GridSearch with SVM producing IndexError

Question

我正在使用SVM构建一个分类器，并希望执行Grid Search来帮助自动找到最优模型。下面是代码:

from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.multiclass import OneVsRestClassifier
X.shape     # (22343, 323)
y.shape     # (22343, 1)
X_train, X_test, y_train, y_test = train_test_split(
  X, Y, test_size=0.4, random_state=0
)
tuned_parameters = [
  {
    'estimator__kernel': ['rbf'],
    'estimator__gamma': [1e-3, 1e-4],
    'estimator__C': [1, 10, 100, 1000]
  },
  {
    'estimator__kernel': ['linear'], 
    'estimator__C': [1, 10, 100, 1000]
  }
]
model_to_set = OneVsRestClassifier(SVC(), n_jobs=-1)
clf = GridSearchCV(model_to_set, tuned_parameters)
clf.fit(X_train, y_train)

，我得到以下错误消息(这不是整个堆栈跟踪)。只是最后3个电话):

----------------------------------------------------
/anaconda/lib/python3.5/site-packages/sklearn/model_selection/_split.py in split(self, X, y, groups)
     88         X, y, groups = indexable(X, y, groups)
     89         indices = np.arange(_num_samples(X))
---> 90         for test_index in self._iter_test_masks(X, y, groups):
     91             train_index = indices[np.logical_not(test_index)]
     92             test_index = indices[test_index]
/anaconda/lib/python3.5/site-packages/sklearn/model_selection/_split.py in _iter_test_masks(self, X, y, groups)
    606 
    607     def _iter_test_masks(self, X, y=None, groups=None):
--> 608         test_folds = self._make_test_folds(X, y)
    609         for i in range(self.n_splits):
    610             yield test_folds == i
/anaconda/lib/python3.5/site-packages/sklearn/model_selection/_split.py in _make_test_folds(self, X, y, groups)
    593         for test_fold_indices, per_cls_splits in enumerate(zip(*per_cls_cvs)):
    594             for cls, (_, test_split) in zip(unique_y, per_cls_splits):
--> 595                 cls_test_folds = test_folds[y == cls]
    596                 # the test split can be too big because we used
    597                 # KFold(...).split(X[:max(c, n_splits)]) when data is not 100%
IndexError: too many indices for array

此外，当我尝试重塑数组以便y是(22343，)时，我发现GridSearch永远不会完成，即使我将tuned_parameters设置为仅默认值。

如果有帮助，这里是所有包的版本:

Python: 3.5.2

scikit-learn: 0.18

熊猫:0.19.0

Answer 1

看来你的实现没有错误。

然而，正如sklearn文档中提到的那样，"拟合时间复杂度与样本数量的关系大于二次，这使得很难扩展到具有多个10000样本的数据集"。查看这里的文档

在您的情况下，您有22343样本，这可能导致一些计算问题/内存问题。这就是为什么当你做你的默认简历需要很多时间。尝试使用10000或更少的样本减少您的训练集。