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System save at 26/05/2025 11:28 by user_client2024

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user_client2024 2025-05-26 05:58:06 +00:00
parent f46e824544
commit e7a5cbaaf5
3 changed files with 168 additions and 22 deletions
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73
.ipynb_checkpoints/main-checkpoint.ipynb
View File

@ -94,8 +94,8 @@
},
{
"cell_type": "code",
"execution_count": 1,
"id": "69d6771d-be1c-4ae1-802a-3ba7b2e8c5fb",
"execution_count": 41,
"id": "87cf157c-3725-4d90-bfb6-91cba5028826",
"metadata": {
"tags": []
},
@ -103,6 +103,35 @@
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from tms_data_interface import SQLQueryInterface"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "b68368ea-9cbb-4060-b138-d8b7c3d8a193",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"query2 = \"\"\"\n",
" SELECT *\n",
"\n",
" FROM percentile_dist\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "69d6771d-be1c-4ae1-802a-3ba7b2e8c5fb",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"query = \"\"\"\n",
" SELECT \n",
" t.transaction_key,\n",
@ -151,7 +180,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 44,
"id": "82c2152f-513c-4fde-a4a9-6ee3a01ef897",
"metadata": {
"tags": []
@ -215,15 +244,42 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 45,
"id": "fdefde12-97ab-4545-9612-153f707b7bc9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"seq = SQLQueryInterface(schema=\"transactionschema\")\n",
"data = seq.execute_raw(query2)\n",
"Columns = ['point_of_percentile', 'value', 'total_event',\n",
" 'true_positive', 'false_positive', 'tpsar']\n",
"percent_dist = pd.DataFrame(data,columns = Columns)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "8cc4b541-75a6-4fa7-978a-12df50f94d32",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# round(int(percent_dist[percent_dist['point_of_percentile']\\\n",
"# == 75]['value'].iloc[0])/100)*100"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "b6c85de2-6a47-4109-8885-c138c289ec25",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"from tms_data_interface import SQLQueryInterface\n",
"\n",
"class Scenario:\n",
" seq = SQLQueryInterface(schema=\"transactionschema\")\n",
@ -252,7 +308,8 @@
" ]\n",
" df = pd.DataFrame(row_list, columns = cols)\n",
" df['Segment'] = 'SME'\n",
" df['MIN_LIMIT'] = 50000\n",
" df['MIN_LIMIT'] = round(int(percent_dist[percent_dist['point_of_percentile']\\\n",
" == 75]['value'].iloc[0])/100)*100\n",
" df['PCT_RANGE'] = 20\n",
" \n",
" scenario_data = scenario9_data(df)\n",
@ -262,7 +319,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 55,
"id": "1f20337b-8116-47e5-8743-1ba41e2df819",
"metadata": {
"tags": []

73
main.ipynb
View File

@ -94,8 +94,8 @@
},
{
"cell_type": "code",
"execution_count": 1,
"id": "69d6771d-be1c-4ae1-802a-3ba7b2e8c5fb",
"execution_count": 41,
"id": "87cf157c-3725-4d90-bfb6-91cba5028826",
"metadata": {
"tags": []
},
@ -103,6 +103,35 @@
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from tms_data_interface import SQLQueryInterface"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "b68368ea-9cbb-4060-b138-d8b7c3d8a193",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"query2 = \"\"\"\n",
" SELECT *\n",
"\n",
" FROM percentile_dist\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "69d6771d-be1c-4ae1-802a-3ba7b2e8c5fb",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"query = \"\"\"\n",
" SELECT \n",
" t.transaction_key,\n",
@ -151,7 +180,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 44,
"id": "82c2152f-513c-4fde-a4a9-6ee3a01ef897",
"metadata": {
"tags": []
@ -215,15 +244,42 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 45,
"id": "fdefde12-97ab-4545-9612-153f707b7bc9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"seq = SQLQueryInterface(schema=\"transactionschema\")\n",
"data = seq.execute_raw(query2)\n",
"Columns = ['point_of_percentile', 'value', 'total_event',\n",
" 'true_positive', 'false_positive', 'tpsar']\n",
"percent_dist = pd.DataFrame(data,columns = Columns)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "8cc4b541-75a6-4fa7-978a-12df50f94d32",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# round(int(percent_dist[percent_dist['point_of_percentile']\\\n",
"# == 75]['value'].iloc[0])/100)*100"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "b6c85de2-6a47-4109-8885-c138c289ec25",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"from tms_data_interface import SQLQueryInterface\n",
"\n",
"class Scenario:\n",
" seq = SQLQueryInterface(schema=\"transactionschema\")\n",
@ -252,7 +308,8 @@
" ]\n",
" df = pd.DataFrame(row_list, columns = cols)\n",
" df['Segment'] = 'SME'\n",
" df['MIN_LIMIT'] = 50000\n",
" df['MIN_LIMIT'] = round(int(percent_dist[percent_dist['point_of_percentile']\\\n",
" == 75]['value'].iloc[0])/100)*100\n",
" df['PCT_RANGE'] = 20\n",
" \n",
" scenario_data = scenario9_data(df)\n",
@ -262,7 +319,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 55,
"id": "1f20337b-8116-47e5-8743-1ba41e2df819",
"metadata": {
"tags": []

44
main.py
View File

@ -87,11 +87,27 @@
# return df
# In[1]:
# In[41]:
import pandas as pd
import numpy as np
from tms_data_interface import SQLQueryInterface
# In[42]:
query2 = """
SELECT *
FROM percentile_dist
"""
# In[43]:
query = """
SELECT
t.transaction_key,
@ -138,7 +154,7 @@ query = """
"""
# In[3]:
# In[44]:
def trx_count_sum_groupwise(data_filt_partywise):
@ -196,10 +212,25 @@ def scenario9_data(data1):
# In[4]:
# In[45]:
from tms_data_interface import SQLQueryInterface
seq = SQLQueryInterface(schema="transactionschema")
data = seq.execute_raw(query2)
Columns = ['point_of_percentile', 'value', 'total_event',
'true_positive', 'false_positive', 'tpsar']
percent_dist = pd.DataFrame(data,columns = Columns)
# In[52]:
# round(int(percent_dist[percent_dist['point_of_percentile']\
# == 75]['value'].iloc[0])/100)*100
# In[53]:
class Scenario:
seq = SQLQueryInterface(schema="transactionschema")
@ -228,7 +259,8 @@ class Scenario:
]
df = pd.DataFrame(row_list, columns = cols)
df['Segment'] = 'SME'
df['MIN_LIMIT'] = 50000
df['MIN_LIMIT'] = round(int(percent_dist[percent_dist['point_of_percentile']\
== 75]['value'].iloc[0])/100)*100
df['PCT_RANGE'] = 20
scenario_data = scenario9_data(df)
@ -236,7 +268,7 @@ class Scenario:
return scenario_data
# In[ ]:
# In[55]:
# sen = Scenario()