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python - 绘制的混淆矩阵值相互重叠,总类别 90

转载 作者:太空宇宙 更新时间:2023-11-03 14:06:25 24 4
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如何增加 x 轴和 y 轴标签之间的间距,以便混淆矩阵内的绘制结果不会重叠?

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最佳答案

我找到了这段代码,经过一些小的修改,我让它工作得很好。

def plot_confusion_matrix_2(cm,
target_names,
title='Confusion matrix',
cmap=None,
normalize=True):
"""
given a sklearn confusion matrix (cm), make a nice plot

Arguments
---------
cm: confusion matrix from sklearn.metrics.confusion_matrix

target_names: given classification classes such as [0, 1, 2]
the class names, for example: ['high', 'medium', 'low']

title: the text to display at the top of the matrix

cmap: the gradient of the values displayed from matplotlib.pyplot.cm
see http://matplotlib.org/examples/color/colormaps_reference.html
plt.get_cmap('jet') or plt.cm.Blues

normalize: If False, plot the raw numbers
If True, plot the proportions


Citiation
---------
http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html

"""
FONT_SIZE = 8

accuracy = np.trace(cm) / float(np.sum(cm))
misclass = 1 - accuracy

if cmap is None:
cmap = plt.get_cmap('Blues')

plt.figure(figsize=(8*2, 6*2)) # 8, 6
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()

if target_names is not None:
tick_marks = np.arange(len(target_names))
plt.xticks(tick_marks, target_names, rotation=90, fontsize=FONT_SIZE)
plt.yticks(tick_marks, target_names, fontsize=FONT_SIZE)

if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]


thresh = cm.max() / 1.5 if normalize else cm.max() / 2
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
if normalize:
plt.text(j, i, "{:0.4f}".format(cm[i, j]),
horizontalalignment="center",
fontsize=FONT_SIZE,
color="white" if cm[i, j] > thresh else "black")
else:
plt.text(j, i, "{:,}".format(cm[i, j]),
horizontalalignment="center",
fontsize=FONT_SIZE,
color="white" if cm[i, j] > thresh else "black")


plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
plt.show()

我就是这么调用它的

plot_confusion_matrix_2(cm, cm_classes, normalize=False, title='Confusion Matrix')

尝试使用 figsizeFONT_SIZE 参数,直到您对结果感到满意为止。

关于python - 绘制的混淆矩阵值相互重叠,总类别 90,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/48855290/

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