Hi Andy,
ipdb> feats.dtype
dtype('float64')
ipdb> type(feats)
<type 'numpy.ndarray'>
ipdb> feats.shape
(1000, 20)
ipdb> labs.dtype
dtype('int8')
ipdb> type(labs)
<type 'numpy.ndarray'>
ipdb> labs.shape
(1000, 1)
I think it could also be related to the values inside the feats matrix but
I don't know what would cause these errors. I made sure that it's not full
of zero but that's the only thing I could think of.
Any ideas?
Thanks,
A
On Mon, Jul 22, 2013 at 4:43 AM, Andreas Mueller
<[email protected]>wrote:
> Hi Ali.
> What is the type and size of your input and output vectors?
> (type, dtype, shape)
>
> Cheers,
> Andy
>
>
> On 07/22/2013 01:24 AM, Arslan, Ali wrote:
>
> Hi,
> I'm trying to use AdaBoostClassifier with a decision tree stump as the
> base classifier. I noticed that the weight adjustment done by
> AdaBoostClassifier has been giving me errors both for SAMME.R and SAMME
> options.
>
> Here's a brief overview of what I'm doing
>
> def train_adaboost(features, labels):
> uniqLabels = np.unique(labels)
> allLearners = []
> for targetLab in uniqLabels:
> runs=[]
> for rrr in xrange(10):
> feats,labs = get_binary_sets(features, labels, targetLab)
> baseClf = DecisionTreeClassifier(max_depth=1,
> min_samples_leaf=1)
> baseClf.fit(feats, labs)
>
> ada_real = AdaBoostClassifier( base_estimator=baseClf,
> learning_rate=1,
> n_estimators=20,
> algorithm="SAMME")
> runs.append(ada_real.fit(feats, labs))
> allLearners.append(runs)
>
> return allLearners
>
> I looked at the fit for every single decision tree classifier and they are
> able to predict some labels. When I look at the AdaBoostClassifier using
> this base classifier, however, I get errors about the weight boosting
> algorithm.
>
> def compute_confidence(allLearners, dada, labbo):
> for ii,thisLab in enumerate(allLearners):
> for jj, thisLearner in enumerate(thisLab):
> #accessing thisLearner's methods here
>
> The methods give errors like these:
>
> ipdb> thisLearner.predict_proba(myData)
>
> PATHTOPACKAGE/lib/python2.7/site-packages/sklearn/ensemble/weight_boosting.py:727:
> RuntimeWarning: invalid value encountered in double_scalars proba /=
> self.estimator_weights_.sum() *** ValueError: 'axis' entry is out of bounds
>
> ipdb> thisLearner.predict(myData)
>
> PATHTOPACKAGE/lib/python2.7/site-packages/sklearn/ensemble/weight_boosting.py:639:
> RuntimeWarning: invalid value encountered in double_scalars pred /=
> self.estimator_weights_.sum() *** IndexError: 0-d arrays can only use a
> single () or a list of newaxes (and a single ...) as an index
>
> I tried SAMME.R algorithm for adaboost but I can't even fit adaboost in
> that case because of this error[...]
>
> File "PATH/sklearn/ensemble/weight_boosting.py", line 388, in fit return
> super(AdaBoostClassifier, self).fit(X, y, sample_weight)
>
> File "PATH/sklearn/ensemble/weight_boosting.py", line 124, in fit
> X_argsorted=X_argsorted)
>
> File "PATH/sklearn/ensemble/weight_boosting.py", line 435, in _boost
> X_argsorted=X_argsorted)
>
> File "PATH/sklearn/ensemble/weight_boosting.py", line 498, in _boost_real
> (estimator_weight < 0)))
>
> ValueError: non-broadcastable output operand with shape (1000) doesn't
> match the broadcast shape (1000,1000)
>
> the data's dimensions are actually compatible with the format that
> classifier is expecting, both before using adaboost and when I try to test
> the trained classifiers. What can these errors indicate?
>
> Thanks,
> Ali
>
>
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--
Ali B Arslan, M.Sc.
Cognitive, Linguistic and Psychological Sciences
Brown University
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