如何在python中使用字符串轴而不是整数来绘制混淆矩阵

Question

我正在关注如何在Matplotlib中绘制混淆矩阵的前一个主题。脚本如下：

from numpy import *
import matplotlib.pyplot as plt
from pylab import *

conf_arr = [[33,2,0,0,0,0,0,0,0,1,3], [3,31,0,0,0,0,0,0,0,0,0], [0,4,41,0,0,0,0,0,0,0,1], [0,1,0,30,0,6,0,0,0,0,1], [0,0,0,0,38,10,0,0,0,0,0], [0,0,0,3,1,39,0,0,0,0,4], [0,2,2,0,4,1,31,0,0,0,2], [0,1,0,0,0,0,0,36,0,2,0], [0,0,0,0,0,0,1,5,37,5,1], [3,0,0,0,0,0,0,0,0,39,0], [0,0,0,0,0,0,0,0,0,0,38] ]

norm_conf = []
for i in conf_arr:
        a = 0
        tmp_arr = []
        a = sum(i,0)
        for j in i:
                tmp_arr.append(float(j)/float(a))
        norm_conf.append(tmp_arr)

plt.clf()
fig = plt.figure()
ax = fig.add_subplot(111)
res = ax.imshow(array(norm_conf), cmap=cm.jet, interpolation='nearest')


for i,j in ((x,y) for x in xrange(len(conf_arr))
            for y in xrange(len(conf_arr[0]))):
    ax.annotate(str(conf_arr[i][j]),xy=(i,j))

cb = fig.colorbar(res)
savefig("confusion_matrix.png", format="png")

我想改变轴来显示字母串，比如说（A，B，C，...）而不是整数（0,1,2,3，。10）。怎么能这样做。感谢。

芭蕉

Answer 1

这是我猜你想要的：

import numpy as np
import matplotlib.pyplot as plt

conf_arr = [[33,2,0,0,0,0,0,0,0,1,3], 
            [3,31,0,0,0,0,0,0,0,0,0], 
            [0,4,41,0,0,0,0,0,0,0,1], 
            [0,1,0,30,0,6,0,0,0,0,1], 
            [0,0,0,0,38,10,0,0,0,0,0], 
            [0,0,0,3,1,39,0,0,0,0,4], 
            [0,2,2,0,4,1,31,0,0,0,2],
            [0,1,0,0,0,0,0,36,0,2,0], 
            [0,0,0,0,0,0,1,5,37,5,1], 
            [3,0,0,0,0,0,0,0,0,39,0], 
            [0,0,0,0,0,0,0,0,0,0,38]]

norm_conf = []
for i in conf_arr:
    a = 0
    tmp_arr = []
    a = sum(i, 0)
    for j in i:
        tmp_arr.append(float(j)/float(a))
    norm_conf.append(tmp_arr)

fig = plt.figure()
plt.clf()
ax = fig.add_subplot(111)
ax.set_aspect(1)
res = ax.imshow(np.array(norm_conf), cmap=plt.cm.jet, 
                interpolation='nearest')

width, height = conf_arr.shape

for x in xrange(width):
    for y in xrange(height):
        ax.annotate(str(conf_arr[x][y]), xy=(y, x), 
                    horizontalalignment='center',
                    verticalalignment='center')

cb = fig.colorbar(res)
alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
plt.xticks(range(width), alphabet[:width])
plt.yticks(range(height), alphabet[:height])
plt.savefig('confusion_matrix.png', format='png')

Answer 2

只需使用matplotlib.pyplot.xticks和matplotlib.pyplot.yticks。

E.g。

import matplotlib.pyplot as plt
import numpy as np

plt.imshow(np.random.random((5,5)), interpolation='nearest')
plt.xticks(np.arange(0,5), ['A', 'B', 'C', 'D', 'E'])
plt.yticks(np.arange(0,5), ['F', 'G', 'H', 'I', 'J'])

plt.show()

Answer 3

这是您想要的：

from string import ascii_uppercase
from pandas import DataFrame
import numpy as np
import seaborn as sn
from sklearn.metrics import confusion_matrix

y_test = np.array([1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5])
predic = np.array([1,2,4,3,5, 1,2,4,3,5, 1,2,3,4,4])

columns = ['class %s' %(i) for i in list(ascii_uppercase)[0:len(np.unique(y_test))]]

confm = confusion_matrix(y_test, predic)
df_cm = DataFrame(confm, index=columns, columns=columns)

ax = sn.heatmap(df_cm, cmap='Oranges', annot=True)

示例图像输出在这里：

---

如果您想将更完整的混淆矩阵作为 matlab 的默认值，并具有总计（最后一行和最后一列）以及每个单元格的百分比，请参见下面的此模块

因为我在Internet上搜索并没有在python上找到像这样的混淆矩阵，所以我开发了一个具有这些改进的矩阵并在git上共享。

---

REF：

https://github.com/wcipriano/pretty-print-confusion-matrix

输出示例在这里：

Answer 4

要获得看起来像 sklearn 为您创建的图表，只需使用他们的代码即可！

from sklearn.metrics import confusion_matrix
# I use the sklearn metric source for this one
from sklearn.metrics import ConfusionMatrixDisplay
classNames = np.arange(1,6)
# Convert to discrete values for confusion matrix
regPredictionsCut = pd.cut(regPredictionsTDF[0], bins=5, labels=classNames, right=False)
cm = confusion_matrix(y_test, regPredictionsCut)
disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=classNames)
disp.plot()

我通过转到 https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html 并点击“来源”链接来解决这个问题。

这是结果图：

Answer 5

如果将结果存储在csv文件中，则可以直接使用此方法，否则可能需要进行一些更改以适合结果的结构。

修改sklearn's website中的示例：

import itertools
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.tight_layout()


#Assumming that your predicted results are in csv. If not, you can still modify the example to suit your requirements
df = pd.read_csv("dataframe.csv", index_col=0)

cnf_matrix = confusion_matrix(df["actual_class_num"], df["predicted_class_num"])

#getting the unique class text based on actual numerically represented classes
unique_class_df = df.drop_duplicates(['actual_class_num','actual_class_text']).sort_values("actual_class_num")

# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=unique_class_df["actual_class_text"],
                      title='Confusion matrix, without normalization')

输出类似于：