Logging updated in loading

project_1.py

@@ -47,6 +47,10 @@ def get_data_paths(data_format, is_linux=False, is_hpc=False):
 
     return data_path, labels_path, saving_path
 
+# Create a logger
+logger = logging.getLogger(__name)
+logging.basicConfig(filename='error_log.txt', level=logging.ERROR)
+
 # Standardize the data
 def standard_scaling(data):
     scaler = StandardScaler()
@@ -75,27 +79,124 @@ def load_data(data_path, labels_path, dataset_size=2, train=True, standardize=Tr
         for seg in dir_list_seg[:len(dir_list_seg)]:  # Limiting to 50 segments
             seg_path = os.path.join(data_path_UID, seg)
 
-            if data_format == 'csv' and seg.endswith('.csv'):
-                time_freq_plot = np.array(pd.read_csv(seg_path, header=None))
-                time_freq_tensor = torch.Tensor(time_freq_plot).reshape(1, 128, 128)
-            elif data_format == 'png' and seg.endswith('.png'):
-                img = Image.open(seg_path)
-                img_data = np.array(img)
-                time_freq_tensor = torch.Tensor(img_data).unsqueeze(0)
-            else:
-                continue  # Skip other file formats
+            try:
+                if data_format == 'csv' and seg.endswith('.csv'):
+                    time_freq_plot = np.array(pd.read_csv(seg_path, header=None))
+                    time_freq_tensor = torch.Tensor(time_freq_plot).reshape(1, 128, 128)
+                elif data_format == 'png' and seg.endswith('.png'):
+                    img = Image.open(seg_path)
+                    img_data = np.array(img)
+                    time_freq_tensor = torch.Tensor(img_data).unsqueeze(0)
+                else:
+                    continue  # Skip other file formats
+
+                X_data.append(time_freq_tensor)
+                X_data_original.append(time_freq_tensor.clone())  # Store a copy of the original data
 
-            X_data.append(time_freq_tensor)
-            X_data_original.append(time_freq_tensor.clone())  # Store a copy of the original data
+                segment_names.append(seg.split('_filt')[0])  # Extract and store segment names
 
-            segment_names.append(seg.split('_filt')[0])  # Extract and store segment names
+            except Exception as e:
+                logger.error(f"Error processing segment: {seg} in UID: {UID}. Exception: {str(e)}")
+                # You can also add more information to the error log, such as the value of time_freq_plot.
+                continue  # Continue to the next segment
 
     X_data = torch.cat(X_data, 0)
     X_data_original = torch.cat(X_data_original, 0)
 
     if standardize:
         X_data = standard_scaling(X_data)  # Standardize the data
+    # Extract labels from CSV files
+    labels = extract_labels(UID_list, labels_path, segment_names)
+
+    important_labels = [0.0, 1.0, 2.0, 3.0]  # List of important labels
+
+    # Initialize labels for segments as unlabeled (-1)
+    segment_labels = {segment_name: -1 for segment_name in segment_names}
+
+    for UID in labels.keys():
+        if UID not in UID_list:
+            # Skip UIDs that are not in the dataset
+            continue
+
+        label_data, label_segment_names = labels[UID]
+
+        for idx, segment_label in enumerate(label_data):
+            segment_name = label_segment_names[idx]
+            if segment_label in important_labels:
+                segment_labels[segment_name] = segment_label
+            else:
+                # Set labels that are not in the important list as -1 (Unlabeled)
+                segment_labels[segment_name] = -1
+
+    # Return all segments along with labels
+    if return_all:
+        return X_data_original, X_data, segment_names, segment_labels, segment_labels.values()
+
+    # Filter out segments that are unlabeled (-1)
+    filtered_segment_names = [segment_name for segment_name, label in segment_labels.items() if label != -1]
+
+    # Filter data to match the filtered segment names
+    filtered_data = torch.stack([X_data[segment_names.index(segment_name)] for segment_name in filtered_segment_names])
+
+    # Return labeled and unlabeled segments along with labels
+    if return_all == 'labeled':
+        return X_data_original, filtered_data, filtered_segment_names, {seg: segment_labels[seg] for seg in filtered_segment_names}, {seg: segment_labels[seg] for seg in filtered_segment_names}.values()
 
+    # Return unlabeled segments along with labels
+    if return_all == 'unlabeled':
+        unlabeled_segment_names = [segment_name for segment_name, label in segment_labels.items() if label == -1]
+        unlabeled_data = torch.stack([X_data[segment_names.index(segment_name)] for segment_name in unlabeled_segment_names])
+        return X_data_original, unlabeled_data, unlabeled_segment_names, {seg: segment_labels[seg] for seg in unlabeled_segment_names}, {seg: segment_labels[seg] for seg in unlabeled_segment_names}.values()
+
+    # By default, return only labeled segments along with labels
+    return X_data_original, filtered_data, filtered_segment_names, {seg: segment_labels[seg] for seg in filtered_segment_names}, {seg: segment_labels[seg] for seg in filtered_segment_names}.values()
+
+def load_data(data_path, labels_path, dataset_size=2, train=True, standardize=True, data_format='csv', return_all=False):
+    if data_format not in ['csv', 'png']:
+        raise ValueError("Invalid data_format. Choose 'csv' or 'png.")
+
+    dir_list_UID = os.listdir(data_path)
+    UID_list = dir_list_UID[:dataset_size] if train else dir_list_UID[dataset_size:]
+
+    X_data = []  # Store all data
+    X_data_original = []  # Store original data without standardization
+    segment_names = []
+
+    for UID in UID_list:
+        data_path_UID = os.path.join(data_path, UID)
+        dir_list_seg = os.listdir(data_path_UID)
+
+        for seg in dir_list_seg[:len(dir_list_seg)]:  # Limiting to 50 segments
+            seg_path = os.path.join(data_path_UID, seg)
+
+            try:
+                if data_format == 'csv' and seg.endswith('.csv'):
+                    time_freq_plot = np.array(pd.read_csv(seg_path, header=None))
+                    time_freq_tensor = torch.Tensor(time_freq_plot).reshape(1, 128, 128)
+                elif data_format == 'png' and seg.endswith('.png'):
+                    img = Image.open(seg_path)
+                    img_data = np.array(img)
+                    time_freq_tensor = torch.Tensor(img_data).unsqueeze(0)
+                else:
+                    continue  # Skip other file formats
+
+                X_data.append(time_freq_tensor)
+                X_data_original.append(time_freq_tensor.clone())  # Store a copy of the original data
+
+                segment_names.append(seg.split('_filt')[0])  # Extract and store segment names
+
+            except Exception as e:
+                logger.error(f"Error processing segment: {seg} in UID: {UID}. Exception: {str(e)}")
+                logger.error(f"Error processing segment: {time_freq_plot.size()} in UID: {UID}. Exception: {str(e)}")
+                # You can also add more information to the error log, such as the value of time_freq_plot.
+                continue  # Continue to the next segment
+
+    X_data = torch.cat(X_data, 0)
+    X_data_original = torch.cat(X_data_original, 0)
+
+    if standardize:
+        X_data = standard_scaling(X_data)  # Standardize the data
+
     # Extract labels from CSV files
     labels = extract_labels(UID_list, labels_path, segment_names)
 
@@ -143,6 +244,7 @@ def load_data(data_path, labels_path, dataset_size=2, train=True, standardize=Tr
     return X_data_original, filtered_data, filtered_segment_names, {seg: segment_labels[seg] for seg in filtered_segment_names}, {seg: segment_labels[seg] for seg in filtered_segment_names}.values()
 
 
+
 def extract_labels(UID_list, labels_path, segment_names):
     labels = {}
     for UID in UID_list: