Hi all,
first of all thanks for all the developers for working on scikit-learn, it
is a wonderful library.
I am struggling for a while now with the following problem:
Trying to use GBR with LR as a BaseEstimator, and I'm getting the following
error:
File "main.py", line 110, in main
score = np.mean(cross_validation.cross_val_score(rd, X, y, cv=4,
scoring='roc_auc'))
File "C:\Python27\lib\site-packages\sklearn\cross_validation.py", line
1152, in cross_val_score
for train, test in cv)
File
"C:\Python27\lib\site-packages\sklearn\externals\joblib\parallel.py", line
517, in __call__
self.dispatch(function, args, kwargs)
File
"C:\Python27\lib\site-packages\sklearn\externals\joblib\parallel.py", line
312, in dispatch
job = ImmediateApply(func, args, kwargs)
File
"C:\Python27\lib\site-packages\sklearn\externals\joblib\parallel.py", line
136, in __init__
self.results = func(*args, **kwargs)
File "C:\Python27\lib\site-packages\sklearn\cross_validation.py", line
1060, in _cross_val_score
estimator.fit(X_train, y_train, **fit_params)
File
"C:\Python27\lib\site-packages\sklearn\ensemble\gradient_boosting.py", line
890, in fit
return super(GradientBoostingClassifier, self).fit(X, y)
File
"C:\Python27\lib\site-packages\sklearn\ensemble\gradient_boosting.py", line
613, in fit
random_state)
File
"C:\Python27\lib\site-packages\sklearn\ensemble\gradient_boosting.py", line
486, in _fit_stage
sample_mask, self.learning_rate, k=k)
File
"C:\Python27\lib\site-packages\sklearn\ensemble\gradient_boosting.py", line
172, in update_terminal_regions
y_pred[:, k])
IndexError: too many indices
I have found a similar problem on stackoverflow (
http://stackoverflow.com/questions/17454139/gradientboostingclassifier-with-a-baseestimator-in-scikit-learn)
and tried to implement the adaptor but it didn't help, the error remained
the same.
Does anyone have any ideas how to resolve this?
Cheers;
Attila
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