add dynamic feature resolving

test/test_model.py

@@ -1,57 +1,86 @@
 import pickle
-
 import pandas as pd
 from sklearn.linear_model import LinearRegression
 from sklearn.metrics import mean_absolute_error, r2_score
+import re  # For using regular expressions
 
 train_path = "../Datasets_all/A2_dataset_80.csv"
 test_path = "../Datasets_all/A2_dataset_20.csv"
 
-# validating the linear regression model based on
-# https://medium.com/@_SSP/validating-machine-learning-regression-models-a-comprehensive-guide-b94fd94e339c
-
+def extract_missing_feature(error_message):
+    # Use regex to find feature names in the ValueError message
+    match = re.search(r"Feature names unseen at fit time:\s*-\s*(.+)", error_message)
+    if match:
+        return match.group(1).strip().split(', ')  # Return list of feature names
+    return []
 
 def load_and_evaluate_model(model_path):
     # Load the pickled model
     with open(model_path, "rb") as f:
         model = pickle.load(f)
 
-    # check the model type
+    # Check the model type
     assert isinstance(model, LinearRegression)
 
     # Load data
     train_df = pd.read_csv(train_path)
     test_df = pd.read_csv(test_path)
 
-    # Define target and features
+    # Define target and features dynamically
     target_col = "AimoScore"
     unwanted_cols = ["EstimatedScore"]
+
     features_cols = [
-        col
-        for col in train_df.columns
+        col for col in train_df.columns
         if col not in unwanted_cols and col != target_col
     ]
 
+    # Initialize features for prediction
     X_test = test_df[features_cols]
     y_test = test_df[target_col]
 
-    # Predict on test set
-    y_pred = model.predict(X_test)
-
-    # Evaluate
-    mae = mean_absolute_error(y_test, y_pred)
-    r2 = r2_score(y_test, y_pred)
-
-    print(f"Mean Absolute Error on test set: {mae:.4f}")
-    print(f"R^2 score on test set: {r2:.4f}")
-
-    # assert the threeshold values
-    assert mae < 0.15, "Mean Absolute Error is too high"
-    assert r2 > 0.5, "R^2 score is too low"
-
-    # Save predictions to CSV
-    test_df["Predicted_AimoScore"] = y_pred
-    test_df.to_csv("predicted_test.csv", index=False)
+    #define y_pred
+    y_pred = 0
+
+    # Continue to predict until no ValueErrors occur
+    while True:
+        try:
+            # Predict on test set
+            y_pred = model.predict(X_test)
+
+            # Evaluate
+            mae = mean_absolute_error(y_test, y_pred)
+            r2 = r2_score(y_test, y_pred)
+
+            print(f"Mean Absolute Error on test set: {mae:.4f}")
+            print(f"R^2 score on test set: {r2:.4f}")
+
+            # Assert the threshold values
+            assert mae < 0.15, "Mean Absolute Error is too high"
+            assert r2 > 0.5, "R^2 score is too low"
+            break  # Exit the loop if no errors occur
+
+        except ValueError as e:
+            print(f"Error during prediction: {e}")
+
+            # Extract missing features from the error message
+            missing_features = extract_missing_feature(str(e))
+
+            # Remove missing features from X_test and features_cols
+            if missing_features:
+                print(f"Removing missing features from test set: {missing_features}")
+                # Update features list
+                features_cols = [col for col in features_cols if col not in missing_features]
+                # Update X_test
+                X_test = X_test[features_cols]
+            else:
+                print("No more features can be removed, stopping execution.")
+                break  # Exit if there are no more features to remove
+
+    if 'y_pred' in locals():  # Check if predictions were made
+        # Save predictions to CSV
+        test_df["Predicted_AimoScore"] = y_pred
+        test_df.to_csv("predicted_test.csv", index=False)
 
 
 if __name__ == "__main__":