Hi Andreas,
Alas you were right.
On Mon, Jul 22, 2013 at 9:07 AM, Andreas Mueller
<[email protected]>wrote:
> That seems to look good to me (I think labs might need to be (1000,) but
> I'm not entirely sure).
>
> Can you reproduce your error on random / generated data?
> A gist to reproduce the problem would be great.
>
> Cheers,
> Andy
>
>
>
> On 07/22/2013 03:02 PM, Arslan, Ali wrote:
>
> 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
>>
>>
>>
>> ------------------------------------------------------------------------------
>> See everything from the browser to the database with AppDynamics
>> Get end-to-end visibility with application monitoring from AppDynamics
>> Isolate bottlenecks and diagnose root cause in seconds.
>> Start your free trial of AppDynamics Pro
>> today!http://pubads.g.doubleclick.net/gampad/clk?id=48808831&iu=/4140/ostg.clktrk
>>
>>
>>
>> _______________________________________________
>> Scikit-learn-general mailing
>> [email protected]https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>>
>>
>>
>>
>> ------------------------------------------------------------------------------
>> See everything from the browser to the database with AppDynamics
>> Get end-to-end visibility with application monitoring from AppDynamics
>> Isolate bottlenecks and diagnose root cause in seconds.
>> Start your free trial of AppDynamics Pro today!
>>
>> http://pubads.g.doubleclick.net/gampad/clk?id=48808831&iu=/4140/ostg.clktrk
>> _______________________________________________
>> Scikit-learn-general mailing list
>> [email protected]
>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>>
>>
>
>
> --
> Ali B Arslan, M.Sc.
> Cognitive, Linguistic and Psychological Sciences
> Brown University
>
>
> ------------------------------------------------------------------------------
> See everything from the browser to the database with AppDynamics
> Get end-to-end visibility with application monitoring from AppDynamics
> Isolate bottlenecks and diagnose root cause in seconds.
> Start your free trial of AppDynamics Pro
> today!http://pubads.g.doubleclick.net/gampad/clk?id=48808831&iu=/4140/ostg.clktrk
>
>
>
> _______________________________________________
> Scikit-learn-general mailing
> [email protected]https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
>
>
>
> ------------------------------------------------------------------------------
> See everything from the browser to the database with AppDynamics
> Get end-to-end visibility with application monitoring from AppDynamics
> Isolate bottlenecks and diagnose root cause in seconds.
> Start your free trial of AppDynamics Pro today!
> http://pubads.g.doubleclick.net/gampad/clk?id=48808831&iu=/4140/ostg.clktrk
> _______________________________________________
> Scikit-learn-general mailing list
> [email protected]
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
>
--
Ali B Arslan, M.Sc.
Cognitive, Linguistic and Psychological Sciences
Brown University
------------------------------------------------------------------------------
See everything from the browser to the database with AppDynamics
Get end-to-end visibility with application monitoring from AppDynamics
Isolate bottlenecks and diagnose root cause in seconds.
Start your free trial of AppDynamics Pro today!
http://pubads.g.doubleclick.net/gampad/clk?id=48808831&iu=/4140/ostg.clktrk
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
