vowpalwabbit

Given a trained contextual bandit model, how can I retrieve a prediction vector on test samples? For example, let's say I have a train set named "train.dat" containing lines formatted as below 1:-1:0.3 | a b c # <action:cost:probability | features> 2:2:0.3 | a d d 3:-1:0.3 | a b e .... And I run below command. vw -d train.dat --cb 30 -f cb.model --save_resume This produces a file, 'cb.model'. Now, let's say I have a test dataset as below | a d d | a b e I'd like to see probabilities as below 0.2 0.7 0.1 The interpretation of these probabilities would be that action 1 should be picked 20% of

I am performing logistic regression using Vowpal Wabbit on a dataset with 25 features and 48 million instances. I have a question on current predict values. Should it be within 0 or 1. average since example example current current current loss last counter weight label predict features 0.693147 0.693147 1 1.0 -1.0000 0.0000 24 0.419189 0.145231 2 2.0 -1.0000 -1.8559 24 0.235457 0.051725 4 4.0 -1.0000 -2.7588 23 6.371911 12.508365 8 8.0 -1.0000 -3.7784 24 3.485084 0.598258 16 16.0 -1.0000 -2.2767 24 1.765249 0.045413 32 32.0 -1.0000 -2.8924 24 1.017911 0.270573 64 64.0 -1.0000 -3.0438 25 0

I got a weird result from vw , which uses online learning scheme for logistic regression. And when I add --l1 or --l2 regularization then I got all predictions at 0.5 (that means all features are 0) Here's my command: vw -d training_data.txt --loss_function logistic -f model_l1 --invert_hash model_readable_l1 --l1 0.05 --link logistic ...and here's learning process info: using l1 regularization = 0.05 final_regressor = model_l1 Num weight bits = 18 learning rate = 0.5 initial_t = 0 power_t = 0.5 using no cache Reading datafile = training_data.txt num sources = 1 average since example example

I am trying to run a program called vowpal-wabbit (version 6.0) in mac os x. I've used homebrew to install some things, such as lib tools and, importantly, boost. However -- I'm finding that, even though /usr/local/Cellar/boost/1.49.0/lib Clearly has all the boost libs, (including lib boost_program_options.dylib)... The error message i get when trying to run a binary is : dyld: Library not loaded: libboost_program_options.dylib I get the same (basically) error when running "make" to build this binary from scratch : ld: library not found for -lboost_program_options So ... I guess my question is

Closed . This question needs to be more focused. It is not currently accepting answers. Learn more . Want to improve this question? Update the question so it focuses on one problem only by editing this post . Following the available docs and resources, it is not really clear how to accomplish a simple getting-started flow where you'd launch Vowpal Wabbit as a daemon (possibly even without any pre-learnt model) and have it online learn and explore ― I'm looking for a flow where I'd feed in a context, get back a recommendation, and feed back a cost/reward. So let me skip the technical

Is there a way to send data to train a model in Vowpal Wabbit without writing it to disk? Here's what I'm trying to do. I have a relatively large dataset in csv (around 2gb) which fits in memory with no problem. I load it in R into a data frame, and I have a function to convert the data in that dataframe into VW format. Now, in order to train a model, I have to write the converted data to a file first, and then feed that file to VW. And the writing to disk part takes way too long, especially since I want to try different various models with different feature transformations, and thus I have to

What parameters should I use in VW for a binary classification task? For example, let's use rcv1_small.dat . I thought it is better to use the logistic loss function (or hinge) and it makes no sense to use --oaa 2 . However, the empirical results (with progressive validation 0/1 loss reported in all 4 experiments) show that best combination is --oaa 2 without logistic loss (i.e. with the default squared loss): cd vowpal_wabbit/test/train-sets cat rcv1_small.dat | vw --binary # average loss = 0.0861 cat rcv1_small.dat | vw --binary --loss_function=logistic # average loss = 0.0909 cat rcv1_small