Merge pull request #103 from Kurorororo/visualize-multiple-policy-values

Add a method to visualize the policy values of multiple policies

obp/ope/meta.py

@@ -524,3 +524,107 @@ def summarize_estimators_comparison(
             index=[metric],
         )
         return eval_metric_ope_df.T
+
+    def visualize_off_policy_estimates_of_multiple_policies(
+        self,
+        policy_name_list: List[str],
+        action_dist_list: List[np.ndarray],
+        estimated_rewards_by_reg_model: Optional[
+            Union[np.ndarray, Dict[str, np.ndarray]]
+        ] = None,
+        alpha: float = 0.05,
+        is_relative: bool = False,
+        n_bootstrap_samples: int = 100,
+        random_state: Optional[int] = None,
+        fig_dir: Optional[Path] = None,
+        fig_name: str = "estimated_policy_value.png",
+    ) -> None:
+        """Visualize policy values estimated by OPE estimators.
+
+        Parameters
+        ----------
+        policy_name_list: List[str]
+            List of the names of policies.
+
+        action_dist_list: List[array-like, shape (n_rounds, n_actions, len_list)]
+            List of action choice probabilities by the evaluation policies (can be deterministic), i.e., :math:`\\pi_e(a_t|x_t)`.
+
+        estimated_rewards_by_reg_model: array-like, shape (n_rounds, n_actions, len_list) or Dict[str, array-like], default=None
+            Expected rewards for each round, action, and position estimated by a regression model, i.e., :math:`\\hat{q}(x_t,a_t)`.
+            When an array-like is given, all OPE estimators use it.
+            When a dict is given, if the dict has the name of an estimator as a key, the corresponding value is used.
+            When it is not given, model-dependent estimators such as DM and DR cannot be used.
+
+        alpha: float, default=0.05
+            Significant level of confidence intervals.
+
+        n_bootstrap_samples: int, default=100
+            Number of resampling performed in the bootstrap procedure.
+
+        random_state: int, default=None
+            Controls the random seed in bootstrap sampling.
+
+        is_relative: bool, default=False,
+            If True, the method visualizes the estimated policy values of evaluation policy
+            relative to the ground-truth policy value of behavior policy.
+
+        fig_dir: Path, default=None
+            Path to store the bar figure.
+            If 'None' is given, the figure will not be saved.
+
+        fig_name: str, default="estimated_policy_value.png"
+            Name of the bar figure.
+
+        """
+        if len(policy_name_list) != len(action_dist_list):
+            raise ValueError(
+                "the length of policy_name_list must be the same as action_dist_list"
+            )
+        if fig_dir is not None:
+            assert isinstance(fig_dir, Path), "fig_dir must be a Path"
+        if fig_name is not None:
+            assert isinstance(fig_name, str), "fig_dir must be a string"
+
+        estimated_round_rewards_dict = {
+            estimator_name: {} for estimator_name in self.ope_estimators_
+        }
+
+        for policy_name, action_dist in zip(policy_name_list, action_dist_list):
+            estimator_inputs = self._create_estimator_inputs(
+                action_dist=action_dist,
+                estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,
+            )
+            for estimator_name, estimator in self.ope_estimators_.items():
+                estimated_round_rewards_dict[estimator_name][
+                    policy_name
+                ] = estimator._estimate_round_rewards(
+                    **estimator_inputs[estimator_name]
+                )
+
+        plt.style.use("ggplot")
+        fig = plt.figure(figsize=(8, 6.2 * len(self.ope_estimators_)))
+
+        for i, estimator_name in enumerate(self.ope_estimators_):
+            estimated_round_rewards_df = DataFrame(
+                estimated_round_rewards_dict[estimator_name]
+            )
+            if is_relative:
+                estimated_round_rewards_df /= self.bandit_feedback["reward"].mean()
+
+            ax = fig.add_subplot(len(action_dist_list), 1, i + 1)
+            sns.barplot(
+                data=estimated_round_rewards_df,
+                ax=ax,
+                ci=100 * (1 - alpha),
+                n_boot=n_bootstrap_samples,
+                seed=random_state,
+            )
+            ax.set_title(estimator_name.upper(), fontsize=20)
+            ax.set_ylabel(
+                f"Estimated Policy Value (± {np.int(100*(1 - alpha))}% CI)", fontsize=20
+            )
+            plt.yticks(fontsize=15)
+            plt.xticks(fontsize=25 - 2 * len(policy_name_list))
+
+        if fig_dir:
+            fig.savefig(str(fig_dir / fig_name))