绘制的混淆矩阵值相互重叠，总类 90答案

【问题标题】：Plotted Confusion Matrix values overlapping each other, total classes 90绘制的混淆矩阵值相互重叠，总类 90
【发布时间】：2018-07-29 01:06:27
【问题描述】：

如何增加 x 轴和 y 轴标签之间的间距，使混淆矩阵内的绘制结果不重叠？

【问题讨论】：

标签： python scikit-learn keras metrics confusion-matrix

【解决方案1】：

我找到了这段代码，经过一些小的修改后，我发现它不能正常工作。

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()

这就是我的称呼

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

使用figsize 和FONT_SIZE 参数，直到您对结果满意为止。

【讨论】：