[tune] Add Initial Parameter Suggestion for HyperOpt (#3944)

Allows users of the HyperOptSearch suggestion algorithm to specify initial experiment values to run (typically already known good baseline parameters within the domain specified)

python/ray/tune/examples/hyperopt_example.py

@@ -43,6 +43,19 @@ if __name__ == '__main__':
         'activation': hp.choice("activation", ["relu", "tanh"])
     }
 
+    current_best_params = [
+        {
+            "width": 1,
+            "height": 2,
+            "activation": 0  # Activation will be relu
+        },
+        {
+            "width": 4,
+            "height": 2,
+            "activation": 1  # Activation will be tanh
+        }
+    ]
+
     config = {
         "my_exp": {
             "run": "exp",
@@ -55,6 +68,10 @@ if __name__ == '__main__':
             },
         }
     }
-    algo = HyperOptSearch(space, max_concurrent=4, reward_attr="neg_mean_loss")
+    algo = HyperOptSearch(
+        space,
+        max_concurrent=4,
+        reward_attr="neg_mean_loss",
+        points_to_evaluate=current_best_params)
     scheduler = AsyncHyperBandScheduler(reward_attr="neg_mean_loss")
     run_experiments(config, search_alg=algo, scheduler=scheduler)