update examples

st-tech · Feb 1, 2021 · 36f544a · 36f544a
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diff --git a/examples/multiclass/evaluate_off_policy_estimators.py b/examples/multiclass/evaluate_off_policy_estimators.py
@@ -48,8 +48,6 @@
     SelfNormalizedInverseProbabilityWeighting(),
     DoublyRobust(),
     SelfNormalizedDoublyRobust(),
-    SwitchInverseProbabilityWeighting(tau=1, estimator_name="switch-ipw (tau=1)"),
-    SwitchInverseProbabilityWeighting(tau=100, estimator_name="switch-ipw (tau=100)"),
     SwitchDoublyRobust(tau=1, estimator_name="switch-dr (tau=1)"),
     SwitchDoublyRobust(tau=100, estimator_name="switch-dr (tau=100)"),
     DoublyRobustWithShrinkage(lambda_=1, estimator_name="dr-os (lambda=1)"),

diff --git a/examples/obd/README.md b/examples/obd/README.md
@@ -5,6 +5,8 @@
 Here, we use the open bandit dataset and pipeline to implement and evaluate OPE.
 Specifically, we evaluate the estimation performances of well-known off-policy estimators using the ground-truth policy value of an evaluation policy, which is calculable with our data using on-policy estimation.
 
+Please clone [the obp repository](https://github.com/st-tech/zr-obp) and download [the small sized Open Bandit Dataset](https://github.com/st-tech/zr-obp/tree/master/obd) to run this example.
+
 ## Evaluating Off-Policy Estimators
 
 We evaluate the estimation performances of off-policy estimators, including Direct Method (DM), Inverse Probability Weighting (IPW), and Doubly Robust (DR).