refactor(ml): clean up model_selection.py by removing unused code and improving function structure

code/src/ml/model_selection.py

@@ -8,7 +8,7 @@ from joblib import load
 def create_ready_data(
     stft_data_path: str,
     stratify: np.ndarray = None,
-) -> tuple:
+) -> tuple[pd.DataFrame, np.ndarray]:
     """
     Create a stratified train-test split from STFT data.
 
@@ -22,7 +22,7 @@ def create_ready_data(
     Returns:
     --------
     tuple
-        (X_train, X_test, y_train, y_test) - Split datasets
+        (pd.DataFrame, np.ndarray) - Combined data and corresponding labels
     """
     ready_data = []
     for file in os.listdir(stft_data_path):
@@ -155,7 +155,7 @@ def train_and_evaluate_model(
     except Exception as e:
         result["error"] = f"Training error: {str(e)}"
         return result
-def plot_confusion_matrix(results_sensor, y_test):
+def plot_confusion_matrix(results_sensor, y_test, title):
     """
     Plot confusion matrices for each model in results_sensor1.
 
@@ -193,8 +193,7 @@ def plot_confusion_matrix(results_sensor, y_test):
         # Plot
         disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)
         disp.plot(cmap=plt.cm.Blues)  # You can change colormap
-        plt.title(f"{i['model']} {i['sensor']} Test")
-        plt.show()
+        plt.title(f"{title}")
 
 def calculate_label_percentages(labels):
     """
@@ -255,9 +254,9 @@ def inference_model(
                             fs=1024, 
                             window=hann(1024), 
                             nperseg=1024, 
-                            noverlap=512
+                            noverlap=1024-512
                             )
     data = pd.DataFrame(np.abs(Zxx).T, columns=[f"Freq_{freq:.2f}" for freq in np.linspace(0, 1024/2, Zxx.shape[1])])
     data = data.rename(columns={"Freq_0.00": "00"}) # To match the model input format
     model = load(models)  # Load the model from the provided path
-    return calculate_label_percentages(model.predict(data))
+    return calculate_label_percentages(model.predict(data.iloc[:21,:]))