def test_meta_evaluate_performance_of_estimators_using_invalid_input_data(
action_dist,
estimated_rewards_by_reg_model,
description_1: str,
metric,
ground_truth_policy_value,
description_2: str,
synthetic_bandit_feedback: BanditFeedback,
) -> None:
"""
Test the response of evaluate_performance_of_estimators using invalid data
"""
ope_ = OffPolicyEvaluation(bandit_feedback=synthetic_bandit_feedback,
ope_estimators=[dm])
with pytest.raises(ValueError, match=f"{description_2}*"):
_ = ope_.evaluate_performance_of_estimators(
ground_truth_policy_value=ground_truth_policy_value,
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
action_dist=action_dist,
metric=metric,
)
# estimate_intervals function is called in summarize_off_policy_estimates
with pytest.raises(ValueError, match=f"{description_2}*"):
_ = ope_.summarize_estimators_comparison(
ground_truth_policy_value=ground_truth_policy_value,
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
action_dist=action_dist,
metric=metric,
)
def test_meta_create_estimator_inputs_using_valid_input_data(
action_dist,
estimated_rewards_by_reg_model,
description: str,
synthetic_bandit_feedback: BanditFeedback,
) -> None:
"""
Test the _create_estimator_inputs using invalid data
"""
ope_ = OffPolicyEvaluation(
bandit_feedback=synthetic_bandit_feedback, ope_estimators=[ipw]
)
estimator_inputs = ope_._create_estimator_inputs(
action_dist=action_dist,
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
)
assert set(estimator_inputs.keys()) == set(["ipw"])
assert set(estimator_inputs["ipw"].keys()) == set(
[
"reward",
"action",
"pscore",
"position",
"action_dist",
"estimated_rewards_by_reg_model",
"estimated_pscore",
"estimated_importance_weights",
"p_e_a",
"pi_b",
"context",
"action_embed",
]
), f"Invalid response of _create_estimator_inputs (test case: {description})"
# _create_estimator_inputs function is called in the following functions
_ = ope_.estimate_policy_values(
action_dist=action_dist,
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
)
_ = ope_.estimate_intervals(
action_dist=action_dist,
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
)
_ = ope_.summarize_off_policy_estimates(
action_dist=action_dist,
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
)
_ = ope_.evaluate_performance_of_estimators(
ground_truth_policy_value=0.1,
action_dist=action_dist,
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
)
_ = ope_.summarize_estimators_comparison(
ground_truth_policy_value=0.1,
action_dist=action_dist,
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
)
def test_meta_evaluate_performance_of_estimators_using_valid_input_data(
action_dist,
estimated_rewards_by_reg_model,
description_1: str,
metric,
ground_truth_policy_value,
description_2: str,
synthetic_bandit_feedback: BanditFeedback,
) -> None:
"""
Test the response of evaluate_performance_of_estimators using valid data
"""
if metric == "relative-ee":
# calculate relative-ee
eval_metric_ope_dict = {
"ipw":
np.abs((mock_policy_value + ipw.eps - ground_truth_policy_value) /
ground_truth_policy_value),
"ipw3":
np.abs((mock_policy_value + ipw3.eps - ground_truth_policy_value) /
ground_truth_policy_value),
}
else:
# calculate se
eval_metric_ope_dict = {
"ipw":
(mock_policy_value + ipw.eps - ground_truth_policy_value)**2,
"ipw3":
(mock_policy_value + ipw3.eps - ground_truth_policy_value)**2,
}
# check performance estimators
ope_ = OffPolicyEvaluation(bandit_feedback=synthetic_bandit_feedback,
ope_estimators=[ipw, ipw3])
performance = ope_.evaluate_performance_of_estimators(
ground_truth_policy_value=ground_truth_policy_value,
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
action_dist=action_dist,
metric=metric,
)
for k, v in performance.items():
assert k in eval_metric_ope_dict, "Invalid key of performance response"
assert v == eval_metric_ope_dict[
k], "Invalid value of performance response"
performance_df = ope_.summarize_estimators_comparison(
ground_truth_policy_value=ground_truth_policy_value,
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
action_dist=action_dist,
metric=metric,
)
assert_frame_equal(
performance_df,
pd.DataFrame(eval_metric_ope_dict,
index=[metric]).T), "Invalid summarization (performance)"
def test_meta_create_estimator_inputs_using_invalid_input_data(
action_dist,
estimated_rewards_by_reg_model,
description: str,
synthetic_bandit_feedback: BanditFeedback,
) -> None:
"""
Test the _create_estimator_inputs using valid data
"""
ope_ = OffPolicyEvaluation(
bandit_feedback=synthetic_bandit_feedback, ope_estimators=[ipw]
)
# raise ValueError when the shape of two arrays are different
with pytest.raises(ValueError, match=f"{description}*"):
_ = ope_._create_estimator_inputs(
action_dist=action_dist,
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
)
# _create_estimator_inputs function is called in the following functions
with pytest.raises(ValueError, match=f"{description}*"):
_ = ope_.estimate_policy_values(
action_dist=action_dist,
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
)
with pytest.raises(ValueError, match=f"{description}*"):
_ = ope_.estimate_intervals(
action_dist=action_dist,
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
)
with pytest.raises(ValueError, match=f"{description}*"):
_ = ope_.summarize_off_policy_estimates(
action_dist=action_dist,
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
)
with pytest.raises(ValueError, match=f"{description}*"):
_ = ope_.evaluate_performance_of_estimators(
ground_truth_policy_value=0.1,
action_dist=action_dist,
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
)
with pytest.raises(ValueError, match=f"{description}*"):
_ = ope_.summarize_estimators_comparison(
ground_truth_policy_value=0.1,
action_dist=action_dist,
estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
)
def test_meta_evaluate_performance_of_estimators(
synthetic_bandit_feedback: BanditFeedback,
random_action_dist: np.ndarray) -> None:
gt = 0.5
# calculate relative-ee
eval_metric_ope_dict = {
"ipw": np.abs((mock_policy_value + ipw.eps - gt) / gt),
"ipw3": np.abs((mock_policy_value + ipw3.eps - gt) / gt),
}
# check performance estimators
ope_ = OffPolicyEvaluation(bandit_feedback=synthetic_bandit_feedback,
ope_estimators=[ipw, ipw3])
performance = ope_.evaluate_performance_of_estimators(
ground_truth_policy_value=gt,
action_dist=random_action_dist,
metric="relative-ee",
)
for k, v in performance.items():
assert k in eval_metric_ope_dict, "Invalid key of performance response"
assert v == eval_metric_ope_dict[
k], "Invalid value of performance response"
# zero division error when using relative-ee
with pytest.raises(ZeroDivisionError, match=r"float division by zero"):
_ = ope_.evaluate_performance_of_estimators(
ground_truth_policy_value=0.0,
action_dist=random_action_dist,
metric="relative-ee",
)
# check summarization
performance_df = ope_.summarize_estimators_comparison(
ground_truth_policy_value=gt,
action_dist=random_action_dist,
metric="relative-ee",
)
assert_frame_equal(
performance_df,
pd.DataFrame(eval_metric_ope_dict,
index=["relative-ee"
]).T), "Invalid summarization (performance)"