README.md

@@ -1,10 +1 @@
-## Overview
+**Hello world!!!**
-This block (`block.py`) is responsible for loading and scoring the model.
-
-## Key Inputs & Outputs
-- **Request**: Refer to `request_schema.json` for detailed input fields and validation rules.  
-- **Response**: Refer to `response_schema.json` for the returned structure and data types.
-
-## Implementation Details
-- All core logic resides in `block.py` within the `__main__` function.
-- Example usage and validation are demonstrated in `test_block.py`.

block.py

@@ -1,65 +1,21 @@
-import logging
+@flowx_block
-import xgboost as xgb
+def example_function(request: dict) -> dict:
-import joblib
-import pandas as pd
 
-# Configure logging
+  # Processing logic here...
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s [%(levelname)s] %(name)s - %(message)s",
-)
-logger = logging.getLogger(__name__)
 
-def __main__(pti : float, score_results : float, BALMAG01 : float, revolving_amount_monthly_payment : float, closed_with_balance_amount_current_balance : float, AT31S : int, AT20S : int, BC21S : int, record_counts_revolving_trade_count : int, record_counts_total_trade_count : int, PAYMNT10 : float, AGG102 : float, total_amount_high_credit : float, revolving_amount_current_balance : float, total_amount_current_balance : float, REV83 : float, revolving_amount_high_credit : float, closed_with_balance_amount_monthly_payment : float, revolving_amount_percent_available_credit : float, AGG101 : float, revolving_amount_credit_limit : float, AT09S : int, US01S : int)->dict:
+  return {
-
+    "meta_info": [
-    input_data = {
+      {
-        "pti": pti, "score_results": score_results, "BALMAG01": BALMAG01,
+        "name": "created_date",
-        "revolving_amount_monthly_payment": revolving_amount_monthly_payment,
+        "type": "string",
-        "closed_with_balance_amount_current_balance": closed_with_balance_amount_current_balance,
+        "value": "2024-11-05"
-        "AT31S": AT31S, "AT20S": AT20S, "BC21S": BC21S,
+      }
-        "record_counts_revolving_trade_count": record_counts_revolving_trade_count,
+    ],
-        "record_counts_total_trade_count": record_counts_total_trade_count, "PAYMNT10": PAYMNT10,
+    "fields": [
-        "AGG102": AGG102, "total_amount_high_credit": total_amount_high_credit,
+      {
-        "revolving_amount_current_balance": revolving_amount_current_balance,
+        "name": "",
-        "total_amount_current_balance": total_amount_current_balance, "REV83": REV83,
+        "type": "",
-        "revolving_amount_high_credit": revolving_amount_high_credit,
+        "value": ""
-        "closed_with_balance_amount_monthly_payment": closed_with_balance_amount_monthly_payment,
+      }
-        "revolving_amount_percent_available_credit": revolving_amount_percent_available_credit,
+    ]
-        "AGG101": AGG101, "revolving_amount_credit_limit": revolving_amount_credit_limit,
-        "AT09S": AT09S, "US01S": US01S, "has_mortgage": None
   }
-
-    # Load model
-    try:
-        model = joblib.load("./xgboost_model.joblib") 
-        # model = joblib.load("C:/Users/cbollu/Downloads/test_blocks/test_blocks/sequence-1/pd_v2_processing/xgboost_model.joblib") 
-    except Exception as e:
-        logger.exception("An unexpected error occurred while loading the model.")
-        raise e
-    df_pre_processed = pd.DataFrame(input_data, index=[0])
-
-    if df_pre_processed.empty:
-        print("PD V2 Pre Processed DataFrame is empty.")
-
-    expected_features = model.feature_names
-    actual_features = df_pre_processed.columns.tolist()
-    missing_features = [feature for feature in expected_features if feature not in actual_features]
-    
-    # Add missing features as None (NaN) values
-    for feature in missing_features:
-        df_pre_processed[feature] = None
-
-    # Convert object columns to categorical
-    for col in df_pre_processed.columns:
-        if df_pre_processed[col].dtype == 'object':
-            df_pre_processed[col] = pd.Categorical(df_pre_processed[col])
-
-    # Prepare data for prediction
-    dmatrix = xgb.DMatrix(df_pre_processed[expected_features], enable_categorical=True)
-    
-    # Make prediction
-    prediction = model.predict(dmatrix)[0]
-
-    logger.info(f"PD V2 Predicted Score: {prediction}")
-
-    return {"probability": float(prediction)}

request_schema.json

@@ -1,99 +1 @@
-{
+{}
-  "$schema": "http://json-schema.org/draft-07/schema#",
-  "type": "object",
-  "properties": {
-    "pti": {
-      "type": ["number", "null"],
-      "description": "external + internal monthly payment to income ratio"
-    },
-    "score_results": {
-      "type": ["number", "null"],
-      "description": "TransUnion score"
-    },
-    "BALMAG01": {
-      "type": ["number", "null"],
-      "description": "Non-mortgage balance magnitude"
-    },
-    "revolving_amount_monthly_payment": {
-      "type": ["number", "null"],
-      "description": "Minimum amount the borrower is required to pay each month to maintain the account in good standing"
-    },
-    "closed_with_balance_amount_current_balance": {
-      "type": ["number", "null"],
-      "description": "The current balance of closed credit accounts"
-    },
-    "AT31S": {
-      "type": ["integer", "null"],
-      "description": "Percentage of open trades > 75% of credit line verified in past 12 months"
-    },
-    "AT20S": {
-      "type": ["integer", "null"],
-      "description": "Months since oldest trade opened"
-    },
-    "BC21S": {
-      "type": ["integer", "null"],
-      "description": "Months since most recent credit card trade opened"
-    },
-    "record_counts_revolving_trade_count": {
-      "type": ["integer", "null"],
-      "description": "Records in the database related to revolving trade accounts (a credit card account)"
-    },
-    "record_counts_total_trade_count": {
-      "type": ["integer", "null"],
-      "description": "Total number of trade-related (transaction) records"
-    },
-    "PAYMNT10": {
-      "type": ["number", "null"],
-      "description": "Number of payments in the last quarter"
-    },
-    "AGG102": {
-      "type": ["number", "null"],
-      "description": "Aggregate non-mortgage balances for month 2"
-    },
-    "total_amount_high_credit": {
-      "type": ["number", "null"],
-      "description": "The highest credit amount extended across all credit accounts"
-    },
-    "revolving_amount_current_balance": {
-      "type": ["number", "null"],
-      "description": "The current owed balance on revolving credit accounts"
-    },
-    "total_amount_current_balance": {
-      "type": ["number", "null"],
-      "description": "The total current balance across all credit accounts"
-    },
-    "REV83": {
-      "type": ["number", "null"],
-      "description": "Months since a revolving account last exceeded 75% utilization"
-    },
-    "revolving_amount_high_credit": {
-      "type": ["number", "null"],
-      "description": "The highest credit amount that has been extended to the borrower in revolving credit accounts"
-    },
-    "closed_with_balance_amount_monthly_payment": {
-      "type": ["number", "null"],
-      "description": "The monthly payment amount for closed credit accounts (loans)"
-    },
-    "revolving_amount_percent_available_credit": {
-      "type": ["number", "null"],
-      "description": "The percentage of available credit that has been utilized in revolving credit accounts"
-    },
-    "AGG101": {
-      "type": ["number", "null"],
-      "description": "Aggregate non-mortgage balances for month 1"
-    },
-    "revolving_amount_credit_limit": {
-      "type": ["number", "null"],
-      "description": "The total credit limit on revolving credit accounts"
-    },
-    "AT09S": {
-      "type": ["integer", "null"],
-      "description": "Number of trades opened in past 24 months"
-    },
-    "US01S": {
-      "type": ["integer", "null"],
-      "description": "Number of unsecured installment trades"
-    }
-  },
-  "required": []
-}

requirements.txt

@@ -1,5 +1 @@
-jsonschema==4.23.0
+{}
-xgboost==1.7.5
-joblib==1.3.2
-pandas==2.2.2
-numpy==1.23.5

response_schema.json

@@ -1,10 +1 @@
-{
+{}
-    "$schema": "http://json-schema.org/draft-07/schema#",
-    "type": "object",
-    "properties": {
-      "probability": {
-        "type": "number",
-        "description": "Model predicted score."
-      }
-    }
-  }

test_block.py

@@ -1,15 +0,0 @@
-import unittest
-from block import __main__
-
-class TestBlock(unittest.TestCase):
-
-    def test_main_success(self):
-        result = __main__(pti= 0.3277136364,score_results= 600.0,BALMAG01= 196.0,revolving_amount_monthly_payment= 56.0,closed_with_balance_amount_current_balance= 8411.0,AT31S= 71.0,AT20S= 166.0,BC21S= 4.0,record_counts_revolving_trade_count= 9.0,record_counts_total_trade_count= 18.0,PAYMNT10= 4.0,AGG102= 24994.0,total_amount_high_credit= 53807.0,revolving_amount_current_balance= 1635.0,total_amount_current_balance= 38353.0,REV83= 0.0,revolving_amount_high_credit= 1720.0,closed_with_balance_amount_monthly_payment= 0.0,revolving_amount_percent_available_credit= 18.0,AGG101= 11043.0,revolving_amount_credit_limit= 2000.0,AT09S= 4.0,US01S= 0.0)
-        self.assertAlmostEqual(result['probability'], 0.33663413, places=7)
-
-    # def test_main_invalid_input(self):
-    #     with self.assertRaises(TypeError):
-    #         __main__(pti= 231,score_results= 600.0,BALMAG01= 196.0,revolving_amount_monthly_payment= 56.0,closed_with_balance_amount_current_balance= 8411.0,AT31S= 71.0,AT20S= 166.0,BC21S= 4.0,record_counts_revolving_trade_count= 9.0,record_counts_total_trade_count= 18.0,PAYMNT10= 4.0,AGG102= 24994.0,total_amount_high_credit= 53807.0,revolving_amount_current_balance= 1635.0,total_amount_current_balance= 38353.0,REV83= 0.0,revolving_amount_high_credit= 1720.0,closed_with_balance_amount_monthly_payment= 0.0,revolving_amount_percent_available_credit= 18.0,AGG101= 11043.0,revolving_amount_credit_limit= 2000.0,AT09S= 4.0,US01S= 0.0)
-
-if __name__ == "__main__":
-    unittest.main()