I'm Harsh Sharma, an undergraduate student from IIIT, Gwalior, pursuing Computer Science and Engineering. I'm interested in participating in the Microsoft RL Open Source fest this year, and I'm specifically interested in working on these projects:
17 - RL-based query planner for open-source SQL engine
20 - AutoML for online learning
Since I've worked with Deep Learning for the NL2SQL task before, I would like to work on 17. Could someone here please clarify what the "query planner" here refers to? Does it mean join query optimization? Also, I'd be really grateful if someone could guide me as to what would be the first step to implement such a query planner in an SQL engine.
I have a question about using VW with
cb_explore_adf and softmax explorer for ranking.
I am trying to use VW to perform ranking using the contextual bandit framework, specifically using
--cb_explore_adf --softmax --lambda X. The choice of softmax is because, according to VW's docs: "This is a different explorer, which uses the policy not only to predict an action but also predict a score indicating the quality of each action." This quality-related score is what I would like to use for ranking.
The scenario is this: I have a list of items [A, B, C, D], and I would like to sort it in an order that maximizes a pre-defined metric (e.g., CTR). One of the problems, as I see, is that we cannot evaluate the items individually because we can't know for sure which item made the user click or not.
To test some approaches, I've created a dummy dataset. As a way to try and solve the above problem, I am using the entire ordered list as a way to evaluate if a click happens or not (e.g., given the context for user X, he will click if the items are [C, A, B, D]). Then, I reward the items individually according to their position on the list, i.e.,
reward = 1/P for 0 < P < len(list). Here, the reward for C, A, B, D is 1, 0.5, and 0.25, 0.125, respectively. If there's no click, the reward is zero for all items. The reasoning behind this is that more important items will stabilize on top and less important on the bottom.
Also, one of the difficulties I found was defining a sampling function for this approach. Typically, we're interested in selecting only one option, but here I have to sample multiple times (4 in the example). Because of that, it's not very clear how I should incorporate exploration when sampling items. I have a few ideas:
copy_pmf. Draw a random number between 0 and
max(copy_pmf)and for each probability value in
copy_pmf, increment the
sum_probvariable (very similar to the tutorial here:https://vowpalwabbit.org/tutorials/cb_simulation.html). When
sum_prob > draw, we add the current item/prob to a list. Then, we remove this probability from
sum_prob = 0, and draw a new number again between 0 and
max(copy_pmf)(which might change or not).
max(pmf)is greater than this number, we exploit. If it isn't, we shuffle the list and return this (explore). This approach requires tuning the
lambdaparameter, which controls the output
pmf(I have seen cases where the max prob is > 0.99, which would mean around a 1% chance of exploring. I have also seen instances where max prob is ~0.5, which is around 50% exploration.
I would like to know if there are any suggestions regarding this problem, specifically sampling and the reward function. Also, if there are any things I might be missing here.
Guys would please help me with two questions:
1) Using a cb_explore_adf, for a pricing agent. I was trying two types of reward: i) Sales and ii) sales X Price where each arm is a Price. I have noticed that the cb_explore_adf converge well when the reward is sales, but when we multiple the sales by the arm price. it simply doesnt converge at all. Is it possible that it is sensitive to scale? sales are in units ( like 40 at most) and price are in cents ( e.g 399).
2) Another quick question? How to pass multiple Namespaces features in the the -q UA parameter.... I mean I want to add more variables from another namespace, something like -q [UA, MA].
-q UA UM MAand so on, depending on how many namespaces you have
--interactionsflag. Here, you could use, for example
--interactions UA UM UAM
-q ::. You should notice that using this is slower because there are lots of features created on-the-fly. Also, you should see a warning saying that some repeated features were ignored (by default in VW).
Hi I was wondering if I could get a pointer to the implementation of the
--cb k --cb_type dr in the source code? Basically I am trying to understand the parameters that are learnt at the end of off-policy CB training in VW. E.g. I did
vw --cb 3 --cb_type ips -f cb.model -d train.txt --invert_hash readable_ips.model vw --cb 3 --cb_type dm -f cb.model -d train.txt --invert_hash readable_dm.model vw --cb 3 --cb_type dr -f cb.model -d train.txt --invert_hash readable_dr.model
dr model obviously contains parameters equal to
dm but I want to know exactly what is the linear regression formula that is being implemented in
--cats? have you experimented with that at all? For cats I would try different combinations of number of discrete actions used by the algorithm (passed in to the --cats arg) and bandwidths (bandwidth being a property of the continuous range). e.g. I would try a grid of num_actions [8, 16, 32, 64, 128, 256, 1024] and e.g. bandwidths [1, 2, 4, 6, 8, 10, 14, 20]. For different number of discrete actions you might need more data for CATS to converge to something sensible. CATS label support in pyvw should be available in the next release (coming soon-ish, we don't want to wait another year for the next vw release). Let me know if you get better results from CATS or not :)