Contextual Bandits#
import vowpalwabbit
import pandas as pd
# generate sample data that could originate from previous random trial, e.g. AB test, for the CB to explore
## data here are equivalent to example in https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Logged-Contextual-Bandit-Example
train_data = [{'action': 1, 'cost': 2, 'probability': 0.4, 'feature1': 'a', 'feature2': 'c', 'feature3': ''},
{'action': 3, 'cost': 0, 'probability': 0.2, 'feature1': 'b', 'feature2': 'd', 'feature3': ''},
{'action': 4, 'cost': 1, 'probability': 0.5, 'feature1': 'a', 'feature2': 'b', 'feature3': ''},
{'action': 2, 'cost': 1, 'probability': 0.3, 'feature1': 'a', 'feature2': 'b', 'feature3': 'c'},
{'action': 3, 'cost': 1, 'probability': 0.7, 'feature1': 'a', 'feature2': 'd', 'feature3': ''}]
train_df = pd.DataFrame(train_data)
## add index to df
train_df['index'] = range(1, len(train_df) + 1)
train_df = train_df.set_index("index")
# generate some test data that you want the CB to make decisions for, e.g. features describing new users, for the CB to exploit
test_data = [{'feature1': 'b', 'feature2': 'c', 'feature3': ''},
{'feature1': 'a', 'feature2': '', 'feature3': 'b'},
{'feature1': 'b', 'feature2': 'b', 'feature3': ''},
{'feature1': 'a', 'feature2': '', 'feature3': 'b'}]
test_df = pd.DataFrame(test_data)
## add index to df
test_df['index'] = range(1, len(test_df) + 1)
test_df = test_df.set_index("index")
# take a look at dataframes
print(train_df)
print(test_df)
action cost probability feature1 feature2 feature3
index
1 1 2 0.4 a c
2 3 0 0.2 b d
3 4 1 0.5 a b
4 2 1 0.3 a b c
5 3 1 0.7 a d
feature1 feature2 feature3
index
1 b c
2 a b
3 b b
4 a b
# create python model - this stores the model parameters in the python vw object; here a contextual bandit with four possible actions
vw = vowpalwabbit.Workspace("--cb 4", quiet=True)
# use the learn method to train the vw model, train model row by row using a loop
for i in train_df.index:
## provide data to cb in requested format
action = train_df.loc[i, "action"]
cost = train_df.loc[i, "cost"]
probability = train_df.loc[i, "probability"]
feature1 = train_df.loc[i, "feature1"]
feature2 = train_df.loc[i, "feature2"]
feature3 = train_df.loc[i, "feature3"]
## do the actual learning
vw.learn(str(action)+":"+str(cost)+":"+str(probability)+" | "+str(feature1)+" "+str(feature2)+" "+str(feature3))
# use the same model object that was trained to perform predictions
# predict row by row and output results
for j in test_df.index:
feature1 = test_df.loc[j, "feature1"]
feature2 = test_df.loc[j, "feature2"]
feature3 = test_df.loc[j, "feature3"]
choice = vw.predict("| "+str(feature1)+" "+str(feature2)+" "+str(feature3))
print(j, choice)
# the CB assigns every instance to action 3 as it should per the cost structure of the train data; you can play with the cost structure to see that the CB updates its predictions accordingly
1 3
2 3
3 3
4 3
# BONUS: save and load the CB model
# save model
vw.save('cb.model')
del vw
# load from saved file
vw = vowpalwabbit.Workspace("--cb 4 -i cb.model", quiet=True)
print(vw.predict('| a b'))
3