[tune] Add SigOpt Integration (#3844)

python/ray/tune/examples/sigopt_example.py

@@ -0,0 +1,78 @@
+"""This test checks that SigOpt is functional.
+
+It also checks that it is usable with a separate scheduler.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import ray
+from ray.tune import run_experiments, register_trainable
+from ray.tune.schedulers import AsyncHyperBandScheduler
+from ray.tune.suggest import SigOptSearch
+
+
+def easy_objective(config, reporter):
+    import time
+    time.sleep(0.2)
+    for i in range(config["iterations"]):
+        reporter(
+            timesteps_total=i,
+            neg_mean_loss=-(config["height"] - 14)**2 +
+            abs(config["width"] - 3))
+        time.sleep(0.02)
+
+
+if __name__ == '__main__':
+    import argparse
+    import os
+
+    assert "SIGOPT_KEY" in os.environ, \
+        "SigOpt API key must be stored as environment variable at SIGOPT_KEY"
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--smoke-test", action="store_true", help="Finish quickly for testing")
+    args, _ = parser.parse_known_args()
+    ray.init(redirect_output=True)
+
+    register_trainable("exp", easy_objective)
+
+    space = [
+        {
+            'name': 'width',
+            'type': 'int',
+            'bounds': {
+                'min': 0,
+                'max': 20
+            },
+        },
+        {
+            'name': 'height',
+            'type': 'int',
+            'bounds': {
+                'min': -100,
+                'max': 100
+            },
+        },
+    ]
+
+    config = {
+        "my_exp": {
+            "run": "exp",
+            "num_samples": 10 if args.smoke_test else 1000,
+            "config": {
+                "iterations": 100,
+            },
+            "stop": {
+                "timesteps_total": 100
+            },
+        }
+    }
+    algo = SigOptSearch(
+        space,
+        name="SigOpt Example Experiment",
+        max_concurrent=1,
+        reward_attr="neg_mean_loss")
+    scheduler = AsyncHyperBandScheduler(reward_attr="neg_mean_loss")
+    run_experiments(config, search_alg=algo, scheduler=scheduler)