Create helpers.py

blockchain_integration/pi_network/daictd/utils/helpers.py

@@ -0,0 +1,66 @@
+import os
+import logging
+import torch
+import numpy as np
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+
+def create_logger(name, level=logging.INFO):
+    logger = logging.getLogger(name)
+    logger.setLevel(level)
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    handler = logging.StreamHandler()
+    handler.setFormatter(formatter)
+    logger.addHandler(handler)
+    return logger
+
+def ensure_dir(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+def save_checkpoint(model, optimizer, epoch, path):
+    torch.save({
+        'model_state_dict': model.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'epoch': epoch
+    }, path)
+
+def load_checkpoint(model, optimizer, path):
+    checkpoint = torch.load(path)
+    model.load_state_dict(checkpoint['model_state_dict'])
+    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+    return checkpoint['epoch']
+
+def calculate_metrics(y_true, y_pred):
+    accuracy = accuracy_score(y_true, y_pred)
+    report = classification_report(y_true, y_pred)
+    matrix = confusion_matrix(y_true, y_pred)
+    return accuracy, report, matrix
+
+def plot_confusion_matrix(matrix, classes, path):
+    import matplotlib.pyplot as plt
+    plt.imshow(matrix, interpolation='nearest', cmap='Blues')
+    plt.title('Confusion Matrix')
+    plt.colorbar()
+    tick_marks = np.arange(len(classes))
+    plt.xticks(tick_marks, classes, rotation=45)
+    plt.yticks(tick_marks, classes)
+    plt.tight_layout()
+    plt.ylabel('True label')
+    plt.xlabel('Predicted label')
+    plt.savefig(path)
+
+def plot_loss_curve(losses, path):
+    import matplotlib.pyplot as plt
+    plt.plot(losses)
+    plt.xlabel('Epoch')
+    plt.ylabel('Loss')
+    plt.title('Loss Curve')
+    plt.savefig(path)
+
+def plot_accuracy_curve(accuracies, path):
+    import matplotlib.pyplot as plt
+    plt.plot(accuracies)
+    plt.xlabel('Epoch')
+    plt.ylabel('Accuracy')
+    plt.title('Accuracy Curve')
+    plt.savefig(path)