How to plot 2x2 confusion matrix with predictions in rows an real values in columns?

Question

I know that we can plot a confusion matrix with sklearn using the following sample code.

from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
import matplotlib.pyplot as plt

y_true = [1, 0, 1, 1, 0, 1]
y_pred = [0, 0, 1, 1, 0, 1]

print(f'y_true: {y_true}')
print(f'y_pred: {y_pred}\n')

cm = confusion_matrix(y_true, y_pred, labels=[0, 1])
print(cm)
disp = ConfusionMatrixDisplay(confusion_matrix=cm)
disp.plot()
plt.show()

What we have:

TN | FP
FN | TP

But I want the prediction label placed in a row or y-axis and the true or real value label in a column or x-axis. How can I plot this using Python?

What I want:

TP | FP
FN | TN

Answer 1

(1) Here is one way of reversing TP/TN.

Code

"""
Reverse True and Prediction labels

References:
    https://github.com/scikit-learn/scikit-learn/blob/0d378913b/sklearn/metrics/_plot/confusion_matrix.py
    https://scikit-learn.org/stable/modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html
"""

from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
import matplotlib.pyplot as plt

y_true = [1, 0, 1, 1, 0, 1]
y_pred = [0, 0, 1, 1, 0, 1]

print(f'y_true: {y_true}')
print(f'y_pred: {y_pred}\n')

# Normal
print('Normal')
cm = confusion_matrix(y_true, y_pred, labels=[0, 1])
print(cm)
disp = ConfusionMatrixDisplay(confusion_matrix=cm)
disp.plot()

plt.savefig('normal.png')
plt.show()

# Reverse TP and TN
print('Reverse TP and TN')
cm = confusion_matrix(y_pred, y_true, labels=[1, 0])  # reverse true/pred and label values
print(cm)
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[1, 0])  # reverse display labels
dp = disp.plot()
dp.ax_.set(ylabel="My Prediction Label")  # modify ylabel of ax_ attribute of plot
dp.ax_.set(xlabel="My True Label")        # modify xlabel of ax_ attribute of plot

plt.savefig('reverse.png')
plt.show()

Output

y_true: [1, 0, 1, 1, 0, 1]
y_pred: [0, 0, 1, 1, 0, 1]

Normal
[[2 0]
 [1 3]]

Reverse TP and TN
[[3 0]
 [1 2]]

(2) Another way is by swapping values and plot it with sns/matplotlib.

Code

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


y_true = [1, 0, 1, 1, 0, 1]
y_pred = [0, 0, 1, 1, 0, 1]

cm = confusion_matrix(y_true, y_pred)
print(cm)
cm_11 = cm[1][1]     # backup value in cm[1][1]
cm[1][1] = cm[0][0]  # swap
cm[0][0] = cm_11     # swap
print(cm)

ax = sns.heatmap(cm, annot=True)

plt.yticks([1.5, 0.5], ['0', '1'], ha='right')
plt.xticks([1.5, 0.5], ['0', '1'], ha='right')

ax.set(xlabel='True Label', ylabel='Prediction Label')
plt.savefig('reverse_tp_tn.png')
plt.show()

Output

[[2 0]
 [1 3]]
[[3 0]
 [1 2]]