Dear scikit experts,
I'm struggling with the implementation of a nested cross validation.
My data: I have 26 subjects (13 per class) x 6670 features. I used a feature
reduction algorithm (you may have heard about Boruta) to reduce the
dimensionality of my data. Problems start now: I defined LOSO as outer
partitioning schema. Therefore, for each of the 26 cv folds I used 24 subjects
for feature reduction. This lead to a different number of features in each cv
fold. Now, for each cv fold I would like to use the same 24 subjects for
hyperparameter optimization (SVM with rbf kernel).
This is what I did:
cv = list(LeaveOneout(len(y))) # in y I stored the labels
inner_train = [None] * len(y)
inner_test = [None] * len(y)
ii = 0
while ii < len(y):
cv = list(LeaveOneOut(len(y)))
a = cv[ii][0]
a = a[:-1]
inner_train[ii] = a
b = cv[ii][0]
b = np.array(b[((len(cv[0][0]))-1)])
inner_test[ii]=b
ii = ii + 1
custom_cv = zip(inner_train,inner_test) # inner cv
pipe_logistic = Pipeline([('scl', StandardScaler()),('clf', SVC(kernel="rbf"))])
parameters = [{'clf__C': np.logspace(-2, 10, 13), 'clf__gamma':np.logspace(-9,
3, 13)}]
scores = [None] * (len(y))
ii = 0
while ii < len(scores):
a = data[ii][0] # data for train
b = data[ii][1] # data for test
c = np.concatenate((a,b)) # shape: number of subjects * number of features
d = cv[ii][0] # labels for train
e = cv[ii][1] # label for test
f = np.concatenate((d,e))
grid_search = GridSearchCV(estimator=pipe_logistic, param_grid=parameters,
verbose=1, scoring='accuracy', cv= zip(([custom_cv[ii][0]]),
([custom_cv[ii][1]])))
scores[ii] = cross_validation.cross_val_score(grid_search, c, y[f],
scoring='accuracy', cv = zip(([cv[ii][0]]), ([cv[ii][1]])))
ii = ii + 1
However, I got the following error message: index 25 is out of bounds for size
25
Would it be so bad if I do not perform a nested LOSO but I use the default
setting for hyperparameter optimization?
Any help would be really appreciated
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