python图元素的百分比格式

Question

我执行下面的代码，我希望第二个图中的数字是百分比格式，具有两位数的精度（0.3333 - > 33.33％）。我已经尝试了大量不同的版本，我在数组上的lambda函数中使用＆＃39; {％，。2％}＆＃39; .format（），但我并没有完全得到它。所有输入都是适用的！

import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn import datasets

%matplotlib inline

iris = datasets.load_iris()
x = iris['data']
y = iris['target']

x = iris_x[:, :2]

clf_tree = DecisionTreeClassifier(random_state = 1)
fit_clf = clf_tree.fit(x, y)

y_pred_proba = fit_clf.predict_proba(x)
y_pred = fit_clf.predict(x)

conf_mat = confusion_matrix(y_true = y, y_pred = y_pred)

fig, ax = plt.subplots(figsize = (15, 9))
ax.matshow(conf_mat, cmap = plt.cm.Blues, alpha = 0.3)

for i in range(conf_mat.shape[0]):
    for j in range(conf_mat.shape[1]):
        ax.text(x = j, y = i,
               s = conf_mat[i, j],
               va = 'center',
                ha = 'center')

plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.show()


conf_mat_prc = conf_mat/len(y)

fig, ax = plt.subplots(figsize = (15, 9))
ax.matshow(conf_mat_prc, cmap = plt.cm.Blues, alpha = 0.3)


for i in range(conf_mat_prc.shape[0]):
    for j in range(conf_mat_prc.shape[1]):
        ax.text(x = j, y = i,
               s = conf_mat_prc[i, j],
               va = 'center',
                ha = 'center')

plt.xlabel('Predicted % dist')
plt.ylabel('Actual % dist')
plt.show()

非常感谢，

- swepab

Answer 1

您的代码中至少存在两个问题：

• line 14中的x[:, :2]是什么？我认为您的意思是iris_x[:, :2]而不是conf_mat_prc

• conf_mat_prc = conf_mat/float(len(y))应该定义为conf_mat_prc = conf_mat/len(y)而不是str(round(conf_mat_prc[i, j]*100,precision)) + "%"来获取浮点而不是0（int）。

最后，对于第二个图（第48行），使用import seaborn as sns import matplotlib.pyplot as plt import matplotlib import numpy as np from sklearn.tree import DecisionTreeClassifier from sklearn import datasets from sklearn.metrics import confusion_matrix # %matplotlib inline iris = datasets.load_iris() x = iris['data'] y = iris['target'] x = x[:, :2] clf_tree = DecisionTreeClassifier(random_state = 1) fit_clf = clf_tree.fit(x, y) y_pred_proba = fit_clf.predict_proba(x) y_pred = fit_clf.predict(x) conf_mat = confusion_matrix(y_true = y, y_pred = y_pred) fig, ax = plt.subplots(figsize = (15, 9)) ax.matshow(conf_mat, cmap = plt.cm.Blues, alpha = 0.3) for i in range(conf_mat.shape[0]): for j in range(conf_mat.shape[1]): ax.text(x = j, y = i, s = conf_mat[i, j], va = 'center', ha = 'center') plt.xlabel('Predicted') plt.ylabel('Actual') plt.show() conf_mat_prc = conf_mat/float(len(y)) fig, ax = plt.subplots(figsize = (15, 9)) ax.matshow(conf_mat_prc, cmap = plt.cm.Blues, alpha = 0.3) precision = 2 for i in range(conf_mat_prc.shape[0]): for j in range(conf_mat_prc.shape[1]): ax.text(x = j, y = i, s = str(round(conf_mat_prc[i, j]*100,precision)) + "%", va = 'center', ha = 'center') plt.xlabel('Predicted % dist') plt.ylabel('Actual % dist') plt.show() ，其中precision定义浮点数。

这是新代码：

{{1}}

这是新的第二张图：