{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
    "#         return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "87cf157c-3725-4d90-bfb6-91cba5028826",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "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",
    "        t.transaction_date,\n",
    "        t.transaction_amount,\n",
    "        t.transaction_desc,\n",
    "        t.benef_account_number,\n",
    "\n",
    "        -- Account data\n",
    "        a.account_number,\n",
    "        a.customer_number AS acc_customer_number,\n",
    "\n",
    "        -- Party data\n",
    "        p.customer_number AS party_customer_number,\n",
    "        p.customer_name,\n",
    "        p.date_of_birth,\n",
    "        p.nationality,\n",
    "        p.business_segment,\n",
    "        CASE\n",
    "            WHEN p.risk_classification = 1 THEN 'Low Risk'\n",
    "            WHEN p.risk_classification = 2 THEN 'Medium Risk'\n",
    "            WHEN p.risk_classification = 3 THEN 'High Risk'\n",
    "            ELSE 'Unknown Risk'\n",
    "        END AS risk_level,\n",
    "\n",
    "        -- Alert data\n",
    "        COALESCE(al.sar_flag, 'N') AS sar_flag\n",
    "\n",
    "    FROM {trans_data} t\n",
    "\n",
    "    -- Join with account data on beneficiary account\n",
    "    LEFT JOIN {acc_data} a\n",
    "        ON t.benef_account_number = a.account_number\n",
    "\n",
    "    -- Join with party/customer data using account's customer number\n",
    "    LEFT JOIN {cust_data} p\n",
    "        ON a.customer_number = p.customer_number\n",
    "\n",
    "    -- Join with alert data using party's customer number\n",
    "    LEFT JOIN {alert_data} al\n",
    "        ON p.customer_number = al.customer_number\n",
    "\n",
    "    WHERE a.account_number IS NOT NULL\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "82c2152f-513c-4fde-a4a9-6ee3a01ef897",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def trx_count_sum_groupwise(data_filt_partywise): \n",
    "    data_filt_partywise = data_filt_partywise.sort_values(by='transaction_amount') \n",
    "    groupeddata = pd.DataFrame(columns=['group_no', 'trxn_cnt', 'trxn_sum_amt', \n",
    "                                        'MIN_LIMIT', 'PCT_RANGE'])\n",
    "    \n",
    "    trxns = data_filt_partywise['transaction_amount'].values\n",
    "    pct_range = data_filt_partywise['PCT_RANGE'].max()\n",
    "    min_value = data_filt_partywise['MIN_LIMIT'].max()\n",
    "\n",
    "    trxns = trxns[trxns >= min_value]\n",
    "    if len(trxns) > 0:\n",
    "        min_value = trxns[0]\n",
    "\n",
    "    group_count = 0\n",
    "    while len(trxns) > 0:\n",
    "        max_value = min_value + (pct_range * 0.01 * min_value)\n",
    "        mask = np.logical_and(trxns >= min_value, trxns <= max_value)\n",
    "        group_filter_trx = trxns[mask]\n",
    "        trx_count = len(group_filter_trx)\n",
    "        trx_sum = np.sum(group_filter_trx)\n",
    "        group_count += 1\n",
    "        groupeddata.loc[len(groupeddata)] = [group_count, trx_count, trx_sum, \n",
    "                                             min_value, pct_range]\n",
    "        trxns = trxns[trxns > max_value]\n",
    "        if len(trxns) > 0:\n",
    "            min_value = trxns[0]\n",
    "\n",
    "    return groupeddata.to_dict('list')\n",
    "\n",
    "# ---------------------------\n",
    "# Function 4: Run scenario 9\n",
    "# ---------------------------\n",
    "def scenario9_data(data1): \n",
    "    grouped = data1.groupby('Focal_id')[['transaction_amount', 'MIN_LIMIT', 'PCT_RANGE']].apply(\n",
    "        trx_count_sum_groupwise).reset_index()\n",
    "\n",
    "    df_list = []\n",
    "    for i in grouped.index:\n",
    "        df_party = pd.DataFrame(grouped.iloc[i, -1])\n",
    "        df_party['Focal_id'] = grouped.loc[i, 'Focal_id']\n",
    "        df_list.append(df_party)\n",
    "\n",
    "    final_df = pd.concat(df_list, ignore_index=True) \n",
    "    Segment = data1.groupby('Focal_id')['Segment'].agg('max').reset_index()\n",
    "    Risk = data1.groupby('Focal_id')['Risk'].agg('max').reset_index()\n",
    "    SAR_FLAG = data1.groupby('Focal_id')['SAR_FLAG'].agg('max').reset_index()\n",
    "    \n",
    "    final_df = final_df.merge(Segment,on = 'Focal_id', how = 'left')\n",
    "    final_df = final_df.merge(Risk,on = 'Focal_id', how = 'left')\n",
    "    final_df = final_df.merge(SAR_FLAG,on = 'Focal_id', how = 'left')\n",
    "    \n",
    "    return final_df\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "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",
    "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 = [\n",
    "                \"transaction_key\",\n",
    "                \"transaction_date\",\n",
    "                \"transaction_amount\",\n",
    "                \"transaction_desc\",\n",
    "                \"benef_account_number\",\n",
    "                \"account_number\",\n",
    "                \"acc_customer_number\",\n",
    "                \"Focal_id\",\n",
    "                \"customer_name\",\n",
    "                \"date_of_birth\",\n",
    "                \"nationality\",\n",
    "                \"Segment\",\n",
    "                \"Risk\", \n",
    "                \"SAR_FLAG\"\n",
    "            ]\n",
    "        df = pd.DataFrame(row_list, columns = cols)\n",
    "        df['Segment'] = 'SME'\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",
    "        \n",
    "        return scenario_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1f20337b-8116-47e5-8743-1ba41e2df819",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# sen = Scenario()\n",
    "# sen.logic()"
   ]
  }
 ],
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