[tune] nevergrad add points_to_evaluate (#12207)

python/ray/tune/tests/test_sample.py

@@ -567,6 +567,49 @@ class SearchSpaceTest(unittest.TestCase):
         self.assertTrue(5 <= config["a"] <= 6)
         self.assertTrue(8 <= config["b"] <= 9)
 
+    def testNevergradBestParams(self):
+        from ray.tune.suggest.nevergrad import NevergradSearch
+        import nevergrad as ng
+
+        config = {
+            "metric": tune.sample.Categorical([1, 2, 3, 4]).uniform(),
+            "a": tune.sample.Categorical(["t1", "t2", "t3", "t4"]).uniform(),
+            "b": tune.sample.Integer(0, 5),
+            "c": tune.sample.Float(1e-4, 1e-1).loguniform()
+        }
+
+        best_params = [{
+            "metric": 1,
+            "a": "t1",
+            "b": 1,
+            "c": 1e-1
+        }, {
+            "metric": 2,
+            "a": "t2",
+            "b": 2,
+            "c": 1e-2
+        }]
+
+        searcher = NevergradSearch(
+            optimizer=ng.optimizers.OnePlusOne, points_to_evaluate=best_params)
+        analysis = tune.run(
+            _mock_objective,
+            config=config,
+            metric="metric",
+            mode="max",
+            search_alg=searcher,
+            num_samples=5)
+
+        for i in range(len(best_params)):
+            trial_config = analysis.trials[i].config
+            trial_config_dict = {
+                "metric": trial_config["metric"],
+                "a": trial_config["a"],
+                "b": trial_config["b"],
+                "c": trial_config["c"]
+            }
+            self.assertDictEqual(trial_config_dict, best_params[i])
+
     def testConvertOptuna(self):
         from ray.tune.suggest.optuna import OptunaSearch, param
         from optuna.samplers import RandomSampler