symmetrized confusion matrix

[yanivboker]

I made a symmetrized confusion matrix so it will sum up the confusion from pairs of labels (shirt->t-shirt and t-shirt->shirt)

from torchmetrics import ConfusionMatrix
from mlxtend.plotting import plot_confusion_matrix

2. Setup confusion matrix instance and compare predictions to targets

confmat = ConfusionMatrix(num_classes=len(class_names), task='multiclass')
confmat_tensor = confmat(preds=y_pred_tensor,
target=test_data.targets)

3. Plot the confusion matrix

fig, ax = plot_confusion_matrix(
conf_mat=confmat_tensor.numpy(), # matplotlib likes working with NumPy
class_names=class_names, # turn the row and column labels into class names
figsize=(10, 7)
);

Function to symmetrize a confusion matrix

def symmetrize_confusion_matrix(matrix):
return matrix + matrix.T - np.diag(matrix.diagonal())

Function to plot the symmetrized confusion matrix

def plot_symmetrized_confusion_matrix(matrix, class_names):
plt.figure(figsize=(10, 10))
plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.Blues)
plt.title('Symmetrized Confusion Matrix')
plt.colorbar()
tick_marks = np.arange(len(class_names))
plt.xticks(tick_marks, class_names, rotation=45)
plt.yticks(tick_marks, class_names)

fmt = 'd'
thresh = matrix.max() / 2.
for i, j in zip(*np.triu_indices_from(matrix)):
    plt.text(j, i, format(matrix[i, j], fmt),
             horizontalalignment="center",
             color="white" if matrix[i, j] > thresh else "black")

plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()

Class names for Fashion-MNIST

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

Create symmetrized confusion matrix and plot it

symmetrized_conf_matrix = symmetrize_confusion_matrix(confmat_tensor)
plot_symmetrized_confusion_matrix(symmetrized_conf_matrix, class_names)