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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