KNN算法的实现

在数据集准备完成之后 开始进行算法的实现

其中本次项目分为3个部分

数据的获取

其中数据已经保存到了txt文件

直接读取

print('(1) load texts...')
train_texts = open('../dataset_train/x_train.txt', encoding='utf-8').read().split('\n')
train_labels = open('../dataset_train/y_train.txt', encoding='utf-8').read().split('\n')
test_texts = open('../dataset_test/x_test.txt', encoding='utf-8').read().split('\n')
test_labels = open('../dataset_test/y_test.txt', encoding='utf-8').read().split('\n')
all_text = train_texts + test_texts

特征值抽取

特征值的抽取

使用的是词袋和TifIdf算法

其中有一点注意就是对于词袋 词语特征的提取是从总文档的词语中抽取

然后对测试集和训练集分别进行特征抽取

然后得到两个矩阵

然后进行Tfidf算法

count_v0 = CountVectorizer();
counts_all = count_v0.fit_transform(all_text);
count_v1 = CountVectorizer(vocabulary=count_v0.vocabulary_);
counts_train = count_v1.fit_transform(train_texts);
print("the shape of train is " + repr(counts_train.shape))
count_v2 = CountVectorizer(vocabulary=count_v0.vocabulary_);
counts_test = count_v2.fit_transform(test_texts);
print("the shape of test is " + repr(counts_test.shape))

tfidftransformer = TfidfTransformer();
train_data = tfidftransformer.fit(counts_train).transform(counts_train);
test_data = tfidftransformer.fit(counts_test).transform(counts_test);

x_train = train_data
y_train = train_labels
x_test = test_data
y_test = test_labels

KNN算法实现

在特征值抽取之后

利用KNN算法建模

使用训练集进行建模

然后使用测试集进行模型的套用

然后的到结果

然后进行结果的比对

然后得到正确率

# KNN算法建模
for x in range(1, 15):
    knnclf = KNeighborsClassifier(n_neighbors=x)
    knnclf.fit(x_train, y_train)
    preds = knnclf.predict(x_test);
    num = 0
    preds = preds.tolist()
    for i, pred in enumerate(preds):
        if int(pred) == int(y_test[i]):
            num += 1
    print('K= ' + str(x) + ', precision_score:' + str(float(num) / len(preds)))