I am using plot_confusion_matrix from sklearn.metrics. I want to represent those confusion matrices next to each other like subplots, how could I do this?
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2 Answers
Let's use the good'ol iris dataset to reproduce this, and fit several classifiers to plot their respective confusion matrices with plot_confusion_matrix:
from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import plot_confusion_matrix
data = load_iris()
X = data.data
y = data.target
Set up -
X_train, X_test, y_train, y_test = train_test_split(X, y)
classifiers = [LogisticRegression(solver='lbfgs'),
AdaBoostClassifier(),
GradientBoostingClassifier(),
SVC()]
for cls in classifiers:
cls.fit(X_train, y_train)
So the way you could compare all matrices at simple sight, is by creating a set of subplots with plt.subplots. Then iterate both over the axes objects and the trained classifiers (plot_confusion_matrix expects the as input) and plot the individual confusion matrices:
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(15,10))
for cls, ax in zip(classifiers, axes.flatten()):
plot_confusion_matrix(cls,
X_test,
y_test,
ax=ax,
cmap='Blues',
display_labels=data.target_names)
ax.title.set_text(type(cls).__name__)
plt.tight_layout()
plt.show()
1 Comment
Tonechas
Function
plot_confusion_matrix is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.if your desired output is that This is my way to see multiple confusion matrices (confusion_matrix) side by side with ConfusionMatrixDisplay.
note: paste your own test and train data names in metrics.confusion_matrix() function.
fig, ax = plt.subplots(1, 2)
ax[0].set_title("test")
ax[1].set_title("train")
metrics.ConfusionMatrixDisplay(
confusion_matrix=metrics.confusion_matrix(y_test, y_pred),
display_labels=[False, True]).plot(ax=ax[0])
metrics.ConfusionMatrixDisplay(
confusion_matrix=metrics.confusion_matrix(y_train, y_train_pred),
display_labels=[False, True]).plot(ax=ax[1])
