first evaluation implementation

test/test_model.py

@@ -1 +1,59 @@
-# test code for the automatic GitHub action
+import pickle
+
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_absolute_error, r2_score
+
+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 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
+    assert isinstance(model, LinearRegression)
+
+    # Load data
+    train_df = pd.read_csv(train_path)
+    test_df = pd.read_csv(test_path)
+
+    # Define target and features
+    target_col = "AimoScore"
+    unwanted_cols = ["EstimatedScore"]
+    features_cols = [
+        col
+        for col in train_df.columns
+        if col not in unwanted_cols and col != target_col
+    ]
+
+    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)
+
+
+if __name__ == "__main__":
+    model_path = "linear_regression_model.pkl"
+    load_and_evaluate_model(model_path)