generated from user_client2024/78
299 lines
11 KiB
Plaintext
299 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# import pandas as pd\n",
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"\n",
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"# query = \"\"\"\n",
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"# select final.CUSTOMER_NUMBER_main as Focal_id,\n",
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"# CAST(final.Cash_deposit_total AS DECIMAL(18, 2)) AS Cash_deposit_total,\n",
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"# final.Cash_deposit_count,\n",
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"# final.SEGMENT,\n",
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"# final.RISK,\n",
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"# final.SAR_FLAG\n",
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"# from \n",
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"# (\n",
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"# (\n",
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"# select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,\n",
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"# subquery.Cash_deposit_total,\n",
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"# subquery.Cash_deposit_count\n",
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"# from \n",
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"# (\n",
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"# select customer_number as CUSTOMER_NUMBER_1, \n",
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"# sum(transaction_amount) as Cash_deposit_total, \n",
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"# count(*) as Cash_deposit_count\n",
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"# from \n",
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"# (\n",
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"# select * \n",
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"# from {trans_data} trans_table \n",
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"# left join {acc_data} acc_table\n",
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"# on trans_table.benef_account_number = acc_table.account_number\n",
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"# ) trans\n",
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"# where account_number not in ('None')\n",
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"# and transaction_desc = 'CASH RELATED TRANSACTION'\n",
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"# group by customer_number\n",
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"# ) subquery\n",
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"# ) main \n",
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"# left join \n",
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"# (\n",
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"# select cd.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,\n",
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"# cd.SEGMENT,\n",
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"# cd.RISK,\n",
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"# case\n",
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"# when ad.SAR_FLAG is NULL then 'N'\n",
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"# else ad.SAR_FLAG\n",
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"# end as SAR_FLAG \n",
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"# from\n",
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"# (\n",
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"# select customer_number as CUSTOMER_NUMBER_3, \n",
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"# business_segment as SEGMENT,\n",
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"# case\n",
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"# when RISK_CLASSIFICATION = 1 then 'Low Risk'\n",
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"# when RISK_CLASSIFICATION = 2 then 'Medium Risk'\n",
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"# when RISK_CLASSIFICATION = 3 then 'High Risk'\n",
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"# else 'Unknown Risk'\n",
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"# end AS RISK\n",
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"# from {cust_data}\n",
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"# ) cd \n",
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"# left join\n",
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"# (\n",
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"# select customer_number as CUSTOMER_NUMBER_4, \n",
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"# sar_flag as SAR_FLAG\n",
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"# from {alert_data}\n",
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"# ) ad \n",
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"# on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4\n",
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"# ) as cust_alert\n",
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"# on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main\n",
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"# ) as final\n",
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"# \"\"\"\n",
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"\n",
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"# from tms_data_interface import SQLQueryInterface\n",
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"\n",
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"# class Scenario:\n",
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"# seq = SQLQueryInterface(schema=\"transactionschema\")\n",
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"\n",
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"# def logic(self, **kwargs):\n",
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"# row_list = self.seq.execute_raw(query.format(trans_data=\"transaction10m\",\n",
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"# cust_data=\"customer_data_v1\",\n",
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"# acc_data=\"account_data_v1\",\n",
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"# alert_data=\"alert_data_v1\")\n",
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"# )\n",
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"# cols = [\"Focal_id\", \"Cash_deposit_total\", \"Cash_deposit_count\",\n",
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"# \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
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"# df = pd.DataFrame(row_list, columns = cols)\n",
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"# df[\"Cash_deposit_total\"] = df[\"Cash_deposit_total\"].astype(float)\n",
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"# return df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "69d6771d-be1c-4ae1-802a-3ba7b2e8c5fb",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"query = \"\"\"\n",
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" SELECT \n",
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" t.transaction_key,\n",
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" t.transaction_date,\n",
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" t.transaction_amount,\n",
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" t.transaction_desc,\n",
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" t.benef_account_number,\n",
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"\n",
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" -- Account data\n",
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" a.account_number,\n",
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" a.customer_number AS acc_customer_number,\n",
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"\n",
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" -- Party data\n",
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" p.customer_number AS party_customer_number,\n",
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" p.customer_name,\n",
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" p.date_of_birth,\n",
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" p.nationality,\n",
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" p.business_segment,\n",
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" CASE\n",
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" WHEN p.risk_classification = 1 THEN 'Low Risk'\n",
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" WHEN p.risk_classification = 2 THEN 'Medium Risk'\n",
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" WHEN p.risk_classification = 3 THEN 'High Risk'\n",
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" ELSE 'Unknown Risk'\n",
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" END AS risk_level,\n",
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"\n",
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" -- Alert data\n",
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" COALESCE(al.sar_flag, 'N') AS sar_flag\n",
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"\n",
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" FROM {trans_data} t\n",
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"\n",
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" -- Join with account data on beneficiary account\n",
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" LEFT JOIN {acc_data} a\n",
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" ON t.benef_account_number = a.account_number\n",
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"\n",
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" -- Join with party/customer data using account's customer number\n",
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" LEFT JOIN {cust_data} p\n",
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" ON a.customer_number = p.customer_number\n",
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"\n",
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" -- Join with alert data using party's customer number\n",
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" LEFT JOIN {alert_data} al\n",
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" ON p.customer_number = al.customer_number\n",
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"\n",
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" WHERE a.account_number IS NOT NULL\n",
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"\"\"\"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "82c2152f-513c-4fde-a4a9-6ee3a01ef897",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# def trx_count_sum_groupwise(data_filt_partywise): \n",
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"# data_filt_partywise = data_filt_partywise.sort_values(by='transaction_amount') \n",
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"# groupeddata = pd.DataFrame(columns=['group_no', 'trxn_cnt', 'trxn_sum_amt', \n",
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"# 'MIN_LIMIT', 'PCT_RANGE'])\n",
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" \n",
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"# trxns = data_filt_partywise['transaction_amount'].values\n",
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"# pct_range = data_filt_partywise['PCT_RANGE'].max()\n",
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"# min_value = data_filt_partywise['MIN_LIMIT'].max()\n",
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"\n",
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"# trxns = trxns[trxns >= min_value]\n",
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"# if len(trxns) > 0:\n",
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"# min_value = trxns[0]\n",
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"\n",
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"# group_count = 0\n",
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"# while len(trxns) > 0:\n",
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"# max_value = min_value + (pct_range * 0.01 * min_value)\n",
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"# mask = np.logical_and(trxns >= min_value, trxns <= max_value)\n",
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"# group_filter_trx = trxns[mask]\n",
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"# trx_count = len(group_filter_trx)\n",
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"# trx_sum = np.sum(group_filter_trx)\n",
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"# group_count += 1\n",
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"# groupeddata.loc[len(groupeddata)] = [group_count, trx_count, trx_sum, \n",
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"# min_value, pct_range]\n",
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"# trxns = trxns[trxns > max_value]\n",
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"# if len(trxns) > 0:\n",
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"# min_value = trxns[0]\n",
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"\n",
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"# return groupeddata.to_dict('list')\n",
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"\n",
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"# # ---------------------------\n",
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"# # Function 4: Run scenario 9\n",
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"# # ---------------------------\n",
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"# def scenario9_data(data1): \n",
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"# grouped = data1.groupby('Focal_id')[['transaction_amount', 'MIN_LIMIT', 'PCT_RANGE']].apply(\n",
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"# trx_count_sum_groupwise).reset_index()\n",
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"\n",
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"# df_list = []\n",
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"# for i in grouped.index:\n",
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"# df_party = pd.DataFrame(grouped.iloc[i, -1])\n",
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"# df_party['Focal_id'] = grouped.loc[i, 'Focal_id']\n",
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"# df_list.append(df_party)\n",
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"\n",
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"# final_df = pd.concat(df_list, ignore_index=True) \n",
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"# Segment = data1.groupby('Focal_id')['Segment'].agg('max').reset_index()\n",
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"# Risk = data1.groupby('Focal_id')['Risk'].agg('max').reset_index()\n",
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"# SAR_FLAG = data1.groupby('Focal_id')['SAR_FLAG'].agg('max').reset_index()\n",
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" \n",
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"# final_df = final_df.merge(Segment,on = 'Focal_id', how = 'left')\n",
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"# final_df = final_df.merge(Risk,on = 'Focal_id', how = 'left')\n",
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"# final_df = final_df.merge(SAR_FLAG,on = 'Focal_id', how = 'left')\n",
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" \n",
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"# return final_df\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "b6c85de2-6a47-4109-8885-c138c289ec25",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"\n",
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"from tms_data_interface import SQLQueryInterface\n",
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"\n",
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"class Scenario:\n",
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" seq = SQLQueryInterface(schema=\"transactionschema\")\n",
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"\n",
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" def logic(self, **kwargs):\n",
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" row_list = self.seq.execute_raw(query.format(trans_data=\"transaction10m\",\n",
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" cust_data=\"customer_data_v1\",\n",
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" acc_data=\"account_data_v1\",\n",
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" alert_data=\"alert_data_v1\")\n",
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" )\n",
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" cols = [\n",
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" \"transaction_key\",\n",
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" \"transaction_date\",\n",
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" \"transaction_amount\",\n",
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" \"transaction_desc\",\n",
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" \"benef_account_number\",\n",
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" \"account_number\",\n",
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" \"acc_customer_number\",\n",
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" \"Focal_id\",\n",
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" \"customer_name\",\n",
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" \"date_of_birth\",\n",
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" \"nationality\",\n",
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" \"Segment\",\n",
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" \"Risk\", \n",
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" \"SAR_FLAG\"\n",
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" ]\n",
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" df = pd.DataFrame(row_list, columns = cols)\n",
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" df['Segment'] = 'SME'\n",
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"# df['MIN_LIMIT'] = 50000\n",
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"# df['PCT_RANGE'] = 20\n",
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" \n",
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"# scenario_data = scenario9_data(df)\n",
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" \n",
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" return df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "1f20337b-8116-47e5-8743-1ba41e2df819",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# sen = Scenario()\n",
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"# sen.logic()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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