Pretty print for sklearn confusion matrix

print_cm.py

from sklearn.metrics import confusion_matrix

def print_cm(cm, labels, hide_zeroes=False, hide_diagonal=False, hide_threshold=None):
    """pretty print for confusion matrixes"""
    columnwidth = max([len(x) for x in labels]+[5]) # 5 is value length
    empty_cell = " " * columnwidth
    # Print header
    print "    " + empty_cell,
    for label in labels: 
        print "%{0}s".format(columnwidth) % label,
    print
    # Print rows
    for i, label1 in enumerate(labels):
        print "    %{0}s".format(columnwidth) % label1,
        for j in range(len(labels)): 
            cell = "%{0}.1f".format(columnwidth) % cm[i, j]
            if hide_zeroes:
                cell = cell if float(cm[i, j]) != 0 else empty_cell
            if hide_diagonal:
                cell = cell if i != j else empty_cell
            if hide_threshold:
                cell = cell if cm[i, j] > hide_threshold else empty_cell
            print cell,
        print

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

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

@hrieke Not really, use it however you want.

Thank you for the fast reply, and the "okay to use statement", but it would be nice to have something from the more standard license.
May I suggest then any of the public domain licenses? CC0 1.0 Universal or Public Domain are two good choices.
If not, MIT License, BSD 3-clause, or Apache License are also very nice.