Hm, maybe I'm doing something wrong but I'm still getting the error:
ValueError: operands could not be broadcast together with shapes (3) (6)
I am using 0.14.1.
Full stacktrace:
Traceback (most recent call last):
File "GB_problem.py", line 46, in <module>
main()
File "GB_problem.py", line 43, 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
466, in _fit_stage
residual = loss.negative_gradient(y, y_pred, k=k)
File
"C:\Python27\lib\site-packages\sklearn\ensemble\gradient_boosting.py", line
349, in negative_gradient
return y - 1.0 / (1.0 + np.exp(-pred.ravel()))
ValueError: operands could not be broadcast together with shapes (3) (6)
On Tue, Oct 22, 2013 at 3:11 PM, Peter Prettenhofer <
peter.prettenho...@gmail.com> wrote:
> Ok, below is the adaptor that will work. The code requires that the output
> of predict is 2d.
>
> Thanks for the test-case.
>
> best,
> Peter
>
> class Adaptor(object):
>
> def __init__(self, est):
> self.est = est
> def predict(self, X):
> return self.est.predict_proba(X)[:, np.newaxis]
> def fit(self, X, y):
> self.est.fit(X, y)
>
>
>
> 2013/10/22 Peter Prettenhofer <peter.prettenho...@gmail.com>
>
>> Right, I thought you were using the multi-class loss function.
>>
>> Please send me a testcase so that I can investigate the issue.
>>
>> thanks,
>> Peter
>>
>>
>> 2013/10/22 Attila Balogh <attila.bal...@gmail.com>
>>
>>> Hi Peter,
>>>
>>> thanks for your answer. I have tried this before also, and the problem
>>> is that in this case I get
>>> ValueError: operands could not be broadcast together with shapes (74)
>>> (148), because the y array is raveled and it has shape (74,2).
>>>
>>> Do you need a self containing testcase which reproduces this error?
>>>
>>> Cheers;
>>> Attila
>>>
>>>
>>> On Tue, Oct 22, 2013 at 1:16 PM, Peter Prettenhofer <
>>> peter.prettenho...@gmail.com> wrote:
>>>
>>>> Hi Attila,
>>>>
>>>> please use the following adaptor::
>>>>
>>>> def __init__(self, est):
>>>> self.est = est
>>>> def predict(self, X):
>>>> return self.est.predict_proba(X)
>>>> def fit(self, X, y):
>>>> self.est.fit(X, y)
>>>>
>>>> The one in the stackoverflow question returns an array of shape
>>>> (n_samples,) but it should rather be (n_samples, n_classes).
>>>>
>>>> PS: I still need to fix the init issue but any solution will most
>>>> likely make the GBRT slower at prediction time (especially for single
>>>> instance prediction).
>>>>
>>>> best,
>>>> Peter
>>>>
>>>>
>>>> 2013/10/22 Attila Balogh <attila.bal...@gmail.com>
>>>>
>>>>> 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
>>>>>
>>>>>
>>>>> ------------------------------------------------------------------------------
>>>>> October Webinars: Code for Performance
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>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> Peter Prettenhofer
>>>>
>>>>
>>>> ------------------------------------------------------------------------------
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>>>> most from
>>>> the latest Intel processors and coprocessors. See abstracts and
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>>>>
>>>> http://pubads.g.doubleclick.net/gampad/clk?id=60135991&iu=/4140/ostg.clktrk
>>>> _______________________________________________
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>>>> Scikit-learn-general@lists.sourceforge.net
>>>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>>>>
>>>>
>>>
>>>
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>>> Free Intel webinars can help you accelerate application performance.
>>> Explore tips for MPI, OpenMP, advanced profiling, and more. Get the most
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>>> the latest Intel processors and coprocessors. See abstracts and register
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>>>
>>
>>
>> --
>> Peter Prettenhofer
>>
>
>
>
> --
> Peter Prettenhofer
>
>
> ------------------------------------------------------------------------------
> October Webinars: Code for Performance
> Free Intel webinars can help you accelerate application performance.
> Explore tips for MPI, OpenMP, advanced profiling, and more. Get the most
> from
> the latest Intel processors and coprocessors. See abstracts and register >
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>
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