plot_confusion_matrix: 可视化混淆矩阵

用于通过matplotlib可视化混淆矩阵的工具函数

> `from mlxtend.plotting import plot_confusion_matrix`

概述

混淆矩阵

有关混淆矩阵的更多信息，请参见mlxtend.evaluate.confusion_matrix。

参考文献

示例 1 - 二进制

from mlxtend.plotting import plot_confusion_matrix
import matplotlib.pyplot as plt
import numpy as np

binary1 = np.array([[4, 1],
                    [1, 2]])

fig, ax = plot_confusion_matrix(conf_mat=binary1)
plt.show()

png

binary2 = np.array([[21, 1],
                    [3, 1]])

fig, ax = plot_confusion_matrix(conf_mat=binary2, figsize=(2, 2))
plt.show()

png

示例 2 - 带颜色条的二进制绝对值和相对值

binary = np.array([[4, 1],
                   [1, 2]])

fig, ax = plot_confusion_matrix(conf_mat=binary,
                                show_absolute=True,
                                show_normed=True,
                                colorbar=True)
plt.show()

png

示例 3 - 多类别相对

multiclass = np.array([[2, 1, 0, 0],
                       [1, 2, 0, 0],
                       [0, 0, 1, 0],
                       [0, 0, 0, 1]])

fig, ax = plot_confusion_matrix(conf_mat=multiclass,
                                colorbar=True,
                                show_absolute=False,
                                show_normed=True)
plt.show()

png

示例 4 - 添加类名

multiclass = np.array([[2, 1, 0, 0],
                       [1, 2, 0, 0],
                       [0, 0, 1, 0],
                       [0, 0, 0, 1]])

class_names = ['class a', 'class b', 'class c', 'class d']

fig, ax = plot_confusion_matrix(conf_mat=multiclass,
                                colorbar=True,
                                show_absolute=False,
                                show_normed=True,
                                class_names=class_names)
plt.show()

png

示例 5 - 更改颜色映射和字体颜色

Matplotlib颜色图可以通过cmap参数作为替代颜色图选择。可以在这里找到颜色图的列表：https://matplotlib.org/stable/tutorials/colors/colormaps.html

multiclass = np.array([[2, 1, 0, 0],
                       [1, 2, 0, 0],
                       [0, 0, 1, 0],
                       [0, 0, 0, 1]])

fig, ax = plot_confusion_matrix(conf_mat=multiclass,
                                colorbar=True,
                                cmap='summer')

plt.show()

png

如上所示，字体颜色阈值可能对某些颜色映射不起作用。默认情况下，所有大于最大单元值的0.5倍的值被转换为白色，而所有等于或小于最大单元值的0.5倍的值被转换为黑色。

如果您想将所有以上的值更改为，例如，白色，您可以将颜色阈值设置为负数。或者，如果您想将所有字体颜色设置为黑色，请选择一个等于或大于1的阈值。

fig, ax = plot_confusion_matrix(conf_mat=multiclass,
                                colorbar=True,
                                fontcolor_threshold=1,
                                cmap='summer')

plt.show()

png

示例 6 - 归一化色图以突出非对角线部分

假设我们有以下高准确率分类器的混淆矩阵：

class_dict = {0: 'airplane',
              1: 'automobile',
              2: 'bird',
              3: 'cat',
              4: 'deer',
              5: 'dog',
              6: 'frog'}

cmat = np.array([[972, 0, 1, 1, 1, 1, 3],
                 [0, 1123, 3, 1, 0, 1, 2],
                 [2, 0, 1025, 0, 0, 0, 1],
                 [0, 0, 0, 1005, 0, 2, 0],
                 [0, 1, 1, 0, 967, 0, 4],
                 [0, 0, 0, 6, 0, 881, 3],
                 [2, 3, 0, 1, 3, 4, 941]])

fig, ax = plot_confusion_matrix(
    conf_mat=cmat,
    class_names=class_dict.values(),
)

png

很难注意到模型出错的单元格。使用对数归一化的彩色图，这些对角线外的错误在一眼看来就更容易被发现：

import matplotlib

fig, ax = plot_confusion_matrix(
    conf_mat=cmat,
    class_names=class_dict.values(),
    norm_colormap=matplotlib.colors.LogNorm()  
)

png

API

plot_confusion_matrix(conf_mat, hide_spines=False, hide_ticks=False, figsize=None, cmap=None, colorbar=False, show_absolute=True, show_normed=False, class_names=None, figure=None, axis=None, fontcolor_threshold=0.5)

Plot a confusion matrix via matplotlib.

Parameters

conf_mat : array-like, shape = [n_classes, n_classes]

Confusion matrix from evaluate.confusion matrix.
hide_spines : bool (default: False)

Hides axis spines if True.
hide_ticks : bool (default: False)

Hides axis ticks if True
figsize : tuple (default: (2.5, 2.5))

Height and width of the figure
cmap : matplotlib colormap (default: None)

Uses matplotlib.pyplot.cm.Blues if None
colorbar : bool (default: False)

Shows a colorbar if True
show_absolute : bool (default: True)

Shows absolute confusion matrix coefficients if True. At least one of show_absolute or show_normed must be True.
show_normed : bool (default: False)

Shows normed confusion matrix coefficients if True. The normed confusion matrix coefficients give the proportion of training examples per class that are assigned the correct label. At least one of show_absolute or show_normed must be True.
class_names : array-like, shape = [n_classes] (default: None)

List of class names. If not None, ticks will be set to these values.
figure : None or Matplotlib figure (default: None)

If None will create a new figure.
axis : None or Matplotlib figure axis (default: None)

If None will create a new axis.
fontcolor_threshold : Float (default: 0.5)

Sets a threshold for choosing black and white font colors for the cells. By default all values larger than 0.5 times the maximum cell value are converted to white, and everything equal or smaller than 0.5 times the maximum cell value are converted to black.

Returns

fig, ax : matplotlib.pyplot subplot objects

Figure and axis elements of the subplot.

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/plotting/plot_confusion_matrix/