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?
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:
confusion_matrixSo 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
import numpy as np ; from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay; disp = ConfusionMatrixDisplay(confusion_matrix=np.asarray([[13099,7004],[27420,544967]]), display_labels= np.asarray([0,1])) ; disp.plot() . The scale of the true negatives here dwarfs everything so the colour scaling is sort of pointless here, unless there is a way to scale the colours logarithmically? Thanks in advance!
ConfusionMatrixDisplay just takes the cm matrix to plot it. Are you saying that you cannot pass a normalized cm matrix in it?The below code is to create confusion matrix from true values and predicted values. If you have already created the confusion matrix you can just run the last line below.
import seaborn as sns
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_true, y_pred)
f = sns.heatmap(cm, annot=True, fmt='d')
You could use a one-line "identity classifier" if that fits your use case.
IC = type('IdentityClassifier', (), {"predict": lambda i : i, "_estimator_type": "classifier"})
plot_confusion_matrix(IC, y_pred, y_test, normalize='true', values_format='.2%');
( see my original answer in: plot_confusion_matrix without estimator )
