你帮我写一个优化的代码

好的，以下是一个简单的优化代码示例，可以供您参考：

import jieba
from collections import Counter
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV

class MyDataset():
    def __init__(self, max_vocab_size=5000, min_word_freq=1):
        self.max_vocab_size = max_vocab_size
        self.min_word_freq = min_word_freq
        self.vocab = {}
        self.stop_words = []

    def set_stopword(self, path='data/scu_stopwords'):
        with open(path, 'r', encoding='utf-8') as fr:
            self.stop_words = [line.strip() for line in fr.readlines()]

    def build_vocab(self, inputs):
        cnt = Counter()
        for data in inputs:
            data = jieba.lcut(data)
            for word in data:
                if word not in self.stop_words:
                    cnt[word] += 1

        cnt = sorted([_ for _ in cnt.items() if _[1] >= self.min_word_freq], key=lambda t:t[1], reverse=True)
        self.vocab['<pad>'] = 0
        for i, (w, _) in enumerate(cnt[:self.max_vocab_size]):
            self.vocab[w] = i + 1

    def transform(self, inputs):
        samples = []
        for doc in inputs:
            doc = jieba.lcut(doc)
            sample = [0] * len(self.vocab)
            for word in doc:
                if word in self.vocab:
                    sample[self.vocab[word]] += 1
            samples.append(sample)
        return samples

def optimize_model(train_X, train_Y):
    # 定义分类器
    clf = SVC()

    # 定义超参数搜索空间
    param_grid = {
        'C': [0.1, 1, 10],
        'kernel': ['linear', 'rbf']
    }

    # 使用网格搜索寻找最优超参数
    grid_search = GridSearchCV(clf, param_grid=param_grid, cv=5)
    grid_search.fit(train_X, train_Y)

    # 输出最优超参数组合和对应的准确率
    print("Best parameters: {}".format(grid_search.best_params_))
    print("Best accuracy: {:.2f}%".format(grid_search.best_score_ * 100))

    # 返回最优模型
    return grid_search.best_estimator_

# 读取数据
train_inputs, train_labels = read_dataset('train.txt')
test_inputs, test_labels = read_dataset('test.txt')

# 创建数据集对象
dataset = MyDataset()
dataset.set_stopword()
dataset.build_vocab(train_inputs)

# 转换文本数据为向量
train_X = dataset.transform(train_inputs)
test_X = dataset.transform(test_inputs)

# 训练模型
clf = optimize_model(train_X, train_labels)

# 预测测试集
pred_Y = clf.predict(test_X)

# 计算准确率
acc = accuracy_score(test_labels, pred_Y)
print("Test accuracy: {:.2f}%".format(acc * 100))

这个示例代码中，我们使用了SVM作为分类器，使用网格搜索方法寻找最优超参数组合，最终输出最优模型的准确率。

在数据预处理阶段，我们使用了jieba进行分词，并通过设定最大词汇量和最小词频等参数，筛选出高频词汇作为词汇表，将文本转换为向量表示。

需要注意的是，这个示例代码并不一定适用于您的具体任务和数据集，您可能需要根据实际情况进行调整和优化。