Hi Arslan.
Have you tried the AdaBoost implementation in the current development
version?
Cheers,
Andy
On 07/24/2013 04:40 AM, Arslan, Ali wrote:
Hi,
I've been running adaboost with DecisionTreeClassifier in a for a
multiclass detection problem (comprises of multiple one-vs-all
problems). The prediction method I'm using is like this:
for ii,thisLab in enumerate(allLearners):
res = np.zeros([dada.shape[0]], dtype='float16')
for jj, thisLearner in enumerate(thisLab):
my_weights = thisLearner.estimator_weights_
#tic = time.time()
for hh, thisEstimator in enumerate(thisLearner):
res = res+thisEstimator.predict(DATA)*my_weights[hh]
I don't know how straightforward this looks but basically I'm
iterating over labels (or classes), then different estimators in the
adaboost to collect their prediction into one result array (after
scaling the results with each individual tree's weight).
The innermost part of the loop is taking a bit too long (~1 sec)
considering it's run about 2600 time for my data.
I was looking for faster/alternative ways of making a prediction and
I've encountered this toolbox for matlab:
http://graphics.cs.msu.ru/en/science/research/machinelearning/adaboosttoolbox
This toolbox's prediction method seems pretty succinct and it runs
very fast (0.0015 sec). The function is something like this:
function y = calc_output(tree_node, XData)
y = XData(tree_node.dim, :) * 0 + 1;
for i = 1 : length(tree_node.parent)
y = y .* calc_output(tree_node.parent, XData); % recursively split
based on its parents' constrain
end
if( length(tree_node.right_constrain) > 0)
y = y .* ((XData(tree_node.dim, :) < tree_node.right_constrain));
end
if( length(tree_node.left_constrain) > 0)
y = y .* ((XData(tree_node.dim, :) > tree_node.left_constrain));
end
I tried to find the analogues of these structures (ie. tree_node.dim ,
tree_node.parent, tree_node. right_constrain) in the "tree object" in
python but I failed to see them.
I was wondering if it's possible to speed up the prediction like this
matlab example?
Thanks!
--
Ali B Arslan, M.Sc.
Cognitive, Linguistic and Psychological Sciences
Brown University
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