研究样本量对文本分类器性能影响的函数运行异常，求代码错误排查

核心错误点 #

• create_model未使用传入参数，硬编码读取全局数据集：函数定义时接收了train_docs/train_y/test_docs/test_y四个入参，但内部所有读取训练、测试数据的逻辑全部调用全局的train、test变量，导致sample_size_impact传入的不同大小训练子集完全不生效，每次运行都用全量训练数据。
• TfidfVectorizer重复fit：代码中先后两次调用tfidf_vect.fit_transform()处理训练文本，属于冗余操作，会造成不必要的性能损耗。
• 返回值处理错误：create_model返回的是(auc_score, prc_score)二元组，sample_size_impact直接将元组存入列表绘图，会同时绘制两条不符合预期的曲线。
• 冗余/笔误问题：sample_size_impact中定义的t_size变量未被使用，x轴标签拼写错误（Smple Size→Sample Size）；SVM分支冗余初始化了SVC对象后又覆盖为LinearSVC；朴素贝叶斯分支的绘图标题错误标注为SVM模型。

修复后代码 #

1. 修复create_model函数 #

def create_model(train_docs, train_y, test_docs, test_y, \
              model_type='svm', stop_words=None, min_df=1, print_result = True, algorithm_para=1.0):
  
  tfidf_vect = TfidfVectorizer(stop_words=stop_words, min_df=min_df)
  # 替换为传入的训练集拟合tfidf，删除重复fit逻辑
  X_train = tfidf_vect.fit_transform(train_docs)
  X_test = tfidf_vect.transform(test_docs)
  # 替换为传入的标签
  y_train = train_y
  y_test = test_y

  if 'svm' in model_type:
      # 删除冗余的SVC初始化
      clf = svm.LinearSVC(C=algorithm_para).fit(X_train, y_train)
      predicted = clf.predict(X_test)
      
      labels = sorted(np.unique(y_train))
      precision, recall, fscore, support = precision_recall_fscore_support(y_test, predicted, labels=labels)
      
      if print_result==True:
        print("labels: ", labels)
        print("precision: ", precision)
        print("recall: ", recall)
        print("f-score: ", fscore)
        print("support: ", support)
        
      predict_p = clf._predict_proba_lr(X_test)
      y_pred = predict_p[:,1]
      
      fpr, tpr, thresholds = roc_curve(y_test,y_pred, pos_label=1)
      precision, recall, thresholds = precision_recall_curve(y_test, y_pred, pos_label=1)
      
      auc_score = auc(fpr, tpr)
      prc_score = auc(recall, precision)

      if print_result==True:
        print("AUC: {:.2%}".format(auc_score), "PRC: {:.2%}".format(prc_score))
        plt.figure()
        plt.plot(fpr, tpr, color='darkorange', lw=2)
        plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
        plt.xlim([0.0, 1.0])
        plt.ylim([0.0, 1.05])
        plt.xlabel('False Positive Rate')
        plt.ylabel('True Positive Rate')
        plt.title('AUC of SVM Model')
        plt.show()
        
        plt.figure()
        plt.plot(recall, precision, color='darkorange', lw=2)
        plt.xlim([0.0, 1.0])
        plt.ylim([0.0, 1.05])
        plt.xlabel('Recall')
        plt.ylabel('Precision')
        plt.title('Precision_Recall_Curve of SVM Model')
        plt.show()

  else:
    clf = MultinomialNB(alpha=algorithm_para).fit(X_train, y_train)
    predicted = clf.predict(X_test)
    
    labels = sorted(np.unique(y_train))
    precision, recall, fscore, support = precision_recall_fscore_support(y_test, predicted, labels=labels)  
      
    if print_result==True:
        print("labels: ", labels)
        print("precision: ", precision)
        print("recall: ", recall)
        print("f-score: ", fscore)
        print("support: ", support)
        
    predict_p = clf.predict_proba(X_test)
    y_pred = predict_p[:,1]
      
    fpr, tpr, thresholds = roc_curve(y_test,y_pred, pos_label=1)
    precision, recall, thresholds = precision_recall_curve(y_test, y_pred, pos_label=1)
      
    auc_score = auc(fpr, tpr)
    prc_score = auc(recall, precision)

    if print_result==True:
        print("AUC: {:.2%}".format(auc_score), "PRC: {:.2%}".format(prc_score))
        plt.figure()
        plt.plot(fpr, tpr, color='darkorange', lw=2)
        plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
        plt.xlim([0.0, 1.0])
        plt.ylim([0.0, 1.05])
        plt.xlabel('False Positive Rate')
        plt.ylabel('True Positive Rate')
        plt.title('AUC of Naive Bayes Model') # 修正标题错误
        plt.show()
        
        plt.figure()
        plt.plot(recall, precision, color='darkorange', lw=2)
        plt.xlim([0.0, 1.0])
        plt.ylim([0.0, 1.05])
        plt.xlabel('Recall')
        plt.ylabel('Precision')
        plt.title('Precision_Recall_Curve of Naive Bayes Model') # 修正标题错误
        plt.show()

  return auc_score, prc_score

2. 修复sample_size_impact函数 #

def sample_size_impact(train_docs, train_y, test_docs, test_y):
  auc_list_svm = []
  sample_sizes = []
  # 按500步长遍历样本量
  max_sample = len(train_docs)
  for i in range(int(max_sample/500)):
    current_size = (i+1)*500
    # 截取对应大小的训练子集
    sub_train_docs = train_docs[:current_size]
    sub_train_y = train_y[:current_size]
    # 只取返回的auc_score
    auc_score_svm, _ = create_model(sub_train_docs, sub_train_y, test_docs, test_y, \
          model_type='svm', stop_words = 'english', min_df = 1, print_result=False, algorithm_para=1.0)
    auc_list_svm.append(auc_score_svm)
    sample_sizes.append(current_size)

  plt.figure()
  plt.plot(sample_sizes, auc_list_svm, color='darkorange')
  plt.xlabel('Sample Size') # 修正拼写错误
  plt.ylabel('AUC')
  plt.title('Sample Size Impact on SVM Performance')
  plt.show()

3. 调用示例 #

# 提前提取数据传入函数，避免硬编码
train_docs = train['text'].values
train_y = train['label'].values
test_docs = test['text'].values
test_y = test['label'].values
sample_size_impact(train_docs, train_y, test_docs, test_y)

内容的提问来源于stack exchange，提问作者Vahid