Scikit-learn 中多标签分类的 GridSearch [英] GridSearch for Multi-label classification in Scikit-learn
问题描述
我正在尝试在十折交叉验证中的每一个中对最佳超参数进行 GridSearch，它在我之前的多类分类工作中工作得很好，但这次在多标签工作中却不是这样.
I am trying to do GridSearch for best hyper-parameters in every individual one of ten folds cross validation, it worked fine with my previous multi-class classification work, but not the case this time with multi-label work.
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
可能是指标签指示器的维度或格式.
Which might refer to the dimension or the format of the label indicator.
print X_train.shape, y_train.shape
获取:
(147, 1024) (147, 6)
似乎GridSearch 固有地实现了StratifiedKFold.该问题出现在具有多标签问题的分层K-fold策略中.
Seems GridSearch implements StratifiedKFold inherently.
The problem raises in the stratified K-fold strategy with multi-label problem.
StratifiedKFold(y_train, 10)
给予
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 折策略效果很好.有没有什么方法可以实现分层 K-fold 到多标签分类?
Current use of conventional K-fold strategy works fine. Is there any method to implement stratified K-fold to multi-label classification?
推荐答案
网格搜索执行 分层交叉验证用于分类问题，但对于多标签任务没有实现；事实上，多标签分层是机器学习中一个未解决的问题.我最近遇到了同样的问题，我能找到的所有文献都是 这篇文章(该文章的作者表示他们也找不到任何其他尝试来解决这个问题).
Grid search performs stratified cross-validation for classification problems, but for multi-label tasks this is not implemented; in fact, multi-label stratification is an unsolved problem in machine learning. I recently faced the same issue, and all the literature that I could find was a proposed method in this article (the authors of which state that they couldn't find any other attempts at solving this either).
这篇关于Scikit-learn 中多标签分类的 GridSearch的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
