Question

我正在尝试将GridSearch用于十个交叉验证中的每一个中的最佳超参数，它与我以前的多类分类工作一起工作得很好，但这次不是多标签工作的情况。

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
clf = OneVsRestClassifier(LinearSVC())

C_range = 10.0 ** np.arange(-2, 9)
param_grid = dict(estimator__clf__C = C_range)

clf = GridSearchCV(clf, param_grid)
clf.fit(X_train, y_train)

我收到错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-65-dcf9c1d2e19d> in <module>()
      6 
      7 clf = GridSearchCV(clf, param_grid)
----> 8 clf.fit(X_train, y_train)

/usr/local/lib/python2.7/site-packages/sklearn/grid_search.pyc in fit(self, X, y)
    595 
    596         """
--> 597         return self._fit(X, y, ParameterGrid(self.param_grid))
    598 
    599 

/usr/local/lib/python2.7/site-packages/sklearn/grid_search.pyc in _fit(self, X, y,   
parameter_iterable)
    357                                  % (len(y), n_samples))
    358             y = np.asarray(y)
--> 359         cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
    360 
    361         if self.verbose > 0:

/usr/local/lib/python2.7/site-packages/sklearn/cross_validation.pyc in _check_cv(cv, X,  
y, classifier, warn_mask)
   1365             needs_indices = None
   1366         if classifier:
-> 1367             cv = StratifiedKFold(y, cv, indices=needs_indices)
   1368         else:
   1369             if not is_sparse:

/usr/local/lib/python2.7/site-packages/sklearn/cross_validation.pyc in __init__(self, 
y, n_folds, indices, shuffle, random_state)
    427         for test_fold_idx, per_label_splits in enumerate(zip(*per_label_cvs)):
    428             for label, (_, test_split) in zip(unique_labels, per_label_splits):
--> 429                 label_test_folds = test_folds[y == label]
    430                 # the test split can be too big because we used
    431                 # KFold(max(c, self.n_folds), self.n_folds) instead of

ValueError: boolean index array should have 1 dimension

可能指的是标签指示符的尺寸或格式。

print X_train.shape, y_train.shape

得到：

(147, 1024) (147, 6)

似乎GridSearch本身就实现了StratifiedKFold。问题出现在具有多标签问题的分层K折叠策略中。

StratifiedKFold(y_train, 10)

给出

ValueError                                Traceback (most recent call last)
<ipython-input-87-884ffeeef781> in <module>()
----> 1 StratifiedKFold(y_train, 10)

/usr/local/lib/python2.7/site-packages/sklearn/cross_validation.pyc in __init__(self,   
y, n_folds, indices, shuffle, random_state)
    427         for test_fold_idx, per_label_splits in enumerate(zip(*per_label_cvs)):
    428             for label, (_, test_split) in zip(unique_labels, per_label_splits):
--> 429                 label_test_folds = test_folds[y == label]
    430                 # the test split can be too big because we used
    431                 # KFold(max(c, self.n_folds), self.n_folds) instead of

ValueError: boolean index array should have 1 dimension

目前使用传统的K-fold策略效果很好。有没有任何方法可以实现分层K-fold到多标签分类？

Answer 1

网格搜索会针对分类问题执行stratified cross-validation，但对于多标签任务，则不执行此操作;实际上，多标签分层是机器学习中尚未解决的问题。我最近遇到了同样的问题，我能找到的所有文献都是this article中提出的方法（其中的作者说他们找不到任何其他尝试来解决这个问题）。

Answer 2

正如Fred Foo指出的那样，多标签任务没有实施分层交叉验证。另一种方法是在变换的标签空间中使用StratifiedKFold类scikit-learn，如建议here。

以下是示例python代码。

from sklearn.model_selection import StratifiedKFold
kf = StratifiedKFold(n_splits=n_splits, random_state=None, shuffle=shuffle)


for train_index, test_index in kf.split(X, lp.transform(y)):
    X_train = X[train_index,:]
    y_train = y[train_index,:]

    X_test = X[test_index,:]
    y_test = y[test_index,:]

    # learn the classifier
    classifier.fit(X_train, y_train)

    # predict labels for test data
    predictions = classifier.predict(X_test)

Answer 3

签出scikit-multilearn package。该文档并不完美，但是this section演示了多标签分层。您可以使用iterative_train_test_split函数。

还有iterative-stratification package，我认为它也实现了相同的想法。

我不确定，但是我认为他们俩都在实施this paper。

GridSearch用于Scikit-learn中的多标签分类

3 个答案: