问题描述
给定一个训练有素的上下文盗贼模型，我该如何检索测试样本上的预测向量?
Given a trained contextual bandit model, how can I retrieve a prediction vector on test samples?
例如，假设我有一个名为"train.dat"的火车，其中包含以下格式的行
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
....
我在命令下运行.
vw -d train.dat --cb 30 -f cb.model --save_resume
这将产生一个文件"cb.model".现在，假设我有一个如下的测试数据集
This produces a file, 'cb.model'. Now, let's say I have a test dataset as below
| a d d
| a b e
我希望看到以下概率
0.2 0.7 0.1
这些概率的解释是，应在20％的时间选择动作1，在2-70％的动作中选择动作3-10％的时间.
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.
有没有办法得到这样的东西?
Is there a way to get something like this?
推荐答案
当您使用"--cb K"时，预测是基于argmax策略(一种静态策略)的最佳操作/动作.
When you use "--cb K", the prediction is the optimal arm/action based on argmax policy, which is a static policy.
使用"--cb_explore K"时，预测输出包含每个手臂/动作的概率.根据您选择的策略，概率的计算方式有所不同.
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.
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