GridSearch for Scikit-learn中的多标签分类

我正在尝试在十个交叉验证的每个人中的每个人中进行最佳超参数GridSearch，它与我以前的多类分类工作很好地工作，但不是这种情况这次与多标签工作。GridSearch for Scikit-learn中的多标签分类

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

这可能指的是尺寸或标签指示的格式。

print X_train.shape, y_train.shape

得到：

(147, 1024) (147, 6)

似乎GridSearch工具StratifiedKFold本质。这个问题在分层K折叠策略中引入了多标签问题。

StratifiedKFold(y_train, 10)

给

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

目前使用的传统的K-倍战略工作正常。是否有任何方法实施分层K折叠到多标签分类？

来源

2014-09-24 Francis

网格搜索对于分类问题执行stratified cross-validation，但对于多标签任务，这没有实现;实际上，多标签分层是机器学习中未解决的问题。我最近面临同样的问题，我能找到的所有文献都是this article中提出的一种方法（其中的作者称他们无法找到解决这个问题的任何其他尝试）。

来源

2014-09-25 20:24:54

谢谢你的评论，我注意到了问题，并更新了线程。也感谢分享纸张，我会通过它。 – Francis 2014-09-26 06:06:53

我刚想出一个想法，对每个类别样本进行分层分割都没有意义吗？既然'GridSearchCV'正在完成'OneVsRestClassifier'，为什么它不能单独处理每个类样本以产生'L'二元问题，因此可以对每个'L'进行分层分割？ – Francis 2015-11-18 14:29:56

正如Fred Foo所指出的那样，多标签任务没有实现分层交叉验证。一种替代方法是在转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)

来源

2017-12-23 06:02:06 Nikhil

GridSearch for Scikit-learn中的多标签分类

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