evaluate_visualization.py

import torch
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns

# Evaluation and Visualization class
class EvaluateVisualization:
  @staticmethod
  def plot_loss_curve(train_losses, val_losses):
    plt.figure(figsize=(10, 5))
    plt.plot(train_losses, label='Training Loss')
    plt.plot(val_losses, label='Validation Loss')
    plt.title('Training and Validation Loss')
    plt.xlabel('Epochs')
    plt.ylabel('Loss')
    plt.legend()
    plt.show()

  @staticmethod
  def plot_confusion_matrix(y_true, y_pred, class_names):
    conf_matrix = confusion_matrix(y_true, y_pred)
    plt.figure(figsize=(8 ,8))
    sns.heatmap(conf_matrix, annot=True, fmt="d", cmap="Oranges", xticklabels=class_names, yticklabels=class_names)
    plt.title('Confusion Matrix')
    plt.xlabel('Predicted')
    plt.ylabel('True')
    plt.show