问题
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 the time, action 2 - 70%, and action 3 - 10% of the time.
Is there a way to get something like this?
回答1:
When you use "--cb K", the prediction is the optimal arm/action based on argmax policy, which is a static policy.
When using "--cb_explore K", the prediction output contains the probability for each arm/action. Depending the policy you pick, the probabilities are calculated differently.
回答2:
If you send those lines to a daemon running your model, you'd get just that. You send a context, and the reply is a probability distribution across the number of allowed actions, presumably comprising the "recommendation" provided by the model.
Say you have 3 actions, like in your example. Start a contextual bandits daemon:
vowpalwabbit/vw -d train.dat --cb_explore 3 -t --daemon --quiet --port 26542
Then send a context to it:
| a d d
You'll get just what you want as the reply.
来源：https://stackoverflow.com/questions/41669363/vowpal-wabbit-how-to-get-prediction-probabilities-from-contextual-bandit-model