根据tf/idf构建的词向量空间和输出的高频特征词以及分好类、具有类标号的文件夹下的中文文本，使用朴素贝叶斯算法训练分类模型，并返回分类模型的准确性、精准度、召回率

好的，下面是具体步骤：

1. 构建词向量空间：

使用中文分词工具（如jieba）对文本进行分词，并过滤停用词等无意义的词语，得到文本中的关键词。然后使用tf/idf算法计算每个关键词在整个文本集合中的重要性，得到每个文本的词向量。

2. 特征选择：

根据tf/idf算法计算每个文本中词语的重要性，选择出高频特征词，作为朴素贝叶斯算法的特征。

3. 训练模型：

将具有类标号的文件夹下的中文文本分为训练集和测试集，使用朴素贝叶斯算法进行训练。训练过程中，将训练集中的每个文本的词向量和对应的类别传入算法中进行训练。

4. 评估模型：

用测试集中的每个文本的词向量作为输入，预测其所属类别，然后将预测结果与真实结果进行比较，计算模型的准确性、精准度和召回率。

代码实现如下：

import jieba
import os
import math
import random

# 读取文件夹中的文本，返回文本内容
def read_file(path):
    with open(path, 'r', encoding='utf-8') as f:
        content = f.read()
    return content

# 分词并返回词语列表
def cut_words(content):
    words = jieba.cut(content)
    return [word for word in words if word.strip()]

# 计算tf值
def calc_tf(word, words):
    return words.count(word) / len(words)

# 计算idf值
def calc_idf(word, texts):
    return math.log(len(texts) / (sum(1 for text in texts if word in text)))

# 计算tf-idf值
def calc_tf_idf(word, words, texts):
    return calc_tf(word, words) * calc_idf(word, texts)

# 构建词向量空间
def build_word_vector_space(texts):
    words_list = set()
    for text in texts:
        words_list.update(text)

    words_list = list(words_list)
    words_list.sort()

    vectors = []
    for text in texts:
        vector = [calc_tf_idf(word, text, texts) for word in words_list]
        vectors.append(vector)

    return vectors

# 特征选择，选择高频特征词作为朴素贝叶斯算法的特征
def feature_selection(vectors, labels, k):
    feature_words = []
    feature_values = []
    for i in range(len(vectors[0])):
        values = [vector[i] for vector in vectors]
        if sum(values) > k:
            feature_words.append(i)
            feature_values.append(values)

    feature_values = list(zip(*feature_values))
    labels = list(zip(labels, vectors))

    return feature_words, feature_values, labels

# 计算条件概率
def calc_conditional_prob(word_index, feature_values, labels):
    probs = {}
    for label, vector in labels:
        if label not in probs:
            probs[label] = []

        value = feature_values[word_index][labels.index((label, vector))]
        probs[label].append(value)

    return probs

# 计算先验概率
def calc_prior_prob(labels):
    n = len(labels)
    prior_probs = {}
    for label in labels:
        if label not in prior_probs:
            prior_probs[label] = 0

        prior_probs[label] += 1

    for label in prior_probs:
        prior_probs[label] /= n

    return prior_probs

# 训练模型
def train(feature_words, feature_values, labels):
    conditional_probs = {}
    for word_index in feature_words:
        probs = calc_conditional_prob(word_index, feature_values, labels)
        for label in probs:
            probs[label] = sum(probs[label]) / len(probs[label])
        conditional_probs[word_index] = probs

    prior_probs = calc_prior_prob([label for label, vector in labels])

    return prior_probs, conditional_probs

# 预测
def predict(vector, prior_probs, conditional_probs):
    probs = dict(prior_probs)
    for i in range(len(vector)):
        if i not in conditional_probs:
            continue

        for label in probs:
            probs[label] *= conditional_probs[i][label] ** vector[i]

    return max(probs, key=probs.get)

# 计算准确性、精准度和召回率
def evaluate(test_vectors, test_labels, prior_probs, conditional_probs):
    correct_count = 0
    positive_count = 0
    true_positive_count = 0

    for vector, label in zip(test_vectors, test_labels):
        predict_label = predict(vector, prior_probs, conditional_probs)

        if predict_label == label:
            correct_count += 1

        if predict_label == '1':
            positive_count += 1

        if label == '1' and predict_label == '1':
            true_positive_count += 1

    accuracy = correct_count / len(test_vectors)
    precision = true_positive_count / positive_count if positive_count > 0 else 0
    recall = true_positive_count / sum(1 for label in test_labels if label == '1')

    return accuracy, precision, recall

# 加载数据集
def load_dataset(path):
    texts = []
    labels = []

    for label in os.listdir(path):
        label_path = os.path.join(path, label)
        for filename in os.listdir(label_path):
            file_path = os.path.join(label_path, filename)
            content = read_file(file_path)
            words = cut_words(content)
            texts.append(words)
            labels.append(label)

    return texts, labels

# 随机划分训练集和测试集
def split_dataset(texts, labels, ratio=0.8):
    dataset = list(zip(texts, labels))
    random.shuffle(dataset)
    n = int(len(dataset) * ratio)
    train_dataset = dataset[:n]
    test_dataset = dataset[n:]
    train_texts, train_labels = zip(*train_dataset)
    test_texts, test_labels = zip(*test_dataset)
    return train_texts, train_labels, test_texts, test_labels

if __name__ == '__main__':
    # 加载数据集
    texts, labels = load_dataset('corpus')

    # 随机划分训练集和测试集
    train_texts, train_labels, test_texts, test_labels = split_dataset(texts, labels)

    # 构建词向量空间
    vectors = build_word_vector_space(train_texts + test_texts)

    # 特征选择
    feature_words, feature_values, labels = feature_selection(vectors, train_labels + test_labels, 10)

    # 训练模型
    prior_probs, conditional_probs = train(feature_words, feature_values, labels)

    # 评估模型
    accuracy, precision, recall = evaluate(vectors[len(train_texts):], test_labels, prior_probs, conditional_probs)

    print('Accuracy:', accuracy)
    print('Precision:', precision)
    print('Recall:', recall)

需要注意的是，上面的代码只是一个简单的示例，对于实际应用中的数据集和问题，需要根据具体情况进行调整和优化。