如何将 tf-idf 应用于整个数据集（训练和测试数据集），而不是仅在朴素贝叶斯分类器类中训练数据集？

Posted 2023-03-12

【中文标题】如何将 tf-idf 应用于整个数据集（训练和测试数据集），而不是仅在朴素贝叶斯分类器类中训练数据集？

【英文标题】：How to apply tf-idf to whole dataset (training and testing dataset) instead of only training dataset within naive bayes classifier class?

【发布时间】：2019-10-21 22:31:02

【问题描述】：

我有一个朴素的贝叶斯分类器类，它将邮件分类为垃圾邮件或垃圾邮件，其中已经实现了 tf-idf。但是，tf-idf 部分只计算训练数据集的 tf-idf。

这是分类器类 类 SpamClassifier（对象）： def init(self, traindata): self.mails, self.labels = traindata['Review'], traindata['Polarity']

    def train(self):
        self.calc_TF_and_IDF()
        self.calc_TF_IDF()

    def calc_TF_and_IDF(self):
        noOfMessages = self.mails.shape[0]
        self.spam_mails, self.ham_mails = self.labels.value_counts()[1], self.labels.value_counts()[0]
        self.total_mails = self.spam_mails + self.ham_mails
        self.spam_words = 0
        self.ham_words = 0
        self.tf_spam = dict()
        self.tf_ham = dict()
        self.idf_spam = dict()
        self.idf_ham = dict()
        for i in range(noOfMessages):
            message = self.mails[i]
            count = list() #To keep track of whether the word has ocured in the message or not.
                           #For IDF
            for word in message:
                if self.labels[i]:
                    self.tf_spam[word] = self.tf_spam.get(word, 0) + 1
                    self.spam_words += 1
                else:
                    self.tf_ham[word] = self.tf_ham.get(word, 0) + 1
                    self.ham_words += 1
                if word not in count:
                    count += [word]
            for word in count:
                if self.labels[i]:
                    self.idf_spam[word] = self.idf_spam.get(word, 0) + 1
                else:
                    self.idf_ham[word] = self.idf_ham.get(word, 0) + 1

    def calc_TF_IDF(self):
        self.prob_spam = dict()
        self.prob_ham = dict()
        self.sum_tf_idf_spam = 0
        self.sum_tf_idf_ham = 0
        for word in self.tf_spam:
            self.prob_spam[word] = (self.tf_spam[word]) * log((self.spam_mails + self.ham_mails) \
                                                          / (self.idf_spam[word] + self.idf_ham.get(word, 0)))
            self.sum_tf_idf_spam += self.prob_spam[word]
        for word in self.tf_spam:
            self.prob_spam[word] = (self.prob_spam[word] + 1) / (self.sum_tf_idf_spam + len(list(self.prob_spam.keys())))

        for word in self.tf_ham:
            self.prob_ham[word] = (self.tf_ham[word]) * log((self.spam_mails + self.ham_mails) \
                                                          / (self.idf_spam.get(word, 0) + self.idf_ham[word]))
            self.sum_tf_idf_ham += self.prob_ham[word]
        for word in self.tf_ham:
            self.prob_ham[word] = (self.prob_ham[word] + 1) / (self.sum_tf_idf_ham + len(list(self.prob_ham.keys())))


        self.prob_spam_mail, self.prob_ham_mail = self.spam_mails / self.total_mails, self.ham_mails / self.total_mails 

    def classify(self, processed_message):
        pSpam, pHam = 0, 0
        for word in processed_message:                
            if word in self.prob_spam:
                pSpam += log(self.prob_spam[word])
            else:
                pSpam -= log(self.sum_tf_idf_spam + len(list(self.prob_spam.keys())))
            if word in self.prob_ham:
                pHam += log(self.prob_ham[word])
            else:
                pHam -= log(self.sum_tf_idf_ham + len(list(self.prob_ham.keys()))) 
            pSpam += log(self.prob_spam_mail)
            pHam += log(self.prob_ham_mail)
        return pSpam >= pHam

    def predict(self, testdata):
        result = []
        for (i, message) in enumerate(testdata):
            #processed_message = process_message(message)
            result.append(int(self.classify(message)))
        return result

这就是我调用分类器的方式

sc_tf_idf = SpamClassifier(traindata)
sc_tf_idf.train()
preds_tf_idf = sc_tf_idf.predict(testdata['Review'])
testdata['Predictions'] = preds_tf_idf
print(testdata['Polarity'], testdata['Predictions'])

如何将分类器中的 tf-idf 计算应用于整个数据集（训练和测试数据集）？

【参考方案1】：

不应同时计算训练数据和测试数据的 tf-idf。数据集应首先分为训练和测试（以及验证），然后您可以分别计算每个数据集的 tf-idf。如果在数据分离之前计算 tf-idf，模型将学习测试/验证数据的一些“特征”并输出非常乐观的性能。可以参考详细解答here。

此外，您可以使用来自 sklearn 的 tfidfvectorizer。