4870 lines
879 KiB
Plaintext
4870 lines
879 KiB
Plaintext
{
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"cells": [
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"id": "7aa09644-4d17-4a7a-841e-3bfcfb8a8901",
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"name": "stdout",
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"text": [
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"Fichiers Flows : ['projet-bdc-data/carmignac/Flows ENSAE V1 -20251027.csv', 'projet-bdc-data/carmignac/Flows ENSAE V2 -20251105.csv']\n",
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"Fichiers AUM : ['projet-bdc-data/carmignac/AUM ENSAE V1 -20251027.csv', 'projet-bdc-data/carmignac/AUM ENSAE V2 -20251105.csv']\n"
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]
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}
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],
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"source": [
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"# Import des données\n",
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"\n",
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"import os\n",
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"import s3fs\n",
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"import pandas as pd\n",
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"\n",
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"s3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n",
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"\n",
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"fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': s3_ENDPOINT_URL})\n",
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"\n",
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"BUCKET = \"projet-bdc-data\"\n",
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"carmignac_path = \"projet-bdc-data/carmignac\"\n",
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"\n",
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"# Liste des fichiers FLOWS\n",
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"all_files = fs.ls(carmignac_path)\n",
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"flows_files = [f for f in all_files if \"Flows\" in f and f.endswith(\".csv\")]\n",
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"print(\"Fichiers Flows :\", flows_files)\n",
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"\n",
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"# Lire tous les fichiers dans un dictionnaire\n",
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"flows_data = {}\n",
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"for file_path in flows_files:\n",
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" with fs.open(file_path, 'r') as f:\n",
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" df = pd.read_csv(f, sep=';',low_memory=False)\n",
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" flows_data[os.path.basename(file_path)] = df\n",
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"\n",
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"\n",
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"# Liste des fichiers AUM\n",
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"all_files = fs.ls(carmignac_path)\n",
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"aum_files = [f for f in all_files if \"AUM\" in f and f.endswith(\".csv\")]\n",
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"print(\"Fichiers AUM :\", aum_files)\n",
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"\n",
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"# Lire tous les fichiers dans un dictionnaire\n",
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"aum_data = {}\n",
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"for file_path in aum_files:\n",
|
||
" with fs.open(file_path, 'r') as f:\n",
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" df = pd.read_csv(f, sep=';',low_memory=False)\n",
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" aum_data[os.path.basename(file_path)] = df\n",
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"\n",
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"df = aum_data['AUM ENSAE V2 -20251105.csv']\n",
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"dg = flows_data['Flows ENSAE V2 -20251105.csv']"
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]
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},
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{
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"cell_type": "code",
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" <th>Agreement - Code</th>\n",
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" <th>Company - Id</th>\n",
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" <th>Company - Ultimate Parent Id</th>\n",
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" <th>Registrar Account - ID</th>\n",
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" <th>Product - Shareclass Currency</th>\n",
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" <th>Centralisation Date</th>\n",
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" <th>Quantity - AUM</th>\n",
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" <th>Value - AUM CCY</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <td>Diversified</td>\n",
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" <td>Patrimoine</td>\n",
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" <td>FCP</td>\n",
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" <td>NO</td>\n",
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" <td>Carmignac Patrimoine</td>\n",
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" <td>France</td>\n",
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" <td>Diversified</td>\n",
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||
" <td>Patrimoine</td>\n",
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||
" <td>FCP</td>\n",
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||
" <td>NO</td>\n",
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||
" <td>Carmignac Patrimoine</td>\n",
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" <td>2015-11-30</td>\n",
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" <td>35.368</td>\n",
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" <td>22413.0553</td>\n",
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" <td>22413.0553</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>003</td>\n",
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" <td>166</td>\n",
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" <td>166</td>\n",
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" <td>200000647</td>\n",
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" <td>France</td>\n",
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" <td>France</td>\n",
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||
" <td>Diversified</td>\n",
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||
" <td>Patrimoine</td>\n",
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||
" <td>FCP</td>\n",
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||
" <td>NO</td>\n",
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||
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" <td>A</td>\n",
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||
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||
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||
" <td>FCP</td>\n",
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||
" <td>NO</td>\n",
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||
" <td>Carmignac Patrimoine</td>\n",
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||
" <td>A</td>\n",
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||
" <td>EUR</td>\n",
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||
" <td>FR0010135103</td>\n",
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" <td>2016-03-31</td>\n",
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" <td>35.368</td>\n",
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" <td>21626.1173</td>\n",
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" <td>21626.1173</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>003</td>\n",
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||
" <td>166</td>\n",
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||
" <td>166</td>\n",
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" <td>200000647</td>\n",
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" <td>France</td>\n",
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" <td>FCP</td>\n",
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||
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],
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"text/plain": [
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||
" Agreement - Code Company - Id Company - Ultimate Parent Id \\\n",
|
||
"0 003 166 166 \n",
|
||
"1 003 166 166 \n",
|
||
"2 003 166 166 \n",
|
||
"3 003 166 166 \n",
|
||
"4 003 166 166 \n",
|
||
"\n",
|
||
" Registrar Account - ID Registrar Account - Region \\\n",
|
||
"0 200000647 France \n",
|
||
"1 200000647 France \n",
|
||
"2 200000647 France \n",
|
||
"3 200000647 France \n",
|
||
"4 200000647 France \n",
|
||
"\n",
|
||
" RegistrarAccount - Country Product - Asset Type Product - Strategy \\\n",
|
||
"0 France Diversified Patrimoine \n",
|
||
"1 France Diversified Patrimoine \n",
|
||
"2 France Diversified Patrimoine \n",
|
||
"3 France Diversified Patrimoine \n",
|
||
"4 France Diversified Patrimoine \n",
|
||
"\n",
|
||
" Product - Legal Status Product - Is Dedie ? Product - Fund \\\n",
|
||
"0 FCP NO Carmignac Patrimoine \n",
|
||
"1 FCP NO Carmignac Patrimoine \n",
|
||
"2 FCP NO Carmignac Patrimoine \n",
|
||
"3 FCP NO Carmignac Patrimoine \n",
|
||
"4 FCP NO Carmignac Patrimoine \n",
|
||
"\n",
|
||
" Product - Shareclass Type Product - Shareclass Currency Product - Isin \\\n",
|
||
"0 A EUR FR0010135103 \n",
|
||
"1 A EUR FR0010135103 \n",
|
||
"2 A EUR FR0010135103 \n",
|
||
"3 A EUR FR0010135103 \n",
|
||
"4 A EUR FR0010135103 \n",
|
||
"\n",
|
||
" Centralisation Date Quantity - AUM Value - AUM CCY Value - AUM € \n",
|
||
"0 2015-03-31 35.368 24648.6666 24648.6666 \n",
|
||
"1 2015-11-30 35.368 22413.0553 22413.0553 \n",
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||
"2 2015-12-31 35.368 22051.2406 22051.2406 \n",
|
||
"3 2016-03-31 35.368 21626.1173 21626.1173 \n",
|
||
"4 2016-11-30 35.368 22489.4502 22489.4502 "
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
||
"df.head()"
|
||
]
|
||
},
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{
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"cell_type": "code",
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||
"execution_count": 222,
|
||
"id": "d5262683-6ae5-4ee6-b949-58a468c7c7b5",
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||
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||
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||
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||
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||
" <td>SICAV</td>\n",
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||
" <td>NO</td>\n",
|
||
" <td>...</td>\n",
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
" <td>Patrimoine</td>\n",
|
||
" <td>FCP</td>\n",
|
||
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|
||
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|
||
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|
||
" <td>144.69</td>\n",
|
||
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|
||
" <td>144.690</td>\n",
|
||
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|
||
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|
||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>Equity</td>\n",
|
||
" <td>Investissement</td>\n",
|
||
" <td>FCP</td>\n",
|
||
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|
||
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|
||
" <td>2016-10-26</td>\n",
|
||
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||
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|
||
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|
||
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||
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||
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|
||
" <th>3</th>\n",
|
||
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|
||
" <td>166</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>FCP</td>\n",
|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
" <th>4</th>\n",
|
||
" <td>003</td>\n",
|
||
" <td>166</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>FCP</td>\n",
|
||
" <td>NO</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>2019-04-08</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>-465.992</td>\n",
|
||
" <td>-465.992</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>-563775.76</td>\n",
|
||
" <td>-563775.76</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>-563775.76</td>\n",
|
||
" <td>-563775.76</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>5 rows × 24 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Agreement - Code Company - Id Company - Ultimate Parent Id \\\n",
|
||
"0 003 166 166 \n",
|
||
"1 003 166 166 \n",
|
||
"2 003 166 166 \n",
|
||
"3 003 166 166 \n",
|
||
"4 003 166 166 \n",
|
||
"\n",
|
||
" Registrar Account - ID Registrar Account - Region \\\n",
|
||
"0 200127202 France \n",
|
||
"1 406533 France \n",
|
||
"2 406533 France \n",
|
||
"3 406533 France \n",
|
||
"4 406533 France \n",
|
||
"\n",
|
||
" RegistrarAccount - Country Product - Asset Type Product - Strategy \\\n",
|
||
"0 France Equity Investissement \n",
|
||
"1 France Diversified Patrimoine \n",
|
||
"2 France Equity Investissement \n",
|
||
"3 France Equity Investissement \n",
|
||
"4 France Equity Investissement \n",
|
||
"\n",
|
||
" Product - Legal Status Product - Is Dedie ? ... Centralisation Date \\\n",
|
||
"0 SICAV NO ... 2020-11-05 \n",
|
||
"1 FCP NO ... 2015-03-09 \n",
|
||
"2 FCP NO ... 2016-10-26 \n",
|
||
"3 FCP NO ... 2018-10-18 \n",
|
||
"4 FCP NO ... 2019-04-08 \n",
|
||
"\n",
|
||
" Quantity - Subscription Quantity - Redemption Quantity - NetFlows \\\n",
|
||
"0 1636.00 0.000 1636.000 \n",
|
||
"1 144.69 0.000 144.690 \n",
|
||
"2 0.00 -8.321 -8.321 \n",
|
||
"3 0.00 -22.083 -22.083 \n",
|
||
"4 0.00 -465.992 -465.992 \n",
|
||
"\n",
|
||
" Value Ccy - Subscription Value Ccy - Redemption Value Ccy - NetFlows \\\n",
|
||
"0 280983.00 0.00 280983.00 \n",
|
||
"1 99985.13 0.00 99985.13 \n",
|
||
"2 0.00 -9384.76 -9384.76 \n",
|
||
"3 0.00 -25227.40 -25227.40 \n",
|
||
"4 0.00 -563775.76 -563775.76 \n",
|
||
"\n",
|
||
" Value € - Subscription Value € - Redemption Value € - NetFlows \n",
|
||
"0 280983.00 0.00 280983.00 \n",
|
||
"1 99985.13 0.00 99985.13 \n",
|
||
"2 0.00 -9384.76 -9384.76 \n",
|
||
"3 0.00 -25227.40 -25227.40 \n",
|
||
"4 0.00 -563775.76 -563775.76 \n",
|
||
"\n",
|
||
"[5 rows x 24 columns]"
|
||
]
|
||
},
|
||
"execution_count": 222,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"dg.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 76,
|
||
"id": "b31d3cb3-479c-4d2b-b6f5-8f78cd69407a",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Filtrer les comptes techniques\n",
|
||
"\n",
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"df['Centralisation Date'] = pd.to_datetime(df['Centralisation Date'])\n",
|
||
"dg['Centralisation Date'] = pd.to_datetime(dg['Centralisation Date'])\n",
|
||
"df = df[~df['Registrar Account - ID'].isin(['Off Distribution','Private Clients', 'Private Client'])]\n",
|
||
"dg = dg[~dg['Registrar Account - ID'].isin(['Off Distribution','Private Clients','Private Client'])]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 77,
|
||
"id": "dec37ff8-0f54-4e3e-ac63-a71f9b3583d9",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"(431, 2)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Registrar Account - ID</th>\n",
|
||
" <th>Value - AUM €</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>3890</th>\n",
|
||
" <td>420350</td>\n",
|
||
" <td>1.623308e+09</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2622</th>\n",
|
||
" <td>364765</td>\n",
|
||
" <td>1.383209e+09</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>956</th>\n",
|
||
" <td>200127454</td>\n",
|
||
" <td>8.784361e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2598</th>\n",
|
||
" <td>312933</td>\n",
|
||
" <td>8.379604e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1099</th>\n",
|
||
" <td>200127809</td>\n",
|
||
" <td>8.342839e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3880</th>\n",
|
||
" <td>420259</td>\n",
|
||
" <td>8.296663e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2634</th>\n",
|
||
" <td>364907</td>\n",
|
||
" <td>8.151083e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2785</th>\n",
|
||
" <td>366441</td>\n",
|
||
" <td>7.707213e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2637</th>\n",
|
||
" <td>364929</td>\n",
|
||
" <td>7.479766e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2710</th>\n",
|
||
" <td>365538</td>\n",
|
||
" <td>7.200408e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2775</th>\n",
|
||
" <td>366403</td>\n",
|
||
" <td>7.092081e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3818</th>\n",
|
||
" <td>418961</td>\n",
|
||
" <td>6.529718e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>336</th>\n",
|
||
" <td>200058108</td>\n",
|
||
" <td>6.110961e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1764</th>\n",
|
||
" <td>200131722</td>\n",
|
||
" <td>5.758019e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>409</th>\n",
|
||
" <td>200073354</td>\n",
|
||
" <td>4.619978e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2741</th>\n",
|
||
" <td>365848</td>\n",
|
||
" <td>4.563625e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>423</th>\n",
|
||
" <td>200075932</td>\n",
|
||
" <td>4.375607e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>925</th>\n",
|
||
" <td>200127410</td>\n",
|
||
" <td>3.920364e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>872</th>\n",
|
||
" <td>200127316</td>\n",
|
||
" <td>3.707238e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>74</th>\n",
|
||
" <td>200001349</td>\n",
|
||
" <td>3.650226e+08</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Registrar Account - ID Value - AUM €\n",
|
||
"3890 420350 1.623308e+09\n",
|
||
"2622 364765 1.383209e+09\n",
|
||
"956 200127454 8.784361e+08\n",
|
||
"2598 312933 8.379604e+08\n",
|
||
"1099 200127809 8.342839e+08\n",
|
||
"3880 420259 8.296663e+08\n",
|
||
"2634 364907 8.151083e+08\n",
|
||
"2785 366441 7.707213e+08\n",
|
||
"2637 364929 7.479766e+08\n",
|
||
"2710 365538 7.200408e+08\n",
|
||
"2775 366403 7.092081e+08\n",
|
||
"3818 418961 6.529718e+08\n",
|
||
"336 200058108 6.110961e+08\n",
|
||
"1764 200131722 5.758019e+08\n",
|
||
"409 200073354 4.619978e+08\n",
|
||
"2741 365848 4.563625e+08\n",
|
||
"423 200075932 4.375607e+08\n",
|
||
"925 200127410 3.920364e+08\n",
|
||
"872 200127316 3.707238e+08\n",
|
||
"74 200001349 3.650226e+08"
|
||
]
|
||
},
|
||
"execution_count": 77,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Date de référence et sélection des 400+ principaux codes\n",
|
||
"\n",
|
||
"ref_date = pd.Timestamp('2025-10-31')\n",
|
||
"\n",
|
||
"df_ref = df[df['Centralisation Date'] == ref_date]\n",
|
||
"\n",
|
||
"aum_account = (\n",
|
||
" df_ref\n",
|
||
" .groupby('Registrar Account - ID')['Value - AUM €']\n",
|
||
" .sum()\n",
|
||
" .reset_index()\n",
|
||
" .sort_values(by='Value - AUM €', ascending=False)\n",
|
||
")\n",
|
||
"aum_account = aum_account[aum_account['Value - AUM €'] > 5_000_000]\n",
|
||
"selected_accounts = aum_account['Registrar Account - ID']\n",
|
||
"\n",
|
||
"print(aum_account.shape)\n",
|
||
"aum_account.head(20)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 44,
|
||
"id": "95238a61-c263-43b4-965f-e09226ec4c73",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_aum = df[df['Registrar Account - ID'].isin(selected_accounts)].copy()\n",
|
||
"df_flows = dg[dg['Registrar Account - ID'].isin(selected_accounts)].copy()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 45,
|
||
"id": "a6dcac05-e9f9-4c25-ade9-7b1f9e35582f",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Clustering\n",
|
||
"\n",
|
||
"# Parse dates\n",
|
||
"df_flows[\"Centralisation Date\"] = pd.to_datetime(df_flows[\"Centralisation Date\"], errors=\"coerce\")\n",
|
||
"df_aum[\"Centralisation Date\"] = pd.to_datetime(df_aum[\"Centralisation Date\"], errors=\"coerce\")\n",
|
||
"\n",
|
||
"ID_COL = \"Registrar Account - ID\"\n",
|
||
"FLOW_COL = \"Quantity - NetFlows\"\n",
|
||
"AUM_COL = \"Quantity - AUM\"\n",
|
||
"\n",
|
||
"# Month key\n",
|
||
"df_flows[\"month\"] = df_flows[\"Centralisation Date\"].dt.to_period(\"M\").dt.to_timestamp(\"M\")\n",
|
||
"df_aum[\"month\"] = df_aum[\"Centralisation Date\"].dt.to_period(\"M\").dt.to_timestamp(\"M\")\n",
|
||
"# Flows sont journaliers, AUM est mensuel → il faut une granularité commune."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 46,
|
||
"id": "5ea26597-af38-41f1-9cde-e9cd115e8678",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"(33972, 6)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Registrar Account - ID</th>\n",
|
||
" <th>month</th>\n",
|
||
" <th>aum_qty</th>\n",
|
||
" <th>net_flow_qty</th>\n",
|
||
" <th>gross_flow_qty</th>\n",
|
||
" <th>n_tx</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>18872</td>\n",
|
||
" <td>2015-01-31</td>\n",
|
||
" <td>179864.637</td>\n",
|
||
" <td>-1524.010</td>\n",
|
||
" <td>15230.010</td>\n",
|
||
" <td>32</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>18872</td>\n",
|
||
" <td>2015-02-28</td>\n",
|
||
" <td>186761.736</td>\n",
|
||
" <td>7247.100</td>\n",
|
||
" <td>18571.880</td>\n",
|
||
" <td>38</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>18872</td>\n",
|
||
" <td>2015-03-31</td>\n",
|
||
" <td>190357.718</td>\n",
|
||
" <td>3655.380</td>\n",
|
||
" <td>9754.040</td>\n",
|
||
" <td>47</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>18872</td>\n",
|
||
" <td>2015-04-30</td>\n",
|
||
" <td>191429.324</td>\n",
|
||
" <td>-218.394</td>\n",
|
||
" <td>12840.950</td>\n",
|
||
" <td>39</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>18872</td>\n",
|
||
" <td>2015-05-31</td>\n",
|
||
" <td>189056.475</td>\n",
|
||
" <td>-4782.849</td>\n",
|
||
" <td>6332.849</td>\n",
|
||
" <td>24</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Registrar Account - ID month aum_qty net_flow_qty gross_flow_qty \\\n",
|
||
"0 18872 2015-01-31 179864.637 -1524.010 15230.010 \n",
|
||
"1 18872 2015-02-28 186761.736 7247.100 18571.880 \n",
|
||
"2 18872 2015-03-31 190357.718 3655.380 9754.040 \n",
|
||
"3 18872 2015-04-30 191429.324 -218.394 12840.950 \n",
|
||
"4 18872 2015-05-31 189056.475 -4782.849 6332.849 \n",
|
||
"\n",
|
||
" n_tx \n",
|
||
"0 32 \n",
|
||
"1 38 \n",
|
||
"2 47 \n",
|
||
"3 39 \n",
|
||
"4 24 "
|
||
]
|
||
},
|
||
"execution_count": 46,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 1) Monthly aggregation for FLOWS : je fais mon etude mensuel parce que aum valeur mensuel \n",
|
||
"\n",
|
||
"ID_COL = \"Registrar Account - ID\"\n",
|
||
"FLOW_COL = \"Quantity - NetFlows\"\n",
|
||
"AUM_COL = \"Quantity - AUM\"\n",
|
||
"\n",
|
||
"df_flows_m = (\n",
|
||
" df_flows\n",
|
||
" .dropna(subset=[ID_COL, \"month\", FLOW_COL])\n",
|
||
" .assign(gross_flow_qty=lambda x: x[FLOW_COL].abs()) # absolute quantity moved\n",
|
||
" .groupby([ID_COL, \"month\"], as_index=False)\n",
|
||
" .agg(\n",
|
||
" net_flow_qty=(FLOW_COL, \"sum\"), # net quantity change over the month\n",
|
||
" gross_flow_qty=(\"gross_flow_qty\", \"sum\"), # total traded quantity (activity intensity)\n",
|
||
" n_tx=(FLOW_COL, \"size\"), # number of transactions\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"# 2) Monthly aggregation for AUM (client-month holdings) ---\n",
|
||
"df_aum_m = (\n",
|
||
" df_aum\n",
|
||
" .dropna(subset=[ID_COL, \"month\", AUM_COL])\n",
|
||
" .groupby([ID_COL, \"month\"], as_index=False)\n",
|
||
" .agg(aum_qty=(AUM_COL, \"sum\")) # total held quantity across ISINs\n",
|
||
")\n",
|
||
"\n",
|
||
"df_month = df_aum_m.merge(df_flows_m, on=[ID_COL, \"month\"], how=\"left\")\n",
|
||
"\n",
|
||
"# 4) Months without transactions => flows are 0 ---\n",
|
||
"df_month[\"net_flow_qty\"] = df_month[\"net_flow_qty\"].fillna(0.0)\n",
|
||
"df_month[\"gross_flow_qty\"] = df_month[\"gross_flow_qty\"].fillna(0.0)\n",
|
||
"df_month[\"n_tx\"] = df_month[\"n_tx\"].fillna(0).astype(int)\n",
|
||
"\n",
|
||
"print(df_month.shape)\n",
|
||
"df_month.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 53,
|
||
"id": "f9c7a0e3-b15a-4404-a99d-b23894cbd3f4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Registrar Account - ID</th>\n",
|
||
" <th>n_months</th>\n",
|
||
" <th>n_active_months</th>\n",
|
||
" <th>flow_freq</th>\n",
|
||
" <th>aum_qty_mean</th>\n",
|
||
" <th>aum_qty_median</th>\n",
|
||
" <th>net_flow_qty_sum</th>\n",
|
||
" <th>gross_flow_qty_sum</th>\n",
|
||
" <th>gross_flow_qty_mean</th>\n",
|
||
" <th>net_flow_qty_vol</th>\n",
|
||
" <th>rel_intensity</th>\n",
|
||
" <th>netflow_to_aum</th>\n",
|
||
" <th>n_tx_total</th>\n",
|
||
" <th>log_aum_qty_mean</th>\n",
|
||
" <th>log_gross_flow_qty_mean</th>\n",
|
||
" <th>gross_flow_to_aum</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>18872</td>\n",
|
||
" <td>130</td>\n",
|
||
" <td>130</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>143505.697354</td>\n",
|
||
" <td>144653.1645</td>\n",
|
||
" <td>-45677.1480</td>\n",
|
||
" <td>1.244126e+06</td>\n",
|
||
" <td>9570.200015</td>\n",
|
||
" <td>9832.357264</td>\n",
|
||
" <td>0.069449</td>\n",
|
||
" <td>-0.003918</td>\n",
|
||
" <td>1926</td>\n",
|
||
" <td>11.874137</td>\n",
|
||
" <td>9.166514</td>\n",
|
||
" <td>8.669523</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>200000076</td>\n",
|
||
" <td>130</td>\n",
|
||
" <td>119</td>\n",
|
||
" <td>0.915385</td>\n",
|
||
" <td>24141.541138</td>\n",
|
||
" <td>19888.8255</td>\n",
|
||
" <td>54791.9840</td>\n",
|
||
" <td>2.314415e+05</td>\n",
|
||
" <td>1780.319492</td>\n",
|
||
" <td>2838.000232</td>\n",
|
||
" <td>0.083230</td>\n",
|
||
" <td>-0.000893</td>\n",
|
||
" <td>518</td>\n",
|
||
" <td>10.091731</td>\n",
|
||
" <td>7.485110</td>\n",
|
||
" <td>9.586858</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>200000082</td>\n",
|
||
" <td>130</td>\n",
|
||
" <td>130</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>422994.464523</td>\n",
|
||
" <td>462973.7880</td>\n",
|
||
" <td>178371.1590</td>\n",
|
||
" <td>2.327246e+06</td>\n",
|
||
" <td>17901.894469</td>\n",
|
||
" <td>13288.481111</td>\n",
|
||
" <td>0.047480</td>\n",
|
||
" <td>0.005194</td>\n",
|
||
" <td>7103</td>\n",
|
||
" <td>12.955117</td>\n",
|
||
" <td>9.792718</td>\n",
|
||
" <td>5.501836</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>200000146</td>\n",
|
||
" <td>130</td>\n",
|
||
" <td>130</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>212108.397869</td>\n",
|
||
" <td>210616.5330</td>\n",
|
||
" <td>457533.3310</td>\n",
|
||
" <td>1.150546e+06</td>\n",
|
||
" <td>8850.350438</td>\n",
|
||
" <td>10074.748210</td>\n",
|
||
" <td>0.051622</td>\n",
|
||
" <td>0.024910</td>\n",
|
||
" <td>4774</td>\n",
|
||
" <td>12.264857</td>\n",
|
||
" <td>9.088325</td>\n",
|
||
" <td>5.424328</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>200000147</td>\n",
|
||
" <td>130</td>\n",
|
||
" <td>130</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>145729.199224</td>\n",
|
||
" <td>79260.8255</td>\n",
|
||
" <td>677492.4351</td>\n",
|
||
" <td>1.213963e+06</td>\n",
|
||
" <td>9338.178685</td>\n",
|
||
" <td>13868.197522</td>\n",
|
||
" <td>0.061164</td>\n",
|
||
" <td>0.022213</td>\n",
|
||
" <td>7585</td>\n",
|
||
" <td>11.889512</td>\n",
|
||
" <td>9.141974</td>\n",
|
||
" <td>8.330268</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Registrar Account - ID n_months n_active_months flow_freq aum_qty_mean \\\n",
|
||
"0 18872 130 130 1.000000 143505.697354 \n",
|
||
"1 200000076 130 119 0.915385 24141.541138 \n",
|
||
"2 200000082 130 130 1.000000 422994.464523 \n",
|
||
"3 200000146 130 130 1.000000 212108.397869 \n",
|
||
"4 200000147 130 130 1.000000 145729.199224 \n",
|
||
"\n",
|
||
" aum_qty_median net_flow_qty_sum gross_flow_qty_sum gross_flow_qty_mean \\\n",
|
||
"0 144653.1645 -45677.1480 1.244126e+06 9570.200015 \n",
|
||
"1 19888.8255 54791.9840 2.314415e+05 1780.319492 \n",
|
||
"2 462973.7880 178371.1590 2.327246e+06 17901.894469 \n",
|
||
"3 210616.5330 457533.3310 1.150546e+06 8850.350438 \n",
|
||
"4 79260.8255 677492.4351 1.213963e+06 9338.178685 \n",
|
||
"\n",
|
||
" net_flow_qty_vol rel_intensity netflow_to_aum n_tx_total \\\n",
|
||
"0 9832.357264 0.069449 -0.003918 1926 \n",
|
||
"1 2838.000232 0.083230 -0.000893 518 \n",
|
||
"2 13288.481111 0.047480 0.005194 7103 \n",
|
||
"3 10074.748210 0.051622 0.024910 4774 \n",
|
||
"4 13868.197522 0.061164 0.022213 7585 \n",
|
||
"\n",
|
||
" log_aum_qty_mean log_gross_flow_qty_mean gross_flow_to_aum \n",
|
||
"0 11.874137 9.166514 8.669523 \n",
|
||
"1 10.091731 7.485110 9.586858 \n",
|
||
"2 12.955117 9.792718 5.501836 \n",
|
||
"3 12.264857 9.088325 5.424328 \n",
|
||
"4 11.889512 9.141974 8.330268 "
|
||
]
|
||
},
|
||
"execution_count": 53,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"eps = 1e-9 \n",
|
||
"\n",
|
||
"# 1) Active month indicator: did the client trade this month?\n",
|
||
"df_month[\"active_month\"] = (df_month[\"gross_flow_qty\"] > 0).astype(int)\n",
|
||
"\n",
|
||
"#client avec beaucoup de mois à 0 → “stable / dormant”\n",
|
||
"#client actif presque tous les mois → “rebalancer / institutionnel actif”\n",
|
||
"\n",
|
||
"\n",
|
||
"# 2) Monthly relative intensity (turnover proxy in quantity terms) : Mesurer l’intensité de trading relativement à la taille et pouvoir ocmparer client petit avec client plus gros\n",
|
||
"df_month[\"rel_intensity_m\"] = df_month[\"gross_flow_qty\"] / (df_month[\"aum_qty\"].abs() + eps)\n",
|
||
"\n",
|
||
"# 3) Monthly net flow ratio (directional change): sert a Capturer la direction de la dynamique\n",
|
||
"df_month[\"netflow_to_aum_m\"] = df_month[\"net_flow_qty\"] / (df_month[\"aum_qty\"].abs() + eps)\n",
|
||
"\n",
|
||
"# 4) Aggregate to client-level features (1 row per client)\n",
|
||
"df_client_feat = (\n",
|
||
" df_month.groupby(ID_COL, as_index=False)\n",
|
||
" .agg(\n",
|
||
" # Coverage / activity\n",
|
||
" n_months=(\"month\", \"nunique\"),\n",
|
||
" n_active_months=(\"active_month\", \"sum\"),\n",
|
||
" flow_freq=(\"active_month\", \"mean\"),\n",
|
||
"\n",
|
||
" # Size in quantity terms\n",
|
||
" aum_qty_mean=(\"aum_qty\", \"mean\"),\n",
|
||
" aum_qty_median=(\"aum_qty\", \"median\"),\n",
|
||
"\n",
|
||
" # Flows in quantity terms\n",
|
||
" net_flow_qty_sum=(\"net_flow_qty\", \"sum\"),\n",
|
||
" gross_flow_qty_sum=(\"gross_flow_qty\", \"sum\"),\n",
|
||
" gross_flow_qty_mean=(\"gross_flow_qty\", \"mean\"),\n",
|
||
"\n",
|
||
" # Dispersion / volatility proxy\n",
|
||
" net_flow_qty_vol=(\"net_flow_qty\", \"std\"),\n",
|
||
" rel_intensity=(\"rel_intensity_m\", \"mean\"),\n",
|
||
" netflow_to_aum=(\"netflow_to_aum_m\", \"mean\"),\n",
|
||
"\n",
|
||
" # Trading frequency proxy\n",
|
||
" n_tx_total=(\"n_tx\", \"sum\"),\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"# 5) Clean NaNs due to std on constant series\n",
|
||
"df_client_feat[\"net_flow_qty_vol\"] = df_client_feat[\"net_flow_qty_vol\"].fillna(0.0)\n",
|
||
"\n",
|
||
"# 6) Log transforms (useful because distributions are heavy-tailed)\n",
|
||
"df_client_feat[\"log_aum_qty_mean\"] = np.log1p(df_client_feat[\"aum_qty_mean\"].clip(lower=0))\n",
|
||
"df_client_feat[\"log_gross_flow_qty_mean\"] = np.log1p(df_client_feat[\"gross_flow_qty_mean\"].clip(lower=0))\n",
|
||
"\n",
|
||
"# 7) Global turnover proxy\n",
|
||
"df_client_feat[\"gross_flow_to_aum\"] = df_client_feat[\"gross_flow_qty_sum\"] / (df_client_feat[\"aum_qty_mean\"].abs() + eps)\n",
|
||
"\n",
|
||
"df_client_feat.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 54,
|
||
"id": "4ddd1305-fe5a-4d0f-b4d3-b07de27b5dc6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(431, 16)"
|
||
]
|
||
},
|
||
"execution_count": 54,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_client_feat.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 55,
|
||
"id": "34a37448-ab63-4fc1-8c93-f9f59db385c2",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Filtered clients: (421, 16)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"dfc = df_client_feat.copy()\n",
|
||
"\n",
|
||
"# Minimal filters (adjust if needed)\n",
|
||
"dfc = dfc[(dfc[\"n_months\"] >= 6)] # at least 6 observed months\n",
|
||
"dfc = dfc[(dfc[\"aum_qty_mean\"].abs() > 0)] # avoid zero holdings\n",
|
||
"print(\"Filtered clients:\", dfc.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 59,
|
||
"id": "2763cc28-f9a7-4ced-8331-c2b79ac7c122",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Q33: 1946.5229933948517 Q66: 8013.920450704226\n",
|
||
"seg_quantiles\n",
|
||
"High-flow 143\n",
|
||
"Low-flow 139\n",
|
||
"Intermediate-flow 139\n",
|
||
"Name: count, dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Baseline Clustering 1\n",
|
||
"\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"# Baseline 1 variable: average monthly gross traded quantity\n",
|
||
"x = dfc[\"gross_flow_qty_mean\"].copy()\n",
|
||
"\n",
|
||
"q33, q66 = x.quantile([0.33, 0.66])\n",
|
||
"\n",
|
||
"dfc[\"seg_quantiles\"] = pd.cut(\n",
|
||
" x,\n",
|
||
" bins=[-np.inf, q33, q66, np.inf],\n",
|
||
" labels=[\"Low-flow\", \"Intermediate-flow\", \"High-flow\"]\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"Q33:\", q33, \" Q66:\", q66)\n",
|
||
"print(dfc[\"seg_quantiles\"].value_counts(dropna=False))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 60,
|
||
"id": "5afe137b-a09c-4fbc-a03c-b54f0422862d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure()\n",
|
||
"plt.hist(dfc[\"gross_flow_qty_mean\"], bins=100)\n",
|
||
"plt.axvline(q33, linestyle=\"--\")\n",
|
||
"plt.axvline(q66, linestyle=\"--\")\n",
|
||
"plt.xlabel(\"Average monthly gross flow (quantity)\")\n",
|
||
"plt.ylabel(\"Count\")\n",
|
||
"plt.title(\"Distribution of average monthly gross flows (quantity) with quantile thresholds\")\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"#X= activite moyenen mensuelle , Y = combien de client ont cette valeurde X"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 61,
|
||
"id": "619559a8-c205-4e25-a810-4d80894644c2",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure()\n",
|
||
"plt.hist(np.log1p(dfc[\"gross_flow_qty_mean\"]), bins=100)\n",
|
||
"plt.axvline(np.log1p(q33), linestyle=\"--\")\n",
|
||
"plt.axvline(np.log1p(q66), linestyle=\"--\")\n",
|
||
"plt.xlabel(\"log(1 + avg monthly gross flow) (quantity)\")\n",
|
||
"plt.ylabel(\"Count\")\n",
|
||
"plt.title(\"Log-distribution of average monthly gross flows (quantity)\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 62,
|
||
"id": "af32c07d-3908-428a-b388-a6e1a8d528bd",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_1011/630006569.py:3: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
|
||
" plt.boxplot(\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"#limite client size \n",
|
||
"plt.figure()\n",
|
||
"plt.boxplot(\n",
|
||
" [dfc.loc[dfc[\"seg_quantiles\"]==s, \"aum_qty_mean\"].dropna()\n",
|
||
" for s in [\"Low-flow\",\"Intermediate-flow\",\"High-flow\"]],\n",
|
||
" labels=[\"Low\",\"Mid\",\"High\"]\n",
|
||
")\n",
|
||
"plt.yscale(\"log\")\n",
|
||
"plt.ylabel(\"Mean AUM (quantity) [log scale]\")\n",
|
||
"plt.title(\"Client size differs widely within flow-intensity segments (quantity AUM)\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 63,
|
||
"id": "0596d9fe-524a-493a-948a-69f37075d1ca",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"count 421.000000\n",
|
||
"mean 80.643705\n",
|
||
"std 37.155098\n",
|
||
"min 7.000000\n",
|
||
"25% 52.000000\n",
|
||
"50% 71.000000\n",
|
||
"75% 130.000000\n",
|
||
"max 130.000000\n",
|
||
"Name: n_months, dtype: float64"
|
||
]
|
||
},
|
||
"execution_count": 63,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Baseline 2\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"dfc[\"n_months\"].describe()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 116,
|
||
"id": "56ac17f8-a25f-4726-a7ac-103a35ac6d6a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"seg_2D\n",
|
||
"Highly active (high int, high freq) 109\n",
|
||
"Small rebalancers (low int, high freq) 106\n",
|
||
"Dormant (low int, low freq) 104\n",
|
||
"Occasional large movers (high int, low freq) 102\n",
|
||
"Name: count, dtype: int64\n",
|
||
"thr_int: 4.174084305917157 thr_freq: 0.9859154929577465\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"dfc[\"rel_intensity_total\"] = dfc[\"gross_flow_to_aum\"] # turnover proxy\n",
|
||
"dfc[\"frequency\"] = dfc[\"flow_freq\"] # share of active months\n",
|
||
"\n",
|
||
"# Thresholds: medians (simple + explainable)\n",
|
||
"thr_int = dfc[\"rel_intensity_total\"].median()\n",
|
||
"thr_freq = dfc[\"frequency\"].median()\n",
|
||
"thr_tx = dfc[\"n_tx_total\"].median()\n",
|
||
"\n",
|
||
"def quadrant(row):\n",
|
||
" low_int = row[\"rel_intensity_total\"] < thr_int\n",
|
||
" low_frq = row[\"frequency\"] < thr_freq\n",
|
||
"\n",
|
||
" if low_int and low_frq:\n",
|
||
" return \"Dormant (low int, low freq)\"\n",
|
||
" if low_int and (not low_frq):\n",
|
||
" return \"Small rebalancers (low int, high freq)\"\n",
|
||
" if (not low_int) and low_frq:\n",
|
||
" return \"Occasional large movers (high int, low freq)\"\n",
|
||
" return \"Highly active (high int, high freq)\"\n",
|
||
"\n",
|
||
"dfc[\"seg_2D\"] = dfc.apply(quadrant, axis=1)\n",
|
||
"\n",
|
||
"print(dfc[\"seg_2D\"].value_counts())\n",
|
||
"print(\"thr_int:\", thr_int, \"thr_freq:\", thr_freq)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 196,
|
||
"id": "8635328a-fb66-45cb-b0fa-d1dc6c3cc1e1",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure()\n",
|
||
"for name, g in dfc.groupby(\"seg_2D\"):\n",
|
||
" plt.scatter(g[\"frequency\"], g[\"rel_intensity_total\"], s=10, label=name)\n",
|
||
"\n",
|
||
"plt.yscale(\"log\")\n",
|
||
"plt.axvline(thr_freq, linestyle=\"--\")\n",
|
||
"plt.axhline(thr_int, linestyle=\"--\")\n",
|
||
"plt.xlabel(\"Activity frequency (share of active months)\")\n",
|
||
"plt.ylabel(\"Gross flow / mean AUM (quantity) [log scale]\")\n",
|
||
"plt.title(\"2D behavioral segmentation: relative intensity vs frequency (400+ Accounts)\")\n",
|
||
"plt.legend(markerscale=2)\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 66,
|
||
"id": "9bf72f4b-95ac-4233-929b-47f6f101db49",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>n_clients</th>\n",
|
||
" <th>aum_qty_med</th>\n",
|
||
" <th>gross_flow_qty_med</th>\n",
|
||
" <th>freq_med</th>\n",
|
||
" <th>rel_int_med</th>\n",
|
||
" <th>n_tx_med</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>seg_2D</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>Highly active (high int, high freq)</th>\n",
|
||
" <td>109</td>\n",
|
||
" <td>106244.381208</td>\n",
|
||
" <td>7877.054714</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>7.201297</td>\n",
|
||
" <td>3861.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Small rebalancers (low int, high freq)</th>\n",
|
||
" <td>106</td>\n",
|
||
" <td>108438.852153</td>\n",
|
||
" <td>4100.832454</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>2.468000</td>\n",
|
||
" <td>2067.5</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Dormant (low int, low freq)</th>\n",
|
||
" <td>104</td>\n",
|
||
" <td>55310.790504</td>\n",
|
||
" <td>1687.835370</td>\n",
|
||
" <td>0.632500</td>\n",
|
||
" <td>1.641374</td>\n",
|
||
" <td>110.5</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Occasional large movers (high int, low freq)</th>\n",
|
||
" <td>102</td>\n",
|
||
" <td>37406.845662</td>\n",
|
||
" <td>2949.680688</td>\n",
|
||
" <td>0.830986</td>\n",
|
||
" <td>8.951903</td>\n",
|
||
" <td>536.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" n_clients aum_qty_med \\\n",
|
||
"seg_2D \n",
|
||
"Highly active (high int, high freq) 109 106244.381208 \n",
|
||
"Small rebalancers (low int, high freq) 106 108438.852153 \n",
|
||
"Dormant (low int, low freq) 104 55310.790504 \n",
|
||
"Occasional large movers (high int, low freq) 102 37406.845662 \n",
|
||
"\n",
|
||
" gross_flow_qty_med freq_med \\\n",
|
||
"seg_2D \n",
|
||
"Highly active (high int, high freq) 7877.054714 1.000000 \n",
|
||
"Small rebalancers (low int, high freq) 4100.832454 1.000000 \n",
|
||
"Dormant (low int, low freq) 1687.835370 0.632500 \n",
|
||
"Occasional large movers (high int, low freq) 2949.680688 0.830986 \n",
|
||
"\n",
|
||
" rel_int_med n_tx_med \n",
|
||
"seg_2D \n",
|
||
"Highly active (high int, high freq) 7.201297 3861.0 \n",
|
||
"Small rebalancers (low int, high freq) 2.468000 2067.5 \n",
|
||
"Dormant (low int, low freq) 1.641374 110.5 \n",
|
||
"Occasional large movers (high int, low freq) 8.951903 536.0 "
|
||
]
|
||
},
|
||
"execution_count": 66,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"profile_2d = (\n",
|
||
" dfc.groupby(\"seg_2D\")\n",
|
||
" .agg(\n",
|
||
" n_clients=(ID_COL, \"count\"),\n",
|
||
" aum_qty_med=(\"aum_qty_mean\",\"median\"),\n",
|
||
" gross_flow_qty_med=(\"gross_flow_qty_mean\",\"median\"),\n",
|
||
" freq_med=(\"frequency\",\"median\"),\n",
|
||
" rel_int_med=(\"rel_intensity_total\",\"median\"),\n",
|
||
" n_tx_med=(\"n_tx_total\",\"median\"),\n",
|
||
" )\n",
|
||
" .sort_values(\"n_clients\", ascending=False)\n",
|
||
")\n",
|
||
"profile_2d\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 225,
|
||
"id": "0434097b-ff04-4fc7-8430-8e3e4c8ab120",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Clustering matrix shape: (421, 6)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Kmeans\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"from sklearn.cluster import KMeans\n",
|
||
"from sklearn.metrics import silhouette_score\n",
|
||
"from sklearn.mixture import GaussianMixture\n",
|
||
"\n",
|
||
"# Safety: ensure baseline-2 columns exist\n",
|
||
"dfc = dfc.copy()\n",
|
||
"dfc[\"frequency\"] = dfc[\"flow_freq\"]\n",
|
||
"dfc[\"rel_intensity_total\"] = dfc[\"gross_flow_to_aum\"]\n",
|
||
"\n",
|
||
"# Choose a compact, interpretable feature set (quantity-based)\n",
|
||
"features = [\n",
|
||
" \"log_aum_qty_mean\", # size (log)\n",
|
||
" \"log_gross_flow_qty_mean\", # activity intensity (log)\n",
|
||
" \"frequency\", # activity frequency\n",
|
||
" \"rel_intensity_total\", # turnover proxy\n",
|
||
" \"net_flow_qty_vol\", # volatility of net flows\n",
|
||
" \"n_tx_total\", # total number of transactions\n",
|
||
"]\n",
|
||
"\n",
|
||
"# Build X (drop NaNs/Infs)\n",
|
||
"X = (dfc[features]\n",
|
||
" .replace([np.inf, -np.inf], np.nan)\n",
|
||
" .dropna()\n",
|
||
" .copy())\n",
|
||
"\n",
|
||
"# Keep IDs aligned\n",
|
||
"ids = dfc.loc[X.index, ID_COL].copy()\n",
|
||
"\n",
|
||
"# Standardize (critical for distance-based clustering)\n",
|
||
"scaler = StandardScaler()\n",
|
||
"X_scaled = scaler.fit_transform(X)\n",
|
||
"\n",
|
||
"print(\"Clustering matrix shape:\", X_scaled.shape)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 239,
|
||
"id": "9980bfb5-4655-44f1-ad46-6532e161a0bf",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>log_aum_qty_mean</th>\n",
|
||
" <th>log_gross_flow_qty_mean</th>\n",
|
||
" <th>frequency</th>\n",
|
||
" <th>rel_intensity_total</th>\n",
|
||
" <th>net_flow_qty_vol</th>\n",
|
||
" <th>n_tx_total</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>11.874137</td>\n",
|
||
" <td>9.166514</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>8.669523</td>\n",
|
||
" <td>9832.357264</td>\n",
|
||
" <td>1926</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>10.091731</td>\n",
|
||
" <td>7.485110</td>\n",
|
||
" <td>0.915385</td>\n",
|
||
" <td>9.586858</td>\n",
|
||
" <td>2838.000232</td>\n",
|
||
" <td>518</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>12.955117</td>\n",
|
||
" <td>9.792718</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>5.501836</td>\n",
|
||
" <td>13288.481111</td>\n",
|
||
" <td>7103</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>12.264857</td>\n",
|
||
" <td>9.088325</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>5.424328</td>\n",
|
||
" <td>10074.748210</td>\n",
|
||
" <td>4774</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>11.889512</td>\n",
|
||
" <td>9.141974</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>8.330268</td>\n",
|
||
" <td>13868.197522</td>\n",
|
||
" <td>7585</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>426</th>\n",
|
||
" <td>11.614810</td>\n",
|
||
" <td>9.116890</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>10.692193</td>\n",
|
||
" <td>13260.634566</td>\n",
|
||
" <td>2921</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>427</th>\n",
|
||
" <td>11.294315</td>\n",
|
||
" <td>9.205628</td>\n",
|
||
" <td>0.430769</td>\n",
|
||
" <td>16.099039</td>\n",
|
||
" <td>28616.097840</td>\n",
|
||
" <td>185</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>428</th>\n",
|
||
" <td>11.638514</td>\n",
|
||
" <td>8.072315</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.412726</td>\n",
|
||
" <td>2193.842219</td>\n",
|
||
" <td>2142</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>429</th>\n",
|
||
" <td>11.200555</td>\n",
|
||
" <td>8.946562</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>13.645709</td>\n",
|
||
" <td>11205.382980</td>\n",
|
||
" <td>2264</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>430</th>\n",
|
||
" <td>10.454506</td>\n",
|
||
" <td>8.009115</td>\n",
|
||
" <td>0.976923</td>\n",
|
||
" <td>11.266566</td>\n",
|
||
" <td>4359.629462</td>\n",
|
||
" <td>1507</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>421 rows × 6 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" log_aum_qty_mean log_gross_flow_qty_mean frequency \\\n",
|
||
"0 11.874137 9.166514 1.000000 \n",
|
||
"1 10.091731 7.485110 0.915385 \n",
|
||
"2 12.955117 9.792718 1.000000 \n",
|
||
"3 12.264857 9.088325 1.000000 \n",
|
||
"4 11.889512 9.141974 1.000000 \n",
|
||
".. ... ... ... \n",
|
||
"426 11.614810 9.116890 1.000000 \n",
|
||
"427 11.294315 9.205628 0.430769 \n",
|
||
"428 11.638514 8.072315 1.000000 \n",
|
||
"429 11.200555 8.946562 1.000000 \n",
|
||
"430 10.454506 8.009115 0.976923 \n",
|
||
"\n",
|
||
" rel_intensity_total net_flow_qty_vol n_tx_total \n",
|
||
"0 8.669523 9832.357264 1926 \n",
|
||
"1 9.586858 2838.000232 518 \n",
|
||
"2 5.501836 13288.481111 7103 \n",
|
||
"3 5.424328 10074.748210 4774 \n",
|
||
"4 8.330268 13868.197522 7585 \n",
|
||
".. ... ... ... \n",
|
||
"426 10.692193 13260.634566 2921 \n",
|
||
"427 16.099039 28616.097840 185 \n",
|
||
"428 1.412726 2193.842219 2142 \n",
|
||
"429 13.645709 11205.382980 2264 \n",
|
||
"430 11.266566 4359.629462 1507 \n",
|
||
"\n",
|
||
"[421 rows x 6 columns]"
|
||
]
|
||
},
|
||
"execution_count": 239,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "c697888b-cb72-4a98-86af-56647f5a5161",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"X_sorted = X.sort_values(by=\"n_tx_total\", ascending=False)\n",
|
||
"X_sorted"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 226,
|
||
"id": "e18be5a6-c8af-47f9-888f-3edf21bd28dd",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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EqVQqODo6CgMGDBBCQ0NVx8vLy4UlS5YILi4ugrW1tRAUFCRcv369xmH3jxqOHRoaKgAQRCKRcOvWrWrLJCQkCNOmTRPkcrlgYWEhNGnSRBg1apSwc+fOWutS3bB7QRCE9PR04YUXXhBcXFwEiUQi+Pv7q903Qfh32P26detqfZ/a3k+hUAgtW7YULC0thbCwsGrPBSDMnz9foxjCwsIEAMIvv/yitv/ixYvC+PHjBWdnZ0EqlQo+Pj7CxIkT1e7/vXv3VHW3tbUVgoKChKtXr2p8/yrfu7IekZGRwqRJk4SmTZsKUqlUcHNzE0aNGiWcP3++tm8XkU6JBOGhtkoiIiIiE8M+RERERGTymBARERGRyWNCRERERCaPCRERERGZPCZEREREZPKYEBEREZHJ48SMGlAqlUhNTYWdnd1jrdpNRERE9UcQBOTl5cHT07PKgswPY0KkgdTU1CqrRRMREVHDcOvWLXh5eT2yjEETopCQEOzatQtXr16FlZUVevfujTVr1qBNmzaqMkVFRXj99dexfft2FBcXIygoCBs2bIC7u7uqTHJyMubOnYuwsDDY2tpi+vTpCAkJUVt7KTw8HIsXL0ZsbCy8vb3x3nvvYcaMGRrFWTkV/61bt2Bvb6+byhMREZFe5ebmwtvbW6MldQyaEB09ehTz58/HE088gbKyMrzzzjsYOnQo4uLiYGNjAwBYtGgRDhw4gF9++QUymQwLFizA+PHjcfLkSQBAeXk5Ro4cCblcjlOnTiEtLQ3Tpk2DhYUFVq1aBaBi3aeRI0fi5Zdfxg8//IDDhw9j1qxZ8PDwQFBQUK1xVj4ms7e3Z0JERETUwGjS3cWolu64c+cO3NzccPToUfTv3x85OTlwdXXFjz/+iKeffhoAcPXqVbRr1w4RERHo1asX/vjjD4waNQqpqamqVqOvvvoKS5YswZ07dyCRSLBkyRIcOHAAMTExqvd67rnnkJ2djYMHD9YaV25uLmQyGXJycpgQERERNRDafH4b1SiznJwcAICTkxMA4MKFCygtLUVgYKCqTNu2bdG0aVNEREQAACIiIuDv76/2CC0oKAi5ubmIjY1VlXnwGpVlKq/xsOLiYuTm5qq9iIiIqPEymoRIqVRi4cKF6NOnDzp06AAAUCgUkEgkcHBwUCvr7u4OhUKhKvNgMlR5vPLYo8rk5uaisLCwSiwhISGQyWSqFztUExERNW5GkxDNnz8fMTEx2L59u6FDwdKlS5GTk6N63bp1y9AhERERkR4ZxbD7BQsWYP/+/Th27JjasDi5XI6SkhJkZ2ertRKlp6dDLperypw9e1bteunp6apjlf9W7nuwjL29PaysrKrEI5VKIZVKdVI3IiIiMn4GbSESBAELFizA7t27ceTIEfj6+qod79atGywsLHD48GHVvvj4eCQnJyMgIAAAEBAQgMuXLyMjI0NVJjQ0FPb29vDz81OVefAalWUqr0FERESmzaCjzObNm4cff/wRv/32m9rcQzKZTNVyM3fuXPz+++/YsmUL7O3t8corrwAATp06BaBi2H3nzp3h6emJtWvXQqFQYOrUqZg1a5basPsOHTpg/vz5ePHFF3HkyBG8+uqrOHDggEbD7jnKjIiIqOHR5vPboAlRTfMCbN68WTVpYuXEjD/99JPaxIyVj8MAICkpCXPnzkV4eDhsbGwwffp0rF69usrEjIsWLUJcXBy8vLzw/vvvazwxIxMiIiKihqfBJEQNBRMiIiKihkebz2+j6FRtqsqVAs4mZiEjrwhudpbo4esEMzEXjyUiIqpvTIgM5GBMGlbsi0NaTpFqn4fMEsGj/TCsg4cBIyMiIjI9RjMPkSk5GJOGudsi1ZIhAFDkFGHutkgcjEkzUGRERESmiQlRPStXClixLw7Vddyq3LdiXxzKlezaRUREVF+YENWzs4lZVVqGHiQASMspwtnErPoLioiIyMQxIapnGXk1J0N1KUdERESPjwlRPXOzs9RpOSIiInp8TIjqWQ9fJ3jILFHT4HoRKkab9fB1qs+wiIiITBoTonpmJhYheHTFGms1JUXBo/04HxEREVE9YkJkAMM6eGDj810hl6k/FpOai7Hx+a6ch4iIiKiecWJGAxnWwQNP+slxNjELcWk5+GD/FZSUKdG1qaOhQyMiIjI5bCEyIDOxCAEtnDGzb3N083GEAOC3qFRDh0VERGRymBAZiXFdmgAAfo28beBIiIiITA8TIiMxqqMHJGZiXFXk4UparqHDISIiMilMiIyEg7UEg9u6AQB2X0wxcDRERESmhQmRERnXteKx2W9RKVzLjIiIqB4xITIig9q4wcHaAum5xTiVcNfQ4RAREZkMJkRGRGIuxqiOFXMQ7YrkYzMiIqL6woTIyIzv6gUAOBijQEFxmYGjISIiMg1MiIxMF28H+LrYoLC0HH/GKgwdDhERkUlgQmRkRCIRxnau6FzN0WZERET1gwmREaqcpPHk9btQ5BQZOBoiIqLGjwmREWrqbI3uPo5QChVD8ImIiEi/mBAZqcrO1XxsRkREpH9MiIzUSP9/l/KIS+VSHkRERPrEhMhIyawtMKRd5VIeXPCViIhIn5gQGbHKztV7olJRVq40cDRERESNFxMiIzawjRscrS1wJ68YJxMyDR0OERFRo8WEyIhJzMUY3ckTALA7ko/NiIiI9IUJkZGrfGz2Z2w6l/IgIiLSEyZERq7zA0t5HIzhUh5ERET6wITIyIlEIlUrEeckIiIi0g8mRA2AaimPhLtIyyk0cDRERESNDxOiBsDbyRo9mjlBEIDfolINHQ4REVGjw4SogRjX9Z/HZpEpEATBwNEQERE1LkyIGogR/h6QmIsRn56HuDQu5UFERKRLTIgaCJmVBQL/WcpjVyQ7VxMREekSE6IGZFwXLwAV/Yi4lAcREZHuMCFqQAa0doWTjQR384tx4vpdQ4dDRETUaBg0ITp27BhGjx4NT09PiEQi7NmzR+24SCSq9rVu3TpVmWbNmlU5vnr1arXrREdHo1+/frC0tIS3tzfWrl1bH9XTOYm5GKM7egDgnERERES6ZNCEqKCgAJ06dcL69eurPZ6Wlqb22rRpE0QiESZMmKBWbuXKlWrlXnnlFdWx3NxcDB06FD4+Prhw4QLWrVuH5cuX45tvvtFr3fRlXNeKx2Z/xiqQz6U8iIiIdMLckG8+fPhwDB8+vMbjcrlcbfu3337DoEGD0Lx5c7X9dnZ2VcpW+uGHH1BSUoJNmzZBIpGgffv2iIqKwieffII5c+Y8fiXqWScvGZq72ODG3QL8cTkNz3T3NnRIREREDV6D6UOUnp6OAwcOYObMmVWOrV69Gs7OzujSpQvWrVuHsrJ/W04iIiLQv39/SCQS1b6goCDEx8fj3r171b5XcXExcnNz1V7Ggkt5EBER6V6DSYi2bt0KOzs7jB8/Xm3/q6++iu3btyMsLAwvvfQSVq1ahbfeekt1XKFQwN3dXe2cym2FovrFUkNCQiCTyVQvb2/jaoUZ+09CFHEjk0t5EBER6UCDSYg2bdqEKVOmwNLSUm3/4sWLMXDgQHTs2BEvv/wyPv74Y3z55ZcoLi6u83stXboUOTk5qtetW7ceN3yd8nayRg/fiqU89lzkUh5ERESPq0EkRMePH0d8fDxmzZpVa9mePXuirKwMN2/eBFDRDyk9PV2tTOV2Tf2OpFIp7O3t1V7GZvw/rUS7Im9zKQ8iIqLH1CASom+//RbdunVDp06dai0bFRUFsVgMN7eKWZ0DAgJw7NgxlJaWqsqEhoaiTZs2cHR01FvM+jb8n6U8rmXkIzbVePo4ERERNUQGTYjy8/MRFRWFqKgoAEBiYiKioqKQnJysKpObm4tffvml2tahiIgIfPbZZ7h06RJu3LiBH374AYsWLcLzzz+vSnYmT54MiUSCmTNnIjY2Fjt27MDnn3+OxYsX10sd9UVmZYEn21X0heJSHkRERI/HoAnR+fPn0aVLF3Tp0gVARX+gLl26YNmyZaoy27dvhyAImDRpUpXzpVIptm/fjgEDBqB9+/b48MMPsWjRIrU5hmQyGf766y8kJiaiW7dueP3117Fs2bIGOeT+YeO7Vjw223uJS3kQERE9DpHADii1ys3NhUwmQ05OjlH1JyotV6LXqsPILCjB5heewKA2boYOiYiIyGho8/ndIPoQUfUszMQY3ckTALCbj82IiIjqjAlRA1c5SeOfsQrkFZXWUpqIiIiqw4SogevoJUNzVxsUlynxR0z1E00SERHRozEhauBEIhEm/LPgKx+bERER1Q0Tokbgqc4V/YhOJ2YiNZtLeRAREWmLCVEj4OVojZ6VS3lEsZWIiIhIW0yIGonKOYl2RaZwKQ8iIiItMSFqJIb7e0BqLsb1jHzEpHApDyIiIm0wIWok7C0t8KTfP0t5XLxt4GiIiIgaFiZEjUjlY7N9XMqDiIhIK0yIGpF+rVzhbCPB3fwSHL9219DhEBERNRhMiBqRB5fy+DWSj82IiIg0xYSokamcpDE0Lh25XMqDiIhII0yIGpkOTezR0s0WxWVKHLzMpTyIiIg0wYSokRGJRKoFXznajIiISDNMiBqhsf8kRKdvZOH2vfsGjoaIiMj4MSFqhJo4WKFXcycAwG9RqQaOhoiIyPgxIWqkxv/TuXpX5G0u5UFERFQLJkSN1PAOckjNxUi4U4DLKTmGDoeIiMioMSFqpOwsLTC0vRwAsD7sOn6LSkFEQibKlWwtIiIiepi5oQMg/WnqZAUA+DM2HX/GpgMAPGSWCB7th2EdPAwZGhERkVFhC1EjdTAmDRvCEqrsV+QUYe62SByMSTNAVERERMaJCVEjVK4UsGJfHKp7OFa5b8W+OD4+IyIi+gcTokbobGIW0nKKajwuAEjLKcLZxKz6C4qIiMiIMSFqhDLyak6G6lKOiIiosWNC1Ai52VnqtBwREVFjx4SoEerh6wQPmSVENRwXoWK0WQ9fp/oMi4iIyGgxIWqEzMQiBI/2A4Aak6Lg0X4wE9d0lIiIyLQwIWqkhnXwwMbnu0IuU38sJhYB6yd35TxERERED+DEjI3YsA4eeNJPjrOJWUjNvo9le2NRUFwOG0vediIiogexhaiRMxOLENDCGRO6eePpfxZ83XnhtoGjIiIiMi5MiEzI0928AQB/xiqQU1hq4GiIiIiMBxMiE9KhiT3ayu1QUqbE/uhUQ4dDRERkNLTqTJKdnY3du3fj+PHjSEpKwv379+Hq6oouXbogKCgIvXv31lecpAMikQhPd/PC/x24gp0XbmNKTx9Dh0RERGQUNGohSk1NxaxZs+Dh4YH/+7//Q2FhITp37owhQ4bAy8sLYWFhePLJJ+Hn54cdO3boO2Z6DE91bgIzsQgXk7NxPSPf0OEQEREZBY1aiLp06YLp06fjwoUL8PPzq7ZMYWEh9uzZg88++wy3bt3CG2+8odNASTdc7aQY1MYVh65kYOeF23h7eFtDh0RERGRwIkEQal3yPDMzE87OzhpfVNvyxi43NxcymQw5OTmwt7c3dDiP7WBMGl7eFgl3eylOvT2EEzQSEVGjpM3nt0aPzLRNbhpTMtQYDW7rDkdrC6TnFuP4tTuGDoeIiMjgNB5lNm/ePOTn/9vn5KeffkJBQYFqOzs7GyNGjNBtdKQXEnMxnurcBADnJCIiIgK0SIi+/vpr3L9/X7X90ksvIT09XbVdXFyMP//8U6s3P3bsGEaPHg1PT0+IRCLs2bNH7fiMGTMgEonUXsOGDVMrk5WVhSlTpsDe3h4ODg6YOXOmWuIGANHR0ejXrx8sLS3h7e2NtWvXahVnY/R0t4pJGv+KS0fOfc5JREREpk3jhOjhrkYadD2qVUFBATp16oT169fXWGbYsGFIS0tTvX766Se141OmTEFsbCxCQ0Oxf/9+HDt2DHPmzFEdz83NxdChQ+Hj44MLFy5g3bp1WL58Ob755pvHjr8ha+/575xE+zgnERERmTiDLmo1fPhwDB8+/JFlpFIp5HJ5tceuXLmCgwcP4ty5c+jevTsA4Msvv8SIESPw0UcfwdPTEz/88ANKSkqwadMmSCQStG/fHlFRUfjkk0/UEidT8/CcRM/34pxERERkuox+purw8HC4ubmhTZs2mDt3LjIzM1XHIiIi4ODgoEqGACAwMBBisRhnzpxRlenfvz8kEomqTFBQEOLj43Hv3r1q37O4uBi5ublqr8ZobJcmMBeLEHUrG9cz8gwdDhERkcFo1UK0bNkyWFtbAwBKSkrw4YcfQiaTAYBa/yJdGTZsGMaPHw9fX18kJCTgnXfewfDhwxEREQEzMzMoFAq4ubmpnWNubg4nJycoFAoAgEKhgK+vr1oZd3d31TFHR8cq7xsSEoIVK1bovD7GxsVWioFt3HDoSjp+uXAbS4e3M3RIREREBqFxQtS/f3/Ex8ertnv37o0bN25UKaNLzz33nOprf39/dOzYES1atEB4eDiGDBmi0/d60NKlS7F48WLVdm5uLry9vfX2fob0dDcvHLqSjt2RKXhzaBuYmxl9oyEREZHOaZwQhYeH6zEMzTRv3hwuLi64fv06hgwZArlcjoyMDLUyZWVlyMrKUvU7ksvlaqPhAKi2a+qbJJVKIZVK9VAD4zO4rRscrS2QkVeM49fvYlAbt9pPIiIiamQeuzmgrKysyjB3fbl9+zYyMzPh4eEBAAgICEB2djYuXLigKnPkyBEolUr07NlTVebYsWMoLf13aHloaCjatGlT7eMyU8M5iYiIiLRIiPbt24ctW7ao7fvwww9ha2sLBwcHDB06tMZOyjXJz89HVFQUoqKiAACJiYmIiopCcnIy8vPz8eabb+L06dO4efMmDh8+jKeeegotW7ZEUFAQAKBdu3YYNmwYZs+ejbNnz+LkyZNYsGABnnvuOXh6egIAJk+eDIlEgpkzZyI2NhY7duzA559/rvZIzNQ9071iTqLQWM5JREREJkrQ0MCBA4X//Oc/qu2TJ08KYrFY+L//+z/h119/Fdq2bSssWrRI08sJgiAIYWFhAoAqr+nTpwv3798Xhg4dKri6ugoWFhaCj4+PMHv2bEGhUKhdIzMzU5g0aZJga2sr2NvbCy+88IKQl5enVubSpUtC3759BalUKjRp0kRYvXq1VnHm5OQIAIScnBytzmtIhn12TPBZsl/4LuKmoUMhIiLSCW0+vzVa3BUA3Nzc8Oeff6JLly4AgMWLFyMuLg4HDx4EAPz+++947bXXcO3aNT2kbYbV2BZ3rc63JxLxwf44dPJ2wG/z+xg6HCIiosem88VdASAvL09t0dYTJ06ojfRq3749UlM543FD9VRnT5iLRbh0KxvX0jknERERmRaNE6ImTZrgypUrACr6/ly6dAm9e/dWHc/MzFTNUUQNj4utFIPaVowwY+dqIiIyNRonRM888wwWLlyI77//HrNnz4ZcLkevXr1Ux8+fP482bdroJUiqH5ULvu66mIKycqWBoyEiIqo/Gs9DtGzZMqSkpODVV1+FXC7Htm3bYGZmpjr+008/YfTo0XoJkurHoDZucLKR4E5eMY5fu6tqMSIiImrsNE6IrKys8N1339V4PCwsTCcBkeFUzEnkic0nb2LnhdtMiIiIyGQ89sSMR48exe+//671HERknJ7pVrFESWhcOrLvlxg4GiIiovqhcUK0Zs0avP/++6ptQRAwbNgwDBo0CKNGjUK7du0QGxurlyCp/vh52sPPwx4l5Ursu8RRg0REZBo0Toh27NiBDh06qLZ37tyJY8eO4fjx47h79y66d+9uEivEm4LKztUcbUZERKZC44QoMTERHTt2VG3//vvvePrpp9GnTx84OTnhvffeQ0REhF6CpPqlmpPodg7+5pxERERkAjROiMrKytRWgI+IiFCbh8jT0xN3797VbXRkEM62UgzmnERERGRCNE6IWrRogWPHjgEAkpOT8ffff6N///6q47dv31abyZoaNtWcRJGck4iIiBo/jYfdz58/HwsWLMDx48dx+vRpBAQEwM/PT3X8yJEjqnXOqOEb1NYNzjYS3M0vxrFrdzC4rbuhQyIiItIbjVuIZs+ejS+++AJZWVno378/fv31V7XjqampePHFF3UeIBmGhZkYT3VuAoCPzYiIqPHTeLV7U2YKq91X50paLoZ/fhwSMzHOvDMEjjYSQ4dERESkMb2sdk+mp52HPdp7/jMnUTTnJCIiosaLCRE9EuckIiIiU8CEiB7pqc5NYGEmQvTtHMQrOCcRERE1TkyI6JGcbCQPzEl0y8DREBER6YdWCVFpaSnMzc0RExOjr3jICD39z4Kvuy+mopRzEhERUSOkVUJkYWGBpk2bory8XF/xkBEa2MYVLrb/zEn09x1Dh0NERKRzWj8ye/fdd/HOO+8gKytLH/GQEbIwE2Ms5yQiIqJGTOOZqiv95z//wfXr1+Hp6QkfHx/Y2NioHY+MjNRZcGQ8JnTzwv9OJOLQlXTcKyjhnERERNSoaJ0QjR07Vg9hkLFr52GPDk3sEZOSi72XUjG9dzNDh0RERKQzWidEwcHB+oiDGoCnu3ohJiUOv1y4xYSIiIgalToNu8/Ozsb//vc/LF26VNWXKDIyEikpKToNjozLmH/mJIpJycWVtFxDh0NERKQzWidE0dHRaN26NdasWYOPPvoI2dnZAIBdu3Zh6dKluo6PjIiTjQRD/ln1/ld2riYiokZE64Ro8eLFmDFjBq5duwZLS0vV/hEjRuDYsWM6DY6MT+VSHnuiUjgnERERNRpaJ0Tnzp3DSy+9VGV/kyZNoFAodBIUGa8BqjmJSnA0nnMSERFR46B1QiSVSpGbW7X/yN9//w1XV1edBEXGy8JMjHFdOCcRERE1LlonRGPGjMHKlStRWloKABCJREhOTsaSJUswYcIEnQdIxmfCP4/NDl9NR1ZBiYGjISIienxaJ0Qff/wx8vPz4ebmhsLCQgwYMAAtW7aEnZ0dPvzwQ33ESEamrdwe/k1kKC0XsDeKIwuJiKjh03oeIplMhtDQUJw8eRKXLl1Cfn4+unbtisDAQH3ER0bq6W5euJySg18u3MaMPr6GDoeIiOixaN1C9N1336G4uBh9+vTBvHnz8NZbbyEwMBAlJSX47rvv9BEjGaExnTxhYSZCbGou4lI5JxERETVsWidEL7zwAnJycqrsz8vLwwsvvKCToMj4OdpIENjunzmJItm5moiIGjatEyJBECASiarsv337NmQymU6CooZBNSfRRc5JREREDZvGfYi6dOkCkUgEkUiEIUOGwNz831PLy8uRmJiIYcOG6SVIMk4DWrvCxVaKu/nF+PpoArydrOFmZ4kevk4wE1dNmomIiIyVxglR5Sr3UVFRCAoKgq2treqYRCJBs2bNOOzexJibidGlqQNC49Lx0V9/q/Z7yCwRPNoPwzp4GDA6IiIizYkEQRC0OWHr1q147rnnIJVK9RWT0cnNzYVMJkNOTg7s7e0NHY7ROBiThpe3RVbZX9k2tPH5rkyKiIjIYLT5/Na6D9HgwYNx586/SzacPXsWCxcuxDfffKN9pNRglSsFrNgXV+2xygx7xb44lCu1yreJiIgMQuuEaPLkyQgLCwMAKBQKBAYG4uzZs3j33XexcuVKra517NgxjB49Gp6enhCJRNizZ4/qWGlpKZYsWQJ/f3/Y2NjA09MT06ZNQ2pqqto1mjVrpurbVPlavXq1Wpno6Gj069cPlpaW8Pb2xtq1a7WtNj3kbGIW0nKKajwuAEjLKcLZxKz6C4qIiKiOtE6IYmJi0KNHDwDAzz//DH9/f5w6dQo//PADtmzZotW1CgoK0KlTJ6xfv77Ksfv37yMyMhLvv/8+IiMjsWvXLsTHx2PMmDFVyq5cuRJpaWmq1yuvvKI6lpubi6FDh8LHxwcXLlzAunXrsHz5crZoPaaMvJqTobqUIyIiMiStZ6ouLS1V9R86dOiQKkFp27Yt0tLStLrW8OHDMXz48GqPVc6I/aD//Oc/6NGjB5KTk9G0aVPVfjs7O8jl8mqv88MPP6CkpASbNm2CRCJB+/btERUVhU8++QRz5szRKl76l5udpU7LERERGZLWLUTt27fHV199hePHjyM0NFQ11D41NRXOzs46D/BBOTk5EIlEcHBwUNu/evVqODs7o0uXLli3bh3KyspUxyIiItC/f39IJBLVvqCgIMTHx+PevXvVvk9xcTFyc3PVXqSuh68TPGSWqGlwvQgVo816+DrVZ1hERER1onVCtGbNGnz99dcYOHAgJk2ahE6dOgEA9u7dq3qUpg9FRUVYsmQJJk2apNZT/NVXX8X27dsRFhaGl156CatWrcJbb72lOq5QKODu7q52rcpthUJR7XuFhIRAJpOpXt7e3nqoUcNmJhYheLQfAFSbFAkAgkf7cT4iIiJqELR+ZDZw4EDcvXsXubm5cHR0VO2fM2cOrK2tdRpcpdLSUkycOBGCIGDjxo1qxxYvXqz6umPHjpBIJHjppZcQEhJS56kBli5dqnbd3NxcJkXVGNbBAxuf74oV++KqdLDu7uPIIfdERNRgaJ0QAYCZmZlaMgRUjPbSh8pkKCkpCUeOHKl1HoGePXuirKwMN2/eRJs2bSCXy5Genq5WpnK7pn5HUqnUpOZZehzDOnjgST85ziZmISOvCAXFZXhndwzOJ93D2cQsPjIjIqIGQeuEyNfXt9q1zCrduHHjsQJ6UGUydO3aNYSFhWnURykqKgpisRhubm4AgICAALz77rsoLS2FhYUFACA0NBRt2rSpktRR3ZiJRQho8e+9iUnNxY9nkhG8Nxb7X+nLx2ZERGT0tE6IFi5cqLZdWlqKixcv4uDBg3jzzTe1ulZ+fj6uX7+u2k5MTERUVBScnJzg4eGBp59+GpGRkdi/fz/Ky8tVfX6cnJwgkUgQERGBM2fOYNCgQbCzs0NERAQWLVqE559/XpXsTJ48GStWrMDMmTOxZMkSxMTE4PPPP8enn36qbdVJQ28MbYMD0Wm4kpaLH88kYWpAM0OHRERE9EhaL91Rk/Xr1+P8+fPYvHmzxueEh4dj0KBBVfZPnz4dy5cvh6+vb7XnhYWFYeDAgYiMjMS8efNw9epVFBcXw9fXF1OnTsXixYvVHnlFR0dj/vz5OHfuHFxcXPDKK69gyZIlGsfJpTu0913ETSz7LRYyKwuEvzEQjjaS2k8iIiLSIW0+v3WWEN24cQOdO3dulEPUmRBpr6xciVFfnsBVRR6m9GyKD8f5GzokIiIyMXpdy6wmO3fuhJMTO9BSBXMzMVaMaQ8A+PFsMmJScgwcERERUc207kPUpUsXtU7VgiBAoVDgzp072LBhg06Do4atZ3NnjO7kiX2XUrF8byx+eTngkR3yiYiIDEXrhGjs2LFq22KxGK6urhg4cCDatm2rq7iokXhnRFscikvH+aR7+C0qFWO7NDF0SERERFXorA9RY8Y+RI9nfdh1rPszHm52Uhx5YyBspXWa/oqIiEgr2nx+a/TJpE1HaSYM9LBZ/Xzx8/lbSMq8j/8cuY63h7MlkYiIjItGCZGDg0OtfT8EQYBIJEJ5eblOAqPGQ2puhmWj/DBz63l8e+IGJnb3QnNXW0OHRUREpKJRQhQWFqbvOKiRG9LOHYPauCIs/g5W7o/D5hlPsIM1EREZDY0SogEDBug7DjIB74/yw4nrxxAefwdHrmZgSDt3Q4dEREQEoA7zEG3evBm//PJLlf2//PILtm7dqpOgqHFq7mqLmX2bAwBW7o9DUSkfrxIRkXHQOiEKCQmBi4tLlf1ubm5YtWqVToKixuuVwS3hbi9FUuZ9fHsi0dDhEBERAahDQpScnFztGmM+Pj5ITk7WSVDUeNlIzbF0eDsAwH+OXEdaTqGBIyIiIqpDQuTm5obo6Ogq+y9dugRnZ2edBEWN21OdPfFEM0cUlpZj1e9XDR0OERGR9gnRpEmT8OqrryIsLAzl5eUoLy/HkSNH8Nprr+G5557TR4zUyIhEIiwf0x5iEbDvUipO38g0dEhERGTitE6IPvjgA/Ts2RNDhgyBlZUVrKysMHToUAwePJh9iEhj7T1lmNSjKQBg+d5YlJUrDRwRERGZsjov3XHt2jVERUXBysoK/v7+8PHx0XVsRoNLd+jHvYISDPo4HNn3S7HyqfaYFtDM0CEREVEjos3nt9YtRJVatWoFT09PPPnkk406GSL9cbSR4PWhbQAAH//1N7IKSgwcERERmao6J0QAMHz4cKSkpOgqFjJBk3s0RTsPe+QUluKjv+INHQ4REZmox0qI6vi0jUjFTCzCijHtAQA/nU1GTEqOgSMiIiJT9FgJEZEu9PB1wlOdPSEIQPDeWCbaRERU7x4rIfr666/h7s71qOjxLR3eDtYSM1xIuoc9UXwMS0RE9euxEqLJkyfDxsZGV7GQCZPLLLFgcEsAQMjvV5FfXGbgiIiIyJRonRAVFBTg/fffR+/evdGyZUs0b95c7UVUVzP7+qKZszUy8orx5eFrhg6HiIhMiLm2J8yaNQtHjx7F1KlT4eHhAZFIpI+4yARJzc2wbLQfXtxyHptOJmLiE95o4Wpr6LCIiMgEaJ0Q/fHHHzhw4AD69Omjj3jIxA1u647Bbd1w5GoGVu6Lw5YXnmDSTUREeqf1IzNHR0c4OTnpIxYiAMCyUX6QmIlx9O87OHQlw9DhEBGRCajTWmbLli3D/fv39REPEZq52GBmP18AwAf741BUWm7giIiIqLHT+pHZxx9/jISEBLi7u6NZs2awsLBQOx4ZGamz4Mh0LRjUErsjU5CcdR//O34DCwa3MnRIRETUiGmdEI0dO1YPYRCps5GaY+mItnhtexTWhyVgfFcveDpYGTosIiJqpOq82r0p4Wr3hiEIAp79+jTO3szCSH85nu/VDBl5RXCzs0QPXyeYidnZmoiIaqbN5zcTIg0wITKcuNRcjPziOB7+IfWQWSJ4tB+GdfAwSFxERGT8tPn81qhTtZOTE+7evQvg31FmNb2IdCk5q6BKMgQAipwizN0WiYMxafUeExERNT4a9SH69NNPYWdnBwD47LPP9BkPkUq5UsCKfXHVHhMAiACs2BeHJ/3kfHxGRESPRaOEaPr06dV+TaRPZxOzkJZTVONxAUBaThHOJmYhoIVz/QVGRESNjtajzABAqVTi+vXryMjIgFKpVDvWv39/nQRGlJFXczJUl3JEREQ10TohOn36NCZPnoykpCQ83B9bJBKhvJyT6JFuuNlZ6rQcERFRTbROiF5++WV0794dBw4c4OKupFc9fJ3gIbOEIqeo2o7VIgByWcUQfCIiosehdUJ07do17Ny5Ey1bttRHPEQqZmIRgkf7Ye62SIiAKkmRACB4tB87VBMR0WPTei2znj174vr16/qIhaiKYR08sPH5rpDLqn8sZmlhVs8RERFRY6TRxIzR0dGqrxMSEvDee+/hzTffhL+/f5W1zDp27Kj7KA2MEzMaXrlSwNnELNVM1Qdj07D1VBKaOFjhz0X9YSut0/gAIiJqxHQ+MWPnzp3RpUsXdO7cGRMmTMCVK1fw4osv4oknnlA71qVLF60CPXbsGEaPHg1PT0+IRCLs2bNH7bggCFi2bBk8PDxgZWWFwMBAXLt2Ta1MVlYWpkyZAnt7ezg4OGDmzJnIz89XKxMdHY1+/frB0tIS3t7eWLt2rVZxkuGZiUUIaOGMpzo3QUALZywZ1hbeTlZIyS7Emj+uGjo8IiJq4DRKiBITE3Hjxg0kJiZW+6o8duPGDa3evKCgAJ06dcL69eurPb527Vp88cUX+Oqrr3DmzBnY2NggKCgIRUX/DrOeMmUKYmNjERoaiv379+PYsWOYM2eO6nhubi6GDh0KHx8fXLhwAevWrcPy5cvxzTffaBUrGRdriTlWj69ojfz+dBLO3Mg0cERERNSgCVo6evSoUFpaWmV/aWmpcPToUW0vpwJA2L17t2pbqVQKcrlcWLdunWpfdna2IJVKhZ9++kkQBEGIi4sTAAjnzp1Tlfnjjz8EkUgkpKSkCIIgCBs2bBAcHR2F4uJiVZklS5YIbdq00Ti2nJwcAYCQk5NT1+qRnizZeUnwWbJfGLguTCgsKTN0OEREZES0+fzWulP1oEGDkJWVVWV/Tk4OBg0a9NgJWqXExEQoFAoEBgaq9slkMvTs2RMREREAgIiICDg4OKB79+6qMoGBgRCLxThz5oyqTP/+/SGRSFRlgoKCEB8fj3v37lX73sXFxcjNzVV7kXF6Z2Q7uNtLkXi3AJ+G/m3ocIiIqIHSOiESBKHauYcyMzNhY2Ojk6AAQKFQAADc3d3V9ru7u6uOKRQKuLm5qR03NzeHk5OTWpnqrvHgezwsJCQEMplM9fL29n78CpFe2Fta4MOx/gCA/x6/gUu3sg0bEBERNUgaD80ZP348gIrZqGfMmAGpVKo6Vl5ejujoaPTu3Vv3ERrA0qVLsXjxYtV2bm4ukyIjFujnjjGdPLH3UiqW/BqNvQv6QmKuda5PREQmTOOESCaTAahoIbKzs4OVlZXqmEQiQa9evTB79mydBSaXywEA6enp8PDwUO1PT09H586dVWUyMjLUzisrK0NWVpbqfLlcjvT0dLUylduVZR4mlUrVEj4yfsGj/XDi+l1cVeRhQ/h1LAxsbeiQiIioAdE4Idq8eTMAoFmzZnjjjTd0+nisOr6+vpDL5Th8+LAqAcrNzcWZM2cwd+5cAEBAQACys7Nx4cIFdOvWDQBw5MgRKJVK9OzZU1Xm3XffRWlpqWrOpNDQULRp0waOjo56rQPVH2dbKZaPaY9Xf7qI9WHXMbyDB9rI7QwdFhERNRBaP1cIDg7WWTKUn5+PqKgoREVFAajoSB0VFYXk5GSIRCIsXLgQ//d//4e9e/fi8uXLmDZtGjw9PTF27FgAQLt27TBs2DDMnj0bZ8+excmTJ7FgwQI899xz8PT0BABMnjwZEokEM2fORGxsLHbs2IHPP/9c7ZEYNQ6jO3ogsJ07SssFvLXzEsrKlYYOiYiIGgiNZqru2rUrDh8+DEdHR3Tp0uWRC7pGRkZq/Obh4eHVjkybPn06tmzZAkEQEBwcjG+++QbZ2dno27cvNmzYgNat/30ckpWVhQULFmDfvn0Qi8WYMGECvvjiC9ja2qrKREdHY/78+Th37hxcXFzwyiuvYMmSJRrHyZmqG4703CIEfnIUeUVleGdEW8zp38LQIRERkYFo8/mtUUK0YsUKvPnmm7C2tsby5csfmRAFBwdrH7GRY0LUsOw4l4wlv16G1FyMgwv7w9dFv493iYjIOOk8ITJ1TIgaFkEQMPXbszhx/S56+Dph++xeEItrTuKJiKhx0vlaZg9atmwZwsLC1JbPIDImIpEIIeP9YWVhhrOJWfjhbLKhQyIiIiOndUIUERGB0aNHw8HBAf369cN7772HQ4cOobCwUB/xEdWJt5M13hrWBgCw+vcrSMnmzycREdVM64QoNDQU2dnZOHz4MEaMGIHz589j/PjxcHBwQN++ffURI1GdTA9ohm4+jigoKcc7uy6DT4eJiKgmdZrO19zcHH369MGECRMwbtw4BAUFQalU4urVq7qOj6jOxGIR1kzoCIm5GEf/voNdkSmGDomIiIyU1gnRN998g8mTJ6NJkybo3bs3Dh48iL59++L8+fO4c+eOPmIkqrOWbrZ4bUgrAMDK/XHIyGPfNyIiqkrrUWZisRiurq54/fXXMW/ePLX5fhorjjJr2ErLlRi7/iRiU3MxvIMcG5/vZuiQiIioHuh1lNmuXbswZcoUbN++Ha6urujduzfeeecd/PXXX7h//36dgybSFwszMdY+3RHmYhH+iFHgj8tphg6JiIiMzGPNQ5STk4Pjx4/jl19+wU8//QSxWNwoh+Ozhahx+OjPePwn7DpcbKU4tLg/HKwlhg6JiIj0SJvPb40Xd31QZmYmjh49ivDwcISHhyM2NhaOjo7o169fnQImqg+vDGmJg7EKXM/Ix8r9cfhkYmdDh0REREZC60dm/v7+cHd3x0svvYSUlBTMnj0bFy9exN27d7F79259xEikE1JzM6yZ0BEiEbArMgVh8RmGDomIiIyE1i1EL7/8MgYMGIAOHTroIx4iverm44gXevti08lEvLvrMv5c1B92lhaGDouIiAxM6xai+fPnMxmiBu2NoNZo6mSN1JwirDnIubOIiEjDhGj16tUaL81x5swZHDhw4LGCItIna4k5Vo/3BwBsO52M0zcyDRwREREZmkYJUVxcHJo2bYp58+bhjz/+UJuAsaysDNHR0diwYQN69+6NZ599FnZ2dnoLmEgXerd0waQe3gCAt3+NRmFJuYEjIiIiQ9IoIfruu+9w6NAhlJaWYvLkyZDL5ZBIJLCzs4NUKkWXLl2wadMmTJs2DVevXkX//v31HTfRY1s6oh3k9pa4mXkfnx7629DhEBGRAWk9D5FSqUR0dDSSkpJQWFgIFxcXdO7cGS4uLvqK0eA4D1HjdfhKOmZuPQ+xCNj5cm8UlymRkVcENztL9PB1gplYZOgQiYiojrT5/H6siRlNBROixu217RfxW1QqzMUilCn//XXwkFkieLQfhnXwMGB0RERUV3pduoOosenbsqJ188FkCAAUOUWYuy0SB2O41AcRUWPHhIhMWrlSwCeh1fcfqkyPVuyLQ7mSDalERI0ZEyIyaWcTs5CWU/P6ewKAtJwinE3Mqr+giIio3jEhIpOWkafZYsSaliMiooaJCRGZNDc7S52WIyKihqlOq92fP38eP//8M5KTk1FSUqJ2bNeuXToJjKg+9PB1gofMEoqcItTUS8jFVoIevk71GhcREdUvrVuItm/fjt69e+PKlSvYvXs3SktLERsbiyNHjkAmk+kjRiK9MROLEDzaDwBQ04xDuYVlCI/PqL+giIio3mmdEK1atQqffvop9u3bB4lEgs8//xxXr17FxIkT0bRpU33ESKRXwzp4YOPzXSGXqT8Wk9tbws/DHiXlSsz+7jy2nU4yUIRERKRvWk/MaGNjg9jYWDRr1gzOzs4IDw+Hv78/rly5gsGDByMtrfHN2cKJGU1DuVLA2cQstZmqlYKAd3dfxs/nbwMAXh7QAm8FtYGYM1gTERk9bT6/te5D5OjoiLy8PABAkyZNEBMTA39/f2RnZ+P+/ft1i5jICJiJRQho4ay+DyKsmdARXo7W+CT0b3x1NAGp2YVY90xHSM3NDBQpERHpmtaPzPr374/Q0FAAwDPPPIPXXnsNs2fPxqRJkzBkyBCdB0hkaCKRCK8OaYWPnukEc7EIey+lYtq3Z5Fzv9TQoRERkY5o/cgsKysLRUVF8PT0hFKpxNq1a3Hq1Cm0atUK7733HhwdHfUVq8HwkRlVOn7tDuZui0R+cRlaudli8wtPwMvR2tBhERFRNbi4q44xIaIHXUnLxQubz0GRWwRXOyk2z3gCHZpwhCURkbHR+eKuubm5al8/6kXU2LXzsMfu+b3RVm6HO3nFePbrCA7LJyJq4DRqITIzM0NaWhrc3NwgFoshElUdYSMIAkQiEcrLy/USqCGxhYiqk1tUirnbLuDk9UyYiUVYNa4Dnn2CU08QERkLnY8yO3LkCJycKmbqDQsLe/wIiRoBe0sLbJ7RA2/visauyBQs+fUyUu4VYtGTrav9TwMRERkvjRKiAQMGqL729fWFt7d3lT/4giDg1q1buo2OyMhJzMX4+JlO8HKwwhdHruOLI9dxO7sQq8d3hMScSwUSETUUWv/F9vX1xZ07d6rsz8rKgq+vr06CImpIRCIRFg9tg9Xj/WEmFmFXZApe3HIOuUUclk9E1FBonRBV9hV6WH5+PiwtuSI4ma7nejTF/6Z3h7XEDCeu38XEryKQllNo6LCIiEgDGs9UvXjxYgAV/xt+//33YW3979wr5eXlOHPmDDp37qzzAIkakkFt3PDzSwF4Ycs5XFXkYdz6U9j8whNo58HO+ERExkzjhOjixYsAKlqILl++DIlEojomkUjQqVMnvPHGG7qPkKiB6dBEht3zemPG5nO4npGPiV9FYOPz3dC3lUu166WZcV00IiKD03pixhdeeAFffPEF7Ozs9BWTmmbNmiEpqeoq4/PmzcP69esxcOBAHD16VO3YSy+9hK+++kq1nZycjLlz5yIsLAy2traYPn06QkJCYG6uWT7IYfdUFzn3SzHn+/M4k5gFc7EIU3o2xV9x6UjLKVKV8ZBZIni0H4Z18DBgpEREjZPOJ2asVFpaiu+//77aBEVfzp07h7S0NNXrwXXUKs2ePVutzNq1a1XHysvLMXLkSJSUlODUqVPYunUrtmzZgmXLltVbHcg0yawt8N3MHhjdyRNlSgFbI5LUkiEAUOQUYe62SByMSTNQlEREBGiZEFlYWKBp06b1Ovmiq6sr5HK56rV//360aNFCbSoAa2trtTIPZoF//fUX4uLisG3bNnTu3BnDhw/HBx98gPXr16OkpKTe6kGmSWpuhk+e6QQbiVm1xyubZ1fsi0O5kqvoEBEZitajzN5991288847yMrK0kc8j1RSUoJt27bhxRdfVBvp9sMPP8DFxQUdOnTA0qVLcf/+fdWxiIgI+Pv7w93dXbUvKCgIubm5iI2NrfZ9iouLuSQJ6cz5pHsoKKn5PxECgLScIpxNrP/fKSIiqqBxp+pK//nPf3D9+nV4enrCx8cHNjY2ascjIyN1FtzD9uzZg+zsbMyYMUO1b/LkyfDx8YGnpyeio6OxZMkSxMfHY9euXQAAhUKhlgwBUG0rFIpq3yckJAQrVqzQTyXI5GTkFdVeSItyRESke1onRGPHjtVDGJr59ttvMXz4cHh6eqr2zZkzR/W1v78/PDw8MGTIECQkJKBFixZ1ep+lS5eqphkAKjpleXt71z1wMmludprNz6VpOSIi0j2tE6Lg4GB9xFGrpKQkHDp0SNXyU5OePXsCAK5fv44WLVpALpfj7NmzamXS09MBAHK5vNprSKVSSKVSHURNBPTwdYKHzBKKnCI8qpfQ/47fQBMHKzR1tn5EKSIi0oc6LbaUnZ2N//3vf1i6dKmqL1FkZCRSUlJ0GtyDNm/eDDc3N4wcOfKR5aKiogAAHh4Vw5gDAgJw+fJlZGRkqMqEhobC3t4efn5+eouXqJKZWITg0RU/aw/POFS5LRYBh69mIPDTo/j4r3gUPqLPERER6Z7W8xBFR0cjMDAQMpkMN2/eRHx8PJo3b4733nsPycnJ+O6773QepFKphK+vLyZNmoTVq1er9ickJODHH3/EiBEj4OzsjOjoaCxatAheXl6quYnKy8vRuXNneHp6Yu3atVAoFJg6dSpmzZqFVatWafT+nIeIdOFgTBpW7Iurdh6ilm62WLEvDsev3QUAeMos8e5IP4zwl1e7VA4REdVOm89vrROiwMBAdO3aFWvXroWdnR0uXbqE5s2b49SpU5g8eTJu3rz5OLFX66+//kJQUBDi4+PRunVr1f5bt27h+eefR0xMDAoKCuDt7Y1x48bhvffeU6t4UlIS5s6di/DwcNjY2GD69OlYvXo1J2akeveomaoFQcCfsen4YH8cUrIr1kALaO6MFU+1R2v3+pkIlYioMdFrQiSTyRAZGYkWLVqoJURJSUlo06YNiooa30gZJkRUnwpLyvHV0QR8dTQBxWVKmIlFmBbgg4WBrSGzsjB0eEREDYbeZqoGKjocVzcvz99//w1XV1dtL0dED7GSmGHRk61xaPEABLV3R7lSwOaTNzHk43D8fO4WlJzAkYhI57ROiMaMGYOVK1eitLQUACASiZCcnIwlS5ZgwoQJOg+QyFR5O1nj66nd8f3MHmjhaoO7+SV469dojNt4ClG3sg0dHhFRo6L1I7OcnBw8/fTTOH/+PPLy8uDp6QmFQoGAgAD8/vvvVSZqbAz4yIwMraRMia2nbuLzw9eQX1wGAJjY3QtvDWsLF1tOEUFEVB299iGqdOLECURHRyM/Px9du3ZFYGBgnYJtCJgQkbHIyC3C6oNXsSuyYooLO0tzLApsjWkBPjA3q2jwfVTHbSIiU1IvCZEpYUJExuZCUhaC98YiJqWiP18bdzssH9MeOYUlNQ7tH9bBw1DhEhEZhN4TosOHD+Pw4cPIyMiAUqlUO7Zp0yZtL2f0mBCRMSpXCthx7hbW/XkV9+6X1liusm1o4/NdmRQRkUnR6yizFStWYOjQoTh8+DDu3r2Le/fuqb2IqH6YiUWY3LMpwt4YiCk9m9ZYrvJ/PCv2xaGcI9SIiKql9VpmX331FbZs2YKpU6fqIx4i0pKDtQSjOnrihzPJNZYRAKTlFOFsYhYCWjjXX3BERA2E1i1EJSUl6N27tz5iIaI6ysjTbEJUTcsREZkarROiWbNm4ccff9RHLERUR252ljotR0RkarR+ZFZUVIRvvvkGhw4dQseOHWFhob6UwCeffKKz4IhIMz18neAhs4QipwiP6iX0fcRNtHSzhasd5y4iInqQ1qPMBg0aVPPFRCIcOXLksYMyNhxlRg3BwZg0zN0WCQBqSZHon22xCFAKgMzKAstG+WF81yYQiTg/ERE1XpyHSMeYEFFDcTAmrcZ5iLwcrbHk12jEplbMXdS/tStWjesAL0drQ4VLRKRXTIh0jAkRNSSPmqm6tFyJ/x6/gc8OXUNJmRLWEjMsGdYWU3v5QMzZrImokdFLQjR+/HiN3nzXrl0alWtImBBRY5NwJx9v/xqNczcr5g7r7uOI1RM6oqWbrYEjIyLSHW0+vzXuVC2TyR47MCIyDi1cbbFjTgB+OJOE1X9cxfmkexjx+XG8FtgKc/o3h4WZ1gNQiYgaND4y0wBbiKgxS8kuxDu7LuPo33cAAH4e9lj7dEd0aML/BBFRw6bXpTuIqHFp4mCFLS88gU+f7QQHawvEpeXiqfUnsebgVRSVlhs6PCKiesGEiIggEokwrosXDi0egFEdPVCuFLAxPAEjPj+Os4lZhg6PiEjv+MhMA3xkRqbmr1gF3tsTg4y8YgDA872aYsmwtrCzrJiI9VEj2YiIjAWH3esYEyIyRTmFpVj9xxX8dPYWAMBTZokPx/mjuKy8xrmOhnXwMFS4RERVMCHSMSZEZMpOXb+Lt3ddRnLW/RrLVLYNbXy+K5MiIjIa7FRNRDrTu6ULDi7shxf7NKuxTOX/qlbsi0O5kv/HIqKGhwkREdXKWmKOJ/3kjywjAEjLKWInbCJqkJgQEZFGMvKKai+kRTkiImPChIiINOJmZ6lRuSupuSgu4/xFRNSwMCEiIo308HWCh8wStQ2u/+rYDfRbE4YN4deRc7+0XmIjInpcTIiISCNmYhGCR/sBQJWkSPTPa1yXJnC3lyIjrxhrD8YjYPVhLN8bi1uPGKFGRGQMOOxeAxx2T/SvgzFpj5yHqKRMiX2XUvHf4zdwVZEHABCLgOH+Hpjdrzk6ezsYKHIiMjWch0jHmBARqdNkpmpBEHDi+l18c+wGjl+7q9rfo5kTZvdvjiFt3SDm7NZEpEdMiHSMCRHR47mSlov/HU/E3kspKC2v+JPT3MUGM/v5YkJXL1hamBk4QiJqjJgQ6RgTIiLdUOQUYcupm/jhTBLyisoAAE42EkwL8MHUXj5wtpWqynK9NCJ6XEyIdIwJEZFu5ReX4edzt/DtiUSkZBcCAKTmYkzo5oWZfX1xLT2P66UR0WNjQqRjTIiI9KOsXImDsQr899gNXLqd88iyXC+NiLTFtcyIqEEwNxNjVEdP7JnfBzvm9MKQtm41luV6aUSkT0yIiMjgRCIRejZ3xqx+zR9ZjuulEZG+MCEiIqOh6Tpo/z1+AzEpOeATfyLSFXNDB0BEVEnT9dKOXM3AkasZaOlmi6c6eWJMZ0/4ONvoOToiaszYQkRERqO29dJEABytLTC8gzsk5mJcz8jHx6F/Y8C6cIzbcBJbTibibn5xfYZMRI2EUSdEy5cvh0gkUnu1bdtWdbyoqAjz58+Hs7MzbG1tMWHCBKSnp6tdIzk5GSNHjoS1tTXc3Nzw5ptvoqysrL6rQkQaqG29NAAIGe+Pjc93x4X3ArHu6Y7o18oFYhFwMTkby/fFoeeqw5i26Sx+vXAb+cX8XScizRj1sPvly5dj586dOHTokGqfubk5XFxcAABz587FgQMHsGXLFshkMixYsABisRgnT54EAJSXl6Nz586Qy+VYt24d0tLSMG3aNMyePRurVq3SOA4OuyeqX7Wtl/awjNwi7I9Ow2+XUnHpVrZqv9RcjEA/d4zt3AQDWrtCYq7+f0BO/kjUuDWaeYiWL1+OPXv2ICoqqsqxnJwcuLq64scff8TTTz8NALh69SratWuHiIgI9OrVC3/88QdGjRqF1NRUuLu7AwC++uorLFmyBHfu3IFEItEoDiZERPWvrslK4t0C7I1KxW9RKbhxt0C1X2ZlgRH+Hniqsyd6NHPCX3EKTv5I1Mhp8/lt9J2qr127Bk9PT1haWiIgIAAhISFo2rQpLly4gNLSUgQGBqrKtm3bFk2bNlUlRBEREfD391clQwAQFBSEuXPnIjY2Fl26dKn2PYuLi1Fc/G8/hNzcXP1VkIiqZSYWIaCFs9bn+brY4LXAVnh1SEvEpORiT1QK9l1KRUZeMX46m4yfzibDwdoC2fdLq5yryCnC3G2RnPyRyAQZdR+inj17YsuWLTh48CA2btyIxMRE9OvXD3l5eVAoFJBIJHBwcFA7x93dHQqFAgCgUCjUkqHK45XHahISEgKZTKZ6eXt767ZiRKR3IpEI/l4yvD/KDxFLh+CHWT0xsbsXbCVm1SZDACd/JDJlRt1CNHz4cNXXHTt2RM+ePeHj44Off/4ZVlZWenvfpUuXYvHixart3NxcJkVEDZiZWIQ+LV3Qp6ULRnb0wPRN52os++/kj5kIaOFSf0ESkUEZdQvRwxwcHNC6dWtcv34dcrkcJSUlyM7OViuTnp4OuVwOAJDL5VVGnVVuV5apjlQqhb29vdqLiBqHmlqHHjbvh0h8sD8O525msbWIyAQ0qIQoPz8fCQkJ8PDwQLdu3WBhYYHDhw+rjsfHxyM5ORkBAQEAgICAAFy+fBkZGRmqMqGhobC3t4efn1+9x09Ehqfp5I/37pfi2xOJeOarCPRcdQhLd13G0b/voKRMqecIicgQjHqU2RtvvIHRo0fDx8cHqampCA4ORlRUFOLi4uDq6oq5c+fi999/x5YtW2Bvb49XXnkFAHDq1CkA/w679/T0xNq1a6FQKDB16lTMmjWLw+6JTFS5UkDfNUegyClCdX/8RADc7S2xbJQfDl1JR+iVdOQV/TufkZ2lOQLbuSOovTv6t3aFtcSoex4QmbRGM8rs9u3bmDRpEjIzM+Hq6oq+ffvi9OnTcHV1BQB8+umnEIvFmDBhAoqLixEUFIQNGzaozjczM8P+/fsxd+5cBAQEwMbGBtOnT8fKlSsNVSUiMrDKyR/nbouECFBLiioH9S8fUzH0fkRHD5SUKXH6RiYOxirwV2w67uYXY/fFFOy+mAJLCzEGtHbFsA5yDG7rDpmVRZX341xHRA2DUbcQGQu2EBE1PtpO/ghUJDcXk+/hYIwCB2MVuH2vUHXM/J9pAoZ1kONJP3e42VnW6T2ISHcazcSMxoIJEVHj9DitN4IgIC4tF3/+kxz9nZ6vOiYSAc1dbJBwp6DKeZVX51xHRPrHhEjHmBARUW1u3MnHn7HpOBirUFs+pDoiAHKZJU4sGczHZ0R6pM3nd4MaZUZEZKyau9pi7sAW+G1+H6yfXP0s+JUq5zo6ce1O/QRHRLUy6k7VREQNUZmG8xbN3Hoe/Vq5YHA7dwxu64YmDvqbcJaIHo0JERGRjmk611GZUkBY/B2Exd/B+wDayu0wuK0bhrRzQ2dvRz5OI6pHTIiIiHSsh68TPGSWj5zrSC6zxLfTn0D43xk4ciUDkcn3cFWRh6uKPGwIT4CjtQUGtnHD4LZu6N/atdoh/Q/i8H6ix8NO1Rpgp2oi0tbBmDTM3RYJoPq5jh4eZXavoARH/76Dw1czcDQ+A7kPTAZpJhbhiWaOGNzWDYPbuqOFqw1Eon+THQ7vJ6oeR5npGBMiIqqLuiYqpeVKXEi6h7CrGTh8NQPXM/LVjvs4W/+THLkh+34JXv0pqkpLFIf3EzEh0jkmRERUV7p4lJWUWYAjVzNw5GoGztzIQkn5v+upPTzb9oM4vJ9MHRMiHWNCRETGIr+4DCeu3cWRq+k4GJOO3KLSWs/5aXYvBLRwrofoiIwL5yEiImqkbKXmGNZBjrVPd8LKp9prdM43xxJw5Go68jRInohMFUeZERE1UO72mg3vrxzabyYWoUMTGQKaOyOghTOeaOYIa4nmHwMcyUaNGRMiIqIGqrbh/QDgYG2BJ9u540xiFpKz7uPSrWxcupWNr44mwFwsQidvB/Ru4YyA5s7o6uMISwuzaq/DkWzU2LEPkQbYh4iIjJU2w/tTsgsRkZCJiIRMnL6RiZTsQrVrSczE6NLUAQH/JEidmzpAam6meg+OZKOGhp2qdYwJEREZs7q03giCgFtZhYi4cbciSbqRifTcYrUylhZidG3qgOjbucgvLqv2OhzJRsaMCZGOMSEiImP3uP17BEFA4t0CRNzIxKmETJy5kYm7+SUan8+RbGSMtPn8Zh8iIqJGwEwseqyERCQSobmrLZq72mJKTx8IgoBrGfn45ugN7Iy8Xev5kcn30NPXCWK2ElEDxRYiDbCFiIhMVURCJib997RGZR2tLdDTt2IEW0ALZ7Rys1VbYoSovrGFiIiIdEKTkWxSczHMRMC9+6U4GKvAwVgFAMDFVopezZ1UnbR9XWxqTZA4tJ8MhS1EGmALERGZMk1Gsg1p547o2zk4faNiFNv5pCwUlSrVriO3t1QlRwEtnOHtZF3lfTi0n3SJnap1jAkREZk6bZOV4rJyRCVnI+KfBOlicrbaGmwA4OVopUqOisvK8c6uGA7tJ51iQqRjTIiIiB7vcVZRaTkik+7h1D9D/C/dykaZUrOPHw7tp7piQqRjTIiIiHSroLgM55PuISIhE6GxCiTcLaj1nHdGtMXYLk3gait9rM7a7KdkOpgQ6RgTIiIi/fktKgWvbY/SuLyTjQSt3W3RVm6PNnI7tJHbobW7HWyltY8TYj8l08JRZkRE1GC42Wm2SK2HvSXS84qQVVCC0zeycPpGltpxL0crtH0gQWort0dzVxtYmIkBoMYlSBQ5RZi7LZL9lEwcEyIiIjKo2ob2P9iHqLRciesZ+biqyEO8IhdXFXn4Oz0P6bnFuH2vELfvFeLQlQzVuRZmIjR3sUVrd1uExd+p9vrCP++xYl8cnvST8/GZiWJCREREBmUmFiF4tB/mbouECNUP7Q8e7QczsQhmYjN0aCJDhyYytWvcKyhBfHoe4hV5qn//VuQhr7isYjs975ExCADScopwNjGLS5CYKPYh0gD7EBER6Z+u+/cIgoCU7EL8nZ6HXZEp2B+dVus5bnZSPOHrBD8Pe/h52KOdhz3c7bXvxM2O28aBnap1jAkREVH90Fcioc0SJA9ztLaAn6c92skrEiQ/T3u0cLWFxFxcbXl23DYeTIh0jAkREVHDVq4U0HfNkUf2U3Kzl2L1+I6IT8/DlbRcxKXm4sbdApRXM1+ShZkILd3s/mlFslO1Jp1JzKy24zYnmDQMJkQ6xoSIiKjh02QJkoeTlaLSclxLz69IkP55XUnLRV5RWbXvIRYBNc03qesJJvlYrnZMiHSMCRERUeOgi8dZlX2T4lJzcSUtT5UsJWfd1+j8CV2boHcLF/g4W6Ops3WdJprkYznNMCHSMSZERESNh75aVnacS8aSXy9rfZ6VhRmaOlUkR02drCsSJSdr+DjboImDVZW+SjXNp8THclVxYkYiIqIamIlFehla39TJRqNyg9u6oqhUiaTM+0jLKURhaXmNUwOIRYCHzEqVJHk5WeF/xxPrbT4lU3osx4SIiIhIBzSdYPK/055QJRUlZUqkZBciKbMAyVn3kZx5H0n//JucdR+FpeVIyS5ESnYhTiVk1hpD5XxK3564gUFt3CCXWcLO0qJO9TG1x3J8ZKYBPjIjIiJN1KXjdk0EQcCd/OKKJOmfROnU9bs4n3RPq5hsJGaQyywhl1nC3d4SHjJLyO0tIZdZ/fOvJZxtJBA/0PLTWB7LsQ+RjjEhIiIiTemzZUXT+ZS8HC2RW1iG3BpGwz3MwkwEN7vKpEmKo/F3UFBSXm3ZhjRajgmRjjEhIiIibejrQ16T+ZQeTFbul5RBkVNU8cotQlpOEdIf+vdufjHqkgmM79oEvZo7w8vBCk0creAhq9oBvDb6fizHhEjHmBAREZGx0OVjOQAoLVfiTl6xKkkKjUvH7ospWsclEgHudpbwcqxIkJo4WMHL0fqBr61gaWFWpR76fCzXaBKikJAQ7Nq1C1evXoWVlRV69+6NNWvWoE2bNqoyAwcOxNGjR9XOe+mll/DVV1+ptpOTkzF37lyEhYXB1tYW06dPR0hICMzNNetTzoSIiIiMiTE8lhvc1g2l5RWdwlPuFaK4TFnrOS62EjRxtEYTmSWO/q3/x3KNZtj90aNHMX/+fDzxxBMoKyvDO++8g6FDhyIuLg42Nv8Ob5w9ezZWrlyp2ra2tlZ9XV5ejpEjR0Iul+PUqVNIS0vDtGnTYGFhgVWrVtVrfYiIiHRhWAcPPOkn18tjOc1Hy3VXvZ8gCLibX6JKjm7fu4+U7ELcvlexnZJdiPziMtzNL8Hd/BJcuvXoGCpHy51NzNLLFAnVMeqE6ODBg2rbW7ZsgZubGy5cuID+/fur9ltbW0Mul1d7jb/++gtxcXE4dOgQ3N3d0blzZ3zwwQdYsmQJli9fDolEotc6EBER6YO+5lMyE4sQPNoPc7dFQoTqH8sFj/ZTS75EIhFc7aRwtZOis7dDlWsKgoCcwtKKBCm7EAdjFBo9lsvIK6q1jK5o1/vJwHJycgAATk5Oavt/+OEHuLi4oEOHDli6dCnu3/93+vSIiAj4+/vD3d1dtS8oKAi5ubmIjY2t9n2Ki4uRm5ur9iIiIjIVwzp4YOPzXSGXWartl8ss69S3RyQSwcFagg5NZAhqL8fE7t4anedmZ1l7IR0x6haiBymVSixcuBB9+vRBhw4dVPsnT54MHx8feHp6Ijo6GkuWLEF8fDx27doFAFAoFGrJEADVtkKhqPa9QkJCsGLFCj3VhIiIyPgZw2O5Hr5O1RzVjwaTEM2fPx8xMTE4ceKE2v45c+aovvb394eHhweGDBmChIQEtGjRok7vtXTpUixevFi1nZubC29vzbJZIiKixsKYHsvpW4N4ZLZgwQLs378fYWFh8PLyemTZnj17AgCuX78OAJDL5UhPT1crU7ldU78jqVQKe3t7tRcRERHpjq4fyz0uo24hEgQBr7zyCnbv3o3w8HD4+vrWek5UVBQAwMOj4hsZEBCADz/8EBkZGXBzcwMAhIaGwt7eHn5+fnqLnYiIiB5Nn4/ltGXUCdH8+fPx448/4rfffoOdnZ2qz49MJoOVlRUSEhLw448/YsSIEXB2dkZ0dDQWLVqE/v37o2PHjgCAoUOHws/PD1OnTsXatWuhUCjw3nvvYf78+ZBKpYasHhERkcnT12M5bRn1xIwiUfUZ4ubNmzFjxgzcunULzz//PGJiYlBQUABvb2+MGzcO7733ntpjrqSkJMydOxfh4eGwsbHB9OnTsXr1ak7MSERE1Ig1mpmqjQUTIiIiooZHm8/vBtGpmoiIiEifmBARERGRyWNCRERERCaPCRERERGZPCZEREREZPKYEBEREZHJY0JEREREJs+oZ6o2FpVTNeXm5ho4EiIiItJU5ee2JlMuMiHSQF5eHgBwxXsiIqIGKC8vDzKZ7JFlOFO1BpRKJVJTU2FnZ1fjciL1JTc3F97e3rh165bJzZrNupte3U213gDrbop1N9V6A/qruyAIyMvLg6enJ8TiR/cSYguRBsRiMby8vAwdhhp7e3uT+4WpxLqbXt1Ntd4A626KdTfVegP6qXttLUOV2KmaiIiITB4TIiIiIjJ5TIgaGKlUiuDgYEilUkOHUu9Yd9Oru6nWG2DdTbHuplpvwDjqzk7VREREZPLYQkREREQmjwkRERERmTwmRERERGTymBARERGRyWNCZGRCQkLwxBNPwM7ODm5ubhg7dizi4+Mfec6WLVsgEonUXpaWlvUUsW4sX768Sh3atm37yHN++eUXtG3bFpaWlvD398fvv/9eT9HqVrNmzarUXSQSYf78+dWWb8j3+9ixYxg9ejQ8PT0hEomwZ88eteOCIGDZsmXw8PCAlZUVAgMDce3atVqvu379ejRr1gyWlpbo2bMnzp49q6ca1M2j6l1aWoolS5bA398fNjY28PT0xLRp05CamvrIa9bld8YQarvnM2bMqFKPYcOG1XrdhnzPAVT7Oy8SibBu3boar9lQ7rkmn2NFRUWYP38+nJ2dYWtriwkTJiA9Pf2R163r3wdNMSEyMkePHsX8+fNx+vRphIaGorS0FEOHDkVBQcEjz7O3t0daWprqlZSUVE8R60779u3V6nDixIkay546dQqTJk3CzJkzcfHiRYwdOxZjx45FTExMPUasG+fOnVOrd2hoKADgmWeeqfGchnq/CwoK0KlTJ6xfv77a42vXrsUXX3yBr776CmfOnIGNjQ2CgoJQVFRU4zV37NiBxYsXIzg4GJGRkejUqROCgoKQkZGhr2po7VH1vn//PiIjI/H+++8jMjISu3btQnx8PMaMGVPrdbX5nTGU2u45AAwbNkytHj/99NMjr9nQ7zkAtfqmpaVh06ZNEIlEmDBhwiOv2xDuuSafY4sWLcK+ffvwyy+/4OjRo0hNTcX48eMfed26/H3QikBGLSMjQwAgHD16tMYymzdvFmQyWf0FpQfBwcFCp06dNC4/ceJEYeTIkWr7evbsKbz00ks6jqz+vfbaa0KLFi0EpVJZ7fHGcL8FQRAACLt371ZtK5VKQS6XC+vWrVPty87OFqRSqfDTTz/VeJ0ePXoI8+fPV22Xl5cLnp6eQkhIiF7iflwP17s6Z8+eFQAISUlJNZbR9nfGGFRX9+nTpwtPPfWUVtdpjPf8qaeeEgYPHvzIMg3xngtC1c+x7OxswcLCQvjll19UZa5cuSIAECIiIqq9Rl3/PmiDLURGLicnBwDg5OT0yHL5+fnw8fGBt7c3nnrqKcTGxtZHeDp17do1eHp6onnz5pgyZQqSk5NrLBsREYHAwEC1fUFBQYiIiNB3mHpVUlKCbdu24cUXX3zkQsKN4X4/LDExEQqFQu2+ymQy9OzZs8b7WlJSggsXLqidIxaLERgY2KB/FnJyciASieDg4PDIctr8zhiz8PBwuLm5oU2bNpg7dy4yMzNrLNsY73l6ejoOHDiAmTNn1lq2Id7zhz/HLly4gNLSUrV72LZtWzRt2rTGe1iXvw/aYkJkxJRKJRYuXIg+ffqgQ4cONZZr06YNNm3ahN9++w3btm2DUqlE7969cfv27XqM9vH07NkTW7ZswcGDB7Fx40YkJiaiX79+yMvLq7a8QqGAu7u72j53d3coFIr6CFdv9uzZg+zsbMyYMaPGMo3hflen8t5pc1/v3r2L8vLyRvWzUFRUhCVLlmDSpEmPXORS298ZYzVs2DB89913OHz4MNasWYOjR49i+PDhKC8vr7Z8Y7znW7duhZ2dXa2PjBriPa/uc0yhUEAikVRJ+B91D+vy90FbXO3eiM2fPx8xMTG1PiMOCAhAQECAart3795o164dvv76a3zwwQf6DlMnhg8frvq6Y8eO6NmzJ3x8fPDzzz9r9L+mxuLbb7/F8OHD4enpWWOZxnC/qXqlpaWYOHEiBEHAxo0bH1m2sfzOPPfcc6qv/f390bFjR7Ro0QLh4eEYMmSIASOrP5s2bcKUKVNqHRzREO+5pp9jxoAtREZqwYIF2L9/P8LCwuDl5aXVuRYWFujSpQuuX7+up+j0z8HBAa1bt66xDnK5vMqIhPT0dMjl8voITy+SkpJw6NAhzJo1S6vzGsP9BqC6d9rcVxcXF5iZmTWKn4XKZCgpKQmhoaGPbB2qTm2/Mw1F8+bN4eLiUmM9GtM9B4Djx48jPj5e6997wPjveU2fY3K5HCUlJcjOzlYr/6h7WJe/D9piQmRkBEHAggULsHv3bhw5cgS+vr5aX6O8vByXL1+Gh4eHHiKsH/n5+UhISKixDgEBATh8+LDavtDQULWWk4Zm8+bNcHNzw8iRI7U6rzHcbwDw9fWFXC5Xu6+5ubk4c+ZMjfdVIpGgW7duaucolUocPny4Qf0sVCZD165dw6FDh+Ds7Kz1NWr7nWkobt++jczMzBrr0VjueaVvv/0W3bp1Q6dOnbQ+11jveW2fY926dYOFhYXaPYyPj0dycnKN97Aufx/qEjgZkblz5woymUwIDw8X0tLSVK/79++rykydOlV4++23VdsrVqwQ/vzzTyEhIUG4cOGC8NxzzwmWlpZCbGysIapQJ6+//roQHh4uJCYmCidPnhQCAwMFFxcXISMjQxCEqnU+efKkYG5uLnz00UfClStXhODgYMHCwkK4fPmyoarwWMrLy4WmTZsKS5YsqXKsMd3vvLw84eLFi8LFixcFAMInn3wiXLx4UTWaavXq1YKDg4Pw22+/CdHR0cJTTz0l+Pr6CoWFhaprDB48WPjyyy9V29u3bxekUqmwZcsWIS4uTpgzZ47g4OAgKBSKeq9fTR5V75KSEmHMmDGCl5eXEBUVpfZ7X1xcrLrGw/Wu7XfGWDyq7nl5ecIbb7whRERECImJicKhQ4eErl27Cq1atRKKiopU12hs97xSTk6OYG1tLWzcuLHaazTUe67J59jLL78sNG3aVDhy5Ihw/vx5ISAgQAgICFC7Tps2bYRdu3aptjX5+/A4mBAZGQDVvjZv3qwqM2DAAGH69Omq7YULFwpNmzYVJBKJ4O7uLowYMUKIjIys/+Afw7PPPit4eHgIEolEaNKkifDss88K169fVx1/uM6CIAg///yz0Lp1a0EikQjt27cXDhw4UM9R686ff/4pABDi4+OrHGtM9zssLKzan+/K+imVSuH9998X3N3dBalUKgwZMqTK98THx0cIDg5W2/fll1+qvic9evQQTp8+XU810syj6p2YmFjj731YWJjqGg/Xu7bfGWPxqLrfv39fGDp0qODq6ipYWFgIPj4+wuzZs6skNo3tnlf6+uuvBSsrKyE7O7vaazTUe67J51hhYaEwb948wdHRUbC2thbGjRsnpKWlVbnOg+do8vfhcYj+eVMiIiIik8U+RERERGTymBARERGRyWNCRERERCaPCRERERGZPCZEREREZPKYEBEREZHJY0JEREREJo8JERHVi5s3b0IkEiEqKsrQoahcvXoVvXr1gqWlJTp37qz1+cZYJyKqGyZERCZixowZEIlEWL16tdr+PXv2QCQSGSgqwwoODoaNjQ3i4+OrrI1nCFu2bIGDg4PB3l8kEmHPnj2q7dLSUkyaNAlNmjRBTEyMweIiqg9MiIhMiKWlJdasWYN79+4ZOhSdKSkpqfO5CQkJ6Nu3L3x8fOq0oKqxKi8vh1KpfKxr3L9/H2PGjMG5c+dw4sQJdOjQQUfRERknJkREJiQwMBByuRwhISE1llm+fHmVx0efffYZmjVrptqeMWMGxo4di1WrVsHd3R0ODg5YuXIlysrK8Oabb8LJyQleXl7YvHlzletfvXoVvXv3hqWlJTp06ICjR4+qHY+JicHw4cNha2sLd3d3TJ06FXfv3lUdHzhwIBYsWICFCxfCxcUFQUFB1dZDqVRi5cqV8PLyglQqRefOnXHw4EHVcZFIhAsXLmDlypUQiURYvnx5jddZu3YtWrZsCalUiqZNm+LDDz+stmx1LTwPt8BdunQJgwYNgp2dHezt7dGtWzecP38e4eHheOGFF5CTkwORSKQWU3FxMd544w00adIENjY26NmzJ8LDw6u87969e+Hn5wepVIrk5GSEh4ejR48esLGxgYODA/r06YOkpKRqY39QdnY2nnzySaSmpuLEiRNVVisnaoyYEBGZEDMzM6xatQpffvklbt++/VjXOnLkCFJTU3Hs2DF88sknCA4OxqhRo+Do6IgzZ87g5ZdfxksvvVTlfd588028/vrruHjxIgICAjB69GhkZmYCqPggHjx4MLp06YLz58/j4MGDSE9Px8SJE9WusXXrVkgkEpw8eRJfffVVtfF9/vnn+Pjjj/HRRx8hOjoaQUFBGDNmDK5duwYASEtLQ/v27fH6668jLS0Nb7zxRrXXWbp0KVavXo33338fcXFx+PHHH+Hu7l7n79uUKVPg5eWFc+fO4cKFC3j77bdhYWGB3r1747PPPoO9vT3S0tLUYlqwYAEiIiKwfft2REdH45lnnsGwYcNUdQEqWnTWrFmD//3vf4iNjYWTkxPGjh2LAQMGIDo6GhEREZgzZ06tj0cVCgUGDBgAADh69Cjkcnmd60rUoOhsmVgiMmrTp08XnnrqKUEQBKFXr17Ciy++KAiCIOzevVt48E9BcHCw0KlTJ7VzP/30U8HHx0ftWj4+PkJ5eblqX5s2bYR+/fqptsvKygQbGxvhp59+EgRBUK3qvnr1alWZ0tJSwcvLS1izZo0gCILwwQcfCEOHDlV771u3bgkAVKtaDxgwQOjSpUut9fX09BQ+/PBDtX1PPPGEMG/ePNV2p06dqqyi/qDc3FxBKpUK//3vf6s9XlmnixcvCoIgCJs3bxZkMplamYe/v3Z2dsKWLVuqvV515yclJQlmZmZCSkqK2v4hQ4YIS5cuVZ0HQIiKilIdz8zMFAAI4eHhNdbvYQAEiUQitG3bVigoKND4PKLGgC1ERCZozZo12Lp1K65cuVLna7Rv3x5i8b9/Qtzd3eHv76/aNjMzg7OzMzIyMtTOCwgIUH1tbm6O7t27q+K4dOkSwsLCYGtrq3q1bdsWQEV/n0rdunV7ZGy5ublITU1Fnz591Pb36dNHqzpfuXIFxcXFGDJkiMbn1Gbx4sWYNWsWAgMDsXr1arV6Vefy5csoLy9H69at1b4vR48eVTtXIpGgY8eOqm0nJyfMmDEDQUFBGD16ND7//HOkpaXVGt+oUaPw999/4+uvv657JYkaICZERCaof//+CAoKwtKlS6scE4vFEARBbV9paWmVchYWFmrbIpGo2n3adO7Nz8/H6NGjERUVpfa6du0a+vfvrypnY2Oj8TUfh5WVlVblNfneLV++HLGxsRg5ciSOHDkCPz8/7N69u8Zr5ufnw8zMDBcuXFD7nly5cgWff/65WqwPPw7bvHkzIiIi0Lt3b+zYsQOtW7fG6dOnH1mHqVOnYtOmTXjjjTfwySefaFp1ogaPCRGRiVq9ejX27duHiIgItf2urq5QKBRqH+y6nGfnwQ/ksrIyXLhwAe3atQMAdO3aFbGxsWjWrBlatmyp9tImCbK3t4enpydOnjyptv/kyZPw8/PT+DqtWrWClZWVxkPyXV1dkZeXh4KCAtW+6r53rVu3xqJFi/DXX39h/Pjxqs7nEokE5eXlamW7dOmC8vJyZGRkVPmeaNK/p0uXLli6dClOnTqFDh064Mcff6z1nOnTp2PLli1466238NFHH9VanqgxYEJEZKL8/f0xZcoUfPHFF2r7Bw4ciDt37mDt2rVISEjA+vXr8ccff+jsfdevX4/du3fj6tWrmD9/Pu7du4cXX3wRADB//nxkZWVh0qRJOHfuHBISEvDnn3/ihRdeqJIo1ObNN9/EmjVrsGPHDsTHx+Ptt99GVFQUXnvtNY2vYWlpiSVLluCtt97Cd999h4SEBJw+fRrffvttteV79uwJa2trvPPOO0hISMCPP/6ILVu2qI4XFhZiwYIFCA8PR1JSEk6ePIlz586pEsJmzZohPz8fhw8fxt27d3H//n20bt0aU6ZMwbRp07Br1y4kJibi7NmzCAkJwYEDB2qMPTExEUuXLkVERASSkpLw119/4dq1a6r3qs3UqVOxdetWvP3221i3bp3G3zOihooJEZEJW7lyZZVHWu3atcOGDRuwfv16dOrUCWfPnq1xBFZdrF69GqtXr0anTp1w4sQJ7N27Fy4uLgCgatUpLy/H0KFD4e/vj4ULF8LBwUGtv5ImXn31VSxevBivv/46/P39cfDgQezduxetWrXS6jrvv/8+Xn/9dSxbtgzt2rXDs88+W6VfVCUnJyds27YNv//+O/z9/fHTTz+pDec3MzNDZmYmpk2bhtatW2PixIkYPnw4VqxYAQDo3bs3Xn75ZTz77LNwdXXF2rVrAVQ8+po2bRpef/11tGnTBmPHjsW5c+fQtGnTGuO2trbG1atXMWHCBLRu3Rpz5szB/Pnz8dJLL2lc9ylTpuD777/H0qVLsWbNGo3PI2qIRMLDD7yJiIiITAxbiIiIiMjkMSEiIiIik8eEiIiIiEweEyIiIiIyeUyIiIiIyOQxISIiIiKTx4SIiIiITB4TIiIiIjJ5TIiIiIjI5DEhIiIiIpPHhIiIiIhMHhMiIiIiMnn/DyhNmS1YxnUpAAAAAElFTkSuQmCC",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Best K by silhouette: 5\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"' Ce que c’est :\\nInertia = somme des distances intra-cluster (SSE).\\nPlus elle baisse, plus les clusters sont “serrés”.\\n\\nComment lire :\\nQuand K augmente, inertia baisse toujours (normal).\\nOn cherche un “coude” : à partir d’un certain K, ajouter des clusters apporte peu\\n'"
|
||
]
|
||
},
|
||
"execution_count": 226,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"k_range = range(2, 21)\n",
|
||
"inertias = []\n",
|
||
"silhouettes = []\n",
|
||
"\n",
|
||
"for k in k_range:\n",
|
||
" km = KMeans(n_clusters=k, n_init=30, random_state=42)\n",
|
||
" labels = km.fit_predict(X_scaled)\n",
|
||
" inertias.append(km.inertia_)\n",
|
||
" silhouettes.append(silhouette_score(X_scaled, labels))\n",
|
||
"\n",
|
||
"# Elbow plot\n",
|
||
"plt.figure()\n",
|
||
"plt.plot(list(k_range), inertias, marker=\"o\")\n",
|
||
"plt.xlabel(\"Number of clusters K\")\n",
|
||
"plt.ylabel(\"Inertia (within-cluster SSE)\")\n",
|
||
"plt.title(\"Elbow curve for K-means\")\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# Silhouette plot\n",
|
||
"plt.figure()\n",
|
||
"plt.plot(list(k_range), silhouettes, marker=\"o\")\n",
|
||
"plt.xlabel(\"Number of clusters K\")\n",
|
||
"plt.ylabel(\"Silhouette score\")\n",
|
||
"plt.title(\"Silhouette score for K-means\")\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"best_k = list(k_range)[int(np.argmax(silhouettes))]\n",
|
||
"print(\"Best K by silhouette:\", best_k)\n",
|
||
"\n",
|
||
"\n",
|
||
"''' Ce que c’est :\n",
|
||
"Inertia = somme des distances intra-cluster (SSE).\n",
|
||
"Plus elle baisse, plus les clusters sont “serrés”.\n",
|
||
"\n",
|
||
"Comment lire :\n",
|
||
"Quand K augmente, inertia baisse toujours (normal).\n",
|
||
"On cherche un “coude” : à partir d’un certain K, ajouter des clusters apporte peu\n",
|
||
"'''"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 227,
|
||
"id": "2759f049-d8fe-4fee-9bc9-856a28b392a9",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>n_clients</th>\n",
|
||
" <th>aum_qty_med</th>\n",
|
||
" <th>freq_med</th>\n",
|
||
" <th>rel_int_med</th>\n",
|
||
" <th>gross_flow_med</th>\n",
|
||
" <th>n_tx_med</th>\n",
|
||
" <th>vol_med</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>cluster_kmeans</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>2.0</th>\n",
|
||
" <td>235</td>\n",
|
||
" <td>3.936071e+04</td>\n",
|
||
" <td>0.986111</td>\n",
|
||
" <td>4.136974</td>\n",
|
||
" <td>2031.883965</td>\n",
|
||
" <td>1069.0</td>\n",
|
||
" <td>2.735326e+03</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1.0</th>\n",
|
||
" <td>105</td>\n",
|
||
" <td>4.528840e+05</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>4.651358</td>\n",
|
||
" <td>28651.252789</td>\n",
|
||
" <td>7585.0</td>\n",
|
||
" <td>3.004524e+04</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>0.0</th>\n",
|
||
" <td>66</td>\n",
|
||
" <td>6.912599e+04</td>\n",
|
||
" <td>0.109903</td>\n",
|
||
" <td>1.632692</td>\n",
|
||
" <td>2773.037334</td>\n",
|
||
" <td>7.5</td>\n",
|
||
" <td>1.080610e+04</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4.0</th>\n",
|
||
" <td>13</td>\n",
|
||
" <td>4.783496e+04</td>\n",
|
||
" <td>0.884615</td>\n",
|
||
" <td>27.093690</td>\n",
|
||
" <td>10629.415385</td>\n",
|
||
" <td>1712.0</td>\n",
|
||
" <td>1.876254e+04</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3.0</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1.470709e+07</td>\n",
|
||
" <td>0.586207</td>\n",
|
||
" <td>5.705179</td>\n",
|
||
" <td>851698.564766</td>\n",
|
||
" <td>2210.5</td>\n",
|
||
" <td>3.218539e+06</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" n_clients aum_qty_med freq_med rel_int_med \\\n",
|
||
"cluster_kmeans \n",
|
||
"2.0 235 3.936071e+04 0.986111 4.136974 \n",
|
||
"1.0 105 4.528840e+05 1.000000 4.651358 \n",
|
||
"0.0 66 6.912599e+04 0.109903 1.632692 \n",
|
||
"4.0 13 4.783496e+04 0.884615 27.093690 \n",
|
||
"3.0 2 1.470709e+07 0.586207 5.705179 \n",
|
||
"\n",
|
||
" gross_flow_med n_tx_med vol_med \n",
|
||
"cluster_kmeans \n",
|
||
"2.0 2031.883965 1069.0 2.735326e+03 \n",
|
||
"1.0 28651.252789 7585.0 3.004524e+04 \n",
|
||
"0.0 2773.037334 7.5 1.080610e+04 \n",
|
||
"4.0 10629.415385 1712.0 1.876254e+04 \n",
|
||
"3.0 851698.564766 2210.5 3.218539e+06 "
|
||
]
|
||
},
|
||
"execution_count": 227,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"km = KMeans(n_clusters=best_k, n_init=50, random_state=42)\n",
|
||
"labels_km = km.fit_predict(X_scaled)\n",
|
||
"\n",
|
||
"dfc.loc[X.index, \"cluster_kmeans\"] = labels_km\n",
|
||
"\n",
|
||
"# Profiling table (medians = robust to outliers)\n",
|
||
"k_profile = (\n",
|
||
" dfc.loc[X.index]\n",
|
||
" .groupby(\"cluster_kmeans\")\n",
|
||
" .agg(\n",
|
||
" n_clients=(ID_COL, \"count\"),\n",
|
||
" aum_qty_med=(\"aum_qty_mean\", \"median\"),\n",
|
||
" freq_med=(\"frequency\", \"median\"),\n",
|
||
" rel_int_med=(\"rel_intensity_total\", \"median\"),\n",
|
||
" gross_flow_med=(\"gross_flow_qty_mean\", \"median\"),\n",
|
||
" n_tx_med=(\"n_tx_total\", \"median\"),\n",
|
||
" vol_med=(\"net_flow_qty_vol\", \"median\"),\n",
|
||
" )\n",
|
||
" .sort_values(\"n_clients\", ascending=False)\n",
|
||
")\n",
|
||
"\n",
|
||
"k_profile\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 74,
|
||
"id": "f7883188-9981-431b-9d33-d330b8b9dfc2",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
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|
||
"\n",
|
||
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|
||
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|
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|
||
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|
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|
||
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|
||
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|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Registrar Account - ID</th>\n",
|
||
" <th>n_months</th>\n",
|
||
" <th>n_active_months</th>\n",
|
||
" <th>flow_freq</th>\n",
|
||
" <th>aum_qty_mean</th>\n",
|
||
" <th>aum_qty_median</th>\n",
|
||
" <th>net_flow_qty_sum</th>\n",
|
||
" <th>gross_flow_qty_sum</th>\n",
|
||
" <th>gross_flow_qty_mean</th>\n",
|
||
" <th>net_flow_qty_vol</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>netflow_to_aum</th>\n",
|
||
" <th>n_tx_total</th>\n",
|
||
" <th>log_aum_qty_mean</th>\n",
|
||
" <th>log_gross_flow_qty_mean</th>\n",
|
||
" <th>gross_flow_to_aum</th>\n",
|
||
" <th>seg_quantiles</th>\n",
|
||
" <th>rel_intensity_total</th>\n",
|
||
" <th>frequency</th>\n",
|
||
" <th>seg_2D</th>\n",
|
||
" <th>cluster_kmeans</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>240</th>\n",
|
||
" <td>200130818</td>\n",
|
||
" <td>58</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>0.172414</td>\n",
|
||
" <td>3.819992e+06</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>9.586849e+06</td>\n",
|
||
" <td>3.429192e+07</td>\n",
|
||
" <td>5.912401e+05</td>\n",
|
||
" <td>2.088032e+06</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-4.619540e+07</td>\n",
|
||
" <td>11</td>\n",
|
||
" <td>15.155759</td>\n",
|
||
" <td>13.289979</td>\n",
|
||
" <td>8.976963</td>\n",
|
||
" <td>High-flow</td>\n",
|
||
" <td>8.976963</td>\n",
|
||
" <td>0.172414</td>\n",
|
||
" <td>Occasional large movers (high int, low freq)</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>246</th>\n",
|
||
" <td>200130906</td>\n",
|
||
" <td>56</td>\n",
|
||
" <td>56</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>2.559419e+07</td>\n",
|
||
" <td>2.814182e+07</td>\n",
|
||
" <td>1.482869e+07</td>\n",
|
||
" <td>6.228080e+07</td>\n",
|
||
" <td>1.112157e+06</td>\n",
|
||
" <td>4.349047e+06</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>4.092506e-03</td>\n",
|
||
" <td>4410</td>\n",
|
||
" <td>17.057876</td>\n",
|
||
" <td>13.921813</td>\n",
|
||
" <td>2.433395</td>\n",
|
||
" <td>High-flow</td>\n",
|
||
" <td>2.433395</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>Small rebalancers (low int, high freq)</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>2 rows × 21 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Registrar Account - ID n_months n_active_months flow_freq \\\n",
|
||
"240 200130818 58 10 0.172414 \n",
|
||
"246 200130906 56 56 1.000000 \n",
|
||
"\n",
|
||
" aum_qty_mean aum_qty_median net_flow_qty_sum gross_flow_qty_sum \\\n",
|
||
"240 3.819992e+06 0.000000e+00 9.586849e+06 3.429192e+07 \n",
|
||
"246 2.559419e+07 2.814182e+07 1.482869e+07 6.228080e+07 \n",
|
||
"\n",
|
||
" gross_flow_qty_mean net_flow_qty_vol ... netflow_to_aum n_tx_total \\\n",
|
||
"240 5.912401e+05 2.088032e+06 ... -4.619540e+07 11 \n",
|
||
"246 1.112157e+06 4.349047e+06 ... 4.092506e-03 4410 \n",
|
||
"\n",
|
||
" log_aum_qty_mean log_gross_flow_qty_mean gross_flow_to_aum \\\n",
|
||
"240 15.155759 13.289979 8.976963 \n",
|
||
"246 17.057876 13.921813 2.433395 \n",
|
||
"\n",
|
||
" seg_quantiles rel_intensity_total frequency \\\n",
|
||
"240 High-flow 8.976963 0.172414 \n",
|
||
"246 High-flow 2.433395 1.000000 \n",
|
||
"\n",
|
||
" seg_2D cluster_kmeans \n",
|
||
"240 Occasional large movers (high int, low freq) 3.0 \n",
|
||
"246 Small rebalancers (low int, high freq) 3.0 \n",
|
||
"\n",
|
||
"[2 rows x 21 columns]"
|
||
]
|
||
},
|
||
"execution_count": 74,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"dfc[dfc['cluster_kmeans']==3.0]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 83,
|
||
"id": "9e26e6c4-ea3e-4aad-9136-a3bbbad8ad47",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
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|
||
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|
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|
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|
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|
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|
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Registrar Account - ID</th>\n",
|
||
" <th>n_months</th>\n",
|
||
" <th>n_active_months</th>\n",
|
||
" <th>flow_freq</th>\n",
|
||
" <th>aum_qty_mean</th>\n",
|
||
" <th>aum_qty_median</th>\n",
|
||
" <th>net_flow_qty_sum</th>\n",
|
||
" <th>gross_flow_qty_sum</th>\n",
|
||
" <th>gross_flow_qty_mean</th>\n",
|
||
" <th>net_flow_qty_vol</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>netflow_to_aum</th>\n",
|
||
" <th>n_tx_total</th>\n",
|
||
" <th>log_aum_qty_mean</th>\n",
|
||
" <th>log_gross_flow_qty_mean</th>\n",
|
||
" <th>gross_flow_to_aum</th>\n",
|
||
" <th>seg_quantiles</th>\n",
|
||
" <th>rel_intensity_total</th>\n",
|
||
" <th>frequency</th>\n",
|
||
" <th>seg_2D</th>\n",
|
||
" <th>cluster_kmeans</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>246</th>\n",
|
||
" <td>200130906</td>\n",
|
||
" <td>56</td>\n",
|
||
" <td>56</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>2.559419e+07</td>\n",
|
||
" <td>2.814182e+07</td>\n",
|
||
" <td>1.482869e+07</td>\n",
|
||
" <td>6.228080e+07</td>\n",
|
||
" <td>1.112157e+06</td>\n",
|
||
" <td>4.349047e+06</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>4.092506e-03</td>\n",
|
||
" <td>4410</td>\n",
|
||
" <td>17.057876</td>\n",
|
||
" <td>13.921813</td>\n",
|
||
" <td>2.433395</td>\n",
|
||
" <td>High-flow</td>\n",
|
||
" <td>2.433395</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>Small rebalancers (low int, high freq)</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>355</th>\n",
|
||
" <td>364765</td>\n",
|
||
" <td>130</td>\n",
|
||
" <td>130</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>2.729485e+06</td>\n",
|
||
" <td>2.268174e+06</td>\n",
|
||
" <td>5.924221e+06</td>\n",
|
||
" <td>3.910049e+07</td>\n",
|
||
" <td>3.007730e+05</td>\n",
|
||
" <td>4.111484e+05</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>9.821006e-03</td>\n",
|
||
" <td>17976</td>\n",
|
||
" <td>14.819624</td>\n",
|
||
" <td>12.614115</td>\n",
|
||
" <td>14.325226</td>\n",
|
||
" <td>High-flow</td>\n",
|
||
" <td>14.325226</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>Highly active (high int, high freq)</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>183</th>\n",
|
||
" <td>200127603</td>\n",
|
||
" <td>71</td>\n",
|
||
" <td>71</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.365998e+07</td>\n",
|
||
" <td>1.293037e+07</td>\n",
|
||
" <td>1.588720e+07</td>\n",
|
||
" <td>3.896140e+07</td>\n",
|
||
" <td>5.487521e+05</td>\n",
|
||
" <td>5.535868e+05</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>2.203899e-02</td>\n",
|
||
" <td>2044</td>\n",
|
||
" <td>16.429981</td>\n",
|
||
" <td>13.215404</td>\n",
|
||
" <td>2.852229</td>\n",
|
||
" <td>High-flow</td>\n",
|
||
" <td>2.852229</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>Small rebalancers (low int, high freq)</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>213</th>\n",
|
||
" <td>200128363</td>\n",
|
||
" <td>67</td>\n",
|
||
" <td>55</td>\n",
|
||
" <td>0.820896</td>\n",
|
||
" <td>1.092064e+07</td>\n",
|
||
" <td>9.611394e+06</td>\n",
|
||
" <td>7.331428e+06</td>\n",
|
||
" <td>3.823942e+07</td>\n",
|
||
" <td>5.707376e+05</td>\n",
|
||
" <td>7.883754e+05</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>4.514853e-02</td>\n",
|
||
" <td>957</td>\n",
|
||
" <td>16.206165</td>\n",
|
||
" <td>13.254687</td>\n",
|
||
" <td>3.501573</td>\n",
|
||
" <td>High-flow</td>\n",
|
||
" <td>3.501573</td>\n",
|
||
" <td>0.820896</td>\n",
|
||
" <td>Dormant (low int, low freq)</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>240</th>\n",
|
||
" <td>200130818</td>\n",
|
||
" <td>58</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>0.172414</td>\n",
|
||
" <td>3.819992e+06</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>9.586849e+06</td>\n",
|
||
" <td>3.429192e+07</td>\n",
|
||
" <td>5.912401e+05</td>\n",
|
||
" <td>2.088032e+06</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-4.619540e+07</td>\n",
|
||
" <td>11</td>\n",
|
||
" <td>15.155759</td>\n",
|
||
" <td>13.289979</td>\n",
|
||
" <td>8.976963</td>\n",
|
||
" <td>High-flow</td>\n",
|
||
" <td>8.976963</td>\n",
|
||
" <td>0.172414</td>\n",
|
||
" <td>Occasional large movers (high int, low freq)</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>210</th>\n",
|
||
" <td>200127901</td>\n",
|
||
" <td>71</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0.028169</td>\n",
|
||
" <td>2.728996e+04</td>\n",
|
||
" <td>2.731466e+04</td>\n",
|
||
" <td>-3.729000e+01</td>\n",
|
||
" <td>3.729000e+01</td>\n",
|
||
" <td>5.252113e-01</td>\n",
|
||
" <td>3.776849e+00</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-1.905423e-05</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>10.214311</td>\n",
|
||
" <td>0.422133</td>\n",
|
||
" <td>0.001366</td>\n",
|
||
" <td>Low-flow</td>\n",
|
||
" <td>0.001366</td>\n",
|
||
" <td>0.028169</td>\n",
|
||
" <td>Dormant (low int, low freq)</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>266</th>\n",
|
||
" <td>200131477</td>\n",
|
||
" <td>34</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>7.623185e+03</td>\n",
|
||
" <td>7.623185e+03</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>8.939081</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>Low-flow</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>Dormant (low int, low freq)</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>238</th>\n",
|
||
" <td>200130743</td>\n",
|
||
" <td>69</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>8.899686e+03</td>\n",
|
||
" <td>8.410000e+03</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>9.093884</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>Low-flow</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>Dormant (low int, low freq)</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>198</th>\n",
|
||
" <td>200127798</td>\n",
|
||
" <td>71</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>2.667762e+04</td>\n",
|
||
" <td>2.790356e+04</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>10.191618</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>Low-flow</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>Dormant (low int, low freq)</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>328</th>\n",
|
||
" <td>200139346</td>\n",
|
||
" <td>13</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>2.908100e+05</td>\n",
|
||
" <td>2.908100e+05</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>12.580429</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>Low-flow</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>Dormant (low int, low freq)</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>421 rows × 21 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Registrar Account - ID n_months n_active_months flow_freq \\\n",
|
||
"246 200130906 56 56 1.000000 \n",
|
||
"355 364765 130 130 1.000000 \n",
|
||
"183 200127603 71 71 1.000000 \n",
|
||
"213 200128363 67 55 0.820896 \n",
|
||
"240 200130818 58 10 0.172414 \n",
|
||
".. ... ... ... ... \n",
|
||
"210 200127901 71 2 0.028169 \n",
|
||
"266 200131477 34 0 0.000000 \n",
|
||
"238 200130743 69 0 0.000000 \n",
|
||
"198 200127798 71 0 0.000000 \n",
|
||
"328 200139346 13 0 0.000000 \n",
|
||
"\n",
|
||
" aum_qty_mean aum_qty_median net_flow_qty_sum gross_flow_qty_sum \\\n",
|
||
"246 2.559419e+07 2.814182e+07 1.482869e+07 6.228080e+07 \n",
|
||
"355 2.729485e+06 2.268174e+06 5.924221e+06 3.910049e+07 \n",
|
||
"183 1.365998e+07 1.293037e+07 1.588720e+07 3.896140e+07 \n",
|
||
"213 1.092064e+07 9.611394e+06 7.331428e+06 3.823942e+07 \n",
|
||
"240 3.819992e+06 0.000000e+00 9.586849e+06 3.429192e+07 \n",
|
||
".. ... ... ... ... \n",
|
||
"210 2.728996e+04 2.731466e+04 -3.729000e+01 3.729000e+01 \n",
|
||
"266 7.623185e+03 7.623185e+03 0.000000e+00 0.000000e+00 \n",
|
||
"238 8.899686e+03 8.410000e+03 0.000000e+00 0.000000e+00 \n",
|
||
"198 2.667762e+04 2.790356e+04 0.000000e+00 0.000000e+00 \n",
|
||
"328 2.908100e+05 2.908100e+05 0.000000e+00 0.000000e+00 \n",
|
||
"\n",
|
||
" gross_flow_qty_mean net_flow_qty_vol ... netflow_to_aum n_tx_total \\\n",
|
||
"246 1.112157e+06 4.349047e+06 ... 4.092506e-03 4410 \n",
|
||
"355 3.007730e+05 4.111484e+05 ... 9.821006e-03 17976 \n",
|
||
"183 5.487521e+05 5.535868e+05 ... 2.203899e-02 2044 \n",
|
||
"213 5.707376e+05 7.883754e+05 ... 4.514853e-02 957 \n",
|
||
"240 5.912401e+05 2.088032e+06 ... -4.619540e+07 11 \n",
|
||
".. ... ... ... ... ... \n",
|
||
"210 5.252113e-01 3.776849e+00 ... -1.905423e-05 2 \n",
|
||
"266 0.000000e+00 0.000000e+00 ... 0.000000e+00 0 \n",
|
||
"238 0.000000e+00 0.000000e+00 ... 0.000000e+00 0 \n",
|
||
"198 0.000000e+00 0.000000e+00 ... 0.000000e+00 0 \n",
|
||
"328 0.000000e+00 0.000000e+00 ... 0.000000e+00 0 \n",
|
||
"\n",
|
||
" log_aum_qty_mean log_gross_flow_qty_mean gross_flow_to_aum \\\n",
|
||
"246 17.057876 13.921813 2.433395 \n",
|
||
"355 14.819624 12.614115 14.325226 \n",
|
||
"183 16.429981 13.215404 2.852229 \n",
|
||
"213 16.206165 13.254687 3.501573 \n",
|
||
"240 15.155759 13.289979 8.976963 \n",
|
||
".. ... ... ... \n",
|
||
"210 10.214311 0.422133 0.001366 \n",
|
||
"266 8.939081 0.000000 0.000000 \n",
|
||
"238 9.093884 0.000000 0.000000 \n",
|
||
"198 10.191618 0.000000 0.000000 \n",
|
||
"328 12.580429 0.000000 0.000000 \n",
|
||
"\n",
|
||
" seg_quantiles rel_intensity_total frequency \\\n",
|
||
"246 High-flow 2.433395 1.000000 \n",
|
||
"355 High-flow 14.325226 1.000000 \n",
|
||
"183 High-flow 2.852229 1.000000 \n",
|
||
"213 High-flow 3.501573 0.820896 \n",
|
||
"240 High-flow 8.976963 0.172414 \n",
|
||
".. ... ... ... \n",
|
||
"210 Low-flow 0.001366 0.028169 \n",
|
||
"266 Low-flow 0.000000 0.000000 \n",
|
||
"238 Low-flow 0.000000 0.000000 \n",
|
||
"198 Low-flow 0.000000 0.000000 \n",
|
||
"328 Low-flow 0.000000 0.000000 \n",
|
||
"\n",
|
||
" seg_2D cluster_kmeans \n",
|
||
"246 Small rebalancers (low int, high freq) 3.0 \n",
|
||
"355 Highly active (high int, high freq) 1.0 \n",
|
||
"183 Small rebalancers (low int, high freq) 1.0 \n",
|
||
"213 Dormant (low int, low freq) 1.0 \n",
|
||
"240 Occasional large movers (high int, low freq) 3.0 \n",
|
||
".. ... ... \n",
|
||
"210 Dormant (low int, low freq) 0.0 \n",
|
||
"266 Dormant (low int, low freq) 0.0 \n",
|
||
"238 Dormant (low int, low freq) 0.0 \n",
|
||
"198 Dormant (low int, low freq) 0.0 \n",
|
||
"328 Dormant (low int, low freq) 0.0 \n",
|
||
"\n",
|
||
"[421 rows x 21 columns]"
|
||
]
|
||
},
|
||
"execution_count": 83,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"dfc.sort_values(by=\"gross_flow_qty_sum\", ascending=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 197,
|
||
"id": "2437e11b-04d2-4c49-b265-32a5681ef96a",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Définition de la correspondance entre les codes numériques et les nouveaux labels\n",
|
||
"mapping = {\n",
|
||
" 0.0: \"Cluster 1 (66) : Dormant\",\n",
|
||
" 1.0: \"Cluster 2 (105) : Highly Active\",\n",
|
||
" 2.0: \"Cluster 3 (235)\",\n",
|
||
" 3.0: \"Cluster 4 (2)\",\n",
|
||
" 4.0: \"Cluster 5 (13) : Large Movers\"\n",
|
||
"}\n",
|
||
"\n",
|
||
"# Création de la nouvelle colonne 'cluster'\n",
|
||
"dfc['cluster'] = dfc['cluster_kmeans'].map(mapping)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 198,
|
||
"id": "2a677706-ae26-4474-8a69-a0e486421feb",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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PGWP4888/ERISAsaY0L0NCgpCRkaG0N9CScXv0+fPn/Hu3Tt88cUXAFDqcaWR5u+tonGX5vvvvxd6jbZs2RJ8Ph/Pnj0r89g9e/agZcuWMDU1FYopMDAQfD4fZ8+eFXmupqamQtcCwL1O9fX1oaOjg9OnTyM9Pb1cz6ekAQMG4H//+x8eP37MlW3fvh329vYICAgo9dhevXrh+vXrQsfu2rULurq66Ny5M4DCvqUAcPz4cWRnZ8slZgBwdnZGUFCQUJm09/vIkSPQ0tLCiBEjuGM1NTVLfa8qiyLek/788080aNAAXbt2Fble0WsyMDAQNjY22L59O7ft9u3buHnzpkif3rJQQiSllJQUdOzYEcbGxti7d6/MnYU/ffoEAKhZs2aZ+9rY2MDQ0FCozN3dHQC4IdEPHz7EnTt3YGFhIfRTtF9RZ7wiDg4OItcxNTUVelPJycnB9OnTYW9vD11dXZibm8PCwgIfPnxARkYGt1+DBg1Qr1497Nq1iyvbtWsXzM3NuYRMnKI30Lp16wqV6+jowMXFReQN1tbWVmznyDt37qBr164wNjaGkZERLCwsuBd+8TilERAQgO7du2PWrFkwNzdH586dsWnTJuTm5nL7PHz4EBkZGbC0tBS5358+feLudVH8JUeD1KpVS+hNvriSv5eiN097e3uR8uK/K2ljknQdQPT3Xxp53nNA8msBADw8PPDu3TtkZWUJlZd8DkX3tCIfjM7OzkKP3759iw8fPmDdunUi97UoOS95b4tLS0vDmDFjYGVlBX19fVhYWHDXKM99AqT7e6to3KWpyH1/+PAhjh07JhJTYGCg2JjKupauri5+/vlnHD16FFZWVmjVqhUWLVqElJSUcj03oDCp0dXV5T5QMzIycOjQIfTr108oERSnR48e0NDQ4H43jDHs2bMHwcHBMDIyAlD4Ghs/fjx+++03mJubIygoCFFRUeV+PRQp+doFpL/fz549Q+3atbkv6kXE/T1KSxHvSY8fPy5zXj4NDQ3069cPBw4c4BLO7du3Q09PDz169JDpOdAoMylkZGQgODgYHz58wLlz52BjYyPzOW7fvg1A9MOyvAQCAby9vbF06VKx20t+oEpK4Bhj3P9Hjx6NTZs2YezYsWjWrBmMjY3B4/HQu3dvCAQCoeN69eqFefPm4d27d6hZsyYOHjyIPn36QEtLfi8pcSMmPnz4gICAABgZGWH27NlwdXWFnp4ebty4gcmTJ4vEWRYej4e9e/fi8uXL+Pvvv3H8+HEMGjQIS5YsweXLl1GjRg0IBAJYWloKfQMpzsLColzPD5D8exFXXvx3JWtM0vz+JZH3PS+vijwHSUq+xoqey3fffSexFtjHx0fi+Xr27ImLFy9i4sSJaNiwIff6ad++fYXuU1l/bxWNuzQVue8CgQDt2rXDpEmTxG4v+gIny7XGjh2LkJAQHDhwAMePH8dPP/2EBQsW4J9//oGvr2+ZMZVkamqKb775Btu3b8f06dOxd+9e5ObmSlW7YGNjg5YtW2L37t348ccfcfnyZSQnJ4uMJl6yZAkGDhyIv/76CydOnEBERAQWLFiAy5cvw87OTuaYAfHvj7Leb2nweDyxv2s+ny9ybWW9J5U0YMAALF68GAcOHECfPn2wY8cOfPPNN9wXTGlRQlSGz58/IyQkBA8ePMCpU6eEqtelxefzsWPHDhgYGODLL78sc/9Xr14hKytLqJaoaDLIovlhXF1dkZCQgLZt25b5LUZae/fuRWhoKJYsWcKVff78Wezkcb169cKsWbPw559/wsrKCpmZmejdu3ep53d0dARQON+Ji4sLV56Xl4ekpCTuW0xpTp8+jffv32Pfvn1o1aoVV56UlFTmsaX54osv8MUXX2DevHnYsWMH+vXrh507d2LIkCFwdXXFqVOn0KJFi1KHtRY9v0ePHgl9e3v//r3cqveLSBuTLCS9jmS559K+Fou/Fkq6d+8ezM3NRWpJlcHCwgI1a9YEn8+X6vVYXHp6OmJiYjBr1ixMnz6dK3/48KHIvrL+zZb191aRuOVB0vNxdXXFp0+f5B6Tq6srJkyYgAkTJuDhw4do2LAhlixZIjIVSlnxFRkwYAA6d+6Mq1evYvv27fD19YWXl5dUsfTq1QsjR47E/fv3sWvXLhgYGCAkJERkP29vb3h7e2PatGm4ePEiWrRogejoaMydO1eq60hD2vvt6OiImJgYfPr0SaiWSNzfo6mpqdim9ZI1+op4T3J1deUqE0pTv359+Pr6Yvv27bCzs0NycjJWrlwp8/WoyawUfD4fvXr1wqVLl7Bnzx40a9asXOeIiIjA3bt3ERERwVWjlqagoABr167lHufl5WHt2rWwsLCAn58fgMJvoi9fvsT69etFjs/JyRFpbpCGpqamSHa+cuVKkW8CQGGzhre3N3bt2oVdu3ahdu3aQh+W4gQGBkJHRwcrVqwQus6GDRuQkZGBjh07ShUjIPwtIi8vD6tXry7zWHHS09NFnnPRxG1FzWY9e/YEn8/HnDlzRI4vKCjgEsa2bdtCS0sLa9asEdpn1apV5YqtNNLGJIuiBKTksbLcc0NDQ6maAmrXro2GDRtiy5YtQte7ffs2Tpw4gQ4dOsgcPwC8e/cO9+7dK3dfDU1NTXTv3h1//vmn2Dfit2/flnosIPoNV9zM3ZLutSRl/b1VJG55MDQ0FPtcevbsiUuXLuH48eMi2z58+ICCggKZrpOdnY3Pnz8Llbm6uqJmzZpCzdzi4iu6pjjBwcEwNzfHzz//jDNnzsjU96R79+7Q1NTEH3/8gT179uCbb74RSuYzMzNFnqe3tzc0NDRKjbk8pL3fHTp0QEFBgdB7FZ/PF5tEuLq64t69e0KvoYSEBFy4cEHk2vJ+T+revTsSEhKwf/9+kW0l/8769++PEydOYNmyZTAzM0NwcLDM16MaolJMmDABBw8eREhICNLS0kS+fZT8o8nIyOD2yc7O5maqfvz4MXr37i32hSKOjY0Nfv75Zzx9+hTu7u7YtWsX4uPjsW7dOm6m3/79+2P37t0YPnw4YmNj0aJFC/D5fNy7dw+7d+/G8ePH4e/vL9Pz/eabb7Bt2zYYGxvD09MTly5dwqlTp2BmZiZ2/169emH69OnQ09PD4MGDy5zl1sLCAlOmTMGsWbPQvn17dOrUCffv38fq1avRuHFjqd6EmjdvDlNTU4SGhiIiIgI8Hg/btm0rd7PJli1bsHr1anTt2hWurq74+PEj1q9fDyMjI+5DOSAgAMOGDcOCBQsQHx+Pr7/+Gtra2nj48CH27NmD5cuX49tvv4WVlRXGjBmDJUuWoFOnTmjfvj0SEhJw9OhRmJuby60mT5aYZNGwYUNoamri559/RkZGBnR1dfHVV1/JdM/9/Pywa9cujB8/Ho0bN0aNGjXEflsGgMWLFyM4OBjNmjXD4MGDkZOTg5UrV8LY2BgzZ84sz23BqlWrMGvWLMTGxpZ7LauFCxciNjYWTZs2xdChQ+Hp6Ym0tDTcuHEDp06dQlpamtjjjIyMuD4t+fn5sLW1xYkTJ8TWpBV9sZk6dSp69+4NbW1thISElForVtbfW3njlgc/Pz+sWbMGc+fOhZubGywtLfHVV19h4sSJOHjwIL755hsMHDgQfn5+yMrKwq1bt7B37148ffoU5ubmUl/nwYMHaNu2LXr27AlPT09oaWlh//79ePPmTak11K6urjAxMUF0dDRq1qwJQ0NDNG3alKvJ1dbWRu/evbFq1SpoamqiT58+UsdkaWmJNm3aYOnSpfj48aPIwJJ//vkHo0aNQo8ePeDu7o6CggJs27aNS2LlSdr7HRISghYtWiAyMhJPnz6Fp6cn9u3bJ/bLzKBBg7B06VIEBQVh8ODBSE1NRXR0NLy8vIQGNCjiPWnixInYu3cvevTogUGDBsHPzw9paWk4ePAgoqOj0aBBA27fvn37YtKkSdi/fz9GjBhRvlnxZRqTVs2Im2iu+E9p+9aoUYPVqVOHfffdd9ycHNJes+TEjI6OjmzVqlUi++bl5bGff/6ZeXl5MV1dXWZqasr8/PzYrFmzWEZGBrcf/p2ArqSSw2zT09NZWFgYMzc3ZzVq1GBBQUHs3r17IvsVKZrDA/9O3laSpIkZV61axerVq8e0tbWZlZUVGzFihMSJGcW5cOEC++KLL5i+vj6zsbFhkyZN4oZeFh8mK82w+xs3brA+ffowBwcHbiKxb775hl27dk1k33Xr1jE/Pz+mr6/Patasyby9vdmkSZPYq1evuH0KCgrYTz/9xKytrZm+vj776quv2N27d5mZmZnQUPqie1NyuL+kYbahoaHM0NCwXDEVTYJWkrjhtOvXr2cuLi5MU1NT6H5Ke88/ffrE+vbty0xMTBhQ9sSMp06dYi1atGD6+vrMyMiIhYSESJyYseQ9KW2eK2mH3Yv7u2CscH6q8PBwZm9vz7S1tZm1tTVr27YtW7duHbePuOf04sUL1rVrV2ZiYsKMjY1Zjx492KtXrxiKTapXZM6cOczW1pZpaGgIPY/y/r1JG7ckkobdl3yNFk10WPwep6SksI4dO7KaNWsyQHhixo8fP7IpU6YwNzc3pqOjw8zNzVnz5s3ZL7/8wk0jUnxixpKK37t3796x8PBwVq9ePWZoaMiMjY1Z06ZN2e7du4WOEffa/uuvv7gJOMW9FouGy5ecY04a69evZwBYzZo1RSZyfPLkCRs0aBBzdXXlJrxt06YNO3XqVJnnLW3Yvbi/acaku9+MMfb+/XvWv39/bmLG/v37i52YkTHGfv/9d26yzIYNG7Ljx49LfH+V93vS+/fv2ahRo7iJOO3s7FhoaKjI9BKMMdahQwcGgF28eFHsvSkLj7EK9EgkhJTpw4cPMDU1xdy5czF16lRVh0MIESMhIQENGzbE1q1b0b9/f1WHoxJPnz6Fs7MzNm3apDbr0smia9euuHXrFh49elSu46kPESFylJOTI1JW1IekvE04hBDFW79+PWrUqIFu3bqpOhRSDq9fv8bhw4crlMxW+T5Ez58/R//+/ZGamgotLS389NNPMs9NQIi0du3ahc2bN6NDhw6oUaMGzp8/jz/++ANff/01WrRooerwCCEl/P3330hMTMS6deswatQolYxuJOWXlJSECxcu4LfffoO2trbQpMayqvIJkZaWFpYtW4aGDRsiJSUFfn5+6NChA73oiUL4+PhAS0sLixYtQmZmJtfRWp5Dawkh8jN69Gi8efMGHTp0wKxZs1QdDpHRmTNnEBYWBgcHB2zZsgXW1tblPle160PUoEEDHDp0SGTiQkIIIYRUX2rfh+js2bMICQmBjY0NeDweDhw4ILJPVFQUnJycoKenh6ZNm+LKlStiz3X9+nXw+XxKhgghhBAiRO0ToqysLDRo0ABRUVFitxfNeTJjxgzcuHEDDRo0QFBQkMi6KWlpaRgwYADWrVunjLAJIYQQUolUqiYzHo+H/fv3o0uXLlxZ06ZN0bhxY242YIFAAHt7e4wePRqRkZEACmccbteuHYYOHVpqD/Tc3FyhmUMFAgHS0tJgZmYm10n1CCGEEKI4jDF8/PgRNjY2ZU4aXKRSd6rOy8vD9evXMWXKFK5MQ0MDgYGBuHTpEoDCmzJw4EB89dVXZQ7HW7BgAXWqI4QQQqqI58+fS72AbqVOiN69ewc+nw8rKyuhcisrK9y7dw8AcOHCBezatQs+Pj5c/6Nt27bB29tb5HxTpkzB+PHjuccZGRlwcHDA8+fPpVqDjBBCCCHCsvMK0GReDADgytS2MNBRfOqRmZkJe3t71KxZU+pjKnVCJI0vv/wSAoFAqn11dXWhq6srUm5kZEQJESGEEFIOWnkF0NA1AFD4eaqMhKiILN1d1L5TdWnMzc2hqamJN2/eCJW/efOmQnMREEIIIaR6qdQJkY6ODvz8/BATE8OVCQQCxMTEoFmzZiqMjBBCCCGVido3mX369EloobakpCTEx8ejVq1acHBwwPjx4xEaGgp/f380adIEy5YtQ1ZWFsLCwlQYNSGEEEIAQFODh+6N7Lj/qyu1H3Z/+vRptGnTRqQ8NDQUmzdvBgCsWrUKixcvRkpKCho2bIgVK1agadOm5b5mVFQUoqKiwOfz8eDBA2RkZJTah4jP5yM/P7/c1yOEVE7a2trQ1NRUdRiEkBIyMzNhbGxc5ud3cWqfEKlSWTeUMYaUlBR8+PBB+cERQtSCiYkJrK2taa4yQtRIeRIitW8yU2dFyZClpSUMDAzoDZGQaoQxhuzsbG5W/Nq1a6s4IkLUE2MMOfl8AIC+tqbaflZSQlROfD6fS4bMzMxUHQ4hRAX09fUBAKmpqbC0tKTmM0LEyMnnw3P6cQBA4uwgpQ67l0WlHmWmSkV9hgwMDFQcCSFElYreA6gfISGVGyVEFaSuVX+EEOWg9wBCqgZKiMSIioqCp6cnGjdurOpQCCGEEM7Ntzfx9+O/cfPtTVWHUuVQQiRGeHg4EhMTcfXqVVWHolI8Ho9b/60qu3//PqytrfHx40e5njcyMhKjR4+W6zkJIdXXr9d+Rb8j/fDj+R/R70g//HrtV1WHVKVQQlRNpaSkYPTo0XBxcYGuri7s7e0REhIiNOu3PJ0+fRo8Hk+hUxTMmzcPzZs3h4GBAUxMTKQ+bsqUKRg9erTQIoCMMfzyyy9wd3eHrq4ubG1tMW/ePKHjcnNzMXXqVDg6OkJXVxdOTk7YuHEjt/2HH37Ali1b8OTJkwo/t9atW4PH44HH43HxhISEYN++fRU+t6op47VBSGV38+1NbLyzUahs452NVFMkR+rZ1buaeZ6WjQNxL/HuUy7Ma+iii68t7GsprrP206dP0aJFC5iYmGDx4sXw9vZGfn4+jh8/jvDwcNy7d09h164oxhj4fD60tERfunl5eejRoweaNWuGDRs2SHW+5ORkHDp0CCtXrhQqHzNmDE6cOIFffvkF3t7eSEtLQ1pamtA+PXv2xJs3b7Bhwwa4ubnh9evXQgsJm5ubIygoCGvWrMHixYvL8WyFDR06FLNnz0ZBQQFevHiB/fv3o3fv3hg4cCDWrVtX7vPm5eVBR0enwvERQhTnWeYzieU+Fj5KjqaKYkSijIwMBoBlZGSIbMvJyWGJiYksJyen3OfPK+CzyL0JzGnyIeYSeZi5TjnMXCIPM6fJh1jk3gSWV8CvSPgSBQcHM1tbW/bp0yeRbenp6dz/AbD9+/czxhiLjY1lAIS2x8XFMQAsKSmJMcbY06dP2TfffMNMTEyYgYEB8/T0ZIcPH2ZJSUkMgNBPaGgoY4wxPp/P5s+fz5ycnJienh7z8fFhe/bs4a5RdN0jR46wRo0aMW1tbRYbG1vq89u0aRMzNjaW6l4sXryY+fv7C5UlJiYyLS0tdu/ePYnHHT16lBkbG7P379+Xev4tW7YwOzs7qWIpTUBAABszZoxI+caNGxkAdvLkSa7s5s2brE2bNkxPT4/VqlWLDR06lH38+JHbHhoayjp37szmzp3LateuzZycnLjf0a5du9iXX37J9PT0mL+/P7t//z67cuUK8/PzY4aGhqx9+/YsNTWVO9eVK1dYYGAgMzMzY0ZGRqxVq1bs+vXrQjECYOvXr2ddunRh+vr6zM3Njf3111+MMVbqa6OykMd7ASFlSUhNYPU31xf5SUhNUHVoZcrJK2Ajfr/GRvx+jeXkFSjlmqV9fktCTWYqNP3Abey89hwMAJ8xFAgY+IyBAdh57TmmH7gt92umpaXh2LFjCA8Ph6Ghoch2WZqaSgoPD0dubi7Onj2LW7du4eeff0aNGjVgb2+PP//8E0Bhf53Xr19j+fLlAIAFCxZg69atiI6Oxp07dzBu3Dh89913OHPmjNC5IyMjsXDhQty9exc+PvL7NnTu3Dn4+/sLlf39999wcXHBoUOH4OzsDCcnJwwZMkSohujgwYPw9/fHokWLYGtrC3d3d/zwww/IyckROleTJk3w4sULPH36VGIMM2fOhJOTU7niDw0NhampKdd0lpWVhaCgIJiamuLq1avYs2cPTp06hVGjRgkdFxMTg/v37+PkyZM4dOgQVz5jxgxMmzYNN27cgJaWFvr27YtJkyZh+fLlOHfuHB49eoTp06dz+3/8+BGhoaE4f/48Ll++jDp16qBDhw4i/bFmzZqFnj174ubNm+jQoQP69euHtLS0Ul8bhJD/+Fj4YJDXIKGywfUHV4raIT1tTazu54fV/fygp62+c3VRk5mKJL/Pxs6rhcmQOIwBO68+x8g2bnJtPnv06BEYY6hXr57czlkkOTkZ3bt3h7e3NwDAxcWF21arVi0AgKWlJZd05ebmYv78+Th16hSaNWvGHXP+/HmsXbsWAQEB3PGzZ89Gu3bt5B7zs2fPRBKiJ0+e4NmzZ9izZw+2bt0KPp+PcePG4dtvv8U///zD7XP+/Hno6elh//79ePfuHUaOHIn3799j06ZN3LlsbGy460hKeszNzeHq6lqu+DU0NODu7s4lXDt27MDnz5+xdetWLuFdtWoVQkJC8PPPP8PKygoAYGhoiN9++41rKis6/ocffkBQUBCAwmbDPn36ICYmBi1atAAADB48mFtDEAC++uoroXjWrVsHExMTnDlzBt988w1XPnDgQPTp0wcAMH/+fKxYsQJXrlxB+/btxb42CCGixvmPQ1vHtniW+QyORo6VIhmqTCghEqP44q6K8lf8S2jweOCXspScBo+HA3EvMbptHbldlylw6bqIiAiMGDECJ06cQGBgILp3715qbc6jR4+QnZ0tkujk5eXB19dXqKxk0iIvOTk50NPTEyoTCATIzc3F1q1b4e7uDgDYsGED/Pz8cP/+fdStWxcCgQA8Hg/bt2+HsbExAGDp0qX49ttvsXr1am4G46J/s7OzJcYwatQokRocWTDGuLlw7t69iwYNGgjV/rVo0QICgQD379/nEiJvb2+x/YaK/76K71u8rGipCgB48+YNpk2bhtOnTyM1NRV8Ph/Z2dlITk6WeF5DQ0MYGRkJnYcQIh0fCx9KhBSEmszEUMaw+3efclHWfG48XuF+8lSnTh3weDyZO05raBS+VIonVCVn5h0yZAiePHmC/v3749atW/D39xfprFzcp0+fAACHDx9GfHw895OYmIi9e/cK7SuueU8ezM3NkZ6eLlRWu3ZtaGlpcckQAHh4eAAA90Ffu3Zt2NracslQ0T6MMbx48YIrK2pms7CwUEj8fD4fDx8+hLOzs0zHSbqf2tra3P+LkqySZcU7joeGhiI+Ph7Lly/HxYsXER8fDzMzM+Tl5Uk8r7jzEEKqruy8AjhFHoZT5GFk5xWoOhyJKCFSEfMauiirskbAGMxr6Mr1urVq1UJQUBCioqKQlZUlsl3S0OeiD/TXr19zZfHx8SL72dvbY/jw4di3bx8mTJiA9evXAwBXG1G81s3T0xO6urpITk6Gm5ub0I+9vX15n6JMfH19kZiYKFTWokULFBQU4PHjx1zZgwcPAACOjo7cPq9eveKSuqJ9NDQ0YGdnx5Xdvn0b2tra8PLyUkj8W7ZsQXp6Orp37w6gMClLSEgQ+t1euHABGhoaqFu3rtyvf+HCBURERKBDhw7w8vKCrq4u3r17J9M5xL02CCFE2SghUpHODW0hKCMjYgzo4msr92sXNQc2adIEf/75Jx4+fIi7d+9ixYoVXF+ekoqSlJkzZ+Lhw4c4fPgwlixZIrTP2LFjcfz4cSQlJeHGjRuIjY3lalYcHR3B4/Fw6NAhvH37Fp8+fULNmjXxww8/YNy4cdiyZQseP36MGzduYOXKldiyZYvMzys5ORnx8fFITk4Gn8/napyKJy0lBQUF4dKlS0IfxoGBgWjUqBEGDRqEuLg4XL9+HcOGDUO7du24WqO+ffvCzMwMYWFhSExMxNmzZzFx4kQMGjSIayYDCjttt2zZUqispFWrVqFt27ZlPr/s7GykpKTgxYsXuHz5MiZPnozhw4djxIgRaNOmDQCgX79+0NPTQ2hoKG7fvo3Y2FiMHj0a/fv355rA5KlOnTrYtm0b7t69i//973/o169fqc9VHHGvDUIIUTZKiFTEwcwAvRvbS2w24/GA3o3tFTIfkYuLC27cuIE2bdpgwoQJqF+/Ptq1a4eYmBisWbNG7DHa2tr4448/cO/ePfj4+ODnn3/G3Llzhfbh8/kIDw+Hh4cH2rdvD3d3d6xevRoAYGtri1mzZiEyMhJWVlZcn5k5c+bgp59+woIFC7jjDh8+LHMTEABMnz4dvr6+mDFjBj59+gRfX1/4+vri2rVrEo8JDg6GlpYWTp06xZVpaGjg77//hrm5OVq1aoWOHTvCw8MDO3fu5PapUaMGTp48iQ8fPsDf3x/9+vVDSEgIVqxYIXT+nTt3YujQoaXG/e7dO6HaKEnWr1+P2rVrw9XVFd26dUNiYiJ27drF3WOgcKHR48ePIy0tDY0bN8a3336Ltm3bYtWqVWWevzw2bNiA9PR0NGrUCP3790dERAQsLS1lOoek1wYhhCgTjymyl20ll5mZCWNjY2RkZMDIyEho2+fPn5GUlARnZ2eRTrnSyucLCofeX30ODR4PPF5hMxljhcnQ7C71oa1JOauiRUVF4eDBgzh+/Lhcz3v06FFMmDABN2/eFDuRJKka5PFeQEhVlp1XAM/phe+vibODYKCj+PfD0j6/JaF3aRXS1tTAgu4+GNnGjZup2qKmLjo3VOxM1UTYsGHD8OHDB3z8+FFo+Y6KysrKwqZNmygZIoSQSoDeqdWAfS0DuQ6tJ7LR0tLC1KlT5X7eb7/9Vu7nJIQQohiUEImhjHmICCGEkOpAg8dDm7oW3P/VFfUhKoWi+xARQio/ei8gRP1QHyJCCCFVxs23N2mZCqI0lBARQghRO79e+xUb72zkHg/yGoRx/uNUGBGp6mhMNyGEELVy8+1NoWQIADbe2Yibb2+qKCJSEdl5BfD46Rg8fjqm1kt3SFVD1KhRI5lOyuPxcPDgQdjayn+WZUIIIVXbs8xnEsup6axyyslX/0FKUiVE8fHxmDBhAmrUqFHmvowxLFy4ELm58l2UlBBCSPXgaOQoUzkh8iB1H6KJEydKPSV/yTWuSOXE4/Gwf/9+dOnSRdWhKFRMTAxGjRqF27dvQ1NTU2nXfffuHTw9PXHjxg2hBWEVTdbf6+nTp9GmTRukp6fDxMRE7D4zZ87EgQMHxC74q2oDBw7Ehw8fcODAAVWHQqTkY+GDQV6DhJrNBtcfTLVDRKGk6kOUlJTErXYujcTERG5VcKKeUlJSMHr0aLi4uEBXVxf29vYICQlBTEyMQq53+vRp8Hg8fPjwQSHnf/r0KQYPHgxnZ2fo6+vD1dUVM2bMQF5eXpnHTpo0CdOmTeOSodevX6Nv375wd3eHhoYGxo4dK/a4PXv2oF69etDT04O3tzeOHDkitH3gwIHg8XhCP+3bt+e2m5ubY8CAAZgxY0b5n/i/Sru/Tk5OWLZsGff49evXCA4OrvA1FWXBggXQ1NTE4sWLZTru6dOn4PF4IknZ8uXLsXnzZvkFSJRinP84bO+wHfO/nI/tHbZjrN9YVYdEqjipEqKi1ailZW9vr9Rv2pVe+lPgzGLgyMTCf9OfKvRyT58+hZ+fH/755x8sXrwYt27dwrFjx9CmTRuEh4cr9NoVxRhDQYFop7x79+5BIBBg7dq1uHPnDn799VdER0fjxx9/LPV858+fx+PHj9G9e3euLDc3FxYWFpg2bRoaNGgg9riLFy+iT58+GDx4MOLi4tClSxd06dIFt2/fFtqvffv2eP36Nffzxx9/CG0PCwvD9u3bkZaWJu0tqDBra2vo6uoq7Xqy2rhxIyZNmoSNGzeWvbMUjI2NJdZsEfXmY+GDENcQqhkiSlGuUWbnzp3Dd999h2bNmuHly5cAgG3btuH8+fNyDU5VoqKi4OnpicaNGyv2Qvx84GAEsLwhcHoBcG1T4b/LGxaW8/MVctmRI0eCx+PhypUr6N69O9zd3eHl5YXx48fj8uXLYo8RVwMRHx8PHo+Hp0+fAgCePXuGkJAQmJqawtDQEF5eXjhy5AiePn2KNm3aAABMTU3B4/EwcOBAAIBAIMCCBQu4mp0GDRpg7969Itc9evQo/Pz8oKurK/Z11r59e2zatAlff/01XFxc0KlTJ/zwww/Yt29fqfdi586daNeundCEek5OTli+fDkGDBgAY2NjscctX74c7du3x8SJE+Hh4YE5c+agUaNGIqvK6+rqwtramvsxNTUV2u7l5QUbGxvs37+/1DjlicfjCTUfXbx4EQ0bNoSenh78/f1x4MABsTUt169fh7+/PwwMDNC8eXPcv39f7PnPnj0LbW1tpKSkCJWPHTsWLVu2LDW2M2fOICcnB7Nnz0ZmZiYuXrwotF0gEGDRokVwc3ODrq4uHBwcMG/ePACAs7MzAMDX1xc8Hg+tW7cGUFhTV9Q8uG7dOtjY2EAgEAidt3Pnzhg0aBD3+K+//kKjRo2gp6cHFxcXzJo1S2wiTgipOmROiP78808EBQVBX18fcXFxXOfpjIwMzJ8/X+4BqkJ4eDgSExNx9epVxV7o8ATgxlYADGB8QJBf+C9YYfnhCXK/ZFpaGo4dO4bw8HAYGhqKbK/IN+nw8HDk5ubi7NmzuHXrFn7++WfUqFED9vb2+PPPPwEA9+/fx+vXr7F8+XIAhc0jW7duRXR0NO7cuYNx48bhu+++w5kzZ4TOHRkZiYULF+Lu3bvw8ZHu22JGRgZq1apV6j7nzp2Dv7+/zM/10qVLCAwMFCoLCgrCpUuXhMpOnz4NS0tL1K1bFyNGjMD79+9FztWkSROcO3eu1OvxeDyFNPtkZmYiJCQE3t7euHHjBubMmYPJkyeL3Xfq1KlYsmQJrl27Bi0tLaEEorhWrVrBxcUF27Zt48ry8/Oxfft2iccU2bBhA/r06QNtbW306dMHGzZsENo+ZcoULFy4ED/99BMSExOxY8cOWFlZAQCuXLkCADh16hRev34tNhnu0aMH3r9/j9jYWK6s6G+iX79+AApfEwMGDMCYMWOQmJiItWvXYvPmzVziRQiRjQaPh6bOtdDUuZbI0h03397E34//VospFWSemHHu3LmIjo7GgAEDsHPnTq68RYsWmDt3rlyDq9LSkv5LhsT6NylqOR4wdZLbZR89egTGGOrVqye3cxZJTk5G9+7d4e3tDQBwcXHhthUlJpaWllzSlZubi/nz5+PUqVNo1qwZd8z58+exdu1aBAQEcMfPnj0b7dq1kzqWR48eYeXKlfjll19K3e/Zs2ewsbGR+rxFUlJSuA/iIlZWVkK1Iu3bt0e3bt3g7OyMx48f48cff0RwcDAuXbok1KRsY2ODuLi4Uq9Xt25dibVVxYnrnJ2dnS1x/x07doDH42H9+vXQ09ODp6cnXr58iaFDh4rsO2/ePO53EhkZiY4dO+Lz589il6sYPHgwNm3ahIkTJwIA/v77b3z+/Bk9e/aUGEtmZib27t3LJZXfffcdWrZsieXLl6NGjRr4+PEjli9fjlWrViE0NBQA4Orqii+//BIAuH6OZmZmsLa2FnsNU1NTBAcHY8eOHWjbti0AYO/evTA3N+dqMWfNmoXIyEjuGi4uLpgzZw4mTZokl/5ehFQ3etqa2DWsmUi5uk2+KXNCdP/+fbRq1Uqk3NjYWGEdZqukW3sBnsa/NUIS8DSAm3uAgIlyu6wil66LiIjAiBEjcOLECQQGBqJ79+6l1uY8evQI2dnZIolOXl4efH19hcpkqcV5+fIl2rdvjx49eoj9YC8uJydHYetP9e7dm/u/t7c3fHx84OrqitOnT3MfxgCgr69fatICFPaRksa5c+dQs2ZNobKipiNx7t+/Dx8fH6F70KRJE7H7Fv9d1q5dGwCQmpoKBwcHkX0HDhyIadOm4fLly/jiiy+wefNm9OzZU2ytZJE//vgDrq6uXL+thg0bwtHREbt27cLgwYNx9+5d5ObmCt278ujXrx+GDh2K1atXQ1dXF9u3b0fv3r2hoVFYYZ6QkIALFy4I1Qjx+Xx8/vwZ2dnZMDAwqND1CSGSJ99s69hWZX3GZE6IrK2t8ejRIzg5OQmVnz9/XqhGgJQhK1W6hCgrVa6XrVOnDng8ntQfsEWKPiyKJ1T5+cJ9nIYMGYKgoCAcPnwYJ06cwIIFC7BkyRKMHj1a7Dk/ffoEADh8+LDIJJ4lO/2W9kFa3KtXr9CmTRs0b94c69atK3N/c3NzpKenS3Xu4qytrfHmzRuhsjdv3kismQAKaxrMzc3x6NEjoQ/1tLQ0mUZxlsbZ2Vmk2VNLSz4r9Ghra3P/LxpkUbIvThFLS0uEhIRg06ZNcHZ2xtGjR3H69OlSz79hwwbcuXNHKF6BQICNGzdi8ODB0NfXr/iTABASEgLGGA4fPozGjRvj3Llz+PXXX7ntnz59wqxZs9CtWzeRY2nxVkLkQx0n35S5D9HQoUMxZswY/O9//wOPx8OrV6+wfft2/PDDDxgxYoQiYqyaDC0BJv7DhMMEhfvJUa1atRAUFISoqChkZWWJbJdUy1f0gf369WuuTNycM/b29hg+fDj27duHCRMmYP369QAAHR0dAIXftIt4enpCV1cXycnJcHNzE/qxt7eX+bm9fPkSrVu3hp+fHzZt2sQlcaXx9fVFYmKizNdq1qyZyBQFJ0+e5Jr+xHnx4gXev3/P1a4UuX37tkiNmLLUrVsXt27dEppIVV5954YMGYJdu3Zh3bp1cHV1RYsWLSTue+vWLVy7dg2nT59GfHw893P69GlcunQJ9+7dQ506daCvry9xaghxrzFx9PT00K1bN2zfvh1//PEH6tatKzQbf6NGjXD//n2R16Sbm5tUrylCiLDsvAI0mnMSjeac5JbuUMfJN2X+646MjETfvn3Rtm1bfPr0Ca1atcKQIUMwbNgwiTUBRAzvb6VLiHx6yP3SUVFR4PP5aNKkCf788088fPgQd+/exYoVKyR+oBclKTNnzsTDhw9x+PBhkQk4x44di+PHjyMpKQk3btxAbGwsPDw8APw3dcOhQ4fw9u1bfPr0CTVr1sQPP/yAcePGYcuWLXj8+DFu3LiBlStXYsuWLTI9p6JkyMHBAb/88gvevn2LlJQUkZFOJQUFBYkdtVb0gfzp0ye8ffsW8fHxQonTmDFjcOzYMSxZsgT37t3DzJkzce3aNYwaNQpAYS3DxIkTcfnyZTx9+hQxMTHo3Lkz3NzcEBQUxJ0nOzsb169fx9dff11qnPXq1VPISLS+fftCIBDg+++/x927d3H8+HGu35UsU22IExQUBCMjI8ydOxdhYWGl7rthwwY0adIErVq1Qv369bmfVq1aoXHjxtiwYQP09PQwefJkTJo0CVu3bsXjx49x+fJlruO1paUl9PX1cezYMbx58wYZGRkSr9evXz8cPnwYGzdu5DpTF5k+fTq2bt2KWbNm4c6dO7h79y527tyJadOmVeh+EFKdpWXlIS3rv3nhiibfLE7lk2+ycsrNzWV37txh//vf/9jHjx/Lexq1lpGRwQCwjIwMkW05OTksMTGR5eTklP8Cf41mbIYxYzOMxPwYF25XkFevXrHw8HDm6OjIdHR0mK2tLevUqROLjY3l9gHA9u/fzz0+f/488/b2Znp6eqxly5Zsz549DABLSkpijDE2atQo5urqynR1dZmFhQXr378/e/fuHXf87NmzmbW1NePxeCw0NJQxxphAIGDLli1jdevWZdra2szCwoIFBQWxM2fOMMYYi42NZQBYenp6qc9n06ZNDIU91EV+SvP+/Xump6fH7t27J1Qu7jyOjo5C++zevZu5u7szHR0d5uXlxQ4fPsxty87OZl9//TWzsLBg2trazNHRkQ0dOpSlpKQInWPHjh2sbt26pcZYFM+mTZskbi/tPjk6OrJff/1V6FzFf68XLlxgPj4+TEdHh/n5+bEdO3YwANw9EXfuuLg4od/9jBkzWIMGDUSu/dNPPzFNTU326tUribHn5uYyMzMztmjRIrHbf/75Z2Zpacny8vIYn89nc+fOZY6OjkxbW5s5ODiw+fPnc/uuX7+e2dvbMw0NDRYQEMAYYyw0NJR17txZ6Jx8Pp/Vrl2bAWCPHz8WueaxY8dY8+bNmb6+PjMyMmJNmjRh69atExufXN4LCKnCsnLzmePkQ8xx8iGWlZsvtC0hNYEdfHSQJaQmyPWapX1+S8JjTIG9bCu5zMxMGBsbIyMjA0ZGRkLbPn/+jKSkJDg7O5e/XwE//7+h9zyNf/sUCQp/Gg0AOi4BNLXLPg+pkIkTJyIzMxNr165V+rW/+OILREREoG/fvkq/tiTbt29HWFgYMjIyKtxvZ/DgwXj79i0OHjwop+jUj1zeCwhRQzff3sSzzGdwNHKsUM1Ndl4BPKcfBwAkzg6CgY58+jWWprTPb0mkikpc50JJypoIjxSjqQ10WlE4tP7mnsIO1DUsAe8ech1qT0o3depUrF69GgKBQKl9RN69e4du3bqhT58+SrumOFu3boWLiwtsbW2RkJCAyZMno2fPnhVKhjIyMnDr1i3s2LGjSidDhFRV6jYkXhmkSoikmf+EVICpk1yH1hPZmJiYlLnEhyKYm5tj0qRJSr9uSSkpKZg+fTpSUlJQu3Zt9OjRo8KTEHbu3BlXrlzB8OHDZZo/ihCieuo4JF4ZpEqINm3apOg4CCEqMmnSJLknZmUNsSeEqC91HBKvDIpvyKuEoqKiuJFYhBBCSHUi7yHxGjwefOyMuf+rq3J1qt67dy92796N5ORk5OXlCW27ceOG3IJTNYV3qiaEVHr0XkCqopJ9iAbXH4yxfmNVF5CMytOpWuYepCtWrEBYWBisrKwQFxeHJk2awMzMDE+ePEFwcLDMQRNCCCFEvYzzH4ftHbZj/pfzsb3D9kqVDJWXzE1mq1evxrp169CnTx9s3rwZkyZNgouLC6ZPn460tDRFxEgIIYQQJfOx8KnSfYZKkrmGKDk5Gc2bNwdQuCjlx48fAQD9+/fHH3/8Id/oCCGEEFKp5eTx0WLhP2ix8B/k5Klv31yZEyJra2uuJsjBwQGXL18GACQlJSl0JXVCCCGEVD4MDC8/5ODlhxwwqG+eIHNC9NVXX3ETrYWFhWHcuHFo164devXqha5du8o9QKI6PB4PBw4cUHUYChcTEwMPDw+5jyr84osv8Oeff8r1nIQQQhRD5oRo3bp1mDp1KgAgPDwcGzduhIeHB2bPno01a9bIPUCiGCkpKRg9ejRcXFygq6sLe3t7hISESFxJvKJOnz4NHo+HDx8+KOT8AODk5AQejyf0s3DhwjKPmzRpEqZNmwZNTU0AwOvXr9G3b1+4u7tDQ0MDY8eOFTlm37598Pf3h4mJCQwNDdGwYUNs27ZNaJ9p06YhMjISAkEZi/hKoTIlp5s3bwaPx+MW9i1uz5494PF4cHJyUn5ghBBSCpk7VWtoaAgtb9C7d2/07t1brkFVNy8+vsDhJ4fx/vN7mOmZoaNLR9jVtFPY9Z4+fYoWLVrAxMQEixcvhre3N/Lz83H8+HGEh4fj3r17Crt2RTHGwOfzoaUl/qU7e/ZsDB06lHtcs2bNUs93/vx5PH78GN27d+fKcnNzYWFhgWnTpuHXX38Ve1ytWrUwdepU1KtXDzo6Ojh06BDCwsJgaWnJrWYfHByMIUOG4OjRo+jYsaOsT1Wh8vLyoKOjo7DzGxoaIjU1FZcuXUKzZs248g0bNsDBwUFh15VWWa8jQuS1jhepPGSuIdq0aRP27NkjUr5nzx5s2bJFLkFVF/mCfMy8OBMd9nXA6oTV2HN/D1YnrEaHfR0w8+JM5AvyFXLdkSNHgsfj4cqVK+jevTvc3d3h5eWF8ePHc33CShJXwxMfHw8ej4enT58CAJ49e4aQkBCYmprC0NAQXl5eOHLkCJ4+fYo2bdoAAExNTcHj8TBw4EAAgEAgwIIFC+Ds7Ax9fX00aNAAe/fuFbnu0aNH4efnB11dXZw/f17ic6tZsyasra25H0NDw1Lvxc6dO9GuXTuh+WOcnJywfPlyDBgwQOKyNa1bt0bXrl3h4eEBV1dXjBkzBj4+PkKxaWpqokOHDti5c2epMVTU+/fv0adPH9ja2sLAwADe3t4iAxxat26NUaNGYezYsTA3N+eStoMHD6JOnTrQ09NDmzZtsGXLFpHf8/nz59GyZUvo6+vD3t4eERERyMrKKjUmLS0t9O3bFxs3/jePyYsXL3D69GmxC9muWbMGrq6u0NHRQd26dYVq2/r27YtevXoJ7Z+fnw9zc3Ns3boVQPlfRwkJCWjTpg1q1qwJIyMj+Pn54dq1a2XccVLV/XrtV/Q70g8/nv8R/Y70w6/XxH8xIlWLzAnRggULYG5uLlJuaWmJ+fPnyyWo6mLe5XnY93AfGBgETIACVgABE4CBYd/DfZh3uWLrSYmTlpaGY8eOITw8XGyyYGJiUu5zh4eHIzc3F2fPnsWtW7fw888/o0aNGrC3t+f60ty/fx+vX7/G8uXLARS+nrZu3Yro6GjcuXMH48aNw3fffYczZ84InTsyMhILFy7E3bt34eMj+dvawoULYWZmBl9fXyxevBgFBQWlxnzu3Dn4+/uX+zkDhbUNMTExuH//Plq1aiW0rUmTJjh37lypx7du3ZpLEMvj8+fP8PPzw+HDh3H79m18//336N+/P65cuSK035YtW6Cjo4MLFy4gOjoaSUlJ+Pbbb9GlSxckJCRg2LBhXHN4kcePH6N9+/bo3r07bt68iV27duH8+fMYNWpUmXENGjQIu3fvRnZ2NoDCprT27dvDyspKaL/9+/djzJgxmDBhAm7fvo1hw4YhLCwMsbGxAIB+/frh77//xqdPn7hjjh8/juzsbK7fYnlfR/369YOdnR2uXr2K69evIzIyEtra2lLeeVIVSVrH6+bbmyqKiCiLzPXFycnJcHZ2Fil3dHREcnKyXIKqDp5/fM4lQ+IUJUVDvIfItfns0aNHYIyhXr16cjtnkeTkZHTv3h3e3t4AABcXF25brVq1ABQmzkVJV25uLubPn49Tp05xzSouLi44f/481q5di4CAAO742bNnl7lIaEREBBo1aoRatWrh4sWLmDJlCl6/fo2lS5dKPObZs2ewsbEp1/PNyMiAra0tcnNzoampidWrV4vEaGNjg+fPn0MgEAg1NRfn4OCA2rVrlysGALC1tcUPP/zAPR49ejSOHz+O3bt3o0mTJlx5nTp1sGjRIu5xZGQk6tati8WLFwMA6tati9u3bwst7LpgwQL069eP60dVp04drFixAgEBAVizZk2pMzP7+vrCxcUFe/fuRf/+/bF582YsXboUT548Edrvl19+wcCBAzFy5EgA4Goqf/nlF7Rp0wZBQUEwNDTE/v370b9/fwDAjh070KlTJ9SsWbNCr6Pk5GRMnDiR+3uoU6eOFHecVGXVdR0vReKBhzqWNbj/qyuZEyJLS0vcvHlTpFNkQkICzMzM5BVXlXfkyRHweLxSpyrg8Xg4/OQwhjUYJrfrKnJqhIiICIwYMQInTpxAYGAgunfvXmptzqNHj5CdnS2SROTl5cHX11eoTJpanPHjx3P/9/HxgY6ODoYNG4YFCxZAV1dX7DE5OTnlXm6hZs2aiI+Px6dPnxATE4Px48fDxcUFrVu35vbR19eHQCBAbm4u9PX1xZ6nqNmnvPh8PubPn4/du3fj5cuXyMvLQ25uLgwMDIT28/PzE3p8//59NG7cWKiseAIFFP5d37x5E9u3b+fKGGMQCARISkoS23G6uEGDBmHTpk1wcHBAVlYWOnTogFWrVgntc/fuXXz//fdCZS1atOBqEbW0tNCzZ09s374d/fv3R1ZWFv766y+uKbIir6Px48djyJAh2LZtGwIDA9GjRw+4urqW+pxI1SbvdbwIoK+jiZPjA8reUcVkToj69OmDiIgI1KxZk2seOHPmDMaMGUOdq2Xw/vN7aEADAkgegaTB08D7z+/let06deqAx+PJ3HG6qHajeEKVny/cx2nIkCEICgrC4cOHceLECSxYsABLlizB6NGjxZ6zqAnk8OHDsLW1FdpWMoEpqy+QOE2bNkVBQQGePn2KunXrit3H3Nwc6enpMp8bKLwnbm5uAICGDRvi7t27WLBggVBClJaWBkNDQ4nJkDwsXrwYy5cvx7Jly+Dt7Q1DQ0OMHTtWZJ3B8tzDT58+YdiwYYiIiBDZJk3n6H79+mHSpEmYOXMm+vfvX+5OzP369UNAQABSU1Nx8uRJ6Ovro3379lyMQPleRzNnzkTfvn1x+PBhHD16FDNmzMDOnTtpCpFqzMfCB4O8Boms40W1Q4qhTp3XZX53mjNnDp4+fYq2bdtyb24CgQADBgygPkQyMNMzKzUZAgABE8BMT761brVq1UJQUBCioqIQEREh8gHx4cMHsf2ILCwsABQOSTc1NQVQ2Km6JHt7ewwfPhzDhw/HlClTsH79eowePZob0VR8rh9PT0/o6uoiOTlZqFlDXuLj46GhoQFLS0uJ+/j6+iIxMVEu1yuqCSru9u3bIrUU8nbhwgV07twZ3333HRfHgwcP4OnpWepxdevWxZEjR4TKrl69KvS4UaNGSExM5BI/WdWqVQudOnXC7t27ER0dLXYfDw8PXLhwAaGhoVzZhQsXhOJv3rw57O3tsWvXLhw9ehQ9evTg+vpU9HXk7u4Od3d3jBs3Dn369MGmTZsoIarmxvmPQ1vHtmrzQV1VlVxAdpDXIIzzH6eyeGROiHR0dLBr1y7MnTsX8fHx0NfXh7e3NxwdqTpRFh1cOiAqPqrUfRhj6Ogi/+HaUVFRaNGiBZo0aYLZs2fDx8cHBQUFOHnyJNasWYO7d++KHOPm5gZ7e3vMnDkT8+bNw4MHD7BkyRKhfcaOHYvg4GC4u7sjPT0dsbGxXJOKo6MjeDweDh06hA4dOkBfXx81a9bEDz/8gHHjxkEgEODLL79ERkYGLly4ACMjI6EPyLJcunQJ//vf/7gRQ5cuXeI61hYlcOIEBQWJHR1ZlOx9+vQJb9++RXx8PHR0dLgP6QULFsDf3x+urq7Izc3FkSNHsG3bNpG5uM6dO4evv/661NgHDBgAW1tbLFiwoNT9kpKSRJLQOnXqoE6dOti7dy8uXrwIU1NTLF26FG/evCkzIRo2bBiWLl2KyZMnY/DgwYiPj8fmzZsBFDbXAsDkyZPxxRdfYNSoURgyZAgMDQ2RmJiIkydPijR9SbJ582asXr1aYpP6xIkT0bNnT/j6+iIwMBB///039u3bh1OnTgnt17dvX0RHR+PBgwdch2sA5X4d5eTkYOLEifj222/h7OyMFy9e4OrVq0JTMJDqq7qt46VIOXl8dFpVOAL34Kgvoa+jKbHzelvHtqq776yCCgoKWFxcHEtLS6voqdTGqlWrmIeHB3N3d2cAWEZGhsg+OTk5LDExkeXk5JT7OjMuzGDem71Z/c31RX68N3uzGRdmVOBZlO7Vq1csPDycOTo6Mh0dHWZra8s6derEYmNjuX0AsP3793OPz58/z7y9vZmenh5r2bIl27NnDwPAkpKSGGOMjRo1irm6ujJdXV1mYWHB+vfvz969e8cdP3v2bGZtbc14PB4LDQ1ljDEmEAjYsmXLWN26dZm2tjazsLBgQUFB7MyZM4wxxmJjYxkAlp6eXurzuX79OmvatCkzNjZmenp6zMPDg82fP599/vy51OPev3/P9PT02L1794TKAYj8ODo6ctunTp3K3NzcmJ6eHjM1NWXNmjVjO3fuFDrHixcvmLa2Nnv+/HmpMQQEBHD3QxJx8QBg586dY+/fv2edO3dmNWrUYJaWlmzatGlswIABrHPnzkLXGDNmjMh5//rrL+bm5sZ0dXVZ69at2Zo1axgAodf1lStXWLt27ViNGjWYoaEh8/HxYfPmzZMY66ZNm5ixsbHE7b/++qvQvWSMsdWrVzMXFxemra3N3N3d2datW0WOS0xM5H4PAoFAaFt5Xke5ubmsd+/ezN7enuno6DAbGxs2atQomf+m5fFeQEhVlpWbzxwnH2KOkw+xrNx8xhhjBx8dFPvZd/DRQblcMyMjQ+LntyQ8xmTrZTt27Fh4e3tj8ODB4PP5CAgIwMWLF2FgYIBDhw4J9Z+o7DIzM2FsbIyMjAwYGRkJbfv8+TOSkpLg7Oxc7k65+YJ8bug9j8eDBk+jcNg9Y+hWpxumfjEV2ho0BFjRJk6ciMzMTKxdu1au5508eTLS09Oxbt06uZ5XkebNm4fo6Gg8f/5c1aFUGvJ4LyCkKsvOK4Dn9OMAgMTZQTDQ0cLNtzfR70g/kX23d9gulxqi0j6/JZG5yWzv3r1cX4W///4bT548wb1797Bt2zZMnToVFy5ckPWU1Za2hjZmNp+JId5DuJmqzfXN0cG5g0JnqibCpk6ditWrV5c6NL48LC0thUa+qaPVq1ejcePGMDMzw4ULF7B48WKp5hgihFQ/8uwArY6d12WuIdLT08OjR49gZ2eH77//HgYGBli2bBmSkpLQoEEDZGZmKipWpVN0DREhqjZu3Djs2rULaWlpcHBwQP/+/TFlyhRa0kIG9F5AqoOSHaC/cfkGzW2aS5UciashKqKoUWZKqSGysrJCYmIiateujWPHjnGdSLOzs7nFMQkhlcOvv/4qcb02QggBxM/efejJIRx6cghAxUaHqVPndZnbB8LCwtCzZ0/Ur18fPB4PgYGBAID//e9/Cpn9mBBCCFGkm29v4u/Hf9PyHBJImr27SFVZ2kTmGqKZM2eifv36eP78OXr06MFNfKapqYnIyEi5B6juZGxxJIRUMSXfA9RpojlSNnWbC0cdSTNLd2lLm/DAg62JPvd/dSVzH6LqpLQ2SD6fjwcPHsDS0pKWLCGkGnv//j1SU1Ph7u6OFXEr6MO1ElH0SKeqpGTiWJK63TOl9CEihTQ1NWFiYoLU1FQAgIGBATeZHSGk6mOMITs7G6mpqTAxMcGdtDvqN9EcKRUt5Cq94rN3X3x5EYeSDnHbVD06TF4oIaoAa2trAOCSIkJI9WNiYgJra2tce3JN7Hb6cFVftJCrbIo6QIe4hqCPR59yNQ2rc5MyJUQVwOPxULt2bVhaWoosdEoIqfq0tbW50bX04Vr5qONcOJWFLKPDPufz0XPtJaRkvUG25VzwNAoAqF+TMiVEcqCpqUlTDhBSzdGHq3oqq0aCFnJVPAFjuPkiA4Aealj+17VE3ZqUZU6IJE28yOPxoKury61qTggh1Q19uKoXaUeQqdNcONWNOjUpy5wQmZiYlNp52M7ODgMHDsSMGTPkugwCIYRUBvThqh7UcjV1IkKdmpRlTog2b96MqVOnYuDAgWjSpAkA4MqVK9iyZQumTZuGt2/f4pdffoGuri5+/PFHuQdMCCGElIVGkKk/dWtSljkh2rJlC5YsWYKePXtyZSEhIfD29sbatWsRExMDBwcHzJs3jxIiQgghFVaekUnUyV09bQzaiDc5z9WySVnmhOjixYuIjo4WKff19cWlS5cAAF9++SWSk5MrHh0hhJBqrbwzSVMnd/VU37w+mug0VHUYYsmcENnb22PDhg1YuHChUPmGDRtgb28PoHDmVlNTU/lESAghpFqqaD8g6uSuPmoZqv+AK5kTol9++QU9evTA0aNH0bhxYwDAtWvXcO/ePezduxcAcPXqVfTq1Uu+kRJCCKlW5NEPiDq5q56BjhZu/NRO1WGUSeaEqFOnTrh37x7Wrl2LBw8eAACCg4Nx4MABODk5AQBGjBgh1yAJIYRUP9QPiCgTLe5aivIsDkcIIUR+SvYhGlx/MMb6jVVdQKRSUNrirh8+fMCGDRtw9+5dAICXlxcGDRoEY2Pj8pxO7URFRSEqKgp8Pl/VoRBCSLVG/YAqv8/5fIRuvAIA2DKoCfS01XNlB5lriK5du4agoCDo6+tz8xBdvXoVOTk5OHHiBBo1aqSQQFWBaogIIYSQisnOK4Dn9OMAgMTZQTDQUfyqYUqpIRo3bhw6deqE9evXQ0ur8PCCggIMGTIEY8eOxdmzZ2U9JSGEEEKISsmcEF27dk0oGQIALS0tTJo0Cf7+/nINjhBCCCGV0ItrwPtHgJkbYNlQuv3sVJtDyJwQGRkZITk5GfXq1RMqf/78OWrWrCm3wAghhBBSCZ2cAVxY9t/jL8YDEJPslNyvxVig3SzFxlYKmVdf7dWrFwYPHoxdu3bh+fPneP78OXbu3IkhQ4agT58+ioiREEIIIZXBi2vCSQ4AXIqSbr8LywrLVaRcEzPyeDwMGDAABQUFAABtbW2MGDFCZPZqQgghhFQj7x9Jt9/Dk5LLVdR0JnNCpKOjg+XLl2PBggV4/PgxAMDV1RUGBgZyD44QQgghlYiZm9hifS0AvGLD7TNfiT9eUrkSlHvsm4GBAby9veUZCyGEEEIqMzv/wr5AxZrDDL4cibvtOhY+KOpELYmRjULDK41UCVG3bt2kPuG+ffvKHQwhpHqJS05H0rssOJsbwteBFoQmpEpoNwvwCBEdPVayE7U4dVS35plUCVFVmYGaEKIc0iQ6C4/eRfSZJ9zj4QEuiAz2UFaIhBBFsvMX7gskrhN1SS3GqXTovVQJ0aZNmxQdByGkipAm0YlLThfaBwCizzxBkJc11RQRUsV8zudjxL6XQN5ErNFeBj1e/n8bAyKBWs5qMQ+RzMPuCSFEEkmJTlxyulBZ0rssscdLKieEVF4CxhD7SguxAl8ISqYdxnaqCUoMqRKiRo0aIT09vewd//Xll1/i5cuX5Q6KEFJxccnp2HfjhUgyokjSJjrO5oZi95NUTkiV9uIakLBTpXPwqEQNK+DgKGD/MOC3toV9jFRIqiaz+Ph4JCQkoFatWlKdND4+Hrm5uRUKjBBSfqrqnyNtouPrYIrhAS5CMY4IcKHmMlL9qNlszUr16Y3w4wvLCjtjq/s8RG3btgVjTKp9eTxeuQMilQeNEFJPquyfI0uiExnsgSAva3oNkepL0mzNKkwKVO79I/VOiJKSkmQ+sZ2d+rQLEvmjEULqq7RmK2UkHbIkOr4OppQIkepL0nw8KkwKFOJlnPT7SpjYURmkSogcHR0VHQepRGiEkHpTh/45lOgQIgVJH/4qTArk7uQM4PwaAOo/Wp1GmRGZ0Qgh9VbUbFUc9c8hRA0VzepcnIrn4pErSXMPBUQW/ogj7VpoClDupTtI9aUONRCkdNQ/h5BKQtKszlXBv8mNAS8XT/X6/ldeNO/QGTELwquwdoxqiIjMqAaicvB1MEW3Rnb0eyFE3dn5Aw16qyYZUuSQ/9KaBNWwdoxqiEi5UA0EIYRUcooe8i9moVehpKfdrMLk6OV1wNYPaNRfftcuBx6Tdiy9GLm5udDV1ZVnPGolMzMTxsbGyMjIgJGRkarDIYQQUlUUrfquqmayF9cKJ0MsaUiM3OP5/PQqxh9+CejUwNKBbaCnrVm4QYEJWXk+v2VqMjt69ChCQ0Ph4uICbW1tGBgYwMjICAEBAZg3bx5evXpVrsAJIYSQauPkjMJkRJUzNJc25F/OBDa+OPJcG0ce50JQVAcjaQ4mFc7WLVVCtH//fri7u2PQoEHQ0tLC5MmTsW/fPhw/fhy//fYbAgICcOrUKbi4uGD48OF4+/atouMmhBBCKh91SQRUPeT/4UnZypVAqj5EixYtwq+//org4GBoaIjmUD179gQAvHz5EitXrsTvv/+OcePGyTdSQggh4qm6+YVIT10mYyyrf4+ivU6QrVwJpEqILl26JNXJbG1tsXChmGF0hBBCFKM6r4VVGam6ZqY4ZQ35FzdTdW6m+H0llSuBzMPuY2NjFREHIYQQWalL8wuRnroNN1f0kP+TM4DNHf97/M+8wn91JXR0llSuBDIPu2/fvj3s7OwQFhaG0NBQ2NvbKyIuQogaoAV81Zy6NL8Q2VTlyRiL4xL2YqPRL0UB9TsCtRsAD46KHlO7gbKiEyFzDdHLly8xatQo7N27Fy4uLggKCsLu3buRl5eniPgIISqy8OhddF19EeN3J6Dr6otYePSuqkMiJalT8wuRjSonY1SW0hL2Ou3Eb5NUrgQyJ0Tm5uYYN24c4uPj8b///Q/u7u4YOXIkbGxsEBERgYQE1XWIIoTIh6QFfOOS01UUERFL3ZpfCCnu38RcH7lI1A1Dom4Y9JFbNWeqbtSoEaytrWFmZoaFCxdi48aNWL16NZo1a4bo6Gh4eXnJK84K6dq1K06fPo22bdti7969qg6HELVX2gK+qmw6oyY8MapL8wuptHg8wAC5ohvUbKbqcq1llp+fj71796JDhw5wdHTE8ePHsWrVKrx58waPHj2Co6MjevToIe9Yy23MmDHYunWrqsOoMuKS07HvxguqLajC1HEBX2rCK0V1aH4hhRS59pi8lTX548kZwMFRwPVNhf+qYoLKYmSuIRo9ejT++OMPMMbQv39/LFq0CPXr1+e2Gxoa4pdffoGNjY1cA62I1q1b4/Tp06oOo0pYePSuUFPK8AAXRAZ7qDAioghFC/gW/12rcgFfSU14QV7WVFNEqo/KNsXCv01muUwLP+YPBgDM194AXTM3ySMkPUJUltjLXEOUmJiIlStX4tWrV1i2bJlQMlTE3NxcbsPzz549i5CQENjY2IDH4+HAgQMi+0RFRcHJyQl6enpo2rQprly5IpdrE2HUr6R6iQz2wP6RzbG0ZwPsH9kck1WY+JbWhEdItVCJp1jgQxN/CgLwpyAAfPy7jpkSlw6RlswJ0YwZM9CjRw+RRV0LCgpw9uxZAICWlhYCAgLkEmBWVhYaNGiAqKgosdt37dqF8ePHY8aMGbhx4wYaNGiAoKAgpKamyuX65D/0oVT9+DqYolsjO5XXwqhjEx4hSqWGCUSZSouZny9+m6RyJZA5IWrTpg3S0tJEyjMyMtCmTRu5BFVccHAw5s6di65du4rdvnTpUgwdOhRhYWHw9PREdHQ0DAwMsHHjRpmvlZubi8zMTKEf8h/6UCKqUtSEV5wqm/AIUbrKOMVCaTFraovfJqlcCWROiBhj4PF4IuXv37+HoaFyPxjz8vJw/fp1BAYGcmUaGhoIDAyUermR4hYsWABjY2PuhyadFEYfSkSV1KkJjxClEzdMvU6QSkKRmriYm40qLFfDBE/qTtXdunUDAPB4PAwcOFCoyYzP5+PmzZto3ry5/CMsxbt378Dn82FlZSVUbmVlhXv37nGPAwMDkZCQgKysLNjZ2WHPnj1o1qyZyPmmTJmC8ePHc48zMzMpKSohMtgDQV7WNPSZqISvgym95kj1VTTFwplFwMPj//2oc+fqdrMAt2+Atf92Y/nqx8J/Vb24rBhSJ0TGxsYACmuIatasCX19fW6bjo4OvvjiCwwdOlT+EcrBqVOnpNpPV1dXpG8UEUUfSoQQokIPjws/VvHorDLZ+gI4LlquZnNoSZ0Qbdq0CQDg5OSEH374QenNY+KYm5tDU1MTb968ESp/8+YNrK2tVRQVIYQQoiCVcf06cavdF7HzV5u4yzXKTB2SIaCwZsrPzw8xMTFcmUAgQExMjNgmMUIIIaRSU8O+N6U6OQP6mwNxXXcYrusOg/7pOaqOSCKpaogaNWqEmJgYmJqawtfXV2yn6iI3btyQW3AA8OnTJzx69F9GnJSUhPj4eNSqVQsODg4YP348QkND4e/vjyZNmmDZsmXIyspCWFiYXOOoTGh5A0IIqaLUsO+NRP/OncTjAWb4WFh2cRngqZ7Ne1IlRJ07d+b61nTu3LnUhEjerl27JjScv6jTc2hoKDZv3oxevXrh7du3mD59OlJSUtCwYUMcO3ZMpKO1LKKiohAVFQU+n1/h+JWNZpImhJAqTs363khUyZr3eIwxpuog1FVmZiaMjY2RkZEBIyMjVYdTprjkdHRdfVGkfP/I5lRTRMpENYuEELl6cQ34rS1ymRbmFnwHAJim9Tt0hx7/LyF6cU0hiV15Pr9lXsvMxcUFV69ehZmZmVD5hw8f0KhRIzx58kTCkUTR1HWFcqL+qGaRECJ3/zbv8c+vwTb+1wCAKS3N/kt81GxtNpk7VT99+lRsU1Jubi5evHghl6BI+dBM0qQ8aI06QojCtJsFDDz83+OieYjUcG02qWuIDh48yP3/+PHj3LxEQOHEjDExMXB2dpZvdEQm6rZCOakcqGaREKJQ4uYhenhS/L4PT6qsf5HUCVGXLl0AFM5UHRoaKrRNW1sbTk5OWLJkiVyDI7KjmaSJrKhmkRCidJmvZCtXAqkTIoFAAABwdnbG1atXYW5urrCgVK0yjzIDaCZpIhuqWSSEKJ2RjWzlSkCjzEpR2UaZEVIRNMqMEKII2XkF8Jxe2GSWODsIBjpa3Ag0EUNi5NJkppRRZgAQExODmJgYpKamcjVHRTZu3FieUxJCVIxqFgkhSqOGE0zKnBDNmjULs2fPhr+/P2rXrq3USRoJKUK1GYQQUjnoaWni3KQ23P8lU22DlcxNZrVr18aiRYvQv39/RcWkNqjJTD3RnDmEEFLJqWGTmczzEOXl5aF58+YyB0eIPNCcOYQQUgWUtqyHisicEA0ZMgQ7duxQRCyElKm0OXPkKS45HftuvKBEixBCKiivQID5R+5i/pG7yCv4t9+xmZv4nSWVK4HMfYg+f/6MdevW4dSpU/Dx8YG2trbQ9qVLl8otOFWp7MPuqzJlzJlDTXKEECI/BQIB1p0tfE8dG1gHOrLXxSiFzAnRzZs30bBhQwDA7du3hbZVlQ7W4eHhCA8P59ogifpQ9Jw5kprkgrysqfM2IYTIS2lNZuo+U3WR2NhYRcRBiNQUORs3LWNBCCFKUBWazAhRB4qaM4eWsSCEkOqpXAnRtWvXsHv3biQnJyMvL09o2759++QSGCGqQMtYEEKIElSFJrOdO3diwIABCAoKwokTJ/D111/jwYMHePPmDbp27aqIGAlRKloglxBCFKwqNJnNnz8fv/76K8LDw1GzZk0sX74czs7OGDZsGGrXrq2IGAlROlrGghBCqheZx749fvwYHTt2BADo6OggKysLPB4P48aNw7p16+QeICGEEEIqLz0tTZwY1wonxrX6b+mOqjAxo6mpKT5+/AgAsLW15Ybef/jwAdnZ2fKNjhBCCCGVmsar63BPOQz3/PvQ0Ph3ep6q0GTWqlUrnDx5Et7e3ujRowfGjBmDf/75BydPnkTbtmLWJamEaGJGQgghRA5Oziixov1YoN2swo7Ttn7Ay+v/bbP1V+lq9zIv7pqWlobPnz/DxsYGAoEAixYtwsWLF1GnTh1MmzYNpqZVp98FLe5KCCGElNO/C7jmMU1EFXQBAIRrHYDO0BOF29VscVeZa4hq1arF/V9DQwORkZGynoIQpYlLTqfRYoQQogr/9gcqgBaW87sDAIZpHYLO+0dAWpL4Yx6erDzD7pOTk0vd7uDgUO5gCJEnWpOMEEJUqLR+Qk8viN+W+Upx8ZRB5oTIycmp1DXLqN8NUQe0JhkhhKiYnX9hn6Hza/4razaqsPz6FpWFJYnMCVFcXJzQ4/z8fMTFxWHp0qWYN2+e3AIjFVPdm4poTTJCCFED7WYBbt8Aa1MLH3/1Y+G/7x6K319SuRLInBA1aNBApMzf3x82NjZYvHgxunXrJpfASPlRUxGtSUYIIWrD1hfAceGy94/F7yupXAlknodIkrp16+Lq1avyOh0pJ0lNRXHJ6SqKSDWK1iQrjtYkI4QQNSHIl61cCWSuIcrMzBR6zBjD69evMXPmTNSpU0dugZHyoaai/6jrmmTVvTlTElXfF1Vfn5BqxcAM+Czmi7qBmfJj+ZfMCZGJiYlIp2rGGOzt7bFz5065BaZKlXliRmoqEqZua5JRc6Z4qr4vqr4+IVWZrpYm/gpvAaTehe6dvYCFG2BeB0gTs0yHueoqVmSemPHMmTNCjzU0NGBhYQE3NzdoacmcX6m1yjoxY8k39xEBLphMb+4qF5ecjq6rL4qU7x/ZXK2SNmVT9X1R9fUJqRZKzlht6QWk3hHdz3cA0HllhS+nlIkZAwICZA6MKJe6NhVVd9ScKZ6q74uqr09IlffimnAyBIhPhgDAyEbh4Ugic0J08OBBqfft1KmTrKcnZZC2n4O6NRURas6URNX3RdXXJ6Sqy0t9hE0F3wAAwjSPQodXSncUQYGSohIlc0LUpUsX8Hg8lGxpK1nG4/EqZR8cdVbRfg7UaVS1ika+lWzOrO6/C1XfF1Vfn5CqrsDUFQsK+gIA+muehA5KyQ3e3lNSVKJk7kN06tQpTJ48GfPnz0ezZs0AAJcuXcK0adMwf/58tGvXTiGBqoI69SGqaD8H6jSqPigxFU/V90XV1yekqsrOK4Dn9MJ5iBJ1w2DAywV0jYDcTNGdLesDIyUs6yEDpfQhGjt2LKKjo/Hll19yZUFBQTAwMMD333+Pu3fvynpKIoWK9HOgZSzUCzVniqfq+6Lq6xNSrYhLhgDgc4Zy4yhG5oTo8ePHMDExESk3NjbG06dP5RASKa7oW2s+XyB2uzT9HKjTqHqhmghCCJHAQHXviTInRI0bN8b48eOxbds2WFlZAQDevHmDiRMnokmTJnIPUB1k5xVAK0+0o5cGjwc9bU2h/SQpz74lm7m8bY1w6+V/WfWQL51Qz1q4KjAnjw8G4VbQ2sZ6Yq9jY6JXahwGOv+9PD7n8yEopXVVUfvqa2ty817lFvDBF8hnXz0tTWhoFO6bVyBAgUB8winrvrpamtAsZd8lJ+5jw/mn3OOipst8vkBi0gsAOpoa0NIsnFheln0L+ALklbKvtqYGtMuxL1/AkFsguR+AloYGdLRk31cgYPgsp301NXjQ1Sr8m2OMISdfPvsq+u++PPuK+7svwgMP+jrl21dVf/fV+T1C0r6V+T2i+Gs5m+kAALRQwHWu5jMeclFYDvOGQInXfnnfI2Qlcx+iR48eoWvXrnjw4AHs7e0BAM+fP0edOnVw4MABuLm5lSsQdVTUBmk/djc0dA1Etrepa4FNYf8lgR4/HZP4RtrUuRZ2DWvGPW405yTSsvLE7utjZ4xZnbzE9hkqqY5lDZwc/99UCO2WnsHD1E9lHjciwAUXHr/HzRfiqydrGergxk//9QfrtfYS/peUJnZffW1N3J3TnnsctukKYu+/lXjtpws7cv8fuf06jtxKkbhv4uwg7s1xwu4E/HnjhcR9r08LhFkNXQDATwduY9vlZxL3PTepDexrFf5O5x+5i3Vnn0jc98S4VnC3qgkA+PXkAyyPkbz44F/hLdDA3gQAsPbMYyw4WnYHwf0jm+PWywxM/0vCMFQAGwf646t6hV9A9lx7jol7b0rcN6pvI3T0qQ0AOHzzNcJ33JC47+JvfdDDv/Dv+J97bzBo8zWJ+87u7IUBzZwAAJcev0ef9Zcl7jsluB6GBbgCABKef0DnKMl9Asa0rYNx7dwBAA/efMTXv56VuO/3rVzwY4fCvm/P07LRclGsxH37f+GIOV3qAwDef8qF39xTEvft3sgOS3oWrtNYvL+DOB28rbG6nx/32CnysMR9FfkecXDUf90WWiz8By8/5IjdV5b3CFsTfVyI/Ip73GnVeXqPgOreI/4Y+gWauRbO3Lz10tOq9R6htQPDtA4BABIELuicN1fivuV5j1BKHyI3NzfcvHkTJ0+exL17hb9IDw8PBAYGisxgTcpPUjNXRVjU1MWU4HpcU02nVeflfg0iO0X8rgkhRKXe3ld1BDKTuYaoOinKMF+/fS82w1Rkdfjd15lia4j+GNqU+3YBUHV4efdVRXV4wvMP6LP+fyLH7B/ZHPVtjSttdbg41GSm3H2pyaxqvEeUtm+lajI7OQP888u5ZjA+4+GaoLCGx1/jATR5THKTWcclQMM+Quctz3tEeWqIpEqIdu7cid69e0t1wufPnyM5ORktWrSQan91VHwtswcPHqhs2D0twVH10O+UEFKlvbgG/Na2/Md3XQs0kC7fKI3CEqKAgACkpqYiLCwMISEh8PAQfgPPyMjAhQsX8Pvvv+PkyZPYsGFDlZilWh3mIaIRSVUP/U4JIVVWwk5g/7DyHz8kBrDzr3AYCutDdObMGRw8eBArV67ElClTYGhoCCsrK+jp6SE9PR0pKSkwNzfHwIEDcfv2bW70Gak4mhul6qHfKSGkyjITHViVzzTxh/M84FEM+mj+A+3Slu5IvSuXhKg8ZO5D9O7dO5w/fx7Pnj1DTk4OzM3N4evrC19fX2holG+om7pShxoiQohiUE0dIQpSYmX77C8mwPN04chMbqZqSRxbAGFHKhyCUkaZmZubo0uXLrIeRgghaoOWsiFy8eIa8P5RYa2Iimo11FK7WYBHyH/3xrIhcPrfqSwGHgYyHwOXooAUyVMDqILMCRGpmujbctVGv9//0FI2RC5K1IKgxdjCRIAUsvP/L0ksPmLS1hdwbgzw84GDo0SPa9BHtExJKCEi9G25iqPfrzBayoZU2ItrwskQUPjYI4RqiqRlKeE9SFK5ElStTj9EZpK+Lcclp6soIiJP9PsVJWn9P2nWBSQEQGFTkCzlRNTDk7KVKwElRNVcad+WSeVHv19Rvg6mGB7gIlQ2IsCFaoeI9MSMpCq1nFQK1GRWzdG35aqNfr/iRQZ7IMjLmvpVkfKx8y/sMyTUh2gcNZfJwthOtnIlkDohmj17tlT7TZ8+vdzBkLLJu3Ns0bflkrMn0wdE1UC/X8loPihSISVHUlEyJJGOpgY2DvTn/g8AeH5F/M7PrwCN+ispMmFSz0OkoaEBGxsbWFpaQtIhPB4PN25IXjW3slG3eYgU2TmWRiFVbfT7JYSoleiW4ofdW/sAw89V+PQKnYcoODgY//zzD/z9/TFo0CB88803VW4iRnWm6KHC9G25aqPfLyFErejWlK1cCaTOaA4fPozHjx+jadOmmDhxImxtbTF58mTcv39fkfGRf1HnWEIIUTMvrhWu3fXimqojUWv5fAH2XHuOPdeeI58vKCx0aCZ+Z0nlSiBTFY+NjQ2mTJmC+/fvY9euXUhNTUXjxo3RokUL5OTkKCpGAuocSwghauXkjMJV3fcPK/z35AxVR6S28vkCTNx7ExP33vwvIdKQ0EAlqVwJyt3m1bhxY7Rp0wYeHh6Ii4tDfn6+POMiJdBQYUKI0lENiHiSJmak+1SpyZyKXbp0CRs3bsTu3bvh7u6OsLAw9O3bVy06HctLVFQUoqKiwOeXsiKvCqjLUGHqoEtINUBLU0hW2sSMNNpMOnXaAWcWii9XEakTokWLFmHz5s149+4d+vXrh3PnzsHHx0eRsalMeHg4wsPDuV7q6kTVnWNpGQhCqgFamqJ0NDFjxV3dKLlcRa8xqROiyMhIODg4oGfPnuDxeNi8ebPY/ZYuXSqv2IiaoUUxCakmqAakdDQxY8W9vCpbuRJInRC1atUKPB4Pd+7ckbgPj8eTS1BEPdGimIRUE1QDUjaamLFiTJyBdw/El6uI1AnR6dOnFRgGqQxopBsh1QTVgEjHzp/uSXnZ+gKPjosvVxFay4xIjZaBIKQaoRoQIic6mhqI6tuI+7+6knrpjm7duoktNzY2hru7O4YMGQILCwu5Bqdq6rZ0h7qgUWbVC/2+CSFy9+Ja4fxNJQ2JkUvyrdClOySNtvrw4QPWr1+PxYsX4+zZs6hfv760pySVlKpHuhHlUfaoQkq+CCGqInVCtGnTJonbBAIBhg4diilTpuDvv/+WS2CEENVS9qhCmtKBkKqpgC/A8TtvAABBXlbQ0tRQy5GMcmnM09DQQEREBK5fvy6P0xFC1IAy18+TlHzFJafL/VqEEOXK4wsQvuMGwnfcQF7R0h3vHorfWVK5Esitd5OhoSGys7PldTpCiIopc1QhLV5MSDWT/V62ciWQW0J08uRJuLu7y+t0hBAVU+b6eTSlAyHVjK2fbOVKIHUfooMHD4otz8jIwPXr1/Hbb7/ht99+k1tghBDVU9b6eTSlAyHVjKWE/oGSypVA6oSoS5cuYstr1qyJunXr4rfffkPv3r3lFReRAxqxQ+RBWaMK1WXxYkKInL2MEy1Tw07VUidEAoFAkXEQOaMRO6QyoikdCKliTs4Azq8B8O9I9X/mAe1nqOXyMHLrQ/ThwwesWrVKXqcjFUAjdgghhKjci2vCy78AwKWownI1VOGEKCYmBn379kXt2rUxY8YMecREKohG7BBCCFG5f5vFtFGAxVrRWKwVDW0UFJaX1mSmIuVKiJ4/f47Zs2fD2dkZX3/9NXg8Hvbv34+UlBR5x0fKgUbsEHUSl5yOfTdeUA0lIdXNv81f2jw+emidRQ+ts9Dm8QvLK3OTWX5+Pvbs2YOgoCDUrVsX8fHxWLx4MTQ0NDB16lS0b98e2traioyVSEmZw6UJKc3Co3fRdfVFjN+dgK6rL2Lh0buqDokQoix2/kCLscJlLcYVlpe2TUWkXtzV0tIS9erVw3fffYcePXrA1LTww1VbWxsJCQnw9PRUaKCqUNkXd6VRZkSV4pLT0XX1RZHy/SOb0+uRkGqkIPkqzt5+ChjZoFWzZoVLdxR5ca2wmczMTa7JkEIXdy0oKACPxwOPx4Ompma5gyTKQyN2iCqV1pdNHV6X9IWBEOXIs/bFoNWpAD4gsYlAOCFKvQu8vA7w81VaOwTIkBC9evUKf/75JzZs2IAxY8YgODgY3333HXg8niLjU4moqChERUWBz+erOhR60yaVljr3ZaNpKQhRA+u/KkyGAOD6psKfof+oLByp+xDp6emhX79++Oeff3Dr1i14eHggIiICBQUFmDdvHk6ePKkWCYQ8hIeHIzExEVevXlVpHJWh/wV1mCWSqGtfNpqWghA1cGPbf8lQkZfXC8tVpFyjzFxdXTF37lw8e/YMhw8fRm5uLr755htYWVnJO75qqzK8aVeGhI2oVmSwB/aPbI6lPRtg/8jmmKwGtTA0LQUhaqBkMlRWuRJUaB4iDQ0NBAcHY+/evXjx4gV+/PFHecVV7an7m3ZlSNiIevB1MEW3RnYqrxkqos5NeYRUG2q4uKvcZqq2sLDA+PHj5XW6ak/d37TVPWEjRBJ1bcojpFqpzIu7EuVS99W/1T1hI6Q0tJAsISr28KTkcnVf3JUonzq/aat7wkZIWWhaCkKUQ1tTA7M7e3H/V1dST8xYHVX2iRmVgaYFIIQQIrMX14Df2oqWD4mRSw2RQidmJEQcWb5lU/JECCEEwH9Ld1xY9l+ZipfukDkh4vP52Lx5M2JiYpCamgqBQCC0/Z9/VDepEpEfeScvFZkIjxIpQgipvPgChitJaQCAJs61oKnx74TO7WYBHiEKWbqjPGROiMaMGYPNmzejY8eOqF+/fpWcqbq6k/csvpKG6Ad5WZeZ4NCMwoQQUrnlFvDRZ/1lAEDi7CAY6BRLPYoWelUDMidEO3fuxO7du9GhQwdFxENUrCLJiyTlXdNKEbEQQggh4sjc3VtHRwdubm6KiIWogdP3U2Uql0Z5h+jTXEeEEEKUReaEaMKECVi+fDlocBqRVnknwqO5jgghpIp7cQ1I2Fn4r4rJ3GR2/vx5xMbG4ujRo/Dy8oK2trbQ9n379sktOKJ8retaYnnMI7HlFVGeOZVoriNCCKnCTs4oMcpsbGFHaxWROSEyMTFB165dFRELUQOKTELKMxGeOk9OSQghld6La6oZ5fXimnAyBBQ+9gipPDNVb9q0SRFxEDWibkkIzShMCCEKoMoamveiLRFceWVJiEjlJu2cPpSEEEJIFabEGhotDQ1MCa7H/R9AYY2UOJLKlaBcCdHevXuxe/duJCcnIy8vT2jbjRs35BIYkT9xc/qoU00QIYQQJVFiDY2OlgaGBbgKF1aFmapXrFiBqVOnYuDAgfjrr78QFhaGx48f4+rVqwgPD1dEjEQOJM3pQ5MeEkJINaQONTRqNlO1zMPuV69ejXXr1mHlypXQ0dHBpEmTcPLkSURERCAjI0MRMRI5kGbunugzTxCXnK6EaAghhKhUUQ1NcQqqoeELGBKef0DC8w/gC9R3yh6Za4iSk5PRvHlzAIC+vj4+fvwIAOjfvz+++OILrFq1Sr4RErmQdu6esmaPJqQyo3XxCClGSTU0uQV8dI66AKDE0h1qNuxe5hoia2trpKUVLtLm4OCAy5cL1ydJSkqiyRrVmLjJEcWhSQ9JVbXw6F10XX0R43cnoOvqi1h49K6qQyJE9ez8gQa9ld9cJalTtwonaJS5huirr77CwYMH4evri7CwMIwbNw579+7FtWvX0K1bN0XESOSk5HD643dSaNLDf1HNQdVG6+IRomYenpRcXlmG3a9btw4CgQAAEB4eDjMzM1y8eBGdOnXCsGHD5B4gka/iw+mP30kR2lZd6/fEjb6jzuVVS3kXGCaEVB8yN5lpaGhAS+u/PKp3795YsWIFRo8eDR0dHbkGR2QXl5yOfTdelNk5WtI35urWqZruQ/VA6+IRombqtJOtXAlkTogA4Ny5c/juu+/QrFkzvHz5EgCwbds2nD9/Xq7BEdnI0keCVpIvRPeheijvAsOEEAVR4ig3acncZPbnn3+if//+6NevH+Li4pCbmwsAyMjIwPz583HkyBG5B0nKJmsfCfrGXIjuQ/WhbkvSEFLtVfZ5iObOnYvo6GisX79eaKX7Fi1a0CzVKiRrTQd9Yy5E96F68XUwRbdGdvT7JUSJtDQ0MKZtHYxpW+e/pTvUkMw1RPfv30erVq1Eyo2NjfHhwwd5xETKoTw1HfSNuRDdB0IIURwdLQ2Ma+cuuqEqzEP06JHoGijnz5+Hi0vZ89wQxShvTQd9Yy5E94EQQpSoKsxDNHToUIwZMwYbN24Ej8fDq1evcOnSJfzwww/46aefFBEjkZK8ajpoTh5CCCHyIhAwPHr7CQDgZlEDGho8pS4uKy2ZE6LIyEgIBAK0bdsW2dnZaNWqFXR1dfHDDz9g9OjRioixQg4dOoQJEyZAIBBg8uTJGDJkiKpDUqji8wyVB83JQwghRJ4+F/Dx9a9nARRbukMdFpctQeYmMx6Ph6lTpyItLQ23b9/G5cuX8fbtW8yZM0cR8VVIQUEBxo8fj3/++QdxcXFYvHgx3r9/r+qw1BbNyUMIIUQpqsKw+yI6Ojrw9PSUZyxyd+XKFXh5ecHW1hYAEBwcjBMnTqBPnz4qjkz+5NHMpczZfKlZjhBCqjk1G3Yvc0L0+fNnrFy5ErGxsUhNTeWW8Sgiz6H3Z8+exeLFi3H9+nW8fv0a+/fvR5cuXYT2iYqKwuLFi5GSkoIGDRpg5cqVaNKkCQDg1atXXDIEALa2ttxEkpVJyeSh5OOKNnMVnS+fLxC7Xd5z8lCzHCGEEACFSZCKE6EiMidEgwcPxokTJ/Dtt9+iSZMm4PF4iogLAJCVlYUGDRpg0KBBYheO3bVrF8aPH4/o6Gg0bdoUy5YtQ1BQEO7fvw9LS0uFxaVMJZOHhvbGiH+ewT3u6muD/XGvhI6RZdHKkucvSd5z8tAim4QQQtSRzAnRoUOHcOTIEbRo0UIR8QgJDg5GcHCwxO1Lly7F0KFDERYWBgCIjo7G4cOHsXHjRkRGRsLGxkaoRujly5dc7ZE4ubm53MzbAJCZmSmHZ1F+4pKH4skQAJFkqMjp+6llJhjizl/S117WUkQqvdP3UyWWU0JECCFEVWTuVG1ra4uaNWsqIhaZ5OXl4fr16wgMDOTKNDQ0EBgYiEuXLgEAmjRpgtu3b+Ply5f49OkTjh49iqCgIInnXLBgAYyNjbkfe3t7hT+P0ih6PS1pzk9rehFCCKkOZE6IlixZgsmTJ+PZs2eKiEdq7969A5/Ph5WVlVC5lZUVUlJSAABaWlpYsmQJ2rRpg4YNG2LChAkwMzOTeM4pU6YgIyOD+3n+/LlCn0NZKtJ3p3XdspsMpTm/vPsPSYpLmngJIYRUPloaGvi+lQu+b+VStZbu8Pf3x+fPn+Hi4gIDAwOh9cwAIC0tTW7ByUOnTp3QqVMnqfbV1dWFrq6ugiOSXtHs06X1IRoR4AIGCO0jbb8fcecvThFreom7Jq0dRgghVZeOlgZ+7KD+A2dkToj69OmDly9fYv78+bCyslJop+rSmJubQ1NTE2/evBEqf/PmDayt5dvvRZXEzT4tbsh6eWeoLnl+AAofDk9rhxFCCFE3PMYYk+UAAwMDXLp0CQ0aNFBUTGLxeDyRYfdNmzZFkyZNsHLlSgCAQCCAg4MDRo0ahcjIyApfMzMzE8bGxsjIyICRkVGFz0cIIYRUNwIBw8sPOQAAWxP9wqU7FKw8n98y1xDVq1cPOTk5MgdXHp8+fRJaSDYpKQnx8fGoVasWHBwcMH78eISGhsLf3x9NmjTBsmXLkJWVxY06K6+oqChERUWBz+dX9CkQQggh1drnAj5aLooFUGzpDjUkcw3RiRMnMGvWLMybNw/e3t4ifYjkWZNy+vRptGnTRqQ8NDQUmzdvBgCsWrWKm5ixYcOGWLFiBZo2bSqX61MNESGEEFIx2XkF8Jx+HIDyEqLyfH7LnBBp/NtDvGTfIcYYeDxelapVoYSIEEIIqZjKkhDJHFVsbKzMgRFCCCGEqDOZE6KAgABFxEEIIYQQojLqO0MSIYQQQoiSUEIkRlRUFDw9PdG4cWNVh0KI0sQlp2PfjReIS05XdSiEEKJ0Mneqrk6oUzWpLhYevSs0e/jwABdEBqv/zLKEEPWXW8DH3EN3AQDTvvGArpamwq+p0E7V2dnZMDAwKHdwhBD1FJecLrJ8S/SZJwjysqZZxFVA3Ez0hFRmulqamNOlvqrDKJPUCZG5uTm++uorbm2wqrQ8BiHVWdK7LInl9IGsXFRTR4jqSN2H6N69ewgKCsLu3bvh5OSEpk2bYt68ebh165Yi4yOEKFjRGnbSlhPFkFRTR326SGXHGMP7T7l4/ykX6txLR+qEyMHBAaNHj8apU6fw5s0bjB07Frdu3ULLli3h4uKCsWPH4p9//qlSEzMSUh34OphieICLUNmIABeqHVKy0mrqCKnMcvL58Jt7Cn5zTyEnX31zhHJNF2lsbIw+ffqgT58+yM/Px+nTp3Hw4EGEhYXh48ePWLlyJfr16yfvWAkhChIZ7IEgL2vqu6JCVFNHiGpVeP5sbW1ttGvXDu3atcPKlSsRFxeHgoICecRGCFEiXwdTSoRUqKimrnizGdXUEaI8cl9QxNfXV96nVDpa7Z4QogpUU0eI6tA8RKWgeYgIIYSQiqksi7vSTNWEEEIIqfYoISKEEEJItSdzQrRx40YkJSUpIhZCCCGEVDGaGjx0b2SH7o3soKnBU3U4Esnch6hOnTp48uQJbG1tERAQgICAALRu3Rpubm6KilFlqA8RIYQQUvkopQ/Rw4cPkZycjAULFsDAwAC//PIL6tatCzs7O3z33XcyB00IIYQQomoVGmWWnZ2Nc+fO4Y8//sD27dvBGKtScxBRDREhhBBSMYwxboZqfW1N8HiKbzZTSg3RiRMn8OOPP6J58+YwMzPDlClTYGpqir179+Lt27cyB62OoqKi4OnpicaNG6s6FEIIIaRSy8nnw3P6cXhOP161lu5o3749LCwsMGHCBBw5cgQmJiYKCEu1wsPDER4ezmWYhBBCCKnaZK4hWrp0KVq0aIFFixbBy8sLffv2xbp16/DgwQNFxEcIIYQQonAyJ0Rjx47Fvn378O7dOxw7dgzNmzfHsWPHUL9+fdjZ2SkiRkIIIYQQhSrX/NmMMcTFxeH06dOIjY3F+fPnIRAIYGFhIe/4CCGEEEIUTuaEKCQkBBcuXEBmZiYaNGiA1q1bY+jQoWjVqlWV7E9ESFUUl5xOC4gSQkgxMidE9erVw7Bhw9CyZUvqcExIJbTw6F1En3nCPR4e4ILIYA8VRkQIIaonc0K0ePFiRcRBCFGCuOR0oWQIAKLPPEGQlzXVFBFCFEKDx0MHb2vu/+qqXIu7njlzBiEhIXBzc4Obmxs6deqEc+fOyTs2QoicJb3LkqmcEEIqSk9bE6v7+WF1Pz/oaWsKb3xxDUjYWfivismcEP3+++8IDAyEgYEBIiIiEBERAX19fbRt2xY7duxQRIyEEDlxNjeUqZwQQhTm5Azgt7bA/mGF/56codJwZF66w8PDA99//z3GjRsnVL506VKsX78ed+/elWuAqhAVFYWoqCjw+Xw8ePCAlu4gVUrJPkQjAlwwmfoQEUKU6cW1wiSopCExgJ1/hU9fnqU7ZE6IdHV1cefOHZHV7R89eoT69evj8+fPspxOrdFaZqSqolFmhBBlyc4rgOf04wCAxNlBMNDRKmwm2z9MdOeua4EGvSt8zfJ8fsvcqdre3h4xMTEiCdGpU6dgb28v6+kIISrg62BKiRAhRHXM3GQrVwKZE6IJEyYgIiIC8fHxaN68OQDgwoUL2Lx5M5YvXy73AAkhhBBSxdj5Ay3GAheW/VfWYpxcmsvKS+aEaMSIEbC2tsaSJUuwe/duAIX9inbt2oXOnTvLPUBCCCGEVEHtZgEeIcD7R4U1QypMhoByLt3RtWtXdO3aVd6xEEIIIaQ6sfNXeSJUpFzzEBFCCCGEVCVS1RCZmpqCJ+XskmlpaRUKiBBCCCFE2aRKiJYtW6bgMAghhBBSFWnweGhT14L7v7qSKiFKSEjAnDlzYGhoiLNnz6J58+bQ0ipX9yNCCCGEVCN62prYFNZE1WGUSao+RCtXrsSnT58AAG3atKFmMUIIIYRUKVJV8zg5OWHFihX4+uuvwRjDpUuXYGoqflK3Vq1ayTVAQgghhBBFk2rpjgMHDmD48OFITU0Fj8eDpEN4PB74fL7cg1Q2WsuMEEIIkY/svAL4zTkFALj+U2Dh0h0KpvC1zD59+gQjIyPcv38flpaWYvcxNjaW9nRqj9YyI4QQQipG7FpmCqbwtcxq1KiB2NhYODs7U6dqQgghhFQZMmc1AQEBioiDEEIIIURlaKZqQgghhFR71O5F5CIuOR1J77LgbG4IXwfxIxAJIYQQdUUJEamwhUfvIvrME+7x8AAXRAZ7qDAiQgghRDYVbjLLzMzEgQMHcPfuXXnEQyqZuOR0oWQIAKLPPEFccrqKIiKEEKJONHg8NHWuhabOtSr/0h3F9ezZE61atcKoUaOQk5MDf39/PH36FIwx7Ny5E927d1dEnERNJb3LklhOTWeEEEL0tDWxa1gzVYdRJplriM6ePYuWLVsCAPbv3w/GGD58+IAVK1Zg7ty5cg+QqDdnc0OZygkhhBB1JHNClJGRgVq1agEAjh07hu7du8PAwAAdO3bEw4cP5R4gUW++DqYYHuAiVDYiwIVqhwghhFQqMjeZ2dvb49KlS6hVqxaOHTuGnTt3AgDS09Ohp6cn9wCJ+osM9kCQlzWNMiOEECIiO68AX/4cCwA4P7mNUmaqLg+Zoxo7diz69euHGjVqwNHREa1btwZQ2JTm7e0t7/hIJeHrYEqJECGEELHSsvJUHUKZZE6IRo4ciSZNmuD58+do164dNDQKW91cXFyoDxEhhBBCKqVy1Vv5+/vD398fAMDn83Hr1i00b94cpqZUQ0AIIYSQykfmTtVjx47Fhg0bABQmQwEBAWjUqBHs7e1x+vRpecenElFRUfD09ETjxo1VHQohhBBClEDmhGjv3r1o0KABAODvv/9GUlIS7t27h3HjxmHq1KlyD1AVwsPDkZiYiKtXr6o6FEIIIYQogcwJ0bt372BtbQ0AOHLkCHr06AF3d3cMGjQIt27dknuAhBBCCCGKJnNCZGVlhcTERPD5fBw7dgzt2rUDAGRnZ0NTU1PuARJCCCGk8tLg8eBjZwwfO+OqtXRHWFgYevbsidq1a4PH4yEwMBAA8L///Q/16tWTe4CEEEIIqbz0tDVxcNSXqg6jTDInRDNnzkT9+vXx/Plz9OjRA7q6ugAATU1NREZGyj1AQgghhBBF4zHGmKqDUFeZmZkwNjZGRkYGjIyMVB0OIYQQQqRQns9vmfsQAcCZM2cQEhICNzc3uLm5oVOnTjh37lx5TkUIIYSQKiwnj48WC/9Bi4X/ICePr+pwJJI5Ifr9998RGBgIAwMDREREICIiAvr6+mjbti127NihiBgJIYQQUkkxMLz8kIOXH3LAoL6NUjL3IZo3bx4WLVqEcePGcWURERFYunQp5syZg759+8o1QEIIIYQQRZO5hujJkycICQkRKe/UqROSkpLkEhQhhBBCiDLJnBDZ29sjJiZGpPzUqVOwt7eXS1CEEEIIIcokc5PZhAkTEBERgfj4eDRv3hwAcOHCBWzevBnLly+Xe4CEEEIIIYomc0I0YsQIWFtbY8mSJdi9ezcAwMPDA7t27ULnzp3lHiAhhBBCiKLJlBAVFBRg/vz5GDRoEM6fP6+omAghhBBSRfDAQx3LGtz/1ZXMEzPWqFEDt2/fhpOTk4JCUh80MSMhhBBS+ShlYsa2bdvizJkzMgdHCCGEEKKuZO5DFBwcjMjISNy6dQt+fn4wNDQU2t6pUye5BUcIIYQQogwyN5lpaEiuVOLxeODz1XdabllRkxkhhBBSMTl5fHRaVdjv+OCoL6Gvo6nwa5bn81vmGiKBQCBzYIRURXHJ6Uh6lwVnc0P4OpiqOhxCCFFLDAwPUz9x/1dXMidEhBBg4dG7iD7zhHs8PMAFkcEeKoyIEEJIRUidEOXk5CAmJgbffPMNAGDKlCnIzc3ltmtqamLOnDnQ09OTf5SEKFlptT9xyelCyRAARJ95giAva6opIoSQSkrqhGjLli04fPgwlxCtWrUKXl5e0NfXBwDcu3cPNjY2Qou+VlZRUVGIioqqUv2hiPTKqv1Jepcl9rikd1mUEBFCSCUl9bD77du34/vvvxcq27FjB2JjYxEbG4vFixdzM1dXduHh4UhMTMTVq1dVHQpRMkm1P3HJ6dxjZ3PDkoeVWk4IIUT9SZ0QPXr0CN7e3txjPT09oRFnTZo0QWJionyjI0TJSqv9KeLrYIrhAS5C20cEuFDtECGEVGJSN5l9+PBBqM/Q27dvhbYLBAKh7YRURtLW/kQGeyDIy5pGmRFCSBl44MHWRJ/7v7qSOiGys7PD7du3UbduXbHbb968CTs7O7kFRogqFNX+FG82k1T74+tgSokQIYSUQV9HExciv1J1GGWSOiHq0KEDpk+fjo4dO4qMJMvJycGsWbPQsWNHuQdIiLJR7Q8hhFQ/Us9U/ebNGzRs2BA6OjoYNWoU3N3dAQD379/HqlWrUFBQgLi4OFhZWSk0YGWimaoJIYSQykehM1VbWVnh4sWLGDFiBCIjI1GUR/F4PLRr1w6rV6+uUskQIYQQQirucz4fPddeAgDsHtYMetqKX7qjPGSaqdrZ2RnHjh1DWloaHj16BABwc3NDrVq1FBIcIYQQQio3AWO4+SKD+7+6KtfSHbVq1UKTJk3kHQshhBBCiEpIPQ8RIYQQQkhVRQkRIYQQQqo9SogIIYQQUu1RQkQIIYSQaq9cnaoJIYQQQqRVy1BH1SGUiRIiQgghhCiMgY4WbvzUTtVhlImazAghhBBS7VFCRAghhJBqjxIiQgghhCjM53w+eq29hF5rL+FzPl/V4UhEfYgIIYQQojACxvC/pDTu/+qKEiKicHHJ6Uh6lwVnc0P4OpiqOhxCCCFEBCVERKEWHr2L6DNPuMfDA1wQGeyhwogIIYQQUdSHiChMXHK6UDIEANFnniAuOV1FERFCCCHiUUJEFCbpXZZM5YQQQoiqUEJEFMbZ3FCmckIIIURVKCEiCuPrYIrhAS5CZSMCXKhjNSGEVDP62prQ19ZUdRil4jGmxmPgVCwzMxPGxsbIyMiAkZGRqsOptGiUGSGEEGUqz+c3jTIjCufrYEqJECGEELVGTWaEEEIIqfYoISKEEEKIwnzO5yNs0xWEbbpCS3cQQgghpHoSMIbY+2+5/6uralFD1LVrV5iamuLbb79VdSiEEEIIUUPVIiEaM2YMtm7dquowCCGEEKKmqkVC1Lp1a9SsWVPVYRBCCCFETak8ITp79ixCQkJgY2MDHo+HAwcOiOwTFRUFJycn6OnpoWnTprhy5YryAyWEEEJIlaXyhCgrKwsNGjRAVFSU2O27du3C+PHjMWPGDNy4cQMNGjRAUFAQUlNTuX0aNmyI+vXri/y8evVKWU+DEEIIIZWYykeZBQcHIzg4WOL2pUuXYujQoQgLCwMAREdH4/Dhw9i4cSMiIyMBAPHx8XKJJTc3F7m5udzjjIwMAIUzXhJCCCFEdtl5BRDkZgMo/Dwt0FF86lH0uS3LYhwqT4hKk5eXh+vXr2PKlClcmYaGBgIDA3Hp0iW5X2/BggWYNWuWSLm9vb3cr0UIIYRUN7WXKfd6Hz9+hLGxsVT7qnVC9O7dO/D5fFhZWQmVW1lZ4d69e1KfJzAwEAkJCcjKyoKdnR327NmDZs2aiew3ZcoUjB8/nnssEAiQlpYGMzMz8Hi88j8RMTIzM2Fvb4/nz5/TOmkKRPdZOeg+Kw/da+Wg+6wcirrPjDF8/PgRNjY2Uh+j1gmRvJw6dUqq/XR1daGrqytUZmJiooCI/mNkZER/bEpA91k56D4rD91r5aD7rByKuM/S1gwVUXmn6tKYm5tDU1MTb968ESp/8+YNrK2tVRQVIYQQQqoatU6IdHR04Ofnh5iYGK5MIBAgJiZGbJMXIYQQQkh5qLzJ7NOnT3j06BH3OCkpCfHx8ahVqxYcHBwwfvx4hIaGwt/fH02aNMGyZcuQlZXFjTqrrHR1dTFjxgyRJjoiX3SflYPus/LQvVYOus/KoU73mcdkGZOmAKdPn0abNm1EykNDQ7F582YAwKpVq7B48WKkpKSgYcOGWLFiBZo2barkSAkhhBBSVak8ISL/b+/Ow5q60j+Af8OSAEpYZYkiURwFlUXFBZCfG5W60DpOKypDsaIdUXEerVZxKY4balulteqMiKCWzbp0XJDBalNE6UiVWGQJEFDHCi4URxSV7f394cMdLwRNKIuR83mePA8599xz3vvmhBzuPZcwDMMwDNPRXus1RAzDMAzDMO2BTYgYhmEYhun02ISIYRiGYZhOj02I2tDOnTshlUphYGCA4cOH49KlSy+t/+2338LR0REGBgZwdnZGcnJyO0Wq3TTJc1RUFLy9vWFmZgYzMzP4+Pi88nVhntN0PDdITEyEQCDAlClT2jbAN4SmeX7w4AEWLFgAW1tbiEQi9O3bl/3uUJOmuY6MjES/fv1gaGgIOzs7LF68GE+fPm2naLVPWloa/Pz8IJFIIBAI8N13371yH5lMhsGDB0MkEqFPnz7czVXtgpg2kZiYSEKhkPbt20c5OTk0d+5cMjU1pTt37qisf+HCBdLV1aWtW7dSbm4urV69mvT19Sk7O7udI9cumuZ55syZtHPnTsrKyqK8vDyaNWsWmZiY0K1bt9o5cu2iaZ4blJSUUPfu3cnb25vefffd9glWi2ma52fPnpG7uztNnDiR0tPTqaSkhGQyGcnl8naOXPtomuu4uDgSiUQUFxdHJSUl9K9//YtsbW1p8eLF7Ry59khOTqZVq1bR0aNHCQAdO3bspfWLi4vJyMiIlixZQrm5ubRjxw7S1dWllJSUdomXTYjayLBhw2jBggXc87q6OpJIJBQREaGy/rRp02jSpEm8suHDh9Nf/vKXNo1T22ma58Zqa2vJ2NiY9u/f31YhvhFakufa2lry9PSkvXv3UlBQEJsQqUHTPO/evZt69+5N1dXV7RXiG0PTXC9YsIDGjh3LK1uyZAl5eXm1aZxvCnUmRJ988gkNGDCAV+bv70++vr5tGNn/sEtmbaC6uhqXL1+Gj48PV6ajowMfHx9kZGSo3CcjI4NXHwB8fX2brc+0LM+NVVVVoaamBubm5m0VptZraZ7XrVsHKysrBAcHt0eYWq8leT5+/Dg8PDywYMECWFtbY+DAgdi0aRPq6uraK2yt1JJce3p64vLly9xlteLiYiQnJ2PixIntEnNn0NGfgx3+n6rfRPfv30ddXR2sra155dbW1sjPz1e5T1lZmcr6ZWVlbRantmtJnhtbvnw5JBJJkzch8z8tyXN6ejqio6Mhl8vbIcI3Q0vyXFxcjHPnziEgIADJyckoKirC/PnzUVNTg/Dw8PYIWyu1JNczZ87E/fv3MXLkSBARamtrMW/ePKxcubI9Qu4UmvscfPjwIZ48eQJDQ8M27Z+dIWI6rc2bNyMxMRHHjh2DgYFBR4fzxqisrERgYCCioqJgaWnZ0eG80err62FlZYU9e/ZgyJAh8Pf3x6pVq/D3v/+9o0N748hkMmzatAm7du3ClStXcPToUZw6dQrr16/v6NCYVsLOELUBS0tL6Orq4s6dO7zyO3fuwMbGRuU+NjY2GtVnWpbnBp9//jk2b96M77//Hi4uLm0ZptbTNM9KpRLXr1+Hn58fV1ZfXw8A0NPTg0KhgIODQ9sGrYVaMp5tbW2hr68PXV1drszJyQllZWWorq6GUChs05i1VUtyvWbNGgQGBmLOnDkAAGdnZzx+/BgfffQRVq1aBR0ddn7h92ruc1AsFrf52SGAnSFqE0KhEEOGDMHZs2e5svr6epw9exYeHh4q9/Hw8ODVB4AzZ840W59pWZ4BYOvWrVi/fj1SUlLg7u7eHqFqNU3z7OjoiOzsbMjlcu7xzjvvYMyYMZDL5bCzs2vP8LVGS8azl5cXioqKuAknABQUFMDW1pZNhl6iJbmuqqpqMulpmIgS+wasVtHhn4PtsnS7E0pMTCSRSESxsbGUm5tLH330EZmamlJZWRkREQUGBtKKFSu4+hcuXCA9PT36/PPPKS8vj8LDw9lt92rQNM+bN28moVBIhw8fptLSUu5RWVnZUYegFTTNc2PsLjP1aJrnmzdvkrGxMS1cuJAUCgWdPHmSrKysaMOGDR11CFpD01yHh4eTsbExJSQkUHFxMaWmppKDgwNNmzatow7htVdZWUlZWVmUlZVFAGjbtm2UlZVFN27cICKiFStWUGBgIFe/4bb7ZcuWUV5eHu3cuZPddv+m2LFjB/Xs2ZOEQiENGzaMfvrpJ27bqFGjKCgoiFf/0KFD1LdvXxIKhTRgwAA6depUO0esnTTJs729PQFo8ggPD2//wLWMpuP5RWxCpD5N83zx4kUaPnw4iUQi6t27N23cuJFqa2vbOWrtpEmua2pqaO3ateTg4EAGBgZkZ2dH8+fPp4qKivYPXEv88MMPKn/fNuQ1KCiIRo0a1WQfNzc3EgqF1Lt3b4qJiWm3eNm33TMMwzAM0+mxNUQMwzAMw3R6bELEMAzDMEynxyZEDMMwDMN0emxCxDAMwzBMp8cmRAzDMAzDdHpsQsQwDMMwTKfHJkQMwzAMw3R6bELEdHqxsbEwNTVVu75MJoNAIMCDBw9aPZYLFy7A2dkZ+vr6mDJlSqu3/yaJjo7G+PHj1aq7du1auLm5tW1ArayjxsL169chEAggl8vbrc/XgVQqRWRkZIv2ra6uhlQqxc8//9y6QTHtik2IGK2TkZEBXV1dTJo0SeN9Vf3S8/f3R0FBgdpteHp6orS0FCYmJgA0n1C9zJIlS+Dm5oaSkhLExsa2SptvoqdPn2LNmjUIDw/v6FDaTHuMhVmzZjWZbNnZ2aG0tBQDBw5skz47Wmu+XxsIhUIsXboUy5cvb9V2mfbFJkSM1omOjkZoaCjS0tJw+/bt392eoaEhrKys1K4vFAphY2MDgUDwu/tuTKlUYuzYsejRo4fKX9pEhNra2lbvV9scPnwYYrEYXl5eHRpHTU1Nm7X9qrHQVnR1dWFjYwM9Pb126/NNEBAQgPT0dOTk5HR0KEwLsQkRo1UePXqEpKQkhISEYNKkSSr/cj5x4gSGDh0KAwMDWFpa4o9//CMAYPTo0bhx4wYWL14MgUDATWhe/IuxoKAAAoEA+fn5vDa3b98OBwcHAPxLZjKZDB9++CH++9//cm2uXbsW69atU/kXtpubG9asWdOkvOEyRXl5OWbPng2BQIDY2Fiur9OnT2PIkCEQiURIT09HfX09IiIi0KtXLxgaGsLV1RWHDx/mtZmcnIy+ffvC0NAQY8aMQWxsLO9Sn6rLSJGRkZBKpbyyvXv3wsnJCQYGBnB0dMSuXbuaxH306FGMGTMGRkZGcHV1RUZGBq+NCxcuYPTo0TAyMoKZmRl8fX1RUVGBAwcOwMLCAs+ePePVnzJlCgIDA5vkqUFiYiL8/Px4ZTKZDMOGDUOXLl1gamoKLy8v3Lhxg1fn4MGDkEqlMDExwfTp01FZWcltS0lJwciRI2FqagoLCwtMnjwZSqWyybEmJSVh1KhRMDAwQFxc3CtzpMqzZ8+waNEiWFlZwcDAACNHjkRmZiavn8ZjQZWDBw/C3d0dxsbGsLGxwcyZM3H37l1enZycHEyePBlisRjGxsbw9vaGUqnE2rVrsX//fvzzn//kxq5MJuNdMquvr0ePHj2we/duXptZWVnQ0dHh8vvgwQPMmTMH3bp1g1gsxtixY3H16tVmj7+hj0OHDsHb2xuGhoYYOnQoCgoKkJmZCXd3d3Tt2hUTJkzAvXv3uP3q6+uxbt069OjRAyKRCG5ubkhJSWnSbnPjsbn3a4OqqirMnj0bxsbG6NmzJ/bs2cNtq66uxsKFC2FrawsDAwPY29sjIiKC225mZgYvLy8kJiY2e9zMa67dvjWNYVpBdHQ0ubu7ExHRiRMnyMHBgerr67ntJ0+eJF1dXfr0008pNzeX5HI5bdq0iYiIysvLqUePHrRu3TruW+6JiGJiYsjExIRrw93dnVavXs3rd8iQIVxZwxcWVlRU0LNnzygyMpLEYjHXZmVlJf3nP/8hHR0dunTpEtfGlStXSCAQkFKpbHJctbW1VFpaSmKxmCIjI6m0tJSqqqq4vlxcXCg1NZWKioqovLycNmzYQI6OjpSSkkJKpZJiYmJIJBKRTCYjouffgi4SiWjJkiWUn59P33zzDVlbW3NxEz3/9m5XV1deHNu3byd7e3vu+TfffEO2trZ05MgRKi4upiNHjpC5uTnFxsYSEVFJSQkBIEdHRzp58iQpFAp67733yN7enmpqaoiIKCsri0QiEYWEhJBcLqdr167Rjh076N69e1RVVUUmJiZ06NAhrs87d+6Qnp4enTt3rtlxYGJiQomJidzzmpoaMjExoaVLl1JRURHl5uZSbGws963a4eHh1LVrV5o6dSplZ2dTWloa2djY0MqVK7k2Dh8+TEeOHKHCwkLKysoiPz8/cnZ2prq6Ot6xSqVSLh+3b99+ZY5UWbRoEUkkEkpOTqacnBwKCgoiMzMzKi8vb3YsqBIdHU3JycmkVCopIyODPDw8aMKECdz2W7dukbm5OU2dOpUyMzNJoVDQvn37KD8/nyorK2natGn09ttvc2P32bNn3HFmZWUREdHSpUtp5MiRvH4//vhjXpmPjw/5+flRZmYmFRQU0Mcff0wWFhZUXl6uMu4Xx01KSgrl5ubSiBEjaMiQITR69GhKT0+nK1euUJ8+fWjevHncftu2bSOxWEwJCQmUn59Pn3zyCenr61NBQUGTdlWNx+ber0TPv/jZ3Nycdu7cSYWFhRQREUE6OjqUn59PRESfffYZ2dnZUVpaGl2/fp3Onz9P8fHxvONavnx5ky8rZbQHmxAxWsXT05MiIyOJ6PmHoKWlJf3www/cdg8PDwoICGh2f3t7e9q+fTuvrPGEaPv27eTg4MA9VygUBIDy8vKIiD8hUrV/gwkTJlBISAj3PDQ0lEaPHv3S4zMxMeF9u3NDX9999x1X9vTpUzIyMqKLFy/y9g0ODqYZM2YQEVFYWBj179+ft3358uUaT4gcHBya/NJfv349eXh4ENH/PoD27t3Lbc/JyeHla8aMGeTl5dXsMYeEhPA+xL/44gvq3bs3b6L7ooqKCgJAaWlpXFl5eTkB4CaEjYWHh5ORkRE9fPiQK1u2bBkNHz682bju3btHACg7O5t3rA3jr8GrctTYo0ePSF9fn+Li4riy6upqkkgktHXrVq6s8VhQR2ZmJgHgPuTDwsKoV69eVF1drbJ+UFAQvfvuu7yyxhOirKwsEggE3OSyrq6OunfvTrt37yYiovPnz5NYLKanT5/y2nFwcKB//OMfKvtVNW4SEhIIAJ09e5Yri4iIoH79+nHPJRIJbdy4kdfW0KFDaf78+c2223g8Nvd+tbe3pz//+c/c8/r6erKysuKOMzQ0lMaOHdvsuCQi+vLLL0kqlTa7nXm9sUtmjNZQKBS4dOkSZsyYAQDQ09ODv78/oqOjuTpyuRzjxo37Xf1Mnz4d169fx08//QQAiIuLw+DBg+Ho6KhRO3PnzkVCQgKePn2K6upqxMfHY/bs2S2Kyd3dnfu5qKgIVVVVeOutt9C1a1fuceDAAe4ST15eHoYPH85rw8PDQ6M+Hz9+DKVSieDgYF4/GzZs4F1KAgAXFxfuZ1tbWwDgLt286jWZO3cuUlNT8euvvwJ4fglz1qxZza7RevLkCQDAwMCAKzM3N8esWbPg6+sLPz8/fPnllygtLeXtJ5VKYWxszIvzxctLhYWFmDFjBnr37g2xWMxdOrx58yavnRdfC01y1ECpVKKmpoa3/klfXx/Dhg1DXl5es3lS5fLly/Dz80PPnj1hbGyMUaNG8WKWy+Xw9vaGvr6+Ru2+yM3NDU5OToiPjwcA/Pjjj7h79y7ef/99AMDVq1fx6NEjWFhY8HJQUlLSbA4avDhurK2tAQDOzs68sobX6OHDh7h9+3aTdWNeXl5N8vay8ahuPAKBADY2Ntx+s2bNglwuR79+/bBo0SKkpqY22d/Q0BBVVVWv7Id5PbFVc4zWiI6ORm1tLSQSCVdGRBCJRPj6669hYmICQ0PD392PjY0Nxo4di/j4eIwYMQLx8fEICQnRuB0/Pz+IRCIcO3YMQqEQNTU1eO+991oUU5cuXbifHz16BAA4deoUunfvzqsnEonUblNHRwdExCt7cZFwQz9RUVFNJle6urq85y9+4DZMZOrr6wHgla/JoEGD4OrqigMHDmD8+PHIycnBqVOnmq1vYWEBgUCAiooKXnlMTAwWLVqElJQUJCUlYfXq1Thz5gxGjBjRJMaGOBtiBJ6/Xvb29oiKioJEIkF9fT0GDhyI6upq3n6qXgt1ctTaHj9+DF9fX/j6+iIuLg7dunXDzZs34evry8XcGu8H4PmC4fj4eKxYsQLx8fF4++23YWFhAeB5DmxtbSGTyZrs96rF4KrGTeOyF18jdb1sPKq7X+P+Bw8ejJKSEpw+fRrff/89pk2bBh8fH97avd9++w3dunXTOF7m9cDOEDFaoba2FgcOHMAXX3wBuVzOPa5evQqJRIKEhAQAz//CO3v2bLPtCIVC1NXVvbK/gIAAJCUlISMjA8XFxZg+fbrGberp6SEoKAgxMTGIiYnB9OnTW+UDqn///hCJRLh58yb69OnDe9jZ2QEAnJyccOnSJd5+DWe8GnTr1g1lZWW8SdGL/3vG2toaEokExcXFTfrp1auX2vG+6jUBgDlz5iA2NhYxMTHw8fHhjkMVoVCI/v37Izc3t8m2QYMGISwsDBcvXsTAgQO5sxqvUl5eDoVCgdWrV2PcuHFwcnJqMuFSpSU5cnBwgFAoxIULF7iympoaZGZmon///mrFCwD5+fkoLy/H5s2b4e3tDUdHxyZnQVxcXHD+/Plm74ZT9/0wc+ZMXLt2DZcvX8bhw4cREBDAbRs8eDDKysqgp6fXJAeWlpZqH8+riMViSCQSXt6A5wv2NcmbusfcXAz+/v6IiopCUlISjhw5gt9++43bfu3aNQwaNKhFbTMdj02IGK1w8uRJVFRUIDg4GAMHDuQ9/vSnP3GXzcLDw5GQkIDw8HDk5eUhOzsbW7Zs4dqRSqVIS0vDr7/+ivv37zfb39SpU1FZWYmQkBCMGTOGd1aqMalUikePHuHs2bO4f/8+75T5nDlzcO7cOaSkpLT4clljxsbGWLp0KRYvXoz9+/dDqVTiypUr2LFjB/bv3w8AmDdvHgoLC7Fs2TIoFArEx8c3uVNp9OjRuHfvHrZu3QqlUomdO3fi9OnTvDp/+9vfEBERga+++goFBQXIzs5GTEwMtm3bpna8YWFhyMzMxPz58/HLL78gPz8fu3fv5uV/5syZuHXrFqKiotTKk6+vL9LT07nnJSUlCAsLQ0ZGBm7cuIHU1FQUFhbCyclJrRjNzMxgYWGBPXv2oKioCOfOncOSJUvU2lfTHHXp0gUhISFYtmwZUlJSkJubi7lz56KqqgrBwcFq9QkAPXv2hFAoxI4dO1BcXIzjx49j/fr1vDoLFy7Ew4cPMX36dPz8888oLCzEwYMHoVAoADwfu7/88gsUCgXu37/f7MRJKpXC09MTwcHBqKurwzvvvMNt8/HxgYeHB6ZMmYLU1FRcv34dFy9exKpVq1r9HxUuW7YMW7ZsQVJSEhQKBVasWAG5XI6//vWvarfxsvfry2zbtg0JCQnIz89HQUEBvv32W9jY2PDOgp0/f17tfxbKvIY6eA0Tw6hl8uTJNHHiRJXb/v3vfxMAunr1KhERHTlyhNzc3EgoFJKlpSVNnTqVq5uRkUEuLi4kEomoYfg3t8hy2rRpBID27dvHK2+8qJqIaN68eWRhYUEAKDw8nFff29ubBgwYoNZxNreo+sW+iJ4v+IyMjKR+/fqRvr4+devWjXx9fenHH3/k6pw4cYL69OlDIpGIvL29ad++fU3a2r17N9nZ2VGXLl3ogw8+oI0bN/IWVRMRxcXFcfk0MzOj//u//6OjR48SUdMFuET/W/T84mJ3mUxGnp6eJBKJyNTUlHx9fZscU2BgIJmbmzdZnKtKTk4OGRoa0oMHD4iIqKysjKZMmUK2trYkFArJ3t6ePv30U+4OMXUWkJ85c4acnJxIJBKRi4sLyWQyAkDHjh1r9ljVyZEqT548odDQULK0tCSRSEReXl68OxKJ1FtUHR8fT1KplEQiEXl4eNDx48ebxHj16lUaP348GRkZkbGxMXl7e3N3Ot69e5feeust6tq1K/eaNXecu3btIgD0wQcfNInj4cOHFBoaShKJhPT19cnOzo4CAgLo5s2bKuNW1Yeqsd74vVlXV0dr166l7t27k76+Prm6utLp06df2q6q8ajq/arqhgtXV1du+549e8jNzY26dOlCYrGYxo0bR1euXOHqXrx4kUxNTZu9I5B5/QmIGi0iYBim1RAR/vCHP2D+/Plqn3FoKzKZDGPGjEFFRUW7/qM/dY0bNw4DBgzAV199pVb9999/H4MHD0ZYWFgbR8Ywr+bv7w9XV1esXLmyo0NhWohdMmOYNnLv3j18/fXXKCsrw4cfftjR4by2KioqcOzYMchkMixYsEDt/T777DN07dq1DSNjGPVUV1fD2dkZixcv7uhQmN+B3WXGMG3EysoKlpaW2LNnD8zMzDo6nNfWoEGDUFFRgS1btqBfv35q7yeVShEaGtqGkTGMeoRCIVavXt3RYTC/E7tkxjAMwzBMp8cumTEMwzAM0+mxCRHDMAzDMJ0emxAxDMMwDNPpsQkRwzAMwzCdHpsQMQzDMAzT6bEJEcMwDMMwnR6bEDEMwzAM0+mxCRHDMAzDMJ0emxAxDMMwDNPp/T+XUK8mgfMhjwAAAABJRU5ErkJggg==",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure()\n",
|
||
"for name, g in dfc[~dfc['cluster_kmeans'].isin([2.0, 3.0])].groupby(\"cluster\"):\n",
|
||
" plt.scatter(g[\"frequency\"], g[\"rel_intensity_total\"], s=10, label=name)\n",
|
||
"\n",
|
||
"plt.yscale(\"log\")\n",
|
||
"plt.axvline(thr_freq, linestyle=\"--\")\n",
|
||
"plt.axhline(thr_int, linestyle=\"--\")\n",
|
||
"plt.xlabel(\"Activity frequency (share of active months)\")\n",
|
||
"plt.ylabel(\"Gross flow / mean AUM (quantity) [log scale]\")\n",
|
||
"plt.title(\"2D behavioral segmentation: relative intensity vs frequency\")\n",
|
||
"plt.legend(markerscale=2)\n",
|
||
"plt.ylim(0.1,100)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 201,
|
||
"id": "41f5ffd2-1c90-48a6-be8f-adcce2d42185",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure()\n",
|
||
"for name, g in dfc[dfc['cluster_kmeans']==2.0].groupby(\"cluster\"): \n",
|
||
" plt.scatter(g[\"frequency\"], g[\"rel_intensity_total\"], s=10, label=name)\n",
|
||
"\n",
|
||
"plt.yscale(\"log\")\n",
|
||
"plt.axvline(thr_freq, linestyle=\"--\")\n",
|
||
"plt.axhline(thr_int, linestyle=\"--\")\n",
|
||
"plt.xlabel(\"Activity frequency (share of active months)\")\n",
|
||
"plt.ylabel(\"Gross flow / mean AUM (quantity) [log scale]\")\n",
|
||
"plt.title(\"2D behavioral segmentation: relative intensity vs frequency\")\n",
|
||
"plt.legend(markerscale=2)\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"\n",
|
||
"#\"log_aum_qty_mean\", # size (log)\n",
|
||
" # \"log_gross_flow_qty_mean\", # activity intensity (log)\n",
|
||
" # \"frequency\", # activity frequency\n",
|
||
" # \"rel_intensity_total\", # turnover proxy\n",
|
||
" # \"net_flow_qty_vol\", # volatility of net flows\n",
|
||
" # \"n_tx_total\", "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 206,
|
||
"id": "ba298e96-5919-44d1-91e3-67d38041349f",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig, axes = plt.subplots(1, 2, figsize=(12,5), sharey=True)\n",
|
||
"\n",
|
||
"# --- Graphique 1 ---\n",
|
||
"for name, g in dfc[~dfc['cluster_kmeans'].isin([2.0, 3.0])].groupby(\"cluster\"):\n",
|
||
" axes[0].scatter(g[\"frequency\"], g[\"rel_intensity_total\"], s=10, label=name)\n",
|
||
"\n",
|
||
"axes[0].set_yscale(\"log\")\n",
|
||
"axes[0].axvline(thr_freq, linestyle=\"--\")\n",
|
||
"axes[0].axhline(thr_int, linestyle=\"--\")\n",
|
||
"axes[0].set_xlabel(\"Activity frequency\")\n",
|
||
"axes[0].set_ylabel(\"Gross flow / mean AUM\")\n",
|
||
"axes[0].set_title(\"2D behavioral segmentation: relative intensity vs frequency\")\n",
|
||
"axes[0].set_ylim(0.1,100)\n",
|
||
"axes[0].legend(markerscale=2)\n",
|
||
"\n",
|
||
"# --- Graphique 2 ---\n",
|
||
"for name, g in dfc[dfc['cluster_kmeans']==2.0].groupby(\"cluster\"):\n",
|
||
" axes[1].scatter(\n",
|
||
" g[\"frequency\"], g[\"rel_intensity_total\"],\n",
|
||
" s=10,\n",
|
||
" label=name,\n",
|
||
" color=\"red\" # 👈 ici\n",
|
||
" )\n",
|
||
"\n",
|
||
"axes[1].set_yscale(\"log\")\n",
|
||
"axes[1].axvline(thr_freq, linestyle=\"--\")\n",
|
||
"axes[1].axhline(thr_int, linestyle=\"--\")\n",
|
||
"axes[1].set_xlabel(\"Activity frequency\")\n",
|
||
"axes[1].set_title(\"2D behavioral segmentation: relative intensity vs frequency\")\n",
|
||
"axes[1].legend(markerscale=2)\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 129,
|
||
"id": "64176145-ee5b-4ea8-98ed-c155146dd2f6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Registrar Account - ID</th>\n",
|
||
" <th>month</th>\n",
|
||
" <th>aum_qty</th>\n",
|
||
" <th>net_flow_qty</th>\n",
|
||
" <th>gross_flow_qty</th>\n",
|
||
" <th>n_tx</th>\n",
|
||
" <th>active_month</th>\n",
|
||
" <th>rel_intensity_m</th>\n",
|
||
" <th>netflow_to_aum_m</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>18872</td>\n",
|
||
" <td>2015-01-31</td>\n",
|
||
" <td>179864.637</td>\n",
|
||
" <td>-1524.010</td>\n",
|
||
" <td>15230.010</td>\n",
|
||
" <td>32</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.084675</td>\n",
|
||
" <td>-0.008473</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>18872</td>\n",
|
||
" <td>2015-02-28</td>\n",
|
||
" <td>186761.736</td>\n",
|
||
" <td>7247.100</td>\n",
|
||
" <td>18571.880</td>\n",
|
||
" <td>38</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.099442</td>\n",
|
||
" <td>0.038804</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>18872</td>\n",
|
||
" <td>2015-03-31</td>\n",
|
||
" <td>190357.718</td>\n",
|
||
" <td>3655.380</td>\n",
|
||
" <td>9754.040</td>\n",
|
||
" <td>47</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.051241</td>\n",
|
||
" <td>0.019203</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>18872</td>\n",
|
||
" <td>2015-04-30</td>\n",
|
||
" <td>191429.324</td>\n",
|
||
" <td>-218.394</td>\n",
|
||
" <td>12840.950</td>\n",
|
||
" <td>39</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.067079</td>\n",
|
||
" <td>-0.001141</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>18872</td>\n",
|
||
" <td>2015-05-31</td>\n",
|
||
" <td>189056.475</td>\n",
|
||
" <td>-4782.849</td>\n",
|
||
" <td>6332.849</td>\n",
|
||
" <td>24</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.033497</td>\n",
|
||
" <td>-0.025299</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>33967</th>\n",
|
||
" <td>422874</td>\n",
|
||
" <td>2025-06-30</td>\n",
|
||
" <td>55540.077</td>\n",
|
||
" <td>1303.393</td>\n",
|
||
" <td>1303.393</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.023468</td>\n",
|
||
" <td>0.023468</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>33968</th>\n",
|
||
" <td>422874</td>\n",
|
||
" <td>2025-07-31</td>\n",
|
||
" <td>55179.460</td>\n",
|
||
" <td>-1013.363</td>\n",
|
||
" <td>2066.489</td>\n",
|
||
" <td>9</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.037450</td>\n",
|
||
" <td>-0.018365</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>33969</th>\n",
|
||
" <td>422874</td>\n",
|
||
" <td>2025-08-31</td>\n",
|
||
" <td>56928.472</td>\n",
|
||
" <td>1749.012</td>\n",
|
||
" <td>2010.564</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.035317</td>\n",
|
||
" <td>0.030723</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>33970</th>\n",
|
||
" <td>422874</td>\n",
|
||
" <td>2025-09-30</td>\n",
|
||
" <td>57042.358</td>\n",
|
||
" <td>113.886</td>\n",
|
||
" <td>3895.248</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.068287</td>\n",
|
||
" <td>0.001997</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>33971</th>\n",
|
||
" <td>422874</td>\n",
|
||
" <td>2025-10-31</td>\n",
|
||
" <td>56522.708</td>\n",
|
||
" <td>-555.680</td>\n",
|
||
" <td>1619.142</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.028646</td>\n",
|
||
" <td>-0.009831</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>33972 rows × 9 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Registrar Account - ID month aum_qty net_flow_qty \\\n",
|
||
"0 18872 2015-01-31 179864.637 -1524.010 \n",
|
||
"1 18872 2015-02-28 186761.736 7247.100 \n",
|
||
"2 18872 2015-03-31 190357.718 3655.380 \n",
|
||
"3 18872 2015-04-30 191429.324 -218.394 \n",
|
||
"4 18872 2015-05-31 189056.475 -4782.849 \n",
|
||
"... ... ... ... ... \n",
|
||
"33967 422874 2025-06-30 55540.077 1303.393 \n",
|
||
"33968 422874 2025-07-31 55179.460 -1013.363 \n",
|
||
"33969 422874 2025-08-31 56928.472 1749.012 \n",
|
||
"33970 422874 2025-09-30 57042.358 113.886 \n",
|
||
"33971 422874 2025-10-31 56522.708 -555.680 \n",
|
||
"\n",
|
||
" gross_flow_qty n_tx active_month rel_intensity_m netflow_to_aum_m \n",
|
||
"0 15230.010 32 1 0.084675 -0.008473 \n",
|
||
"1 18571.880 38 1 0.099442 0.038804 \n",
|
||
"2 9754.040 47 1 0.051241 0.019203 \n",
|
||
"3 12840.950 39 1 0.067079 -0.001141 \n",
|
||
"4 6332.849 24 1 0.033497 -0.025299 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"33967 1303.393 5 1 0.023468 0.023468 \n",
|
||
"33968 2066.489 9 1 0.037450 -0.018365 \n",
|
||
"33969 2010.564 3 1 0.035317 0.030723 \n",
|
||
"33970 3895.248 7 1 0.068287 0.001997 \n",
|
||
"33971 1619.142 6 1 0.028646 -0.009831 \n",
|
||
"\n",
|
||
"[33972 rows x 9 columns]"
|
||
]
|
||
},
|
||
"execution_count": 129,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Analyse temporelle \n",
|
||
"df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 130,
|
||
"id": "17787765-a4bb-4e23-80f0-92c91d16f1cc",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Registrar Account - ID</th>\n",
|
||
" <th>month</th>\n",
|
||
" <th>aum_qty</th>\n",
|
||
" <th>net_flow_qty</th>\n",
|
||
" <th>gross_flow_qty</th>\n",
|
||
" <th>n_tx</th>\n",
|
||
" <th>active_month</th>\n",
|
||
" <th>rel_intensity_m</th>\n",
|
||
" <th>netflow_to_aum_m</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>18872</td>\n",
|
||
" <td>2015-01-31</td>\n",
|
||
" <td>179864.637</td>\n",
|
||
" <td>-1524.010</td>\n",
|
||
" <td>15230.010</td>\n",
|
||
" <td>32</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>8.467484e-02</td>\n",
|
||
" <td>-8.473094e-03</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>27192</th>\n",
|
||
" <td>365377</td>\n",
|
||
" <td>2015-01-31</td>\n",
|
||
" <td>0.000</td>\n",
|
||
" <td>3640.020</td>\n",
|
||
" <td>7687.660</td>\n",
|
||
" <td>63</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>7.687660e+12</td>\n",
|
||
" <td>3.640020e+12</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>650</th>\n",
|
||
" <td>200000201</td>\n",
|
||
" <td>2015-01-31</td>\n",
|
||
" <td>17072.819</td>\n",
|
||
" <td>-494.780</td>\n",
|
||
" <td>800.440</td>\n",
|
||
" <td>20</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>4.688388e-02</td>\n",
|
||
" <td>-2.898057e-02</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25893</th>\n",
|
||
" <td>365172</td>\n",
|
||
" <td>2015-01-31</td>\n",
|
||
" <td>67707.000</td>\n",
|
||
" <td>11917.000</td>\n",
|
||
" <td>11957.000</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1.765992e-01</td>\n",
|
||
" <td>1.760084e-01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>33712</th>\n",
|
||
" <td>422691</td>\n",
|
||
" <td>2015-01-31</td>\n",
|
||
" <td>60705.316</td>\n",
|
||
" <td>3724.160</td>\n",
|
||
" <td>6372.040</td>\n",
|
||
" <td>24</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1.049668e-01</td>\n",
|
||
" <td>6.134817e-02</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>26152</th>\n",
|
||
" <td>365236</td>\n",
|
||
" <td>2025-10-31</td>\n",
|
||
" <td>74195.145</td>\n",
|
||
" <td>-29100.206</td>\n",
|
||
" <td>32046.852</td>\n",
|
||
" <td>98</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>4.319265e-01</td>\n",
|
||
" <td>-3.922117e-01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13522</th>\n",
|
||
" <td>200127403</td>\n",
|
||
" <td>2025-10-31</td>\n",
|
||
" <td>17711.000</td>\n",
|
||
" <td>197.000</td>\n",
|
||
" <td>197.000</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1.112303e-02</td>\n",
|
||
" <td>1.112303e-02</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13593</th>\n",
|
||
" <td>200127404</td>\n",
|
||
" <td>2025-10-31</td>\n",
|
||
" <td>44881.000</td>\n",
|
||
" <td>3099.500</td>\n",
|
||
" <td>3539.500</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>7.886411e-02</td>\n",
|
||
" <td>6.906040e-02</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18584</th>\n",
|
||
" <td>200128363</td>\n",
|
||
" <td>2025-10-31</td>\n",
|
||
" <td>7491447.864</td>\n",
|
||
" <td>0.000</td>\n",
|
||
" <td>0.000</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" <td>0.000000e+00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>33971</th>\n",
|
||
" <td>422874</td>\n",
|
||
" <td>2025-10-31</td>\n",
|
||
" <td>56522.708</td>\n",
|
||
" <td>-555.680</td>\n",
|
||
" <td>1619.142</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2.864587e-02</td>\n",
|
||
" <td>-9.831093e-03</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>33972 rows × 9 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Registrar Account - ID month aum_qty net_flow_qty \\\n",
|
||
"0 18872 2015-01-31 179864.637 -1524.010 \n",
|
||
"27192 365377 2015-01-31 0.000 3640.020 \n",
|
||
"650 200000201 2015-01-31 17072.819 -494.780 \n",
|
||
"25893 365172 2015-01-31 67707.000 11917.000 \n",
|
||
"33712 422691 2015-01-31 60705.316 3724.160 \n",
|
||
"... ... ... ... ... \n",
|
||
"26152 365236 2025-10-31 74195.145 -29100.206 \n",
|
||
"13522 200127403 2025-10-31 17711.000 197.000 \n",
|
||
"13593 200127404 2025-10-31 44881.000 3099.500 \n",
|
||
"18584 200128363 2025-10-31 7491447.864 0.000 \n",
|
||
"33971 422874 2025-10-31 56522.708 -555.680 \n",
|
||
"\n",
|
||
" gross_flow_qty n_tx active_month rel_intensity_m netflow_to_aum_m \n",
|
||
"0 15230.010 32 1 8.467484e-02 -8.473094e-03 \n",
|
||
"27192 7687.660 63 1 7.687660e+12 3.640020e+12 \n",
|
||
"650 800.440 20 1 4.688388e-02 -2.898057e-02 \n",
|
||
"25893 11957.000 4 1 1.765992e-01 1.760084e-01 \n",
|
||
"33712 6372.040 24 1 1.049668e-01 6.134817e-02 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"26152 32046.852 98 1 4.319265e-01 -3.922117e-01 \n",
|
||
"13522 197.000 4 1 1.112303e-02 1.112303e-02 \n",
|
||
"13593 3539.500 15 1 7.886411e-02 6.906040e-02 \n",
|
||
"18584 0.000 0 0 0.000000e+00 0.000000e+00 \n",
|
||
"33971 1619.142 6 1 2.864587e-02 -9.831093e-03 \n",
|
||
"\n",
|
||
"[33972 rows x 9 columns]"
|
||
]
|
||
},
|
||
"execution_count": 130,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_month.sort_values(by=\"month\", ascending=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 150,
|
||
"id": "0ff98dd4-cd21-443a-a603-6dbdee066b87",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Filtered clients: (154, 18)\n",
|
||
"Filtered clients: (355, 18)\n",
|
||
"Filtered clients: (421, 18)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"# 1. Définir les fenêtres temporelles (ex: 3 ans glissants)\n",
|
||
"windows = [\n",
|
||
" (\"2016-10-31\", \"2019-10-31\"),\n",
|
||
" (\"2019-10-31\", \"2022-10-31\"),\n",
|
||
" (\"2022-10-31\", \"2025-10-31\")\n",
|
||
"]\n",
|
||
"\n",
|
||
"stability_results = []\n",
|
||
"\n",
|
||
"for start, end in windows:\n",
|
||
" # FILTRAGE : On recalcule les variables sur la période (simulation)\n",
|
||
" # Note : Tu dois adapter cette partie à tes données brutes par date\n",
|
||
" df_period = df_month[(df_month['month'] > start) & (df_month['month'] <= end)].copy()\n",
|
||
"\n",
|
||
" eps = 1e-9 \n",
|
||
"\n",
|
||
" # 1) Active month indicator: did the client trade this month?\n",
|
||
" df_period[\"active_month\"] = (df_period[\"gross_flow_qty\"] > 0).astype(int)\n",
|
||
"\n",
|
||
" #client avec beaucoup de mois à 0 → “stable / dormant”\n",
|
||
" #client actif presque tous les mois → “rebalancer / institutionnel actif”\n",
|
||
"\n",
|
||
"\n",
|
||
" # 2) Monthly relative intensity (turnover proxy in quantity terms) : Mesurer l’intensité de trading relativement à la taille et pouvoir ocmparer client petit avec client plus gros\n",
|
||
" df_period[\"rel_intensity_m\"] = df_period[\"gross_flow_qty\"] / (df_period[\"aum_qty\"].abs() + eps)\n",
|
||
"\n",
|
||
" # 3) Monthly net flow ratio (directional change): sert a Capturer la direction de la dynamique\n",
|
||
" df_period[\"netflow_to_aum_m\"] = df_period[\"net_flow_qty\"] / (df_period[\"aum_qty\"].abs() + eps)\n",
|
||
"\n",
|
||
" # 4) Aggregate to client-level features (1 row per client)\n",
|
||
" dft_client_feat = (\n",
|
||
" df_period.groupby(ID_COL, as_index=False)\n",
|
||
" .agg(\n",
|
||
" # Coverage / activity\n",
|
||
" n_months=(\"month\", \"nunique\"),\n",
|
||
" n_active_months=(\"active_month\", \"sum\"),\n",
|
||
" flow_freq=(\"active_month\", \"mean\"),\n",
|
||
"\n",
|
||
" # Size in quantity terms\n",
|
||
" aum_qty_mean=(\"aum_qty\", \"mean\"),\n",
|
||
" aum_qty_median=(\"aum_qty\", \"median\"),\n",
|
||
"\n",
|
||
" # Flows in quantity terms\n",
|
||
" net_flow_qty_sum=(\"net_flow_qty\", \"sum\"),\n",
|
||
" gross_flow_qty_sum=(\"gross_flow_qty\", \"sum\"),\n",
|
||
" gross_flow_qty_mean=(\"gross_flow_qty\", \"mean\"),\n",
|
||
"\n",
|
||
" # Dispersion / volatility proxy\n",
|
||
" net_flow_qty_vol=(\"net_flow_qty\", \"std\"),\n",
|
||
" rel_intensity=(\"rel_intensity_m\", \"mean\"),\n",
|
||
" netflow_to_aum=(\"netflow_to_aum_m\", \"mean\"),\n",
|
||
"\n",
|
||
" # Trading frequency proxy\n",
|
||
" n_tx_total=(\"n_tx\", \"sum\"),\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
" # 5) Clean NaNs due to std on constant series\n",
|
||
" dft_client_feat[\"net_flow_qty_vol\"] = dft_client_feat[\"net_flow_qty_vol\"].fillna(0.0)\n",
|
||
"\n",
|
||
" # 6) Log transforms (useful because distributions are heavy-tailed)\n",
|
||
" dft_client_feat[\"log_aum_qty_mean\"] = np.log1p(dft_client_feat[\"aum_qty_mean\"].clip(lower=0))\n",
|
||
" dft_client_feat[\"log_gross_flow_qty_mean\"] = np.log1p(dft_client_feat[\"gross_flow_qty_mean\"].clip(lower=0))\n",
|
||
"\n",
|
||
" # 7) Global turnover proxy\n",
|
||
" dft_client_feat[\"gross_flow_to_aum\"] = dft_client_feat[\"gross_flow_qty_sum\"] / (dft_client_feat[\"aum_qty_mean\"].abs() + eps)\n",
|
||
"\n",
|
||
" dfct = dft_client_feat.copy()\n",
|
||
"\n",
|
||
" # Minimal filters (adjust if needed)\n",
|
||
" dfct = dfct[(dfct[\"n_months\"] >= 6)] # at least 6 observed months\n",
|
||
" dfct = dfct[(dfct[\"aum_qty_mean\"].abs() > 0)] # avoid zero holdings\n",
|
||
"\n",
|
||
" dfct[\"rel_intensity_total\"] = dfct[\"gross_flow_to_aum\"] # turnover proxy\n",
|
||
" dfct[\"frequency\"] = dfct[\"flow_freq\"] \n",
|
||
" \n",
|
||
" if start == \"2016-10-31\":\n",
|
||
" df_2016 = dfct.copy()\n",
|
||
" if start == \"2019-10-31\":\n",
|
||
" df_2019 = dfct.copy()\n",
|
||
" if start == \"2022-10-31\":\n",
|
||
" df_2022 = dfct.copy()\n",
|
||
" print(\"Filtered clients:\", dfct.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 180,
|
||
"id": "39bcd74a-5828-47d5-9709-a1c1eb4540f7",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"ids_2016 = set(df_2016['Registrar Account - ID'])\n",
|
||
"ids_2019 = set(df_2019['Registrar Account - ID'])\n",
|
||
"ids_2022 = set(df_2022['Registrar Account - ID'])\n",
|
||
"\n",
|
||
"common_ids = ids_2016 & ids_2019 & ids_2022\n",
|
||
"\n",
|
||
"common_id = ids_2019 & ids_2022\n",
|
||
"\n",
|
||
"df_2016_common = df_2016[df_2016['Registrar Account - ID'].isin(common_ids)].copy()\n",
|
||
"df_2019_common = df_2019[df_2019['Registrar Account - ID'].isin(common_ids)].copy()\n",
|
||
"df_2022_common = df_2022[df_2022['Registrar Account - ID'].isin(common_ids)].copy()\n",
|
||
"\n",
|
||
"df_2019_common2 = df_2019[df_2019['Registrar Account - ID'].isin(common_id)].copy()\n",
|
||
"df_2022_common2 = df_2022[df_2022['Registrar Account - ID'].isin(common_id)].copy()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 181,
|
||
"id": "c9526723-3aaf-4a7a-961a-a426e8aa7a9b",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Evolution des clusters dans le temps\n",
|
||
"\n",
|
||
"X_2016 =(df_2016_common[features]\n",
|
||
" .replace([np.inf, -np.inf], np.nan)\n",
|
||
" .dropna()\n",
|
||
" .copy())\n",
|
||
"X_2019 = (df_2019_common[features]\n",
|
||
" .replace([np.inf, -np.inf], np.nan)\n",
|
||
" .dropna()\n",
|
||
" .copy())\n",
|
||
"X_2022 = (df_2022_common[features]\n",
|
||
" .replace([np.inf, -np.inf], np.nan)\n",
|
||
" .dropna()\n",
|
||
" .copy())\n",
|
||
"\n",
|
||
"X_2019_2 = (df_2019_common2[features]\n",
|
||
" .replace([np.inf, -np.inf], np.nan)\n",
|
||
" .dropna()\n",
|
||
" .copy())\n",
|
||
"X_2022_2 = (df_2022_common2[features]\n",
|
||
" .replace([np.inf, -np.inf], np.nan)\n",
|
||
" .dropna()\n",
|
||
" .copy())\n",
|
||
"\n",
|
||
"X_2016_scaled = scaler.transform(X_2016)\n",
|
||
"X_2019_scaled = scaler.transform(X_2019)\n",
|
||
"X_2022_scaled = scaler.transform(X_2022)\n",
|
||
"\n",
|
||
"X_2019_scaled2 = scaler.transform(X_2019_2)\n",
|
||
"X_2022_scaled2 = scaler.transform(X_2022_2)\n",
|
||
"\n",
|
||
"labels_2016 = km.predict(X_2016_scaled)\n",
|
||
"labels_2019 = km.predict(X_2019_scaled)\n",
|
||
"labels_2022 = km.predict(X_2022_scaled)\n",
|
||
"\n",
|
||
"labels_2019_2 = km.predict(X_2019_scaled2)\n",
|
||
"labels_2022_2 = km.predict(X_2022_scaled2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 182,
|
||
"id": "85fde921-cad6-4528-b27f-261522304e33",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_2016_common[\"cluster_kmeans\"] = labels_2016\n",
|
||
"df_2019_common[\"cluster_kmeans\"] = labels_2019\n",
|
||
"df_2022_common[\"cluster_kmeans\"] = labels_2022\n",
|
||
"\n",
|
||
"df_2019_common2[\"cluster_kmeans\"] = labels_2019_2\n",
|
||
"df_2022_common2[\"cluster_kmeans\"] = labels_2022_2"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 208,
|
||
"id": "6a1351c8-5819-493e-80b6-7274bc6d8b03",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Définition de la correspondance entre les codes numériques et les nouveaux labels\n",
|
||
"mapping = {\n",
|
||
" 0.0: 1,\n",
|
||
" 1.0: 2,\n",
|
||
" 2.0: 3,\n",
|
||
" 3.0: 4,\n",
|
||
" 4.0: 5\n",
|
||
"}\n",
|
||
"\n",
|
||
"# Création de la nouvelle colonne 'cluster'\n",
|
||
"df_2016_common['cluster'] = df_2016_common['cluster_kmeans'].map(mapping)\n",
|
||
"df_2019_common['cluster'] = df_2019_common['cluster_kmeans'].map(mapping)\n",
|
||
"df_2022_common['cluster'] = df_2022_common['cluster_kmeans'].map(mapping)\n",
|
||
"\n",
|
||
"df_2019_common2['cluster'] = df_2019_common2['cluster_kmeans'].map(mapping)\n",
|
||
"df_2022_common2['cluster'] = df_2022_common2['cluster_kmeans'].map(mapping)\n",
|
||
"\n",
|
||
"clusters_keep = [1, 2, 3]\n",
|
||
"\n",
|
||
"df_2016_f = df_2016_common[df_2016_common[\"cluster\"].isin(clusters_keep)]\n",
|
||
"df_2019_f = df_2019_common[df_2019_common[\"cluster\"].isin(clusters_keep)]\n",
|
||
"df_2022_f = df_2022_common[df_2022_common[\"cluster\"].isin(clusters_keep)]\n",
|
||
"\n",
|
||
"df_2019_f2 = df_2019_common2[df_2019_common2[\"cluster\"].isin(clusters_keep)]\n",
|
||
"df_2022_f2 = df_2022_common2[df_2022_common2[\"cluster\"].isin(clusters_keep)]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 211,
|
||
"id": "c50a2b85-6124-4df2-8b92-590bbc4865ba",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Merge\n",
|
||
"\n",
|
||
"df_evo = (\n",
|
||
" df_2016_f[[ID_COL, \"cluster\"]]\n",
|
||
" .rename(columns={\"cluster\": \"cluster_2016\"})\n",
|
||
" .merge(\n",
|
||
" df_2019_f[[ID_COL, \"cluster\"]]\n",
|
||
" .rename(columns={\"cluster\": \"cluster_2019\"}),\n",
|
||
" on=ID_COL\n",
|
||
" )\n",
|
||
" .merge(\n",
|
||
" df_2022_f[[ID_COL, \"cluster\"]]\n",
|
||
" .rename(columns={\"cluster\": \"cluster_2022\"}),\n",
|
||
" on=ID_COL\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"df_evo2 = (\n",
|
||
" df_2019_f2[[ID_COL, \"cluster\"]]\n",
|
||
" .rename(columns={\"cluster\": \"cluster_2019\"})\n",
|
||
" .merge(\n",
|
||
" df_2022_f2[[ID_COL, \"cluster\"]]\n",
|
||
" .rename(columns={\"cluster\": \"cluster_2022\"}),\n",
|
||
" on=ID_COL\n",
|
||
" )\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 212,
|
||
"id": "9d78a9ee-8b17-457d-8b4c-8f93ad1584b3",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
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|
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" vertical-align: middle;\n",
|
||
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|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
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|
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|
||
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|
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" text-align: right;\n",
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"</style>\n",
|
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th>cluster_2019</th>\n",
|
||
" <th>1</th>\n",
|
||
" <th>2</th>\n",
|
||
" <th>3</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>cluster_2016</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>8.0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>10.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>38.0</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>82.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
"cluster_2019 1 2 3\n",
|
||
"cluster_2016 \n",
|
||
"1 8.0 2.0 10.0\n",
|
||
"2 1.0 38.0 5.0\n",
|
||
"3 NaN 5.0 82.0"
|
||
]
|
||
},
|
||
"execution_count": 212,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_evo.groupby([\"cluster_2016\", \"cluster_2019\"]).size().unstack()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 213,
|
||
"id": "9aaf2863-dbd2-43d1-861c-98ddd9354881",
|
||
"metadata": {},
|
||
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|
||
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|
||
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||
"text/html": [
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|
||
"<style scoped>\n",
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||
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|
||
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|
||
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|
||
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||
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|
||
" text-align: right;\n",
|
||
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|
||
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|
||
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th>cluster_2022</th>\n",
|
||
" <th>1</th>\n",
|
||
" <th>2</th>\n",
|
||
" <th>3</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>cluster_2019</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>34.0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>7.0</td>\n",
|
||
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|
||
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|
||
" <th>2</th>\n",
|
||
" <td>NaN</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
" <th>3</th>\n",
|
||
" <td>6.0</td>\n",
|
||
" <td>16.0</td>\n",
|
||
" <td>206.0</td>\n",
|
||
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|
||
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|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
"cluster_2022 1 2 3\n",
|
||
"cluster_2019 \n",
|
||
"1 34.0 2.0 7.0\n",
|
||
"2 NaN 59.0 15.0\n",
|
||
"3 6.0 16.0 206.0"
|
||
]
|
||
},
|
||
"execution_count": 213,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_evo2.groupby([\"cluster_2019\", \"cluster_2022\"]).size().unstack()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 218,
|
||
"id": "82e96b64-0d48-4bbf-b334-0dcb311440d0",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x600 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import seaborn as sns\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# matrice de transition\n",
|
||
"transition1 = df_evo.groupby([\"cluster_2016\", \"cluster_2019\"]).size().unstack(fill_value=0)\n",
|
||
"\n",
|
||
"plt.figure(figsize=(8,6))\n",
|
||
"sns.heatmap(transition1, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
|
||
"\n",
|
||
"plt.ylabel(\"Cluster 2016-2019\")\n",
|
||
"plt.xlabel(\"Cluster 2019-2022\")\n",
|
||
"plt.title(\"Matrice de transition des clusters (2016 → 2019)\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 219,
|
||
"id": "aac4fc2b-8f5c-49d5-aaa6-8c563dfc0ef3",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x600 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"transition1 = df_evo.groupby([\"cluster_2016\", \"cluster_2019\"]).size().unstack(fill_value=0)\n",
|
||
"transition2 = df_evo2.groupby([\"cluster_2019\", \"cluster_2022\"]).size().unstack(fill_value=0)\n",
|
||
"\n",
|
||
"transition_pct1 = transition1.div(transition1.sum(axis=1), axis=0)\n",
|
||
"transition_pct2 = transition2.div(transition2.sum(axis=1), axis=0)\n",
|
||
"\n",
|
||
"plt.figure(figsize=(8,6))\n",
|
||
"sns.heatmap(transition_pct2, annot=True, fmt=\".2f\", cmap=\"Blues\")\n",
|
||
"\n",
|
||
"plt.xlabel(\"Cluster 2019-2022\")\n",
|
||
"plt.ylabel(\"Cluster 2016-2019\")\n",
|
||
"plt.title(\"Probabilité de transition (2016 → 2019)\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 169,
|
||
"id": "0f5cc8dd-4f9b-4c66-9e21-951b05b506e0",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.plotly.v1+json": {
|
||
"config": {
|
||
"plotlyServerURL": "https://plot.ly"
|
||
},
|
||
"data": [
|
||
{
|
||
"link": {
|
||
"source": [
|
||
0,
|
||
0,
|
||
1,
|
||
1,
|
||
2,
|
||
2,
|
||
3
|
||
],
|
||
"target": [
|
||
4,
|
||
5,
|
||
5,
|
||
6,
|
||
5,
|
||
6,
|
||
6
|
||
],
|
||
"value": {
|
||
"bdata": "CQEgDQZcAQ==",
|
||
"dtype": "i1"
|
||
}
|
||
},
|
||
"node": {
|
||
"label": [
|
||
"2019_0",
|
||
"2019_1",
|
||
"2019_2",
|
||
"2019_4",
|
||
"2022_0",
|
||
"2022_1",
|
||
"2022_2",
|
||
"2022_4"
|
||
]
|
||
},
|
||
"type": "sankey"
|
||
}
|
||
],
|
||
"layout": {
|
||
"template": {
|
||
"data": {
|
||
"bar": [
|
||
{
|
||
"error_x": {
|
||
"color": "#2a3f5f"
|
||
},
|
||
"error_y": {
|
||
"color": "#2a3f5f"
|
||
},
|
||
"marker": {
|
||
"line": {
|
||
"color": "#E5ECF6",
|
||
"width": 0.5
|
||
},
|
||
"pattern": {
|
||
"fillmode": "overlay",
|
||
"size": 10,
|
||
"solidity": 0.2
|
||
}
|
||
},
|
||
"type": "bar"
|
||
}
|
||
],
|
||
"barpolar": [
|
||
{
|
||
"marker": {
|
||
"line": {
|
||
"color": "#E5ECF6",
|
||
"width": 0.5
|
||
},
|
||
"pattern": {
|
||
"fillmode": "overlay",
|
||
"size": 10,
|
||
"solidity": 0.2
|
||
}
|
||
},
|
||
"type": "barpolar"
|
||
}
|
||
],
|
||
"carpet": [
|
||
{
|
||
"aaxis": {
|
||
"endlinecolor": "#2a3f5f",
|
||
"gridcolor": "white",
|
||
"linecolor": "white",
|
||
"minorgridcolor": "white",
|
||
"startlinecolor": "#2a3f5f"
|
||
},
|
||
"baxis": {
|
||
"endlinecolor": "#2a3f5f",
|
||
"gridcolor": "white",
|
||
"linecolor": "white",
|
||
"minorgridcolor": "white",
|
||
"startlinecolor": "#2a3f5f"
|
||
},
|
||
"type": "carpet"
|
||
}
|
||
],
|
||
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"linecolor": "white",
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"ticks": ""
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},
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"bgcolor": "#E5ECF6",
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"caxis": {
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"gridcolor": "white",
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"linecolor": "white",
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"ticks": ""
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}
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},
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"title": {
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"x": 0.05
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},
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"xaxis": {
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"automargin": true,
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"gridcolor": "white",
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"linecolor": "white",
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"ticks": "",
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"title": {
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"standoff": 15
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},
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"zerolinecolor": "white",
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"zerolinewidth": 2
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},
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"yaxis": {
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"automargin": true,
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"gridcolor": "white",
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"linecolor": "white",
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"ticks": "",
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"title": {
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"standoff": 15
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},
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"zerolinecolor": "white",
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}
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}
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}
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}
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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import plotly.graph_objects as go\n",
|
||
"\n",
|
||
"# Préparer les flux\n",
|
||
"flows = df_evo.groupby([\"cluster_2019\", \"cluster_2022\"]).size().reset_index(name=\"count\")\n",
|
||
"\n",
|
||
"labels = sorted(set(flows[\"cluster_2019\"]).union(set(flows[\"cluster_2022\"])))\n",
|
||
"\n",
|
||
"label_map = {k: i for i, k in enumerate(labels)}\n",
|
||
"\n",
|
||
"fig = go.Figure(data=[go.Sankey(\n",
|
||
" node=dict(label=[f\"2019_{l}\" for l in labels] + [f\"2022_{l}\" for l in labels]),\n",
|
||
" link=dict(\n",
|
||
" source=[label_map[s] for s in flows[\"cluster_2019\"]],\n",
|
||
" target=[label_map[t] + len(labels) for t in flows[\"cluster_2022\"]],\n",
|
||
" value=flows[\"count\"]\n",
|
||
" )\n",
|
||
")])\n",
|
||
"\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 221,
|
||
"id": "4e139972-f4de-4f18-b9b3-601819f06d3f",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1400x600 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig, axes = plt.subplots(1, 2, figsize=(14,6), sharey=True)\n",
|
||
"\n",
|
||
"# --- Heatmap 1 : 2016 → 2019 ---\n",
|
||
"sns.heatmap(\n",
|
||
" transition_pct1,\n",
|
||
" annot=True,\n",
|
||
" fmt=\".2f\",\n",
|
||
" cmap=\"Blues\",\n",
|
||
" ax=axes[0]\n",
|
||
")\n",
|
||
"\n",
|
||
"axes[0].set_xlabel(\"Cluster 2019-2022\")\n",
|
||
"axes[0].set_ylabel(\"Cluster 2016-2019\")\n",
|
||
"axes[0].set_title(\"Transition des clusters (2016-2019 → 2019-2022)\")\n",
|
||
"\n",
|
||
"# --- Heatmap 2 : 2019 → 2022 ---\n",
|
||
"sns.heatmap(\n",
|
||
" transition_pct2,\n",
|
||
" annot=True,\n",
|
||
" fmt=\".2f\",\n",
|
||
" cmap=\"Blues\",\n",
|
||
" ax=axes[1]\n",
|
||
")\n",
|
||
"\n",
|
||
"axes[1].set_xlabel(\"Cluster 2022-2025\")\n",
|
||
"axes[1].set_ylabel(\"Cluster 2019-2022\")\n",
|
||
"axes[1].set_title(\"Transition des clusters (2019-2022 → 2022-2025)\")\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 228,
|
||
"id": "d2701546-abaf-42e2-9070-7ef43824096c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Best K by silhouette: 3\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"' Ce que c’est :\\nInertia = somme des distances intra-cluster (SSE).\\nPlus elle baisse, plus les clusters sont “serrés”.\\n\\nComment lire :\\nQuand K augmente, inertia baisse toujours (normal).\\nOn cherche un “coude” : à partir d’un certain K, ajouter des clusters apporte peu\\n'"
|
||
]
|
||
},
|
||
"execution_count": 228,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Evolution des clusters\n",
|
||
"\n",
|
||
"k_range = range(2, 21)\n",
|
||
"inertias = []\n",
|
||
"silhouettes = []\n",
|
||
"\n",
|
||
"for k in k_range:\n",
|
||
" km = KMeans(n_clusters=k, n_init=30, random_state=42)\n",
|
||
" labels = km.fit_predict(X_2022_scaled2)\n",
|
||
" inertias.append(km.inertia_)\n",
|
||
" silhouettes.append(silhouette_score(X_2022_scaled2, labels))\n",
|
||
"\n",
|
||
"# Elbow plot\n",
|
||
"plt.figure()\n",
|
||
"plt.plot(list(k_range), inertias, marker=\"o\")\n",
|
||
"plt.xlabel(\"Number of clusters K\")\n",
|
||
"plt.ylabel(\"Inertia (within-cluster SSE)\")\n",
|
||
"plt.title(\"Elbow curve for K-means\")\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# Silhouette plot\n",
|
||
"plt.figure()\n",
|
||
"plt.plot(list(k_range), silhouettes, marker=\"o\")\n",
|
||
"plt.xlabel(\"Number of clusters K\")\n",
|
||
"plt.ylabel(\"Silhouette score\")\n",
|
||
"plt.title(\"Silhouette score for K-means\")\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"best_k = list(k_range)[int(np.argmax(silhouettes))]\n",
|
||
"print(\"Best K by silhouette:\", best_k)\n",
|
||
"\n",
|
||
"\n",
|
||
"''' Ce que c’est :\n",
|
||
"Inertia = somme des distances intra-cluster (SSE).\n",
|
||
"Plus elle baisse, plus les clusters sont “serrés”.\n",
|
||
"\n",
|
||
"Comment lire :\n",
|
||
"Quand K augmente, inertia baisse toujours (normal).\n",
|
||
"On cherche un “coude” : à partir d’un certain K, ajouter des clusters apporte peu\n",
|
||
"'''"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 236,
|
||
"id": "aa314cec-891d-4f97-af49-75c8869df105",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>n_clients</th>\n",
|
||
" <th>aum_qty_med</th>\n",
|
||
" <th>freq_med</th>\n",
|
||
" <th>rel_int_med</th>\n",
|
||
" <th>gross_flow_med</th>\n",
|
||
" <th>n_tx_med</th>\n",
|
||
" <th>vol_med</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>cluster2_kmeans</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>3.0</th>\n",
|
||
" <td>133</td>\n",
|
||
" <td>112893.522820</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>5.640443</td>\n",
|
||
" <td>7428.045000</td>\n",
|
||
" <td>2433.0</td>\n",
|
||
" <td>8849.651088</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>0.0</th>\n",
|
||
" <td>130</td>\n",
|
||
" <td>22171.725988</td>\n",
|
||
" <td>0.984615</td>\n",
|
||
" <td>5.229762</td>\n",
|
||
" <td>1395.668979</td>\n",
|
||
" <td>1043.5</td>\n",
|
||
" <td>1577.856556</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2.0</th>\n",
|
||
" <td>52</td>\n",
|
||
" <td>816081.008680</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>4.501734</td>\n",
|
||
" <td>51749.188856</td>\n",
|
||
" <td>11492.5</td>\n",
|
||
" <td>49834.562115</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1.0</th>\n",
|
||
" <td>31</td>\n",
|
||
" <td>65939.622642</td>\n",
|
||
" <td>0.200000</td>\n",
|
||
" <td>3.489589</td>\n",
|
||
" <td>2369.314778</td>\n",
|
||
" <td>22.0</td>\n",
|
||
" <td>9971.798124</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4.0</th>\n",
|
||
" <td>9</td>\n",
|
||
" <td>27289.956085</td>\n",
|
||
" <td>0.028169</td>\n",
|
||
" <td>0.030004</td>\n",
|
||
" <td>6.280154</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>28.914546</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" n_clients aum_qty_med freq_med rel_int_med \\\n",
|
||
"cluster2_kmeans \n",
|
||
"3.0 133 112893.522820 1.000000 5.640443 \n",
|
||
"0.0 130 22171.725988 0.984615 5.229762 \n",
|
||
"2.0 52 816081.008680 1.000000 4.501734 \n",
|
||
"1.0 31 65939.622642 0.200000 3.489589 \n",
|
||
"4.0 9 27289.956085 0.028169 0.030004 \n",
|
||
"\n",
|
||
" gross_flow_med n_tx_med vol_med \n",
|
||
"cluster2_kmeans \n",
|
||
"3.0 7428.045000 2433.0 8849.651088 \n",
|
||
"0.0 1395.668979 1043.5 1577.856556 \n",
|
||
"2.0 51749.188856 11492.5 49834.562115 \n",
|
||
"1.0 2369.314778 22.0 9971.798124 \n",
|
||
"4.0 6.280154 2.0 28.914546 "
|
||
]
|
||
},
|
||
"execution_count": 236,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"km = KMeans(n_clusters=5, n_init=50, random_state=42)\n",
|
||
"labels_km = km.fit_predict(X_2022_scaled2)\n",
|
||
"\n",
|
||
"dfc.loc[X_2022_2.index, \"cluster2_kmeans\"] = labels_km\n",
|
||
"\n",
|
||
"# Profiling table (medians = robust to outliers)\n",
|
||
"k_profile = (\n",
|
||
" dfc.loc[X_2022_2.index]\n",
|
||
" .groupby(\"cluster2_kmeans\")\n",
|
||
" .agg(\n",
|
||
" n_clients=(ID_COL, \"count\"),\n",
|
||
" aum_qty_med=(\"aum_qty_mean\", \"median\"),\n",
|
||
" freq_med=(\"frequency\", \"median\"),\n",
|
||
" rel_int_med=(\"rel_intensity_total\", \"median\"),\n",
|
||
" gross_flow_med=(\"gross_flow_qty_mean\", \"median\"),\n",
|
||
" n_tx_med=(\"n_tx_total\", \"median\"),\n",
|
||
" vol_med=(\"net_flow_qty_vol\", \"median\"),\n",
|
||
" )\n",
|
||
" .sort_values(\"n_clients\", ascending=False)\n",
|
||
")\n",
|
||
"\n",
|
||
"k_profile\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 238,
|
||
"id": "fd2eb9a4-9289-437f-b360-87d2accd9737",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure()\n",
|
||
"for name, g in dfc[~dfc['cluster2_kmeans'].isin([4.0])].groupby(\"cluster2_kmeans\"):\n",
|
||
" plt.scatter(g[\"frequency\"], g[\"rel_intensity_total\"], s=10, label=name)\n",
|
||
"\n",
|
||
"plt.yscale(\"log\")\n",
|
||
"plt.axvline(thr_freq, linestyle=\"--\")\n",
|
||
"plt.axhline(thr_int, linestyle=\"--\")\n",
|
||
"plt.xlabel(\"Activity frequency (share of active months)\")\n",
|
||
"plt.ylabel(\"Gross flow / mean AUM (quantity) [log scale]\")\n",
|
||
"plt.title(\"2D behavioral segmentation: relative intensity vs frequency\")\n",
|
||
"plt.legend(markerscale=2)\n",
|
||
"plt.ylim(0.1,100)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "cda42f1d-d725-412b-8897-3728a4a5fc4a",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.13.12"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|