLIXTbiM1hEFERERERETEY4zrUzjB9seN8ZxIz3VxNZVbuVKy119/nX/+85/ExMTQqVMnTUG9jNmXn/r7mAn2U+gqIiIiIiIi4ik61ImgU90I1h5K4fPlB3nmqmauLqnSKldi8sEHH9C/f3/mzp2rTrfL3Mmzlp+aTCYXVyMiIiIiIiIiZXFfnwasPbSOr1cdYXy/hoT6+7i6pEqpXEtQk5OTGTZsmMI3OTOAQfu/iYiIiIiIiHicfk2iaRQdTEZeAV+vPuLqciqtcgVwbdq04ejRo46uRTyQPYCL0gRUEREREREREY9jNpu4t3d9AD5fdpC8AquLK6qcyhXAvfLKK3zyySds3LjR0fWIh0nKsADqgBMRERERERHxVEPa1iAm1J+TGXn8vDHB1eVUSuVaQzp9+nRiY2Pp3Lkz3bp1o169enh5eRU7x2QyMXnyZIcUKe4rMbNwSkqUJqCKiIiIiIiIeCRfbzN396zHK7/t5OMl+xnWoSZms/Z5d6RyBXBTp04t+vuyZctYtmzZOecogLs8FHXAaQmqiIiIiIiIiMe6tXMt3v9rLwcSs1iw8wQDW8S4uqRKpVxLUG0220X/pKWlObpWcUNFe8BpCaqIiIiIiIiIxwrx92FU1zoAfLx4P4ZhuLiiyqVcAdyFrFixgrvvvpsaNWo4+tLihhLtU1DVASciIiIiIiLi0cb0qIuvt5kNR1JZdzjF1eVUKg4J4E6ePMmbb75Js2bN6NWrF1OmTKFevXqOuLS4uaSM0wGcOuBEREREREREPFp0iD83ta8JwKTF+11cTeVS7gDOZrMxZ84chg4dSq1atXj66afx9fXltddeY+/evWzatMmBZYo7yrFYybIUjieO1BAGEREREREREY93T696mEywYOdJ9pzIcHU5lUaZhzDs3buXzz//nC+++IJjx45RvXp1brvtNqZPn87zzz/PjTfe6Iw6xQ3Z93/z9zET7FeueR4iIiIiIiIi4kbqRwUzqHkM87Yf54VftnNFs2r4+Zjx8/bC/yIv/bzN+PsUvtQU1eJKnZp88cUXTJ48maVLl+Ln58f111/PmDFjGDRoEAcPHuSLL75wZp3ihk6etfzUZNInloiIiIiIiEhlcF/fBszbfpwV+0+xYv+pcl3D18uMn7cZPx97MFcY0j11VVP6NI5ycMXur9QB3JgxY2jYsCEfffQRt956K2FhYc6sSzyAvQNO+7+JiIiIiIiIVB5ta4XzytCWbDySSl6Bjbx8K7l/e5l31svc0y8LbGcmp1qsNixWGxl5BcWunWMp+PvtLgulDuD8/f3Zv38/3333HYGBgdx0000EBgY6sza3MXHiRCZOnIjVanV1KW7FHsBFaQKqiIiIiIiISKUysksdRnapU6bnFFhthcHcWaHc3182qx7ipIrdW6mHMBw/fpwPPviAjIwMRo8eTUxMDHfddRdLlizBMIyLX8CDjR8/nh07drB27VpXl+JWEjUBVURERERERERO8/YyE+TnTZUgX2LDA6gXGUSz6qG0rRVO1/pV6dM4iugQf1eX6RKlDuBCQ0O5//77Wbt2LZs2bWLMmDH88ssv9OvXj549e2IymUhJSXFmreJmijrgNAFVREREREREROS8Sh3Ana1169a8//77JCQk8NVXX9GqVSsA7r33Xlq1asVLL73E9u3bHVqouJ+kDAsAkVqCKiIiIiIiIiJyXuUK4Ox8fX259dZb+eOPPzhw4ADPPvssGRkZvPDCC7Rp08ZRNYqbOtMBpwBOREREREREROR8LimAO1udOnV48cUXOXjwIHPnzuWmm25y1KXFTSXap6CqA05ERERERERE5LxKPQW1tEwmE4MGDWLQoEGOvrS4mSQNYRARERERERERuSiHdcDJ5SXHYiXLYgUgSh1wIiIiIiIiIiLnpQBOysW+/5u/j5kgXy8XVyMiIiIiIiIi4r4UwEm5nDxr+anJZHJxNSIiIiIiIiIi7ksBnJSLvQNO+7+JiIiIiIiIiFyYAjgpF3sAp/3fREREREREREQuTAGclEuiJqCKiIiIiIiIiJSKAjgpl6IOuGBfF1ciIiIiIiIiIuLeFMBJuSRlWACI1BJUEREREREREZELUgAn5XKmA04BnIiIiIiIiIjIhSiAk3JJtE9BVQeciIiIiIiIiMgFKYCTcknSEAYRERERERERkVJRACdllm0pIMtiBSBKHXAiIiIiIiIiIhekAE7KzD6Awd/HTJCvl4urERERERERERFxbwrgpMyK9n8L9sNkMrm4GhERERERERER96YATsosKfP/27vr6Ciuvw3g35tk48SFECQEDw3uwd3d3aFFixUppbhD0faHS/FCkUKB4rTFixR31+CQhNjz/pF3p7sRSIBkI8/nnB7IzOz27mUye+eZK5z/jYiIiIiIiIgovhjAUYLpAzjO/0ZERERERERE9GEM4CjBnnAFVCIiIiIiIiKieGMARwmm9YCztzRxSYiIiIiIiIiIkj8GcJRg+lVQ3TgElYiIiIiIiIjogxjAUYL91wOOARwRERERERER0YcwgKMEe6JfBZU94IiIiIiIiIiIPogBHCVYIBdhICIiIiIiIiKKNwZwlCBBoeHyNjRCRETc2QOOiIiIiIiIiOiDGMDFw5w5c8TPz0+KFi1q6qKYnH4BBmudmdhZmpu4NEREREREREREyR8DuHjo0aOHnD9/Xo4dO2bqopicNv+bvZUopUxcGiIiIiIiIiKi5I8BHCVI4BvO/0ZERERERERElBAM4ChB9AEc538jIiIiIiIiIoofBnCUIE+4AioRERERERERUYIwgKME0XrA2VuauCRERERERERERCkDAzhKEP0qqByCSkREREREREQUPwzgKEGecBEGIiIiIiIiIqIEYQBHCaKtgsoecERERERERERE8cIAjhIkkIswEBERERERERElCAM4ireg0HB5GxohIpwDjoiIiIiIiIgovhjAUbzpF2Cw1pmJnaW5iUtDRERERERERJQyMICjeDNcgEEpZeLSEBERERERERGlDAzgKN4CuQIqEREREREREVGCMYCjeNMHcJz/jYiIiIiIiIgo/hjAUbw94QqoREREREREREQJxgCO4k3rAWdvaeKSEBERERERERGlHAzgKN70q6ByCCoRERERERERUfwxgKN4e8JFGIiIiIiIiIiIEowBHMWbtgoqe8AREREREREREcUbAziKt0AuwkBERERERERElGAM4ChegkLD5W1ohIhwDjgiIiIiIiIiooRgAEfxol+AwVpnJnaW5iYuDRERERERERFRysEAjuLFcAEGpZSJS0NERERERERElHIwgKN4CeQKqEREREREREREH4UBHMXLk/9fgIHzvxERERERERERJQwDOIoX9oAjIiIiIiIiIvo4DOAoXvQBnLu9pYlLQkRERERERESUsjCAo3jRr4LKIahERERERERERAnDAI7i5QmHoBIRERERERERfRQGcBQv2hxw7AFHRERERERERJQgDOAoXgJfswccEREREREREdHHYABHHxQUGi5vQyNEhHPAERERERERERElFAM4+iD9AgzWOjOxszQ3cWmIiIiIiIiIiFIWBnD0QYYLMCilTFwaIiIiIiIiIqKUhQEcfZB+AQYOPyUiIiIiIiIiSjgGcPRBT7gAAxERERERERHRR2MARx8U+IYBHBERERERERHRx2IARx+kDUG1tzRxSYiIiIiIiIiIUh4GcPRB+lVQOQccEREREREREVHCMYCjD3rCIahERERERERERB+NARx9kDYHHHvAERERERERERElGAM4+qBAroJKRERERERERPTRGMDFw5w5c8TPz0+KFi1q6qIkuaDQcHkbGiEinAOOiIiIiIiIiOhjMICLhx49esj58+fl2LFjpi5KktMvwGCtMxM7S3MTl4aIiIiIiIiIKOVhAEfvZbgAg1LKxKUhIiIiIiIiIkp5GMDRe+kXYODwUyIiIiIiIiKij8MAjt7rCRdgICIiIiIiIiL6JAzg6L0C3zCAIyIiIiIiIiL6FAzg6L20Iaj2liYuCRERERERERFRysQAjt5LPwSVc8AREREREREREX0cBnD0XoFvQkWEQ1CJiIiIiIiIiD4WAzh6L20OOPaAIyIiIiIiIiL6KAzg6L0C9UNQ2QOOiIiIiIiIiOijMICjOAWFhsvb0AgRYQ84IiIiIiIiIqKPxQCO4hT4Omr+N2udmdhZmpu4NEREREREREREKRMDOIrTE/38b/ZWopQycWmIiIiIiIiIiFImBnAUJ/0CDO4cfkpERERERERE9NEYwFGcnrz+rwccERERERERERF9HAZwFKfANwzgiIiIiIiIiIg+FQM4ipM2BNXe0sQlISIiIiIiIiJKuRjAUZz0Q1A5BxwRERERERER0cdjAEdxCnwTKiIcgkpERERERERE9CkYwFGctDng2AOOiIiIiIiIiOijMYCjOAXqh6CyBxwRERERERER0UdjAEexCgoNl7ehESLCHnBERERERERERJ+CARzFKvB11Pxv1jozsbM0N3FpiIiIiIiIiIhSLgZwFKsn+vnf7K1EKWXi0hARERERERERpVwM4ChWT/Tzv3H4KRERERERERHRJ2EAR7EKNOgBR0REREREREREH48BHMWKARwRERERERER0efBAI5ipQ/g3O0tTVwSIiIiIiIiIqKUjQEcxYpzwBERERERERERfR4M4ChWgW9CRYRDUImIiIiIiIiIPhUDOIqVNgcce8AREREREREREX0SBnAUK20IKnvAERERERERERF9EgZwFENQaLgEhUaICHvAERERERERERF9KgZwFEPg66j536x1ZmJnaW7i0hARERERERERpWwM4CiGJ/r53+ytRCll4tIQEREREREREaVsDOAoBm3+Nw4/JSIiIiIiIiL6ZAzgKIZAgx5wRERERERERET0aRjAUQwM4IiIiIiIiIiIPh8GcBSDPoDjEFQiIiIiIiIiok/HAI5i0OaAs7c0cUmIiIiIiIiIiFI+BnAUQ+CbUBHhEFQiIiIiIiIios+BARzFoM0BxyGoRERERERERESfjAEcxfDfEFQGcEREREREREREn4oBHBkJCg2XoNAIEWEPOCIiIiIiIiKiz4EBHBkJfB01/5u1zkzsLM1NXBoiIiIiIiIiopSPARwZeaKf/83eSpRSJi4NEREREREREVHKxwCOjGjzv3H4KRERERERERHRZ8EAjowEGvSAIyIiIiIiIiKiT8cAjowwgCMiIiIiIiIi+rwYwJERDkElIiIiIiIiIvq8GMCREX0POHd7SxOXhIiIiIiIiIgodWAAR0YC34SKCIegEhERERERERF9LgzgyIg2BxyHoBIRERERERERfRYM4MiINgcce8AREREREREREX0WDOBIExQaLkGhESLCHnBERERERERERJ8LAzjSBL6Omv/NWmcmdpbmJi4NEREREREREVHqwACONE/0K6CmsxKllIlLQ0RERERERESUOjCAI41+/jeugEpERERERERE9PkwgCONtgIqAzgiIiIiIiIios+GARxpGMAREREREREREX1+DOBIox+C6s4VUImIiIiIiIiIPhsGcKTR94Bzt7c0cUmIiIiIiIiIiFIPBnCkCXwTKiIcgkpERERERERE9DkxgCONNgcch6ASEREREREREX02aSqAa9++vVhaWoq9vb323/bt201drGRDmwOOPeCIiIiIiIiIiD6bNBXAiYh07dpV3rx5o/1XvXp1UxcpWQgKDZeg0AgRYQ84IiIiIiIiIqLPKc0FcBS7wNdR879Z68zEztLcxKUhIiIiIiIiIko9kmUAN378eGnSpIn4+vqKUkp8fHzee/yqVaukcOHCYmNjI25ubtKiRQu5detWrMeuWLFCXFxcJE+ePDJ27FgJDw9PhE+Q8jx5EyIiIu7prEQpZeLSEBERERERERGlHskygBs6dKjs2bNHsmXLJs7Ozu89dvbs2dKyZUuxsbGR6dOnS9++feWPP/6QUqVKyf37942O7d27t1y6dEkCAwNl+fLlsmTJEhk5cmRifpQU48lrroBKRERERERERJQYkmUAd+3aNXn69Kn88ccfkiFDhjiPe/r0qQwZMkQKFSok+/btk+7du8u3334r27dvlwcPHsh3331ndHyhQoXEw8NDzMzMpEiRIjJy5EhZvXp1Yn+cFEFbAZUBHBERERERERHRZ5UsAzhfX994Hbdp0yZ58+aN9O7dWywsLLTtRYoUkbJly8ratWslNDQ0ztebmZkJgE8ub2rAAI6IiIiIiIiIKHEkywAuvo4ePSoiIqVKlYqxr1SpUvL69Wu5ePGitm3NmjXy8uVLASBnzpyRkSNHSqNGjeJ8/3fv3smrV6+M/kutnryOCuDcuQIqEREREREREdFnlaIDuHv37omISMaMGWPs02+7e/eutm3u3Lni4+Mj6dKlk0aNGkmzZs1k9OjRcb7/+PHjxdHRUfsvU6ZMn/kTJB/6HnDu9pYmLgkRERERERERUepi8eFDkq+goCAREbGyitlry9ra2ugYEZH9+/cn6P2HDBki/fr1035+9epVqg3hAt9wEQYiIiIiIiIiosSQogM4W1tbEYkaKmpjY2O0Lzg42OiYj2FlZRVruJcaaXPAcQgqEREREREREdFnlaKHoHp7e4uI8TBTvfcNT6WYtDng2AOOiIiIiIiIiOizStEBXNGiRUVE5O+//46x7++//xZ7e3vJnTt3UhcrxQkKDZeg0AgRYQ84IiIiIiIiIqLPLUUHcPXq1RNbW1uZOXOmhIeHa9uPHz8uBw4ckKZNm4qlJRcV+JDA11Hzv1nrzMTO0tzEpSEiIiIiIiIiSl2S5Rxwy5cvl1u3bomIyJMnTyQ0NFTGjBkjIiJOTk7Ss2dPERFxc3OTcePGSd++faV8+fLSpk0bCQwMlOnTp4unp6eMGjXKZJ8hJXnyJkRERNzTWYlSysSlISIiIiIiIiJKXRQAmLoQ0ZUvXz7OFUuzZMkiN2/eNNq2YsUKmTp1qly4cEFsbW2lSpUqMn78eMmaNetnLderV6/E0dFRXr58KQ4ODp/1vU1p+9mH0v3nE1Iws5P8+lWAqYtDRERERERERJTsJSQnSpY94Pbt25eg41u1aiWtWrVKnMKkAdoKqFyAgYiIiIiIiIjos0vRc8DR58EAjoiIiIiIiIgo8TCAI3nyOiqAc+cKqEREREREREREnx0DONJ6wLnbc8VYIiIiIiIiIqLPjQFcPMyZM0f8/PykaNGipi5Kogh8EyoiHIJKRERERERERJQYGMDFQ48ePeT8+fNy7NgxUxclUXAIKhERERERERFR4mEAR1yEgYiIiIiIiIgoETGAS+OCQsMlKDRCRETc2AOOiIiIiIiIiOizYwCXxgW+jpr/zVpnJnaW5iYuDRERERERERFR6sMALo178iZERKLmf1NKmbg0RERERERERESpDwO4NO7Ja66ASkRERERERESUmBjApXFcgIGIiIiIiIiIKHExgEvjnrxmAEdERERERERElJgYwKVx+h5w7lwBlYiIiIiIiIgoUTCAS+O0AM7e0sQlISIiIiIiIiJKnRjApXGBb7gIAxERERERERFRYmIAFw9z5swRPz8/KVq0qKmL8tnp54DjEFQiIiIiIiIiosTBAC4eevToIefPn5djx46ZuiifHVdBJSIiIiIiIiJKXAzg0rCg0HAJCo0QERE39oAjIiIiIiIiIkoUDODSsMDXUfO/WevMxM7S3MSlISIiIiIiIiJKnRjApWFP3oSISNT8b0opE5eGiIiIiIiIiCh1YgCXhj15zRVQiYiIiIiIiIgSGwO4NIwLMBARERERERERJT4GcGnYk9cM4IiIiIiIiIiIEhsDuDRM3wPOnSugEhERERERERElGgtTF4BMp2Ehb8npmU7yZXQ0dVGIiIiIiIiIiFItBnBpWOEsLlI4i4upi0FERERERERElKpxCCoREREREREREVEiYgBHRERERERERESUiBjAERERERERERERJSIGcPEwZ84c8fPzk6JFi5q6KERERERERERElMIoADB1IVKKV69eiaOjo7x8+VIcHBxMXRwiIiIiIiIiIjKRhORE7AFHRERERERERESUiBjAERERERERERERJSIGcERERERERERERImIARwREREREREREVEiYgBHRERERERERESUiBjAERERERERERERJSIGcERERERERERERImIARwREREREREREVEiYgBHRERERERERESUiBjAERERERERERERJSIGcERERERERERERImIARwREREREREREVEisjB1AVISACIi8urVKxOXhIiIiIiIiIiITEmfD+nzovdhAJcAr1+/FhGRTJkymbgkRERERERERESUHLx+/VocHR3fe4xCfGI6EhGRyMhIuX//vqRLl06UUqYuToK8evVKMmXKJHfu3BEHBwdTFyfVYj1/OtZh0mA9Jx7WbdJgPSce1m3SYD0nDdZz4mHdJg3Wc+Jh3SaN1F7PAOT169eSIUMGMTN7/yxv7AGXAGZmZpIxY0ZTF+OTODg4pMqTPrlhPX861mHSYD0nHtZt0mA9Jx7WbdJgPScN1nPiYd0mDdZz4mHdJo3UXM8f6vmmx0UYiIiIiIiIiIiIEhEDOCIiIiIiIiIiokTEAC6NsLKykhEjRoiVlZWpi5KqsZ4/HeswabCeEw/rNmmwnhMP6zZpsJ6TBus58bBukwbrOfGwbpMG6/k/XISBiIiIiIiIiIgoEbEHHBERERERERERUSJiAEdERERERERERJSIGMARERERERERERElIgZwREREREREREREiYgBHBERERERERERUSJiAEdElApwQWsiIiIiIqLkiwFcChMREWHqIhBRMrF48WLp3bu3iIgopRjCEVEMhtcFXiMSV2RkpKmLQERElKiYR3waBnApjLm5uYiI7N6928QlSRvYmKbk6uXLlzJ//nyZPXu2DBkyREQYwiUW1mniYd0mroiICFFKaT8b/p0+D8Nz2Mwsqln96NEjUxUnTYh+88e22uejr9ugoCATlyT1i37e8vvw82FdJi7mEZ+GAVwK1KpVK+nevbu8ffvW1EVJ1QBojekHDx6YuDRExhwdHWXBggVStWpVmTZtmgwaNEhEGMJ9boYBxrt370xcmtQlejgUEhKi3ZDwHP489I3ktm3byqpVq0SEdfs5hYaGyubNm+XIkSPatvr168ukSZMkJCTEhCVLvSIjI7Xz+rvvvpM7d+5obTX6NPq6PXnypJQtW1YuXbpk6iKlWpGRkWJmZibHjx+XCRMmiAgfkHwu+rbFhQsXZPPmzaYuTqrFPOLj8RsrBSpWrJjcuHFD/vzzT1MXJVXTfxE2adJEvv32WxOXJnXgjd+nu3r1qvZ3Pz8/+eGHH6Rs2bIyY8YMhnCJQH+j17p1a9m0aZOI8Dz+HCIiIrS6HT16tNSvX1+KFi0qbdq0kT179vBG5DO6fv26/Pzzz3L8+HER4U3e5/To0SOZPn26dOvWTQ4dOiSVK1eWAwcOSKVKlUSn05m6eKmSPmxr2LChLFy4UA4fPsxr8mdiZmYmDx8+lPbt20tkZKQ8e/bM1EVKtczMzOT69etSv359+fXXX+XixYumLlKqYW5uLpcvX5by5cvL+vXr5c6dO6YuUqrEPOLjMYBLgUqXLi3Ozs4yc+ZMefnypamLk6o9e/ZMnj17Jlu2bJGzZ8+aujjJnmEjWD+MITw8XNvOG79Ps27dOvH395eZM2eKSNQT1Ny5c8usWbMYwiWie/fuycqVK2Xr1q0iwvP4Uxn2YKlZs6bMmjVLnj9/Ljly5JCjR49K5cqVZfHixSYuZeoAQHx9faV169Yyf/58LYSjz8PT01OGDh0q9+/fl9q1a8vJkydl0aJFUqNGDe0cp88jPDxc+/uVK1fk1q1bMmnSJKlRowavyZ9I3/M4LCxMAIi5ublMnTpVSpQoYeKSpT6Gw6f37t0r2bNnlzlz5kju3LlNWKrUQV+3ERERsmjRIsmXL5/07t1bMmXKZOKSpU7MIz4eA7hkLPrNs/7nwoULS9OmTeXgwYMSGBgoIpz/IrG4uLhI+/btJTAwUI4dOyYirOu43L59W5RSEhkZKeHh4WJubi5XrlyRdu3aScWKFaVLly5y4sQJCQ0NNXVRUyxHR0ej+tP3BGAIl7jc3Nykbdu2snHjRjlw4ICpi5Pi6c/bnj17yj///COzZ8+WzZs3y4YNG+Srr74SEZFTp05xDqLPQB9M1KlTR968eSP79u0TEU6g/LlYWlpK1apVJX369PL8+XNxdXUVOzs7rd55/f18LCwsRERkzJgxMmfOHHnx4oVUrlxZ7O3tTVyylM/MzExu3rwpnp6e0r17d3Fzc5MKFSqwDZEI9L2zvv76a/nll18kd+7cUqRIERHh9eJTmZuby40bN2Tt2rWyZ88eKVOmjBQuXNjUxUrxmEckAlCyFBEREev2kJAQAMCJEyeQLl06dOjQISmLlWZERkYiMjISABAaGopSpUohZ86ceP78uWkLlky1bdsWLi4u+Pfff7Vt58+fh5ubG7y8vJAzZ044ODggW7ZsWLJkCd6+fWvC0qZM+vPx1q1b2rY//vjD6JgLFy6gcuXKsLS0xMCBA2O8lj7e3r17oZTC6NGjAbBOP9Xdu3eRN29e9O/fH69fvwYA7Nu3D7a2tujUqZPReQ6wvt8nLCzM6M/Y9gFA1apVkSVLFrx48SLJypZaGZ6Px48fR+PGjTF06FC4u7ujSJEi2Lt3r9aOi6s9Rwm3detWKKXg4+ODOnXqaNt5ffh0O3fuhK+vL6ytrVGwYEEEBgbGek2hT9eyZUsopeDl5YUZM2YA+O/+jj5eaGgoChYsCAsLC/j4+ODo0aMAYv9upPhhHpE4GMAlQ4YNiQYNGqBZs2ZYu3at0TGvXr1CpUqVkDFjRly5ciXG6yhh4ro46+t09OjRUErh559/BsAGdXTDhw+Hm5sb8ubNi1OnTgEA6tSpgypVquDIkSN4/vw5/vzzT/j7+8PFxQVz585lCJdA+nNRf+516tQJSiksXLjQ6DiGcB8vtutARESEVnf169eHq6srrl27ltRFS3WOHj0KMzMzbN26FUBUmGxjY4PWrVvj/v372nGzZ8/GiRMnTFXMZE1fd0DUjYfeihUrcOfOHaNrRmRkJGbPng2lFGbPng2A14SPFR4erv39wYMHAIC3b98iKCgIv//+Ozw8PFC4cGHs27fP6NigoCC8fPkyycub2ujPY6WU0e8Az+dPt2nTJhQvXhxKKSxduhSA8QNp+jyCgoLQoEEDKKWQP39+rT3Me4tPd/r0aeTPnx9KKXTo0AHBwcEAeH34GMwjEg8DuGRGfwMYHh6OxYsXo3379nBxcYGZmRlKly6N5cuXazd/f/75J6ytrTF+/HhTFjlV+eqrrzB58mS8ePHCqOH8+vVrZMqUCfXq1TNd4ZIhw4vs5MmT4eLigjx58uDff/9F06ZNsXjxYqPjb926hQIFCsDV1RU//vgjQ7gEMLw2AFGBRebMmZExY0YsWLDA6FjDEG7w4MFJXtaUbtasWbh//752furP859++glKKfz0008A2Fj+FBcvXoROp8PPP/+M06dPw8bGBq1atdICDSCq16GPjw+WLFliwpImT0ePHoVSCqVKldK2RUREaA+LsmTJgrZt2+LSpUtaz+1Xr17B19cXlSpVMlGpUz7DkL5fv36oW7eu0UOQsLAwbN682SiEA6ICuh9++AGdOnViD8R4in59Nfz5f//7H5RSKFq0KP766y9tO2/84ud9310bN25Evnz5oJTCpk2bADCE+xRx1VtwcDAaNmwIpRTatGmDV69eAWC74nM4f/48/Pz84ODggHnz5uHdu3cAeH1ICOYRiYsBXDJVp04d1KtXD9evX8fNmzcxfPhwFChQAEopZM6cGdOnT8fmzZtRtWpV+Pv74/Lly6Yucoqkv8BERERg48aNsLa2hlIKuXPnRo8ePXDv3j28efMGAPDNN99AKYWNGzeassjJjmFjYdKkSXB2dkaWLFmQPn16bUiqYZh58+ZNFChQAC4uLvjf//6n1S/FzbCOW7ZsiV9//RVA1JC9jBkzIkOGDLGGcNWqVYNSCj169EjK4qZoY8aMgVIKGTJkQLNmzXDkyBGjoLhgwYIoVKiQCUuYshieu/rGb3h4OB4+fIgSJUogffr0sLOzQ7t27XDv3j3t2Lt376JHjx7Ily8fe8DF4tmzZ+jXrx/Mzc1Rvnx5bfvFixdx8OBB1KxZE46OjnBwcECDBg2wYcMGAMCMGTNgZWWF5cuXm6roKZbh91itWrXg7e2NHj164ObNm0bHhYWFYcuWLfDw8EDBggUxduxYDBo0CEopTJkyJamLnSIZ1vWjR49w9erVGMPSZ8yYAaUUKlSogEOHDmnbeZP9fvq6ffDgAbZs2YJJkyZh8+bNRvcRmzZtwhdffAGlFDZv3gyAIdzH0Nf169evce/ePZw5c8aozRsUFIQ6derA0tISnTt3ZgiXAPq6DQsLQ1BQkFZ3emfPnkW2bNng7e2NZcuWab3EeQ4nDPOIxMEALpkwbGz89NNP8Pb2xurVq7UnpfqL8Zw5c1C3bl0opZAzZ06kS5cO9vb2+P33342Oow8zvAhXqVIF8+bNw8mTJ7Fjxw6ULl0a1tbWcHV1RceOHfH333/j5MmTsLW1xaBBg2K8Pq0zrIuxY8ciY8aMsLCw0J6eRj8vb926hSJFikAphUWLFiVpWVMaw7r96quvYG1tjeXLl2vzL+zduzfOEO78+fMoVqwYZs6cmaRlTsnu3r2Lv/76C82bN4ezszPMzMxQr149zJs3DwAwceJEpEuXTvuZ4mbYWygoKCjGHJpz586FUgrOzs5aqAwAV65cwbfffgtbW1uttyH9R389ff78uRbslCtXzuiY4OBgnDx5Er1790amTJmglEKNGjXQuHFjeHt748svv0RkZCTbDB+hTZs2yJAhA9asWYNnz57FekxISAh27NgBb29v7RyfNGmStp/th7gZtod79OiBAgUKwNbWFh4eHhgyZAguXryo7deHcOXLl8fhw4dNUdwURf/7fu7cOeTJkweurq6wsbGBUgolSpTA//73P+3Y3377TQvhtmzZAoDnbULoz+MLFy6gevXq8Pb2hoODA3LkyIGlS5fixo0bAIxDuE6dOmlzovLaHDd93V6+fBnt2rVDvnz54O/vjzp16uDKlSvaeXr27Fn4+vrC29sbS5cuZQgXD8wjkgYDuGTm6NGj6NChA9q1a6c9JYmtkbx792506NABefPm1YahRE//KW6G9TlkyBA4OztjwYIFWjflkJAQ7NmzB926dYOjoyOUUmjSpAk8PT2RLl06nD9/3lRFT1b0F+qgoCA8fvxY2z5lyhRYWVnB09MTp0+fBhDzYnz9+nWUKVPGqDFNxgzr7PXr1yhdujSmTp2q/a7rGxHvC+HiukGkDzcQjh8/jrFjxyJz5sxQSqFy5cro2LEjbGxs0LVr1yQqZcpk2Ijr3r078ufPD29vb4wcOdLo+jly5EgopWBjY4MuXbqgU6dOKFGiBGxtbTFx4kTtODaYjenrwzCEM+wJZ1hfFy5cwKJFi+Dn54eMGTNCKQVbW1ujRXMofvbv34/06dNj8uTJCAoKAhA1tPfChQuYOnUqJkyYYDTP24sXL7Bjxw78888/2jbemMTN8LytVasW3N3d0bVrV0ybNg1fffWV1hbThxcAMHPmTCilEBAQgIMHD5qg1CnL1atX4enpiYoVK2LdunV48OABfv/9dyilULBgQVy4cEE7duvWrShUqBCUUti2bZsJS52y6M/jCxcuwNXVFcWKFcOwYcMwe/ZsbWTC6NGjtVAjODgYderUgb29PZo1a6aFcBSTYYjs7u4OPz8/tGrVCi1btkTGjBnh6uqK3bt3a8fpQzgfHx/MmzfPaL5UihvziMTFAC4Zad++PRwcHODn56cNDzHsQaC/oOv/fPv2LQIDA9GkSRPY2dlh7969ANi4+xDDBl5YWBiaNGmCwYMHa41mwxtHIOoiNHPmTGTJkgVOTk5QSmH48OGIiIhI03Wtr6crV66gbdu26NKli9FQsSlTpmhzwp05cwZAzHOTKxPFT4MGDdC5c2cULlxYu/HQT6wePYTLnDmz0VNsPQYYxgzPvTNnzmDbtm04efJkrJOkX7p0CStXrkT+/Pnh4+OjTQCuv+ZS3OrWrQtHR0eUK1cOZcuWhZmZGWrWrIk9e/ZoxyxatAjVq1eHp6cnPDw80LhxY6xevVrbn5avs7GJvrpmXCFc9FX17t27h99//x0tWrTQhqa/e/eO14YEWL58OZRS2L17NwDgxo0b6NKlCzw9PbXrQvbs2REYGBjr63kux8/o0aPh4eGB1atXa2HEli1boJRC9+7d8fTpU6O6nD59OpRS+OWXX0xV5BQhPDwc3bp1Q758+YyG7U6YMAGWlpZYunQp3r59a3RN2LBhAwICAnDu3DlTFDnFevHiBcqVK4fSpUvj2LFj2vavv/4aFhYWWLNmDUJDQ7W2SHBwMMqWLQtPT0+jhYgopkePHqFAgQIxer42btwYSils2LDB6Ppw7tw5ODk5wc/PjwvhxAPziMTHAC4ZOXDggDZUpGXLllrAEVfjWH9iP3z4EG5ubujWrVuSlTU1aNiwIfLnz4/8+fNrT00N6zr6heP+/ftYvXo1ihUrhjx58sQI6tISwydQ3t7eKFy4MMaNGxfjuEmTJsHFxQW5c+fWQri0XG8f49q1ayhdujR0Oh1sbGy0Sb0N6c/bffv2wcPDAzY2Njh79mxSFzXFMDwH27ZtCw8PD61XUNmyZbU5naI/KdXP7dS3b18opdC7d28O44vGsJG2a9cuZMuWDatWrUJwcDDevHmD+fPnQ6fToUyZMti1a5d27OvXr/HkyRM8f/5cW7UMYAMuurjqI64QLq4hN3Xq1EHOnDm1XlwUU2wPiI4ePQorKytUr14dPXv2hIuLC/LmzYs+ffrg/v37Wo/O6dOnJ32BU4mwsDBUr14dderU0Xpe7N27FzY2Nmjbti1u374d6+v0K7BT3MLDw5EvXz60a9dO2zZgwADodDrMmzdPCztfv35tNJcT5+pNuEuXLiF9+vRGU4AMHDgQOp0O8+fP14Igw3ZGcHBwnOc3/fc9tn37dmTIkAGrVq3S9g0ePBg6nQ4LFy6MNWS7cOGCtkonvR/ziMTHAC6Z0J/Ux44dg4+PDxwcHLB06VLtpH7fE+o3b96gSJEiKFeuHMONeHrx4gV69+6tPbWeM2fOe2/09PtCQ0OxYsUKKKUwf/78pCpusnTnzh1kz54dFSpUMHoCFb1noD6E8/f3x8mTJ01Q0pTv8OHDaNGiBczMzDB48OBYz1X9NWLnzp0xhqHSfwzrrnbt2vD09MSAAQOwe/duDBs2DLa2tsiYMSOuX78OIObqs3rt27eHh4cHHj58mHSFT0HmzZuHefPmoV69ejFWk129ejV0Oh1Kly6t9SQyFJ/vvbSuXr16CAgIMNoWVwhnGCTpp1lYt24dewzF09ixY7W/v3z5EpMnT0aGDBng6OiIzp07G/WE3bJlC2xsbLSJ6ynhAgMDkTVrVgwYMABAVJBva2uLVq1aGfUMGjp0qFE7TH+9YGgft7dv38LHxwf9+/cHAAwaNAgWFhaYN2+eURjfuHFjjBw5UrteUMLt3LkTSimt99vAgQNj1HVERARatmyprSZJ8TN58mQ4OjpqU9/og8158+Zp7Y3g4GD07t07Rk9wej/mEUmDAZwJfOikPHLkCNKnT4/s2bNj3bp1Mbp6Rnf16lXkzZsXtWvX5tj2BLh37x6+++47ODk5oWLFikZzmMVGX//37t2DnZ0dhg0blhTFTLZmzpwJd3d3o1Vhow/v1Zs8eTKUUihevDiHPL3H+24cDh06hPr167+3d0X0euWNiDHDa+8333yDrFmzYvXq1drT0t27d8POzg5KKaRPn14b7mv4Ov15vWbNGiiljJ7AUpQpU6ZAKQU3NzdtrrzojWB9CFe+fHmj4aj0YS9evECbNm2glEL9+vWN9sUnhAOA9evXQ6fTMSj6gIULF0IphTp16mjbQkJC8PDhwxhzwd65cwd9+/ZFjhw5+LDpEwQFBcHf3x+NGjXC0aNHYWtri5YtWxqFb6dOnYKfn582jJpiF31UR0hICIoVK4ZixYqhc+fOsLCwwIIFC4zmHNu2bRuyZ8+OyZMn8yb6E/z9999QSmH58uUYMmRIrEHn4sWL4eDgwPn1EmjBggWwtLTEzZs3MXr0aC18M6zbKVOmwNvbm6uovwfzCNNhAJfEDBvBO3bswMKFC7Fy5UrcvXvX6LjDhw/D09MTOXLkeO9JHxYWhq+//ho6nY5DzuLwvhDi/v37GD58OHQ6HRo0aKA9OYlLZGQk/vnnH2TKlAm9e/fW5uFKC6J/ziZNmsDb21u7sY5t+K5h3c+YMYMLLryH4bXhxIkT2L17N06cOIEnT55o2w8fPqytOjRjxgxTFDPFefLkCb7//nvtPI2IiMDly5dRtmxZ9OrVC0+fPgUQFb7pb/RGjRoFpRQyZcqkDUeNHmD8/vvvsLa2xsqVK5P2AyVD0a+xd+/eRZMmTWBlZYX8+fNr26PX4dq1a6GUQr58+WJ8B1Ls9NfZhw8fonfv3lBKoW7dukbH6EM4c3PzGL3kgKigSN/7U39+U+wePHiAPn36QCmFWrVqxXncxYsXMWjQINjY2HDV6Xh6X9ts4sSJUErB3NwcnTp1woMHD7R9Dx48QL9+/ZA9e3b88ccfSVHUFOd9dbtu3TpYWFhAKYURI0YYXZePHz+OypUrI3/+/BwKGU/vuweoWLEibGxsYGZmhkWLFhndYxw9ehTly5dHlSpVjNp5FDd9YHTq1Cl4enoiffr0MDc3x8qVK41WWT906BACAgLQuHFjbaELMsY8wrQYwCUhw6S5efPmcHNz0ybtLVKkCJYtW2Z0vP6kz5MnD9asWRPnF+rNmzdx6dKlRC17SmV4gTl79iwOHz5sNBkq8F8IZ25ujoYNG743hHv79i26du0KpVSaqnP9uWv4mVu2bAl3d3etYRzbRXnatGn466+/krCkKZPh73arVq20a4OlpSUqVqyIxYsXa/sPHz6MOnXqQCmFWbNmmaC0KcejR4+0VR979+6t9ZS4cuUKmjdvrvVg+ffff7XVx/SNuMqVK0MpBScnJ6O5cICoXrABAQHw8vIyWo0vLYrre+nhw4do1qyZNoeIXvQQbtmyZTyP3yO2J9SG86307NkzzhCuf//+sQ4zffXqFapVq8aeAdHE1YP40aNHWthZu3Ztbb/+XF6wYAEKFiwIV1dXTJ48Oc73o//qzPA6oO9NqH8YAkQ9hAoICIBSCv3799d6KZ8+fVqb64kPof5jONej/ppx7949LFu2DDNnzsTixYvx4sULbdL/oUOHQqfToWrVqvj5558RGBiI2bNno3z58nB2duYNdDzp6/rly5e4ceMGrly5YhQWr1y5Enny5EG6dOmwadMmLQzasmULqlWrBg8PD6NVZ+m/666+bkNCQhASEoK3b98aTQnSo0cPKKWQI0cO3Lt3T3v9rl27UK1aNWTIkCFN3aclBPMI02MAl0QMG2L169eHh4cHxo4di71792Lv3r1wcHBA3rx5MXfuXKPXHTlyBM7OznB3d4918kgOMYub4QWmc+fOyJIlC5RSsLKyQpcuXfDgwQPt38UwhGvSpEmcE86GhYXht99+S5ONk4sXL0IphS+//BIA8O2330IphZEjR2qhpWGX4/Xr16Nw4cLYtGkTb0Tew7BumjRpAjc3N/Tq1QszZsxAv379YGFhASsrK0yaNEk77siRI2jQoAGUUpg4caIpip0irFy5UltcwcHBAb169dJ6wulv9p49e4bSpUujTJkyuHTpktbAa9GiBQoXLgxLS0vMmzcvxns3a9YMR48eTboPk8zVrVs3Rm+rR48eoWnTprC0tESbNm207XGtfpzWrxP676zYQrc9e/YYTSwdWwjXqFEjo9c8ffoUf/75p9G26Dc3FMWwLRXbIiCGIZzhcNTbt29jwIABaNSoEdatWxfr+xHwyy+/aOGDYf127NhRe0ji5eWFJk2aaL2B9u3bh1KlSkEphYwZM8Lf3x9eXl5wcXEx+j5M69eNzp07Y8mSJUbD786ePQsfHx/odDrtxtrf3x9z5sxBUFAQnj17hrFjx8LGxkbbb29vj5IlS+Lff/814adJ3pYuXar9XX8NPX/+PIoXLw5HR0copeDp6YkpU6bgzZs3iIyMxI8//ohs2bJBKYWsWbPC19cXbm5u8PX1xenTp031UZIdw7k09Q9LL126hNq1a8PX1xcZM2ZEkyZNsHXrVgBR9xv6Fb1dXV3RvHlzVKxYET4+PsiQIQPrNg7MI5IHBnBJbMSIEciZMyeWL1+uhTz//PMPLC0tYWlpCU9PT/z0009Gr/nzzz/T/IT/CRV9onX9qiwLFixAjx49YG5ujubNmxv1bLl//z5GjBgBpRSqVKkS501iWmK4+EStWrVQvXp1/P777wCinvjlzp0b6dOnx9y5c40a1ceOHUONGjWQP39+Di17D8Nz7P79+yhdujQWLVpkVJf79u2Dq6sr7OzsjOYb0w9f4Gp7cXvw4AEqVqyIGjVqIGfOnDF6wkVGRuLevXtwd3fHwIEDtdddvXoVZcuWxYoVK2I8zWMjI6b3zUn24MEDNGnSBDqdDm3bttW28/oau8ePH6N3795GPbWHDBkCpRQWL15sNFeT/lx88OABqlatCqUUWrRoEev7Rj9v03poEZc2bdpg5syZsdbzw4cP0aVLFyil0LRpU23/o0ePjHpv8Rph7PTp01BKwdfX1yhEbtSoEVxcXNCsWTOMHTtWC9vy5Mmjnf9nzpzB/PnzUbt2bVSpUgXDhw/Hjh07tPdI63V97do1eHl5IWPGjFi7di3CwsLw4sUL5MyZExUqVMDSpUtx8uRJTJ06Fblz54atrS0mTJigtTHOnTuHVatWYc6cOThy5AiHQr7HnDlzoJSK0Vbw8PBAvnz50Lt3bwwePBi5c+eGUgpt2rTRHvQfO3YMw4YNQ+3atdG4cWPMnDkTt27dMuGnSV70DzcMA87z58/Dzc0N2bJlQ/Xq1VGzZk0tLNb3mg8NDcWMGTNQp04dZMmSBSVKlED//v1x9epVU32UFIN5hGkxgEtCd+7cQd68edG9e3etsbZ//37Y2Niga9eu2LdvH5ycnJA1a9YYybNeWm9sJFS/fv2QNWtWrFq1Snv6unHjRm3+i9q1a+PKlStGPeH69+/PoQ347wbt5s2buHDhAho0aGD05QhELVWdLVs26HQ6NGzYEKtWrUL//v1RuHBhODs780lqPH311Vfo2rWr0XBHw9Vk9+zZo/VwMZzM/tGjRyYpb0rStWtXFCpUCKdOnULJkiWhlEKfPn20EO7kyZNQSuHrr79GSEgIbt68iREjRsDDwwOHDh3S3ofX3tjFZ04yfQhna2sbZ0BEUfQL1rRo0QKnTp0CEDVEOiAgAG5ubli0aJFROKQPMvfs2aMNIzFcfIHi786dO8iTJw9cXFywaNEivHr1Stun//2/efOmtnp6lSpVYrwHg82YQkJCMHr0aNjb2yNPnjx4+fIlrl69Ch8fHyxdulQ7n58/f47x48fDxcUFefPmjdHjM/o1mNfkKCdOnEDBggXh5eWF9evXY8eOHfD398e+ffu0Y8LCwvDvv//C398fHh4e2LVrlwlLnDKdO3cOnTt31toLADB69GgUL17c6IHJqVOn8OWXX8LMzAy9evUyeg8uGBK7DRs2IG/evHB2dtamXOnUqRPKlCljNNJg3bp1+OKLL6CUwpIlS4zeQz/0lw/3Pox5hOkxgEtEsTXEvv32W/z9998Aoi7m9vb2aNq0qfYkpGPHjjA3N0emTJkYAn2is2fPIl++fBg4cKAWvu3duxe2trZo2rQphg8fDqWU1hNO/+9lOAdcWm9MP336FFmzZoWDgwNcXV21G0LD5agvXLiA8uXLw9LSEkopODo6okqVKjh37pwpi55iXL16FUopODs7w9/fX7sZiT4crXXr1kiXLh2uX78e4z3S+nkaG/05ev/+fTg5OWHs2LEIDg5GoUKFtBBOH2a2b98eOp0OxYoVQ4ECBWBtbW00lxP952PnJHv48KG2iu/OnTuTpKwpUWBgIIYOHQqlFJo0aYIzZ84AiJoCoFSpUlo4ZBjCAcCiRYuQJUsWtGrVCtOmTTNF0VOc6DdqEREROH/+vDYP1sKFC7UQzjAAql+/PooXLw6lFDZt2pTk5U5J9HUWEhKC8ePHawuzrFixAuXKldPOY/30Fa9evcLIkSOhlELnzp0BGH+/8bsudsePH0e+fPng6+uLzp07o0SJEto+wxvlgwcPavcdlHCXL19Gp06doJTCd999h169eqF79+6xHqefA1U/aoTeb9u2bciTJw8cHBywbNkytGzZEuPHj49x3NatW+Hp6QkPDw/t+xGIfR5qisI8IvlhAJdIDL/wYluB5dWrV6hXrx7y589vtJR9//79UbZsWZibm+PHH39MkrKmVhcuXEDHjh21+j1z5gxsbW3RrFkzBAYG4vr168icObO2uhknjozdoEGD4OPjA0tLS2zcuBFAzJtw/cqS+/fvx507d4yeXKd1+royvCZE33bixAk4OTlBKYVRo0ZpxxneIPbq1Qu2trZpftL/uAQFBWnDZ/SNjfDwcAQHB6NTp07IlSuXdky+fPm0EC4sLAyPHz/GsGHDkCNHDtSuXdvoySqf8n2+Ocnu37+Pbdu2JW5hU4Fnz55h8ODB7w3hFi5cqD25vn//Pnr27ImhQ4ca9bDgjUj8/Pjjj9p1VR/ClS1bVgvhDNtwly5dQpEiRbBmzRouMBRP0UO4dOnSwdzcHPnz58erV6+MHugBUSF03rx5UaBAAfZmSYDjx49rD5g8PT1x//79GMe8ffsWpUqVgo+PT6z76cMuXbqEjh07wsLCAmZmZujdu7e2z/Cau23bNiilMGzYMFMUM8WIXmd58uSBu7s77OzstFE3YWFhRsfpe4rr54OjuDGPSJ4YwCWyxo0bo0+fPnj48KHR9ocPH2pPqvSuXbuG0qVL45dffuFNXwLFNaH048ePtT9LlCihTbSu16xZM5QtWxZKKWzYsCFJyppSGNbpmDFjYGFhgfTp02srNsUWLJExw+F5AwYMwPz5841uKCIiIrSfT506BXt7ezg6OmLhwoVG73Pv3j1Uq1YNefLkwe3bt3ljHc3Dhw9RtmxZlChRAr///rvRKmRA1FN/pZTWiHjy5Any588PpRT69eun9b54/vy50UIiPLejrp29evUyGmKjD4c+55xkZCyuEO7ChQsoU6YMbGxs0KFDB8ydOxcdOnSAtbU1FixYoL2e14i4GX63/fjjj1BKoVu3btqTf30IV6ZMGTg7O2PSpEl4/Pgx7ty5g++++w4+Pj5GcwzxXP4wwxBuzJgxyJw5MxwdHXHkyBGj/fo/W7duDScnJ85JlgD6ucYqVKgApRSmTJkSo0c9EDUvsp+fn9EQa0qYCxcu4Msvv4SdnR1Klixp1OYwbOPZ29ujS5cupihiimL4fbVlyxbtIanhEN6IiAitfXbmzBkopTB27NgkL2tKxTwieWEAl4hCQkJQvnx56HQ6fPfdd0Yn/cOHD5ErVy4ULlwYAHDlyhWMGTMG7u7u2Lx5s3YcT/yE6dSpEw4cOBBj+7lz5+Dq6mrUnfn8+fPImTMn5s+fb7QYQ1r1oXNt3LhxsLe3h7+/vxZi8vx8v8jISISEhMDb2xtKKWTLlg25c+fG/PnzY+1xefz4cdjZ2cHW1haDBw/GrVu3sG3bNnzzzTcwNzfH7NmzTfApkrfg4GCtsZYuXTqYmZmhcuXKGDNmDIKCgrRhpo0bN0a+fPlw7do1AFG9LAoUKAClFL766iujufUABhh6nJPMdOIK4Z48eYImTZrAzMwMFhYWcHZ2xpQpU0xc2pTB8OZ4x44d2LBhA+zs7ODi4oKuXbvi9u3bAKK+2y5duqRN/J0xY0b4+vpCKcXh6fEUV/sgODgYY8eOhb29PXLmzBmjJ9bt27dRpEgRBAQEGE0JQv+J6/spLCwMx44dQ6FCheDu7o5Fixbh2bNn2v4jR44gc+bMqFevnjbxOr2fYV0bntPnz59H165doZRCjx49EBQUZBQkb926FTqdjivVv4dh3Rpem7dt26YtnKVfhd7w2GXLlsHc3Bxr165NusKmYMwjkh8GcIns7du3aNy4MczNzTF8+HDtpA8NDcXAgQOhlIKTkxMyZswICwsLTJgwwcQlTrkOHToEBwcHeHl54fDhw0b7du/eDaUUBg8eDCCqh8aoUaPg4+ODkydPasel1QuM/unogwcPsHHjRkyZMgW//fYbAgMDjY4bM2YMnJyc4O/vj4sXLxq9luLWsWNHODs7Y+7cuahfvz5sbGzg5uaG0aNHa3Mw6B09ehT29vbaqnFubm6oXr260Q02w6H/hIaG4ocffoCjoyNKlCiBPn36oF69enBwcECOHDnQt29fPHz4EIsXL0b69Omxfft27bVPnz6Fn58flFLYv3+/CT9F8vWxc5ItXLhQm5OMK/V+vOghnD4EBaJCzv379xt936XV77D4MKybunXrIlOmTKhatSpq1KiBbNmyQSmFrl274s6dO0avGzVqFOrVq4d69eph2bJl2nZeh+NmeDN97do13L1716i+QkJCMG7cOFhbWyNHjhzYsWMHHjx4gJMnT2LYsGEwMzPD//73P1MUPdnTt7mePn2Ks2fPYv369Th//rzWXgsLC8Px48dRoEABODo6onnz5ti1axe+++47VKtWDS4uLkZDzShu+rp+9uwZ7t27h5s3bxrtv3DhgrYwQ8eOHbFz506EhYVhw4YNqFmzJjw8PLSHfmRMX7evX79GSEhIjDbE9u3bkStXLpibm2PWrFlaGL9v3z5UrlwZmTJl0h6Y0Icxj0heGMB9JrHNiaX39u1bNGzYUDvp7927ByDqojN79my0aNECnTt3xpo1a2J9PcVPREQEfvnlF+TKlQuenp5GNyXh4eEoWbIkzM3NUbFiRZQtWxY6nY4XGPx37p47dw65c+eGtbU1lFLQ6XQoVKhQjAbHmDFj4OjoiEKFCnGhhQ/Q33CsWrUK3t7e2tDSNWvWoHPnztDpdPD09ETLli3x119/aQ3oU6dOwcHBAe7u7pgwYQKCg4O19+S1IaZ3795hzpw52mq8GzZswJUrV9C6dWtkzpwZ7u7u+Prrr6GUQvXq1QH892/z5MkTrFy50pTFT/Y4J5lpRa//48ePx3ocrw3x061bNzg6OmLVqlXatfX+/fvaIiGxhXAAYkwfQLEzrJvWrVvD09MTXl5eqFq1qjbMF4jqCTdu3Dg4ODjAxsYGvr6+KFOmDHLkyIGpU6dqx/G68R99e+38+fMoXry49rDO1tYWxYsX11aeDw8Px/Hjx7WVvzNmzIgSJUqgV69ebLfFk/48PnfuHPz9/eHi4gIXFxcMGzZMu48Dor4Hu3TpAp1OBwsLCxQoUABffPEFihUrZrRIAP1HX7cXL15E5cqVUbBgQVSvXj1Gj7bff/8duXLlglIKBQsWRJEiRVC0aFH4+vri9OnTpih6ssc8ImVgAPeZ6eezAGKe9A0aNICFhQWGDx9uNMwBAOcdSqDodaRvGEdGRmLdunXIkSNHjBDuxYsXqFevntbIM5xnK63Wub5he+nSJXh6eqJixYpYsmQJAgMDMW7cOCil4O3tHWPlzfHjx0MphYCAAISGhrKB/AHh4eEoXLgwAgICjLafOXNGe3qaOXNmlChRQuvyfenSJdjb2yNz5sxYvny59pq0eq5+SGhoKGbNmgVLS0uULl0a//zzD4Co1cgGDRqEnDlzwsHBAfXq1dOGPMW2CiLFjnOSmZZh/Tdt2pQ3Hx/p2bNnyJUrF2rUqKEtImJ4btarV08L4fTttMjISO2/6MdT3Fq3bg0XFxe0bdsWTZo0Qbp06ZAtWzbs3r1bC+aDg4MxZswYZM+eHRYWFjh27JjRfFq8Jv9Hf95dvnwZbm5uCAgIwJgxY/DLL7+gRYsWWg+WQ4cOAYj6TjScE27z5s1GD0Tow27duoUsWbKgSJEi6Nq1q3Z9aNy4sdbGAKIC0a+++grOzs4oVKgQ7t69qz2QothduXIFXl5e8PHxQfny5eHh4QGlVIwhu9u3b4efnx/c3NxQunRp3Lp1yygApdgxj0jeGMB9Rt27d4dSSlspEjA+eZ89e4ayZcvCxcUF33//faxPWOnDDBu/ht2P9am/YQiXPn16o4sQEBXExbZ6X1phuEIkEDWMoVq1aqhUqRKOHj2qHTdo0CBYW1vDzMwM6dOnj7H65pQpU7RhqBQ3fT2vXbsWOp1OW9UJiPqiK1WqFLJmzYoWLVpoT/py5syJRYsW4dSpU7C1tUWmTJnYSyseQkNDMXv2bFhZWSEgIAAHDx7U9p08eRLbt2+PcT2g+OOcZKZlWP9t27bl8JuPcPv2beh0Om3lwuiT/58/fx7p06eHs7MzunXrxpUiP9Ldu3dRqlQpLFmyRGtz/Prrr8ibNy8yZsyIXbt2aTd6wcHB+P7776GUMvqeY9AZU1BQEBo1aoS8efPG+C6bOXMmHB0d4eXlpY1ciIiIwOHDh1GqVCmjhUPo/fTXg+XLl6NMmTJaXT99+hSzZs2ClZUVateubTSFzblz59CyZUukS5fOaN49Mqb/vR43bhxq1Kih1e3hw4fRpEkTKKUwcuRIo9ds3boVGTNmRObMmTkvZDwwj0j+GMB9RkuWLEG2bNng6ekZ46TXX3BGjx6tdRfv378/goKCTFXcFK9+/fqoVq0azp49q20zXJnz559/hr29PbJmzWq0gh+ANPske+PGjejatas24Xx4eDj279+P/PnzY86cOdpxgwYNgoWFBf73v/9h7NixUEohU6ZMMUI4ir8rV64gU6ZMaNmyJQDg0aNHKF++PNzd3bF9+3aEhITgxo0bGD9+PKysrDBmzBgAwLFjx+Dk5AQ7OztOOBsPhiFc2bJlsWvXrliPS2u/+58L5yQzrWfPnqF///5QSuGHH34AwHM5LtF7uAJRKx3nypUL2bNnN1p8yfA8LV68uNYb49tvv40xNxHFFH3Y07Fjx+Dj42MUYIaFhWHnzp3ImzcvMmXKhD/++MMohIveTqOYgoOD4ePjg8aNG2vbDHu16YPMQYMGxZhzjz4seiA/dOhQtGnTxuiY4OBg/PTTT1oIZ9gT7vLlywzt4xC9LdCwYUPtQYje2bNn0bJly1hDuJ07dzJEjifmEckfA7iPZPjFZtjwWLt2LbJmzQp3d3ftpI+MjNQuPD/99BNq166NKlWqaI1nih99Yzo8PByRkZEYNGgQlFJo1aqVNu+Ffr/+z7p160IpBVdXV6PeMGnR6tWroZSCjY0N+vTpozXaTp06pS1OAUT1bDMzM8OsWbMARJ2/hQsXhlIK5ubmDOE+wZQpU2BjY4MdO3agZs2a2pdj9C++u3fvAvjvOnPo0CF4e3tztd54ih7C7dmzR9vHsOLTcU4y07p16xZKly4NDw8PPHnyxNTFSfYMf/8BaOfu119/HePJ//nz51GyZEmcPHkSjRo1gpubmzbcl9eO2Bm2gRcsWICRI0di9uzZqFq1KsLDwxEREaG13wxDOB8fH+zYsSPGsEheN/4TPUS+fv06LC0t0alTJwD/DRfT19m7d++QM2dOVKpUKWkLmgroz+MbN25g9OjRmDx5Mlq2bImePXsCMA4xDUO4+vXrG40eoZj0dXv37l2sX78eGzZsQJ8+fbB+/XoAMJrn+N9//9WGVI8ePdok5U1JmEekTAzgPkL0J33RvyBXr16NrFmzwsPDA7/++qu2/f79+2jUqBGGDBmSFMVMVQwbZJ07d8bu3bsRERGhJfgtWrQwCuH0/yYDBw5E2bJl4eXlleZX1Jo8ebI2R4idnR169eqlNSgMw7iMGTOib9++Whf6iIgI5MmTB9WqVUPOnDlx6dIlk32GlO7kyZPIlCmT1qPw119/Nbr50J/nhsOE9dcbPsFOGMMQrkKFCti9e7epi5SqcE6ypGfY0G7atCkyZ86MV69embBEyd+IESOglMLixYu1be/evUOdOnVgZWWFHj16aG2Hq1evYsSIEfDx8cHZs2dx8OBBKKXQt29fE5U+ZalTpw6UUjAzM4NSCkoprFixQttv+HD0jz/+QM6cOWFnZ8eh1NE8fvwYjx8/1n7fL1y4gIkTJ+Ldu3cIDw9H+fLl4eTkhAsXLgAwvqkODQ1F3rx5UaZMGZOVPyU7f/48PDw8YGtrq53DmTNn1hbIMpwfKzg4GPPmzYNSCs2bN2cbLQ768/jcuXPw9vY2uj6UL19eO87wXvrff/9FmzZtYp0Tjv7DPCLlYgCXQIYn+7Bhw9CgQQM0bdpUmzhdb9WqVciRIwcsLCwwbdo0rFu3Dn379oWdnR1Wr16tHccnqgmjX2lo9erV2hfhyJEjoZRCy5YtjVYcunPnDqpVq4affvrJaOWttOr06dMoXrw4OnToAD8/Pyil0Lt3b6MAaN++fTFuVnbt2oWSJUvizJkzvNn7DPRzMyxbtixBr+O1IuFCQ0Mxd+5cKKVQoEABrWchfR6ckyzpRUZG4vTp0yhQoAAqVarE4ZEfsGfPHlSpUgUWFhZG32uXL1/WJlR3dXVFlSpVkDNnTpiZmWk3fE+fPkW6dOnw5Zdfmqj0yZthe3ju3Lnw9fXFwoULce/ePSxZsgRubm5wdHTEL7/8EuM1YWFh2Lp1q9ECQwS8fPkSgwYNQufOnfH06VOcO3cO1tbWaNeunRYCTZ06FUopVKpUKcY8vH/++ScyZ86Mfv36AWC7IT4Mew9WrVoVNWrUwM6dO/H48WN0794dOp0OBQoU0HppGYZwQUFBWLx4sRaGkjH9+ff8+XMUKVIE1atXx7Jly3DgwAEUKlRIuw/R16lh3Z46dQqdO3fmqr1xYB6RsjGA+0i1atWCtbU1fH194eTkpHWVNVz1ZuvWrdrqQ0opWFlZYdKkSSYsdcoTfdLI4sWLY+bMmTGCIP28F9WqVcP27dtx9uxZDBkyBM7Ozti7d2+s75cWNWrUCAEBAbh9+zZKlCgBpZTRcNTt27dr5/Ldu3exf/9+1KhRA7lz5+ZQp0+k/3I7e/YsXF1d0bZtWxOXKG0IDQ3F1KlTMXv2bFMXJVXinGRJ7/z587C0tDSa/5SMGX7X//XXX6hUqRLMzMywaNEio+PGjRuHSpUqIWvWrKhVq5bRyr0rVqyAnZ0dpk2bBoDndVyOHj2KLl26oFOnTkaB8C+//ILs2bPD1dVVG2oGGC+YpZfW22Z67969w/Tp06GUQq1atZAuXTpUr14d58+fN6qjDh06QCmFL774Atu2bcPly5exfv16VK5cGZ6enpwrK5705+C9e/ewf/9+lC5dGuvWrdO2v3r1CqNGjYKFhQUKFiwYawhHsdOfr69evcKff/6JggULYseOHdr+p0+fokKFCrC1tUWfPn20OjXsxcVVez+MeUTKxAAungyT5sWLF8PX1xc///wz3r59i6NHj6J9+/ZQSmHgwIHaUyogqpvn33//jTVr1uDvv//WtrOxkTBNmzZF//79UaRIEW2C0+h1OGPGDO3iYmtrCzMzM0yYMMEUxU02oq94eu7cOdjY2OCnn37C69evUbBgQS2E03efb9GiBczMzODp6QkPDw94eXkZ9SykT/PixQtUrlwZLi4uOHDggKmLkyYYXit4E/35cU6ypPfmzRtTFyFZiW3BBcNtf/75Z5whXGhoKF6+fGm0ut6RI0dQoUIFZM2alT0736NDhw5wdHRE/vz5tZDNcCjeunXrkC1bNri6umLDhg2mKmaK07dvXyil4O3tjS1btmjbDeu2T58+sLGxgbm5OczNzeHk5IRs2bJxOoAEiIyMxNOnT2FnZwd/f3/kzJkTL1++BPBf+PP27dtYQ7jYrjlk7N69e8iYMSPq1q2LgIAAbbv+PA4MDET58uVhY2ODvn37Gs0VSbFjHpE6MIBLoH379mHgwIFo27at0ZO+GzduoGfPnrGe9NHxZE+Y8+fPw9/fH2ZmZnB0dIxzwm8A2Lt3LyZMmIBhw4bFufxyWnDhwoUYw+0iIyPx/Plz1KxZEyVKlAAAPHnyBPny5dPmuQkLC8OLFy8wZcoUNGzYEN988w2uXLliio+QqnFuIUoNOCcZJTeDBw/G1q1btZ+jh3BlypSBmZkZVq5cGevrIyIi8N1336Fq1apwd3fnw6cPOHDggDavaadOnYyGmOqtW7cOuXLlQrp06bBq1SpTFTXZ019PX7x4gaJFi8LHxwfm5uZo2bKl0QJMhr2vdu7ciZkzZ6J3795YunRpjEVFKH6GDRumPcA3HMKnP4/1IZyNjQ18fHw431s83bt3DzVr1tQ6RsQ2V7c+hHNwcEDnzp0ZvsUT84iUjQFcArRt2xZKKfj5+WH+/PkAjJ9G3bx5Uzvpv/nmG20Se/p0e/bs0eZriW1Czvf1aklrF5hly5bB2dkZOXPmxM8//xxjboqtW7dCKaU1hJ88eYL8+fNrq8Lpn/rxSzDxvHr1Cn5+ftrQJqKUinOSUXJx+PBhKKVQqlQpo0VXDL/L9KuBW1lZxegJBwBnzpyBm5sbypYti/PnzydJuVMqfbvr2LFjyJIlC9zd3bF69WqtzWVY72vWrIGrqyt++uknk5Q1uTNcRCEiIgJ79uzByZMnMXHiRJibm6NFixZGbbm01q5NLIbnqH7ob8mSJY0e9BuGcIMHD4abmxuuX7+e5GVNqW7evInOnTtDKYXhw4fj8ePH2j593T59+hQFChSAl5cXHj58aKqiphjMI1I+BnAJcPbsWXh4eGgT/usZdge9efMmevfuDaUUevTowfHrCfS+RsXu3btRoUIFWFhYcOLeOFy/fh3m5ubaKptWVlYoXLgw+vXrh7t37yIoKAgAULFiRVSsWBH37t0DEPUEqkCBAjAzM0PHjh153iYBw/kZiFIyzklGScmwnRB9FbgNGzbA1tYWJUuWxK5du7Tthr2GSpQogezZs0MpZTTBtz5QunPnznt7DaRF0es5usOHD8PT0xM5cuQwmkPLMOBgb/rY6ev2+vXr6N27N8aNG6ftCw4O1kK45s2bG4XCV69exapVq9gbKwGi32NEn8tt3LhxUEqhevXqOHHihLZdfx4HBQVxmoU4vO/+7caNG2jatCksLS0xYcIEozo0DOFu3ryZ6OVMDZhHpHwM4OJJf5G+du0avLy8YGZmhh9//FHbb9jIuHHjBjp06IAZM2YkeTlTqoiICKM6PHfuHP7++29cuXIFL1680Lbv2bMH5cqVg4WFhdHy9hTl3bt3mDNnDhwcHFCpUiV8++236Nq1K1xdXeHt7Y2WLVvi+vXrGDlyJLy8vHDq1CnttU+fPkWWLFng4ODAJ1BJiHOSUWrAOckoKTx8+BADBgzAvHnzjNoMkZGR2rV0/fr1sLKyQokSJYx6wgFRc7t5e3tj3rx5RnNrUdwM63nu3Lno3r07mjdvjm+++UabLwsADh06FGsIFz3kYO+t/+jr4ty5c8iSJQuKFi2Kdu3aGR3z5s0bTJo0SRuOeuLECfz777+oXbs28uTJw/ZaPBkGnaNGjUKDBg3QokULLFmyxOg8Hjt2bKwh3IdC6LRMXzd37tzB8uXL0b9/f8ydOxcHDx7Ujrl9+zaaNGny3hCOPox5ROrAAC4WH7rIXr58GW5ubvDw8MC8efNifR3nwfmwX375xWhlLL22bdvCy8sLSik4ODigfv36RpP3GoZwnE8kpnfv3mH27NnQ6XSoW7cutm/fjidPnuDrr79G7ty54ejoiEaNGmlztgD/NQKfPXuGa9eumbL4REREMYSEhCBDhgxQSiFbtmzIlSsX5s+fj4sXL8Y4dv369bC2tkbx4sW19sP9+/cxcuRIlC5d2qiHGwOhuBnWTe3ateHs7IzcuXPDz88PdnZ2yJkzJw4cOKC1f/U94fLkyYM1a9awbuPhxo0byJgxIypXroy//vpL225Yd2/evMHkyZNhYWEBZ2dn+Pj4wMnJCf/8848pipzi6Ovy7Nmz8PLyQpYsWZAvXz588cUXUEqhefPmRotX6HvC1a5dG0eOHDFVsVMEw7rNmjUrXF1d4ejoCFtbW1hbW2P69OnasXfu3NFCuEmTJhkNR6WYmEekXgzgojFMjrdt24ZZs2ZhyZIlRsMUAODSpUtwc3ODu7u7Nv46+usB9m6JTWRkJO7cuQOlFHx9fY2eQjds2BAuLi7o2rUrxo4dqy217uzsbFTPe/fuRcWKFaGUinUel7QuNDQUs2bNgqWlJQICArSneM+ePcOECRNQokQJ+Pj4YPjw4drKb3y6R0REyVnHjh3h7OyMuXPnon79+rCxsYGbmxtGjRplFF4AwK+//gobGxtYWVmhfPnyKFasGMzMzDBlyhQTlT7l6tixIzw9PbXV9gDgu+++0+blNRzedOTIETg7O8Pd3Z3DTt8jMjIS4eHhGDJkCLy8vPD7779r+2ILLsPDw7F582bUrl0b3bp1izV4prjduXMH2bJlQ9WqVbF//34AwMuXL1G8eHE4ODjgr7/+MrqHmzBhApRSaNy4MYf5fsD169fh7e2NKlWq4LfffgMQ1SPWzc0N9vb2RkOn79y5gxYtWkAphR9++IEhfRyYR6RuDOAMGAYQTZs2hbOzs7YqTq5cuTBr1iyj4/UnvZeXFyeW/Qi//fYbdDod8uTJg82bN+PevXsoVqwYli5davRl98svv8Da2hru7u5Gq5vt3bsXBQsWZNfaOISGhmL27NmwsrJCQECA1uAAorounz59Gvfv3zdhCYmIiD5Mf/OwcuVKeHt7aw/e1q5diy5dukCn08HT0xMtW7bEX3/9pU06feLECdSqVQt58+ZF4cKF8b///S/Ge9L7/fvvv8iZMye+//57rTfFwYMHYWtri44dO8Y6b9Off/5p1COD4laxYkUULlxY+9nwvIztwei7d+84ZC8B9PU5f/58+Pj4YNu2bVq9Dhw4ENbW1pg/f77RMFS9adOmcW7T94iIiEBkZCSGDBmCL774Avv379fqe8iQIbC3t8f8+fNj9MK6ceMG2rdvz4Vu4sA8IvVjAPf/DL/wGjRoAA8PD0yePBnHjx/H8ePH4eHhAS8vL6PJUYGo7p/p0qWDhYUFL9IJoH/isX37diil4O/vj379+sHNzQ03btwwOgaIamQrpdChQwejhod+EQGKnWEIV7ZsWaNJqYmIiFKS8PBwFC5cGKVLlzbafubMGW2lvcyZM6NEiRLYvHmzNl/Ou3fv8ODBA+149rqIW/RgcuvWrTA3N8eBAwcARE0DYmtri5YtWxo9xPv5559jfb+0XtfRP7/hAhUvXrxAnjx5UKJECQAw6klo+O+wcOHCJChp6ta1a1dkzZpV+3ngwIGwsLDAvHnztF6dL168wPbt201VxBSrXLlyqFq1qvazYd3q54d99uwZjh49qh3DEDl2zCPShjQdwMX2ZGn06NHIkSMHli9frl00zpw5AysrKzg5OSFdunSYPHmy0WvOnz9v9FSV4kd/kfn999+hlIKHhweKFi2q/bvon6zoGy916tSBp6cnHj16FOd7UUzRQ7i9e/dq+1hvRESUEujbBmvXroVOp8PSpUu1faGhoShVqhSyZs2KFi1aIFeuXFBKIXfu3Bg5ciSA/77v+L33n+h1EVu7WL+oxbVr1/DPP//AxsYGrVq1Mgrfdu7cCUdHR2zatCnRy5wSBQUF4enTp3j+/HmMfZ06dYJSSpvPLSIiwii0W716NdKnT4+///47qYqb4unPY/2fkZGR6Ny5M/LkyQMAGDBggBYQBQUFaa/r1asXXF1dubBFPIWHhyMiIgL58+dH69atAUSFbzqdLkbddunSBXXq1OGcZLFgHpH2pNkA7vHjx+jVqxeOHTumbbt79y5KlSqFrl27ahP07t+/HzY2NujUqRP279+PdOnSwd7eHhMmTIj1fdP6k76E0tfXjh07tO61hl1rDZ+QdOzYEQ4ODhw2+REMQ7iKFSti586dpi4SERFRgl25cgWZMmVCy5YtAQCPHj1C+fLl4e7uju3btyMkJAQ3btzA+PHjYW1tHWd7jaKEhITg4MGDRgswNWvWTFtZ7+bNm/D29oa/vz/s7OzQvn17o2Gnt27dQvv27VGiRAlcuHAhycufnO3duxfdunWDt7c3HBwc4OHhgXbt2hktQLZy5UqYm5sje/bsMXqunDhxAjVq1EBAQAAnrE+gCxcuYMiQIVrgs379eiilUKFCBVhaWmL+/Pl4/fq1dvzu3bsREBCArl27GgVH9B/9PVtwcLDR9t69e8PJyQlNmzaFubl5jGGnO3bsgL+/P4YPHx5jVeS0jnlE2pRmA7jJkydDKYUWLVrg1KlT2vZhw4bh8OHDAKKSZAcHBzRp0kRrbAwdOhTp0qWDh4cHvvvuO5OUPSV73wVh9+7dUEohQ4YMWLt2rdG+e/fuoUKFCsifPz8ePnzIJ9gfITQ0FD/++COUUqhVqxYbGERElCJNmTIFNjY22LFjB2rWrAlPT09s3Lgxxvfa3bt3TVTClOPIkSPw9/dH586dcevWLVStWhUeHh7aULzQ0FD07NkTSilkypTJKHy7du0aRowYAXt7eyxYsMBUHyFZWrZsGVxdXZE3b160bNkS7dq1Q9myZbWHzYb3EAMGDNBGgsybNw9//PEHpk6dimLFisHV1ZVDyj5Cs2bNYGFhoQUYV65cQYUKFWBhYYHGjRsbHXvo0CFUr14d2bJlw9WrV01R3GRP30vr2rVr6NatG7p3767t2759O6ytraGUQp8+fYxed/ToUVSvXh1+fn6xzheZ1jGPSJvSbAAXGBiIoUOHQimFpk2baqtE6r19+xaNGjVCvnz5jFYcGTBgAPLmzQsvLy/t6SDFj2Fvtps3b+Kff/7BmTNnjI7RD0d1d3fHxIkT8eTJE+zZswdDhgyBmZkZ5s6dm9TFTlVCQ0OxYMECPqUmIqIU6+TJk8iUKZMWCv36669G82fpH/bpH9axN0DcwsPDMXDgQK0u3dzc8PvvvxvV58uXL1GrVi0opeDn54cpU6bgu+++Q8WKFWFlZYXx48drx/IBKbBhwwZYWVmhd+/eRjfVkZGRWL16NTJkyAClFPr376/tGzVqFLJly6YFdE5OTihatCjDt4+0Y8cOWFlZoW/fvtq2bdu2wd/fH0opNGvWDBMmTED37t3xxRdfwM3NDadPnzZhiZMv/fXz3LlzyJQpE0qVKoUuXboYHTNnzhwopeDs7IwpU6Zg165dGD16NIoWLQoXFxf8+++/pih6ssc8Im1KswEcEDUh5ODBg7WT/uTJk9q+p0+fIkeOHNoQByBq1ZYaNWpg2bJlRg0T+jDD8e2dOnVC7ty5oZSCnZ0dmjVrhqNHj2p1ajgcNXfu3PD09ES5cuUwdepU7T3YwCMiIkq7unfvDqUUli1bZuqipAoeHh4wNzdHlSpVjEIf/ZCxV69e4ZtvvoGfnx+srKzg4OCAypUrY/HixdqxDDqjhuSWKFEC9evXx/Xr17Xthu3W3bt3I2fOnFBKYfbs2dr2S5cuYdOmTZg7dy727dvHYaef4OnTpyhUqBBy5cqFW7duadsPHjyIbt26wdnZGVZWVvDx8UGLFi1w8eJFE5Y2+bt16xayZMmCSpUq4dChQ9p2wyGlixYtQu7cuaHT6bQenVWqVDEKjigm5hFpT5oO4IC4T/rg4GB88cUXKFCgAEJDQ3H27FmMGjUKzs7O2LBhg/Z6BkExxbXiEwDUrl0bnp6e6N+/P1auXInx48dDp9OhTJkyuHDhgvbaPXv2QKfTwcvLCxMnTjSaS4ANPCIiorRJ36Y4e/YsXF1d0bZtWxOXKOXbtm0bChcurPVy69q1Ky5duqTt199kh4WF4fnz5zh06BBu3LihDe8D2DbTO3z4MCwtLTFv3rwY+wzbw1u3boVSCrly5cKVK1eSsoipiuF5p69f/UP/jRs3QimFOXPmGL0mPDwc9+7dwz///IPHjx9zSpb30C+IN3r0aHh4eGDLli0xFrQx/De4cuUKDh8+jBUrVuDcuXOxLjxCMTGPSFvSfAAHxH3ST5kyBZaWlrCxsYGnpycsLS0xceJE0xY2mdN/6T158gR//fWX0UV52rRp8PDwwIoVK/Dy5UsAUfMGmJubo3PnztqTPv1rtm3bBqWU0dL2vMAQERHRixcvULlyZbi4uODAgQOmLk6KEr0t9ezZM9y+fRshISEYNGgQlFLo3LkzLl++bHRc9N4WXFk2pmnTpkEppfWoir7CoWFdDRo0CObm5lzh9CPp6/L27dt4/PixNtWNfvutW7dQqFAh+Pn5GS0ywvM14erUqYNcuXJpPxvWYWyreFLCMY9IOyyExNnZWQYNGiQiIhMnTpTIyEgZP3689OrVS7JmzSqbNm0SFxcXKV26tDRq1EhERCIjI8XMzMyUxU52IiIixNzcXB4+fCi+vr7SoEEDyZcvn9jb24uIyN9//y25c+eWevXqiZ2dnezdu1caNmwoLVu2lBEjRoi7u7uIiFavNWrUkBs3bkiWLFm0/4dSKuk/GBERESUrjo6OMmLECClbtqxs2LBBypQpY+oipQj6tppeeHi4ODs7i7Ozs4iIjBs3TsLDw2X69OkiIjJo0CDJkSOHvHjxQlatWiXv3r2Tvn37ish/bTK2zf6j0+lEROSff/6RXLlyGdW1SFRdARCllBQoUEAiIyPl8uXLUrJkSVMUN0VTSsmjR4+kUKFCEhwcLG3btpU2bdpodZk5c2Zp1qyZDB48WE6dOiW+vr4xzn96v8jISAkLC5PAwEAxMzOTsLAwERGxsIiKEABo9Tl16lTp37+/ycqa0jGPSDsYwP2/6Cc9ABkzZow0bNhQGjZsaHTB5skek2H4VqRIESlQoID06tVLC9/evHkj165dk/z584udnZ3s2rVL6tWrJw0aNJCJEyeKl5eXiIh8++23EhoaKpMmTRKRqC9PEdY5ERERGcufP7/kyZNHayvQ+xm2ZUeOHCmnTp2Su3fvSrly5aRmzZpSsWJFMTc3lylTpohSSqZNmybh4eFSu3ZtuXz5sgwbNkymTZtm4k+RvBUoUEB0Op0cPnxYWrRoISKiBW56kZGRYm5uLv7+/tp++jh2dnYyfvx42bNnj/z000+ycOFCad++vVSuXFmaNGkiPXr0kLVr18rIkSOlRo0aYmNjY+oipyhKKbGyspJy5crJxIkTZefOnVKrVi0BYHQ92bRpk0ydOlX8/f2latWqJi51ysU8Io0wYe+7ZMmw+2eTJk2MVi+i2Om7Hj948AAZMmRA8eLFcezYsRjH1atXDwUKFMD+/ftha2uLFi1a4P79+9r+f//9FyVLlkS7du04HwMRERF90NOnT01dhBSnVq1asLGxga+vL7744gsopWKsNB8ZGYkhQ4ZAKQVra2tYW1tj7NixJix1yvDixQsULVoUSin8+uuv2vbYhj2OGTMGTk5ORvPt0fu9b/joxo0b0bNnT9ja2kIphZo1a2L9+vXo0KEDvL29sXbt2iQsacrzvnkct27dCkdHR7i6uuKPP/4w2nfs2DHUqFEDRYsWxcOHDxO7mGkC84jUTQF87BLd8+fPZdKkSTJx4kRp06aNjBkzRjJlymTqYiVL+P+neo8ePZKSJUuKp6en/Pjjj5IvXz4tlb9165b8/vvvYm9vLx06dJCIiAhp27atTJs2TVxcXERE5OHDhzJt2jRZt26dzJkzR2rWrGnKj0VEREQpCKL1MqL/GPaamDlzptbGrVy5snh6esrKlStlzJgxcvHiRZk3b5507txZe+3GjRslMDBQMmbMKNWrVxcR9rz4kFOnTkmhQoXE3t5eVq9eHWub9tSpU9KzZ0+xs7OTNWvWiJOTU9IXNIXRn8cvX76UBw8eSGBgoLi7u0uuXLmMjjtz5ozMmzdPNm/eLC9evJD06dPL1atX5csvv5RZs2bx3I2Fvm5v3rwpq1atkmPHjkn69Oklb9680qNHDxER+eGHH2TgwIFiZmYmQ4cOlTx58silS5dk48aNcuvWLdm/f7/kzZvXxJ8k9WAekYqZNv9Lvp49e4b+/ftDKYUffvgBACftjEtwcDDy588PpVSMVZ+uX78Ob29vtGjRAqdOnUK9evWglEKjRo1w584dAMCJEycwYMAAWFhYYPr06Sb4BERERESp2+HDh/HDDz+gdevWCA4ONtq3fft2+Pr6wsbGBn/99Vec78HVTuPnwIEDUEpBKYVZs2ZpK51GRkZi8+bNqFGjBlxcXHD+/HkTlzRl0I+2uXDhAsqVKwd3d3copaDT6dC7d28cPHjQ6PiQkBC8ePEC/fr1Q/HixWFra4szZ86YoujJnv53+uzZs8iQIQMyZswIf39/ZMmSBWZmZqhTp462EvKCBQtQtGhRmJubQykFV1dXlC5dGmfPnjXlR0i1mEekTuwB9x63b9+WVq1ayeXLl+XcuXPi5uZm6iIlS2FhYTJ79mwZMWKEFCxYUEaNGiXlypWTO3fuSOnSpSVz5syyYMECyZUrl1y6dEmGDh0qGzduFGtra3FxcZE3b96IpaWlDBo0SJu8E3ySTURERPRZ9O3bV2bOnCkuLi7SsWNHmTRpkkRERIhSSusRtGDBAunatatMnz5d+vTpw7bYJzpy5IjUrFlTnj9/LunTp5ds2bLJixcv5Pnz52Jvby/r1q3T5oGjuOnPw8uXL0tAQID4+PhI1apVxcHBQQ4ePCjbt2+X4sWLy8iRI6Vy5cpGrxERuXnzptjZ2WmLvVFMd+7ckfLly4uvr698++23Uq5cOXn+/LlUrFhRTp8+LXv27JHy5cuLiMjdu3fl9u3bcuPGDcmdO7dkzZpVG9FEnx/ziNSHAVwsDC/azZo1k8OHD8vZs2clXbp0Ji5Z8hUWFibz58+Xfv36SUBAgHTu3Fm++eYb8fX1lXnz5knOnDm1Yx88eCD//POPbNq0Sd68eSOFCxeWokWLStmyZUWEQxuIiIiIPqdNmzbJqFGj5OzZs1K/fn1Zs2aNiES1ufD/Kxm+efNGMmfOLBUrVpRffvnFxCVOHW7duiWrVq2StWvXSlhYmHh5eUmNGjWkcePGHE72AYb3A2/fvpWmTZvK7du3ZdGiRVK0aFEREXnx4oUsXbpUvv76a6lWrZrMmzdPq1cGyB+mr6OZM2fKzJkzZe7cudoiCt9++61MmzZN5syZI40aNRIHBwcTlzZtYR6RenEV1Fjolwj/999/5fLly5IjRw5ewD9Ap9NJly5dBIAMGDBA9u7dKwUKFJCFCxdKtmzZROS/C4mXl5fUqlVLatWqFeN9GL4RERERfTzDG7fw8HCxsLCQevXqibW1tfTr10/WrVsnZcuWlR49ehi1uS5duiTh4eEx5tSij5clSxYZPHiw9O/fXywsLHg/EQ9Hjx4VHx8f8fDw0O4LXrx4IadOnZK6detq4ZuIiJOTk/Ts2VNevXolI0aMkIMHD0rLli1FRFjX0RjeY+n73+jr6OjRo2JnZ6eFb4MGDZIffvhB5syZI02bNhU7Ozt5/fq1bN68WVq1amWaD5DGMI9IvZh0xEEpJTqdTs6fPy8zZswQe3t7Uxcp2dPpdNK1a1eZMWOG2NraipmZmdy5cyfWYw07Xhr+neEbERER0cfRDyvVi4yM1P5erVo1mT59uuTOnVv69Okjs2bN0vbdvn1bfv31VwkJCZEiRYokaZnTAsPwjYOP4rZ//34pUaKE9O7dW548eaLdF7x69UqePHkilpaW2rH6ejQ3N5c6deqITqeTjRs3SlhYGOs4Fob3WOHh4UbXiYiICK3Ovv32Wy18a926tdjZ2YmIyMiRI2XWrFny8OHDpC14GsY8InViD7j3yJMnjzx79ky78NCH6XQ6ad++vYSHh0u/fv3k+++/lxEjRkiFChW0JF8pZXTRZ5pPRERE9GkMVzudNm2aHDt2TG7duiUVKlSQ+vXrS9GiRaVq1aoyffp0+frrr6VPnz6yefNmcXR0lJcvX8rx48dl1KhR0qBBAxN/ktSH7d74KVeunJQqVUrWrl0rVlZWMmXKFHF3dxcnJydxcnKS/fv3y5UrV7TeQPoengUKFBA3NzfR6XSi0+lM/TGSncuXL8uhQ4dk0aJF8vz5c1FKSfv27aVUqVJSvHhxbdh52bJl5e+//5YFCxZI/fr1xcbGRkREDhw4IPv375dixYpxxd4kxjwi9eEccJQowsLCZN68edK/f39tYlT95J2ck4GIiIjo8zEcXla7dm05evSoZMqUSdzd3eXAgQOSIUMG6dOnj/Tq1UtERHbv3i19+vSR69evS4YMGWTu3LmilJIqVarEeD+ipKAP00REqlSpIrt375Y2bdrIxIkTJX369DJlyhQZPHiw9OjRQ4YOHSqenp4iEhU879mzR5o3by79+/eXIUOGiAiDTr3NmzfLuHHj5Pz585IxY0axsbGRGzduyIsXLyRXrlwyadIkqVixohQtWlQuXrwojRo1knXr1mmvP3TokIwaNUquXLkiO3fuFF9fXxN+GqKUjz3gKFHoh6OKiPTv319GjRolkZGRUrFiRX4hEhEREX1G+rCsW7du8s8//8j06dOlTp064uDgID/99JN89dVXEhQUJG/evBF7e3upVKmSTJkyRYYMGSI3btyQp0+fSosWLUQk6iEqexFRUrOwsNDOvT/++EOqVKkiy5cvF5GoHp3t27eXY8eOyaxZs+T169fStGlTqVatmmzdulXmzp0r1tbW0qJFC95nGFi+fLn06tVLqlSpIgMGDJDGjRtLRESEnD59Wn799VcZO3asNGzYULZt2yY7duyQQoUKyfr166Vx48ZSrVo1OX36tBw8eFDu378ve/bsYfhG9BmwBxwlqrCwMFmwYIH06NFD8ufPL7/99pt4e3ubulhEREREKc77RhHcvn1bKleuLA0bNpTBgweLk5OT7Nq1Sxo2bCj16tWTMWPGSJYsWYwCtu3bt8uAAQPkzp07Mm3aNOnUqdMH/z9En8vjx4/FyspKHB0dtW3v3r0TKysrEfmvJ1y7du1kxowZ8vLlSxk9erQsWLBARESsra3FzMxMXFxc5LfffpN8+fKZ5HMkR4sXL5ZOnTpJr169pGfPnpIjRw5tn/73e8GCBdK1a1dxcXGR/fv3i62trfTt21eOHTsmDx8+lEyZMkmJEiVk1KhRXJyF6DNhAEeJLiwsTGbNmiVWVlbSo0cPUxeHiIiIKMXRDwsNDg6WEydOiKenp9FN9f79+6VChQpy+PBhKVasmOzZs0fq1KkjDRo0kMmTJ4uXl5eIiGzZskUKFSqkPRDduXOn9O/fXx48eCAjR45kW42SxNKlS2XkyJHi4eEh7dq1k2zZsknVqlVjDH+uVKmS7N27V9q1ayc//PCDODo6yt69e2XLli0SHh4ufn5+UrNmTcmcObMJP03ysnz5cmnXrp3069dPvvnmG3F3dxeR2IP1adOmyYABA6Rly5by888/y/PnzyU0NFSuXr0quXLlEhsbG84/RvQZMYCjJBF96Ws+VSUiIiKKH307KjAwUL7++ms5ePCgVKxYUWbPni22trYiInLkyBEpU6aMbNq0SaysrKROnTpSv359mTJliha+7dixQ+rWrSu//vqr1KxZU3v/P/74Q9q3by/m5uZy5swZcXR0ZFuNEs2NGzckZ86cEhERIdbW1hIeHi7h4eFSoEABKVq0qLRv315cXFy0Xlf16tWTLVu2SJs2bWTChAni5eXFodJxuHHjhmTPnl1sbW1l2bJl2qIqhou0iPx3P/b8+XNp0KCBnDp1Sk6ePClZs2Y1VdGJ0gTOrkpJwvBJFht0RERERPGjD98ePXok5cuXlzNnzki9evVk2rRpWvgmIpI7d25xcnKS7777TurWrSv169eXadOmaeHbrVu3ZNOmTfLFF19IpkyZRCTqJlxEtPm29u7dK05OTmyrUaLy9vaWGTNmiKOjo/j4+MjQoUNlzJgxki5dOlm0aJEEBARIuXLlpE2bNrJixQqZN2+eFClSRFavXi2DBw+W+/fva+Eb+5IY8/b2lh9++EFERGbMmCH79+8XERFzc3OjutL/jjs7O0vRokXl1atXcufOnSQvL1FawwCOiIiIiCiZMjMzk2fPnknlypXF3t5epkyZItOmTRMnJycJCwsTkagVJB0dHWX48OFy7do1sbe3ly+//FI8PDxEROT69euyePFi+fnnn6V79+7i7+8vIlE34fqb8ooVK0q2bNlM8yEpTbG0tJTOnTvLuHHj5OrVq3LixAkpW7as7N+/Xy5duiSzZs2SgIAA2bp1q7Rp00ZKlCghOp1OwsPDZfny5TJq1CiJiIgQET7Yj87S0lK6d+8uEyZMkMOHD8vw4cNl3759ImL8+y4iEhoaKiKiDd+NjIxM8vISpTUcgkpERERElAwBEAAyePBg+e2332TatGlStWpVo5EFIlET1yul5PXr1zJ+/Hj58ccfJVeuXFK+fHlxdnaW3bt3y7Fjx2T48OEyePBg7b0ZXpAphYWFyf/+9z8ZMGCAFC5cWCZOnCilS5fW9j98+FAOHTokW7ZskXPnzsmxY8fEyspKTp06xUUBPiAsLEzmzZsn/fv3l+LFi8vIkSOlfPnyIvJfr0H973/dunXl5s2bWv0SUeJhAEdERERElIwVK1ZMXF1dZevWrUZz6t6/f18WL14sW7dulWfPnsmXX34p2bNnl6dPn8rYsWPl1q1bopSS8uXLS6tWraRNmzYiIjEmuicylehB0YgRI6RixYoxjgsMDJTr16+Lq6sre2rG0/tCOL3NmzfL119/LW3atJFvv/1WzM3NGcwTJSIGcEREREREydTjx4/F19dXGjduLEuWLBGRqABt8eLFsmDBAjly5Ig4OjrKu3fvJDQ0VBo2bCjTpk0TV1dXefz4sVhYWIiLi4vY2Nhor2X4RsnJ+4Ki8PBwsbCwYI/NjxS9br///nupUKGCiIicPn1avvnmG3n06JFs2rSJK8kSJQELUxeAiIiIiIhi5+rqKhUqVJBly5aJv7+/+Pn5ybx582TTpk2SPn16mTJlilSvXl1evXolc+bMkRUrVkiVKlWkS5cukiVLFqP3AsDwjZIdnU4nXbt2FRGR/v37y4gRI7QQjuHbp4letyNHjhQzMzNJnz69fPfdd3Ls2DE5cOAAwzeiJMIecEREREREydilS5ekTp06cvXqVRGJuqkuXbq0zJ8/X3x9fbXjdu3aJVWrVpXmzZvLypUr2duNUhTD3loBAQEyePBgqVKliqmLlSoY1m3+/PlFROTcuXPy119/aT8TUeJjDzgiIiIiomQsV65csnv3btm1a5fcvHlTAgICpEyZMmJjYyMRERFibm4uIlE93KytraVMmTIiIgzfKEXR99YyNzeXr776SmxsbKR06dLa8Gn6eNHr1szMTE6ePKmtiExESYMBHBERERFRMpcpUybp0KGD0Tb9/FgiIg8ePJDVq1eLu7u7FCxY0BRFJPpkOp1OOnXqJDqdTgICAhi+fUY6nU46duwoNjY2Urx4ccmdO7epi0SU5nAIKhERERFRCqJvvuvnxbp9+7YsWbJERo8eLdOnT5eePXuasnhElIxxTj0i02EPOCIiIiKiFMTw5nnz5s3yyy+/yG+//SZjxozRwjfeZBNRbHhdIDIdTgxBRERERJTCvH37VgYOHCjNmjWTCxcuyNSpU+Wbb74REZHIyEjeZBMRESUzHIJKRERERJQCXbp0Sf766y8pXry45M2bV0SEK58SERElUwzgiIiIiIhSKMOhphx2SkRElHwxgCMiIiIiIiIiIkpE7J9ORERERERERESUiBjAERERERERERERJSIGcERERERERERERImIARwREREREREREVEiYgBHRERERERERESUiBjAERERERERERERJSIGcERERERERERERImIARwRERERmcSSJUtEKSX79u0zdVGIiIiIEhUDOCIiIqJUYt++faKUEqWU9OzZM9ZjHj9+LJaWlqKUkvLlyyd6mW7evCnff/+9nDp1KtH/X0RERETJFQM4IiIiolTG2tpaVq5cKe/evYuxb/ny5QJALCwskqQsN2/elJEjRzKAIyIiojSNARwRERFRKtOgQQN5/vy5bNq0Kca+xYsXS82aNcXKysoEJSMiIiJKmxjAEREREaUy+fLlk0KFCsnixYuNth89elTOnTsnHTp0iPV1W7ZskTJlyki6dOnEzs5OihUrJqtWrYpxXPny5cXHx0fu3r0rTZs2FWdnZ7Gzs5Nq1arJ5cuXteO+//57qVChgoiIdOjQQRse2759e6P3i4yMlIkTJ4qvr69YWVlJzpw5ZenSpZ9YC0RERETJR9KMPSAiIiKiJNWhQwfp06eP3L17VzJmzCgiIosWLRIPDw+pXbt2jOPnzZsn3bp1kxw5csiQIUPE0tJSfv75Z2nZsqXcuHFDhg4danT827dvpVy5clKyZEkZN26c3LhxQ2bMmCH16tWTs2fPirm5uTRs2FDCwsJk3Lhx0rVrVylTpoyIiGTLls3ovYYMGSIhISHSvXt3sbS0lJ9++knat28v2bNnl4CAgESqISIiIqKkwwCOiIiIKBVq2bKlDBgwQJYtWyZDhw6V4OBgWb16tXTu3DnG/G8vXryQfv36iY+Pjxw7dkwcHR1FROSrr76SkiVLyogRI6R169aSOXNm7TWBgYEycOBAGTRokLbN3d1dBg0aJLt27ZJq1apJvnz55NmzZzJu3DgpWbKktG7dOtayhoaGyrFjx8TS0lJERJo0aSK+vr4ye/ZsBnBERESUKnAIKhEREVEq5OLiIvXq1ZMlS5aIiMiGDRvk5cuX0rFjxxjH/vHHH/L27Vvp1auXFr6JiNja2sqAAQMkPDxcNm/ebPQaMzMz6d27t9G2ihUriojIlStXElTWr776SgvfRES8vb0lZ86cCX4fIiIiouSKARwRERFRKtWhQwe5cuWK/Pnnn7Jo0SIpVqyY+Pn5xTju+vXrIiKSN2/eGPv8/f2NjtHLkCGDWFtbG21zdXUVEZGnT58mqJy+vr4xtrm6uib4fYiIiIiSKwZwRERERKlU1apVJWPGjDJy5EjZu3dvrL3fREQAxPkece0zNzdP8GviEtd7JfR9iIiIiJIrBnBEREREqZSZmZm0bdtWdu3aJdbW1tK8efNYj9MvinDu3LkY+/Tboi+cEF9KqY96HREREVFqwkUYiIiIiFKxbt26iU6nE19fX6P53QxVqVJF7OzsZPbs2dK5c2dxcHAQEZGQkBCZOnWqWFhYSJ06dT7q/29vby8iIs+fP/+4D0BERESUCjCAIyIiIkrFMmfOLN9///17j3FycpKpU6dK9+7dpWjRotKhQwfR6XTy888/y6lTp2Ts2LFGK6AmhJ+fn9jb28vcuXPFzs5OHBwcJGvWrFK8ePGPej8iIiKilIhDUImIiIhIunXrJps2bRJ3d3cZPXq0DB8+XHQ6naxYsUKGDh360e9rY2MjK1euFFtbW+nVq5e0aNFCfvzxx89YciIiIqLkT4Gz2xIRERERERERESUa9oAjIiIiIiIiIiJKRAzgiIiIiIiIiIiIEhEDOCIiIiIiIiIiokTEAI6IiIiIiIiIiCgRMYAjIiIiIiIiIiJKRAzgiIiIiIiIiIiIEhEDOCIiIiIiIiIiokTEAI6IiIiIiIiIiCgRMYAjIiIiIiIiIiJKRAzgiIiIiIiIiIiIEhEDOCIiIiIiIiIiokTEAI6IiIiIiIiIiCgR/R/L6qWt7zSL+gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "monthly_data = data.groupby(pd.Grouper(key='Date',freq='W')).size()\n", + "monthly_data_amount = data.groupby(pd.Grouper(key='Date',freq='W'))\n", + "x = monthly_data_amount['Amount'].sum()\n", + "print(x)\n", + "plt.plot(monthly_data.index, x)\n", + "ax = plt.gca()\n", + "ax.xaxis.set_major_locator(mdates.MonthLocator())\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter('%B'))\n", + "plt.yscale('log')\n", + "plt.xticks(rotation=45,ha='right')\n", + "plt.xlabel('Month')\n", + "plt.ylabel('Amount')\n", + "plt.title('Weekly Spent Amount Trend')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.dates as mdates\n", + "weekly_data = data.groupby(pd.Grouper(key='Date',freq='W')).size()\n", + "plt.plot(weekly_data.index, weekly_data.values)\n", + "plt.xlabel('Week')\n", + "plt.ylabel('Number of transactions')\n", + "plt.title('Weekly Transactions')\n", + "ax = plt.gca()\n", + "ax.xaxis.set_major_locator(mdates.MonthLocator())\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter('%B'))\n", + "plt.xticks(rotation=45,ha='right')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.dates as mdates\n", + "daily_data = data.groupby(pd.Grouper(key='Date',freq='D')).size()\n", + "plt.plot(daily_data.index, daily_data.values)\n", + "plt.xlabel('Day')\n", + "plt.ylabel('Number of transactions')\n", + "plt.title('Daily Transactions')\n", + "ax = plt.gca()\n", + "ax.xaxis.set_major_locator(mdates.MonthLocator())\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n", + "plt.xticks(rotation=45,ha='right')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13092" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(data['Merchnum'].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "930090121224 9310\n", + "5509006296254 2131\n", + "9900020006406 1714\n", + "602608969534 1092\n", + "4353000719908 1020\n", + "410000971343 982\n", + "9918000409955 956\n", + "5725000466504 872\n", + "9108234610000 817\n", + "602608969138 783\n", + "Name: Merchnum, dtype: int64" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Merchnum'].value_counts().head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[646723.66 621548.95 542669.07 424271. 381320.16 368019.01 341880.09\n", + " 305034.7 284846.24 283336.56]\n" + ] + }, + { + "data": { + "image/png": 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zgFm3bt0j35dHDf/S+7P3XrbX072PX0Tkv0zhn0gGs2rVKtO2bVvj7+9vvL29Te7cuc37779vIiIijDHG/gXi0KFDJleuXCY0NNQcOHBAvxmVp0py4V/nzp0NYLp27ZrkebYvUe+//36C7bYvGQMHDkx0zq1bt4yvr2+SXzRSKiXh38aNGw1gcufOnewx77zzjgHMq6++muJr3xv+GWNMtWrVjJOTk7l48aIxxpgpU6YYwIwfP94Yk3z4Z+t1+PXXXyd5nU8++cQApl+/fgm22+7t/V/mbUqWLGkA880336To8djqy5s3b4KwyMYWHsycOTNF7Rnz93394osvEmy3vc48PT3t9+tePXv2tIcvSdWY3BdQW43J9dpJDVuPut9//z3J/feGEkn98fT0NJMmTUr1Z7yth9+AAQMSbHd3dzfe3t4pau+nn36yv4+TOv7cuXPGxcXF+Pr62vffunXL3vNn69atybaZmvBv/fr19oD6/lDJGGO+/vprA5gCBQok2G4LWCpVqpRku7bn5v4QJaVSEv4VK1YsQeiYUun5mt+2bZsBTNOmTVNdx/0OHTpkABMSEpJg+//+9z8DmCJFipiYmJgUtZXW8C9//vwGMN9++22ic65du2bvoXhv2Gh7rhwcHMxff/2V6LyxY8cawHTq1ClFtdscO3bMWCwW4+bmluCXIFu2bDFwt/dkUvfD9vqoUqVKon27d++235tp06Yl2v/GG28k+bmWlvvyqOFfen/23qtt27YGMDNmzHjosSIi/wVa8k0kA5k/fz5vvfUWAM8++yxBQUGcOHECb29v+6IexhgsFgtz5szhwoULTJ48mSJFigAQExODs7MzFosFq9VqnwtQ5GmxYcMG4O/5fO7XuXNn1q9fz/r163n33XcT7W/fvn2ibe7u7jz//PNMmzaNDRs2UK1atfQt+v8z/39OsXvnpHocOnbsSFhYGHPmzOHNN99k5syZODk50a5du2TPsVqt/Prrrzg6OtKiRYskj6lRowZAsvMRNm/ePNG28+fPs3fvXtzd3ZOc5/BBateujaura6LtRYsW5eeff+bcuXOJ9t2+fZtffvmFHTt2cOXKFft8cocPHwbuzsOXlPLly5M9e/YkrwUkea0HCQkJ4eeff6ZHjx6MHDmSGjVq4Obmlqo2bC5dugSAj4/PA4/z8PCgVatW9r/HxMRw+vRpNm/ezJAhQ3BycuLVV19NdF5sbCyrVq1iy5YtXLx4kTt37mCM4fz580DiexYSEsKGDRvo0KEDb7zxBmXLlk32Z8Xy5csBaNWqVZKv+1y5clGoUCH++usvjhw5QqFChdi5cye3bt2iYMGCVKxYMdE5TZo0wdvbm+vXrz/wftzL9rnRvHlzPD09E+1/+eWX6d69O0ePHuXs2bPkzp07wf4GDRok2W7RokXZt29fql8fqdGsWbMH/iz+J17zRYsWJXPmzCxbtowPP/yQdu3akS9fvofWvn37dtauXcvJkyeJiorC3O10kGRdK1asAO4+F87Ozg9tO63OnDnDsWPHcHFxoU2bNon2e3t707x5c2bOnMn69eupWrVqgv158+alePHiic5L62fFzJkzMcbw/PPP2+fmBKhUqRJFixblwIEDLF26NMnPV4Dnnnsu0baCBQva/79u3brJ7r+31ke9L2mV3p+997J9Zto+Q0VE/usU/on8w2z/+L1/YupHac9isbBlyxb69u1LYGAg06ZNIygoCC8vr0THOzg4cOXKFdauXUuhQoV4/vnnuXz5Mv/73/84deoUmTNnJjg4mGbNmj1ybSLp7ezZswDkz58/yf0FChRIcNz9goKCHrj9zJkzj1bgA9hCh5s3byZ7jG0i/KQCipRq3bo1ffv2ZdasWTRs2JCtW7fStGlT/Pz8kj0nPDycGzduADx05eLkvkjlzZs30bZTp04BkC9fvlR/oQ8ICEhyu+3e3L+oycaNG2nduvUDvyxGRkamy7Ue5q233mLbtm0sX76c+vXr4+LiQkhICLVr16ZDhw4UKlQoxW3ZJuZ/2GvC19c3yQUCDh8+TM2aNenevTve3t60bt3avu/gwYM0bdo02YAIEt+zKVOm0KJFC7799lu+/fZbsmTJQmhoKPXq1aNDhw5kzZrVfuyJEycA6NOnD3369Hlg/ZcuXaJQoUL2925y71WAwMDAVIV/D/vccHJyIm/evMmGf+n9+kiNpN5XNv/Ua97T05OZM2fStWtX3n33Xd59911y585N9erVadasGS1btkywkNDNmzdp06YNy5YtS3Fdts8K2y8jHxfbayFv3rw4OjomecyDfo6k52vBGGNfWCepBTM6duzI4MGDmTFjRrLhX548eRJt8/DwSNH+e2t91PuSVo/zvWX7N3BqPitERDIyhX8i/wBbQAekW+hnY7FYiImJYdasWVy9epWVK1dSqlSpRMfFx8fb/0Hn4uLCX3/9RaZMmdi2bRsvvvgiJ0+eJFOmTMTHxxMTE8NLL73EjBkzkv1HoMjTyNarJK3nPc5eeYGBgcDdLyI3b94kc+bMiY6xhY+2Y9PC09OT5s2bM2fOHPr37w/c/RL5ILaewS4uLg/toefr65vk9kyZMiV7Tlrua2p6HkdFRdGyZUsuXrxIt27deO211yhQoACZM2fGwcGBqVOn0r1792RfH+ndy9nDw8PeG2vZsmWsX7+eLVu2sGnTJkaPHs2UKVPo1q1bitry9vbm8uXLREZGPjSYTUqhQoV4++236du3L2PHjk0Q/r3wwgscOnSI559/nkGDBlGkSBG8vLxwdHRk5cqV1KtXL9E9K168OHv37mX16tUsX76csLAwVqxYwS+//MLIkSNZsWIFISEhwN+vq9q1ayf7Jd/m/p6N6fleTMnnwoOOeZK94JN7X/3Tr/mWLVtSp04dli1bxq+//kpYWBjz5s1j3rx5lCxZkrCwMHvPtcGDB7Ns2TKCg4P56KOPCAkJIVu2bDg7OxMTE5Nkj16bx90z+ml6Laxdu9YekH/44YeMHj06wX5b8P/LL79w6dKlJHvIPayelNb7qPclObbPgOQ8zveWLWC+9xcSIiL/ZQr/RNLBw4ID2/aLFy/yxx9/sH//fuLj46lWrRoVKlR45Ovfvn2bOXPmULZsWUqXLs3JkyfZsmUL165dI0+ePOTLl4/g4GD78V5eXsTFxXHt2jU6depE8eLF+eGHHwgODmbz5s28++67fPvtt5QvX54ePXo88B/qIv+k3Llzc/ToUY4dO5aodw7A8ePH7ccl5eTJk0mG4ydPnnzgeekhS5Ys5MuXj+PHj7Nr1y6qV6+e6JgdO3YAUKZMmUe6VqdOnZgzZw7Lli3Dx8eHxo0bP/B4Hx8fMmXKRGxsLF9++WW6vedtIeaxY8eIjY19bMP5NmzYwMWLFylfvjxTp05NtP/IkSOP5boPExISYg/CoqOjmTp1Km+88QZ9+vShdevWCYb5JSd79uxcvnyZq1evPrAX2IPYeuwcOHDAvu3AgQPs3buXHDlysHDhwkS/6HnQPXN2dqZ+/frUr18fuNtrb9CgQcycOZM+ffqwefNm4O9ePe3ataNLly4pqtX2HrSFIkmxvV9Tytb76dixY0nuj4uL4/Tp0wmu/7R7Eq95b29v2rdvb58+Yd++fXTs2JEdO3YwZswYe3i1YMECAL777jtKlCiRorpsnxUHDx5M97rvZXstnDp1KsEvRe/1sJ8j6eXenrphYWHJHhcXF8e3335Lv379Hlstab0vLi4uAPae4/ez9eh8EsLDwwGSDE1FRP6LNKGXSBrcO28NPLg3X1RUFHPmzKF+/foULVqUJk2aMHLkSIYNG0aNGjWoX79+ssNyUuratWvExMSQL18+/ve//9GiRQvatm1Lr169aNq0KXXr1uW7775LMHyiYMGCREVFceXKFQYPHkzZsmWxWCzUqFGD999/n4CAAL799ttkvyyJPAm2wMw2VOp+M2bMAP6en+5+c+fOTbQtKiqKxYsXJ2j/cWnatClw90vx/bZu3crJkyfx8/MjNDT0ka5Tu3ZtSpQogY+PD507d7Z/QUuOk5MTdevWJS4uzn4v0kPOnDkpVaoUUVFRzJ8/P93avd/Vq1eBpIeQxcTE8MMPP6Tr9Wz3My4uLsXnuLm50bdvXwoWLMidO3ceONT2XmXLlgXuBi1pZQtc7u1tartnuXLlSvKLflKv0eRkz56dDz/8EIA9e/bYt9vmyvvf//6X4rbKly+Ph4cHhw8ftofh91q2bFmqh/HZ3tc//vhjkiHFnDlziI2NpUCBAv+a8O+ffs0npXjx4rz55ptAwuf9QbXNmzcvybbq1asHwOzZs4mNjU3R9W2/TEjN+9D2C9GYmJgkX+MRERH8+OOPQPI/R9LDjRs37O+LPXv22P9def8fW41JDelPT2m9L7b3S3Kh7cqVK9O1ztR89to+M22foSIi/3UK/0SSYPtHV3x8fJJDFmxhnzGG/fv38/PPP7N+/Xr7RNv3un37NqNGjeLUqVN069aN2bNns3DhQhYuXEijRo1YuXIlQ4YMeaR6z549i4eHB+fPn6dv3774+fmxZs0aTp48yahRo4iLi+Pll19m7dq19nNsvZ9q1apl/8287Qtg6dKlCQ0N5eDBg/bwL63DKUXSU9++fXF0dOSbb77h559/TrBvypQprFu3Di8vL7p27Zrk+ZMmTUqwYEV8fDwDBw7k8uXLlC1b9rEt9mHzxhtv4OLiwrRp01izZo19+/Xr1+nZsycAAwYMeOTh9g4ODuzdu5crV67w8ccfp+icoUOH4uzsTM+ePVm0aFGi/bGxsSxZssTeqyulhg0bBtx97u79DLJZt26dfXhbWtkmh1+zZk2C3m2xsbG88cYbHD169JHav5/tC+/+/fuT3D958mT7ggv32rt3LydPnsTBwSHJubiS8rCFVh7m8OHDjBkzBri7WIZNoUKFcHBw4M8//0zQ68gYw+jRo5PsiRQVFcW4ceO4cuVKon1LliwBEs5R16xZM8qXL88vv/zCm2++meQvuv788097aA93F+CxvX/79OmTIOg7f/48AwYMSOlDt6tevTrly5fn2rVr9O3bN0G4dPjwYfvPYNsw+X+Df/I1//vvv/P9998THR2dYLsxxj6v373Pu622yZMnJzh+1apVfPrpp0leo1mzZpQpU4YDBw7wyiuvJJob9cqVK/z2228Jtj3sfZgcW2A5ePDgBPcpJiaG3r17c/36dSpWrJhui1ok5fvvvycqKopSpUpRsmTJZI9r1qwZXl5e7N27l507dz62eiBt96VChQp4enry119/JQgNrVYrI0aMSPXPi4dJ6XN+8+ZN/vzzT7Jly/bA+ysi8l+iYb/yn3dvLz5bqGfrxZfUF/D4+Hg2bNjAN998w6pVqzh37hwuLi44OjpSoUIF3n///QQBgo+PDzNnzqRo0aKJJmwvVqwY69evZ+vWrVy9epVs2bKlunaLxYKDgwO5c+fmt99+o3jx4nz88cf2f+y8/fbbZM+ena5duzJnzhyee+45HBwcqFKlCjNnzuTGjRv236TaHrebmxu5c+cmPj6e27dvJ9gn8iSVKVOGcePG8frrr9OoUSNCQ0MJCgpi37597N69G1dXV2bNmkXOnDmTPP+VV16hatWq1KxZk+zZs7N161aOHTtG1qxZk+1NmJz333/f/sXX1qs2IiKCZ555xn5M165dEwSRQUFBTJkyha5du1K3bl1q1apF1qxZWbt2LeHh4Tz33HOPdWjXg5QvX55Zs2bRuXNnmjdvToECBShatCiZMmXi9OnTHDhwgIiICKZMmULlypVT3G6LFi0YMWIEw4YNo3bt2pQtW5aiRYty7do19u3bx6lTpzh+/HiKhsAmp1y5cjRs2JCff/6ZMmXKULt2bTw9Pdm8eTPh4eH06dOHiRMnprn9+9WrVw93d3d++OEHqlevToECBXB0dKRp06Y0bdqUqVOn0qtXLwoWLEiJEiVwd3fn7NmzbNy4kbi4OAYOHEiuXLlSdK2GDRvi5OSUICxOypUrVxIsGnDvar/x8fEEBwczatQo+34/Pz969OjB5MmTqVWrFjVr1sTPz49du3Zx9OhRBgwYwCeffJLgGjExMfTr14+BAwdSunRpChYsiMVi4eDBg+zevRsnJyc++ugj+/EODg4sWrSIBg0aMH78eGbMmEHp0qXx9/fn0qVLHD9+nOPHj1OpUiU6d+5sP+/9999nw4YNbNmyhQIFClCrVi3i4uJYvXo1xYoVo3LlyqkOFebOnUutWrWYOXMmq1evJjQ0lMjISNasWcOdO3do27YtPXr0SFWbT9I/+Zo/efIkL774Ih4eHpQvX57cuXMTHR3Njh07OH36NDly5OCtt96yH//uu+/y4osv8s4777BgwQKKFCnCyZMn2bx5M2+//bY9jL6Xg4MDP/zwA3Xr1uXbb79l6dKlVK1aFQ8PD06cOMHvv/9O27ZtEwRPzZs3Z9y4cdSpU4fatWvbe7ZOmzbtgY+nV69ehIWFsWDBAkqUKEGtWrXw8vJi48aNnDlzhjx58jBnzpx0uXfJsQXeL7300gOPc3Nzo0WLFsycOZOZM2dSvnz5x1ZTWu6Lu7s7Q4YM4e2336Zdu3ZMnjwZX19fdu/ezeXLl//xz16bdevWER8fT6NGjfTvVxERGyMiCURGRprffvvNjBs3znTp0sU888wzZsGCBfb9P/74o8mZM6fx8vIyHTp0MFOmTDFTp041r732mrFYLMbLy8scP348Rdfav3+/yZUrl2nSpIm5evVqqmu1Wq3GGGMOHTpkGjRoYCwWi+nSpYsxxpi4uDgTHx9vjDFm3759pmDBgqZ48eLmzJkzxhhjDh48aNzd3U1QUJDZv39/gnbj4+NNtWrVjKOjozl69Giq6xJ5VDNmzDCAqVGjRpL7169fb5o2bWp8fX2Ns7OzyZUrl2nXrp3Zs2dPkscDBjBWq9VMnDjRlChRwri5uRkfHx/Tpk0bc+TIkVTX2LFjR3u7yf0ZNmxYkueuXbvW1KtXz3h7exs3NzcTHBxsxo4da2JjY1NdR2BgoAHM3r17U3T8sGHDDGD69++f5P7Dhw+bXr16mcKFC5tMmTIZDw8PU7BgQdOkSRMzdepUEx4enuB422N9mPXr15sWLVqYnDlzGmdnZ5M9e3YTGhpqPvroI3P79u1E9SV375LbHx0dbUaOHGmKFStm3NzcTPbs2U3r1q3Nvn377K+njh07Jjgnue0p2b9mzRpTs2ZNkyVLFmOxWBLUtGTJEvPqq6+a0qVLGx8fH+Pq6moCAwNN48aNzc8///zQe3W/li1bGsDs3Lkz0b61a9cm+dqzWCzG29vbVK5c2Xz88ccmKioq0bnx8fFm0qRJplSpUsbd3d1ky5bNNGrUyGzatMne7r3vwdjYWDN58mTTunVrU7hwYePp6Wk8PDxMkSJFzCuvvJLsazAqKspMmDDBVKlSxXh7extnZ2fj7+9vnnnmGfPuu++aP/74I9E5ERERZsCAASYgIMC4uLiYvHnzmjfffNPcuHHD1KhRwwBm7dq1qbqPFy9eNG+++aYpWLCgcXFxMZ6enqZq1apm5syZ9p+n97K9x2fMmJFkew/b/zC2exwYGJimtv+p1/z58+fNqFGjTL169UxgYKBxc3MzWbNmNaVLlzbvvfeeuXjxYqJ2fv31V1OtWjXj7e1tMmfObCpVqmS++eYbY8yDPzMiIiLMyJEjTenSpY2Hh4dxd3c3BQsWNB07djSbN29OcGxUVJTp16+fyZcvn3F2dk7U7oM+S+Lj483XX39tqlSpYjw9PY2Li4spWLCg6d+/v7l06VKi45N6P6Rm/70OHz5sAOPg4GD/N9mDrF692gDGx8fH3Llzxxjz8NfHg+7xg14Dqb0vNv/3f/9nSpQoYVxcXIyPj49p2bKl2b9//z/+2WvzwgsvGMBs2rQp2ZpFRP5rLMZoLJ/8t/36668sXryYXbt2cfDgQa5duwbcXR3M9v8bN26093T58ssv2b17N6+++mqieUT69+/PuHHjGD16NG+99VaSv22MjIzk0qVLHDp0iE8++YR169Yxa9YsXnrppQSrAqdGZGQkw4YNY8KECbRs2ZIFCxZgtVrtq6hFRUVRvXp1Dh48yPHjx/H19SU+Pp4hQ4YwduxY2rZta/9tbkREBL/++iutW7fmueeeY9GiRbi5uaW6JpGnie19pR958m+0YcMGatSoQa9evfjiiy+edDkiIk+tq1evkjt3boKDg5OcN1RE5L9Kw34lQ7BarQ9cdCMptqBtwYIFrFq1isDAQFq1akWBAgUICAggZ86c9OvXjyNHjiSYJL1x48a0adMmwRC1mJgYXFxcqFWrFhMnTuTAgQNJ1nLkyBFGjRrFjh07OHToEDExMbz77ru0a9cOSPvQWi8vL5599lkmTJiQ5Dworq6uXL9+HWOMfYJsR0dH3n77bXbs2MG8efPYu3cvlSpV4urVqyxdupSQkBDGjBmj4E9E5AmrXr06jRs3Zvr06bzzzjv4+/s/6ZJERJ5KH3/8MdHR0YwdO/ZJlyIi8lRRzz/JUG7cuMHBgwcJDw+nUKFC5M+fP9ljbeHfiRMnuHXrFj4+Pnh7e9vDrkOHDtGgQQPy5MnD/Pnzk51DDLD3sluxYgUNGjSgc+fOTJ8+nfj4eBwdHe3XOnbsGF26dMHLy4uiRYty584dFixYQObMmZk5c2aS82ilpjdg6dKl2bt3L99++609UIS7vRVfe+01XnvtNSZMmICTk5O95hMnTjB//nwWLFjA0aNH8fLyom7duvTq1UsrpEmGoZ5/8m936NAhSpQoQbdu3Zg0adKTLkdE5Klz+fJl8uXLx7PPPpvkwlUiIv9lCv/kqZWa0Gv27Nl8/vnn/P7777i6uuLg4ECHDh0YPnw4fn5+qbquLRRbunQpTZs25cUXX2TevHn2IO9BOnbsyJw5c1i6dCn169d/6LViYmI4cOAA1atXx93dnUOHDiXoZZiWmvv27cvp06d54YUXKF68OEePHuX7778nf/78zJ8/n+LFiyc6D+5OFu/l5WVf/EMkI1H4JyIiIiIi/1Ua9itPpXtDqTNnzuDq6oqfn1+iQDA2NpYJEyYwdOhQihQpwtChQwkICCA8PJwiRYrg7u6eouvd267tuuHh4QD23oMPC/7Wrl3LvHnzqFOnDs8999wDrxUfH4+DgwMuLi6UKlWKJk2aMGfOHP744w+qVKliPzY6Oppt27ZRoEABcufObb8vR44c4aeffqJGjRqUL1/evmJx48aNcXV1ZfLkyaxatYrvvvuOzJkz06hRI4YMGZIg+Lv3sQL4+vra67MNo753v8i/mUI/ERERERH5r1L4J/8Iq9VqD9geFigZY3BwcOC3336jU6dOHDt2jDfffJNPP/2U+Ph4nJyc7G2FhYUxfPhw6tevz6xZs9LUaw4Sz7VnjGH58uUAKerBd+PGDd555x28vLwYPXr0Ax+jxWKxP4aoqCjc3d3tvRNPnDhBlSpV7HMIDh8+nLFjxzJo0CBGjx5NTEwMbm5u/PzzzwwYMIAWLVqwcOFCrFarPZysW7cuNWvWZN++fWTNmpW8efOm+l48LOgUERERERERkX8HhX/yj7g3DDPGcO3aNTJnzpzkEFNbELdy5UqOHTsGwNGjRxPss1gs3LlzhxkzZhAXF8e4cePswd/t27fJlCkTkLAHYUrZgsU///wT4IHhWVxcHE5OTowcOZKtW7fy8ccfU65cuRRdx2Kx4O7uzpUrV9i8eTNeXl7kyZMnweOsWLEiALdu3QL+vo+hoaHkz5+f6tWrA9gX8bBxdnamdOnS9r9brdYE54uIiIiIiIjIf4PCv38Jq9XKuXPn8PT0TPOKsE9KTEwMP//8M4sWLWLPnj3ExsaSP39+unTpQtOmTZMM6M6cOcPs2bN55ZVXWLhwIbt37yYyMjLB8NyoqCgWL15McHAwWbNmZfPmzaxatYpz587h6+tLkSJFaNq0aZpqPnr0KDdu3KBcuXLcvn2byMjIRMfYgr+lS5cyceJEnnvuOdq3b5+oTrj7/N24cYO4uDg8PDy4cuUK165dY/fu3cycOZOdO3fy0ksvUbZsWfu1bt++zc2bNwEoV66cfXt0dDSFCxdm165dAEnWBqmbM1Gefrah3SntQSsiIiIiIiIZlzGGGzdu4O/v/9Dvh1rw41/izJkzBAQEPOkyRERERERERETkKXH69Gn7KMLkqOffv4Snpydw90n18vJ6wtXAnTt3mDBhAlevXmXMmDHJ9jKbN28er732Gh06dKBz587kz58fLy+vB/ZIi42NpWzZsjRu3JgxY8bQpk0bli9fTlhYGCVLlrSvurt9+3Y6dOiAp6cnFy9epHHjxgwbNozs2bPzyy+/MGrUKPbu3cvEiRN5+eWXUzQE2HbMt99+S+/evenXrx9Dhw5N9tzu3buzYMEChg4dSvfu3dm3bx9Xr17l5MmTXLhwgfr16xMSEgLcfe4+/fRTzpw5A0CePHmoVKkSlSpVIl++fAnuie1+Dh48mKtXr/LJJ5/YXwOScdh68sGDh2TfuXOHrVu3snXrVnbs2MFff/3F2bNn7edu2rQp0WIuIiIiIiIiknFFRkYSEBCQoqxA4d+/hC0Y8vLyeirCv4iICNzd3dm5cycrVqzA0dGRHDlyULlyZXuIcfv2bYYPH065cuWYOnVqkvP73csWeH399dfcuHGDli1b4uXlRYkSJVi+fDnHjh2jSpUqxMbG4uzsbF/MYuvWrVSqVIkxY8aQO3du4uLiePHFF8mSJQsNGzZk6dKldO3aFVdXV/t1bAuHJCU6Opo///wTV1dXatasmez9XrBgAUuWLCFz5sycPXuWpk2bcvr0aS5dumSfY69EiRLUrFkTBwcHgoODmTp1arLXTcqkSZNSfKz8+yUXMv/666/2IexlypShSpUqFChQgAIFCuDr60uxYsWeis8FERERERER+WelZLovhX+San/99RdffPEFixcv5uLFi/bwLz4+nj/++IOSJUsC2PePGjUKFxcX9u/fz759+3B0dKRIkSLkzZsXDw8Pe+hnsVgwxjB79mwqVapEzZo1AewLV2zfvp2XX36ZuLg4nJ2d8fPzI0+ePGzdupV8+fKRO3due69AgOLFi1O6dGn++usvLl26ZB82bVttNzlubm6sX78eYwwVKlRI9ri1a9cSFRVlf6zFihXj5ZdfpmLFipQrV458+fIlOsd23fj4+ARztz3ozaq5+zImq9XKH3/8wY8//sjGjRu5ffs21apVo127dpQqVQqLxWIPA3Pnzk3JkiXx9vbms88+o1ixYri6umpVZhEREREREXkohX+SKlu3bqVZs2bcvn2b2rVrExERQfv27alVqxbLli2zr7IL2FfqjYmJYdmyZfTs2ZMzZ87Yhyq2aNGCkSNHUrx4cWJiYnBxcWHlypVs2rSJqVOn2oON4OBgAPbv3w9gv4avry/BwcH873//IyIiAiBBkBYQEICLiwvnz5+39/qDu/Mn9uvXj7i4OObNm5dgH8C1a9e4fPmy/fz72cK4N954gz59+lCwYMFEq+3ef+z9UhPaKPjLWGyB3sqVK3n99dc5ffo05cqVIyoqik8++YSvv/6acePG8dJLL9mf+2zZspEjRw4uX75Mnjx5cHd3f8KPQkRERERERP4tFP5JqsyZM4dLly4xffp06taty6effoqnpyf58+enT58+APbed25ubgAsXLiQkydPEhoayiuvvEJMTAzTpk3jhx9+4PLly/z666+4uLhw584d5s+fT5kyZejYsaP9mrZ5Ardv387bb7/Nrl27WLRoEe7u7jRq1Ij333+fnTt3Juj1B3fnDrx+/ToeHh4JArTo6Gh+/fVXjDGcPXuW/PnzA38HdcuXLycmJobWrVsnCgbh7zCucOHC9m22ocRwN4C0Dd1UcPfvl9J5+VLaloODA7t27aJr167cuXOHWbNmERISgr+/P2FhYbRu3Zp3332XoKAgqlatCtyd87NQoUL8/vvvbN68GTc3N06dOoWnpycFChSgSJEiZMmS5ZEfq4iIiIiIiGQ8Cv8kxeLi4oiMjCRnzpxUrlyZPHny4OLiwqFDh4DEc+nlyJEDgNWrV/Pcc88xY8YMeyD43HPP8eyzz7Jp0yYWL17MCy+8wJUrV5g9ezYNGzZk7NixbNmyhT179nDixAmsVisWi4XJkyfj6enJ5cuXCQwMpEKFCtSvX59ffvmFkSNHMmTIEFxcXLhy5Qrjx4/n8OHDDBo0CD8/P3u45+LiQr58+fDw8EjQg8oW1DVv3py2bdum6J7cO2Q5NXP5yb+H7flNr7bgbg/ac+fOMWzYMFq1amXfX6NGDcaOHUu3bt2YOHGiPfxzc3OjYMGChIeH07NnT/u8krah8q1atWL06NEUKFAgXeoUERERERGRjENphaSYo6MjZcuWZdasWXzzzTe8/PLLeHl5cePGDeDuAh/3hmn3zrH3+uuv24M/AGdnZ1555RW2bNnCxo0badasGVFRUeTKlYulS5eyfPly/Pz8CAoKok6dOmzevJk///yTUaNG0aJFC3x8fOzDJ0ePHk1kZCTvv/8+S5cuJTg4mFOnTrF582YaNWpE7969EzyOmzdvcuPGDcqWLUvOnDkT9Ri01ZmS1YHVs+/fydab72HB3vXr19m1axdbt27l4sWLBAQE0LRpUwoVKpTma8fExLB3717c3NyoXLmyvR5bgBwSEoLFYuGnn37i9u3bZMqUCUdHR/Lly0dISAgVK1akYcOGBAYGcvLkSaZOncrChQsJDw9nzpw55MyZM821iYiIiIiISMaj8E9SzGKx0KFDB6ZNm8ZHH33E999/b+8NuGzZMoKDg2nWrBnNmzfHzc2NAgUKEBAQwOnTp/H29ra3Yws6bEtSX758mejoaPz9/RkwYACxsbEUK1aMgIAAfH19yZUrF59++ikDBw7E1dUVf39/exvGGEqVKsWMGTOYM2cOP/74I7/88gv+/v4MHDiQrl27kjt37gSP49atWxw7dowhQ4YAyc+/96hDPOXplZLefJs2baJfv37s27cPDw8PnJ2dOXPmDGPGjGHQoEEMGDAgTYuxuLi44OrqSnR0NFevXiU+Ph4HBwd72Hz58mWyZctGeHg4O3bsoFq1agCUKlWKiRMnUr58eXsv0+DgYBo0aEC7du2YP38+q1at4qWXXtIiMSIiIiIiImKndENSJWvWrKxfv54RI0ZQqVIlChYsiLu7Ozdv3mTdunW0b9+eUaNGcf36dXLnzk1ISAgA+/btA+7OBxgXFwdAZGQkV69exdPTEy8vL9zd3enbty/9+/enYcOGlCxZkly5cgHYhzMuW7YMwN6GLeAoWLAgw4YNY926dVy+fJk//viDDz74gKCgIHvttmOLFSvGtGnTaNmy5WO+W/JPslqt9rn5Huavv/6iV69edO7cmQsXLiTa//vvv1O7dm2OHz/O8OHD+d///seSJUtYsmQJN2/eZMiQIdy6dSvVAZutvlKlSuHm5sacOXOIjIy0r/oMMH/+fCIjIwHYvXu3/bz8+fNTqVIlnJyc7O3Ex8djsVho3rw5AOvXr09VPSIiIiIiIpLxqeefpFq2bNl47733gLtzl40fP57u3bsTHBxMq1atmDBhApUqVaJRo0a0atWKpUuX8tVXX1GlShWKFi1q72m3evVqACpWrJigfavVCtwN66xWK46OjpQqVYoqVarY50BLbnVdWw9DWxB0b6hikzlzZjp37pw+N0OeiHtfI7YALqU9Ne/cucObb77JqlWrAOjTp0+iobKzZs0iJiaGr7/+mkaNGtm3ly5dmubNm/Pdd99x+PBhypQpk6b6mzRpwpIlS1i0aBFdu3alUaNGODs78+OPP/L7779TpUoV1q1bZ59P0/Y+uH+osu2/RYsWBf5eYVs9/0RERERERMRG4Z+kSVxcHE5OTuTMmZPs2bNz6NAhatasSfv27QkLC+PEiRMA1K9fn0GDBvHBBx/Qpk0b2rVrh6enJytXrmTx4sW0bdvWvuBBUiGOLSjMnz8/YWFhKa7vYUGQwpF/t3uf3xs3brBnzx62bdvG7t27eeeddyhSpEiy5+7bt4+wsDAqV67Mli1bOHXqFOXKlQP+nufx3LlzAPj6+iY6Pzo6msyZM+Pi4pLqum1D1X19fXnvvfdwcHBg1apV/PjjjwD4+PgwceJE8ubNS9WqVbl69ar9PNt/bb397g22baGfn59fovsjIiIiIiIi/20K/yRNbHOOZc+eHT8/P86fPw/cnScN7g5rhLvDhN955x3i4uJYvHgx77zzDlarFS8vL3r06MGIESPw8vJK0TWtVitWqzVdVtVV8PfvdeXKFT755BP7IjDXrl0DwMvLi6xZs9KrV68kz7Mt7DJs2DBq1qxJjRo12Lp1K/v37+f5558H/g7/nnnmGRYsWMCECRMYMGAAnp6enD9/noULF7Jy5UpGjx5N8eLFEy0WkxK2117ZsmVZuHAhq1ev5vLlywQEBFCyZEm8vLz4+eefAciTJw+QdCBuc+/8lT179kxVLSIiIiIiIpLxKfyTVNuzZw/h4eFky5YNuNsTasuWLbz00kvMnTuXNm3aUKFCBfvxbm5ujBo1ig4dOnD27Fly5MhB0aJFUx3iOTg4qEdTBpDUkN3UuHTpEuPGjQOgbdu2lCxZEn9/fwICAuwrRCfF0dGRXbt2sWnTJsaPH0++fPlwdnZm//79xMbG4uzsbH99de7cmdOnTzN+/HiWLFmCn58fERERXLt2jZIlS5I/f34iIiLIkiXLI/UitVqt1KlTJ9H25cuXA3eHB99rwoQJODo64uHhQVxcHH/99Rfz5s0jNjaWd9991756sIiIiIiIiIiNwj9JtQMHDtCpUyfy5cuHm5sbR48eJTIykoCAAF5//XWGDRuGm5tbgnOsVitFixa1z00Gfy9+oF54/y33Brh37tzBxcUlVa+BHDlyEBISQnR0NG+99RbFixd/6Dm2Hn2LFi3Czc2Nxo0bc+7cOXLmzMnBgweJiooiS5Ys9tq8vb357LPPaNy4MT169CAwMJCqVavi5+fH6tWradKkCfXr12fRokWJhv+mJgxMKsz+888/+fbbb6lcubK9B61tmP3KlSv55Zdf8PT05MaNGzg5OVGxYkU6d+5Mp06dUt0LUURERERERDI+hX+Sas8//zyrVq1ix44dODg4cOPGDbZu3cro0aMpVqxYkufYQg6r1Wrv8aXQL2Mwxtif1wf1zIyPj+fMmTNs3ryZH3/8keXLl1O5cmWmTJlCvnz5UhyaeXh4EBQUxIYNG4iOjgburhydKVOmJBeCMcbg4ODAhQsXmDlzJk2bNsXb25s7d+4QGBjI/v37uXbtGlmyZElw3v79+2nTpg0tW7ZkwoQJ9pDvlVdeoXfv3nz99dfMmjWLLl26JKg7Na/rU6dOceDAAaxWKzExMezYsYOpU6disVj46KOP7EPibfd17NixtG/f3n4PihYtiqura4LHqveViIiIiIiI3Evhn6Sai4sLoaGhhIaGAnD27FnOnDnD4cOHKVasmL2XUlI0bDfjsVgsKepx9vvvvzNkyBB+/fVX+7ZLly5x7do18uXLl+Lrubi4EBwczI8//sivv/7K5s2b2bNnD87OzuTJk4d69epRtmzZBPUB/PTTT1y8eJFBgwZhtVrJli0bAQEBbNiwgbNnzyYaLjx48GBu377NRx99hIuLi32hDTc3N9q0acOPP/7IunXraNWqFd7e3hhjuHPnDtu2baNAgQLkzp3b3uPwyJEjLFmyhOrVq1O+fHliYmJwcXFh9erVvPvuu0RHR3Pr1i2sVivVq1dn1KhRCVbBtr1vgoODCQ4OTlCnMcYecCr4ExERERERkfsp/JM0sy12EBsbi9Vq5fjx40DiBQnk38dqtdp7kSUX2BpjOHXqFGFhYaxevZpDhw7h5eVF48aNadiwoT3QswVgN2/e5ObNmwwYMIBnn32W2bNns2TJEi5cuJCq2hwcHChUqBDR0dEMHjwYuLsqr4uLC+fOnWPatGl89NFHtGzZMkGtn376KU2bNiUgIMA+f2TZsmWZM2cOZ86c4dKlS0RHR5MrVy6cnZ05fvw4fn5+XLp0CS8vrwS9G11cXOy99WxBt8ViYfjw4YwdO5ZBgwYxevRoYmJicHNz4+eff6Z///60aNGChQsX2oe8N2nSBFdXVy5fvkxgYCBlypSxh5C2+5bUc2O7nnrRioiIiIiIyMMo/JM0s4V8AQEBfPjhh/YFQBRE/PvdGzoZY7h27RqZM2e2D329c+cOCxcuZMKECRw9epRcuXLh5eXFvn37WLFiBZMnT2b69Ok888wz9nZsK+j6+/sDdxeOmTt3LmfOnAFS97opWLAgLVq0oEWLFjRq1AgvLy9OnTrFjz/+yDvvvMPLL79M6dKlKViwIADfffcdly9f5u2338bBwYEtW7Zw5MgRfvrpJwC6dOlCVFQUHTp0YOLEiRhjCAoKYsmSJfz+++8ULFgwwZDiXbt2ERERQUBAAJkzZ7b35LP11rt161aC+xgaGkr+/PmpXr06gH2orq+vL+3atUv0+Gw9+R723IiIiIiIiIg8jMI/eWSOjo724E/+/WJiYvjpp59YsGABv//+OzExMRQqVIjXXnuNFi1aAHD69GkGDBiAr68vI0eOpHr16uTKlQuAL774gpEjR9K/f382btxoD/Xc3NzswZ/VaiVv3rwAnDhxItVz1RUsWJBPP/2UvHnzYrFYMMYQGBjIG2+8wYULFxg7diw//PADAwcOxGKxEBYWxvXr12nQoAFXrlyxt+Pp6Wl//b788ss0aNAAJycnXFxcaNy4MUuWLOHDDz/EwcGB3Llzc/36dZYvX87nn39OsWLF7L0LbaGorVde1apVE2wPCQnhyJEjST6WpOZMVIAuIiIiIiIi6UXhn8h/QFRUFCtXriQmJobWrVsnO6Q0JiaG8ePHM2bMGHLmzEm5cuXIlCkTt27d4vr16/bjAgICGDFiBF26dEk0zHv48OH83//9H5s3b+bWrVt4eHgk2G/r1ZYnTx68vLw4fvw4kZGRiRbceBBPT088PT3tf7dYLPZh6LVq1eKLL75g+/btxMXF4ezsTI0aNVi+fDklSpQgT5489sUyYmNjGTx4MM7OzowZMyZBDa1bt+bAgQOMGzeOF154gaxZsxIVFUVsbCy1atVi+PDh9nkvbc6ePYurqyt16tRJVHN8fDzGmETzYaZ0zkQRERERERGRtFD4J09U0NvLnnQJGdKJMY0S/N3R0ZG9e/cSHh5O69atk+1Z9t133/H222/TtWtXunfvTsGCBfHy8kp0vKurK6+++ipwN9SyLTZhW+ylQIECXLp0idOnT1O0aNEEPfts//Xz88Pf358TJ04QHh5OlixZUtUD8P5jbfPo5c2bl6ioKC5cuGAfqtuwYUOCg4MJCAhIFDLOmjWLsLAwzp8/n2BflixZGDt2LG3atGH58uXcvn2bAgUKUKFCBYoXL46Tk5O9Btt/T5w4wQsvvGDv8Xf/cyAiIiIiIiLyT1P4J/IfEB0djaOjIwcPHmTBggU4OjqSI0cOKleubO8BePv2bQYNGkS5cuX44osvkgywknJvqOXk5MTBgwc5ffo01apVw8/PD0h6GKu3tzf58uVjz549XLx4kfz58yfZvjGG+Pj4JHvM2Xr72a4NsHnzZgBy585tP9bT05MSJUokaNPWA9Hd3Z3r169z9uxZihYtmuixVahQgQoVKiRZ2/2B5rhx45I8TkRERERERORJUfgnksH99ddffPHFFyxevJiLFy+yYsUKHB0diY+P548//qBkyZIA9v2jRo3CxcWF/fv3s2/fPhwdHSlSpAh58+bFw8Mj2d55tiBu0aJFnDlzhn79+uHj45Ps8R4eHhQqVIi1a9dy7ty5ZOu3WCyJgj+4O5TZ3d3dfu0rV66wceNGhg0bBmBfCdjm/t6Htv//4IMPGDNmDIGBgcnWYJuXD7D3cnzQsZqzT0RERERERJ4WCv9EMrCtW7fSrFkzbt++Te3atYmIiKB9+/bUqlWLZcuWkSlTJvuxx44dA+7O+7ds2TJ69uzJmTNn7MNpW7RowciRIylevHiCHndwd6ELR0dH9u3bx9ChQ6latSodOnQAkl+8ws3NjYIFC3L79m1OnjyZoJ172YLEuLg45s2bZ18pd9CgQZw7d86+0Mj+/fvZuHEjAQEBzJ8/n9KlSydoJ7k6ChQo8ND7mJp5+RT8iYiIiIiIyNNE4Z9IBjZnzhwuXbrE9OnTqVu3Lp9++imenp7kz5+fPn36AH/32HNzcwNg4cKFnDx5ktDQUF555RViYmKYNm0aP/zwA5cvX2bVqlX2ufRsHBwcMMbw2muv4ezszPvvv//AFaBtC44EBgbi7OzMmTNngKTnxYuOjubXX3/FGMPZs2ftw4Pd3NzYvn07d+7cITY2Fn9/f3r27Mnzzz9P9erV0+X+iYiIiIiIiPzbKfwTyaDi4uKIjIwkZ86cVK5cmTx58uDi4sKhQ4eAxHPp5ciRA4DVq1fz3HPPMWPGDHsg+Nxzz/Hss8+yadMmFi1axAsvvJDoeh988AFhYWGMHj2aGjVqJJhX7362bYGBgWTLlo19+/axevVqbt26xc2bN3n22WfJnj07AC4uLuTLlw8PDw/7MF/AvjCJt7e3vXYRERERERERSSjxt3IRyRAcHR0pW7YsFy5c4JtvvmHfvn14eXlx8+ZN4O4CH/fOpRcQEADcHbb6+uuv24M/AGdnZ1555RUcHBzYuHEjd+7cSXCt9evXM2zYMOrXr0/37t3t7SQV/EVFRREWFsbUqVP5v//7P6Kjo1m5ciWtWrXi+eefp3///uzatct+/M2bN7lx4waFCxcmZ86cxMfHA+Dj40ORIkXswZ/VaiUuLs4+N5+IiIiIiIiIqOefSIZlsVjo0KED06ZN46OPPuL777+39wZctmwZwcHBNGvWjObNm+Pm5kaBAgUICAjg9OnTeHt729uxLWAREBCAp6cnV65c4fbt2/a5965du0b//v0JCgpi7NixeHt7c/v2bW7evMmFCxfIlCkTBQsWtLdz+vRp3nvvPTZs2ADc7dlXsmRJSpQoQdmyZQkNDSUkJMQ+NPjWrVscO3aMIUOGAAmHBt+7uIaDg0OSYaOIiIiIiIjIf5nCP5EMLGvWrKxfv55Jkyaxf/9+Ll68yIEDB7h58ybr1q3j+++/591336Vfv37kzp2bkJAQTp8+zb59+6hcuTLx8fFYrVacnZ2JjIzk6tWrZM6cGW9vb+Li4nBycmLs2LHs2rWLl156iSNHjrB8+XKOHTvGsWPH2LFjB5UrV2bJkiX2oC5Hjhz06NGD119/nXLlyj1wlV2AYsWKMW3aNFq2bJlonxbXEBEREREREXkwi7Et5SlPtcjISLJkyUJERAReXl5Pupx0E/T2siddQoZ0YkyjJLdv3bqV8ePH0717d4KDg2nVqhW7d+9m7ty5NGrUiHnz5tGpUyfKli3LzJkzKVq0qP3cPn36MGnSJKZNm8Yrr7wCwK5duwgJCQHAycmJ+Ph4jDF4eXlRuHBhSpQoQb169WjduvUDgzrbeRaLJdnhwiIiIiIiIiJyV2pyIvX8E/kPsPXSy5kzJ9mzZ+fQoUPUrFmT9u3bExYWxokTJwCoX78+gwYN4oMPPqBNmza0a9cOT09PVq5cyeLFi2nbti2tWrVKMBS4SpUqFCxYkNKlS1O6dGnKlClD1qxZH1jP/YuBJLXK7/3Hq5efiIiIiIiISOop/BP5D7At7JE9e3b8/Pw4f/48AJs2bQKgVKlSwN1hwu+88w5xcXEsXryYd955B6vVipeXFz169GDEiBEJfqPg5+dHWFhYkte8tzff/eGerYdfSin4ExEREREREUkbhX8iGdyePXsIDw8nW7ZsAERHR7NlyxZeeukl5s6dS5s2bahQoYL9eDc3N0aNGkWHDh04e/YsOXLkoGjRoglWBr6XMYb4+PhEQ3Yf1ptPRERERERERB4/hX8iGdyBAwfo1KkT+fLlw83NjaNHjxIZGUlAQACvv/46w4YNw83NLcE5VquVokWLJpjzzzY96P298CwWS7LBoIiIiIiIiIg8WfrGLpLBPf/886xatYodO3bg4ODAjRs32Lp1K6NHj6ZYsWJJnmPrvWe1Wu09+jT0VkREREREROTfR+GfSAbn4uJCaGgooaGhAJw9e5YzZ85w+PBhihUrZl8MJCladVdERERERETk303f7EX+I+Lj4wGIjY3FarVy/PhxQHPziYiIiIiIiGRk6vkn8h9hC/kCAgL48MMP7QuAaDiviIiIiIiISMalnn8i/zGOjo724E9EREREREREMjaFfyIiIiIiIiIiIhmUhv2KSIoEvb3sSZeQYZ0Y0+hJlyAiIiIiIiIZlHr+iYiIiIiIiIiIZFAK/0RERERERERERDIohX8iIiIiIiIiIiIZlMI/ERERERERERGRDErhn4iIiIiIiIiISAal8E9ERERERERERCSDUvgnIiIiIiIiIiKSQSn8ExERERERERERyaAU/omIiIiIiIiIiGRQCv/+YZ06dcLFxYXMmTPb/yxfvvxJlyUiIiIiIiIiIhmQwr8n4NVXX+XmzZv2P/Xr13/SJYmIiIiIiIiISAak8E9ERERERERERCSDemrDv4iICAYPHkyRIkVwc3MjW7ZshIaG8uOPP/4j1x89ejQvvPAC+fPnx2KxEBQU9NBz5s2bR/ny5cmUKRO+vr60bduWkydPJjpuzpw5ZMuWjWLFivHhhx8SFxf3GB6BiIiIiIiIiIj81zk96QKScvr0aWrVqsXVq1fp3LkzxYsXJyoqigMHDnDq1Kl/pIZ33nmHbNmyUa5cOa5fv/7Q47/44gv69OlDlSpVGDduHFeuXGH8+PFs2LCB7du34+/vD0Dfvn0ZO3Ysvr6+7Nq1i7Zt2xIdHc3777//mB+RiIiIiIiIiIj81zyV4d/LL7/MrVu3+OOPPwgICEjVucYYvvrqK7p06YKjo2OSx0ybNo2XXnoJNze3ZNs5evQo+fPnB6BEiRLcvHkz2WPDw8MZPHgw5cqVY926dTg53b2t9evXp2LFigwdOpRp06YBUK5cOft5ISEhjBgxgmHDhin8ExERERERERGRdPfUDfsNCwtj/fr1DBo0iICAAOLi4rh161aKz1+zZg3du3enXbt2SQ6n7devH926dWP27NkPbMcW/KXE4sWLuXnzJn379rUHf3A33KtevTrff/89MTExSZ7r4OCAMSbF1xIREREREREREUmppy78+/nnn4G74VuLFi3IlCkTmTNnJigoiC+++OKh59epU4eJEyfy/fff07Zt2wQB4Jtvvsm4cePsAWB62bZtGwChoaGJ9oWGhnLjxg0OHDgAwPz584mIiMAYw549exgxYgQtW7ZMt1pERERERERERERsnrphv7aQrGvXruTLl4/p06djsViYPHkyffr04dq1a7z33nsPbKN3795YLBZ69+6NMYZ58+YxYMAAPv/8cwYMGMDHH3+crjWfPXsWgDx58iTaZ9t25swZSpUqxeTJk+nRowexsbHkypWL9u3b88477yTb9qRJk5g0aRLx8fHpWrOIiIiIiIiIiGR8T134d+PGDQA8PDzYsGEDrq6uALz44osUL16c0aNH07t3b7JmzfrAdnr16mUPAIsUKcLx48d56623+Oijj9K95qioKAB7rfeyzStoO2b9+vWpartXr1706tWLyMhIsmTJ8oiVioiIiIiIiIjIf8lTN+w3U6ZMALRr1y5BmObi4kL79u25ffs2W7duTVFbr732GtWqVeP48eMUKFDgsS2q4e7uDsCdO3cS7bt9+3aCY0RERERERERERP4pT134ZxsmmytXrkT7bNuuXr360HaMMbz22mts2LCBRo0acezYMVq2bJlkQPeocufODdwd2nu/Bw0JFhEREREREREReZyeuvDvmWeeAeD06dOJ9p06dQqAHDlyPLANYwzdu3fnyy+/ZOjQoSxdupSvvvqKZcuW0bx583QPACtUqADApk2bEu3btGkTmTNnpmjRoul6TRERERERERERkYd56sK/Zs2a4eXlxaxZs4iIiLBvv3HjBt988w1Zs2alcuXKyZ5vjKFbt2589dVXjBgxghEjRgDQpUsXvv76a1asWEGzZs2Ijo5O15rd3d35/PPPE6wuvGPHDjZs2EDr1q1xcXFJt+uJiIiIiIiIiIikxFO34Ie3tzfjxo2jS5cuVKxYka5du2KxWJg+fTrnz59n5syZD5w/b82aNUyfPp0PPviAIUOGJNjXqVMnHBwc6Ny5M7Nnz6Zbt27JtjN79mxOnjwJwOXLl4mJieGDDz6w19i7d2/7sb6+vowaNYo33niDmjVr8vLLL3PlyhXGjRtHjhw5GDly5KPcEhERERERERERkTSxGGPMky4iKUuWLOGjjz5i9+7dGGMoX748gwcPpkGDBg89d8eOHYSEhKR5P0DNmjWTXZk3MDCQEydOJNo+Z84cPv30U/bv34+7uzt169Zl9OjR5MuX76E1P4xttd+IiAi8vLweub2nRdDby550CRnSiTGN0r1NPVePz+N4vkRERERERCTjSk1O9NSGf5KQwj9JDYV//y4K/0RERERERCQ1UpMTPXVz/omIiGRUxhj0OzcREREREfknPXVz/omIiPzb2UI+YwwWiwUHh7u/a7NYLE+4MhERERER+a9R+CciIpLOLBZLoqDv4MGD/Pnnn/z++++0bt2aUqVKPaHqRERERETkv0Thn4iISApZrVYAe0++pFy6dImdO3eybds2tm/fzt69ezlz5kyC4b4NGzZ87LWKiIiIiIiAwj8REZEUuzf0s1qtCf5uG+Lbtm1b1q5di6+vL3nz5qVy5coULFiQnDlzMnnyZC5cuJChFm4SEREREZGnm8I/ERGRFDh27Bjz58/n119/JTIykkqVKtGxY0dCQkJwcHAgPj4eJycnRowYQe/evcmTJw/Zs2fH29ubLFmysGfPHj766CPKli2Lv7//k344IiIiIiLyH6HwT0RE5CFOnz5Nz5492bBhAyEhIWTKlIkZM2Ywa9Ysxo0bR9euXXFyuvsjtWrVqkm2cebMGc6ePUv16tXJli0b8fHxODo6/pMPQ0RERERE/oOSn7RIRETkX8YYg9VqTTC/Xnr44IMPWLlyJcOGDWPevHmsXbuW1atX4+Pjw/Dhw1m7di3w95yA9672a3PmzBkAChYsCKDgT0RERERE/hEK/0REJMOwWCw4ODgkWmk3LWzB3f79+/nmm2+oWbMmgwYNInfu3Dg5OVG5cmWmTJnCuXPnmDRpkv36tv/eu+KvMYbVq1cDUL9+/UeuTUREREREJKU07FdERJ5qth5094ZpSQkPD2fnzp3s3r0bgGeffZZy5cql+bq2a0VERBATE0Px4sWBhAt9NGjQgICAAJYuXcrt27fJlClTkvVbLBb27NkDQJ48edJck4iIiIiISGqp55+IiDzVUtKb76effqJy5co0atSIUaNGMWzYMJ599lleffXVR77+yZMncXR0JE+ePMTFxdmDP1vPwDp16hATE8O2bduSrX/fvn1ER0dTtWpVDfcVEREREZF/lHr+iYjIP84WnD1seO6pU6fYvHkzO3fu5MqVKxQpUoTXXnsNLy8v+zGrVq2iRYsW+Pv7M2HCBEqUKEFUVBSffPIJX3/9Nb6+vowaNSpNNVosFqKjozHG2P97//4SJUoA8Pvvv1OjRo0Ebdh6Ce7bt4+TJ09Sr149/P397eeKiIiIiIg8bgr/RETkH5eS4Gv27NmMHDmSCxcu4OPjQ+bMmVm2bBm1atWiYsWK9uNGjhyJ1Wrlp59+okyZMvbtzzzzDCVKlGDMmDF07tyZQoUKpapGW0CXNWtWXF1dOXXqFHFxcTg7Oyc4zraAx759+4CEw4Jtj/PatWsAZM+ePUHbIiIiIiIij5vCPxER+cdERUWxZ88e9u7dy5UrVyhUqBCtWrVKdNy4ceN46623qFChAkOHDqVYsWJ4e3vj4eFBzpw57cfduHGDkydPUqRIEXLkyAFAfHw8cXFxeHt7U7t2bb799lt++ukn+vXrl6rAzXZsnjx5yJ49OwcPHiQyMtI+r9+9+wHOnj0LkKB3oMVi4fbt2+zcuRM3NzdCQ0MB7OGgiIiIiIjI46bwT0RE/hFfffUVn332GYcPH8bT05MsWbJw584dqlWrZg/uAE6cOMEXX3xBlSpVWLlyJS4uLonasvWcu3TpEt7e3lgsFq5fv06uXLnsC4QA5MuXD4BNmzbRtm1b/P39U1yvLdwLCAggODiYrVu3cujQIXuttv1+fn4A9h6B98/plylTJlavXk1MTEyCHosiIiIiIiL/BHU9EBGRJBljsFqtWK3WRHPdpaYNgA8//JDu3bvj4+PDpEmTmD9/PnPmzGHRokX4+PgAd4fLAixcuJDjx4/To0cPXFxcOH/+PAcOHODChQv2dm3BW5YsWShSpAiHDx/m999/B8DJyQk3NzdiYmLYsWMHAAcOHODSpUtpug8+Pj4899xzhIeHs2zZsgS1AuzduxeAypUrJ3lvwsPDiYmJoWTJkgnOExERERER+Seo55+IiCTJYrEkGCYbHR0NgJubW6ra2LZtG6NHj6Zt27Z88803ODkl/aPHdq2TJ0/i7OxMrly52LdvHyNHjmTv3r3ExcUREBBAz549adGiBQC+vr68+OKLLFy4kDFjxpA/f36yZs3KxYsX+eqrr/j1118pVKgQp0+fJjIyMtE1UzL3noODA+3bt+ejjz7iyy+/pF27dpQqVcq+f8qUKQBUr149QVu2tn/44QdOnz5No0aNEixUIiIiIiIi8k9Q+Cci8h/0sNArLi6OAwcOsGLFCn7++WcOHz6Mu7s71apVo3Xr1lSrVu2hIaBt4YtFixYRExND+/btcXJyIiIigqioKLy9ve3z58Hd8C8yMpKrV6+SNWtW1q5dy7hx48iRIwehoaFER0fz/fffs3v3bq5fv84rr7wCQJMmTRg4cCAff/wxoaGhBAYGEhUVhZubG/PmzWPu3LkcPnw4yXpTMgeg1WrFx8eHwYMH88Ybb9CkSRPatWtHnjx5WLp0KatWreKNN95IsNjIverWrcuuXbsoUaJEssGniIiIiIjI46JvISIi/zH3rkZ75swZXF1d8fPzsweC8fHxLFiwgJEjR3LlyhWKFi1K9erVOXnyJNOnT+frr79m4sSJ9OzZ84EhooODA+Hh4ezcuZOAgACqVq3Kn3/+yeTJkzl8+DCOjo4ULVqUgQMHkjt3bgBcXFxwdHTk0qVLfPbZZ1SvXp158+bh6elJXFwcHTt2pGnTpgwePJi2bduSKVMmXFxc+PDDD2ncuDELFy7k8uXLFClShNq1a1OtWjVmzJgBgKura4L6oqOj2bZtGwUKFCB37tz2+3LkyBGWLFlC9erVKV++vH177969yZYtG+PHj+fzzz/n9u3b+Pr6MmDAAAYNGpQgyIS/g8WgoKD0fPpERERERERSRXP+iYj8i9nm5YuPj0/RfHLGGBwcHPjtt98oWLAgefPmZcyYMcDd3n4AFy9e5H//+x/Ozs58/vnnzJo1i6+++oqwsDBWrlyJxWJh3LhxHD9+/KE955ydnTl8+DDXr19n3bp1lClThoULFxIdHc3Fixf5/PPPqV69OqdPnwbuDin29fXFxcWFmzdvMm7cOHvw5+TkRIMGDWjQoAGXL19m8+bN9sfk5OREtWrVmDBhAnPnzmXYsGFUq1YNgIiICPLmzYuHh0eCxzl8+HBq1qzJF198AUBMTAwAP//8M/3792f06NH29m3atWvHihUrWL16NceOHePSpUuMHj0ab2/vB86FmJp5EkVERERERNKTwj8RkX8xi8WCg4MDjo6OODg4YLVauXz5MkCSYaAtrFu5ciXHjh0D4OjRown2ZcmShddff51169bRtm1b8uXLZ+/VFhoaSseOHTl//jz79+9/aH1eXl7ExcVx7do1XnnlFTp37syRI0cICwvj119/pVevXhw/fpyhQ4dy5coVAAoUKICHhwfZsmWzB3KOjo7Ex8cDdxfWAPjzzz8fev0ff/yRjRs38vzzz1OwYMEEPRVtK+/eunULwN4bMjQ0lPz581O9enXg71V8bbJmzcozzzxj79FnWxDlQUFoSoYXi4iIiIiIPA4K/0RE/qXOnDnD0qVLeffdd6lfvz5BQUE4OTnRuXNnIPneZqdPn2bOnDn06NEDLy8v9uzZA9wN2AA8PDyoVq0a2bJlIz4+PkE77u7uxMbGEhUVRc6cOR9Yny2sy5MnD3B32O2bb76Jl5cXd+7cwdfXl7feeouQkBCWLl3KoUOHAChdujR58uTBarVy7tw54G5vPVuPPT8/PwCuXr0K/B1yxsfHExkZyblz5/jjjz8YP348nTp1okCBAvTs2RO4G8LZHqftvKpVqwJ3hxwDhISEcOTIEfr27ZvsY7v3njg4OCjcExERERGRp5bCPxGRp0xUVBSLFi3i+++/BxL34Lt27RqdOnUib968NG3alGnTpnH16lWqVq3KkCFD7Ath2EKu+/3vf//j4sWLjB49mpw5c3LixAmio6OTDLAcHR3t240xxMXFsWPHDvz8/MiWLdsDH4ctILP1sCtbtqx9tVtb0BYQEMAzzzxDeHi4vQdi2bJlqVevHtevX2f16tXA3d53tjn7Nm3ahIuLCyVKlLDXGBkZSb9+/QgODqZ169a0aNGC9957j/Lly/PTTz9RuHDhRPWdPXsWV1dX6tSpk2hffHy8PWxMisI+ERERERH5t9CCHyIiTxlHR0f27t1LeHg4rVu3ThQ0eXp64uPjg5OTE6NGjaJGjRr4+fnh4+ODp6fnA9uOjY1l3LhxdO3alSxZslC4cGEOHTrEwYMHKV26dLLDV21z7n3xxRccOHCA9957j6CgoAcOd7Vtr169Op9//jnXr1+3B2q2RTSsViu+vr5YLBZ7yOnh4UHHjh2ZMWMG06ZNIygoiBo1anD9+nX+97//MXPmTCpVqkSrVq3s1/Ly8qJu3bqcO3eOmzdvUrJkSWrUqEGNGjXIlStXgrpsNZ84cYIXXnjBHkTe/xyIiIiIiIhkBAr/RESeMtHR0Tg6OnLw4EEWLFiAo6MjOXLkoHLlyjg4OODk5ES+fPmIi4ujQIECVKhQ4aFt2gKv2bNnExkZSZMmTQAoWrQoS5cuZc+ePZQuXRqr1Zoo+IqPj8fJyYn9+/czefJkChYsSMeOHYHEPeCMMfbjbXPohYSEEBISwsGDB9m1axdBQUH2azg6OrJs2TLc3d3tPQQBgoOD+eGHH2jRogU9e/YkR44cWK1Wrl69SuPGjfnqq68SPcZGjRrRuHHjh94LW83jxo176LEiIiIiIiL/dgr/RESeIn/99RdffPEFixcv5uLFi6xYscK+2MUff/xByZIlgbvDZd3c3Ni1axflypXjwoULxMXFUbRoUbJkyYKTU8KPd4vFgjGG2bNnU6lSJWrWrAncnV8PYPv27bz88svExcUlCP+MMTg6OhIXF8fo0aM5cuQIc+fOJX/+/Pbee/dfx3ZtW8gWEBDAoEGDeOGFF3jnnXcoU6YMHh4eXL58me+++47t27fTvHlzihQpkqCtatWqceDAAebOncuePXvIkSMHVatWpVKlSmTNmjXRvbNdzzZPoW0xlAcN0X3YQh0iIiIiIiL/dgr/RESeElu3bqVZs2bcvn2b2rVrExERQfv27alVqxbLli2zr7gLkCtXLnLlysXcuXPZsGED+/bt486dO8TExFCnTh2GDh1KpUqVgL+H2K5cuZJNmzYxdepUe8AXHBwMYF+51zavno0tGJs2bRrffvstXbp0oU2bNsTGxiYKGOHuIiT9+vUjLi6OefPm2dtr2bIlQ4YM4cMPP6Rw4cKULl2aW7ducfjwYVq2bMnEiRMTBYkAPj4+9OnTJ1X3MTVDdhX8iYiIiIhIRqfwT0TkKTFnzhwuXbrE9OnTqVu3Lp9++imenp7kz5/fHoDZgjxfX1+KFSvG1q1b8fb25s0338TDw4P169ezaNEi/vjjD+bNm0e1atVwcHAgJiaG+fPnU6ZMGfuQXYD8+fPj5eXF9u3befvtt9m1axeLFi3C3d3dfsy2bdsYOXIkhQsXZuLEicDdBTiSEh0dza+//ooxhrNnz5I/f35777phw4ZRt25dvvvuO/766y8KFy7MW2+9Rf369VO8crCtN5+IiIiIiIikjMI/EZGnQFxcHJGRkeTMmZPKlSuTJ08eXFxcOHToEHB3eOq98/Flz56dDh06MHTo0ARz5XXr1o2ZM2fSu3dvhg4dytq1awG4cuUKs2fPpmHDhowdO5YtW7awZ88eTpw4gdVqxWKxMHnyZDw9Pbl8+TKBgYEAXL9+nREjRhAZGcmiRYtwc3MjPDyciIgIzp07h4ODA6VKlSJz5szA3VV88+XLh4eHhz1AtIV1Tk5OVK9enerVq6f6/mgBDhERERERkbRR9wkRkaeAo6MjZcuW5cKFC3zzzTfs27cPLy8vbt68CcDt27cTBGCZM2emdevW9uDPtlKuu7s7PXv2JHfu3Kxfv54rV64AEBERgb+/P0uXLuW9995j27Zt5MyZky5dulCiRAmMMYwaNYrt27eTM2dOjDEATJ06lV9++YVq1aqxe/duBgwYQLdu3WjRogV16tShb9++HDhwwF7XzZs3uXHjBoULFyZnzpz2Hnv3s1qtxMXF2efnExERERERkcdDPf9ERJ4CFouFDh06MG3aND766CO+//57e2/AZcuWERwcTLNmzWjRokWiefng7951cXFxODk5UbZsWc6ePcuRI0fw9fUlR44cDBgwgJiYGIoVK0ZAQAC+vr7kypWLTz/9lIEDB+Lq6oq/v7+9J+DRo0eZM2cOACtWrGDFihU4OzsTGBhImTJlePHFF6lZsyZlypSxD+29desWx44dY8iQIUDyPfYcHBw0fFdEREREROQfoPBPROQpkTVrVtavX8+kSZPYv38/Fy9e5MCBA9y8eZN169bx/fff8+677zJw4EA8PT3tgZut59y9K9fGx8fj6upKdHS0ve3kFs4oUKAAAMuWLaNbt27Exsbi6uqKp6cnbdu2pXnz5oSEhBAcHEy+fPke+BiKFSvGtGnTaNmyZXrdFhEREREREXkECv9ERJ4i2bJl47333gPurv47fvx4unfvTnBwMK1atWLChAlUqVKFevXq2cM+W+Bn+++iRYv45ZdfqFOnDuXLl0+wzzY82GKx2OcQLFWqFFWqVKFq1arA3yv+Zs+enbfffjtRjbahurZr39uDL3PmzHTu3Plx3BoRERERERFJA4V/IiJPGdvQ3Zw5c5I9e3YOHTpEzZo1ad++PWFhYRw7dgyACxcucPToUfz8/Lh+/TpXr15l5cqVTJ06FX9/fzp37oynp2eCtu8N6mxDcvPnz09YWFiStRhjiI+PTzBM92GLb9zbA1FERERERESeLIV/IiJPGSenux/N2bNnx8/Pj/PnzwOwadMmAEqUKAHA8ePHqVGjBrly5cJisRAeHk5sbCxVq1Zl0KBBNGjQIMXXtFqtWK1W+7VtLBZLom0Po+BPRERERETk6aHwT0TkKbJnzx7Cw8PJli0bANHR0WzZsoWXXnqJuXPn0qZNGypUqABA6dKlGTlyJBcvXsTT05MiRYoQEhJCsWLFcHBwSFUPPC3AISIiIiIikjEp/BMReYocOHCATp06kS9fPtzc3Dh69CiRkZEEBATw+uuvM2zYMNzc3IC78+u9++67ybalHngiIiIiIiKi8E9E5Cny/PPPs2rVKnbs2IGDgwM3btxg69atjB49mmLFiiV5jtVqta/46+DgoNBPRERERERE7BT+iYg8RVxcXAgNDSU0NBSAs2fPcubMGQ4fPkyxYsXsi4HcS8N1RUREREREJDn6xigi8hSKj48HIDY2FqvVyvHjx4GHr7QrIiIiIiIici/1/BMReQrZQr6AgAA+/PBD+wIgGtIrIiIiIiIiqaGefyIiTzFHR0d78CciIiIiIiKSWur5JyKSAQW9vexJl5BhnRjT6EmXICIiIiIikmLq+SciIiIiIiIiIpJBKfwTERERERERERHJoBT+iYiIiIiIiIiIZFAK/0RERERERERERDIohX8iIiIiIiIiIiIZlMI/ERERERERERGRDErhn4iIiIiIiIiISAal8E9ERERERERERCSDUvgnIiIiIiIiIiKSQSn8ExERERERERERyaAU/omIiIiIiIiIiGRQCv9EREREREREREQyKIV/IiIiIiIiIiIiGZTCPxERERERERERkQxK4Z+IiIiIiIiIiEgGpfBPREREREREREQkg1L4JyIiIiIiIiIikkEp/BMREREREREREcmgFP6JiIiIiIiIiIhkUAr/REREREREREREMiiFfyIiIiIiIiIiIhmUwj8REREREREREZEMSuGfiIiIiIiIiIhIBqXwT0REREREREREJINS+CciIiIiIiIiIpJBKfwTERERERERERHJoBT+iYiIiIiIiIiIZFAK/0RERERERERERDIohX8iIiIiIiIiIiIZlMI/ERERERERERGRDErhn4iIiIiIiIiISAaVpvDvlVdeYevWrcnu37ZtG6+88kqaixIREREREREREZFHl6bwb+bMmRw9ejTZ/cePH+ebb75Jc1EZWadOnXBxcSFz5sz2P8uXL3/SZYmIiIiIiIiISAb0WIb9RkZG4uLi8jiazhBeffVVbt68af9Tv379J12SiIiIiIiIiIhkQE4pPXDPnj3s3r3b/vewsDDi4uISHXft2jUmT55M0aJF06VAERERERERERERSZsUh38//vgjI0aMAMBisfDll1/y5ZdfJnls5syZmTdvXpqLslgsye67du0a3t7eaW47pUaPHs2uXbvYuXMnx48fJzAwkBMnTjzwnHnz5vHJJ5+wb98+PDw8qFu3LmPGjCEwMDDBcXPmzGHu3LnkyJGDl156iUGDBuHklOKnQkREREREREREJEVSnDh16tSJmjVrYoyhdu3aDBkyhGeffTbBMRaLhcyZM1O8eHHc3NweqbBq1arx6quvJtru4eHxSO2m1DvvvEO2bNkoV64c169ff+jxX3zxBX369KFKlSqMGzeOK1euMH78eDZs2MD27dvx9/cHoG/fvowdOxZfX1927dpF27ZtiY6O5v3333/Mj0hERERERERERP5rUhz+BQYG2nuwDRs2jJYtW1KiRInHVlj+/Pl56aWXUn2eMYavvvqKLl264OjomOQx06ZN46WXXnpgQHn06FHy588PQIkSJbh582ayx4aHhzN48GDKlSvHunXr7L346tevT8WKFRk6dCjTpk0DoFy5cvbzQkJCGDFiBMOGDVP4JyIiIiIiIiIi6S5NC34MGzbssQZ/NjExMdy4cSNV56xZs4bu3bvTrl27JOck7NevH926dWP27NkPbMcW/KXE4sWLuXnzJn379k0wfDckJITq1avz/fffExMTk+S5Dg4OGGNSfC0REREREREREZGUSvNEc8YYVq1axeHDhwkPD08UYFksFt577700F7Zw4UK+/fZb4uPjyZYtG82bN+eDDz4gZ86cDzyvTp06TJw4kT59+mC1Wpk3b549kHvzzTcZP368PQBML9u2bQMgNDQ00b7Q0FDWr1/PgQMHKFWqFPPnz6d+/fp4eXmxd+9eRowYQcuWLdOtFhEREREREREREZs0hX+HDx/m+eef58CBA8n2WnuU8K9ChQq0atWKQoUKERUVxdq1a5kxYwYrV65k69at5MqV64Hn9+7dG4vFQu/evTHGMG/ePAYMGMDnn3/OgAED+Pjjj9NUV3LOnj0LQJ48eRLts207c+YMpUqVYvLkyfTo0YPY2Fhy5cpF+/bteeedd9K1HhEREREREREREUhj+NenTx+OHj3KRx99RO3atfHx8UnXomw96Wzat29PjRo16NChA8OGDWPq1KkPbaNXr172ALBIkSIcP36ct956i48++ihdawWIiooCwNXVNdE+27yCtmPWr1+fqrYnTZrEpEmTiI+Pf8QqRURERERERETkvyZN4d9vv/3GG2+8wYABA9K7nmS9/PLLDB06lGXLlqX4nNdee4358+ezYcMGChQo8NgW1XB3dwfgzp07ZMqUKcG+27dvJzgmtXr16kWvXr2IjIwkS5Ysj1aoiIiIiIiIiIj8p6RpwQ8XFxfy5cuX3rU8VFBQEJcvX07RscYYXnvtNTZs2ECjRo04duwYLVu25M6dO+leV+7cuYG7Q3vv96AhwSIiIiIiIiIiIo9TmsK/Z599lo0bN6Z3LQ9kjOHIkSMPXfDDdmz37t358ssvGTp0KEuXLuWrr75i2bJlNG/ePN0DwAoVKgCwadOmRPs2bdpE5syZKVq0aLpeU0RERERERERE5GHSFP6NGzeOTZs28emnnxITE5OuBV28eDHJ7RMnTuTMmTM0bdr0gecbY+jWrRtfffUVI0aMYMSIEQB06dKFr7/+mhUrVtCsWTOio6PTreZmzZrh7u7O559/TlxcnH37jh072LBhA61bt8bFxSXdriciIiIiIiIiIpISaZrzr1q1aty6dYu33nqLt99+G39/fxwdHRMcY7FYOHr0aKrbHj16NKtWraJx48YEBgZy+/Zt1q1bx5IlSyhUqBDDhw9/4Plr1qxh+vTpfPDBBwwZMiTBvk6dOuHg4EDnzp2ZPXs23bp1S7ad2bNnc/LkSQAuX75MTEwMH3zwAQDe3t707t3bfqyvry+jRo3ijTfeoGbNmrz88stcuXKFcePGkSNHDkaOHJnq+yAiIiIiIiIiIvKo0hT+5c2bF4vFkt61AFC7dm0OHDjAt99+y5UrV7BYLBQoUIAhQ4YwcODAhy56UadOHbZv305ISEiS+zt06EDx4sWT3W8zffr0RCvzvvfeewAEBgYmCP8AXn/9dXx9ffn000954403cHd3p27duowePdo+J6CIiIiIiIiIiMg/KU3h37p169K5jL81bdr0oUN7H+Zhwd7D9kPaHmP79u1p3759qs8TERERERERERF5HNI055+IiIiIiIiIiIg8/RT+iYiIiIiIiIiIZFBpGvbr4ODw0Dn/LBZLgpVvRURERERERERE5J+VpvCvQ4cOicK/uLg4jh49ytatWylVqhRlypRJj/pEREREREREREQkjdIU/s2cOTPZfZs2baJp06ZMmTIlrTWJiIiIiIiIiIhIOkj3Of9CQ0Pp3Lkzb731Vno3LSIiIiIiIiIiIqnwWBb8KFSoELt27XocTYuIiIiIiIiIiEgKPZbwb926dWTKlOlxNC0iIiIiIiIiIiIplKY5/2bNmpXk9qtXr7Jq1Sp++eUXunbt+kiFiYiIiIiIiIiIyKNJU/jXqVMnLBYLxpjEDTo50aVLFz777LNHLk5ERERERERERETSLk3h39q1axNts1gsZMuWjXz58uHh4fHIhYmIiIiIiIiIiMijSVP4V6NGjfSuQ0RERERERERERNJZmsK/e928eZMTJ04AEBQURObMmR+1SREREREREREREUkHaV7t9+DBg9SvX5+sWbNSunRpSpcuTdasWWnYsCEHDx5MzxpFREREREREREQkDdLU8+/w4cM888wzREREULt2bUqWLIkxhj///JMVK1YQGhrKli1bKFSoUHrXKyIiIiIiIiIiIimUpvDvvffeIzY2lt9++43Q0NAE+zZv3sxzzz3HsGHDmDt3broUKSIiIiIiIiIiIqmXpmG/a9asoXfv3omCP4DKlSvTs2dPVq1a9cjFiYiIiIiIiIiISNqlKfyLjIwkMDAw2f1BQUFERkamuSgRERERERERERF5dGkK//LmzcuaNWuS3b9mzRry5s2b5qJERERERERERETk0aUp/GvdujU//PADAwYM4Nq1a/bt165d46233uKHH37gxRdfTLciRUREREREREREJPXStODHkCFDCAsL47PPPmP8+PHkyJEDgIsXL2K1WqlevTpDhgxJ10JFREREREREREQkddIU/mXKlIk1a9YwY8YMFi1axLFjxzDGULZsWZo3b06nTp1wdHRM71pFREREREREREQkFdIU/gE4OjrStWtXunbtmp71iIiIiIiIiIiISDpJ05x/IiIiIiIiIiIi8vRLc8+/mzdvMnfuXI4cOUJ4eDjGmAT7LRYL06dPf+QCRUREREREREREJG3SFP799ttvNGvWLMFKv/dT+CciIiIiIiIiIvJkpWnYb9++fXF0dGTx4sVcvXoVq9Wa6E98fHx61yoiIiIiIiIiIiKpkKaef/v27eP999+nSZMm6V2PiIiIiIiIiIiIpJM09fzz9/fH2dk5vWsRERERERERERGRdJSm8K9r167MmzcPq9Wa3vWIiIiIiIiIiIhIOknTsN/Bgwdz7tw5QkND6dGjB4GBgTg6OiY6rnr16o9coIiIiIiIiIiIiKRNmsK/27dvc+XKFbZv38727dsT7TfGYLFYtOiHiIiIiIiIiIjIE5Sm8K9Xr14sWLCAFi1aUKVKFbJmzZredYmIiIiIiIiIiMgjSlP4t3jxYrp27cqXX36Z3vWIiIiIiIiIiIhIOknTgh/GGMqXL5/etYiIiIiIiIiIiEg6SlP4V7NmTbZt25betYiIiIiIiIiIiEg6SlP4N378eNasWcOECROIjY1N75pEREREHhur1YrVan3SZYiIiIiI/CPSNOdfrVq1uHXrFv369WPgwIHkypULR0fHBMdYLBaOHj2aLkWKiIiIpBcHh79/92m1WhP8XUREREQko0lT+Jc3b14sFkt61yIiIiLyWB07doz58+fz66+/EhkZSaVKlejYsSMhISEKAUVEREQkQ0pT+Ldu3bp0LkNERETkwYwxGGPSHNKdPn2anj17smHDBkJCQsiUKRMzZsxg1qxZfPrpp7z66qvpXLGIiIiIyJP32H7FrYBQRERE0pPFYnmk3nkffPABK1euZNiwYcybN4+1a9eyevVqfHx8GDlyJGvXrgXuhowiIiIiIhlFuoZ/586dY9SoURQsWJA6deqkZ9MiIiLyH3b27Fl+/PFH+vfvz6JFiwCIj49/6Hm2IG///v1888031KpVi0GDBpE7d26cnJyoXLkyU6ZM4dy5c0yaNOlxPgQRERERkSciTcN+7xUXF8dPP/3E9OnTWblyJfHx8eTJk4c+ffqkR30iIiKSAdiG7NqG7aZk7mBjDL///jvTp0/n559/5uTJkwBcuHCB559/PkXXtV0nMjKSmJgYihUrBiRc6KNBgwYEBASwdOlSoqKicHd3T8MjFBERERF5OqW559++ffsYMGAAuXPn5oUXXmD58uW88MILbNmyhVOnTjF+/Ph0LFNERET+LYwxxMfHY7Va7dtsQ3YdHR2xWCzExsY+tJ07d+7w008/MWXKFDw9PenevTsA58+fB0jVEOATJ07g6OhInjx5iIuLs59r6xlYp04dYmJi2L59e4rbFBERERH5N0hV+Hfz5k2mTZtG5cqVKVmyJJMmTaJmzZpMnDgRYwytWrWiYsWKj6tWERER+RewWCw4Ojri4OCAMYZ9+/Yxe/Zs+vTpQ82aNcmTJw+9evUCHjy/npubG40bN2bDhg3s2bOHKVOm4OHhwcGDB4mNjU1x70GA6OhojDH2/96/v2TJkgDs2rUrzY9bRERERORplOJhv6+88goLFizg1q1bVKxYkS+++IK2bdvi7e3N0aNHH2eNIiIi8oTZhuxaLJYEodu9w2dtx4WFhTFixAiOHj3K6dOnMcbg7OxMjhw5iIyMJDIyktKlSwM8NMArX748FouFuLg4nJycKFCgAHv27OHkyZMULFjQXtOD6rZYLGTNmhVXV1dOnz5NfHw8zs7OCY4rUKAAcHduwHvPExERERH5t0tx+Ddz5kwKFy7MnDlzKF++/OOsSURERJ6ABwVe94Z+N27c4PTp0zg7O1OoUKFEx128eJGbN29SuXJlOnbsSFBQELlz56Zw4cK0b9+ezZs3U7Vq1RTVdH89ZcqUYc+ePezfvz9F4Z9tX0BAANmzZ+fAgQNERETg5uaWaD/AmTNnHnovRERERET+TVIc/j3zzDNs2bKFWrVq8cILL9CxY0eqV6/+OGsTERGRf1ByYVdcXBzbtm3ju+++Y/ny5Rw7dgxXV1fy589PjRo1GD58OL6+vvbj69evT2hoKF5eXnh6eiZoy2q14u3tbe95l9KQzXZMuXLlmDVrFn/99RdNmjR54LDhe8/LkycPwcHBbN26lcOHD5MjR44E+231u7i4AKmbT1BERERE5GmW4n/Zbtq0iX379vHqq6+ybNkyatWqRYECBRg5ciTHjh17nDWKiIjIY3bx4kWWL1/OunXriIuLS7Bv+vTpvPDCC8ybN49SpUoxbNgwevTogYuLC5MnT6Zv376cPn0auBvmeXp6kjt3bnvwZ1v4IywsjCNHjlCpUiVy5coFPHzYr43tuLJlywLw559/purx+fj48NxzzxEeHs7SpUsT1AWwd+9eAEJDQx8aKIqIiIiI/Juk6tfaRYsW5ZNPPuHMmTMsWLCAokWL8v7771O/fn0sFgu//fYbly9ffly1ioiIyGNw5coVQkJCaNiwIZ999hnh4eEA9hV5//jjD4KDg/nhhx+YMWMG7733Hp9++inLli2jYcOGfPfdd2zYsAFIGKjd79q1a4SHh5MjRw6yZs1KfHx8imu09cQrVqwY8PfcfI6Ojik+v3379vj7+/Pll1+yZ8+eBL37Jk+eDEC1atU03FdEREREMpQ0jWlxcnKiRYsWLFu2jJMnTzJy5Ejy58/P+PHj8ff3p3bt2vZ/RIuIiEj6MsYQHx//wKAtJWzh29SpU4mIiCBbtmxERERw/fp14O/A7Y033mDWrFlUq1bN3psvNjaWnDlz0r59e+DBPfFs7Zw9exaAIkWKACkP7u7l6+uLn58fx48fJzIyEnhw4GhjtVrx8fHh7bff5saNGzRp0oTBgwczadIkGjRowIoVK3jjjTcoU6ZMqmsSEREREXmaPfKENv7+/gwZMoTDhw+zevVqXnzxRbZs2UKfPn3Soz4RERG5j8ViwdHR8ZHnpXN0dOTChQssXryYmjVr0qxZM/bv38+lS5eAv0O7woULkzNnzgTDYW1z9kVHRwPg5eVlbzM5P//8M3B3TsC0sA1HLlq0KNevX+fIkSMJ6rzXkSNH+Oyzz9i5cyfwd0DYu3dvZs2aRc6cOfn888/p06cPO3fuZMCAAQwdOpRMmTKlqTYRERERkadVihf8SIlatWpRq1YtIiIimDt3bno2LSIikuEZYzDGJBvqGWM4deoUYWFhrFq1isOHD+Pl5UXjxo1p2LAh+fLlS9W1bFN2HDx4kI8//phffvmFiIgIzp07ByScjy+phTni4uKYPXs2AG3atHnotQ4dOoSrqyt+fn4prvNeTk53/9lSqVIlwsLCOHr0KOXKlePUqVO4urqSI0cOYmNjcXZ2ZunSpQwYMIAWLVqwcOHCBMFlu3btqF+/PocOHSJnzpwEBQU98HGKiIiIiPybpWv4Z5MlSxZee+21x9G0iIhIhmQLnSwWC8YYoqOjE/RCu3PnDgsXLmTChAkcPXqUXLly4eXlxb59+1ixYgWTJ09m+vTpPPPMMykKsGzXmTx5MmXLlqV69ers27eP2NhY++Id9x9/P9sCId26dSMgIOCB1/rjjz+IioqicuXK9l6Dybl06RIuLi54e3sneCyXLl2y93oEGD9+PMuXLycsLIxnnnmGWbNmER8fj7OzM1WrViV//vxUr14dINE1s2XLxjPPPGP/u9Vqtd9/EREREZGM5JGH/YqIiMijs1gsbN68GV9fX/z9/Zk5cybw97x8p0+fZsCAAdy+fZuRI0cyb948fvrpJ3bu3MnQoUPZv38//fv3T9U1N2/ezLp163j33XcByJs3L25ubhw/fpzbt28/8Nzr16/Tr18/8ubNS//+/e298u5nG267b98+zp49S4kSJciRI0eS8/Rdv36dzp07kzNnTr799lsAe4+9bdu2Ua9ePapWrcpHH31kr3/u3LnExMTYhx27ubkBEBISwpEjR+jbt2+yj+He3oAODg4K/kREREQkQ3osPf9ERET+66Kioli5ciUxMTG0bt0aq9X6wDn6bL3wrl69ipubG1euXAH+ns8uICCAESNG0KVLl0Tz6g0fPpz/+7//Y/Pmzdy6dQsPD48H1mar5ZNPPqFChQqUKFECgOzZs+Pr68vRo0e5fPkyefPmTfbcwYMHc+TIEb799lsKFy6c7LVsgVpERATAA4f8urm54e/vj4ODgz1MtD1+JycnTp48ScWKFWnVqhWVK1emVKlSSdZoEx8fjzEm2WBSYZ+IiIiI/Bco/BMREXkMHB0d2bt3L+Hh4bRu3fqhQdOhQ4f47rvvGDRoEB9//DEnTpwgPj7eHvS5urry6quvAndDLVtPtbi4OJycnChQoACXLl3i9OnTFC1a9IFDfx0cHPjjjz9YtWoVgwYNIkeOHAAEBgZSoEABzp8/bx9ybGsf/g7+fvjhB7788ku6dOnCiy++mGDf/SwWC1FRUWzZsgV3d3eqVatmr+F+rq6u/4+9+46OqmrfPv6dSYckkIQUIIEUWghFugSlV5WiKCDtQQRBERBEQJGmKCI/pYtUBQQElCbwSJMmSEdAegsl9BBIAqkz8/7BO8fEhBbhoV2ftVwmc9o+J0GHa+69b5KTkzGZTDz33HMZtpUtW5YrV65keT9WqxWr1Zop5MtON2ERERERkSeNpv2KiIg8AElJSTg4OHDo0CHmzZvHggUL2LhxY5bTXQG+/vprwsPDeemllwgICODEiRPExsZmua+Dg4MR7Dk6OnLo0CFOnz7N888/b1TW3Sr4s1//xx9/pGDBgvTr18/YdurUKaxWK0ePHqVmzZpEREQwffp04O/A8eTJk3To0IHixYszdOhQI2C7XVVjjhw5WLFiBYmJiZQuXTrLfexhZXR0NKGhoQQGBma5T1paGmlpaRmeY/pKQRERERERyUjvlEVERO6zffv2MXbsWBYtWsSFCxdYvnw5Dg4OWCwWdu/eTcmSJTNU5p07d4758+fTu3dvypQpQ968eTlz5gznzp0jT548t6zis1cGLly4kDNnztCzZ098fHzuWPV35swZFi1axJUrV2jZsiVbt27l5MmTxvqCJpOJixcvUrFiRQICAoCbgaPNZqNLly5cu3aNmTNn4ubmxrFjx7h48SJnz57l2LFjdO/eHRcXlwzXPHXqFHnz5qVevXqZttlZrVYcHBw4cuQItWvXxsPDI9M+JpNJIZ+IiIiIyD3SO2gREZH7aMuWLTRu3JjExERq1qzJtWvXaNWqFTVq1GDp0qXGdFqTyWSEdxMmTMDR0ZEmTZqQI0cOihYtyi+//EJ0dHSmoNDOHpbt37+fAQMG8Nxzz9G2bVvj3P/c176/yWTC29sbPz8/4uPj2bBhAwULFqRKlSqEhoZy9uxZJk+eTIsWLRg0aBBeXl7GecaOHcvKlSspUaIEK1euZPLkyRw5coQzZ84QFxeH1WqlZs2alC9fPsP1CxQowI4dO2773OwVhL/99pvRvENERERERP69bId/P/74I2PGjOHIkSPExMRk2m5fh0hERORpMnPmTC5evMiUKVOoU6cOX331FR4eHoSGhtK1a9cM+zo4OHD27FkWLVpEzZo1KVy4MCkpKURERDBr1ixOnDgBZL2entlsxmaz8fbbb+Pk5MSnn36Kt7d3lmMym80Zjs+RIwe9e/cmNTWV4OBg8uXLh5eXF46OjmzdupX58+eTlJRkdM5NTU3FycmJKVOmkJqayt69ezlw4AAFCxakdOnStGrVikqVKhEREYGvr+8tKw/T0tIyTFnOioI/EREREZH7K1vh3/Dhw+nbty8+Pj48++yz+Pj43O9xiYiIPHbS0tKIi4sjICCAypUrExgYiLOzM4cPHwZurllnr8Cz27ZtG7t372bMmDHAzQ/PChUqBMD58+cBbjnVdciQIWzYsIGhQ4dSrVo1bDYbNpstU1C4b98+pkyZQnh4OG3atMHV1ZUXXnghy3M6OztjtVrZsGEDZ8+eJSwszNj21Vdfce3aNSpUqEBQUNAtn8Otwj1N2RURERER+d/L1rvwcePGUalSJVavXm1MXxIREXnaOTg4UKZMGaZPn860adNo06YNnp6exMfHA5CYmEiOHDkyHPP555/ToEEDo7utk5MTJUuWxMXFhT/++IPRo0eTlJTEO++8g7u7u1FVt27dOgYOHEj9+vXp1KkTcDN0Sx+82SsGt2zZwsiRI6lbty6NGzfG1dXVCArtx9i/DgoK4rPPPiNv3rzkz5/fGJPNZqNWrVoZxm6xWDKc43ZNP0RERERE5OHIVvh3/vx5evfureBPREQkHZPJRNu2bZk8eTLDhg1j7ty5RjXg0qVLiYiIoHHjxrz88su4urqyatUqdu7cyfDhw5k3bx5btmxh586d7N27l+TkZFatWsWqVat49tlnadq0Ke7u7phMJmJjY3n//fcJDg7myy+/JHfu3CQmJpKQkMD58+dxc3MzqgcBQkNDyZEjB15eXvj5+RmhYPqg0P61j48PnTt3zvLe4O8mI0CGCkYREREREXk0ZSv8CwsL49q1a/d7LCIiIo89Ly8v1q1bx7hx4zhw4AAXLlzg4MGDJCQksHbtWubOncuHH37IZ599xpkzZ/Dw8KBnz57AzbX4AgMDKV26NIcOHeL69et88MEHNG3alLx58xpVdl9++SU7d+6kdevWHD16lF9//ZXjx49z/Phxtm/fTuXKlfnll1+w2WwAuLu7c+PGDYoVKwbcelquXVbTk+0U+ImIiIiIPF6yFf716NGDoUOH8u677+Lh4XG/xyQiIvJY8/b2pn///gDGlNtOnToRERHBq6++yqhRo3jhhRd4/vnneeONN3Bzc6No0aIEBQVRoEABPD09GTRoEN999x1ly5alSJEixrl37tzJsGHDgJvNt2bOnInNZsPT05MiRYrQqFEj6tWrh81mM4K6xMREAF5//XXgzuGfyWRSyCciIiIi8oTIVvjn7OyMr68v4eHhtG/fnpCQkCz/ktC2bdt/PUAREZHHUVpaGo6OjgQEBODn58fhw4epXr06rVq1YsOGDfz1119UqVKFL774Aicnp0zHFytWjMTERLZt20a9evWM8wUFBVGlShUKFSpE6dKlKV26NM888wxeXl63HEvOnDn5+OOPCQ4OfoB3LCIiIiIij6JshX/t2rUzvh4yZEiW+9jXPRIREXka2Tvb+vn54evry7lz5wDYtGkTAMWLFwcwgj+r1QpgNM0ICwujQIEC+Pn5ZTifr68vGzZsyPKa6RtwpP9QrmzZspQtW/a+3p+IiIiIiDweshX+rVmz5n6PQ0RE5ImxZ88eYmJi8Pb2BiApKYnNmzfTunVrZs2aRYsWLahQoUKGY/7ZKbdBgwZERUVleX6bzYbFYsnUZfd2U3XtTT5EREREROTpkq3wr1q1avd7HCIiIk+MgwcP0q5dO0JCQnB1deXYsWPExcURFBRE9+7dGThwIK6urnc8z60ab5hMJqMS8G4p+BMREREReTplK/z7p4SEBOBmN0EREZGnXZMmTVi1ahXbt2/HbDYTHx/Pli1bGDp0KOHh4Xd9HjXeEBERERGRfyvb4d+lS5fo378/CxYs4PLlywDkyZOHpk2bMnjwYHx9fe/bIEVERB4nzs7OREZGEhkZCUB0dDRnzpzhyJEjhIeHG807REREREREHrRs/c3j/PnzPPvss5w6dYpChQoRGRmJzWZj//79fPvttyxbtozNmzcTEBBwv8crIiLy2LBYLDg4OJCamorVauXEiRPA7dfmExERERERuZ+yFf7179+f6OhofvjhB1q2bJlh248//kjbtm0ZOHAgEyZMuC+DFBEReRzZQ76goCA+++wzowGIyWR6mMMSEREREZGnSLbCv2XLltG5c+dMwR9AixYt+P3331mwYMG/HpyIiMiTwMHBwQj+RERERERE/pey1frv8uXLlChR4pbbS5YsSUxMTLYHJSIiIiIiIiIiIv9etir/AgIC2LZtG506dcpy+7Zt2/D39/9XAxMREXlaBPdd+rCH8ESK+uLFhz0EEREREZGHLluVf40bN+b7779n7NixWCwW43WLxcI333zD999/T5MmTe7XGEVERERERERERCQbslX5N2jQIFauXEn37t0ZPHgwRYoUAeDw4cNcuXKFIkWKMGjQoPs5ThEREREREREREblH2ar88/b2ZuvWrXz44Yf4+/uza9cudu3ahb+/Px999BFbt27Fy8vrfo9VRERERERERERE7kG2Kv8APDw8GDJkCEOGDLmf4xEREREREREREZH7JFuVfyIiIiIiIiIiIvLou6vKv/Xr1wNQtWrVDN/fiX1/ERERERERERER+d+7q/CvevXqmEwmEhMTcXZ2Nr6/FZvNhslkytAJWERERERERERERP637ir8mzp1KiaTCScnpwzfi4iIiIiIiIiIyKPrrsK/du3a3fZ7ERERERERERERefRkq+HHJ598wl9//XXL7fv27eOTTz7J9qBERERERERERETk38tW+Ddo0CD27Nlzy+1//fUXgwcPzvagRERERERERERE5N/LVvh3Jzdu3MDR8a5mFIuIiIiI3BObzYbVan3YwxARERF5LNx1Qnfq1CmioqKM7w8ePMj69esz7RcbG8uECRMIDQ29LwMUEREREUnPZDIZzeeOHj3KuXPnyJMnD+Hh4Q95ZCIiIiKPnrsO/7777jsGDx5svNn67LPP+OyzzzLtZ7PZMJvNTJ48+b4OVERERESePDabDZvNBoDZfHeTUpYvX87o0aNZu3YtFouFXLlyERERwZw5c/D19X2QwxURERF57Nx1+NekSROCg4Ox2Wy0b9+et956i8qVK2fYx2Qy4e7uTvny5SlQoMB9H6yIiIiIPFnSV/HdjXnz5tGlSxc8PDx46623CA4Oxmq14uTkhIODwwMcqYiIiMjj6a7Dv9KlS1O6dGkA1q1bxxtvvEGlSpUe2MBERERE5MkWHx/PoUOHWLFiBcuXLyc8PJzBgwfj7++f5f5Hjhyhe/fuFC5cmKlTp1KkSJF7Cg5FREREnkbZ6srx3Xff3e9xiIiIiMhTZN++fYwcOZIFCxZw5coVABITE4mJicHf3x+bzWYEe1arFbPZzMyZMzl//jzffPMNRYsWBSAlJQUnJydMJpOxn4iIiIj8LVvh37hx41iwYAGrVq3KcnvdunVp2rQpnTp1+leDExEREZGHz74u371O0c2KPaA7cuQIy5cvp0yZMtSvX58VK1awd+9eLl68SPHixTMcYzabuXz5MmvWrKFw4cI0adKES5cu8fPPP3Pq1Cnc3d2JiIigcePG/2psIiIiIk+ibIV/33//PeXLl7/l9iJFijB16lSFfyIiIiJPgPsR+tnZK/OqVavGsmXLKFGiBADXr19nzZo1REdHG9dMz9nZmX379uHm5sbWrVtp3rw5J0+exM3NDYvFQkpKCq1bt+a7777T2n8iIiIi6WRrXsSRI0coWbLkLbdHRERw5MiRbA9KRERERB6s9F12b7Ud4Nq1a6xevZqhQ4cyePBgNm3alGmf7PDy8jKCv9TUVPLnz09aWhonT57Mcn9PT0/S0tKIjY2lXbt2FC9enB07dhAbG8vy5cupUqUKP/zwA2PHjiU5OTnb4xIRERF50mQr/EtNTb3tm6qkpCSSkpKyPagnWbt27XB2dsbd3d3459dff33YwxIREZEn3D/DvttV89mn+B4/fpwmTZpQv359vv76a0aMGEHt2rVp1aoVVqv1vlUDOjk5ERwcjKurK1FRUdy4cSPTeAAKFSrEjRs3uHz5Mh9++CFlypTBZDJRrVo1Pv30U4KCgvjhhx84fvz4fRmXiIiIyJMgW+FfkSJFWLly5S23r1ixgrCwsGwP6kn31ltvkZCQYPxTv379hz0kERERecLZwz6bzcaBAwdYtmwZ69atIyUlJct94+Pjady4MVu2bGHw4MEsWrSIRYsW8eKLL/Ljjz/y/vvv39fx+fv74+fnR1RUFFevXs00HoBSpUoBUKNGDaNq0D7Ft3Tp0kRGRnLo0CEj/Ps3lYkiIiIiT4pshX+vv/46y5cvZ+DAgRneMKakpDBgwABWrFhBy5Yt79sgRURERCQjm82G1Wq9q4DLYrGwZs0a2rVrR1BQEBEREbzyyiu88MIL1K1blw0bNmQ65r///S8HDhzgvffe46OPPiIyMpJq1aoxa9YsihcvzqhRozh06NB9ux8vLy8KFizIqVOnuHjxonGPcLNJCMBzzz0HQHx8PM7OzsDfwaCrqyv58+fHYrGQmJiYYZuIiIjI0yxb4V+PHj2oWrUqn376KQEBAVSuXJnKlSsTEBDAkCFDeO655+7bp8E3btwgNDQUk8lE586d78s578bQoUN57bXXjGsHBwff8ZjZs2dTrlw53NzcyJMnD6+//nqW69bMnDkTb29vwsPD+eyzz0hLS3sAdyAiIiJPMpPJhNlsNqr0/vrrL3bv3p3lvr/88gstW7ZkwYIF1KpVi2+++YYxY8bwn//8h/Xr1/PSSy8RFRUF/B20LVmyBKvVStOmTQFIS0sjJSUFJycn3njjDQDmzZt336rrPDw8KFSoEBcuXODcuXOZ7hXg+eefx83NjQMHDnDq1KkM21xcXNi6dSvJycmULVv2voxJRERE5EmQrfDPycmJFStW8MUXX1CgQAH27NnDnj17KFiwIF9++SWrVq0yPo39twYMGMClS5fuy7nuxUcffcRvv/1GWFgYXl5ed9x/7NixtGzZEjc3N0aMGMF7773HypUriYyM5OzZs8Z+3bp149ChQ1y+fJkZM2bw/fffM3jw4Ad5KyIiIvKEiY+P55dffqF9+/YUKlQIHx8fqlatygsvvECjRo3YsmVLhv0vXLhAkyZNWLt2LdOmTaNz58507NiRb775hh49ehAfH8+cOXOMtf4AEhMTMZvNXL9+Hbg5vdbJyQmAKlWqULBgQdauXcuFCxfuyz25ublRuHBh4uLiOHPmDPB3EGkfU2hoKF27duXkyZN8+umnxrHXrl1j/vz5/P7779SuXZt8+fLdlzGJiIiIPAkcs3ugk5MTvXv3pnfv3vdzPBns2rWLkSNHMmzYMHr16nVXx9hsNiZNmsSbb75prAHzT5MnT6Z169a4urre8jzHjh0jNDQUgBIlSpCQkHDLfWNiYvjwww8pW7Ysa9euxdHx5mOtX78+FStWZMCAAUyePBkgwyfR5cuXZ/DgwQwcODDDG1gRERF5sqUPtbIzNXXt2rW89957WK1WKlasSJs2bXB2dmbfvn3MmjWLffv2MX/+fEqXLg3ASy+9RIsWLciVK5dxjpSUFJydnalRowZjxozh4MGDxlhSUlJwd3cnZ86cGZpv2LcHBQVRtGhRDhw4wMWLFwkICMj2s4Cb798cHBwoWLAggFHV98/3cg4ODvTt25ft27cze/Zs9u7dS6VKlbhy5QpLliyhfPnyfPHFF7d9jyciIiLytMl2+PegWSwWOnbsSL169WjatOldh3+//fYbnTp1YvXq1cycOdMI4ux69uzJiBEjsNlsdOzY8ZbnsQd/d2PRokUkJCTQrVu3DNcrX748VatWZe7cuXzzzTdZVkOazWYtRi0iIvKUMZv/nnyRnJyMs7PzXYWA9so8b29vevToQaVKlQgPD8fd3d3YJzg4mM8//5x169ZRunRp0tLSyJ8/f6Zz2d+zODk5kZaWlmFMVquVPHnykJycTGxsbKZjvby8CA4OZsOGDcTExNxynHfLHoIWK1YMT09Pjh07xuHDh0lKSiIuLo5nnnkGd3d3LBYLuXPnZvLkycyZM4d58+bx008/4enpSZs2bejSpQvPPPPMXV9XRERE5Gnwr8K/CxcusH37dmJjY41PsNNr27Ztts89cuRI9u/fz7x58+7puFq1ajFmzBi6du2K1Wpl9uzZxpvbHj16MHLkSHr27Hnb4O9ebd26FYDIyMhM2yIjI1m3bh0HDx6kVKlSzJkzh/r16+Pp6cnevXsZPHiwsZaOiIiIPJ7szTfs6/DdisVi4cyZM2zevJn58+fz66+/UrlyZcaPH09ISMgdQzP7tipVqlClSpUs97F3wbU3zbjVeOyvz5o1C7PZzGuvvWZsc3Z2Jjg4mJSUlExr68HNKbre3t4kJibeslvw3UpLSyMqKopTp06xefNmXFxcWLFiBfv27SM6OhpfX18mTpzI888/j9lsxmq1EhwcTJ8+fXjzzTfx9PS8b8vNiIiIiDyJshX+Wa1WunTpwuTJk7MM/eyyG/6dPHmSgQMH0r9/f0JCQowFqO/Wu+++i8lk4t1338VmszF79mx69erF6NGj6dWrF8OHD8/WuG4lOjoagMDAwEzb7K+dOXOGUqVK8c0339C5c2dSU1PJmzcvrVq14qOPPrqv4xEREZH/LZPJlGGKakJCglGNlz7Q27VrF/369WPlypXGvhcvXiQ2NpaQkJB7uqZ95oD93NevX2f//v2MHz8eV1dXmjdvDtw6/ANYs2YNs2fPplatWtStW9d43Ww2U6RIEVxcXNi9ezc3btwgR44cGe7HYrEAZJplkZSUxNatWwkLCyN//vxYrVbMZjNHjx5l8eLFVKtWjXLlypGWloajoyNHjx7lgw8+YOnSpcY57NWIVatWpXbt2hQtWtS41/TBYp48eYwx3U34KiIiIvI0ylb493//939MmDCB1q1bU7duXdq2bcuwYcPw8PBg5MiR5MqVi6FDh2Z7UG+//TYFCxa866m+WenSpYsRABYtWpQTJ07Qu3dvhg0blu1z3op9LRwXF5dM2+xrztj3Wbdu3T2de9y4cYwbN854gy0iIiIPns1myxCu3a6SzWq1snHjRubPn8/GjRu5cuUKgYGB1KtXj4YNG1KiRAlSU1NxcnIiISGBhIQEevXqRe3atZkxYwa//PIL58+fv+cx2se0c+dOli5dyoEDB1i3bh2XL19mzpw5lCxZ8rbHx8fH89FHH+Hp6cnQoUMzhWZly5YlLCyMtWvXcvLkScLDw0lNTcVqteLi4sLVq1cz7G+/x0GDBvHll1/Sp08fhg4dSkpKCq6urixbtoxevXrxyiuv8NNPP2GxWHB0dMTd3Z3SpUtTtGhRqlSpQpkyZQgODr7nZ3GrtZ7l4Ur/Z0nBrIiIyMORrfBv2rRp1KtXj+nTpxvrvJQrV46aNWvSpk0bSpUqxY4dO6hZs+Y9n3vWrFn897//Zd26dUZHuex6++23mTNnDuvXrycsLOyBNdWwfxKenJyMm5tbhm2JiYkZ9rlXXbp0oUuXLsTFxWVYpFtEREQenLtpxGGvgJs/fz79+vXj8uXLREZGUqBAAQ4cOEC/fv345Zdf+Pnnn8mbNy8Azz77LPPmzTO60e7Zs4dZs2YZ3W2z2/xjyJAheHp6EhYWhq+vL6NHjyY+Pp7mzZtnmhJrr7j75JNP2LJlC8OHD8/QkMx+bz4+PjRq1IgvvviCuXPnMnDgQOO92aVLl9i6dSvPPPMMxYoVyzD2ihUrAhhdgu2BT2RkJKGhoVStWhX4+0PTwMBAhgwZkum+rFZrhmq+7Dwbefiy29RGRERE7p9sffx2/PhxGjRocPME//8NXWpqKgA5c+bkjTfeMLrb3ouUlBR69OjBSy+9RIECBYiKiiIqKsp4QxwfH09UVBTXrl2747lsNhtvv/0269ev58UXX+T48eM0bdqU5OTkex7XndgX0baPM73bTQkWERGRR09MTAwrV65k8ODBtGnThg4dOjBr1iwuXLgA/F3JZDKZ+Ouvv2jevDlpaWn8/PPPTJo0iZ9++ok///yT5s2bs3PnTn755Rfj3K6urkbwZ7VaKVCgAABRUVHZbgDWvn174uPjuXTpEps3b+a7777DZrPxn//8hxkzZmTY1x78LVy4kDFjxvDCCy/w9ttvG/dlZ//69ddfp1KlSgwfPpwBAwawdetWfv31Vzp27Mju3btp2LAh+fPnx2azGdN/7UvCPPfccwBG+Fi+fHmOHj1Kt27dMt2DzWYjLS0tw3IyZrMZR0dHHBwcFB49YuzB7J1cunSJlStX0q1bN8LDw/H19eXKlSv/gxGKiIhIetkK/9zc3Iw3cu7u7phMJmNRaYCAgABOnz59z+e9ceMGFy9eZMmSJYSEhBj/PP/888DNqsCQkBDGjx9/2/PYbDY6derEhAkTGDBgAEuWLGHSpEksXbqUl19++b4HgBUqVABg06ZNmbZt2rQJd3d341NxEREReThu3LjBwoULmTt3LkCm8OL69et89dVX1KpVi1dffZUJEyYYU2pbt25NZGQkBw8ezFDJtHXrVmw2G+3bt6d69eoEBAQAN9esq1u3LikpKVk2o7DZbJjNZgIDA/H09OTEiRPExcVl675y586Ns7MzNpuN1NRUypQpwxdffIG/vz+zZ8823qPZg7/Tp0/Tr18/8ubNy1dffWXMTjCZTEaoY/9wt2TJkkyYMIECBQowfPhw6tWrR9OmTVm7di39+vVj8ODBmZqUREdH4+LiQq1atTKN1WKxkJaWlul1k8mEo6OjpoU+Jsxms/GzulUIGB0dTe/evXnppZcYO3Yshw4dIiYmhuPHjwNkO+wWERGRe5etab8FCxbkxIkTwM03t4UKFeLXX3+lTZs2AKxatQp/f/97Pm/OnDlZsGBBptcvXrxIp06dqFevHp07dyYiIuKW57DZbHTs2JEpU6YwePBgBgwYAMCbb76Jg4MDb775Jo0bN2bhwoXGenz/VuPGjenWrRujR4+mVatWxiff27dvZ/369bzxxhvqQiciIvKQOTg4sHfvXmJiYmjWrFmmajKTycSsWbPw9vamQ4cOlC1bFl9fX9zc3Jg4cSJDhw5lwIABzJw505j+ag/xsgruVq9eTd68eTNNqbVfC8DX15d8+fIRFRVFTEwMuXLlumPH31tJ3+wiX7585MuXj9OnTxthm/39Sb9+/Th06BCff/45BQoUYNu2bVy+fJmoqCjOnj3LSy+9RKVKlYzzlipVypiefOLECQIDA4mMjMzQhMP+LEwmE1FRUbz22mtZvvfRunyPv+PHjzNnzhxWrlxJXFwclSpV4j//+Q/ly5c3ujGbzWZiY2O5ePEi9erVo0+fPkyfPp1JkyZx4MABypcvj9Vq1e+DiIjI/0i2wr+aNWsyf/58o3lGmzZtGDBgAGfPnsVms7Fhw4ZsNetwcnKiSZMmmV63d/sNDg7Ocnt6v/32G1OmTGHIkCH069cvw7Z27dphNpt54403mDFjBh07drzleWbMmMHJkyeBm1MWUlJSjPVocufOzbvvvmvsmydPHj7//HPee+89qlevTps2bbh8+TIjRozA39+fTz755C7uXkRERB6kpKQkHBwcOHToEPPmzcPBwQF/f38qV66M2WwmR44crFy5Em9v70zHdu/enRkzZnDw4EEuXbpkTN2tVq0apUuX5v/+7/9IS0ujZMmSJCQksHjxYtasWcOIESMoVarULceUO3duQkJC2LNnDxcuXCA0NDTL/Ww2m9Eg458sFosRotj/nZCQwN69ewkMDDTGCjBv3jzmz5+Pu7s7x44do3r16pw+fZqLFy8aFVyFCxemYsWKGQJIR0dH2rZte9vna99/xIgRt91PHl+nT5/mnXfeYf369ZQvXx43Nze+++47pk+fzogRI+jQoYMRQIeFhTF58mRjvcuzZ88yadIk9u7d+zBvQURE5KmUrfCvV69e1K1bl+TkZFxcXPjwww+5cOECM2fOxMHBgbfeeovBgwff77HelVq1arFt2zbKly+f5fa2bdtSvHjxW263mzJlSqbOvP379wduVj6mD//g5l8K8uTJw1dffcV7771Hjhw5qFOnDkOHDjXWBBQREZGHY9++fYwdO5ZFixZx4cIFli9fjoODAxaLhd27d1OyZEmsVmuWwR/crHa6fv065cqVw9PTE7g53bFQoUJ8/fXXjB8/nqlTp2ZYl7hYsWL8+eefLFy48JYfXubMmZPChQuzZs0azp49e8vx26fF/tP169fJmTMncDMEvHLlCrt372bo0KGkpaXRu3fvDPuvWbOGGzduALBo0SLCw8Np06YNFStWpGzZsoSEhNz6If7/awB3bMCR3epFuT/Sr0t5P38OQ4YMYcWKFQwdOpTWrVvj7+/Ptm3beP311xk0aBBhYWHUqFEDq9WKm5sbbm5uxjjslaL79u0DstfcRkRERLInW+Ff3rx5jU/x4OanzGPGjGHMmDH3bWDpBQcH39O6IHcK9u60HW52zrtXrVq1olWrVvd8nIiIiDw4W7ZsoXHjxiQmJlKzZk2uXbtGq1atqFGjBkuXLsXNzQ0gw3pzqampREdHc/r0aXbv3s24ceNISkqiQ4cOuLu7Z9i/WrVqJCUlcebMGerVq0eTJk3w8/Nj+fLlfPTRR8yePZupU6fSvHnzTKGYq6srhQoVIjEx0ZhxkNV0yDNnztCzZ0/S0tKYPXu20Sm3S5cuxMfHG2sNHj16lJ07d2Kz2fjyyy954403gL/DuPfee4+uXbtSqFAhY+ryP90uuLvbaZoKdh6u+xn62X8fDhw4wLRp06hevTp9+vQxtleuXJnx48fz4osvMm7cOGrUqJHh2vavixYtitls5vDhwwBa31FEROR/KFvhn4iIiMjjYubMmVy8eJEpU6ZQp04dvvrqKzw8PAgNDaVr165ZHhMVFUXnzp3ZsWMHcXFxmM1munXrluUU3n379tGmTRuaNGnChx9+aFTotW3bljJlylC6dGnGjBlD8+bNM4Qi9rXRChYsiJOTE2fOnAGyDtiSkpJYuXIlNpuN6OhoY3qwl5cXGzdu5I8//iA5OZmAgACaNWtGw4YNqVGjhhES2q9bpEgR45z2qcSQsYGDgrvHW0xMDDt27ODPP/8EoHbt2lmuO3m37L8P165dIyUlheLFiwNkaAzToEEDgoKCWLJkCYmJiUagbmez2XBxcSEkJIRTp05x9uzZDNPRRURE5MHK1kduGzduZOzYsRlemzNnDiEhIeTKlYvu3burg5eIiIg8dGlpacTFxREQEEDlypUJDAzE2dnZqD5KH4Cl5+3tTYkSJejbty9z5sxh3LhxLFu2jJCQEL7//vsM+06ZMoXLly/Tt2/fDFNzLRYLJUuWJG/evBw8eJDLly9nOM4enBQsWBBvb2/279/P6tWrWbx4MbNmzTK69AI4OzsTEhJCyZIlje68AAMHDuTXX39l165dxMTEsG/fPsaNG0f9+vWN4O+f7O/R7FOJ1WX30WSfunsvFi9eTOXKlXnxxRf5/PPPGThwILVr1+att9761+M5efIkDg4OBAYGkpaWZvzO2MdYq1YtUlJS2Lp1a6Zj7etJlixZktTUVOPP3606BYuIiMj9la13eoMHD2b16tXG91FRUbRr146UlBQKFy7M2LFjmTRp0n0bpIiIiEh2ODg4UKZMGc6fP8+0adPYv38/np6eJCQkAJCYmJhlpZ2Pjw+jRo2ib9++vPbaa3Tq1Ildu3ZRsGBBOnbsmGF9vlOnTgEQHx8P/L0unoODA5cvXyZnzpx4e3tz9epV45gbN26wYcMGJk6cyLfffktSUhIrVqzg1VdfpUmTJrz//vvs3LnT2D8hIYH4+HiKFClCQECAcY3cuXMTFhaGv78/cDNMSUtLu22oosq+x4N96q7NZuPgwYMsXbrUaIKXlVWrVvHKK6+QlJTEqFGjWLx4MQsWLKBs2bJMnTqVjz76KFvjsId7SUlJ2Gw249//3F6iRAkAdu3adctzlClTBvh73T8VC4iIiPxvZGva7969e+nRo4fx/Y8//ojJZGLnzp34+/vTqFEjpkyZcl8+ZRQRERHJLpPJRNu2bZk8eTLDhg1j7ty5RjXg0qVLiYiIoHHjxrzyyiuZKuWsVis2mw2z2YzNZsPNzY369eszduxYdu7caUxbLFasGADjx4/n66+/JkeOHMY6aWPGjOHo0aO8+eabFCpUyJgqefr0afr378/69euBm5V9JUuWpESJEpQpU4bIyEjKly9v7H/9+nWOHz9Ov379gFuvvZd++q48etJXXd5pv507d7Js2TJ+/vln9uzZA8D06dMJDg7O8phPPvkEq9XK4sWLeeaZZ4zXn332WUqUKMEXX3zBG2+8QeHChe95zCaTCS8vL1xcXDh16hRpaWmZ1owsVKgQAPv37wcyTgu2/9s+/dh+PxaL5Y7NY0REROTfy1b4Fxsbi5+fn/H9ihUrqFatmvGp8wsvvGC8ORURERF5mLy8vFi3bh3jxo3jwIEDXLhwgYMHD5KQkMDatWuZO3cuH3/8MR988AEeHh4ZuqTaQ4nU1FRcXFyMtczSV/41bNiQVatWMXHiRI4fP06lSpVwc3NjzZo1rF27lsjISLp06QL8Hfr4+/vTuXNnunfvTtmyZSlYsOBt7yE8PJzJkyfTtGnTB/GI5H8gfSOV+Ph4jhw5gsVioUKFCpn2tTdn2bhxI15eXlSsWJEdO3YQExODxWLJFP7Gx8dz8uRJihYtarwft1gspKWlkTt3bmrWrMkPP/zA4sWL6dmz5z2FbfZ9AwMD8fPz49ChQ8TFxRl/FtJvB4iOjjbu184eBNrXCzx48CBwM/QWERGRBy9bHw17eXlx/vx5AJKTk9m8eTNVq1bNsE9SUtK/H52IiIjIfeDt7U3//v2ZNWsWn3/+OVWrVuW7775j7969PP/884waNYpNmzYBGauz7P+4uLhw6NAhFi9ejK+vL+XKlTPOXalSJUaNGkWDBg3466+/+Pzzz/n44485cOAAHTp0YOzYsUYllj0oyZ07Ny1atODll182gj97WGOxWDJN23V3d+eNN97Aw8PjQT8qeUBMJhMbN26kfv36+Pj4ULlyZerXr8+rr77KsWPHjP3sP/vGjRsza9YsYmJi+Pzzz8mRIwcHDx4kMTEx07kvXrxI7ty5cXFxMaaXp18zMCQkBIBNmzZx7ty5ex43QFBQEBERERw+fNhYsy/9dl9fXwCjIjB9QGlfCzMkJAR3d3eOHDnCnj17WLNmDePHj8/ynkREROT+yVblX5kyZZgyZQp169Zl/vz5JCcnU69ePWP7iRMnjE8dRURERB4FaWlpODo6EhAQgJ+fH4cPH6Z69eq0atWKDRs2cOLECeBm5dLp06fx9vYmNjaWq1evsnHjRqZPn87Fixfp06dPhvAPbgaAS5cuZevWrTg5ORESEkLu3LlvOx57OGOfEnmrqbzp99f0yIfD/rNKXw16r/7880+6devG4cOH6d69O4ULF2br1q1MnTqVo0ePMmvWLKMyDqBr167GVHR3d3d8fHw4fvw48fHxuLu7Zzh3rly5KFq0KEuXLmXXrl2Eh4cbzVxSUlLYvn07cLPi7uLFi9nqtOvj40PdunVZtmwZS5cu5fnnn88wtXfv3r0AVK5cOdPv6o4dO9i3bx9Xr17FxcWF8+fPU79+fa5fv058fDzFixenWrVq9zwmERERuTvZCv8+/vhj6tatS4UKFbDZbNSvX99YwwNgyZIlVKpU6b4NUkREROTfslcf+fn54evra1RA2Sv+7A0L9uzZQ8OGDQkMDMRqtRITE0NKSgplypRh0KBBtG/fPsvz22w2KlasmOE1i8WCyWTKch2+ew2SFPw9POl/VtevX+fixYvkzZsXV1fXOx5rD8imTJnCrl27+P7772nbti0Ab731FoULF+bDDz9k0qRJjBgxwvhdSb8Gpb+/PwUKFCAqKoorV66QN2/eDAFbnjx5aN68OT/99BNffPEFoaGheHl5ceHCBSZNmsTKlSspXLgwp0+fJi4uLtMY7yZYNpvNtGrVimHDhjFhwgRatmxJqVKljO3jx48HoGrVqkajEvs5Bw0axNKlS419g4KCKFasGOXLl6d48eJGIxARERF5MLIV/kVGRrJz506WL19uTFuxi4mJoW7durz88sv3bZAiIiIi/8aePXuIiYnB29sbuLk8yebNm2ndujWzZs2iRYsWlC9fHoAqVaowcuRIoqKicHNzo0iRIpQvX55ixYrdtjrPHnjYv4Y7V/PJw2OfXns3DVJWrVrF9OnTWbNmDdevX8fDw4Ny5crRoUMHGjRocNvgzGw2c/bsWbZs2UJERAT169cHbq4j6eTkRLNmzViyZAkLFy6kZcuWxofr6QM0Ly8vgoOD2bVrFxcvXiQiIiLTNRs2bMgHH3zA8OHDiYyMpGDBgty4cQNXV1dmz57NrFmzOHLkSJaB5d0Ey1arFR8fHz788EPee+89GjZsSMuWLQkMDGTJkiWsWrWK9957L9MUd4BWrVpRr149qlSpQvHixTM11xEREZEHK1vhH0CRIkUoUqRIptd9fHwYMWLEvxqUiIiIyP108OBB2rVrR0hICK6urhw7doy4uDiCgoLo3r07AwcONEKR3Llz061bt2xdR9V5jz57oJY+9Ltw4QIxMTHkz5+fXLlyZdh/8eLFdOnShdTUVOrXr4+/vz+HDx9m4cKFbNy4kUmTJtGoUaMsG3HYrxUbG8vevXupVKkSfn5+WCwWY228/Pnz06hRI/r06cOmTZuoUKGC8Xtk/7e7uzshISEkJCQYDTX+ydnZmc8++4yXXnqJn376iUuXLlG0aFFq1qzJ888/z3fffQeQKXhLSkpi69athIWFkT9/fqNS8ejRo/zyyy9UrVqVcuXKGa+/++67eHt7M3LkSEaPHk1iYiJ58uShV69e9OnTx2gEkl76QgH7c7FYLMY9KiQXERF5sLId/omIiIg8Lpo0acKqVavYvn07ZrOZ+Ph4tmzZwtChQwkPD8/yGKvValTymc1mBXuPsPTNLe40ndpkMpGWlsaPP/7I999/z44dO4wAqkePHnz00UdGVd6RI0fo3r07KSkpLFq0iBIlShjr7c2cOZM2bdqwcOFCGjVqlOU10zfDSE5ONhpbpA+7nJ2dqVixIiaTib1792ZYR8/ObDYTHByM2WzmxIkTxvj+ydHRkeeff57nn38+07Zr165RoEABcubMCfy9BuagQYP48ssv6dOnD0OHDiUlJQVXV1eWLVvG+++/zyuvvMJPP/2UoXtvy5YtadCgAYcOHSIgIIDg4OAMP4usnoXFYjH+HJlMJmMavoiIiDx42f6/7o8//siYMWM4cuQIMTExmbbb31iJiIiIPGzOzs5ERkYSGRkJ3GzqcebMGY4cOUJ4eLgRhKR3N9NB5dFwL+snWiwWhg0bxldffUVQUBBt2rTB39+fkydPGk1a7OHc1atXOXnyJC1atODZZ5/NcB77+tb2697u9yVXrly4ubmRkJDA9evXjQDOLigoCF9fX06cOEFsbCw+Pj7GNnuYlj9/fjw9PTly5AjXr1+/Y0OZ9BYsWMDGjRvp1q0bhQoVyhDQ2depvH79eob7iIyMJDQ0lKpVqwJkChu9vLwyPBOr1Xrbn4Oq+0RERB6ebIV/w4cPp2/fvvj4+PDss89meIMiIiIi8qiyT81MTU3FarUaHX4VTDy+YmJi2LlzJ5s2beLo0aO4uLhQs2ZNatWqhb+/f6ZKtMWLF9O/f386dOhA3759yZ8/f6apsPYAzM/PD4AjR45k2B4bG8ukSZPIly/fLRvApOfi4kJoaCjnzp0jOjqaIkWKZBhXzpw5yZs3L1evXuXq1av4+PhkGrefnx8BAQEcO3aM2NjYW4Z/FouF69evk5CQwKVLl1izZg0DBw4kLCyMd955B8g41da+9uFzzz0H3AzKAcqXL8/Ro0dve1/px6iwXERE5NGVrfBv3LhxVKpUidWrV2e5roeIiIjIo8geeAQFBfHZZ58ZDUA0pffRc+PGDVasWEFKSgrNmjXLNB32+vXrfPvtt8yYMYMTJ06QM2dOvLy8uHLlClOnTiUkJISlS5dSrFgx45iUlBQGDx5MgQIF+Pzzz8mTJw9AllNtbTYbBQsW5JVXXmH+/PnUqlWLKlWqAPDHH3+wevVqevXqZbx2K/aArFSpUuzbt4+DBw9SpEgRrFar8fuYlpaGp6cncXFxRgWq/XfSfny+fPkIDQ1l7969pKamGvdjs9mM8DIuLo7+/fszf/58ChYsyLlz57h48SIVKlRg3LhxWa7XHR0djYuLC7Vq1cq0zWKxYLPZbjlFV39uREREHg/ZCv/Onz9P7969FfyJiIjIY8nBwcEI/uTR5ODgwN69e4mJiaFZs2aZgiaTycSsWbPw9vamQ4cOlC1bFl9fX9zc3Jg4cSJDhw5lwIABzJw505iyGhUVxaFDh2jfvj158uTh4MGDbNiwgYSEBEJCQihZsiRhYWEZrvPtt99Su3Ztvv76a4YMGQJAnjx5CA8PZ+vWrXz++ed06NABPz+/LNe7s79WvXp1Zs+ezdq1a2nUqBE2m80IHa1WK+fOnSNnzpwULFgwy4q6lJQUcuXKRUxMDN988w05cuTg1KlTvPDCC7Rs2RIAT09P6tSpw9mzZ0lISKBkyZJUq1aNatWqkTdv3izHFRUVxWuvvWZU/P3zZyAiIiKPv2yFf2FhYVy7du1+j0VEREREBLjZhdbBwYFDhw4xb948HBwc8Pf3p3LlypjNZnLkyMHKlSuzDHG7d+/OjBkzOHjwIJcuXSJfvnwAbN++neTkZPLmzcvEiRMZOHAgFy5cwNnZmZSUFEJCQhgxYoTRwMNms5EnTx7CwsLw9fWlXbt2dOrUCR8fH3bs2MEnn3zCxx9/TExMDF999ZURqNm72To6OhohXrVq1ciVKxfLli2jd+/eBAQEGOONj4/n6NGjNGzYEMhYUbdo0SIWL17M8ePH2bJlC0lJSYwePdrYr2TJkhk6Db/44ou89NJLd3y+9muMGDEiOz8eEREReYxkK/zr0aMHQ4cO5d1338XDw+N+j0lERETkkRXcd+nDHsITKeqLF42v9+3bx9ixY1m0aBEXLlxg+fLlODg4YLFY2L17NyVLlsRqtd6yevP48eNcv36dcuXK4enpaYRy9umyy5Yt49q1a1SrVo1BgwaRI0cOpk+fzuDBg+nZsydeXl48//zzmEwmjh49yjvvvEPRokX56KOPgJvTYcuVK8fkyZOpXLky8+bN44MPPjACvfTdbO0hW1hYGL1796Zfv3506dKFadOmkZyczMmTJxkwYAAAvXv3znQv69evZ8aMGeTJk4dq1arx7LPPUqZMGUqVKpWhy66d/Xr2Kbsmk+mO3apv1aFXREREngzZCv+cnZ3x9fUlPDyc9u3bExISkuW0gLZt2/7rAYqIiIjI02PLli00btyYxMREatasybVr12jVqhU1atRg6dKlxrIz6dfoS01NJTo6mtOnT7N7927GjRtHUlISHTp0wN3d3ejmnDdvXpycnNi8eTOVKlVi2rRpxnp5H330Ee7u7vTs2ZMZM2bw/PPPA7B161aOHTtmBH/w93TY3LlzExERwa+//kpsbKwR/p05c4aePXuSlpbG7NmzcXFxwcHBgZ49e7Jjxw7mz5/Pxo0bKVGiBKdOneLs2bN89dVXmToKA/Tr148vvvgiU7ddu6zWK0w/xruh4E9EROTJlq3wr127dsbX9rVP/slkMin8ExEREZF7MnPmTC5evMiUKVOoU6cOX331FR4eHoSGhtK1a9csj4mKiqJz587s2LGDuLg4zGYz3bp1o1SpUsDfQWHRokXJkSMH169fp1q1ari4uGSojmvWrBk9e/Zky5YtxrnPnj2Lo6MjcXFxRoho5+TkxIULF0hNTSVnzpzG60lJSaxcuRKbzUZ0dDShoaFYLBZcXFyYOnUqr776Kj/++CPR0dFUqlSJxo0b89JLL2UZ2NmrG61Wq9GZ12w2G/ekLrsiIiJyJ9kK/9asWXO/xyEiIiIiT7m0tDTi4uIICAigcuXKBAYG4uzszOHDhwGMJhn/DMm8vb0pUaIEderUISwsjCtXrjBixAjGjRvHhAkTjA+uCxQoQLFixdiyZQs+Pj4ZwjybzUa+fPnw8/Pj2rVrXL58mTx58lC0aFFcXV0ZO3YsjRo1IiQkhNTUVGJjY/nhhx/YunUrL774Irly5TLG4+zsTEhICDlz5iRHjhzAzUo8q9WKp6cnLVq0oEWLFvf0bNIHfiIiIiL3IlvhX7Vq1e73OERERETkKefg4ECZMmWYPn0606ZNo02bNnh6ehIfHw9AYmKiEaal5+Pjw6hRozK81rZtW0qXLk3Hjh2pW7cu+fLlw2QyUb9+fbZt28aBAwe4fv06uXLlMqbO/vXXX1gsFooUKYLFYgGgbt26vPzyy8ZU4CpVquDn58eePXvYuHEjlSpVYsiQIRnOk5CQQHx8PGXKlCEgIMBoyPHP8M5ezWcymdRZV0RERB4YfXwoIiIiIo8E+7IxERERDBs2jIYNGzJx4kQmTJhAyZIlad++PbNnzyY5OTnTsVar1WhyYbVacXNzo379+lgsFnbu3Gns16xZM0qXLs2MGTPYsGEDcLOqzmazMWnSJGJiYqhduzb+/v6kpqbi4uLC4MGD+eyzz8ifPz8rVqzg+++/5+rVq3zwwQdMmTKF0qVLG+MHuH79OsePHzfWDbxVsGc2m3F0dFTwJyIiIg9Utir/7LZv386WLVuIjY011iCxM5lM9O/f/18NTkRERESeLl5eXqxbt45x48Zx4MABLly4wMGDB0lISGDt2rXMnTuXjz/+mA8++AAPDw9jzT77P4AR2tmbg5w9e9Z4PTw8nN69e/Of//yHpk2b0qxZMwoVKsSmTZtYs2YNTZs2pXXr1gBGk43g4GA+/PBD2rZti5OTE35+flmO3X798PBwJk+eTNOmTR/osxIRERG5G9kK/xITE3nllVdYsWKF8YbLZrMBGF8r/BMRERGR7PD29jbeR27ZsoWRI0fSqVMnIiIiePXVVxk1ahRVqlShXr16GcI/OxcXFw4dOsTixYvx9fWlXLlyAMb6fq+99hq+vr6MGjWKpUuXkpCQgK+vL126dKFr167ky5cv05hsNhv58+c3vrdPC7Y3C0nP3d2dN9544/4+FBEREZFsylb498knn7BixQr69etHrVq1qFGjBtOmTcPPz4+hQ4eSmJjI9OnT7/dYRUREROQpYW/GERAQgJ+fH4cPH6Z69eq0atWKDRs2cOLECQCio6M5ffo03t7exMbGcvXqVTZu3Mj06dO5ePEiffr0McI/e0BoNpupWbMmzz77LGfOnMHf3z9Dww57oJjeP7+/01TdrM4hIiIi8jBkK/z76aefeO211/jkk0+IiYkBIH/+/NSsWZNatWpRoUIFvv/+e4YOHXpfBysiIiIiTwd7lZ6fnx++vr6cO3cOgE2bNgFQokQJAPbs2UPDhg0JDAzEarUSExNDSkoKZcqUYdCgQbRv3/6W18iRIwdFihQxvrdYLJjN5vsS2in4ExERkUdFtsK/06dP07NnT+DvTz1TUlJuntDRkddff53x48cr/BMRERGRe7Znzx5iYmLw9vYGICkpic2bN9O6dWtmzZpFixYtKF++PABVqlRh5MiRREVF4ebmRpEiRShfvjzFihW750YaarwhIiIiT6JshX8eHh7GOiceHh6YzWZjIWWAXLlycf78+fszQhERERF5qhw8eJB27doREhKCq6srx44dIy4ujqCgILp3787AgQNxdXUFIHfu3HTr1u0hj1hERETk0ZWt8C8sLIyjR48CNz8hjYiI4KeffqJ9+/bYbDbmz59PUFDQfR2oiIiIiDwdmjRpwqpVq9i+fTtms5n4+Hi2bNnC0KFDCQ8Pz/IYq9VqNKC7X1N3RURERJ4E2Qr/ateuzXfffcfXX3+N2WymU6dOvPvuu4SFhWEymThx4gSff/75/R6riIiIiDwFnJ2diYyMJDIyErjZ1OPMmTMcOXKE8PBwoxlIev/suCsiIiIiN2Ur/Ovbty9t2rTBarViNpt55513SExMZObMmTg4ONCxY0d69+59v8cqIiIiIk8Ri8WCg4MDqampWK1Wo8Ov1uYTeTBsNhtWqxWTyWQE6upcLSLy+MtW+Ofu7k7RokUzvPb+++/z/vvv35dBiYiIiIjYQ76goCA+++wzowGIggiRB8NkMmUI1+Pj4/Hw8HiIIxIRkfvhnudHXL9+nbCwMEaOHPkAhiMiIiIikpGDg4MR/InIvbFarVit1rva9/fff+ett96iSJEiBAUF0bRpU6ZPn05ycvIDHqWIiDxI91z5lzNnTmJiYnB3d38Q4xEREREREZH7JP16mPZlm7KyevVqOnXqxJUrV3j++edxcXFh69attGvXjr/++ou+ffsqhBcReUxla9rvs88+y44dO+jQocP9Ho+IiIiIyH0T3Hfpwx7CEynqixcf9hDkLhw/fpw5c+awcuVK4uLiqFSpEv/5z38oX748ZrPZWM/vwoULvPvuu1y6dIkpU6ZQp04dcuXKxdmzZ+nSpQv/93//R8GCBenSpYvWABQReQxlqy3aF198wbx585g+ffr9Ho+IiIiIiIj8S6dPn+add97h008/JS0tDTc3N7777jtq1arF1KlTgZvNPABWrlzJoUOHaN++Pa+++iq5cuUCIF++fPTs2ROAWbNmceXKFQV/IiKPobuu/Dt16hS+vr64ubnRs2dPcuXKxRtvvEGvXr0IDQ0lR44cGfY3mUysXr36vg9YRERERETkSWGz2YxquvsZrA0ZMoQVK1YwdOhQWrdujb+/P9u2beP1119n0KBBhIWFUaNGDQDS0tIAjNDPYrFgNpsxmUwUKVKEXLlycfDgQX7//XcaNWp038YoIiL/G3dd+RcSEsLChQuBm+XjVquVAgUKkDNnTi5cuMCJEycy/HP8+PEHNWYREREREZEngslkMoK2f8teyXfgwAGmTZtG9erV6dOnD/nz58fR0ZHKlSszfvx4zp49y7hx44Cb6wDa1/I7ffp0hnEBODs7k5SURGxsLHv27Lnr5iEiIvLouOvKP/snUgBRUVEPajwiIiIiIiKPNfvfm24V6Nkr/a5du8b27dvZunUrKSkp1KlTh8jIyAz73Av7/teuXSMlJYXixYsDGRt9NGjQgKCgIJYsWUJiYiJubm6UL18eDw8PFi5cSJ8+fShSpIgxhs8++4wcOXKQnJzMyZMniY+PNyoERUTk8ZCtNf9EREREREQka7ebwmsP9Y4dO0aTJk2oX78+X3/9NSNGjKB27dq0atUKq9X6ryoBT548iYODA4GBgaSlpRnBnz2UrFWrFikpKWzduhW4ubbfm2++SWxsLC+88AK9evVi+PDhNGvWjO+//57q1avj4+PDhQsXSE5Ozva4RETk4chWt18REREREZGnyd1U4tlsNg4fPszu3bu5dOkSBQsWpF69ejg5ORn7mEwm4uPjadKkCceOHWPw4MFUr16d1NRUxo4dy48//oifnx8jRozI9hiTkpKw2WzGv/+5vUSJEgDs2rWLatWqAfDpp5+SL18+pkyZwqhRo7BYLPj5+fH1119TsGBB1q9fT1paGl5eXvc8LhERebjuKfybP38+R48evat9TSYT/fv3z9agREREREREHqZ/Tt21//tWIeCqVasYMGAAe/fuxWKx4OLiQlJSEiEhIcybN4+IiAhj3//+978cOHCA3r1789FHHxmvR0ZGUrZsWUaNGkXnzp0pWrToPY/ZZDLh5eWFi4sLp06dIi0tLUP4CFCoUCEA9u/fD9xs8JEzZ0569epF8+bNiY6OJjAwkMDAQAB2795NTEwMjo6OODk5ZZhGLCIij757Cv8WLFjA/Pnz72pfhX8iIiIiIvK4sgd88fHxHDp0iEuXLlGgQIEMIZ7dL7/8wttvv01ycjK9e/emTJkymEwmli1bxvjx4+nZsyc//vijUTW3ZMkSrFYrTZs2BW5227VarTg7O/PGG2/Qq1cv5s2bR79+/e5p+q9938DAQPz8/Dh06BBxcXG4ubll2g4QHR2d4XibzUZQUBBBQUEZXt+0aRMADRs2BFDwJyLymLmn8O+jjz6idu3aD2osIiIiIiIij4RffvmFMWPG8PvvvwM3q+NCQkKoVKkSo0aNInfu3EYFnL2D7saNG6lcubJxjjp16vDnn3+ya9cuoqOjjfAvMTERs9nM9evXAXBwcMDBwQGAKlWqULBgQdauXUuHDh0ICAi46zHbw72goCAiIiLYsmULhw8fxt/fP8N2X19fAKMi0H7trILGAwcO0L9/f0qVKsXLL79812MREZFHxz2Ff+Hh4caaECIiIiIiIo8aq9UK3L7pxp3MmDGDDz74ABcXF959912KFStGXFwcM2fOZMaMGURERNCjRw+cnJxIS0vjxo0b+Pr64uzsnOE89jEEBgbi4eEBQEpKCu7u7uTMmZMbN25kunZQUBBFixblwIEDXLx48Z7CPzsfHx/q1q3LsmXLWLp0Kc8//3yGqbp79+4FoHLlyhmmMZ8+fZpff/0Vq9WKk5MT+/btY/r06eTMmZOhQ4eSJ0+eex6LiIg8fGr4ISIiIiIiT4z0U1KTk5Nxdna+pxAwOTmZb7/9lsTERH788UeqV69ubKtQoQLNmzfnt99+44033sDX1xdHR0fKli3L+vXr+fXXXylXrhxnz54lJiaGKVOmcODAAb788ksKFiwI3AwnfX19SU5O5sqVK8a57WP08vIiJCSEDRs2EBMTk2l8d9N4xGw206pVK4YNG8aECRNo2bIlpUqVMraPHz8egKpVq2IymYxg8MqVK0yYMIG//vqLtLQ0nJ2dqV69Ov369aNKlSp3/QxFROTRovBPREREREQeeXcKvSwWC2fOnOGPP/5gwYIF/Prrr1SuXJnx48cTEhJyx+Pt2/ft28e5c+coV64czzzzTIZ98ufPT2JiIsnJyfj6+mKxWHBwcKBjx45s3LiR/v37M3HiREJCQjh27BjR0dHUq1ePKlWqGOd3dnamYMGCpKSkcPr0aSDjdFs3Nze8vLxITEwkJSUl0zjvJsi0Wq34+Pjw4Ycf8t5779GwYUNatmxJYGAgS5YsYdWqVbz33nvG/dkD0+LFi/PNN99w8eJFAgMDCQ8Px8XF5Y7XExGRR5vCPxEREREReaSln7J65swZXFxc8PX1zRDo7dq1i379+rFy5UrjuIsXLxIbG0tISMhdX8vX15egoCD27dvHli1bqFevHteuXePKlStMnDgRk8lEz549gb/XygsPD2fVqlXMmjWLvn37EhMTQ40aNciTJw8rV66kXLlyfP3113Tq1Amz2UyRIkVwcXFh9+7d3Lhxgxw5cgB/B5AWiwUAR8eMf11LSkpi69athIWFkT9/fuO5HD16lF9++YWqVatSrlw54/V3330Xb29vRo4cyejRo0lMTCRPnjz06tWLPn36GI1A7JycnKhYsWKG12w2GzabTU0+REQeY3f9X3Cr1UrLli0f5FhEREREROQJZ7PZsFqtWK1WbDbbXe1vNpv5/fffKVSoEAUKFOCLL74Ablb72df4S0hIICEhgV69evHrr7/SqlUrjh8/zvnz5+9qXOk74b7//vtcunSJ1157jRdeeIFOnTrRqFEjhg0bRqNGjahTp06m43ft2kX//v3p2rUrf/75J9OnT+frr79m0aJFhISE8Pbbb7Nr1y4AnnnmGcLCwli7di0nT54EIDU11aj0u3r1aoZzp6amAjBo0CCqV6/O2LFjAYz9ly1bxvvvv8/QoUONZ2bXsmVLli9fzurVqzl+/DgXL15k6NCh5M6d+5bPP/3PxmQyKfgTEXnM6b/iIiIiIiLyP2MPk8xm811NYbXvs2LFCo4fPw7AsWPHMpwL4Nlnn2XevHl8+eWX1K1bl9KlSxMfH8+ZM2cynOdurteoUSP+/PNPmjZtyooVK1i9ejVeXl40aNCALVu2UKZMGSPIg5vh3ODBgwkKCqJr1644ODiQmpqKxWIhNDSUtm3bArB27VrgZnVhw4YNOX/+PHPnzgVuVt25uLhw6dIltm7dyjPPPEOxYsUyjN1elWfvEmy/98jISEJDQ6latapxrvS8vLx49tlnCQ4OBv4O9271TO72ZyMiIo8HTfsVEREREZH/iZiYGHbu3MmmTZs4evQoLi4u1KxZk1q1auHv73/LQOr06dPMnDmTzp07M2vWLPbs2QNkbO7h6upKvnz5gJvhVoECBQCIioq6qyYZ/3T+/Hk2bdrEp59+Sp8+fTCbzSQnJzN58mT69+9Pt27dmDhxIuHh4ezfv5+DBw9SqVIlo+OvPQB0cHAwQjd7eAk3K/LWrl3L8OHDsVgsvPTSS1y5coVvv/2W3bt3079/f/Lnz4/NZjOm/9qrHJ977jkA41rly5fn6NGjt72f9M9AlXwiIk8XhX8iIiIiIvKv3LhxgxUrVpCSkkKzZs0yrNEHNyvVvv32W2bMmMGJEyfImTMnXl5eXLlyhalTpxISEsLSpUuNSrd/+vnnn7lw4QJDhw7lt99+4/DhwyQlJeHq6pppX/s04cDAQDw9PTlx4gRxcXHkypXrru/nwoULfPDBB4SHh/PBBx9gNptJTU3FxcWFLl26cODAAb755hu2b99OeHg4qamp5MiRg6ioKE6dOkXJkiUxm81Gs4x9+/YBGOEkQMmSJZkwYQItWrRg+PDhjBkzhpSUFJycnOjXrx+DBw/OFFpGR0fj4uJCrVq1Mo3ZYrFkCAr/SZV8IiJPL4V/IiIiIiLyrzg4OLB3715iYmJo1qxZpqDJZDIxa9YsvL296dChA2XLlsXX1xc3NzcmTpzI0KFDGTBgADNnzsw0ZTU1NZURI0bQoUMHcuXKRZEiRTh8+DCHDh2idOnSmQIy+9e+vr7ky5ePqKgoYmJiyJUr1x0rAO2h5c6dO9m7dy/PPfccjo6OpKam4uTkRFpaGo6OjkRERAAY6wmGhIRQq1YtJk6cyMCBA43mGpcvX2bRokUMHz6cwMBA/vOf/2S4XqlSpdizZw+zZs3ixIkTBAYGEhkZSdGiRTPci33cUVFRvPbaa0bF3z9/BiIiIllR+CciIiIiIv9KUlISDg4OHDp0iHnz5uHg4IC/vz+VK1fGbDaTI0cOVq5cibe3d6Zju3fvzowZMzh48CCXLl0yquPsgdeMGTOIi4ujYcOGABQrVowlS5awZ88eSpcujdVqzTL4yp07NyEhIezZs4cLFy4QGhqa5dhtNhsWiwVHR0cjbAsICABg7969wN9r6Nmr6lasWIGLiwtFihQBwMfHh759+7J69WoWLlzI4sWLCQsL4+rVq1y5coUqVaowbty4DJV/do6OjsaagLdiH9eIESNuu5+IiEhWFP6JiIiIiEi27du3j7Fjx7Jo0SIuXLjA8uXLcXBwwGKxsHv3bkqWLInVas0y+IOb6+Bdv36dcuXK4enpabxuMpmw2WzMmDGDSpUqUb16dQBKly4NwLZt22jTpg1paWlZhn85c+akcOHCrFmzhrNnz95y/CaTyQj17CFbsWLF8Pf3Z9u2bYwdO5a33nqLuLg4zp07x88//8wvv/xCgQIFqF27NnAzQAwODub3339n0aJFbNiwgRs3blC0aFGqVq1KhQoV8PHxue1ztFgswJ2bbWRn/UIREXm6KfwTEREREZFs2bJlC40bNyYxMZGaNWty7do1WrVqRY0aNVi6dClubm5AxgYTqampREdHc/r0aXbv3s24ceNISkqiQ4cOuLu7A39Pv12xYgWbNm1i4sSJRsBnn3J74MABAGNdvX9ydXWlUKFCJCYmcvLkSeO8/wwKz5w5Q8+ePUlLS2P27Nm4uLjg5ubG119/Tbt27ejWrRujR48mf/78nDt3jsOHD1OtWjXGjRtHzpw5jTDOZrPh7+/PW2+9xVtvvXXPz/Jup+0q+BMRkXul8E9ERERERLJl5syZXLx4kSlTplCnTh2++uorPDw8CA0NpWvXrlkeExUVRefOndmxYwdxcXGYzWa6detGqVKljH3MZjMpKSnMmTOHZ555JsNaeaGhoXh6erJt2zb69u3Lzp07WbhwITly5DD2sYeHBQsWxMnJiTNnzgBZB2xJSUmsXLkSm81GdHQ0oaGhWK1WXn/9dQIDA5k/fz4bN27k2rVrlClTht69e1OzZk2jg689jPtnKGdvwGE2m9VdV0REHiqFfyIiIiIics/S0tKIi4sjICCAypUrExgYiLOzM4cPHwZuTk/NqtLO29ubEiVKUKdOHcLCwrhy5QojRoxg3LhxTJgwgXbt2gFw+fJlZsyYwQsvvMCXX37J5s2b2bNnD1FRUVitVkwmE9988w0eHh5cunSJggULGtewh20FCxbE29ub/fv3s3r1aq5fv05CQgK1a9fGz88PAGdnZ0JCQsiZM6cRINqPf/7556lcufItO+jejhpwiIjIo0Lhn4iIiIiI3DMHBwfKlCnD9OnTmTZtGm3atMHT05P4+HgAEhMTM1Tj2fn4+DBq1KgMr7Vt25bSpUvTsWNH6tSpQ/78+bl27Rr58uVjyZIl/Prrr/j6+hIcHEytWrX4448/+Ouvv/j888955ZVXMqynd+PGDXbs2MGBAwfYvXs3SUlJrFixgi1btnDt2jX8/f357rvvqF+/PgAJCQnEx8dTpkwZAgICsFgsGYI7e/BntVqN0PFO6/KJiIg8ShT+iYiIiIjIPTOZTLRt25bJkyczbNgw5s6da1QDLl26lIiICBo3bswrr7ySaV0+q9VqTIm12Wy4ublRv359xo4dy86dO8mfPz/+/v706tWLlJQUwsPDCQoKIk+ePOTNm5evvvqKDz74ABcXF/Lly4fNZjPOffr0afr378/69euBm5V9JUuWpESJEpQpU4bIyEjKly9vTA2+fv06x48fp1+/fsCtK/Y0fVdERB5XCv9ERERERCRbvLy8WLduHePGjePAgQNcuHCBgwcPkpCQwNq1a5k7dy4ff/wxH3zwAR4eHkZzDPs/cLMBiL3JBsC5c+cAyJUr1y3XDQwLCwNg6dKldOzYkbS0NJycnADw9/enc+fOdO/enbJly2aYDpyV8PBwJk+eTNOmTe/LMxEREXnUKPwTEREREZFs8/b2pn///sDN7r8jR46kU6dORERE8OqrrzJq1CiqVKlCvXr1MoR/di4uLhw6dIjFixfj6+tLuXLlgL8r8KxWK3Cz0tC+hmCpUqWoUqUKzz33HIAR/AHkzp2bFi1aZBijvfmG/drpK/jc3d154403HsCTEREReTQo/BMRERERkX8lLS0NR0dHAgIC8PPz4/Dhw1SvXp1WrVqxYcMGTpw4AUB0dDSnT5/G29ub2NhYrl69ysaNG5k+fToXL16kT58+Rvhnlz6osweCoaGhbNiw4ZbjsdlsxrTi9Mfdbn+t4SciIk8qhX8iIiIiIvKv2Jti+Pn54evra0zd3bRpEwAlSpQAYM+ePTRs2JDAwECsVisxMTGkpKRQpkwZBg0aRPv27e/6mvYGHFl14v1ndeGdKPgTEZEnmcI/ERERERHJtj179hATE4O3tzcASUlJbN68mdatWzNr1ixatGhB+fLlAahSpQojR44kKioKNzc3ihQpQvny5SlWrNgdq/P+SQ04RERE7o7CPxERERERybaDBw/Srl07QkJCcHV15dixY8TFxREUFET37t0ZOHAgrq6uwM31+Lp16/aQRywiIvJ0UfgnIiIiIiLZ1qRJE1atWsX27dsxm83Ex8ezZcsWhg4dSnh4eJbHWK1WbDYbcLOCT9NuRUREHhyFfyIiIiIikm3Ozs5ERkYSGRkJ3GzqcebMGY4cOUJ4eLjRDCQ9TdcVERH539H/dUVERERE5F+zWCwApKamYrVajQ6/97qWn4iIiNxfqvwTEREREZF/zR7yBQUF8dlnnxkNQDSlV0RE5OFS5Z+IiIiIiNw3Dg4ORvAnIiIiD5/CPxERERERERERkSeUpv2KiIiIiMgjIbjv0oc9hCdS1BcvPuwhiIjIQ6TKPxERERERERERkSeUwj8REREREREREZEnlMI/ERERERERERGRJ5TCPxERERERERERkSeUwj8REREREREREZEnlMI/ERERERERERGRJ5TCPxERERERERERkSeUwj8REREREREREZEnlMI/ERERERERERGRJ5TCPxERERERERERkSeUwj8REREREREREZEnlMI/ERERERERERGRJ5TCPxERERERERERkSeUwj8REREREREREZEnlMI/ERERERERERGRJ5TCPxERERERERERkSeUwj8RERERERGRx4zNZnvYQxCRx4Tjwx6AiIiIiIiIiGTNZrMZQZ/JZMJkMhlfi4jcDVX+iYiIiIiIiDyiTCYTZrMZs9mMyWTi+vXr7Ny5k6lTp3Lt2rWHPTwReQyo8k9ERERERETkEbR//37+/PNPNm/ezM6dOzl48CBXrlwxtkdERFCpUqWHOEIReRwo/BMRERERERF5QKxWKzabDQcHh7s+JiYmhpdffpnff/8dV1dXfH19CQkJ4bXXXgNg9uzZlCtXjgIFCjyoYYvIE0Thn4iIiIiIiMgDYLPZMJvNGb6/m7X6PDw86NmzJy1atKBIkSL4+/vj7e1N/vz5+e2335gwYQLe3t7kzZsXq9Wa4RoiIv+k8E9ERERERETkATCZTGzdupVevXpx+fJl+vbtS9u2bbFYLLetBHR2dqZJkyZZbouOjgagYMGCxjVERG5H4Z+IiIiIiIjIP6TvsPtv/PDDD/z+++84ODhw/Pjxez5n+nFYLBbWrl2LyWSiXr1692V8IvLkU22wiIiIiIiIPPVsNpsRtMHNUO3fBmsHDx5kzpw5tG/fHovFwokTJwDuaZqufRz2dQO3bduGzWajcOHC/2psIvL0UPgnIiIiIiIiT730IdvBgwdZunQpixcv5sKFC9k+59y5czGZTLz33nu4urpy/PhxkpOTsz2+qKgokpKSKFOmjNb5E5G7pmm/IiIiIiIi8kS626m7iYmJ/PzzzyxYsIANGzZw+fJlPD09uXHjBsWKFaNZs2Z8/PHH93Td5ORkRo8eTdeuXSlevDhBQUGcOnWKc+fOERwcfNfNPwCjqceePXs4evQobdu2VadfEblrCv9ERERERETksXS7cC99uBYfH8+RI0ewWCxUqFAh075XrlzhnXfeIX/+/DRu3Jhy5crh4eFBTEwMw4cPZ8CAAVSoUMFYZ+927EHd6NGjMZlMNGrUCLPZTNGiRVmxYgVRUVH3HP7Z97t69SoAvr6+AHdsHCIiAgr/RERERERE5DFgs9mwWq2YTCZjyuvtwjOTycTGjRv59NNP+e233zCZTLi7u1OjRg2GDRtGWFiYsa+Pjw9Tp06lbNmyhISEZDhv7ty56dixI//973+pVq0arq6utx2n2WwmPj6ehQsXUqNGDcqUKQNA6dKlWbJkCUePHqV69epYLJa7nrprMplISUlhz549ODk5UbFiRQAFfyJyVxT+iYiIiIiIyCPPZDJlCLvOnDnDn3/+yf79+3nnnXdwd3fPsP+ff/5Jt27dOHz4MN27d6dw4cJs27aNKVOmcPToUWbNmkXx4sWx2Wy4urry6quvZnldf39/UlNTSUtLw9XV9bYVe/aqv7Vr17J9+3Z69+5tbCtevDiA0fTDycnpnu7f2dmZX3/9ldTUVKpWrXpPx4rI003hn4iIiIiIiDzSoqOj+fXXX/ntt9/YvXs3x44dMxpnBAYG8tJLLxnhmj2AmzJlCrt27eL777+nbdu2ALz11lsULlyYvn37MmnSJEaMGJEhyLMHeykpKcTGxnLkyBE+//xznJ2djSm/t6s2NJvNWCwWxo8fT6lSpWjSpImxrXr16gDs2bOHdevWERUVxX/+85+7fgbXrl0jLi6OsLCwf92FWESeLgr/RERERERE5IHKasru3bAHeYsWLeLdd9/FxcWFBg0aULduXcLCwggKCiJ//vwEBwcbx5jNZs6ePcuWLVuIiIigfv36AKSmpuLk5MRrr73GL7/8wsKFC2nZsiUVKlQwQj+TycSxY8cYMmQI58+f548//iAuLo7JkyfTsGHDuxrzn3/+yZo1axg9ejQ7duxg586dbNu2ja1bt2IymVi6dClLly6lQIECNGjQAD8/vzs+O5PJxJYtW7h27RqvvvoqHh4ed/0MRUQU/omIiIiIiMgD9c8pu/faqKJEiRLkzZuXChUqMG3aNFxcXHB2ds60nz0oi42NZe/evVSqVAk/Pz8sFosxzTZ//vw0atSIPn36sGnTJipUqJChki4xMZGZM2cSERFB7dq1SUpKYtCgQaxatYrx48eTK1eu2451//79mEwmOnXqZLzm5eVFUFAQuXLlIiUlhffee486deqQI0cOY5+jR4+yePFiqlWrRrly5YxnZL+nzZs3Ex8fT968eXFzc1OzDxG5awr/RERERERE5J7YbLYMnXbvNA11x44dTJs2jd9//520tDTq1q1L69ateeaZZ257nP28gYGB5MqVizNnzhhVb+nDr/SVewB58uQhOTmZxMREIGNjDGdnZypWrIjJZGLv3r1GdaFdiRIlSElJMb6PiYlh9OjRfPHFF7i7uzN8+PDbBoChoaG0bt0aq9VK0aJFCQ4OJigoiJCQEHr27Mns2bOpWbMm1apVy3AfS5YsoVevXrzyyiv89NNPWK1WHBwcjLG9+OKLFC5c2Jh+rOBPRO6Wwj8RERERERG5J3cT+NkDuW3bttGxY0eOHj1KzZo1SUhI4Ouvv+a7775j4sSJNG3aNFMAl/46AH5+foSEhPDHH3+wceNGDh48yMWLF8mZMychISHUqVMnQxfe3Llz4+bmRkJCAtevXydnzpwZzhsUFISvry8nTpwgNjYWHx+fTNe2WCzYbDZ8fHzo27cva9euZcGCBbz55ptUqlQpw5gvXryIk5MTXl5elCtXjtKlS2dqQAJQoUIFZs+ezbZt26hZsyZpaWnGOZ577jlCQ0ONZh7/bAhSrlw5ypUrd9tnLiKSFYV/IiIiIiIictdiYmLYuXMnmzZt4ujRo7i4uFCzZk1q1aqFv79/hiq8K1eu0KtXL/bv38+8efOIjIwkV65c/Pnnn9SuXZuuXbtSpUoVAgICbntNd3d3AgMDuXr1KvXr1+f69es4Ojpis9mwWCzUrVuX8ePHExISAtys7gsNDeXcuXNER0dTpEiRDF16c+bMSd68ebl69SpXr17Fx8cnUxdfBwcHrFYrFosFR0dHQkJC2LFjB1euXAFuri149epVevTowbRp0xg9erSxLqE9iExfIWk2mwkNDQUgLS0NAEfHv/9KXr58eY4ePXrb53CrkFRE5Hb0Xw0RERERERHhxo0bLFy4kLlz5wI3g6b0rl+/zldffUWtWrV49dVXmTBhAjt37mTp0qW0bt2ayMhIDh48mCFAO3nyJBs2bKBFixY0btwYX19fY9ptv379OH/+PFOmTDHCsKzYw7Pw8HCaNGnC4MGDOXDgABcuXGDZsmU0bdqUFStW0Lt3b06ePAncrBgsVaoUV65c4eDBg5nuJy0tDU9PT6xWqxHA2cdtvx7cDOwcHBw4d+4cS5cuJTAwkGLFihnbXV1dyZcvH2azOdN57F+bzeYMU3etViv9+vXL8l4tFsttn4WCPxHJDv2XQ0RERERERHBwcGDv3r1s2rQJINO0XpPJxKxZs/D29uazzz7jp59+YuHChWzbto2PP/6YU6dOMWDAAFJTU41j1qxZA5ChU649XGvatCkFChRg0aJFnDt3LsO2rDRo0IAvvviCnj17UrRoUXLnzk2dOnWYMGECTZs25eeff2b9+vXG/tWrVwdg7dq1xrntAaDVajWuWbBgQeBm8Hbt2jXjvuPi4oiKimL+/Pm8/vrrxMTEUKdOHaO6EMDFxYXk5GRMJhPPPffcXT1j+/VvtT19NaCIyP2g/6qIiIiIiIgISUlJODg4cOjQIebNm4eDgwP+/v5UrlwZs9lMjhw5WLlyJd7e3pmO7d69OzNmzODgwYNcunSJfPnyATfXwoO/169LP221UKFClClThl9//ZUjR44QFBSUaeot/B1CFi1aNNPrVqsVb29vmjdvzs8//8yOHTto06YNANWqVSNXrlwsW7aM3r17Z5haHB8fz9GjRzOEkgAjR45k7ty5FClSBIvFwtmzZzl27BhOTk4MHTqUPn36GPvaxxodHU1oaCiBgYF3/axVwSci/0sK/0RERERERJ5y+/btY+zYsSxatIgLFy6wfPlyHBwcsFgs7N69m5IlSxpBW1aOHz/O9evXKVeunDGd1mw2G11xz5w5A2Su7CtXrhyLFi3iyJEj1KxZ847j/Gc4aP+6QIECAMTGxhrbwsLC6N27N/369aNLly5MmzaN5ORkTp48yYABAwDo3bu3sb+DgwMREREULFiQI0eOYLPZCAwMpEmTJtStW5fSpUtnGIu9G++RI0eoXbu20YVYRORRo/BPRERERETkKbZlyxYaN25MYmIiNWvW5Nq1a7Rq1YoaNWqwdOlS3NzcgIzVaqmpqURHR3P69Gl2797NuHHjSEpKokOHDri7uxtdbIsUKQLAoUOHsrx2eHg4AKdPnwb+DvOsVmuG9fjsTCYTFosFs9mcoePw4sWLAXjmmWeMfR0cHOjZsyc7duxg/vz5bNy4kRIlSnDq1CnOnj3LV199xbPPPpvh/I0aNaJOnTq4u7sbU3Rvxb79t99+w9PT87b7iog8TAr/REREREREnmIzZ87k4sWLTJkyhTp16vDVV1/h4eFBaGgoXbt2zfKYqKgoOnfuzI4dO4iLi8NsNtOtWzdKlSoF/B0U2r/ftWsXkHkdQR8fHwCSk5MzbE/fJMPOXk2YPpS7fv06mzdvZsyYMXh5eVG/fn1jm81mw8XFhalTp/Lqq6/y448/Eh0dTaVKlWjcuDEvvfRSpoDP2dkZZ2dn43ir1YrNZstyPHYK/kTkUafwT0RERERE5CmVlpZGXFwcAQEBVK5cmcDAQJydnTl8+DDwdwD2z5DM29ubEiVKUKdOHcLCwrhy5QojRoxg3LhxTJgwgXbt2gE31/Xz9/dn+/bt3Lhxgxw5cmQ4z9WrV3FwcMDPzy/DeoD79u1jypQphIeH07p1a9zc3IiJieH777/n0qVL+Pn5kZiYyF9//cXChQvJly8fX375pVFJCH+vCejp6UmLFi1o0aLFPT0bk8l0x+o/EZHHgcI/ERERERGRp5SDgwNlypRh+vTpTJs2jTZt2uDp6Ul8fDwAiYmJmQI7uFmxN2rUqAyvtW3bltKlS9OxY0fq1q1Lvnz5MJlMNGjQgO+//56lS5fStGlTzGYzFovF6C5ssVgICwvDbDaTmpqKk5MTW7ZsYeTIkdStW5dGjRrh5uaGi4sL27dvZ/78+eTIkYPr16/j4+NDs2bNeOONN7JcMzCr6kGr1apgT0SeKgr/REREREREnlImk4m2bdsyefJkhg0bxty5c41qwKVLlxIREUHjxo155ZVXcHFxyXBs+imxNpsNNzc36tevz9ixY9m5c6fR8bdly5asWLGCgQMHEhERQfHixXFwcODSpUssWrQIPz8/SpYsCfwd1oWGhpIjRw68vLzw9/fHZrPh6enJ559/zltvvUXu3LkJDQ3Fy8vrnu73dtN3RUSeVAr/REREREREnmJeXl6sW7eOcePGceDAAS5cuMDBgwdJSEhg7dq1zJ07l48//pgPPvgADw8Po+Nu+oYbqampuLi4GM1Bzp49C9wMCGvXrk2PHj344IMPeOmll2jWrBk+Pj7MmzePnTt3MnbsWAoXLgz8Hf65u7tz48YNihUrlmGsYWFhhIWFZXjNYrFgMpkU6omI3ILCPxERERERkaect7c3/fv3BzCm3Hbq1ImIiAheffVVRo0aRZUqVahXr16G8M/OxcWFQ4cOsXjxYnx9fSlXrhzwdwOPzp074+3tzYQJExg1ahQpKSkUKlSIsWPH8s477xjnse+fmJgIwOuvv57hdbi5DmH61zR9V0Tk9hT+iYiIiIiICGlpaTg6OhIQEICfnx+HDx+mevXqtGrVig0bNnDixAkAoqOjOX36NN7e3sTGxnL16lU2btzI9OnTuXjxIn369MkU/uXMmZM33niDunXrEhsbS2BgILlz577lWHLmzMnHH39McHBwpm3/7BgsIiK3p/BPREREREREcHS8+ddDPz8/fH19OXfuHACbNm0CoESJEgDs2bOHhg0bEhgYiNVqJSYmhpSUFMqUKcOgQYNo3759lue32Wzkz5+f/PnzG6+l7/CbXtmyZSlbtux9vT8RkaeVwj8REREREZGn3J49e4iJicHb2xuApKQkNm/eTOvWrZk1axYtWrSgfPnyAFSpUoWRI0cSFRWFm5sbRYoUoXz58hQrVuy2U3DtFXv2acOQuRtvercKBkVE5N4o/BMREREREXnKHTx4kHbt2hESEoKrqyvHjh0jLi6OoKAgunfvzsCBA3F1dQUgd+7cdOvWLdvXuttpuwr+RETuD4V/IiIiIiIiT7kmTZqwatUqtm/fjtlsJj4+ni1btjB06FDCw8OzPMZqtRrNN8xms9biE7kHR48e5dKlS1SuXPlhD0WeAgr/REREREREnnLOzs5ERkYSGRkJ3GzqcebMGY4cOUJ4eLjRDCQ9VebJ08xms2UIv+/2GJPJROnSpdm7dy/BwcFs27YNHx+fBzlUyQb7z/dJ+e/ck3EXIiIiIiIi8q9ZLBYAUlNTsVqtRoff263lJ/I0MplMmM3mew7+5s6dy8WLF3F2dsbBwYGzZ88a2+XRYf/5ws0qzQ0bNnDgwIGHPKrsU+WfiIiIiIiIAH+HfEFBQXz22WdGAxBN6RX525UrV9i+fTurVq1i7969ODo6UqdOHV588UXCwsKyPMYe/n333XdERkbi6urK7NmzOXHiBCVLlszQCEfur/RVmiaT6a6e8/Llyxk9ejRr167FYrGQK1cuIiIimDNnDr6+vg96yPedwj8RERERERHJwMHBwQj+RORvv/32G8OGDWPnzp3kzJmTgIAAzpw5w9KlSxk5ciRjxozhxRdfzBDm2aeP/v7772zdupVRo0aRkpLC7NmzOXbs2EO+oyffPwO/hIQEoqKiKFKkCM7Ozpn2nzdvHl26dMHDw4O33nqL4OBgrFYrTk5Oj20VtMK//7F27doxa9asDL9gP/30E/Xr13+IoxIRERERERF5smSn4ut2zpw5w/Dhw/njjz/o0aMH9erVo0CBAri5uTF37ly6dOnCkCFDiIyMxMvLyzjOft05c+bg4eFB69at+e9//wvA4cOHM+wj99fRo0fZunUrmzdvZufOnRw+fJjLly9ToEABli5dSkRERIb9jxw5Qvfu3SlcuDBTp06lSJEiT8TPRuHfQ/DWW28xduzYhz0MERERERGRbAvuu/RhD+GJFPXFiw97CE+MrAK/fzO9NkeOHHTs2JFx48YRGhqaYVvz5s1ZsWIF69atY+fOndSqVSvD9tOnTzNjxgz69+8PQNGiRQE4fvy4pvzeZ/bnOWzYMD788EMAQkNDyZ8/P02aNCEkJIRixYqRN29e4xir1YrZbGbmzJmcP3+eb775xvgZpaSk4OTkhMlkMvZ73Cj8ExEREREREZHHgr2a706VfDabjT179rB8+XK2bNlCbGwshQoVol27dkRGRmYrcPP29uaVV14BbjbHsY/BZDLh5eVFrly5uHr1aoaqP7vp06djMplo1qwZAH5+fuTPn5+oqCjOnDlDUFDQUx0C3s8qTavVioODA8WKFcPJyYkGDRrw7bffkpycjK+vLzly5Mh0jNls5vLly6xZs4bChQvTpEkTLl26xM8//8ypU6dwd3cnIiKCxo0bZ3tcD9MjF1ceOnSIVq1aER4eTq5cuciZMyfh4eG8//77nD9//n82jqFDh/Laa68RGhqKyWQiODj4jsfMnj2bcuXK4ebmRp48eXj99dc5efJkpv1mzpyJt7c34eHhfPbZZ6SlpT2AOxARERERERF5sti7sN4pHPr55595+eWXGT58OCdPnsRisbB79262bt1qnOffcHBwMMZhDwJ37NiBm5tbpoYQSUlJjB07lubNm5MvXz4sFgvu7u4UL16c8+fPG122n9bgDzJ2T06/VmJ22CvzChcujJubGzExMQQEBFCwYMEMwZ/Vas1wnLOzM/v27ePGjRts3bqVihUr8s477zBq1Cg++eQTXn75Zdq2bWv8vB4nj1zl35kzZzh//jwvv/wygYGBODo6snfvXiZMmMDs2bPZtWsX/v7+D3wcH330Ed7e3pQtW5arV6/ecf+xY8fStWtXqlSpwogRI7h8+TIjR45k/fr1bNu2jXz58gHQrVs3vvzyS/LkycPOnTt5/fXXSUpK4tNPP33AdyQiIiIiIiLy6LmXird9+/Yxbtw4EhMTGTp0KAEBAZn2WbJkCc2aNaN69ep069aNEiVKkCtXLry9ve97w4a0tDQcHR2ZOnUq+/bto2fPnuTPnx/4eyrphAkTcHJyonfv3hmu7+PjQ3x8PG+++SaJiYk0b96c7t27P7ZTS/8pfSXfnfa7n1Wa9n2DgoIoUKAABw4cID4+nh07dhAfH0/evHkpUKAAfn5+GY7z9PQkLS2N2NhY2rVrR/HixZk/fz4RERH88ccffPzxx/zwww+UK1eOzp074+Lico9P5OF55MK/WrVqZZobD/D888/TvHlzpkyZwkcffXTL4202G5MmTeLNN9+85R/qyZMn07p1a1xdXW95nmPHjhlz+EuUKEFCQsIt942JieHDDz+kbNmyrF27FkfHm4+1fv36VKxYkQEDBjB58mQAypYtaxxXvnx5Bg8ezMCBAxX+iYiIiIiIyFPhn6FQ+kqv24U8ycnJ9OjRg1WrVgHQtWvXTOFfcnIy7733HoULF2bp0qW4ubk9iFsAboZ7jo6OnDlzhm+++QZfX19atWqF2Ww2AryEhATWrFlDYmIiY8aM4a+//mLfvn3GzEaTycQff/xBsWLFjMDvcQ3+bvVzvZOff/6Z3r17Ex8fT8GCBcmZM6dRpRkZGZntikgPDw+Cg4P566+/aNCgAfv27SMuLs7Y9vHHH/POO++QI0cO4+dVqFAhdu7cyeXLl5k4cSJlypQhNTWVatWq8emnn/Kf//yHH374gbp16xIeHp6tcT0Mj1z4dyshISEAxMbG3na/3377jU6dOrF69WpmzpxpBHF2PXv2ZMSIEdhsNjp27HjL8/xz8c7bWbRoEQkJCXTr1i3D9cqXL0/VqlWZO3cu33zzTZYtpM1mc7ZLWUVEREREREQeN/YwJz4+nkOHDnHp0iUKFCiQqfPqP+3fv58NGzZQuXJlNm/ezKlTpzIU2AAsX76c48ePM378eNzc3Dh79iynTp3CycmJiIgIowjofqyvZw/p/u///o+dO3cyevRoypQpY1QDws0w0svLixs3bvDdd9+RJ08eIiIiaNKkCSaTifHjx1O9enW+//57PD09/9V4Hrb0Ie6hQ4c4duwYFouFSpUq3XIG54Oq0rSHeWFhYRQpUgQ/Pz+aNWtGkSJF2LJlC7NmzaJ3797cuHGDDz74wAiJS5Uqxc6dO6lRowYlSpQAMMZRunRpIiMjWbp0KcePHyc8PPyxWafxkQ3/kpKSSEhIICkpiYMHD9K3b18AXnjhhdseV6tWLcaMGUPXrl2xWq3Mnj3b+EPXo0cPRo4cSc+ePW8b/N0r+5oBkZGRmbZFRkaybt06Dh48SKlSpZgzZw7169fH09OTvXv3MnjwYJo2bXrfxiIiIiIiIiLyKPvll18YM2YMv//+O3CzeUZISAiVKlVi1KhR5M6dO8P+FosFBwcHBg4cSPXq1alWrRpbtmzhwIEDNGnSBPh7+u2OHTuAm8059u/fz3vvvceff/5JamoqTk5OdO7cmZ49e5I7d+77Mr123rx5jB8/nkaNGvHuu+8a17Hz9vamZcuWVK5cmbCwMIKCgvD39ydXrlxcu3aNGTNmkJycnGkK6qPgbpurACQmJvLzzz+zYMECNmzYwOXLl/H09OTGjRsUK1aMZs2a8fHHH2c45n9RpdmkSRNeeumlDDNM69evT4MGDWjfvj0TJ07k2WefpW7dugA899xzfP/998THxxsFXPZ7d3V1JX/+/FgsFhITEzNse9Q9suHf5MmT6dq1q/F9UFAQ06ZNo0aNGnc89t1338VkMvHuu+9is9mYPXs2vXr1YvTo0fTq1Yvhw4ff17FGR0cDEBgYmGmb/bUzZ85QqlQpvvnmGzp37kxqaip58+alVatWt53GLCIiIiIiIvKkmDFjBh988AEuLi506dKF8PBw4uLimDlzJjNmzCAiIoIePXpkCNAcHBzYuXMnmzZtYuTIkYSEhODk5MSBAweMsM0ewtirtBYuXMiQIUOw2Wy89957pKWlMW/ePIYMGcKJEyeYMWPGvw7+Dh8+TP/+/fHy8uLbb78FyDBuuBkO1alTJ8vj3dzcyJkzJxs3buTQoUMULVr0kaokSx/6xcfHG41TSpcunWnfK1eu8M4775A/f34aN25MuXLl8PDwICYmhuHDhzNgwAAqVKhAvXr1jGMeZJWm/WdbrVo14zV7mGk2mylXrhxvvvkm77//Pjt27DDCv+effx43NzcOHDjAqVOnKFasmHFtFxcXtm7dSnJycqaK00fdIxv+NWnShGLFipGQkMCuXbv45Zdf7jjlN70uXboYAWDRokU5ceIEvXv3ZtiwYfd9rDdu3ADIcrFH+y+rfZ9169bd07nHjRvHuHHjHstuMiIiIiIiIvJkunHjBrt37yZHjhyUKFHirqZoJicn8+2335KYmMiPP/5I9erVjW3ly5enRYsW/Pbbb7Rv3548efIAf0/fXLBgAW5ubjRs2JDo6GgCAgI4dOgQN27cIFeuXMb17RV0c+bMoWLFiixbtsyoJGzbti116tRh8eLFrFmzJlNx0b2ETGlpaUaQ+MsvvxAQEIDFYiEhIYHLly9js9koUKCAUT2WvooObgZrzs7OdOnSBavVatzvoxL8xcfHs3btWhYsWMD69es5deoU7u7uuLm5Ua5cOfr160elSpWM/X18fJg6dSply5YlJCQkw33kzp2bjh078t///pdq1aoZOcn/qkozffVi+pC4aNGiAFy4cMHYNzQ0lK5du/Lll1/y6aefMnPmTACuXbvGypUr+f3336lbt67R1PVx8ciGf4GBgUbVXJMmTWjatCkVKlTgxo0bfPjhh3d1jrfffps5c+awfv16wsLCHlhTDXur6OTk5ExlqvZS0PTtpO9Fly5d6NKlC3FxceTKlevfDVRERERERETk/7PZbFitVkwm0z0FKydPnqR+/focOnSIFi1aMHbsWLy9vW97HZPJxL59+zh37hxly5blmWeeybBPYGAgiYmJJCcnkydPHmNtfLPZzPnz55k2bRoNGzYkV65cJCUlUbBgQfbv309sbGyGvysXKFAAwOiumz44Cg4O5u2336Z3796sWbOGqlWrZggt7yZ4s59rypQpzJw5k5o1a5KSksKkSZM4fvw4R48eZc+ePRQuXJgvv/yS4sWL33bq7D+nwt4P9qDRfk9321AlvbVr1/Lee+9htVqpWLEibdq0wdnZmX379jFr1iz27dvH/PnzKV26NFarFVdXV1599dUsz+Xv709qaiqpqam4uroaU7T/V1WaJpMpy/D1zz//BDBCQLgZCvbt25ft27cze/Zs9u7dS6VKlbhy5QpLliyhfPnyfPHFF7dtIPsoemTDv38qVaoUZcqU4Ztvvrmr8M9ms/H222+zfv16XnzxRZYtW0bTpk356aef7ns7Znsb7zNnzlC4cOEM2243JVhERERERETkYTGZTBnCr4SEBNzd3YGsgyL72ns//fQThw4dwtPTk0uXLnHjxo3bhn92vr6+BAUFsX//frZs2UK9evW4du0aV65cYeLEiZhMJnr27GmMzW7x4sVcuHCBPn36YLVa8fb2JigoiPXr1xMdHU1wcLCxb1hYGF5eXsTGxhqvpw+OwsPDyZEjB9HR0dy4cQMPDw9sNhvJycls3bqVsLAw8ufPb4R8R48eZfHixVSrVo1y5cphNps5fvw4EydOxGazsXXrVpo3b05iYiKOjo6EhIRQuHBhGjVqZFSH3SlwS0tLw8HB4b5V/f0zaLSHqv9cSzEr9p+7t7c3PXr0oFKlSoSHhxu/FwDBwcF8/vnnrFu3jtKlS2doYmo/PiUlhdjYWI4cOcLnn3+Os7Mz9evXBzD6MvyvqjSzeiZnz57l22+/xcnJicqVK2c4b+7cuZk8eTJz5sxh3rx5/PTTT3h6etKmTRu6dOmSKbh+HDw24R/c/IW9cuXKHfez2Wx06tSJSZMmMWDAAAYPHsyUKVPo2LEjL7/8MgsWLLivAWCFChWYMGECmzZtyhT+bdq0CXd3d4oVK3bfriciIiIiIiLyT+krvu5UKWW1Wtm4cSPz589n48aNXLlyhcDAQOrVq0fDhg0pUaJEpqmWDg4OXLx4kXnz5lGnTh18fHxYt26dceyt2EOXwMBA3n//fZo0acJrr73Gc889h6enJ/v27WPfvn20a9cu0/p4NpuNr776ikaNGhEUFITZbMZsNlOmTBlmzpzJmTNnuHjxIklJSeTLl4+iRYsSERHB77//TlRUFM888wwWiwWr1YqTk5PRXNTNzQ0PDw8j0Bw0aBBffvklffr0YejQoaSkpODq6sqyZcvo1asXr7zyCj/99JNxPyVKlCBPnjxUqFCBkiVLUqFCBUJDQ7P1c7OHYbdzt1WaN27cYO/evaxevZpVq1YRFRWFzWajZMmS1K9fn1deeYWAgIBbhmf216pUqUKVKlWyvIa9C+7FixczHGP/+tixYwwZMoTz58/zxx9/EBcXx6RJk2jYsGGG8zzoKk2A8+fPc/jwYRITE3FwcODatWvs2bOHKVOmcP78eb744osMXaZNJhNWq5Xg4GD69OnDm2++iaenpzF9+3H171a3fADOnz+f5etr1qzhr7/+4tlnn73t8TabjY4dOzJp0iQGDx7M4MGDAXjzzTeZOnUqy5cvp3HjxiQlJd23MTdu3JgcOXIwevRo0tLSjNe3b9/O+vXradas2WP/iyIiIiIiIiKPNnsw9M9w6J+VWQDz58+nQ4cOTJ8+HX9/f5555hkuXbpEv379eOuttzh37lyWIdOGDRv466+/GDJkCB4eHly5ciXDmml3Gl+jRo34888/adq0KStWrGD16tV4eXnRoEEDtmzZQpkyZdi1a5dxzI8//silS5fo27cvZrOZzZs388MPP7B48WLg5t/1AwICGDhwIFevXgWgadOmAMyePZvLly/j4OBgNOKwnzs8PBzAWF+/YsWKAFy/fh34OzyNjIwkNDSUqlWrGmMKCQlh2rRpLF++nCFDhtC8eXMj+LNaraSlpd33dfvtVZr2cSUkJBjb0oe+P/74Iy+99BITJ04kOTmZatWqUaZMGXbv3s27777L4MGDSUxMvKvwLP154eaz2bZtG+PHj8fV1ZXmzZsDmYPmGzduMHPmTM6fP0/t2rV54YUXGDx4MC1btiQuLs7Yz16lmZycfFdVmvYxJSUlGVWfcPOZAxw9epSvv/7aWEvQPpaPPvqIBg0a0KpVK15//XU+/fRT8uTJw9SpU3n//fczNWlJP4Y8efLg7OyMzWYzQuTH0SNX+ff/2LvLwKiOrg/gZ3ZjxN2IQQQJECQ4FAhOCEUe3L04FFqsFIoUJ7h7gQAtJUiKu7sVCykJEiQkJEQgtvt/P+S9t1kSKBLP+X0pXNneu8PMnT13Zs6AAQPo+fPn5OPjQ87OzpSYmEhXrlyhrVu3kpGREc2dO/ej5x89epTWrFlDU6dOpfHjx2vs69GjBykUCurZsyf99ttv1Ldv3w9+zm+//UaPHj0iIqJXr15RcnIyTZ06lYjSFqscPHiwfKylpSX9+uuvNHz4cKpbty517dqVIiMjyd/fn2xsbGjy5Mlf+nUwxhhjjDHGGGP/KTo6mq5du0YnT56k0NBQsrS0JF9fX6pZsybp6upqrDv3999/U/v27cnFxYV27NhBJUuWJFtbW0pJSaFu3brRzp07ac+ePdSvX78M/58FCxZQ7dq1qXLlynT06FFKSkqix48ff9Y0zBcvXtDZs2dpypQpNHr0aFIoFJSUlESrV6+mCRMm0NChQ2nZsmVUpkwZOnHiBMXExFDTpk0pMjJS/gwjIyNSKpVkbm5OXbt2pWbNmslr7f/vf/+jy5cv0+bNm0kIQT179iSVSkUHDhygRYsWUZ06daht27ZERPJAHSmoU6tWLY3t3t7eFBISkul9SIN/0gdcMwu+ZiarR2kmJyeTjo4O6enpUaVKlWjIkCHy6EQDAwN69uwZtW7dmnbs2EFt27YlHx+f/ywzad/Vq1cpKCiI7t69SydOnKDIyEjatm0blS1bNtPzypYtS8nJyfLfo6KiaOHChTRjxgwyNDSkWbNmkampKZUoUYLKlClDp06dypZRmmq1mooWLUq//PILXbx4kXR0dMjV1ZW8vLyoWLFiGmXxX/92358in9/kueBfx44dacOGDfTbb7/Rq1evSAhBzs7O1L9/f/rhhx/kYaEfUr9+fbp06RJ5e3tnur9bt25UunTpD+6XrFmzJkNmZDxrfgAAnC5JREFU3gkTJhARkbOzs0bwj4ho2LBhZGlpSXPnzqXhw4eTvr4+NWzYkKZPny6vCcgYY4wxxhhjjH2Ot2/f0sGDByk5OZnatWuXYSpuUlISbd++nVauXEl///036enpkZ6eHj1+/JgWLVpEvr6+tHXrVo2lry5evEgAqFevXhoZd7W1talRo0a0bdu2TGevnT59mk6fPk379u0jorT177W1tSkkJISSkpI+KQnCy5cv6YcffqBSpUrRDz/8QAqFglJSUkhXV5cGDRpEd+/epaVLl9KVK1eoTJky9M0339DBgwepTJky5ODgQC4uLlSyZElKSUmhsWPHkra2Ns2YMUMj6UfRokVp+vTp9ObNG9q/fz/98ccfpK2tTSkpKeTn50fz58+X15uThIeHk66uLtWvXz/DNatUKgKQYXrup0zX/ZAPJQBJH4iS/vznn3/S+PHjKTIykmrUqEFOTk509+5dGj9+PO3Zs4d27NhBdnZ2RETUqVMn6tSpU4bPtbW1pRo1atClS5fkoOXnJP+YOnUqGRsbk6urK1lZWdHChQspLi6O2rdv/8GZjtL3ZmFhQWPGjKHjx49TYGAg9e7dW84U3KZNGzp16hQFBARQrVq1yNLSUg6yZTZKU6lUfvIoTYVCIZdpZuX6sUQsBU2eC/61a9eO2rVr91Wf8V+Bvf/aT5T2j/tzde7cmTp37vzZ5zHGGGOMMcYYY5lRKpV069YtioqKonbt2mUIVNy/f5+6d+9OxYsXp9GjR9M333xDZmZmpFKpqHfv3rRr1y6aP3++HGgjSgt6KBQKjSmYkiNHjpCdnR1VrFhR3iYFHOfOnUvVqlWjChUqEFHaGn5mZmYUHBxMb968+WjwT/qMq1ev0q1bt6hWrVqkpaVFKSkppK2tLWeAldZfk9aT8/X1pXLlypGjo6NGgI+IaOPGjXTq1Cl6/vx5hn0ODg60e/duOnjwIP39999kbm5OFStWJE9PT40RXFIAKCwsjNq2bZtpICurR3xl9yhNlUqVYW3AiIgICgoKIisrqw+O2PuQXr160eDBg+Xv5tq1a/T9999T9+7dKTk5mXr37p3peUqlktRqNalUKjkZypUrVzRyObRp04YuXbqU7aM01Wq1/L1K30thCPpJ8lzwjzHGGGOMMcYYY2kSExNJqVTS/fv36ffffyelUkk2NjZUvXp1UigUZGVlRTNmzKARI0ZkWLtszpw51LZtWwoKCqLu3buTra0tERHVqVOHvLy8aM6cOZSamkply5al+Ph4ObOqv78/lStXjoj+DRReunSJTpw4QSNGjJBHzTk7O5OTkxM9ffqUzMzMMly7tE6alpaWHGiRruHWrVtERPI1S6PoDh48SLq6uuTh4UFERCYmJhqBPWm6rEKhIH19fYqJiaHw8PAPJtls1KgRNWrU6IPfr3Rd/v7+HzzmU+WVUZpSsPL8+fN0//59unfvHm3evJnevXtHK1eulEcJfiopAy8ASk1NpQoVKtCMGTOoZcuWFBAQQH5+fhojKdOPXpTuPzw8nIKCgsjBwUGjrHJqlOanTMUuyDj4xxhjjDHGGGOM5UG3b9+mxYsX065du+jly5d04MABUiqVpFKp6MaNG1S2bFmytramkSNHytsVCoUcHHNyciJLS0tKTk6mpKQkIkoLjri5udG8efNo2bJltHbtWnrz5o38/yxZsiRdv36dAgMDqWXLlnIgZ+/evWRtbS0vh0WUFpjU0dGhmzdvUoMGDSgpKYnGjh1LLVu2JKK0wJoUhJGCQSVLliQbGxu6dOkSLV68mPr160exsbH0/Plz2rFjB+3Zs4ecnJyoQYMG8v8nfTAp/TTNqVOn0owZM8jZ2fmj36M06kuhUHx0tNfnrFuYmbwwSlO6X4VCQfPnz6ft27eTlpYW6ejoUPny5en69etUunRpKlGixGffb/pRc/b29mRvb09PnjyRpxGrVCqKj4+Xg7WxsbH0+vVrunr1Ks2dO5eioqKoQ4cOGuvtEeXtUZoFBQf/GGOMMcYYY4yxPObChQv07bff0rt378jHx4fevHlDnTt3pnr16lFQUBAVKVKEiDSDHdKfpWmRDx8+pLt371Lbtm3J2dmZAMjH1KlThxITE+np06fUuHFjatmyJVlbW9OBAwdo3LhxFBAQQGvWrKEOHTrQs2fPaO3ataRQKGjIkCF0+vRpCgkJkddcE0LQnTt3qHTp0mRkZCRfz9OnT+n777+n1NRUCggIIF1dXSpSpAjNmzePevToQUOHDqWFCxdS0aJF6fnz5xQcHEx16tShJUuWkIGBwX+uyebq6vpJ3+Wnjvr62mmguT1KUyIlEhk9ejSNGDGCSpQoQXFxcbRx40aaN28e7d27l5YtW0ZVqlSRA4XpR2m+T1prj+jff2Px8fF069YtcnBwIHt7e/lYKeDo4eFBKpWKnj17Rv/88w9pa2vT9OnTafTo0R/8/nJylGZhw8E/xhhjjDHGGGMsj9m8eTNFRETQmjVrqGHDhjR37lwyMjKi4sWL05AhQz56rlKpJAC0b98+AiAHVNIHt27fvk1du3alli1b0tixY+WgT7du3ahChQrk5eVFixcvpg4dOpC9vT0ZGRnR69evKSAggBwdHalRo0bk5uZGoaGh9Mcff1Dv3r1p+PDhGlNKExMT6dChQwSAwsPDqXjx4qRWq6ljx47k4OAgZ6998+YNVahQgX788Ufy8fEhFxeXDNeb1+WFUZoSqfyltRmJ0qbujh8/ntzd3alDhw60fv16OXEGkeYozfQSEhLIwMBAvp7Xr1/TjRs3aPr06ZSamko//vijxv/X09OTnJ2d6cGDBwSAHBwcqGXLltSoUSPy8vL6z+8xp0ZpFjYc/GOMMcYYY4wxxvKQ1NRUio2NJVtbW6pevTo5ODiQjo4OBQcHE1Fa4EOtVn90iuOFCxdo3rx5VLt2berVq1eGYMmaNWsoMjKSxowZoxH0UalUVLZsWbKzs6N79+5RRESEPN0XALm6ulLRokXJ3Nyc9PX16cCBA3To0CFSq9VkZWUlX58QgnR0dKhYsWJkYGBA+vr6RPTvKLzatWtT9erVvypjbl6RF0Zprl27ltq3b59htOT75V6vXj0i+nfNRak8MhulSUQ0aNAgiouLk0cihoSE0NWrVwkAzZo1i3r27KnxXbRo0YIaNmxIhoaGXzQFN6dGaRY2+b+WMcYYY4wxxhhjBYhSqaQKFSrQxo0bacOGDdS1a1cyNjamuLg4IiJ69+6dHEzLTExMjDzCbM2aNUREGYJBjx8/JiKSP1Oa2qlUKikyMpIMDAzIwMCAYmNjydramjp27Jjp/8vAwIBSU1Pp+PHjFBERQfb29vL/Iz4+nuLi4qhChQpka2urMX2U6N8kH2q1mtRqtbymXH4L7OSFUZqLFi2i9u3ba5wnfafpXbp0ibS0tMjOzo7evn0r/zvKbJQmEZGZmRmdOXOGzp07R0lJSWRra0vt2rUjPz8/qlevnkZyEqK0zLvSWnxSkFoayVfYk27kJg7+McYYY4wxxhhjeYgQgrp160arV6+mmTNn0vbt2+XRgEFBQeTp6UnffvsttW7dOkPwhYho3rx5dO7cOVq9ejW5ubnJa8AJIeQRg1LG1WXLltG8efNIX19fDtotWrSIQkJCqHfv3uTm5iZ/rjRKTQghB3Sk5BUuLi5kbm4u7ydKmzL68OFDGj9+PBF9OBlDfg4M5aVRmpGRkWRpaSkHWdN/p8nJyfTkyROaPHkypaamUtu2bUlfX19e8y+zUZpERBMnTqTBgweToaEh2djYfNZ3I4TgBBx5BAf/GGOMMcYYY4yxPMbMzIxOnDhBS5Ysobt379LLly/p3r17FB8fT8ePH6ft27fTTz/9RD/88AMZGRnJQZy//vqLpk6dSm3atKEuXboQUeZTJP38/Ojw4cO0cuVKevjwIVWtWpWKFClCx44do+PHj1ONGjVo0KBBGuekDyZJn2lvb08//fSTxnHSvlKlStHq1aupTZs2Wfrd5CV5aZRmTEwMWVpa0sOHD+no0aMUExNDpqamFB8fT8HBwfTnn39SSkoKTZw4kZo3b67x//rQKE1TU1MyNTWVr1capZmfA7aFEQf/GGOMMcYYY4yxPMjc3JwmTJhARGmjw+bPn0/9+/cnT09P+t///kcLFiygWrVqUaNGjUihUNCTJ09o7NixVKxYMVq4cKE8/TIhIYFiYmJIS0tLHr1VtWpVWrBgAU2ePJmuXbtGR44cIQBkb29Pffr0of79+1P58uU/6To/NLrN0NAww5pwBU1eHKWpUChoz549dPjwYdLT06O4uDjS19en2rVrU+fOnal169byOoSSwjBKszDj4B9jjDHGGGOMMZZHpaamkpaWFtna2pK1tTUFBwdT3bp1qXPnznTq1CkKCQmhRo0aUWJiIs2ePZtu3bpFo0aNorCwMDpw4AA9fvyYgoOD5dF9/v7+8sitqlWrUlBQEF28eJG0tbWpWLFiGqO8PtXHpncWhqyseW2UpouLC/n7+1NISAgVKVKEihcvTk5OThqf+X5ikMIwSrMw4+AfY4wxxhhjjDGWR0lrvFlbW5OVlRU9f/6ciIjOnj1LRERly5YlorREDosXLyYioj///JMWLVpESUlJRERka2tLnp6e5OnpSUSa03cBUJUqVTT+nyqVSk6+8bUKeuBPkpdGaSqVSnJ3dyd3d3eNa5SSbyiVygzlUhhGaRZmHPxjjDHGGGOMMcbyoJs3b1JUVJScSCMxMZHOnz9PXbp0oS1btlCHDh2ocuXKRJQWZLOzsyM3NzcqVaoUVa5cmby9vcnT01MjScT7pOQd0p+JPjzlk31cXhulCUBOzEJE/xnMLQyjNAsrDv4xxhhjjDHGGGN50L1796hHjx5UrFgx0tPTo3/++YdiY2PJ0dGRhg0bRhMnTiQ9PT0iIqpZsyaFh4dn+jlSkoYPTc/lgE/WyGujNNNP6/0U/O+g4OLgH2OMMcYYY4wxlge1bNmSDh8+TJcvXyaFQkFxcXF04cIFmj59OpUqVUrjWClwk5qaKv9doVDI/+UkDdmLR2myvIyDf4wxxhhjjDHGWB6ko6NDNWrUoBo1ahARUXh4OD19+pQePHhApUqVkqeZpvex4BHLPjxKk+Vl3CowxhhjjDHGGGN5mEqlIqVSSSkpKaRWqyk0NJSIeNRXXsKjNFlexsE/xhhjjDHGGGMsD5OCfI6OjjRt2jR5aimPAss7eJQmy8s4nMwYY4wxxhhjjOUDSqVSDvyxvEmlUhER8ShNlqdwmJkxxhhjjDHGGCvAXMYE5fYlFFhhM3w1/s6jNFlexCP/GGOMMcYYY4wxxrIQj9JkeQkH/xhjjDHGGGOMMcYYK6B42i9jjDHGGGOMMcZYHsHTtLPH+1O0CxMe+ccYY4wxxhhjjDHGWAHFwT/GGGOMMcYYY4wxxgooDv4xxhhjjDHGGGOMMVZAcfCPMcYYY4wxxhhjjLECioN/jDHGGGOMMcYYY4wVUBz8Y4wxxhhjjDHGGGOsgOLgH2OMMcYYY4wxxhhjBRQH/xhjjDHGGGOMMcYYK6A4+McYY4wxxhhjjDHGWAHFwT/GGGOMMcYYY4wxxgooDv4xxhhjjDHGGGOMMVZAcfCPMcYYY4wxxhhjjLECioN/jDHGGGOMMcYYY4wVUBz8Y4wxxhhjjDHGGGOsgOLgH2OMMcYYY4wxxhhjBRQH/xhjjDHGGGOMMcYYK6A4+McYY4wxxhhjjDHGWAHFwT/GGGOMMcYYY4wxxgooDv4xxhhjjDHGGGOMMVZAaeX2BbBPA4CIiGJjY3P5SrKWOultbl9CgZQd/064rLIPl1f+wuWVf2TXM5PLK3tweeUvXF75C5dX/sJ9jfyFyyv/KGjxFOl+pHjRxwh8ylEs1z19+pQcHR1z+zIYY4wxxhhjjDHGWB7x5MkTcnBw+OgxHPzLJ9RqNT179oyMjIxICJHbl1PoxMbGkqOjIz158oSMjY1z+3LYf+Dyyj+4rPIXLq/8hcsrf+Hyyl+4vPIXLq/8g8sqf+Hyyl0AKC4ujuzt7Umh+PiqfjztN59QKBT/Gcll2c/Y2JgbtXyEyyv/4LLKX7i88hcur/yFyyt/4fLKX7i88g8uq/yFyyv3mJiYfNJxnPCDMcYYY4wxxhhjjLECioN/jDHGGGOMMcYYY4wVUBz8Y+wT6Orq0sSJE0lXVze3L4V9Ai6v/IPLKn/h8spfuLzyFy6v/IXLK3/h8so/uKzyFy6v/IMTfjDGGGOMMcYYY4wxVkDxyD/GGGOMMcYYY4wxxgooDv4xxhhjjDHGGGOMMVZAcfCPMcYYY4wxxhhjjLECioN/jDHGGGOMMcYYY4wVUBz8Y4wxxhhjjGUrzjHIGGOM5R4O/jHGWD6iUqmIiEitVufylTDGGGOfTggh/5kDgYwxxljO4uAfY4XY8+fP6fr160TEHfG87uHDh9SlSxdatWoVEREpFNx852V3796lBQsW5PZlsA+Qgujc7jGW/Z4+fUqrV6+mVatW0Z9//klEaYFArn95Q2RkJE2ZMoVu376d25fCvhLXqbxF6mtIuHzyj4JaVlq5fQGMsdyxbNky+v7776lJkya0fPlysrGxIbVazUGlPCg2NpaqVq1KUVFRFBUVRdWrVycvLy8CoDGSguUNM2fOpLFjx1LlypWpe/fuZGpqmtuXxNJZsWIF7dy5kwIDA0lPTy+3L4f9h+DgYNLS0iITExOysLDI7cthn2nevHk0e/ZsSk5OpujoaCIiGjlyJM2ePZufX3nA2rVracCAAZSSkkJ+fn5ERNwXzEf27t1LYWFhFBUVRY0bN6Zy5cqRvr4+9w/zgMDAQDp48CDFxcWRtbU1fffdd+Tu7s71K486evQoPXjwgBISEqhVq1bk5ORESqWywNUlgYIa1mSMfVStWrXo7NmzZGdnR6NGjaIRI0bk9iWxTAAgAFSrVi2KjIykkJAQGjp0KE2bNo0MDAwK3EMpPwsLC6N27drR33//TaNHj6YOHTpQiRIlcvuyWDonTpygevXqERHRL7/8QhMmTMjlK2IfEh0dTf369aPz589TTEwM2dvbk5+fH02YMIFMTExy+/LYf3j79i2NHDmStm3bRoMGDaKaNWuShYUFdevWjSIjI+nKlSvk5OSU25dZaL1+/Zo6dOhAx48fpzJlytD169dpypQpNH78+Ny+NPYJnjx5Qt26daPbt2+TQqGgiIgIMjExocGDB9OYMWPIwMAgty+x0IqJiaEuXbrQ+fPnydHRkWJjYyk0NJTc3Nzo3r17HPjLY2JiYqhbt250/vx5UqvV9Pr1aypRogTNmjVLfiFSkPC/PsYKGbVaTampqWRjY0NdunQhhUJBS5cupePHj+f2pbFMCCEoNjaWnjx5QiNHjiQ/Pz9atWoV7dixQ97Pct+CBQuodOnSpKenR9u3b6ehQ4dy4C8Pev36NRERVa9enSZNmkRnz54looI7vSO/unDhApUvX57u379PPXr0oOnTp5OtrS3NmzePunfvTufOnSMiLre87MaNGxQUFEQjRoygESNGUJMmTahy5crUokULUqvVpKurm9uXWGht2LCBHB0d6dmzZ7Rz505asWIFEaUtV5GYmMj1Ko87evQoNWjQgFJSUsjf358OHjxI+/fvJw8PD1q0aBEdOHAgty+x0Dpz5gx988039PjxY1q0aBH9+eef9M8//9CPP/5IISEhtHr16ty+RJbOzZs3qV69evT06VOaPn067dixg3777Td6/vw5HTlyhIgKXj+Dp/0yVsgoFApSKBQUGxtL5ubmtGbNGmrSpAmtWLGCSpYsSba2tjwkPQ9RqVRkampKdnZ2FB0dTTNnziRvb29atmwZlS1blipUqMDllctu3bpFI0aMIGdnZ5o+fTrVrFmTiIhSU1Pp4MGDZGpqSpaWluTq6kpKpZLLKxcVLVqUXFxcqFmzZhQcHEyDBw+ms2fP8vTfPEStVtOaNWuIiGj+/PlUvXp1KlKkCDVp0oTmzJlDK1eupNTUVFq8eDG5uLjw6Oc8asOGDfTs2TPy9fUlc3NzOUnV69evqVevXpSSkkIJCQk8QimHAKCkpCTy8fGhq1evUu/evWngwIHk4eFBqampVKlSJbpx4wa3hXlceHg4zZgxgywsLGjGjBlUrVo10tLSkqf71qlThx4+fEhExG1jDnvx4gUtXLiQbGxs6Mcff6R69eqRllZaqKVEiRKkUCjIysqKiP4NKHH55K4dO3ZQREQErVmzhnx8fEhHR4diYmJo5syZ9OzZMyL6d33aglJW/OuDsUJGeuAYGxsTAGrUqBF16tSJtm3bRps2bSIiTiaRlyiVSiIiioqKIi0tLSpZsiRNmjSJLly4QGvWrKHY2FhSKBSc/TeXqNVqKlu2LA0ePJgePXpEV69eJSKizZs3k7OzM7Vv355q1apFFStWpJ49e1JERASXVy6KjIykuLg46t27N/Xr14+uX79Ov/zyS25fFkvnyZMnFBgYSHXr1iUfHx8qUqQIERG5ubmRn58fWVhY0MmTJ2nWrFlExD+e8hqpbXN1dSWitJcjL168oLi4OBo/fjytWbOGVq9eTU5OTlS1alXavn17bl5uoSGEoNTUVFKr1bR69WqaOnUqeXp6kra2Nuno6JClpSXdvn2bTp06lduXyj7i4cOH9OLFC/r++++pVq1apKWlJffrLSwsSKlU0r1794iI28acZmtrS6VKlaKRI0dSw4YN5cDfu3fv6NixY+Tk5ERxcXH06tUrEkJw+eSyN2/e0JYtW6hkyZLUpEkT0tHRIaK0JUd0dXWpU6dOFB4eTkQFqy7xL3zGCoH0Q5alP2tra9PTp0+JiGj27Nnk5OREy5cvl4c5R0dHF7ihznnVq1ev6OTJk0SUcXi5Wq0mlUpFTk5O8luooUOHUsOGDWn9+vW0Z88eevjwIW3cuJHevHmT49deGMXGxtLz58+J6N8fugsXLiQXFxeaM2cONWrUiLp160Y+Pj40Z84c2rBhA9WrV482bdpEPXr0ICIOsOeWokWLElHaGi99+vQhHx8fmjlzJp04cULjOG77cp70ncfFxVFkZCSlpKRQYmIiEf2bMdHDw4Pi4uLo3bt39Oeff9L+/ftz7XpZmpcvX9KIESPkqdhS29a4cWMqWbIk9ezZk5o1a0aVK1emZcuW0dSpU2ndunW0bNkySkhIoD59+sj9DpZ9kpOTydDQkA4ePEgdO3YkMzMzIiJKSUkhpVJJrVq1IqK08iQifkGVR1WoUIGWLFlC//vf/4hIc3SfmZkZ6ejokL29PRFxGWa31NRUSkpKIqK0ekRE9PPPP1OTJk3kY4KDg6lp06YUEBBAurq6tH37dipXrhy1aNGCrly5kivXzdKkpKSQiYkJXb9+nZ4+fUpxcXF09+5d6tSpE12/fp169OhBHh4e1LFjxw/+RsuP+NcHYwXQ1atX6eLFi/TgwQNSqVQabywUCgUBIG1tbXm6jZ2dHc2dO5cePnxIy5YtowkTJlDnzp3p2rVruXULhcaFCxfIxsaG6tatS2FhYRneLikUClIqlRQXF0epqalERKSjo0OLFi0ipVJJ48ePJzc3N9qzZw/Fx8fnxi0UKsuXL6fSpUtTUFAQERFpaWnJ5bJx40Z68uQJPXr0iLZs2UKLFi2i/v37U9euXSkwMJDatGlD+/fvp/Xr1xNRwehE5DX/9WNHrVZTdHQ0aWtrU7FixWjAgAFkaWlJQ4YMocTERNqwYQNduXKlQL3lzcvOnTsnd6olZcqUocqVK9PNmzfp8uXLRPTvCOizZ8+St7c3zZo1iyIiIuj06dNy/WM5b9WqVVS0aFE6c+YM6evra+wrV64c/f7779StWzcyNzent2/f0qJFi2jEiBHUsmVL6t+/Py1cuJAMDAxo9uzZuXQHBdvmzZtp7NixRETyqBZjY2O5PhGlvQgmIjn5ijTyj19Q5b6EhIQM2wwNDalGjRry39M/qx4+fEiJiYlka2ubI9dXmO3du5dq165NK1euJKJ/65FUbwBQcnIy+fn5UUJCAgUFBdHhw4dp8+bN9OOPP9KRI0doxowZ8otklr0y6xtaWlpSmzZtKDo6msqUKUP169enChUqEBHRmjVraOXKlTRmzBjatm0bjRkzhkJDQ+UpwPkaGGMFRnR0NPz8/GBlZQUDAwOYm5ujS5cuePDggXxMSkoKAGD48OGoWLGixvlt27aFQqGAEALDhg3D69evoVarc/QeCpvp06dDCAFdXV00b948w/7U1FQAQOPGjdGrVy95e1hYGEqUKAGFQoFy5crh0qVLAMDllU2ioqLQqFEjaGtrY+DAgXj48KHGfpVKBQAYO3YsBg0ahLdv38r7kpKSAACXLl2CEAJ+fn549+5dzl18IZCSkoLp06ejV69eaNiwIdauXYvw8HAAmnXi1q1bsLa2xsGDBwGkldvEiRMhhECpUqUghMD48eNz5R4KC6k8Bg4cCCEEWrVqJZeVSqVCSkoKVqxYAR0dHdSoUQPbt2/HrVu3sHz5cujr66NPnz54/PgxypYtixo1auTmrRRar1+/RqNGjaBUKjF8+HDcvHlTbgMl6f9+6dIlDBo0CHFxcRr73r59i5o1a0JfXz9Dm8q+XHJyMjp27AghBDp16iR/7x/z5MkT6Ovrw9vbGxERETlwlexDoqOj0alTJ/z6669y2X1K327Tpk3Q1tbGiRMnMt3P/cOsERERASEEhBBo2LAhrly5AkDz+5XauLt37+L58+ca58fHx6N79+4wMDDA7du3c+7CC6GP9Q2BtLZy6dKl6NmzJzw9PVG5cmXcuXNHoyzHjRsHIQRmzpyZG7eQ5fi1DmMFxMOHD6lBgwYUFhZGEyZMoNWrV1PLli1p27Zt1KFDBzn7l/RWKi4ujoyNjYkobUrV8uXLaf/+/aStrU3W1tZUp04dMjMz4xEw2czKyoqMjIyoS5cuFBQUJGfdw/+/WZLe0L97904e4bJkyRIqV64caWlpUYUKFSgsLEwepcnllfU2btxIzs7OFB4eTlu2bKFffvmFihUrJu8HIJfXr7/+SosXL5bXKSNKeyOsVqupUqVKVKJECUpMTORF1bPQgQMHqHjx4rR69Wq6desW3b9/n/r27UuzZ8+md+/eadQJXV1dioqKkr9/hUIhT5W6d+8ejR49msaPH59bt1IoSOVx8eJFIkobabR582YiSisPLS0tatu2LU2aNInOnTtH7du3J29vbxo6dCgNGjSIVq1aRY6OjuTo6EghISH06tWrXLuXwigwMJBsbW0pIiKCAgICaNy4cVS2bNkMI8XS17u//vqLTp48SYaGhpScnCzvk9pJJycnMjExybmbKMD27NlDdnZ2dO7cOdqwYQPNnTuXDA0NP3qOWq0mBwcHatKkCb169UoeJchy3rlz56hGjRoUEBBAGzZskNvJj/XtpGUR/v77b9LT06NSpUrJ+549eyaP5uT+4ddTq9Wko6ND3t7eVLVqVTp8+DCtXbuW4uLiSAghjzCT2kMpkaJ0LgAyMDAgOzs7evv2LT169CjX7qWg+6++IVFa/3zAgAG0du1a6tq1Kw0aNIhKlSpFQgh5KveAAQPIxMREXioLPPKPMZYXbNq0Cfr6+ti+fbs8ui8xMRF//PEHhBCoUKEC7t+/Lx//888/o1KlSrh9+zb8/PygVCoxZMgQLF26FAqFAg0aNMCjR49y63YKjdmzZ6Nu3bo4fvw4SpUqBRMTE42Rmmq1GqmpqWjXrh2++eYbNGrUCAqFAgMHDsSDBw9w+/ZtedTSkydPcvFOCh6VSoVJkyZBCIG+ffvi5s2b8ig+AIiNjdV4O6hWq+W3vdKIzfT7L126BF1dXfTo0SPDPvZlZsyYAWtra3To0AHHjx/H69evERsbi7p168LV1RXPnj2Tj1Wr1QgODoaFhQUOHTqEFy9eyKNj6tatCysrK5QpU0Zj1CbLemq1GvHx8ahbty6+//57uLq6wsXFBYcOHcpw7P79+7F8+XJMmzYNDx8+1Kh/HTp0QIkSJXLy0gu9R48ewcfHB0IIBAYGauzbtm0bNm/ejMDAQDx9+lRj3/z58yGEwLFjx+RtsbGx2Lx5MwwNDTFu3LicuPwCr1u3bhBCoFevXrhw4cJntWUqlQqDBw+GEAK7du3KxqtkH3Lt2jVUrVoVVlZW+O6772Bqaopvv/32k0fFNmnSBOXKlQOQVr9Onz4NPz8/2Nvb4+bNm9l56YVKZGQkTE1NsWXLFvzvf/+Dnp4e1q5d+9Fz0o+EfvfuHWrVqoXSpUvj1atX2X25hdLn9A1TU1MRHx+PatWqYcCAAQA0++fnzp2TfwcUBBz8Y6yAaNeuHezt7TWmd0gPm19++QVCCPzvf/+T933//fcQQsDY2BgeHh7Ytm0boqOjAQCdO3dGqVKlNBpHlrWkstm0aRMsLS2RmJiI1atXQ0dHB02aNMlw/NChQyGEQOXKlTXKCkj70XXx4sWcuvRCIzU1FVOnToW+vj6GDBkibz9y5Aj8/PxQr149NG7cGNOmTct0Gm/6zl5wcDA6d+4MJycnnD59Okeuv6A7f/48SpcujSFDhiAkJETe/u7dOzRv3hyurq7ydBupLF6/fg1TU1P069cPzs7OcHR0xPr16/Hq1Sv8/PPPEELgp59+ypX7KWxq1qyJESNG4OjRo/LzSQoaJScnf/TcM2fOwMLCAqNGjcqJS2Xp/PnnnyhatKi8TMXu3bvh4OAALS0teSqcu7s7Dh06JAdrd+7cCUNDQ9jb28Pf3x8bN27ETz/9BDs7O1SrVg2hoaG5eEcFw9WrVyGEQLly5eRpiAAQEhKCFStWYOPGjThz5gxiY2MBaD6fpB+6O3fuhBAC06ZN45dTuWDr1q2wsbHBqlWroFarMWXKFCgUCsycORPx8fEAPvzSMDY2FqVKlUKHDh0QFRWF+fPnw9bWFk5OTjhz5kxO3kaBlpqairdv38LT0xMrV67EvXv3YGxsjEqVKsnL73xsCYRnz57h119/hZmZGWbNmgWVSpXhePZ1PqdvKNWnlJQUeHh4oHbt2rh8+bJ8ztOnTzFgwABYWVnh1KlTOXsj2YSDf4zlY+lHGg0aNAgGBgbyj6f3Hyg+Pj5QKpXYsGEDAODOnTuwsrLCwIEDcfv2bXmkEgC5k8G+XkpKChITEwFk7BAAwOrVq+Hs7AyVSoXXr1+jT58+EEJg0aJFACAHYJOSkjB79mxcvXpVo6xY9pDK6unTp+jatSu0tLRw6tQpDBo0CEIIlClTBlWqVIGlpSWEEOjQoQNu3bqV4XPCw8Oxa9cuNG/eHDo6OpgzZ05O30qB9fDhQ8yePRuPHz/W2H79+nV4eHigcePGOH/+PF6+fCkHIR48eABvb2+5zK5cuSIHbkNDQ7Fw4UKuXzlApVKhTp06+PHHHwEAPXr0gBAC06dPz/RYSXx8PC5cuIC2bdvCw8MD58+fz7FrLuykgGx8fLy8TmatWrVgYGCATp06ISAgANeuXcNPP/0Ee3t7eHh44K+//pLPnzx5MpycnOSXjkWLFsWIESNy63YKFKmO/PDDDxr9hx9++AE6OjowNDSEEAJKpRLNmjWTZwlIs0QkISEhsLe3R7t27XL2Bgo5qfzCw8Px999/y9vv378PHx8f2Nvby+vUfsitW7dgbGyMLl26oHfv3hBCyKOYWNazsbHB8uXLAQALFiyAEAL9+/eXX8y//wLr+fPnCAwMRPv27aGlpYXBgwdz0C+bfEnfEEgbLSgNsggMDMTSpUvRrVs3aGlpYdy4cQXmhQgH/xjLZzZt2oQxY8Zk2C41Wu8vSCr9kL1y5QqEEPD19ZXfeAQHB+Ply5fZf9GF1J49e1CtWjUsXLgwwz7pIbJ//34YGhrKU6wvXryIihUrwtDQEDNmzIBCocCmTZsAZOyos6z1/hReycmTJ+Hp6QkhBFxdXbFhwwa5vEJDQ+XFgH/88Ud5VMW7d++wfPlyuLu7w9bWFiVKlMDx48dz9oYKmL///ltjhFBmHbE1a9bA3Nwctra28Pb2RsOGDWFlZYWhQ4fKx2zbtg3bt2/PMCWYZa33y0si/eDx9fVF27ZtAQAvX76Up/9KP3JDQ0M1ArEHDhxAz5494e7uDhsbG+zfvz/7b6IQu3//Pm7duoULFy5k2Hfv3j20atUKFhYWmD17tkZysHfv3mH37t0oUqQIevXqJbeJSUlJePjwIQICAvDnn3/i7t27OXo/BVn6IIKHhwcsLCxQqlQpmJub45dffkFQUBBOnDghTwuuW7eufHz6tu/ly5coUqQIXF1dkZCQwO1iNjp79qycmOP971n6u1qtxv79+2FhYQFfX1+NUUzvCwoKkkfeOjg44MiRI9l38QVcfHw8VCqV/OL+/b5hbGwsKlWqhGnTpgFI65v7+vqiSJEiWLNmDfbt24fZs2cjJiYGarUa+/btg5GREVxdXeHu7o6dO3fmxm0VGl/aN4yKipLbSCEEDAwMUKZMmQJXXhz8Yyyf+FD2NqnTFxUVBVNTU9SoUUMegSQ1gNIxvXv3hpWVFf75559cuIPC5VOygQHA5s2bYW1trZENbPbs2dDS0oK2tja6dOmi8SaYZb2UlBRMmjQJ3bt3R6dOnRAYGKhRHiqVCgsWLECNGjWwfv16jTeFQFpZN2nSBMWLF8eLFy/k7du2bcPAgQOxYMGCHLuXgqpPnz4wMzPD9u3bM+yT6tSJEyfg4uKCfv364fLly3j9+jVevnwJX19fCCEwb948AGltKQfSs9fHygtIq1NNmzZFz5495W27du2CEALNmjVD69at8c0332i0fXPnzkXjxo0xePBg+UcZy3opKSno27cv3N3dYWpqCiEE2rRpo7FeH5A2RXHKlCkabZ7U14iOjkbz5s1hYWGBN2/eaOxjXy8yMjLDNqlNu3jxIoQQqFatGk6ePJlhSYpOnTpBCCGvUZY+0AQAO3bs4Gz02eS/Mp1nJi4uDpMnT5anY0vB9Pdt27YNVlZWPJr2K02fPh1+fn6oWbMm2rdvjxs3bgBAhhkBJUqU0FirNDQ0FKamprCysoIQAsOHD5fr6fPnzzF+/HgsWLAgQ/+RfZ3M2kLJp/YN58+fL5+TmpqKoKAg7NmzB3v37v3PJUjyIw7+MZYP7N69GxYWFnBxccGGDRsypI2XOg2//vqrvGaVFBxMPzX4t99+gxACQUFBOXsDhYxKpUJMTAwqV66MatWqQQiBQYMGyZ229AHA8+fPo0iRIvIoiD/++APW1tYwNjaGEAK//vprrtxDYXHs2DHY29vD0dERXl5ecHBwkEdGpE+gEhYWhsOHD2ucm36tkPHjx0MIgaNHj8r7VSoVT6H/SqdPn4azszNsbW3h7++PiIiIDx4bFRWFe/fuZVjk/saNGzA0NET58uU/+MOJZY1PKS/pR1TPnj3h4+Ojsc/X1xdKpRL6+vpYuHAh3r17Jwc14uPjNQJNLOtdvHgRZcqUgbu7O8aPHw9/f3/0798fQgg0adIEYWFh8rH/9aOoQ4cO0NPTw507d7L7sguNixcvwsvLC5MmTcq0P5G+L/j+EhNSeUmzQDp06KBRhjzKL+dIS09YWlpi1qxZ8vYPlcHDhw/RuHFj2NjYaPTfw8PD5YQRb9++1Ujqxz7PxYsX4e7uDmdnZzRu3Bi1a9eGEALOzs4a9USlUiE5ORlVqlTB6NGj5e2HDx+GpaUlFAoFatSoIffppTItiEGk3PRfbWF6n9o3LCxtIAf/GMvjPid725MnT1CrVi1YWlpi3bp18napQduyZQuEEDxdKgd8ajYwaUrHxYsX5TfyPXv2xNatW1GlShUIIeQ3jyxr3b17F6VKlUKTJk1w8uRJxMTEICEhAd27d4cQAt98802m0+KlH1jpOwpSUp1z587l2PUXdCNGjIAQAn5+fjh58uRnB1LTv6n39vZGxYoVecRfNvrc8mrVqhX8/PwApLWX0nqaRkZGsLOz44yjOSw4OBj169dHvXr1cPToUXn0V3JyMoYNG4YiRYpkusZiZu1hbGwsPDw8UL16dX4BkkUCAgLkNWbd3d0zXQPuY6MrpX1xcXEoWrQoWrVqlW3XyjL3OZnO33fgwAFYWVmhWbNmuH79Onbv3o1GjRph9uzZnKH+K23atAlOTk5o1qwZjh49ipiYGADA2LFjIYTA+vXrM5zj7e2NoUOHQq1WY8CAAfKzr06dOjA0NJTX3WRZ71Pawo/5lL5hQQ4EcvCPsTzsU7O3SQ+q1NRUXLhwAWZmZrC3t8e2bdvkc8LDw9G5c2eULVuWs/hms0/JBiY9fC5dugRtbW0IIeDo6IjVq1fLw9iXL18ODw8PjcxTLOtIizT/+eefGtuTk5Ph7u4OIQSGDh2qkVlZkr5jcP36dbi6uqJ+/frcCc8C8fHx6NmzpzzyNf2izbGxsXj+/DnevHnz0c5Z+n1Hjx6Frq4uBg0alK3XXVh9bnlJnewRI0agXr16CAoKQokSJWBpaYnFixdj8+bNEEKgZs2aGiPNWPZRq9VYt24dDAwMsGXLFo3tKpUKAQEBEEJg48aNHzxfEh4ejkmTJsHY2Bhr1qzJsJ99HrVajWPHjsHR0RFubm6YOHEiLCws0KxZMzx48OCj52YWmJUy+v7yyy8Z9rGc8bFM5+8HcNO3mzNnzoSOjg7q1q0LMzMzFClSRF43kH2ZR48eoUmTJmjfvj2uXr2qsW/27NkwNjbG2bNn5W0qlQrv3r1DgwYNUKlSJbi7u8Pc3Bz+/v549eoVHjx4ACEEqlev/tEpqezzfU1bmP4zJIW1b8jBP8byKCk49KnZ29L/4Nq1axcMDAygq6uLAQMGYMKECejSpQt0dXUxY8aMXLmfwui/soEBadl8u3btioEDB+LSpUsaUwOSkpK4Y56NpDUwpTWpUlNT5XonTeNVKBTyumXvd8oTEhJw+vRptG/fHhYWFggICMjZGyigVCqV3OlO3ynz9/dHuXLl4O7ujjJlymDYsGGIiorSODd9fZFehvj5+cHDwyPTxAXs631OeaX/MdS3b18IIaCjo4PGjRvj9OnT8mizPn36oHXr1ry2Xw46ffq0xsLm6evSX3/9BSEEduzYkWGf5OnTp9ixY4eczXLYsGH8/Moi69atQ9myZXHmzBkAwLRp0yCEwNSpUz865e399vD8+fPw8fFBmTJlcPv27Zy5eKbhczKdp5eUlIS1a9dCX18fQgh0796dR7JnkQULFmSoD0+fPkWdOnXg7OyMZcuW4erVqxrf97BhwyCEQNu2bXHmzBmNZ9WRI0fkQRksa2VVW1iY+4Yc/GMsD7l//778AEofBPrc7G1AWme9a9eu0NXVRdGiRVG2bNkCl7EoN504ceKDP4Q+NRvYzJkzkZSUhOfPn8vrtrDsp1KpoFKp5GmKgYGBADTXZmnUqBGqVq0Ke3t7lC9fXj5XrVYjNTUVc+fOxbBhw1CsWDFYWVlhz549uXIvBcH7nTIgLSjes2dPKBQKbNmyBfXq1YOuri6aNWuGjh07olSpUhBCoHnz5hne1gNpo6aXL1+OcuXKwdzcnNu+LPb+KL7PKS9p9PODBw9Qvnx5zJ49W170XsKLomevhISETKeIJiQkAMj4TFu+fDm0tLQ+mLH81KlTUCgUcHZ2hpubG9e3LCK1hxERERrrXYaEhMhrwP3111//+TkXL17E8uXLUbZsWZibm2P37t3Zds0s6zOdR0REYMKECbCysoKVlRWv251FPjTKcufOnXB0dISlpSXq1KmDtm3bwsDAALVr15YTH6WkpCAgICDTcmZZI7P1TL+2LeS+IQf/GMszRo0aBRcXF9SqVUseGfal2dvSe/nyJf755x9+Q5iFfv/9dzmTb0hIyAeP+69sYJmNXGJZ78SJEzh79iz++ecfjc5eUFAQhBBwcXHBkydP5M72xo0bYWlpiU2bNqFt27YQQmDfvn3yedHR0bC1tYWLiwtnHs0CHwr0nD59GuXLl4cQQu50v379GgDw4sULTJ8+HUIIjBo1Sk5w9ObNGwwaNAiurq6wtraGr6/vR5OEsM83dOhQDBgwIMP2zykv6Rn37NkzuewAnoKYEyZNmgRfX1/4+PhgzJgxH80mL/UbRo8eDVNT00zXQJX89NNPmDdvHgduv9KGDRvkH7GZvViUHDp0CNbW1mjUqNEHEz3ExcVh4sSJsLGxgYWFBZo3b87tYTbLjkznjx8/Rvny5dG0aVPub3yFf/75B0DGzL3pJSYmws3NDZ06dcKNGzeQmJiIlJQUbNy4EcbGxmjdurWcdJGfV9nrY8+Sz20LuW/4Lw7+MZbL7t69i/Lly8POzg7Tp0/XWKcP+PLsbfxQyj6jRo2CQqGAiYkJGjZsmGH/l2YDY1nr3Llz8ts9IQTMzc3RtWtXjXXEhg8fDiEELCws0KhRI9SpUwdKpRKjRo0CkDZ9QwiB33//HcC/P4YfPHjA06a+0t27d9GpUyfUq1cP1apVw/LlyzXWbVGr1Vi0aBGaNm2Kffv2Zagnr169QtOmTVGsWDGNqfQbNmzApEmTPuktMPt0t2/fhpeXFwwMDDBx4sQML6G+tLxYzvj7779RunRp2Nraon79+nJCKSsrK6xevVperzT9CxKpDH19feHp6anxozkmJkZjxPrHflCzj1Or1Xj37h3q1q0LIQTatGkjL+XyoSQe8fHxckB90qRJH5xmuGfPHkyZMoXbw2yWHZnOpbJPTU3FkydPsv8mCqiQkBB4e3vD2tpafiGVWXslbQsPD9d4KQWkBdI7dOgAIyMjOYjIssfH+oaZtYef2hauX7+e+4bg4B9juSo5ORm9evWCt7c39u/fL689lt7HRuxx9racJf0Q8vf3h4ODA7777jsIIbBw4UKN/RLOBpZ7fv/9dxgbG6N58+ZYuXIl9u/fj1atWkEIgfr168vZ9WJjY/Hbb7+hfv36cHd3h4+PD3bv3i3/ED579iyEENiwYUNu3k6Bs3TpUujo6MDb2xuNGjWCt7c3hBAoX768RgAwPDwct27dyvQzUlNTMWTIkEyzLHMgImtNnjwZQgjUqVMHhw4dktfWkUht37Nnz76ovFj2iomJQcuWLeHh4YGDBw/KP2w3bNgAV1dX2NraYsGCBZmeGx8fDw8PD7Rp0wZA2miMv//+G4MHD4afn1+GIDD7cuXKlYOWlhaMjY011oD70AvCsLAw+Pr6wtLSUmPpibt378pT6aWlKlj24UznedfRo0flZSeEEJ+d3CF9sKlfv34QQuD06dNZfZns/31q3/B9H2sLpWQ63Bam4eAfY7no4sWL0NbWhr+/v7zt2bNnCAgIwKFDhzTeHEpBQM7elvtGjx4NX19fXLlyBWXLloWBgQHu3LkD4N/vnrOB5Z53797B19cX5cqVw82bN+WHfVhYGAYOHCgHAKWRl0DaD9rM1l1cvHgxd/ay2NmzZ+Hg4IC+ffvK9QYABg8eDCEEqlSpopHASCLVrfTtW9++faGvr6/xOSzrhIeHo1y5ctDR0cGMGTMQHBycaTlkhssr77hw4QK0tbXx66+/amyPjo7GuHHjoFQq4eHhIS9vkP4H7507d2BqaopZs2YBSHuxUrZsWejp6WHz5s05dxMFXFRUFLy8vPDrr7+idOnScHBw0Fhu4kMOHz4MOzs7NGzYECdPnsSyZctQokQJzJ49m6dgZzPOdJ63/f333/D09ISNjQ3mzZuHKlWqQEtLC3/++SeA/35JmL4dDA0NhZubG+rXr8/LKGWTL+0bSrgt/DQc/GMsF61ZswaWlpbyEOVp06ZBT08Purq6EELA1dVV4+2vSqXi7G25SOoILFu2DM7OzkhNTcW6deugp6eH+vXrZzh+6NChnA0sF1y/fh26uroYM2aMvE2qN7t27YKRkREMDQ3Rt29fjfMyq1uVK1fG//73P15nJwsNHz4c2tramY7W8/DwgBAC3333nTw9R5K+fNRqNQ4ePAgLCwv06NGDX3hkk8uXL0MIgapVq2qsQRUUFITx48dj/PjxCAwMlEcZZTZlVPozl1fu2bBhA4QQWLduHQDNvsSmTZsghICWlhZatGiRYc3hwMBACCEwf/58jBkzBgqFAo0aNcpQP9mXk8qiYsWKmDRpEk6cOAEhBFq2bIlHjx4ByDjdLf2L4FmzZkFXVxelS5eGQqGAl5dXhiQ6LOtxpvO8LSEhAU5OTvLMjcOHD8PIyAglS5aUy+O/AoBqtRp37tzBd999BysrK/z222/ydpa1vrRvyG3h5+HgH2M5JCEhQX7ISP9dv349hBB4+PAh/P39oaenhwkTJuCPP/7A0qVLUbx4cQghMG/ePACaDxvO3pZ7Zs2aBXd3dwBp06mk6b9z584FkLYGI5D2QNqyZQtnA8tGmXXApIBF79695ek30lqYFy9ehK6uLgwMDGBlZZXpyIqTJ09i3bp1qFChAhwcHOTsbixrtG/fHsWKFdNoD6U/S3VJCIGtW7fK+9N7/vw5/vjjD1SrVg0uLi44efJkzt5AISF97z/99BOEEFiwYAFev36NJk2aQKFQwNLSUi6r6tWry+sgvT8qgssr90hlKK0LPGjQIDlphzQSYvbs2fDx8UGdOnVgYGCQIXP5nDlzIISAmZkZdHV1sWLFipy9iUIiNTUVZcuWlV/49u/fH0IITJky5YPr/gFpa2nOnDkTQggoFAq5H8Ky3tdmpudM59nr2bNn2Lhxo8Z6ie+/ZP/xxx/l/uF/uXTpEpYtW4aqVatCV1cXy5Yty5brZmm+tm/IbeGn4eAfYzlg8eLF6N+/f4Z1PKQh/qNGjUK1atUwfvx4ea0xALh16xZMTExgY2Mjj+p78+YNZ2/LRh/LBiZ1JgICAmBtbS132q5evYoqVapAT08PvXr1ghACO3bsAMBvB7NbdHS0HNiTvHv3DlWqVEHp0qVx5MgRjX0TJ05E/fr1sXz5cnmqfPoy+ueff2BlZQUbGxv4+vpmOhWYfboDBw7gwoULANLqT2pqqrxujlRHJPHx8ahevTqaNWsGe3t7eHl5yftUKhWioqLQq1cv/O9//4OpqSkqV67MC29nsRcvXgBIa7fSBxw8PT2hp6cHa2trlC1bFr/99huuX7+OkJAQeYRz9erV5eNTU1O5vHLB3r17ERAQgBUrVmiMNHrx4gVatGgBY2NjzJo1C3FxcUhKSsLhw4dhYmKCH374Abt374ZCoZCDTykpKUhNTZV/LHNf4+ulbw8zCyTVq1dPHpH+6tUrlCxZEg4ODjhw4ACAtBdb6Z93YWFh6Ny5MxQKBapVq8YvGrNZVmSm50zn2WPp0qXy2nDJyckZvk+pjsXGxqJGjRpQKpVygsX3+/vJycny7zMnJyc0bNgQISEhOXMjhURW9Q0l3BZ+Og7+MZaNgoODUalSJVhbW6N9+/Zy8C/9unCWlpYwMzODoaGh3MED/h2pNGPGDHnUhWTv3r2cvS2LfWo2MABYvnw57OzsNBa9Hz9+PJRKJXR1dTF58mQeZp7N7ty5g+bNm6N8+fJwdXXFTz/9hIsXLwJIq1dr1qyBnp4eypQpgwULFmDfvn3yen/z5s3Ds2fPULJkSdSpU0f+TKle7tu3DwcPHsyN2yownj17hqZNm6Jo0aKoXbs2YmJi5O/34MGD0NLSQtGiRXHnzh3ExMQgISEBP/74I8zMzHD06FG0bt0aQgiN4O2bN29QsmRJVK5cGUuWLMmtWyuQoqOjUbFiRbi4uGj8IJVG8V2/fl0OAN2+fTtD29i9e3cIIbBmzRp5G5dXzrl79y5q1KgBR0dHmJiYQAiBSpUqabxwPH36NDw9PSGEQMmSJVG2bFkIIdC1a1cAwOPHj1G0aFE0btxY47OPHz/OMwu+Umbt4ftUKhWqVauGoUOHytv27dsHIQS++eYbeHp6om7duhqL3oeGhsovjln2ya7M9OzrhYeHo2bNmtDW1oaJiQm0tbXlJSo+FAA8fPgwTE1N4ebmhufPn2f6ua9evcLSpUszjIRmXyc7+oYAt4Wfg4N/jGWTGzduwMvLC9988w327t2LJ0+eaOyXflTNnz9fHsosTYeS3rgDadMDihQpgilTpmiczxmLss6nZgNLHxwyNTVFeHg4EhMTMXHiRGhpacHBwQFCCEybNi0nL7/QCQgIgIGBAerUqYOePXvi22+/hRACNjY28lohCQkJWLhwobx+pvQGd/Xq1QDSguu1a9eGm5sb4uLiPjqtin2ev/76CzY2NqhUqRI2btyYaWbXcePGQVtbGwqFAqVLl4aTkxNMTU2xfv16AGlrygkh5BciUnv57NkzREVF5dzNFAJqtRorVqyQ68nIkSM19kvPmiVLlsgLpUukcpGm2vfr1w8qlYrLKwetWrUK5ubmqFu3LjZt2oQzZ85g69at0NbWRr9+/TRmE4SEhKBv375o1KgRmjRpgt27d2usn1S8eHE5GMhtYtb4lPZQqmOtWrVC69atNfbVrl0bSqUStra22LVrl8aoXJVKpRGsZ1kvuzPTsy+3ZMkS6OjooFKlSjh27Ji8RMXatWv/89zx48dDCIFu3brJ2y5evIhr164BSHsu8mjMrJUdfUOpjLgt/HQc/GMsm0ybNk2eriE1TklJSRmyuz558gStWrWCEAKNGjXK8Dk7duyQF9sGeGpAVvuSbGB//PEHrK2t8ddff6FSpUowNDTEzJkzcfDgQVSvXh1CCFy9ejWnb6VQiIqKwjfffIO6devi3r17cn1YsWIF7O3tUbx4cVy6dEnefuXKFQQFBWH79u149+6dRv2pW7euxlRF9vXi4+Ph6+uLpk2b4tKlSxmmZEsSExNx4MAB9OjRA3Xr1sXgwYNx48YNOWgkdfDeXyqBZY/ly5dDqVSiRYsWEELII1/fn/77Pmnf06dPYWRkhB49euTI9bK0stmxYweKFy+OwYMH49atW3L79ubNGzg5OaF+/fpyHUy/FmNm2SpPnToFhUIhZ/VlX+9T2sP3n0mdO3cGkLbsS7169aCtrQ0rKyvY2dkhMDAwx66dcWb6vCoiIgI1atRAkSJFMHLkSPk7vXz5MnR1dTFp0iQAmb/AkLbFxcWhTp06EEJg+fLl2LlzJ0qXLo0SJUpovDBhWYP7hnkHB/8YywYpKSlwc3PDgAED5G1r1qyBt7c3KleujBYtWmDTpk3yvuvXr8ujxn755Re5Ubx16xbatWsHDw8PjTeMLOt8STawI0eOQKlUQgiBWrVq4eDBg0hISAAArF69Gn5+frw2Uja5cOEChBBYunQpAM1O9oYNG2Bubo6mTZt+dH0W6UezoaGh/Dksa+zduxdCCI0RYidOnMDs2bOxYsUKPHjwQM5qKMmsoz1y5EgYGRnh3r172X7NhZlUf9avX4+qVati3bp1sLa2RsmSJTWWNXj/nPdfQi1YsAAKhULOhMiyX2pqKoKCgtChQwfcvXtXY9+5c+fg4uKCcePGZahfarVa45kmZbPs3LkzypYty2slZaFPaQ/TryPXq1cv+Pr6Yt68eTA0NISXlxf++usv7N69+6PBJpY9ODN93rRy5UoULVoUO3fu1JhCf/36dejq6qJixYofDeBJgaTjx4/DwsIC+vr6UCgU8PT0lEf+sazFfcO8g4N/jGUxlUqFiIgI2NraYuTIkUhOTkb79u2hUChQt25d+Pj4yIGjBQsWyA+uY8eOwd7eHkIIeHl5oWnTpvDy8kKRIkXk4c4sa0md7s/NBvb48WMMGzYMM2fOzLCAPU/Hzl7SGkj+/v7yNqkzHRMTg8mTJ8tZvqSySN/ZDg8Px549e1CtWjVUqVIFwcHBOXr9Bd2sWbNQtGhRAGkd7Pbt20MIASMjI3lq9pgxY+TjVSqVRvnExcVh7969cHd3x9ChQ/mHUg5Zt24dbGxs8PbtW0yZMkVj+YP3yyD932NjY7F79255PbL3l7dgWSt9Nksg7ft/fxTf0aNHUaJECWhpaaFs2bL49ttv0adPHzmAkf4Zdf36dcyaNQvffPMNDA0NOXibxT63PezUqROEEDA2NsaQIUNw//59ubxGjBghj2hiOYMz0+cNmfUDHj9+nOnIPh8fH7i4uODhw4cf/cyoqCjs3LkTpqamEEJg4sSJWXW5LBPcN8w7OPjH2FdKnx0xPVdXV3Tv3h179+6Fvb09tm/fLq99dODAAdSuXRsWFhYICgqSH2AXL17ExIkTUbJkSVSvXh3t2rXjDFNZ6MyZMzh27BgOHz6c6f7PyQYWGRmJ+Pj47L3gQi6zuvXixQuYmZmhf//+GfYBaQtzf/PNN3B2dpbrm9SJWL16NRo3bgwLCwtUrVoVYWFhOXQnBc+HAkILFy6EEAL//PMPRo4cCQcHB2zcuBFnz57F7t27Ubp0aQghMGPGjAyfuWHDBkyePBk2NjaoUqUKZ4bNQb///jucnZ0RGxuL0NBQ+Pr6QgiBoKAgABmnsalUKvz+++8YP348LC0tUbZsWZ7Ols3ez2aZntSH+OOPPyCEQLNmzbBt2zZcvnwZ06ZNg5GREWxtbeURSmq1Gk+ePEGPHj1QrFgxzmb5lb62PZTWCX748CH8/Pywbdu2DC8leQ3G7MWZ6fOHD71gl0akjxkzBkIInD17FkDm9SYyMhJjx46Fjo4OSpQowcv0ZCHuG+Z9HPxj7At9KDuiWq1GSkoK+vXrBx0dHdSsWRP169eX9wFpD69du3bB0tISHTt2zPTz37x5k/03UUg8e/YMzZo1g4ODA7S1tSGEwIABAzKd3vQ52cBY9vhQ3QLS1nr59ttvoaurK4/ae38K26JFizKMDgTSfjx37tyZp/p+peTkZERHR8tT3dNbs2YNhBAYOHAgihcvjgULFmh0voODg2FhYQEbGxs5Ix8A3L9/H1ZWVnBycsLo0aNz5D4Ki8TERDx69CjDUgTpO+kHDx6Ejo4OXr16BSBtik7RokXh5uaGq1evomHDhnJ2vdevX2PgwIEwMzODnZ0dl1c2+5xslqdPn8bOnTszrC08efJkCCHwww8/aGy/deuW/COZfZmsag9v3LgBIK1+ZbYmI8senJk+74qPj4e/vz9mzJiBefPm4cWLFxrJbjIjJa/6r6yvnTp1ytAesq/DfcP8gYN/jH2BD2VHTN8R37BhA2xsbCCEQOPGjeXtUqDixYsXqF69OoyMjDJdH46HNGeNgwcPwsXFBZUrV8bChQuxZs0adOvWDdra2hlSxb/vQ9nApE46v4nPep9St1avXp2hXqU/5t69ezAxMUHXrl01pukkJiZm2ilhn+6PP/5AjRo1ULJkSTg6OuKXX37ReGv+7t07WFtbo0iRIrC2tpbrCvDvOjszZsyAEELOvCzVo3PnzvE07Cy2fft2lClTBg4ODtDX10fv3r3lkc/p69TOnTthb2+v8f1PnToVQggYGhrC0dERe/fulfcdP34cq1evzrDWHMtan5rN8kP9hcTERADAo0ePoKuriyFDhgDgZ1dWyY72kOUczkyfd+3cuROWlpZwcHCQp+YWK1Ys05FhwL9t4LNnz2BmZoa+ffsCyNjWSX/npB5Zi/uG+QcH/xj7Qh/KjigFGuLj49GqVStoaWmhSpUqciOY/kHk5+cHZ2dnTg6RTd69ewc/Pz9Uq1ZNIwPsxYsXIYTAunXrMj2Ps4Hlrg/VLamDkJCQIL9tX7RokXxe+rrl4eGBVq1a5eyFF2CpqakYOnQotLS00KpVK/Tp0wctW7aEEAIuLi64efOmfKw0vSP91JvU1FS5/C5fvgylUskZzLORWq3G1KlToauri549e2LSpEkYNmwYdHR0YGhomGHpg8uXL0OhUOD27dsA0qZFNW7cGAqFAkIILF68WP5clv2+JpulJP2+gwcPQgiBqVOnZu+FFxLZ2R6ynMHZR/OuwMBA2NjYoFOnTjhx4gRCQ0Nx9OhRGBsbQwiBFStWfPDcJ0+eoFSpUihatCgvzZMDuG+Y/3Dwj7HP9CnZEaWG7OzZsyhfvjyUSiXGjRuHZ8+eyfsvXrwIe3t7+e0Uy3qXLl3SmP4pBWZ37NiBChUq4Pnz5xqZ9tI/aDgbWM77nLp14cIFeHp6QltbG3v37pU7eWq1GkePHoWhoSHmzp2bOzdSAO3ZswdWVlaYPHmy3I4B/ybHKV++vJyR/Pnz52jXrh2EEPKSB+lJ07J37tyZU5df6Dx+/BilSpVCx44dNcpr8+bNcHV1haOjI06fPq0xvc3c3Bz37t3DunXrYGlpCQ8PD4wePRouLi5wdHTkUSw56GuyWb6fjfnGjRvw9fVFqVKl8OjRo2y/9sKA28P8j7OP5l2dOnWCg4MDbt26pbF9+/btcpKIoKCgDwaH2rZtCx0dHZw4cSInLrdQ47Yw/+HgH2Nf6GPZEdO/cf/9999RoUIFKJVKtGrVCocOHcLKlSvRpEkTWFhYyKOa2NfJrBNw8uRJCCHw3XffAUgrlytXrsDT0xPm5uZwcHBAhQoVsGLFCvkHVvqy42xgueNz6lbp0qVhamqK4cOH49KlS/j999/RrFkzFCtWjIO0WUAKJLRq1Qrm5ubyQsvpEz+ULFkSQgj06NED0dHRANLWaXF1dZWnbkdERCAuLg7Hjh1DrVq1UK1aNV5LMxvt379foxOdfl3Mbdu2wdraGvXr15c75bdv34aOjg48PT2hVCrRvXt33L59G2q1Gr/88guEENiyZUtu3EqhkB3ZLF+8eIG9e/eiQYMGMDExyTBVmH0+bg8LDs4+mje9e/cOtra28swNKfmKpGLFihBCoFq1ahrBJulYIG3ZpfSJWniZg6zHbWH+xcE/xr7Qf2VHlEYopaSk4NatW/Dx8ZGHOxsaGqJcuXK4fPlybt5CgSU9fKKjo1G9enUIIVCrVi34+flBV1cXNWrUwKxZszBx4kRUqVIFQgj8/PPPGp/B2cByz6fWreTkZFy9ehU1a9aEEAJKpRLGxsYoXbo0rly5kpu3kK+l/xEj/blp06YoXbq0vE2lUskd6jZt2sjTQwMCAuRzL168KGdws7W1RcWKFWFjYwMnJydOMpDNfv/9dwghsGrVKrmcpLKMjY3Fr7/+CoVCgdmzZwNIC/7Vrl0blSpVwvr16xEVFSUf//LlS40FuFn2+lAQ4VOzWUZFRWHKlCmoV68e7OzsULp0aVy6dCnbr7ug4vawYOHso3mXSqXCq1ev4OXlBRcXF43tABASEgJHR0dUrVo101k9ksDAQI2XxixrcFtYMHDwj7FMZFV2xPTrKsXFxeHq1as4ePAgj/bLQvHx8fj5558xcuRIjBkzBrdu3dLIlHfu3Dn07NkT1apVg5GREdq1a6cx9Sk2NhYODg6wt7fPsNgzZwPLellVtw4dOiQfHxMTg9OnT2PHjh0IDAzMmRspwBITEzWmNyUnJ6Nbt24QQmDr1q0ax0ZERMDLywtdu3aFra0tvLy8NPYHBwdjyZIlaNq0Kdq0aYMJEybwCIlsJP0ICgsLg5mZGQYMGAAgY0Dp3r17qFOnDhwcHOQp9WfPnsWtW7c+uBQCy3ofe359aTbLiIgIdOjQAc2bN8e8efOy7doLC24P868P9TcAzj6aF5w9exZ///03nj59qrG9b9++EEJg1KhRGttnzZqFsmXLYu3atbCwsIC7uzvi4uIyfG58fDzXrWzAbWHBwME/xt6TXdkRWdb766+/YGlpiWLFiqF48eJQKpUwNDTE0KFDMxz78OFD9OnTJ0P2KQBYvHgxlEplhsQSnNQja3HdyttOnDiB//3vf6hZsyY8PDywbds2eZ03KSBrbW2NM2fO4NGjR3j48CGaNWsGR0dH3L17F99++y2EEDhz5kymn/+hBdXZlzl//jxmzJiBGTNmICgoSGNx8/DwcPj6+kJbWxv3798HkHF0hL+/P4QQvLh2Lvmc5xfwadkspT9HRkbi9evX2XwHBRu3h/nbx/obAGcfzU2HDx9GyZIlYW9vDx0dHbi7u2PNmjV48eIFgLQRftKU0RYtWuDHH39E06ZNNZJPderUCWZmZggLC9P4bJ7im/W4LSxYOPjH2P/LruyILHuEhISgTJkyaNq0Ka5cuYLo6GiEhISgQYMG8qiIN2/eyMffu3cPjo6OOH78OADNh400/WPbtm05fh+FAdetvG/UqFHQ0dFBnTp1UL9+fTg4OMDS0lKeag0A06ZNk9e/NDc3h56eHkqXLi1P09i6davGdESWfQYOHAg9PT3Y29tDS0sLQgjUrFkTp06dko9ZtWoVhBCoV6+exrnSj6O7d+9CX18fQ4YMAcDBv5z0Kc8vaUTm+zibZfbj9jD/+pT+xvtTfzn7aM7ZsmULTE1N0bZtW6xatQr+/v6oWrUqFAoF+vXrJ6/3duzYMfTu3RuWlpbQ1tZGrVq1sGfPHrlsJk6cCH19fZ5ync24LSx4OPjH2P/j7Ij5S0BAAIQQOHDggMb227dvo3379tDW1sbq1auRmJgIIK0jIYTA1KlTNTLDhoSEoH79+vD09JQXpGVZi+tW3jZ16lTY29tjwYIFCA0NBZA2qkxfX18jY3JKSgouXbqEn3/+Gd27d8fy5csRHh4ul9uGDRugra3NGfayiVqtRmJiIoYPHw5TU1PMnz8fN27cQGxsLEaPHg1jY2NUqFBBXuT87du3+N///gchhLy2n/Q50n+trKzQq1evXLmfwuxzn1/v42yW2Yfbw/ztU/sbQNp6pm3btuXsozmoRYsWKF++PEJCQuRtr1+/lrPADh48WN6empqKp0+fZjrKsnXr1nB1dcXLly9z5LoLI24LCyYO/jH2/zg7Yv4yefJkCCHkjK7py+vSpUuoXLkyXF1d5cQPKSkpqFChAkxMTDBx4kSEhITg999/R9u2bVGkSBEsWrQoN26jUOC6lXe9evUKHh4eaNGihUbw+8KFCzAzM8sQnJC8PwJCpVKhbdu2KF68OHfGs1FoaCgcHBzQrVs3jbWOwsPDMXjwYDkLotTuXbx4EZUqVYJCoUBAQIA8GjopKQkBAQHQ19fH77//niv3Uph96vPr/URTnM0ye3F7mP99Sn/Dx8dHzpbN2UdzzosXL6Crq4thw4YBgEZyiJSUFFSsWBHa2trYuHHjBz8jMTFRXgd6ypQpOXHZhRK3hQWXghgrhABk2BYXF0dERJGRkaRWq0mpVMrHNW3alIYPH07Hjh2jwMBA+ZyqVauSnp4erVmzhubNm0elSpUiIQR99913dOvWLerYsWOO3E9holariYioWLFiREQUFhZGRKRRXhUqVKChQ4dSeHg4bdy4kYiIhBA0b948AkCTJ08md3d36tOnD926dYt27dpFgwcPzvmbKYC4buUvERERFBoaSmXKlCFTU1N5+6lTp8jOzo5OnTpFs2fPpocPH8r7pDooefnyJa1bt47OnDlDffv2JWtr60z/HbCvd/36dQoPD6f69euToaEhqVQqAkD29vZUvXp1IiK6efMmLV68mFQqFVWuXJl+/vlnqlixInXr1o0GDBhA27dvp6VLl9K0adPI09OTqlSpkst3VXh87vNrw4YNGucpFGnddhMTEyIiOnr0qMZ29nW4PcxfvrS/cfz4cfrjjz9IpVKRh4cHBQQEUKlSpWjevHlUrlw5qlOnDnXo0IEeP35M8+bNI1tb2xy9r4Lg/bKR/m5jY0NPnjwhorR2S6FQUGpqKmlpadGCBQtIW1ub/P396dmzZ0SkWb/+/vtvCggIoNGjR5OlpSW1adMmh+6m8OG2sADLhYAjY3kKZ0fM2z6UDezChQuwtrZGy5YtMz3v6dOnaNWqFfT09BAeHi5vv3TpEubOnYsFCxZg/fr12XrthR3XrbxL+i7DwsJQvHhx6OnpYenSpQgMDESHDh0ghEDFihXh6uoKfX19uLm5YfPmzRqf8fr1a0yYMAG9evWCrq4uOnfuLCfRYVlLGh1x4sQJOQtiTEwMAMhTQzdt2gQnJyc4OjrC0tIS+/fvB5BW1qGhoWjZsiWMjY1RpEgRmJmZoVWrVplmSmRZJyueX9LUxfTtH2ezzFrcHuZ/n9vfcHR01FgXmrOPZr+3b9+iYsWKsLS0lEenv/8df//99xBCYOHChRrb165dC3Nzc1hZWaFly5b87Mom3BYWfBz8Y4XKqVOnMGLECAwfPhwLFy6UM0sBnB0xr/lQNrBXr14BSEsCIT2I9uzZAyBjeS1evBhCCCxZsiTHr7+w4bqVt4WFheHq1atyEpX0Vq9eDScnJxgYGEBfXx/W1tbYsmULnj9/joSEBBw6dAgKhQJlypTB3bt35fPu3bsHJycnVKhQIUPnj32dD5VXREQEqlWrBnd3d411qKKjo1G9enV0795dnhIq1aX02cvv37+Ps2fPZphOyrJWdj6/eIrv1+P2MH/Lqv7G+wEmCWcf/XJXr15Fp06d0Lp1a3To0AFHjx7VCPwsXboUQgj89NNP8osrtVott2sxMTGwsrJCly5dAPzb3r179w4TJ06UX2qxrMFtYeHDwT9WaIwYMQLa2tooXbo0bGxsIIRAyZIlsX37dvkYzo6YN/xXNjCpo7d3716YmprC1tZWIzOi1HF78uQJhBBYsWJFrtxHYcF1K28bN24cbGxsYGxsDCEEmjVrJq8VJvn7778REBCAJk2aYPr06Xj79q3GfmmNst9++w3Avz+m7t69i4SEhJy5kULiY+WVlJSErVu3wsTEBKampujatStGjBgBJycnuLq6IjQ0FI8ePYKjoyOaN2+ey3dSOPHzK2/j9jB/4/5G3jV37lxoa2ujevXqqFmzJqysrKBQKNC9e3f5mLi4OHh7e8PJyQl79+7VOD8lJQWJiYlo1qwZnJyc5HonlRuXU9bitrBw4uAfKxSkjKFz587FP//8g+TkZGzevBnFihWDlZUV1qxZA4CzI+YVn5MNbNy4cRBCoFWrVhpTOAAgMDAQQggEBATk2LUXNly38q63b9+id+/eMDU1xdSpU7F+/XrMnTsXpqam0NLSwq5duzKMIvrf//6HS5cuAUjrxEmBiKNHj0IIgQULFuT4fRQW/1VegYGB8rE7d+5EyZIlYW1tDWNjYwwYMAB37twBACQkJMDd3V0O/vFIsZzFz6+8idvD/I/7G3nXlStX4OTkhH79+snJVF68eIHWrVtDCIERI0bIiSMCAwOhq6uLxo0b4/r16wCgscRLo0aNUK5cObx+/ZoDftmA28LCjYN/rEBTq9VQq9Vo3Lgx3NzcMmTsunz5MvT09ODk5ISzZ88C4OyIue1Ts4GtW7cOAPDy5Uv06dMHQgh06tQJFy5cAJDWEWndujU8PT011vxjWYPrVt5369Yt2NjYYOTIkRqBhV27dqFs2bKwtbXFkSNH5M71pUuXoK+vj+nTp2fo+I0ZMwZKpRLHjx/P0XsoTP6rvGxsbHDkyBH5zfqbN28QFhaGiIgIjc+JjIyEhYUFvvvuuxy9fsbPr7yM28P8i/sbed+CBQsghJBfQkkePHiAHj16QKlUYvHixUhKSoJKpcKkSZPk0Wbps8CeP38exYsXx9ChQ3P6FgoNbgsLNw7+sULBy8sLvr6+AP5d/0hqwKT1J5o2bSr/qAoMDIS3tze0tbXRqVMnbNu2Df7+/ihTpgwqV66MR48e5c6NFDDvv9FTq9V48eIFnJyc0Lp1a419UrmdOnUK+vr6qFChAh4/fgwgbaHm8ePHQwgBpVIJNzc3ODg4wMLCAkFBQTlzM4UU1628a8uWLRBC4MyZMwD+LR8gbeSYvb096tSpgwcPHgBIextsZ2eHSpUqYd++fQDS1k/atm0bihUrhqZNmyIqKirnb6SQ+NTyCg4Olre/34ZGRkZiwoQJsLW15XX9shk/v/IXbg/zP+5v5F3Dhg2DlpYW/v77bwCaI86vXr2K6tWrw8XFReO51L17dwgh4OXlhQkTJmDq1KmoWrUqihYtKgdxWdbjtrBw4+AfK7CkjnlsbCx8fHxgYWGBJ0+eyPvSd9z9/PwghMDatWvl/ZwdMWdJ5fE52cCkBe0le/fuxdixYzFgwACMGzeOyyqbcN3K26TvX0r8sHnz5gxr5sTHx2PWrFlQKBSYOXOmvF9KMqClpYX69eujdu3aMDMzQ7169TKMMGNZ63PK632JiYk4dOgQRo8eDRMTEwwePFheTJ3lHH5+5T3cHuZv3N/I26RA65o1ayCEkF9YpC8blUqFLVu2aKyzCKStb7pw4UJ4enqiSJEicHR0RJMmTXi0czbhtpABHPxjBcjHMhb9/PPPEEJg3rx5Gtulh9a9e/cghEC7du0QExMj7+fsiNnjzp07mDx5MkaNGoWhQ4ciODhYIxvbsmXLPisbWPq3VkDGzG7s63DdytsePXqEUaNGyWvCSfXkzp07MDU1xcCBAzW2S+7fv4+6deuiaNGiGgszz507F+XLl0fFihXh4+Mj/5BiWeP9oJxUV76kvFQqFV69eoVBgwbB1dUVlpaWGYJKLGtldTZLfn5lLW4P8zfub+Rd4eHh2LFjBw4ePCiP8JOcOHEClpaWaNGihcZ2Kaj09OlTfPvtt9DT08OzZ880jomLi0NERESGKcPs63BbyDLDwT9WIPxXxqKIiAjY2NigSpUquHz5ssa5UqPXqVMnjYaOF5nNHhMnToSRkRHc3d1hYWEhZ2rbunWrfMybN29QuXLlz8oGlh6XXdbhupW3zZs3D6ampihXrhw6deqkEYR49uwZmjZtCqVSKXeq3w8szJkzB0IILF68WGN7bGws3rx5w9nastjy5cvRpk0btGzZEqNHj9ZICvHkyZNPLq8lS5ZobN+1axf8/f15xEQ2y65slulx+/jluD3M37i/kXdJdcva2hpCCJibm2PWrFny1OnXr1+jQ4cOEELIZfZ+/Zo/fz6EENiwYYPGdi6jrMdtIfsQDv6xfO1zMhbNnTsXQggMGzYMkZGRANIaO+mhM27cOOjp6cmZp1jWio6ORu/evVG0aFFMmzYNV65cwbNnz/Dbb79BT08PDRo00PjhumvXLujp6XE2sFzCdStvS05Oxvfffw8LCwuMGTMGV65c0agfknXr1kEIgVq1amlsTz/aTFtbGxMnTgTAmWGzS1RUFBo3bgxDQ0P4+PigbNmy0NLSgqOjI1atWiV/76tXr/6s8np/3SuWfTibZd7F7WH+xv2NvEn6TmfMmAFTU1OMHTsWBw8exJ9//glfX18IIdC5c2c8ffoUALBv3z6Ym5vD0tJSLhsAcmbY4OBgCCE0XvazrMVtIfsvHPxj+dqnZCw6evQogLQfX35+ftDV1cWvv/6a4bM6deqEYsWK8aKl2UCtVmPjxo0wMzPDr7/+Kv9AkgwYMABGRkYawb+kpCT88ssvnA0sl3Ddytvu3r0LNzc3jB07ViPzYfq3u0BaR7Bjx44QQmDy5MkZPicqKgq6urr48ccfs/2aC7MZM2bA1tYW27dvx+vXrwEABw4cQKlSpWBkZCSP5EtKSuLyyqM4m2Xexe1h/sb9jbwrNTUVlSpVgo+PT4aAXo8ePSCEQJ8+feTtUrvXpEkTjXZPrVbL68adPHkyR++hMOG2kP0XDv6xfO1TMhZ98803+OeffwCkrSXi6uoKIQQmTpyIN2/e4NmzZ/jjjz/g4OCAYcOGZVh/h329pKQkjB07Fp06ddL4fqW3imPHjtUox/Q4G1ju4LqVN0l1Ztq0aTAxMZE71zExMRgyZAj8/PzQokUL7NmzRw6mX716FTVq1IAQAitXrpSDT7GxsZg/fz5MTEwyrXvs66lUKiQkJKB06dKoWbNmhmmet2/fhrGxMezs7HDixAkAXF55FWezzHu4PSwYuL+Rd4WGhkIIgRkzZgBIKxtpdJharUa1atWgVCqxatUqAGlBoyFDhkBLSwtNmjTBgQMH8OLFC+zbtw9Vq1ZFnTp1MgwAYF+P20L2qTj4x/K1T81YNGPGDHn7jRs3UKVKFQghYGlpiXLlysHIyAgNGjTQeKvFstb9+/flh5FUVlLnbMKECTA2NkZYWJh8vHQMZwPLHVy38rbevXujTJkyANJGR5iamsLe3h5ly5aFgYEBdHR00LVrVznhwP79+1G3bl0IIeDj44OpU6di6NChsLS0RJs2bTRGW7Cs5+HhgXbt2gH4dwrU+1kSGzVqJJfXgQMHuLzyCM5mmfdxe5i/cX8j73rw4AGsrKzk55dEahcvXLgAAwMDlC9fXu7DP3r0CHPmzIFCoZDLx8jICOXLl9dY55ZlPW4L2X/h4B/LFx48eIBhw4bJb9ylh//nZCyKj4+Xtz99+hSbNm1Cnz590KdPH6xfvz6H7qTgi4uL08hw+Ck6dOgAJycnREdHayw6m748ORtY9oiJiUFkZKTG9AyA61ZelD7YMHLkSBgbG+PKlSto0qQJOnXqhDt37iA6OhphYWGoVasWhBAYN26cfH5kZCT69u2LYsWKwdbWFs7OzvJ6LizrSWUVFRWFKlWqwNraGo8fP5b3pW8fW7ZsCSGERva8qKgoLq8c9vLlS5w4cQKnTp3CrVu3APxbjidPnuRslnkIt4f5T2hoKC5dugRAs/y4v5H7EhMTkZqaKr+ckrx79w5lypSBnZ2dnGRFKjfpv6NGjco0OcS5c+ewdOlSTJ8+ncsmG3FbyD4HB/9Ynjdr1iwIIdCwYUMEBwdr7PucjEXvZ0eU8CKmWcff3x/e3t44cODAJ5+jUqlQo0YN1KlTB0DmAUOVSsWLomeDJUuWyFPVTExMsHTpUvmNeXh4ONetXHb79m3cuXMnQ1ZD4N+seX5+fihSpAgOHz6ssf/p06ewtraGra0tbt68KW9PSUnBq1ev8ODBA431YNjXO378OIKCgnDo0KEMP6AmTJgAIQTmzZunsV2qUyEhIVAoFOjSpQvi4+M1RkdzeeWMBQsWwM3NTc5maWVlhd27d8v7Y2JiOJtlLuL2MH/bt2+fXK+k6YVSveC+fO7auHEjatasiYoVK6JGjRrYtGkT4uLi5P3Lli2DEALjx4+Xl65Qq9Xydx4dHQ0rKyv06tULAHjKdTbjtpB9DQ7+sTzr0aNHqFq1KooUKYIJEybg/v37mR73uRmLuBOe9WJjYzFs2DAIISCEQNeuXfHo0aNPOvf58+ewtbXF4MGD5W3x8fE4efIkzp8/n12XXKi9evUKvr6+MDY2RufOndG9e3d4eXnB1NRUo6PAdSt3xMTEoGPHjnB3d4ehoSGEEOjRowcuXrwoH/P27Vs4OTlBCIFy5cohNjYWQFoZSOUzbdo0CCGwb98+eR/LerGxsWjevDlMTU2hr68PIQS8vb2xbt06+ZiXL1/Czs4O3t7eGuUI/PujtXXr1nBycsoQOGTZ6+XLl2jatCkMDQ3x/fffY9OmTVi5ciXMzMxgZ2ensU7joUOHOJtlDuP2MP9LTU2V+4hKpRK9e/fOcAz3N3JeUlIS+vTpAz09PbRr1w6dO3dGqVKlIIRAq1at5Cyxb968kdcqDQwM1PiM1NRUJCQkoHbt2ihdurQ884dlPW4LWVZQEGN50Lp168jFxYW0tLRo27ZtNHz4cPLw8Mj02M6dO1PHjh3pzJkzNGXKFHm7UqkkIiIbGxtSKBT07t07IiISQmT/DRQib9++pSVLltDixYupRYsW1KtXL9q8eTPt3LmT3r59S0READ54fmhoKEVERFDVqlWJiCgkJITmzZtHzZs3p9WrV5Narc6R+ygsVCoVTZo0ie7fv09r166lRYsW0fr162nlypWUkJBAISEh8rFct3LekSNHqEKFCnT79m3q2LEj/fLLL9S5c2fasGEDzZs3jyIjI4mIqEiRIjR+/HgiIrp16xbdvn2biIhSU1PlcihevDgREcXHxxMRl092iIyMpLZt29Ldu3dp8eLFFBQURFu2bKEXL15Qr169aMuWLZSamkrW1tY0ZswYunLlCq1bt45evnxJREQpKSmkUKR1xWxtbSkhIYHCwsJy8Y4Kl4cPH9KAAQPo5cuXtGrVKvrpp5+oc+fO1LdvX2rZsiW9ePGCbty4IT/DGjRoQMOHD6eoqCjq0qULRUREEBGRtrY2AaCDBw8SEZG9vX2u3VNBwu1hwaBUKsnc3JxsbGyoY8eOtHbtWgoMDCSitDIi4v5Gbjh58iQFBgbS+PHjacmSJbRp0yY6f/489ejRgwIDA2no0KEUFRVFxsbG9PPPP1N0dDTNnz+fLl26RERpzy+lUkn6+vqUkJBA1tbWRPTxPj/7MtwWsiyTu7FHxjK6c+cOKlSokCEdfGJiIrZs2YItW7Zgz549Gm/dr1+/zhmLcklkZCS6desGX19fvH37Fs+ePUPNmjXh7OyMY8eO/ef5a9asgZ6eHg4ePIhjx47Bx8cHQgj8/PPP2X/xhVBISAiMjY0xaNAgjbd9Bw8ehL29Pe7evatxPNetnHPt2jVUqVIFfn5+OH/+vDx1JiEhAR07doSlpSXu3bsnHx8REYHevXtDCIEaNWpofNa7d+/Qs2dP2NjY4MmTJzl6H4XJsWPHoK+vj4ULF2qM2Dt48CCqVKkCCwsL7N+/H2q1GtHR0WjTpg2USiV++uknjc9JTk6Gj48PqlatyiP/ctCNGzdQs2ZNrF+/XiOD5bt379C2bVvUrl07w4Ln0dHRnM0yB3B7WLCMGjUKrVu3xv79+1G0aFE4Ozvj1atXAP6dJnrz5k3ub+QAqe83duxY6OjoyO2V1AaGhYXhu+++g0KhgL+/vzwCcMaMGRBCoHbt2ggNDQWQVrcCAwNhZ2cnZwRmWYvbQpaVOPjH8qT169fD1NQUffv2BZC2HoWlpSW0tLTkqaWVKlXC6dOn5SlTnB0x57w/RPzu3bvytqSkJOzatQsmJiZo3bq13EF4n1RuI0aMgKGhIcaOHQtLS0t4enrKiV1Y1rt48SKEEJg8ebK8LSkpCf3794e7uzs6deqEwYMH48KFC3JHkLOBZb/U1FTMmTMHRkZG8lQMIK2eqFQqLFy4EEKIDOtpvnjxAhUrVoQQAu3atcORI0dw5swZzJw5E5aWlvjuu++QmJjI0zqyyaJFiyCEkJNDpA/cBQUFwcnJCTVq1JAzHL548QKenp4QQmDYsGEICQnBzZs3MX/+fJibm8vZLLm8sk/6tawAZJqE48cff4QQAg4ODhg4cCD69++P27dvy1PaHj9+zNkssxG3hwWH1NebMWMGPDw8oFar5bW8u3fvrnEMwH35nNS1a1cYGBjIgaP09eLmzZuoXbs2HB0dcfXqVXn74MGDIYSAi4sLunTpgv79+6No0aLw9vaWs/2yrMNtIctqHPxjeYr0NiMyMhJDhw6FEALffPMNDAwM0L9/f2zbtg1Xr17F8OHDYW5ujkqVKmmMDuTsiNnr/REp7z80pGDRmzdvMH78eCgUCsydOxcJCQmZHq9Wq9GqVSs5oPvjjz9m49UXLh96oD98+BDFixeHlpYWxo4di3nz5qF8+fIwMDBAixYtUKtWLVhYWMDOzg6rVq2Sz+NsYNkjfTnt378fBw8e1Ngn7V+9ejWEEDhx4kSGfSEhIXKWUWlBdT09PY11NFnWeD/LobQQemBgoPwDVtqXkJCAuXPnQqFQYPr06fJnBAcHo0mTJvL6V05OTtDV1cWwYcNy9mYKmaioKHz//feYNGlSpmsIp6amIjw8HG5ubrCwsMDIkSOxdu1aTJs2DTY2NnB2dsbvv/+ucQ5ns8xa3B4WDJn1P6ZOnQovLy8AacHzNm3aQAgh1ylpFJJareb+RjaTnlVLliyBlpaWXM/Sl5tarUZAQAD09fU16k5qaiq2bduGOnXqoFixYihbtiy+//77nL2BQoDbQpZdOPjHctXHMhZduXIF9evXh42NDVauXCkvWgqkJYTYsGEDlEolfvjhByQnJ8uBJ85YlD0CAgLQokUL1K9fH23atMHx48fx7t07ABmzsgFpP3Dr1KkDBweHDNmmJO/evUO3bt1Qq1YteeQMyxrSNI70WdekDsHmzZtRrlw5WFlZQUdHB+XKlcOpU6fkIO3Fixeho6ODUqVKaYzC5LqVdSIjI5GcnJyhfKQ6lX4bAEyaNAn6+voZ6onUiY+Pj8eJEyewePFi+Pv7Z9qmsi/3fnlJbd7ly5dhYmKCgQMHAsiYcfL+/fuoV69ehsQRb968waFDhzB79mxMmTIFFy5cyKE7KZw2btwIc3NzlCpVCsOHD8+wvIFErVZj4MCBuHDhgkZ5nT17Fubm5mjfvj0SEhIyfeaxL8ftYf525coVnDlzBocOHcrQBkp/X7NmDWxsbBATEwMAOHz4MFxdXWFvb4+pU6dCCIGjR4/K53F/I2s8evTogy/gjxw5AnNzc/j6+mpsl44LDw+Hr68v9PT0EB4ernFMSkoKUlJSEBERkY1XX/hwW8iyGwf/WK74lIxFALBq1SrMnj1b7iwA/zZ4L168QJUqVeDh4ZGhs8GyTkJCAtq1awcdHR00aNAAtWrVgr29PZRKJQYNGvTB89RqNYKCgmBmZoYWLVrI0wFev36N4OBgxMfHA0h7A8yyTmxsLL799ltUrFhR3vb+iCQgLcvywYMH0bp1a2zevFn+MSv995dffoFCoZAzu/HUgKyRmpqKgQMHonr16qhQoQI6d+6Mq1evfrANkzqAPXr0gJOTE2fSy2EfKi+pnjx+/BiNGzfWmPr7fmBIWicp/Uhalv3Sj860trbGgAEDcP78efmH8PvebwPTb3/9+jUqV64MBwcHnnaYhbg9zN9iYmLQtm1bFCtWTO7Lt2jRItN1+ebOnQtXV1eNafejR4+GEAI6OjoYMGAAT5nPQiqVCl27doWLiwv279+f6TGxsbHo0KGDRoby99s/aYr2+1l+uU+YtbgtZDmFs/2yHPepGYuI0rJ/jRo1ikxMTDJ8jo2NDenp6VFcXByFh4fn5C0UKrNmzaJTp07R4sWLadOmTXTq1Ck6c+YM2dvb09KlS2n69OmUnJyc4TwhBNWqVYuGDBlCe/fupa1bt9K5c+dowoQJ1LlzZ7px4wYRETk6Oub0LRVY165dIx8fH9q9ezddu3aNpk+fTkT/ZvJKn9HL1taWqlatSpGRkeTp6UlKpVLOukdEVLp0aQJA0dHRGc5lX+bWrVvk6elJf/31F7m5uZGNjQ3t3r2bGjVqRPPnz5e///QZrrW0tIiI6M6dO1SsWDHS1dWV90VHR1NMTEyGc1jW+Fh5LViwgFJSUsjR0ZHat29PRER9+vQhon+zU0rl6efnR0QkZz9nOUNqszZs2EA1atSgH374gapWrUr6+voUGRlJqampcr1Rq9VyuUn/RdoLclIqlWRmZkaxsbFkZWVFKpWK61sW4PYwfwsKCiJPT0+6e/cuDRkyhFavXk3Tpk2jPXv2UGBgIKWkpBDRv5lfXV1dKTIyUs7Wu379elq2bBkZGxtTSkoKVa5cmVxdXXPtfgqS0NBQatOmDW3atIkePXpE69evz5BFHgAZGRlR//79ydLSkr777jt69uyZ3P5J5degQQMiItLR0dE4n/uEWYfbQpajcjX0yAqdz81YJMls5NKLFy9gaWmJFi1aaAyPZllDrVbj9evXcHJyQr169eQpUNJbwV27dslvbH///fcMb6fSTxuoV68erKys4ObmBiGEPEWOZZ07d+6gdu3asLKywqhRo+Dq6goTExN5OmFm09SOHz8OLS0t+Pv7a+yXpmObm5sjODg4x+6hoBs6dCiKFy+OEydOyN/3xYsXUaFCBejo6GD+/PmZnvf8+XNYW1tjyJAhANLK58qVK+jZsye6dOmSY9df2PxXec2dOxdA2hv4rl27QgiBcePGZfic27dvQwiBOXPm5Oj1M+DEiRMQQsgL1iclJWHYsGHw9vZGnTp18N1332ksUi89t9I/z968eYM5c+bAwMAAS5YsydkbKMC4Pcy/du3aBXd3d3Tp0gUXLlyQ++CxsbGwsbFB06ZNM/TL161bB0dHR1y7dg1NmzaFUqnE0KFDsWHDBpQsWRI6Ojp4+vRpbtxOgfL69Ws5KcegQYMwYsQIKBQKzJkzR55x8/6oPWl0et26dTWS9CUmJmLs2LEwNjbmvmA24raQ5SQO/rEck5yc/EUZiyTpH1ZhYWEYMWIETE1NsXPnzgz72deRfvi8fPkSBgYG+OGHHwCkPVik7zkuLg7Ozs4QQqBcuXIfXPfjxo0b+N///gchBEqUKJHpdBD29Z48eaIRYNi0aRO0tLRQu3btTH/QAmlrgdjZ2cHT0xM7duwAkBaomDNnDszNzdGnTx+8ffuW69ZXUqlUiIyMlLMVvu/hw4ewsrKCpaWlvGhz+mDsmTNnUKRIEWzcuBHJycnYsGEDihcvDlNTU/z11185dh+FhbTg/KeU15EjRwAAt27dgo+PD4QQmD17Nl6+fAkg7SXVmDFjYGdnl+mLLZa9tmzZAm1tbVy5cgV3796Fvb09bG1tUa9ePTnrsqenJ86ePSufk769u3PnDubOnQtbW1v4+fnx2mNZgNvD/E2lUmHbtm1o0KBBhvXDLly4ACcnJ/z8888Zztu/f7+c3M3DwwMBAQHyWt7Tpk1D6dKl8eDBgxy5h4IsIiICPXr0kBPoPXv27IPrb0t9wri4OIwePRp6enrw9vbGb7/9hosXL2L16tVwcXFB+/btuS+YDbgtZLmBg38sWyUnJ+PgwYPyA+NzMhZlJjQ0FAEBAfDz84OWlhYmTZqUvTdQiCQlJWHHjh0aAaL79+/DyckJjo6O8gNHKq9Dhw7BwcFBDuz5+/sD0ByleeDAAZQvXx5CCAwfPjxnb6gQkcom/RtbAOjSpQuEEB/Nkrdq1Sq5Q+7u7o4SJUrA0NBQfpPIvtyrV6/kP8fHx8PJyQndu3cHgAyJI3777TcIIdCwYUN5YWcpu/aKFSsghMD69evx3XffQQiBtm3bZsi+zb5O+rbvU8urQYMG8qjoEydOoHnz5nJAqUePHvDz84Ouri5Gjx7NP5xykFROUsBhz549GDhwIKpUqYK///5brjvTp0+HqakpmjdvLq8/q1arcfbsWfTr1w8NGjSArq4uj1bPAtweFhwvX76Uk4pJ7ty5g5o1a0IIgcqVK2PgwIGYOXMmHj58CAC4evUq2rdvjxEjRuDmzZsaQQxpRBr7MpcuXdL4u5Q5GUjr26dff/uff/7ROFZ6Lr169Qpr166FqakphBAwNjZGkSJF0LFjR55dlcW4LWS5iYN/LNsEBQXB3NwcderUkRci/ZKMRZKdO3dCCAEnJyeUKVNGIysY+zo7d+6Evr4+WrRokSGjl/RAkUaBvX37Fg8fPpSnbx86dAj6+vrw9vbW6MBJb4ednJw+mO2XfZmEhASNQML7U3qlzkNoaChKlCgBIyMjnD59OtNjAWDt2rVo3rw5WrZsiR49euDcuXPZePUF37lz59C6dWuMGjUKd+7cAZDW2StbtiyKFi0qTzN8PxjUpk0bCCGwYcMGje0//PADhBAwMzODkZERtm/fnjM3UkhERkZCpVIhKSlJ3vby5ctPLq9169bJ25KSkjBhwgRUr14d5cqVQ7Vq1fDnn3/myH0UVidPnsTChQuxZs0anDx5UmPf/fv34eDgAC8vL7i5uWHx4sUANH/wjho1CkqlEnv27AGQNtVtzpw5sLe3R/PmzeW2k30Zbg/zt+DgYAQHB2eaIVt6YbJv3z4IIVCpUiX4+/tj586d6NmzJ5RKJSpUqCAfHxoaiqioqBy79oLu2rVrcHd3R8WKFTPN4iqVT2xsLCZNmgSFQoEZM2bISVcyeyF19+5d7NmzB5s2beKZOlmM20KWF3Dwj2W51NRUtG/fXh5NZGJiIgeUMnvQfGrGopSUFIwcORJLly7NvosvZFJTU+VMX926dcOVK1fkzoL03+fPn8tvc21tbeHt7Q0jIyNUqFABjx49AgA0bdoUbm5uGlncAHwwqyL7MqmpqRg1ahSaN2+OBg0a4Oeff5bLANCsX1L5bdy4Edra2qhevfp/vr3lt4Vfb+TIkdDV1UW9evUwY8YMjenwEyZM0BglK5ECsv/88w+0tbXRu3dvpKSkQKVS4d27d+jRoweEEOjcubM8yoxljbFjx8Lb2xtubm6oWbMmtm7dKmdzHTt27CeXV1JSksbo6KSkJERERPCIiWykUqnQrVs36OnpoWjRohBCQEtLC6NHj5anWL958wbdu3eHrq6uRkbL9KM8pcBF79695W3JycmZBjvY5+H2MP9KTU1F//794eHhAQMDAwgh8Ouvv2a6xMvu3buxaNEiPH78WKNuDRo0CEIIzJw5MycvvdD46aef5N9Zw4YNk/vgmWWIDQkJQf369WFnZ4cDBw58MIssyx7cFrK8goN/LEsFBQXBysoKzs7O2Lp1K/r16wchBLZs2fKf51apUgV16tTR2Pb69WuNqQX8QyrrHDhwADY2NnB2dsbatWszjPgD/u1APHjwAAsWLEDz5s1RrVo1+Pv748mTJ/L+Dh06wMPDg9/oZqOzZ8/CyckJTk5OqF+/Pry8vCCEgKurKwICAjJMy04fCOzZsyeEEBg7dqy8LTg4WC5zKejH0xK/jFqthkqlwvjx4+Hk5ITZs2fj/v37GY578eIFHBwcUKFChQyjK1NTU6FWq9G0aVO4urpqdMz37t2LQ4cOZft9FCbBwcGoUKECrKys0KNHD3Tp0gWlS5eGEAKdOnUCkDb6z8nJCeXLl/+s8uJ6lL2k5UKGDBkCe3t7rFy5Enfu3MG5c+fktq5ly5byMgj79u2Dm5sbFAoFfvjhB7lPIbV7CQkJEELIa2Rx+X0dbg/zv8uXL6NMmTIoVqwYxo4di59++gn16tWDsbGxxmgwqa6kpqZqlJH0Ev/69esQQmDkyJE5ewMFnPS9//rrr6hbty6qV68OfX19jVHomZ1z4MABWFpaomnTpvIos9DQUFy+fJmnXmcDbgtZXsTBP5ZlunTpAoVCgc6dO8sZRk+ePAmlUomFCxcCyHzKIcAZi3La0aNHoaurC0dHR1y/fl3jjdHt27cRFhaW6cLm6afFSd6+fQsXFxe0bt2a3yRmk+fPn+Obb76Bt7c3Tp06JZfDkiVLYGNjA1dX1wzTAYB/61tYWBhKly4NAwMD7Nu3D8ePH0ft2rVRs2bNHL2PgiwqKgrOzs4YMGAAIiMj5e3BwcGIjo6WR8EuWrRIHmUkZTZMX6/at28PFxcXPH36lIMQ2ahv375wdXXF3r175bJ59eqVnJH8559/Rnx8PJYvX/7J5cVyTmRkJOzt7dGhQ4cMswUGDhwIIQS6du0qb5syZQqMjIzg6uqqMQ07Pj4eCxYsgK6urjztl309bg/zr3fv3qF169bw9vbGyZMn5SD5qVOnNEbPfkj6fuCZM2cghMDkyZOz9ZoLq19//RUlS5bE7du3YWJigipVqsiZzTOrL2/evMHUqVMhhMCkSZOwa9cuNGnSBKVKlcK1a9dy+OoLB24LWV7DwT+WJaZOnQqlUolVq1bhxYsX8vbTp0/L6eM/hjMW5ayQkBB06dIFenp68lvcs2fPokaNGrCxsYGJiQlKlCiBFStWyFPgJOkfOuHh4ZgyZQrs7OwQFBSUo/dQmEjT0tauXaux/eXLl/juu++gUCjg5eUlB93Td76l0bK///479PX1UbRoUZiZmcHa2hq7du3KuZso4AIDA6FQKOSpHHfu3EHDhg3h6OgINzc3fPvtt7hz5w5iY2PlqRrff/+9xme8ePECXl5e+Pbbb3PhDgoHtVqNhw8fQldXF507d5a3S53soKAgCCGgVCqxdetWREZGyolzuLxyn/T8uXv3LoQQWLVqFYC08kvf7tWpUwdCCHmZkJiYGIwZMwZGRkaws7PD7NmzceDAAcyfPx9OTk7w8fGRMzSzr8ftYf516dIlCCEwa9YsAP/Wue3bt6NkyZIayQrS73//zyEhIWjdujWKFSuWIRkZyxrz58+Hl5cXVCoVZs+eDSEEBg4cKPfbMyub8PBw+Pj4wNjYGEZGRjA0NMTq1atz5foLA24LWV7DwT+WZW7evJnpyLDy5cujVKlScia99KTOOmcsyn7vZwM7duwY3Nzc4O3tjZ9//hkKhQJ169bF4MGDMXz4cNjb20OhUODnn3+Wp0lJnYcnT55g7dq16NatG3R1dTFixAge9ZfF0n+fs2bNghAC+/btA6A5gnbOnDkQQkBbWxvdu3fPtA4mJibi8OHDsLS0hBAC3bt357qVxfz9/VGkSBHcvHkTe/bsgZaWFqpUqYJevXqhYcOGEELAzc0Nx48fR1RUFKpVqyavtXnixAkcOnQIQ4cOhbGxsTyKk9/uZo/Q0FDo6enhl19+AQCNJFRhYWGwtraGEAKlS5dGdHQ03rx5g0qVKnF55aL3n19Xr16FoaGhRgAX+LdtvHLlCgwMDFC+fHk522hERAQ2bdoEKysruc00NDREp06duD3MYtwe5l+HDh2S1/eTPHr0CHXr1oWjoyOqVauG1q1bY9++ffLLxfczpZ8+fRqdOnWCsbEx5syZk+P3UNBJdWHVqlWwtLSU+32NGzeGvr6+/FIkNTVVY2ZPamoqjhw5Iq/j3bJlS7x+/Trnb6AQ4baQ5TUc/GNf5Pjx47C0tMSpU6c+eIxKpZLX5dHW1pYzG2UWJOKMRdnnQ9nAUlNTsWjRIhgZGcHExAT+/v4aozZv3bqFZs2awcLCIsN0qGnTpsHAwACenp7YsWNHjt1LYZBZ5tG9e/dCCIGJEyfKgVhpqtvo0aPh6+sLLy8v2NjYZKiTr1+/xsyZM2FlZZVpWbKssWvXLgghEBQUhPbt26NFixYaU0GXLFkCc3NzNGrUCPHx8Xjy5Am6dOkiJyKwsrKCubk51qxZk4t3UThERUXB1tYWDg4OGbLPb926Fe7u7ujWrRuEEPIP19DQUC6vXPCh51dycjJKliwJR0dHOTD4/nqnY8aMgRACy5cv1/jM0NBQHD58GLt378bFixdz6E4KF24P84cHDx7gwIEDOHnyJB48eAAgbTqiu7u7PHV+8ODBsLS0hKurKwYPHowuXbrA0dERJiYmGgn44uPjsXbtWnTo0AHu7u6wsrLiTOfZbPny5XB3d5dHY96+fRumpqbw9vbG0qVL0aJFC7mPnpqais2bN8PU1BSGhoYZZpKw7MFtIctrOPjHPtvAgQOhVCrRsGHDT1ojYsaMGRpTCN6XmJjIGYuy0YeygQFpb3P79++PsWPHZpqs4/jx49DX18fo0aMBQCOpRFBQ0AczM7Mvk1nm0bi4OERHR6N69eqwsbHBsmXLkJiYiMTERPzxxx/Q1dXFnDlzsG7dOnn0LPBvWSUkJKBhw4Zo27Yt161sFBoaCnt7e7i6usLa2lqeBi+97IiKisLo0aPlTiCQNlXx2rVrCAwMxMaNGzlhTg5auHChnNzj8ePHiIiIwMGDB2FpaYkBAwbg3Llz0NXVRZ06dRAbGwsgrU5xeeWsjz2/pPUYx44dKy9WLy2wDqSt4WhhYYGBAwcC+PCawyzrcXuY940aNQr29vYwMjKCEAJly5bFkydPAKQtE+Lj4wM3Nzfo6OjA19dXI/v13bt3oaenh1KlSuH27dsA0gKJ9evXR9WqVTF8+HDub2Qj6QVHUFAQihQporGWnL+/P4QQUCgUqFatGkJCQuSELFu3bkWjRo00XvSz7MVtIctrOPjHPtn58+fh7OwMKysrLFy4MNNpvOlJD6fg4GDo6urKaxhkNvKPMxZlvU/NBnbv3r0MWZSlc8PCwmBgYIAGDRrIx/Bw86z3scyjPXr0AJCWndnBwQFCCJQrVw6lSpWCUqmUk+Rcv34dhoaG8vHAv3WN17LKfmq1Gv369YOenh6EEPLb9vczs2W2ngvLeSqVCq1atYIQAjo6OrC1tYVSqUT79u3l0YB16tSBl5dXplPpWfb6lOdXVFQUateuDTs7O/zxxx8a56pUKsTHx6NixYqoVq0al2EO4/Yw7woLC0Pt2rXh4OCASZMmYcuWLRgyZIi8XpwkMTERFy5cQKdOnXDy5El5u1SXJk2aBB0dHRw7dkzeFxoaKmeRZdnvt99+g42NjdzHO3v2LLy8vKCtrQ0dHR3MmDFD43huB3Met4Usr1EQY59gzJgxVL16dfLw8KC//vqLunXrRo6Ojh89RwhBRERaWlpkb29PBw8eJCIiheLff3YAiIjI19eXGjRokE1XXzhJ3z8R0YsXL2j16tWkra1Ny5Yto2vXrsn7SpQoQVpaWqRWqzOcq6+vT2/fvqVSpUqRlpZWhs9lWWP27NkUGxtL69atoyVLltBvv/1GJ06cIFdXV9qwYQNNmTKF6tatS3v37qWuXbuSubk5eXh40P79+2nq1KlERFSsWDHS0tIiOzs7+XOlumZtbZ0r91WYCCFo0qRJ5OjoSAqFgi5fvkwxMTGkUCgoNTWViIi8vLyIiMjW1jY3L5VRWt3YtGkTBQQEUL9+/ahBgwYUFBREK1asIF1dXSIi0tbWJj09PUpOTs7lqy18Pvb8unr1KhERmZqa0oQJEyg+Pp7mzZtHZ86cISIitVpNCoWCihQpQlFRUeTs7Ew6Ojpyf4NlP24P867169fT8+fPacGCBTRixAjq2LEjLVy4kCwsLEipVJJarSaVSkW6urpkZWVF169fJ3NzcyIiSklJIR0dHSIisrS0pJSUFIqJiZE/28XFhZydnXPjtgqlokWLUnJyMj179ox+/fVX+uabb8jMzIxWrVpFZmZmtG7dOjp9+rR8vFR2LOdwW8jynNyOPrL8oXfv3hBCYNy4cfK20NBQjBgxAn369EH37t1x7NgxjelR6TVo0AAGBgZyNlKWcz4nG1j6N1HPnj1Dv379YGpqitOnT+f4dRcGn5N5dNOmTfL+99cpA4Ddu3dDCIGNGzdm/4WzD/rzzz9hZmYGZ2dnbNmyRd4eGRmJH374Aebm5twO5jGZjUb/559/YGpqiqFDh+bCFTHJh55fMTExANKymUtTuL28vHDr1i0AaaMCly1bBisrqwyj3VnO4fYw71CpVHj58iWKFi2KevXqydulfkjFihVx+PBhpKSkyP1Cab2yRYsWacwQef78OZo0aQI3NzdOGJGLdu7cCSEE7O3toa+vj4kTJ8qZlRcvXgwhBI4ePZq7F8kAcFvI8g4O/rFP5u7ujuLFi+PChQtYtGgRtLW1YWtri6JFi0JHRwfa2tro1auXfLxarZaDgAsWLIBSqZSn9nJm2Oz3pdnAkpKScOLECQwcOBAGBgYYOXIkZ0LMRp+aebRkyZLyuh/pyyMlJQXXrl2Dn58fqlevrrH2C8t5arUac+bMgbm5OYoUKYLvv/8eS5YswbBhw2BoaIiePXvyWkh5mFqtxt27d9G/f38UK1YM58+fz+1LKpT+6/m1cuVKAJADElOmTIGuri7MzMxQq1YtNG36f+3df3SO9/3H8dcl5c4PP5pbEhaxRicYpiMdijXRjhQjSU/a2HaoOJnOkHTSHnRKVYkfpbb6tR4lp6WOsjTdTC3m19azlKZjrSniYGu6FrGESI2Q9/cP39zr7Q4Vv+6I5+OcHOf+XJ/rc7+vK7nv6/a+r8/nPcCCgoJs6NChvN78iPfDuuXcuXMWHR1tDz/8sCdJdOLECRs6dKg5jmM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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#data['Merchnum'].value_counts().head(15).plot(kind = 'bar')\n", + "\n", + "\n", + "monthly_data_amount = data.groupby(pd.Grouper(key='Merchnum'))\n", + "monthly_data =monthly_data_amount['Amount'].sum().sort_values(ascending = False).head(10)\n", + "print(monthly_data.values)\n", + "\n", + "plt.bar(monthly_data.index, monthly_data.values)\n", + "for i,j in enumerate(monthly_data):\n", + " plt.text(i,j*1.01,'$'+three_commas(str(j)),ha = 'center', fontsize = 14, rotation = 10)\n", + "plt.xticks(rotation=34)\n", + "plt.yscale('log')\n", + "plt.title('Top 10 Merchants (Based on Transaction Amount)')\n", + "plt.ylabel('Transaction Amount')\n", + "plt.xlabel('Merchant Number')\n", + "plt.show()\n", + "#plt.rcParams[\"figure.figsize\"] = (15,6)\n", + "#\n", + "#\n", + "\n", + "#plt.ylim(bottom = 100)\n", + "#plt.ylim(top = 15000)" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Merchant Number')" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data['Merchnum'].value_counts().head(10).plot(kind = 'bar')\n", + "plt.rcParams[\"figure.figsize\"] = (15,6)\n", + "for i,j in enumerate(data['Merchnum'].value_counts().head(10)):\n", + " plt.text(i,j*1.02,three_commas(str(j)),ha = 'center', fontsize = 14)\n", + "#plt.yscale('log')\n", + "plt.xticks(rotation=45)\n", + "plt.title('Top 10 Merchants (Based on Transaction Count)')\n", + "plt.ylabel('Number of Transactions')\n", + "plt.xlabel('Merchant Number')\n", + "#plt.ylim(bottom = 100)\n", + "#plt.ylim(top = 2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "monthly_data_amount = data.groupby(pd.Grouper(key='Merch description'))\n", + "monthly_data =monthly_data_amount['Amount'].sum().round(2).sort_values(ascending = False).head(10)\n", + "plt.bar(monthly_data.index, monthly_data.values)\n", + "for i,j in enumerate(monthly_data):\n", + " plt.text(i,j*1.01,'$'+three_commas(str(j)),ha = 'center', fontsize = 14, rotation = 10)\n", + "plt.xticks(rotation=34)\n", + "plt.yscale('log')\n", + "plt.title('Top 10 Merchants (Based on Transaction Amount)')\n", + "plt.ylabel('Transaction Amount')\n", + "plt.xlabel('Merchant Description')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13126" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(data['Merch description'].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GSA-FSS-ADV 1688\n", + "SIGMA-ALDRICH 1635\n", + "STAPLES #941 1174\n", + "FISHER SCI ATL 1093\n", + "MWI*MICRO WAREHOUSE 958\n", + " ... \n", + "HBD INC 1\n", + "SALES MARKETING MAGAZINE 1\n", + "PATTERSON'S TRUE VALUE 1\n", + "FLOPPY COPY 1\n", + "BEST BUY 00001610 1\n", + "Name: Merch description, Length: 13126, dtype: int64" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Merch description'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "228" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(data['Merch state'].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Merchant State')" + ] + }, + "execution_count": 171, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data['Merch state'].value_counts().head(15).plot(kind = 'bar')\n", + "plt.rcParams[\"figure.figsize\"] = (15,6)\n", + "for i,j in enumerate(data['Merch state'].value_counts().head(15)):\n", + " plt.text(i,j+100,three_commas(str(j)),ha = 'center', fontsize = 14)\n", + "#plt.yscale('log')\n", + "plt.xticks(rotation=0)\n", + "plt.title('Top 15 Merchant State (Based on Transaction Count)')\n", + "plt.ylabel('Number of Transactions')\n", + "plt.xlabel('Merchant State')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "monthly_data_amount = data.groupby(pd.Grouper(key='Merch state'))\n", + "monthly_data =monthly_data_amount['Amount'].sum().round(2).sort_values(ascending = False).head(15)\n", + "plt.bar(monthly_data.index, monthly_data.values)\n", + "for i,j in enumerate(monthly_data):\n", + " plt.text(i,j*1.01,'$'+three_commas(str(j)),ha = 'left', fontsize = 14, rotation = 45)\n", + "plt.xticks(rotation=0)\n", + "plt.yscale('log')\n", + "plt.title('Amount distribution based on States')\n", + "plt.ylabel('Transaction Amount')\n", + "plt.xlabel('Merchant State')\n", + "plt.ylim(top = 5050000)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "TN 12035\n", + "VA 7872\n", + "CA 6817\n", + "IL 6508\n", + "MD 5398\n", + " ... \n", + "495 1\n", + "376 1\n", + "458 1\n", + "546 1\n", + "116 1\n", + "Name: Merch state, Length: 227, dtype: int64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Merch state'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "text() missing 1 required positional argument: 's'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_49732\\1602548894.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m#plt.rcParams[\"figure.figsize\"] = (15,6)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m 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+ }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data['Merch state'].value_counts().head(15).plot(kind = 'pie')\n", + "#plt.rcParams[\"figure.figsize\"] = (15,6)\n", + "for i,j in enumerate(data['Merch state'].value_counts().head(10)):\n", + " plt.text(i,j,ha = 'center', fontsize = 14)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "merchstate_counts = data['Merch state'].value_counts()\n", + "\n", + "merchstate_counts_pct = (merchstate_counts/merchstate_counts.sum())*100\n", + "print(merchstate_counts_pct)\n", + "\n", + "# Sort the merchstates by the number of transactions in descending order and select the top 10\n", + "top_10_merchstates = merchstate_counts_pct.sort_values(ascending=False).head(10)\n", + "\n", + "# Plot the top 10 merchstates as a pie chart\n", + "plt.pie(top_10_merchstates, labels=top_10_merchstates.index)\n", + "plt.title('Top 10 Merchstates by Number of Transactions')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4568" + ] + }, + "execution_count": 184, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(data['Merch zip'].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "38118 11868\n", + "0 4656\n", + "63103 1650\n", + "8701 1267\n", + "22202 1250\n", + " ... \n", + "89125 1\n", + "46225 1\n", + "27025 1\n", + "6076 1\n", + "36043 1\n", + "Name: Merch zip, Length: 4568, dtype: int64" + ] + }, + "execution_count": 186, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Merch zip'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 38118\n", + "1 1803\n", + "2 20706\n", + "3 38118\n", + "4 38118\n", + " ... \n", + "96748 41042\n", + "96749 45248\n", + "96750 45150\n", + "96751 92656\n", + "96752 7606\n", + "Name: Merch zip, Length: 96753, dtype: int32\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Merchant Zip')" + ] + }, + "execution_count": 182, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Merchzip = data['Merch zip'].fillna(value = '00000')\n", + "\n", + "Merchzip = data['Merch zip'].dropna()\n", + "data['Merch zip'] = Merchzip.astype(int)\n", + "print(Merchzip)\n", + "data['Merch zip'] = data['Merch zip'].fillna(value = '00000').astype(int).astype(str).str.zfill(5)\n", + "\n", + "\n", + "#plt.yscale('log')\n", + "data['Merch zip'].value_counts().head(15).plot(kind = 'bar')\n", + "plt.rcParams[\"figure.figsize\"] = (15,6)\n", + "for i,j in enumerate(data['Merch zip'].value_counts().head(15)):\n", + " plt.text(i,j*1.02,three_commas(str(j)),ha = 'center', fontsize = 14)\n", + "plt.xticks(rotation=0)\n", + "plt.title('Transaction Trends based on Merchant Zip')\n", + "plt.ylabel('Number of Transactions')\n", + "plt.xlabel('Merchant Zip')" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "monthly_data_amount = data.groupby(pd.Grouper(key='Merch zip'))\n", + "monthly_data =monthly_data_amount['Amount'].sum().round(2).sort_values(ascending = False).head(15)\n", + "plt.bar(monthly_data.index, monthly_data.values)\n", + "for i,j in enumerate(monthly_data):\n", + " plt.text(i,j*1.01,'$'+three_commas(str(j)),ha = 'left', fontsize = 14, rotation = 45)\n", + "plt.xticks(rotation=0)\n", + "plt.yscale('log')\n", + "plt.title('Amount distribution based on Merchant Zip')\n", + "plt.ylabel('Transaction Amount')\n", + "plt.xlabel('Merchant Zip')\n", + "plt.ylim(top = 9000000)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "P 96398\n", + "A 181\n", + "D 173\n", + "Y 1\n", + "Name: Transtype, dtype: int64" + ] + }, + "execution_count": 187, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Transtype'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Transaction Type')" + ] + }, + "execution_count": 190, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data['Transtype'].value_counts().plot(kind = 'bar')\n", + "plt.rcParams[\"figure.figsize\"] = (15,6)\n", + "for i,j in enumerate(data['Transtype'].value_counts()):\n", + " plt.text(i,j*1.1,j,ha = 'center', fontsize = 14)\n", + "plt.yscale('log')\n", + "plt.xticks(rotation=0)\n", + "plt.title('Transaction Count on Transaction Type')\n", + "plt.ylabel('Number of Transactions')\n", + "plt.xlabel('Transaction Type')" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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7vo0bNxrp6urHa2W+f/IieImMjDSSzLZt26xtqamp1lWFWa2Oy4qzAdz48eONJHP//fdnuV3fvn2NJDNw4ECbdme/4x2VXQB34cIFExAQYBeyGWPM9OnTjSTzyCOP2LRf+/5ZsmSJ3b5+/vlnI8n4+PjYhPJ16tQxkrJcUW6MMU888YSRZN555x2njhEAYAzXgAMA5ImUlBQtWLBAktSvXz+bvsyfc7q7W9GiRdWiRQu79jvvvFPS1et1XX8R/BIlSqhUqVK6cuWKzfWY1q5dK0l65JFH5OZm/0/dgAEDJF29xk56evqNDs1hDzzwgF1b6dKlVbJkSaWmpubZNaM2b96s8+fP684771T9+vXt+tu3b29zrbUbqVevniTptdde09y5c3X69Ok8qVOSOnfu7PS2vXv3tmvz9va2Xgcr83XOD5n77ty5s3x9fe36+/Tpo6JFi2r37t06dOiQXX9W7xVJ1ptSHD58ONc1ZfV8WSwW9erVy6bma/3555966623NGzYMA0YMEAxMTF67LHH5OHhoZMnT+rMmTPWsZnvk9GjR2vhwoVKSUnJtpYtW7bo+PHjql27tvUzfL3mzZtLkn7++Wdr25o1a7I9Fk9PT3Xr1i3bfWbl4MGD+vvvv+Xh4aGePXva9ZcoUcL6Hs3c97WCgoIUHh5u134zr5Oj7rjjDjVs2DDHMQkJCXr99dcVGxur/v37KyYmRhMnTpQk7dy5M9vtsnr/ZXdMma97r169tG7dOus1OXNy/PhxzZo1S88884wGDRqkmJgYxcTE6OjRo3a1bdq0SadPn9add96pZs2a3XDum5H5Gejbt2+W/Zn/NmT1XpD+ue/4TF5eXurfv78uX76sDz/80KZvxowZkqTHHnssy20DAgL04IMP2rU3aNBAd955p1JSUqzX7ztx4oS2bNmiwMDAbO/Im9XnFQCQO+75XQAAoHCYP3++zp8/r4YNG+quu+6y6evUqZNKlCih33//XVu2bFGdOnXstr/jjjtUpEgRu/bixYtLkipWrJjlfosXL66TJ0/q8uXL1rbMwKNKlSpZblOpUiV5eHjo0qVLOnXqlMqUKePYQd5ApUqVsmz39fXV6dOnbWq8GZnHl90FxiUpODhYZ8+edWi+Fi1aaMyYMXr99dfVr18/WSwW3X333YqKilKPHj1u6pfioKAgp7fN7vgy2w8ePOj03DfrRu8xd3d3BQUFWQO4ChUq2PTn9F6R5NR7JTfPV1pamoYOHapZs2blOGdycrICAgIkXQ3S16xZozlz5qhr164qUqSIatWqpRYtWuiRRx7Rvffea90u84YVmzdvvuGNRY4fP2797xu9t3N6z2clc76goKAsv18kWe+UmVVQ6orXyVE5fXZSUlLUs2fPHG9Ck5ycnG1fVseVeUypqak27ZMnT9b27dv13Xff6bvvvpO3t7caNGigVq1aqV+/fipfvrzN+Pfee09PP/20Ll265FBt+/fvlyS7fzdc4Uaf25zeC9I/9x1/rccff1xTp07VjBkz9Oyzz8piseinn37Sb7/9ppo1ayoyMjLL7YKDg7OdMyQkRH/99Zf1OyHz83ry5Mks/2h1rWs/rwCA3CGAAwDkidmzZ0u6+otLdn9Bl66ugssqgLvR//TfqP9aJou7Tzoz5noZGRk59uemxryQmzum3sirr76qwYMH69tvv9XKlSu1bt06TZ8+XdOnT1ffvn2tdx3MLS8vrzyrMVPma5eb47/Ra+dsDc6OccV7JbvnI6vn6+2339asWbNUoUIFTZ06VY0aNVKZMmXk4eEhSSpfvryOHDlicwxubm6Kj4/XqFGjtHjxYq1atUobNmzQli1b9Oabb2rs2LGaMGGCpP97voOCgrJc2XqtBg0aOHwsuXUrvk6OyumzM2bMGC1ZskT33HOPXnvtNdWrV08lS5ZU0aJFlZqaKk9Pzxznzs1xlStXTj/99JPWrVunpUuXau3atVq3bp1WrVqlV155RfPnz1e7du0kXV3NFhsbK3d3d02ZMkUPPfSQKlasaD2WXr16ad68eVk+53n5fZadG70fbtSfH++H0NBQtW3bVkuXLtWyZcvUpk0bvffee5KyX/0m5fx8Xv+dkPl5LVmypNq3b59jPZkrJQEAuUcABwC4abt379a6deskSQcOHNCBAweyHfvpp5/q3//+t/UXfVfIXC33999/Z9l/8OBBXblyRcWKFVPJkiWt7Zk1nTt3zm6btLQ0HTlyxAXV5l7maqrMVQtZ2bdvX67nDQkJ0bBhwzRs2DAZY7R8+XJFR0dr7ty56tWrl9q0aeNsyU7Zt2+fatasmWW7JJtVZTm9dtL/rbLJKzd6j6WlpVk/B9evfnOVvXv3Ovx8zZ8/X5L0/vvv66GHHrIZf+HCBeupglkJDw9XeHi4Ro0apbS0NC1YsMB66mN0dLTuvvtu60qhoKCgHE89v16FChX0999/a+/evXYrqzKPMTcyX6f9+/crPT09y1Vwe/bsse67oMh8/T777DNVr17dpu+vv/7K8/25ubmpWbNm1tWwycnJmjRpkiZPnqxBgwZZvxsXLFggY4yGDx+up556ym6erGrLXKm1Y8eOPK/7ehUrVtSOHTv0999/q3Hjxnb9t+p74YknntDSpUv13nvvKSIiQvPnz5evr2+Wp2pncuTfh8zjzPy8ent75+rzCgDIHa4BBwC4afHx8TLGqEuXLjJXb/CT5ePuu+/WqVOntHjxYpfWk/lL4ieffJLlyqfM1XqNGzeWu/v//S0q85eRrH4RXLFihUPXPsqNzGva5XbeunXrqnjx4tq1a5c2bdpk179kyRKHTz/NjsViUevWrdW1a1dJ0u+//27tywy78vr5uN6nn35q13bhwgUtWrRIkmxOjc3ptZOkZcuWZdnu7LFk7vurr77KMvT75JNPdOXKFYWGhv5jv8xn9XwZY/TZZ59Jsn2+Mq/zl9UpddmtUMqKu7u7evbsqWbNmskYo23btkmSIiIiVKpUKW3cuDHHQP56mdeZyupYUlNTrdeZdFTFihVVuXJlpaamWp+HayUlJemrr76y2XdBcKPXz9X8/Pz06quvytPTU0ePHtWJEyduWNf27dv166+/2rXXrVtXgYGB2rVrl/UPOTdys5/buXPnZtmf+W/DrfZeaNu2rUJDQ7V48WJNnDhRly5d0iOPPJLl9ScznTlzRt99951d+6ZNm/TXX3/Jx8fHuhq9fPnyqlmzpg4ePKiNGze67DgA4HZHAAcAuCnGGOsvM4888kiOYzP/Wp/5S46rdOvWTRUqVNCOHTs0btw4mzDhl19+0ZtvvilJevrpp222a9mypSTp9ddftwlVduzYoWHDhuV5nZnBzJ9//pmr7by9vTVo0CBJ0rBhw2zCtiNHjmjkyJG5mu+rr77SunXr7EKXpKQk/fjjj5Jsr0flbN25FRcXZ3PB7/T0dD377LM6ceKEateuraZNm1r7IiIi5Ovrq//97382QUtGRobGjx+vn376Kct9OHsszZo1U926dXXmzBkNHz5cV65csfbt2rVLL7zwgiTpmWeeydW8N2PBggXWcDLTa6+9pm3btql8+fJ6+OGHre2Zp5G99957Nq/71q1bNWbMmCznnzt3bpYBysGDB7V161ZJ//c+KVq0qF566SWlpqaqY8eO1v5rnT17VjNnztSxY8esbbGxsXJzc9OsWbO0evVqa3tGRoZGjx6d7bW5cpK5EmvMmDHavXu3tT01NVVPPPGEzp49q/r166tJkya5nju/ZL5+06dPt2n/4YcfrN9veeXNN9/M8nqLy5Yt0+XLl+Xn52e96UtmXXPnzrW5ScepU6fUv3//LAOzokWLWt9zvXv3tgn7pauf++v/aOPs5/bRRx+Vj4+PfvjhB33wwQc2fd98840+/vhjubu7a/jw4bma19Xc3Nz02GOPKT09XVOnTpWU8+mnmUaOHGnz+Tpz5oz137JBgwbZnOacefp4z549s7wJxYULFzRv3jyXf+8DQKH2T9xqFQBQeC1fvtxIMgEBAeby5cs5jt27d6+xWCzG3d3dHD161BhjzJ49e4wkExwcnOU248aNM5LMuHHjsuwPDg42ksyePXts2tetW2f8/PyMJHPXXXeZ6Oho07JlS1OkSBEjyYwePdpurlOnTpkKFSoYSaZcuXKmS5cuplmzZqZYsWKmV69e2e4ru/Yb9T/11FNGkildurTp0aOHGThwoBk4cGCWc1wvOTnZ1K5d20gyJUuWNF27djUdO3Y0Pj4+JiIiwjRq1MhIMqtWrbLZrnnz5nbtTz75pJFkypQpY9q2bWt69+5tHnzwQevz17hxY5Oammodf+TIEePt7W0kmaZNm5qYmBgzcOBAs2jRIusYSeZG/5uR3ZjM9uHDh5siRYqY++67z0RHR5sqVapY32t//PGH3XaTJ082kozFYjFNmzY1nTt3NpUrVzY+Pj5m2LBhRpLp16+fzTaXLl0yd9xxh5Fk6tata/r27WsGDhxoPvzwQ+uY7F6/HTt2mPLlyxtJplKlSqZHjx7mgQceMJ6enkaSiY6ONhkZGTbb9OvXz0gys2fPzvI5uVF/VjLrGz58uPX1io6ONtWrVzeSjKenp1mxYoXNNuvXrzdFixa1fj569OhhWrRoYYoUKWKio6OzPOaOHTtaj/Whhx4yvXv3Nq1atTLFihUzkkz37t3tahsxYoT1Naldu7bp2rWrad++valVq5bx8PAwksyff/5ps83EiRONJOPm5maioqJMdHS0qVq1qvH09DRDhgzJ8nXMSXp6uunWrZuRZIoVK2YeeOAB06NHD1OxYkUjyVSsWNHs2rXLZptVq1YZSaZ58+ZZznmjfkdkPsfXf0Ydmfvzzz+3fk5q165tevbsaf3Mjx49+oafrexk1e/v728sFou55557TNeuXU10dLRp2LChsVgsRpKJi4uzjj19+rT1eS1Tpozp0qWL6dChg/H19TV33XWX6dSpU5bv74yMDDNw4EDr6x4ZGWmio6P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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "monthly_data_amount = data.groupby(pd.Grouper(key='Transtype'))\n", + "monthly_data =monthly_data_amount['Amount'].sum().round(2).sort_values(ascending = False).head(15)\n", + "plt.bar(monthly_data.index, monthly_data.values)\n", + "for i,j in enumerate(monthly_data):\n", + " plt.text(i,j*1.01,'$'+three_commas(str(j)),ha = 'center', fontsize = 14, rotation = 0)\n", + "plt.xticks(rotation=0)\n", + "plt.yscale('log')\n", + "plt.title('Amount distribution based on Transaction Type')\n", + "plt.ylabel('Transaction Amount')\n", + "plt.xlabel('Transaction Type')\n", + "plt.ylim(top = 90000000)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.62 4283\n", + "3.67 1620\n", + "3.74 913\n", + "3.80 827\n", + "4.37 378\n", + " ... \n", + "949.29 1\n", + "2303.00 1\n", + "303.72 1\n", + "183.36 1\n", + "554.64 1\n", + "Name: Amount, Length: 34909, dtype: int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Amount'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data['Amount'].value_counts().head(15).plot(kind = 'bar')\n", + "for i,j in enumerate(data['Amount'].value_counts().head(15)):\n", + " plt.text(i,j*1.05,j,ha = 'center', fontsize = 14)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.1, 101589.6)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data['Amount'],bins=50,range=[0,4000000])\n", + "#plt.yscale('log')\n", + "plt.ylim(bottom = .1)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3102045.53" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Amount'].max()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Amount (Interval of $100)')" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data['Amount'],bins=50,range=[0,4000],edgecolor='black',linewidth=1)\n", + "plt.yscale('log')\n", + "plt.ylim(bottom = 1)\n", + "plt.title('Transaction Trends based on Amount')\n", + "plt.ylabel('Number of Transactions')\n", + "plt.xlabel('Amount (Interval of $100)')\n", + "#plt.savefig('amount distribution')" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Fraud Indicator')" + ] + }, + "execution_count": 174, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data['Fraud'].value_counts().plot(kind = 'bar')\n", + "plt.yscale('log')\n", + "for i,j in enumerate(data['Fraud'].value_counts()):\n", + " plt.text(i,j*1,three_commas(str(j)),ha = 'center', fontsize = 14)\n", + "plt.xticks(rotation=0)\n", + "plt.title('Fraud Transactions')\n", + "plt.ylabel('Number of Transactions')\n", + "plt.xlabel('Fraud Indicator')" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "monthly_data_amount = data.groupby(pd.Grouper(key='Fraud'))\n", + "monthly_data =monthly_data_amount['Amount'].sum().round(2).sort_values(ascending = False).head(15)\n", + "plt.bar(['0','1'], monthly_data.values)\n", + "for i,j in enumerate(monthly_data):\n", + " plt.text(i,j*1.02,'$'+three_commas(str(j)),ha = 'center', fontsize = 14, rotation = 0)\n", + "plt.xticks(rotation=0)\n", + "plt.yscale('log')\n", + "plt.title('Amount distribution based on Fraud Indicator')\n", + "plt.ylabel('Transaction Amount')\n", + "plt.xlabel('Fraud Indicator')\n", + "plt.ylim(top = 50000000)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## OK, that's what the raw data looks like. Now let's remove the exclusions and explore the data a bit more" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(96753, 11)\n", + "3102045.53\n" + ] + } + ], + "source": [ + "data['DOW'] = data['Date'].dt.day_name()\n", + "print(data.shape)\n", + "print(data['Amount'].max())" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(96397, 11)\n", + "47900.0\n" + ] + } + ], + "source": [ + "# remove exclusions: only keep the type P's and remove one high transaction outlier\n", + "temp = data[data['Transtype'] == 'P']\n", + "data = temp[temp['Amount'] <= 3000000]\n", + "data = data.reset_index(drop=True)\n", + "print(data.shape)\n", + "print(data['Amount'].max())" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RecnumCardnumDateMerchnumMerch descriptionMerch stateMerch zipTranstypeAmountFraudDOW
0151421904392010-01-015509006296254FEDEX SHP 12/23/09 AB#TN38118.0P3.620Friday
1251421839732010-01-0161003026333SERVICE MERCHANDISE #81MA1803.0P31.420Friday
2351421317212010-01-014503082993600OFFICE DEPOT #191MD20706.0P178.490Friday
3451421484522010-01-015509006296254FEDEX SHP 12/28/09 AB#TN38118.0P3.620Friday
4551421904392010-01-015509006296254FEDEX SHP 12/23/09 AB#TN38118.0P3.620Friday
\n", + "
" + ], + "text/plain": [ + " Recnum Cardnum Date Merchnum Merch description \\\n", + "0 1 5142190439 2010-01-01 5509006296254 FEDEX SHP 12/23/09 AB# \n", + "1 2 5142183973 2010-01-01 61003026333 SERVICE MERCHANDISE #81 \n", + "2 3 5142131721 2010-01-01 4503082993600 OFFICE DEPOT #191 \n", + "3 4 5142148452 2010-01-01 5509006296254 FEDEX SHP 12/28/09 AB# \n", + "4 5 5142190439 2010-01-01 5509006296254 FEDEX SHP 12/23/09 AB# \n", + "\n", + " Merch state Merch zip Transtype Amount Fraud DOW \n", + "0 TN 38118.0 P 3.62 0 Friday \n", + "1 MA 1803.0 P 31.42 0 Friday \n", + "2 MD 20706.0 P 178.49 0 Friday \n", + "3 TN 38118.0 P 3.62 0 Friday \n", + "4 TN 38118.0 P 3.62 0 Friday " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data['DOW'].value_counts().plot(kind = 'bar')\n", + "for i,j in enumerate(data['DOW'].value_counts().head(15)):\n", + " plt.text(i,j*1.01,j,ha = 'center', fontsize = 14)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#goods: 95338 #bads: 1059\n" + ] + } + ], + "source": [ + "goods = data[data['Fraud'] == 0]\n", + "bads = data[data['Fraud'] == 1]\n", + "print(\"#goods:\", len(goods), \" #bads:\", len(bads))" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(goods['Amount'],bins=50,range=[0,35000], density = True, color = 'green')\n", + "plt.hist(bads['Amount'],bins=50,range=[0,35000], density = True, color = 'red', alpha = .5)\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(goods['Amount'],bins=50,range=[0,4000], density = True, color = 'green')\n", + "plt.hist(bads['Amount'],bins=50,range=[0,4000], density = True, color = 'red', alpha = .5)\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ngoods = len(goods)\n", + "nbads = len(bads)\n", + "goods_series = goods.assign(trx = np.ones(ngoods)).set_index(goods['Date']).resample(timedelta(days = 1)).count().trx\n", + "norm_goods_series = goods_series / ngoods\n", + "norm_goods_series.plot(title = 'Daily Transactions', color = 'green')\n", + "bads_series = bads.assign(trx = np.ones(nbads)).set_index(bads['Date']).resample(timedelta(days = 1)).count().trx\n", + "norm_bads_series = bads_series / nbads\n", + "norm_bads_series.plot(color = 'red')" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "goods_series = goods.assign(trx = np.ones(ngoods)).set_index(goods['Date']).resample(timedelta(days = 7)).count().trx\n", + "norm_goods_series = goods_series / ngoods\n", + "norm_goods_series.head(52).plot(title = 'Weeky Transactions', color = 'green')\n", + "bads_series = bads.assign(trx = np.ones(nbads)).set_index(bads['Date']).resample(timedelta(days = 7)).count().trx\n", + "norm_bads_series = bads_series / nbads\n", + "norm_bads_series.head(52).plot(color = 'red')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "goods_series = goods.assign(trx = np.ones(ngoods)).set_index(goods['Date']).resample(timedelta(days = 30)).count().trx\n", + "norm_goods_series = goods_series / ngoods\n", + "norm_goods_series.head(12).plot(title = 'Approximately Monthly Transactions', color = 'green')\n", + "bads_series = bads.assign(trx = np.ones(nbads)).set_index(bads['Date']).resample(timedelta(days = 30)).count().trx\n", + "norm_bads_series = bads_series / nbads\n", + "norm_bads_series.head(12).plot(color = 'red')" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "duration: 0:00:10.556533\n" + ] + } + ], + "source": [ + "# takes about 20 seconds to run\n", + "stop_time = datetime.now()\n", + "print('duration: ', stop_time - start_time)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/transaction_models.ipynb b/transaction_models.ipynb new file mode 100644 index 0000000..087851e --- /dev/null +++ b/transaction_models.ipynb @@ -0,0 +1,7797 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modeling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook uses a wide variety of modeling algorithms for a binary classification problem. It reads a file created from a feature selection process that has a reasonably small number of good variables, and we know their order of multivariate importance because we used a proper wrapper method. We can explore # input variables, model algorithms and tune model hyperparameters. At the end we can select our favorite algorithm, run it again and build the final model performace score percentile tables.\n", + "\n", + "Here we call the larger fraction population the goods and the smaller fraction the bads. This notebook was originally\n", + "written for fraud detection but can be used for any binary classification. It uses detection rate as an appropriate measure of goodness.\n", + "\n", + "Rather than use a built-in CV, we do a \"manual CV\" by running each model multiple (nitermax) times and average the performance on the training (trn), testing (tst) and out of time (oot) data sets.\n", + "\n", + "Some of the ML algorithms are very fast and some are slow. Feel free to comment out any cells/models you want. At the bottom of the notebook you can select your final model/hyperparameters to run one time only and then make the business perfoemance tables for that final model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Things to add: better calculation of FDR@3% when building a model using sampled training data. Right now I just approximate it by using the entire population trntst for a model built with sampled data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from datetime import datetime\n", + "start_time = datetime.now()\n", + "\n", + "import pandas as pd\n", + "import random\n", + "import xgboost as xgb\n", + "from sklearn import svm\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.ensemble import GradientBoostingClassifier\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from catboost import CatBoostClassifier\n", + "from sklearn.decomposition import PCA\n", + "import gc\n", + "import lightgbm as lgb\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt \n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(96397, 42)\n", + "CPU times: user 369 ms, sys: 68.9 ms, total: 438 ms\n", + "Wall time: 440 ms\n" + ] + }, + { + "data": { + "text/html": [ + "
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RecnumFraudcard_merch_total_14card_zip3_max_14card_merch_avg_14Card_Merchdesc_Zip_med_14Card_Merchdesc_Zip_max_60Card_Merchnum_Zip_avg_60Card_Merchnum_desc_avg_60Merchdesc_dow_avg_30Card_Merchnum_desc_med_0Card_Merchdesc_max_60
count96397.00000096397.00000096397.00000096397.00000096397.00000096397.00000096397.00000096397.00000096397.00000096397.00000096397.00000096397.000000
mean48365.4818200.010986769.967286529.353162401.927811394.564839551.295774404.503353404.444849396.465357393.500463552.096524
std27945.0038830.1042364168.2145171086.568630790.425618783.3448331104.370845778.326998778.414021696.170708790.6414171104.598654
min1.0000000.0000000.0100000.0100000.0100000.0100000.0100000.0100000.0100000.0100000.0100000.010000
25%24154.0000000.00000077.00000057.19000039.99000039.18000052.50000044.90000044.97000053.03500034.90000052.770000
50%48365.0000000.000000236.920000212.660000157.065000149.970000223.550000171.030000171.000000200.000000140.000000224.300000
75%72578.0000000.000000676.860000609.000000452.940000438.450000658.000000472.000000472.000000478.200000431.550000659.340000
max96753.0000001.000000306633.41000047900.00000028392.84000028392.84000047900.00000028392.84000028392.84000028392.84000028392.84000047900.000000
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RecnumFraudcard_merch_total_14card_zip3_max_14card_merch_avg_14Card_Merchdesc_Zip_med_14Card_Merchdesc_Zip_max_60Card_Merchnum_Zip_avg_60Card_Merchnum_desc_avg_60Merchdesc_dow_avg_30Card_Merchnum_desc_med_0Card_Merchdesc_max_60
83966842960789.32609.34394.660394.660609.34394.660000394.660000271.69250394.66609.34
83967842970235.00235.00235.000235.000235.00235.000000235.000000235.00000235.00235.00
83968842980600.00600.00600.000600.000600.00600.000000600.000000600.00000600.00600.00
8396984299030.0030.0030.00030.000365.32197.660000197.660000294.1040030.00365.32
83970843000182.00182.00182.000182.000182.00182.000000182.000000182.00000182.00182.00
83971843010399.61225.00199.805199.805225.00199.805000199.805000174.61000174.61225.00
8397284302053.00206.9053.00075.500659.76224.701429224.701429128.4587553.00659.76
8397384303025.00531.2525.00025.00028.4826.74000026.74000025.0000025.0028.48
8397484304097.1797.1797.17097.17097.1797.17000097.17000097.1700097.1797.17
839758430506.516.516.5106.5106.5111.7333334.5000006.510006.516.51
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" + ], + "text/plain": [ + " Recnum Fraud card_merch_total_14 card_zip3_max_14 \\\n", + "83966 84296 0 789.32 609.34 \n", + "83967 84297 0 235.00 235.00 \n", + "83968 84298 0 600.00 600.00 \n", + "83969 84299 0 30.00 30.00 \n", + "83970 84300 0 182.00 182.00 \n", + "83971 84301 0 399.61 225.00 \n", + "83972 84302 0 53.00 206.90 \n", + "83973 84303 0 25.00 531.25 \n", + "83974 84304 0 97.17 97.17 \n", + "83975 84305 0 6.51 6.51 \n", + "\n", + " card_merch_avg_14 Card_Merchdesc_Zip_med_14 \\\n", + "83966 394.660 394.660 \n", + "83967 235.000 235.000 \n", + "83968 600.000 600.000 \n", + "83969 30.000 30.000 \n", + "83970 182.000 182.000 \n", + "83971 199.805 199.805 \n", + "83972 53.000 75.500 \n", + "83973 25.000 25.000 \n", + "83974 97.170 97.170 \n", + "83975 6.510 6.510 \n", + "\n", + " Card_Merchdesc_Zip_max_60 Card_Merchnum_Zip_avg_60 \\\n", + "83966 609.34 394.660000 \n", + "83967 235.00 235.000000 \n", + "83968 600.00 600.000000 \n", + "83969 365.32 197.660000 \n", + "83970 182.00 182.000000 \n", + "83971 225.00 199.805000 \n", + "83972 659.76 224.701429 \n", + "83973 28.48 26.740000 \n", + "83974 97.17 97.170000 \n", + "83975 6.51 11.733333 \n", + "\n", + " Card_Merchnum_desc_avg_60 Merchdesc_dow_avg_30 \\\n", + "83966 394.660000 271.69250 \n", + "83967 235.000000 235.00000 \n", + "83968 600.000000 600.00000 \n", + "83969 197.660000 294.10400 \n", + "83970 182.000000 182.00000 \n", + "83971 199.805000 174.61000 \n", + "83972 224.701429 128.45875 \n", + "83973 26.740000 25.00000 \n", + "83974 97.170000 97.17000 \n", + "83975 4.500000 6.51000 \n", + "\n", + " Card_Merchnum_desc_med_0 Card_Merchdesc_max_60 \n", + "83966 394.66 609.34 \n", + "83967 235.00 235.00 \n", + "83968 600.00 600.00 \n", + "83969 30.00 365.32 \n", + "83970 182.00 182.00 \n", + "83971 174.61 225.00 \n", + "83972 53.00 659.76 \n", + "83973 25.00 28.48 \n", + "83974 97.17 97.17 \n", + "83975 6.51 6.51 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# find the row i vars that corresponds to 11/1 (Recnum=84300)\n", + "test = vars[vars['Recnum'] > 84295]\n", + "test.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Fraud
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" + ], + "text/plain": [ + " Fraud\n", + "0 0\n", + "1 0\n", + "2 0\n", + "3 0\n", + "4 0" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "record_save = vars['Recnum']\n", + "Y_save = pd.DataFrame(vars.loc[:,'Fraud'])\n", + "Y_save.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scale and truncate field values" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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card_merch_total_14card_zip3_max_14card_merch_avg_14Card_Merchdesc_Zip_med_14Card_Merchdesc_Zip_max_60Card_Merchnum_Zip_avg_60Card_Merchnum_desc_avg_60Merchdesc_dow_avg_30Card_Merchnum_desc_med_0Card_Merchdesc_max_60
count96397.00000096397.00000096397.00000096397.00000096397.00000096397.00000096397.00000096397.00000096397.00000096397.000000
mean769.967286529.353162401.927811394.564839551.295774404.503353404.444849396.465357393.500463552.096524
std4168.2145171086.568630790.425618783.3448331104.370845778.326998778.414021696.170708790.6414171104.598654
min0.0100000.0100000.0100000.0100000.0100000.0100000.0100000.0100000.0100000.010000
25%77.00000057.19000039.99000039.18000052.50000044.90000044.97000053.03500034.90000052.770000
50%236.920000212.660000157.065000149.970000223.550000171.030000171.000000200.000000140.000000224.300000
75%676.860000609.000000452.940000438.450000658.000000472.000000472.000000478.200000431.550000659.340000
max306633.41000047900.00000028392.84000028392.84000047900.00000028392.84000028392.84000028392.84000028392.84000047900.000000
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" + ], + "text/plain": [ + " card_merch_total_14 card_zip3_max_14 card_merch_avg_14 \\\n", + "count 96397.000000 96397.000000 96397.000000 \n", + "mean 769.967286 529.353162 401.927811 \n", + "std 4168.214517 1086.568630 790.425618 \n", + "min 0.010000 0.010000 0.010000 \n", + "25% 77.000000 57.190000 39.990000 \n", + "50% 236.920000 212.660000 157.065000 \n", + "75% 676.860000 609.000000 452.940000 \n", + "max 306633.410000 47900.000000 28392.840000 \n", + "\n", + " Card_Merchdesc_Zip_med_14 Card_Merchdesc_Zip_max_60 \\\n", + "count 96397.000000 96397.000000 \n", + "mean 394.564839 551.295774 \n", + "std 783.344833 1104.370845 \n", + "min 0.010000 0.010000 \n", + "25% 39.180000 52.500000 \n", + "50% 149.970000 223.550000 \n", + "75% 438.450000 658.000000 \n", + "max 28392.840000 47900.000000 \n", + "\n", + " Card_Merchnum_Zip_avg_60 Card_Merchnum_desc_avg_60 \\\n", + "count 96397.000000 96397.000000 \n", + "mean 404.503353 404.444849 \n", + "std 778.326998 778.414021 \n", + "min 0.010000 0.010000 \n", + "25% 44.900000 44.970000 \n", + "50% 171.030000 171.000000 \n", + "75% 472.000000 472.000000 \n", + "max 28392.840000 28392.840000 \n", + "\n", + " Merchdesc_dow_avg_30 Card_Merchnum_desc_med_0 Card_Merchdesc_max_60 \n", + "count 96397.000000 96397.000000 96397.000000 \n", + "mean 396.465357 393.500463 552.096524 \n", + "std 696.170708 790.641417 1104.598654 \n", + "min 0.010000 0.010000 0.010000 \n", + "25% 53.035000 34.900000 52.770000 \n", + "50% 200.000000 140.000000 224.300000 \n", + "75% 478.200000 431.550000 659.340000 \n", + "max 28392.840000 28392.840000 47900.000000 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_no_scaling = vars.drop(columns = ['Recnum','Fraud'])\n", + "X_no_scaling.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "X = (X_no_scaling - X_no_scaling.mean()) / X_no_scaling.std()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# use this to cap variables. For some problems it helps\n", + "Clip = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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card_merch_total_14card_zip3_max_14card_merch_avg_14Card_Merchdesc_Zip_med_14Card_Merchdesc_Zip_max_60Card_Merchnum_Zip_avg_60Card_Merchnum_desc_avg_60Merchdesc_dow_avg_30Card_Merchnum_desc_med_0Card_Merchdesc_max_60
count9.639700e+049.639700e+049.639700e+049.639700e+049.639700e+049.639700e+049.639700e+049.639700e+049.639700e+049.639700e+04
mean-4.580238e-161.642357e-159.159462e-15-1.424971e-151.927932e-161.083906e-141.434370e-156.285712e-157.526706e-163.751579e-15
std1.000000e+001.000000e+001.000000e+001.000000e+001.000000e+001.000000e+001.000000e+001.000000e+001.000000e+001.000000e+00
min-1.847211e-01-4.871696e-01-5.084828e-01-5.036796e-01-4.991854e-01-5.196959e-01-5.195626e-01-5.694801e-01-4.976851e-01-4.998073e-01
25%-1.662504e-01-4.345452e-01-4.579024e-01-4.536761e-01-4.516561e-01-4.620209e-01-4.618042e-01-4.933134e-01-4.535564e-01-4.520434e-01
50%-1.278838e-01-2.914617e-01-3.097860e-01-3.122441e-01-2.967715e-01-2.999682e-01-2.998981e-01-2.822086e-01-3.206263e-01-2.967562e-01
75%-2.233745e-027.330125e-026.453762e-025.602279e-029.661992e-028.672017e-028.678563e-021.174060e-014.812490e-029.708818e-02
max7.337997e+014.359655e+013.541246e+013.574195e+014.287392e+013.595961e+013.595567e+014.021481e+013.541345e+014.286435e+01
\n", + "
" + ], + "text/plain": [ + " card_merch_total_14 card_zip3_max_14 card_merch_avg_14 \\\n", + "count 9.639700e+04 9.639700e+04 9.639700e+04 \n", + "mean -4.580238e-16 1.642357e-15 9.159462e-15 \n", + "std 1.000000e+00 1.000000e+00 1.000000e+00 \n", + "min -1.847211e-01 -4.871696e-01 -5.084828e-01 \n", + "25% -1.662504e-01 -4.345452e-01 -4.579024e-01 \n", + "50% -1.278838e-01 -2.914617e-01 -3.097860e-01 \n", + "75% -2.233745e-02 7.330125e-02 6.453762e-02 \n", + "max 7.337997e+01 4.359655e+01 3.541246e+01 \n", + "\n", + " Card_Merchdesc_Zip_med_14 Card_Merchdesc_Zip_max_60 \\\n", + "count 9.639700e+04 9.639700e+04 \n", + "mean -1.424971e-15 1.927932e-16 \n", + "std 1.000000e+00 1.000000e+00 \n", + "min -5.036796e-01 -4.991854e-01 \n", + "25% -4.536761e-01 -4.516561e-01 \n", + "50% -3.122441e-01 -2.967715e-01 \n", + "75% 5.602279e-02 9.661992e-02 \n", + "max 3.574195e+01 4.287392e+01 \n", + "\n", + " Card_Merchnum_Zip_avg_60 Card_Merchnum_desc_avg_60 \\\n", + "count 9.639700e+04 9.639700e+04 \n", + "mean 1.083906e-14 1.434370e-15 \n", + "std 1.000000e+00 1.000000e+00 \n", + "min -5.196959e-01 -5.195626e-01 \n", + "25% -4.620209e-01 -4.618042e-01 \n", + "50% -2.999682e-01 -2.998981e-01 \n", + "75% 8.672017e-02 8.678563e-02 \n", + "max 3.595961e+01 3.595567e+01 \n", + "\n", + " Merchdesc_dow_avg_30 Card_Merchnum_desc_med_0 Card_Merchdesc_max_60 \n", + "count 9.639700e+04 9.639700e+04 9.639700e+04 \n", + "mean 6.285712e-15 7.526706e-16 3.751579e-15 \n", + "std 1.000000e+00 1.000000e+00 1.000000e+00 \n", + "min -5.694801e-01 -4.976851e-01 -4.998073e-01 \n", + "25% -4.933134e-01 -4.535564e-01 -4.520434e-01 \n", + "50% -2.822086e-01 -3.206263e-01 -2.967562e-01 \n", + "75% 1.174060e-01 4.812490e-02 9.708818e-02 \n", + "max 4.021481e+01 3.541345e+01 4.286435e+01 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# push in any outlier values, then rescale\n", + "X.clip(-1*Clip,Clip,inplace=True)\n", + "X = (X_no_scaling - X_no_scaling.mean()) / X_no_scaling.std()\n", + "X.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Fraud 179\n", + "dtype: int64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# separate data into modeling (traintest) and out of time. Here I'm using the record number to do this separation.\n", + "# you need to change this oot record number to whatever is appropriate for your data\n", + "# oot_recnum=84300\n", + "oot_recnum = 83970\n", + "X_trntst = X[0:oot_recnum]\n", + "Y_trntst = Y_save[0:oot_recnum]\n", + "X_oot = X[oot_recnum:]\n", + "Y_oot = Y_save[oot_recnum:]\n", + "Y_oot.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Explore regularization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve a linear regression with ridge and lasso regularization and watch how the variable weights evolve with the regularization strength" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import Ridge, RidgeCV, Lasso, LassoCV \n", + "from sklearn.metrics import mean_squared_error" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "alphas = 10**np.linspace(2,9,30)*0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(30, 10)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ridge = Ridge()\n", + "coefs = []\n", + "for a in alphas: \n", + " ridge.set_params(alpha=a) \n", + " ridge.fit(X_trn,Y_trn.values.ravel()) \n", + " coefs.append(ridge.coef_) \n", + "np.shape(coefs)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.04 s, sys: 31.3 ms, total: 1.07 s\n", + "Wall time: 523 ms\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "%matplotlib inline\n", + "ax = plt.gca() # Get the current Axes instance\n", + "ax.plot(alphas, coefs)\n", + "ax.set_xscale('log')\n", + "plt.xlabel('alpha') \n", + "plt.ylabel('standadized coef') \n", + "plt.title('Ridge')\n", + "plt.savefig('ridge.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "alphas = 10**np.linspace(-5,0,30)*0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# %%time\n", + "# # sometimes this cell takes a long time\n", + "# lasso = Lasso(max_iter=10000) \n", + "# coefs = [] \n", + "# for a in alphas: \n", + "# lasso.set_params(alpha=a) \n", + "# lasso.fit(X_trn,Y_trn.values.ravel()) \n", + "# coefs.append(lasso.coef_) \n", + "# # print('Shape:',np.shape(coefs)\n", + "# print('Selected Features:', list(vars.columns[np.where(lasso.coef_!=0)[0]]))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "ax = plt.gca() # Get the current Axes instance \n", + "ax.plot(alphas, coefs)\n", + "ax.set_xscale('log')\n", + "plt.xlabel('alpha')\n", + "plt.ylabel('standerdized coef') \n", + "plt.title('Lasso')\n", + "plt.legend()\n", + "plt.savefig('lasso.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Look at PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(X_trntst.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "X_trntst_save = X_trntst.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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mean0.0030150.0019600.0029970.0028090.0029980.0023150.0023180.0022520.0032630.002961
std1.0588331.0107350.9994060.9986801.0173700.9963890.9963970.9887240.9993101.017333
min-0.184721-0.487170-0.508483-0.503680-0.499185-0.519696-0.519563-0.569480-0.497685-0.499807
25%-0.166286-0.436791-0.460066-0.456017-0.453920-0.465389-0.464977-0.497610-0.456238-0.454551
50%-0.127625-0.293058-0.309824-0.312231-0.299171-0.300694-0.300629-0.282209-0.320664-0.298847
75%-0.0220930.0747990.0672200.0580010.0984310.0905750.0906400.1191300.0514360.098319
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" + ], + "text/plain": [ + " PC1 PC2 PC3 PC4\n", + "count 12427.000000 12427.000000 12427.000000 12427.000000\n", + "mean -0.022501 -0.006851 0.008091 -0.009948\n", + "std 0.961810 0.610333 0.941264 0.887663\n", + "min -0.532474 -18.785671 -18.706326 -32.193259\n", + "25% -0.445516 -0.097902 -0.195838 -0.120168\n", + "50% -0.282872 0.060170 -0.015967 0.164146\n", + "75% 0.066763 0.125096 0.091078 0.246899\n", + "max 32.088195 10.658845 31.192750 20.159147" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_oot_orig_pca.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(83970, 4)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_trntst_pca.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(12427, 4)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_oot_orig_pca.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Subsample the larger class if desired" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.010479933309515303\n", + "(1751, 10) 1751\n" + ] + } + ], + "source": [ + "# set the ratio of goods to bads that you would like. This next line is the ratio of goods to bads that you want for modeling\n", + "sample_ratio_desired = 1\n", + "\n", + "temp = X_trntst.copy()\n", + "temp['Fraud'] = Y_trntst['Fraud']\n", + "temp.head()\n", + "goods = temp[temp['Fraud']==0]\n", + "bads = temp[temp['Fraud']==1]\n", + "actual_bad_fraction = len(bads)/len(temp)\n", + "actual_good_fraction = 1 - actual_bad_fraction\n", + "print(actual_bad_fraction)\n", + "fraction = sample_ratio_desired * actual_bad_fraction\n", + "goods_sampled = goods.sample(frac = fraction)\n", + "all_sampled = pd.concat([goods_sampled,bads])\n", + "all_sampled.sort_index(inplace=True)\n", + "Y_trntst_sampled = pd.DataFrame(all_sampled['Fraud'])\n", + "X_trntst_sampled = all_sampled.drop(columns=['Fraud'])\n", + "del [temp,goods,bads,all_sampled]\n", + "gc.collect()\n", + "print(X_trntst_sampled.shape,len(Y_trntst_sampled))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "niter = 0\n", + "nitermax = 10\n", + "jittersize = .1\n", + "X_oot_orig = X_oot.copy()\n", + "pd.options.mode.chained_assignment = None # default='warn'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can comment in/out any of these model cells and just explore one model type. You can also just rerun that single cell multiple times (hit shift-enter on that cell) as you manually explore different model hyperparameters." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "Modeling_output = pd.DataFrame(columns=['Model','Trn','Tst','OOT'],index=range(1000))\n", + "counter = 0\n", + "model_counter = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 0.6329113924050633 0.6411290322580645 0.5418994413407822\n", + "1 0.6197411003236246 0.6068702290076335 0.44692737430167595\n", + "2 0.635 0.6178571428571429 0.5586592178770949\n", + "3 0.6477272727272727 0.6022727272727273 0.49162011173184356\n", + "4 0.6220839813374806 0.6582278481012658 0.553072625698324\n", + "5 0.6247960848287113 0.6441947565543071 0.5027932960893855\n", + "6 0.6472545757071547 0.5734767025089605 0.5307262569832403\n", + "7 0.6395348837209303 0.6007194244604317 0.547486033519553\n", + "8 0.6270096463022508 0.6201550387596899 0.5083798882681564\n", + "9 0.6116504854368932 0.6526717557251909 0.5083798882681564\n", + "trn 0.630771\n", + "tst 0.621757\n", + "oot 0.518994\n", + "dtype: float64\n", + "CPU times: user 6.8 s, sys: 137 ms, total: 6.94 s\n", + "Wall time: 1.77 s\n" + ] + } + ], + "source": [ + "%%time\n", + "# Logistic regression\n", + "\n", + "FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n", + "for niter in range(nitermax): \n", + " X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n", + "\n", + " model = LogisticRegression()\n", + " \n", + " X_oot = X_oot_orig.copy()\n", + " X_trn_save = X_trn.copy()\n", + " Y_trn_save = Y_trn.copy()\n", + "\n", + " model.fit(X_trn, Y_trn.values.ravel()) \n", + "\n", + " predictions = model.predict_proba(X_trn_save)[:,1]\n", + " X_trn['predicted'] = predictions\n", + " X_trn['Fraud'] = Y_trn_save['Fraud']\n", + " topRows = int(round(X_trn.shape[0]*0.03))\n", + " temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_tst)[:,1]\n", + " X_tst['predicted']=predictions\n", + " X_tst['Fraud'] = Y_tst['Fraud']\n", + " topRows = int(round(X_tst.shape[0]*0.03))\n", + " temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_oot)[:,1]\n", + " X_oot['predicted']=predictions\n", + " X_oot['Fraud'] = Y_oot['Fraud']\n", + " topRows = int(round(X_oot.shape[0]*0.03))\n", + " temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + " print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n", + " Modeling_output.iloc[counter] = ['log reg',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n", + " counter = counter + 1\n", + " \n", + "print(FDR3.mean())\n", + "model_counter = model_counter + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1.0 0.5864661654135338 0.29608938547486036\n", + "1 1.0 0.6174242424242424 0.31843575418994413\n", + "2 1.0 0.5795053003533569 0.21787709497206703\n", + "3 1.0 0.5793650793650794 0.33519553072625696\n", + "4 1.0 0.551094890510949 0.2737430167597765\n", + "5 1.0 0.5605536332179931 0.2737430167597765\n", + "6 1.0 0.6190476190476191 0.3407821229050279\n", + "7 1.0 0.5902255639097744 0.3128491620111732\n", + "8 1.0 0.536 0.35195530726256985\n", + "9 1.0 0.6245353159851301 0.2737430167597765\n", + "trn 1.000000\n", + "tst 0.584422\n", + "oot 0.299441\n", + "dtype: float64\n", + "CPU times: user 7.42 s, sys: 45.6 ms, total: 7.46 s\n", + "Wall time: 6.92 s\n" + ] + } + ], + "source": [ + "%%time\n", + "# Single DT\n", + "\n", + "FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n", + "for niter in range(nitermax): \n", + " X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n", + "\n", + " model = DecisionTreeClassifier()\n", + " \n", + " X_oot = X_oot_orig.copy()\n", + " X_trn_save = X_trn.copy()\n", + " Y_trn_save = Y_trn.copy()\n", + "\n", + " model.fit(X_trn, Y_trn.values.ravel()) \n", + "\n", + " predictions = model.predict_proba(X_trn_save)[:,1]\n", + " X_trn['predicted'] = predictions\n", + " X_trn['Fraud'] = Y_trn_save['Fraud']\n", + " topRows = int(round(X_trn.shape[0]*0.03))\n", + " temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_tst)[:,1]\n", + " X_tst['predicted']=predictions\n", + " X_tst['Fraud'] = Y_tst['Fraud']\n", + " topRows = int(round(X_tst.shape[0]*0.03))\n", + " temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_oot)[:,1]\n", + " X_oot['predicted']=predictions\n", + " X_oot['Fraud'] = Y_oot['Fraud']\n", + " topRows = int(round(X_oot.shape[0]*0.03))\n", + " temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + " print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n", + " Modeling_output.iloc[counter] = ['DT',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n", + " counter = counter + 1\n", + "\n", + "print(FDR3.mean())\n", + "model_counter = model_counter + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1.0 0.803030303030303 0.3687150837988827\n", + "1 1.0 0.8142292490118577 0.4245810055865922\n", + "2 1.0 0.8577405857740585 0.3687150837988827\n", + "3 1.0 0.8482142857142857 0.4301675977653631\n", + "4 1.0 0.8269896193771626 0.36312849162011174\n", + "5 1.0 0.8201438848920863 0.35195530726256985\n", + "6 1.0 0.8206106870229007 0.5195530726256983\n", + "7 1.0 0.8127659574468085 0.5307262569832403\n", + "8 1.0 0.8611111111111112 0.35195530726256985\n", + "9 1.0 0.8414634146341463 0.41899441340782123\n", + "trn 1.000000\n", + "tst 0.830630\n", + "oot 0.412849\n", + "dtype: float64\n", + "CPU times: user 2min 10s, sys: 617 ms, total: 2min 10s\n", + "Wall time: 2min 11s\n" + ] + } + ], + "source": [ + "%%time\n", + "# RF\n", + "\n", + "FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n", + "for niter in range(nitermax): \n", + " X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n", + "\n", + " model = RandomForestClassifier()\n", + " X_oot = X_oot_orig.copy()\n", + " X_trn_save = X_trn.copy()\n", + " Y_trn_save = Y_trn.copy()\n", + "\n", + " model.fit(X_trn, Y_trn.values.ravel()) \n", + "\n", + " predictions = model.predict_proba(X_trn_save)[:,1]\n", + " X_trn['predicted'] = predictions\n", + " X_trn['Fraud'] = Y_trn_save['Fraud']\n", + " topRows = int(round(X_trn.shape[0]*0.03))\n", + " temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_tst)[:,1]\n", + " X_tst['predicted']=predictions\n", + " X_tst['Fraud'] = Y_tst['Fraud']\n", + " topRows = int(round(X_tst.shape[0]*0.03))\n", + " temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_oot)[:,1]\n", + " X_oot['predicted']=predictions\n", + " X_oot['Fraud'] = Y_oot['Fraud']\n", + " topRows = int(round(X_oot.shape[0]*0.03))\n", + " temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + " print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n", + " Modeling_output.iloc[counter] = ['RF',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n", + " counter = counter + 1\n", + " \n", + "print(FDR3.mean())\n", + "model_counter = model_counter + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 0.9888712241653418 0.7649402390438247 0.3128491620111732\n", + "1 0.9804241435562806 0.7865168539325843 0.3463687150837989\n", + "2 0.9867768595041322 0.8 0.3128491620111732\n", + "3 0.9841269841269841 0.808 0.3463687150837989\n", + "4 0.9834437086092715 0.8043478260869565 0.3407821229050279\n", + "5 0.9851485148514851 0.791970802919708 0.37988826815642457\n", + "6 0.9886914378029079 0.7854406130268199 0.35195530726256985\n", + "7 0.9839486356340289 0.7937743190661478 0.3463687150837989\n", + "8 0.9826771653543307 0.7877551020408163 0.3463687150837989\n", + "9 0.9773095623987034 0.7870722433460076 0.35195530726256985\n", + "trn 0.984142\n", + "tst 0.790982\n", + "oot 0.343575\n", + "dtype: float64\n", + "CPU times: user 28.9 s, sys: 880 ms, total: 29.8 s\n", + "Wall time: 4.09 s\n" + ] + } + ], + "source": [ + "%%time\n", + "# LGBM\n", + "\n", + "FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n", + "for niter in range(nitermax): \n", + " X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n", + "\n", + " model = lgb.LGBMClassifier()\n", + "\n", + " X_oot = X_oot_orig.copy()\n", + " X_trn_save = X_trn.copy()\n", + " Y_trn_save = Y_trn.copy()\n", + "\n", + " model.fit(X_trn, Y_trn.values.ravel()) \n", + "\n", + " predictions = model.predict_proba(X_trn_save)[:,1]\n", + " X_trn['predicted'] = predictions\n", + " X_trn['Fraud'] = Y_trn_save['Fraud']\n", + " topRows = int(round(X_trn.shape[0]*0.03))\n", + " temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter,'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_tst)[:,1]\n", + " X_tst['predicted']=predictions\n", + " X_tst['Fraud'] = Y_tst['Fraud']\n", + " topRows = int(round(X_tst.shape[0]*0.03))\n", + " temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter,'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_oot)[:,1]\n", + " X_oot['predicted']=predictions\n", + " X_oot['Fraud'] = Y_oot['Fraud']\n", + " topRows = int(round(X_oot.shape[0]*0.03))\n", + " temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter,'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + " print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n", + " Modeling_output.iloc[counter] = ['LGBM',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n", + " counter = counter + 1\n", + " \n", + "print(FDR3.mean())\n", + "model_counter = model_counter + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 (58779, 10) (58779, 1)\n", + "0 (116300, 10) (116300, 1)\n", + "Fraud 629\n", + "dtype: int64\n", + "Fraud 58150\n", + "dtype: int64\n", + "0 0.9109697933227345 0.8207171314741036 0.31843575418994413\n", + "1 (58779, 10) (58779, 1)\n", + "1 (116280, 10) (116280, 1)\n", + "Fraud 639\n", + "dtype: int64\n", + "Fraud 58140\n", + "dtype: int64\n", + "1 0.9280125195618153 0.7966804979253111 0.33519553072625696\n", + "2 (58779, 10) (58779, 1)\n", + "2 (116306, 10) (116306, 1)\n", + "Fraud 626\n", + "dtype: int64\n", + "Fraud 58153\n", + "dtype: int64\n", + "2 0.9217252396166135 0.7992125984251969 0.3016759776536313\n", + "3 (58779, 10) (58779, 1)\n", + "3 (116312, 10) (116312, 1)\n", + "Fraud 623\n", + "dtype: int64\n", + "Fraud 58156\n", + "dtype: int64\n", + "3 0.9293739967897271 0.8132295719844358 0.31843575418994413\n", + "4 (58779, 10) (58779, 1)\n", + "4 (116318, 10) (116318, 1)\n", + "Fraud 620\n", + "dtype: int64\n", + "Fraud 58159\n", + "dtype: int64\n", + "4 0.9016129032258065 0.823076923076923 0.3407821229050279\n", + "5 (58779, 10) (58779, 1)\n", + "5 (116372, 10) (116372, 1)\n", + "Fraud 593\n", + "dtype: int64\n", + "Fraud 58186\n", + "dtype: int64\n", + "5 0.924114671163575 0.7979094076655052 0.3128491620111732\n", + "6 (58779, 10) (58779, 1)\n", + "6 (116326, 10) (116326, 1)\n", + "Fraud 616\n", + "dtype: int64\n", + "Fraud 58163\n", + "dtype: int64\n", + "6 0.9188311688311688 0.7954545454545454 0.3240223463687151\n", + "7 (58779, 10) (58779, 1)\n", + "7 (116296, 10) (116296, 1)\n", + "Fraud 631\n", + "dtype: int64\n", + "Fraud 58148\n", + "dtype: int64\n", + "7 0.9239302694136292 0.8232931726907631 0.3240223463687151\n", + "8 (58779, 10) (58779, 1)\n", + "8 (116338, 10) (116338, 1)\n", + "Fraud 610\n", + "dtype: int64\n", + "Fraud 58169\n", + "dtype: int64\n", + "8 0.9180327868852459 0.7666666666666667 0.30726256983240224\n", + "9 (58779, 10) (58779, 1)\n", + "9 (116334, 10) (116334, 1)\n", + "Fraud 612\n", + "dtype: int64\n", + "Fraud 58167\n", + "dtype: int64\n", + "9 0.9035947712418301 0.8171641791044776 0.33519553072625696\n", + "trn 0.918020\n", + "tst 0.805340\n", + "oot 0.321788\n", + "dtype: float64\n", + "CPU times: user 52.2 s, sys: 2.63 s, total: 54.8 s\n", + "Wall time: 8.25 s\n" + ] + } + ], + "source": [ + "%%time\n", + "# LGBM with SMOTE\n", + "from imblearn.over_sampling import SMOTE\n", + "\n", + "FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n", + "for niter in range(nitermax): \n", + " X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n", + " \n", + " sm = SMOTE()\n", + " \n", + " X_trn_sm, Y_trn_sm = sm.fit_resample(X_trn,Y_trn)\n", + " \n", + " print(niter, X_trn.shape,Y_trn.shape)\n", + " print(niter, X_trn_sm.shape,Y_trn_sm.shape)\n", + " print(Y_trn.sum())\n", + " print(Y_trn_sm.sum())\n", + "\n", + " model = lgb.LGBMClassifier()\n", + " \n", + " X_oot = X_oot_orig.copy()\n", + " X_trn_save = X_trn.copy()\n", + " Y_trn_save = Y_trn.copy()\n", + "\n", + " model.fit(X_trn_sm, Y_trn_sm.values.ravel()) \n", + "\n", + " predictions = model.predict_proba(X_trn_save)[:,1]\n", + " X_trn['predicted'] = predictions\n", + " X_trn['Fraud'] = Y_trn_save['Fraud']\n", + " topRows = int(round(X_trn.shape[0]*0.03))\n", + " temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter,'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_tst)[:,1]\n", + " X_tst['predicted']=predictions\n", + " X_tst['Fraud'] = Y_tst['Fraud']\n", + " topRows = int(round(X_tst.shape[0]*0.03))\n", + " temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter,'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_oot)[:,1]\n", + " X_oot['predicted']=predictions\n", + " X_oot['Fraud'] = Y_oot['Fraud']\n", + " topRows = int(round(X_oot.shape[0]*0.03))\n", + " temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter,'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + " print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n", + " Modeling_output.iloc[counter] = ['LGBM with SMOTE',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n", + " counter = counter + 1\n", + " \n", + "print(FDR3.mean())\n", + "model_counter = model_counter + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# %%time\n", + "# # LGBM with jitter\n", + "\n", + "# jittersize = .1\n", + "# FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n", + "# for niter in range(nitermax): \n", + "# X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n", + " \n", + "# print(niter, X_trn.shape,Y_trn.shape)\n", + " \n", + "# X_trn_bads = X_trn[Y_trn == 1]\n", + "# Y_trn_bads = Y_trn[Y_trn == 1]\n", + " \n", + "# print(X_trn_bads.head())\n", + "# for i in range(2):\n", + "# X_trn_more = X_trn_bads*(1+jittersize*random.uniform(-1,1))\n", + "# X_trn = X_trn.append(X_trn_more,ignore_index=True)\n", + "# Y_trn = Y_trn.append(Y_trn_bads,ignore_index=True)\n", + " \n", + "# print(niter, X_trn.shape,Y_trn.shape)\n", + "\n", + "# model = lgb.LGBMClassifier()\n", + "\n", + "# X_oot = X_oot_orig.copy()\n", + "# X_trn_save = X_trn.copy()\n", + "# Y_trn_save = Y_trn.copy()\n", + "\n", + "# model.fit(X_trn, Y_trn.values.ravel()) \n", + "\n", + "# predictions = model.predict_proba(X_trn_save)[:,1]\n", + "# X_trn['predicted'] = predictions\n", + "# X_trn['Fraud'] = Y_trn_save['Fraud']\n", + "# topRows = int(round(X_trn.shape[0]*0.03))\n", + "# temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter,'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + "# predictions = model.predict_proba(X_tst)[:,1]\n", + "# X_tst['predicted']=predictions\n", + "# X_tst['Fraud'] = Y_tst['Fraud']\n", + "# topRows = int(round(X_tst.shape[0]*0.03))\n", + "# temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter,'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + "# predictions = model.predict_proba(X_oot)[:,1]\n", + "# X_oot['predicted']=predictions\n", + "# X_oot['Fraud'] = Y_oot['Fraud']\n", + "# topRows = int(round(X_oot.shape[0]*0.03))\n", + "# temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter,'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + "# print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n", + "# Modeling_output.iloc[counter] = ['LGBM with jitter',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n", + "# counter = counter + 1\n", + " \n", + "# print(FDR3.mean())\n", + "# model_counter = model_counter + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 0.7328990228013029 0.7255639097744361 0.44692737430167595\n", + "1 0.7178683385579937 0.7396694214876033 0.5307262569832403\n", + "2 0.7615262321144675 0.7091633466135459 0.40782122905027934\n", + "3 0.7288961038961039 0.678030303030303 0.547486033519553\n", + "4 0.7166392092257001 0.7106227106227107 0.39664804469273746\n", + "5 0.7138047138047138 0.7027972027972028 0.39664804469273746\n", + "6 0.7515527950310559 0.6949152542372882 0.3687150837988827\n", + "7 0.7433774834437086 0.6956521739130435 0.39106145251396646\n", + "8 0.7096247960848288 0.7153558052434457 0.43575418994413406\n", + "9 0.7335526315789473 0.7169117647058824 0.4692737430167598\n", + "trn 0.730974\n", + "tst 0.708868\n", + "oot 0.439106\n", + "dtype: float64\n", + "CPU times: user 9min 15s, sys: 8.27 s, total: 9min 23s\n", + "Wall time: 2min 23s\n" + ] + } + ], + "source": [ + "%%time\n", + "# NN\n", + "\n", + "FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n", + "for niter in range(nitermax): \n", + " X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n", + "\n", + " model = MLPClassifier()\n", + " \n", + " X_oot = X_oot_orig.copy()\n", + " X_trn_save = X_trn.copy()\n", + " Y_trn_save = Y_trn.copy()\n", + "\n", + " model.fit(X_trn, Y_trn.values.ravel()) \n", + "\n", + " predictions = model.predict_proba(X_trn_save)[:,1]\n", + " X_trn['predicted'] = predictions\n", + " X_trn['Fraud'] = Y_trn_save['Fraud']\n", + " topRows = int(round(X_trn.shape[0]*0.03))\n", + " temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_tst)[:,1]\n", + " X_tst['predicted']=predictions\n", + " X_tst['Fraud'] = Y_tst['Fraud']\n", + " topRows = int(round(X_tst.shape[0]*0.03))\n", + " temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_oot)[:,1]\n", + " X_oot['predicted']=predictions\n", + " X_oot['Fraud'] = Y_oot['Fraud']\n", + " topRows = int(round(X_oot.shape[0]*0.03))\n", + " temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + " print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n", + " Modeling_output.iloc[counter] = ['NN',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n", + " counter = counter + 1\n", + " \n", + "print(FDR3.mean())\n", + "model_counter = model_counter + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# %%time\n", + "# # NN on pc's\n", + "\n", + "# FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n", + "# for niter in range(nitermax): \n", + "# X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst_pca, Y_trntst, test_size = .3)\n", + "\n", + "# model = MLPClassifier()\n", + "\n", + "# X_oot = X_oot_orig_pca.copy()\n", + "# X_trn_save = X_trn.copy()\n", + "# Y_trn_save = Y_trn.copy()\n", + "\n", + "# model.fit(X_trn, Y_trn.values.ravel()) \n", + "\n", + "# predictions = model.predict_proba(X_trn_save)[:,1]\n", + "# X_trn['predicted'] = predictions\n", + "# X_trn['Fraud'] = Y_trn_save['Fraud']\n", + "# topRows = int(round(X_trn.shape[0]*0.03))\n", + "# temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + "# predictions = model.predict_proba(X_tst)[:,1]\n", + "# X_tst['predicted']=predictions\n", + "# X_tst['Fraud'] = Y_tst['Fraud']\n", + "# topRows = int(round(X_tst.shape[0]*0.03))\n", + "# temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + "# predictions = model.predict_proba(X_oot)[:,1]\n", + "# X_oot['predicted']=predictions\n", + "# X_oot['Fraud'] = Y_oot['Fraud']\n", + "# topRows = int(round(X_oot.shape[0]*0.03))\n", + "# temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + "# print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n", + "# Modeling_output.iloc[counter] = ['NN_PCs',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n", + "# counter = counter + 1\n", + " \n", + "# print(FDR3.mean())\n", + "# model_counter = model_counter + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# %%time\n", + "# # GBC\n", + "\n", + "# FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n", + "# for niter in range(nitermax): \n", + "# X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n", + "\n", + "# model = GradientBoostingClassifier()\n", + "\n", + "# X_oot = X_oot_orig.copy()\n", + "# X_trn_save = X_trn.copy()\n", + "# Y_trn_save = Y_trn.copy()\n", + "\n", + "# model.fit(X_trn, Y_trn.values.ravel()) \n", + "\n", + "# predictions = model.predict_proba(X_trn_save)[:,1]\n", + "# X_trn['predicted'] = predictions\n", + "# X_trn['Fraud'] = Y_trn_save['Fraud']\n", + "# topRows = int(round(X_trn.shape[0]*0.03))\n", + "# temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + "# predictions = model.predict_proba(X_tst)[:,1]\n", + "# X_tst['predicted']=predictions\n", + "# X_tst['Fraud'] = Y_tst['Fraud']\n", + "# topRows = int(round(X_tst.shape[0]*0.03))\n", + "# temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + "# predictions = model.predict_proba(X_oot)[:,1]\n", + "# X_oot['predicted']=predictions\n", + "# X_oot['Fraud'] = Y_oot['Fraud']\n", + "# topRows = int(round(X_oot.shape[0]*0.03))\n", + "# temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + "# print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n", + "# Modeling_output.iloc[counter] = ['GBC',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n", + "# counter = counter + 1\n", + " \n", + "# print(FDR3.mean())\n", + "# model_counter = model_counter + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 0.9292763157894737 0.8088235294117647 0.3743016759776536\n", + "1 0.9392 0.8352941176470589 0.3687150837988827\n", + "2 0.9185303514376997 0.8464566929133859 0.3407821229050279\n", + "3 0.9336650082918739 0.8050541516245487 0.3575418994413408\n", + "4 0.9297385620915033 0.8059701492537313 0.4301675977653631\n", + "5 0.9408866995073891 0.7749077490774908 0.3575418994413408\n", + "6 0.9247135842880524 0.7955390334572491 0.3687150837988827\n", + "7 0.9320388349514563 0.8015267175572519 0.3743016759776536\n", + "8 0.9298531810766721 0.8089887640449438 0.4748603351955307\n", + "9 0.927536231884058 0.8262548262548263 0.3743016759776536\n", + "trn 0.930544\n", + "tst 0.810882\n", + "oot 0.382123\n", + "dtype: float64\n", + "CPU times: user 5min 53s, sys: 9.71 s, total: 6min 3s\n", + "Wall time: 58.9 s\n" + ] + } + ], + "source": [ + "%%time\n", + "# Catboost\n", + "\n", + "FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n", + "for niter in range(nitermax): \n", + " X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n", + "\n", + " model = CatBoostClassifier(verbose=0)\n", + "\n", + " X_oot = X_oot_orig.copy()\n", + " X_trn_save = X_trn.copy()\n", + " Y_trn_save = Y_trn.copy()\n", + "\n", + " model.fit(X_trn, Y_trn.values.ravel()) \n", + "\n", + " predictions = model.predict_proba(X_trn_save)[:,1]\n", + " X_trn['predicted'] = predictions\n", + " X_trn['Fraud'] = Y_trn_save['Fraud']\n", + " topRows = int(round(X_trn.shape[0]*0.03))\n", + " temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_tst)[:,1]\n", + " X_tst['predicted']=predictions\n", + " X_tst['Fraud'] = Y_tst['Fraud']\n", + " topRows = int(round(X_tst.shape[0]*0.03))\n", + " temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_oot)[:,1]\n", + " X_oot['predicted']=predictions\n", + " X_oot['Fraud'] = Y_oot['Fraud']\n", + " topRows = int(round(X_oot.shape[0]*0.03))\n", + " temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + " print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n", + " Modeling_output.iloc[counter] = ['cat boost',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n", + " counter = counter + 1\n", + " \n", + "print(FDR3.mean())\n", + "model_counter = model_counter + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "94.42045454545455\n", + "0 0.8227272727272728 0.8227272727272728 0.4581005586592179\n", + "1 0.8284090909090909 0.8284090909090909 0.5027932960893855\n", + "2 0.8 0.8 0.5139664804469274\n", + "3 0.8420454545454545 0.8420454545454545 0.3743016759776536\n", + "4 0.8227272727272728 0.8227272727272728 0.4022346368715084\n", + "5 0.8284090909090909 0.8284090909090909 0.39106145251396646\n", + "6 0.8022727272727272 0.8022727272727272 0.4860335195530726\n", + "7 0.8227272727272728 0.8227272727272728 0.36312849162011174\n", + "8 0.8159090909090909 0.8159090909090909 0.3743016759776536\n", + "9 0.8125 0.8125 0.4748603351955307\n", + "trn 0.819773\n", + "tst 0.819773\n", + "oot 0.434078\n", + "dtype: float64\n", + "CPU times: user 40.5 s, sys: 901 ms, total: 41.4 s\n", + "Wall time: 5.87 s\n" + ] + } + ], + "source": [ + "%%time\n", + "# NOTE this cell has been substantially modified to evaluate a sampled trn/tst data set. \n", + "# Only use this cell if you do downsampling of the goods.\n", + "# each good needs to have a weight of (1-actual_ratio)/sample_ratio_desired\n", + "# it's hard to get the correct FDR@3% for the actual train and test, so I just use the original trntst after the model is built for evaluation\n", + "\n", + "xmult = actual_good_fraction / (actual_bad_fraction * sample_ratio_desired)\n", + "print(xmult)\n", + "FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n", + "for niter in range(nitermax): \n", + " X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst_sampled, Y_trntst_sampled, test_size = .3)\n", + "\n", + " model = lgb.LGBMClassifier()\n", + "\n", + " model.fit(X_trn, Y_trn.values.ravel()) \n", + " \n", + " X_oot = X_oot_orig.copy()\n", + " X_trn = X_trntst.copy()\n", + " Y_trn = Y_trntst.copy()\n", + " X_tst = X_trntst.copy()\n", + " Y_tst = Y_trntst.copy()\n", + "\n", + "\n", + " predictions = model.predict_proba(X_trntst)[:,1]\n", + " X_trn['predicted'] = predictions\n", + " X_trn['Fraud'] = Y_trntst['Fraud']\n", + " topRows = int(round(X_trn.shape[0]*0.03))\n", + " temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_trntst)[:,1]\n", + " X_tst['predicted']=predictions\n", + " X_tst['Fraud'] = Y_trntst['Fraud']\n", + " topRows = int(round(X_tst.shape[0]*0.03))\n", + " temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_oot)[:,1]\n", + " X_oot['predicted']=predictions\n", + " X_oot['Fraud'] = Y_oot['Fraud']\n", + " topRows = int(round(X_oot.shape[0]*0.03))\n", + " temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + " print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n", + " Modeling_output.iloc[counter] = ['LGBM sampled',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n", + " counter = counter + 1\n", + " \n", + "print(FDR3.mean())\n", + "model_counter = model_counter + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# %%time\n", + "# # Catboost on pc's\n", + "\n", + "# FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n", + "# for niter in range(nitermax): \n", + "# X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst_pca, Y_trntst, test_size = .3)\n", + "\n", + "# model = CatBoostClassifier()\n", + "\n", + "# X_oot = X_oot_orig_pca.copy()\n", + "# X_trn_save = X_trn.copy()\n", + "# Y_trn_save = Y_trn.copy()\n", + "\n", + "# model.fit(X_trn, Y_trn.values.ravel()) \n", + "\n", + "# predictions = model.predict_proba(X_trn_save)[:,1]\n", + "# X_trn['predicted'] = predictions\n", + "# X_trn['Fraud'] = Y_trn_save['Fraud']\n", + "# topRows = int(round(X_trn.shape[0]*0.03))\n", + "# temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + "# predictions = model.predict_proba(X_tst)[:,1]\n", + "# X_tst['predicted']=predictions\n", + "# X_tst['Fraud'] = Y_tst['Fraud']\n", + "# topRows = int(round(X_tst.shape[0]*0.03))\n", + "# temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + "# predictions = model.predict_proba(X_oot)[:,1]\n", + "# X_oot['predicted']=predictions\n", + "# X_oot['Fraud'] = Y_oot['Fraud']\n", + "# topRows = int(round(X_oot.shape[0]*0.03))\n", + "# temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + "# print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n", + "# Modeling_output.iloc[counter] = ['cat boost_PCs',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n", + "# counter = counter + 1\n", + " \n", + "# print(FDR3.mean())\n", + "# model_counter = model_counter + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# %%time\n", + "# # unsupervised model using pc's. \n", + "\n", + "# FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n", + "# for niter in range(nitermax): \n", + "# X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst_pca, Y_trntst, test_size = .3)\n", + "\n", + "# X_oot = X_oot_orig_pca.copy()\n", + "# X_trn_save = X_trn.copy()\n", + "# Y_trn_save = Y_trn.copy()\n", + "\n", + "# pow = 2\n", + "# oop = 1/pow\n", + "# predictions = ((X_trn.abs()**pow).sum(axis=1))**oop\n", + "# X_trn['predicted'] = predictions\n", + "# X_trn['Fraud'] = Y_trn_save['Fraud']\n", + "# topRows = int(round(X_trn.shape[0]*0.03))\n", + "# temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + "# predictions = ((X_tst.abs()**pow).sum(axis=1))**oop\n", + "# X_tst['predicted']=predictions\n", + "# X_tst['Fraud'] = Y_tst['Fraud']\n", + "# topRows = int(round(X_tst.shape[0]*0.03))\n", + "# temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + "# predictions = ((X_oot.abs()**pow).sum(axis=1))**oop\n", + "# X_oot['predicted']=predictions\n", + "# X_oot['Fraud'] = Y_oot['Fraud']\n", + "# topRows = int(round(X_oot.shape[0]*0.03))\n", + "# temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + "# print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n", + "# Modeling_output.iloc[counter] = ['unsupervised outliers',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n", + "# counter = counter + 1\n", + " \n", + "# print(FDR3.mean())\n", + "# model_counter = model_counter + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# %%time\n", + "# # XGB\n", + "\n", + "# FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n", + "# for niter in range(nitermax): \n", + "# X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n", + "\n", + "# model = xgb.XGBClassifier()\n", + "\n", + "# X_oot = X_oot_orig.copy()\n", + "# X_trn_save = X_trn.copy()\n", + "# Y_trn_save = Y_trn.copy()\n", + "\n", + "# model.fit(X_trn, Y_trn.values.ravel()) \n", + "\n", + "# predictions = model.predict_proba(X_trn_save)[:,1]\n", + "# X_trn['predicted'] = predictions\n", + "# X_trn['Fraud'] = Y_trn_save['Fraud']\n", + "# topRows = int(round(X_trn.shape[0]*0.03))\n", + "# temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + "# predictions = model.predict_proba(X_tst)[:,1]\n", + "# X_tst['predicted']=predictions\n", + "# X_tst['Fraud'] = Y_tst['Fraud']\n", + "# topRows = int(round(X_tst.shape[0]*0.03))\n", + "# temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + "# predictions = model.predict_proba(X_oot)[:,1]\n", + "# X_oot['predicted']=predictions\n", + "# X_oot['Fraud'] = Y_oot['Fraud']\n", + "# topRows = int(round(X_oot.shape[0]*0.03))\n", + "# temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + "# print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n", + "# Modeling_output.iloc[counter] = ['XGB',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n", + "# counter = counter + 1\n", + " \n", + "# print(FDR3.mean())\n", + "# model_counter = model_counter + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "# %%time\n", + "# # Knn\n", + "# # Knn can be very slow with a lot of records.\n", + "\n", + "# FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n", + "# for niter in range(nitermax): \n", + "# X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n", + "\n", + "# model = KNeighborsClassifier()\n", + " \n", + "# X_oot = X_oot_orig.copy()\n", + "# X_trn_save = X_trn.copy()\n", + "# Y_trn_save = Y_trn.copy()\n", + "\n", + "# model.fit(X_trn, Y_trn.values.ravel()) \n", + "\n", + "# predictions = model.predict_proba(X_trn_save)[:,1]\n", + "# X_trn['predicted'] = predictions\n", + "# X_trn['Fraud'] = Y_trn_save['Fraud']\n", + "# topRows = int(round(X_trn.shape[0]*0.03))\n", + "# temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + "# predictions = model.predict_proba(X_tst)[:,1]\n", + "# X_tst['predicted']=predictions\n", + "# X_tst['Fraud'] = Y_tst['Fraud']\n", + "# topRows = int(round(X_tst.shape[0]*0.03))\n", + "# temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + "# predictions = model.predict_proba(X_oot)[:,1]\n", + "# X_oot['predicted']=predictions\n", + "# X_oot['Fraud'] = Y_oot['Fraud']\n", + "# topRows = int(round(X_oot.shape[0]*0.03))\n", + "# temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + "# print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n", + "# Modeling_output.iloc[counter] = ['Knn',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n", + "# counter = counter + 1\n", + " \n", + "# print(FDR3.mean())\n", + "# model_counter = model_counter + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "# %%time\n", + "# # SVM\n", + "# # SVM can be very slow. It scales like the # training records cubed\n", + "\n", + "# FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n", + "# for niter in range(nitermax): \n", + "# X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n", + "\n", + "# model = svm.SVC()\n", + " \n", + "# X_oot = X_oot_orig.copy()\n", + "# X_trn_save = X_trn.copy()\n", + "# Y_trn_save = Y_trn.copy()\n", + "\n", + "# model.fit(X_trn, Y_trn.values.ravel()) \n", + "\n", + "# predictions = model.predict_proba(X_trn_save)[:,1]\n", + "# X_trn['predicted'] = predictions\n", + "# X_trn['Fraud'] = Y_trn_save['Fraud']\n", + "# topRows = int(round(X_trn.shape[0]*0.03))\n", + "# temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + "# predictions = model.predict_proba(X_tst)[:,1]\n", + "# X_tst['predicted']=predictions\n", + "# X_tst['Fraud'] = Y_tst['Fraud']\n", + "# topRows = int(round(X_tst.shape[0]*0.03))\n", + "# temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + "# predictions = model.predict_proba(X_oot)[:,1]\n", + "# X_oot['predicted']=predictions\n", + "# X_oot['Fraud'] = Y_oot['Fraud']\n", + "# topRows = int(round(X_oot.shape[0]*0.03))\n", + "# temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + "# needed = temp.loc[:,'Fraud']\n", + "# FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + "# print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n", + "# Modeling_output.iloc[counter] = ['SVM',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n", + "# counter = counter + 1\n", + " \n", + "# print(FDR3.mean())\n", + "# model_counter = model_counter + 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model comparison plots" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ModelTrnTstOOT
0log reg0.6329110.6411290.541899
1log reg0.6197410.606870.446927
2log reg0.6350.6178570.558659
3log reg0.6477270.6022730.49162
4log reg0.6220840.6582280.553073
5log reg0.6247960.6441950.502793
6log reg0.6472550.5734770.530726
7log reg0.6395350.6007190.547486
8log reg0.627010.6201550.50838
9log reg0.611650.6526720.50838
10DT1.00.5864660.296089
11DT1.00.6174240.318436
12DT1.00.5795050.217877
13DT1.00.5793650.335196
14DT1.00.5510950.273743
15DT1.00.5605540.273743
16DT1.00.6190480.340782
17DT1.00.5902260.312849
18DT1.00.5360.351955
19DT1.00.6245350.273743
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" + ], + "text/plain": [ + " Model Trn Tst OOT\n", + "0 log reg 0.632911 0.641129 0.541899\n", + "1 log reg 0.619741 0.60687 0.446927\n", + "2 log reg 0.635 0.617857 0.558659\n", + "3 log reg 0.647727 0.602273 0.49162\n", + "4 log reg 0.622084 0.658228 0.553073\n", + "5 log reg 0.624796 0.644195 0.502793\n", + "6 log reg 0.647255 0.573477 0.530726\n", + "7 log reg 0.639535 0.600719 0.547486\n", + "8 log reg 0.62701 0.620155 0.50838\n", + "9 log reg 0.61165 0.652672 0.50838\n", + "10 DT 1.0 0.586466 0.296089\n", + "11 DT 1.0 0.617424 0.318436\n", + "12 DT 1.0 0.579505 0.217877\n", + "13 DT 1.0 0.579365 0.335196\n", + "14 DT 1.0 0.551095 0.273743\n", + "15 DT 1.0 0.560554 0.273743\n", + "16 DT 1.0 0.619048 0.340782\n", + "17 DT 1.0 0.590226 0.312849\n", + "18 DT 1.0 0.536 0.351955\n", + "19 DT 1.0 0.624535 0.273743" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = Modeling_output.dropna()\n", + "df.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(80, 4)" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ModelTypeValue
0log regTrn0.632911
1log regTrn0.619741
2log regTrn0.635
3log regTrn0.647727
4log regTrn0.622084
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" + ], + "text/plain": [ + " Model Type Value\n", + "0 log reg Trn 0.632911\n", + "1 log reg Trn 0.619741\n", + "2 log reg Trn 0.635\n", + "3 log reg Trn 0.647727\n", + "4 log reg Trn 0.622084" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_unpivot = df.melt( id_vars='Model', value_vars=['Trn','Tst','OOT'], var_name=['Type'], value_name='Value')\n", + "df_unpivot.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ModelTypeValue
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2log regTrn0.635
3log regTrn0.647727
4log regTrn0.622084
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" + ], + "text/plain": [ + " Model Type Value\n", + "0 log reg Trn 0.632911\n", + "1 log reg Trn 0.619741\n", + "2 log reg Trn 0.635\n", + "3 log reg Trn 0.647727\n", + "4 log reg Trn 0.622084" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_compare = df_unpivot[(df_unpivot['Type']=='Trn') | (df_unpivot['Type']=='Tst') | (df_unpivot['Type']=='OOT')]\n", + "df_compare.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TrnTstOOT
meanstdmeanstdmeanstd
Model
DT1.0000000.0000000.5844220.0298330.2994410.040731
LGBM0.9841420.0035620.7909820.0120780.3435750.019397
LGBM sampled0.8197730.0126340.8197730.0126340.4340780.058773
LGBM with SMOTE0.9180200.0096740.8053400.0177530.3217880.012685
NN0.7309740.0171700.7088680.0173030.4391060.060526
RF1.0000000.0000000.8306300.0201770.4128490.066282
cat boost0.9305440.0065320.8108820.0204960.3821230.039957
log reg0.6307710.0118840.6217570.0269920.5189940.034383
\n", + "
" + ], + "text/plain": [ + " Trn Tst OOT \n", + " mean std mean std mean std\n", + "Model \n", + "DT 1.000000 0.000000 0.584422 0.029833 0.299441 0.040731\n", + "LGBM 0.984142 0.003562 0.790982 0.012078 0.343575 0.019397\n", + "LGBM sampled 0.819773 0.012634 0.819773 0.012634 0.434078 0.058773\n", + "LGBM with SMOTE 0.918020 0.009674 0.805340 0.017753 0.321788 0.012685\n", + "NN 0.730974 0.017170 0.708868 0.017303 0.439106 0.060526\n", + "RF 1.000000 0.000000 0.830630 0.020177 0.412849 0.066282\n", + "cat boost 0.930544 0.006532 0.810882 0.020496 0.382123 0.039957\n", + "log reg 0.630771 0.011884 0.621757 0.026992 0.518994 0.034383" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output = df.groupby('Model').agg({'Trn':['mean','std'],'Tst':['mean','std'],'OOT':['mean','std']})\n", + "output" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize=(15,10))\n", + "ax = sns.boxplot(x='Model',y='Value',hue='Type', data=df_compare, palette=['navy','r','g'])\n", + "\n", + "plt.ylim(.3,.9)\n", + "plt.ylabel('Score (FDR3%)')\n", + "plt.grid(axis='y')\n", + "plt.savefig('modeling.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "duration: 0:06:08.764642\n" + ] + } + ], + "source": [ + "print('duration: ', datetime.now() - start_time)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 0.6261829652996845 0.6097560975609756 0.4301675977653631\n", + "1 0.6266233766233766 0.6325757575757576 0.441340782122905\n", + "2 0.022764227642276424 0.033962264150943396 0.055865921787709494\n", + "3 0.6218905472636815 0.6064981949458483 0.5027932960893855\n", + "4 0.04019292604501608 0.015503875968992248 0.055865921787709494\n", + "5 0.0416 0.03137254901960784 0.061452513966480445\n", + "6 0.038523274478330656 0.019455252918287938 0.061452513966480445\n", + "7 0.6330128205128205 0.6015625 0.43575418994413406\n", + "8 0.6377049180327868 0.5925925925925926 0.4581005586592179\n", + "9 0.6261980830670927 0.5984251968503937 0.4245810055865922\n", + "loop trn tst oot 1 0.39146931389650663 0.3741704281583399 0.2927374301675978\n", + "0 0.7330097087378641 0.7519083969465649 0.4245810055865922\n", + "1 0.7634069400630915 0.6666666666666666 0.3854748603351955\n", + "2 0.7728026533996684 0.7184115523465704 0.3743016759776536\n", + "3 0.7712 0.7450980392156863 0.5307262569832403\n", + "4 0.750814332247557 0.7255639097744361 0.547486033519553\n", + "5 0.7666666666666667 0.7214285714285714 0.35195530726256985\n", + "6 0.7193548387096774 0.6923076923076923 0.5251396648044693\n", + "7 0.7088815789473685 0.7058823529411765 0.5363128491620112\n", + "8 0.7483974358974359 0.71875 0.5251396648044693\n", + "9 0.7775919732441472 0.7163120567375887 0.3854748603351955\n", + "loop trn tst oot 21 0.7512126127913478 0.7162329238364953 0.45865921787709485\n", + "0 0.7852459016393443 0.774074074074074 0.37988826815642457\n", + "1 0.7710437710437711 0.7342657342657343 0.3743016759776536\n", + "2 0.774247491638796 0.7163120567375887 0.3575418994413408\n", + "3 0.8019323671497585 0.7258687258687259 0.37988826815642457\n", + "4 0.785234899328859 0.7394366197183099 0.3687150837988827\n", + "5 0.8183361629881154 0.718213058419244 0.3463687150837989\n", + "6 0.7882736156351792 0.7180451127819549 0.3463687150837989\n", + "7 0.7916666666666666 0.7535714285714286 0.3575418994413408\n", + "8 0.815359477124183 0.7126865671641791 0.3407821229050279\n", + "9 0.7632450331125827 0.7681159420289855 0.4245810055865922\n", + "loop trn tst oot 41 0.7894585386327255 0.7360589319630224 0.3675977653631285\n", + "0 0.8279742765273312 0.7674418604651163 0.31843575418994413\n", + "1 0.8357963875205254 0.7859778597785978 0.36312849162011174\n", + "2 0.7903494176372712 0.7777777777777778 0.35195530726256985\n", + "3 0.8327974276527331 0.7364341085271318 0.33519553072625696\n", + "4 0.8564437194127243 0.7528089887640449 0.3463687150837989\n", + "5 0.8243021346469622 0.7416974169741697 0.3463687150837989\n", + "6 0.8294314381270903 0.74822695035461 0.329608938547486\n", + "7 0.8258706467661692 0.7653429602888087 0.35195530726256985\n", + "8 0.8363047001620746 0.7566539923954373 0.3240223463687151\n", + "9 0.819935691318328 0.7868217054263565 0.33519553072625696\n", + "loop trn tst oot 61 0.827920583977121 0.761918362075205 0.3402234636871508\n", + "0 0.7987421383647799 0.7827868852459017 0.36312849162011174\n", + "1 0.8246205733558178 0.7700348432055749 0.33519553072625696\n", + "2 0.8801996672212978 0.7383512544802867 0.29608938547486036\n", + "3 0.8164251207729468 0.749034749034749 0.3743016759776536\n", + "4 0.8680781758957655 0.7443609022556391 0.22346368715083798\n", + "5 0.8070175438596491 0.7351778656126482 0.3743016759776536\n", + "6 0.8811410459587956 0.7269076305220884 0.33519553072625696\n", + "7 0.815831987075929 0.7931034482758621 0.3407821229050279\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/stevecoggeshall/opt/anaconda3/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8 0.8879598662207357 0.7553191489361702 0.30726256983240224\n", + "9 0.8022328548644339 0.766798418972332 0.3687150837988827\n", + "loop trn tst oot 81 0.8382248973590152 0.7561875146541253 0.33184357541899445\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1h 18min 28s, sys: 1min 1s, total: 1h 19min 30s\n", + "Wall time: 20min 33s\n" + ] + } + ], + "source": [ + "%%time\n", + "training = []\n", + "testing = []\n", + "oot = []\n", + "results = pd.DataFrame(np.zeros((niter,3)),columns=['trn','tst','oot'])\n", + "for i in range(1,101,20):\n", + " for niter in range(nitermax):\n", + " X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n", + "\n", + " model = MLPClassifier(hidden_layer_sizes=(i,i))\n", + "\n", + " model.fit(X_trn, Y_trn.values.ravel()) \n", + "\n", + " X_oot = X_oot_orig.copy()\n", + " X_trn_save = X_trn.copy()\n", + " Y_trn_save = Y_trn.copy()\n", + " \n", + " predictions = model.predict_proba(X_trn_save)[:,1]\n", + " X_trn['predicted'] = predictions\n", + " X_trn['Fraud'] = Y_trn_save['Fraud']\n", + " topRows = int(round(X_trn.shape[0]*0.03))\n", + " temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " results.loc[niter,'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_tst)[:,1]\n", + " X_tst['predicted']=predictions\n", + " X_tst['Fraud'] = Y_tst['Fraud']\n", + " topRows = int(round(X_tst.shape[0]*0.03))\n", + " temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " results.loc[niter,'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_oot)[:,1]\n", + " X_oot['predicted']=predictions\n", + " X_oot['Fraud'] = Y_oot['Fraud']\n", + " topRows = int(round(X_oot.shape[0]*0.03))\n", + " temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " results.loc[niter,'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + " print(niter, results.loc[niter,'trn'],results.loc[niter,'tst'],results.loc[niter,'oot'],)\n", + "\n", + " results_mean_trn = results['trn'].mean()\n", + " results_mean_tst = results['tst'].mean()\n", + " results_mean_oot = results['oot'].mean()\n", + " print('loop', 'trn', 'tst', 'oot', i, results_mean_trn, results_mean_tst, results_mean_oot)\n", + " training.append(results_mean_trn)\n", + " testing.append(results_mean_tst)\n", + " oot.append(results_mean_oot)\n", + "\n", + "table=pd.DataFrame({'n': range(1,len(training)+1),'training':training,'testing':testing,'oot':oot})\n", + "table.set_index('n',inplace=True) \n", + "table.plot()\n", + "plt.savefig('complexity_NN.pdf', format='pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The rest of the notebook makes the tables for your final model of choice. You need to run that final model only once (no CV). If you want you can run the below cell over and over by itself until it gives you a model you like (due to the stochastic nature of some ML algorithms, but you can't change from your best hyperparameters." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 0.7129629629629629 0.7025862068965517 0.39664804469273746\n", + "1 0.7255216693418941 0.7198443579766537 0.5586592178770949\n", + "CPU times: user 1min 27s, sys: 1.57 s, total: 1min 29s\n", + "Wall time: 23.1 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "for niter in range(100): \n", + " X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n", + "\n", + "# here's where you put your final model of choice\n", + "# I run this loop a large number of times with an unreasonably high stopping criterion (the break condition)\n", + "# I then look at all these runs and select a value of the oot performance where I want to break out this loop\n", + "# and that will be my final model run of choice\n", + "\n", + " model = MLPClassifier()\n", + "\n", + " X_oot = X_oot_orig.copy()\n", + " X_trn_save = X_trn.copy()\n", + " Y_trn_save = Y_trn.copy()\n", + "\n", + " model.fit(X_trn, Y_trn.values.ravel()) \n", + "\n", + " predictions = model.predict_proba(X_trn_save)[:,1]\n", + " X_trn['predicted'] = predictions\n", + " X_trn['Fraud'] = Y_trn_save['Fraud']\n", + " topRows = int(round(X_trn.shape[0]*0.03))\n", + " temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_tst)[:,1]\n", + " X_tst['predicted']=predictions\n", + " X_tst['Fraud'] = Y_tst['Fraud']\n", + " topRows = int(round(X_tst.shape[0]*0.03))\n", + " temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n", + "\n", + " predictions = model.predict_proba(X_oot)[:,1]\n", + " X_oot['predicted']=predictions\n", + " X_oot['Fraud'] = Y_oot['Fraud']\n", + " topRows = int(round(X_oot.shape[0]*0.03))\n", + " temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n", + " needed = temp.loc[:,'Fraud']\n", + " FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n", + " print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n", + " Modeling_output.iloc[counter] = ['LGBM',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n", + " counter = counter + 1\n", + "# choose a good break point\n", + " if(FDR3.loc[niter, 'oot'] > .55): break\n", + " \n", + "model_counter = model_counter + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "X_trn_eval = X_trn.copy()\n", + "X_tst_eval = X_tst.copy()\n", + "X_oot_eval = X_oot.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " card_merch_total_14 card_zip3_max_14 card_merch_avg_14 \\\n", + "89206 -0.119305 23.262218 -0.163517 \n", + "90369 1.283442 23.262218 7.233701 \n", + "89186 9.768838 7.148446 1.131783 \n", + "89183 9.722223 7.148446 1.176766 \n", + "89174 9.315610 7.148446 1.161467 \n", + "89130 9.112811 7.148446 1.242556 \n", + "89121 6.919649 2.844300 0.990067 \n", + "89128 7.032132 2.844300 0.955244 \n", + "89120 6.526073 2.844300 0.966029 \n", + "89117 6.440982 2.844300 1.010629 \n", + "87173 0.559000 10.016530 3.413442 \n", + "89114 6.341771 2.844300 1.055899 \n", + "89083 5.388373 2.844300 1.038297 \n", + "89077 5.249519 2.844300 1.177200 \n", + "89082 5.296705 2.844300 1.097374 \n", + "89074 5.006737 2.844300 1.316607 \n", + "89068 4.294732 2.844300 1.178781 \n", + "89060 3.537990 2.844300 1.001603 \n", + "89057 3.313470 2.844300 1.028780 \n", + "86673 1.019346 4.131784 5.841020 \n", + "\n", + " Card_Merchdesc_Zip_med_14 Card_Merchdesc_Zip_max_60 \\\n", + "89206 -0.155595 -0.252285 \n", + "90369 7.308487 5.042087 \n", + "89186 0.742253 7.013345 \n", + "89183 0.793514 7.013345 \n", + "89174 0.742253 7.013345 \n", + "89130 0.793514 7.013345 \n", + "89121 0.793514 2.778581 \n", + "89128 0.742253 2.778581 \n", + "89120 0.742253 2.778581 \n", + "89117 0.793514 2.778581 \n", + "87173 3.453696 2.307834 \n", + "89114 0.893042 2.778581 \n", + "89083 0.793514 2.778581 \n", + "89077 0.992571 2.778581 \n", + "89082 0.893042 2.778581 \n", + "89074 1.020113 2.778581 \n", + "89068 0.992571 2.778581 \n", + "89060 0.893042 2.778581 \n", + "89057 0.992571 2.778581 \n", + "86673 5.903218 4.045312 \n", + "\n", + " Card_Merchnum_Zip_avg_60 Card_Merchnum_desc_avg_60 \\\n", + "89206 -0.169368 -0.169273 \n", + "90369 7.342835 7.342089 \n", + "89186 1.146067 1.382985 \n", + "89183 1.191749 1.454852 \n", + "89174 1.176212 1.445630 \n", + "89130 1.258561 1.505739 \n", + "89121 1.002148 1.126262 \n", + "89128 0.966783 1.074085 \n", + "89120 0.977736 1.100613 \n", + "89117 1.023029 1.169116 \n", + "87173 3.463193 3.462881 \n", + "89114 1.069004 1.241456 \n", + "89083 1.051127 1.179062 \n", + "89077 1.192190 1.383197 \n", + "89082 1.111123 1.265332 \n", + "89074 1.333764 1.485234 \n", + "89068 1.193795 1.320888 \n", + "89060 1.013864 1.099718 \n", + "89057 1.041462 1.146056 \n", + "86673 5.928506 5.927919 \n", + "\n", + " Merchdesc_dow_avg_30 Card_Merchnum_desc_med_0 Card_Merchdesc_max_60 \\\n", + "89206 -0.142688 -0.152813 -0.252958 \n", + "90369 8.220921 7.242385 5.040323 \n", + "89186 1.557548 0.095302 7.011174 \n", + "89183 1.742342 0.144831 7.011174 \n", + "89174 1.728709 0.095302 7.011174 \n", + "89130 1.917739 0.060318 7.011174 \n", + "89121 0.648317 0.005451 2.777283 \n", + "89128 0.570554 0.025333 2.777283 \n", + "89120 0.420585 -0.014432 2.777283 \n", + "89117 0.540736 0.005451 2.777283 \n", + "87173 3.883436 3.423169 2.306633 \n", + "89114 0.712811 -0.014432 2.777283 \n", + "89083 -0.153813 -0.131685 2.777283 \n", + "89077 1.558065 0.984757 2.777283 \n", + "89082 -0.286978 -0.248938 2.777283 \n", + "89074 1.672156 1.012045 2.777283 \n", + "89068 1.488396 0.984757 2.777283 \n", + "89060 1.241097 0.886147 2.777283 \n", + "89057 1.292910 0.984757 2.777283 \n", + "86673 1.219521 5.850085 4.043752 \n", + "\n", + " predicted Fraud \n", + "89206 0.999926 1 \n", + "90369 0.999913 1 \n", + "89186 0.999303 1 \n", + "89183 0.999245 1 \n", + "89174 0.998893 1 \n", + "89130 0.998456 1 \n", + "89121 0.983969 1 \n", + "89128 0.983460 1 \n", + "89120 0.982486 1 \n", + "89117 0.982083 1 \n", + "87173 0.979089 0 \n", + "89114 0.978847 1 \n", + "89083 0.954761 1 \n", + "89077 0.941451 1 \n", + "89082 0.925749 1 \n", + "89074 0.924850 1 \n", + "89068 0.873528 1 \n", + "89060 0.795388 1 \n", + "89057 0.721146 1 \n", + "86673 0.688786 0 " + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cols = ['bin','#recs','#g','#b','%g','%b','tot','cg','cb','%cg','FDR','KS','FPR']\n", + "FDR_trn = pd.DataFrame(np.zeros((101, 13)), columns = cols)\n", + "FDR_tst = pd.DataFrame(np.zeros((101, 13)), columns = cols)\n", + "FDR_oot = pd.DataFrame(np.zeros((101, 13)), columns = cols)\n", + "trn_sorted = X_trn_eval.sort_values('predicted',ascending=False)\n", + "tst_sorted = X_tst_eval.sort_values('predicted',ascending=False)\n", + "oot_sorted = X_oot_eval.sort_values('predicted',ascending=False)\n", + "bad_tot_trn = sum(X_trn_eval.loc[:, 'Fraud'])\n", + "bad_tot_tst = sum(X_tst_eval.loc[:, 'Fraud'])\n", + "bad_tot_oot = sum(X_oot_eval.loc[:, 'Fraud'])\n", + "num_tot_trn = len(X_trn_eval)\n", + "num_tot_tst = len(X_tst_eval)\n", + "num_tot_oot = len(X_oot_eval)\n", + "good_tot_trn = num_tot_trn - bad_tot_trn\n", + "good_tot_tst = num_tot_tst - bad_tot_tst\n", + "good_tot_oot = num_tot_oot - bad_tot_oot\n", + "oot_sorted.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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..........................................
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101 rows × 13 columns

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" + ], + "text/plain": [ + " bin #recs #g #b %g %b tot cg \\\n", + "0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 0.0 \n", + "1 1.0 124.0 64.0 60.0 51.612903 48.387097 124.0 64.0 \n", + "2 2.0 125.0 107.0 18.0 85.600000 14.400000 249.0 171.0 \n", + "3 3.0 124.0 102.0 22.0 82.258065 17.741935 373.0 273.0 \n", + "4 4.0 124.0 123.0 1.0 99.193548 0.806452 497.0 396.0 \n", + ".. ... ... ... ... ... ... ... ... \n", + "96 96.0 124.0 121.0 3.0 97.580645 2.419355 11930.0 11752.0 \n", + "97 97.0 124.0 123.0 1.0 99.193548 0.806452 12054.0 11875.0 \n", + "98 98.0 124.0 124.0 0.0 100.000000 0.000000 12178.0 11999.0 \n", + "99 99.0 125.0 125.0 0.0 100.000000 0.000000 12303.0 12124.0 \n", + "100 100.0 124.0 124.0 0.0 100.000000 0.000000 12427.0 12248.0 \n", + "\n", + " cb %cg FDR KS FPR \n", + "0 0.0 0.000000 0.000000 0.000000 0.000000 \n", + "1 60.0 0.522534 33.519553 32.997019 1.066667 \n", + "2 78.0 1.396146 43.575419 42.179273 2.192308 \n", + "3 100.0 2.228935 55.865922 53.636986 2.730000 \n", + "4 101.0 3.233181 56.424581 53.191400 3.920792 \n", + ".. ... ... ... ... ... \n", + "96 178.0 95.950359 99.441341 3.490982 66.022472 \n", + "97 179.0 96.954605 100.000000 3.045395 66.340782 \n", + "98 179.0 97.967015 100.000000 2.032985 67.033520 \n", + "99 179.0 98.987590 100.000000 1.012410 67.731844 \n", + "100 179.0 100.000000 100.000000 0.000000 68.424581 \n", + "\n", + "[101 rows x 13 columns]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cost_fraud = 1000\n", + "cost_fp = 30\n", + "for i in range(101):\n", + " percent_rows_trn = int(round(X_trn_eval.shape[0]*0.01*i))\n", + " percent_rows_tst = int(round(X_tst_eval.shape[0]*0.01*i))\n", + " percent_rows_oot = int(round(X_oot_eval.shape[0]*0.01*i))\n", + " temp_trn = trn_sorted.head(percent_rows_trn)\n", + " temp_tst = tst_sorted.head(percent_rows_tst)\n", + " temp_oot = oot_sorted.head(percent_rows_oot)\n", + " num_bad_trn = sum(temp_trn.loc[:,'Fraud'])\n", + " num_bad_tst = sum(temp_tst.loc[:,'Fraud'])\n", + " num_bad_oot = sum(temp_oot.loc[:,'Fraud'])\n", + " num_tot_trn = len(temp_trn)\n", + " num_tot_tst = len(temp_tst)\n", + " num_tot_oot = len(temp_oot)\n", + " num_good_trn = num_tot_trn - num_bad_trn\n", + " num_good_tst = num_tot_tst - num_bad_tst\n", + " num_good_oot = num_tot_oot - num_bad_oot\n", + " \n", + " FDR_trn.loc[i, 'bin'] = i\n", + " FDR_trn.loc[i,'#recs'] = 0\n", + " FDR_trn.loc[i, 'tot'] = num_tot_trn\n", + " FDR_trn.loc[i, 'cg'] = num_good_trn\n", + " FDR_trn.loc[i, 'cb'] = num_bad_trn\n", + " FDR_trn.loc[i, 'Fraud Savings'] = FDR_trn.loc[i, 'cb'] * cost_fraud\n", + " FDR_trn.loc[i, 'FP Loss'] = FDR_trn.loc[i, 'cg'] * cost_fp\n", + " FDR_trn.loc[i, 'Overall Savings'] = FDR_trn.loc[i, 'Fraud Savings'] - FDR_trn.loc[i, 'FP Loss']\n", + " FDR_tst.loc[i, 'bin'] = i\n", + " FDR_tst.loc[i, 'tot'] = num_tot_tst\n", + " FDR_tst.loc[i, 'cg'] = num_good_tst\n", + " FDR_tst.loc[i, 'cb'] = num_bad_tst\n", + " FDR_tst.loc[i, 'Fraud Savings'] = FDR_tst.loc[i, 'cb'] * cost_fraud\n", + " FDR_tst.loc[i, 'FP Loss'] = FDR_tst.loc[i, 'cg'] * cost_fp\n", + " FDR_tst.loc[i, 'Overall Savings'] = FDR_tst.loc[i, 'Fraud Savings'] - FDR_tst.loc[i, 'FP Loss']\n", + " FDR_oot.loc[i, 'bin'] = i\n", + " FDR_oot.loc[i, 'tot'] = num_tot_oot\n", + " FDR_oot.loc[i, 'cg'] = num_good_oot\n", + " FDR_oot.loc[i, 'cb'] = num_bad_oot\n", + "\n", + " if i != 0:\n", + " FDR_trn.loc[i, '#g'] = num_good_trn - FDR_trn.loc[i-1, 'cg']\n", + " FDR_trn.loc[i, '#b'] = num_bad_trn - FDR_trn.loc[i-1, 'cb']\n", + " FDR_trn.loc[i,'#recs'] = FDR_trn.loc[i, '#g'] + FDR_trn.loc[i, '#b']\n", + " FDR_trn.loc[i, '%g'] = 100* (num_good_trn - FDR_trn.loc[i-1, 'cg']) / (num_tot_trn - FDR_trn.loc[i-1, 'tot'])\n", + " FDR_trn.loc[i, '%b'] = 100 - FDR_trn.loc[i, '%g']\n", + " FDR_trn.loc[i, '%cg'] = 100 * num_good_trn / good_tot_trn\n", + " FDR_trn.loc[i, 'FDR'] = 100 * num_bad_trn / bad_tot_trn\n", + " FDR_trn.loc[i, 'KS'] = FDR_trn.loc[i, 'FDR'] - FDR_trn.loc[i, '%cg']\n", + " FDR_trn.loc[i, 'FPR'] = num_good_trn / num_bad_trn\n", + " FDR_tst.loc[i, '#g'] = num_good_tst - FDR_tst.loc[i-1, 'cg']\n", + " FDR_tst.loc[i, '#b'] = num_bad_tst - FDR_tst.loc[i-1, 'cb']\n", + " FDR_tst.loc[i,'#recs'] = FDR_tst.loc[i, '#g'] + FDR_tst.loc[i, '#b']\n", + " FDR_tst.loc[i, '%g'] = 100* (num_good_tst - FDR_tst.loc[i-1, 'cg']) / (num_tot_tst - FDR_tst.loc[i-1, 'tot'])\n", + " FDR_tst.loc[i, '%b'] = 100 - FDR_tst.loc[i, '%g']\n", + " FDR_tst.loc[i, '%cg'] = 100 * num_good_tst / good_tot_tst\n", + " FDR_tst.loc[i, 'FDR'] = 100 * num_bad_tst / bad_tot_tst\n", + " FDR_tst.loc[i, 'KS'] = FDR_tst.loc[i, 'FDR'] - FDR_tst.loc[i, '%cg']\n", + " FDR_tst.loc[i, 'FPR'] = num_good_tst / num_bad_tst\n", + " FDR_oot.loc[i, '#g'] = num_good_oot - FDR_oot.loc[i-1, 'cg']\n", + " FDR_oot.loc[i, '#b'] = num_bad_oot - FDR_oot.loc[i-1, 'cb']\n", + " FDR_oot.loc[i,'#recs'] = FDR_oot.loc[i, '#g'] + FDR_oot.loc[i, '#b']\n", + " FDR_oot.loc[i, '%g'] = 100* (num_good_oot - FDR_oot.loc[i-1, 'cg']) / (num_tot_oot - FDR_oot.loc[i-1, 'tot'])\n", + " FDR_oot.loc[i, '%b'] = 100 - FDR_oot.loc[i, '%g']\n", + " FDR_oot.loc[i, '%cg'] = 100 * num_good_oot / good_tot_oot\n", + " FDR_oot.loc[i, 'FDR'] = 100 * num_bad_oot / bad_tot_oot\n", + " FDR_oot.loc[i, 'KS'] = FDR_oot.loc[i, 'FDR'] - FDR_oot.loc[i, '%cg']\n", + " FDR_oot.loc[i, 'FPR'] = num_good_oot / num_bad_oot\n", + "\n", + "FDR_oot" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Max possible savings: 20,724,000.0\n" + ] + }, + { + "data": { + "image/png": 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sBeCsOmfxaJdHiYyI5MzaZ2oZdEn2++9uhjkpyd3HfM45XicSkQCl0iwiIiJ+YU/CHqZvmE5UdBSzNs7i4NGDhIaE0rNpTx7o+AD9WvSjXqV6XscUf/DDD+4e5rJlYfFiOPVUrxOJSABTaRYRERFPWGtZt2dd5rLr5bHLSbNp1KlQh2GnDSMyPJIeTXtQrlQ5r6OKP5k7FwYNgtq1Yc4caNLE60QiEuBUmkVERMRnklOTWbxtceay6837NwPQpnYbHu70MJERkbSt05YgE+RxUvFLkybBsGEQEQGzZkGdOl4nEpESQKVZREREitS+I/syl13P3DiT+KR4ygSXoXuT7txz/j30D+9Pg8oNvI4p/m7MGLj2Wnfv8rRpUK2a14lEpIRQaRYREZFCt37Peqasn0JUdBTLYpaRZtOoVb4Wg1sOJjIikl5Ne1G+dHmvY0px8eqrcOed0KuXm20ur987IuI7Ks0iIiJSYNZa/j74N9F7o1m/dz1/7PqDWZtmsWHfBgBOr3U6D3R8gMjwSM6ud7aWXUv+WAuPPQZPPAGXXAKffQZlynidSkRKGJVmEREROaH4pHii90a7crxnPdH70h/3RnM4+XDmdWVDytK5UWduP/d2+of3p1GVRh6mlmItLQ3uuAPeeAOuuQbeew9C9K2riPie/uQRERERwG3StXn/5sxZ46yPOw7tyLzOYGhcpTER1SPo1LATEdUjCA8LJyIsgnqV6mk2WU5ecrK7f/nTT+Guu2D0aNCZ3CLiEZVmERGREsRay45DO/5XirPMGm/ev5lUm5p5bfVy1QkPC6dv876ZpTg8LJxm1ZoRGhLq4aeQgJaYCEOHwpQp8NRT8OCDKswi4imVZhERkQB0MOkgG/ZtyFxCnVGSo/dGc/DowczrQkNCaVGtBafXOp0hrYa4cpw+c1ytrHYnFh87eBAGDoQFC+DNN+GWW7xOJCKi0iwiIlJcpaSlsPXA1mOKcUY5/vvg35nXGQyNqjQiPCycqxpcdcyscYPKDbScWvzDnj3Qty/88guMHw/Dh3udSEQEUGkWERHxa9Zadh3eleN9xpv2bSI5LTnz2mplqxERFkGvpr0yS3F4WDjNqzWnbKmyHn4KkRPYvh1694ZNm9yRUpGRXicSEcmk0iwiIuIHEpIT2LB3wzHFOGMGOS4pLvO60sGlaVGtBa1qtGJQxKBjNuEKKxfm4ScQKaCNG935y3v3wsyZ0LWr14lERI6h0iwiIuJDOw7t4Ncdv/7rXuOY+JhjrmtQqQER1SMY3no4EdUjMmeOG1ZuSHBQsEfpRQrZmjVuhjklBebPh3btvE4kIvIvKs0iIiJFyFrLLzt+IWp9FFHRUfz0z0+ZX6sSWoWIsAi6Nu6aWYojqkfQvFpzypUq52FqER9YvhwuvBDKl3cbf7Vs6XUiEZEcqTSLiIgUsiPJR5i/ZT5R0VFMjZ7K9oPbMRjOq38ez3R/hk6NOhERFkH1ctUxOkpHSqIZM+CSS6BePZgzBxo39jqRiEiuVJpFREQKwY5DO5gaPZWo6Cjmbp5LQnICFUpXoHez3kSGR3JhiwupWb6m1zFFvPfZZ3DVVdC6tSvPtWp5nUhE5LhUmkVERArAWsvqnaszl13/+PePADSs3JCr21xNZHgkXRt3pUxIGY+TiviR11+H22+HLl1g8mSoXNnrRCIiJ6TSLCIikkeJKYks2LIgc9l1THwMBsM59c7hqW5PERkRSeuarbXkWiQ7a+G//4WnnoJBg+CLLyA01OtUIiJ5otIsIiJyHDsP7WTahmlERUcxZ9McDicfplypcvRu1pvHuj5Gvxb9qFVBy0tFcpWaCrfcAu+9B9deC+++CyH6FlREig/9iSUiIpKFtZbfd/1OVLRbdv1D7A9YLPUr1efKM64kMjySbk26ERqiWTKRE0pKgiuugAkT4L774NlnQSsxRKSYUWkWEZESLykliUXbFmXen7wtbhsAZ9c9m8e7Pk5kRCRn1DpDy65F8uPgQbjoIpg3D0aPhrvv9jqRiEiBqDSLiEiJ9cs/v/DKileY9OckDh09RNmQsvRq1ouHOz9Mvxb9qFOxjtcRRYqn3bvdGcy//AJjxsDIkV4nEhEpMJVmEREpUay1zNw4k9HLRzN/y3wqlK7AZaddxoCIAfRo0oOypcp6HVGkePvrL+jdG7Ztg0mTIDLS60QiIidFpVlEREqEpJQkPvvtM15a/hJrd6+lbsW6PN/zeW446waqhFbxOp5IYFi71hXmQ4dg9mzo1MnrRCIiJ02lWUREAtrehL28u+pd3lj5BjsP7+T0WqczbtA4hp42lNLBpb2OJxI4VqyAfv2gdGlYtAjOOMPrRCIihUKlWUREAtKmfZt4ZcUrfPLrJyQkJ3BBswu4p/099GjSQxt6iRS2WbPg4ouhdm2YMweaNvU6kYhIoVFpFhGRgLI8Zjmjl49m0rpJhASFMPz04dx13l20rtXa62gigenLL+HKK6FVK5g50xVnEZEAotIsIiLFXmpaKpPXT2b096NZHrucqqFVub/j/dx6zq3UrVjX63gigeutt+C226BjR5gyBapU8TqRiEihU2kWEZFi6/DRw4z5dQyvrHiFTfs30aRKE17v8zpXn3k1FUpX8DqeSOCyFh5/3P01YICbbS6rnedFJDCpNIuISLGz49AO3lz5Ju+seod9R/Zxbr1zea7nc1x0ykUEBwV7HU8ksKWmwqhR8PbbcNVV8MEHEKJvKUUkcOlPOBERKTb+2PUHLy9/mfG/jSc5NZmBpwzknvPvoX2D9trcS8QXjh519y9/9RXccw+88ALovz0RCXAqzSIi4testSzYuoDR349mxsYZlA0py7VnXsud591Ji7AWXscTKTkOHXI7ZM+Z48ry//2f14lERHxCpVlERPxScmoyX//xNaOXj+bXHb9Ss3xNnuj6BDeffTPVy1X3Op5IybJnjzuDedUq+PhjuPpqrxOJiPiMSrOIiPiNNJtGbHwsX//xNa/98Bqx8bGcUv0UPoj8gCtOv4LQkFCvI4qUPDEx0Ls3bNkC334LAwd6nUhExKdUmkVExOf2H9lP9N5oovdGs37v+szHDXs3cCTlCABdG3fl3X7v0rdFX4JMkMeJRUqoP/90hTkuDmbNgi5dvE4kIuJzKs0iIlIkklKS2LR/kyvEe9YfU5B3J+zOvC7YBNO0alPCw8Lp0aQHEWERnFf/PM6ofYaH6UWElSvhwgshOBgWLoQzz/Q6kYiIJ1SaRUSkwKy1bD+4/V+leP3e9Ww9sJU0m5Z5ba3ytYioHsHAiIFEVI8gPCyciLAImlRtQung0h5+ChH5lzlz4KKLoGZNmD0bmjf3OpGIiGdUmkVE5ITiEuP+VYozllcnJCdkXleuVDnCw8I5u+7ZDG89nIgwV47Dw8KpHFrZw08gInn29ddwxRVwyiluSXadOl4nEhHxlEqziIjkatO+Tdwx6w6mRk/NfC7IBNGkShPCw8Lp2qirmzFOnzmuV7GezksWKc7eeQduuQXat4eoKKha1etEIiKeU2kWEZF/OZJ8hOeWPsfzy56ndHBpHu70MGfVPYuIsAiaVm1KmZAyXkcUkcJkLTz1FPz3v+5oqa+/hnLlvE4lIuIXVJpFROQYU6OnMmrGKLYc2MLlrS/nxV4vUrdiXa9jiUhRSUuD22+HN9+EESPgo4+gVCmvU4mI+A2VZhERAWDz/s3cMfMOoqKjaFWjFQtGLqBr465exxKRonT0KFx1FXzxBdx1F7z4IgTpiDcRkaxUmkVESrjElEReWPYCzy59lpCgEEb3Gs2oc0dRKlgzTSIB7fBhuOQSt9nXs8/CffeB9iQQEfkXlWYRkRJsWvQ0Rs0cxeb9mxl22jBG9xpNvUr1vI4lIkVt3z537/LKlfDBB3DddV4nEhHxWyrNIiIl0NYDW7lj5h1MXj+ZltVbMu/KeXRv0t3rWCLiC7GxcMEFsHEjfPMNXHyx14lERPyaSrOISAmSmJLI6O9H8/SSpwk2wbzQ8wVuP+92SgeX9jqaiPjC+vXQuzfs3w8zZ0K3bl4nEhHxeyrNIiIlxMyNM7ltxm1s3LeRS0+9lJd6v0T9SvW9jiUivrJqFfTt6+5bXrgQ2rb1OpGISLGg0iwiEuC2HdjGnbPuZNKfk4gIi2DOiDn0bNrT61gi4kvz5sGgQRAWBrNnQ3i414lERIoNlWYRkQCVlJLES8tf4qnFT2GM4bkez3Hn+XdqKbZISTNhAgwfDi1auJ2y62mzPxGR/FBpFhEJQLM2zuK2GbexYd8GBrcazEu9X6Jh5YZexxIRX3v/fbjpJjj/fIiKgmrVvE4kIlLs6PR6EZEA8lfcXwz+ejB9PusDwKwrZvHNkG9UmEVKGmvhmWfgxhuhTx+YM0eFWUSkgDTTLCISAI6mHuXl5S/z5OInsdbydPenufv8uykTUsbraCLia2lpcPfd8Oqrbln2J59AqVJepxIRKbZUmkVEirm5m+dy6/RbWb93PRedchGvXPAKjao08jqWiHghORmuuQbGj4fbb4eXX4YgLSwUETkZKs0iIsVUbHwsd826i2/WfkOzqs2Yfvl0+rbo63UsEfFKQgJceilMmwZPPw0PPOCOlxIRkZOi0iwicpKSUpL4fdfvpNpUn73ngi0LeHLxk6TaVJ7s9iT3tL+H0JBQn72/iPiZ/fuhf39YsQLeew9uuMHrRCIiAUOlWUSkAHYd3sX0DdOJio5i9qbZHDp6yOcZBkYM5NU+r9K4SmOfv7eI+JG//4YLLoDoaPj6a7jkEq8TiYgEFJVmEZE8sNbyx+4/iFofRVR0FCtiV2Cx1KtYj+Gth9OzaU/KlSrnszw1ytXg7Hpn++z9RMRPbdgAvXvDnj0wYwZ07+51IhGRgKPSLCKSi6OpR1m0dRFR0a4obz2wFYCz6pzFo10eJTIikjNrn4nRPYMi4oVffnHHSaWlwYIF0K6d14lERAKSSrOISBZ7EvZkLruetXEWB48eJDQklJ5Ne/JAxwfoH96fuhXreh1TREq6hQthwACoWtWdwRwe7nUiEZGApdIsIiWatZZ1e9ZlLrteHrucNJtGnQp1GHbaMCLDI+nRtIdPl16LiBzXd9/BsGHQrBnMng316nmdSEQkoKk0i0iJk5yazOJtizOXXW/evxmAtnXa8nCnh4mMiKRtnbYEGZ1tKiJ+5qOP3M7Y55zjjpaqVs3rRCIiAU+lWURKhL0Je5mxcQZR0VHM3DiT+KR4QkNC6dGkB/e2v5f+4f2pV0mzNSLip6yFF16A++939zFPmADly3udSkSkRFBpFpGAZK1l/d71mcuul8UsI82mUat8LYa0GkJkeCQ9m/akfGl90ykifi4tDe69F156CS67DMaMgdKlvU4lIlJiqDSLSMBITk1m6V9LiYqOYmr0VDbs2wBAm9pteKjTQ0SGR3JW3bO07FpEio/kZLj+ehg7Fm67DV59FYL0Z5iIiC+pNItIsbb/yP5jll0fSDxAmeAydGvSjTvPu5P+4f1pULmB1zFFRPLvyBEYOhSiouCJJ+Dhh0FH3ImI+FyeSrMxJgy4COgHtAbqAUeB34BPgE+stWlZrm8MbDnOS35lrR2Wy3uNBG4BWgGpwC/AaGvt1FyuLwvcDwwDGgHxwELgUWvtulzG1AeeAPoAYcA/wHfA49ba/bmMaQ88DJwHhAIbgY+BN6y1qV59FpGSKHpvdOay66V/LSXVplKzfE0uPuViIiPcsusKpSt4HVNEpOAOHIDISFi2DN5+G26+2etEIiIllrHWnvgiY24C3sGVywXAX0At4GKgMjARGGLTXyxLaV6NK6PZ/W6tnZDD+4wG7gZigQlAaVyBrAbcZq19M9v1ZYB5QAdgFTAfaAAMwZX67tbaH7KNaQZ8D9QEJgN/AucA3YD1QAdr7d5sYwamf8ZE4CtgHxAJRAATrLVDvPgsOWnXrp1dtWrViS4TKVZS0lJY9teyzN2uo/dGA9C6ZmsiwyOJjIjknHrnaNm1iASGf/5xm32tWwfjx8Oll3qdSESkWDHG/GStbVdor5fH0twdKA9MyzajXBtYiSt3g621E9Ofb4wrzWOttVflKYibyV0GbALOzpjxTX+tn9Lf/xRr7dYsYx4AnsGV0qEZ2dJL7nfAWqB1tsyzgN7AKGvtG1mefxm4E3jPWntTlucr4WaVK+MK9ar050NxxfZ84DJr7Ze+/iw5UWmWQHEg8QAzN84kKjqKGRtmsD9xP6WDS9O1cVdXlMMjaVSlkdcxRUQK16ZN0Ls37NzpzmPu2dPrRCIixU5hl+Y8TctYa+dba6OyFzZr7Q7g3fRfdj3JLBlF9emsS6TTi+VbQBng6oznjTEmy5h7s2az1k4GluCWRXfJMqYprjBnvGZWjwKHgRHGmKzb6Q4GagBfZhTm9PdIxC3XBsi+ZqrIP4tIINq4byOvLH+F7mO7U+PFGlw28TJmb5rNgIgBTBgygT3/t4dZV8zi1nNuVWEWkcDz66/QoQPExcH8+SrMIiJ+ojA2AktOf0zJ4Wt1jTE34u4b3gsst9auyeV1uqc/zszhazOAR9KveTT9uWZAQyDaWpvT/dMzgE7pYxZke4/ZOfwA4KAxZhmuVJ+HWyp9olyLgQSgvTGmjLU2yYefRaTYS0lLYXnM8sxl13/u+ROAU2ucyj3n30NkRCTn1juX4KBgj5OKiBSxxYvdPcyVKsGCBdCypdeJREQk3UmVZmNMCHBl+i9zKoi90v/KOmYhMNJa+1eW58rjNhc7ZK39J4fX2ZD+GJ7luYj0x+hc4hV0TO/0MRmlOdcx1toUY8wW4FSgKbDOh59FpFiKS4xj1qZZREVHMX3DdPYd2UepoFJ0adyFm9vdTGR4JE2qNvE6poiI70yZ4nbJbtwYZs+GBtrxX0TEn5zsTPNzwGnAdGvtrCzPJwBP4u7F3Zz+3OnAY7gNt+YZY9pYaw+nf61y+mNcLu+T8XyVLM/56xhf5RIpNjbv35y52/WibYtISUshrGwY/Vr0IzI8kguaX0ClMpW8jiki4ntjxsB118FZZ8G0aVC9uteJREQkmwKXZmPMKNzu0H8CI7J+zVq7C/hvtiGLjTG9gaXAucB1wGv5fNsT71qWJaIfjynU9zDG3ADcANCwYcN8xhApfKlpqayIXZG57Hrt7rUAtKzekrvOu4vIiEjOr3++ll2LSMn24otw773Qqxd8+y1U0FF5IiL+qECl2RhzC67wrgV6WGv35WVc+nLmD3GluTP/K80ZM6mVcxyY80zsicZUynadr8b46rNksta+D7wPbvfsXF5DpEjFJ8Uze9PszGXXexL2EBIUQudGnbm+7fX0D+9P82rNvY4pIuI9a+G++1xpvvRSGDcOypTxOpWIiOQi36XZGHMH8ArwO64w78rnS+xOf8zcodpae9gYsx2oZ4ypk8O9wC3SH7Pe87s+/TG3+3wLc0y79DE/Zb04/Z7uJrhN0Db7+LOIeG7rga2Zy64Xbl1IcloyVUOrcmGLCzOXXVcJreJ1TBER/5GSAjfcAJ98AjffDG+8AcFadSMi4s/yVZqNMffh7mP+Fehlrd1TgPc8L/1xc7bn5+OWefcBPsn2tb5ZrsmwCfgLCDfGNMlh1+mcxmTsPN3bGBOU7fzmikAH4AiwIluu4em5vsj2Hp2BcsDiLDtn++qziPhcaloqK7evzFx2/fuu3wGICIvg9nNvJzIikvYN2hMSVBgb84uIBJgjR+Cyy2DyZHj0UfeXMSceJyIinjLW5m01rzHmEeAJ3Gxr7+MtyTbGnAv8Yq09mu357sB03DnFHay132f5WntgGa5Anp1xvrExpnH6e5YHTkk/6zhjzAPAM8AEYGhGCTbGDMRtQrYWaJ2tHM/C7ZA9ylr7RpbnXwbuBN6z1t6U5flK6ZkqpWdelf58KK7Eng9cZq390tefJSft2rWzq1atOt4lIvly6OihzGXX06KnsTthN8EmmI4NOxIZHklkRCThYdrYXUTkuOLiYMAAWLIEXn8dbr3V60QiIgHLGPOTtbZdob1eXkqzMWYkMAZIBd4g53trt1prx6RfvxB3DNNCIDb966fzv/OLH7HWPpXD+7wE3JU+ZgJQGhiKO+f5Nmvtm9muL4Mrru2BVbhjohoCQ4CjQHdr7Q/ZxjQDvgdqApOBdbh7rLvhlj+3t9buzTZmUHqeROBLYB8wAHdU1ATgUpvtH6QvPktOVJqlMPwV91fmsusFWxdwNPUolctUpm+LvkSGR9K3eV+qlq3qdUwRkeJh507o0wd+/93dv3zZZV4nEhEJaF6V5seAR09w2SJrbdf0668FLsIdR1UdKAXsBJYDb1prlxznvUYCtwKtgDTgZ+BFa+3UXK4vC9wPXI4rmfG4sv6otXZtLmMa4GbN++BK7D+42dzHc5tBN8Z0AB7CzSyHAhuBj4HXrbWpXn2W7FSapSDSbBo/bv8xc9n1mp1rAGherbmbTQ6PpGPDjpQKLuVxUhGRYmbzZujdG/75x+2QfcEFXicSEQl4npRmKT5UmiWvDh89zNzNc4mKjmJq9FR2Ht5JkAmiQ4MOmcuuI8IiMLrfTkSkYNascSX56FF3BvN55514jIiInLTCLs3arUckgFlr2X5wO+v3rCd6bzTr9/7vceuBraTZNCqVqUSf5n0yl12HlQvzOraISPG3dCn07+/OXl6yBFq18jqRiIgUkEqzSACIS4z7VymO3htN9N5oEpITMq8rV6oc4WHhnF33bIa3Hk6XRl3o1KgTpYNLe5heRCTATJ0KQ4ZAw4YwezY0auR1IhEROQkqzSLFxNHUo2zZv+V/xXjP+sy/33l4Z+Z1QSaIJlWaEB4WTtdGXQkPCyeiegThYeHUq1hPy61FRIrSuHFwzTVw5pkwfTrUqOF1IhEROUkqzSJ+xFrLP4f+ySzFWWeNN+/fTGqWPedqlq9JeFg4/Vr0yyzFEWERNK3alDIhZTz8FCIiJdTLL8Pdd0OPHjBpElSs6HUiEREpBCrNIh44mHQwc/l09iXVh44eyryubEhZWoS1oE3tNlx66qVEhLlyHB4WriOfRET8hbXw4IPw3HMweDCMHw9l9MNLEZFAodIsUkSSU5PZemDrMcupo/e5x38O/ZN5ncHQuEpjwsPC6dig4zHLqetXqk+QCfLwU4iIyHGlpMDNN8OHH8KNN8Jbb0FwsNepRESkEKk0i5wEay07D+/McTn1pv2bSElLybw2rGwYEdUjuKD5BZkzxhFhETSr1ozQkFAPP4WIiBRIYiJcfrlbiv3ww/DEE6B9I0REAo5Ks0geHD56ONfl1PFJ8ZnXlQkuQ4uwFpxa81QubnnxMcupdZSTiEgAiY+HQYNgwQJ47TUYNcrrRCIiUkRUmkVy8c6P7zBh3QTW71nP9oPbj/law8oNiQiLYMTpI/43a1w9ggaVGhAcpGV5IiIBbdcu6NsX1qxx9y8PH+51IhERKUIqzSI5SEhO4K7Zd1G3Yl16NO1BeLX/3WfcvFpzypUq53VEERHxwtat0KsXbN8OU6a48iwiIgFNpVkkB3M3zyUxJZH3+r9Hz6Y9vY4jIiL+4I8/oHdvSEiAuXOhfXuvE4mIiA9oW16RHEStj6JSmUp0btTZ6ygiIuIPfvwROnd2x0stWaLCLCJSgqg0i2STZtOIio6iT/M+lA4u7XUcERHx2sKF0L07VK4MS5fCaad5nUhERHxIpVkkm1V/r2Ln4Z0MCB/gdRQREfHatGnuvuUGDdwMc9OmXicSEREfU2kWySZqfRTBJpi+LbS5i4hIifbll+5YqVNPhcWLoV49rxOJiIgHVJpFspkSPYUODTtQrWw1r6OIiIhX3n8fLr/c3bs8fz5Ur+51IhER8YhKs0gW2w5sY83ONVqaLSJSko0eDTfeCH36wIwZUKmS14lERMRDKs0iWURFRwEQGRHpcRIREfE5a+GRR+D//g8uvRS++w7KlfM6lYiIeEznNItkERUdRXhYOOFh4V5HERERX0pLgzvugDfegOuug3ffheBgr1OJiIgf0EyzSLr4pHgWbFmgpdkiIiVNSgpcc40rzHfd5e5nVmEWEZF0mmkWSTd702yS05K1NFtEpCRJSnIbfn37LTzxBDz8MBjjdSoREfEjKs0i6aKio6gaWpX2Ddp7HUVERHzh8GG4+GKYPRtefRVuv93rRCIi4odUmkWA1LRUpkVPo194P0KC9J+FiEjAO3AA+vWDFSvg44/h6qu9TiQiIn5K7UAEWB67nL1H9hIZrqXZIiIBb9cuuOAC+OMP+PpruOQSrxOJiIgfU2kWAaLWRxESFMIFzS7wOoqIiBSlmBjo1Qv++guiolx5FhEROQ6VZhHc/cxdG3elcmhlr6OIiEhR2bABevZ0S7Nnz4aOHb1OJCIixYCOnJISb+O+jazbs05Ls0VEAtmaNdCpEyQkwIIFKswiIpJnKs1S4kWtjwJQaRYRCVQrVkCXLhASAkuWQNu2XicSEZFiRKVZSryo6ChOq3kaTao28TqKiIgUtvnz3ZLssDBYuhROOcXrRCIiUsyoNEuJtv/IfhZvW6xZZhGRQDR5Mlx4ITRp4maYGzf2OpGIiBRDKs1Sos3cOJNUm8qAiAFeRxERkcL02WfuKKkzzoBFi6BOHa8TiYhIMaXSLCXalOgp1Cxfk3PqneN1FBERKSzvvAMjRkDnzjB3LlSr5nUiEREpxlSapcRKTk1mxoYZ9GvRjyCj/xRERALCc8/Bf/4D/fvD9OlQsaLXiUREpJhTU5ASa+lfS4lLitPSbBGRQGAt3H8/PPAAXH45TJwIoaFepxIRkQAQ4nUAEa9MWT+FMsFl6NW0l9dRRETkZKSlwa23umXZN90Eb70FQZoXEBGRwqH/o0iJZK0lKjqK7k26U750ea/jiIhIQSUnw5VXusJ8773w9tsqzCIiUqj0fxUpkf7c8yeb9m/S0mwRkeIsMRGGDHE7ZT/7LDz/PBjjdSoREQkwWp4tJdKU9VMA6B/e3+MkIiJSIHFxMHAgLF4Mb74Jt9zidSIREQlQKs1SIkVFR3Fm7TOpX6m+11FERCS/du6EPn3g99/dLPNll3mdSEREApiWZ0uJs/vwbpbHLtfSbBGR4mjLFujYEaKjYepUFWYRESlymmmWEmf6humk2TQiwyO9jiIiIvnx229wwQWQlATz5sF553mdSERESgDNNEuJExUdRd2KdWlbp63XUUREJK+WLoXOnd3O2EuWqDCLiIjPqDRLiZKUksSsTbOIDI/EaIdVEZHiYdo06NULataEZcugVSuvE4mISAmi0iwlysKtCzl09JCWZouIFBeffup2yT7tNDfb3KiR14lERKSEUWmWEiUqOopypcrRvUl3r6OIiMiJvPIKXHkldO0K8+dDjRpeJxIRkRJIpVlKDGstU9ZPoVfTXpQtVdbrOCIikhtr4cEH4a67YPBgtzy7YkWvU4mISAml0iwlxpqda4iJj9HSbBERf5aaCjfeCM8+6x6//BLKlPE6lYiIlGAqzVJiREVHYTD0D+/vdRQREclJYiJceil88AE8/DC88w4EB3udSkRESjid0ywlxpT1Uzin3jnUqlDL6ygiIpJdfDwMGgQLFsBrr8GoUV4nEhERATTTLCXEPwf/4ce/f9TSbBERf7RrF3Tr5s5fHj9ehVlERPyKZpqlRJi2YRoAAyIGeJxERESOsXUr9O4NsbEwZQr07et1IhERkWOoNEuJMGX9FBpVbsRpNU/zOoqIiGT4/Xe44AJISIC5c6F9e68TiYiI/IuWZ0vAO5J8hLmb5xIZHokxxus4IiIC8P330Lmz+/slS1SYRUTEb6k0S8Cbt2UeR1KOaGm2iIi/mDEDevaE6tVh2TI4TauARETEf6k0S8Cbsn4KFUtXpEvjLl5HERGRzz6DAQOgZUtYuhQaN/Y6kYiIyHGpNEtAS7NpTI2eSp/mfSgdXNrrOCIiJdvrr8MVV0CnTu5oqZo1vU4kIiJyQirNEtB+/udn/jn0j46aEhHxkrXwyCNw++1w8cUwfTpUquR1KhERkTzR7tkS0Kasn0KQCeLCFhd6HUVEpGRKTYVbboH33oPrroN334XgYK9TiYiI5JlmmiWgRUVH0aFBB8LKhXkdRUSk5ElKgmHDXGF+4AF4/30VZhERKXZUmiVgxcTF8OuOX7U0W0TECwcPQr9+MGECvPwyPPMM6Ng/EREphrQ8WwJWVHQUAJERKs0iIj61ezdceCH88guMHQtXXul1IhERkQJTaZaAFRUdRYtqLYgIi/A6iohIyfHXX9C7N2zbBt99B/37e51IRETkpGh5tgSkg0kHmb9lPpHhkRgtBxQR8Y1166BDB9i5E+bMUWEWEZGAoNIsAWnO5jkcTT2qpdkiIr6ycqU7fzklBRYtgo4dvU4kIiJSKFSaJSBFRUdRNbQqHRp08DqKiEjgmzcPuneHypVh6VI4/XSvE4mIiBQalWYJOKlpqUyNnkrfFn0pFVyqwK8THQ2xsYUYTEQkEH37rdv0q2lTV5ibNfM6kYiISKFSaZaA88P2H9iTsOekjppKSnK35Z16qtvHRkREcvDRRzBkCJx1lluSXaeO14lEREQKnUqzBJyo9VGEBIXQp3mfAr/Gd9/Bnj1QrRpcdBHcd5+7TU9ERNK9+CJcd53bKXvOHKha1etEIiIiRUKlWQLOlOgpdG7UmSqhVQr8Gh98AI0bw9q1cNNN8MIL0KuX2xBWRKREs9b9JPHee2HYMJg8GcqX9zqViIhIkVFploCyef9m1u5ey4DwAQV+jU2b3J42114LZcvCO+/A2LHwww9w5pmwbFkhBhYRKU5SU+GGG9xPEm++GcaPh9KlvU4lIiJSpFSaJaBErY8COKmjpj7+GIKC4Oqr//fclVfCihVuMqVrV3j1VTfZIiJSYiQlwdCh8OGH8PDD8NZbEBzsdSoREZEip9IsAWVK9BRa1WhF06pNCzQ+JQU++cRtBFuv3rFfO/10+PFH6NcP7rzTrUo8eLAQQouI+LtDh6B/f5g4EV55BZ58EozxOpWIiIhPqDRLwIhLjGPxtsUntTR72jT45x+4/vqcv16lCkyaBM8/DxMmwLnnwrp1BX47ERH/t3cv9OgBCxa4e1XuuMPrRCIiIj6Vp9JsjAkzxlxnjJlkjNlojDlijIkzxiw1xlxrjMnxdYwx7Y0x040x+4wxCcaYNcaYO4wxua7nMsaMNMasNMYcSn+PhcaY/se5vqwx5nFjzHpjTKIxZpcx5mtjTMvjjKlvjPnYGPO3MSbJGLPVGPOqMSbXrT/99bPI/8zcOJOUtJSTWpr9wQfuxJQLL8z9GmPc/jdz57rvJc8+G77+usBvKSLiv2JjoVMnWL3ancd85ZVeJxIREfG5vM40DwE+AM4FfgBeBSYCpwEfAl8bc+w6LWPMQGAx0BmYBLwFlAZeAb7M6U2MMaOBMUCd9PcbD7QGoowxt+ZwfRlgDvBfIB54DZgLXASsMsacm8OYZsBPwNXAyvQ8m4HbgeXGmLAcxvjlZ5FjTYmeQvVy1Tm3XsH+UcXGwowZ7l7mkJATX9+tG/z8M5xxhrvN74474OjRAr21iIj/iY6Gjh3dH46zZsGAgq/iERERKc6MzcNuRsaY7kB5YJq1Ni3L87VxxbMBMNhaOzH9+UrARqAy0MFauyr9+VBgPnA+cJm19sssr9UeWAZsAs621u5Pf74xruSWB06x1m7NMuYB4BlgAjA0I1t6yf0OWAu0zpZ5FtAbGGWtfSPL8y8DdwLvWWtvyvK8336WnLRr186uWrXqeJcEpOTUZGqOrsmgUwbxycBPCvQaTz4J//2v2z27aT5uiU5OdjPPr74K7du7Wefs90OLiBQrv/wCF1zg/n7mTGjb1ts8IiIi+WCM+cla266wXi9PM83W2vnW2qjshc1auwN4N/2XXbN8aTBQA/gyo2SmX58IPJz+y5uzvU1GUX06o2Smj9mKm9ktg5sdBiB9ZjtjzL1Zs1lrJwNLgFZAlyxjmuIKc8ZrZvUocBgYYYzJeuCkX34WOdaymGUcSDxAZHjBlmanpcFHH0HPnvkrzAClSrl9cb780q1gbNvW3fonIlIsLV7sjgkoWxaWLlVhFhGREq8wNgJLTn9MyfJc9/THmTlcvxhIANqnL0nOy5gZ2a4BaAY0BKKttVvyOCbj72fn8AOAg7jZ4XLAeXnM5eVnkSyi1kdROrg0vZv1LtD4uXNh2za47rqCZxg61O2uXa2aK9/PP69jqUSkmImKcjPMdeu6whwe7nUiERERz51UaTbGhAAZu4JkLYgR6Y/R2cdYa1OALUAI0DT9dcoD9YBD1tp/cnirDemPWf/vnet7+GqMx59F0llrmRI9he5NulOhdIUCvcYHH0BYGAwadHJZWraElSth8GC4/364+GKIizu51xQR8YlPP4WLLoLWrWHJEmjQwOtEIiIifuFkZ5qfw20GNt1aOyvL85XTH3OrCxnPVyng9f48xle5MhljbjDGrDLGrNq9e3cuLxG41u9dz8Z9Gwu8NHvXLpg8GUaOhDJlTnz9iVSs6JZqv/oqTJ0K7drBmjUn/7oiIkXmtdfczthdusC8eVC9uteJRERE/EaBS7MxZhRwN/AnMCK/w9Mf87t4NT/XF+Q9fDWmUN/DWvu+tbadtbZdjRo18hmj+ItaHwVA//BcT/M6rnHj3GZeJ7M0Oztj4PbbYeFCSEiA885zkzgiIn7FWrcD4h13uFnmadPcT/5EREQkU4FKszHmFtyRSGuBbtbafdkuyZgZrUzOKmW77kTX5zQTm9/38NUYX30WSRcVHUWb2m1oWLlhvsdaCx9+CB06uKXVha1DB3cs1bnnukmcm2+GpKTCfx8RkXxLS4PbbnNHB1x7rdv6PzTU61QiIiJ+J9+l2RhzB/Am8DuuMO/I4bL16Y//ugc3/T7oJriNwzYDWGsPA9uBCsaYOjm8Xov0x6z3/Ob6Hr4a4/FnEWBvwl6WxSwr8NLsJUtg/Xq4/vpCDpZFrVowZw7cdx+8+y506gR//VV07ycickJHj8IVV8Bbb8H//Z/b2CEvB9SLiIiUQPkqzcaY+4BXgF9xhXlXLpfOT3/sk8PXOuN2qP7eWpt1zu14Y/pmuwbcGch/AeHGmCZ5HJNxEFBvY8wxn90YUxHoABwBVuQxl5efRYDpG6aTZtMYEDGgQOM/+AAqVYIhQwo5WDYhIfDcczBpkivpbdvCrFknHiciUugSEtyuh1984f5geuEFd0+JiIiI5CjPpdkY8whu46+fgB7W2j3HuXwCsAcYZozJPFTaGBMKPJX+y3eyjck47/khY0zVLGMaA7cAScAnGc9ba22WMS9kLcHGmIFAJ9zy8UVZxmwCZgMZr5nV40B5YFz6bLFffxZxoqKjqFOhDm3r5P8c0f37YcIEGD4cypUrgnA5GDQIVq1yp7n07QtPPOFWSIqI+MT+/dCrl/up3fvvuyUwIiIiclzG5uEgWWPMSGAMkAq8Qc731m611o7JMmYQrnAmAl8C+4ABuOOVJgCX2mxvbox5CbgLiE2/pjQwFAgDbrPWvpnt+jK42df2wCpgHu684yHAUaC7tfaHbGOaAd8DNYHJwDrgXKAbbvlze2vt3mxj/PKz5KRdu3Z21apVJ7osIBxNPUr1F6oz7LRhvB/5fr7Hv/mmu53v55/hzDOLIOBxJCTATTe5zcH69oXx4935ziIiReaff6BPH1i3Dj7/3J2NJyIiEoCMMT9Za9ud+Mo8vl4eS/NjwKMnuGyRtbZrtnEdgIeA84FQYCPwMfC6tTY1l/caCdwKtALSgJ+BF621U3O5vixwP3A5rmTGAwuBR621a3MZ0wB4Ard8Ogz4B/gOeDyHTc38+rNkV5JK85xNc+g9vjdRl0Xle+dsa6FNGyhVys38esFaeO89t8t2nTowcSKcdZY3WUQkwG3e7GaYd+5094n06uV1IhERkSLjSWmW4qMkleZRM0bx4c8fsvfevZQtVTZfY3/8Ec45B955x834emnlSjfhs2sXvPGGO/pKtxeKSKH57Tfo3dtt/jV9utvOX0REJIAVdmku8DnNIl6y1jJl/RR6Nu2Z78IMbgOwcuXg8suLIFw+nXOOWyLepQvccIM7+eXIEa9TiUhAWL4cOneGoCBYvFiFWUREpABUmqVY+n3X72yL21agXbMPHXKbxg4d6nbO9gfVq7sJoP/+Fz75BNq3h02bvE4lIsXarFnQs6f7A2bZMjj1VK8TiYiIFEsqzVIsTVk/BYB+Lfrle+yXX7rifN11hZ3q5AQHw+OPw7RpsG2bu785KsrrVCJSLH31FURGQng4LF0KjRt7nUhERKTYUmmWYikqOoqz655NnYp18j32ww+hVSs4//wiCFYILrwQfvoJmjWDAQPgoYcgNcet5kREcvDuu3DZZXDeebBwIdSq5XUiERGRYk2lWYqdHYd2sHL7ygItzf7tN/jhB7j+ev/ebKtJE7ea8vrr4Zln4IILYPdur1OJiF+zFp5+Gm6+Gfr1c8uzK1f2OpWIiEixp9Isxc606GlYLJHhkfke+8EHULo0jBhRBMEKWWgovP8+fPyxK9Bt28KKFV6nEhG/lJYGd98NDz8MV1wB334LZfO/SaKIiIj8m0qzFDtR0VE0qNSA02udnq9xR47Ap5/CxRdDWFgRhSsCV18N33/vyn7nzvDWW25CSUQEgJQUuOYaeOUVGDUKxo51h9CLiIhIoVBplmLlSPIR5myew4CIAZh8rq/+9ls4cMAteS5uzjwTVq1yy7RvvdVNJB0+7HUqEfFcYiJccokryk88Aa++6o6XEhERkUKj/7NKsTJ/y3wSkhMKvDS7WTPo2rXwc/lC1aowebK7ZfHLL91xq+vXe51KRDwTHw99+sCUKfDmm/DII/69WYOIiEgxpdIsxUpUdBQVSlega+Ou+RoXHQ2LFsG11xbvSZigIHjwQbe/z86dcPbZMHGi16lExOd27YJu3dyGB599Brfc4nUiERGRgFWM64OUNNZaoqKjuKDZBZQJKZOvsR995M5Bvuqqosnmaz17ws8/w6mnwuDBcM89kJzsdSoR8Ylt26BTJ1i3zi0/ufxyrxOJiIgENJVmKTZ+/udn/j74d76XZh89CmPGQGQk1Mn/sc5+q0EDN3t+663w0kvQowf884/XqUSkSK1bBx07uqUms2e7g91FRESkSKk0S7ERFR1FkAniwhb5+yYxKsqtZLzuuiIK5qHSpeGNN9zqzJ9+csdSLV7sdSoRKRI//uhmmJOT3U/MOnb0OpGIiEiJoNIsxYK1lonrJnJ+/fOpUb5GvsZ++CHUr+/2ywlUl18OP/wAlSpB9+5u5lnHUokEkHnz3H/cFSvC0qVwxhleJxIRESkxVJqlWPh1x6/8vut3hrcenq9x27a5TbOuucbd0xzITjvNTUQNGuTucR4yxG2uKyLF3KRJbhl248Zu46/mzb1OJCIiUqKoNEuxMHb1WEoHl2boaUPzNe7jj93jNdcUQSg/VKkSfPMNjB4N333ndtf+4w+vU4lIgX38sdvtr21btyS7bl2vE4mIiJQ4Ks3i95JTk/n8t88ZEDGAamWr5Xlcaqr7frN3b2jUqAgD+hlj4O67Yf58N9N8zjnw+edepxKRfBs92p2T17MnzJ0L1fL+55+IiIgUHpVm8XszN85kd8Jurjz9ynyNmzULYmPh+uuLKJif69zZHUt11lkwfDjcdpvbSVxE/Jy1cP/98H//B5de6nYzLF/e61QiIiIllkqz+L1xa8ZRo1wN+jTP305eH3wANWq4o6ZKqjp13P5B99wDb74JXbpATIzXqUQkV6mpcOON8Pzz7vHzz902+SIiIuIZlWbxa/uO7GPK+ilc3vpySgWXyvO4HTvc5MxVV+n7zVKl4MUXYcIEd39z27auSIuIn0lKgmHD3E/8HnoI3nkn8HcwFBERKQZUmsWvff3H1xxNPcrIM0bma9yYMW7CJhDPZi6oSy5xu2vXquXu837mGUhL8zqViABw6JBbFjNhgjsz7qmn3AYFIiIi4jmVZvFrY1eP5bSap9Gmdps8j0lLc2czd+kC4eFFl604iohw5zkPG+Ymsrp1g3XrvE4lUsLt3Qs9erjd+z75BO66y+tEIiIikoVKs/it9XvWsyJ2BSPPGInJx4zLwoWwaZNmmXNTvjyMHw8ffQS//QZnnAH//S8kJnqdTKQE2r7d7dq3ejVMnOjuKRERERG/otIsfuvTNZ8SZIIY3np4vsZ9+CFUqeKWI0vOjHFnV//5JwwdCk8+Ca1bu1NtRMRHNmyADh3c7nwzZ8LAgV4nEhERkRyoNItfSrNpfLrmU3o3602dinXyPG7vXjdZM2IElC1bhAEDRM2a8OmnMGeO+3WvXu6f3a5d3uYSCXi//AIdO8Lhw7BgAXTt6nUiERERyYVKs/ilRVsX8VfcX/k+m/nTT91ZxFqanT89e7ql2o88Al99Baec4pZva6MwkSKwZIkryWXKwNKl7jB1ERER8VsqzeKXxq4eS6UylRh0yqA8j7HWLc0+5xw4/fSiyxaoQkPhiSfcrZWnneZ+8NC1K6xd63UykQAydarbvr5uXVi2zO3OJyIiIn5NpVn8zuGjh5mwdgKXtrqUsqXyvsZ6xQp3DvH11xdhuBKgZUu3mdpHH7l/nm3awMMPw5EjXicTKebGj4dBg+DUU2HxYmjQwOtEIiIikgcqzeJ3vl33LYeTD3PlGflbmv3BB25n6KFDiyhYCRIU9L+Nwi67DJ5+2m0UlnHvs4jk0+uvuw0DOnd29zDXqOF1IhEREckjlWbxO+PWjKNJlSZ0bNgxz2Pi4929uJddBhUrFmG4EqZGDRg7FubNc0W6d28YPhx27vQ6mUgxYS08+ijcfrubZZ4+XX9IiYiIFDMqzeJXYuJimLd5HleecWW+zmb+4gtISNDS7KLSvTusWePOc/7mG7dR2AcfaKMwkeNKS4NRo9xmAVdf7f7jCQ31OpWIiIjkk0qz+JXxa8ZjsQVamt26NZx9dhEFE0JD4fHHXXk+/XS44Qa30vSPP7xOJuKHkpPhiivgzTfh7rvdJgEhIV6nEhERkQJQaRa/Ya1l3JpxdGzYkaZVm+Z53C+/wE8/uVnmfExOSwGdcorbKOyTT2DdOrdR2IMPaqMwkUwJCTBwoFsC8+yz8OKL+sNJRESkGFNpFr/x498/8ueePxl5xsh8jfvwQzcLesUVRRRM/sUYuOoqt1HY8OGuF5x2Gsya5XUyEY8dOOBu/p85E957D+6/X4VZRESkmFNpFr8xbvU4QkNCGdJqSJ7HJCTAZ5/B4MFQtWoRhpMc1agBY8bA/Plu5WmfPnD55bBjh9fJRDywYwd06QIrV7qdCW+4wetEIiIiUghUmsUvJKUk8cXvXzDolEFUDq2c53HffANxcXDddUUYTk6oWzdYvdptEjxxojvr+b33tFGYlCBbtkDHjrBxI0ydCkPy/sM/ERER8W8qzeIXpm+Yzr4j+7jy9PxtAPbhhxAe7jakEm+FhsJjj7mNwtq0gZtuch3it9+8TiZSxH77DTp0gH373PlsvXt7nUhEREQKkUqz+IWxq8dSu0JtejXrlecx69bB0qVullm3DPqPiAi3XHvMGIiOhrZt4YEH3FJ6kYCzfLn7qZ0xsGQJnHee14lERESkkKk0i+f2JOxh2oZpXNH6CkKC8n4ky4cfuvtor8zf5LT4gDEwcqTbKOyKK+C559xGYTNmeJ1MpBDNmgU9e0L16u4neKee6nUiERERKQIqzeK5L377gpS0lHydzZyUBGPHulNdatUqwnByUqpXd0dTLVgApUvDhRe6Wz23b/c6mchJ+uoriIyEFi1cYW7SxOtEIiIiUkRUmsVzY1eP5czaZ9K6Vus8j5k8GfbudWczi//r2tVtFPbkk26PpFNOgddeg5QUr5OJFMB778Fll8G557pDy/WTOxERkYCm0iye+mPXH/z0z0/5mmUG+OADaNjQrYyU4qFMGXj4Yfj9d7dn0h13wDnnuNN5RIoFa+GZZ9wudxde6JZnV6nidSoREREpYirN4qlxq8cRbIK5vPXleR6zeTPMnQvXXgvBwUUYTopEs2bu3uavv3bH2p53HtxyCxw44HUykeNIS4N77oGHHoLhw2HSJChXzutUIiIi4gMqzeKZ1LRUxv82nr4t+lKzfM08j/v4YwgKgquvLsJwUqSMcfc2//kn3HYbvPuuW7L9xRduMk/Er6SkwDXXwMsvu9+w48ZBqVJepxIREREfUWkWz8zbMo+/D/7NyDNG5nlMSorbWKpPH2jQoAjDiU9UquTubV650v37vPxyd8Tthg1eJxNJl5gIgwe7nQcfe8z9hg3S/zpFRERKEv2fXzwzbvU4qoRWoX94/zyPmT4d/v5bG4AFmrPOghUr4M03XYE+7TTXTxITvU4mJVp8PPTt63YefP11ePRRHQovIiJSAqk0iyfik+L5dt23DDt1GKEhoXke98EHULs29OtXhOHEE8HB7t7mP/+ESy6Bxx+H1q1hzhyvk0mJtHs3dO/ujpMaP94tyxYREZESSaVZPDFx7USOpBzJ167Z27e7mearr9bthIGsTh34/HOYPdv9undvt2x7xw5vc0kJ8tdf0LEj/PEHfPed2/hLRERESiyVZvHE2NVjaVGtBefVPy/PYz75xG1ge+21RRhM/EavXvDbb25F7MSJEBEBb70FqaleJ5OA9uef7ky0nTvdMgctaxERESnxVJrF57bs38KibYsYecZITB7vDzx82BWmnj3dkUVSMoSGunubf/sNzj4bbr3VHVH1889eJ5OAtGqVm2FOToaFC93fi4iISImn0iw+N37NeACuOP2KPI958023PPexx4oolPi18HA36ffZZ27l7Nlnw+23u32aRArF/PnQrRtUrOjuY27TxutEIiIi4idUmsWnrLWMWzOObo270ahKozyNOXAAnn/erZLs0KFo84n/Msbd27x+Pdx0E7zxhjvb+ZtvdLaznKRJk9wu2Y0awbJl0Ly514lERETEj6g0i08tj13Oxn0b87UB2OjRsH8/PPVUEQaTYqNKFbdUf8UKqFULLr0ULrwQNm3yOpkUSx9+6M5hPvNMWLwY6tb1OpGIiIj4GZVm8amxv46lXKlyXNLykjxdv3MnvPoqDBum1ZJyrHPOgR9/dL8/li51Zzs//TQkJXmdTIoFa91vmOuvd1u0z5sH1ap5nUpERET8kEqz+ExiSiJf/fEVF7e8mIplKuZpzNNPQ2IiPPFEEYeTYikkxN3b/OefEBkJDz/sfriycKHXycSvpaW53zgPPwxXXAFTpkD58l6nEhERET+l0iw+M2X9FOKS4hh5xsg8Xb9tG7z7LlxzDbRoUcThpFirVw++/tqd452U5PZzuvJK2LXL62Tid44edecuv/EG3HUXjB2rg99FRETkuFSaxWfGrR5HvYr16Na4W56uf+wxCAqC//63aHNJ4OjbF37/HR56CL780m0U9v77bmJRhIMHoX9/95vj+efdhglB+t+giIiIHJ++WxCf2HloJzM3zmTE6SMIDgo+4fXr1sG4cXDLLVC/vg8CSsAoV85tGrd6NZx+Otx4oztud/Vqr5OJp3bvhu7d3dFSH38M997rtmQXEREROQGVZvGJz377jFSbmuddsx95xJWf++8v4mASsFq2hAUL3OrbDRvgrLPgnnvg0CGvk4nPbd3qzqv7/Xd3vNTVV3udSERERIoRlWbxiXGrx3F23bNpWaPlCa9dtQomToS774YaNXwQTgKWMe7e5vXr3b3xL73kyvR33+ls5xLjt9+gfXs30zx3rtsxTkRERCQfVJqlyK3esZrVO1fneQOwhx6CsDC3R49IYahWzd3bvGwZVK0KF10EAwe6zeYkgC1dCp07u5+eLFniZptFRERE8kmlWYrcuNXjKBVUimGnDTvhtQsXwuzZ8MADUKlS0WeTkqV9e/jpJ3jxRXcsb6tW8MILkJzsdTIpdFOmQK9eUKsWfP+9O8hbREREpABUmqVIpaSl8Nlvn9E/vD9h5cKOe6218OCD7vig//zHRwGlxClVyt3bvG4d9O4N990HZ57pJiUlQHz8sVtO0Lq1+xfbqJHXiURERKQYU2mWIjV702x2Ht6Zpw3Apk2D5cvdEVNly/ognJRoDRu6PaEmT3YnEXXqBNdeC3v2eJ1MCsxaeO459y+yZ0+3U3b16l6nEhERkWJOpVmK1NjVYwkrG8aFLS487nVpae5e5ubNtbGt+NaAAbB2rTuBaNw4d7bzJ5/obOdiJy3NbYTwwANw2WUQFQUVKnidSkRERAKASrMUmf1H9jP5z8lcdtpllA4ufdxrv/oK1qyBJ55wy2dFfKl8eXj+efjlF1ear7kGunaFP/7wOpnkydGjMGIEvPoq3H47jB8PpY//Z46IiIhIXqk0S5H5Zu03JKUmMbLN8XfNTk525zKffjoMHeqjcCI5OO00WLwYPvrIFeY2bdxZ4YcPe51McnXokFsu8Pnn8Oyz8MorEKT/tYmIiEjh0XcWUmTGrh5Ly+otOavOWce97pNPYNMmePppfa8r3gsKcjPN69e7ycvnn4dTT4WpU71OJv+yZw/06AFz5sCHH7qfcBjjdSoREREJMKooUiQ27tvI9zHfM/KMkZjjfBN75Ag8/rg7CqhfPx8GFDmB6tXdJsyLFrnl25GRcPHFEBPjdTIB3CHbHTu6+zq+/dZt/iUiIiJSBFSapUiMWz0Og2H46cOPe93bb8Pff8Mzz2iCSPxT587uXudnn4WZM6FlS3j5ZUhJ8TpZCfbHH9ChA+zY4Q52HzjQ60QiIiISwFSapdCl2TQ+XfMpPZv2pH6l+rleFx/visgFF0CXLj4MKJJPpUu7lb9//OF+r959N7RrBytWeJ2sBPr+e3c+WFoaLFni/l5ERESkCKk0S6Fbsm0JWw9sZeQZx98A7OWXYe9edy+zSHHQpIm7t3niRHc7bfv2cNNNsH+/18lKiKlT3fnL1au78ty6tdeJREREpATIU2k2xgw2xrxhjFlijIk3xlhjzPhcrm2c/vXc/vryOO8z0hiz0hhzyBgTZ4xZaIzpf5zryxpjHjfGrDfGJBpjdhljvjbGtDzOmPrGmI+NMX8bY5KMMVuNMa8aY6oeZ0x7Y8x0Y8w+Y0yCMWaNMeYOY0ywl5/FX41bPY4KpSsw6JRBuV6zeze89BIMHgxnHX+fMBG/Yoy7t3ndOrjzTrf/VESEO+XIWq/TBbAxY2DQILcr27Jl0Lixx4FERESkpAjJ43UPA2cAh4BY4JQ8jFkNfJfD87/ndLExZjRwd/rrfwCUBoYBUcaY26y1b2a7vgwwB+gArAJeAxoAQ4B+xpju1tofso1pBnwP1AQmA38C5wC3A32MMR2stXuzjRkITAQSga+AfUAk8Er6ew/x4rP4q4TkBL5Z+w1DWg2hfOnyuV733HOQkODOZRYpjipWdD/4GTHCzTaPGOH2pHr+ed2fX+hefBHuvdfNMn/7rfuHLyIiIuIjeS3Nd+IK4EagC7AgD2N+tdY+lpcXN8a0x5XMTcDZ1tr96c+/CPwEjDbGTLXWbs0y7C5cyZwADLXWpqWP+QpX1j82xrTOeD7d27jCPMpa+0aW9385/TM+DdyU5flKuNKbCnS11q5Kf/4RYD4w2BgzzFr7ZZYxvvosfmnSukkcPHqQK8+4MtdrYmLgrbdg5Ei3qZJIcdamjZv4HDXKdbv9++HddyE413Uokmdpaa4sv/SSO8R93Dh3g7mIiIiID+Vpeba1doG1doO1Rbb4MKOoPp1RMtPfdyvwFlAGuDrjeePOMMoYc2/WMmmtnQwsAVrhCn7GmKZAbyDjNbN6FDgMjDDGZJ0eHQzUAL7MKMzp75GIm30HuNnXn8WfjVszjkaVG9G5Uedcr3nySbeM9dFHfRhMpAgFB8Obb8JDD7nl2pddBklJXqcq5pKT4aqrXGG+7Tb4/HMVZhEREfFEUW4EVtcYc6Mx5sH0x9OPc2339MeZOXxtRrZrAJoBDYFoa+2WPI7J+PvZ2WdsrbUHgWVAOeC8POZaDCQA7dOXV+dlTGF9Fr+0PX47czfPZcTpIwgyOf/Wio52Z9/edBM0auTjgCJFyBh46ikYPRq++QYGDIDDh71OVUwdPuyOkfr0U/cP9bXXIEj7VoqIiIg38ro8uyB6pf+VyRizEBhprf0ry3PlgXrAIWvtPzm8zob0x/Asz0WkP0bn8t4FHdM7fcy8E42x1qYYY7YApwJNgXU+/Cx+6bPfPiPNph13afZ//wuhofDggz4MJuJDd98NVavC9ddD795uw+equW4zKP+ybx/07w8//ADvv+/+QYqIiIh4qCh+dJ8APAmcBVRN/yvjPuiuwLxsS6Arpz/G5fJ6Gc9XKQZjfJXrGMaYG4wxq4wxq3bv3p3bZUXKWsvY1WM5v/75tAhrkeM1v/4KX30Fd9wBtWr5NJ6IT11zDXz9Nfz4I3TtCjt3ep2omPj7b3cQ9k8/uel6FWYRERHxA4Vemq21u6y1/7XW/mytPZD+12LcLO4PQHPguoK8dD6uzdi71h/HFPp7WGvft9a2s9a2q1GjRj6jFI6f//mZtbvXHvds5ocecjNu99zjw2AiHrnkEpg2DTZuhI4dYetWrxP5uaz/oGbMcOd6iYiIiPgBn90kZq1NAT5M/2XWXaIyZlIrk7OcZmJPNKaSR2N89Vn8ztjVYykTXIZLT700x68vXQrTp8N990GVKr7NJuKVXr1g7lzYs8f1wXXrvE7kp9ascf+A4uNh/nzo7vdbOIiIiEgJ4uudVTLWDmcuz7bWHga2AxWMMXVyGJOx1jfrPb/r0x9zu8+3yMcYY0KAJkAKsBl8+ln8ytHUo3zx+xcMiBhA1bL/vnnTWncPc+3abhNckZLk/PNh0SJISYFOnWDVqhOPKVGWLYPOnaFUKViyBM4+2+tEIiIiIsfwdWnO2Jl6c7bn56c/9slhTN9s14A7A/kvINwY0ySPYzLOlu5tzLFbOxtjKuLOST4CrMhjrs643ba/t9ZmPVzGF5/Fr8zYMIM9CXtyXZo9a5b7XviRR6BcOR+HE/EDp5/uVltUrOgmURcu9DqRn5g+3U3H16rlyrMObhcRERE/VOil2RhzrjHmX4dpGmO6A3em/3J8ti+/m/74kDGmapYxjYFbgCTgk4zn08+LzhjzQtYSbIwZCHQC1gKLsozZBMwGMl4zq8dxs9/j0meLM0wA9gDDjDHtsrxHKPBU+i/f8fVn8TdjV4+lZvma9G7W+19fS0tzs8xNmsB1BbmTXSRANG/uinODBtCnD0RFeZ3IY1984Y6VatnS/VStYUOvE4mIiIjkKE9HThljBgGD0n9ZO/3xfGPMmPS/32Otzdje6Xng1PTjpWLTnzud/50z/Ii19vusr2+t/d4Y8zJwF7DGGDMBKA0MBaoBt1lrt2aL9TLQHxgM/GCMmYc773gIbgfva7Kfxwz8B/geeN0Y0wNYB5wLdMMtf34oW654Y8z1uPK80BjzJbAPGIA7KmoC8JVHn8Uv7E3Yy9Toqdx6zq2UCi71r69PnAi//ALjxkHpf/0oRaRkqVcPFi+Gvn3hootgzBi44gqvU3ng7bfh1lvdsuwpU6BSpROPEREREfGIcROdJ7jImMeAR49zyTZrbeP0a68FLgJOA6oDpYCdwHLgTWvtkuO8z0jgVqAVkAb8DLxorZ2ay/VlgfuBy3ElMx5YCDxqrV2by5gGwBO45dNhwD/Ad8Dj1tp9uYzpgCvU5wOhwEbgY+B1a22qV58lJ+3atbOrfHjT5Fsr3+LWGbfyy42/0KZ2m2O+lpICp50GISGwejUEB/sslohfO3jQTbIuWABvvOH6Y4lgLTz5JDz6KAwY4M6gCw31OpWIiIgEGGPMT9badie+Mo+vl5fSLMWHr0vzuR+eS2JKIqtvWv2vr338MVx7LUyaBIMG+SySSLGQmAjDhsHkya5HPvQQGHPiccVWWhrceSe8/jqMHAkffuh+oiYiIiJSyAq7NPt6IzAJIH/u+ZOV21dy5elX/utriYnw2GNwzjluRk1EjhUaChMmwIgRbpO8u+92E7EBKTnZFeXXX4c77nA/UVNhFhERkWJC37VIgX26+lOCTBDDTx/+r6+99x7ExMAnnwT47JnISQgJcfc1V6kCr7wCBw7A++8HWJ88cgSGDnU7nz31lNsZUH8oiIiISDESSN+aiY/9vONnzqh1BrUr1D7m+YMH4emnoUcP95eI5C4oCF57DapVg8cfh7g4+PxzKFPG62SFIC4OIiPdtuFvvw033+x1IhEREZF80/JsKbDY+FgaVv73MTGvvQa7d7viLCInZoy7neHVV+Hbb6F/fzh0yOtUJ2nnTujaFZYvdz8FUGEWERGRYkqlWQosNj6WBpUaHPPc3r3w4otu469zz/Uml0hxdfvt7paG+fOhVy/Yl+N+/sXA1q3QsSNER7tl2cOGeZ1IREREpMBUmqVADh09xIHEA9SvVP+Y5194wS3PfvJJj4KJFHNXXeU2CPv5Z+jSBf75x+tE+bR2rSvMe/bAnDnQp4/XiUREREROikqzFEhsfCzAMaX577/d5rhXXOHOZxaRgrnoIpg+HbZscf1zyxavE+XRDz9Ap06QmgqLF0P79l4nEhERETlpKs1SIBmluUHl/y3Pfuop973y4497lUokcPToAfPmwf790KED/PGH14lOYO5cF7pKFVi2DFq39jqRiIiISKFQaZYCiYmLAf4307xpE3zwAVx/PTRp4mUykcBx7rluwhagc2dYudLbPLmaOBH69YOmTd1O2U2bep1IREREpNCoNEuBZMw016tYD3A7/5YqBQ8/7GEokQB02mmuh1au7CZy58/3OlE2H3wAl14K7drBokVQp47XiUREREQKlUqzFEhsfCw1y9ekTEgZfvsNPvsMRo3S98siRSFjArdxY+jbF777zutE6Z5/Hm64AXr3htmzoWpVrxOJiIiIFDqVZimQmPiYzKXZjzwClSrBvfd6HEokgNWt6yZyzzwTBg92E7wpKR6Fsdb9B3///XDZZTB5MpQv71EYERERkaKl0iwFknFG84oV7vvl//s/qFbN61Qiga1aNbffVteuboK3Vi248kp3S/GhQz4KkZLiNi948UX4z39g/HgoXdpHby4iIiLieyrNUiAx8THUq1ifBx+EmjXh9tu9TiRSMlSoADNmuLOc+/WDqVPdzHP16u7X779fhGc7JybC0KHw0Uduicmbb0KQ/jciIiIigU3f7Ui+HTp6iAOJB0ja0IEFC+Chh9w38iLiG6VKwSWXwLhxsGsXLFgAN98M69bBjTe6pdznnQfPPgtr17rV1Cft4EHXyr/9Fl59FZ54AowphBcWERER8W/GFsp3U+Iv2rVrZ1etWlWk77F+z3pOeesUzlu+hS2/NmbbNihTpkjfUkTywFr4/Xd3y8TkyZDxR0Hz5jBwoPurfXsIDs7nC+/ZAxdeCD//DB9/7NaEi4iIiPgpY8xP1tp2hfV6mmmWfIuJd2c0x+2oxumnqzCL+AtjoHVrd/Tbjz9CTAy8/TY0awavv+7Oeq5dG66+2u3AffhwHl70r7+gY0dYs8bNMqswi4iISAmj0iz5lnFG894d5WjY0OMwIpKr+vXdsu2ZM91k8VdfudOhJk2Ciy5y90EPGOBuUd65M4cX+OMPNzW9Y4c7UmrAAJ9/BhERERGvhXgdQIqf2PhYSCnNrp0hKs0ixUSlSnDppe6v5GRYvPh/y7ijotws9fnnuyXcAwbAKfu+h/793VKSxYvh9NO9/ggiIiIintBMs+RbTFwM1ZLdN9AqzSLFT6lS0KOHW7K9dSv88gs8+igcOQL33QctW0JEx+rcy/Mse/0nUk9VYRYREZGSS6VZ8i32YCzVjrYBVJpFijtjoE0bV5p//hm2vTSBN8woGlbYzysHr6PjpXWpWxfuuAMSErxOKyIiIuJ7Ks2Sb7HxsVRMPBVQaRYJKC++SMO7h3Br97XM2d6K3bsNn38OXbu6WenzzoMNG7wOKSIiIuJbKs2SbzFxMZQ62AxwGw2JSDGXlgb33AP33utuep42DSpWpEoVuOwyt4HYjBmwfTu0a+d23hYREREpKVSaJV8OHz3M/sT92LgG1KoFoaFeJxKRk5KcDFddBS+9BLfeCl98keM5chdc4JZvh4e7nbfvuw9SUnwfV0RERMTXVJolXzKOm0rcW1NLs0WKu8OHYdAg+PRTeOIJtwY7KPf/LTRqBEuXwk03wQsvQK9euRxVJSIiIhJAVJolXzJKc/yuyirNIsXZ3r3Qs6c7xPm99+CRR9yuYCdQpgy88w6MHQsrVsCZZ8KyZT7IKyIiIuIRlWbJl9j4WLCw+5+yKs0ixVVMDHTq5M6a+uYbuOGGfL/ElVe60lyunNso7NVXwdpCTyoiIiLiOZVmyZeY+Bg4UpWEw0EqzSLF0bp10L6929Vr5ky4+OICv9QZZ8CqVdCvH9x5JwwbBgcPFmJWERERET+g0iz5EhsfS5WjZwA6bkqk2FmxAjp2dJt/LVrkpohPUpUq8O238NxzMGECnHOO6+UiIiIigUKlWfIlNj6WakkqzSLFzowZ0KMHVK3qbkJu06bQXjooyO2mPXcu7NsHZ5/tjqkSERERCQQqzZIvMfExlEtoCag0ixQb48fDgAEQEeEKc7NmRfI23bq5Y6nOOMMt1b7jDjh6tEjeSkRERMRnVJolX2LjYwk52JQyZaBGDa/TiMgJvfwyjBjhNv5auBBq1SrSt6tXz73N7bfDa6+5Ir19e5G+pYiIiEiRUmmWPEtITmDfkX2kHqhHgwZ5Op1GRLxirVszfffdMHgwTJ8OlSr55K1LlXK7aX/5JaxeDW3bwoIFPnlrERERkUKn0ix5lnFG85E91bU0W8SfpaTANdfACy/AzTe79hoa6vMYQ4fCypVQrZo7Evr553UslYiIiBQ/Ks2SZxml+cCuyirNIv4qIQEuugjGjIHHHoO33oLgYM/itGrlivPgwXD//S5aXJxncURERETyTaVZ8iwmLgZSQ9i7s7RKs4g/2r8feveGadPg7bfh0Uf94j6KihXdZPcrr7ho7drBmjVepxIRERHJG5VmybPY+FiIr4e1RqVZxN9s3+42+/rxR/j6a7cs248Y43bTXrAADh+G886DceO8TiUiIiJyYirNkmex8bFUTjod0HFTIn7lzz+hfXv46y93HvPgwV4nylXHjvDLL3DuuTBypOv2SUlepxIRERHJnUqz5FlMfAyVk1oDKs0ifmPlStdEExPdWU/du3ud6IRq1YI5c9zm3u++6+Jv2+Z1KhEREZGcqTRLnsXGx1L2cAQADRp4HEZEYNYsV5IrV4Zly9zZTsVESAg89xxMmgTR0S76rFlepxIRERH5N5VmybPY+FiC45tQvTqUK+d1GpES7vPPoX9/aN7cFebmzb1OVCCDBsGqVVCvHvTtC088AWlpXqcSERER+R+VZsmThOQE9h7ZS8r+OlqaLeK1N96A4cOhQwdYtAhq1/Y60Ulp0QJWrIArrnAbfvfvD3v3ep1KRERExFFpljzZHr8dgMN7wlSaRbxirZuKHTUKBg6EmTPd0uwAUK4cjB0L77wD8+ZBy5bw5JMqzyIiIuI9lWbJk9j4WAAO7Kyo0izihbQ0uPNONxU7ciRMmAChoV6nKlTGwE03wfLlcM458N//uk0HR42CLVu8TiciIiIllUqz5ElMfAwkVuLwoRCVZhFfS0mBa6+F116D22+Hjz92O2kFqLZtYepU+P13uPRSt8N28+Zw2WXw889epxMREZGSRqVZ8iQ2PhbiXFtWaRbxocREGDIExoyBxx+HV16BoJLxR/epp8Inn7hZ5rvvhmnT4KyzoGdPt9O2tV4nFBERkZKgZHznJSctJi6GikdOA1SaRXzm4EG3K9Z337lZ5v/+161hLmHq1YMXXoCYGPe4bh306QNt2sD48ZCc7HVCERERCWQqzZInsQdjqZSo0iziM/v2uSnVhQvdDlmjRnmdyHOVK8P//Z+bef7kE7dqfcQIaNbMTcAfPOh1QhEREQlEKs2SJ7HxsYQmhFOqFNSq5XUakQD399/QuTP8+itMnAhXXul1Ir9SujRcdRX89pu797lpU7jrLvcDvQcfhB07vE4oIiIigUSlWfIkJi4GE9eIBg1KzO2UIt7YvBk6doRt22DGDHe0lOQoKAj69XOT8T/84Cbmn3sOGjWC66+H9eu9TigiIiKBQPVHTuhI8hH2HtlL8r7aWpotUpR+/90V5rg4d1hx9+5eJyo2zjkHvvkGoqPdRuPjx7uzngcNgmXLvE4nIiIixZlKs5zQ9oPbATi0p5pKs0hR+eEHtyTbGFi82LVAybfmzeHtt91E/SOPwJIl7ucQHTq4/dTS0rxOKCIiIsWNSrOcUExcDKQGs39XOZVmkaIwdy706AFVq8LSpe6sJTkpNWu6E7r++gtef93dJn7RRdCqFXzwgTvJS0RERCQvVJrlhGLjY+FQHdJSg1SaRQrbpEnuxtymTV1hbtLE60QBpXx5uO022LABvvzS/fqGG6BxY3jmGdi/3+uEIiIi4u9UmuWEYuJjIM61ZZVmkUI0ZgwMHgxt27rdrOrU8TpRwAoJgaFDYdUqd7t4mzbw0EPQoAHceSfExnqdUERERPyVSrOcUGx8LOWPtAJUmkUKzauvwtVXu2XZc+ZAtWpeJyoRjHH7q82c6U70uugiePNNiIiAl15yZz+LiIiIZKXSLCcUGx9LpSPuHssGDTwOI1LcWQuPPuqmNy++GKKioEIFr1OVSGecAZ9+6pZu9+gB99wD7dq5PdlEREREMqg0ywnFxMdQ+lBzqlbV9/YiJyUtDW6/HZ54ws0yf/UVlCnjdaoSr3FjmDwZvv0W9uyB88+HW291J3+JiIiIqDTLCcXGx2LjGmpptsjJSE6Gq66CN96Au+6Cjz5yN9qKXzDGLdVeu9ZtHPb22+6c5wkT3OIAERERKblUmuW4ElMS2ZOwh6P7aqk0ixRUYqLb8OvTT+Gpp2D0aNfSxO9UqgSvveaWaNeuDUOGQGQkbN3qdTIRERHxikqzHFdsvNtSNn5XFZVmkYI4eBAuvBCmTHE7Tj30kApzMXD22bByJbz8stvY/NRT4cUX3YIBERERKVlUmuW4YuNjIbEiCQfLqDSL5NeePW6r5sWLYfx4uOUWrxNJPoSEuP3a1q6Fnj3h3nvdRmErVnidTERERHxJpVmOKzY+FuLdltkqzSL5sH07dOkCv/0GkybB8OFeJ5ICatjQbRQ2aRLs3Qvt28N//gMHDnidTERERHxBpVmOKyYuBuJcW1ZpFsmjjRuhY0eIiXEHAkdGep1ICsGgQbBuHYwaBe+95zYK+/prbRQmIiIS6FSa5bhi42Mpl3AKoNIskidr1rjCfPAgzJ8PXbt6nUgKUcWK8OqrbqOwunVh6FDo1w+2bPE6mYiIiBQVlWY5rpj4GCocaUVwMNSp43UaET+3fLlbkh0SAkuWuBtgJSC1a+eK86uvun/Vp54Kzz+vjcJEREQCkUqzHFdsfCwhB5tRvz4EB3udRsSPzZnjdouqXh2WLnVrdyWghYTA7be7jcIuuADuvx/OOsv97EREREQCh0qzHFdsfCz2QH0tzRY5ngkT3Brd5s3dtGPjxl4nEh9q0MBtEjZpEuzfDx06wM03a6MwERGRQKHSLLlKTElkd8JujuytqdIskpv33oNLL3UH+y5cCLVre51IPDJokJt1vv12eP99OOUU+OorbRQmIiJS3Kk0S662x2+HtCAO7qmk0iySnbXw1FNw001w4YVueXbVql6nEo9VrAivvAI//gj168OwYdC3L2ze7HUyERERKSiVZslVbHwsHKpFakqQSrNIVmlpcMcd8MgjMGKEW5dbrpzXqcSPtG3rNgp7/XVYtsxtFPbcc9ooTEREpDhSaZZcxcTrjGaRf0lOhiuvdG3ozjthzBgoVcrrVOKHgoPhttvc2c4XXggPPABnnulKtIiIiBQfeSrNxpjBxpg3jDFLjDHxxhhrjBl/gjHtjTHTjTH7jDEJxpg1xpg7jDG57sFsjBlpjFlpjDlkjIkzxiw0xvQ/zvVljTGPG2PWG2MSjTG7jDFfG2Ny3bbWGFPfGPOxMeZvY0ySMWarMeZVY0yu6yr99bMUtdj4WJVmkawOH4aBA+Gzz+CZZ+CllyBIP3uU46tfHyZOhMmTIT7eHeN9++1w9KjXyURERCQv8vrd3sPArUAbYPuJLjbGDAQWA52BScBbQGngFeDLXMaMBsYAdYAPgPFAayDKGHNrDteXAeYA/wXigdeAucBFwCpjzLk5jGkG/ARcDaxMz7MZuB1YbowJKy6fxRdi42Mpm3AKoNIswr590KsXzJrldnl64AEwxutUUowMGOA2Crv1VrdQoWtXiI31OpWIiIiciLF52NbTGNMNiAU2Al2ABcBn1torcri2Uvp1lYEO1tpV6c+HAvOB84HLrLVfZhnTHlgGbALOttbuT3++Ma7klgdOsdZuzTLmAeAZYAIw1Fqblv78QOA7YC3QOuP59K/NAnoDo6y1b2R5/mXgTuA9a+1NxeGz5KZdu3Z21apVJ7osTwZ+OZBl711Kyi/DdXSKlGzbt7uDeDdsgC++gIsv9jqRFHNffw3XXgtly7rfUj16eJ1IREQkcBhjfrLWtius18vTTLO1doG1doPNS8OGwUAN4MuMkpn+Gom4GWuAm7ONySiqT2eUzPQxW3Ezu2Vws8MAGGNMljH3Zi2T1trJwBKgFa7gZ4xpiivMGa+Z1aPAYWCEMaa8v38WX4mNjyX4YFPNMkvJFh3tDt7dtg1mzFBhlkJx6aWwciVUrw69e8Ozz7r95URERMT/FMXNeN3TH2fm8LXFQALQPn1Jcl7GzMh2DUAzoCEQba3dkscxGX8/O/uMrbX2IG52uBxwXh5zeflZfCImLobU/fVUmqXk+ukndwNqQoI7g7m7z/8zlADWsqUrzkOGwIMPwkUXoVU9IiIifqgoSnNE+mN09i9Ya1OALUAI0BQgfWa3HnDIWvtPDq+3If0xPC/v4asxHn+WIpeYksjuhN0c2VNdpVlKpgULoFs3d5TU0qVw1lleJ5IAVKGCW5792mswfbr7bfbrr16nEhERkayKojRXTn+My+XrGc9XKeD1/jzGV7mK3N8H/4aj5UiIL6fSLCXPt99Cnz5uB7xlyyDcpz+zkhLGGBg1ChYtgsREOP98GDvW61QiIiKSwYuzUjK2m83L/dFZ5ef6gryHr8YU+nsYY24wxqwyxqzavXt3PqPkLCYuBuIaANo5W0qYDz5w62XPOgsWL4Z69bxOJCVE+/bwyy+uNF91Fdx4oyvRIiIi4q2iKM0ZM6OVc/l6pWzXnej6nGZi8/sevhrjq89yDGvt+9badtbadjVq1MjtsnzRGc1S4ljrdmO64Qa3U/acOVCtmteppISpWRNmz4b773cnm3XsCFu3ep1KRESkZCuK0rw+/fFf6xmNMSFAEyAFdz4y1trDuLOfKxhj6uTwei3SH7Pe85vre/hqjMefpchlLc0NGvjynUU8kJYGd9/tdmO6/HKYPBnKlz/xOJEiEBLifn7z3XfulLOzzoKZOW0tKSIiIj5RFKV5fvpjnxy+1hm3Q/X31tqkPI7pm+0acGcg/wWEG2Oa5HHMgvTH3saYYz63MaYi0AE4AqzIYy4vP0uRi4mPoczhcIKCoG5dX76ziI8lJ7u1sK+84m4s/fRTKFXK61QiDBzoNnCvXx8uvBAee0zHUomIiHihKErzBGAPMMwYk3mgtDEmFHgq/ZfvZBvzbvrjQ8aYqlnGNAZuAZKATzKeTz8vOmPMC1lLsDFmINAJWAssyjJmEzAbyHjNrB4HygPj0meL/fqz+EJsfCxlEyKoW1f9QQJYQoI75+fTT+HJJ+HVVyHIi60eRHLWvDksXw4jRsDjj0O/frB3r9epRERESpaQvFxkjBkEDEr/Ze30x/ONMWPS/36PtfYeAGttvDHmelzhXGiM+RLYBwzAHa80Afgq6+tba783xrwM3AWsMcZMAEoDQ4FqwG3W2q3ZYr0M9AcGAz8YY+bhzjsegjs/+Zrs5zED/wG+B143xvQA1gHnAt1wy58fypbLnz9LkYqNjyUovrHuZ5bAtX8/REbC99/DO+/ATTd5nUgkR+XKwZgx0KED3HYbtG0LEybA2Wd7nUxERKRkMG6i8wQXGfMY8OhxLtlmrW2cbUwHXAk9HwgFNgIfA69ba1NzeZ+RwK1AKyAN+Bl40Vo7NZfrywL3A5fjSmY8sBB41Fq7NpcxDYAncMunw4B/gO+Ax621+3IZ45efJSft2rWzq1atyuvluao1uhaJL/3GhV1r8sUXJ/1yIv7l77/dkVJ//gmffeZ2yxYpBn78EQYPhh074I034Prr3ZFVIiIi8j/GmJ+ste1OfGUeXy8vpVmKj8IozUkpSYQ+WZbgZ49y950hPP98IYUT8QcbN0KvXrB7t9tpqWdPrxOJ5MvevTB8OMyaBSNHwttvu9loERERcQq7NOvmPfmX7Qe3w+GapCaHaHm2BJZffnFrXA8dggULVJilWAoLg2nT4NFHYdw4d77zxo1epxIREQlcKs3yLzqjWQLSokXQtSuUKQNLluiGUCnWgoPdbtrTpkFMDLRrB1OmeJ1KREQkMKk0y7+oNEvA+e47uOACqFfPbfx1yileJxIpFH37umOpmjd3R1Q9+CCkpHidSkREJLCoNMu/xMTFqDRL4Pj4Y7jkEmjTxs0w16/vdSKRQtW4MSxdCjfcAM8+634+tGuX16lEREQCh0qz/EtsfCylD7WgQgWoUsXrNCIn4YUX4Npr3b3Lc+e6m0FFAlBoKLz3HnzyiVtM0batO99ZRERETp5Ks/xL7MFYQg+3oGFDHWUixZS18H//B/fdB8OGQVQUVKjgdSqRInfVVa4sh4ZC587uWCodkiEiInJyVJrlX2LiYiC+oZZmS/GUkgJXXw2jR8Mtt7hzmEuX9jqViM+0aQOrVrn7nUeNgssvdxvGi4iISMGoNMu/xMbHcnRfbZVmKX6OHIGLL4axY93Wwm+8AUH6Y05KnipV3P53zzwDX38N554Lf/7pdSoREZHiSd9NyjGOph5l54E4EuMqqTRL8XLggNsBaepUeOstd4it7i+QEiwoCB54AGbPht273SlrEyZ4nUpERKT4UWmWY2yP3w5xDQDtnC3FyI4d7gzmFSvgiy/gP//xOpGI3+jRA37+GU47DYYMgbvvhuRkr1OJiIgUHyrNcozY+FiIV2mWYmTTJujQATZudLPMQ4d6nUjE79SvD4sWwW23wcsvQ/fu8M8/XqcSEREpHlSa5Rgx8TqjWYqR1atdYT5wAObNg969vU4k4rdKl4bXX4fPP3czz2ee6Yq0iIiIHJ9KsxwjNj4W4hpijKVePa/TiBzHkiXQpQuUKgVLl7qdjkTkhC67DFaudJuF9ejhNprXsVQiIiK5U2mWY8TGx1LqUHPq1DE6pUf8V1SUm1WuXRuWLYOWLb1OJFKsnHqqK84XXeSONB88GOLivE4lIiLin1Sa5Rgx8TGUPtRcS7PFf40Z477Tb93azTDrN6tIgVSq5I6jevllmDzZ7a79++9epxIREfE/Ks1yjNj4WOyBBuoh4p9Gj4arr4Zu3dw9zNWre51IpFgzBu68ExYsgIMH3V0On33mdSoRERH/otIsx4iJiyVpX02VZvEv1sJ99/1vHenUqVCxotepRAJGp07wyy/Qrh1ccQXccgskJXmdSkRExD+oNEumo6lH2bkrldSjpVWaxX+kpMB118ELL8BNN8GXX0KZMl6nEgk4tWu7BRz33ANvvw2dO8Nff3mdSkRExHsqzZLp74N/Q5zOaBY/kpgIQ4bAxx/DI4+47+SDg71OJRKwQkLgxRdh4kRYtw7atoU5c7xOJSIi4i2VZskUE/e/M5obNPA4jEhcHPTpA999B6+9Bk884W7AFJEid/HFsGoV1KkDF1wATz0FaWlepxIREfGGSrNkyjijGTTTLB7buRO6dnXHSX32GYwa5XUikRInPBxWrIDLL3cLPQYMgP37vU4lIiLieyrNkimjNJctawkL8zqNlFhbtkDHjrB+PUyZ4r5jFxFPlC8Pn34Kb70Fs2e75do//+x1KhEREd9SaZZMMfExhBxsRsOGRqtgxRu//QYdOsDevW5Hor59vU4kUuIZA//5DyxZ4vbla98ePvrI61QiIiK+o9IsmWLjYwk52FRLs8UbS5e67XqNcd+dn3++14lEJItzz3WzzJ06uQ3tr70WjhzxOpWIiEjRU2mWTLHxsaQdqK/SLL43bRr06gU1arj7mE891etEIpKDGjVg5kx4+GG3qX2HDrB5s9epREREipZKs2T6a99OjsZVU2kW3/r0Uxg4EFq1crPNjRt7nUhEjiM4GJ58EqZOdVsQnHWW+3sREZFApdIsABxNPcrOv0sB2jlbfOiVV+DKK6FLF1iwAGrW9DqRiORRv37w00/QpAlERrodtlNTvU4lIiJS+FSaBYB/Dv4Dce5wZpVmKXLWwkMPwV13uQNhp02DSpW8TiUi+dS0qbuj4ppr3FnOffrA7t1epxIRESlcKs0CuJ2zdUaz+ERqKtx0EzzzDFx/PXz9NYSGep1KRAqobFm3m/aHH7o9/Nq2dQtHREREAoVKswD/O6MZoH59j8NI4EpKgqFD4f334cEH4b333A2SIlLsXXstfP89lCkD3bu7Oy927fI6lYiIyMlTaRYAYuLcTHPNWmma9JOicfAgXHghTJzo7mV++ml0ILhIYGnbFtascT8T+/JLOOUU9zOytDSvk4mIiBScSrMAbqY5+GBTGjXUbwkpArt3Q7dusGgRjBsHd9zhdSIRKSLlyrmfif36K7RuDTfeCB07wurVXicTEREpGDUkASD2YCzB8Y11P7MUvr/+ct8x//EHTJ4MI0Z4nUhEfKBVK1i4EMaMgQ0b3NFU99wDhw55nUxERCR/VJoFgL8OxJCyv55KsxSudeugQwfYuRPmzHFn1IhIiWEMjBwJ69e7HbZfeglatoTvvnOb6IuIiBQHKs0CQMyOw6QdDVVplsKzahV06gTJyW5ZdseOXicSEY9Uq+bubV62DKpWhYsugoEDYds2r5OJiIicmEqzkJyazM6/ywA6bkoKyfz57h7mihVh6VI44wyvE4mIH2jfHn76CV58EebNc0u4X3jB/WxNRETEX6k0C38f/Bvi3DlTKs1y0iZNgr59oVEjN63UvLnXiUTEj5Qq5e5tXrcOeveG++6DM890P18TERHxRyrNcswZzSrNclI+/hgGD3bnzixeDHXrep1IRPxUw4buZ2yTJ7sT6Tp1cmc979njdTIREZFjqTRLZmkuXSaNGjW8TiPF1ujR7jvenj1h7lx3E6OIyAkMGABr18K997oT6U45BT75RBuFiYiI/1BpFmLiYyCuIQ0aWIzxOo0UO9bCAw/A//0fDBkCU6ZA+fJepxKRYqR8eXj+efj5Z1ear7kGunRxJ9WJiIh4TaVZiI2PJehgExo30m8HyafUVLjxRnjuOff4xRdQpozXqUSkmGrd2t3Z8eGHrjC3aeN+JpeQ4HUyEREpydSShJj4GILiG9GwoaaZJR+SkmDYMPjgA3jwQXjnHQgO9jqViBRzQUHuTo8//4QrrnA/kzv1VJg2zetkIiJSUqk0C3/t+4eUuJraBEzy7tAhiIyECRPgpZfg6afR2n4RKUw1arh7mxcuhLJloX9/uOQSiI31OpmIiJQ0Ks3CX7GpYINo0MDrJFIs7N3rNvuaN8/tln3XXV4nEpEA1qUL/PorPPsszJgBLVvCK69ASorXyUREpKRQaS7hklOT2bU9FNBxU5IH27dD587uO9iJE+Hqq71OJCIlQOnScP/97j7nzp3dz+ratYMVK7xOJv/f3n2HWVFlex//LnIGSZIzAoI6ChiIgooYUPCCF3MaUQdFFGeujs4gZq+OiGFGcMQABkzARUFRQQFFFEwvikqmyUmaZGND7/ePfVrbnm7ohtO9zzn1+zwPT3XXqaqz2i6rz6raey0RkShQ0hxx63aug3T/iFlJs+zX4sXQuTOkpfnHPX37ho5IRCKmaVN46y1/z27zZujUCa691vd5FhERKSpKmiMuu0czoOHZkr+vvoIuXWDXLpg5E3r0CB2RiESUGZx3HixaBDfd5GsRnnoqbN0aOjIREUlVSpojLi3d92g+rMZeKlQIHY0kpNmz/aTCsmX91+3bh45IRITKlX0dwjff9Pf1uneHdetCRyUiIqlISXPEZT9p1tBsydNbb0GvXlC3LsyZA61bh45IROR3zj0Xpk6F5cuha1dYsSJ0RCIikmqUNEfc6u2rKbG9CU0bq7+u5PLii37ecrt2/gmz7qyISII65RR4/31f3L9LF9/jWUREJF6UNEfcqvQ0SG9Mo0bqsSs5PP44XHyxL1M7Y4ZvmCoiksBOPBE++si3ouraFb74InREIiKSKpQ0R9zKDdvI2lNRDxHFcw7uvBOGDPFPmadO9RMHRUSSwNFH+4ExFSr4eoVz5oSOSEREUoGS5ohbtcovlTQLWVk+WR4xwvdffu01KFcudFQiIoXSsqVPluvU8SUZ3n03dEQiIpLslDRHWOa+TDatLQ8oaY68zEy45BJ44gkYNgyeeQZKlQodlYjIQWnY0D9xbtUK+vTxfZ1FREQOlpLmCFu/cz2k++bMSpojbPduPxT7pZfgvvvgoYd8I1QRkSRWu7ZvK9+xI5x/Pjz7bOiIREQkWSlpjrC07b5Hc6nSWRx+eOhoJIht2+D002HaNBg9Gm67TQmziKSMatVg+nRfXfvKK2HUqNARiYhIMlLSHGHZPZrr1MukhM6E6Fm/Hk4+GebNgwkTYNCg0BGJiMRdxYowZQr06wdDh8Jdd/mahyIiIgWlVCnCspPmJo11GkTO8uW+menixfDWWzBgQOiIRESKTNmy8OqrcNllMHw43HKLEmcRESk4VfqJsLT0NGz7AJo10WkQKQsX+pKyGRnwwQe+uamISIorVQrGjoUqVeCRRyA93c9KKVkydGQiIpLolC1F2Kpta3Hb69GokeawRsbcuXDWWb6V1KxZ0K5d6IhERIpNiRJ+XnPVqnDPPbBjB4wbB2XKhI5MREQSmZLmCFu+ag+4kqqcHRXTp/tJffXq+a+bNg0dkYhIsTODu+/2ifOf/+wT59dfhwoVQkcmIiKJSpNZIyxtlX/CrKQ5Al59Fc4+G1q2hDlzlDCLSOTdcguMGQPvvANnnAHbt4eOSEREEpWS5ojam7WXLesrAkqaU97o0TBwIJxwAnz4IeovJiLiXX21b1H/ySfQsyds3hw6IhERSURKmiNq3Y51uPT6ADRsGDgYKRrOwX33wbXXwplnwrvv+qalIiLyq4EDYdIk+PZb6N4d1qwJHZGIiCQaJc0Rld1uqnK1X6hUKXQ0EnfO+bGHt98OF14IEydqwp6ISD7OOssP0161Crp2hWXLQkckIiKJRElzRGUnzfUa7AsdisTb3r1w5ZW+p8r11/vSsKVLh45KRCShde8OM2b4VlRduvgnzyIiIqCkObLStqdBeiOaNVaDypSSkQH9+8Nzz8Gdd8Jjj/keKyIickAdO/pufADdusHnn4eNR0REEoM+TUdU9pPmZk30BDJlbN/u5y5PnuyT5eHDfW8VEREpsLZtfZOBqlXhlFPgo49CRyQiIqEpaY6o5eu3QMZhNGqkpColbNrkS7/OmgXjx8MNN4SOSEQkaTVrBrNnQ4MG0Ls3vP126IhERCQkJc0RtWxlJqB2Uykhu3LNt9/6p8wXXRQ6IhGRpFe/vr8P2bYt9O0LEyaEjkhEREJR0hxRa1b7ucxKmpPc999D586wbh1Mn+5LwIqISFzUrOmLg3XqBBdcAE8/HToiEREJQUlzBO3N2svWdb7PlJLmJDZ/vn/CnJnpJ9117Ro6IhGRlFOlCkyb5odpDxoEDz8cOiIRESluSpojaP3O9bj0BpQomUXduqGjkYMycyb06AGVKvmKNX/4Q+iIRERSVoUKMGkSDBgAf/6zH669alXoqEREpLgUWdJsZivMzOXzb30++3Qys6lmttXMdpvZN2Y21Mzy7YtkZpeZ2WdmttPM0s3sQzM7ez/blzezEWb2g5llmNlGM3vVzNrsZ58GZjbWzNaa2Z7Yz/aomR22n32K/Gc5WNmVs2vWyaCkOk4ln0mT/COPxo19wtyiReiIRERSXpky8PLL8OCDfjZMmzb+qXNmZujIRESkqBX1k+Z0YEQe//5jcJOZnQvMAroBE4EngTLASOCVvA5uZg8DzwF1gaeB8cBRwBQzuz6P7csC7wF/B7YDo4D3gX7AfDM7IY99mgMLgCuAz2LxLANuBOaaWY0QP8uhSEv3PZobNMyK52GlODz7LPzXf8Gxx/oKNfXrh45IRCQySpaEv/wFvvvONyz485+hfXv45JPQkYmISFEy51zRHNhsBYBzrkkBtq0CLAGqAp2dc/Nj68sBM4CTgAucc6/k2KcT8DGwFOjonPsptr4JPsmtCLR2zq3Isc9twH3A68B/O+eyYuvPBSYB3wFHZa+PvfYu0AsY4px7PMf6R4CbgNHOuWuL+2fJT4cOHdz8+fP3u83IuSO5+cy+9O9dj9deLnugQ0qi+Mc/4JZb4LTT4M03/dBsEREJwjnfsOCGG2D1arj6anjgAahePXRkIiJiZguccx3idbxEmdPcH6gFvJKdZAI45zKAO2LfXpdrn+xE9d7sJDO2zwr8k92y+KfDAJiZ5djnLzkTY+fcZGA2cCTQPcc+zfAJc/YxcxoO7AIuMbOKxfmzHKpVP62B7Q1o0aRMvA4pRck5+OtffcI8YABMmaKEWUQkMDM/t3nRIhg2DMaOhVat4IUX/GVbRERSR1EnzWXN7GIz+6uZ3WhmPfKZ09sztnwnj9dmAbuBTrHh1QXZZ1qubQCaA42AH51zywu4T/bX03Mm2QDOuR34p8MVgBMLGFe8fpZDsiRtB2SVpnFji9chpajs2wfXXgv33+/Ltr78MpTV6AARkURRqZKf2/zFF9CyJVx2ma/TuGhR6MhERCReijpprgOMA+4FHsUPT15sZt1zbdcqtvwx9wGcc3uB5UApoBlA7MlufWCnc25dHu+7OLY8oiDvUVz7xPFnOSQrVvpb4Go3leB++cU3Bh0zBm67DZ56ClVuExFJTEcf7WszjhkD33wDxxwDt98Ou3eHjkxERA5VUSbNzwKn4BPniviiVqOBJsA0Mzsmx7ZVY8v0fI6Vvb7aQW6fyPsczHv8jpkNMrP5ZjZ/06ZN+W32q3WrSwNKmhPazp3Qpw+89pp/hHHffX4soIiIJKwSJfzc5u+/9/c877sP2rXzfZ5FRCR5FVnS7Jwb4Zyb4Zzb4Jzb7ZxbGCuY9QhQHrizEIfLzhYKO0uoMNsfzHsU1z773d45N8Y518E516FWrVr7Pci+rH1sXe/nwyppTlBbt/piX++/7yfJDRsWOiIRESmE2rXh+edh5kw/o+bMM31JijVrQkcmIiIHI0QhsKdiy2451mU/Ta1K3qrk2u5A2+f19Law71Fc+xzMz3LQ1u9cj0tvQPlKe6hS5cDbSzFbswa6dfOT415/Ha6IW/03EREpZiefDF9/DffeC2+9Ba1bw6hRsHdv6MhERKQwQiTNG2PLnBWnf4gt/2PerpmVApoCe/H9kXHO7QLWAJXMrG4e79Eytsw5rzjf9yiufeL4sxy0tO2+R/Ph9fbE43AST0uWQJcusHIlvPMO9OsXOiIRETlEZcr4Bgjffusv8UOHwvHHw2efhY5MREQKKkTSfFJsuSzHuhmxZe88tu+Gr1D9iXMuZ6a3v33OyLUN+B7Iq4AjzKxpAfeZGVv2MrPf/bcys8pAZ+Bn4NMCxhWvn+Wgrd6+GtIb0VBDsxPL11/7T1M7dvjxfD16hI5IRETiqFkzmDrVl6rYsAFOPBH+9CfYti10ZCIiciBFkjSbWVszq57H+sbAE7Fvx+d46XVgMzDQzDrk2L4ccE/s23/lOlz2MO/bzeywHPs0AQYDe/DFyABwzrkc+/xvziTYzM4FugLfAR/l2GcpMB1fvGxwrvcfgX9a/kLsaXGx/SyHIjtpbtFUPZoTxuzZ0L07lC7tv+4Qtz7sIiKSQMygf3/fjmrIEBg92g/Zfukl9XYWEUlkRfWkeQCw1symmdk/zexBM3sd+B5oAUwFHs7e2Dm3HbgaKAl8aGb/NrP/Bb7CP5l+HZiQ8w2cc5/gi4o1B74xs5Fm9iQwH6gO3OKcW5ErrkeAT4D+wDwze8DMXoodfzdwZe5+zMCf8EPKHzOzSWZ2v5nNAG7CD5m+PVdcxfWzHJSl6zfAzzVo2VS9fhPC229Dr15Qpw58/DG0aRM6IhERKWJVqsCjj8Lnn/uinBdd5Os//hiXiVgiIhJvRZU0zwQm4ufvXgjcDHQH5gCXAWc7537JuYNzblJsm1nAfwE3AJmxfQfGnhSTa59hwOXAemAQcCnwLdDHOfdEHtvvAU4F7sK3cLoJOA2YBHR0zs3LY5+lQAfgOeAEYBg+uX0MOMk5tyWPfYr8ZzlYS1b4UeGNG6t9UXAvvgh9+8KRR/onzCpnLiISKccdB3PnwpNP+gT6qKPgzjshIyN0ZCIikpPlkb9JEuvQoYObP39+vq8fefNNLBo5ktmz/RRaCeSJJ+CGG3xp1cmTUSlzEZFoW7/edxh86SVo0QL++U//9FlERArPzBY45+I25zFEITAJaP1qP5dZDzUDcQ5GjPAJ87nnwrRpSphFRIQ6dfwApPfe83Ofe/WCCy/0ybSIiISlpDlC9mXtY9vGyliJLOrVCx1NBGVlwY03+rF3l1/u+zCXKxc6KhERSSCnngrffOP/VLzxBrRq5Ydv79sXOjIRkehS0hwhG3ZtwG1rQLVauyhVKnQ0EZOZCZdeCo8/DjffDM88g34JIiKSl3LlYPhwWLjQ93S+/nrfouqLL0JHJiISTUqaIyQtPQ3SG1GnfmboUKJl927o18+Pu7v3Xnj4YSih//VERGT/WraE6dPh5ZchLQ06dvQDlrZvDx2ZiEi06JN7hGT3aFbl7GK0bRucfjpMnQpPPQV//aufrCYiIlIAZjBwIHz/PVx3nR+w1Lo1vPaaejuLiBQXJc0RsnJbGmxvSMsmmkdbLDZs8NWx582DV16Ba64JHZGIiCSpatV844V583zRsPPPhzPPhKVLQ0cmIpL6lDRHyOJV6bCvLEc0U9Jc5Fas8D29Fi+GKVP8pxsREZFD1LEjfPYZjBoFH38M7drBPffAnj2hIxMRSV1KmiNkyfJfADQ8u6gtXAidO8OWLfD++354toiISJyUKgVDhsCiRdCnD/ztb3DMMTBzZujIRERSk5LmCFmV5pfq0VyEPv0UunXzE81mzYKTTgodkYiIpKj69eHVV33ZjF9+gZ49faOGjRtDRyYiklqUNEfIxrV+WLaS5iLy3nu+wWb16r+NmRMRESliZ5wB334Lt9/uS2i0agVjxkBWVujIRERSg5LmiNiXtY/0DVUpU34P1aqFjiYFvfYanHUWNG8Oc+ZA06ahIxIRkQgpX97Pbf76a/jDH3ztyc6d/fciInJolDRHxIZdG3DbGlCjzi51PIq3p5+G//5vOP54+OgjX9ZUREQkgDZtYMYMeOEFX1m7fXsYNgx27gwdmYhI8lLSHBHZPZrrNtgbOpTU4Rw88AAMGuTHxk2fjh7ji4hIaGZwySW+t/NVV8Ejj/hkeuJE9XYWETkYSpojIjtpbtpYv/K4cA7+8he47Ta48EKYNAkqVAgdlYiIyK+qV4fRo32ZjerV4bzz4JxzfFdEEREpOGVQEbF0w1rYXZsjmpcPHUry27vX37p/+GEYPBjGjYPSpUNHJSIikqdOnWDBAvjHP3xbqiOPhAcf9BW3RUTkwJQ0R8T3y3YB0LqZnoYekowMGDAAnn0Whg+Hxx+HEvrfSEREElupUnDzzb63c+/ecOutcOyxMHt26MhERBKfPu1HxNIVmQA0bqwqYAdtxw5fIXvSJBg1Cu68E1VVExGRZNKwIbz5Jvzf/8GuXdCtG1x5JWzeHDoyEZHEpaQ5ItaklQTUo/mgbdoEPXv66tjjxsGQIaEjEhEROWh9+vjezv/zP/7PWqtWMHasejuLiORFSXNEbFpXHiyL+vVDR5KE0tL8rfiFC/1T5osvDh2RiIjIIatY0TeB+PJLP8/5qquge3f/505ERH6jpDkC9mXtY/vGalSqvoMyZUJHk2R++AE6d4a1a+Hdd+Hss0NHJCIiElft2vmBVM884+c8H3usn/O8a1foyEREEoOS5gjYuGsjblsDatX9OXQoyWXBAujSBfbsgQ8/9E+bRUREUlCJEn5u8/ffw6WX+urabdvCW2+FjkxEJDwlzRGQ3aO5XsN9oUNJHuPGQdeufuzanDn+truIiEiKq1nTP3GeNcv/CezTx/d3TksLHZmISDhKmiNgVXoapDeiWeNSoUNJfBkZcO21/jb78cfDp59Cy5ahoxIRESlWXbv6uc4PPADvvANt2vg+z5mZoSMTESl+Spoj4PuVW2BfOdo0V4/m/Vq+3M9fHj3alxN9/32oUyd0VCIiIkGUKeP/HH73HZx8MtxyC3ToAHPnho5MRKR4KWmOgB+W+bnMbVpUChxJApsyBY47DpYuhcmT/a31UnoyLyIi0qSJ/zP55puwdSt06gTXXOO/FhGJAiXNEbBsxV4AGje2wJEkoL174bbb4JxzoFkz+OIL/7WIiIj8ygz69fNPnW++2c97bt3alwBxLnR0IiJFS0lzBKxd7Z+YNmoUOJBEs349nHaaf6p89dXw8cc+cRYREZE8Va7s5zYvWADNm/sSID17+qrbIiKpSklzBGxeV4GSZfdQvXroSBLIrFm+Iva8efDcczBmDJQrFzoqERGRpHDMMf5e8+jR8NVXcPTRcMcd8LO6W4pIClLSnOKyXBY7N1ejWu10TKOz/Riyhx7yt8UrV/ZJ82WXhY5KREQk6ZQoAYMGwQ8/wMCBcO+90K6dr7YtIpJKlDSnuI27NuK2NaRW3YzQoYS3bZufkPWXv/jl/Plw1FGhoxIREUlqtWvDCy/AjBlQujSccQacfz6sWRM6MhGR+FDSnOLSYj2aGzSKeJWOL7+E9u3h7bdh5Eh49VWoUiV0VCIiIimjRw/4+mu4+25fbbtNG3jsMV9zU0QkmSlpTnHLN6+FnXVp3qR06FDCcM6X+DzpJNizBz76CIYORWPVRURE4q9sWT+3eeFC35rqxhvhhBPg889DRyYicvCUNKe4hUu2AXBkFHs0794NV14Jf/wjdO3qnzZ36hQ6KhERkZTXvDlMm+YHdq1b5xPnwYP9TCkRkWSjpDnF/bjMz2Vu26Jy4EiK2eLF/uny88/D3//uq5LUqhU6KhERkcgwgwEDfDuqG26Ap57yvZ1fflm9nUUkuShpTnErVmUB0LhxhIYjv/GGn7+8ejVMnQojRkDJkqGjEhERiaQqVWDUKPjsM2jYEC68EHr18ve3RUSSgZLmFLd+tZ/L3KBB4ECKQ2Ym3Hwz9O/vq498+SX07h06KhEREcHfz/70U3jySZ9AH3UU3HMP7NsXOjIRkf1T0pzitqyvRLlq2yhXLnQkRWzNGjj5ZF8Z+/rrYdYsaNQodFQiIiKSQ8mS8Kc/+SHbffvC3/7mW1Rt3hw6MhGR/ClpTmFZLotdm6pz2OE7QodStN5/H4491ve5ePllePxxX75TREREElLduvDKK/D00/4+d/v2MH9+6KhERPKmpDmFbdy1EZfekMPr7QkdStHIyvLjunr1gpo1fT+LgQNDRyUiIiIF9Mc/wpw5/usuXXyXSBGRRKOkOYWlpa+G9EapOUp5yxY4+2w/ruuCC/zkqDZtQkclIiIihdShAyxYAN26+ST66qshIyN0VCIiv1HSnMIWrdoAmRVp0TTFhip/9hkcdxx88AH8858wfjxUimAfahERkRRRs6bv63z77fDvf/unzitXho5KRMRT0pzCFi7eDkC7llUCRxInzvkkuUsX3/xxzhy47jr/tYiIiCS1kiX9rKvJk307qvbt4b33QkclIqKkOaUtWf4LAG1bVg4cSRzs3g2XXAKDB8Npp/lxXB07ho5KRERE4uycc3xRsLp14fTT4b77fBkTEZFQlDSnsFVpftmkcZL/mleu9E+XX3oJ7r4bpkyBGjVCRyUiIiJFpGVL39N54EA/ZLtfP9i2LXRUIhJVSZ5Nyf6sX1MGK7WHWrVCR3IIZs70FUKWLvXJ8h13QAmdtiIiIqmuYkV48UUYNQqmTvUDzP7f/wsdlYhEkbKPFLZtfWUq1dqanFN+nYPHHvNDsWvW9MW/zjordFQiIiJSjMxgyBB/D33nTjjxRHj55dBRiUjUKGlOUVkui92ba1K9zs7QoRReRgZccQXceKNPlOfNg1atQkclIiIigXTpAl984YuDXXih/4iQmRk6KhGJCiXNKWrTrk249IbUrZ9kf1FWr/aNGp9/HoYPh4kToUqKVP8WERGRg1a3ru82OXSoH4zWowesWxc6KhGJAiXNKWr55jWwoy6Nk6kI2Jw5/hbyokU+Wb7zTs1fFhERkV+VLg0jR/oh2l9+CccdB7Nnh45KRFKdMpIU9fWSzUAJjmhWLnQoBfPUU/6WcZUqfjh2376hIxIREZEENXCgL3dSubL/+PDoo74ciohIUVDSnKK+W+LnMh/VsmrgSA5gzx645hq47jpf9Ovzz+HII0NHJSIiIgmubVv/saFPH7jpJrjgAl8sTEQk3pQ0p6ily/1c5qOOSOCked066NkTxoyB227zLaWqVQsdlYiIiCSJqlXhjTfg/vvhtdd8de0ffwwdlYikGiXNKSotzfeZatwoQX/Fn37q+y9/9RVMmAD33QclS4aOSkRERJJMiRJw663w7ruwYYP/eDFpUuioRCSVJGhGJYdq49pylK78E+XLh44kD2PHQvfuULYszJ0L558fOiIRERFJcqeeCgsWQOvW0K+fH8S2b1/oqEQkFShpTlHpG6tQqda20GH8XmYmXH89XHWVbyv1+edw9NGhoxIREZEU0agRzJoFgwbBAw9A796waVPoqEQk2SlpTkFZLouMLbWoWWdX6FB+s3GjvwX85JMwbBhMmwY1aoSOSkRERFJMuXIwejQ884xvR9W+va+0LSJysJQ0p6BNuzbjtjWiXoO9oUPxFizwE4w++wzGj4eHH4ZSpUJHJSIiIinsyivh44/9nOeuXX3dUbWlEpGDoaQ5BS1KWwu/VKZpkwRITMePhy5d/NcffwwXXRQ2HhEREYmM9u39vfsePXyHy2uugb0J8kxBRJKHkuYU9PWPPwHQqlnAKmB79/ph2JdcAiecAPPnw3HHhYtHREREIqlGDXj7bV8Y7OmnYcAAyMgIHZWIJBMlzSlo0VI/l/noI6qFCWDzZl9545FH4IYb4L33oHbtMLGIiIhI5JUs6btbjhrl21GdfTbs2BE6KhFJFkqaU9CyFX7c0TGtDiv+N//6a+jYEebMgWefhcceg9Kliz8OERERkVyGDIHnn4cPP/T1SbduDR2RiCQDJc0paE1aSSj5C3XrFPOvd8IEOOkk31pq1iy4/PLifX8RERGRA7j0UnjjDfjqK98Bc+3a0BGJSKJT0pyCNq8rT7nqmyhRXL/dffvg1lth4EA49lg/f/n444vpzUVEREQK59xzfffLlSt9Ze1ly0JHJCKJTElzCkrfWI0qtbcVz5v99BOcdRY8+KAvSTlzJtSpUzzvLSIiInKQevaEDz6Abdt8o4+FC0NHJCKJSklzCtqztTa16v5c9G+0YIF/ojxjBjz1lP9XpkzRv6+IiIhIHBx/vJ9RZuaHas+bFzoiEUlESppTTOa+vbC9PvUbZhXdm3z6KfTpAx06+NKTM2f6p8wiIiIiSaZtW1+/9LDD4JRT/NNnEZGclDSnmN0ZmeBK0rxJnCtWO+eT41NP9cW+PvkERoyARYugc+f4vpeIiIhIMWra1CfOzZrBmWfCxImhIxKRRKKkOcX8vGcfAK2bV4jPAZ2Dt9/2iXHPnn7Cz0MPwYoV8Pe/+9uyIiIiIkmubl3fiuq446B/f3juudARiUiiUNKcYjL2+GHZf2hV/dAOlJUFr78O7dvD2WfDmjXwxBOwfDnccgtUrhyHaEVEREQSR/Xq8N57fpj2FVfAqFGhIxKRRKCkOcXs+cUv/9CqxsEdIDMTXnjBT/AZMAB27oSxY2HxYhg8GMqXj1+wIiIiIgmmUiWYMgXOOw+GDoXhw/3AOxGJLiXNKSbzFyhR4SeqVC7kr3bPHhg9Go44Ai67DEqXhlde8XOWr7hCVbFFREQkMsqWhQkT/Eegu+6CG2/0g/BEJJpKhQ5A4mtvZknK19oEFHCu8a5dMGYMPPwwrF3rey+MGuWHZJfQPRURERGJplKl4JlnfPmWRx7x/ZzHjvXrRSRa9L99gjGzBsBdQG+gBrAOmASMcM79dKD9s/aWpFrt7Qd+o/R0ePJJGDkSNm+G7t3h+ef9JB6zQ/oZRERERFKBmX+uUL063HGH//g0YQKUKxc6MhEpTkqaE4iZNQc+AWoDk4HvgeOBG4HeZtbZObdlf8dwe0tTu15G/hts3gyPPuqLeqWnwxlnwO23q22UiIiISB7M/EelatXg+ut9S6rJk1UTVSRKNP42sfwTnzAPcc71dc7d6pzrCYwEWgH3HvAIriQNG+Wxfu1aGDYMGjeGe+/1T5Tnz4epU5Uwi4iIiBzA4MEwfjzMmuU/Rm3Z72MMEUklSpoThJk1A3oBK4Anc708HNgFXGJmFQ90rBZNS//2zYoVcN110LSpn6t83nnw7bfwxhu+nZSIiIiIFMhFF8HEifDNN9Ctm+/IKSKpT0lz4ugZW053zv2uPqNzbgfwMVABOPFABzqyRWX44Qe4/HJo0cJXsbj8cvjxRxg3Do48Mt6xi4iIiERCnz7wzjuQlgZdusCSJaEjEpGipqQ5cbSKLX/M5/XFseURBzpQ93/9L7RpA6++6iffLFvm20k1axaXQEVERESi7OSTYcYM2LHDJ87ffBM6IhEpSkqaE0fV2DI9n9ez11fb/2EcTeZMgltv9UOzH30UGjSIQ3giIiIikq1DB5g927eg6t4d5s4NHZGIFBVVz04e2X2g3H+8YDYIGBT7dk/pnSzk/vvh/vuLLThJWjWBzaGDkKSgc0UKQ+eLFFTKnCudOoWOIBJS5nyRItfqwJsUnJLmxJH9JLlqPq9XybXdr5xzY4AxAGY23znXIf7hSSrS+SIFpXNFCkPnixSUzhUpDJ0vUlBmNj+ex9Pw7MTxQ2yZ35zllrFlfnOeRUREREREJM6UNCeOmbFlLzP73e/FzCoDnYGfgU+LOzAREREREZGoUtKcIJxzS4HpQBNgcK6XRwAVgRecc7sOcKgx8Y9OUpjOFykonStSGDpfpKB0rkhh6HyRgorruWLO/UddKQnEzJoDnwC1gcnAIuAEoAd+WHYn59yWcBGKiIiIiIhEi5LmBGNmDYG7gN5ADWAdMAkY4ZzbGjA0ERERERGRyFHSLCIiIiIiIpIPzWlOAWbWwMzGmtlaM9tjZivM7FEzOyx0bJJYYueGy+ff+tDxSfEzs/5m9riZzTaz7bFzYfwB9ulkZlPNbKuZ7Tazb8xsqJmVLK64pfgV5lwxsyb7udY4M3uluOOX4mNmNczsj2Y20cyWmNnPZpZuZnPM7KrcBU9z7KdrS8QU9lzRtUXM7EEz+8DM0mLny1Yz+9LMhptZjXz2OeRri/o0J7k85kF/DxwP3Aj0NrPOmgctuaQDj+axfmcxxyGJ4Q7gGPzvfzXQen8bm9m5wBtABjAB2Ar0AUbiq/wPKMpgJahCnSsxX+OnGOW2MH5hSQIaAPwLP8VsJrAKOBw4D/g3cIaZDXA5hjvq2hJZhT5XYnRtia6bgC+A94CN+GLJJwJ3AoPM7ETnXFr2xvG6tmh4dpIzs3eBXsAQ59zjOdY/gj+pRjvnrg0VnyQWM1sB4JxrEjYSSRRm1gOfAC0BuuM/tLzonLs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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "cost_fraud = 400\n", + "cost_fp = 20\n", + "# xmult: oot is only 2 out of 12 months. 100,000 sample transactions out of 10 million/year\n", + "xoot = 12/2 * 10000000/100000\n", + "Financials_trn = pd.DataFrame(np.zeros((101, 3)), columns = ['Fraud Savings','FP Loss','Overall Savings'])\n", + "Financials_tst = pd.DataFrame(np.zeros((101, 3)), columns = ['Fraud Savings','FP Loss','Overall Savings'])\n", + "Financials_oot = pd.DataFrame(np.zeros((101, 3)), columns = ['Fraud Savings','FP Loss','Overall Savings'])\n", + "for i in range(101):\n", + " Financials_trn.loc[i, 'Fraud Savings'] = FDR_trn.loc[i, 'cb'] * cost_fraud * xoot\n", + " Financials_trn.loc[i, 'FP Loss'] = FDR_trn.loc[i, 'cg'] * cost_fp * xoot\n", + " Financials_trn.loc[i, 'Overall Savings'] = Financials_trn.loc[i, 'Fraud Savings'] - Financials_trn.loc[i, 'FP Loss']\n", + " Financials_tst.loc[i, 'Fraud Savings'] = FDR_tst.loc[i, 'cb'] * cost_fraud * xoot\n", + " Financials_tst.loc[i, 'FP Loss'] = FDR_tst.loc[i, 'cg'] * cost_fp * xoot\n", + " Financials_tst.loc[i, 'Overall Savings'] = Financials_tst.loc[i, 'Fraud Savings'] - Financials_tst.loc[i, 'FP Loss']\n", + " Financials_oot.loc[i, 'Fraud Savings'] = FDR_oot.loc[i, 'cb'] * cost_fraud * xoot\n", + " Financials_oot.loc[i, 'FP Loss'] = FDR_oot.loc[i, 'cg'] * cost_fp * xoot\n", + " Financials_oot.loc[i, 'Overall Savings'] = Financials_oot.loc[i, 'Fraud Savings'] - Financials_oot.loc[i, 'FP Loss']\n", + "\n", + "max_savings = Financials_oot['Overall Savings'].max(0)\n", + "print('Max possible savings: '+'{:,}'.format(max_savings))\n", + "yupper = max_savings * 1.5\n", + "plt.rcParams.update({'font.size':20})\n", + "plt.figure(figsize=(15,10))\n", + "plt.plot(Financials_oot['Fraud Savings'], color='green')\n", + "plt.plot(Financials_oot['FP Loss'], color='red')\n", + "plt.plot(Financials_oot['Overall Savings'], color='blue')\n", + "plt.xlim(0,30)\n", + "plt.ylim(0,yupper)\n", + "plt.ticklabel_format(style='plain')\n", + "plt.savefig('savings.png', format='png')" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "FDR3.to_csv('FDR3.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "FDR_trn.to_csv('FDR_trn.csv', index=False)\n", + "FDR_tst.to_csv('FDR_tst.csv', index=False)\n", + "FDR_oot.to_csv('FDR_oot.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "duration: 0:27:07.295743\n" + ] + } + ], + "source": [ + "print(\"duration: \", datetime.now() - start_time)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/stevecoggeshall/Documents/Teaching/Data sets/done/transactions/transactions 2023 new/give to students'" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%pwd" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 191 ms, sys: 46.5 ms, total: 237 ms\n", + "Wall time: 239 ms\n" + ] + }, + { + "data": { + "text/plain": [ + "(96397, 10)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "df = pd.read_csv('card transactions.csv')\n", + "df.dropna(how='all', axis=1, inplace=True)\n", + "df = df[df['Transtype'] == 'P']\n", + "df = df[df['Amount'] <= 3000000]\n", + "df['Date'] = pd.to_datetime(df['Date'])\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "df['prediction'] = model.predict_proba(X)[:,1]" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "pred_scale = 1/df['prediction'].max()\n", + "df['prediction'] = df['prediction']*pred_scale" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0151421904392010-01-015509006296254FEDEX SHP 12/23/09 AB#TN38118.0P3.6200.000873
1251421839732010-01-0161003026333SERVICE MERCHANDISE #81MA1803.0P31.4200.000876
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4551421904392010-01-015509006296254FEDEX SHP 12/23/09 AB#TN38118.0P3.6200.000886
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" + ], + "text/plain": [ + " Recnum Cardnum Date Merchnum Merch description \\\n", + "0 1 5142190439 2010-01-01 5509006296254 FEDEX SHP 12/23/09 AB# \n", + "1 2 5142183973 2010-01-01 61003026333 SERVICE MERCHANDISE #81 \n", + "2 3 5142131721 2010-01-01 4503082993600 OFFICE DEPOT #191 \n", + "3 4 5142148452 2010-01-01 5509006296254 FEDEX SHP 12/28/09 AB# \n", + "4 5 5142190439 2010-01-01 5509006296254 FEDEX SHP 12/23/09 AB# \n", + "\n", + " Merch state Merch zip Transtype Amount Fraud prediction \n", + "0 TN 38118.0 P 3.62 0 0.000873 \n", + "1 MA 1803.0 P 31.42 0 0.000876 \n", + "2 MD 20706.0 P 178.49 0 0.000897 \n", + "3 TN 38118.0 P 3.62 0 0.000873 \n", + "4 TN 38118.0 P 3.62 0 0.000886 " + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 96397 entries, 0 to 96752\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Recnum 96397 non-null int64 \n", + " 1 Cardnum 96397 non-null int64 \n", + " 2 Date 96397 non-null datetime64[ns]\n", + " 3 Merchnum 93199 non-null object \n", + " 4 Merch description 96397 non-null object \n", + " 5 Merch state 95377 non-null object \n", + " 6 Merch zip 92097 non-null float64 \n", + " 7 Transtype 96397 non-null object \n", + " 8 Amount 96397 non-null float64 \n", + " 9 Fraud 96397 non-null int64 \n", + " 10 prediction 96397 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(3), int64(3), object(4)\n", + "memory usage: 8.8+ MB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RecnumCardnumDateMerchnumMerch descriptionMerch stateMerch zipTranstypeAmountFraudprediction
600476004851421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P1664.8711.000000
600316003251421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P24778.9911.000000
600276002851421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P3058.5611.000000
600046000551421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P173.1911.000000
599985999951421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P9117.3411.000000
599885998951421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P3437.7111.000000
599335993451421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P1941.0711.000000
599175991851421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P5454.9211.000000
599145991551421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P324.5011.000000
599115991251421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P10598.0911.000000
599105991151421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P2096.0811.000000
599025990351421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P11368.5911.000000
598925989351421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P4764.7011.000000
598785987951421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P24366.7811.000000
598435984451421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P23382.4811.000000
598175981851421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P7360.4211.000000
597915979251421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P5376.4211.000000
597495975051421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P6927.2710.999999
597445974551421381352010-08-04900009045549KRACKELER SCIENTIFICNY12202.0P12193.3610.999998
825628256351421528572010-10-219662801069973KIESUB CORPORATIONNV89109.0P1976.3310.999997
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" + ], + "text/plain": [ + " Recnum Cardnum Date Merchnum Merch description \\\n", + "60047 60048 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "60031 60032 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "60027 60028 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "60004 60005 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "59998 59999 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "59988 59989 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "59933 59934 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "59917 59918 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "59914 59915 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "59911 59912 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "59910 59911 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "59902 59903 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "59892 59893 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "59878 59879 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "59843 59844 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "59817 59818 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "59791 59792 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "59749 59750 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "59744 59745 5142138135 2010-08-04 900009045549 KRACKELER SCIENTIFIC \n", + "82562 82563 5142152857 2010-10-21 9662801069973 KIESUB CORPORATION \n", + "\n", + " Merch state Merch zip Transtype Amount Fraud prediction \n", + "60047 NY 12202.0 P 1664.87 1 1.000000 \n", + "60031 NY 12202.0 P 24778.99 1 1.000000 \n", + "60027 NY 12202.0 P 3058.56 1 1.000000 \n", + "60004 NY 12202.0 P 173.19 1 1.000000 \n", + "59998 NY 12202.0 P 9117.34 1 1.000000 \n", + "59988 NY 12202.0 P 3437.71 1 1.000000 \n", + "59933 NY 12202.0 P 1941.07 1 1.000000 \n", + "59917 NY 12202.0 P 5454.92 1 1.000000 \n", + "59914 NY 12202.0 P 324.50 1 1.000000 \n", + "59911 NY 12202.0 P 10598.09 1 1.000000 \n", + "59910 NY 12202.0 P 2096.08 1 1.000000 \n", + "59902 NY 12202.0 P 11368.59 1 1.000000 \n", + "59892 NY 12202.0 P 4764.70 1 1.000000 \n", + "59878 NY 12202.0 P 24366.78 1 1.000000 \n", + "59843 NY 12202.0 P 23382.48 1 1.000000 \n", + "59817 NY 12202.0 P 7360.42 1 1.000000 \n", + "59791 NY 12202.0 P 5376.42 1 1.000000 \n", + "59749 NY 12202.0 P 6927.27 1 0.999999 \n", + "59744 NY 12202.0 P 12193.36 1 0.999998 \n", + "82562 NV 89109.0 P 1976.33 1 0.999997 " + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sorted = df.sort_values(by=['prediction'],ascending=False)\n", + "df_sorted.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5142140316 46\n", + "5142847398 45\n", + "5142199009 45\n", + "5142160778 41\n", + "5142189341 41\n", + "5142181728 39\n", + "5142212038 39\n", + "5142220919 38\n", + "5142214614 37\n", + "5142202847 37\n", + "5142138135 36\n", + "5142271065 34\n", + "5142152857 32\n", + "5142179617 32\n", + "5142235211 32\n", + "5142197711 32\n", + "5142182128 31\n", + "5142189113 30\n", + "5142197563 30\n", + "5142116864 28\n", + "Name: Cardnum, dtype: int64" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bads = df[df['Fraud']==1]\n", + "bads['Cardnum'].value_counts().head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4353000719908 107\n", + "930090121224 50\n", + "8834000695423 46\n", + "4503738417400 45\n", + "4620009957157 39\n", + "618901687330 36\n", + "900009045549 36\n", + "9108234610000 33\n", + "253052983001 33\n", + "938909877224 32\n", + "4503082476300 31\n", + "6006333528866 30\n", + "997674930332 29\n", + "6070095870009 27\n", + "9900020006406 25\n", + "Name: Merchnum, dtype: int64" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bads['Merchnum'].value_counts().head(15)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "107" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "card = 5142140316\n", + "# card = 5142847398\n", + "# card = 5142199009\n", + "# card = 5142160778\n", + "# card = 5142189341\n", + "# card = 5142181728\n", + "# card = 5142212038\n", + "# card = 5142220919\n", + "# card = 5142214614\n", + "# card = 5142202847\n", + "# card = 5142138135\n", + "# card = 5142271065\n", + "# card = 5142152857\n", + "# card = 5142179617\n", + "# card = 5142235211\n", + "# card = 5142197711\n", + "# card = 5142182128\n", + "# card = 5142189113\n", + "# card = 5142197563 \n", + "\n", + "merch = 4353000719908\n", + "# merch = 930090121224 \n", + "# merch = 48834000695423\n", + "# merch = 44503738417400\n", + "# merch = 44620009957157\n", + "# merch = 4618901687330\n", + "# merch = 4900009045549\n", + "# merch = 49108234610000\n", + "# merch = 4253052983001\n", + "# merch = 4938909877224\n", + "# merch = 44503082476300\n", + "# merch = 46006333528866\n", + "# merch = 4997674930332\n", + "# merch = 46070095870009\n", + "# merch = 49900020006406\n", + "# sample = df[df['Cardnum'] == card]\n", + "sample = df[df['Merchnum'] == str(merch)]\n", + "sample['Fraud'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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counterindexRecnumCardnumDateMerchnumMerch descriptionMerch stateMerch zipTranstypeAmountFraudprediction
01863878638851421804322010-11-104353000719908ACI*AMAZON.COM INCWA98101.0P237.6000.002190
12864068640751421394832010-11-104353000719908ACI*AMAZON.COM INCWA98101.0P178.3600.001906
23864858648651421788482010-11-104353000719908ACI*AMAZON.COM INCWA98101.0P15.1100.001160
34868028680351421462172010-11-124353000719908ACI*AMAZON.COM INCWA98101.0P63.4500.000849
45869078690851422236592010-11-124353000719908ACI*AMAZON.COM INCWA98101.0P73.9000.000859
\n", + "
" + ], + "text/plain": [ + " counter index Recnum Cardnum Date Merchnum \\\n", + "0 1 86387 86388 5142180432 2010-11-10 4353000719908 \n", + "1 2 86406 86407 5142139483 2010-11-10 4353000719908 \n", + "2 3 86485 86486 5142178848 2010-11-10 4353000719908 \n", + "3 4 86802 86803 5142146217 2010-11-12 4353000719908 \n", + "4 5 86907 86908 5142223659 2010-11-12 4353000719908 \n", + "\n", + " Merch description Merch state Merch zip Transtype Amount Fraud \\\n", + "0 ACI*AMAZON.COM INC WA 98101.0 P 237.60 0 \n", + "1 ACI*AMAZON.COM INC WA 98101.0 P 178.36 0 \n", + "2 ACI*AMAZON.COM INC WA 98101.0 P 15.11 0 \n", + "3 ACI*AMAZON.COM INC WA 98101.0 P 63.45 0 \n", + "4 ACI*AMAZON.COM INC WA 98101.0 P 73.90 0 \n", + "\n", + " prediction \n", + "0 0.002190 \n", + "1 0.001906 \n", + "2 0.001160 \n", + "3 0.000849 \n", + "4 0.000859 " + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tsample = sample[sample['Date'] > '2010-11-09']\n", + "tsample = tsample[tsample['Date'] < '2010-12-01']\n", + "tsample.reset_index(inplace=True)\n", + "tsample.reset_index(inplace=True)\n", + "tsample.rename(columns={'level_0':'counter'},inplace=True)\n", + "tsample['counter'] = tsample['counter']+1\n", + "tsample.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,5))\n", + "plt.xticks(rotation=90, ha='right')\n", + "plt.plot(tsample['Date'],tsample['prediction'])\n", + "plt.plot_date(tsample['Date'],tsample['Fraud'])" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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23864858648651421788482010-11-104353000719908ACI*AMAZON.COM INCWA98101.0P15.1100.001160
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45869078690851422236592010-11-124353000719908ACI*AMAZON.COM INCWA98101.0P73.9000.000859
56869848698551421374162010-11-134353000719908ACI*AMAZON.COM INCWA98101.0P35.9000.001019
67870008700151422367992010-11-144353000719908ACI*AMAZON.COM INCWA98101.0P98.6000.000878
78872828728351422219622010-11-164353000719908ACI*AMAZON.COM INCWA98101.0P32.2800.000859
89873178731851422269792010-11-164353000719908ACI*AMAZON.COM INCWA98101.0P35.0600.000955
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1314875048750551422236592010-11-164353000719908ACI*AMAZON.COM INCWA98101.0P70.9000.001509
1415875098751051422269792010-11-164353000719908ACI*AMAZON.COM INCWA98101.0P179.3200.001725
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2526883008830151421217082010-11-194353000719908ACI*AMAZON.COM INCWA98101.0P24.9200.000850
2627883878838851422376072010-11-194353000719908ACI*AMAZON.COM INCWA98101.0P81.8800.000807
2728885378853851422152862010-11-224353000719908ACI*AMAZON.COM INCWA98101.0P18.9500.000864
2829885878858851422725112010-11-224353000719908ACI*AMAZON.COM INCWA98101.0P48.9000.000876
2930887528875351422152862010-11-224353000719908ACI*AMAZON.COM INCWA98101.0P17.0200.000932
3031888168881751422833222010-11-234353000719908ACI*AMAZON.COM INCWA98101.0P15.1500.000944
3132888848888551421774862010-11-234353000719908ACI*AMAZON.COM INCWA98101.0P67.6200.000875
3233889998900051422833222010-11-234353000719908ACI*AMAZON.COM INCWA98101.0P43.4000.001005
3334893148931551421774862010-11-244353000719908ACI*AMAZON.COM INCWA98101.0P35.7800.001067
3435893648936551422352112010-11-254353000719908AMAZON.COM *SUPERSTORWA98101.0P472.5310.001981
3536893658936651422352112010-11-254353000719908ACI*AMAZON.COM INCWA98101.0P1397.7710.029239
3637893668936751422352112010-11-254353000719908AMAZON.COM *SUPERSTORWA98101.0P497.0310.046437
3738893688936951422352112010-11-254353000719908AMAZON.COM *SUPERSTORWA98101.0P890.5310.069032
3839893748937551422352112010-11-254353000719908AMAZON.COM *SUPERSTORWA98101.0P1288.9010.074207
3940893778937851422352112010-11-254353000719908AMAZON.COM *SUPERSTORWA98101.0P3619.8810.239189
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0.001725 \n", + "15 0.001403 \n", + "16 0.001217 \n", + "17 0.002228 \n", + "18 0.000889 \n", + "19 0.000949 \n", + "20 0.000859 \n", + "21 0.000862 \n", + "22 0.000907 \n", + "23 0.000879 \n", + "24 0.001023 \n", + "25 0.000850 \n", + "26 0.000807 \n", + "27 0.000864 \n", + "28 0.000876 \n", + "29 0.000932 \n", + "30 0.000944 \n", + "31 0.000875 \n", + "32 0.001005 \n", + "33 0.001067 \n", + "34 0.001981 \n", + "35 0.029239 \n", + "36 0.046437 \n", + "37 0.069032 \n", + "38 0.074207 \n", + "39 0.239189 " + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tsample.head(40)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "duration: 0:27:08.262268\n" + ] + } + ], + "source": [ + "print(\"duration: \", datetime.now() - start_time)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/transactions feature selection.ipynb b/transactions feature selection.ipynb new file mode 100644 index 0000000..1834f74 --- /dev/null +++ b/transactions feature selection.ipynb @@ -0,0 +1,1533 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This feature selection notebook does a filter followed by a wrapper for a binary dependent variable (binary classification). It's capable of doing the filter on more than one file. The variable files are called vars1.csv, vars2.csv ... Or you can make the input file name(s) anything you want.\n", + "\n", + "The filter runs separately on each vars file and keeps the top num_filter variables from each file. If there are more than one vars files we'll again select the top num_filter variables across all the vars.csv files.\n", + "\n", + "If balance = 0 the entire files are used. If balance != 0 then balance is the RATIO OF BADS TO GOODS retained for the rest of the feature selection. We keep all the rare class (bads) and downsample the goods. I think in general it's better to keep balance = 0.\n", + "\n", + "I've got an annoying warning message from the wrapper and I can't figure out how to get rid of it. If anybody figures this out please send a message to stevecoggeshall@gmail.com" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting lightgbm\n", + " Downloading lightgbm-3.3.5-py3-none-win_amd64.whl (1.0 MB)\n", + " ---------------------------------------- 1.0/1.0 MB 3.1 MB/s eta 0:00:00\n", + "Requirement already satisfied: scipy in c:\\users\\rijul\\anaconda3\\lib\\site-packages (from lightgbm) (1.9.1)\n", + "Requirement already satisfied: numpy in c:\\users\\rijul\\anaconda3\\lib\\site-packages (from lightgbm) (1.21.5)\n", + "Requirement already satisfied: wheel in c:\\users\\rijul\\anaconda3\\lib\\site-packages (from lightgbm) (0.37.1)\n", + "Requirement already satisfied: scikit-learn!=0.22.0 in c:\\users\\rijul\\anaconda3\\lib\\site-packages (from lightgbm) (1.0.2)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\rijul\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (2.2.0)\n", + "Requirement already satisfied: joblib>=0.11 in c:\\users\\rijul\\anaconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (1.1.0)\n", + "Installing collected packages: lightgbm\n", + "Successfully installed lightgbm-3.3.5\n" + ] + } + ], + "source": [ + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import scipy.stats as sps\n", + "import matplotlib.pyplot as plt\n", + "import datetime as dt\n", + "import gc\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n", + "from lightgbm import LGBMClassifier\n", + "%matplotlib inline\n", + "start_time = dt.datetime.now()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# set some parameters\n", + "num_files = 1\n", + "# I recommend set num_filter to be about 10 to 20% of the original # variables\n", + "num_filter = 200\n", + "# I recommend set num_wrapper to be about 50, then look for a saturation of the model performance as variables are added\n", + "# Then you can run it again with num_wrapper just a bit above this saturation point, not more than about twice this saturation number\n", + "num_wrapper = 20\n", + "balance = 0\n", + "detect_rate = .03\n", + "index_name = 'Recnum'\n", + "y_name = 'Fraud'\n", + "good_label = 0\n", + "bad_label = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run a filter on all the files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "filter_score_df_list = []\n", + "for i in range(num_files):\n", + "# file_name = \"vars\"+str(i+1)+'.csv'\n", + " file_name = 'candidate_variables.csv'\n", + " df = pd.read_csv(file_name)\n", + " print(\"********** working on\",file_name,\"size is\",df.shape)\n", + " df = df.set_index(index_name) \n", + "# df = df[df.index <= 84300] # remove the last two months as the out-of-time data (OOT)\n", + " df = df[df.index >= 2995] # remove the first 2 weeks of records since their variables aren't well formed\n", + " df['RANDOM'] = np.random.ranf(len(df)) # add a random number variable to make sure it doesn't come up as important\n", + " goods = df[df[y_name] == good_label]\n", + " bads = df[df[y_name] == bad_label]\n", + " del df # don't need this file anymore\n", + " num_goods = len(goods)\n", + " num_bads = len(bads)\n", + " num_vars = len(bads.columns)-2\n", + " if(balance != 0):\n", + " if(i == 0):\n", + " num_goods_desired = int(min(num_goods,num_bads*balance))\n", + " goods = goods.sample(n=num_goods_desired,random_state=1)\n", + " goods_keep = list(goods.index)\n", + " goods_keep.sort()\n", + " \n", + " if(i > 0):\n", + " goods = goods.loc[goods_keep] \n", + " \n", + " df_sampled = pd.concat([goods,bads])\n", + " df_sampled.sort_index(inplace=True)\n", + " filter_score = pd.DataFrame(np.zeros((num_vars+1,2)))\n", + " filter_score.columns = ['variable','filter score'] \n", + " j = 0\n", + " for column in df_sampled:\n", + " filter_score.loc[j,'variable'] = column\n", + " filter_score.loc[j,'filter score'] = sps.ks_2samp(goods[column],bads[column])[0]\n", + " j = j+1\n", + " if j%100 == 0:\n", + " print(j)\n", + "\n", + " filter_score.sort_values(by=['filter score'], ascending=False, inplace=True)\n", + " vars_keep = list(filter_score['variable'][1:num_filter+1]) \n", + " print(file_name,filter_score.head(20))\n", + " if(i == 0): # if first time through need to initialize some stuff\n", + " Y = pd.DataFrame(df_sampled[y_name], index=df_sampled.index)\n", + " df_top = df_sampled.filter(vars_keep, axis=1)\n", + " \n", + " if(i > 0): # if more than one variable file we use this loop\n", + " data_new_top = df_sampled.filter(vars_keep, axis=1)\n", + " df_top = pd.concat([df_top,data_new_top], axis=1)\n", + "\n", + " filter_score_df_list.append(filter_score)\n", + " \n", + " del goods # delete these before starting the next file, if any\n", + " del bads\n", + " gc.collect()\n", + "filter_score = pd.concat(filter_score_df_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'filter_score' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_29952\\633183133.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfilter_score\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mby\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'filter score'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mfilter_score\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'filter_score' is not defined" + ] + } + ], + "source": [ + "filter_score.sort_values(by=['filter score'], ascending=False, inplace=True)\n", + "filter_score.reset_index(drop=True,inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filter_score.head(30)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filter_score.tail(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filter_score.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "filter_score.head(80).to_csv('filter_top.csv')\n", + "vars_keep = list(filter_score['variable'][num_files:num_filter+3])\n", + "print(i,' vars_keep:',vars_keep)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "vars_keep_df = pd.DataFrame({'col':vars_keep})\n", + "vars_keep_df.to_csv('vars_keep_filter.csv',index=False)\n", + "df_keep = df_top.filter(vars_keep, axis=1)\n", + "df_keep.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_keep.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Y.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Y = Y.values\n", + "Y_save = Y.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Y = np.array(Y)\n", + "X = df_keep\n", + "print(Y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('time to here:', dt.datetime.now() - start_time)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(X.shape,Y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(type(X),type(Y))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# I'd like to define a scoring for the wrapper that's KS, but I haven't gotten around to this yet.\n", + "# def KSscore(classifier, x,y)\n", + "# goods = " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def fdr(classifier, x, y, cutoff=detect_rate):\n", + "# Calculates FDR score for the given classifier on dataset x and y with cutoff value\n", + "# get the probability list from the given classifier\n", + " return fdr_prob(y, classifier.predict_proba(x), cutoff)\n", + "def fdr_prob(y, y_prob, cutoff=detect_rate):\n", + " if len(y_prob.shape) != 1: # sometimes the proba list can contain many columns, one for each category\n", + " y_prob = y_prob[:, -1:] # only the last one (fraud_label==1) is used here.\n", + " num_fraud = len(y[y == 1]) # count the total nunber of frauds \n", + "# sort the proba list from high to low while retain the true (not predicted) fraud label\n", + " sorted_prob = np.asarray(sorted(zip(y_prob, y), key=lambda x: x[0], reverse=True))\n", + " cutoff_bin = sorted_prob[0:int(len(y) * cutoff), 1:] # 3% cutoff\n", + "# return the FDR score (#fraud_in_cutoff / #total_fraud)\n", + " return len(cutoff_bin[cutoff_bin == 1]) / num_fraud " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run a wrapper on the remaining top variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "%%time\n", + "import warnings \n", + "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n", + "# If you're doing forward selection it's enough to stop at num_wrapper variables. \n", + "# If you're doing backward selection you need to go through all the variables to get a sorted list of num_wrapper variables.\n", + "\n", + "# I can't figure out how to get rid of this annoying warning! I don't know what I'm doing wrong...\n", + "\n", + "nfeatures = len(X.columns)\n", + "# clf = RandomForestClassifier(n_estimators=5) # simple, fast nonlinear model for the wrapper\n", + "clf = LGBMClassifier(n_estimators=40,num_leaves=4) # simple, fast nonlinear model for the wrapper\n", + "sfs = SFS(clf,k_features=num_wrapper,forward=True,verbose=0,scoring=fdr,cv=4,n_jobs=-1) # use for forward selection\n", + "# sfs = SFS(clf,k_features=1,forward=False,verbose=0,scoring=fdr,cv=4,n_jobs=-1) # use for backward selection\n", + "sfs.fit(X,Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time to here: 0:10:29.394128\n" + ] + } + ], + "source": [ + "print('time to here:', dt.datetime.now() - start_time)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs\n", + "fig1 = plot_sfs(sfs.get_metric_dict(),kind='std_dev', figsize=(15, 6))\n", + "# plt.xticks(np.arange(0, len(X.columns), step=5))\n", + "plt.xticks(np.arange(0, num_wrapper, step=5))\n", + "plt.yticks(np.arange(0,1,step=.1))\n", + "plt.ylim([.5, .8])\n", + "plt.xlim(0,num_wrapper)\n", + "plt.title('Stepwise Selection')\n", + "plt.grid()\n", + "plt.savefig('performance_nvars.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "vars_FS = pd.DataFrame.from_dict(sfs.get_metric_dict()).T" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "ordered_vars_FS = vars_FS.copy()\n", + "for i in range(len(ordered_vars_FS)):\n", + " ordered_vars_FS.loc[i+1,'add variables in this order'] = int(i+1)\n", + " if i+1 == 1:\n", + " ordered_vars_FS.loc[i+1,'variable name'] = (list(ordered_vars_FS.loc[i+1,'feature_names'])[0])\n", + " else:\n", + " ordered_vars_FS.loc[i+1,'variable name'] = (list(set(ordered_vars_FS.loc[i+1,'feature_names']) - set(ordered_vars_FS.loc[i,'feature_names'])))[0]\n", + "# You might also need this following line. It converts a list to a string\n", + "# ordered_vars_FS.loc[i+1,'variable name'] = ordered_vars_FS.loc[i+1,'variable name'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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feature_idxcv_scoresavg_scorefeature_namesci_boundstd_devstd_erradd variables in this ordervariable name
1(15,)[0.6908396946564885, 0.5670498084291188, 0.709...0.595961(card_merch_total_14,)0.1883080.1174740.0678231.0card_merch_total_14
2(15, 16)[0.7404580152671756, 0.6360153256704981, 0.744...0.660912(card_merch_total_14, card_zip3_max_14)0.1454740.0907520.0523962.0card_zip3_max_14
3(15, 16, 173)[0.7595419847328244, 0.632183908045977, 0.7786...0.676176(card_merch_total_14, card_zip3_max_14, card_m...0.1592840.0993670.057373.0card_merch_avg_14
4(15, 16, 153, 173)[0.7900763358778626, 0.6896551724137931, 0.797...0.696269(card_merch_total_14, card_zip3_max_14, state_...0.1874820.1169580.0675264.0state_des_max_1
5(15, 16, 45, 153, 173)[0.767175572519084, 0.685823754789272, 0.82061...0.701036(card_merch_total_14, card_zip3_max_14, card_z...0.1755520.1095160.0632295.0card_zip_max_60
6(15, 16, 45, 153, 169, 173)[0.7977099236641222, 0.6934865900383141, 0.812...0.709631(card_merch_total_14, card_zip3_max_14, card_z...0.178180.1111550.0641766.0Card_Merchnum_desc_avg_30
7(15, 16, 45, 64, 153, 169, 173)[0.7862595419847328, 0.6973180076628352, 0.824...0.711543(card_merch_total_14, card_zip3_max_14, card_z...0.1766680.1102120.0636317.0Card_Merchnum_Zip_total_60
8(15, 16, 45, 48, 64, 153, 169, 173)[0.7748091603053435, 0.7049808429118773, 0.824...0.709642(card_merch_total_14, card_zip3_max_14, card_z...0.175910.109740.0633588.0Card_Merchdesc_Zip_total_30
9(15, 16, 45, 48, 64, 153, 169, 173, 192)[0.8053435114503816, 0.6896551724137931, 0.816...0.711536(card_merch_total_14, card_zip3_max_14, card_z...0.1823320.1137460.0656719.0card_zip_avg_1
10(15, 16, 45, 48, 64, 153, 169, 173, 174, 192)[0.8053435114503816, 0.6819923371647509, 0.828...0.713437(card_merch_total_14, card_zip3_max_14, card_z...0.18510.1154720.06666810.0Card_Merchnum_Zip_avg_14
11(15, 16, 45, 48, 64, 149, 153, 169, 173, 174, ...[0.8053435114503816, 0.6934865900383141, 0.832...0.716311(card_merch_total_14, card_zip3_max_14, card_z...0.187890.1172130.06767311.0card_merch_avg_3
12(15, 16, 45, 48, 62, 64, 149, 153, 169, 173, 1...[0.8053435114503816, 0.6934865900383141, 0.839...0.720127(card_merch_total_14, card_zip3_max_14, card_z...0.1862540.1161920.06708412.0card_merch_total_60
13(15, 16, 45, 48, 62, 64, 149, 151, 153, 169, 1...[0.8053435114503816, 0.6934865900383141, 0.839...0.720127(card_merch_total_14, card_zip3_max_14, card_z...0.1862540.1161920.06708413.0Card_Merchnum_Zip_avg_3
14(15, 16, 45, 48, 62, 64, 149, 151, 153, 169, 1...[0.7977099236641222, 0.6934865900383141, 0.828...0.715356(card_merch_total_14, card_zip3_max_14, card_z...0.1793290.1118720.0645914.0Card_Merchdesc_Zip_avg_7
15(15, 16, 45, 48, 62, 64, 149, 151, 153, 169, 1...[0.7977099236641222, 0.6934865900383141, 0.828...0.715356(card_merch_total_14, card_zip3_max_14, card_z...0.1793290.1118720.0645915.0Card_Merchdesc_avg_7
16(15, 16, 45, 48, 62, 64, 149, 151, 153, 169, 1...[0.8015267175572519, 0.6934865900383141, 0.828...0.716311(card_merch_total_14, card_zip3_max_14, card_z...0.1804710.1125850.06500116.0Card_Merchnum_Zip_avg_1
17(15, 16, 45, 48, 62, 64, 149, 151, 153, 168, 1...[0.8015267175572519, 0.6934865900383141, 0.828...0.716311(card_merch_total_14, card_zip3_max_14, card_z...0.1804710.1125850.06500117.0Card_Merchnum_desc_avg_7
18(15, 16, 45, 48, 62, 64, 96, 149, 151, 153, 16...[0.7977099236641222, 0.6934865900383141, 0.828...0.717265(card_merch_total_14, card_zip3_max_14, card_z...0.1746050.1089250.06288818.0Card_Merchnum_desc_max_1
19(15, 16, 45, 48, 62, 64, 96, 149, 151, 153, 16...[0.7977099236641222, 0.6934865900383141, 0.828...0.717265(card_merch_total_14, card_zip3_max_14, card_z...0.1746050.1089250.06288819.0card_merch_avg_1
20(15, 16, 45, 48, 62, 64, 96, 149, 151, 153, 15...[0.7977099236641222, 0.7011494252873564, 0.824...0.714409(card_merch_total_14, card_zip3_max_14, card_z...0.1821340.1136220.065620.0merch_state_max_1
\n", + "
" + ], + "text/plain": [ + " feature_idx \\\n", + "1 (15,) \n", + "2 (15, 16) \n", + "3 (15, 16, 173) \n", + "4 (15, 16, 153, 173) \n", + "5 (15, 16, 45, 153, 173) \n", + "6 (15, 16, 45, 153, 169, 173) \n", + "7 (15, 16, 45, 64, 153, 169, 173) \n", + "8 (15, 16, 45, 48, 64, 153, 169, 173) \n", + "9 (15, 16, 45, 48, 64, 153, 169, 173, 192) \n", + "10 (15, 16, 45, 48, 64, 153, 169, 173, 174, 192) \n", + "11 (15, 16, 45, 48, 64, 149, 153, 169, 173, 174, ... \n", + "12 (15, 16, 45, 48, 62, 64, 149, 153, 169, 173, 1... \n", + "13 (15, 16, 45, 48, 62, 64, 149, 151, 153, 169, 1... \n", + "14 (15, 16, 45, 48, 62, 64, 149, 151, 153, 169, 1... \n", + "15 (15, 16, 45, 48, 62, 64, 149, 151, 153, 169, 1... \n", + "16 (15, 16, 45, 48, 62, 64, 149, 151, 153, 169, 1... \n", + "17 (15, 16, 45, 48, 62, 64, 149, 151, 153, 168, 1... \n", + "18 (15, 16, 45, 48, 62, 64, 96, 149, 151, 153, 16... \n", + "19 (15, 16, 45, 48, 62, 64, 96, 149, 151, 153, 16... \n", + "20 (15, 16, 45, 48, 62, 64, 96, 149, 151, 153, 15... \n", + "\n", + " cv_scores avg_score \\\n", + "1 [0.6908396946564885, 0.5670498084291188, 0.709... 0.595961 \n", + "2 [0.7404580152671756, 0.6360153256704981, 0.744... 0.660912 \n", + "3 [0.7595419847328244, 0.632183908045977, 0.7786... 0.676176 \n", + "4 [0.7900763358778626, 0.6896551724137931, 0.797... 0.696269 \n", + "5 [0.767175572519084, 0.685823754789272, 0.82061... 0.701036 \n", + "6 [0.7977099236641222, 0.6934865900383141, 0.812... 0.709631 \n", + "7 [0.7862595419847328, 0.6973180076628352, 0.824... 0.711543 \n", + "8 [0.7748091603053435, 0.7049808429118773, 0.824... 0.709642 \n", + "9 [0.8053435114503816, 0.6896551724137931, 0.816... 0.711536 \n", + "10 [0.8053435114503816, 0.6819923371647509, 0.828... 0.713437 \n", + "11 [0.8053435114503816, 0.6934865900383141, 0.832... 0.716311 \n", + "12 [0.8053435114503816, 0.6934865900383141, 0.839... 0.720127 \n", + "13 [0.8053435114503816, 0.6934865900383141, 0.839... 0.720127 \n", + "14 [0.7977099236641222, 0.6934865900383141, 0.828... 0.715356 \n", + "15 [0.7977099236641222, 0.6934865900383141, 0.828... 0.715356 \n", + "16 [0.8015267175572519, 0.6934865900383141, 0.828... 0.716311 \n", + "17 [0.8015267175572519, 0.6934865900383141, 0.828... 0.716311 \n", + "18 [0.7977099236641222, 0.6934865900383141, 0.828... 0.717265 \n", + "19 [0.7977099236641222, 0.6934865900383141, 0.828... 0.717265 \n", + "20 [0.7977099236641222, 0.7011494252873564, 0.824... 0.714409 \n", + "\n", + " feature_names ci_bound std_dev \\\n", + "1 (card_merch_total_14,) 0.188308 0.117474 \n", + "2 (card_merch_total_14, card_zip3_max_14) 0.145474 0.090752 \n", + "3 (card_merch_total_14, card_zip3_max_14, card_m... 0.159284 0.099367 \n", + "4 (card_merch_total_14, card_zip3_max_14, state_... 0.187482 0.116958 \n", + "5 (card_merch_total_14, card_zip3_max_14, card_z... 0.175552 0.109516 \n", + "6 (card_merch_total_14, card_zip3_max_14, card_z... 0.17818 0.111155 \n", + "7 (card_merch_total_14, card_zip3_max_14, card_z... 0.176668 0.110212 \n", + "8 (card_merch_total_14, card_zip3_max_14, card_z... 0.17591 0.10974 \n", + "9 (card_merch_total_14, card_zip3_max_14, card_z... 0.182332 0.113746 \n", + "10 (card_merch_total_14, card_zip3_max_14, card_z... 0.1851 0.115472 \n", + "11 (card_merch_total_14, card_zip3_max_14, card_z... 0.18789 0.117213 \n", + "12 (card_merch_total_14, card_zip3_max_14, card_z... 0.186254 0.116192 \n", + "13 (card_merch_total_14, card_zip3_max_14, card_z... 0.186254 0.116192 \n", + "14 (card_merch_total_14, card_zip3_max_14, card_z... 0.179329 0.111872 \n", + "15 (card_merch_total_14, card_zip3_max_14, card_z... 0.179329 0.111872 \n", + "16 (card_merch_total_14, card_zip3_max_14, card_z... 0.180471 0.112585 \n", + "17 (card_merch_total_14, card_zip3_max_14, card_z... 0.180471 0.112585 \n", + "18 (card_merch_total_14, card_zip3_max_14, card_z... 0.174605 0.108925 \n", + "19 (card_merch_total_14, card_zip3_max_14, card_z... 0.174605 0.108925 \n", + "20 (card_merch_total_14, card_zip3_max_14, card_z... 0.182134 0.113622 \n", + "\n", + " std_err add variables in this order variable name \n", + "1 0.067823 1.0 card_merch_total_14 \n", + "2 0.052396 2.0 card_zip3_max_14 \n", + "3 0.05737 3.0 card_merch_avg_14 \n", + "4 0.067526 4.0 state_des_max_1 \n", + "5 0.063229 5.0 card_zip_max_60 \n", + "6 0.064176 6.0 Card_Merchnum_desc_avg_30 \n", + "7 0.063631 7.0 Card_Merchnum_Zip_total_60 \n", + "8 0.063358 8.0 Card_Merchdesc_Zip_total_30 \n", + "9 0.065671 9.0 card_zip_avg_1 \n", + "10 0.066668 10.0 Card_Merchnum_Zip_avg_14 \n", + "11 0.067673 11.0 card_merch_avg_3 \n", + "12 0.067084 12.0 card_merch_total_60 \n", + "13 0.067084 13.0 Card_Merchnum_Zip_avg_3 \n", + "14 0.06459 14.0 Card_Merchdesc_Zip_avg_7 \n", + "15 0.06459 15.0 Card_Merchdesc_avg_7 \n", + "16 0.065001 16.0 Card_Merchnum_Zip_avg_1 \n", + "17 0.065001 17.0 Card_Merchnum_desc_avg_7 \n", + "18 0.062888 18.0 Card_Merchnum_desc_max_1 \n", + "19 0.062888 19.0 card_merch_avg_1 \n", + "20 0.0656 20.0 merch_state_max_1 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ordered_vars_FS" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "ordered_vars_FS.to_csv('Wrapper_selection_info.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1 card_merch_total_14\n", + "2 card_zip3_max_14\n", + "3 card_merch_avg_14\n", + "4 state_des_max_1\n", + "5 card_zip_max_60\n", + "6 Card_Merchnum_desc_avg_30\n", + "7 Card_Merchnum_Zip_total_60\n", + "8 Card_Merchdesc_Zip_total_30\n", + "9 card_zip_avg_1\n", + "10 Card_Merchnum_Zip_avg_14\n", + "11 card_merch_avg_3\n", + "12 card_merch_total_60\n", + "13 Card_Merchnum_Zip_avg_3\n", + "14 Card_Merchdesc_Zip_avg_7\n", + "15 Card_Merchdesc_avg_7\n", + "16 Card_Merchnum_Zip_avg_1\n", + "17 Card_Merchnum_desc_avg_7\n", + "18 Card_Merchnum_desc_max_1\n", + "19 card_merch_avg_1\n", + "20 merch_state_max_1\n", + "Name: variable name, dtype: object" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vars_keep = ordered_vars_FS['variable name']\n", + "vars_keep_list = ordered_vars_FS['variable name'].tolist()\n", + "vars_keep.to_csv('final_vars_list.csv',index=False)\n", + "vars_keep" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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filter score
variable
card_zip3_total_70.676549
card_zip_total_70.666816
card_zip3_total_30.660260
card_zip3_total_140.659257
card_state_total_70.654135
......
merch_zip_unique_count_for_merch_state_70.000022
Card_Merchdesc_Zip_unique_count_for_card_state_10.000022
card_zip3_unique_count_for_card_state_10.000017
card_merch_unique_count_for_card_state_10.000011
card_merch_unique_count_for_Cardnum_10.000000
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2343 rows × 1 columns

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" + ], + "text/plain": [ + " filter score\n", + "variable \n", + "card_zip3_total_7 0.676549\n", + "card_zip_total_7 0.666816\n", + "card_zip3_total_3 0.660260\n", + "card_zip3_total_14 0.659257\n", + "card_state_total_7 0.654135\n", + "... ...\n", + "merch_zip_unique_count_for_merch_state_7 0.000022\n", + "Card_Merchdesc_Zip_unique_count_for_card_state_1 0.000022\n", + "card_zip3_unique_count_for_card_state_1 0.000017\n", + "card_merch_unique_count_for_card_state_1 0.000011\n", + "card_merch_unique_count_for_Cardnum_1 0.000000\n", + "\n", + "[2343 rows x 1 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filter_score.set_index('variable',drop=True,inplace=True)\n", + "filter_score = filter_score.iloc[1:,:]\n", + "filter_score" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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variable
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card_zip3_max_14
card_merch_avg_14
state_des_max_1
card_zip_max_60
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: []\n", + "Index: [card_merch_total_14, card_zip3_max_14, card_merch_avg_14, state_des_max_1, card_zip_max_60]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vars_keep_sorted = pd.DataFrame(vars_keep_list)\n", + "vars_keep_sorted.columns=['variable']\n", + "vars_keep_sorted.set_index('variable',drop=True,inplace=True)\n", + "vars_keep_sorted.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "vars_keep_sorted = pd.concat([vars_keep_sorted,filter_score],axis=1,join='inner')" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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wrapper ordervariablefilter score
01card_merch_total_140.630048
12card_zip3_max_140.629515
23card_merch_avg_140.518386
34state_des_max_10.524301
45card_zip_max_600.605193
56Card_Merchnum_desc_avg_300.519394
67Card_Merchnum_Zip_total_600.597034
78Card_Merchdesc_Zip_total_300.604455
89card_zip_avg_10.514891
910Card_Merchnum_Zip_avg_140.518122
1011card_merch_avg_30.525502
1112card_merch_total_600.598597
1213Card_Merchnum_Zip_avg_30.524558
1314Card_Merchdesc_Zip_avg_70.517607
1415Card_Merchdesc_avg_70.516608
1516Card_Merchnum_Zip_avg_10.514052
1617Card_Merchnum_desc_avg_70.519505
1718Card_Merchnum_desc_max_10.560029
1819card_merch_avg_10.515008
1920merch_state_max_10.522978
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filter score
variable
card_zip3_total_70.676549
card_zip_total_70.666816
card_zip3_total_30.660260
card_zip3_total_140.659257
card_state_total_70.654135
......
merch_zip_unique_count_for_merch_state_70.000022
Card_Merchdesc_Zip_unique_count_for_card_state_10.000022
card_zip3_unique_count_for_card_state_10.000017
card_merch_unique_count_for_card_state_10.000011
card_merch_unique_count_for_Cardnum_10.000000
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2343 rows × 1 columns

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" + ], + "text/plain": [ + " filter score\n", + "variable \n", + "card_zip3_total_7 0.676549\n", + "card_zip_total_7 0.666816\n", + "card_zip3_total_3 0.660260\n", + "card_zip3_total_14 0.659257\n", + "card_state_total_7 0.654135\n", + "... ...\n", + "merch_zip_unique_count_for_merch_state_7 0.000022\n", + "Card_Merchdesc_Zip_unique_count_for_card_state_1 0.000022\n", + "card_zip3_unique_count_for_card_state_1 0.000017\n", + "card_merch_unique_count_for_card_state_1 0.000011\n", + "card_merch_unique_count_for_Cardnum_1 0.000000\n", + "\n", + "[2343 rows x 1 columns]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filter_score" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 12.5 s, sys: 1.78 s, total: 14.3 s\n", + "Wall time: 14.5 s\n" + ] + }, + { + "data": { + "text/plain": [ + "(96397, 2344)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "df = pd.read_csv(file_name)\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(96397, 22)\n" + ] + } + ], + "source": [ + "df_keep = df.filter(vars_keep_list, axis=1)\n", + "# df_keep = df[df.index.isin(vars_keep_list)]\n", + "print(df_keep.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "df_keep.to_csv('vars_final.csv',index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "duration: 0:10:44.857541\n" + ] + } + ], + "source": [ + "print(\"duration: \", dt.datetime.now() - start_time)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/stevecoggeshall/Documents/Teaching/Data sets/done/transactions/transactions 2023 new'" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%pwd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/transactions variable creation.ipynb b/transactions variable creation.ipynb new file mode 100644 index 0000000..917e7af --- /dev/null +++ b/transactions variable creation.ipynb @@ -0,0 +1,11355 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import datetime\n", + "import calendar\n", + "import timeit\n", + "import datetime as dt\n", + "import re\n", + "from math import exp\n", + "import matplotlib.pyplot as plt\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "start_time = datetime.datetime.now()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(96753, 18)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('card transactions.csv')\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RecnumCardnumDateMerchnumMerch descriptionMerch stateMerch zipTranstypeAmountFraudUnnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17
0151421904391/1/105509006296254FEDEX SHP 12/23/09 AB#TN38118.0P3.620NaNNaNNaNNaNNaNNaNNaNNaN
1251421839731/1/1061003026333SERVICE MERCHANDISE #81MA1803.0P31.420NaNNaNNaNNaNNaNNaNNaNNaN
2351421317211/1/104503082993600OFFICE DEPOT #191MD20706.0P178.490NaNNaNNaNNaNNaNNaNNaNNaN
3451421484521/1/105509006296254FEDEX SHP 12/28/09 AB#TN38118.0P3.620NaNNaNNaNNaNNaNNaNNaNNaN
4551421904391/1/105509006296254FEDEX SHP 12/23/09 AB#TN38118.0P3.620NaNNaNNaNNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " Recnum Cardnum Date Merchnum Merch description \\\n", + "0 1 5142190439 1/1/10 5509006296254 FEDEX SHP 12/23/09 AB# \n", + "1 2 5142183973 1/1/10 61003026333 SERVICE MERCHANDISE #81 \n", + "2 3 5142131721 1/1/10 4503082993600 OFFICE DEPOT #191 \n", + "3 4 5142148452 1/1/10 5509006296254 FEDEX SHP 12/28/09 AB# \n", + "4 5 5142190439 1/1/10 5509006296254 FEDEX SHP 12/23/09 AB# \n", + "\n", + " Merch state Merch zip Transtype Amount Fraud Unnamed: 10 Unnamed: 11 \\\n", + "0 TN 38118.0 P 3.62 0 NaN NaN \n", + "1 MA 1803.0 P 31.42 0 NaN NaN \n", + "2 MD 20706.0 P 178.49 0 NaN NaN \n", + "3 TN 38118.0 P 3.62 0 NaN NaN \n", + "4 TN 38118.0 P 3.62 0 NaN NaN \n", + "\n", + " Unnamed: 12 Unnamed: 13 Unnamed: 14 Unnamed: 15 Unnamed: 16 \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " Unnamed: 17 \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 96753 entries, 0 to 96752\n", + "Data columns (total 10 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Recnum 96753 non-null int64 \n", + " 1 Cardnum 96753 non-null int64 \n", + " 2 Date 96753 non-null datetime64[ns]\n", + " 3 Merchnum 93378 non-null object \n", + " 4 Merch description 96753 non-null object \n", + " 5 Merch state 95558 non-null object \n", + " 6 Merch zip 92097 non-null float64 \n", + " 7 Transtype 96753 non-null object \n", + " 8 Amount 96753 non-null float64 \n", + " 9 Fraud 96753 non-null int64 \n", + "dtypes: datetime64[ns](1), float64(2), int64(3), object(4)\n", + "memory usage: 7.4+ MB\n" + ] + } + ], + "source": [ + "data.dropna(how='all', axis=1, inplace=True)\n", + "data['Date'] = pd.to_datetime(data['Date'])\n", + "data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(96397, 10)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = data[data['Transtype'] == 'P']\n", + "data = data[data['Amount'] <= 3000000]\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Recnum 0\n", + "Cardnum 0\n", + "Date 0\n", + "Merchnum 3198\n", + "Merch description 0\n", + "Merch state 1020\n", + "Merch zip 4300\n", + "Transtype 0\n", + "Amount 0\n", + "Fraud 0\n", + "dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "data_orig = data.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Clean and impute merchnum" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "data['Merchnum'] = data['Merchnum'].replace({'0':np.nan})" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3251" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Merchnum'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "merchdes_merchnum = {}\n", + "for index, merchdes in data[data['Merch description'].notnull()][data['Merchnum'].notnull()]['Merch description'].items():\n", + " if pd.isnull(merchdes) == True:\n", + " continue\n", + " elif merchdes not in merchdes_merchnum:\n", + " merchdes_merchnum[merchdes] = data.loc[index, 'Merchnum']" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# fill in by mapping with Merch description\n", + "data['Merchnum'] = data['Merchnum'].fillna(data['Merch description'].map(merchdes_merchnum))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2094" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Merchnum'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# assign unknown for adjustments transactions\n", + "data['Merchnum'] = data['Merchnum'].mask(data['Merch description'] == 'RETAIL CREDIT ADJUSTMENT', 'unknown')\n", + "data['Merchnum'] = data['Merchnum'].mask(data['Merch description'] == 'RETAIL DEBIT ADJUSTMENT', 'unknown')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1403" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Merchnum'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['MONTGOMERY COLLEGE-PHONE', 'PACKAGE PLACE THE',\n", + " 'CUBIX CORPORATION', 'SIGNAL GRAPHICS PRINTING',\n", + " 'C & M OFFICE EQUIPMENT', \"TOMMY'S TRAILERS\",\n", + " 'Z WORLD/RABBIT SEMICONDUC', 'IMPAC/TRI-COUNTY/FREED',\n", + " 'REPROGRPHC TECHNLGIES INC', 'STP SPECIALITY TECH',\n", + " 'VANGARD INTERNAITONAL', 'BLACKWELL SCIENCE', 'CDN ISOTOPES INC',\n", + " 'INTERACTIVE SOFTWARE S', 'H R WILLIAMS MILL SUPP',\n", + " 'ELSEVIER SCIENCE BV', 'COLORADO GARDEN SHOW',\n", + " 'PEARSON EDUCATION CANADA', 'PONTOTOC AREA VO-TECH',\n", + " 'NATIONAL BAG COMPANY'], dtype=object)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.loc[data.Merchnum.isna(), 'Merch description'].unique()[:20]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "508" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 1403 NULL Merchnums with 508 unique Descriptions\n", + "data.loc[data.Merchnum.isna(), 'Merch description'].nunique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create new Merchnums using the description field" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# adding new merchnums\n", + "# each new unique merchnum will be max(merchnum) + 1\n", + "merchnum_create = {}\n", + "max_merchnum = pd.to_numeric(data.Merchnum, errors='coerce').max()\n", + "for merch_desc in data.loc[data.Merchnum.isna(), 'Merch description'].unique():\n", + " merchnum_create[merch_desc] = str(int(max_merchnum + 1))\n", + " max_merchnum += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# fill in by mapping with Merch description (newly created merchnums)\n", + "data['Merchnum'] = data['Merchnum'].fillna(data['Merch description'].map(merchnum_create))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recnum 0\n", + "Cardnum 0\n", + "Date 0\n", + "Merchnum 0\n", + "Merch description 0\n", + "Merch state 1020\n", + "Merch zip 4300\n", + "Transtype 0\n", + "Amount 0\n", + "Fraud 0\n" + ] + } + ], + "source": [ + "for i in data.columns:\n", + " print(i, data[i].isnull().sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Clean and impute State" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1020" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Merch state'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([9.2600e+02, 9.2900e+02, 1.4000e+03, 6.5132e+04, 8.6899e+04,\n", + " 2.3080e+04, 6.0528e+04, 9.3400e+02, 9.0200e+02, 7.3800e+02,\n", + " 9.0805e+04, 7.6302e+04, 9.0000e+00, 9.1400e+02, 6.0000e+00,\n", + " 9.5461e+04, 5.0823e+04, 2.0000e+00, 4.8700e+04, 6.8000e+02,\n", + " 1.0000e+00, 6.8100e+02, 6.2300e+02, 7.2600e+02, 9.3600e+02,\n", + " 1.2108e+04, 7.9100e+02, 9.0700e+02, 9.2200e+02, 9.2000e+02,\n", + " 3.0000e+00, 8.0100e+02, 8.0000e+00, 3.1040e+04, 3.8117e+04,\n", + " 4.1160e+04])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[(data['Merch state'].isnull()) & (data['Merch zip'].notnull())]['Merch zip'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# dict for mapping\n", + "zip_state = {}\n", + "for index, zip5 in data[data['Merch zip'].notnull()]['Merch zip'].items():\n", + " if zip5 not in zip_state:\n", + " zip_state[zip5] = data.loc[index, 'Merch state']\n", + " \n", + "zip_state['00926'] = 'PR'\n", + "zip_state['00929'] = 'PR'\n", + "zip_state['00934'] = 'PR'\n", + "zip_state['00902'] = 'PR'\n", + "zip_state['00738'] = 'PR'\n", + "zip_state['90805'] = 'CA'\n", + "zip_state['76302'] = 'TX'\n", + "zip_state['00914'] = 'PR'\n", + "zip_state['95461'] = 'CA'\n", + "zip_state['00680'] = 'PR'\n", + "zip_state['00623'] = 'PR'\n", + "zip_state['00726'] = 'PR'\n", + "zip_state['00936'] = 'PR'\n", + "zip_state['12108'] = 'NY'\n", + "zip_state['00791'] = 'PR'\n", + "zip_state['00907'] = 'PR'\n", + "zip_state['00922'] = 'PR'\n", + "zip_state['00920'] = 'PR'\n", + "zip_state['00801'] = 'VI'\n", + "zip_state['31040'] = 'GA'\n", + "zip_state['41160'] = 'KY'\n", + "zip_state['00681'] = 'PR'" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "merchnum_state = {}\n", + "for index, merchnum in data[data['Merchnum'].notnull()]['Merchnum'].items():\n", + " if merchnum not in merchnum_state :\n", + " merchnum_state [merchnum] = data.loc[index, 'Merch state']" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "merchdes_state = {}\n", + "for index, merchdes in data[data['Merch description'].notnull()]['Merch description'].items():\n", + " if merchdes not in merchdes_state :\n", + " merchdes_state [merchdes] = data.loc[index, 'Merch state']" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# fill in by mapping with zip, merchnum and merch description\n", + "data['Merch state'] = data['Merch state'].fillna(data['Merch zip'].map(zip_state))\n", + "data['Merch state'] = data['Merch state'].fillna(data['Merchnum'].map(merchnum_state))\n", + "data['Merch state'] = data['Merch state'].fillna(data['Merch description'].map(merchdes_state))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# assign unknown for adjustments transactions\n", + "data['Merch state'] = data['Merch state'].mask(data['Merch description'] == 'RETAIL CREDIT ADJUSTMENT', 'unknown')\n", + "data['Merch state'] = data['Merch state'].mask(data['Merch description'] == 'RETAIL DEBIT ADJUSTMENT', 'unknown')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "346" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Merch state'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# change non-US states\n", + "# might actually be useful cus fraud could be foreign transactions\n", + "# maybe put a 'foreign' tag or just leave them as is\n", + "\n", + "states = [\"AL\", \"AK\", \"AZ\", \"AR\", \"CA\", \"CO\", \"CT\", \"DC\", \"DE\", \"FL\", \"GA\", \n", + " \"HI\", \"ID\", \"IL\", \"IN\", \"IA\", \"KS\", \"KY\", \"LA\", \"ME\", \"MD\", \n", + " \"MA\", \"MI\", \"MN\", \"MS\", \"MO\", \"MT\", \"NE\", \"NV\", \"NH\", \"NJ\", \n", + " \"NM\", \"NY\", \"NC\", \"ND\", \"OH\", \"OK\", \"OR\", \"PA\", \"RI\", \"SC\", \n", + " \"SD\", \"TN\", \"TX\", \"UT\", \"VT\", \"VA\", \"WA\", \"WV\", \"WI\", \"WY\", \n", + " 'VI', 'PR', np.nan, 'unknown']\n", + "\n", + "for index, state in data['Merch state'].items():\n", + " if state not in states:\n", + " data.loc[index, 'Merch state'] = 'foreign'" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "data['Merch state'].fillna('unknown',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Merch state'].isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Clean and impute zip" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4300" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Merch zip'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "merchnum_zip = {}\n", + "for index, merchnum in data[data['Merchnum'].notnull()]['Merchnum'].items():\n", + " if merchnum not in merchnum_zip :\n", + " merchnum_zip [merchnum] = data.loc[index, 'Merch zip']" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "merchdes_zip = {}\n", + "for index, merchdes in data[data['Merch description'].notnull()]['Merch description'].items():\n", + " if merchdes not in merchdes_zip :\n", + " merchdes_zip [merchdes] = data.loc[index, 'Merch zip']" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# fill in by mapping with merchnum and merch description\n", + "data['Merch zip'] = data['Merch zip'].fillna(data['Merchnum'].map(merchnum_zip))\n", + "data['Merch zip'] = data['Merch zip'].fillna(data['Merch description'].map(merchdes_zip))" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2658" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Merch zip'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# assign unknown for adjustments transactions\n", + "data['Merch zip'] = data['Merch zip'].mask(data['Merch zip'] == 'RETAIL CREDIT ADJUSTMENT', 'unknown')\n", + "data['Merch zip'] = data['Merch zip'].mask(data['Merch zip'] == 'RETAIL DEBIT ADJUSTMENT', 'unknown')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2658" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Merch zip'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RecnumCardnumDateMerchnumMerch descriptionMerch stateMerch zipTranstypeAmountFraud
515251422043842010-01-025000006000095IBM INTERNET 01000025NYNaNP20.150
545551421463402010-01-025000006000095IBM INTERNET 01000025NYNaNP23.900
555651422609842010-01-025000006000095IBM INTERNET 01000025NYNaNP19.950
585951422043842010-01-025000006000095IBM INTERNET 01000025NYNaNP20.150
596051422043842010-01-025000006000095IBM INTERNET 01000025NYNaNP20.150
606151422043842010-01-025000006000095IBM INTERNET 01000025NYNaNP20.150
616251422043842010-01-025000006000095IBM INTERNET 01000025NYNaNP20.150
626351422533562010-01-025000006000095IBM INTERNET 01000025NYNaNP27.410
646551422043842010-01-025000006000095IBM INTERNET 01000025NYNaNP20.150
656651422043842010-01-025000006000095IBM INTERNET 01000025NYNaNP20.150
666751422609842010-01-025000006000095IBM INTERNET 01000025NYNaNP19.950
686951422609842010-01-025000006000095IBM INTERNET 01000025NYNaNP37.510
697051422609842010-01-025000006000095IBM INTERNET 01000025NYNaNP19.950
717251422043842010-01-025000006000095IBM INTERNET 01000025NYNaNP28.130
727351422043842010-01-025000006000095IBM INTERNET 01000025NYNaNP20.150
757651422533562010-01-025000006000095IBM INTERNET 01000025NYNaNP12.500
777851422043842010-01-025000006000095IBM INTERNET 01000025NYNaNP20.150
787951421499942010-01-025000006000095IBM INTERNET 01000025NYNaNP101.400
798051421532012010-01-025000006000095IBM INTERNET 01000025NYNaNP19.950
878851422554162010-01-038053478940091MCGHEE & COMPANY INCWVNaNP55.800
19920051422573562010-01-032000049710067LASER ACCESS 42760017GANaNP1940.000
21821951421729952010-01-036700046420068MARYS GIFTS 41480013ILNaNP17.970
23023151422215712010-01-036005030600003FORMA SCIENTIFICOHNaNP72.000
25825951421715822010-01-044900000004673WALGREEN 00004179ILNaNP21.280
26226351422575752010-01-04unknownRETAIL DEBIT ADJUSTMENTunknownNaNP320.000
27227351421247912010-01-04unknownRETAIL DEBIT ADJUSTMENTunknownNaNP970.000
29329451421715822010-01-044900000004673WALGREEN 00004179ILNaNP6.760
37938051421839042010-01-041700000096481AMES DEPT STOR 0021436MANaNP13.000
40040151422760992010-01-04unknownRETAIL DEBIT ADJUSTMENTunknownNaNP82.590
41641751421736172010-01-046100020004006USGPO SUPT DOCS-SUB/PUDCNaNP98.000
47647751422677932010-01-05unknownRETAIL DEBIT ADJUSTMENTunknownNaNP17.590
48248351422573562010-01-052000049710067LASER ACCESS 42760017GANaNP600.000
48748851422677932010-01-05unknownRETAIL CREDIT ADJUSTMENTunknownNaNP19.690
51551651422677932010-01-05unknownRETAIL CREDIT ADJUSTMENTunknownNaNP17.590
54454551421937302010-01-05unknownRETAIL CREDIT ADJUSTMENTunknownNaNP105.000
55755851422344712010-01-05unknownRETAIL CREDIT ADJUSTMENTunknownNaNP1149.970
73773851422677932010-01-05unknownRETAIL CREDIT ADJUSTMENTunknownNaNP18.640
73974051422677932010-01-05unknownRETAIL CREDIT ADJUSTMENTunknownNaNP17.000
74474551422677932010-01-05unknownRETAIL CREDIT ADJUSTMENTunknownNaNP17.590
75875951422677932010-01-05unknownRETAIL CREDIT ADJUSTMENTunknownNaNP24.680
81781851422306692010-01-069996060597906TOMMY'S TRAILERSOKNaNP48.970
85285351421743052010-01-06unknownRETAIL CREDIT ADJUSTMENTunknownNaNP379.420
86886951422055002010-01-065000006000095IBM INTERNET 01000025NYNaNP14.090
90690751422888972010-01-069996060597908IMPAC/TRI-COUNTY/FREEDMDNaNP467.580
93193251422145512010-01-066176269MUNKSGAARDS FORLAGunknownNaNP1790.000
1040104151422301812010-01-069996060597910STP SPECIALITY TECHforeignNaNP486.180
1082108351421325742010-01-069900020008506UNICOR FED PRISON INDKYNaNP1090.000
1162116351421791592010-01-07674906173338G B SCIENTIFICCANaNP849.890
1174117551421390112010-01-077300020006306GENERAL SERVICES ADMINMDNaNP609.670
1195119651422606892010-01-07679960185332BUSINESS WIRECANaNP532.000
\n", + "
" + ], + "text/plain": [ + " Recnum Cardnum Date Merchnum Merch description \\\n", + "51 52 5142204384 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "54 55 5142146340 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "55 56 5142260984 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "58 59 5142204384 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "59 60 5142204384 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "60 61 5142204384 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "61 62 5142204384 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "62 63 5142253356 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "64 65 5142204384 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "65 66 5142204384 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "66 67 5142260984 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "68 69 5142260984 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "69 70 5142260984 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "71 72 5142204384 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "72 73 5142204384 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "75 76 5142253356 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "77 78 5142204384 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "78 79 5142149994 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "79 80 5142153201 2010-01-02 5000006000095 IBM INTERNET 01000025 \n", + "87 88 5142255416 2010-01-03 8053478940091 MCGHEE & COMPANY INC \n", + "199 200 5142257356 2010-01-03 2000049710067 LASER ACCESS 42760017 \n", + "218 219 5142172995 2010-01-03 6700046420068 MARYS GIFTS 41480013 \n", + "230 231 5142221571 2010-01-03 6005030600003 FORMA SCIENTIFIC \n", + "258 259 5142171582 2010-01-04 4900000004673 WALGREEN 00004179 \n", + "262 263 5142257575 2010-01-04 unknown RETAIL DEBIT ADJUSTMENT \n", + "272 273 5142124791 2010-01-04 unknown RETAIL DEBIT ADJUSTMENT \n", + "293 294 5142171582 2010-01-04 4900000004673 WALGREEN 00004179 \n", + "379 380 5142183904 2010-01-04 1700000096481 AMES DEPT STOR 0021436 \n", + "400 401 5142276099 2010-01-04 unknown RETAIL DEBIT ADJUSTMENT \n", + "416 417 5142173617 2010-01-04 6100020004006 USGPO SUPT DOCS-SUB/PU \n", + "476 477 5142267793 2010-01-05 unknown RETAIL DEBIT ADJUSTMENT \n", + "482 483 5142257356 2010-01-05 2000049710067 LASER ACCESS 42760017 \n", + "487 488 5142267793 2010-01-05 unknown RETAIL CREDIT ADJUSTMENT \n", + "515 516 5142267793 2010-01-05 unknown RETAIL CREDIT ADJUSTMENT \n", + "544 545 5142193730 2010-01-05 unknown RETAIL CREDIT ADJUSTMENT \n", + "557 558 5142234471 2010-01-05 unknown RETAIL CREDIT ADJUSTMENT \n", + "737 738 5142267793 2010-01-05 unknown RETAIL CREDIT ADJUSTMENT \n", + "739 740 5142267793 2010-01-05 unknown RETAIL CREDIT ADJUSTMENT \n", + "744 745 5142267793 2010-01-05 unknown RETAIL CREDIT ADJUSTMENT \n", + "758 759 5142267793 2010-01-05 unknown RETAIL CREDIT ADJUSTMENT \n", + "817 818 5142230669 2010-01-06 9996060597906 TOMMY'S TRAILERS \n", + "852 853 5142174305 2010-01-06 unknown RETAIL CREDIT ADJUSTMENT \n", + "868 869 5142205500 2010-01-06 5000006000095 IBM INTERNET 01000025 \n", + "906 907 5142288897 2010-01-06 9996060597908 IMPAC/TRI-COUNTY/FREED \n", + "931 932 5142214551 2010-01-06 6176269 MUNKSGAARDS FORLAG \n", + "1040 1041 5142230181 2010-01-06 9996060597910 STP SPECIALITY TECH \n", + "1082 1083 5142132574 2010-01-06 9900020008506 UNICOR FED PRISON IND \n", + "1162 1163 5142179159 2010-01-07 674906173338 G B SCIENTIFIC \n", + "1174 1175 5142139011 2010-01-07 7300020006306 GENERAL SERVICES ADMIN \n", + "1195 1196 5142260689 2010-01-07 679960185332 BUSINESS WIRE \n", + "\n", + " Merch state Merch zip Transtype Amount Fraud \n", + "51 NY NaN P 20.15 0 \n", + "54 NY NaN P 23.90 0 \n", + "55 NY NaN P 19.95 0 \n", + "58 NY NaN P 20.15 0 \n", + "59 NY NaN P 20.15 0 \n", + "60 NY NaN P 20.15 0 \n", + "61 NY NaN P 20.15 0 \n", + "62 NY NaN P 27.41 0 \n", + "64 NY NaN P 20.15 0 \n", + "65 NY NaN P 20.15 0 \n", + "66 NY NaN P 19.95 0 \n", + "68 NY NaN P 37.51 0 \n", + "69 NY NaN P 19.95 0 \n", + "71 NY NaN P 28.13 0 \n", + "72 NY NaN P 20.15 0 \n", + "75 NY NaN P 12.50 0 \n", + "77 NY NaN P 20.15 0 \n", + "78 NY NaN P 101.40 0 \n", + "79 NY NaN P 19.95 0 \n", + "87 WV NaN P 55.80 0 \n", + "199 GA NaN P 1940.00 0 \n", + "218 IL NaN P 17.97 0 \n", + "230 OH NaN P 72.00 0 \n", + "258 IL NaN P 21.28 0 \n", + "262 unknown NaN P 320.00 0 \n", + "272 unknown NaN P 970.00 0 \n", + "293 IL NaN P 6.76 0 \n", + "379 MA NaN P 13.00 0 \n", + "400 unknown NaN P 82.59 0 \n", + "416 DC NaN P 98.00 0 \n", + "476 unknown NaN P 17.59 0 \n", + "482 GA NaN P 600.00 0 \n", + "487 unknown NaN P 19.69 0 \n", + "515 unknown NaN P 17.59 0 \n", + "544 unknown NaN P 105.00 0 \n", + "557 unknown NaN P 1149.97 0 \n", + "737 unknown NaN P 18.64 0 \n", + "739 unknown NaN P 17.00 0 \n", + "744 unknown NaN P 17.59 0 \n", + "758 unknown NaN P 24.68 0 \n", + "817 OK NaN P 48.97 0 \n", + "852 unknown NaN P 379.42 0 \n", + "868 NY NaN P 14.09 0 \n", + "906 MD NaN P 467.58 0 \n", + "931 unknown NaN P 1790.00 0 \n", + "1040 foreign NaN P 486.18 0 \n", + "1082 KY NaN P 1090.00 0 \n", + "1162 CA NaN P 849.89 0 \n", + "1174 MD NaN P 609.67 0 \n", + "1195 CA NaN P 532.00 0 " + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp = data[data['Merch zip'].isna()]\n", + "temp.head(50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## New imputation logic added" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'TN': 38118.0, 'MA': 1803.0, 'MD': 20706.0, 'OH': 45429.0, 'GA': '31040', 'IL': 60061.0, 'TX': '76302', 'WA': 98101.0, 'WI': 53052.0, 'NJ': 8701.0, 'FL': 32514.0, 'NY': '12108', 'CA': '90805', 'MS': 39201.0, 'VA': 20190.0, 'MO': 63026.0, 'PA': 17201.0, 'NC': 27560.0, 'WV': nan, 'CT': 6513.0, 'KS': 66202.0, 'OR': 97015.0, 'SC': 29407.0, 'MI': 48080.0, 'MN': 55802.0, 'KY': '41160', 'ME': 4032.0, 'CO': 80134.0, 'DC': 20001.0, 'AK': 99501.0, 'IA': 50309.0, 'NH': 3087.0, 'MT': 59601.0, 'OK': 74145.0, 'unknown': nan, 'NV': 89103.0, 'RI': 2882.0, 'NE': 69160.0, 'AZ': 85225.0, 'NM': 87114.0, 'UT': 84112.0, 'ID': 83814.0, 'SD': 57049.0, 'AL': 36601.0, 'DE': 19713.0, 'foreign': nan, 'LA': 70433.0, 'IN': 46229.0, 'VT': 5146.0, 'ND': 20740.0, 'AR': 72221.0, 'HI': 96813.0, 'WY': 82609.0, 'PR': '00926', 'VI': '00801'}\n", + "1150\n" + ] + } + ], + "source": [ + "# my imputation logic\n", + "# not the best\n", + "\n", + "state_zip = {}\n", + "for index, zip5 in data[data['Merch state'].notnull()]['Merch state'].items():\n", + " if zip5 not in state_zip:\n", + " state_zip[zip5] = data.loc[index, 'Merch zip']\n", + "\n", + "state_zip['PR']='00926'\n", + "\n", + "state_zip['CA']='90805'\n", + "\n", + "state_zip['TX']='76302'\n", + "\n", + "state_zip['NY']='12108'\n", + "\n", + "state_zip['VI']='00801'\n", + "\n", + "state_zip['GA']='31040'\n", + "\n", + "state_zip['KY']='41160'\n", + "\n", + "\n", + "print(state_zip)\n", + "data['Merch zip'] = data['Merch zip'].fillna(data['Merch state'].map(state_zip))\n", + "print(data['Merch zip'].isnull().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Merch zip'].fillna('unknown', inplace=True)\n", + "data['Merch zip'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "df = data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "data.to_csv('transactions_clean.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 96397 entries, 0 to 96752\n", + "Data columns (total 10 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Recnum 96397 non-null int64 \n", + " 1 Cardnum 96397 non-null int64 \n", + " 2 Date 96397 non-null datetime64[ns]\n", + " 3 Merchnum 96397 non-null object \n", + " 4 Merch description 96397 non-null object \n", + " 5 Merch state 96397 non-null object \n", + " 6 Merch zip 96397 non-null object \n", + " 7 Transtype 96397 non-null object \n", + " 8 Amount 96397 non-null float64 \n", + " 9 Fraud 96397 non-null int64 \n", + "dtypes: datetime64[ns](1), float64(1), int64(3), object(5)\n", + "memory usage: 10.1+ MB\n" + ] + } + ], + "source": [ + "data.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Target encoded variables" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Fraud
Dow
Monday0.008711
Tuesday0.007127
Wednesday0.009788
Thursday0.018626
Friday0.025994
Saturday0.010095
Sunday0.009630
\n", + "" + ], + "text/plain": [ + " Fraud\n", + "Dow \n", + "Monday 0.008711\n", + "Tuesday 0.007127\n", + "Wednesday 0.009788\n", + "Thursday 0.018626\n", + "Friday 0.025994\n", + "Saturday 0.010095\n", + "Sunday 0.009630" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_dow=y_dow.reset_index()\n", + "cats=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n", + "y_dow['Dow']=pd.Categorical(y_dow['Dow'], categories=cats, ordered=True)\n", + "y_dow=y_dow.sort_values('Dow')\n", + "y_dow=y_dow.set_index('Dow')\n", + "y_dow" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use('ggplot')\n", + "fig, ax = plt.subplots(figsize=(20,10))\n", + "plt.bar(data = y_dow, \n", + " x = y_dow.index, \n", + " height = 'Fraud',\n", + " color = 'darkblue'\n", + " )\n", + "#ax.set_ylim(bottom = 0.013)\n", + "ax.axhline(y = y_avg, ls = '--', lw = 2, label=\"Average Fraud Population\")\n", + "\n", + "for i, v in enumerate(y_dow.index):\n", + " ax.text(v,y_dow.loc[v,'Fraud']+0.0001,round(y_dow.loc[v,'Fraud'],5),horizontalalignment='center',fontsize=15)\n", + "\n", + "plt.legend(['Average Fraud Population'], fontsize=15)\n", + "plt.xlabel(\"Day of Week\",fontsize=20)\n", + "plt.ylabel(\"Percentage\",fontsize=20)\n", + "plt.xticks(fontsize=15)\n", + "plt.yticks(fontsize=15)\n", + "plt.title(\"Fraud Proportion of Each Weekday\", fontsize=30)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Month of Transaction (New variable created)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# find the month of each transaction\n", + "df['Month'] = df.Date.apply(lambda x: calendar.month_abbr[x.month])\n", + "df['Month'].value_counts()\n", + "\n", + "train_test = df[df.Date < '2010-11-01']\n", + "c = 4; nmid = 20; y_avg = train_test['Fraud'].mean()\n", + "y_month = train_test.groupby('Month')['Fraud'].mean()\n", + "num = train_test.groupby('Month').size()\n", + "y_month_smooth = y_avg + (y_mth - y_avg)/(1 + np.exp(-(num - nmid)/c))\n", + "df['Month_Risk'] = df.Month.map(y_mth_smooth)\n", + "y_month=y_month.reset_index()\n", + "cats=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct']\n", + "y_month['Month']=pd.Categorical(y_month['Month'], categories=cats, ordered=True)\n", + "y_month=y_month.sort_values('Month')\n", + "y_month=y_month.set_index('Month')\n", + "\n", + "plt.style.use('ggplot')\n", + "fig, ax = plt.subplots(figsize=(20,10))\n", + "plt.bar(data=y_month, \n", + " x=y_month.index, \n", + " height='Fraud',\n", + " color='darkblue'\n", + " )\n", + "\n", + "ax.axhline(y=y_avg, ls='--', lw=2, label=\"Average Fraud Population\")\n", + "\n", + "for i, v in enumerate(y_month.index):\n", + " ax.text(v, y_month.loc[v, 'Fraud'] + 0.0001, round(y_month.loc[v, 'Fraud'], 5), horizontalalignment='center', fontsize=15)\n", + "\n", + "plt.legend(['Average Fraud Population'], fontsize=15)\n", + "plt.xlabel(\"Month\", fontsize=20)\n", + "plt.ylabel(\"Percentage\", fontsize=20)\n", + "plt.xticks(fontsize=15)\n", + "plt.yticks(fontsize=15)\n", + "plt.title(\"Fraud Proportion of Each Month\", fontsize=30)\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RecnumCardnumDateMerchnumMerch descriptionMerch stateMerch zipTranstypeAmountFraudDowDow_RiskMonthMonth_Risk
0151421904392010-01-015509006296254FEDEX SHP 12/23/09 AB#TN38118.0P3.620Friday0.025994January0.003235
1251421839732010-01-0161003026333SERVICE MERCHANDISE #81MA1803.0P31.420Friday0.025994January0.003235
2351421317212010-01-014503082993600OFFICE DEPOT #191MD20706.0P178.490Friday0.025994January0.003235
3451421484522010-01-015509006296254FEDEX SHP 12/28/09 AB#TN38118.0P3.620Friday0.025994January0.003235
4551421904392010-01-015509006296254FEDEX SHP 12/23/09 AB#TN38118.0P3.620Friday0.025994January0.003235
\n", + "
" + ], + "text/plain": [ + " Recnum Cardnum Date Merchnum Merch description \\\n", + "0 1 5142190439 2010-01-01 5509006296254 FEDEX SHP 12/23/09 AB# \n", + "1 2 5142183973 2010-01-01 61003026333 SERVICE MERCHANDISE #81 \n", + "2 3 5142131721 2010-01-01 4503082993600 OFFICE DEPOT #191 \n", + "3 4 5142148452 2010-01-01 5509006296254 FEDEX SHP 12/28/09 AB# \n", + "4 5 5142190439 2010-01-01 5509006296254 FEDEX SHP 12/23/09 AB# \n", + "\n", + " Merch state Merch zip Transtype Amount Fraud Dow Dow_Risk Month \\\n", + "0 TN 38118.0 P 3.62 0 Friday 0.025994 January \n", + "1 MA 1803.0 P 31.42 0 Friday 0.025994 January \n", + "2 MD 20706.0 P 178.49 0 Friday 0.025994 January \n", + "3 TN 38118.0 P 3.62 0 Friday 0.025994 January \n", + "4 TN 38118.0 P 3.62 0 Friday 0.025994 January \n", + "\n", + " Month_Risk \n", + "0 0.003235 \n", + "1 0.003235 \n", + "2 0.003235 \n", + "3 0.003235 \n", + "4 0.003235 " + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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935cWbN2yZYvd9k888YTT7SpDcGrTpk2l/oFY9Khbt66xY8eOEt+7Ny634FRSUpIRExPjVtsFBQUZn3zyiWEY3genli5dahf8LOnRunVrIzEx0Z0ms3rttdc8+m1hsViMKVOmuPV9tWPHDqcBFncexc9dV0twau/evUbr1q1dvh9nwan27duXqW1uu+026x/Mnrx3XwSnfv/9d7f3ncjISCMuLq7SB6f27NnjUHdXwakff/zRqF27tkef5w033GCcPXu21HqU1+8+wzCMTz/91AgNDfV433PnnFuR56aKPteW5Xh01S4PP/xwmfLq2LGjkZCQUGpb2Jo/f77LmzGLP9q0aWMcOHCgzL97K3IfB3yNYf0AXDVeeOEFPf/88w7rY2Ji1KJFCwUEBFi70huGIUkqKCjQCy+8oOPHj+uTTz7xqLz4+HhNmTJFZ8+elSSFhISoZ8+eqlOnjs6ePatt27bp1KlT1u1TU1M1ePBgrVy5Ul27di3z+6yMPvroI7388svat2+fTp8+rSpVqqhmzZpq1KiR+vbtq6FDh+q6664zu5rl5tChQ5oyZYrS09MlSdWqVVO3bt0UHR2tCxcu6ODBg07TFRYW2i3XqVNHbdq0UWRkpKpWraqMjAzFx8dr//79KigosG43a9Ys5ebm6v3336+4N1UGa9as0S233KKsrCy79Y0aNVL79u1VrVo1JSUladOmTcrPz9fRo0c1ZMgQPfLIIybVWBo+fLhq165tPTbnzp1b4vE4d+5c6/ObbrrJq+HrNm/erJtuuklpaWl262vWrKlrr71WtWrVUk5Ojg4ePKjdu3dbXz9y5Ij69OmjNWvWqFOnTm6Xl5OTo5EjR2rTpk2SJH9/f3Xp0kXXXHONJCkxMVG//fabQ7pvvvlGkydPVk5Ojt36oKAgdenSRdHR0QoKCrIOl3fs2DG362Tr0Ucf1QcffCBJslgs6tChg5o2baqgoCAdPXpUW7ZsUX5+vnX7Dz74QDExMbr//vtLzLf4cebv768WLVqoSZMmCgsLk8Vi0enTp7Vr1y67ucROnz6tm266SWvXrlWvXr08ei+FhYWaMGGCvv/+e2uZRW3t7++v+Ph4u+8eSXrqqafUvn173XjjjR6VVdGaNGmi6Ohou7a57777tGzZMrVp08bEmrl2uZxbs7KyNGLECC1fvtxufZUqVdSpUyc1aNBAQUFBSklJ0ebNm3Xx4kVJl4b+e+yxx3TmzBmn1zll1a1bN9WoUcN6HfPzzz/rtddec7n9pk2bdO7cOYf1P//8s4YOHeoyXfH3O2jQoLJVuIL98ccfGj9+vPW7PSIiQl27dlWtWrWUmZmprVu32g17eeLECY0cOVI7d+4s81BMV4pTp06pX79+SkhIsFsfGhqq7t27W6+Nf//9d504cUI5OTm69957rd9HZbV+/Xrdeuut1u+rOnXqqHPnzoqIiNDZs2e1adMmnT592rr9/v37NW7cOMXFxcnPr+TpuQsLC3XXXXfpiy++sFvv5+en9u3bq3HjxqpWrZpSU1O1efNm67FhGIZmzpypU6dO6fPPP3eZf3p6ugYPHqyTJ0/arY+Ojla7du1Us2ZN+fv7KyMjQ8eOHdP+/fuVnZ3tSfNccdLT0zV8+HAdOnRIkhQYGKhu3bqpfv36ys3N1aFDh5SYmOiQrvh3RGRkpNq2batatWopNDRUmZmZOnLkiHbv3m03x9E333yjCxcu6Pvvv6+Q4X/Lau/evRo4cKDdvi1d2v+vvfZa1ahRQydPntTGjRuVlZVlbbeZM2eaVGP3JCUlOayrVauWw7pPPvlE999/v933uSQ1btxYMTExCg8P1/nz57Vjxw67a9QVK1aob9++2rBhg0fD3JX1d99jjz2md99912F9ZGSkOnXqpKioKBUUFCg1NVU7d+7UmTNn3KpPRZ+bnKnIc623ih/fVatWVZs2bVS3bl2FhYUpNzdXKSkp2rFjhzIyMqzb7dixQ/3799f27dsVGRlZajkLFizQn//8Z4f9rmnTpmrbtq2qVaum5ORkbd68WTk5Odq3b59uueUWPfjggx6/J1/v40CFMzc2BgBl58mdV0uXLnW4c6R///5OexscOXLEuPXWWx22/+ijj0qsT/G7qCIiIgzpUhf8l156yeHOuvz8fOObb75xGNqkRYsWbnfVL08V2XPKnUfXrl2Nn3/+ufzekAu+6DlVdJd+RESE8a9//cvIzs52SHPo0CGHddWrVzeGDh1qzJkzx0hOTnZZXnJysvH0008bgYGBduWWdne6L3tOnTlzxuEOwpYtWxorVqxw2DYtLc2uh0zRsVMe+6IrrnpOGYZhPP7443Z1cTWsw4YNG+zyWLx4sfU1T3tOnThxwqhbt65dmh49ehjLly93egfjwYMHHYbPatGihXH+/HmXZRS/I7ZoP/Xz8zP+/ve/G2lpaQ5pEhMT7e6+3rhxo8NdmA0bNjTmzp3r8rx17Ngx45133jHatm3rds8p2+FH7r77buPYsWMOaZKSkoxhw4bZpQsLCyv1LuZvv/3WqFGjhvHQQw8ZP/30U4nDdmzYsMHo37+/w/vNyckpsYzibV30fvz9/Y2pU6c6bev9+/cb3bt3t0vXvHlzp5//mTNnjMOHDxuHDx82evToYZemaL2zhydDE5bkiSeecDiPBgYGGhMnTjT+97//GRkZGV7ln5GRYa3zqFGj7MpZt26dy/fnbD8xjIo7t5b353DnnXfa5REeHm7MmjXL6XF98eJF480337S7Q9disRg//PCDGy3sPtvrIYvFYpw6dcrlts8//7zT7/d27dqVWEZsbKx12+DgYJfnEnfuLk5NTbW2s+13UP369Uv8TFJTUx3yKt5boeg4btSokfHtt986DGdZWFhozJs3z2E4oxdffLHE919Wl1PPqZEjR9ptX61aNeOdd95xOP8WFhYaS5YssfbMLT4Ulac9p4rSd+jQwek1Zl5enjFr1izD39/fLt2///3vEssxDMf9PSgoyHjuueecHiN5eXnGZ5995nB98+GHH7rMf+rUqXbbdurUyYiLi3O5fV5envHLL78YTzzxhFGzZk2HnlPleWyUVfFzQ3n3nCq6rgkODjZeffVVp99Fzq6/27VrZ/Tt29d4//33nb5eJD093Zg5c6ZDb9x//etfpdbVk/24OE96TuXm5hqdOnWy275u3brGggULjIKCArttMzIyjOeff946/Fjx/bOy9Zwqfu0RGhpq5Obm2m2zcuVKh+N54sSJxp49e5zmuXr1aoehlm+77bYS61Eev/vefvtth+OhV69exsqVKx0+pyK///678fTTTxtRUVElnnMr+txkGBV/ri06/8yfP99u+0cffbTE85Wza/oHH3zQaNasmfHCCy8YW7duddm+ubm5xoIFC4ymTZvalTlu3LgS28IwLv1eKj58ZseOHY3169c7bHv+/Hlj+vTp1mHhi3/Plfa71xf7OOBrBKcAXLbcvbjNzs52+NN3zJgxpc6RUXw4qapVq5b4p4yzgIy/v7+xaNGiEsv5448/HAJUM2bMKO3tlzuzg1PSpT++nn766QodD9lZcMrThzvvNzIy0ti1a5fHdfPEzz//bDffUWk/8HwZnCo+hELbtm2d/iFva9asWU7b29fBqd27d9u9Nn/+fKd53H333dZt6tSpY/dHkKfBqeJBlr/85S9uzePzyCOP2KV74YUXXG5bPGBS9Pjqq69KLccwLo3ZXnw4qz59+rg9X15hYaGRkpLi9DVXx+U///nPEvPMy8szunTpYpfm008/LTHNqVOnjMzMTLfqXFTv4kGD0vZJZ23t7+9f6hwV586dcxgP31lA11ZZ54LzRmpqaonD1/j7+xsdOnQw7r77buOjjz4yduzY4fLPgNKUda4NWxV9bjUM7z+HBQsW2KVv1KiRW/Vev369XYCqefPmZW5rZ/71r3/Z1evrr792uW3v3r2t2xU/p7kKCl64cMEuGHjDDTe4zN/ToW9sz1euhnwsibNh0GJiYkq8FjQMw1i0aJFdmoYNG1bIdU3x+pX2p11Jj+JB4PIMTi1btsxu25CQkBKDLIZxKVDcrFkzh/b3NDglXboZrbSbFopff/Tt27fE7Tdu3Gg39GZERITdPFmuHDhwwG7Yq/DwcJc3lbRp08a6XWRkpEdz05Y2V4q3x0ZZufrOcPdR/JzobAjYwMBAj4cT9/Q74vfff7cbwrRp06alnnc92Y+L8+R78K233rLbtm7duqUOTbZw4UKnQ8lWpuDUiRMnHIaNHT58uN02GRkZdr+pLRaLMWfOnFLzzsjIcBjieu3atS639/Z33549e+yuMaRLQ1O7+9198eJFl0Oz+eLcZBi+O9eWx1CTR48e9ei6KD093S7A6+fnV+oxNG7cOLt69urVq9Qbtb755hun896WdG3jq30c8DWCUwAuW+5e3H722Wd22zVq1Mit8cHz8/Md7jwr6Y9fZxeqjz32mFvvpfg8N7Vq1XJ611VFqojgVP369Y177rnH+OSTT4y4uDhj7969xv79+43169cbs2fPNoYMGeL0x+fUqVPL740V46vglDt33ZaH4kHUkiZg91Vw6vz583bjp/v7+xvbt293q4zhw4d7dJFeViUFpwzDMLp162Z9bfDgwQ7pMzMz7e6cLT5HiifBqeLzrfTq1cvtH1H5+flGhw4drGmjoqJcnjucBUzuuusut8oxDMP4+OOP7dLWr1+/1ICju5wdlxMmTHAr7ZIlS8qUzhOZmZl247qX9Ae6YThv66efftqtst5//32P0pkRnDKMS3/OlRSgKv6IiIgwRo8ebfzf//2fw53OJSmP4FRZeHJuNQzvPofCwkK7u1oDAgI8mqvIdu47ScZ3333nUfklSUhIsMv7zjvvdLrduXPn7P5sW7Vqld0fKF988YXTdMV7tpc015rZwamAgABj7969bqXt2rWrXdo//vjD4/I9rV95PsozOFU8UFnSZ2wrLi7O4Y87T4NTNWrUsM4FWZK8vDwjOjra7rMuaRSD4t/xnvRYLB6Ifuutt5xuZ9sDb9SoUW7n744rOThVUT0Vi3vzzTftyi0t4OrJflycu9+DBQUFDvOBlnZTTBFnc3xWluDU8ePHHW5EkmT89NNPdtvNnDnT7vUnn3zS7XolJibajQxw8803u9zW2999EyZMsEt74403ltsNDL44NxmG7861vpgHzZmtW7falfvyyy+73Pb48eN21z9Vq1Z1+1r1nnvucdiXSrq28dU+DvhaxQ7uCQCVQPHxlp9++mm3xv739/fXyy+/bLfOk/GXAwMDNWPGDLe2HTlypDp37mxdTktL09KlS90uq7Lp3r27fvrpJx07dkwff/yx7r77bvXp00dt2rRRq1at1Lt3bz300EP68ccftWXLFrVo0cIu/WuvvabFixebVHvvXXPNNZo4caJPyho1apTd8oYNG3xSbkm+++47Xbhwwbp86623uj0XUvFjzix33XWX9fmKFSsc5k369ttv7cYlt93eU++9957d8quvvur2+Ov+/v569NFHrcupqan69ddf3S576tSpbm/7r3/9y2755ZdfVs2aNd1O76lnnnnGre0GDx6swMBA6/L27dvLvS5Vq1a1m/tp06ZNDmPYlyQkJER///vf3dp2+PDhdssV8X7KQ8eOHbV9+3bdfffdCggofRrbM2fOaOHChRoxYoTatm2rRYsW+aCWZefLc+vq1avt5pGbOHGiOnTo4Hb6Bx98UMHBwdblornNykPTpk3VtGlT6/LPP//sdLvVq1db54CrVq2a+vTpoxtuuMH6evF5pVzlV1nnm5Iu7RPuzqt2uRzHFe3s2bP68ccfrcvh4eF6+OGH3Urbp08fDRgwwKvy77vvPrfmggwICLCbFy0/P1+7du1yum1CQoLdNXq/fv08mhtw9OjRatSokXXZnePVdo5auFa1alWfzVtaGa+/f/nlFx05csS63K1bN4dzkSvPPvusqlSpUkE1cy07O1tHjhxxeOzdu1c//vijHn/8cbVp08ZhDtQRI0Zo8ODBdutmz55tfR4aGur2daR06bfb2LFjrcvLly93mFu1pLTu/u5LT0/Xf/7zH+uyn5+fZs+eXS5zlplxbipSEedaM3Xp0sWuLUo6vhcuXGg3B+7EiRPVuHFjt8qZMWOGR3NumbWPAxWN4BSAK1peXp42b95sXQ4ICNBtt93mdvqhQ4cqKirKunz48GG7ya5LMmTIEI/+uJ0wYYLdclxcnNtpK5thw4Zp8ODBbl1od+3aVRs3blTLli3t1k+dOtVhks+KcvjwYY8epbnlllvKdXLX/Px8nT17VseOHXP48Va8jfft21du5ZZV8X13/Pjxbqft2LGjYmJiyrtKHhs/frxCQkIkXZpIt3iQ+7PPPrM+79Gjh9t/WDqzYsUK6/Po6Gj169fPo/T9+/e3W163bp1b6WJiYhwCw66kp6fr999/ty6Hh4d79Ll6qmnTpmrdurVb21apUkXNmjWzLnvzJ152drZSU1N19OhRh2PNduLgjIwMpxNzu9KrVy+3JlOWpEaNGqlq1arW5cr8p2RUVJQ++eQTJSQk6NVXX1Xnzp3dOvfFx8fr1ltv1SOPPOJRkK+8VZZza/EAzbhx4zxKX7VqVXXv3t267O45wF22fwAmJSVp7969DtvYBp/69eunwMBAu3QrVqyQYRglpouKinL7RgYz3HTTTW5vW/w7oTIfxxVp48aNdsf48OHDrd+t7rD9I60sKuIzs/3Oljw/Xi0Wi933/KZNm5Sbm+uwne134Pr167VgwQKPyrkcrFu3zqPr7wYNGpSYX//+/RUeHl5u9SsoKNC5c+eUlJTk8B1R/DfK5X79HRUV5RDs8YVNmzapSZMmDo+YmBjdeOONmjVrls6fP2+X5rrrrtNXX31lt+7AgQNKTEy0Lg8bNkyhoaEe1cX2ejonJ8fuP4SSePK7b+3atXb7zqBBg+yuYb3hq3OTM5fj96NhGLpw4YJOnDjhNEBq+x9QScd38cCVJ99bDRs2VI8ePdza1sx9HKhopd/mCACXsT179igrK8u6HBMTo7CwMLfT+/v7q0ePHlqyZIl13ZYtW/SnP/2p1LTuXmi42n7Lli0epb+cRUZGav78+eratav1z6v9+/dr9erVdndeVxR3725y17XXXutV+pSUFH377bf66aeftGvXLrsL0dKcOXPGq7LLw9atW+2Wy3Is7Nmzpzyr5LHw8HCNHDlSX3/9taRLvSanT58ui8Wi+Ph4/fLLL9Ztvek1lZCQoBMnTliXmzdvrqNHj3qUR/EfjgkJCW6l82Q//fXXX+3+WO7Zs6eCgoLcTu+ptm3berR9RESE9fm5c+fcTrdp0yZ9++23+vXXX7Vnzx6P0p45c0YNGzZ0a1tP30+NGjV08eJFSZ69H7M0bNhQ06ZN07Rp03Tu3Dn9+uuv+u2337R9+3Zt2rTJZSBv9uzZCgsL81mPycp6bi3+h2JkZKTd3e/usL22OXLkiAoLC8vtJolBgwbpww8/tC7//PPPDvu0bYCt6A9O215QJ0+e1M6dO9WxY0fruuTkZLtA1w033FAud49XFE+OY9tzkuSb4/i5557T888/X6a0kydPdrgJozzs2LHDbrlr164epe/WrZtX5VfEZ1b8eK1du7bHx6vtDQjZ2dlKTk52uBadMGGCtm3bJunSTTLjxo3T559/rj//+c8aOnSo2zc8VGYNGjQo12twb6+/z5w5o0WLFmnp0qXauXOnDh065PYNFFfK9XdlHrkjLCxMU6ZM0dSpUx16bBc/Lhs2bOjxcVn8OzMhIUHXXXddqek82e+KBzJiY2PdTlsaX52bnKns34/Spd9Ly5cv13//+19t27ZN+/fvd7vnUEnHd3l8z7kz6oWZ+zhQ0QhOAbiiFb8Tp3jvHHe0bt3aLjjl7t09npZVvAfD1XaXbefOnTV48GD99NNP1nU//vijT4JT5c2dYQ2cuXjxop5//nm9++67bt+pVlxl+CP75MmT1udVq1ZVvXr1PEpfluO0Itx1113W4FRCQoJ++eUX9evXz67XVEhIiEe9MYsrPlxgXFycmjRpUub8pEu9nNzhyX5qG0CTVOG924r/cC2N7VA0tkNruLJ79249+OCDdkFGT3lyrHnzfvLy8jxKa7bw8HANHTrUbtiW+Ph4/ec//9Hs2bPtzg+S9I9//EMTJkzwOIDnicp+bi1+HujZs6dX+RUWFurs2bPl9uf1wIED5e/vb73be/ny5XbDiR49elQHDx60LhcFp+rWrat27dpZhyxcvny5XXCq+FB/lXlIP8mz47j48FiX23FcXtLS0uyWbYdJcoe7NwC4UhGfWfHjdfTo0Z5XrJj09HSHP4AfeughLVy4UBs3brSuW7ZsmZYtWyaLxaKYmBj16tVLffr0Ub9+/cr9RqvLUVmvvwsKCjRz5ky98sordsNSe6KyXX9Ljr8tS1NZrr8lKTg4WOHh4YqOjlaXLl10/fXXa/To0S6H5i9+XL755pt68803varD5XY97atzkzOV/ftx8eLF+tvf/ubWCCjOlHR8237PhYWFqUaNGh7l7e73nJn7OFDRGNYPwBWt+F0uZRnqoXgad7/EPemh5U05VxLbPzMlaefOnSbVxDu2w3+568KFCxo6dKhmzpxZ5j9PJZk6RFYR2+PO0+NAKttxWhEGDBhg94Ns7ty5Kigo0L///W/rulGjRpXpPRY5ffq0N1V0ynYurJJ4sp8Wr6enwRZPleewmMXFxcWpd+/eXgWmJM+OtYp8P5eD5s2ba/r06YqPj3cY7qSwsFCzZs2qsLIvh3OrmecBd4SHh9v1YFm7dq1dW9oGmRo0aGA3ZI/tMFHFg1GX03xTEsdxWZw9e9Zu2dPrI2++X6WK+cx8dbwGBQVpxYoVuvfee+Xv72/3mmEY2r17tz755BNNnjxZTZo0UceOHTVr1ixrr9urUVmuv/Pz83Xbbbdp2rRpZQ5MSZXv+lvy/reoL/Tr10+GYTg8srKylJKSot9//11z5szRpEmTSpwzmutpc9ugMn8/vvPOOxoxYkSZA1OSnA5LXMT2e64s5yB3j9PKfq0IeKPynkEAoBwUv5Aoj+Fi3M3D07KKb1+Zh7apKMXvzEpNTTWnIiZ46qmn7OYJsVgsGjRokN59913FxcXpyJEjysjIUG5urt2PN28utH3hct6PLRaLJk+ebF1euHChvvvuOx0/fty6zpsh/STHIfnKQ0k/oMrL5fq5nj9/XmPHjrX7MRYeHq777rtP8+fP1/bt23Xy5EllZmaqoKDA7lh77rnnTKz5lSE0NFRfffWVunTpYre+eNCiPF0O59bL4TxgGzjKzMy0G5rI2ZB+ztLFxcUpOzvbWj/b+THatGlT6nwyuPwUH/7V0329Io4Nb/nyeK1WrZo++ugjHTx4UC+88IK6devmMJxZkZ07d+rxxx9XixYttHr16nKv45Vq1qxZWrhwod26nj17aubMmVq9erXi4+N17tw55eTkOARQUHlcDt+jzpTn9fTl2gYVadOmTfr73/9ut65x48Z6+umntWTJEu3du1fp6enKyspSYWGh3fHt7hzAtt9zZfkM3E3D54srGcP6AbiiFR/SpixDLhRP4+4dTp6WVfzu0orumVAZFZ8k23a+sCvZiRMn7ObzCA4O1uLFi92amLj4JMEVqfjkz65EREQoJSVFUvkcc2aaPHmyXnjhBRmGoczMTP31r3+1vtakSROvx4qvVauW3fK9996rjz76yKs8K0Lxel6uPTs//PBDuyFVevToof/97392kx674stj7UoWEBCgRx99VHfccYd1XWJiorKyshy+A7x1uZxba9WqpeTkZEmX6njx4sVKFwAePHiwXnrpJevy8uXLFRsbq8LCQq1cudJuO1v9+vVTUFCQcnJylJ2drV9++UWDBw/Wjh077IYvruy9plA2xa9lPZ2XpzJ+19SqVUt//PGHdTk5OVl169at0DKbNGmiZ599Vs8++6wyMzO1ZcsWrV+/Xr/88ovWrl1rN29KcnKyhg0bpjVr1ng859DVJjc3V6+88op12WKxaO7cuZo0aVKpaSvr9bet8+fPu3V9U6QyXX97qvh16tdff63x48ebVBvXKvJ62oxzU2X3wgsv2PVqvPfee/XBBx+4DPLbcvcYj4iIUGZmpqRLx5Cnc366uw9cLvs4UBb0nAJwRSs+BrTtBZu7Dhw4UGKernhalu18DZ6UcyUpPjdB8YuwK9WSJUvsLpyffPJJt/48lWT9Q9MdxS/E3Zmbx5a7fyrVqVPH+vzixYse1VEq23FaURo1aqQBAwZYl23bYPLkyV7/gWzbVlLleu+2iv+43bt3r0k18c7ixYutzy0Wi77++mu3/7jxdD+Ga506dXJYVxGTyfvq3Oot2/NAdna2EhMTfVa2u3r27Gk39ExRb7fffvvN+seKn5+fwzyRISEh6tu3r0O6y21IP5RN8Tmmdu3a5VF6T7f3BbO/t6tVq6bY2FhNnz5dP/30k06fPq2PP/7Y7ns6OzvbobcAHK1du9YuIHP77be7FZiSPP+OsB2a0RfX35Ljb8vSVNZrUHeYfVy6qyKvpy+XNvCVzMxMu5tnmjZt6nZgSnKcH8wV2++53Nxcj9vd3e85Pl9cyQhOAbiixcTE2N2JvXv3bo/udCsoKNCmTZvs1tnOu1AS2wmM3VHWcq4kxdugXr16JtXEt4r/eBw+fLjbaW2HVipN8TGti/fWK0lOTo4SEhLc2rZr1652y94eC2ZzNnSfxWJx+w+MksTExNiN8f/rr79WyvG/e/XqZXcX4K+//loph1sqje2x1qZNGzVt2tTttL/++mtFVMlrla2HjTuKz58iuZ7rwpv356tzq+RdPXv37m23XJHDHJZVQECAXU/R7du36/Tp03Z1vfbaa1WzZk2HtLaBp6KglG26KlWqeN0L1ZnL8di40vTs2dNu2dPh5irj8HSV7XitVq2a7rnnHm3YsMFuTp4NGzbY9U60xbFxiS+/I2yvwT25/pakPXv2uLXdlXb97YnKdly60qdPH7vlNWvWlFvel0sbeMKbc9XRo0ftfqsMHTrU7cDUoUOHrKOAlMab77mCggK7oadLciV+vkARglMArmhVqlRR9+7drcv5+flasGCB2+mXL19u98OuSZMmbgdMli9f7tHElV9//bXdsu2dxleD7Oxs/fe//7VbVxF/VlVGxX+kujshcUFBgebNm+d2OdWrV7cL1npyt97KlSutc4WUpvi+O3/+fLfL2bFjh9s/wn3l1ltvVY0aNezWDRw40OGO8LLw9/fXwIEDrcs5OTn68ssvvc63vEVERKhz587W5XPnzumbb74xsUZlY3useTLx96pVqyplbxbJcU4X2+GdKqvi557w8HCXE5178/58dW6VvKvnkCFD7JY//fRTj8r2FdsgU2FhoVasWFHifFPO1u/cuVNHjhxRXFycdV2vXr0UGhpa7vW1/Uwuh+PiStS2bVu7ngKbN2/Wzp073UqblZVVKb8Pix+vX375pdvXRxWpcePGdj29DcPQkSNHnG7LsXFJWb8jJOnzzz/3qCzbETEOHjyovLw8t9Lt27fP5edYnDfX36mpqZf1n92dO3e2G3Hj119/1e7du02skXP9+vWzC5AsX7683Oa4rKznJm+YcQ0oeXZ8F+8x/umnn7o9l9P333/vdhDsctnHgbIgOAXgimc7r4Ukvfrqq7p48WKp6QoKCjRjxgy7dZ70lMjNzdXLL7/s1raLFi3Stm3brMu1atXSTTfd5HZZV4LXX39dx48fty77+/tfNW1QfIz4/fv3u5Xu/fff9+gHjcVisRtO6/Dhw24FggoLC+3G5C/NrbfeavdH46JFi7Rjxw630hY/5iqD4OBgxcXFad26ddbHZ599Vm75P/zww3bLL7zwgttDSfjSgw8+aLc8Y8aMChmKrSLZHmsHDx60G/LNlby8PE2bNq0iq+WV4oHTit53MjIyFB8f71UeH3/8sd2y7R+qxXnz/nx1bpW8q+eNN96oZs2aWZc3b95crueY8lI8+LRo0SK7HoWuglOdOnWyGz7zmWeesfvDrKKG9LP9TNLS0tz+Mxjlx8/PT/fee6/dugcffNCtOXSeffZZnTx5sqKqVmbt27dXv379rMvHjh3Tq6++amKN/r/ivQKK/7FbhGPjkrJ+RyxevNjt3g5FbG/wyc3NdTsQ9OKLL7pdxvXXX6/GjRtbl7du3aolS5a4Xc7lvB/4+fnpgQcesC4bhqFHHnnE4yEUK1qNGjU0ceJE63JhYaEeeeSRcsm7Mp+bysqMa8CjR4/qvffec7ucwYMHq0mTJtblbdu26ZNPPik13YULFzwafvVy2ceBsiA4BeCKN3HiREVHR1uXDx8+rLvvvrvUPyX//ve/2wWMQkJCdP/993tU9uzZs+3mOHEmPj7e7kJDku677z6XPygruy+//NLjPxM++eQTvfDCC3brJk+eXC49Uy4HHTt2tFt+8803S/3jZtmyZXrqqac8Lsu2l47kXjDoySef9Hj4wMmTJ1uXCwoKNHHixFInfH3nnXfc/hHtazExMerbt6/1cc0115Rb3rGxsXZ/zp46dUrDhg1TUlKSR/lkZGQ49MAsT3/+85/t/kA/duyYRowY4XaAyjAM0/9otD3W0tLSSu2hUlBQoPvuu0+bN2+u6KqVWZs2beyWK/rO59OnT6t169a64447ytTL8fnnn3eYb2jChAkut/fm/fny3OpNPQMCAhz+gLz//vsdehO7Y8WKFTp06JDH6dzRsmVLu+/lBQsWWIfMqVatmsOQM0UsFovdncVfffWV3esVFZyy/Uzy8/Mr5RBxV4MHHnhAkZGR1uW4uDhNnDjR5Y1ihmFo5syZevPNN31VRY+98sordsNNvfTSS3r//fc9zmfTpk3avn27w/rExER98cUXHvUSOHHihFasWGFdDgwMdDl0LcfGJcW/I95//31lZmaWmGbr1q268847PS6r+PX3888/X+qfyrNnz/aol7qfn58eeughu3X33XdfqTdb/Pe//9U///lPt8uprP7+97/b9SxZvXq17rzzTo97DyUmJmrZsmXlXT2radOmqUqVKtblJUuW6LHHHnPrhinpUq9S27nSbFX0ucnXmjZtqsDAQOvy6tWr3Q6iNmvWzO5mySVLlpTa0ygtLU233nqry/Z1xs/PT9OnT7db99BDD5V47KalpWnYsGEe3wh1uezjgKcITgG44gUFBTncpT1//nwNGTJE+/btc9g+MTFRY8aM0bvvvmu3/u2337YbkqE0ERERKigo0NixY/XKK684/NgpKCjQggUL1LdvX7vu3M2bN9fTTz/tdjmeuHDhgo4cOeL0UfyiJi0tzeW2Jf2YmjNnjpo0aaJJkyZp6dKlJf7I27p1q2699Vbde++9dt3f69ev73avsyvBjTfeqOrVq1uX161bp5EjR+rYsWMO254+fVpTp07VLbfcopycHLu70d1x11132c0d9H//93+66667nAYY9u3bpz/96U966623JDnevVaSl156yW4IzD179qhXr152E9MWSU9P1yOPPKLHH39ckuOdbleDL774Qg0aNLAu//777+rQoYPeeOMNpaWluUyXkZGh77//Xn/5y19Uv379Cjt3SJf+QP/mm28UHBxsXffLL7+oc+fO+ve//+3yh1FSUpLee+89tW/fXv/6178qrH7uGDdunN3yQw89pPfee8/p/FlbtmzRgAEDNHfuXEny+Fjzlf79+9stT5kyRW+88YY2btyo+Ph4u3O3u0OHlKagoEBffvml2rVrp27duum9997Tnj17XA5jUlBQoJUrV2rgwIEONyL069dPo0ePdllWv3797P5oeeutt/TMM88oLi5OBw8etHt/xQO6vjy3evs5TJgwwW5+u9zcXI0aNUoTJ07Ub7/95rLcgoICbd++XS+88ILatm2rQYMGVegQlLaBJNvPOzY21u4PpOJse1XZpouIiHCYJ6W8FP9M7rzzTv3zn//Ub7/9pkOHDtl9JiWdZ+Gd2rVr64MPPrBb95///Edt2rTRq6++qvXr1+vgwYPaunWrPv74Y/Xq1UtPPvmkJOm2224zo8ql6tOnj55//nm7dQ8//LBuvPFGrVmzxuWfzIZhaP/+/Zo5c6a6d++unj17Ou1Znp6ersmTJ6thw4Z66KGHtHLlSmVlZbnM8+eff1ZsbKzdnJW33nqr3fnPFsfGJT169LALuB88eFCDBg1y+vvwwoULeuONNxQbG6szZ854/B0xbtw4u3mntm7dqhEjRig5Odlh22PHjunuu++29qjx5Lr4kUcesQu6JScnq0+fPlq4cKHDfpmZmakXX3xRt912mwoLCy/76++wsDD95z//setBOG/ePHXu3FlfffVViX/gnzhxQnPnztUtt9yipk2b6j//+U+F1bNVq1Z6++237da9++676tevn1avXu3y/LFjxw5Nnz5djRo1chk4quhzk68FBgbazdOVmJioW265RYsXL9bevXtL/D8jMDBQI0aMsC7n5eVpyJAh+vHHHx3KKSgo0Hfffadu3bpp27ZtslgsTufQdOUvf/mL3bCKeXl5Gj9+vIYMGaJ58+Zp+/btOnDggNasWaPp06erVatWWrdunSwWi8aOHet2OZfLPg54zACAy9TcuXMNSdZHv379Stx+xowZdtsXPdq3b2/ceuutxpgxY4yuXbsaFovFYZs777yz1Po899xzdmk++ugjIzw83LpctWpVY8CAAcb48eONYcOGGXXq1HEoJywszNi8eXM5tZCj4m1W1sfhw4ddltGvXz+7bf38/IxWrVoZQ4YMMcaOHWuMHz/eGDx4sNP3L8mIjIw0du3aVWFtcPjwYYcyvVX8s1+9erXHebz++usO9fL39ze6d+9u3Hbbbcbo0aONHj16GP7+/tbXQ0NDjUWLFtmlmTRpUqllTZkyxaGs4OBgo3///saECROMP/3pT0br1q3tXn/66acdPtvSrF692ggODnYoq1G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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# statistical smoothing\n", + "c = 4\n", + "nmid = 20\n", + "y_avg = train_test['Fraud'].mean()\n", + "y_state = train_test.groupby('Merch state')['Fraud'].mean()\n", + "num = train_test.groupby('Merch state').size()\n", + "y_state_smooth = y_avg + (y_state - y_avg)/(1 + np.exp(-(num-nmid)/c))\n", + "df['state_risk'] = df['Merch state'].map(y_state_smooth)\n", + "top15_states = pd.DataFrame(y_state.sort_values(ascending=False).head(15))\n", + "plt.style.use('ggplot')\n", + "fig, ax = plt.subplots(figsize=(20,10))\n", + "plt.bar(data=top15_states, x=top15_states.index, height='Fraud', color='darkblue')\n", + "\n", + "plt.title('Top 15 Fraud Merchant State with Highest Fraud Percentage', fontsize=30)\n", + "\n", + "plt.xticks(fontsize=20)\n", + "plt.yticks(fontsize=20)\n", + "plt.xlabel('Merchant State',fontsize=25, labelpad=20)\n", + "plt.ylabel('Fraud Percentage',fontsize=25, labelpad=20)\n", + "\n", + "ax.axhline(y=y_avg, lw = 4, ls='--')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# statistical smoothing\n", + "c = 4\n", + "nmid = 20\n", + "y_avg = train_test['Fraud'].mean()\n", + "y_cardnum = train_test.groupby('Cardnum')['Fraud'].mean()\n", + "num = train_test.groupby('Cardnum').size()\n", + "y_cardnum_smooth = y_avg + (y_cardnum - y_avg)/(1 + np.exp(-(num-nmid)/c))\n", + "top15_cardnum = pd.DataFrame(y_cardnum\\\n", + " .sort_values(ascending=False).head(15))\n", + "plt.style.use('ggplot')\n", + "fig, ax = plt.subplots(figsize=(20,10))\n", + "plt.bar(data=top15_cardnum, x=top15_cardnum.index.astype(str), height='Fraud', color='darkblue')\n", + "plt.title('Top 15 Card Numbers with Highest Fraud Percentage', fontsize=30)\n", + "\n", + "plt.xticks(fontsize=20)\n", + "plt.yticks(fontsize=20)\n", + "plt.xlabel('Card Number',fontsize=25, labelpad=20)\n", + "plt.ylabel('Fraud Percentage',fontsize=25, labelpad=20)\n", + "plt.xticks(rotation = 80)\n", + "\n", + "ax.axhline(y=y_avg, lw = 4, ls='--')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Fraud\n", + "Merchnum \n", + "450730006NOT0 1.000000\n", + "6006333528866 1.000000\n", + "4503738417400 1.000000\n", + "600660007477 1.000000\n", + "19908503337 1.000000\n", + "7000330100777 1.000000\n", + "8834000695423 1.000000\n", + "7593860080752 1.000000\n", + "92891948003 1.000000\n", + "6070095870009 0.931034\n", + "938909877224 0.780488\n", + "8292309000040 0.689655\n", + "6929 0.666667\n", + "6005030600003 0.615385\n", + "3831009006589 0.500000" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# statistical smoothing\n", + "c = 4\n", + "nmid = 20\n", + "y_avg = train_test['Fraud'].mean()\n", + "y_merchnum = train_test.groupby('Merchnum')['Fraud'].mean()\n", + "num = train_test.groupby('Merchnum').size()\n", + "y_merchnum_smooth = y_avg + (y_merchnum - y_avg)/(1 + np.exp(-(num-nmid)/c))\n", + "\n", + "# comment this out so we don't include this variable because it overfits\n", + "# data['merchnum_risk'] = data['Merchnum'].map(y_merchnum_smooth)\n", + "top15_merchnum = pd.DataFrame(y_merchnum\\\n", + " .sort_values(ascending=False).head(15))\n", + "top15_merchnum.head(20)\n", + "\n", + "# plt.style.use('ggplot')\n", + "# fig, ax = plt.subplots(figsize=(20,10))\n", + "# plt.bar(data=top15_merchnum, x=top15_merchnum.index.astype(str), height='Fraud', color='darkblue')\n", + "# plt.title('Top 15 Merchant Numbers with Highest Fraud Percentage', fontsize=30)\n", + "\n", + "# plt.xticks(fontsize=20)\n", + "# plt.yticks(fontsize=20)\n", + "# plt.xlabel('Merchant Number',fontsize=25, labelpad=20)\n", + "# plt.ylabel('Fraud Percentage',fontsize=25, labelpad=20)\n", + "# plt.xticks(rotation = 80)\n", + "\n", + "# ax.axhline(y=y_avg, lw = 4, ls='--')\n", + "\n", + "# plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Other variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1. Data types" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 96397 entries, 0 to 96752\n", + "Data columns (total 15 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Recnum 96397 non-null int64 \n", + " 1 Cardnum 96397 non-null object \n", + " 2 Date 96397 non-null datetime64[ns]\n", + " 3 Merchnum 96397 non-null object \n", + " 4 Merch description 96397 non-null object \n", + " 5 Merch state 96397 non-null object \n", + " 6 Merch zip 96397 non-null object \n", + " 7 Transtype 96397 non-null object \n", + " 8 Amount 96397 non-null float64 \n", + " 9 Fraud 96397 non-null int64 \n", + " 10 Dow 96397 non-null object \n", + " 11 Dow_Risk 96397 non-null float64 \n", + " 12 Month 96397 non-null object \n", + " 13 Month_Risk 83970 non-null float64 \n", + " 14 state_risk 96397 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(4), int64(2), object(8)\n", + "memory usage: 13.8+ MB\n" + ] + } + ], + "source": [ + "df['Cardnum'] = df['Cardnum'].apply(str)\n", + "df['Merchnum'] = df['Merchnum'].apply(str)\n", + "df['Merch zip'] = df['Merch zip'].apply(str)\n", + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "### add leading 0 to zips\n", + "### note: there are some zips that are state abbrv. as we imputed them ealier, so pandas read the column as str\n", + "\n", + "def leading_0(x):\n", + " \n", + " if '.0' in x:\n", + " x = x[:-2]\n", + " if len(x) == 5:\n", + " return x\n", + " else: \n", + " return '0'*(5-len(x)) + x\n", + " else:\n", + " return '0'*(5-len(x)) + x\n", + "\n", + "# df['Merch zip'] = df['Merch zip'].apply(leading_0)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "### delete white spaces in merch description\n", + "df['Merch description'] = df['Merch description'].str.replace(r'\\s', '')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create entities" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 96397 entries, 0 to 96752\n", + "Data columns (total 15 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Recnum 96397 non-null int64 \n", + " 1 Cardnum 96397 non-null object \n", + " 2 Date 96397 non-null datetime64[ns]\n", + " 3 Merchnum 96397 non-null object \n", + " 4 Merch description 96397 non-null object \n", + " 5 Merch state 96397 non-null object \n", + " 6 Merch zip 96397 non-null object \n", + " 7 Transtype 96397 non-null object \n", + " 8 Amount 96397 non-null float64 \n", + " 9 Fraud 96397 non-null int64 \n", + " 10 Dow 96397 non-null object \n", + " 11 Dow_Risk 96397 non-null float64 \n", + " 12 Month 96397 non-null object \n", + " 13 Month_Risk 83970 non-null float64 \n", + " 14 state_risk 96397 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(4), int64(2), object(8)\n", + "memory usage: 13.8+ MB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "df['card_merch'] = df['Cardnum'] + df['Merchnum']\n", + "df['card_zip'] = df['Cardnum'] + df['Merch zip']\n", + "df['card_state'] = df['Cardnum'] + df['Merch state']\n", + "df['merch_zip'] = df['Merchnum'] + df['Merch zip']\n", + "df['merch_state'] = df['Merchnum'] + df['Merch state']\n", + "df['state_des'] = df['Merch state'] + df['Merch description']\n", + "\n", + "# these next entity take a long time to calculate the variables for, and I don't know why\n", + "# df['state_zip'] = df['Merch state'] + df['Merch zip']\n", + "\n", + "df['zip3'] = df['Merch zip'].str[:3]\n", + "df['card_zip3'] = df.Cardnum + df['zip3']\n", + "# df['merchnum_zip'] = df.Merchnum + df['Merch zip']\n", + "# df['merchnum_zip3'] = df.Merchnum + df['zip3']\n", + "df['Card_Merchdesc'] = df['Cardnum'] + df['Merch description']\n", + "df['Card_dow'] = df['Cardnum'] + df['Dow']\n", + "df['Merchnum_desc'] = df['Merchnum'] + df['Merch description']\n", + "df['Merchnum_dow'] = df['Merchnum'] + df['Dow']\n", + "# df['Merchdesc_State'] = df['Merch description'] + df['Merch state']\n", + "# df['Merchdesc_Zip'] = df['Merch description'] + df['Merch zip']\n", + "df['Merchdesc_dow'] = df['Merch description'] + df['Dow']\n", + "df['Card_Merchnum_desc'] = df['Cardnum'] + df['Merchnum'] + df['Merch description']\n", + "# df['Card_Merchnum_State'] = df['Cardnum'] + df['Merchnum'] + df['Merch state']\n", + "df['Card_Merchnum_Zip'] = df['Cardnum'] + df['Merchnum'] + df['Merch zip']\n", + "# df['Card_Merchdesc_State'] = df['Cardnum'] + df['Merch description'] + df['Merch state']\n", + "df['Card_Merchdesc_Zip'] = df['Cardnum'] + df['Merch description'] + df['Merch zip']\n", + "df['Merchnum_desc_State'] = df['Merchnum'] + df['Merch description'] + df['Merch state']\n", + "# df['Merchnum_desc_Zip'] = df['Merchnum'] + df['Merch description'] + df['Merch zip']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Recnum', 'Cardnum', 'Date', 'Merchnum', 'Merch description',\n", + " 'Merch state', 'Merch zip', 'Transtype', 'Amount', 'Fraud', 'Dow',\n", + " 'Dow_Risk', 'Month', 'Month_Risk', 'state_risk', 'card_merch',\n", + " 'card_zip', 'card_state', 'merch_zip', 'merch_state', 'state_des',\n", + " 'zip3', 'card_zip3', 'Card_Merchdesc', 'Card_dow', 'Merchnum_desc',\n", + " 'Merchnum_dow', 'Merchdesc_dow', 'Card_Merchnum_desc',\n", + " 'Card_Merchnum_Zip', 'Card_Merchdesc_Zip', 'Merchnum_desc_State'],\n", + " dtype='object')" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "entities = list(df.iloc[:, np.r_[1, 3, 12:len(df.columns)]].columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Cardnum',\n", + " 'Merchnum',\n", + " 'Month',\n", + " 'Month_Risk',\n", + " 'state_risk',\n", + " 'card_merch',\n", + " 'card_zip',\n", + " 'card_state',\n", + " 'merch_zip',\n", + " 'merch_state',\n", + " 'state_des',\n", + " 'zip3',\n", + " 'card_zip3',\n", + " 'Card_Merchdesc',\n", + " 'Card_dow',\n", + " 'Merchnum_desc',\n", + " 'Merchnum_dow',\n", + " 'Merchdesc_dow',\n", + " 'Card_Merchnum_desc',\n", + " 'Card_Merchnum_Zip',\n", + " 'Card_Merchdesc_Zip',\n", + " 'Merchnum_desc_State']" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "entities" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Variables" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "df.Date = pd.to_datetime(df.Date)\n", + "df1 = df.copy()\n", + "final = df.copy()\n", + "df1['check_date'] = df1.Date\n", + "df1['check_record'] = df1.Recnum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make the Benford's law top 40 tables and variables" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 26603\n", + "3 18670\n", + "2 16178\n", + "4 8278\n", + "5 6955\n", + "6 6017\n", + "7 5027\n", + "8 4534\n", + "9 4135\n", + "Name: first_digit, dtype: int64" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# another way to get the first digit\n", + "bf = data.copy()\n", + "bf['amount_100'] = (bf['Amount'] * 100).astype(str)\n", + "bf['first_digit'] = bf['amount_100'].str[0]\n", + "bf['first_digit'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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RecnumCardnumDateMerchnumMerch descriptionMerch stateMerch zipTranstypeAmountFraudamount_100first_digit
0151421904392010-01-015509006296254FEDEX SHP 12/23/09 AB#TN38118.0P3.620362.03
3451421484522010-01-015509006296254FEDEX SHP 12/28/09 AB#TN38118.0P3.620362.03
4551421904392010-01-015509006296254FEDEX SHP 12/23/09 AB#TN38118.0P3.620362.03
5651421498742010-01-015509006296254FEDEX SHP 12/22/09 AB#TN38118.0P3.670367.03
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" + ], + "text/plain": [ + " Recnum Cardnum Date Merchnum Merch description \\\n", + "0 1 5142190439 2010-01-01 5509006296254 FEDEX SHP 12/23/09 AB# \n", + "3 4 5142148452 2010-01-01 5509006296254 FEDEX SHP 12/28/09 AB# \n", + "4 5 5142190439 2010-01-01 5509006296254 FEDEX SHP 12/23/09 AB# \n", + "5 6 5142149874 2010-01-01 5509006296254 FEDEX SHP 12/22/09 AB# \n", + "6 7 5142189277 2010-01-01 5509006296254 FEDEX SHP 12/28/09 AB# \n", + "\n", + " Merch state Merch zip Transtype Amount Fraud amount_100 first_digit \n", + "0 TN 38118.0 P 3.62 0 362.0 3 \n", + "3 TN 38118.0 P 3.62 0 362.0 3 \n", + "4 TN 38118.0 P 3.62 0 362.0 3 \n", + "5 TN 38118.0 P 3.67 0 367.0 3 \n", + "6 TN 38118.0 P 3.62 0 362.0 3 " + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dropfedex = bf[bf['Merch description'].str.contains('FEDEX')]\n", + "dropfedex.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 3, 4, 5, 6, 9, 10, 11, 12, 15]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "droplist = dropfedex.index.tolist()\n", + "droplist[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[96246, 96291, 96292, 96319, 96397, 96415, 96426, 96433, 96459, 96727]" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "droplist[-10:]" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "11775" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(droplist)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RecnumCardnumDateMerchnumMerch descriptionMerch stateMerch zipTranstypeAmountFraudamount_100first_digit
0151421904392010-01-015509006296254FEDEX SHP 12/23/09 AB#TN38118.0P3.620362.03
1251421839732010-01-0161003026333SERVICE MERCHANDISE #81MA1803.0P31.4203142.03
2351421317212010-01-014503082993600OFFICE DEPOT #191MD20706.0P178.49017849.01
3451421484522010-01-015509006296254FEDEX SHP 12/28/09 AB#TN38118.0P3.620362.03
4551421904392010-01-015509006296254FEDEX SHP 12/23/09 AB#TN38118.0P3.620362.03
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" + ], + "text/plain": [ + " Recnum Cardnum Date Merchnum Merch description \\\n", + "0 1 5142190439 2010-01-01 5509006296254 FEDEX SHP 12/23/09 AB# \n", + "1 2 5142183973 2010-01-01 61003026333 SERVICE MERCHANDISE #81 \n", + "2 3 5142131721 2010-01-01 4503082993600 OFFICE DEPOT #191 \n", + "3 4 5142148452 2010-01-01 5509006296254 FEDEX SHP 12/28/09 AB# \n", + "4 5 5142190439 2010-01-01 5509006296254 FEDEX SHP 12/23/09 AB# \n", + "\n", + " Merch state Merch zip Transtype Amount Fraud amount_100 first_digit \n", + "0 TN 38118.0 P 3.62 0 362.0 3 \n", + "1 MA 1803.0 P 31.42 0 3142.0 3 \n", + "2 MD 20706.0 P 178.49 0 17849.0 1 \n", + "3 TN 38118.0 P 3.62 0 362.0 3 \n", + "4 TN 38118.0 P 3.62 0 362.0 3 " + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(96397, 12)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bf.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(84622, 12)" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bf1 = bf.drop(droplist)\n", + "bf1.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "# datefilter = datetime.datetime(2010,11,1)\n", + "# bf1 = bf1[bf1['Date'] < datefilter]\n", + "# bf1.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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131451421247912010-01-015725000466504CDW*GOVERNMENT INCIL60061.0P106.89010689.01low
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" + ], + "text/plain": [ + " Recnum Cardnum Date Merchnum Merch description \\\n", + "1 2 5142183973 2010-01-01 61003026333 SERVICE MERCHANDISE #81 \n", + "2 3 5142131721 2010-01-01 4503082993600 OFFICE DEPOT #191 \n", + "7 8 5142191182 2010-01-01 6098208200062 MIAMI COMPUTER SUPPLY \n", + "8 9 5142258629 2010-01-01 602608969534 FISHER SCI ATL \n", + "13 14 5142124791 2010-01-01 5725000466504 CDW*GOVERNMENT INC \n", + "\n", + " Merch state Merch zip Transtype Amount Fraud amount_100 first_digit bin \n", + "1 MA 1803.0 P 31.42 0 3142.0 3 high \n", + "2 MD 20706.0 P 178.49 0 17849.0 1 low \n", + "7 OH 45429.0 P 230.32 0 23032.0 2 low \n", + "8 GA 30091.0 P 62.11 0 6211.0 6 high \n", + "13 IL 60061.0 P 106.89 0 10689.0 1 low " + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bf1['bin']= bf1['first_digit'].apply(lambda x: \"low\" if x == \"1\" else (\"low\" if x == \"2\" else \"high\"))\n", + "bf1.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 25697\n", + "2 15827\n", + "3 10297\n", + "4 7686\n", + "5 6749\n", + "6 5469\n", + "7 4699\n", + "8 4152\n", + "9 4046\n", + "Name: first_digit, dtype: int64" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bf1['first_digit'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Merch zipbincount
01.0high6
11.0low5
22.0high7
32.0low9
43.0high14
............
709276302low14
709390805high76
709490805low41
7095unknownhigh688
7096unknownlow462
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7097 rows × 3 columns

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" + ], + "text/plain": [ + " Merch zip bin count\n", + "0 1.0 high 6\n", + "1 1.0 low 5\n", + "2 2.0 high 7\n", + "3 2.0 low 9\n", + "4 3.0 high 14\n", + "... ... ... ...\n", + "7092 76302 low 14\n", + "7093 90805 high 76\n", + "7094 90805 low 41\n", + "7095 unknown high 688\n", + "7096 unknown low 462\n", + "\n", + "[7097 rows x 3 columns]" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# calculating n_low and n_high\n", + "zip_bf = bf1.groupby(['Merch zip','bin']).agg({'bin': ['count']}).reset_index()\n", + "zip_bf.columns=['Merch zip', 'bin', 'count']\n", + "zip_bf" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Merch zipn_highn_low
01.06.05.0
12.07.09.0
23.014.04.0
35.01.01.0
46.05.02.0
............
45683104044.049.0
45694116059.067.0
45707630222.014.0
45719080576.041.0
4572unknown688.0462.0
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4573 rows × 3 columns

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" + ], + "text/plain": [ + " Merch zip n_high n_low\n", + "0 1.0 6.0 5.0\n", + "1 2.0 7.0 9.0\n", + "2 3.0 14.0 4.0\n", + "3 5.0 1.0 1.0\n", + "4 6.0 5.0 2.0\n", + "... ... ... ...\n", + "4568 31040 44.0 49.0\n", + "4569 41160 59.0 67.0\n", + "4570 76302 22.0 14.0\n", + "4571 90805 76.0 41.0\n", + "4572 unknown 688.0 462.0\n", + "\n", + "[4573 rows x 3 columns]" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# if either n_low or n_high is zero, set it to 1\n", + "zip_bf = zip_bf.fillna(1)\n", + "zip_bf" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Merch zipn_highn_low
01.06.05.0
12.07.09.0
23.014.04.0
35.0NaN1.0
46.05.02.0
............
45683104044.049.0
45694116059.067.0
45707630222.014.0
45719080576.041.0
4572unknown688.0462.0
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4573 rows × 3 columns

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" + ], + "text/plain": [ + " Merch zip n_high n_low\n", + "0 1.0 6.0 5.0\n", + "1 2.0 7.0 9.0\n", + "2 3.0 14.0 4.0\n", + "3 5.0 NaN 1.0\n", + "4 6.0 5.0 2.0\n", + "... ... ... ...\n", + "4568 31040 44.0 49.0\n", + "4569 41160 59.0 67.0\n", + "4570 76302 22.0 14.0\n", + "4571 90805 76.0 41.0\n", + "4572 unknown 688.0 462.0\n", + "\n", + "[4573 rows x 3 columns]" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "zip_bf = zip_bf.pivot_table(index='Merch zip',columns='bin',values='count',aggfunc='sum').reset_index()\n", + "zip_bf.columns=['Merch zip', 'n_high', 'n_low']\n", + "zip_bf" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Cardnumbincount
05142110002low1
15142110081high4
25142110313high1
35142110313low2
45142110402high8
............
31285142310598low2
31295142310768high2
31305142310768low2
31315142847398high35
31325142847398low10
\n", + "

3133 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " Cardnum bin count\n", + "0 5142110002 low 1\n", + "1 5142110081 high 4\n", + "2 5142110313 high 1\n", + "3 5142110313 low 2\n", + "4 5142110402 high 8\n", + "... ... ... ...\n", + "3128 5142310598 low 2\n", + "3129 5142310768 high 2\n", + "3130 5142310768 low 2\n", + "3131 5142847398 high 35\n", + "3132 5142847398 low 10\n", + "\n", + "[3133 rows x 3 columns]" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# calculating n_low and n_high\n", + "card_bf = bf1.groupby(['Cardnum','bin']).agg({'bin': ['count']}).reset_index()\n", + "card_bf.columns=['Cardnum', 'bin', 'count']\n", + "card_bf" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Cardnumn_highn_low
05142110002NaN1.0
151421100814.0NaN
251421103131.02.0
351421104028.03.0
45142110434NaN1.0
............
163551423103971.0NaN
163651423105253.01.0
16375142310598NaN2.0
163851423107682.02.0
1639514284739835.010.0
\n", + "

1640 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " Cardnum n_high n_low\n", + "0 5142110002 NaN 1.0\n", + "1 5142110081 4.0 NaN\n", + "2 5142110313 1.0 2.0\n", + "3 5142110402 8.0 3.0\n", + "4 5142110434 NaN 1.0\n", + "... ... ... ...\n", + "1635 5142310397 1.0 NaN\n", + "1636 5142310525 3.0 1.0\n", + "1637 5142310598 NaN 2.0\n", + "1638 5142310768 2.0 2.0\n", + "1639 5142847398 35.0 10.0\n", + "\n", + "[1640 rows x 3 columns]" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "card_bf = card_bf.pivot_table(index='Cardnum',columns='bin',values='count',aggfunc='sum').reset_index()\n", + "card_bf.columns=['Cardnum', 'n_high', 'n_low']\n", + "card_bf" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Cardnumn_highn_low
051421100021.01.0
151421100814.01.0
251421103131.02.0
351421104028.03.0
451421104341.01.0
............
163551423103971.01.0
163651423105253.01.0
163751423105981.02.0
163851423107682.02.0
1639514284739835.010.0
\n", + "

1640 rows × 3 columns

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" + ], + "text/plain": [ + " Cardnum n_high n_low\n", + "0 5142110002 1.0 1.0\n", + "1 5142110081 4.0 1.0\n", + "2 5142110313 1.0 2.0\n", + "3 5142110402 8.0 3.0\n", + "4 5142110434 1.0 1.0\n", + "... ... ... ...\n", + "1635 5142310397 1.0 1.0\n", + "1636 5142310525 3.0 1.0\n", + "1637 5142310598 1.0 2.0\n", + "1638 5142310768 2.0 2.0\n", + "1639 5142847398 35.0 10.0\n", + "\n", + "[1640 rows x 3 columns]" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# if either n_low or n_high is zero, set it to 1\n", + "card_bf = card_bf.fillna(1)\n", + "card_bf" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "# calclating R, 1/R, U, n, t U_smoothed\n", + "c=3\n", + "n_mid=15\n", + "card_bf['R'] = (1.096 * card_bf['n_low']/card_bf['n_high'])\n", + "card_bf['1/R'] = (1/card_bf['R'])\n", + "card_bf['U'] = list(map(lambda x, y : max(x,y),card_bf['R'],card_bf['1/R']))\n", + "card_bf['n'] = card_bf['n_high'] + card_bf['n_low']\n", + "card_bf['t'] = ((card_bf['n']-n_mid)/c)\n", + "card_bf['U_smoothed']= list(map(lambda x, y : (1 + (x-1)/(1+exp(-y))),card_bf['U'],card_bf['t']))" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Cardnumn_highn_lowR1/RUntU_smoothed
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Merchnumn_highn_low
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" + ], + "text/plain": [ + " Merchnum n_high n_low\n", + "0 003100006NOT6 1.0 NaN\n", + "1 004740006ABC6 NaN 1.0\n", + "2 005590006PNB6 1.0 NaN\n", + "3 014430619 14 NaN 1.0\n", + "4 014938913 51 1.0 NaN" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# calculating n_low and n_high\n", + "merch_bf = bf1.groupby(['Merchnum','bin']).agg({'bin': ['count']}).reset_index()\n", + "merch_bf.columns=['Merchnum', 'bin', 'count']\n", + "merch_bf = merch_bf.pivot_table(index='Merchnum',columns='bin',values='count',aggfunc='sum').reset_index()\n", + "merch_bf.columns=['Merchnum', 'n_high', 'n_low']\n", + "merch_bf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Merchnumn_highn_low
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" + ], + "text/plain": [ + " Merchnum n_high n_low\n", + "0 003100006NOT6 1.0 1.0\n", + "1 004740006ABC6 1.0 1.0\n", + "2 005590006PNB6 1.0 1.0\n", + "3 014430619 14 1.0 1.0\n", + "4 014938913 51 1.0 1.0\n", + "... ... ... ...\n", + "13586 DU49038320006 1.0 1.0\n", + "13587 JCPENNE9 CO 2.0 1.0\n", + "13588 PENNE9 CO #05 1.0 1.0\n", + "13589 PENNE9 CO #68 1.0 1.0\n", + "13590 unknown 417.0 274.0\n", + "\n", + "[13591 rows x 3 columns]" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# if either n_low or n_high is zero, set it to 1\n", + "merch_bf = merch_bf.fillna(1)\n", + "merch_bf" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "# calclating R, 1/R, U, n, t U_smoothed\n", + "merch_bf['R'] = (1.096 * merch_bf['n_low']/merch_bf['n_high'])\n", + "merch_bf['1/R'] = (1/merch_bf['R'])\n", + "merch_bf['U'] = list(map(lambda x, y : max(x,y),merch_bf['R'],merch_bf['1/R']))\n", + "merch_bf['n'] = merch_bf['n_high'] + merch_bf['n_low']\n", + "merch_bf['t'] = ((merch_bf['n']-n_mid)/c)\n", + "merch_bf['U_smoothed']= list(map(lambda x, y : (1 + (x-1)/(1+exp(-y))),merch_bf['U'],merch_bf['t']))" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Merchnum n_high n_low R 1/R U n \\\n", + "0 991808369338 181.0 1.0 0.006055 165.145985 165.145985 182.0 \n", + "1 8078200641472 1.0 59.0 64.664000 0.015465 64.664000 60.0 \n", + "2 308904389335 53.0 1.0 0.020679 48.357664 48.357664 54.0 \n", + "3 3523000628102 1.0 34.0 37.264000 0.026836 37.264000 35.0 \n", + "4 808998385332 36.0 1.0 0.030444 32.846715 32.846715 37.0 \n", + "5 55158027 1.0 27.0 29.592000 0.033793 29.592000 28.0 \n", + "6 8916500620062 31.0 1.0 0.035355 28.284672 28.284672 32.0 \n", + "7 3910694900001 1.0 25.0 27.400000 0.036496 27.400000 26.0 \n", + "8 881145544 1.0 24.0 26.304000 0.038017 26.304000 25.0 \n", + "9 8889817332 1.0 24.0 26.304000 0.038017 26.304000 25.0 \n", + "10 5600900060992 27.0 1.0 0.040593 24.635036 24.635036 28.0 \n", + "11 6844000608436 1.0 23.0 25.208000 0.039670 25.208000 24.0 \n", + "12 92891948003 24.0 1.0 0.045667 21.897810 21.897810 25.0 \n", + "13 5803301245621 1.0 21.0 23.016000 0.043448 23.016000 22.0 \n", + "14 3433000017263 3.0 53.0 19.362667 0.051646 19.362667 56.0 \n", + "15 467615916337 22.0 1.0 0.049818 20.072993 20.072993 23.0 \n", + "16 817004638227 1.0 19.0 20.824000 0.048022 20.824000 20.0 \n", + "17 2376700063599 2.0 30.0 16.440000 0.060827 16.440000 32.0 \n", + "18 993620816222 19.0 1.0 0.057684 17.335766 17.335766 20.0 \n", + "19 993620810220 76.0 5.0 0.072105 13.868613 13.868613 81.0 \n", + "20 465614140337 18.0 1.0 0.060889 16.423358 16.423358 19.0 \n", + "21 8999000079657 18.0 1.0 0.060889 16.423358 16.423358 19.0 \n", + "22 8317600900099 2.0 24.0 13.152000 0.076034 13.152000 26.0 \n", + "23 5000006000095 23.0 253.0 12.056000 0.082946 12.056000 276.0 \n", + "24 9420966064460 17.0 1.0 0.064471 15.510949 15.510949 18.0 \n", + "25 5186264200136 17.0 1.0 0.064471 15.510949 15.510949 18.0 \n", + "26 600000201284 50.0 4.0 0.087680 11.405109 11.405109 54.0 \n", + "27 5600000060302 16.0 1.0 0.068500 14.598540 14.598540 17.0 \n", + "28 7080606900600 16.0 1.0 0.068500 14.598540 14.598540 17.0 \n", + "29 6070095870009 3.0 26.0 9.498667 0.105278 9.498667 29.0 \n", + "30 999960264339 28.0 3.0 0.117429 8.515815 8.515815 31.0 \n", + "31 555400670006 15.0 1.0 0.073067 13.686131 13.686131 16.0 \n", + "32 881894855 15.0 1.0 0.073067 13.686131 13.686131 16.0 \n", + "33 1960400470068 3.0 23.0 8.402667 0.119010 8.402667 26.0 \n", + "34 993620559229 43.0 5.0 0.127442 7.846715 7.846715 48.0 \n", + "35 2586000448258 14.0 1.0 0.078286 12.773723 12.773723 15.0 \n", + "36 8100544800098 14.0 1.0 0.078286 12.773723 12.773723 15.0 \n", + "37 604901367333 14.0 1.0 0.078286 12.773723 12.773723 15.0 \n", + "38 6000330043193 1.0 13.0 14.248000 0.070185 14.248000 14.0 \n", + "39 6005300190068 1.0 13.0 14.248000 0.070185 14.248000 14.0 \n", + "\n", + " t U_smoothed \n", + "0 55.666667 165.145985 \n", + "1 15.000000 64.663981 \n", + "2 13.000000 48.357557 \n", + "3 6.666667 37.217908 \n", + "4 7.333333 32.825921 \n", + "5 4.333333 29.221627 \n", + "6 5.666667 28.190609 \n", + "7 3.666667 26.741995 \n", + "8 3.333333 25.432399 \n", + "9 3.333333 25.432399 \n", + "10 4.333333 24.328875 \n", + "11 3.000000 24.059914 \n", + "12 3.333333 21.177981 \n", + "13 2.333333 21.069793 \n", + "14 13.666667 19.362645 \n", + "15 2.666667 18.833836 \n", + "16 1.666667 17.674579 \n", + "17 5.666667 16.386771 \n", + "18 1.666667 14.740518 \n", + "19 22.000000 13.868613 \n", + "20 1.333333 13.205914 \n", + "21 1.333333 13.205914 \n", + "22 3.666667 12.849118 \n", + "23 87.000000 12.056000 \n", + "24 1.000000 11.608354 \n", + "25 1.000000 11.608354 \n", + "26 13.000000 11.405086 \n", + "27 0.666667 9.985322 \n", + "28 0.666667 9.985322 \n", + "29 4.666667 9.419493 \n", + "30 5.333333 8.479703 \n", + "31 0.333333 8.390562 \n", + "32 0.333333 8.390562 \n", + "33 3.666667 8.218159 \n", + "34 11.000000 7.846601 \n", + "35 0.000000 6.886861 \n", + "36 0.000000 6.886861 \n", + "37 0.000000 6.886861 \n", + "38 -0.333333 6.530110 \n", + "39 -0.333333 6.530110 " + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top40_merch_bf = merch_bf.sort_values(['U_smoothed'], ascending = False).head(40).reset_index(drop = True)\n", + "top40_merch_bf.head(40)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "# calclating R, 1/R, U, n, t U_smoothed\n", + "c=3\n", + "n_mid=15\n", + "zip_bf['R'] = (1.096 * zip_bf['n_low']/zip_bf['n_high'])\n", + "zip_bf['1/R'] = (1/zip_bf['R'])\n", + "zip_bf['U'] = list(map(lambda x, y : max(x,y),zip_bf['R'],zip_bf['1/R']))\n", + "zip_bf['n'] = zip_bf['n_high'] + zip_bf['n_low']\n", + "zip_bf['t'] = ((zip_bf['n']-n_mid)/c)\n", + "zip_bf['U_smoothed']= list(map(lambda x, y : (1 + (x-1)/(1+exp(-y))),zip_bf['U'],zip_bf['t']))" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Merch zipn_highn_lowR1/RUntU_smoothed
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" + ], + "text/plain": [ + " Merch zip n_high n_low R 1/R U n \\\n", + "0 55435.0 1.0 28.0 30.688000 0.032586 30.688000 29.0 \n", + "1 7054.0 2.0 25.0 13.700000 0.072993 13.700000 27.0 \n", + "2 77023.0 18.0 1.0 0.060889 16.423358 16.423358 19.0 \n", + "3 91372.0 2.0 24.0 13.152000 0.076034 13.152000 26.0 \n", + "4 75230.0 26.0 2.0 0.084308 11.861314 11.861314 28.0 \n", + "5 16258.0 17.0 1.0 0.064471 15.510949 15.510949 18.0 \n", + "6 2881.0 17.0 1.0 0.064471 15.510949 15.510949 18.0 \n", + "7 7645.0 17.0 1.0 0.064471 15.510949 15.510949 18.0 \n", + "8 22091.0 4.0 42.0 11.508000 0.086896 11.508000 46.0 \n", + "9 20015.0 3.0 30.0 10.960000 0.091241 10.960000 33.0 \n", + "10 22033.0 68.0 6.0 0.096706 10.340633 10.340633 74.0 \n", + "11 1020.0 3.0 25.0 9.133333 0.109489 9.133333 28.0 \n", + "12 39466.0 15.0 1.0 0.073067 13.686131 13.686131 16.0 \n", + "13 92649.0 3.0 23.0 8.402667 0.119010 8.402667 26.0 \n", + "14 64133.0 3.0 20.0 7.306667 0.136861 7.306667 23.0 \n", + "15 79764.0 1.0 13.0 14.248000 0.070185 14.248000 14.0 \n", + "16 59860.0 1.0 13.0 14.248000 0.070185 14.248000 14.0 \n", + "17 12108 44.0 262.0 6.526182 0.153229 6.526182 306.0 \n", + "18 21140.0 5.0 29.0 6.356800 0.157312 6.356800 34.0 \n", + "19 48184.0 41.0 6.0 0.160390 6.234793 6.234793 47.0 \n", + "20 46229.0 13.0 1.0 0.084308 11.861314 11.861314 14.0 \n", + "21 29634.0 1.0 12.0 13.152000 0.076034 13.152000 13.0 \n", + "22 14625.0 1.0 12.0 13.152000 0.076034 13.152000 13.0 \n", + "23 95616.0 15.0 2.0 0.146133 6.843066 6.843066 17.0 \n", + "24 41001.0 6.0 25.0 4.566667 0.218978 4.566667 31.0 \n", + "25 22310.0 24.0 5.0 0.228333 4.379562 4.379562 29.0 \n", + "26 15222.0 104.0 22.0 0.231846 4.313205 4.313205 126.0 \n", + "27 75050.0 14.0 2.0 0.156571 6.386861 6.386861 16.0 \n", + "28 20057.0 14.0 2.0 0.156571 6.386861 6.386861 16.0 \n", + "29 95030.0 2.0 13.0 7.124000 0.140371 7.124000 15.0 \n", + "30 18041.0 4.0 16.0 4.384000 0.228102 4.384000 20.0 \n", + "31 11701.0 4.0 16.0 4.384000 0.228102 4.384000 20.0 \n", + "32 31210.0 18.0 4.0 0.243556 4.105839 4.105839 22.0 \n", + "33 6611.0 18.0 4.0 0.243556 4.105839 4.105839 22.0 \n", + "34 66212.0 18.0 4.0 0.243556 4.105839 4.105839 22.0 \n", + "35 20250.0 11.0 38.0 3.786182 0.264118 3.786182 49.0 \n", + "36 10305.0 33.0 8.0 0.265697 3.763686 3.763686 41.0 \n", + "37 19428.0 3.0 14.0 5.114667 0.195516 5.114667 17.0 \n", + "38 22151.0 6.0 20.0 3.653333 0.273723 3.653333 26.0 \n", + "39 46032.0 7.0 23.0 3.601143 0.277690 3.601143 30.0 \n", + "\n", + " t U_smoothed \n", + "0 4.666667 30.411428 \n", + "1 4.000000 13.471575 \n", + "2 1.333333 13.205914 \n", + "3 3.666667 12.849118 \n", + "4 4.333333 11.720619 \n", + "5 1.000000 11.608354 \n", + "6 1.000000 11.608354 \n", + "7 1.000000 11.608354 \n", + "8 10.333333 11.507658 \n", + "9 6.000000 10.935373 \n", + "10 19.666667 10.340633 \n", + "11 4.333333 9.027976 \n", + "12 0.333333 8.390562 \n", + "13 3.666667 8.218159 \n", + "14 2.666667 6.896928 \n", + "15 -0.333333 6.530110 \n", + "16 -0.333333 6.530110 \n", + "17 97.000000 6.526182 \n", + "18 6.333333 6.347303 \n", + "19 10.666667 6.234671 \n", + "20 -0.333333 5.533836 \n", + "21 -0.666667 5.122489 \n", + "22 -0.666667 5.122489 \n", + "23 0.666667 4.860843 \n", + "24 5.333333 4.549530 \n", + "25 4.666667 4.348078 \n", + "26 37.000000 4.313205 \n", + "27 0.333333 4.138225 \n", + "28 0.333333 4.138225 \n", + "29 0.000000 4.062000 \n", + "30 1.666667 3.846387 \n", + "31 1.666667 3.846387 \n", + "32 2.333333 3.831284 \n", + "33 2.333333 3.831284 \n", + "34 2.333333 3.831284 \n", + "35 11.333333 3.786148 \n", + "36 8.666667 3.763210 \n", + "37 0.666667 3.718792 \n", + "38 3.666667 3.587201 \n", + "39 5.000000 3.583734 " + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top40_zip_bf = zip_bf.sort_values(['U_smoothed'], ascending = False).head(40).reset_index(drop = True)\n", + "top40_zip_bf.head(40)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "# Here are the tables for the Benford's law. They would be useful for a forensic analysis\n", + "top40_card_bf.to_csv('Benford top cards.csv')\n", + "top40_merch_bf.to_csv('Benford top merchs.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "top40_zip_bf.to_csv('Benford top zips.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1640 entries, 0 to 1639\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Cardnum 1640 non-null object \n", + " 1 n_high 1640 non-null float64\n", + " 2 n_low 1640 non-null float64\n", + " 3 R 1640 non-null float64\n", + " 4 1/R 1640 non-null float64\n", + " 5 U 1640 non-null float64\n", + " 6 n 1640 non-null float64\n", + " 7 t 1640 non-null float64\n", + " 8 U_smoothed 1640 non-null float64\n", + "dtypes: float64(8), object(1)\n", + "memory usage: 115.4+ KB\n" + ] + } + ], + "source": [ + "card_bf['Cardnum'] = card_bf['Cardnum'].apply(str)\n", + "merch_bf['Merchnum'] = merch_bf['Merchnum'].apply(str)\n", + "card_bf.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 4573 entries, 0 to 4572\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Merch zip 4573 non-null object \n", + " 1 n_high 4573 non-null float64\n", + " 2 n_low 4573 non-null float64\n", + " 3 R 4573 non-null float64\n", + " 4 1/R 4573 non-null float64\n", + " 5 U 4573 non-null float64\n", + " 6 n 4573 non-null float64\n", + " 7 t 4573 non-null float64\n", + " 8 U_smoothed 4573 non-null float64\n", + "dtypes: float64(8), object(1)\n", + "memory usage: 321.7+ KB\n" + ] + } + ], + "source": [ + "zip_bf['Merch zip'] = zip_bf['Merch zip'].apply(str)\n", + "zip_bf.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "card_bf.set_index('Cardnum',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RecnumCardnumDateMerchnumMerch descriptionMerch stateMerch zipTranstypeAmountFraud...Merchnum_descMerchnum_dowMerchdesc_dowCard_Merchnum_descCard_Merchnum_ZipCard_Merchdesc_ZipMerchnum_desc_StateU*_cardnumU*_merchnumU*_zip
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1251421839732010-01-0161003026333SERVICEMERCHANDISE#81MA1803.0P31.420...61003026333SERVICEMERCHANDISE#8161003026333FridaySERVICEMERCHANDISE#81Friday514218397361003026333SERVICEMERCHANDISE#815142183973610030263331803.05142183973SERVICEMERCHANDISE#811803.061003026333SERVICEMERCHANDISE#81MA1.6048571.0012441.155442
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3451421484522010-01-015509006296254FEDEXSHP12/28/09AB#TN38118.0P3.620...5509006296254FEDEXSHP12/28/09AB#5509006296254FridayFEDEXSHP12/28/09AB#Friday51421484525509006296254FEDEXSHP12/28/09AB#5142148452550900629625438118.05142148452FEDEXSHP12/28/09AB#38118.05509006296254FEDEXSHP12/28/09AB#TN1.044105NaN2.944592
4551421904392010-01-015509006296254FEDEXSHP12/23/09AB#TN38118.0P3.620...5509006296254FEDEXSHP12/23/09AB#5509006296254FridayFEDEXSHP12/23/09AB#Friday51421904395509006296254FEDEXSHP12/23/09AB#5142190439550900629625438118.05142190439FEDEXSHP12/23/09AB#38118.05509006296254FEDEXSHP12/23/09AB#TN2.178008NaN2.944592
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+ "source": [ + "final['U*_cardnum'].isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "11775" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final['U*_merchnum'].isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final['U*_zip'].isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [], + "source": [ + "final['U*_cardnum'].fillna(1,inplace=True)\n", + "final['U*_merchnum'].fillna(1,inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final['U*_cardnum'].isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final['U*_merchnum'].isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(96397, 35)\n", + "(96397, 32)\n" + ] + } + ], + "source": [ + "print(final.shape)\n", + "final.drop(columns=['U*_cardnum','U*_merchnum', 'U*_zip'],inplace=True)\n", + "print(final.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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..................................................................
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178.49 0 ... 5142131721207 \n", + "3 TN 38118.0 P 3.62 0 ... 5142148452381 \n", + "4 TN 38118.0 P 3.62 0 ... 5142190439381 \n", + "... ... ... ... ... ... ... ... \n", + "96392 KY 41042.0 P 84.79 0 ... 5142276053410 \n", + "96393 OH 45248.0 P 118.75 0 ... 5142225701452 \n", + "96394 OH 45150.0 P 363.56 0 ... 5142226486451 \n", + "96395 CA 92656.0 P 2202.03 0 ... 5142244619926 \n", + "96396 NJ 7606.0 P 554.64 0 ... 5142243247760 \n", + "\n", + " Card_Merchdesc Card_dow \\\n", + "0 5142190439FEDEXSHP12/23/09AB# 5142190439Friday \n", + "1 5142183973SERVICEMERCHANDISE#81 5142183973Friday \n", + "2 5142131721OFFICEDEPOT#191 5142131721Friday \n", + "3 5142148452FEDEXSHP12/28/09AB# 5142148452Friday \n", + "4 5142190439FEDEXSHP12/23/09AB# 5142190439Friday \n", + "... ... ... \n", + "96392 5142276053BESTBUY00001610 5142276053Friday \n", + "96393 5142225701MARKUSOFFICESUPPLIES 5142225701Friday \n", + "96394 5142226486TECHPAC,INC 5142226486Friday \n", + "96395 5142244619BUY.COM 5142244619Friday \n", + "96396 5142243247STAPLESNATIONAL#471 5142243247Friday \n", + "\n", + " Merchnum_desc Merchnum_dow \\\n", + "0 5509006296254FEDEXSHP12/23/09AB# 5509006296254Friday \n", + "1 61003026333SERVICEMERCHANDISE#81 61003026333Friday \n", + "2 4503082993600OFFICEDEPOT#191 4503082993600Friday \n", + "3 5509006296254FEDEXSHP12/28/09AB# 5509006296254Friday \n", + "4 5509006296254FEDEXSHP12/23/09AB# 5509006296254Friday \n", + "... ... ... \n", + "96392 3500000006160BESTBUY00001610 3500000006160Friday \n", + "96393 8090710030950MARKUSOFFICESUPPLIES 8090710030950Friday \n", + "96394 4503057341100TECHPAC,INC 4503057341100Friday \n", + "96395 8834000695412BUY.COM 8834000695412Friday \n", + "96396 9108347680006STAPLESNATIONAL#471 9108347680006Friday \n", + "\n", + " Merchdesc_dow \\\n", + "0 FEDEXSHP12/23/09AB#Friday \n", + "1 SERVICEMERCHANDISE#81Friday \n", + "2 OFFICEDEPOT#191Friday \n", + "3 FEDEXSHP12/28/09AB#Friday \n", + "4 FEDEXSHP12/23/09AB#Friday \n", + "... ... \n", + "96392 BESTBUY00001610Friday \n", + "96393 MARKUSOFFICESUPPLIESFriday \n", + "96394 TECHPAC,INCFriday \n", + "96395 BUY.COMFriday \n", + "96396 STAPLESNATIONAL#471Friday \n", + "\n", + " Card_Merchnum_desc \\\n", + "0 51421904395509006296254FEDEXSHP12/23/09AB# \n", + "1 514218397361003026333SERVICEMERCHANDISE#81 \n", + "2 51421317214503082993600OFFICEDEPOT#191 \n", + "3 51421484525509006296254FEDEXSHP12/28/09AB# \n", + "4 51421904395509006296254FEDEXSHP12/23/09AB# \n", + "... ... \n", + "96392 51422760533500000006160BESTBUY00001610 \n", + "96393 51422257018090710030950MARKUSOFFICESUPPLIES \n", + "96394 51422264864503057341100TECHPAC,INC \n", + "96395 51422446198834000695412BUY.COM \n", + "96396 51422432479108347680006STAPLESNATIONAL#471 \n", + "\n", + " Card_Merchnum_Zip Card_Merchdesc_Zip \\\n", + "0 5142190439550900629625438118.0 5142190439FEDEXSHP12/23/09AB#38118.0 \n", + "1 5142183973610030263331803.0 5142183973SERVICEMERCHANDISE#811803.0 \n", + "2 5142131721450308299360020706.0 5142131721OFFICEDEPOT#19120706.0 \n", + "3 5142148452550900629625438118.0 5142148452FEDEXSHP12/28/09AB#38118.0 \n", + "4 5142190439550900629625438118.0 5142190439FEDEXSHP12/23/09AB#38118.0 \n", + "... ... ... \n", + "96392 5142276053350000000616041042.0 5142276053BESTBUY0000161041042.0 \n", + "96393 5142225701809071003095045248.0 5142225701MARKUSOFFICESUPPLIES45248.0 \n", + "96394 5142226486450305734110045150.0 5142226486TECHPAC,INC45150.0 \n", + "96395 5142244619883400069541292656.0 5142244619BUY.COM92656.0 \n", + "96396 514224324791083476800067606.0 5142243247STAPLESNATIONAL#4717606.0 \n", + "\n", + " Merchnum_desc_State \n", + "0 5509006296254FEDEXSHP12/23/09AB#TN \n", + "1 61003026333SERVICEMERCHANDISE#81MA \n", + "2 4503082993600OFFICEDEPOT#191MD \n", + "3 5509006296254FEDEXSHP12/28/09AB#TN \n", + "4 5509006296254FEDEXSHP12/23/09AB#TN \n", + "... ... \n", + "96392 3500000006160BESTBUY00001610KY \n", + "96393 8090710030950MARKUSOFFICESUPPLIESOH \n", + "96394 4503057341100TECHPAC,INCOH \n", + "96395 8834000695412BUY.COMCA \n", + "96396 9108347680006STAPLESNATIONAL#471NJ \n", + "\n", + "[96397 rows x 32 columns]" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Cardnum',\n", + " 'Merchnum',\n", + " 'Month',\n", + " 'Month_Risk',\n", + " 'state_risk',\n", + " 'card_merch',\n", + " 'card_zip',\n", + " 'card_state',\n", + " 'merch_zip',\n", + " 'merch_state',\n", + " 'state_des',\n", + " 'zip3',\n", + " 'card_zip3',\n", + " 'Card_Merchdesc',\n", + " 'Card_dow',\n", + " 'Merchnum_desc',\n", + " 'Merchnum_dow',\n", + " 'Merchdesc_dow',\n", + " 'Card_Merchnum_desc',\n", + " 'Card_Merchnum_Zip',\n", + " 'Card_Merchdesc_Zip',\n", + " 'Merchnum_desc_State']" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "entities" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Cardnum',\n", + " 'Merchnum',\n", + " 'Month',\n", + " 'Month_Risk',\n", + " 'card_merch',\n", + " 'card_zip',\n", + " 'card_state',\n", + " 'merch_zip',\n", + " 'merch_state',\n", + " 'state_des',\n", + " 'card_zip3',\n", + " 'Card_Merchdesc',\n", + " 'Card_dow',\n", + " 'Merchnum_desc',\n", + " 'Merchnum_dow',\n", + " 'Merchdesc_dow',\n", + " 'Card_Merchnum_desc',\n", + " 'Card_Merchnum_Zip',\n", + " 'Card_Merchdesc_Zip',\n", + " 'Merchnum_desc_State']" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# If you want, remove some entities that take a long time\n", + "# these take a long time and don't add much\n", + "entities.remove('state_risk')\n", + "entities.remove('zip3')\n", + "entities" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Cardnum',\n", + " 'Merchnum',\n", + " 'card_merch',\n", + " 'card_zip',\n", + " 'card_state',\n", + " 'merch_zip',\n", + " 'merch_state',\n", + " 'state_des',\n", + " 'card_zip3',\n", + " 'Card_Merchdesc',\n", + " 'Card_dow',\n", + " 'Merchnum_desc',\n", + " 'Merchnum_dow',\n", + " 'Merchdesc_dow',\n", + " 'Card_Merchnum_desc',\n", + " 'Card_Merchnum_Zip',\n", + " 'Card_Merchdesc_Zip',\n", + " 'Merchnum_desc_State']" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# removing my custom variables\n", + "# these take a long time and don't add much\n", + "entities.remove('Month')\n", + "entities.remove('Month_Risk')\n", + "entities" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "final.shape\n", + "numstart = len(final.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cardnum_day_since ---> Done\n", + "# Day-since variables is 1\n", + "Cardnum_count_0 ---> Done\n", + "Cardnum amount variables over past 0 ---> Done\n", + "Cardnum_count_1 ---> Done\n", + "Cardnum amount variables over past 1 ---> Done\n", + "Cardnum_count_3 ---> Done\n", + "Cardnum amount variables over past 3 ---> Done\n", + "Cardnum_count_7 ---> Done\n", + "Cardnum amount variables over past 7 ---> Done\n", + "Cardnum_count_14 ---> Done\n", + "Cardnum amount variables over past 14 ---> Done\n", + "Cardnum_count_30 ---> Done\n", + "Cardnum amount variables over past 30 ---> Done\n", + "Cardnum_count_60 ---> Done\n", + "Cardnum amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 64\n", + "Merchnum Run time for the this entity ----------------- 4.889977334000001s\n", + "\n", + "Merchnum_day_since ---> Done\n", + "# Day-since variables is 65\n", + "Merchnum_count_0 ---> Done\n", + "Merchnum amount variables over past 0 ---> Done\n", + "Merchnum_count_1 ---> Done\n", + "Merchnum amount variables over past 1 ---> Done\n", + "Merchnum_count_3 ---> Done\n", + "Merchnum amount variables over past 3 ---> Done\n", + "Merchnum_count_7 ---> Done\n", + "Merchnum amount variables over past 7 ---> Done\n", + "Merchnum_count_14 ---> Done\n", + "Merchnum amount variables over past 14 ---> Done\n", + "Merchnum_count_30 ---> Done\n", + "Merchnum amount variables over past 30 ---> Done\n", + "Merchnum_count_60 ---> Done\n", + "Merchnum amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 128\n", + "card_merch Run time for the this entity ----------------- 34.025245625s\n", + "\n", + "card_merch_day_since ---> Done\n", + "# Day-since variables is 129\n", + "card_merch_count_0 ---> Done\n", + "card_merch amount variables over past 0 ---> Done\n", + "card_merch_count_1 ---> Done\n", + "card_merch amount variables over past 1 ---> Done\n", + "card_merch_count_3 ---> Done\n", + "card_merch amount variables over past 3 ---> Done\n", + "card_merch_count_7 ---> Done\n", + "card_merch amount variables over past 7 ---> Done\n", + "card_merch_count_14 ---> Done\n", + "card_merch amount variables over past 14 ---> Done\n", + "card_merch_count_30 ---> Done\n", + "card_merch amount variables over past 30 ---> Done\n", + "card_merch_count_60 ---> Done\n", + "card_merch amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 192\n", + "card_zip Run time for the this entity ----------------- 1.661040290999999s\n", + "\n", + "card_zip_day_since ---> Done\n", + "# Day-since variables is 193\n", + "card_zip_count_0 ---> Done\n", + "card_zip amount variables over past 0 ---> Done\n", + "card_zip_count_1 ---> Done\n", + "card_zip amount variables over past 1 ---> Done\n", + "card_zip_count_3 ---> Done\n", + "card_zip amount variables over past 3 ---> Done\n", + "card_zip_count_7 ---> Done\n", + "card_zip amount variables over past 7 ---> Done\n", + "card_zip_count_14 ---> Done\n", + "card_zip amount variables over past 14 ---> Done\n", + "card_zip_count_30 ---> Done\n", + "card_zip amount variables over past 30 ---> Done\n", + "card_zip_count_60 ---> Done\n", + "card_zip amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 256\n", + "card_state Run time for the this entity ----------------- 1.886241959000003s\n", + "\n", + "card_state_day_since ---> Done\n", + "# Day-since variables is 257\n", + "card_state_count_0 ---> Done\n", + "card_state amount variables over past 0 ---> Done\n", + "card_state_count_1 ---> Done\n", + "card_state amount variables over past 1 ---> Done\n", + "card_state_count_3 ---> Done\n", + "card_state amount variables over past 3 ---> Done\n", + "card_state_count_7 ---> Done\n", + "card_state amount variables over past 7 ---> Done\n", + "card_state_count_14 ---> Done\n", + "card_state amount variables over past 14 ---> Done\n", + "card_state_count_30 ---> Done\n", + "card_state amount variables over past 30 ---> Done\n", + "card_state_count_60 ---> Done\n", + "card_state amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 320\n", + "merch_zip Run time for the this entity ----------------- 2.188245875s\n", + "\n", + "merch_zip_day_since ---> Done\n", + "# Day-since variables is 321\n", + "merch_zip_count_0 ---> Done\n", + "merch_zip amount variables over past 0 ---> Done\n", + "merch_zip_count_1 ---> Done\n", + "merch_zip amount variables over past 1 ---> Done\n", + "merch_zip_count_3 ---> Done\n", + "merch_zip amount variables over past 3 ---> Done\n", + "merch_zip_count_7 ---> Done\n", + "merch_zip amount variables over past 7 ---> Done\n", + "merch_zip_count_14 ---> Done\n", + "merch_zip amount variables over past 14 ---> Done\n", + "merch_zip_count_30 ---> Done\n", + "merch_zip amount variables over past 30 ---> Done\n", + "merch_zip_count_60 ---> Done\n", + "merch_zip amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 384\n", + "merch_state Run time for the this entity ----------------- 32.95245154100001s\n", + "\n", + "merch_state_day_since ---> Done\n", + "# Day-since variables is 385\n", + "merch_state_count_0 ---> Done\n", + "merch_state amount variables over past 0 ---> Done\n", + "merch_state_count_1 ---> Done\n", + "merch_state amount variables over past 1 ---> Done\n", + "merch_state_count_3 ---> Done\n", + "merch_state amount variables over past 3 ---> Done\n", + "merch_state_count_7 ---> Done\n", + "merch_state amount variables over past 7 ---> Done\n", + "merch_state_count_14 ---> Done\n", + "merch_state amount variables over past 14 ---> Done\n", + "merch_state_count_30 ---> Done\n", + "merch_state amount variables over past 30 ---> Done\n", + "merch_state_count_60 ---> Done\n", + "merch_state amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 448\n", + "state_des Run time for the this entity ----------------- 34.023255459s\n", + "\n", + "state_des_day_since ---> Done\n", + "# Day-since variables is 449\n", + "state_des_count_0 ---> Done\n", + "state_des amount variables over past 0 ---> Done\n", + "state_des_count_1 ---> Done\n", + "state_des amount variables over past 1 ---> Done\n", + "state_des_count_3 ---> Done\n", + "state_des amount variables over past 3 ---> Done\n", + "state_des_count_7 ---> Done\n", + "state_des amount variables over past 7 ---> Done\n", + "state_des_count_14 ---> Done\n", + "state_des amount variables over past 14 ---> Done\n", + "state_des_count_30 ---> Done\n", + "state_des amount variables over past 30 ---> Done\n", + "state_des_count_60 ---> Done\n", + "state_des amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 512\n", + "card_zip3 Run time for the this entity ----------------- 4.609189666999995s\n", + "\n", + "card_zip3_day_since ---> Done\n", + "# Day-since variables is 513\n", + "card_zip3_count_0 ---> Done\n", + "card_zip3 amount variables over past 0 ---> Done\n", + "card_zip3_count_1 ---> Done\n", + "card_zip3 amount variables over past 1 ---> Done\n", + "card_zip3_count_3 ---> Done\n", + "card_zip3 amount variables over past 3 ---> Done\n", + "card_zip3_count_7 ---> Done\n", + "card_zip3 amount variables over past 7 ---> Done\n", + "card_zip3_count_14 ---> Done\n", + "card_zip3 amount variables over past 14 ---> Done\n", + "card_zip3_count_30 ---> Done\n", + "card_zip3 amount variables over past 30 ---> Done\n", + "card_zip3_count_60 ---> Done\n", + "card_zip3 amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 576\n", + "Card_Merchdesc Run time for the this entity ----------------- 1.964401874999993s\n", + "\n", + "Card_Merchdesc_day_since ---> Done\n", + "# Day-since variables is 577\n", + "Card_Merchdesc_count_0 ---> Done\n", + "Card_Merchdesc amount variables over past 0 ---> Done\n", + "Card_Merchdesc_count_1 ---> Done\n", + "Card_Merchdesc amount variables over past 1 ---> Done\n", + "Card_Merchdesc_count_3 ---> Done\n", + "Card_Merchdesc amount variables over past 3 ---> Done\n", + "Card_Merchdesc_count_7 ---> Done\n", + "Card_Merchdesc amount variables over past 7 ---> Done\n", + "Card_Merchdesc_count_14 ---> Done\n", + "Card_Merchdesc amount variables over past 14 ---> Done\n", + "Card_Merchdesc_count_30 ---> Done\n", + "Card_Merchdesc amount variables over past 30 ---> Done\n", + "Card_Merchdesc_count_60 ---> Done\n", + "Card_Merchdesc amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 640\n", + "Card_dow Run time for the this entity ----------------- 1.0719121670000078s\n", + "\n", + "Card_dow_day_since ---> Done\n", + "# Day-since variables is 641\n", + "Card_dow_count_0 ---> Done\n", + "Card_dow amount variables over past 0 ---> Done\n", + "Card_dow_count_1 ---> Done\n", + "Card_dow amount variables over past 1 ---> Done\n", + "Card_dow_count_3 ---> Done\n", + "Card_dow amount variables over past 3 ---> Done\n", + "Card_dow_count_7 ---> Done\n", + "Card_dow amount variables over past 7 ---> Done\n", + "Card_dow_count_14 ---> Done\n", + "Card_dow amount variables over past 14 ---> Done\n", + "Card_dow_count_30 ---> Done\n", + "Card_dow amount variables over past 30 ---> Done\n", + "Card_dow_count_60 ---> Done\n", + "Card_dow amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 704\n", + "Merchnum_desc Run time for the this entity ----------------- 1.566243416000006s\n", + "\n", + "Merchnum_desc_day_since ---> Done\n", + "# Day-since variables is 705\n", + "Merchnum_desc_count_0 ---> Done\n", + "Merchnum_desc amount variables over past 0 ---> Done\n", + "Merchnum_desc_count_1 ---> Done\n", + "Merchnum_desc amount variables over past 1 ---> Done\n", + "Merchnum_desc_count_3 ---> Done\n", + "Merchnum_desc amount variables over past 3 ---> Done\n", + "Merchnum_desc_count_7 ---> Done\n", + "Merchnum_desc amount variables over past 7 ---> Done\n", + "Merchnum_desc_count_14 ---> Done\n", + "Merchnum_desc amount variables over past 14 ---> Done\n", + "Merchnum_desc_count_30 ---> Done\n", + "Merchnum_desc amount variables over past 30 ---> Done\n", + "Merchnum_desc_count_60 ---> Done\n", + "Merchnum_desc amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 768\n", + "Merchnum_dow Run time for the this entity ----------------- 4.009386583000008s\n", + "\n", + "Merchnum_dow_day_since ---> Done\n", + "# Day-since variables is 769\n", + "Merchnum_dow_count_0 ---> Done\n", + "Merchnum_dow amount variables over past 0 ---> Done\n", + "Merchnum_dow_count_1 ---> Done\n", + "Merchnum_dow amount variables over past 1 ---> Done\n", + "Merchnum_dow_count_3 ---> Done\n", + "Merchnum_dow amount variables over past 3 ---> Done\n", + "Merchnum_dow_count_7 ---> Done\n", + "Merchnum_dow amount variables over past 7 ---> Done\n", + "Merchnum_dow_count_14 ---> Done\n", + "Merchnum_dow amount variables over past 14 ---> Done\n", + "Merchnum_dow_count_30 ---> Done\n", + "Merchnum_dow amount variables over past 30 ---> Done\n", + "Merchnum_dow_count_60 ---> Done\n", + "Merchnum_dow amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 832\n", + "Merchdesc_dow Run time for the this entity ----------------- 5.192437458000001s\n", + "\n", + "Merchdesc_dow_day_since ---> Done\n", + "# Day-since variables is 833\n", + "Merchdesc_dow_count_0 ---> Done\n", + "Merchdesc_dow amount variables over past 0 ---> Done\n", + "Merchdesc_dow_count_1 ---> Done\n", + "Merchdesc_dow amount variables over past 1 ---> Done\n", + "Merchdesc_dow_count_3 ---> Done\n", + "Merchdesc_dow amount variables over past 3 ---> Done\n", + "Merchdesc_dow_count_7 ---> Done\n", + "Merchdesc_dow amount variables over past 7 ---> Done\n", + "Merchdesc_dow_count_14 ---> Done\n", + "Merchdesc_dow amount variables over past 14 ---> Done\n", + "Merchdesc_dow_count_30 ---> Done\n", + "Merchdesc_dow amount variables over past 30 ---> Done\n", + "Merchdesc_dow_count_60 ---> Done\n", + "Merchdesc_dow amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 896\n", + "Card_Merchnum_desc Run time for the this entity ----------------- 1.7312168329999906s\n", + "\n", + "Card_Merchnum_desc_day_since ---> Done\n", + "# Day-since variables is 897\n", + "Card_Merchnum_desc_count_0 ---> Done\n", + "Card_Merchnum_desc amount variables over past 0 ---> Done\n", + "Card_Merchnum_desc_count_1 ---> Done\n", + "Card_Merchnum_desc amount variables over past 1 ---> Done\n", + "Card_Merchnum_desc_count_3 ---> Done\n", + "Card_Merchnum_desc amount variables over past 3 ---> Done\n", + "Card_Merchnum_desc_count_7 ---> Done\n", + "Card_Merchnum_desc amount variables over past 7 ---> Done\n", + "Card_Merchnum_desc_count_14 ---> Done\n", + "Card_Merchnum_desc amount variables over past 14 ---> Done\n", + "Card_Merchnum_desc_count_30 ---> Done\n", + "Card_Merchnum_desc amount variables over past 30 ---> Done\n", + "Card_Merchnum_desc_count_60 ---> Done\n", + "Card_Merchnum_desc amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 960\n", + "Card_Merchnum_Zip Run time for the this entity ----------------- 1.0461267080000027s\n", + "\n", + "Card_Merchnum_Zip_day_since ---> Done\n", + "# Day-since variables is 961\n", + "Card_Merchnum_Zip_count_0 ---> Done\n", + "Card_Merchnum_Zip amount variables over past 0 ---> Done\n", + "Card_Merchnum_Zip_count_1 ---> Done\n", + "Card_Merchnum_Zip amount variables over past 1 ---> Done\n", + "Card_Merchnum_Zip_count_3 ---> Done\n", + "Card_Merchnum_Zip amount variables over past 3 ---> Done\n", + "Card_Merchnum_Zip_count_7 ---> Done\n", + "Card_Merchnum_Zip amount variables over past 7 ---> Done\n", + "Card_Merchnum_Zip_count_14 ---> Done\n", + "Card_Merchnum_Zip amount variables over past 14 ---> Done\n", + "Card_Merchnum_Zip_count_30 ---> Done\n", + "Card_Merchnum_Zip amount variables over past 30 ---> Done\n", + "Card_Merchnum_Zip_count_60 ---> Done\n", + "Card_Merchnum_Zip amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 1024\n", + "Card_Merchdesc_Zip Run time for the this entity ----------------- 1.649755749999997s\n", + "\n", + "Card_Merchdesc_Zip_day_since ---> Done\n", + "# Day-since variables is 1025\n", + "Card_Merchdesc_Zip_count_0 ---> Done\n", + "Card_Merchdesc_Zip amount variables over past 0 ---> Done\n", + "Card_Merchdesc_Zip_count_1 ---> Done\n", + "Card_Merchdesc_Zip amount variables over past 1 ---> Done\n", + "Card_Merchdesc_Zip_count_3 ---> Done\n", + "Card_Merchdesc_Zip amount variables over past 3 ---> Done\n", + "Card_Merchdesc_Zip_count_7 ---> Done\n", + "Card_Merchdesc_Zip amount variables over past 7 ---> Done\n", + "Card_Merchdesc_Zip_count_14 ---> Done\n", + "Card_Merchdesc_Zip amount variables over past 14 ---> Done\n", + "Card_Merchdesc_Zip_count_30 ---> Done\n", + "Card_Merchdesc_Zip amount variables over past 30 ---> Done\n", + "Card_Merchdesc_Zip_count_60 ---> Done\n", + "Card_Merchdesc_Zip amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 1088\n", + "Merchnum_desc_State Run time for the this entity ----------------- 1.060368416000017s\n", + "\n", + "Merchnum_desc_State_day_since ---> Done\n", + "# Day-since variables is 1089\n", + "Merchnum_desc_State_count_0 ---> Done\n", + "Merchnum_desc_State amount variables over past 0 ---> Done\n", + "Merchnum_desc_State_count_1 ---> Done\n", + "Merchnum_desc_State amount variables over past 1 ---> Done\n", + "Merchnum_desc_State_count_3 ---> Done\n", + "Merchnum_desc_State amount variables over past 3 ---> Done\n", + "Merchnum_desc_State_count_7 ---> Done\n", + "Merchnum_desc_State amount variables over past 7 ---> Done\n", + "Merchnum_desc_State_count_14 ---> Done\n", + "Merchnum_desc_State amount variables over past 14 ---> Done\n", + "Merchnum_desc_State_count_30 ---> Done\n", + "Merchnum_desc_State amount variables over past 30 ---> Done\n", + "Merchnum_desc_State_count_60 ---> Done\n", + "Merchnum_desc_State amount variables over past 60 ---> Done\n", + "# Frequency & Amount variables is 1152\n", + "Total run time: 2.3256242618166665mins\n", + "CPU times: user 1min 52s, sys: 31.3 s, total: 2min 23s\n", + "Wall time: 2min 19s\n" + ] + } + ], + "source": [ + "%%time\n", + "start = timeit.default_timer()\n", + "for entity in entities: \n", + " try: print(entity,'Run time for the this entity ----------------- {}s'.format(timeit.default_timer() - st))\n", + " except: print('')\n", + " st = timeit.default_timer() \n", + "\n", + "# Day-since variables: \n", + " df_l = df1[['Recnum', 'Date', entity]]\n", + " df_r = df1[['check_record', 'check_date', entity, 'Amount']] \n", + " temp = pd.merge(df_l, df_r, left_on = entity, right_on = entity) \n", + " temp1 = temp[temp.Recnum > temp.check_record][['Recnum','Date','check_date']]\\\n", + " .groupby('Recnum')[['Date', 'check_date']].last()\n", + " mapper = (temp1.Date - temp1.check_date).dt.days\n", + " final[entity + '_day_since'] = final.Recnum.map(mapper)\n", + " final[entity + '_day_since'].fillna((final.Date - pd.to_datetime('2006-01-01')).dt.days, inplace = True)\n", + " print('\\n' + entity + '_day_since ---> Done') \n", + " daysince = len(final.columns) - numstart\n", + " print('# Day-since variables is ', daysince)\n", + " \n", + "# Frequency & Amount variables: \n", + "\n", + "## Velocity\n", + "\n", + " for time in [0,1,3,7,14,30,60]: \n", + " temp2 = temp[(temp.check_date >= (temp.Date - dt.timedelta(time))) &\\\n", + " (temp.Recnum >= temp.check_record)][['Recnum', entity, 'Amount']] \n", + " col_name = entity + '_count_' + str(time) \n", + " mapper2 = temp2.groupby('Recnum')[entity].count() \n", + " final[col_name] = final.Recnum.map(mapper2) \n", + " print(col_name + ' ---> Done') \n", + " final[entity + '_avg_' + str(time)] = final.Recnum.map(temp2.groupby('Recnum')['Amount'].mean())\n", + " final[entity + '_max_' + str(time)] = final.Recnum.map(temp2.groupby('Recnum')['Amount'].max())\n", + " final[entity + '_med_' + str(time)] = final.Recnum.map(temp2.groupby('Recnum')['Amount'].median())\n", + " final[entity + '_total_' + str(time)] = final.Recnum.map(temp2.groupby('Recnum')['Amount'].sum())\n", + " final[entity + '_actual/avg_' + str(time)] = final['Amount'] / final[entity + '_avg_' + str(time)]\n", + " final[entity + '_actual/max_' + str(time)] = final['Amount'] / final[entity + '_max_' + str(time)]\n", + " final[entity + '_actual/med_' + str(time)] = final['Amount'] / final[entity + '_med_' + str(time)]\n", + " final[entity + '_actual/toal_' + str(time)] = final['Amount'] / final[entity + '_total_' + str(time)] \n", + " print(entity + ' amount variables over past ' + str(time) + ' ---> Done')\n", + " print('# Frequency & Amount variables is ', len(final.columns) - numstart)\n", + "\n", + " del df_l\n", + " del df_r\n", + " del temp\n", + " del temp1\n", + " del temp2\n", + " del mapper2 \n", + "\n", + "print('Total run time: {}mins'.format((timeit.default_timer() - start)/60))" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(96397, 1184)\n", + "# new variables is 1152\n" + ] + } + ], + "source": [ + "print(final.shape)\n", + "print('# new variables is ',len(final.columns) - numstart)\n", + "numstart = len(final.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "run time: 0.22058900000001813s\n", + "CPU times: user 189 ms, sys: 32.6 ms, total: 222 ms\n", + "Wall time: 221 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "start = timeit.default_timer()\n", + "\n", + "## Relative Velocity\n", + "for ent in entities:\n", + " for d in ['0', '1']:\n", + " for dd in ['7', '14', '30', '60']:\n", + " final[ent + '_count_' + d + '_by_' + dd] =\\\n", + " final[ent + '_count_' + d]/(final[ent + '_count_' + dd])/float(dd)\n", + " final[ent + '_total_amount_'+d+'_by_' + dd]=\\\n", + " final[ent +'_total_'+d]/(final[ent+'_total_'+dd])/float(dd)\n", + " \n", + "print('run time: {}s'.format(timeit.default_timer() - start))" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(96397, 1472)" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(96397, 1472)\n", + "# new variables is 288\n" + ] + } + ], + "source": [ + "print(final.shape)\n", + "print('# new variables is ',len(final.columns) - numstart)\n", + "numstart = len(final.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "run time: 0.11195449999999596s\n" + ] + } + ], + "source": [ + "## Velocity-density\n", + "start = timeit.default_timer()\n", + "for ent in entities:\n", + " for d in ['0', '1']:\n", + " for dd in ['7', '14', '30', '60']:\n", + " final[ent + '_vdratio_' + d +'by' + dd] =\\\n", + " final[ent + '_count_' + d + '_by_' + dd]/(final[ent + '_day_since']+1)\n", + " \n", + "print('run time: {}s'.format(timeit.default_timer() - start))" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(96397, 1616)" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(96397, 1616)\n", + "# new variables is 144\n" + ] + } + ], + "source": [ + "print(final.shape)\n", + "print('# new variables is ',len(final.columns) - numstart)\n", + "numstart = len(final.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run time for entity Cardnum in field Merchnum ---> Done\n", + "Run time for entity Cardnum in field card_merch ---> Done\n", + "Run time for entity Cardnum in field card_zip ---> Done\n", + "Run time for entity Cardnum in field card_state ---> Done\n", + "Run time for entity Cardnum in field merch_zip ---> Done\n", + "Run time for entity Cardnum in field merch_state ---> Done\n", + "Run time for entity Cardnum in field state_des ---> Done\n", + "Run time for entity Cardnum in field card_zip3 ---> Done\n", + "Run time for entity Cardnum in field Card_Merchdesc ---> Done\n", + "Run time for entity Cardnum in field Card_dow ---> Done\n", + "Run time for entity Cardnum in field Merchnum_desc ---> Done\n", + "Run time for entity Cardnum in field Merchnum_dow ---> Done\n", + "Run time for entity Cardnum in field Merchdesc_dow ---> Done\n", + "Run time for entity Cardnum in field Card_Merchnum_desc ---> Done\n", + "Run time for entity Cardnum in field Card_Merchnum_Zip ---> Done\n", + "Run time for entity Cardnum in field Card_Merchdesc_Zip ---> Done\n", + "Run time for entity Cardnum in field Merchnum_desc_State ---> Done\n", + "Run time for entity Merchnum in field Cardnum ---> Done\n", + "Run time for entity Merchnum in field card_merch ---> Done\n", + "Run time for entity Merchnum in field card_zip ---> Done\n", + "Run time for entity Merchnum in field card_state ---> Done\n", + "Run time for entity Merchnum in field merch_zip ---> Done\n", + "Run time for entity Merchnum in field merch_state ---> Done\n", + "Run time for entity Merchnum in field state_des ---> Done\n", + "Run time for entity Merchnum in field card_zip3 ---> Done\n", + "Run time for entity Merchnum in field Card_Merchdesc ---> Done\n", + "Run time for entity Merchnum in field Card_dow ---> Done\n", + "Run time for entity Merchnum in field Merchnum_desc ---> Done\n", + "Run time for entity Merchnum in field Merchnum_dow ---> Done\n", + "Run time for entity Merchnum in field Merchdesc_dow ---> Done\n", + "Run time for entity Merchnum in field Card_Merchnum_desc ---> Done\n", + "Run time for entity Merchnum in field Card_Merchnum_Zip ---> Done\n", + "Run time for entity Merchnum in field Card_Merchdesc_Zip ---> Done\n", + "Run time for entity Merchnum in field Merchnum_desc_State ---> Done\n", + "Run time for entity card_merch in field Cardnum ---> Done\n", + "Run time for entity card_merch in field Merchnum ---> Done\n", + "Run time for entity card_merch in field card_zip ---> Done\n", + "Run time for entity card_merch in field card_state ---> Done\n", + "Run time for entity card_merch in field merch_zip ---> Done\n", + "Run time for entity card_merch in field merch_state ---> Done\n", + "Run time for entity card_merch in field state_des ---> Done\n", + "Run time for entity card_merch in field card_zip3 ---> Done\n", + "Run time for entity card_merch in field Card_Merchdesc ---> Done\n", + "Run time for entity card_merch in field Card_dow ---> Done\n", + "Run time for entity card_merch in field Merchnum_desc ---> Done\n", + "Run time for entity card_merch in field Merchnum_dow ---> Done\n", + "Run time for entity card_merch in field Merchdesc_dow ---> Done\n", + "Run time for entity card_merch in field Card_Merchnum_desc ---> Done\n", + "Run time for entity card_merch in field Card_Merchnum_Zip ---> Done\n", + "Run time for entity card_merch in field Card_Merchdesc_Zip ---> Done\n", + "Run time for entity card_merch in field Merchnum_desc_State ---> Done\n", + "Run time for entity card_zip in field Cardnum ---> Done\n", + "Run time for entity card_zip in field Merchnum ---> Done\n", + "Run time for entity card_zip in field card_merch ---> Done\n", + "Run time for entity card_zip in field card_state ---> Done\n", + "Run time for entity card_zip in field merch_zip ---> Done\n", + "Run time for entity card_zip in field merch_state ---> Done\n", + "Run time for entity card_zip in field state_des ---> Done\n", + "Run time for entity card_zip in field card_zip3 ---> Done\n", + "Run time for entity card_zip in field Card_Merchdesc ---> Done\n", + "Run time for entity card_zip in field Card_dow ---> Done\n", + "Run time for entity card_zip in field Merchnum_desc ---> Done\n", + "Run time for entity card_zip in field Merchnum_dow ---> Done\n", + "Run time for entity card_zip in field Merchdesc_dow ---> Done\n", + "Run time for entity card_zip in field Card_Merchnum_desc ---> Done\n", + "Run time for entity card_zip in field Card_Merchnum_Zip ---> Done\n", + "Run time for entity card_zip in field Card_Merchdesc_Zip ---> Done\n", + "Run time for entity card_zip in field Merchnum_desc_State ---> Done\n", + "Run time for entity card_state in field Cardnum ---> Done\n", + "Run time for entity card_state in field Merchnum ---> Done\n", + "Run time for entity card_state in field card_merch ---> Done\n", + "Run time for entity card_state in field card_zip ---> Done\n", + "Run time for entity card_state in field merch_zip ---> Done\n", + "Run time for entity card_state in field merch_state ---> Done\n", + "Run time for entity card_state in field state_des ---> Done\n", + "Run time for entity card_state in field card_zip3 ---> Done\n", + "Run time for entity card_state in field Card_Merchdesc ---> Done\n", + "Run time for entity card_state in field Card_dow ---> Done\n", + "Run time for entity card_state in field Merchnum_desc ---> Done\n", + "Run time for entity card_state in field Merchnum_dow ---> Done\n", + "Run time for entity card_state in field Merchdesc_dow ---> Done\n", + "Run time for entity card_state in field Card_Merchnum_desc ---> Done\n", + "Run time for entity card_state in field Card_Merchnum_Zip ---> Done\n", + "Run time for entity card_state in field Card_Merchdesc_Zip ---> Done\n", + "Run time for entity card_state in field Merchnum_desc_State ---> Done\n", + "Run time for entity merch_zip in field Cardnum ---> Done\n", + "Run time for entity merch_zip in field Merchnum ---> Done\n", + "Run time for entity merch_zip in field card_merch ---> Done\n", + "Run time for entity merch_zip in field card_zip ---> Done\n", + "Run time for entity merch_zip in field card_state ---> Done\n", + "Run time for entity merch_zip in field merch_state ---> Done\n", + "Run time for entity merch_zip in field state_des ---> Done\n", + "Run time for entity merch_zip in field card_zip3 ---> Done\n", + "Run time for entity merch_zip in field Card_Merchdesc ---> Done\n", + "Run time for entity merch_zip in field Card_dow ---> Done\n", + "Run time for entity merch_zip in field Merchnum_desc ---> Done\n", + "Run time for entity merch_zip in field Merchnum_dow ---> Done\n", + "Run time for entity merch_zip in field Merchdesc_dow ---> Done\n", + "Run time for entity merch_zip in field Card_Merchnum_desc ---> Done\n", + "Run time for entity merch_zip in field Card_Merchnum_Zip ---> Done\n", + "Run time for entity merch_zip in field Card_Merchdesc_Zip ---> Done\n", + "Run time for entity merch_zip in field Merchnum_desc_State ---> Done\n", + "Run time for entity merch_state in field Cardnum ---> Done\n", + "Run time for entity merch_state in field Merchnum ---> Done\n", + "Run time for entity merch_state in field card_merch ---> Done\n", + "Run time for entity merch_state in field card_zip ---> Done\n", + "Run time for entity merch_state in field card_state ---> Done\n", + "Run time for entity merch_state in field merch_zip ---> Done\n", + "Run time for entity merch_state in field state_des ---> Done\n", + "Run time for entity merch_state in field card_zip3 ---> Done\n", + "Run time for entity merch_state in field Card_Merchdesc ---> Done\n", + "Run time for entity merch_state in field Card_dow ---> Done\n", + "Run time for entity merch_state in field Merchnum_desc ---> Done\n", + "Run time for entity merch_state in field Merchnum_dow ---> Done\n", + "Run time for entity merch_state in field Merchdesc_dow ---> Done\n", + "Run time for entity merch_state in field Card_Merchnum_desc ---> Done\n", + "Run time for entity merch_state in field Card_Merchnum_Zip ---> Done\n", + "Run time for entity merch_state in field Card_Merchdesc_Zip ---> Done\n", + "Run time for entity merch_state in field Merchnum_desc_State ---> Done\n", + "Run time for entity state_des in field Cardnum ---> Done\n", + "Run time for entity state_des in field Merchnum ---> Done\n", + "Run time for entity state_des in field card_merch ---> Done\n", + "Run time for entity state_des in field card_zip ---> Done\n", + "Run time for entity state_des in field card_state ---> Done\n", + "Run time for entity state_des in field merch_zip ---> Done\n", + "Run time for entity state_des in field merch_state ---> Done\n", + "Run time for entity state_des in field card_zip3 ---> Done\n", + "Run time for entity state_des in field Card_Merchdesc ---> Done\n", + "Run time for entity state_des in field Card_dow ---> Done\n", + "Run time for entity state_des in field Merchnum_desc ---> Done\n", + "Run time for entity state_des in field Merchnum_dow ---> Done\n", + "Run time for entity state_des in field Merchdesc_dow ---> Done\n", + "Run time for entity state_des in field Card_Merchnum_desc ---> Done\n", + "Run time for entity state_des in field Card_Merchnum_Zip ---> Done\n", + "Run time for entity state_des in field Card_Merchdesc_Zip ---> Done\n", + "Run time for entity state_des in field Merchnum_desc_State ---> Done\n", + "Run time for entity card_zip3 in field Cardnum ---> Done\n", + "Run time for entity card_zip3 in field Merchnum ---> Done\n", + "Run time for entity card_zip3 in field card_merch ---> Done\n", + "Run time for entity card_zip3 in field card_zip ---> Done\n", + "Run time for entity card_zip3 in field card_state ---> Done\n", + "Run time for entity card_zip3 in field merch_zip ---> Done\n", + "Run time for entity card_zip3 in field merch_state ---> Done\n", + "Run time for entity card_zip3 in field state_des ---> Done\n", + "Run time for entity card_zip3 in field Card_Merchdesc ---> Done\n", + "Run time for entity card_zip3 in field Card_dow ---> Done\n", + "Run time for entity card_zip3 in field Merchnum_desc ---> Done\n", + "Run time for entity card_zip3 in field Merchnum_dow ---> Done\n", + "Run time for entity card_zip3 in field Merchdesc_dow ---> Done\n", + "Run time for entity card_zip3 in field Card_Merchnum_desc ---> Done\n", + "Run time for entity card_zip3 in field Card_Merchnum_Zip ---> Done\n", + "Run time for entity card_zip3 in field Card_Merchdesc_Zip ---> Done\n", + "Run time for entity card_zip3 in field Merchnum_desc_State ---> Done\n", + "Run time for entity Card_Merchdesc in field Cardnum ---> Done\n", + "Run time for entity Card_Merchdesc in field Merchnum ---> Done\n", + "Run time for entity Card_Merchdesc in field card_merch ---> Done\n", + "Run time for entity Card_Merchdesc in field card_zip ---> Done\n", + "Run time for entity Card_Merchdesc in field card_state ---> Done\n", + "Run time for entity Card_Merchdesc in field merch_zip ---> Done\n", + "Run time for entity Card_Merchdesc in field merch_state ---> Done\n", + "Run time for entity Card_Merchdesc in field state_des ---> Done\n", + "Run time for entity Card_Merchdesc in field card_zip3 ---> Done\n", + "Run time for entity Card_Merchdesc in field Card_dow ---> Done\n", + "Run time for entity Card_Merchdesc in field Merchnum_desc ---> Done\n", + "Run time for entity Card_Merchdesc in field Merchnum_dow ---> Done\n", + "Run time for entity Card_Merchdesc in field Merchdesc_dow ---> Done\n", + "Run time for entity Card_Merchdesc in field Card_Merchnum_desc ---> Done\n", + "Run time for entity Card_Merchdesc in field Card_Merchnum_Zip ---> Done\n", + "Run time for entity Card_Merchdesc in field Card_Merchdesc_Zip ---> Done\n", + "Run time for entity Card_Merchdesc in field Merchnum_desc_State ---> Done\n", + "Run time for entity Card_dow in field Cardnum ---> Done\n", + "Run time for entity Card_dow in field Merchnum ---> Done\n", + "Run time for entity Card_dow in field card_merch ---> Done\n", + "Run time for entity Card_dow in field card_zip ---> Done\n", + "Run time for entity Card_dow in field card_state ---> Done\n", + "Run time for entity Card_dow in field merch_zip ---> Done\n", + "Run time for entity Card_dow in field merch_state ---> Done\n", + "Run time for entity Card_dow in field state_des ---> Done\n", + "Run time for entity Card_dow in field card_zip3 ---> Done\n", + "Run time for entity Card_dow in field Card_Merchdesc ---> Done\n", + "Run time for entity Card_dow in field Merchnum_desc ---> Done\n", + "Run time for entity Card_dow in field Merchnum_dow ---> Done\n", + "Run time for entity Card_dow in field Merchdesc_dow ---> Done\n", + "Run time for entity Card_dow in field Card_Merchnum_desc ---> Done\n", + "Run time for entity Card_dow in field Card_Merchnum_Zip ---> Done\n", + "Run time for entity Card_dow in field Card_Merchdesc_Zip ---> Done\n", + "Run time for entity Card_dow in field Merchnum_desc_State ---> Done\n", + "Run time for entity Merchnum_desc in field Cardnum ---> Done\n", + "Run time for entity Merchnum_desc in field Merchnum ---> Done\n", + "Run time for entity Merchnum_desc in field card_merch ---> Done\n", + "Run time for entity Merchnum_desc in field card_zip ---> Done\n", + "Run time for entity Merchnum_desc in field card_state ---> Done\n", + "Run time for entity Merchnum_desc in field merch_zip ---> Done\n", + "Run time for entity Merchnum_desc in field merch_state ---> Done\n", + "Run time for entity Merchnum_desc in field state_des ---> Done\n", + "Run time for entity Merchnum_desc in field card_zip3 ---> Done\n", + "Run time for entity Merchnum_desc in field Card_Merchdesc ---> Done\n", + "Run time for entity Merchnum_desc in field Card_dow ---> Done\n", + "Run time for entity Merchnum_desc in field Merchnum_dow ---> Done\n", + "Run time for entity Merchnum_desc in field Merchdesc_dow ---> Done\n", + "Run time for entity Merchnum_desc in field Card_Merchnum_desc ---> Done\n", + "Run time for entity Merchnum_desc in field Card_Merchnum_Zip ---> Done\n", + "Run time for entity Merchnum_desc in field Card_Merchdesc_Zip ---> Done\n", + "Run time for entity Merchnum_desc in field Merchnum_desc_State ---> Done\n", + "Run time for entity Merchnum_dow in field Cardnum ---> Done\n", + "Run time for entity Merchnum_dow in field Merchnum ---> Done\n", + "Run time for entity Merchnum_dow in field card_merch ---> Done\n", + "Run time for entity Merchnum_dow in field card_zip ---> Done\n", + "Run time for entity Merchnum_dow in field card_state ---> Done\n", + "Run time for entity Merchnum_dow in field merch_zip ---> Done\n", + "Run time for entity Merchnum_dow in field merch_state ---> Done\n", + "Run time for entity Merchnum_dow in field state_des ---> Done\n", + "Run time for entity Merchnum_dow in field card_zip3 ---> Done\n", + "Run time for entity Merchnum_dow in field Card_Merchdesc ---> Done\n", + "Run time for entity Merchnum_dow in field Card_dow ---> Done\n", + "Run time for entity Merchnum_dow in field Merchnum_desc ---> Done\n", + "Run time for entity Merchnum_dow in field Merchdesc_dow ---> Done\n", + "Run time for entity Merchnum_dow in field Card_Merchnum_desc ---> Done\n", + "Run time for entity Merchnum_dow in field Card_Merchnum_Zip ---> Done\n", + "Run time for entity Merchnum_dow in field Card_Merchdesc_Zip ---> Done\n", + "Run time for entity Merchnum_dow in field Merchnum_desc_State ---> Done\n", + "Run time for entity Merchdesc_dow in field Cardnum ---> Done\n", + "Run time for entity Merchdesc_dow in field Merchnum ---> Done\n", + "Run time for entity Merchdesc_dow in field card_merch ---> Done\n", + "Run time for entity Merchdesc_dow in field card_zip ---> Done\n", + "Run time for entity Merchdesc_dow in field card_state ---> Done\n", + "Run time for entity Merchdesc_dow in field merch_zip ---> Done\n", + "Run time for entity Merchdesc_dow in field merch_state ---> Done\n", + "Run time for entity Merchdesc_dow in field state_des ---> Done\n", + "Run time for entity Merchdesc_dow in field card_zip3 ---> Done\n", + "Run time for entity Merchdesc_dow in field Card_Merchdesc ---> Done\n", + "Run time for entity Merchdesc_dow in field Card_dow ---> Done\n", + "Run time for entity Merchdesc_dow in field Merchnum_desc ---> Done\n", + "Run time for entity Merchdesc_dow in field Merchnum_dow ---> Done\n", + "Run time for entity Merchdesc_dow in field Card_Merchnum_desc ---> Done\n", + "Run time for entity Merchdesc_dow in field Card_Merchnum_Zip ---> Done\n", + "Run time for entity Merchdesc_dow in field Card_Merchdesc_Zip ---> Done\n", + "Run time for entity Merchdesc_dow in field Merchnum_desc_State ---> Done\n", + "Run time for entity Card_Merchnum_desc in field Cardnum ---> Done\n", + "Run time for entity Card_Merchnum_desc in field Merchnum ---> Done\n", + "Run time for entity Card_Merchnum_desc in field card_merch ---> Done\n", + "Run time for entity Card_Merchnum_desc in field card_zip ---> Done\n", + "Run time for entity Card_Merchnum_desc in field card_state ---> Done\n", + "Run time for entity Card_Merchnum_desc in field merch_zip ---> Done\n", + "Run time for entity Card_Merchnum_desc in field merch_state ---> Done\n", + "Run time for entity Card_Merchnum_desc in field state_des ---> Done\n", + "Run time for entity Card_Merchnum_desc in field card_zip3 ---> Done\n", + "Run time for entity Card_Merchnum_desc in field Card_Merchdesc ---> Done\n", + "Run time for entity Card_Merchnum_desc in field Card_dow ---> Done\n", + "Run time for entity Card_Merchnum_desc in field Merchnum_desc ---> Done\n", + "Run time for entity Card_Merchnum_desc in field Merchnum_dow ---> Done\n", + "Run time for entity Card_Merchnum_desc in field Merchdesc_dow ---> Done\n", + "Run time for entity Card_Merchnum_desc in field Card_Merchnum_Zip ---> Done\n", + "Run time for entity Card_Merchnum_desc in field Card_Merchdesc_Zip ---> Done\n", + "Run time for entity Card_Merchnum_desc in field Merchnum_desc_State ---> Done\n", + "Run time for entity Card_Merchnum_Zip in field Cardnum ---> Done\n", + "Run time for entity Card_Merchnum_Zip in field Merchnum ---> Done\n", + "Run time for entity Card_Merchnum_Zip in field card_merch ---> Done\n", + "Run time for entity Card_Merchnum_Zip in field card_zip ---> Done\n", + "Run time for entity Card_Merchnum_Zip in field card_state ---> Done\n", + "Run time for entity Card_Merchnum_Zip in field merch_zip ---> Done\n", + "Run time for entity Card_Merchnum_Zip in field merch_state ---> Done\n", + "Run time for entity Card_Merchnum_Zip in field state_des ---> Done\n", + "Run time for entity Card_Merchnum_Zip in field card_zip3 ---> Done\n", + "Run time for entity Card_Merchnum_Zip in field Card_Merchdesc ---> Done\n", + "Run time for entity Card_Merchnum_Zip in field Card_dow ---> Done\n", + "Run time for entity Card_Merchnum_Zip in field Merchnum_desc ---> Done\n", + "Run time for entity Card_Merchnum_Zip in field Merchnum_dow ---> Done\n", + "Run time for entity Card_Merchnum_Zip in field Merchdesc_dow ---> Done\n", + "Run time for entity Card_Merchnum_Zip in field Card_Merchnum_desc ---> Done\n", + "Run time for entity Card_Merchnum_Zip in field Card_Merchdesc_Zip ---> Done\n", + "Run time for entity Card_Merchnum_Zip in field Merchnum_desc_State ---> Done\n", + "Run time for entity Card_Merchdesc_Zip in field Cardnum ---> Done\n", + "Run time for entity Card_Merchdesc_Zip in field Merchnum ---> Done\n", + "Run time for entity Card_Merchdesc_Zip in field card_merch ---> Done\n", + "Run time for entity Card_Merchdesc_Zip in field card_zip ---> Done\n", + "Run time for entity Card_Merchdesc_Zip in field card_state ---> Done\n", + "Run time for entity Card_Merchdesc_Zip in field merch_zip ---> Done\n", + "Run time for entity Card_Merchdesc_Zip in field merch_state ---> Done\n", + "Run time for entity Card_Merchdesc_Zip in field state_des ---> Done\n", + "Run time for entity Card_Merchdesc_Zip in field card_zip3 ---> Done\n", + "Run time for entity Card_Merchdesc_Zip in field Card_Merchdesc ---> Done\n", + "Run time for entity Card_Merchdesc_Zip in field Card_dow ---> Done\n", + "Run time for entity Card_Merchdesc_Zip in field Merchnum_desc ---> Done\n", + "Run time for entity Card_Merchdesc_Zip in field Merchnum_dow ---> Done\n", + "Run time for entity Card_Merchdesc_Zip in field Merchdesc_dow ---> Done\n", + "Run time for entity Card_Merchdesc_Zip in field Card_Merchnum_desc ---> Done\n", + "Run time for entity Card_Merchdesc_Zip in field Card_Merchnum_Zip ---> Done\n", + "Run time for entity Card_Merchdesc_Zip in field Merchnum_desc_State ---> Done\n", + "Run time for entity Merchnum_desc_State in field Cardnum ---> Done\n", + "Run time for entity Merchnum_desc_State in field Merchnum ---> Done\n", + "Run time for entity Merchnum_desc_State in field card_merch ---> Done\n", + "Run time for entity Merchnum_desc_State in field card_zip ---> Done\n", + "Run time for entity Merchnum_desc_State in field card_state ---> Done\n", + "Run time for entity Merchnum_desc_State in field merch_zip ---> Done\n", + "Run time for entity Merchnum_desc_State in field merch_state ---> Done\n", + "Run time for entity Merchnum_desc_State in field state_des ---> Done\n", + "Run time for entity Merchnum_desc_State in field card_zip3 ---> Done\n", + "Run time for entity Merchnum_desc_State in field Card_Merchdesc ---> Done\n", + "Run time for entity Merchnum_desc_State in field Card_dow ---> Done\n", + "Run time for entity Merchnum_desc_State in field Merchnum_desc ---> Done\n", + "Run time for entity Merchnum_desc_State in field Merchnum_dow ---> Done\n", + "Run time for entity Merchnum_desc_State in field Merchdesc_dow ---> Done\n", + "Run time for entity Merchnum_desc_State in field Card_Merchnum_desc ---> Done\n", + "Run time for entity Merchnum_desc_State in field Card_Merchnum_Zip ---> Done\n", + "Run time for entity Merchnum_desc_State in field Card_Merchdesc_Zip ---> Done\n", + "Total run time: 0.29769826318333326mins\n" + ] + } + ], + "source": [ + "start = timeit.default_timer()\n", + "# # Cross entity uniqueness variables\n", + "for entity in entities: \n", + " for field in entities:\n", + " st = timeit.default_timer()\n", + " if entity != field:\n", + " new_attributes = f'{entity}_{field}_nunique'\n", + " if new_attributes not in list(final.columns):\n", + " mapper3 = final.groupby(entity)[field].nunique()\n", + " final[new_attributes] = final[entity].map(mapper3)\n", + " print(f'Run time for entity {entity} in field {field}'+ ' ---> Done')\n", + "print('Total run time: {}mins'.format((timeit.default_timer() - start)/60))" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(96397, 1922)" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(96397, 1922)\n", + "# new variables is 306\n" + ] + } + ], + "source": [ + "print(final.shape)\n", + "print('# new variables is ',len(final.columns) - numstart)\n", + "numstart = len(final.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "# %%time\n", + "# print(final.shape)\n", + "# final = final.T.drop_duplicates().T\n", + "# final.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [], + "source": [ + "# df2 = data.copy()\n", + "# df2['check_date'] = df2.Date\n", + "# df2['check_recnum'] = df2.Recnum\n", + "# df_2 = df2[['Recnum', 'Date', 'Amount', 'Cardnum', 'Merchnum']]\n", + "# df_s = df2[['check_recnum', 'check_date', 'Amount', 'Cardnum', 'Merchnum']]\n", + "# temp2 = pd.merge(df_2, df_s, left_on = 'Cardnum', right_on = 'Cardnum')\n", + "\n", + "# #Frequency Mappers\n", + "# # groupers = ['Cardnum', 'Merchnum']\n", + "# groupers = ['Cardnum']\n", + "# for grouper in groupers: \n", + "# for d in [0,1]:\n", + "# for dd in [3,7,14,30]:\n", + "# numerator_df = temp2[(temp2.check_date >= (temp2.Date - dt.timedelta(d)))\n", + "# & (temp2.Recnum >= temp2.check_recnum)]\n", + "# denominator_df = temp2[(temp2.check_date >= (temp2.Date - dt.timedelta(dd)))\n", + "# & (temp2.Recnum >= temp2.check_recnum)]\n", + "\n", + "# numerator = numerator_df.groupby(grouper)['Recnum'].count()\n", + "# denominator = denominator_df.groupby(grouper)['Recnum'].count()/dd\n", + "\n", + "# colname = 'relative_velocity_count_by_' + grouper + '_' + str(d) + '_days_over_' + str(dd)\n", + "\n", + "# final[colname] = final[grouper].map(numerator)/final[grouper].map(denominator)" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(96397, 1922)\n", + "# new variables is 0\n" + ] + } + ], + "source": [ + "print(final.shape)\n", + "print('# new variables is ',len(final.columns) - numstart)\n", + "numstart = len(final.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run time for the last entity ----------------- 0.02721662499999411s\n", + "Cardnum_variability_avg_0 ---> Done\n", + "Cardnum_variability_max_0 ---> Done\n", + "Cardnum_variability_med_0 ---> Done\n", + "Cardnum amount variables over past 0 ---> Done\n", + "Cardnum_variability_avg_1 ---> Done\n", + "Cardnum_variability_max_1 ---> Done\n", + "Cardnum_variability_med_1 ---> Done\n", + "Cardnum amount variables over past 1 ---> Done\n", + "Cardnum_variability_avg_3 ---> Done\n", + "Cardnum_variability_max_3 ---> Done\n", + "Cardnum_variability_med_3 ---> Done\n", + "Cardnum amount variables over past 3 ---> Done\n", + "Cardnum_variability_avg_7 ---> Done\n", + "Cardnum_variability_max_7 ---> Done\n", + "Cardnum_variability_med_7 ---> Done\n", + "Cardnum amount variables over past 7 ---> Done\n", + "Cardnum_variability_avg_14 ---> Done\n", + "Cardnum_variability_max_14 ---> Done\n", + "Cardnum_variability_med_14 ---> Done\n", + "Cardnum amount variables over past 14 ---> Done\n", + "Cardnum_variability_avg_30 ---> Done\n", + "Cardnum_variability_max_30 ---> Done\n", + "Cardnum_variability_med_30 ---> Done\n", + "Cardnum amount variables over past 30 ---> Done\n", + "Run time for the last entity ----------------- 2.5443698329999904s\n", + "Merchnum_variability_avg_0 ---> Done\n", + "Merchnum_variability_max_0 ---> Done\n", + "Merchnum_variability_med_0 ---> Done\n", + "Merchnum amount variables over past 0 ---> Done\n", + "Merchnum_variability_avg_1 ---> Done\n", + "Merchnum_variability_max_1 ---> Done\n", + "Merchnum_variability_med_1 ---> Done\n", + "Merchnum amount variables over past 1 ---> Done\n", + "Merchnum_variability_avg_3 ---> Done\n", + "Merchnum_variability_max_3 ---> Done\n", + "Merchnum_variability_med_3 ---> Done\n", + "Merchnum amount variables over past 3 ---> Done\n", + "Merchnum_variability_avg_7 ---> Done\n", + "Merchnum_variability_max_7 ---> Done\n", + "Merchnum_variability_med_7 ---> Done\n", + "Merchnum amount variables over past 7 ---> Done\n", + "Merchnum_variability_avg_14 ---> Done\n", + "Merchnum_variability_max_14 ---> Done\n", + "Merchnum_variability_med_14 ---> Done\n", + "Merchnum amount variables over past 14 ---> Done\n", + "Merchnum_variability_avg_30 ---> Done\n", + "Merchnum_variability_max_30 ---> Done\n", + "Merchnum_variability_med_30 ---> Done\n", + "Merchnum amount variables over past 30 ---> Done\n", + "Run time for the last entity ----------------- 24.904272332999994s\n", + "card_merch_variability_avg_0 ---> Done\n", + "card_merch_variability_max_0 ---> Done\n", + "card_merch_variability_med_0 ---> Done\n", + "card_merch amount variables over past 0 ---> Done\n", + "card_merch_variability_avg_1 ---> Done\n", + "card_merch_variability_max_1 ---> Done\n", + "card_merch_variability_med_1 ---> Done\n", + "card_merch amount variables over past 1 ---> Done\n", + "card_merch_variability_avg_3 ---> Done\n", + "card_merch_variability_max_3 ---> Done\n", + "card_merch_variability_med_3 ---> Done\n", + "card_merch amount variables over past 3 ---> Done\n", + "card_merch_variability_avg_7 ---> Done\n", + "card_merch_variability_max_7 ---> Done\n", + "card_merch_variability_med_7 ---> Done\n", + "card_merch amount variables over past 7 ---> Done\n", + "card_merch_variability_avg_14 ---> Done\n", + "card_merch_variability_max_14 ---> Done\n", + "card_merch_variability_med_14 ---> Done\n", + "card_merch amount variables over past 14 ---> Done\n", + "card_merch_variability_avg_30 ---> Done\n", + "card_merch_variability_max_30 ---> Done\n", + "card_merch_variability_med_30 ---> Done\n", + "card_merch amount variables over past 30 ---> Done\n", + "Run time for the last entity ----------------- 1.3704199990000063s\n", + "card_zip_variability_avg_0 ---> Done\n", + "card_zip_variability_max_0 ---> Done\n", + "card_zip_variability_med_0 ---> Done\n", + "card_zip amount variables over past 0 ---> Done\n", + "card_zip_variability_avg_1 ---> Done\n", + "card_zip_variability_max_1 ---> Done\n", + "card_zip_variability_med_1 ---> Done\n", + "card_zip amount variables over past 1 ---> Done\n", + "card_zip_variability_avg_3 ---> Done\n", + "card_zip_variability_max_3 ---> Done\n", + "card_zip_variability_med_3 ---> Done\n", + "card_zip amount variables over past 3 ---> Done\n", + "card_zip_variability_avg_7 ---> Done\n", + "card_zip_variability_max_7 ---> Done\n", + "card_zip_variability_med_7 ---> Done\n", + "card_zip amount variables over past 7 ---> Done\n", + "card_zip_variability_avg_14 ---> Done\n", + "card_zip_variability_max_14 ---> Done\n", + "card_zip_variability_med_14 ---> Done\n", + "card_zip amount variables over past 14 ---> Done\n", + "card_zip_variability_avg_30 ---> Done\n", + "card_zip_variability_max_30 ---> Done\n", + "card_zip_variability_med_30 ---> Done\n", + "card_zip amount variables over past 30 ---> Done\n", + "Run time for the last entity ----------------- 1.0757113749999974s\n", + "card_state_variability_avg_0 ---> Done\n", + "card_state_variability_max_0 ---> Done\n", + "card_state_variability_med_0 ---> Done\n", + "card_state amount variables over past 0 ---> Done\n", + "card_state_variability_avg_1 ---> Done\n", + "card_state_variability_max_1 ---> Done\n", + "card_state_variability_med_1 ---> Done\n", + "card_state amount variables over past 1 ---> Done\n", + "card_state_variability_avg_3 ---> Done\n", + "card_state_variability_max_3 ---> Done\n", + "card_state_variability_med_3 ---> Done\n", + "card_state amount variables over past 3 ---> Done\n", + "card_state_variability_avg_7 ---> Done\n", + "card_state_variability_max_7 ---> Done\n", + "card_state_variability_med_7 ---> Done\n", + "card_state amount variables over past 7 ---> Done\n", + "card_state_variability_avg_14 ---> Done\n", + "card_state_variability_max_14 ---> Done\n", + "card_state_variability_med_14 ---> Done\n", + "card_state amount variables over past 14 ---> Done\n", + "card_state_variability_avg_30 ---> Done\n", + "card_state_variability_max_30 ---> Done\n", + "card_state_variability_med_30 ---> Done\n", + "card_state amount variables over past 30 ---> Done\n", + "Run time for the last entity ----------------- 1.2538705419999872s\n", + "merch_zip_variability_avg_0 ---> Done\n", + "merch_zip_variability_max_0 ---> Done\n", + "merch_zip_variability_med_0 ---> Done\n", + "merch_zip amount variables over past 0 ---> Done\n", + "merch_zip_variability_avg_1 ---> Done\n", + "merch_zip_variability_max_1 ---> Done\n", + "merch_zip_variability_med_1 ---> Done\n", + "merch_zip amount variables over past 1 ---> Done\n", + "merch_zip_variability_avg_3 ---> Done\n", + "merch_zip_variability_max_3 ---> Done\n", + "merch_zip_variability_med_3 ---> Done\n", + "merch_zip amount variables over past 3 ---> Done\n", + "merch_zip_variability_avg_7 ---> Done\n", + "merch_zip_variability_max_7 ---> Done\n", + "merch_zip_variability_med_7 ---> Done\n", + "merch_zip amount variables over past 7 ---> Done\n", + "merch_zip_variability_avg_14 ---> Done\n", + "merch_zip_variability_max_14 ---> Done\n", + "merch_zip_variability_med_14 ---> Done\n", + "merch_zip amount variables over past 14 ---> Done\n", + "merch_zip_variability_avg_30 ---> Done\n", + "merch_zip_variability_max_30 ---> Done\n", + "merch_zip_variability_med_30 ---> Done\n", + "merch_zip amount variables over past 30 ---> Done\n", + "Run time for the last entity ----------------- 24.96129054100001s\n", + "merch_state_variability_avg_0 ---> Done\n", + "merch_state_variability_max_0 ---> Done\n", + "merch_state_variability_med_0 ---> Done\n", + "merch_state amount variables over past 0 ---> Done\n", + "merch_state_variability_avg_1 ---> Done\n", + "merch_state_variability_max_1 ---> Done\n", + "merch_state_variability_med_1 ---> Done\n", + "merch_state amount variables over past 1 ---> Done\n", + "merch_state_variability_avg_3 ---> Done\n", + "merch_state_variability_max_3 ---> Done\n", + "merch_state_variability_med_3 ---> Done\n", + "merch_state amount variables over past 3 ---> Done\n", + "merch_state_variability_avg_7 ---> Done\n", + "merch_state_variability_max_7 ---> Done\n", + "merch_state_variability_med_7 ---> Done\n", + "merch_state amount variables over past 7 ---> Done\n", + "merch_state_variability_avg_14 ---> Done\n", + "merch_state_variability_max_14 ---> Done\n", + "merch_state_variability_med_14 ---> Done\n", + "merch_state amount variables over past 14 ---> Done\n", + "merch_state_variability_avg_30 ---> Done\n", + "merch_state_variability_max_30 ---> Done\n", + "merch_state_variability_med_30 ---> Done\n", + "merch_state amount variables over past 30 ---> Done\n", + "Run time for the last entity ----------------- 24.878636416999996s\n", + "state_des_variability_avg_0 ---> Done\n", + "state_des_variability_max_0 ---> Done\n", + "state_des_variability_med_0 ---> Done\n", + "state_des amount variables over past 0 ---> Done\n", + "state_des_variability_avg_1 ---> Done\n", + "state_des_variability_max_1 ---> Done\n", + "state_des_variability_med_1 ---> Done\n", + "state_des amount variables over past 1 ---> Done\n", + "state_des_variability_avg_3 ---> Done\n", + "state_des_variability_max_3 ---> Done\n", + "state_des_variability_med_3 ---> Done\n", + "state_des amount variables over past 3 ---> Done\n", + "state_des_variability_avg_7 ---> Done\n", + "state_des_variability_max_7 ---> Done\n", + "state_des_variability_med_7 ---> Done\n", + "state_des amount variables over past 7 ---> Done\n", + "state_des_variability_avg_14 ---> Done\n", + "state_des_variability_max_14 ---> Done\n", + "state_des_variability_med_14 ---> Done\n", + "state_des amount variables over past 14 ---> Done\n", + "state_des_variability_avg_30 ---> Done\n", + "state_des_variability_max_30 ---> Done\n", + "state_des_variability_med_30 ---> Done\n", + "state_des amount variables over past 30 ---> Done\n", + "Run time for the last entity ----------------- 2.8472433749999766s\n", + "card_zip3_variability_avg_0 ---> Done\n", + "card_zip3_variability_max_0 ---> Done\n", + "card_zip3_variability_med_0 ---> Done\n", + "card_zip3 amount variables over past 0 ---> Done\n", + "card_zip3_variability_avg_1 ---> Done\n", + "card_zip3_variability_max_1 ---> Done\n", + "card_zip3_variability_med_1 ---> Done\n", + "card_zip3 amount variables over past 1 ---> Done\n", + "card_zip3_variability_avg_3 ---> Done\n", + "card_zip3_variability_max_3 ---> Done\n", + "card_zip3_variability_med_3 ---> Done\n", + "card_zip3 amount variables over past 3 ---> Done\n", + "card_zip3_variability_avg_7 ---> Done\n", + "card_zip3_variability_max_7 ---> Done\n", + "card_zip3_variability_med_7 ---> Done\n", + "card_zip3 amount variables over past 7 ---> Done\n", + "card_zip3_variability_avg_14 ---> Done\n", + "card_zip3_variability_max_14 ---> Done\n", + "card_zip3_variability_med_14 ---> Done\n", + "card_zip3 amount variables over past 14 ---> Done\n", + "card_zip3_variability_avg_30 ---> Done\n", + "card_zip3_variability_max_30 ---> Done\n", + "card_zip3_variability_med_30 ---> Done\n", + "card_zip3 amount variables over past 30 ---> Done\n", + "Run time for the last entity ----------------- 1.1229678340000078s\n", + "Card_Merchdesc_variability_avg_0 ---> Done\n", + "Card_Merchdesc_variability_max_0 ---> Done\n", + "Card_Merchdesc_variability_med_0 ---> Done\n", + "Card_Merchdesc amount variables over past 0 ---> Done\n", + "Card_Merchdesc_variability_avg_1 ---> Done\n", + "Card_Merchdesc_variability_max_1 ---> Done\n", + "Card_Merchdesc_variability_med_1 ---> Done\n", + "Card_Merchdesc amount variables over past 1 ---> Done\n", + "Card_Merchdesc_variability_avg_3 ---> Done\n", + "Card_Merchdesc_variability_max_3 ---> Done\n", + "Card_Merchdesc_variability_med_3 ---> Done\n", + "Card_Merchdesc amount variables over past 3 ---> Done\n", + "Card_Merchdesc_variability_avg_7 ---> Done\n", + "Card_Merchdesc_variability_max_7 ---> Done\n", + "Card_Merchdesc_variability_med_7 ---> Done\n", + "Card_Merchdesc amount variables over past 7 ---> Done\n", + "Card_Merchdesc_variability_avg_14 ---> Done\n", + "Card_Merchdesc_variability_max_14 ---> Done\n", + "Card_Merchdesc_variability_med_14 ---> Done\n", + "Card_Merchdesc amount variables over past 14 ---> Done\n", + "Card_Merchdesc_variability_avg_30 ---> Done\n", + "Card_Merchdesc_variability_max_30 ---> Done\n", + "Card_Merchdesc_variability_med_30 ---> Done\n", + "Card_Merchdesc amount variables over past 30 ---> Done\n", + "Run time for the last entity ----------------- 0.5881499999999846s\n", + "Card_dow_variability_avg_0 ---> Done\n", + "Card_dow_variability_max_0 ---> Done\n", + "Card_dow_variability_med_0 ---> Done\n", + "Card_dow amount variables over past 0 ---> Done\n", + "Card_dow_variability_avg_1 ---> Done\n", + "Card_dow_variability_max_1 ---> Done\n", + "Card_dow_variability_med_1 ---> Done\n", + "Card_dow amount variables over past 1 ---> Done\n", + "Card_dow_variability_avg_3 ---> Done\n", + "Card_dow_variability_max_3 ---> Done\n", + "Card_dow_variability_med_3 ---> Done\n", + "Card_dow amount variables over past 3 ---> Done\n", + "Card_dow_variability_avg_7 ---> Done\n", + "Card_dow_variability_max_7 ---> Done\n", + "Card_dow_variability_med_7 ---> Done\n", + "Card_dow amount variables over past 7 ---> Done\n", + "Card_dow_variability_avg_14 ---> Done\n", + "Card_dow_variability_max_14 ---> Done\n", + "Card_dow_variability_med_14 ---> Done\n", + "Card_dow amount variables over past 14 ---> Done\n", + "Card_dow_variability_avg_30 ---> Done\n", + "Card_dow_variability_max_30 ---> Done\n", + "Card_dow_variability_med_30 ---> Done\n", + "Card_dow amount variables over past 30 ---> Done\n", + "Run time for the last entity ----------------- 0.8715843749999976s\n", + "Merchnum_desc_variability_avg_0 ---> Done\n", + "Merchnum_desc_variability_max_0 ---> Done\n", + "Merchnum_desc_variability_med_0 ---> Done\n", + "Merchnum_desc amount variables over past 0 ---> Done\n", + "Merchnum_desc_variability_avg_1 ---> Done\n", + "Merchnum_desc_variability_max_1 ---> Done\n", + "Merchnum_desc_variability_med_1 ---> Done\n", + "Merchnum_desc amount variables over past 1 ---> Done\n", + "Merchnum_desc_variability_avg_3 ---> Done\n", + "Merchnum_desc_variability_max_3 ---> Done\n", + "Merchnum_desc_variability_med_3 ---> Done\n", + "Merchnum_desc amount variables over past 3 ---> Done\n", + "Merchnum_desc_variability_avg_7 ---> Done\n", + "Merchnum_desc_variability_max_7 ---> Done\n", + "Merchnum_desc_variability_med_7 ---> Done\n", + "Merchnum_desc amount variables over past 7 ---> Done\n", + "Merchnum_desc_variability_avg_14 ---> Done\n", + "Merchnum_desc_variability_max_14 ---> Done\n", + "Merchnum_desc_variability_med_14 ---> Done\n", + "Merchnum_desc amount variables over past 14 ---> Done\n", + "Merchnum_desc_variability_avg_30 ---> Done\n", + "Merchnum_desc_variability_max_30 ---> Done\n", + "Merchnum_desc_variability_med_30 ---> Done\n", + "Merchnum_desc amount variables over past 30 ---> Done\n", + "Run time for the last entity ----------------- 2.4318592500000022s\n", + "Merchnum_dow_variability_avg_0 ---> Done\n", + "Merchnum_dow_variability_max_0 ---> Done\n", + "Merchnum_dow_variability_med_0 ---> Done\n", + "Merchnum_dow amount variables over past 0 ---> Done\n", + "Merchnum_dow_variability_avg_1 ---> Done\n", + "Merchnum_dow_variability_max_1 ---> Done\n", + "Merchnum_dow_variability_med_1 ---> Done\n", + "Merchnum_dow amount variables over past 1 ---> Done\n", + "Merchnum_dow_variability_avg_3 ---> Done\n", + "Merchnum_dow_variability_max_3 ---> Done\n", + "Merchnum_dow_variability_med_3 ---> Done\n", + "Merchnum_dow amount variables over past 3 ---> Done\n", + "Merchnum_dow_variability_avg_7 ---> Done\n", + "Merchnum_dow_variability_max_7 ---> Done\n", + "Merchnum_dow_variability_med_7 ---> Done\n", + "Merchnum_dow amount variables over past 7 ---> Done\n", + "Merchnum_dow_variability_avg_14 ---> Done\n", + "Merchnum_dow_variability_max_14 ---> Done\n", + "Merchnum_dow_variability_med_14 ---> Done\n", + "Merchnum_dow amount variables over past 14 ---> Done\n", + "Merchnum_dow_variability_avg_30 ---> Done\n", + "Merchnum_dow_variability_max_30 ---> Done\n", + "Merchnum_dow_variability_med_30 ---> Done\n", + "Merchnum_dow amount variables over past 30 ---> Done\n", + "Run time for the last entity ----------------- 3.050272791000026s\n", + "Merchdesc_dow_variability_avg_0 ---> Done\n", + "Merchdesc_dow_variability_max_0 ---> Done\n", + "Merchdesc_dow_variability_med_0 ---> Done\n", + "Merchdesc_dow amount variables over past 0 ---> Done\n", + "Merchdesc_dow_variability_avg_1 ---> Done\n", + "Merchdesc_dow_variability_max_1 ---> Done\n", + "Merchdesc_dow_variability_med_1 ---> Done\n", + "Merchdesc_dow amount variables over past 1 ---> Done\n", + "Merchdesc_dow_variability_avg_3 ---> Done\n", + "Merchdesc_dow_variability_max_3 ---> Done\n", + "Merchdesc_dow_variability_med_3 ---> Done\n", + "Merchdesc_dow amount variables over past 3 ---> Done\n", + "Merchdesc_dow_variability_avg_7 ---> Done\n", + "Merchdesc_dow_variability_max_7 ---> Done\n", + "Merchdesc_dow_variability_med_7 ---> Done\n", + "Merchdesc_dow amount variables over past 7 ---> Done\n", + "Merchdesc_dow_variability_avg_14 ---> Done\n", + "Merchdesc_dow_variability_max_14 ---> Done\n", + "Merchdesc_dow_variability_med_14 ---> Done\n", + "Merchdesc_dow amount variables over past 14 ---> Done\n", + "Merchdesc_dow_variability_avg_30 ---> Done\n", + "Merchdesc_dow_variability_max_30 ---> Done\n", + "Merchdesc_dow_variability_med_30 ---> Done\n", + "Merchdesc_dow amount variables over past 30 ---> Done\n", + "Run time for the last entity ----------------- 0.9844405409999695s\n", + "Card_Merchnum_desc_variability_avg_0 ---> Done\n", + "Card_Merchnum_desc_variability_max_0 ---> Done\n", + "Card_Merchnum_desc_variability_med_0 ---> Done\n", + "Card_Merchnum_desc amount variables over past 0 ---> Done\n", + "Card_Merchnum_desc_variability_avg_1 ---> Done\n", + "Card_Merchnum_desc_variability_max_1 ---> Done\n", + "Card_Merchnum_desc_variability_med_1 ---> Done\n", + "Card_Merchnum_desc amount variables over past 1 ---> Done\n", + "Card_Merchnum_desc_variability_avg_3 ---> Done\n", + "Card_Merchnum_desc_variability_max_3 ---> Done\n", + "Card_Merchnum_desc_variability_med_3 ---> Done\n", + "Card_Merchnum_desc amount variables over past 3 ---> Done\n", + "Card_Merchnum_desc_variability_avg_7 ---> Done\n", + "Card_Merchnum_desc_variability_max_7 ---> Done\n", + "Card_Merchnum_desc_variability_med_7 ---> Done\n", + "Card_Merchnum_desc amount variables over past 7 ---> Done\n", + "Card_Merchnum_desc_variability_avg_14 ---> Done\n", + "Card_Merchnum_desc_variability_max_14 ---> Done\n", + "Card_Merchnum_desc_variability_med_14 ---> Done\n", + "Card_Merchnum_desc amount variables over past 14 ---> Done\n", + "Card_Merchnum_desc_variability_avg_30 ---> Done\n", + "Card_Merchnum_desc_variability_max_30 ---> Done\n", + "Card_Merchnum_desc_variability_med_30 ---> Done\n", + "Card_Merchnum_desc amount variables over past 30 ---> Done\n", + "Run time for the last entity ----------------- 0.5769458340000142s\n", + "Card_Merchnum_Zip_variability_avg_0 ---> Done\n", + "Card_Merchnum_Zip_variability_max_0 ---> Done\n", + "Card_Merchnum_Zip_variability_med_0 ---> Done\n", + "Card_Merchnum_Zip amount variables over past 0 ---> Done\n", + "Card_Merchnum_Zip_variability_avg_1 ---> Done\n", + "Card_Merchnum_Zip_variability_max_1 ---> Done\n", + "Card_Merchnum_Zip_variability_med_1 ---> Done\n", + "Card_Merchnum_Zip amount variables over past 1 ---> Done\n", + "Card_Merchnum_Zip_variability_avg_3 ---> Done\n", + "Card_Merchnum_Zip_variability_max_3 ---> Done\n", + "Card_Merchnum_Zip_variability_med_3 ---> Done\n", + "Card_Merchnum_Zip amount variables over past 3 ---> Done\n", + "Card_Merchnum_Zip_variability_avg_7 ---> Done\n", + "Card_Merchnum_Zip_variability_max_7 ---> Done\n", + "Card_Merchnum_Zip_variability_med_7 ---> Done\n", + "Card_Merchnum_Zip amount variables over past 7 ---> Done\n", + "Card_Merchnum_Zip_variability_avg_14 ---> Done\n", + "Card_Merchnum_Zip_variability_max_14 ---> Done\n", + "Card_Merchnum_Zip_variability_med_14 ---> Done\n", + "Card_Merchnum_Zip amount variables over past 14 ---> Done\n", + "Card_Merchnum_Zip_variability_avg_30 ---> Done\n", + "Card_Merchnum_Zip_variability_max_30 ---> Done\n", + "Card_Merchnum_Zip_variability_med_30 ---> Done\n", + "Card_Merchnum_Zip amount variables over past 30 ---> Done\n", + "Run time for the last entity ----------------- 0.9053837090000343s\n", + "Card_Merchdesc_Zip_variability_avg_0 ---> Done\n", + "Card_Merchdesc_Zip_variability_max_0 ---> Done\n", + "Card_Merchdesc_Zip_variability_med_0 ---> Done\n", + "Card_Merchdesc_Zip amount variables over past 0 ---> Done\n", + "Card_Merchdesc_Zip_variability_avg_1 ---> Done\n", + "Card_Merchdesc_Zip_variability_max_1 ---> Done\n", + "Card_Merchdesc_Zip_variability_med_1 ---> Done\n", + "Card_Merchdesc_Zip amount variables over past 1 ---> Done\n", + "Card_Merchdesc_Zip_variability_avg_3 ---> Done\n", + "Card_Merchdesc_Zip_variability_max_3 ---> Done\n", + "Card_Merchdesc_Zip_variability_med_3 ---> Done\n", + "Card_Merchdesc_Zip amount variables over past 3 ---> Done\n", + "Card_Merchdesc_Zip_variability_avg_7 ---> Done\n", + "Card_Merchdesc_Zip_variability_max_7 ---> Done\n", + "Card_Merchdesc_Zip_variability_med_7 ---> Done\n", + "Card_Merchdesc_Zip amount variables over past 7 ---> Done\n", + "Card_Merchdesc_Zip_variability_avg_14 ---> Done\n", + "Card_Merchdesc_Zip_variability_max_14 ---> Done\n", + "Card_Merchdesc_Zip_variability_med_14 ---> Done\n", + "Card_Merchdesc_Zip amount variables over past 14 ---> Done\n", + "Card_Merchdesc_Zip_variability_avg_30 ---> Done\n", + "Card_Merchdesc_Zip_variability_max_30 ---> Done\n", + "Card_Merchdesc_Zip_variability_med_30 ---> Done\n", + "Card_Merchdesc_Zip amount variables over past 30 ---> Done\n", + "Run time for the last entity ----------------- 0.578162041999974s\n", + "Merchnum_desc_State_variability_avg_0 ---> Done\n", + "Merchnum_desc_State_variability_max_0 ---> Done\n", + "Merchnum_desc_State_variability_med_0 ---> Done\n", + "Merchnum_desc_State amount variables over past 0 ---> Done\n", + "Merchnum_desc_State_variability_avg_1 ---> Done\n", + "Merchnum_desc_State_variability_max_1 ---> Done\n", + "Merchnum_desc_State_variability_med_1 ---> Done\n", + "Merchnum_desc_State amount variables over past 1 ---> Done\n", + "Merchnum_desc_State_variability_avg_3 ---> Done\n", + "Merchnum_desc_State_variability_max_3 ---> Done\n", + "Merchnum_desc_State_variability_med_3 ---> Done\n", + "Merchnum_desc_State amount variables over past 3 ---> Done\n", + "Merchnum_desc_State_variability_avg_7 ---> Done\n", + "Merchnum_desc_State_variability_max_7 ---> Done\n", + "Merchnum_desc_State_variability_med_7 ---> Done\n", + "Merchnum_desc_State amount variables over past 7 ---> Done\n", + "Merchnum_desc_State_variability_avg_14 ---> Done\n", + "Merchnum_desc_State_variability_max_14 ---> Done\n", + "Merchnum_desc_State_variability_med_14 ---> Done\n", + "Merchnum_desc_State amount variables over past 14 ---> Done\n", + "Merchnum_desc_State_variability_avg_30 ---> Done\n", + "Merchnum_desc_State_variability_max_30 ---> Done\n", + "Merchnum_desc_State_variability_med_30 ---> Done\n", + "Merchnum_desc_State amount variables over past 30 ---> Done\n", + "Total run time: 1.62297104235mins\n" + ] + } + ], + "source": [ + "start = timeit.default_timer()\n", + "for entity in entities:\n", + " try: print('Run time for the last entity ----------------- {}s'.format(timeit.default_timer() - st))\n", + " except: \n", + " print('')\n", + " st = timeit.default_timer() \n", + " df_l = df1[['Recnum', 'Date', entity,'Amount']]\n", + " df_r = df1[['check_record', 'check_date', entity, 'Amount']]\n", + " temp = pd.merge(df_l, df_r, left_on = entity, right_on = entity)\n", + " \n", + " for time in [0,1,3,7,14,30]:\n", + " temp2 = temp[(temp.check_date >= (temp.Date - dt.timedelta(time))) &\\\n", + " (temp.Recnum >= temp.check_record)][['Recnum', 'check_record',entity, 'Amount_x','Amount_y']]\n", + " temp2['Amount_diff']=temp2['Amount_y']-temp2['Amount_x']\n", + "\n", + " col_name = entity + '_variability_avg_' + str(time)\n", + " mapper2 = temp2.groupby('Recnum')['Amount_diff'].mean()\n", + " final[col_name] = final.Recnum.map(mapper2) \n", + " print(col_name + ' ---> Done')\n", + " \n", + " col_name = entity + '_variability_max_' + str(time)\n", + " mapper2 = temp2.groupby('Recnum')['Amount_diff'].max()\n", + " final[col_name] = final.Recnum.map(mapper2) \n", + " print(col_name + ' ---> Done')\n", + " \n", + " col_name = entity + '_variability_med_' + str(time)\n", + " mapper2 = temp2.groupby('Recnum')['Amount_diff'].median()\n", + " final[col_name] = final.Recnum.map(mapper2) \n", + " print(col_name + ' ---> Done')\n", + " \n", + " print(entity + ' amount variables over past ' + str(time) + ' ---> Done')\n", + " del df_l\n", + " del df_r\n", + " del temp\n", + " del temp2\n", + " \n", + "print('Total run time: {}mins'.format((timeit.default_timer() - start)/60))" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(96397, 2246)" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(96397, 2246)\n", + "# new variables is 324\n" + ] + } + ], + "source": [ + "print(final.shape)\n", + "print('# new variables is ',len(final.columns) - numstart)\n", + "numstart = len(final.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total run time: 36.16697837916666mins\n", + "CPU times: user 28min 26s, sys: 8min 47s, total: 37min 13s\n", + "Wall time: 36min 10s\n" + ] + } + ], + "source": [ + "%%time\n", + "# this cell can take a long time.\n", + "## Counts by Entity\n", + "start = timeit.default_timer()\n", + "for i in entities:\n", + " for v in entities:\n", + " if i==v:\n", + " continue\n", + " else:\n", + " df_c=df1[['Recnum','Date',i]]\n", + " df_d=df1[['check_record','check_date',i,v]]\n", + " temp=pd.merge(df_c,df_d,left_on=i,right_on=i)\n", + " \n", + " for t in [1,3,7,14,30,60]:\n", + " count_day_df=temp[(temp.check_date>=(temp.Date-dt.timedelta(t)))&(temp.Recnum>=temp.check_record)]\n", + " col_name=f'{i}_unique_count_for_{v}_{t}'\n", + " mapper=count_day_df.groupby(['Recnum'])[v].nunique()\n", + " final[col_name]=final.Recnum.map(mapper)\n", + "\n", + "print('Total run time: {}mins'.format((timeit.default_timer() - start)/60))" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(96397, 4082)" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cardnum\n", + "Merchnum\n", + "card_merch\n", + "card_zip\n", + "card_state\n", + "merch_zip\n", + "merch_state\n", + "state_des\n", + "card_zip3\n", + "Card_Merchdesc\n", + "Card_dow\n", + "Merchnum_desc\n", + "Merchnum_dow\n", + "Merchdesc_dow\n", + "Card_Merchnum_desc\n", + "Card_Merchnum_Zip\n", + "Card_Merchdesc_Zip\n", + "Merchnum_desc_State\n", + "run time: 0.18228908300034163s\n" + ] + } + ], + "source": [ + "## Relative Velocity (squared)\n", + "start = timeit.default_timer()\n", + "for ent in entities:\n", + " print(ent)\n", + " for d in ['0', '1']:\n", + " for dd in ['7', '14', '30', '60']:\n", + " final[ent + '_count_' + d + '_by_' + dd + \"_sq\"] =\\\n", + " final[ent + '_count_' + d]/(final[ent + '_count_' + dd])/pow(float(dd),2)\n", + "print('run time: {}s'.format(timeit.default_timer() - start))" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(96397, 4226)" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Binning Amounts" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Amount bins\n", + "AMOUNT = True\n", + "if AMOUNT:\n", + " final['amount_cat'] = pd.qcut(final.Amount, q=5,labels=[1,2,3,4,5])\n", + " \n", + " final['amount_cat'].value_counts().plot(kind='barh')\n", + " plt.show()\n", + " \n", + " qcut_series, qcut_intervals = pd.qcut(final.Amount, q=5,labels=[1,2,3,4,5],retbins=True)\n", + " \n", + " qcut_series.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 0.01\n", + "2 21.74\n", + "3 85.0\n", + "4 216.0\n", + "5 550.57\n" + ] + } + ], + "source": [ + "bins = [1,2,3,4,5]\n", + "for bin, interval in zip(bins, qcut_intervals):\n", + " print(bin, round(interval,2))" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [], + "source": [ + "if AMOUNT:\n", + " final[['Amount', 'amount_cat']].head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [], + "source": [ + "if AMOUNT:\n", + " final['amount_cat'] = final['amount_cat'].astype(str)" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['501.0', '544.0', '601.0', ..., '99928.0', '99929.0', '99950.0'],\n", + " dtype=object)" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Foreign zipcode\n", + "zip_state = pd.read_csv('zip_code_database.csv')[['zip','state']]\n", + "zip_state.sample(5)\n", + "# Check if the zipcode of merchant is in the US\n", + "zip_state = pd.read_csv('zip_code_database.csv')['zip'].astype(float).astype(str).values\n", + "zip_state" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [], + "source": [ + "mapping = list(map(lambda x: x not in zip_state, final['Merch zip']))\n", + "final = pd.concat([final, pd.DataFrame({'foreign': mapping})], axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [], + "source": [ + "final.fillna(0,inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RecnumCardnumDateMerchnumMerch descriptionMerch stateMerch zipTranstypeAmountFraud...Merchnum_desc_State_count_0_by_7_sqMerchnum_desc_State_count_0_by_14_sqMerchnum_desc_State_count_0_by_30_sqMerchnum_desc_State_count_0_by_60_sqMerchnum_desc_State_count_1_by_7_sqMerchnum_desc_State_count_1_by_14_sqMerchnum_desc_State_count_1_by_30_sqMerchnum_desc_State_count_1_by_60_sqamount_catforeign
0151421904392010-01-015509006296254FEDEXSHP12/23/09AB#TN38118.0P3.620...0.0204080.0051020.0011110.0002780.0204080.0051020.0011110.0002781False
1251421839732010-01-0161003026333SERVICEMERCHANDISE#81MA1803.0P31.420...0.0204080.0051020.0011110.0002780.0204080.0051020.0011110.0002782False
2351421317212010-01-014503082993600OFFICEDEPOT#191MD20706.0P178.490...0.0204080.0051020.0011110.0002780.0204080.0051020.0011110.0002783False
3451421484522010-01-015509006296254FEDEXSHP12/28/09AB#TN38118.0P3.620...0.0204080.0051020.0011110.0002780.0204080.0051020.0011110.0002781False
4551421904392010-01-015509006296254FEDEXSHP12/23/09AB#TN38118.0P3.620...0.0204080.0051020.0011110.0002780.0204080.0051020.0011110.0002781False
\n", + "

5 rows × 4228 columns

\n", + "
" + ], + "text/plain": [ + " Recnum Cardnum Date Merchnum Merch description \\\n", + "0 1 5142190439 2010-01-01 5509006296254 FEDEXSHP12/23/09AB# \n", + "1 2 5142183973 2010-01-01 61003026333 SERVICEMERCHANDISE#81 \n", + "2 3 5142131721 2010-01-01 4503082993600 OFFICEDEPOT#191 \n", + "3 4 5142148452 2010-01-01 5509006296254 FEDEXSHP12/28/09AB# \n", + "4 5 5142190439 2010-01-01 5509006296254 FEDEXSHP12/23/09AB# \n", + "\n", + " Merch state Merch zip Transtype Amount Fraud ... \\\n", + "0 TN 38118.0 P 3.62 0 ... \n", + "1 MA 1803.0 P 31.42 0 ... \n", + "2 MD 20706.0 P 178.49 0 ... \n", + "3 TN 38118.0 P 3.62 0 ... \n", + "4 TN 38118.0 P 3.62 0 ... \n", + "\n", + " Merchnum_desc_State_count_0_by_7_sq Merchnum_desc_State_count_0_by_14_sq \\\n", + "0 0.020408 0.005102 \n", + "1 0.020408 0.005102 \n", + "2 0.020408 0.005102 \n", + "3 0.020408 0.005102 \n", + "4 0.020408 0.005102 \n", + "\n", + " Merchnum_desc_State_count_0_by_30_sq Merchnum_desc_State_count_0_by_60_sq \\\n", + "0 0.001111 0.000278 \n", + "1 0.001111 0.000278 \n", + "2 0.001111 0.000278 \n", + "3 0.001111 0.000278 \n", + "4 0.001111 0.000278 \n", + "\n", + " Merchnum_desc_State_count_1_by_7_sq Merchnum_desc_State_count_1_by_14_sq \\\n", + "0 0.020408 0.005102 \n", + "1 0.020408 0.005102 \n", + "2 0.020408 0.005102 \n", + "3 0.020408 0.005102 \n", + "4 0.020408 0.005102 \n", + "\n", + " Merchnum_desc_State_count_1_by_30_sq Merchnum_desc_State_count_1_by_60_sq \\\n", + "0 0.001111 0.000278 \n", + "1 0.001111 0.000278 \n", + "2 0.001111 0.000278 \n", + "3 0.001111 0.000278 \n", + "4 0.001111 0.000278 \n", + "\n", + " amount_cat foreign \n", + "0 1 False \n", + "1 2 False \n", + "2 3 False \n", + "3 1 False \n", + "4 1 False \n", + "\n", + "[5 rows x 4228 columns]" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(96397, 4228)" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove any redundant columns" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [], + "source": [ + "final.set_index('Recnum', inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(96397, 4227)\n", + "CPU times: user 1min 40s, sys: 19.8 s, total: 2min\n", + "Wall time: 2min 5s\n" + ] + }, + { + "data": { + "text/plain": [ + "(96397, 3047)" + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "# if the kernel dies in this cell it's likely due to memory problems.\n", + "# In that case, just write out the data file as is and you can read it in another notebook that just does deduping\n", + "print(final.shape)\n", + "final = final.T.drop_duplicates().T\n", + "final.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Cardnum',\n", + " 'Date',\n", + " 'Merchnum',\n", + " 'Merch description',\n", + " 'Merch state',\n", + " 'Merch zip',\n", + " 'Transtype',\n", + " 'Amount',\n", + " 'Fraud',\n", + " 'Dow',\n", + " 'Dow_Risk',\n", + " 'Month',\n", + " 'Month_Risk',\n", + " 'state_risk',\n", + " 'card_merch',\n", + " 'card_zip',\n", + " 'card_state',\n", + " 'merch_zip',\n", + " 'merch_state',\n", + " 'state_des',\n", + " 'zip3',\n", + " 'card_zip3',\n", + " 'Card_Merchdesc',\n", + " 'Card_dow',\n", + " 'Merchnum_desc',\n", + " 'Merchnum_dow',\n", + " 'Merchdesc_dow',\n", + " 'Card_Merchnum_desc',\n", + " 'Card_Merchnum_Zip',\n", + " 'Card_Merchdesc_Zip',\n", + " 'Merchnum_desc_State',\n", + " 'Cardnum_day_since',\n", + " 'Cardnum_count_0',\n", + " 'Cardnum_avg_0',\n", + " 'Cardnum_max_0',\n", + " 'Cardnum_med_0',\n", + " 'Cardnum_total_0',\n", + " 'Cardnum_actual/avg_0',\n", + " 'Cardnum_actual/max_0',\n", + " 'Cardnum_actual/med_0',\n", + " 'Cardnum_actual/toal_0',\n", + " 'Cardnum_count_1',\n", + " 'Cardnum_avg_1',\n", + " 'Cardnum_max_1',\n", + " 'Cardnum_med_1',\n", + " 'Cardnum_total_1',\n", + " 'Cardnum_actual/avg_1',\n", + " 'Cardnum_actual/max_1',\n", + " 'Cardnum_actual/med_1',\n", + " 'Cardnum_actual/toal_1',\n", + " 'Cardnum_count_3',\n", + " 'Cardnum_avg_3',\n", + " 'Cardnum_max_3',\n", + " 'Cardnum_med_3',\n", + " 'Cardnum_total_3',\n", + " 'Cardnum_actual/avg_3',\n", + " 'Cardnum_actual/max_3',\n", + " 'Cardnum_actual/med_3',\n", + " 'Cardnum_actual/toal_3',\n", + " 'Cardnum_count_7',\n", + " 'Cardnum_avg_7',\n", + " 'Cardnum_max_7',\n", + " 'Cardnum_med_7',\n", + " 'Cardnum_total_7',\n", + " 'Cardnum_actual/avg_7',\n", + " 'Cardnum_actual/max_7',\n", + " 'Cardnum_actual/med_7',\n", + " 'Cardnum_actual/toal_7',\n", + " 'Cardnum_count_14',\n", + " 'Cardnum_avg_14',\n", + " 'Cardnum_max_14',\n", + " 'Cardnum_med_14',\n", + " 'Cardnum_total_14',\n", + " 'Cardnum_actual/avg_14',\n", + " 'Cardnum_actual/max_14',\n", + " 'Cardnum_actual/med_14',\n", + " 'Cardnum_actual/toal_14',\n", + " 'Cardnum_count_30',\n", + " 'Cardnum_avg_30',\n", + " 'Cardnum_max_30',\n", + " 'Cardnum_med_30',\n", + " 'Cardnum_total_30',\n", + " 'Cardnum_actual/avg_30',\n", + " 'Cardnum_actual/max_30',\n", + " 'Cardnum_actual/med_30',\n", + " 'Cardnum_actual/toal_30',\n", + " 'Cardnum_count_60',\n", + " 'Cardnum_avg_60',\n", + " 'Cardnum_max_60',\n", + " 'Cardnum_med_60',\n", + " 'Cardnum_total_60',\n", + " 'Cardnum_actual/avg_60',\n", + " 'Cardnum_actual/max_60',\n", + " 'Cardnum_actual/med_60',\n", + " 'Cardnum_actual/toal_60',\n", + " 'Merchnum_day_since',\n", + " 'Merchnum_count_0',\n", + " 'Merchnum_avg_0',\n", + " 'Merchnum_max_0',\n", + " 'Merchnum_med_0',\n", + " 'Merchnum_total_0',\n", + " 'Merchnum_actual/avg_0',\n", + " 'Merchnum_actual/max_0',\n", + " 'Merchnum_actual/med_0',\n", + " 'Merchnum_actual/toal_0',\n", + " 'Merchnum_count_1',\n", + " 'Merchnum_avg_1',\n", + " 'Merchnum_max_1',\n", + " 'Merchnum_med_1',\n", + " 'Merchnum_total_1',\n", + " 'Merchnum_actual/avg_1',\n", + " 'Merchnum_actual/max_1',\n", + " 'Merchnum_actual/med_1',\n", + " 'Merchnum_actual/toal_1',\n", + " 'Merchnum_count_3',\n", + " 'Merchnum_avg_3',\n", + " 'Merchnum_max_3',\n", + " 'Merchnum_med_3',\n", + " 'Merchnum_total_3',\n", + " 'Merchnum_actual/avg_3',\n", + " 'Merchnum_actual/max_3',\n", + " 'Merchnum_actual/med_3',\n", + " 'Merchnum_actual/toal_3',\n", + " 'Merchnum_count_7',\n", + " 'Merchnum_avg_7',\n", + " 'Merchnum_max_7',\n", + " 'Merchnum_med_7',\n", + " 'Merchnum_total_7',\n", + " 'Merchnum_actual/avg_7',\n", + " 'Merchnum_actual/max_7',\n", + " 'Merchnum_actual/med_7',\n", + " 'Merchnum_actual/toal_7',\n", + " 'Merchnum_count_14',\n", + " 'Merchnum_avg_14',\n", + " 'Merchnum_max_14',\n", + " 'Merchnum_med_14',\n", + " 'Merchnum_total_14',\n", + " 'Merchnum_actual/avg_14',\n", + " 'Merchnum_actual/max_14',\n", + " 'Merchnum_actual/med_14',\n", + " 'Merchnum_actual/toal_14',\n", + " 'Merchnum_count_30',\n", + " 'Merchnum_avg_30',\n", + " 'Merchnum_max_30',\n", + " 'Merchnum_med_30',\n", + " 'Merchnum_total_30',\n", + " 'Merchnum_actual/avg_30',\n", + " 'Merchnum_actual/max_30',\n", + " 'Merchnum_actual/med_30',\n", + " 'Merchnum_actual/toal_30',\n", + " 'Merchnum_count_60',\n", + " 'Merchnum_avg_60',\n", + " 'Merchnum_max_60',\n", + " 'Merchnum_med_60',\n", + " 'Merchnum_total_60',\n", + " 'Merchnum_actual/avg_60',\n", + " 'Merchnum_actual/max_60',\n", + " 'Merchnum_actual/med_60',\n", + " 'Merchnum_actual/toal_60',\n", + " 'card_merch_day_since',\n", + " 'card_merch_count_0',\n", + " 'card_merch_avg_0',\n", + " 'card_merch_max_0',\n", + " 'card_merch_med_0',\n", + " 'card_merch_total_0',\n", + " 'card_merch_actual/avg_0',\n", + " 'card_merch_actual/max_0',\n", + " 'card_merch_actual/med_0',\n", + " 'card_merch_actual/toal_0',\n", + " 'card_merch_count_1',\n", + " 'card_merch_avg_1',\n", + " 'card_merch_max_1',\n", + " 'card_merch_med_1',\n", + " 'card_merch_total_1',\n", + " 'card_merch_actual/avg_1',\n", + " 'card_merch_actual/max_1',\n", + " 'card_merch_actual/med_1',\n", + " 'card_merch_actual/toal_1',\n", + " 'card_merch_count_3',\n", + " 'card_merch_avg_3',\n", + " 'card_merch_max_3',\n", + " 'card_merch_med_3',\n", + " 'card_merch_total_3',\n", + " 'card_merch_actual/avg_3',\n", + " 'card_merch_actual/max_3',\n", + " 'card_merch_actual/med_3',\n", + " 'card_merch_actual/toal_3',\n", + " 'card_merch_count_7',\n", + " 'card_merch_avg_7',\n", + " 'card_merch_max_7',\n", + " 'card_merch_med_7',\n", + " 'card_merch_total_7',\n", + " 'card_merch_actual/avg_7',\n", + " 'card_merch_actual/max_7',\n", + " 'card_merch_actual/med_7',\n", + " 'card_merch_actual/toal_7',\n", + " 'card_merch_count_14',\n", + " 'card_merch_avg_14',\n", + " 'card_merch_max_14',\n", + " 'card_merch_med_14',\n", + " 'card_merch_total_14',\n", + " 'card_merch_actual/avg_14',\n", + " 'card_merch_actual/max_14',\n", + " 'card_merch_actual/med_14',\n", + " 'card_merch_actual/toal_14',\n", + " 'card_merch_count_30',\n", + " 'card_merch_avg_30',\n", + " 'card_merch_max_30',\n", + " 'card_merch_med_30',\n", + " 'card_merch_total_30',\n", + " 'card_merch_actual/avg_30',\n", + " 'card_merch_actual/max_30',\n", + " 'card_merch_actual/med_30',\n", + " 'card_merch_actual/toal_30',\n", + " 'card_merch_count_60',\n", + " 'card_merch_avg_60',\n", + " 'card_merch_max_60',\n", + " 'card_merch_med_60',\n", + " 'card_merch_total_60',\n", + " 'card_merch_actual/avg_60',\n", + " 'card_merch_actual/max_60',\n", + " 'card_merch_actual/med_60',\n", + " 'card_merch_actual/toal_60',\n", + " 'card_zip_day_since',\n", + " 'card_zip_count_0',\n", + " 'card_zip_avg_0',\n", + " 'card_zip_max_0',\n", + " 'card_zip_med_0',\n", + " 'card_zip_total_0',\n", + " 'card_zip_actual/avg_0',\n", + " 'card_zip_actual/max_0',\n", + " 'card_zip_actual/med_0',\n", + " 'card_zip_actual/toal_0',\n", + " 'card_zip_count_1',\n", + " 'card_zip_avg_1',\n", + " 'card_zip_max_1',\n", + " 'card_zip_med_1',\n", + " 'card_zip_total_1',\n", + " 'card_zip_actual/avg_1',\n", + " 'card_zip_actual/max_1',\n", + " 'card_zip_actual/med_1',\n", + " 'card_zip_actual/toal_1',\n", + " 'card_zip_count_3',\n", + " 'card_zip_avg_3',\n", + " 'card_zip_max_3',\n", + " 'card_zip_med_3',\n", + " 'card_zip_total_3',\n", + " 'card_zip_actual/avg_3',\n", + " 'card_zip_actual/max_3',\n", + " 'card_zip_actual/med_3',\n", + " 'card_zip_actual/toal_3',\n", + " 'card_zip_count_7',\n", + " 'card_zip_avg_7',\n", + " 'card_zip_max_7',\n", + " 'card_zip_med_7',\n", + " 'card_zip_total_7',\n", + " 'card_zip_actual/avg_7',\n", + " 'card_zip_actual/max_7',\n", + " 'card_zip_actual/med_7',\n", + " 'card_zip_actual/toal_7',\n", + " 'card_zip_count_14',\n", + " 'card_zip_avg_14',\n", + " 'card_zip_max_14',\n", + " 'card_zip_med_14',\n", + " 'card_zip_total_14',\n", + " 'card_zip_actual/avg_14',\n", + " 'card_zip_actual/max_14',\n", + " 'card_zip_actual/med_14',\n", + " 'card_zip_actual/toal_14',\n", + " 'card_zip_count_30',\n", + " 'card_zip_avg_30',\n", + " 'card_zip_max_30',\n", + " 'card_zip_med_30',\n", + " 'card_zip_total_30',\n", + " 'card_zip_actual/avg_30',\n", + " 'card_zip_actual/max_30',\n", + " 'card_zip_actual/med_30',\n", + " 'card_zip_actual/toal_30',\n", + " 'card_zip_count_60',\n", + " 'card_zip_avg_60',\n", + " 'card_zip_max_60',\n", + " 'card_zip_med_60',\n", + " 'card_zip_total_60',\n", + " 'card_zip_actual/avg_60',\n", + " 'card_zip_actual/max_60',\n", + " 'card_zip_actual/med_60',\n", + " 'card_zip_actual/toal_60',\n", + " 'card_state_day_since',\n", + " 'card_state_count_0',\n", + " 'card_state_avg_0',\n", + " 'card_state_max_0',\n", + " 'card_state_med_0',\n", + " 'card_state_total_0',\n", + " 'card_state_actual/avg_0',\n", + " 'card_state_actual/max_0',\n", + " 'card_state_actual/med_0',\n", + " 'card_state_actual/toal_0',\n", + " 'card_state_count_1',\n", + " 'card_state_avg_1',\n", + " 'card_state_max_1',\n", + " 'card_state_med_1',\n", + " 'card_state_total_1',\n", + " 'card_state_actual/avg_1',\n", + " 'card_state_actual/max_1',\n", + " 'card_state_actual/med_1',\n", + " 'card_state_actual/toal_1',\n", + " 'card_state_count_3',\n", + " 'card_state_avg_3',\n", + " 'card_state_max_3',\n", + " 'card_state_med_3',\n", + " 'card_state_total_3',\n", + " 'card_state_actual/avg_3',\n", + " 'card_state_actual/max_3',\n", + " 'card_state_actual/med_3',\n", + " 'card_state_actual/toal_3',\n", + " 'card_state_count_7',\n", + " 'card_state_avg_7',\n", + " 'card_state_max_7',\n", + " 'card_state_med_7',\n", + " 'card_state_total_7',\n", + " 'card_state_actual/avg_7',\n", + " 'card_state_actual/max_7',\n", + " 'card_state_actual/med_7',\n", + " 'card_state_actual/toal_7',\n", + " 'card_state_count_14',\n", + " 'card_state_avg_14',\n", + " 'card_state_max_14',\n", + " 'card_state_med_14',\n", + " 'card_state_total_14',\n", + " 'card_state_actual/avg_14',\n", + " 'card_state_actual/max_14',\n", + " 'card_state_actual/med_14',\n", + " 'card_state_actual/toal_14',\n", + " 'card_state_count_30',\n", + " 'card_state_avg_30',\n", + " 'card_state_max_30',\n", + " 'card_state_med_30',\n", + " 'card_state_total_30',\n", + " 'card_state_actual/avg_30',\n", + " 'card_state_actual/max_30',\n", + " 'card_state_actual/med_30',\n", + " 'card_state_actual/toal_30',\n", + " 'card_state_count_60',\n", + " 'card_state_avg_60',\n", + " 'card_state_max_60',\n", + " 'card_state_med_60',\n", + " 'card_state_total_60',\n", + " 'card_state_actual/avg_60',\n", + " 'card_state_actual/max_60',\n", + " 'card_state_actual/med_60',\n", + " 'card_state_actual/toal_60',\n", + " 'merch_zip_day_since',\n", + " 'merch_zip_count_0',\n", + " 'merch_zip_avg_0',\n", + " 'merch_zip_max_0',\n", + " 'merch_zip_med_0',\n", + " 'merch_zip_total_0',\n", + " 'merch_zip_actual/avg_0',\n", + " 'merch_zip_actual/max_0',\n", + " 'merch_zip_actual/med_0',\n", + " 'merch_zip_actual/toal_0',\n", + " 'merch_zip_count_1',\n", + " 'merch_zip_avg_1',\n", + " 'merch_zip_max_1',\n", + " 'merch_zip_med_1',\n", + " 'merch_zip_total_1',\n", + " 'merch_zip_actual/avg_1',\n", + " 'merch_zip_actual/max_1',\n", + " 'merch_zip_actual/med_1',\n", + " 'merch_zip_actual/toal_1',\n", + " 'merch_zip_count_3',\n", + " 'merch_zip_avg_3',\n", + " 'merch_zip_max_3',\n", + " 'merch_zip_med_3',\n", + " 'merch_zip_total_3',\n", + " 'merch_zip_actual/avg_3',\n", + " 'merch_zip_actual/max_3',\n", + " 'merch_zip_actual/med_3',\n", + " 'merch_zip_actual/toal_3',\n", + " 'merch_zip_count_7',\n", + " 'merch_zip_avg_7',\n", + " 'merch_zip_max_7',\n", + " 'merch_zip_med_7',\n", + " 'merch_zip_total_7',\n", + " 'merch_zip_actual/avg_7',\n", + " 'merch_zip_actual/max_7',\n", + " 'merch_zip_actual/med_7',\n", + " 'merch_zip_actual/toal_7',\n", + " 'merch_zip_count_14',\n", + " 'merch_zip_avg_14',\n", + " 'merch_zip_max_14',\n", + " 'merch_zip_med_14',\n", + " 'merch_zip_total_14',\n", + " 'merch_zip_actual/avg_14',\n", + " 'merch_zip_actual/max_14',\n", + " 'merch_zip_actual/med_14',\n", + " 'merch_zip_actual/toal_14',\n", + " 'merch_zip_count_30',\n", + " 'merch_zip_avg_30',\n", + " 'merch_zip_max_30',\n", + " 'merch_zip_med_30',\n", + " 'merch_zip_total_30',\n", + " 'merch_zip_actual/avg_30',\n", + " 'merch_zip_actual/max_30',\n", + " 'merch_zip_actual/med_30',\n", + " 'merch_zip_actual/toal_30',\n", + " 'merch_zip_count_60',\n", + " 'merch_zip_avg_60',\n", + " 'merch_zip_max_60',\n", + " 'merch_zip_med_60',\n", + " 'merch_zip_total_60',\n", + " 'merch_zip_actual/avg_60',\n", + " 'merch_zip_actual/max_60',\n", + " 'merch_zip_actual/med_60',\n", + " 'merch_zip_actual/toal_60',\n", + " 'merch_state_day_since',\n", + " 'merch_state_count_0',\n", + " 'merch_state_avg_0',\n", + " 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'merch_state_actual/med_60',\n", + " 'merch_state_actual/toal_60',\n", + " 'state_des_day_since',\n", + " 'state_des_count_0',\n", + " 'state_des_avg_0',\n", + " 'state_des_max_0',\n", + " 'state_des_med_0',\n", + " 'state_des_total_0',\n", + " 'state_des_actual/avg_0',\n", + " 'state_des_actual/max_0',\n", + " 'state_des_actual/med_0',\n", + " 'state_des_actual/toal_0',\n", + " 'state_des_count_1',\n", + " 'state_des_avg_1',\n", + " 'state_des_max_1',\n", + " 'state_des_med_1',\n", + " 'state_des_total_1',\n", + " 'state_des_actual/avg_1',\n", + " 'state_des_actual/max_1',\n", + " 'state_des_actual/med_1',\n", + " 'state_des_actual/toal_1',\n", + " 'state_des_count_3',\n", + " 'state_des_avg_3',\n", + " 'state_des_max_3',\n", + " 'state_des_med_3',\n", + " 'state_des_total_3',\n", + " 'state_des_actual/avg_3',\n", + " 'state_des_actual/max_3',\n", + " 'state_des_actual/med_3',\n", + " 'state_des_actual/toal_3',\n", + " 'state_des_count_7',\n", + " 'state_des_avg_7',\n", + " 'state_des_max_7',\n", + " 'state_des_med_7',\n", + " 'state_des_total_7',\n", + " 'state_des_actual/avg_7',\n", + " 'state_des_actual/max_7',\n", + " 'state_des_actual/med_7',\n", + " 'state_des_actual/toal_7',\n", + " 'state_des_count_14',\n", + " 'state_des_avg_14',\n", + " 'state_des_max_14',\n", + " 'state_des_med_14',\n", + " 'state_des_total_14',\n", + " 'state_des_actual/avg_14',\n", + " 'state_des_actual/max_14',\n", + " 'state_des_actual/med_14',\n", + " 'state_des_actual/toal_14',\n", + " 'state_des_count_30',\n", + " 'state_des_avg_30',\n", + " 'state_des_max_30',\n", + " 'state_des_med_30',\n", + " 'state_des_total_30',\n", + " 'state_des_actual/avg_30',\n", + " 'state_des_actual/max_30',\n", + " 'state_des_actual/med_30',\n", + " 'state_des_actual/toal_30',\n", + " 'state_des_count_60',\n", + " 'state_des_avg_60',\n", + " 'state_des_max_60',\n", + " 'state_des_med_60',\n", + " 'state_des_total_60',\n", + " 'state_des_actual/avg_60',\n", + " 'state_des_actual/max_60',\n", + " 'state_des_actual/med_60',\n", + " 'state_des_actual/toal_60',\n", + " 'card_zip3_day_since',\n", + " 'card_zip3_count_0',\n", + " 'card_zip3_avg_0',\n", + " 'card_zip3_max_0',\n", + " 'card_zip3_med_0',\n", + " 'card_zip3_total_0',\n", + " 'card_zip3_actual/avg_0',\n", + " 'card_zip3_actual/max_0',\n", + " 'card_zip3_actual/med_0',\n", + " 'card_zip3_actual/toal_0',\n", + " 'card_zip3_count_1',\n", + " 'card_zip3_avg_1',\n", + " 'card_zip3_max_1',\n", + " 'card_zip3_med_1',\n", + " 'card_zip3_total_1',\n", + " 'card_zip3_actual/avg_1',\n", + " 'card_zip3_actual/max_1',\n", + " 'card_zip3_actual/med_1',\n", + " 'card_zip3_actual/toal_1',\n", + " 'card_zip3_count_3',\n", + " 'card_zip3_avg_3',\n", + " 'card_zip3_max_3',\n", + " 'card_zip3_med_3',\n", + " 'card_zip3_total_3',\n", + " 'card_zip3_actual/avg_3',\n", + " 'card_zip3_actual/max_3',\n", + " 'card_zip3_actual/med_3',\n", + " 'card_zip3_actual/toal_3',\n", + " 'card_zip3_count_7',\n", + " 'card_zip3_avg_7',\n", + " 'card_zip3_max_7',\n", + " 'card_zip3_med_7',\n", + " 'card_zip3_total_7',\n", + " 'card_zip3_actual/avg_7',\n", + " 'card_zip3_actual/max_7',\n", + " 'card_zip3_actual/med_7',\n", + " 'card_zip3_actual/toal_7',\n", + " 'card_zip3_count_14',\n", + " 'card_zip3_avg_14',\n", + " 'card_zip3_max_14',\n", + " 'card_zip3_med_14',\n", + " 'card_zip3_total_14',\n", + " 'card_zip3_actual/avg_14',\n", + " 'card_zip3_actual/max_14',\n", + " 'card_zip3_actual/med_14',\n", + " 'card_zip3_actual/toal_14',\n", + " 'card_zip3_count_30',\n", + " 'card_zip3_avg_30',\n", + " 'card_zip3_max_30',\n", + " 'card_zip3_med_30',\n", + " 'card_zip3_total_30',\n", + " 'card_zip3_actual/avg_30',\n", + " 'card_zip3_actual/max_30',\n", + " 'card_zip3_actual/med_30',\n", + " 'card_zip3_actual/toal_30',\n", + " 'card_zip3_count_60',\n", + " 'card_zip3_avg_60',\n", + " 'card_zip3_max_60',\n", + " 'card_zip3_med_60',\n", + " 'card_zip3_total_60',\n", + " 'card_zip3_actual/avg_60',\n", + " 'card_zip3_actual/max_60',\n", + " 'card_zip3_actual/med_60',\n", + " 'card_zip3_actual/toal_60',\n", + " 'Card_Merchdesc_day_since',\n", + " 'Card_Merchdesc_count_0',\n", + " 'Card_Merchdesc_avg_0',\n", + " 'Card_Merchdesc_max_0',\n", + " 'Card_Merchdesc_med_0',\n", + " 'Card_Merchdesc_total_0',\n", + " 'Card_Merchdesc_actual/avg_0',\n", + " 'Card_Merchdesc_actual/max_0',\n", + " 'Card_Merchdesc_actual/med_0',\n", + " 'Card_Merchdesc_actual/toal_0',\n", + " 'Card_Merchdesc_count_1',\n", + " 'Card_Merchdesc_avg_1',\n", + " 'Card_Merchdesc_max_1',\n", + " 'Card_Merchdesc_med_1',\n", + " 'Card_Merchdesc_total_1',\n", + " 'Card_Merchdesc_actual/avg_1',\n", + " 'Card_Merchdesc_actual/max_1',\n", + " 'Card_Merchdesc_actual/med_1',\n", + " 'Card_Merchdesc_actual/toal_1',\n", + " 'Card_Merchdesc_count_3',\n", + " 'Card_Merchdesc_avg_3',\n", + " 'Card_Merchdesc_max_3',\n", + " 'Card_Merchdesc_med_3',\n", + " 'Card_Merchdesc_total_3',\n", + " 'Card_Merchdesc_actual/avg_3',\n", + " 'Card_Merchdesc_actual/max_3',\n", + " 'Card_Merchdesc_actual/med_3',\n", + " 'Card_Merchdesc_actual/toal_3',\n", + " 'Card_Merchdesc_count_7',\n", + " 'Card_Merchdesc_avg_7',\n", + " 'Card_Merchdesc_max_7',\n", + " 'Card_Merchdesc_med_7',\n", + " 'Card_Merchdesc_total_7',\n", + " 'Card_Merchdesc_actual/avg_7',\n", + " 'Card_Merchdesc_actual/max_7',\n", + " 'Card_Merchdesc_actual/med_7',\n", + " 'Card_Merchdesc_actual/toal_7',\n", + " 'Card_Merchdesc_count_14',\n", + " 'Card_Merchdesc_avg_14',\n", + " 'Card_Merchdesc_max_14',\n", + " 'Card_Merchdesc_med_14',\n", + " 'Card_Merchdesc_total_14',\n", + " 'Card_Merchdesc_actual/avg_14',\n", + " 'Card_Merchdesc_actual/max_14',\n", + " 'Card_Merchdesc_actual/med_14',\n", + " 'Card_Merchdesc_actual/toal_14',\n", + " 'Card_Merchdesc_count_30',\n", + " 'Card_Merchdesc_avg_30',\n", + " 'Card_Merchdesc_max_30',\n", + " 'Card_Merchdesc_med_30',\n", + " 'Card_Merchdesc_total_30',\n", + " 'Card_Merchdesc_actual/avg_30',\n", + " 'Card_Merchdesc_actual/max_30',\n", + " 'Card_Merchdesc_actual/med_30',\n", + " 'Card_Merchdesc_actual/toal_30',\n", + " 'Card_Merchdesc_count_60',\n", + " 'Card_Merchdesc_avg_60',\n", + " 'Card_Merchdesc_max_60',\n", + " 'Card_Merchdesc_med_60',\n", + " 'Card_Merchdesc_total_60',\n", + " 'Card_Merchdesc_actual/avg_60',\n", + " 'Card_Merchdesc_actual/max_60',\n", + " 'Card_Merchdesc_actual/med_60',\n", + " 'Card_Merchdesc_actual/toal_60',\n", + " 'Card_dow_day_since',\n", + " 'Card_dow_count_7',\n", + " 'Card_dow_avg_7',\n", + " 'Card_dow_max_7',\n", + " 'Card_dow_med_7',\n", + " 'Card_dow_total_7',\n", + " 'Card_dow_actual/avg_7',\n", + " 'Card_dow_actual/max_7',\n", + " 'Card_dow_actual/med_7',\n", + " 'Card_dow_actual/toal_7',\n", + " 'Card_dow_count_14',\n", + " 'Card_dow_avg_14',\n", + " 'Card_dow_max_14',\n", + " 'Card_dow_med_14',\n", + " 'Card_dow_total_14',\n", + " 'Card_dow_actual/avg_14',\n", + " 'Card_dow_actual/max_14',\n", + " 'Card_dow_actual/med_14',\n", + " 'Card_dow_actual/toal_14',\n", + " 'Card_dow_count_30',\n", + " 'Card_dow_avg_30',\n", + " 'Card_dow_max_30',\n", + " 'Card_dow_med_30',\n", + " 'Card_dow_total_30',\n", + " 'Card_dow_actual/avg_30',\n", + " 'Card_dow_actual/max_30',\n", + " 'Card_dow_actual/med_30',\n", + " 'Card_dow_actual/toal_30',\n", + " 'Card_dow_count_60',\n", + " 'Card_dow_avg_60',\n", + " 'Card_dow_max_60',\n", + " 'Card_dow_med_60',\n", + " 'Card_dow_total_60',\n", + " 'Card_dow_actual/avg_60',\n", + " 'Card_dow_actual/max_60',\n", + " 'Card_dow_actual/med_60',\n", + " 'Card_dow_actual/toal_60',\n", + " 'Merchnum_desc_day_since',\n", + " 'Merchnum_desc_count_0',\n", + " 'Merchnum_desc_avg_0',\n", + " 'Merchnum_desc_max_0',\n", + " 'Merchnum_desc_med_0',\n", + " 'Merchnum_desc_total_0',\n", + " 'Merchnum_desc_actual/avg_0',\n", + " 'Merchnum_desc_actual/max_0',\n", + " 'Merchnum_desc_actual/med_0',\n", + " 'Merchnum_desc_actual/toal_0',\n", + " 'Merchnum_desc_count_1',\n", + " 'Merchnum_desc_avg_1',\n", + " 'Merchnum_desc_max_1',\n", + " 'Merchnum_desc_med_1',\n", + " 'Merchnum_desc_total_1',\n", + " 'Merchnum_desc_actual/avg_1',\n", + " 'Merchnum_desc_actual/max_1',\n", + " 'Merchnum_desc_actual/med_1',\n", + " 'Merchnum_desc_actual/toal_1',\n", + " 'Merchnum_desc_count_3',\n", + " 'Merchnum_desc_avg_3',\n", + " 'Merchnum_desc_max_3',\n", + " 'Merchnum_desc_med_3',\n", + " 'Merchnum_desc_total_3',\n", + " 'Merchnum_desc_actual/avg_3',\n", + " 'Merchnum_desc_actual/max_3',\n", + " 'Merchnum_desc_actual/med_3',\n", + " 'Merchnum_desc_actual/toal_3',\n", + " 'Merchnum_desc_count_7',\n", + " 'Merchnum_desc_avg_7',\n", + " 'Merchnum_desc_max_7',\n", + " 'Merchnum_desc_med_7',\n", + " 'Merchnum_desc_total_7',\n", + " 'Merchnum_desc_actual/avg_7',\n", + " 'Merchnum_desc_actual/max_7',\n", + " 'Merchnum_desc_actual/med_7',\n", + " 'Merchnum_desc_actual/toal_7',\n", + " 'Merchnum_desc_count_14',\n", + " 'Merchnum_desc_avg_14',\n", + " 'Merchnum_desc_max_14',\n", + " 'Merchnum_desc_med_14',\n", + " 'Merchnum_desc_total_14',\n", + " 'Merchnum_desc_actual/avg_14',\n", + " 'Merchnum_desc_actual/max_14',\n", + " 'Merchnum_desc_actual/med_14',\n", + " 'Merchnum_desc_actual/toal_14',\n", + " 'Merchnum_desc_count_30',\n", + " 'Merchnum_desc_avg_30',\n", + " 'Merchnum_desc_max_30',\n", + " 'Merchnum_desc_med_30',\n", + " 'Merchnum_desc_total_30',\n", + " 'Merchnum_desc_actual/avg_30',\n", + " 'Merchnum_desc_actual/max_30',\n", + " 'Merchnum_desc_actual/med_30',\n", + " 'Merchnum_desc_actual/toal_30',\n", + " 'Merchnum_desc_count_60',\n", + " 'Merchnum_desc_avg_60',\n", + " 'Merchnum_desc_max_60',\n", + " 'Merchnum_desc_med_60',\n", + " 'Merchnum_desc_total_60',\n", + " 'Merchnum_desc_actual/avg_60',\n", + " 'Merchnum_desc_actual/max_60',\n", + " 'Merchnum_desc_actual/med_60',\n", + " 'Merchnum_desc_actual/toal_60',\n", + " 'Merchnum_dow_day_since',\n", + " 'Merchnum_dow_count_7',\n", + " 'Merchnum_dow_avg_7',\n", + " 'Merchnum_dow_max_7',\n", + " 'Merchnum_dow_med_7',\n", + " 'Merchnum_dow_total_7',\n", + " 'Merchnum_dow_actual/avg_7',\n", + " 'Merchnum_dow_actual/max_7',\n", + " 'Merchnum_dow_actual/med_7',\n", + " 'Merchnum_dow_actual/toal_7',\n", + " 'Merchnum_dow_count_14',\n", + " 'Merchnum_dow_avg_14',\n", + " 'Merchnum_dow_max_14',\n", + " 'Merchnum_dow_med_14',\n", + " 'Merchnum_dow_total_14',\n", + " 'Merchnum_dow_actual/avg_14',\n", + " 'Merchnum_dow_actual/max_14',\n", + " 'Merchnum_dow_actual/med_14',\n", + " 'Merchnum_dow_actual/toal_14',\n", + " 'Merchnum_dow_count_30',\n", + " 'Merchnum_dow_avg_30',\n", + " 'Merchnum_dow_max_30',\n", + " 'Merchnum_dow_med_30',\n", + " 'Merchnum_dow_total_30',\n", + " 'Merchnum_dow_actual/avg_30',\n", + " 'Merchnum_dow_actual/max_30',\n", + " 'Merchnum_dow_actual/med_30',\n", + " 'Merchnum_dow_actual/toal_30',\n", + " 'Merchnum_dow_count_60',\n", + " 'Merchnum_dow_avg_60',\n", + " 'Merchnum_dow_max_60',\n", + " 'Merchnum_dow_med_60',\n", + " 'Merchnum_dow_total_60',\n", + " 'Merchnum_dow_actual/avg_60',\n", + " 'Merchnum_dow_actual/max_60',\n", + " 'Merchnum_dow_actual/med_60',\n", + " 'Merchnum_dow_actual/toal_60',\n", + " 'Merchdesc_dow_day_since',\n", + " 'Merchdesc_dow_count_0',\n", + " 'Merchdesc_dow_avg_0',\n", + " 'Merchdesc_dow_max_0',\n", + " 'Merchdesc_dow_med_0',\n", + " 'Merchdesc_dow_total_0',\n", + " 'Merchdesc_dow_actual/avg_0',\n", + " 'Merchdesc_dow_actual/max_0',\n", + " 'Merchdesc_dow_actual/med_0',\n", + " 'Merchdesc_dow_actual/toal_0',\n", + " 'Merchdesc_dow_count_7',\n", + " 'Merchdesc_dow_avg_7',\n", + " 'Merchdesc_dow_max_7',\n", + " 'Merchdesc_dow_med_7',\n", + " 'Merchdesc_dow_total_7',\n", + " 'Merchdesc_dow_actual/avg_7',\n", + " 'Merchdesc_dow_actual/max_7',\n", + " 'Merchdesc_dow_actual/med_7',\n", + " 'Merchdesc_dow_actual/toal_7',\n", + " 'Merchdesc_dow_count_14',\n", + " 'Merchdesc_dow_avg_14',\n", + " 'Merchdesc_dow_max_14',\n", + " 'Merchdesc_dow_med_14',\n", + " 'Merchdesc_dow_total_14',\n", + " 'Merchdesc_dow_actual/avg_14',\n", + " 'Merchdesc_dow_actual/max_14',\n", + " 'Merchdesc_dow_actual/med_14',\n", + " 'Merchdesc_dow_actual/toal_14',\n", + " 'Merchdesc_dow_count_30',\n", + " 'Merchdesc_dow_avg_30',\n", + " 'Merchdesc_dow_max_30',\n", + " 'Merchdesc_dow_med_30',\n", + " 'Merchdesc_dow_total_30',\n", + " 'Merchdesc_dow_actual/avg_30',\n", + " 'Merchdesc_dow_actual/max_30',\n", + " 'Merchdesc_dow_actual/med_30',\n", + " 'Merchdesc_dow_actual/toal_30',\n", + " 'Merchdesc_dow_count_60',\n", + " 'Merchdesc_dow_avg_60',\n", + " 'Merchdesc_dow_max_60',\n", + " 'Merchdesc_dow_med_60',\n", + " 'Merchdesc_dow_total_60',\n", + " 'Merchdesc_dow_actual/avg_60',\n", + " 'Merchdesc_dow_actual/max_60',\n", + " 'Merchdesc_dow_actual/med_60',\n", + " 'Merchdesc_dow_actual/toal_60',\n", + " 'Card_Merchnum_desc_day_since',\n", + " 'Card_Merchnum_desc_count_0',\n", + " 'Card_Merchnum_desc_avg_0',\n", + " 'Card_Merchnum_desc_max_0',\n", + " 'Card_Merchnum_desc_med_0',\n", + " 'Card_Merchnum_desc_total_0',\n", + " 'Card_Merchnum_desc_actual/avg_0',\n", + " 'Card_Merchnum_desc_actual/max_0',\n", + " 'Card_Merchnum_desc_actual/med_0',\n", + " 'Card_Merchnum_desc_actual/toal_0',\n", + " 'Card_Merchnum_desc_count_1',\n", + " 'Card_Merchnum_desc_avg_1',\n", + " 'Card_Merchnum_desc_max_1',\n", + " 'Card_Merchnum_desc_med_1',\n", + " 'Card_Merchnum_desc_total_1',\n", + " 'Card_Merchnum_desc_actual/avg_1',\n", + " 'Card_Merchnum_desc_actual/max_1',\n", + " 'Card_Merchnum_desc_actual/med_1',\n", + " 'Card_Merchnum_desc_actual/toal_1',\n", + " 'Card_Merchnum_desc_count_3',\n", + " 'Card_Merchnum_desc_avg_3',\n", + " 'Card_Merchnum_desc_max_3',\n", + " 'Card_Merchnum_desc_med_3',\n", + " 'Card_Merchnum_desc_total_3',\n", + " 'Card_Merchnum_desc_actual/avg_3',\n", + " 'Card_Merchnum_desc_actual/max_3',\n", + " 'Card_Merchnum_desc_actual/med_3',\n", + " 'Card_Merchnum_desc_actual/toal_3',\n", + " 'Card_Merchnum_desc_count_7',\n", + " 'Card_Merchnum_desc_avg_7',\n", + " 'Card_Merchnum_desc_max_7',\n", + " 'Card_Merchnum_desc_med_7',\n", + " 'Card_Merchnum_desc_total_7',\n", + " 'Card_Merchnum_desc_actual/avg_7',\n", + " 'Card_Merchnum_desc_actual/max_7',\n", + " 'Card_Merchnum_desc_actual/med_7',\n", + " 'Card_Merchnum_desc_actual/toal_7',\n", + " 'Card_Merchnum_desc_count_14',\n", + " 'Card_Merchnum_desc_avg_14',\n", + " 'Card_Merchnum_desc_max_14',\n", + " 'Card_Merchnum_desc_med_14',\n", + " 'Card_Merchnum_desc_total_14',\n", + " 'Card_Merchnum_desc_actual/avg_14',\n", + " 'Card_Merchnum_desc_actual/max_14',\n", + " 'Card_Merchnum_desc_actual/med_14',\n", + " 'Card_Merchnum_desc_actual/toal_14',\n", + " 'Card_Merchnum_desc_count_30',\n", + " 'Card_Merchnum_desc_avg_30',\n", + " 'Card_Merchnum_desc_max_30',\n", + " 'Card_Merchnum_desc_med_30',\n", + " 'Card_Merchnum_desc_total_30',\n", + " 'Card_Merchnum_desc_actual/avg_30',\n", + " 'Card_Merchnum_desc_actual/max_30',\n", + " 'Card_Merchnum_desc_actual/med_30',\n", + " 'Card_Merchnum_desc_actual/toal_30',\n", + " 'Card_Merchnum_desc_count_60',\n", + " 'Card_Merchnum_desc_avg_60',\n", + " 'Card_Merchnum_desc_max_60',\n", + " 'Card_Merchnum_desc_med_60',\n", + " 'Card_Merchnum_desc_total_60',\n", + " 'Card_Merchnum_desc_actual/avg_60',\n", + " 'Card_Merchnum_desc_actual/max_60',\n", + " 'Card_Merchnum_desc_actual/med_60',\n", + " 'Card_Merchnum_desc_actual/toal_60',\n", + " 'Card_Merchnum_Zip_day_since',\n", + " 'Card_Merchnum_Zip_count_0',\n", + " 'Card_Merchnum_Zip_avg_0',\n", + " 'Card_Merchnum_Zip_max_0',\n", + " 'Card_Merchnum_Zip_med_0',\n", + " 'Card_Merchnum_Zip_total_0',\n", + " 'Card_Merchnum_Zip_actual/avg_0',\n", + " 'Card_Merchnum_Zip_actual/max_0',\n", + " 'Card_Merchnum_Zip_actual/med_0',\n", + " 'Card_Merchnum_Zip_actual/toal_0',\n", + " 'Card_Merchnum_Zip_count_1',\n", + " 'Card_Merchnum_Zip_avg_1',\n", + " 'Card_Merchnum_Zip_max_1',\n", + " 'Card_Merchnum_Zip_med_1',\n", + " 'Card_Merchnum_Zip_total_1',\n", + " 'Card_Merchnum_Zip_actual/avg_1',\n", + " 'Card_Merchnum_Zip_actual/max_1',\n", + " 'Card_Merchnum_Zip_actual/med_1',\n", + " 'Card_Merchnum_Zip_actual/toal_1',\n", + " 'Card_Merchnum_Zip_count_3',\n", + " 'Card_Merchnum_Zip_avg_3',\n", + " 'Card_Merchnum_Zip_max_3',\n", + " 'Card_Merchnum_Zip_med_3',\n", + " 'Card_Merchnum_Zip_total_3',\n", + " 'Card_Merchnum_Zip_actual/avg_3',\n", + " 'Card_Merchnum_Zip_actual/max_3',\n", + " 'Card_Merchnum_Zip_actual/med_3',\n", + " 'Card_Merchnum_Zip_actual/toal_3',\n", + " 'Card_Merchnum_Zip_count_7',\n", + " 'Card_Merchnum_Zip_avg_7',\n", + " 'Card_Merchnum_Zip_max_7',\n", + " 'Card_Merchnum_Zip_med_7',\n", + " 'Card_Merchnum_Zip_total_7',\n", + " 'Card_Merchnum_Zip_actual/avg_7',\n", + " 'Card_Merchnum_Zip_actual/max_7',\n", + " 'Card_Merchnum_Zip_actual/med_7',\n", + " 'Card_Merchnum_Zip_actual/toal_7',\n", + " 'Card_Merchnum_Zip_count_14',\n", + " 'Card_Merchnum_Zip_avg_14',\n", + " 'Card_Merchnum_Zip_max_14',\n", + " 'Card_Merchnum_Zip_med_14',\n", + " 'Card_Merchnum_Zip_total_14',\n", + " 'Card_Merchnum_Zip_actual/avg_14',\n", + " 'Card_Merchnum_Zip_actual/max_14',\n", + " 'Card_Merchnum_Zip_actual/med_14',\n", + " 'Card_Merchnum_Zip_actual/toal_14',\n", + " 'Card_Merchnum_Zip_count_30',\n", + " 'Card_Merchnum_Zip_avg_30',\n", + " 'Card_Merchnum_Zip_max_30',\n", + " 'Card_Merchnum_Zip_med_30',\n", + " 'Card_Merchnum_Zip_total_30',\n", + " 'Card_Merchnum_Zip_actual/avg_30',\n", + " 'Card_Merchnum_Zip_actual/max_30',\n", + " 'Card_Merchnum_Zip_actual/med_30',\n", + " 'Card_Merchnum_Zip_actual/toal_30',\n", + " 'Card_Merchnum_Zip_count_60',\n", + " 'Card_Merchnum_Zip_avg_60',\n", + " 'Card_Merchnum_Zip_max_60',\n", + " 'Card_Merchnum_Zip_med_60',\n", + " 'Card_Merchnum_Zip_total_60',\n", + " 'Card_Merchnum_Zip_actual/avg_60',\n", + " 'Card_Merchnum_Zip_actual/max_60',\n", + " 'Card_Merchnum_Zip_actual/med_60',\n", + " 'Card_Merchnum_Zip_actual/toal_60',\n", + " 'Card_Merchdesc_Zip_day_since',\n", + " 'Card_Merchdesc_Zip_count_0',\n", + " 'Card_Merchdesc_Zip_avg_0',\n", + " 'Card_Merchdesc_Zip_max_0',\n", + " 'Card_Merchdesc_Zip_med_0',\n", + " 'Card_Merchdesc_Zip_total_0',\n", + " 'Card_Merchdesc_Zip_actual/avg_0',\n", + " 'Card_Merchdesc_Zip_actual/max_0',\n", + " 'Card_Merchdesc_Zip_actual/med_0',\n", + " 'Card_Merchdesc_Zip_actual/toal_0',\n", + " 'Card_Merchdesc_Zip_count_1',\n", + " 'Card_Merchdesc_Zip_avg_1',\n", + " 'Card_Merchdesc_Zip_max_1',\n", + " 'Card_Merchdesc_Zip_med_1',\n", + " 'Card_Merchdesc_Zip_total_1',\n", + " 'Card_Merchdesc_Zip_actual/avg_1',\n", + " 'Card_Merchdesc_Zip_actual/max_1',\n", + " ...]" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final.columns.values.tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [], + "source": [ + "# careful about this line. Modify it so you only keep the variables (including the record # and dependent variable)\n", + "final_vars = final.iloc[:, np.r_[8, 10, 11, len(entities)+10:len(final.columns)]]" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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FraudDow_RiskMonthCard_Merchnum_ZipCard_Merchdesc_ZipMerchnum_desc_StateCardnum_day_sinceCardnum_count_0Cardnum_avg_0Cardnum_max_0...Merchnum_desc_State_count_0_by_7_sqMerchnum_desc_State_count_0_by_14_sqMerchnum_desc_State_count_0_by_30_sqMerchnum_desc_State_count_0_by_60_sqMerchnum_desc_State_count_1_by_7_sqMerchnum_desc_State_count_1_by_14_sqMerchnum_desc_State_count_1_by_30_sqMerchnum_desc_State_count_1_by_60_sqamount_catforeign
Recnum
100.025994January5142190439550900629625438118.05142190439FEDEXSHP12/23/09AB#38118.05509006296254FEDEXSHP12/23/09AB#TN1461.013.623.62...0.0204080.0051020.0011110.0002780.0204080.0051020.0011110.0002781False
200.025994January5142183973610030263331803.05142183973SERVICEMERCHANDISE#811803.061003026333SERVICEMERCHANDISE#81MA1461.0131.4231.42...0.0204080.0051020.0011110.0002780.0204080.0051020.0011110.0002782False
300.025994January5142131721450308299360020706.05142131721OFFICEDEPOT#19120706.04503082993600OFFICEDEPOT#191MD1461.01178.49178.49...0.0204080.0051020.0011110.0002780.0204080.0051020.0011110.0002783False
400.025994January5142148452550900629625438118.05142148452FEDEXSHP12/28/09AB#38118.05509006296254FEDEXSHP12/28/09AB#TN1461.013.623.62...0.0204080.0051020.0011110.0002780.0204080.0051020.0011110.0002781False
500.025994January5142190439550900629625438118.05142190439FEDEXSHP12/23/09AB#38118.05509006296254FEDEXSHP12/23/09AB#TN0.023.623.62...0.0204080.0051020.0011110.0002780.0204080.0051020.0011110.0002781False
\n", + "

5 rows × 3022 columns

\n", + "
" + ], + "text/plain": [ + " Fraud Dow_Risk Month Card_Merchnum_Zip \\\n", + "Recnum \n", + "1 0 0.025994 January 5142190439550900629625438118.0 \n", + "2 0 0.025994 January 5142183973610030263331803.0 \n", + "3 0 0.025994 January 5142131721450308299360020706.0 \n", + "4 0 0.025994 January 5142148452550900629625438118.0 \n", + "5 0 0.025994 January 5142190439550900629625438118.0 \n", + "\n", + " Card_Merchdesc_Zip \\\n", + "Recnum \n", + "1 5142190439FEDEXSHP12/23/09AB#38118.0 \n", + "2 5142183973SERVICEMERCHANDISE#811803.0 \n", + "3 5142131721OFFICEDEPOT#19120706.0 \n", + "4 5142148452FEDEXSHP12/28/09AB#38118.0 \n", + "5 5142190439FEDEXSHP12/23/09AB#38118.0 \n", + "\n", + " Merchnum_desc_State Cardnum_day_since Cardnum_count_0 \\\n", + "Recnum \n", + "1 5509006296254FEDEXSHP12/23/09AB#TN 1461.0 1 \n", + "2 61003026333SERVICEMERCHANDISE#81MA 1461.0 1 \n", + "3 4503082993600OFFICEDEPOT#191MD 1461.0 1 \n", + "4 5509006296254FEDEXSHP12/28/09AB#TN 1461.0 1 \n", + "5 5509006296254FEDEXSHP12/23/09AB#TN 0.0 2 \n", + "\n", + " Cardnum_avg_0 Cardnum_max_0 ... Merchnum_desc_State_count_0_by_7_sq \\\n", + "Recnum ... \n", + "1 3.62 3.62 ... 0.020408 \n", + "2 31.42 31.42 ... 0.020408 \n", + "3 178.49 178.49 ... 0.020408 \n", + "4 3.62 3.62 ... 0.020408 \n", + "5 3.62 3.62 ... 0.020408 \n", + "\n", + " Merchnum_desc_State_count_0_by_14_sq \\\n", + "Recnum \n", + "1 0.005102 \n", + "2 0.005102 \n", + "3 0.005102 \n", + "4 0.005102 \n", + "5 0.005102 \n", + "\n", + " Merchnum_desc_State_count_0_by_30_sq \\\n", + "Recnum \n", + "1 0.001111 \n", + "2 0.001111 \n", + "3 0.001111 \n", + "4 0.001111 \n", + "5 0.001111 \n", + "\n", + " Merchnum_desc_State_count_0_by_60_sq \\\n", + "Recnum \n", + "1 0.000278 \n", + "2 0.000278 \n", + "3 0.000278 \n", + "4 0.000278 \n", + "5 0.000278 \n", + "\n", + " Merchnum_desc_State_count_1_by_7_sq \\\n", + "Recnum \n", + "1 0.020408 \n", + "2 0.020408 \n", + "3 0.020408 \n", + "4 0.020408 \n", + "5 0.020408 \n", + "\n", + " Merchnum_desc_State_count_1_by_14_sq \\\n", + "Recnum \n", + "1 0.005102 \n", + "2 0.005102 \n", + "3 0.005102 \n", + "4 0.005102 \n", + "5 0.005102 \n", + "\n", + " Merchnum_desc_State_count_1_by_30_sq \\\n", + "Recnum \n", + "1 0.001111 \n", + "2 0.001111 \n", + "3 0.001111 \n", + "4 0.001111 \n", + "5 0.001111 \n", + "\n", + " Merchnum_desc_State_count_1_by_60_sq amount_cat foreign \n", + "Recnum \n", + "1 0.000278 1 False \n", + "2 0.000278 2 False \n", + "3 0.000278 3 False \n", + "4 0.000278 1 False \n", + "5 0.000278 1 False \n", + "\n", + "[5 rows x 3022 columns]" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_vars.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(96397, 3022)" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_vars.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#final_vars.to_csv('candidate_variables.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duration: 0:50:06.742062\n" + ] + } + ], + "source": [ + "print('Duration: ', pd.datetime.now() - start_time)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}