predict = output.argmax(dim = 1)confusion_matrix =torch.zeros(2,2)for t, p in zip(predict.view(-1), target.view(-1)):    confusion_matrix[t.long(), p.long()] += 1a_p =(confusion_matrix.diag() / confusion_matrix.sum(1))[0]b_p = (confusion_matrix.diag() / confusion_matrix.sum(1))[1]a_r =(confusion_matrix.diag() / confusion_matrix.sum(0))[0]b_r = (confusion_matrix.diag() / confusion_matrix.sum(0))[1]

def eval():    net.eval()    test_loss = 0    correct = 0    total = 0    classnum = 9    target_num = torch.zeros((1,classnum))    predict_num = torch.zeros((1,classnum))    acc_num = torch.zeros((1,classnum))    for batch_idx, (inputs, targets) in enumerate(testloader):        if use_cuda:            inputs, targets = inputs.cuda(), targets.cuda()        inputs, targets = Variable(inputs, volatile=True), Variable(targets)        outputs = net(inputs)        loss = criterion(outputs, targets)        # loss is variable , if add it(+=loss) directly, there will be a bigger ang bigger graph.        test_loss += loss.data[0]        _, predicted = torch.max(outputs.data, 1)        total += targets.size(0)        correct += predicted.eq(targets.data).cpu().sum()        pre_mask = torch.zeros(outputs.size()).scatter_(1, predicted.cpu().view(-1, 1), 1.)        predict_num += pre_mask.sum(0)        tar_mask = torch.zeros(outputs.size()).scatter_(1, targets.data.cpu().view(-1, 1), 1.)        target_num += tar_mask.sum(0)        acc_mask = pre_mask*tar_mask        acc_num += acc_mask.sum(0)    recall = acc_num/target_num    precision = acc_num/predict_num    F1 = 2*recall*precision/(recall+precision)    accuracy = acc_num.sum(1)/target_num.sum(1)#精度调整    recall = (recall.numpy()[0]*100).round(3)    precision = (precision.numpy()[0]*100).round(3)    F1 = (F1.numpy()[0]*100).round(3)    accuracy = (accuracy.numpy()[0]*100).round(3)# 打印格式方便复制    print("recall"," ".join("%s" % id for id in recall))    print("precision"," ".join("%s" % id for id in precision))    print("F1"," ".join("%s" % id for id in F1))    print("accuracy",accuracy)

from sklearn.datasets import fetch_20newsgroupsfrom sklearn.model_selection import train_test_splitfrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.naive_bayes import MultinomialNBfrom sklearn.metrics import classification_report # 加载数据集 从scikit-learn官网下载新闻数据集(共20个类别)news = fetch_20newsgroups(subset="all")  # all表示下载训练集和测试集 # 进行数据分割 (划分训练集和测试集)x_train, x_test, y_train, y_test = train_test_split(news.data, news.target, test_size=0.25) # 对数据集进行特征抽取 (进行特征提取，将新闻文档转化成特征词重要性的数字矩阵)tf = TfidfVectorizer()  # tf-idf表示特征词的重要性# 以训练集数据统计特征词的重要性 (从训练集数据中提取特征词)x_train = tf.fit_transform(x_train) print(tf.get_feature_names())  # ["condensed", "condescend", ...] x_test = tf.transform(x_test)  # 不需要重新fit()数据，直接按照训练集提取的特征词进行重要性统计。 # 进行朴素贝叶斯算法的预测mlt = MultinomialNB(alpha=1.0)  # alpha表示拉普拉斯平滑系数，默认1print(x_train.toarray())  # toarray() 将稀疏矩阵以稠密矩阵的形式显示。"""[[ 0.     0.          0.   ...,  0.04234873  0.   0. ] [ 0.     0.          0.   ...,  0.          0.   0. ] ..., [ 0.     0.03934786  0.   ...,  0.          0.   0. ]"""mlt.fit(x_train, y_train)  # 填充训练集数据 # 预测类别y_predict = mlt.predict(x_test)print("预测的文章类别为：", y_predict)  # [4 18 8 ..., 15 15 4] # 准确率print("准确率为：", mlt.score(x_test, y_test))  # 0.853565365025 print("每个类别的精确率和召回率：", classification_report(y_test, y_predict, target_names=news.target_names))"""                precision  recall  f1-score  support    alt.atheism   0.86      0.66     0.75      207  comp.graphics   0.85      0.75     0.80      238 sport.baseball   0.96      0.94     0.95      253 ...,"""