Add hd_score_g2 support and reason-based scoring

README.md

@@ -1 +1 @@
-**Hello world!!!**
+# Block for assigning a composite score

block.py

@@ -1,21 +1,37 @@
-@flowx_block
-def example_function(request: dict) -> dict:
+import logging
+from score_processing import processing
 
-  # Processing logic here...
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(name)s - %(message)s",
+)
+logger = logging.getLogger(__name__)
 
-  return {
-    "meta_info": [
-      {
-        "name": "created_date",
-        "type": "string",
-        "value": "2024-11-05"
-      }
-    ],
-    "fields": [
-      {
-        "name": "",
-        "type": "",
-        "value": ""
-      }
-    ]
+def __main__(
+        hd_score_m1: float,
+        hd_score_g1: float,
+        hd_score_s1: float,
+        hd_score_s2: float,
+        hd_score_s3: float,
+        hd_score_iso_m2: float,
+        hd_score_g2: float
+    ) -> dict:
+
+    data = {
+        "hd_score_m1": hd_score_m1,
+        "hd_score_g1": hd_score_g1,
+        "hd_score_s1": hd_score_s1,
+        "hd_score_s2": hd_score_s2,
+        "hd_score_s3": hd_score_s3,
+        "hd_score_iso_m2": hd_score_iso_m2,
+        "hd_score_g2": hd_score_g2
     }
+
+    final = processing(data)
+    logger.info(f"Scores of application: {final}")
+
+    return final
+
+# testing :
+# __main__

request_schema.json

@@ -1 +1,35 @@
-{}
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "type": "object",
+  "properties": {
+    "hd_score_m1": {
+      "type": ["number", "null"],
+      "description": "HD Fraud Score M1"
+    },
+    "hd_score_g1": {
+      "type": ["number", "null"],
+      "description": "HD Fraud Score G1"
+    },
+    "hd_score_g2": {
+      "type": ["number", "null"],
+      "description": "HD Fraud Score G1"
+    },
+    "hd_score_s1": {
+      "type": ["number", "null"],
+      "description": "HD Fraud Score S1"
+    },
+    "hd_score_s2": {
+      "type": ["number", "null"],
+      "description": "HD Fraud Score S2"
+    },
+    "hd_score_s3": {
+      "type": ["number", "null"],
+      "description": "HD Fraud Score S3"
+    },
+    "hd_score_iso_m2": {
+      "type": ["number", "null"],
+      "description": "HD Fraud Score M2"
+    }
+  },
+  "required": []
+}

requirements.txt

@@ -1 +1 @@
-{}
+

response_schema.json

@@ -1 +1,25 @@
-{}
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "type": "object",
+  "properties": {
+    "hd_score": {
+      "type": ["number", "null"],
+      "description": "HD fraud Score"
+    },
+    "recommended_action": {
+      "type": ["string", "null"],
+      "description": "Recommended Action"
+    },
+    "description": {
+      "type": ["string", "null"],
+      "description": "Description of Recommended Action"
+    },
+    "hd_action_reasoncode": {
+      "type": ["string", "null"],
+      "description": "HD Action Reasoncode"
+    }
+  }
+}
+
+
+

score_processing.py

@@ -0,0 +1,102 @@
+import logging
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(name)s - %(message)s",
+)
+logger = logging.getLogger(__name__)
+
+
+def processing(data: dict) -> dict:
+    score_threshold = 1200
+    score_threshold2 = 1225
+
+    # ---- Compute hd_score based on hierarchy ----
+    try:
+        reason_hierarchy = [
+            ("hd_score_s1", "S1 Triggered", score_threshold, True),
+            ("hd_score_s2", "S2 Triggered", score_threshold, True),
+            ("hd_score_g1", "G1 Triggered", score_threshold, True),
+            ("hd_score_s3", "S3 Triggered", score_threshold, True),
+            ("hd_score_m1", "M1 Triggered", score_threshold, True),
+            ("hd_score_iso_m2", "M2 Triggered", score_threshold2, False),
+            ("hd_score_g2", "G2 Triggered", score_threshold2, False),
+        ]
+
+        winning_score = None
+        winning_reason = "Pass"
+        winning_threshold = None
+
+        for key, reason, threshold, use_gt in reason_hierarchy:
+            score = data.get(key)
+            if score is None:
+                continue
+            passes = (score > threshold) if use_gt else (score >= threshold)
+            if passes:
+                winning_score = score
+                winning_reason = reason
+                winning_threshold = threshold
+                break
+
+        if winning_score is None:
+            winning_score = max([
+                data.get("hd_score_s1", 0),
+                data.get("hd_score_s2", 0),
+                data.get("hd_score_g1", 0),
+                data.get("hd_score_s3", 0),
+                data.get("hd_score_m1", 0),
+                data.get("hd_score_iso_m2", 0),
+                data.get("hd_score_g2", 0),
+            ])
+            winning_reason = "Pass"
+
+        hd_score = winning_score
+
+    except Exception as e:
+        logging.error(f"Error calculating hd_score values: {e}")
+        return {}
+
+    # ---- recommended_action ----
+    try:
+        if winning_reason != "Pass":
+            recommended_action = "Decline Application"
+        else:
+            recommended_action = "Pass Application"
+
+        logging.info(f"recommended_action: {recommended_action}")
+
+    except Exception as e:
+        logging.error(f"Error assigning recommended_action: {e}")
+        return {}
+
+    # ---- description ----
+    try:
+        if winning_reason != "Pass":
+            threshold_used = winning_threshold if winning_threshold is not None else score_threshold
+            description = (
+                f"HD Fraud Score is above the risk threshold {threshold_used}, "
+                f"Recommended action: Decline Application"
+            )
+        else:
+            description = (
+                "HD Fraud Score is below the risk threshold, Recommended action: Pass Application"
+            )
+
+        logging.info(f"description: {description}")
+
+    except Exception as e:
+        logging.error(f"Error creating description: {e}")
+        return {}
+
+    # ---- hd_action_reasoncode ----
+    hd_action_reasoncode = winning_reason
+    logging.info(f"hd_action_reasoncode: {hd_action_reasoncode}")
+
+    # ---- Return only original 4 fields ----
+    return {
+        "hd_score": hd_score,
+        "recommended_action": recommended_action,
+        "description": description,
+        "hd_action_reasoncode": hd_action_reasoncode,
+    }

test_block.py

@@ -0,0 +1,17 @@
+import unittest
+from block import __main__
+
+data = {'hd_score_m1': 1173.0, 'hd_score_g1': 1203.0, 'hd_score_s1': 1240.0, 'hd_score_s2': 0, 'hd_score_s3': 0, 'hd_score_iso_m2': 1001.0, 'hd_score_g2': 0.0}
+
+class TestBlock(unittest.TestCase):
+    def test_main_success(self):
+        # blockResult = main(data)
+        blockResult = __main__(**data) 
+
+        # breakpoint()
+        self.assertIsInstance(blockResult, dict, "Result should be a dictionary.")
+        self.assertIn("hd_score", blockResult, "Result dictionary should contain 'hd_score' if success.")
+
+
+if __name__ == "__main__":
+    unittest.main()