Advanced S Series Models/Rules

README.md

@@ -1 +1 @@
-**Hello world!!!**
+# S Series Model

block.py

@@ -1,21 +1,34 @@
-@flowx_block
+import logging
-def example_function(request: dict) -> dict:
+from rules_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 {
+def __main__(
-    "meta_info": [
+        hd_score_m1: float,
-      {
+        hd_score_g1: float,
-        "name": "created_date",
+        cluster_size_users_v2: int,
-        "type": "string",
+        target_connected_30_sum: float,
-        "value": "2024-11-05"
+        email_cnt: int,
-      }
+        rejected_app_count: float,
-    ],
+        app_dt_day_cnt: int,
-    "fields": [
+    ) -> dict:
-      {
+    # Create a dictionary instead of using pandas DataFrame
-        "name": "",
+    data = {
-        "type": "",
+        "hd_score_m1": hd_score_m1,
-        "value": ""
+        "hd_score_g1": hd_score_g1,
-      }
+        "cluster_size_users_v2": cluster_size_users_v2,
-    ]
+        "target_connected_30_sum": target_connected_30_sum,
-  }
+        "email_cnt": email_cnt,
+        "rejected_app_count": rejected_app_count,
+        "app_dt_day_cnt": app_dt_day_cnt,
+    }
+
+    final = processing(data)
+    logger.info(f"scores of application: {final}")
+
+    return final

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"
+      },
+    "cluster_size_users_v2": {
+      "type": ["number", "null"],
+      "description": "Size of the user cluster in version 2."
+    },
+    "target_connected_30_sum": {
+      "type": ["number", "null"],
+      "description": "Sum of target connections within 30 days."
+    },
+    "email_cnt": {
+      "type": ["number", "null"],
+      "description": "Count of emails associated with the application."
+    },
+    "rejected_app_count": {
+      "type": ["number", "null"],
+      "description": "Count of rejected applications for the applicant."
+    },
+    "app_dt_day_cnt": {
+      "type": ["number", "null"],
+      "description": "Number of application days counted."
+    }
+  },
+  "required": []
+}

requirements.txt

@@ -1 +1,3 @@
-{}
+
+
+

response_schema.json

@@ -1 +1,29 @@
-{}
+{
+  "$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_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"
+    }
+  }
+}
+
+
+

rules_processing.py

@@ -0,0 +1,54 @@
+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:
+    try:
+        hd_score_s1 = (
+            min(1225 + (data["cluster_size_users_v2"] * 5), 1390)
+            if data["cluster_size_users_v2"] >= 3 and data["hd_score_m1"] >= 1140
+            else 0
+        )
+        logger.info(f"score_s1 calculated: {hd_score_s1}")
+    except Exception as e:
+        logger.error(f"Error processing score_s1 calculations: {e}")
+        return {}
+
+    try:
+        hd_score_s2 = (
+            min(1215 + (data["cluster_size_users_v2"] * 5), 1380)
+            if data["cluster_size_users_v2"] >= 2 and data["app_dt_day_cnt"] == 1
+            else 0
+        )
+        logger.info(f"score_s2 calculated: {hd_score_s2}")
+    except Exception as e:
+        logger.error(f"Error processing score_s2 calculations: {e}")
+        return {}
+
+    try:
+        target_connected_30_sum = data.get("target_connected_30_sum", 0) or 0  # Handling None case
+        hd_score_s3 = (
+            min(1250 + (target_connected_30_sum * 5), 1400)
+            if target_connected_30_sum >= 1
+            else 0
+        )
+        logger.info(f"score_s3 calculated: {hd_score_s3}")
+    except Exception as e:
+        logger.error(f"Error processing score_s3 calculations: {e}")
+        return {}
+
+    # Return the final results as a dictionary
+    return {
+        "hd_score_m1": data["hd_score_m1"],
+        "hd_score_g1": data["hd_score_g1"],
+        "hd_score_s1": hd_score_s1,
+        "hd_score_s2": hd_score_s2,
+        "hd_score_s3": hd_score_s3,
+    }
+
+

test_block.py

@@ -0,0 +1,16 @@
+import unittest
+from block import __main__
+
+data = {'hd_score_m1': 1173.0, 'hd_score_g1': 1203.0, 'cluster_size_users_v2': 3.0, 'target_connected_30_sum': 0.0, 'email_cnt': 1.0, 'rejected_app_count': 2.0, 'app_dt_day_cnt': 3.0}
+
+class TestBlock(unittest.TestCase):
+    def test_main_success(self):
+        blockResult = __main__(**data) 
+
+        # breakpoint()
+        self.assertIsInstance(blockResult, dict, "Result should be a dictionary.")
+        self.assertIn("hd_score_s1", blockResult, "Result dictionary should contain 'hd_score_s1' if success.")
+
+
+if __name__ == "__main__":
+    unittest.main()