Python 3.x 在sklearn中对pickled数据执行预测

我试图通过在newsgroup20数据集上进行实验来学习。我的训练模型运行良好，预测部分是我遇到问题的地方。现在我要做的是在一个函数中保存训练模型（使用pickle），并在另一个函数中对pickle数据执行预测。我找到的所有教程都告诉我如何保存和加载pickle文件，但没有告诉我如何提取X_train和y_train。如果有人能帮忙，那就太好了。这是我的密码

def classifier(): 
    twenty_train = fetch_20newsgroups(subset='train', shuffle=True, random_state=42)
    X_train, X_test, y_train, y_test = train_test_split(twenty_train.data, twenty_train.target, test_size=0.4, random_state=0)

    naive_clf = Pipeline([('vect', CountVectorizer()),
                         ('tfidf', TfidfTransformer()),
                         ('clf', MultinomialNB()),
    ])
    naive_clf.fit(X_train, y_train)  
    filename = 'finalized_model.sav'
    pickle.dump(naive_clf, open(filename, 'wb'))


def predictions(): # need help in first 3 lines and last print statement

    loaded_model = pickle.load(open('finalized_model.sav', 'rb'))
    result = loaded_model.score(X_test, y_test)
    print(result)

    #parsing my file as string for prediction(works fine)
    with open("/home/ubuntu/Desktop/text_classifier/dataset/predict/file,txt", "r") as myfile:
        file=myfile.readlines()
        file = ''.join(file)

    print('belongs to class {} according to naive bayes'.format(twenty_train.target_names[loaded_model.predict([file])[0]]))`

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使用pickle保存模型时，只保存模型本身，而不保存用于培训的数据。因此，如果要使用pickle加载数据，则需要单独保存。例如：

data = {'train': X_train, 'target': y_train}
with open('data.pkl', 'wb') as f:
    pickle.dump(data, f)

with open('data.pkl', 'rb') as f:
    data = pickle.load(f)
X_train = data['train']
y_train = data['target']

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使用pickle保存模型时，只保存模型本身，而不保存用于培训的数据。因此，如果要使用pickle加载数据，则需要单独保存。例如：

data = {'train': X_train, 'target': y_train}
with open('data.pkl', 'wb') as f:
    pickle.dump(data, f)

with open('data.pkl', 'rb') as f:
    data = pickle.load(f)
X_train = data['train']
y_train = data['target']

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谢谢你的快速回复，但我怎样才能做到同样的效果呢？添加了一个例子。效果很好。谢谢你的回复谢谢你的快速回复，但我如何才能做到同样的效果？添加了一个例子。效果很好。谢谢你的回复