绘制混淆矩阵 #confusion_matrix

plot_confusion.py

%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix



def print_confusion_matrix(true_cls, pred_cls, cls_name):
    """
    true_cls、true_cls、pred_cls、cls_name都不是标签的id，而是原始标签文本
    true_cls = [u'真实标签1', u'真实标签2', u'真实标签3',..]
    pred_cls = [u'预测标签1', u'预测标签2', u'预测标签3',..]
    cls_name: 类别标签列表，传给confusion_matrix便于重建混淆矩阵坐标轴上的标签
    """
    # Get the true classifications for the test-set.
    # Get the confusion matrix using sklearn.
    n_classes = len(cls_name)
    cm = confusion_matrix(y_true=true_cls,
                          y_pred=pred_cls,
                          labels=cls_name
                         )

    # Print the confusion matrix as text.
    print(cm)
    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)

    # Make various adjustments to the plot.
    plt.tight_layout()
    plt.colorbar()
    tick_marks = np.arange(n_classes)
    plt.xticks(tick_marks, cls_name)
    plt.yticks(tick_marks, cls_name)
    plt.xlabel('Predicted')
    plt.ylabel('True')

    # Ensure the plot is shown correctly with multiple plots
    # in a single Notebook cell.
    plt.show()


cls_name = np.array(tid2topic.values())
print_confusion_matrix(df['topic'].tolist(), topic_pred, cls_name)