sklearn confusion_matrix displaying with wrong dimensions / tick marks at wrong spots

Question

I'm trying to display a confusion matrix and can't for the life of my figure out why it refuses to display in an appropriate manner. Here's my code:

import numpy as np
import itertools
from sklearn.metrics import confusion_matrix

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.winter):
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title, fontsize=30)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, fontsize=20)
    plt.yticks(tick_marks, classes, fontsize=20)

    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.

    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", 
                 color="white" if cm[i, j] < thresh else "black", fontsize=40)

    plt.tight_layout()
    plt.ylabel('True label', fontsize=30)
    plt.xlabel('Predicted label', fontsize=30)

    return plt

cm = confusion_matrix(y_test, y_predicted_counts)
fig = plt.figure(figsize=(10, 10))
plot = plot_confusion_matrix(cm, classes=['Unsure','No','Yes'], normalize=False, title='Confusion matrix')
plt.show()
print(cm)

And this is what is displayed:

Any help would be appreciated. Thanks in advance.

Answer 1

For the call to imshow you need to specify origin='lower' (the default is 'upper'; they probably changed this at some time and the scikit-learn docs didn't update their example). So the following should do the trick:

plt.imshow(cm, interpolation='nearest', cmap=cmap, origin='lower')
#                                                    ^
#                                                    |
# added origin='lower'  ------------------------------