Update example

package/visualization/plot_pyribs/README.md

@@ -30,20 +30,23 @@ from optuna.study import StudyDirection
 module = optunahub.load_module("samplers/cmamae")
 CmaMaeSampler = module.CmaMaeSampler
 
-plot_pyribs = optunahub.load_module(package="visualization/plot_pyribs",)
+plot_pyribs = optunahub.load_module(package="visualization/plot_pyribs")
 plot_grid_archive_heatmap = plot_pyribs.plot_grid_archive_heatmap
 
 
-def objective(trial: optuna.trial.Trial) -> tuple[float, float, float]:
+def objective(trial: optuna.trial.Trial) -> float:
     """Returns an objective followed by two measures."""
     x = trial.suggest_float("x", -10, 10)
     y = trial.suggest_float("y", -10, 10)
-    return x**2 + y**2, x, y
+    trial.set_user_attr("m0", 2 * x)
+    trial.set_user_attr("m1", x + y)
+    return x**2 + y**2
 
 
 if __name__ == "__main__":
     sampler = CmaMaeSampler(
         param_names=["x", "y"],
+        measure_names=["m0", "m1"],
         archive_dims=[20, 20],
         archive_ranges=[(-1, 1), (-1, 1)],
         archive_learning_rate=0.1,
@@ -56,21 +59,12 @@ if __name__ == "__main__":
         emitter_sigma0=0.1,
         emitter_batch_size=20,
     )
-    study = optuna.create_study(
-        sampler=sampler,
-        directions=[
-            StudyDirection.MINIMIZE,
-            # The remaining directions are for the measures, which do not have
-            # an optimization direction. However, we set MINIMIZE as a
-            # placeholder direction.
-            StudyDirection.MINIMIZE,
-            StudyDirection.MINIMIZE,
-        ],
-    )
+    study = optuna.create_study(sampler=sampler)
     study.optimize(objective, n_trials=10000)
 
     fig, ax = plt.subplots(figsize=(8, 6))
     plot_grid_archive_heatmap(study, ax=ax)
+    plt.savefig("archive.png")
     plt.show()
 ```
 

package/visualization/plot_pyribs/example.py

@@ -0,0 +1,44 @@
+import matplotlib.pyplot as plt
+import optuna
+import optunahub
+
+
+module = optunahub.load_module("samplers/cmamae")
+CmaMaeSampler = module.CmaMaeSampler
+
+plot_pyribs = optunahub.load_module(package="visualization/plot_pyribs")
+plot_grid_archive_heatmap = plot_pyribs.plot_grid_archive_heatmap
+
+
+def objective(trial: optuna.trial.Trial) -> float:
+    """Returns an objective followed by two measures."""
+    x = trial.suggest_float("x", -10, 10)
+    y = trial.suggest_float("y", -10, 10)
+    trial.set_user_attr("m0", 2 * x)
+    trial.set_user_attr("m1", x + y)
+    return x**2 + y**2
+
+
+if __name__ == "__main__":
+    sampler = CmaMaeSampler(
+        param_names=["x", "y"],
+        measure_names=["m0", "m1"],
+        archive_dims=[20, 20],
+        archive_ranges=[(-1, 1), (-1, 1)],
+        archive_learning_rate=0.1,
+        archive_threshold_min=-10,
+        n_emitters=1,
+        emitter_x0={
+            "x": 0,
+            "y": 0,
+        },
+        emitter_sigma0=0.1,
+        emitter_batch_size=20,
+    )
+    study = optuna.create_study(sampler=sampler)
+    study.optimize(objective, n_trials=10000)
+
+    fig, ax = plt.subplots(figsize=(8, 6))
+    plot_grid_archive_heatmap(study, ax=ax)
+    plt.savefig("archive.png")
+    plt.show()