利用Rocchio算法实现测试文档分类的python代码

Rocchio算法是一种基于向量空间模型的文本分类算法，其思想是将测试文档的向量表示与已知类别的训练文档的向量表示进行比较，根据最相似的训练文档的类别来预测测试文档的类别。以下是一个基于Rocchio算法的测试文档分类的Python代码示例：

import numpy as np

class RocchioClassifier:
    def __init__(self, alpha=1, beta=0.75, threshold=0):
        self.alpha = alpha  # 加权因子
        self.beta = beta  # 减权因子
        self.threshold = threshold  # 判断阈值

    def fit(self, X, y):
        # 计算各个类别的文档向量的平均值
        self.class_means = {}
        for label in np.unique(y):
            self.class_means[label] = np.mean(X[y == label], axis=0)

    def predict(self, X):
        y_pred = []
        for x in X:
            # 计算测试文档向量与各个类别的文档向量的余弦相似度
            similarities = {}
            for label, mean in self.class_means.items():
                similarities[label] = np.dot(x, mean) / (np.linalg.norm(x) * np.linalg.norm(mean))

            # 根据余弦相似度最大的类别来预测测试文档的类别
            max_label = max(similarities, key=similarities.get)
            if similarities[max_label] >= self.threshold:
                y_pred.append(max_label)
            else:
                y_pred.append(None)

        return y_pred

    def fit_predict(self, X_train, y_train, X_test):
        self.fit(X_train, y_train)
        return self.predict(X_test)

使用示例：

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

# 加载数据集
newsgroups = fetch_20newsgroups(subset='all')

# 特征提取
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(newsgroups.data)
y = newsgroups.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练并预测
clf = RocchioClassifier()
y_pred = clf.fit_predict(X_train, y_train, X_test)

# 评估分类器性能
print(classification_report(y_test, y_pred, target_names=newsgroups.target_names))