Update paths and file names

project/src/README.md

@@ -7,7 +7,7 @@
 This directory contains the scripts used for the project:
 - `classify_with_baseline.py` - Classify images using a random and a majority classifier.
 - `classify_with_basic_classifiers.py` - Classify images using a decision tree, random forest or a naive bayes classifier.
-  - `find_optimal_parameters.py` - Helper script to find the optimal parameters for a decision tree, random forest or a naive bayes classifier.
+  - `find_optimal_parameters_basic.py` - Helper script to find the optimal parameters for a decision tree, random forest or a naive bayes classifier.
 - `classify_with_CNN.py` - Classify images using a convolutional neural network.
   - `evaluate_cnn.py` Evaluate the trained models and create plots
 

project/src/classify_with_basic_classifiers.py

@@ -26,7 +26,7 @@ from sklearn.metrics import accuracy_score, confusion_matrix, precision_recall_f
 
 
 # Local imports for hyperparameter optimization
-from find_optimal_parameters import find_best_params_for_decision_tree, find_best_params_for_random_forest, find_best_params_for_naive_bayes
+from find_optimal_parameters_basic import find_best_params_for_decision_tree, find_best_params_for_random_forest, find_best_params_for_naive_bayes
 
 
 def read_and_resize_image(image_path: str, resize: tuple) -> np.ndarray:
@@ -324,7 +324,7 @@ def train_and_evaluate_classifier(classifier, dataset, feature_description, resi
     logging.info(f"Confusion matrix saved to ../figures/{classifier_name[classifier.__name__]}/{classifier.__name__}_{feature_description}_confusion_matrix_{params_as_string}.png")
         
     # Save the results to a simple text file
-    with open ("results.txt", "a") as f:
+    with open ("results_basic_classifiers.txt", "a") as f:
         f.write(f"Results for {classifier.__name__} classifier on {feature_description} images:\n")
         f.write(f"Optimal parameters: {params}\n")
         f.write(str(results) + "\n\n")