Create utils/validation.py

utils/validation.py

@@ -0,0 +1,56 @@
+import pandas as pd
+
+
+class InputValidationError(Exception):
+    """Custom exception for input validation errors."""
+    pass
+
+
+def validate_and_prepare_input(input_df: pd.DataFrame, model):
+    """
+    Validates input dataframe against model expected features.
+    Returns a clean dataframe ready for prediction.
+    """
+
+    if not isinstance(input_df, pd.DataFrame):
+        raise InputValidationError("Input must be a pandas DataFrame.")
+
+    # Get expected feature names from trained XGBoost model
+    try:
+        expected_features = model.get_booster().feature_names
+    except Exception:
+        raise InputValidationError("Unable to retrieve model feature names.")
+
+    # -------------------------
+    # 1️⃣ Check missing columns
+    # -------------------------
+    missing_cols = set(expected_features) - set(input_df.columns)
+    if missing_cols:
+        raise InputValidationError(
+            f"Missing required columns: {list(missing_cols)}"
+        )
+
+    # -------------------------
+    # 2️⃣ Check extra columns
+    # -------------------------
+    extra_cols = set(input_df.columns) - set(expected_features)
+    if extra_cols:
+        raise InputValidationError(
+            f"Unexpected columns provided: {list(extra_cols)}"
+        )
+
+    # -------------------------
+    # 3️⃣ Enforce numeric types
+    # -------------------------
+    for col in expected_features:
+        if not pd.api.types.is_numeric_dtype(input_df[col]):
+            raise InputValidationError(
+                f"Column '{col}' must be numeric."
+            )
+
+    # -------------------------
+    # 4️⃣ Reorder columns safely
+    # -------------------------
+    input_df = input_df[expected_features]
+
+    return input_df