Plot Confusion Matrix with scikit-learn without a Classifier

Question

I have a confusion matrix created with sklearn.metrics.confusion_matrix.

Now, I would like to plot it with sklearn.metrics.plot_confusion_matrix, but the first parameter is the trained classifier, as specified in the documentation. The problem is that I don't have a classifier; the results were obtained doing manual calculations.

Is it still possible to plot the confusion matrix in one line via scikit-learn, or do I have to code it myself with matplotlib?

Answer 1

The fact that you can import plot_confusion_matrix directly suggests that you have the latest version of scikit-learn (0.22) installed. So you can just look at the source code of plot_confusion_matrix() to see how its using the estimator.

From the latest sources here, the estimator is used for:

1. computing confusion matrix using confusion_matrix
2. getting the labels (unique values of y which correspond to 0,1,2.. in the confusion matrix)

So if you have those two things already, you just need the below part:

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

disp = ConfusionMatrixDisplay(confusion_matrix=cm,
                              display_labels=display_labels)


# NOTE: Fill all variables here with default values of the plot_confusion_matrix
disp = disp.plot(include_values=include_values,
                 cmap=cmap, ax=ax, xticks_rotation=xticks_rotation)

plt.show()

Do look at the NOTE in comment.

For older versions, you can look at how the matplotlib part is coded here