实验六: 基于集成学习的 Amazon 用户评论质量预测
1 案例简介
随着电商平台的兴起，以及疫情的持续影响，线上购物在我们的日常生活中扮演着越来越重要的角色。在进行线上商品挑选时，评论往往是我们十分关注的一个方面。然而目前电商网站的评论质量参差不齐，甚至有水军刷好评或者恶意差评的情况出现，严重影响了顾客的购物体验。因此，对于评论质量的预测成为电商平台越来越关注的话题，如果能自动对评论质量进行评估，就能根据预测结果避免展现低质量的评论。本案例中我们将基于集成学习的方法对 Amazon 现实场景中的评论质量进行预测。
2 作业说明
本案例中需要大家完成两种集成学习算法的实现（Bagging、AdaBoost.M1），其中基分类器要求使用 SVM 和决策树两种，因此，一共需要对比四组结果（AUC 作为评价指标）：
- Bagging + SVM
- Bagging + 决策树
- AdaBoost.M1 + SVM
- AdaBoost.M1 + 决策树
注意集成学习的核心算法需要手动进行实现，基分类器可以调库。
2.1 基本要求
- 根据数据格式设计特征的表示
- 汇报不同组合下得到的 AUC
- 结合不同集成学习算法的特点分析结果之间的差异
- （使用 sklearn 等第三方库的集成学习算法会酌情扣分）
2.2 扩展要求
- 尝试其他基分类器（如 k-NN、朴素贝叶斯）
- 分析不同特征的影响
- 分析集成学习算法参数的影响
3 数据概览
import pandas as pd
train_df = pd.read_csv('./data/train.csv', sep='\t')
test_df = pd.read_csv('./data/test.csv', sep='\t')
testlabels_df = pd.read_csv('./data/groundTruth.csv')
train_df.head()
| reviewerID | asin | reviewText | overall | votes_up | votes_all | label | |
|---|---|---|---|---|---|---|---|
| 0 | 7885 | 3901 | First off, allow me to correct a common mistak... | 5.0 | 6 | 7 | 0 |
| 1 | 52087 | 47978 | I am really troubled by this Story and Enterta... | 3.0 | 99 | 134 | 0 |
| 2 | 5701 | 3667 | A near-perfect film version of a downright glo... | 4.0 | 14 | 14 | 1 |
| 3 | 47191 | 40892 | Keep your expectations low. Really really low... | 1.0 | 4 | 7 | 0 |
| 4 | 40957 | 15367 | "they dont make em like this no more..."well..... | 5.0 | 3 | 6 | 0 |
test_df.head()
| Id | reviewerID | asin | reviewText | overall | |
|---|---|---|---|---|---|
| 0 | 0 | 82947 | 37386 | I REALLY wanted this series but I am in SHOCK ... | 1.0 |
| 1 | 1 | 10154 | 23543 | I have to say that this is a work of art for m... | 4.0 |
| 2 | 2 | 5789 | 5724 | Alien 3 is certainly the most controversal fil... | 3.0 |
| 3 | 3 | 9198 | 5909 | I love this film...preachy? Well, of course i... | 5.0 |
| 4 | 4 | 33252 | 21214 | Even though I previously bought the Gamera Dou... | 5.0 |
testlabels_df.head()
| Id | Expected | |
|---|---|---|
| 0 | 0 | 0 |
| 1 | 1 | 0 |
| 2 | 2 | 0 |
| 3 | 3 | 0 |
| 4 | 4 | 0 |
本次数据来源于 Amazon 电商平台，包含超过 50,000 条用户在购买商品后留下的评论，各列的含义如下：
- reviewerID：用户 ID
- asin：商品 ID
- reviewText：英文评论文本
- overall：用户对商品的打分（1-5）
- votes_up：认为评论有用的点赞数（只在训练集出现）
- votes_all：该评论得到的总评价数（只在训练集出现）
- label：评论质量的 label，1 表示高质量，0 表示低质量（只在训练集出现）
评论质量的 label 来自于其他用户对评论的 votes，votes_up/votes_all ≥ 0.9 的作为高质量评论。此外测试集包含一个额外的列 ID，标识了每一个测试的样例。
4 特征提取
- 采用
TfidfVectorizer进行特征提取 - 划分数据集
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer# 创建 TfidfVectorizer 实例，并指定停用词为 'english'
vectorizer = TfidfVectorizer(stop_words='english')# 训练集
train_X_all = vectorizer.fit_transform(train_df['reviewText'])
train_y_all = np.array(train_df['label'])# 测试集
test_X_all = vectorizer.transform(test_df['reviewText'])
test_y_all = np.array(testlabels_df['Expected'])train_X_all.shape,train_y_all.shape,test_X_all.shape,test_y_all.shape
结果：
((57039, 153747), (57039,), (11208, 153747), (11208,))
因为训练集，测试集数据量较大，采用训练集前2000个数据、测试集前200个数据进行代码编写
# 简单训练集
train_X = vectorizer.fit_transform(train_df.reviewText[0:2000])
train_y = np.array(train_df.label[0:2000])# 简单测试集
test_X = vectorizer.transform(test_df.reviewText[0:200])
test_y = np.array(testlabels_df.Expected[0:200])train_X.shape,train_y.shape,test_X.shape,test_y.shape
结果：
((2000, 28961), (2000,), (200, 28961), (200,))
5 模型构建
- 采用
sklearn自带的BaggingClassifier,AdaBoostClassifier进行训练 - 自己构建 Bagging 算法与 AdaBoost 算法，进行训练
5.1 利用sklearn自带库实现集成学习
from sklearn.ensemble import BaggingClassifier, AdaBoostClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import roc_auc_scoreRANDOM_SEED = 2024# 定义基分类器
svm_classifier = SVC(C=200,kernel='rbf',probability=True)
tree_classifier = DecisionTreeClassifier()
- Bagging + SVM
# Bagging + SVM
bagging_svm = BaggingClassifier(estimator=svm_classifier, n_estimators=50, random_state=RANDOM_SEED)
bagging_svm.fit(train_X, train_y)
y_pred_bagging_svm = bagging_svm.predict_proba(test_X)[:, 1]
auc_bagging_svm = roc_auc_score(test_y, y_pred_bagging_svm)
print("Bagging + SVM AUC:", auc_bagging_svm)
Bagging + SVM AUC: 0.747968827723429
- Bagging + 决策树
# Bagging + 决策树
bagging_tree = BaggingClassifier(estimator=tree_classifier, n_estimators=50, random_state=RANDOM_SEED)
bagging_tree.fit(train_X, train_y)
y_pred_bagging_tree = bagging_tree.predict_proba(test_X)[:, 1]
auc_bagging_tree = roc_auc_score(test_y, y_pred_bagging_tree)
print("Bagging + Decision Tree AUC:", auc_bagging_tree)
Bagging + Decision Tree AUC: 0.7278229149394795
- AdaBoost.M1 + SVM
# AdaBoost.M1 + SVM
adaboost_svm = AdaBoostClassifier(estimator=svm_classifier, n_estimators=50, random_state=RANDOM_SEED)
adaboost_svm.fit(train_X, train_y)
y_pred_adaboost_svm = adaboost_svm.predict_proba(test_X)[:, 1]
auc_adaboost_svm = roc_auc_score(test_y, y_pred_adaboost_svm)
print("AdaBoost.M1 + SVM AUC:", auc_adaboost_svm)
AdaBoost.M1 + SVM AUC: 0.7449842480517327
- AdaBoost.M1 + 决策树
# AdaBoost.M1 + 决策树
adaboost_tree = AdaBoostClassifier(estimator=tree_classifier, n_estimators=50, random_state=RANDOM_SEED)
adaboost_tree.fit(train_X, train_y)
y_pred_adaboost_tree = adaboost_tree.predict_proba(test_X)[:, 1]
auc_adaboost_tree = roc_auc_score(test_y, y_pred_adaboost_tree)
print("AdaBoost.M1 + Decision Tree AUC:", auc_adaboost_tree)
AdaBoost.M1 + Decision Tree AUC: 0.5928535897861051
5.2 手动实现集成学习
- Bagging算法实现
# Bagging 手动实现
def bagging(X, y,test_X, base_classifier,num_classifiers):result = np.zeros(test_X.shape[0]) # 记录测试集的预测结果for i in range(num_classifiers):# 随机采样，有放回indices = np.random.choice(len(y), len(y), replace=True)X_sampled, y_sampled = X[indices], y[indices]print('Model {:<2d} finish!'.format(i))# 训练基分类器base_classifier.fit(X_sampled, y_sampled)# 进行预测predict_proba = base_classifier.predict_proba(test_X)[:, 1]result += predict_proba # 累加不同分类器的预测概率result /= num_classifiers # 取平均（投票）return resultnp.random.seed(RANDOM_SEED)
num_classifiers=10# 使用 Bagging + SVM
y_pred_bagging_svm_self = bagging(train_X, train_y, test_X, svm_classifier,num_classifiers)
auc_bagging_svm = roc_auc_score(test_y, y_pred_bagging_svm_self)
print("Bagging + SVM AUC:", auc_bagging_svm)
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Bagging + SVM AUC: 0.7456474879787763
# 使用 Bagging + 决策树
y_pred_bagging_tree_self = bagging(train_X, train_y, test_X, tree_classifier,num_classifiers)
auc_bagging_tree = roc_auc_score(test_y, y_pred_bagging_tree_self)
print("Bagging + Decision Tree AUC:", auc_bagging_tree)
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Bagging + Decision Tree AUC: 0.6983916431769193
- AdaBoost算法实现
def adaboost(X, y, test_X, base_classifier,num_classifiers):result_lst, beta_lst = list(), list() # 记录每次迭代的预测结果和投票权重num_samples = len(y)weight = np.ones(num_samples) # 样本权重，注意总和应为 num_samplesfor i in range(num_classifiers):# 使用样本权重训练基分类器base_classifier.fit(X, y, sample_weight=weight) print('Model {:<2d} finish!'.format(i))# 在训练集上进行预测train_predict = base_classifier.predict(X) # 计算错误率并更新权重error_flag = train_predict != y # 预测错误的位置error = np.sum(weight[error_flag]) / num_samples # 计算错误率if error > 0.5:break# 存储 beta 值和预测结果beta = error / (1 - error)weight *= (1.0 - error_flag) * beta + error_flag # 调整权重，正确位置乘上 beta，错误位置还是原来的weight /= np.sum(weight) / num_samples # 归一化，让权重和等于 num_samplesbeta_lst.append(beta)predict_proba = base_classifier.predict_proba(test_X)[:, 1]result_lst.append(predict_proba)# 计算最终预测结果，作为加权和beta_lst = np.log(1 / np.array(beta_lst))beta_lst /= np.sum(beta_lst) # 归一化投票权重result = (np.array(result_lst) * beta_lst[:, None]).sum(0) # 每一轮的预测结果加权求和return result
svm_classifier = SVC(kernel='linear', probability=True)
# 使用 AdaBoost.M1 + SVM
y_pred_adaboost_svm_self = adaboost(train_X, train_y, test_X, svm_classifier,num_classifiers)
auc_adaboost_svm = roc_auc_score(test_y, y_pred_adaboost_svm_self)
print("AdaBoost.M1 + SVM AUC:", auc_adaboost_svm)
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AdaBoost.M1 + SVM AUC: 0.7541037970485822
# 使用 AdaBoost.M1 + SVM
tree_classifier=DecisionTreeClassifier(max_depth=10, class_weight='balanced')y_pred_adaboost_tree_self = adaboost(train_X, train_y, test_X, tree_classifier,num_classifiers)
auc_adaboost_tree = roc_auc_score(test_y, y_pred_adaboost_tree_self)
print("AdaBoost.M1 + Decision Tree AUC:", auc_adaboost_tree)
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AdaBoost.M1 + Decision Tree AUC: 0.5791742662908307
5.3 绘制ROC曲线
- 根据sklearn自带的集成学习，绘制的ROC曲线
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc# 绘制 ROC 曲线
def plot_all_roc_curves(y_true, y_pred_probs, labels):plt.figure(figsize=(8, 8))for y_pred_prob, label in zip(y_pred_probs, labels):fpr, tpr, _ = roc_curve(y_true, y_pred_prob)roc_auc = auc(fpr, tpr)plt.plot(fpr, tpr, lw=2, label='{} (AUC = {:.2f})'.format(label, roc_auc))plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')plt.xlabel('False Positive Rate')plt.ylabel('True Positive Rate')plt.title('Receiver Operating Characteristic')plt.legend(loc='lower right')plt.show()# 绘制所有 ROC 曲线
plot_all_roc_curves(test_y, [y_pred_bagging_svm, y_pred_bagging_tree, y_pred_adaboost_svm, y_pred_adaboost_tree],['Bagging + SVM', 'Bagging + Decision Tree', 'AdaBoost.M1 + SVM', 'AdaBoost.M1 + Decision Tree'])
- 根据手动实现的集成学习代码绘制ROC图
# 绘制所有 ROC 曲线
plot_all_roc_curves(test_y, [y_pred_bagging_svm_self, y_pred_bagging_tree_self, y_pred_adaboost_svm_self, y_pred_adaboost_tree_self],['Bagging + SVM', 'Bagging + Decision Tree', 'AdaBoost.M1 + SVM', 'AdaBoost.M1 + Decision Tree'])
6 云平台提交格式
提交文件需要对测试集中每一条评论给出预测为高质量的概率，每行包括一个 ID（和测试集对应）以及预测的概率 Prediction（0-1的浮点数），用逗号分隔。示例提交格式如下：
ID,Prediction
0,0.9
1,0.45
2,0.78
...
文件命名没有要求。
from sklearn import svm
from sklearn.calibration import CalibratedClassifierCVsvm_classifier=svm.LinearSVC(dual='auto')
svm_classifier = CalibratedClassifierCV(svm_classifier, method='sigmoid')
num_classifiers = 100# 使用 Bagging + SVM
y_pred_bagging_svm_self = bagging(train_X_all, train_y_all, test_X_all, svm_classifier,num_classifiers)
auc_bagging_svm = roc_auc_score(test_y_all, y_pred_bagging_svm_self)
print("Bagging + SVM AUC:", auc_bagging_svm)
Bagging + SVM AUC: 0.7812187133616998
# 生成提交文件
result_df = pd.DataFrame()
result_df['ID'] = test_df['Id'].values
result_df['Prediction'] = y_pred_bagging_svm_self.round(2) # 保留两位小数
result_df.to_csv('./result.csv', index=False)
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