def process(i: int):
# synthetic data generator
dataset = SyntheticBanditDataset(
n_actions=n_actions,
dim_context=dim_context,
reward_function=logistic_reward_function,
behavior_policy_function=linear_behavior_policy,
random_state=i,
)
# define evaluation policy using IPWLearner
evaluation_policy = IPWLearner(
n_actions=dataset.n_actions,
base_classifier=base_model_dict[base_model_for_evaluation_policy](
**hyperparams[base_model_for_evaluation_policy]
),
)
# sample new training and test sets of synthetic logged bandit feedback
bandit_feedback_train = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds)
bandit_feedback_test = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds)
# train the evaluation policy on the training set of the synthetic logged bandit feedback
evaluation_policy.fit(
context=bandit_feedback_train["context"],
action=bandit_feedback_train["action"],
reward=bandit_feedback_train["reward"],
pscore=bandit_feedback_train["pscore"],
)
# predict the action decisions for the test set of the synthetic logged bandit feedback
action_dist = evaluation_policy.predict(
context=bandit_feedback_test["context"],
)
# estimate the mean reward function of the test set of synthetic bandit feedback with ML model
regression_model = RegressionModel(
n_actions=dataset.n_actions,
action_context=dataset.action_context,
base_model=base_model_dict[base_model_for_reg_model](
**hyperparams[base_model_for_reg_model]
),
)
estimated_rewards_by_reg_model = regression_model.fit_predict(
context=bandit_feedback_test["context"],
action=bandit_feedback_test["action"],
reward=bandit_feedback_test["reward"],
n_folds=3, # 3-fold cross-fitting
random_state=random_state,
)
# evaluate estimators' performances using relative estimation error (relative-ee)
ope = OffPolicyEvaluation(
bandit_feedback=bandit_feedback_test,
ope_estimators=ope_estimators,
)
relative_ee_i = ope.evaluate_performance_of_estimators(
ground_truth_policy_value=dataset.calc_ground_truth_policy_value(
expected_reward=bandit_feedback_test["expected_reward"],
action_dist=action_dist,
),
action_dist=action_dist,
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
)
return relative_ee_i
def test_synthetic_obtain_batch_bandit_feedback():
# n_rounds
with pytest.raises(ValueError):
dataset = SyntheticBanditDataset(n_actions=2)
dataset.obtain_batch_bandit_feedback(n_rounds=0)
with pytest.raises(ValueError):
dataset = SyntheticBanditDataset(n_actions=2)
dataset.obtain_batch_bandit_feedback(n_rounds="3")
# bandit feedback
n_rounds = 10
n_actions = 5
dataset = SyntheticBanditDataset(n_actions=n_actions)
bandit_feedback = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds)
assert bandit_feedback["n_rounds"] == n_rounds
assert bandit_feedback["n_actions"] == n_actions
assert (bandit_feedback["context"].shape[0] == n_rounds # n_rounds
and bandit_feedback["context"].shape[1] == 1 # default dim_context
)
assert (bandit_feedback["action_context"].shape[0] == n_actions
and bandit_feedback["action_context"].shape[1] == n_actions)
assert (bandit_feedback["action"].ndim == 1
and len(bandit_feedback["action"]) == n_rounds)
assert (bandit_feedback["position"].ndim == 1
and len(bandit_feedback["position"]) == n_rounds)
assert (bandit_feedback["reward"].ndim == 1
and len(bandit_feedback["reward"]) == n_rounds)
assert (bandit_feedback["expected_reward"].shape[0] == n_rounds
and bandit_feedback["expected_reward"].shape[1] == n_actions)
assert (bandit_feedback["pscore"].ndim == 1
and len(bandit_feedback["pscore"]) == n_rounds)
def process(i: int):
# synthetic data generator with uniformly random policy
dataset = SyntheticBanditDataset(
n_actions=n_actions,
dim_context=dim_context,
reward_function=logistic_reward_function,
behavior_policy_function=None, # uniformly random
random_state=i,
)
# sample new data of synthetic logged bandit feedback
bandit_feedback = dataset.obtain_batch_bandit_feedback(
n_rounds=n_rounds)
# simulate the evaluation policy
action_dist = run_bandit_simulation(bandit_feedback=bandit_feedback,
policy=evaluation_policy)
# estimate the ground-truth policy values of the evaluation policy
# by Monte-Carlo Simulation using p(r|x,a), the reward distribution
ground_truth_policy_value = calc_ground_truth_policy_value(
bandit_feedback=bandit_feedback,
reward_sampler=dataset.sample_reward, # p(r|x,a)
policy=evaluation_policy,
n_sim=n_sim, # the number of simulations
)
# evaluate estimators' performances using relative estimation error (relative-ee)
ope = OffPolicyEvaluation(
bandit_feedback=bandit_feedback,
ope_estimators=ope_estimators,
)
metric_i = ope.evaluate_performance_of_estimators(
ground_truth_policy_value=ground_truth_policy_value,
action_dist=action_dist,
)
return metric_i
def test_synthetic_obtain_batch_bandit_feedback():
# n_rounds
with pytest.raises(ValueError):
dataset = SyntheticBanditDataset(n_actions=2)
dataset.obtain_batch_bandit_feedback(n_rounds=0)
with pytest.raises(TypeError):
dataset = SyntheticBanditDataset(n_actions=2)
dataset.obtain_batch_bandit_feedback(n_rounds="3")
# bandit feedback
n_rounds = 10
n_actions = 5
for n_deficient_actions in [0, 2]:
dataset = SyntheticBanditDataset(
n_actions=n_actions,
beta=0,
n_deficient_actions=n_deficient_actions)
bandit_feedback = dataset.obtain_batch_bandit_feedback(
n_rounds=n_rounds)
assert bandit_feedback["n_rounds"] == n_rounds
assert bandit_feedback["n_actions"] == n_actions
assert (bandit_feedback["context"].shape[0] == n_rounds # n_rounds
and
bandit_feedback["context"].shape[1] == 1 # default dim_context
)
assert (bandit_feedback["action_context"].shape[0] == n_actions
and bandit_feedback["action_context"].shape[1] == n_actions)
assert (bandit_feedback["action"].ndim == 1
and len(bandit_feedback["action"]) == n_rounds)
assert bandit_feedback["position"] is None
assert (bandit_feedback["reward"].ndim == 1
and len(bandit_feedback["reward"]) == n_rounds)
assert (bandit_feedback["expected_reward"].shape[0] == n_rounds
and bandit_feedback["expected_reward"].shape[1] == n_actions)
assert (bandit_feedback["pi_b"].shape[0] == n_rounds
and bandit_feedback["pi_b"].shape[1] == n_actions)
# when `beta=0`, behavior_policy should be uniform
if n_deficient_actions == 0:
uniform_policy = np.ones_like(bandit_feedback["pi_b"]) / n_actions
assert np.allclose(bandit_feedback["pi_b"], uniform_policy)
assert np.allclose(bandit_feedback["pi_b"][:, :, 0].sum(1),
np.ones(n_rounds))
assert (bandit_feedback["pi_b"] == 0
).sum() == n_deficient_actions * n_rounds
assert (bandit_feedback["pscore"].ndim == 1
and len(bandit_feedback["pscore"]) == n_rounds)
def synthetic_bandit_feedback() -> BanditFeedback:
n_actions = 10
dim_context = 5
random_state = 12345
n_rounds = 10000
dataset = SyntheticBanditDataset(
n_actions=n_actions,
dim_context=dim_context,
reward_function=logistic_reward_function,
behavior_policy_function=linear_behavior_policy,
random_state=random_state,
)
bandit_feedback = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds)
return bandit_feedback
def process(i: int):
# synthetic data generator
dataset = SyntheticBanditDataset(
n_actions=n_actions,
dim_context=dim_context,
reward_function=logistic_reward_function,
behavior_policy_function=linear_behavior_policy,
random_state=i,
)
# define evaluation policy using IPWLearner
ipw_policy = IPWLearner(
n_actions=dataset.n_actions,
base_classifier=base_model_dict[base_model_for_evaluation_policy](
**hyperparams[base_model_for_evaluation_policy]),
)
# baseline method 1. RandomPolicy
random_policy = RandomPolicy(n_actions=dataset.n_actions)
# baseline method 2. UniformSampleWeightLearner
uniform_sample_weight_policy = UniformSampleWeightLearner(
n_actions=dataset.n_actions,
base_classifier=base_model_dict[base_model_for_evaluation_policy](
**hyperparams[base_model_for_evaluation_policy]),
)
# sample new training and test sets of synthetic logged bandit feedback
bandit_feedback_train = dataset.obtain_batch_bandit_feedback(
n_rounds=n_rounds)
bandit_feedback_test = dataset.obtain_batch_bandit_feedback(
n_rounds=n_rounds)
# train the evaluation policy on the training set of the synthetic logged bandit feedback
ipw_policy.fit(
context=bandit_feedback_train["context"],
action=bandit_feedback_train["action"],
reward=bandit_feedback_train["reward"],
pscore=bandit_feedback_train["pscore"],
)
uniform_sample_weight_policy.fit(
context=bandit_feedback_train["context"],
action=bandit_feedback_train["action"],
reward=bandit_feedback_train["reward"],
pscore=bandit_feedback_train["pscore"],
)
# predict the action decisions for the test set of the synthetic logged bandit feedback
ipw_action_dist = ipw_policy.predict(
context=bandit_feedback_test["context"], )
random_action_dist = random_policy.predict(
context=bandit_feedback_test["context"], )
uniform_sample_weight_action_dist = uniform_sample_weight_policy.predict(
context=bandit_feedback_test["context"], )
# get the ground truth policy value for each learner
gt_ipw_learner = dataset.calc_ground_truth_policy_value(
expected_reward=bandit_feedback_test["expected_reward"],
action_dist=ipw_action_dist,
)
gt_random_policy = dataset.calc_ground_truth_policy_value(
expected_reward=bandit_feedback_test["expected_reward"],
action_dist=random_action_dist,
)
gt_uniform_sample_weight_learner = dataset.calc_ground_truth_policy_value(
expected_reward=bandit_feedback_test["expected_reward"],
action_dist=uniform_sample_weight_action_dist,
)
return gt_ipw_learner, gt_random_policy, gt_uniform_sample_weight_learner
InverseProbabilityWeighting(),
SelfNormalizedInverseProbabilityWeighting(),
DoublyRobust(),
SelfNormalizedDoublyRobust(),
SwitchDoublyRobust(),
]
# a base ML model for regression model used in Direct Method and Doubly Robust
base_model = CalibratedClassifierCV(RandomForest(**hyperparams))
evaluation_of_ope_results = {
est.estimator_name: np.zeros(n_runs)
for est in ope_estimators
}
for i in np.arange(n_runs):
# sample a new set of logged bandit feedback
bandit_feedback = dataset.obtain_batch_bandit_feedback(
n_rounds=n_rounds)
# run a counterfactual bandit algorithm on logged bandit feedback data
selected_actions = run_bandit_simulation(
bandit_feedback=bandit_feedback, policy=policy)
# estimate the ground-truth policy values of the counterfactual policy
# using the full expected reward contained in the bandit feedback dictionary
ground_truth_policy_value = bandit_feedback["expected_reward"][
np.arange(n_rounds),
selected_actions.flatten()].mean()
# evaluate the estimation performance of OPE estimators
ope = OffPolicyEvaluation(
bandit_feedback=bandit_feedback,
action_context=dataset.action_context,
regression_model=RegressionModel(base_model=base_model),
ope_estimators=ope_estimators,
)
def process(i: int):
# synthetic data generator
dataset = SyntheticBanditDataset(
n_actions=n_actions,
dim_context=dim_context,
reward_function=logistic_reward_function,
behavior_policy_function=linear_behavior_policy,
random_state=i,
)
# estimate the mean reward function of the train set of synthetic bandit feedback with ML model
regression_model = RegressionModel(
n_actions=dataset.n_actions,
action_context=dataset.action_context,
base_model=base_model_dict[base_model_for_reg_model](
**hyperparams[base_model_for_reg_model]),
)
ope_estimator = DoublyRobust()
# define evaluation policy using NNPolicyLearner
nn_policy = NNPolicyLearner(
n_actions=dataset.n_actions,
dim_context=dim_context,
off_policy_objective=ope_estimator.estimate_policy_value_tensor,
)
# baseline method 1. RandomPolicy
random_policy = RandomPolicy(n_actions=dataset.n_actions)
# baseline method 2. UniformSampleWeightLearner
uniform_sample_weight_policy = UniformSampleWeightLearner(
n_actions=dataset.n_actions,
base_classifier=base_model_dict[base_model_for_evaluation_policy](
**hyperparams[base_model_for_evaluation_policy]),
)
# sample new training and test sets of synthetic logged bandit feedback
bandit_feedback_train = dataset.obtain_batch_bandit_feedback(
n_rounds=n_rounds)
bandit_feedback_test = dataset.obtain_batch_bandit_feedback(
n_rounds=n_rounds)
# estimate the mean reward function of the train set of synthetic bandit feedback with ML model
estimated_rewards_by_reg_model = regression_model.fit_predict(
context=bandit_feedback_train["context"],
action=bandit_feedback_train["action"],
reward=bandit_feedback_train["reward"],
n_folds=3, # 3-fold cross-fitting
random_state=12345,
)
# train the evaluation policy on the training set of the synthetic logged bandit feedback
nn_policy.fit(
context=bandit_feedback_train["context"],
action=bandit_feedback_train["action"],
reward=bandit_feedback_train["reward"],
pscore=bandit_feedback_train["pscore"],
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
)
uniform_sample_weight_policy.fit(
context=bandit_feedback_train["context"],
action=bandit_feedback_train["action"],
reward=bandit_feedback_train["reward"],
pscore=bandit_feedback_train["pscore"],
)
# predict the action decisions for the test set of the synthetic logged bandit feedback
nn_policy_action_dist = nn_policy.predict(
context=bandit_feedback_test["context"], )
random_action_dist = random_policy.predict(
context=bandit_feedback_test["context"], )
uniform_sample_weight_action_dist = uniform_sample_weight_policy.predict(
context=bandit_feedback_test["context"], )
# get the ground truth policy value for each learner
gt_nn_policy_learner = dataset.calc_ground_truth_policy_value(
expected_reward=bandit_feedback_test["expected_reward"],
action_dist=nn_policy_action_dist,
)
gt_random_policy = dataset.calc_ground_truth_policy_value(
expected_reward=bandit_feedback_test["expected_reward"],
action_dist=random_action_dist,
)
gt_uniform_sample_weight_learner = dataset.calc_ground_truth_policy_value(
expected_reward=bandit_feedback_test["expected_reward"],
action_dist=uniform_sample_weight_action_dist,
)
return gt_nn_policy_learner, gt_random_policy, gt_uniform_sample_weight_learner
activation = args.activation
solver = args.solver
batch_size = args.batch_size if args.batch_size else "auto"
early_stopping = args.early_stopping
random_state = args.random_state
# synthetic data generator
dataset = SyntheticBanditDataset(
n_actions=n_actions,
dim_context=dim_context,
reward_function=logistic_reward_function,
behavior_policy_function=linear_behavior_policy,
random_state=random_state,
)
# sample new training and test sets of synthetic logged bandit feedback
bandit_feedback_train = dataset.obtain_batch_bandit_feedback(
n_rounds=n_rounds)
bandit_feedback_test = dataset.obtain_batch_bandit_feedback(
n_rounds=n_rounds)
# estimate the mean reward function of the train set of synthetic bandit feedback with ML model
regression_model = RegressionModel(
n_actions=dataset.n_actions,
action_context=dataset.action_context,
base_model=base_model_dict[base_model_for_reg_model](
**hyperparams[base_model_for_reg_model]),
)
estimated_rewards_by_reg_model = regression_model.fit_predict(
context=bandit_feedback_train["context"],
action=bandit_feedback_train["action"],
reward=bandit_feedback_train["reward"],
n_folds=3, # 3-fold cross-fitting
random_state=random_state,
def process(i: int):
# synthetic data generator
dataset = SyntheticBanditDataset(
n_actions=n_actions,
dim_context=dim_context,
reward_function=logistic_reward_function,
behavior_policy_function=linear_behavior_policy,
random_state=i,
)
# sample new training and test sets of synthetic logged bandit data
bandit_feedback_train = dataset.obtain_batch_bandit_feedback(
n_rounds=n_rounds)
bandit_feedback_test = dataset.obtain_batch_bandit_feedback(
n_rounds=n_rounds)
# defining policy learners
ipw_policy = IPWLearner(
n_actions=dataset.n_actions,
base_classifier=base_model_dict[base_model_for_evaluation_policy](
**hyperparams[base_model_for_evaluation_policy]),
)
q_policy = QLearner(
n_actions=dataset.n_actions,
base_model=base_model_dict[base_model_for_evaluation_policy](
**hyperparams[base_model_for_evaluation_policy]),
)
nn_policy = NNPolicyLearner(
n_actions=dataset.n_actions,
dim_context=dim_context,
off_policy_objective="ipw",
)
# baseline method 1. RandomPolicy
random_policy = RandomPolicy(n_actions=dataset.n_actions)
# baseline method 2. UniformSampleWeightLearner
uniform_sample_weight_policy = UniformSampleWeightLearner(
n_actions=dataset.n_actions,
base_classifier=base_model_dict[base_model_for_evaluation_policy](
**hyperparams[base_model_for_evaluation_policy]),
)
# policy training
ipw_policy.fit(
context=bandit_feedback_train["context"],
action=bandit_feedback_train["action"],
reward=bandit_feedback_train["reward"],
pscore=bandit_feedback_train["pscore"],
)
q_policy.fit(
context=bandit_feedback_train["context"],
action=bandit_feedback_train["action"],
reward=bandit_feedback_train["reward"],
pscore=bandit_feedback_train["pscore"],
)
nn_policy.fit(
context=bandit_feedback_train["context"],
action=bandit_feedback_train["action"],
reward=bandit_feedback_train["reward"],
pscore=bandit_feedback_train["pscore"],
)
uniform_sample_weight_policy.fit(
context=bandit_feedback_train["context"],
action=bandit_feedback_train["action"],
reward=bandit_feedback_train["reward"],
pscore=bandit_feedback_train["pscore"],
)
# prediction/making decisions
ipw_action_dist = ipw_policy.predict(
context=bandit_feedback_test["context"], )
q_action_dist = q_policy.predict(
context=bandit_feedback_test["context"], )
nn_action_dist = nn_policy.predict(
context=bandit_feedback_test["context"], )
random_action_dist = random_policy.predict(
context=bandit_feedback_test["context"], )
uniform_sample_weight_action_dist = uniform_sample_weight_policy.predict(
context=bandit_feedback_test["context"], )
# evaluation
gt_ipw_learner = dataset.calc_ground_truth_policy_value(
expected_reward=bandit_feedback_test["expected_reward"],
action_dist=ipw_action_dist,
)
gt_q_learner = dataset.calc_ground_truth_policy_value(
expected_reward=bandit_feedback_test["expected_reward"],
action_dist=q_action_dist,
)
gt_nn_learner = dataset.calc_ground_truth_policy_value(
expected_reward=bandit_feedback_test["expected_reward"],
action_dist=nn_action_dist,
)
gt_random_policy = dataset.calc_ground_truth_policy_value(
expected_reward=bandit_feedback_test["expected_reward"],
action_dist=random_action_dist,
)
gt_uniform_sample_weight_learner = dataset.calc_ground_truth_policy_value(
expected_reward=bandit_feedback_test["expected_reward"],
action_dist=uniform_sample_weight_action_dist,
)
return (
gt_ipw_learner,
gt_q_learner,
gt_nn_learner,
gt_random_policy,
gt_uniform_sample_weight_learner,
)
