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user_client2024 2024-10-09 03:33:57 +00:00
parent 5560bd0b1b
commit ea7475b57c
3 changed files with 512 additions and 33 deletions
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197
.ipynb_checkpoints/main-checkpoint.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f35b1262-3c20-44a6-bbd3-2679a15551e6",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from tms_data_interface import SQLQueryInterface\n",
"seq = SQLQueryInterface(schema=\"transactionschema\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e52124e8-4f62-449d-8852-1e04f8c01ecc",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"[['account_data_v1'],\n",
" ['account_data_v2'],\n",
" ['alert_data_v1'],\n",
" ['alert_data_v2'],\n",
" ['customer_data_v1'],\n",
" ['customer_data_v2'],\n",
" ['transaction10m'],\n",
" ['transaction60m']]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"seq.execute_raw(\"show tables\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "dda35e8d-8997-42d4-a472-844c208d0f49",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"query = \"\"\"\n",
" select final.CUSTOMER_NUMBER_main as Focal_id,\n",
" final.Credit_transaction_amount,\n",
" final.SEGMENT,\n",
" final.RISK,\n",
" final.SAR_FLAG\n",
" from \n",
" (\n",
" (\n",
" select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,\n",
" subquery.Credit_transaction_amount\n",
" from \n",
" (\n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_1, \n",
" sum(transaction_amount) as Credit_transaction_amount\n",
" from \n",
" (\n",
" select * \n",
" from {trans_data} as trans_table \n",
" left join {acc_data} as acc_table\n",
" on trans_table.benef_account_number = acc_table.account_number\n",
" where trans_table.transaction_desc = 'WIRE RELATED TRANSACTION'\n",
" )\n",
" where account_number not in ('None')\n",
" group by 1\n",
" ) credit\n",
" ) subquery\n",
" ) main left join \n",
" (\n",
" select subquery.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,\n",
" subquery.SEGMENT,\n",
" subquery.RISK,\n",
" case\n",
" when subquery.SAR_FLAG is NULL then 'N'\n",
" else subquery.SAR_FLAG\n",
" end as SAR_FLAG \n",
" from\n",
" (\n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_3, \n",
" business_segment as SEGMENT,\n",
" case\n",
" when RISK_CLASSIFICATION = 1 then 'Low Risk'\n",
" when RISK_CLASSIFICATION = 2 then 'Medium Risk'\n",
" when RISK_CLASSIFICATION = 3 then 'High Risk'\n",
" else 'Unknown Risk'\n",
" end AS RISK\n",
" from {cust_data}\n",
" ) cd left join\n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_4, \n",
" sar_flag as SAR_FLAG\n",
" from {alert_data}\n",
" ) ad on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4\n",
" ) subquery\n",
" ) cust_alert on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main\n",
" ) final\n",
"\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "fb0405fe-cd10-4da1-9f06-fe52cff942b4",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from tms_data_interface import SQLQueryInterface\n",
"\n",
"class Scenario:\n",
" seq = SQLQueryInterface(schema=\"transactionschema\")\n",
"\n",
" def logic(self, **kwargs):\n",
" row_list = self.seq.execute_raw(query.format(trans_data=\"transaction10m\",\n",
" cust_data=\"customer_data_v1\",\n",
" acc_data=\"account_data_v1\",\n",
" alert_data=\"alert_data_v1\")\n",
" )\n",
" cols = [\"Focal_id\", \"Total_Wire_Deposit_Amt\",\n",
" \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
" df = pd.DataFrame(row_list, columns = cols)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ddc11b42-6cbb-419b-9e26-73e7606e18a6",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# sen = Scenario()\n",
"# sen.logic()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "157c4e46-2cff-4f6f-acba-faf4d73538cf",
"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": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

230
main.ipynb
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@ -1,33 +1,197 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
"metadata": {},
"outputs": [],
"source": "from tms_data_interface import SQLQueryInterface\n\nclass Scenario:\n\tseq = SQLQueryInterface()\n\n\tdef logic(self, **kwargs):\n\t\t# Write your code here\n"
}
],
"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": "3.8.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f35b1262-3c20-44a6-bbd3-2679a15551e6",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from tms_data_interface import SQLQueryInterface\n",
"seq = SQLQueryInterface(schema=\"transactionschema\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e52124e8-4f62-449d-8852-1e04f8c01ecc",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"[['account_data_v1'],\n",
" ['account_data_v2'],\n",
" ['alert_data_v1'],\n",
" ['alert_data_v2'],\n",
" ['customer_data_v1'],\n",
" ['customer_data_v2'],\n",
" ['transaction10m'],\n",
" ['transaction60m']]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"seq.execute_raw(\"show tables\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "dda35e8d-8997-42d4-a472-844c208d0f49",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"query = \"\"\"\n",
" select final.CUSTOMER_NUMBER_main as Focal_id,\n",
" final.Credit_transaction_amount,\n",
" final.SEGMENT,\n",
" final.RISK,\n",
" final.SAR_FLAG\n",
" from \n",
" (\n",
" (\n",
" select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,\n",
" subquery.Credit_transaction_amount\n",
" from \n",
" (\n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_1, \n",
" sum(transaction_amount) as Credit_transaction_amount\n",
" from \n",
" (\n",
" select * \n",
" from {trans_data} as trans_table \n",
" left join {acc_data} as acc_table\n",
" on trans_table.benef_account_number = acc_table.account_number\n",
" where trans_table.transaction_desc = 'WIRE RELATED TRANSACTION'\n",
" )\n",
" where account_number not in ('None')\n",
" group by 1\n",
" ) credit\n",
" ) subquery\n",
" ) main left join \n",
" (\n",
" select subquery.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,\n",
" subquery.SEGMENT,\n",
" subquery.RISK,\n",
" case\n",
" when subquery.SAR_FLAG is NULL then 'N'\n",
" else subquery.SAR_FLAG\n",
" end as SAR_FLAG \n",
" from\n",
" (\n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_3, \n",
" business_segment as SEGMENT,\n",
" case\n",
" when RISK_CLASSIFICATION = 1 then 'Low Risk'\n",
" when RISK_CLASSIFICATION = 2 then 'Medium Risk'\n",
" when RISK_CLASSIFICATION = 3 then 'High Risk'\n",
" else 'Unknown Risk'\n",
" end AS RISK\n",
" from {cust_data}\n",
" ) cd left join\n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_4, \n",
" sar_flag as SAR_FLAG\n",
" from {alert_data}\n",
" ) ad on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4\n",
" ) subquery\n",
" ) cust_alert on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main\n",
" ) final\n",
"\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "fb0405fe-cd10-4da1-9f06-fe52cff942b4",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from tms_data_interface import SQLQueryInterface\n",
"\n",
"class Scenario:\n",
" seq = SQLQueryInterface(schema=\"transactionschema\")\n",
"\n",
" def logic(self, **kwargs):\n",
" row_list = self.seq.execute_raw(query.format(trans_data=\"transaction10m\",\n",
" cust_data=\"customer_data_v1\",\n",
" acc_data=\"account_data_v1\",\n",
" alert_data=\"alert_data_v1\")\n",
" )\n",
" cols = [\"Focal_id\", \"Total_Wire_Deposit_Amt\",\n",
" \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
" df = pd.DataFrame(row_list, columns = cols)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ddc11b42-6cbb-419b-9e26-73e7606e18a6",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# sen = Scenario()\n",
"# sen.logic()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "157c4e46-2cff-4f6f-acba-faf4d73538cf",
"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": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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main.py Normal file
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
# In[2]:
from tms_data_interface import SQLQueryInterface
seq = SQLQueryInterface(schema="transactionschema")
# In[3]:
seq.execute_raw("show tables")
# In[4]:
query = """
select final.CUSTOMER_NUMBER_main as Focal_id,
final.Credit_transaction_amount,
final.SEGMENT,
final.RISK,
final.SAR_FLAG
from
(
(
select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,
subquery.Credit_transaction_amount
from
(
(
select customer_number as CUSTOMER_NUMBER_1,
sum(transaction_amount) as Credit_transaction_amount
from
(
select *
from {trans_data} as trans_table
left join {acc_data} as acc_table
on trans_table.benef_account_number = acc_table.account_number
where trans_table.transaction_desc = 'WIRE RELATED TRANSACTION'
)
where account_number not in ('None')
group by 1
) credit
) subquery
) main left join
(
select subquery.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,
subquery.SEGMENT,
subquery.RISK,
case
when subquery.SAR_FLAG is NULL then 'N'
else subquery.SAR_FLAG
end as SAR_FLAG
from
(
(
select customer_number as CUSTOMER_NUMBER_3,
business_segment as SEGMENT,
case
when RISK_CLASSIFICATION = 1 then 'Low Risk'
when RISK_CLASSIFICATION = 2 then 'Medium Risk'
when RISK_CLASSIFICATION = 3 then 'High Risk'
else 'Unknown Risk'
end AS RISK
from {cust_data}
) cd left join
(
select customer_number as CUSTOMER_NUMBER_4,
sar_flag as SAR_FLAG
from {alert_data}
) ad on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4
) subquery
) cust_alert on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main
) final
"""
# In[5]:
from tms_data_interface import SQLQueryInterface
class Scenario:
seq = SQLQueryInterface(schema="transactionschema")
def logic(self, **kwargs):
row_list = self.seq.execute_raw(query.format(trans_data="transaction10m",
cust_data="customer_data_v1",
acc_data="account_data_v1",
alert_data="alert_data_v1")
)
cols = ["Focal_id", "Total_Wire_Deposit_Amt",
"Segment", "Risk", "SAR_FLAG"]
df = pd.DataFrame(row_list, columns = cols)
return df
# In[7]:
# sen = Scenario()
# sen.logic()
# In[ ]: