在网格搜索中返回numpy.core.memmap而不是numpy.Number的评分

当在较大的数据集上使用

GridSearchCV

和

LogisticRegression

时，我们能够（仅在我们的应用程序atm的上下文中）在Ubuntu 15.04和带有scikit 0.17的OS X上重现以下问题

...........................................................................
/Users/samuelhopkins/.virtualenvs/cpml/lib/python2.7/site-packages/sklearn/pipeline.py in fit(self=Pipeline(steps=[('cpencoder', <cpml.whitebox.Lin...s', refit=True, scoring=u'roc_auc', verbose=1))]), X=                   Unnamed: 0  member_id  loan_a...          42.993346  

[152536 rows x 45 columns], y=array([0, 1, 0, ..., 1, 1, 0]), **fit_params={})
    160         y : iterable, default=None
    161             Training targets. Must fulfill label requirements for all steps of
    162             the pipeline.
    163         """
    164         Xt, fit_params = self._pre_transform(X, y, **fit_params)
--> 165         self.steps[-1][-1].fit(Xt, y, **fit_params)
        self.steps.fit = undefined
        Xt = array([[  0.00000000e+00,   1.29659900e+06,   5....000000e+00,   0.00000000e+00,   4.29933458e+01]])
        y = array([0, 1, 0, ..., 1, 1, 0])
        fit_params = {}
    166         return self
    167 
    168     def fit_transform(self, X, y=None, **fit_params):
    169         """Fit all the transforms one after the other and transform the

...........................................................................
/Users/samuelhopkins/.virtualenvs/cpml/lib/python2.7/site-packages/sklearn/grid_search.py in fit(self=GridSearchCV(cv=None, error_score='raise',
     ...jobs', refit=True, scoring=u'roc_auc', verbose=1), X=array([[  0.00000000e+00,   1.29659900e+06,   5....000000e+00,   0.00000000e+00,   4.29933458e+01]]), y=array([0, 1, 0, ..., 1, 1, 0]))
    799         y : array-like, shape = [n_samples] or [n_samples, n_output], optional
    800             Target relative to X for classification or regression;
    801             None for unsupervised learning.
    802 
    803         """
--> 804         return self._fit(X, y, ParameterGrid(self.param_grid))
        self._fit = <bound method GridSearchCV._fit of GridSearchCV(...obs', refit=True, scoring=u'roc_auc', verbose=1)>
        X = array([[  0.00000000e+00,   1.29659900e+06,   5....000000e+00,   0.00000000e+00,   4.29933458e+01]])
        y = array([0, 1, 0, ..., 1, 1, 0])
        self.param_grid = {'C': [1], 'class_weight': ['auto'], 'fit_intercept': [False], 'intercept_scaling': [1], 'penalty': ['l2']}
    805 
    806 
    807 class RandomizedSearchCV(BaseSearchCV):
    808     """Randomized search on hyper parameters.

...........................................................................
/Users/samuelhopkins/.virtualenvs/cpml/lib/python2.7/site-packages/sklearn/grid_search.py in _fit(self=GridSearchCV(cv=None, error_score='raise',
     ...jobs', refit=True, scoring=u'roc_auc', verbose=1), X=array([[  0.00000000e+00,   1.29659900e+06,   5....000000e+00,   0.00000000e+00,   4.29933458e+01]]), y=array([0, 1, 0, ..., 1, 1, 0]), parameter_iterable=<sklearn.grid_search.ParameterGrid object>)
    548         )(
    549             delayed(_fit_and_score)(clone(base_estimator), X, y, self.scorer_,
    550                                     train, test, self.verbose, parameters,
    551                                     self.fit_params, return_parameters=True,
    552                                     error_score=self.error_score)
--> 553                 for parameters in parameter_iterable
        parameters = undefined
        parameter_iterable = <sklearn.grid_search.ParameterGrid object>
    554                 for train, test in cv)
    555 
    556         # Out is a list of triplet: score, estimator, n_test_samples
    557         n_fits = len(out)

...........................................................................
/Users/samuelhopkins/.virtualenvs/cpml/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=Parallel(n_jobs=2), iterable=<generator object <genexpr>>)
    807             if pre_dispatch == "all" or n_jobs == 1:
    808                 # The iterable was consumed all at once by the above for loop.
    809                 # No need to wait for async callbacks to trigger to
    810                 # consumption.
    811                 self._iterating = False
--> 812             self.retrieve()
        self.retrieve = <bound method Parallel.retrieve of Parallel(n_jobs=2)>
    813             # Make sure that we get a last message telling us we are done
    814             elapsed_time = time.time() - self._start_time
    815             self._print('Done %3i out of %3i | elapsed: %s finished',
    816                         (len(self._output), len(self._output),

---------------------------------------------------------------------------
Sub-process traceback:
---------------------------------------------------------------------------
ValueError                                         Mon Jan 18 11:58:09 2016
PID: 71840 Python 2.7.10: /Users/samuelhopkins/.virtualenvs/cpml/bin/python
...........................................................................
/Users/samuelhopkins/.virtualenvs/cpml/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self=<sklearn.externals.joblib.parallel.BatchedCalls object>)
     67     def __init__(self, iterator_slice):
     68         self.items = list(iterator_slice)
     69         self._size = len(self.items)
     70 
     71     def __call__(self):
---> 72         return [func(*args, **kwargs) for func, args, kwargs in self.items]
     73 
     74     def __len__(self):
     75         return self._size
     76 

...........................................................................
/Users/samuelhopkins/.virtualenvs/cpml/lib/python2.7/site-packages/sklearn/cross_validation.pyc in _fit_and_score(estimator=LogisticRegression(C=1, class_weight='auto', dua...         tol=0.0001, verbose=0, warm_start=False), X=memmap([[  0.00000000e+00,   1.29659900e+06,   5...000000e+00,   0.00000000e+00,   4.29933458e+01]]), y=memmap([0, 1, 0, ..., 1, 1, 0]), scorer=make_scorer(roc_auc_score, needs_threshold=True), train=array([ 49100,  49101,  49102, ..., 152533, 152534, 152535]), test=array([    0,     1,     2, ..., 57517, 57522, 57532]), verbose=1, parameters={'C': 1, 'class_weight': 'auto', 'fit_intercept': False, 'intercept_scaling': 1, 'penalty': 'l2'}, fit_params={}, return_train_score=False, return_parameters=True, error_score='raise')
   1545                              " numeric value. (Hint: if using 'raise', please"
   1546                              " make sure that it has been spelled correctly.)"
   1547                              )
   1548 
   1549     else:
-> 1550         test_score = _score(estimator, X_test, y_test, scorer)
   1551         if return_train_score:
   1552             train_score = _score(estimator, X_train, y_train, scorer)
   1553 
   1554     scoring_time = time.time() - start_time

...........................................................................
/Users/samuelhopkins/.virtualenvs/cpml/lib/python2.7/site-packages/sklearn/cross_validation.pyc in _score(estimator=LogisticRegression(C=1, class_weight='auto', dua...         tol=0.0001, verbose=0, warm_start=False), X_test=memmap([[  0.00000000e+00,   1.29659900e+06,   5...000000e+01,   0.00000000e+00,   4.29933458e+01]]), y_test=memmap([0, 1, 0, ..., 1, 1, 1]), scorer=make_scorer(roc_auc_score, needs_threshold=True))
   1604         score = scorer(estimator, X_test)
   1605     else:
   1606         score = scorer(estimator, X_test, y_test)
   1607     if not isinstance(score, numbers.Number):
   1608         raise ValueError("scoring must return a number, got %s (%s) instead."
-> 1609                          % (str(score), type(score)))
   1610     return score
   1611 
   1612 
   1613 def _permutation_test_score(estimator, X, y, cv, scorer):

ValueError: scoring must return a number, got 0.998981811748 (<class 'numpy.core.memmap.memmap'>) instead.

更多信息：

• 我们使用的是Python2.7
• 我们正在使用

  管道

  ，以确保所有输入都是数字

我的问题如下：

• 我们如何重新生成此问题，从而使

  记分器返回
  memmap

• 还有其他人有这个问题吗
• 我们在

  cross\u validation.py中所做的更改实际上是一个不错的解决方案吗

是的，有类似的病例

我爱上了<代码> MEMMAP/<代码> -S，由于内存分配的O/S限制，我考虑<代码> .MMMAP> <代码> -S是一个用于大规模机器学习的智能工具，使用EM在<强> >代码>。FIE（）/<代码> <强> -S和其他<代码> SkEXEX/COD>方法。（

GridSearchCV（）

目前还不是这种情况，因为在具有

n_作业=-1的大型
HyperPARAMETER
s'网格上预分配内存会产生不利影响）


我们如何。。。复制…？据我记忆所及，我的情况与此相似，从“普通”的
numpy.ndarray
到
numpy.memmap（）
的变化启动了这些工件。因此，如果您试图人为地创建一个这样的数组，请将数据包装到数组的
.memmap（）
-ed表示形式中，并使其返回，即使它包含一个数据单元，而不是一个普通的数字。用户应能看到该单元的通用数组表示的
.memmap（）
-ed子范围


是变化。。。一个合适的解决方案？好吧，我通过引用结果的
[0]组件显式返回一个单元格值，摆脱了
.memmap（）
-ed包装器。通过
.float（）
强制转换似乎很好。
似乎scikit的人也认为这是一个不错的解决方案。带有转换的解决方案将包含在
0.17中。
...

if isinstance(score, np.core.memmap):
   score = np.float(score)
if not isinstance(score, numbers.Number):
    raise ValueError("scoring must return a number, got %s (%s) instead."

...