wabbit | Golang AMQP | Pub Sub library

You can use the > selector to the child div's having the date class within the parent, and then hide the parent:

This is simpler to do than it might seem, because of some handy ways Pandas lets you treat sequences almost the same as you would single values in Python.

First we'll import your CSV file, treating all values as strings to make our formatting easier:

Missed what the default learner is for batch processing or,

The default learner in VW is SGD on a linear representation, but this can be modified using command line arguments.

Don’t understand how VW is actually learning in this cost-sensitive framework?

In contextual bandit learning, the reward associated with the taken action is presented for learning. VW in ips mode converts this into a reward for each action by putting zeros at the actions not taken and importance-weighting the reward for the action taken. Having imputed the missing data, it then treats the problem as a supervised learning problem.

Right now vowpalwabbit explicitly uses TCP sockets in deamon mode so the only thing you can define is the port you wish to use via --port.

PS: I'm a maintainer for this project and we would be more than happy to accept a patch with this if you need it, as we currently don't have plans to support this feature. Here is the related TCP code: https://github.com/VowpalWabbit/vowpal_wabbit/blob/master/vowpalwabbit/parser.cc#L208

Given the following types:

Olga's response on the repo: https://github.com/VowpalWabbit/vowpal_wabbit/issues/2555

--cb does not do any exploration and just trains the model given the input so the output will be what the model (that has been trained so far) predicted

--cb_explore includes exploration using epsilon-greedy by default if nothing else is specified. You can take a look at all the available exploration methods here

cb_explore's output is the PMF given by the exploration strategy (see here for more info).

Epsilon-greedy will choose, with probability e, an action at random from a uniform distribution (exploration), and with probability 1-e epsilon-greedy will use the so-far trained model to predict the best action (exploitation).

So the output will be the pmf over the actions (prob. 1-e OR e for the chosen action) and then the remaining probability will be equally split between the remaining actions. Therefore cb_explore will not provide you with a ranking.

One option for ranking would be to use CCB. Then you get a ranking and can provide feedback on any slot, but it is more computationally expensive. CCB runs CB for each slot, but the effect is a ranking since each slot draws from the overall pool of actions.

And my follow up:

I think CCB is a good option if computational limits allow. I'd just like to add that if you do cb_explore or cb_explore_adf then the resulting PMF should be sorted by score so it is a ranking of sorts. However, it's worth verifying that the ordering is in fact sorted by scores (--audit will help here) as I don't know if there is a test covering this.

I'm assuming that currently there is no support for problems with an infinity-sized action set

Correct, this isn't supported at the moment.

But are there any limits on how large a finite set of actions can be? Or is that limited only by the hardware the library runs on?

I don't believe there are concrete/artificial limits for action set size, so hardware is probably the limit. Internally, the action ID is a 32 bit number so there is definitely a limit at 2^32. As far as other issues, if you face anything like that please feel free to open an issue and we can work with you to get them sorted out. It's definitely the kind of thing that should be fixed.

Vowpal Wabbit is based on online learning (SGD-like updates, but there is also --bfgs if you really need batch optimization) and (machine learning) reductions. See some of the tutorials or papers to understand the idea of reductions. Many VW papers are also about Contextual Bandit, which is implemented as a reduction to a cost-sensitive one-against-all (OAA) classification (which is further reduced to regression). See a simple intro into reductions or a simple example how binary classification is reduced into regression.

As far as I know, VowpalWabbit does not support Decision trees nor ensembles, but see --boosting and --bootstrap. It does not support SVM, but see --loss_function hinge (hinge loss is one of the two key concepts of SVM) and --ksvm. It does not support NN, but --nn (and related options) provides a very limited support simulating a single hidden layer (feed-forward with tanh activation function), which can be added into the reduction stack.

From your description, I am assuming you want to print the outputs line by line. So use '\n' instead of comma:

std::cout << i.Name << "\n" << i.Number1 << "\n" << i.Number2 << endl;