System save at 22/09/2025 11:40 by user_client2024

.ipynb_checkpoints/main-checkpoint.ipynb

@@ -148,6 +148,7 @@
     "        cols = [\"Focal_id\", \"Total_Wire_Deposit_Amt\",\n",
     "                \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
     "        df = pd.DataFrame(row_list, columns = cols)\n",
+    "        df['Segment'] = 'Individual'\n",
     "        return df"
    ]
   },
@@ -170,7 +171,9 @@
    "id": "157c4e46-2cff-4f6f-acba-faf4d73538cf",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "#tst cmt"
+   ]
   }
  ],
  "metadata": {

.ipynb_checkpoints/main-checkpoint.py

@@ -0,0 +1,119 @@
+#!/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]:
+#tst
+
+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)
+        df['Segment'] = 'Individual'
+        return df
+
+
+# In[7]:
+
+
+# sen = Scenario()
+# sen.logic()
+
+
+# In[ ]:
+
+
+
+

main.ipynb

@@ -148,6 +148,7 @@
     "        cols = [\"Focal_id\", \"Total_Wire_Deposit_Amt\",\n",
     "                \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
     "        df = pd.DataFrame(row_list, columns = cols)\n",
+    "        df['Segment'] = 'Individual'\n",
     "        return df"
    ]
   },
@@ -170,7 +171,9 @@
    "id": "157c4e46-2cff-4f6f-acba-faf4d73538cf",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "#tst cmt"
+   ]
   }
  ],
  "metadata": {

main.py

@@ -101,6 +101,7 @@ class Scenario:
         cols = ["Focal_id", "Total_Wire_Deposit_Amt",
                 "Segment", "Risk", "SAR_FLAG"]
         df = pd.DataFrame(row_list, columns = cols)
+        df['Segment'] = 'Individual'
         return df
 
 
@@ -114,5 +115,5 @@ class Scenario:
 # In[ ]:
 
 
-
+#tst cmt