--lambda or --epsilon value and then load with another?
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Hello all,
I'm NG Sai, final year UG @ IIIT Sri City. I got to know about Microsoft RLOS programme via LinkedIn. My experience with open source includes contributing to C++ ML libraries such as: shogun, tensorflow-lite support and, mlpack where I've done GSoC'21 and currently serve as a member. My github.
I came across the Safe Contextual Bandits. Is this topic taken for this summer or will it be available for the year 2022. My forte is implementing algorithms from research papers so I wanted to inquire about this.
Thanks in advance!
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vw -h) but didn't find and it's neither in the documentation. I'm interesting in knowing the values for gamma_scale (I believe I saw in the presentation that it's set to 1000 but would be good to confirm) and gamma_exponent.Using
vw.get_arguments() on the model also doesn't show the default values.Thanks
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I thought it shouldn't be due to exploration which inherently brings randomization to the process, but since VW does this under the hood for CCB, I'm not sure.
To further explain: I'm training a new policy every time (with the same hyperparameters) on the same data ordered the same way. The results (even the action distribution) is always the same.
Is this expected?
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My goal is to estimate the average reward of different models and compare that with the current online policy (which can be uniformly random) to assess if it's appropriate to deploy a new policy or not.
I've been using a custom pipeline that trains for multiple passes using rejection sampling and then assessing the model's performance on a test set, but I'm a bit unsure due to the contexts' distribution shifted induced by the rejection sampling.
ccb shared | s_1 s_2
ccb action | a:1 b:1 c:1
ccb action | a:0.5 b:2 c:1
ccb action | a:0.5
ccb action | c:1
ccb slot | d:4
ccb slot 1:0.8:0.8,0:0.2 0,1,3 | d:7[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.
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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?
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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.
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Union [ example, Costs] instead of using kwargs, should I go by that method too then?
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from_example(..) if example type is what it claims to be. No?
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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
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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
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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!
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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
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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
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 :)
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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.
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