print_cm.py

from sklearn.metrics import confusion_matrix

def print_cm(cm, labels):
    """pretty print for confusion matrixes"""
    columnwidth = max([len(x) for x in labels])
    # Print header
    print(" " * columnwidth, end="\t")
    for label in labels:
        print("%{0}s".format(columnwidth) % label, end="\t")
    print()
    # Print rows
    for i, label1 in enumerate(labels):
        print("%{0}s".format(columnwidth) % label1, end="\t")
        for j in range(len(labels)):
            print("%{0}d".format(columnwidth) % cm[i, j], end="\t")
        print()


# first generate with specified labels
labels = [ ... ]
cm = confusion_matrix(ypred, y, labels)

# then print it in a pretty way
print_cm(cm, labels)

Adapted for Python 3