Hi Ali,
Can you describe the shapes/contents of those structures?
Am I right in thinking that this evaluates the entire tree for every
sample, rather than just the path from root to a single leaf? I can see
that as bringing speed gains if the process is vectorised over samples...?
- Joel
On Wed, Jul 24, 2013 at 12:40 PM, Arslan, Ali <ali_ars...@brown.edu> 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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