added unit tests for model

Author: ol1g3

src/utils/logger.py

@@ -4,8 +4,7 @@
 
 class PipelineLogger(logging.Logger):
     def __init__(self, name, log_file=None, level=logging.INFO):
-        self.logger = logging.getLogger(name)
-        self.logger.setLevel(level)
+        super().__init__(name, level)
 
         # Configure handlers based on parameters
         self._setup_handlers(log_file, level)
@@ -26,6 +25,6 @@ def _setup_handlers(self, log_file, level):
                 "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
             )
             f_handler.setFormatter(f_format)
-            self.logger.addHandler(f_handler)
+            self.addHandler(f_handler)
 
-        self.logger.addHandler(c_handler)
+        self.addHandler(c_handler)

tests/test_data_loader.py

@@ -77,6 +77,12 @@ def main():
         print(f"Test failed: {e}", file=sys.stderr)
         sys.exit(1)
 
+    try:
+        test_preprocessor()
+    except Exception as e:
+        print(f"Test failed: {e}", file=sys.stderr)
+        sys.exit(1)
+
     print("All DataLoader & Preprocessor tests passed.")
     sys.exit(0)
 

tests/test_model.py

@@ -0,0 +1,76 @@
+import sys
+import os
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../src")))
+
+import pandas as pd
+from src.data.loader import DataLoader
+from src.data.preprocessor import DataPreprocessor
+from src.model.model import FraudDetectionModel
+import numpy as np
+
+
+def detect_date_columns(df):
+    date_columns = []
+    for col in df.columns:
+        try:
+            temp_series = pd.to_datetime(df[col], errors="coerce")
+            if temp_series.notna().any():
+                date_columns.append(col)
+        except Exception:
+            pass
+    return date_columns
+
+
+def test_model() -> float:
+
+    d = DataLoader("data/processed")
+
+    d.load_data("train.csv", "test.csv")
+
+    train, val, test = d.train_valid_split(0.2)
+
+    date_columns = detect_date_columns(train.copy())
+
+    categorical = [
+        col
+        for col in train.select_dtypes(include=["object"]).columns
+        if col not in date_columns
+    ]
+    numerical = train.select_dtypes(include=["float64", "int64"]).columns
+
+    p = DataPreprocessor(categorical, numerical, "is_fraud", date_columns)
+
+    p = p.fit(train)
+    X, y = p.transform(train)
+    y = np.array(y).astype(np.float64)
+
+    fd = FraudDetectionModel(
+        X.shape[1],
+    )
+
+    fd.train(X, y)
+    ans = fd.evaluate(X, y)
+
+    return ans["accuracy"]
+
+
+def main():
+    accuracy = -1
+    try:
+        accuracy = test_model()
+    except Exception as e:
+        print(f"Test failed: {e}", file=sys.stderr)
+        sys.exit(1)
+
+    # Regression test
+    if accuracy < 0.8:
+        print("Model accuracy too low:", accuracy, file=sys.stderr)
+        sys.exit(1)
+
+    print("All model tests passed.")
+    sys.exit(0)
+
+
+if __name__ == "__main__":
+    main()