sklearn管道适合：AttributeError：未找到下限

Posted 2023-03-12

【中文标题】sklearn管道适合：AttributeError：未找到下限

【英文标题】：sklearn pipeline fit: AttributeError: lower not found

【发布时间】：2017-03-24 20:43:46

【问题描述】：

我想在 sklearn 中使用管道，如下所示：

corpus = load_files('corpus/train')

stop_words = [x for x in open('stopwords.txt', 'r').read().split('\n')]  # Uppercase!

countvec = CountVectorizer(stop_words=stop_words, ngram_range=(1, 2))

X_train, X_test, y_train, y_test = train_test_split(corpus.data, corpus.target, test_size=0.9,
                                                    random_state=0)
x_train_counts = countvec.fit_transform(X_train)
x_test_counts = countvec.transform(X_test)

k_fold = KFold(n=len(corpus.data), n_folds=6)
confusion = np.array([[0, 0], [0, 0]])

pipeline = Pipeline([
    ('vectorizer',  CountVectorizer(stop_words=stop_words, ngram_range=(1, 2))),
    ('classifier',  MultinomialNB()) ])

for train_indices, test_indices in k_fold:

    pipeline.fit(x_train_counts, y_train)
    predictions = pipeline.predict(x_test_counts)

但是，我收到此错误：

AttributeError: lower not found

我看过这篇文章：

AttributeError: lower not found; using a Pipeline with a CountVectorizer in scikit-learn

但我将字节列表传递给矢量化器，所以这不应该是问题。

编辑

corpus = load_files('corpus')

stop_words = [x for x in open('stopwords.txt', 'r').read().split('\n')]

X_train, X_test, y_train, y_test = train_test_split(corpus.data, corpus.target, test_size=0.5,
                                                    random_state=0)

k_fold = KFold(n=len(corpus.data), n_folds=6)
confusion = np.array([[0, 0], [0, 0]])

pipeline = Pipeline([
    ('vectorizer', CountVectorizer(stop_words=stop_words, ngram_range=(1, 2))),
    ('classifier', MultinomialNB())])

for train_indices, test_indices in k_fold:
    pipeline.fit(X_train[train_indices], y_train[train_indices])
    predictions = pipeline.predict(X_test[test_indices])

现在我得到了错误：

TypeError: only integer arrays with one element can be converted to an index

第二次编辑

corpus = load_files('corpus')

stop_words = [y for x in open('stopwords.txt', 'r').read().split('\n') for y in (x, x.title())]

k_fold = KFold(n=len(corpus.data), n_folds=6)
confusion = np.array([[0, 0], [0, 0]])

pipeline = Pipeline([
    ('vectorizer', CountVectorizer(stop_words=stop_words, ngram_range=(1, 2))),
    ('classifier', MultinomialNB())])

for train_indices, test_indices in k_fold:
    pipeline.fit(corpus.data, corpus.target)

【问题讨论】：

等等，你为什么要进行训练，测试拆分然后kfold？当你没有足够的测试时，通常使用折叠。此外，您正在折叠 corpus.data，这可能与您的 X_train 不同，后者将缩短 50%。

@Ale：好吧，我想我误解了 kfold。我认为这是一种打乱测试和训练数据的方法。第二次编辑没有抛出错误，但我现在如何使用 predict() 函数？

那是您在 kfold 中使用索引来提取训练和测试数据的地方，请参阅我编辑的答案。或者不要使用 kfold 并在 X_train 上进行训练并在 X_test 上进行测试

@Ale 非常感谢！！我想我现在明白了：D

【参考方案1】：

您没有正确使用管道。你不需要传递向量化的数据，想法是管道对数据进行向量化。

# This is done by the pipeline
# x_train_counts = countvec.fit_transform(X_train)
# x_test_counts = countvec.transform(X_test)

k_fold = KFold(n=len(corpus.data), n_folds=6)
confusion = np.array([[0, 0], [0, 0]])

pipeline = Pipeline([
    ('vectorizer',  CountVectorizer(stop_words=stop_words, ngram_range=(1, 2))),
    ('classifier',  MultinomialNB()) ])

# also you are not using the indices...
for train_indices, test_indices in k_fold:

    pipeline.fit(corpus.data[train_indices], corpus.target[train_indices])
    predictions = pipeline.predict(corpus.data[test_indices])

【讨论】：

感谢您的回复！我更改了代码，但现在出现了新错误！你能再看看吗？

@user3813234 应该对 corpus.data 和 corpus.target 进行折叠