co-training代码

以下是一个简单的co-training代码示例，用于二分类问题：

from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import accuracy_score

# 初始化两个分类器
clf1 = MultinomialNB()
clf2 = MultinomialNB()

# 初始化两个特征提取器
vec1 = CountVectorizer()
vec2 = CountVectorizer()

# 初始化训练数据和标签
X_train_1 = []
X_train_2 = []
y_train = []

# 加载训练数据和标签
with open('data.txt', 'r') as f:
    for line in f:
        x1, x2, y = line.strip().split('\t')
        X_train_1.append(x1)
        X_train_2.append(x2)
        y_train.append(y)

# 训练分类器1和特征提取器1
X_train_vec1 = vec1.fit_transform(X_train_1)
clf1.fit(X_train_vec1, y_train)

# 训练分类器2和特征提取器2
X_train_vec2 = vec2.fit_transform(X_train_2)
clf2.fit(X_train_vec2, y_train)

# 开始co-training过程
for i in range(10):
    # 分别使用分类器1和分类器2进行预测
    X_train_vec1 = vec1.transform(X_train_2)
    y_pred1 = clf1.predict(X_train_vec1)

    X_train_vec2 = vec2.transform(X_train_1)
    y_pred2 = clf2.predict(X_train_vec2)

    # 找到分类器1和分类器2都预测正确的样本，并将其加入训练集
    X_new_1 = []
    X_new_2 = []
    y_new = []
    for j in range(len(y_train)):
        if y_train[j] == y_pred1[j] and y_train[j] == y_pred2[j]:
            X_new_1.append(X_train_1[j])
            X_new_2.append(X_train_2[j])
            y_new.append(y_train[j])

    # 将新样本加入训练集并重新训练分类器和特征提取器
    X_train_1 += X_new_1
    X_train_2 += X_new_2
    y_train += y_new

    X_train_vec1 = vec1.fit_transform(X_train_1)
    clf1.fit(X_train_vec1, y_train)

    X_train_vec2 = vec2.fit_transform(X_train_2)
    clf2.fit(X_train_vec2, y_train)

# 在测试集上测试分类器1和分类器2的准确率
X_test_1 = []
X_test_2 = []
y_test = []
with open('test_data.txt', 'r') as f:
    for line in f:
        x1, x2, y = line.strip().split('\t')
        X_test_1.append(x1)
        X_test_2.append(x2)
        y_test.append(y)

X_test_vec1 = vec1.transform(X_test_1)
y_pred1 = clf1.predict(X_test_vec1)
acc1 = accuracy_score(y_test, y_pred1)
print("Classifier 1 accuracy:", acc1)

X_test_vec2 = vec2.transform(X_test_2)
y_pred2 = clf2.predict(X_test_vec2)
acc2 = accuracy_score(y_test, y_pred2)
print("Classifier 2 accuracy:", acc2)

这个示例代码使用了两个朴素贝叶斯分类器和两个基于词频的特征提取器，分别对两个不同的特征进行建模。在co-training过程中，每次从分类器1和分类器2都预测正确的样本中选择一部分加入训练集，并利用所有训练数据重新训练分类器和特征提取器。最后，在测试集上测试分类器1和分类器2的准确率。