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System save at 07/11/2024 15:35 by user_client2024

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user_client2024 2024-11-07 10:05:54 +00:00
parent 0d638798e8
commit 79bc7cb401
3 changed files with 127 additions and 155 deletions
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108
.ipynb_checkpoints/main-checkpoint.ipynb
View File

@ -7,44 +7,14 @@
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "77134554-e1dc-4e5b-aaa2-bb432789aa01",
"metadata": {},
"outputs": [],
"source": [
"from tms_data_interface import SQLQueryInterface\n",
"seq = SQLQueryInterface(schema=\"transactionschema\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9a13f2ec-d02f-4151-9d9a-17edd4f29063",
"metadata": {},
"outputs": [],
"source": [
"seq.execute_raw(\"show tables\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "32c443d5-c51f-425c-be6f-bcd8c9908ef4",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"query = \"\"\"\n",
" select final.CUSTOMER_NUMBER_main as Focal_id,\n",
" final.Credit_transaction_amount,\n",
" final.Total_no_of_credit_transactions,\n",
" final.Debit_transaction_amount,\n",
" final.Total_no_of_debit_transactions,\n",
" final.Wash_Ratio,\n",
" final.SEGMENT,\n",
" final.RISK,\n",
" final.SAR_FLAG\n",
@ -61,12 +31,7 @@
" case\n",
" when subquery.Total_no_of_debit_transactions is NULL then 0\n",
" else Total_no_of_debit_transactions\n",
" end as Total_no_of_debit_transactions,\n",
" case\n",
" when subquery.Debit_transaction_amount = 0\n",
" or subquery.Debit_transaction_amount is NULL then 0\n",
" else subquery.Credit_transaction_amount / subquery.Debit_transaction_amount\n",
" end as Wash_Ratio\n",
" end as Total_no_of_debit_transactions\n",
" from \n",
" (\n",
" (\n",
@ -76,7 +41,7 @@
" from \n",
" (\n",
" select * \n",
" from {trans_data} as trans_table left join {acc_data} as acc_table\n",
" from {trans_data} trans_table left join {acc_data} acc_table\n",
" on trans_table.benef_account_number = acc_table.account_number\n",
" )\n",
" where account_number not in ('None')\n",
@ -89,7 +54,7 @@
" from \n",
" (\n",
" select * \n",
" from {trans_data} as trans_table left join {acc_data} as acc_table\n",
" from {trans_data} trans_table left join {acc_data} acc_table\n",
" on trans_table.orig_account_number = acc_table.account_number\n",
" )\n",
" where account_number not in ('None')\n",
@ -126,34 +91,57 @@
" ) subquery\n",
" ) cust_alert on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main\n",
" ) 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\", \"Credit_transaction_amount\", \"Total_no_of_credit_transactions\",\n",
" \"Debit_transaction_amount\", \"Total_no_of_debit_transactions\",\n",
" \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
" df = pd.DataFrame(row_list, columns = cols)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "77134554-e1dc-4e5b-aaa2-bb432789aa01",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "9a13f2ec-d02f-4151-9d9a-17edd4f29063",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "32c443d5-c51f-425c-be6f-bcd8c9908ef4",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "c34dc466-7bb5-471e-b0c5-0bfcd81b761c",
"metadata": {},
"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\", \"Credit_transaction_amount\",\n",
" \"Total_no_of_credit_transactions\",\n",
" \"Debit_transaction_amount\", \"Total_no_of_debit_transactions\",\n",
" \"Wash_Ratio\", \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
" df = pd.DataFrame(row_list, columns = cols)\n",
" return df"
]
"source": []
},
{
"cell_type": "code",

108
main.ipynb
View File

@ -7,44 +7,14 @@
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "77134554-e1dc-4e5b-aaa2-bb432789aa01",
"metadata": {},
"outputs": [],
"source": [
"from tms_data_interface import SQLQueryInterface\n",
"seq = SQLQueryInterface(schema=\"transactionschema\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9a13f2ec-d02f-4151-9d9a-17edd4f29063",
"metadata": {},
"outputs": [],
"source": [
"seq.execute_raw(\"show tables\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "32c443d5-c51f-425c-be6f-bcd8c9908ef4",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"query = \"\"\"\n",
" select final.CUSTOMER_NUMBER_main as Focal_id,\n",
" final.Credit_transaction_amount,\n",
" final.Total_no_of_credit_transactions,\n",
" final.Debit_transaction_amount,\n",
" final.Total_no_of_debit_transactions,\n",
" final.Wash_Ratio,\n",
" final.SEGMENT,\n",
" final.RISK,\n",
" final.SAR_FLAG\n",
@ -61,12 +31,7 @@
" case\n",
" when subquery.Total_no_of_debit_transactions is NULL then 0\n",
" else Total_no_of_debit_transactions\n",
" end as Total_no_of_debit_transactions,\n",
" case\n",
" when subquery.Debit_transaction_amount = 0\n",
" or subquery.Debit_transaction_amount is NULL then 0\n",
" else subquery.Credit_transaction_amount / subquery.Debit_transaction_amount\n",
" end as Wash_Ratio\n",
" end as Total_no_of_debit_transactions\n",
" from \n",
" (\n",
" (\n",
@ -76,7 +41,7 @@
" from \n",
" (\n",
" select * \n",
" from {trans_data} as trans_table left join {acc_data} as acc_table\n",
" from {trans_data} trans_table left join {acc_data} acc_table\n",
" on trans_table.benef_account_number = acc_table.account_number\n",
" )\n",
" where account_number not in ('None')\n",
@ -89,7 +54,7 @@
" from \n",
" (\n",
" select * \n",
" from {trans_data} as trans_table left join {acc_data} as acc_table\n",
" from {trans_data} trans_table left join {acc_data} acc_table\n",
" on trans_table.orig_account_number = acc_table.account_number\n",
" )\n",
" where account_number not in ('None')\n",
@ -126,34 +91,57 @@
" ) subquery\n",
" ) cust_alert on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main\n",
" ) 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\", \"Credit_transaction_amount\", \"Total_no_of_credit_transactions\",\n",
" \"Debit_transaction_amount\", \"Total_no_of_debit_transactions\",\n",
" \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
" df = pd.DataFrame(row_list, columns = cols)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "77134554-e1dc-4e5b-aaa2-bb432789aa01",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "9a13f2ec-d02f-4151-9d9a-17edd4f29063",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "32c443d5-c51f-425c-be6f-bcd8c9908ef4",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "c34dc466-7bb5-471e-b0c5-0bfcd81b761c",
"metadata": {},
"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\", \"Credit_transaction_amount\",\n",
" \"Total_no_of_credit_transactions\",\n",
" \"Debit_transaction_amount\", \"Total_no_of_debit_transactions\",\n",
" \"Wash_Ratio\", \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
" df = pd.DataFrame(row_list, columns = cols)\n",
" return df"
]
"source": []
},
{
"cell_type": "code",

66
main.py
View File

@ -6,30 +6,12 @@
import pandas as pd
# In[ ]:
from tms_data_interface import SQLQueryInterface
seq = SQLQueryInterface(schema="transactionschema")
# In[ ]:
seq.execute_raw("show tables")
# In[ ]:
query = """
select final.CUSTOMER_NUMBER_main as Focal_id,
final.Credit_transaction_amount,
final.Total_no_of_credit_transactions,
final.Debit_transaction_amount,
final.Total_no_of_debit_transactions,
final.Wash_Ratio,
final.SEGMENT,
final.RISK,
final.SAR_FLAG
@ -46,12 +28,7 @@ query = """
case
when subquery.Total_no_of_debit_transactions is NULL then 0
else Total_no_of_debit_transactions
end as Total_no_of_debit_transactions,
case
when subquery.Debit_transaction_amount = 0
or subquery.Debit_transaction_amount is NULL then 0
else subquery.Credit_transaction_amount / subquery.Debit_transaction_amount
end as Wash_Ratio
end as Total_no_of_debit_transactions
from
(
(
@ -61,7 +38,7 @@ query = """
from
(
select *
from {trans_data} as trans_table left join {acc_data} as acc_table
from {trans_data} trans_table left join {acc_data} acc_table
on trans_table.benef_account_number = acc_table.account_number
)
where account_number not in ('None')
@ -74,7 +51,7 @@ query = """
from
(
select *
from {trans_data} as trans_table left join {acc_data} as acc_table
from {trans_data} trans_table left join {acc_data} acc_table
on trans_table.orig_account_number = acc_table.account_number
)
where account_number not in ('None')
@ -113,25 +90,20 @@ query = """
) final
"""
# In[ ]:
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", "Credit_transaction_amount",
"Total_no_of_credit_transactions",
cols = ["Focal_id", "Credit_transaction_amount", "Total_no_of_credit_transactions",
"Debit_transaction_amount", "Total_no_of_debit_transactions",
"Wash_Ratio", "Segment", "Risk", "SAR_FLAG"]
"Segment", "Risk", "SAR_FLAG"]
df = pd.DataFrame(row_list, columns = cols)
return df
@ -141,3 +113,27 @@ class Scenario:
# In[ ]:
# In[ ]:
# In[ ]:
# In[ ]: