Offline policy evaluation

Implementations and examples of common offline policy evaluation methods in Python. For more information on offline policy evaluation see this tutorial.

Installation

pip install offline-evaluation

Usage

from ope.methods import doubly_robust

Get some historical logs generated by a previous policy:

df = pd.DataFrame([
	{"context": {"p_fraud": 0.08}, "action": "blocked", "action_prob": 0.90, "reward": 0},
	{"context": {"p_fraud": 0.03}, "action": "allowed", "action_prob": 0.90, "reward": 20},
	{"context": {"p_fraud": 0.02}, "action": "allowed", "action_prob": 0.90, "reward": 10},
	{"context": {"p_fraud": 0.01}, "action": "allowed", "action_prob": 0.90, "reward": 20},     
	{"context": {"p_fraud": 0.09}, "action": "allowed", "action_prob": 0.10, "reward": -20},
	{"context": {"p_fraud": 0.40}, "action": "allowed", "action_prob": 0.10, "reward": -10},
 ])

Define a function that computes P(action | context) under the new policy:

def action_probabilities(context):
    epsilon = 0.10
    if context["p_fraud"] > 0.10:
        return {"allowed": epsilon, "blocked": 1 - epsilon}    
    return {"allowed": 1 - epsilon, "blocked": epsilon}

Conduct the evaluation:

doubly_robust.evaluate(df, action_probabilities)
> {'expected_reward_logging_policy': 3.33, 'expected_reward_new_policy': -28.47}

This means the new policy is significantly worse than the logging policy. Instead of A/B testing this new policy online, it would be better to test some other policies offline first.

See examples for more detailed tutorials.

Supported methods

• Inverse propensity scoring
• Direct method
• Doubly robust (paper)