Input

The input for thompson is a pd.DataFrame with rows as samples and columns as features. For demonstration purposes we will load a dataset with ads, with 10000 samples and 10 ads.

# Import library
import thompson as th

# Load example data
df = th.import_example()

# Show data
print(df)

#             Ad 1  Ad 2  Ad 3  Ad 4  Ad 5  Ad 6  Ad 7  Ad 8  Ad 9  Ad 10
#       0        1     0     0     0     1     0     0     0     1      0
#       1        0     0     0     0     0     0     0     0     1      0
#       2        0     0     0     0     0     0     0     0     0      0
#       3        0     1     0     0     0     0     0     1     0      0
#       4        0     0     0     0     0     0     0     0     0      0
#          ...   ...   ...   ...   ...   ...   ...   ...   ...    ...
#       9995     0     0     1     0     0     0     0     1     0      0
#       9996     0     0     0     0     0     0     0     0     0      0
#       9997     0     0     0     0     0     0     0     0     0      0
#       9998     1     0     0     0     0     0     0     1     0      0
#       9999     0     1     0     0     0     0     0     0     0      0


# Compute which add is best using multi-armed bandits.
results = th.thompson(df)

Output

The output of thompson thompson.thompson() is a dictionary with the following keys:

• columns : Name of the columns

• total_reward : Total rewards

• cols_selected : Vector that decribes for each sample the linked feature

• cols_rewards_0 : The rewards per feature

• cols_rewards_1 : The rewards per feature

• methodtype : Method that is used

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