empty search space (#295)

fix the error when an empty dictionary is passed to BlendSearch as the search space.

flaml/searcher/blendsearch.py

@@ -219,7 +219,7 @@ class BlendSearch(Searcher):
         else:
             self._candidate_start_points = None
         self._time_budget_s, self._num_samples = time_budget_s, num_samples
-        if space:
+        if space is not None:
             self._init_search()
 
     def set_search_properties(

test/tune/test_searcher.py

@@ -5,16 +5,22 @@ try:
 
     assert ray_version >= "1.0.0"
     from ray.tune import sample
+
+    use_ray = True
 except (ImportError, AssertionError):
     from flaml.tune import sample
 
+    use_ray = False
+
 from flaml.searcher.suggestion import OptunaSearch, Searcher, ConcurrencyLimiter
 from flaml.searcher.blendsearch import BlendSearch, CFO, RandomSearch
 
+
 def define_search_space(trial):
     trial.suggest_float("a", 6, 8)
     trial.suggest_float("b", 1e-4, 1e-2, log=True)
 
+
 def test_searcher():
     searcher = Searcher()
     searcher = Searcher(metric=["m1", "m2"], mode=["max", "min"])
@@ -45,20 +51,20 @@ except (ImportError, AssertionError):
         mode="max",
     )
     config = {"a": sample.uniform(6, 8), "b": sample.loguniform(1e-4, 1e-2)}
-        searcher = OptunaSearch(
-            config,
-            points_to_evaluate=[{"a": 6, "b": 1e-3}],
-            evaluated_rewards=[{"m": 2}],
-            metric="m",
-            mode="max",
-        )
+    # searcher = OptunaSearch(
+    #     config,
+    #     points_to_evaluate=[{"a": 6, "b": 1e-3}],
+    #     evaluated_rewards=[{"m": 2}],
+    #     metric="m",
+    #     mode="max",
+    # )
     searcher = OptunaSearch(
         define_search_space,
         points_to_evaluate=[{"a": 6, "b": 1e-3}],
         # evaluated_rewards=[{'m': 2}], metric='m', mode='max'
         mode="max",
     )
-        searcher = OptunaSearch()
+    # searcher = OptunaSearch()
     # searcher.set_search_properties('m', 'min', define_search_space)
     searcher.set_search_properties("m", "min", config)
     searcher.suggest("t1")
@@ -78,9 +84,7 @@ except (ImportError, AssertionError):
     c = searcher.suggest("t1")
     searcher.on_trial_complete("t1", {"config": c}, True)
     c = searcher.suggest("t2")
-        searcher.on_trial_complete(
-            "t2", {"config": c, "m2": 1, "c": 2, "time_total_s": 1}
-        )
+    searcher.on_trial_complete("t2", {"config": c, "m2": 1, "c": 2, "time_total_s": 1})
     config1 = config.copy()
     config1["_choice_"] = 0
     searcher._expand_admissible_region(
@@ -155,3 +159,10 @@ except (ImportError, AssertionError):
     print(searcher.suggest("t1"))
     print(searcher.suggest("t2"))
     print(searcher.suggest("t3"))
+    searcher = RandomSearch(space={})
+    print(searcher.suggest("t1"))
+    searcher = BlendSearch(space={})
+    print(searcher.suggest("t1"))
+    from flaml import tune
+
+    tune.run(lambda x: 1, config={}, use_ray=use_ray)