python - 显示截止值的混淆矩阵

Question

混淆矩阵是用切割值来表示的。

skplt.metrics.plot_confusion_matrix(
        y_test,
        predictions,
        figsize=(25, 25),title="Confusion matrix")
    plt.show()

混淆矩阵图片如下:

Answer 1

首先，skplt.metrics.plot_confusion_matrix不存在。仅存在 sklearn.metrics.confusion_matrix。

要绘制混淆矩阵，您需要 sklearn 网站中定义的 plot_confusion_matrix 函数。

为了使其在您的情况下工作，请在 plot_confusion_matrix 函数中添加以下行:

plt.xlim(-0.5, len(np.unique(y))-0.5)
plt.ylim(len(np.unique(y))-0.5, -0.5)

原码:https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html

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完整示例:

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels

# import some data to play with
iris = datasets.load_iris()
X = iris.data
y = np.repeat(np.arange(0,2),75)
class_names = np.array(['1', '2'])

# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

# Run classifier, using a model that is too regularized (C too low) to see
# the impact on the results
classifier = svm.SVC(kernel='linear', C=0.01)
y_pred = classifier.fit(X_train, y_train).predict(X_test)

def plot_confusion_matrix(y_true, y_pred, classes,
                          normalize=False,
                          title=None,
                          cmap=plt.cm.Blues):

    if not title:
        if normalize:
            title = 'Normalized confusion matrix'
        else:
            title = 'Confusion matrix, without normalization'

    # Compute confusion matrix
    cm = confusion_matrix(y_true, y_pred)
    # Only use the labels that appear in the data
    classes = classes[unique_labels(y_true, y_pred)]
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    fig, ax = plt.subplots()
    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
    ax.figure.colorbar(im, ax=ax)
    # We want to show all ticks...
    ax.set(xticks=np.arange(cm.shape[1]),
           yticks=np.arange(cm.shape[0]),
           # ... and label them with the respective list entries
           xticklabels=classes, yticklabels=classes,
           title=title,
           ylabel='True label',
           xlabel='Predicted label')

    # Rotate the tick labels and set their alignment.
    plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
             rotation_mode="anchor")

    # Loop over data dimensions and create text annotations.
    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i in range(cm.shape[0]):
        for j in range(cm.shape[1]):
            ax.text(j, i, format(cm[i, j], fmt),
                    ha="center", va="center",
                    color="white" if cm[i, j] > thresh else "black")
    fig.tight_layout()
    plt.xlim(-0.5, len(np.unique(y))-0.5) # ADD THIS LINE
    plt.ylim(len(np.unique(y))-0.5, -0.5) # ADD THIS LINE
    return ax

np.set_printoptions(precision=2)

# Plot non-normalized confusion matrix
plot_confusion_matrix(y_test, y_pred, classes=class_names,
                      title='Confusion matrix, without normalization')

# Plot normalized confusion matrix
plot_confusion_matrix(y_test, y_pred, classes=class_names, normalize=True,
                      title='Normalized confusion matrix')

plt.show()