chore: import upstream snapshot with attribution

python/ray/tune/examples/optuna_multiobjective_example.py

@@ -0,0 +1,79 @@
+"""This example demonstrates the usage of Optuna with Ray Tune for
+multi-objective optimization.
+
+Please note that schedulers may not work correctly with multi-objective
+optimization.
+
+Requires the Optuna library to be installed (`pip install optuna`).
+"""
+import time
+
+import ray
+from ray import tune
+from ray.tune.search import ConcurrencyLimiter
+from ray.tune.search.optuna import OptunaSearch
+
+
+def evaluation_fn(step, width, height):
+    return (0.1 + width * step / 100) ** (-1) + height * 0.1
+
+
+def easy_objective(config):
+    # Hyperparameters
+    width, height = config["width"], config["height"]
+
+    for step in range(config["steps"]):
+        # Iterative training function - can be any arbitrary training procedure
+        intermediate_score = evaluation_fn(step, width, height)
+        # Feed the score back back to Tune.
+        tune.report(
+            {
+                "iterations": step,
+                "loss": intermediate_score,
+                "gain": intermediate_score * width,
+            }
+        )
+        time.sleep(0.1)
+
+
+def run_optuna_tune(smoke_test=False):
+    algo = OptunaSearch(metric=["loss", "gain"], mode=["min", "max"])
+    algo = ConcurrencyLimiter(algo, max_concurrent=4)
+    tuner = tune.Tuner(
+        easy_objective,
+        tune_config=tune.TuneConfig(
+            search_alg=algo,
+            num_samples=10 if smoke_test else 100,
+        ),
+        param_space={
+            "steps": 100,
+            "width": tune.uniform(0, 20),
+            "height": tune.uniform(-100, 100),
+            # This is an ignored parameter.
+            "activation": tune.choice(["relu", "tanh"]),
+        },
+    )
+    results = tuner.fit()
+
+    print(
+        "Best hyperparameters for loss found were: ",
+        results.get_best_result("loss", "min").config,
+    )
+    print(
+        "Best hyperparameters for gain found were: ",
+        results.get_best_result("gain", "max").config,
+    )
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--smoke-test", action="store_true", help="Finish quickly for testing"
+    )
+    args, _ = parser.parse_known_args()
+
+    ray.init(configure_logging=False)
+
+    run_optuna_tune(smoke_test=args.smoke_test)