Question

我想使用LeaveOneGroupOut策略来评估我的模型。根据{{3}}，cross_val_score似乎很方便。

但是，以下代码不起作用。

import sklearn
from sklearn import datasets
iris = datasets.load_iris()
from sklearn.model_selection import cross_val_score
clf = sklearn.svm.SVC(kernel='linear', C=1)
# cv = ShuffleSplit(n_splits=3, test_size=0.3, random_state=0) # => this works
cv = LeaveOneGroupOut # => this does not work
scores = cross_val_score(clf, iris.data, iris.target, cv=cv)

错误消息是：

ValueError                                Traceback (most recent call last)
<ipython-input-40-435a3a7fa16c> in <module>()
      4 from sklearn.model_selection import cross_val_score
      5 clf = sklearn.svm.SVC(kernel='linear', C=1)
----> 6 scores = cross_val_score(clf, iris.data, iris.target, cv=LeaveOneGroupOut())
      7 scores

/Users/xxx/.pyenv/versions/anaconda-2.0.1/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)
    138                                               train, test, verbose, None,
    139                                               fit_params)
--> 140                       for train, test in cv.split(X, y, groups))
    141     return np.array(scores)[:, 0]
    142 

/Users/xxx/.pyenv/versions/anaconda-2.0.1/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
    756             # was dispatched. In particular this covers the edge
    757             # case of Parallel used with an exhausted iterator.
--> 758             while self.dispatch_one_batch(iterator):
    759                 self._iterating = True
    760             else:

/Users/xxx/.pyenv/versions/anaconda-2.0.1/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in dispatch_one_batch(self, iterator)
    601 
    602         with self._lock:
--> 603             tasks = BatchedCalls(itertools.islice(iterator, batch_size))
    604             if len(tasks) == 0:
    605                 # No more tasks available in the iterator: tell caller to stop.

/Users/xxx/.pyenv/versions/anaconda-2.0.1/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __init__(self, iterator_slice)
    125 
    126     def __init__(self, iterator_slice):
--> 127         self.items = list(iterator_slice)
    128         self._size = len(self.items)
    129 

/Users/xxx/.pyenv/versions/anaconda-2.0.1/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc in <genexpr>(***failed resolving arguments***)
    135     parallel = Parallel(n_jobs=n_jobs, verbose=verbose,
    136                         pre_dispatch=pre_dispatch)
--> 137     scores = parallel(delayed(_fit_and_score)(clone(estimator), X, y, scorer,
    138                                               train, test, verbose, None,
    139                                               fit_params)

/Users/xxx/.pyenv/versions/anaconda-2.0.1/lib/python2.7/site-packages/sklearn/model_selection/_split.pyc 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]

/Users/xxx/.pyenv/versions/anaconda-2.0.1/lib/python2.7/site-packages/sklearn/model_selection/_split.pyc in _iter_test_masks(self, X, y, groups)
    770     def _iter_test_masks(self, X, y, groups):
    771         if groups is None:
--> 772             raise ValueError("The groups parameter should not be None")
    773         # We make a copy of groups to avoid side-effects during iteration
    774         groups = np.array(groups, copy=True)

ValueError: The groups parameter should not be None
scores

Answer 1

您没有定义组参数，该参数是您要拆分数据的组。

错误来自 cross_val_score ，它在参数中使用此参数：在您的情况下，它等于无。

尝试按照以下示例进行操作：

from sklearn.model_selection import LeaveOneGroupOut
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
y = np.array([1, 2, 1, 2])
groups = np.array([1, 1, 2, 2])
lol = LeaveOneGroupOut()

你有：

[In] lol.get_n_splits(X, y, groups)
[Out] 2

然后你就可以使用：

lol.split(X, y, groups)

在sklearn中的cross_val_score上使用LeaveOneGroupOut策略

1 个答案: