#####模块说明######
'''
根据传入的文件true_label和predict_label来求模型预测的精度、召回率和F1值，另外给出微观和宏观取值。
powered by wangjianing 2019.3.2
'''
import numpy as np
def getLabelData(file_dir):
'''
模型的预测生成相应的label文件，以及真实类标文件，根据文件读取并加载所有label
1、参数说明：
file_dir：加载的文件地址。
文件内数据格式：每行包含两列，第一列为编号1,2，...，第二列为预测或实际的类标签名称。两列以空格为分隔符。
需要生成两个文件，一个是预测，一个是实际类标，必须保证一一对应，个数一致
2、返回值：
返回文件中每一行的label列表，例如['true','false','false',...,'true']
'''
labels = []
with open(file_dir,'r',encoding="utf-8") as f:
for i in f.readlines():
labels.append(i.strip().split(' ')[1])
return labels
def getLabel2idx(labels):
'''
获取所有类标
返回值：label2idx字典，key表示类名称，value表示编号0,1,2...
'''
label2idx = dict()
for i in labels:
if i not in label2idx:
label2idx[i] = len(label2idx)
return label2idx
def buildConfusionMatrix(predict_file,true_file):
'''
针对实际类标和预测类标，生成对应的矩阵。
矩阵横坐标表示实际的类标，纵坐标表示预测的类标
矩阵的元素(m1,m2)表示类标m1被预测为m2的个数。
所有元素的数字的和即为测试集样本数，对角线元素和为被预测正确的个数，其余则为预测错误。
返回值：返回这个矩阵numpy
'''
true_labels = getLabelData(true_file)
predict_labels = getLabelData(predict_file)
label2idx = getLabel2idx(true_labels)
confMatrix = np.zeros([len(label2idx),len(label2idx)],dtype=np.int32)
for i in range(len(true_labels)):
true_labels_idx = label2idx[true_labels[i]]
predict_labels_idx = label2idx[predict_labels[i]]
confMatrix[true_labels_idx][predict_labels_idx] += 1
return confMatrix,label2idx
def calculate_all_prediction(confMatrix):
'''
计算总精度：对角线上所有值除以总数
'''
total_sum = confMatrix.sum()
correct_sum = (np.diag(confMatrix)).sum()
prediction = round(100*float(correct_sum)/float(total_sum),2)
return prediction
def calculate_label_prediction(confMatrix,labelidx):
'''
计算某一个类标预测精度：该类被预测正确的数除以该类的总数
'''
label_total_sum = confMatrix.sum(axis=0)[labelidx]
label_correct_sum = confMatrix[labelidx][labelidx]
prediction = 0
if label_total_sum != 0:
prediction = round(100*float(label_correct_sum)/float(label_total_sum),2)
return prediction
def calculate_label_recall(confMatrix,labelidx):
'''
计算某一个类标的召回率：
'''
label_total_sum = confMatrix.sum(axis=1)[labelidx]
label_correct_sum = confMatrix[labelidx][labelidx]
recall = 0
if label_total_sum != 0:
recall = round(100*float(label_correct_sum)/float(label_total_sum),2)
return recall
def calculate_f1(prediction,recall):
if (prediction+recall)==0:
return 0
return round(2*prediction*recall/(prediction+recall),2)
def main(predict_file,true_file):
'''
该为主函数，可将该函数导入自己项目模块中
打印精度、召回率、F1值的格式可自行设计
'''
#读取文件并转化为混淆矩阵,并返回label2idx
confMatrix,label2idx = buildConfusionMatrix(predict_file,true_file)
total_sum = confMatrix.sum()
all_prediction = calculate_all_prediction(confMatrix)
label_prediction = []
label_recall = []
print('total_sum=',total_sum,',label_num=',len(label2idx),'\n')
for i in label2idx:
print(' ',i)
print(' ')
for i in label2idx:
print(i,end=' ')
label_prediction.append(calculate_label_prediction(confMatrix,label2idx[i]))
label_recall.append(calculate_label_recall(confMatrix,label2idx[i]))
for j in label2idx:
labelidx_i = label2idx[i]
label2idx_j = label2idx[j]
print(' ',confMatrix[labelidx_i][label2idx_j],end=' ')
print('\n')
print('prediction(accuracy)=',all_prediction,'%')
print('individual result\n')
for ei,i in enumerate(label2idx):
print(ei,'\t',i,'\t','prediction=',label_prediction[ei],'%,\trecall=',label_recall[ei],'%,\tf1=',calculate_f1(label_prediction[ei],label_recall[ei]))
p = round(np.array(label_prediction).sum()/len(label_prediction),2)
r = round(np.array(label_recall).sum()/len(label_prediction),2)
print('MACRO-averaged:\nprediction=',p,'%,recall=',r,'%,f1=',calculate_f1(p,r))
