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.ipynb_checkpoints/main-checkpoint.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"query = \"\"\"\n",
" select final.CUSTOMER_NUMBER_main as Focal_id,\n",
" CAST(final.Cash_deposit_total AS DECIMAL(18, 2)) AS Cash_deposit_total,\n",
" final.Cash_deposit_count,\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.Cash_deposit_total,\n",
" subquery.Cash_deposit_count\n",
" from \n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_1, \n",
" sum(transaction_amount) as Cash_deposit_total, \n",
" count(*) as Cash_deposit_count\n",
" from \n",
" (\n",
" select * \n",
" from {trans_data} trans_table \n",
" left join {acc_data} acc_table\n",
" on trans_table.benef_account_number = acc_table.account_number\n",
" ) trans\n",
" where account_number not in ('None')\n",
" and transaction_desc = 'CASH RELATED TRANSACTION'\n",
" group by customer_number\n",
" ) subquery\n",
" ) main \n",
" left join \n",
" (\n",
" select cd.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,\n",
" cd.SEGMENT,\n",
" cd.RISK,\n",
" case\n",
" when ad.SAR_FLAG is NULL then 'N'\n",
" else ad.SAR_FLAG\n",
" end as SAR_FLAG \n",
" from\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 \n",
" left join\n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_4, \n",
" sar_flag as SAR_FLAG\n",
" from {alert_data}\n",
" ) ad \n",
" on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4\n",
" ) as cust_alert\n",
" on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main\n",
") as final\n",
"\"\"\"\n",
"\n",
"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\", \"Cash_deposit_total\", \"Cash_deposit_count\",\n",
" \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
" df = pd.DataFrame(row_list, columns = cols)\n",
" df[\"Cash_deposit_total\"] = df[\"Cash_deposit_total\"].astype(float)\n",
" \n",
" \n",
"\n",
" # Step 1: Compute 90th percentiles per Segment for all 3 fields\n",
" percentiles = (\n",
" df.groupby(\"Segment\")[[\"Cash_deposit_total\",\n",
" \"Cash_deposit_count\"]]\n",
" .quantile(0.98)\n",
" .reset_index()\n",
" )\n",
"\n",
" # Rename columns for clarity\n",
" percentiles = percentiles.rename(columns={\n",
" \"Cash_deposit_total\": \"P90_Credit\",\n",
" \"Cash_deposit_count\": \"P90_Credit_count\"\n",
" })\n",
"\n",
" # Step 2: Merge back to main df\n",
" df = df.merge(percentiles, on=\"Segment\", how=\"left\")\n",
"\n",
" # Step 3: Identify customers above 90th percentile in ANY of the 3 metrics\n",
" high_pop = (\n",
" (df[\"Cash_deposit_total\"] > df[\"P90_Credit\"]) &\n",
" (df[\"Cash_deposit_count\"] > df[\"P90_Credit_count\"])\n",
" )\n",
"\n",
" # Step 4: Randomly select 0.1% sample from high-risk population\n",
" sample_fraction = 0.1 # 0.1%\n",
" high_pop_indices = df[high_pop].sample(frac=sample_fraction, random_state=42).index\n",
"\n",
" # Step 5: Set SAR_FLAG values\n",
" df[\"SAR_FLAG\"] = \"N\" # default for all\n",
" df.loc[high_pop_indices, \"SAR_FLAG\"] = \"Y\" # assign Y to 0.1% random high-risk population\n",
"\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1f20337b-8116-47e5-8743-1ba41e2df819",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# sen = Scenario()\n",
"# a = sen.logic()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "6de62b37-00d1-4c88-b27b-9a70e05add91",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# a[a[\"SAR_FLAG\"] == \"Y\"]"
]
}
],
"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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{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"query = \"\"\"\n",
" select final.CUSTOMER_NUMBER_main as Focal_id,\n",
" CAST(final.Cash_deposit_total AS DECIMAL(18, 2)) AS Cash_deposit_total,\n",
" final.Cash_deposit_count,\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.Cash_deposit_total,\n",
" subquery.Cash_deposit_count\n",
" from \n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_1, \n",
" sum(transaction_amount) as Cash_deposit_total, \n",
" count(*) as Cash_deposit_count\n",
" from \n",
" (\n",
" select * \n",
" from {trans_data} trans_table \n",
" left join {acc_data} acc_table\n",
" on trans_table.benef_account_number = acc_table.account_number\n",
" ) trans\n",
" where account_number not in ('None')\n",
" and transaction_desc = 'CASH RELATED TRANSACTION'\n",
" group by customer_number\n",
" ) subquery\n",
" ) main \n",
" left join \n",
" (\n",
" select cd.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,\n",
" cd.SEGMENT,\n",
" cd.RISK,\n",
" case\n",
" when ad.SAR_FLAG is NULL then 'N'\n",
" else ad.SAR_FLAG\n",
" end as SAR_FLAG \n",
" from\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 \n",
" left join\n",
" (\n",
" select customer_number as CUSTOMER_NUMBER_4, \n",
" sar_flag as SAR_FLAG\n",
" from {alert_data}\n",
" ) ad \n",
" on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4\n",
" ) as cust_alert\n",
" on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main\n",
") as final\n",
"\"\"\"\n",
"\n",
"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\", \"Cash_deposit_total\", \"Cash_deposit_count\",\n",
" \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
" df = pd.DataFrame(row_list, columns = cols)\n",
" df[\"Cash_deposit_total\"] = df[\"Cash_deposit_total\"].astype(float)\n",
" \n",
" \n",
"\n",
" # Step 1: Compute 90th percentiles per Segment for all 3 fields\n",
" percentiles = (\n",
" df.groupby(\"Segment\")[[\"Cash_deposit_total\",\n",
" \"Cash_deposit_count\"]]\n",
" .quantile(0.98)\n",
" .reset_index()\n",
" )\n",
"\n",
" # Rename columns for clarity\n",
" percentiles = percentiles.rename(columns={\n",
" \"Cash_deposit_total\": \"P90_Credit\",\n",
" \"Cash_deposit_count\": \"P90_Credit_count\"\n",
" })\n",
"\n",
" # Step 2: Merge back to main df\n",
" df = df.merge(percentiles, on=\"Segment\", how=\"left\")\n",
"\n",
" # Step 3: Identify customers above 90th percentile in ANY of the 3 metrics\n",
" high_pop = (\n",
" (df[\"Cash_deposit_total\"] > df[\"P90_Credit\"]) &\n",
" (df[\"Cash_deposit_count\"] > df[\"P90_Credit_count\"])\n",
" )\n",
"\n",
" # Step 4: Randomly select 0.1% sample from high-risk population\n",
" sample_fraction = 0.1 # 0.1%\n",
" high_pop_indices = df[high_pop].sample(frac=sample_fraction, random_state=42).index\n",
"\n",
" # Step 5: Set SAR_FLAG values\n",
" df[\"SAR_FLAG\"] = \"N\" # default for all\n",
" df.loc[high_pop_indices, \"SAR_FLAG\"] = \"Y\" # assign Y to 0.1% random high-risk population\n",
"\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1f20337b-8116-47e5-8743-1ba41e2df819",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# sen = Scenario()\n",
"# a = sen.logic()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "6de62b37-00d1-4c88-b27b-9a70e05add91",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# a[a[\"SAR_FLAG\"] == \"Y\"]"
]
}
],
"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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#!/usr/bin/env python
# coding: utf-8
# In[7]:
import pandas as pd
query = """
select final.CUSTOMER_NUMBER_main as Focal_id,
CAST(final.Cash_deposit_total AS DECIMAL(18, 2)) AS Cash_deposit_total,
final.Cash_deposit_count,
final.SEGMENT,
final.RISK,
final.SAR_FLAG
from
(
(
select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,
subquery.Cash_deposit_total,
subquery.Cash_deposit_count
from
(
select customer_number as CUSTOMER_NUMBER_1,
sum(transaction_amount) as Cash_deposit_total,
count(*) as Cash_deposit_count
from
(
select *
from {trans_data} trans_table
left join {acc_data} acc_table
on trans_table.benef_account_number = acc_table.account_number
) trans
where account_number not in ('None')
and transaction_desc = 'CASH RELATED TRANSACTION'
group by customer_number
) subquery
) main
left join
(
select cd.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,
cd.SEGMENT,
cd.RISK,
case
when ad.SAR_FLAG is NULL then 'N'
else ad.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
) as cust_alert
on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main
) as final
"""
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", "Cash_deposit_total", "Cash_deposit_count",
"Segment", "Risk", "SAR_FLAG"]
df = pd.DataFrame(row_list, columns = cols)
df["Cash_deposit_total"] = df["Cash_deposit_total"].astype(float)
# Step 1: Compute 90th percentiles per Segment for all 3 fields
percentiles = (
df.groupby("Segment")[["Cash_deposit_total",
"Cash_deposit_count"]]
.quantile(0.98)
.reset_index()
)
# Rename columns for clarity
percentiles = percentiles.rename(columns={
"Cash_deposit_total": "P90_Credit",
"Cash_deposit_count": "P90_Credit_count"
})
# Step 2: Merge back to main df
df = df.merge(percentiles, on="Segment", how="left")
# Step 3: Identify customers above 90th percentile in ANY of the 3 metrics
high_pop = (
(df["Cash_deposit_total"] > df["P90_Credit"]) &
(df["Cash_deposit_count"] > df["P90_Credit_count"])
)
# Step 4: Randomly select 0.1% sample from high-risk population
sample_fraction = 0.1 # 0.1%
high_pop_indices = df[high_pop].sample(frac=sample_fraction, random_state=42).index
# Step 5: Set SAR_FLAG values
df["SAR_FLAG"] = "N" # default for all
df.loc[high_pop_indices, "SAR_FLAG"] = "Y" # assign Y to 0.1% random high-risk population
return df
# In[8]:
# sen = Scenario()
# a = sen.logic()
# In[10]:
# a[a["SAR_FLAG"] == "Y"]