1 reply
[warning] Unlabeled example in train set, was this intentional?, I didn't understandWhat exactly is the
slot argument? ccb slot | d:4 what does it mean? Does it mean this is the fourth slot? In L7, what's happening? + the error term. Would be really grateful for an explanation, thanks
¿Has anyone experienced issues like this? I am very disappointed because of this.
I am using it currently for NLP Intent detection, (OAA Classifier on POS-tagged + morphological analyzed text)
Although it runs smooth and fine, the F1 score must be always re-calculated afterward, and the loss is tricky, you can get a really low loss, and a bad F1 and vice-versa! Precision-Recall behavior is also an issue.
12 replies
In CCB, what's the difference between the weight of an example vs it's cost. I thought weight is just an inverse of cost?
I tried feeding the training data from CCB page, and the weight seems to be equal to the example counter, is that intentional?
1 reply
As it stands now, by looking at the source code, seems that the selection of the action on a slot does not take into account the previous action selected as "features". I will try to explain me better:
Suppose that there are N slots and M actions (M >= N).
The algorithms choose in slot N_1 action M_1, then a decision on N_2 must be taken, and other actions are similar to M_1.
There is a high chance that in slot M_2 an action similar to M_1 will be taken.
I would like to pass as "context" the decision made at the previous step, to promote "diversity".
I am not an expert, but I would like to know if someone has some experience regarding that.
4 replies
Union [ example, Costs] instead of using kwargs, should I go by that method too then?
1 reply
from_example(..) if example type is what it claims to be. No?
4 replies
Version 8.10.1
Id
Min label:-1
Max label:1
bits:18
lda:0
0 ngram:
0 skip:
options: --cb_adf --cb_type dr --csoaa_ldf multiline --csoaa_rank
Checksum: 4264491651
event_sum 0
action_sum 0
:0
s^age:7950:0.108916
:7951:0.242307
s^year:39846:-1.6944
:39847:-1.82654I had a few questions if you guys don't mind.
a) Are you guys conducting RLOS this year? Not that I'd stop contributing if you aren't, but it certainly gives motivation to contribute :)
b) On the topic of AutoML, I read the wiki and some merged PRs. I’d like to contribute, how can I help since its quite volatile right now, my PRs maybe slow so I don’t want to end up being the bottleneck. Other project which interests me: Python model introspection, CB in python.
c) I’m currently in a 6 month internship, but I think RLOS allows for this to be part-time? Would that be okay?
Thanks for your answers
1 reply
Hello everyone, I'm trying a toy example to try and understand the model weights in the context of CATS algorithm
Training data : ca 1.23:-1:0.7 | a:1
Test data : | a:1
The vw command vw --cats 4 --bandwidth 1 --min_value 0 --max_value 32 --d train.vw --invert_hash m.ih -f model.vw --noconstant
the output is :
Version 8.11.0
Id
Min label:-1
Max label:0
bits:18
lda:0
0 ngram:
0 skip:
options: --bandwidth 1 --binary --cats 4 --cats_pdf 4 --cats_tree 4 --cb_explore_pdf --get_pmf --max_value 32 --min_value 0 --pmf_to_pdf 4 --sample_pdf --tree_bandwidth 0 --random_seed 2147483647
Checksum: 2730770910
:1
initial_t 0
norm normalizer 0.357143
t 1
sum_loss 0
sum_loss_since_last_dump 0
dump_interval 2
min_label -1
max_label 0
weighted_labeled_examples 1
weighted_labels 1
weighted_unlabeled_examples 0
example_number 1
total_features 1
total_weight 0.357143
sd::oec.weighted_labeled_examples 1
current_pass 1
a:108232:-0.190479 0.714286 1
a[1]:108233:-0.190479 0.714286 1Can anyone please help to use the weights to get the final results ? Thank you
1 reply
Hey guys, just trying out the new VW release (it looks awesome, by the way)!
I was wondering what's the proper way of using --automl. I tried using --cb_explore_adf --automl 5 --oracle_type one_diff and it didn't give me any warning, is that correct?
Moreover, is there a way for me to know which interactions the model found to be the best after training with --automl?
Thank you!
12 replies
I just tried to use another experimental feature, experimental_full_name_interactions, but wasn't able to.
Scenario: I've changed one of the namespaces in my dataset to begin with the same letter (changed Session to Usession - just for testing purposes, and I also have the User namespace).
Then, I tried running the following command:vw --cb_explore_adf -c train.dat --passes 5 experimental_full_name_interactions Usession|User -f regular.vw
And got the following output:User: command not found
Is this a bug or am I doing something wrong?
Thanks
18 replies
Version 9.0.0
Id
Min label:-2
Max label:0.189197
bits:18
lda:0
0 ngram:
0 skip:
options: --cb 2 --cb_force_legacy --cb_type dr --csoaa 2 --random_seed 123
Checksum: 955399906
:1
initial_t 0
norm normalizer 508
t 32
sum_loss -32.2039
sum_loss_since_last_dump 0
dump_interval 64
min_label -2
max_label 0.189197
weighted_labeled_examples 32
weighted_labels 0
weighted_unlabeled_examples 0
example_number 32
total_features 128
total_weight 127
sd::oec.weighted_labeled_examples 32
current_pass 1
l1_state 0
l2_state 1
d:20940:0.0795723 0.676124 1
d[1]:20941:-0.253962 17.0199 1
d[2]:20942:0.0793786 0.305578 1
d[3]:20943:-0.253947 8.55648 1
e:69020:0.0795723 0.676124 1
e[1]:69021:-0.253962 17.0199 1
e[2]:69022:0.0793786 0.305578 1
e[3]:69023:-0.253947 8.55648 1
a:108232:-0.253785 17.065 1
a[1]:108233:0.0793965 0.706623 1
a[2]:108234:-0.253947 8.55648 1
a[3]:108235:0.0793786 0.305578 1
b:129036:-0.253785 17.065 1
b[1]:129037:0.0793965 0.706623 1
b[2]:129038:-0.253947 8.55648 1
b[3]:129039:0.0793786 0.305578 1
f:139500:0.0795723 0.676124 1
f[1]:139501:-0.253962 17.0199 1
f[2]:139502:0.0793786 0.305578 1
f[3]:139503:-0.253947 8.55648 1
Constant:202096:-0.238701 17.7411 1
Constant[1]:202097:-0.238162 17.7265 1
Constant[2]:202098:-0.238136 8.86206 1
Constant[3]:202099:-0.238136 8.86206 1
c:219516:-0.253785 17.065 1
c[1]:219517:0.0793965 0.706623 1
c[2]:219518:-0.253947 8.55648 1
c[3]:219519:0.0793786 0.305578 1
3 replies
I was attempting to use the VW estimators library for some bias correction on a pandas dataframe before building a Q function (model) outside of VW, but I noticed there is no Doubly Robust or MTR methods in this python library? Is that intentional or are they named as something else and I am missing them?
Also a small Usage snippet in the README might be useful. I've been reading through the basic-usage.py, but it is somewhat difficult to figure out with just this script.
Hello VW community,
We are evaluating to use of Vowpal Wabbit for our recommender system (multi-arm-bandit). We want to show different images (combinatorial) and predict with which the user will interact.
Going through the documentation, Vowpal Wabbit supports with Slates the combinatorial setup. For the reward, it is stated that:
"A single, global, reward is produced that signals the outcome of an entire slate of decisions. There’s a linearity assumption on the impact of each action on the observed reward."
I.e. the semi-feedback we need is not supported by default.
Our question: Is there a way to work with semi-feedback and Slots in Vowpal Wabbit?
Thank you :)
2 replies
Hello all, I am trying to install Vowpal Wabbit on my local machine and have succesfully built and also ran the tests without any failures. However whenever I try to run make install it gives an error.
make: *** No rule to make target 'install'. Stop.I am unable to resolve it . Any help would be appreciated.
5 replies
num_actions = 20
vw = pyvw.vw("--cats " + str(num_actions) + " --bandwidth " + str(bandwidth) + " -d data/cats.acpx --min_value 0 --max_value 20000 --json --chain_hash --coin --epsilon 0.2 -f saved_model2.model --save_resume -q :: --quiet")
I want to generate the model file uisng invert-hash and i use pyvw.vw(" -d data/cats.acpx -t -i saved_model2.model --invert_hash model2.humanreadable"). But i am getting ouput in the model2.humanreadable as ['Version 8.11.0\n', 'Id \n', 'Min label:0\n', 'Max label:0\n', 'bits:18\n', 'lda:0\n', '0 ngram:\n', '0 skip:\n', 'options: --bandwidth 1000 --binary --cats 20 --cats_pdf 20 --cats_tree 20 --cb_explore_pdf --coin --epsilon 0.200000002980232 --get_pmf --max_value 20000 --min_value 0 --pmf_to_pdf 20 --quadratic :: --sample_pdf --tree_bandwidth 1 --random_seed 11542015123243797559\n', 'Checksum: 1406756192\n', ':0\n']. Is something wrong in the way I pass the params?
1 reply
I printed the actions and probs which are like [(0.0, 0.5625, 0.0020000000949949026), (0.5625, 2.5625, 0.4020000100135803), (2.5625, 100.0, 0.0020000000949949026)]. The last value in the each tuple corresponds to pdf. Now how do you sample using this pdf? If I am not wrong to sample from pdf, we sample from x ~ unif(0,1) and then cdf-inv(x) will be the sample. Is similar thing is done here? If yes then there should be a parameter for this pdf. for eg guassian has mean and variance. How to get that?
2 replies
8 replies
Hello. I had a question about the Compiler Optimisation project for RLOS Fest.
I wanted to clarify the aims for the project. From the project description: "We will develop RL agents using VowpalWabbit" and "Implement VW agents for CompilerGym".
Now the tasks in CompilerGym such as Optimisation Phase Ordering are multi-state MDPs which are usually tackled with full RL approaches (as opposed to Contextual Bandits).
The closest method I could find to full RL in VW is learning to search. However these are all imitation style learning algorithms which require an oracle as a reference policy to learn from which is not available in the Compiler Optimisation case.
One approach that came to mind is to modify the reward and observation space of CompilerGym so that each observation would contain context from previous states and reward would be reward to go to termination. This would allow for the use of Contextual Bandits but would be a little awkward compared to using a full RL agent.
The other approach I thought of was using core VW classification algorithms to learn a Q-function within a larger RL agent. If this is the case then is there a particular reason for using VW algorithms (such as for scale and performance) as opposed to neural nets or other ML models?
In general I am just trying to get an idea of the expected approach since CompilerGym hosts full-RL problems whereas VW is targetted towards contextual bandits.
1 reply
Hello. I want to work on the project "Introduce Feature Engineering Language in VowpalWabbit" for RLOS Fest.
I have completed part 1 and 2 of the screening exercise and I wanted to clarify something about part 3. In the DataBase of DataRows where each row is is map<string,float>
Can i assume the key values will be disjoint for the different rows?
What will be the number of keys in a row and the number of rows with respect to the length of the queries? I am asking because this part asks for some ideas about how to optimize the function select() but the constraints are a little vague.
3 replies
Hello Everyone!!
I have applied for the "Native CSV parsing" project in RLOSF,
One thing I noticed related to the screening exercise was that the output for the second example had some discrepancy, I
am not sure if it is the case or not, it'd be really great if someone can confirm that. I think shouldn't the first line of output should have C:1 rather than C:2 ?
Anyways, I hope I too get selected this year...
Best of luck to @threewisemonkeys-as , @rajuthegr8, and all other applicants 👍
1 reply
Thinking about Contextual Bandits (and variations thereof) in production, how could one identify when it's appropriate to reset a model (i.e., start training from scratch)? My premise is that the model gradually becomes less sensitive to new data, so resetting from time to time seems appropriate (correct?).
I'm not sure if this should be based on a given period (e.g., every week), amount of data, or something else.
I know this is somewhat subjective but I would love to hear if anyone has any thoughts on the subject.
6 replies
1 reply
