GridSearchCV: "TypeError: 'StratifiedKFold' object is not iterable"

Question

I want to perform GridSearchCV in a RandomForestClassifier, but data is not balanced, so I use 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)

But I get an error:

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

When I write cv=StratifiedKFold(y_train) I have ValueError: The number of folds must be of Integral type. But when I write `cv=5, it works.

I don't understand what is wrong with StratifiedKFold

Answer 1

I had exactly the same problem. The solution that worked for me is to replace:

from sklearn.grid_search import GridSearchCV

with

from sklearn.model_selection import GridSearchCV

---

Then it should work fine.

Answer 2

The problem here is an API change as mentioned in other answers, however the answers could be more explicit.

The cv parameter documentation states:

cv : int, cross-validation generator or an iterable, optional

Determines the cross-validation splitting strategy. Possible inputs for cv are:

• None, to use the default 3-fold cross-validation, integer, to specify the number of folds.

• An object to be used as a cross-validation generator.

• An iterable yielding train/test splits.

For integer/None inputs, if y is binary or multiclass, StratifiedKFold used. If the estimator is a classifier or if y is neither binary nor multiclass, KFold is used.

So, whatever the cross validation strategy used, all that is needed is to provide the generator using the function split, as suggested:

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

Answer 3

It seems that cv=StratifiedKFold()).fit(X_train, y_train) should be changed to cv=StratifiedKFold()).split(X_train, y_train).