Question

我想在RandomForestClassifier中执行GridSearchCV，但数据不平衡，所以我使用StratifiedKFold：

from sklearn.model_selection import StratifiedKFold
from sklearn.grid_search import GridSearchCV
from sklearn.ensemble import RandomForestClassifier

param_grid = {'n_estimators':[10, 30, 100, 300], "max_depth": [3, None],
          "max_features": [1, 5, 10], "min_samples_leaf": [1, 10, 25, 50], "criterion": ["gini", "entropy"]}

rfc = RandomForestClassifier()

clf = GridSearchCV(rfc, param_grid=param_grid, cv=StratifiedKFold()).fit(X_train, y_train)

但是我收到了一个错误：

TypeError                                 Traceback (most recent call last)
<ipython-input-597-b08e92c33165> in <module>()
     9 rfc = RandomForestClassifier()
     10 
---> 11 clf = GridSearchCV(rfc, param_grid=param_grid, cv=StratifiedKFold()).fit(X_train, y_train)

c:\python34\lib\site-packages\sklearn\grid_search.py in fit(self, X, y)
    811 
    812         """
--> 813         return self._fit(X, y, ParameterGrid(self.param_grid))

c:\python34\lib\site-packages\sklearn\grid_search.py in _fit(self, X, y, parameter_iterable)
    559                                     self.fit_params, return_parameters=True,
    560                                     error_score=self.error_score)
--> 561                 for parameters in parameter_iterable
    562                 for train, test in cv)

c:\python34\lib\site-packages\sklearn\externals\joblib\parallel.py 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:

c:\python34\lib\site-packages\sklearn\externals\joblib\parallel.py 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.

c:\python34\lib\site-packages\sklearn\externals\joblib\parallel.py in __init__(self, iterator_slice)
    125 
    126     def __init__(self, iterator_slice):
--> 127         self.items = list(iterator_slice)
    128         self._size = len(self.items)

c:\python34\lib\site-packages\sklearn\grid_search.py in <genexpr>(.0)
    560                                     error_score=self.error_score)
    561                 for parameters in parameter_iterable
--> 562                 for train, test in cv)
    563 
    564         # Out is a list of triplet: score, estimator, n_test_samples

TypeError: 'StratifiedKFold' object is not iterable

当我写cv=StratifiedKFold(y_train)时我有ValueError: The number of folds must be of Integral type.但是当我写`cv = 5时，它有效。

我不明白StratifiedKFold有什么问题

Answer 1

我有完全相同的问题。对我有用的解决方案是替换：

from sklearn.grid_search import GridSearchCV

的与

from sklearn.model_selection import GridSearchCV

然后它应该可以正常工作。

Answer 2

似乎应将cv=StratifiedKFold()).fit(X_train, y_train)更改为cv=StratifiedKFold()).split(X_train, y_train).

Answer 3

api在最新版本中发生了变化。您曾经传递y，现在只需在创建stratifiedKFold对象时传递数字。你稍后通过了。

Answer 4

这里的问题是其他答案中提到的API更改，但答案可能更明确。

cv参数文档说明：

cv：int，交叉验证生成器或可迭代的可选

确定交叉验证拆分策略。可能的输入   对于简历是：



无，使用默认的3倍交叉验证，整数，   指定折叠数。



要用作的对象   交叉验证生成器。



可迭代的火车/测试分裂。



对于整数/无输入，如果y是二进制或多类，则为StratifiedKFold   用过的。如果估计量是分类器，或者y既不是二进制也不是   多类，使用KFold。

因此，无论使用cross validation strategy，所需的只是使用函数split提供生成器，如下所示：

kfolds = StratifiedKFold(5)
clf = GridSearchCV(estimator, parameters, scoring=qwk, cv=kfolds.split(xtrain,ytrain))
clf.fit(xtrain, ytrain)

GridSearchCV：“TypeError：'StratifiedKFold'对象不可迭代”

4 个答案:

我有完全相同的问题。对我有用的解决方案是替换：

的与