[BUG] import dict metric data in SMAC and BOHB

src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py

@@ -641,6 +641,7 @@ def handle_import_data(self, data):
             if not _value:
                 logger.info("Useless trial data, value is %s, skip this trial data.", _value)
                 continue
+            _value = extract_scalar_reward(_value)
             budget_exist_flag = False
             barely_params = dict()
             for keys in _params:

src/sdk/pynni/nni/smac_tuner/smac_tuner.py

@@ -309,6 +309,7 @@ def import_data(self, data):
             if not _value:
                 self.logger.info("Useless trial data, value is %s, skip this trial data.", _value)
                 continue
+            _value = extract_scalar_reward(_value)
             # convert the keys in loguniform and categorical types
             valid_entry = True
             for key, value in _params.items():

src/sdk/pynni/tests/test_builtin_tuners.py

@@ -34,7 +34,7 @@ class BuiltinTunersTestCase(TestCase):
         - [ ] save_checkpoint
         - [X] update_search_space
         - [X] generate_multiple_parameters
-        - [ ] import_data
+        - [X] import_data
         - [ ] trial_end
         - [x] receive_trial_result
     """
@@ -141,50 +141,119 @@ def search_space_test_all(self, tuner_factory, supported_types=None, ignore_type
             logger.info("Full supported search space: %s", full_supported_search_space)
             self.search_space_test_one(tuner_factory, full_supported_search_space)
 
+    def import_data_test(self, tuner_factory, stype="choice_str"):
+        # import data at the beginning with number value and dict value
+        # import data in the middle also with number value and dict value, and duplicate data record
+        # generate parameters after data import
+        if stype == "choice_str":
+            search_space = {
+                "choice_str": {
+                    "_type": "choice",
+                    "_value": ["cat", "dog", "elephant", "cow", "sheep", "panda"]
+                }
+            }
+        elif stype == "choice_num":
+            search_space = {
+                "choice_num": {
+                    "_type": "choice",
+                    "_value": [10, 20, 30, 40, 50, 60]
+                }
+            }
+        else:
+            raise RuntimeError("Unexpected stype")
+        tuner = tuner_factory()
+        self.assertIsInstance(tuner, Tuner)
+        tuner.update_search_space(search_space)
+        # import data at the beginning
+        if stype == "choice_str":
+            data = [{"parameter": {"choice_str": "cat"}, "value": 1.1},
+                    {"parameter": {"choice_str": "dog"}, "value": {"default": 1.2, "tmp": 2}}]
+        else:
+            data = [{"parameter": {"choice_num": 20}, "value": 1.1},
+                    {"parameter": {"choice_num": 60}, "value": {"default": 1.2, "tmp": 2}}]
+        tuner.import_data(data)
+        logger.info("Imported data successfully at the beginning")
+        # generate parameters
+        parameters = tuner.generate_multiple_parameters(list(range(3)))
+        for i in range(3):
+            tuner.receive_trial_result(i, parameters[i], random.uniform(-100, 100))
+        # import data in the middle
+        if stype == "choice_str":
+            data = [{"parameter": {"choice_str": "cat"}, "value": 1.1},
+                    {"parameter": {"choice_str": "dog"}, "value": {"default": 1.2, "tmp": 2}},
+                    {"parameter": {"choice_str": "cow"}, "value": 1.3}]
+        else:
+            data = [{"parameter": {"choice_num": 20}, "value": 1.1},
+                    {"parameter": {"choice_num": 60}, "value": {"default": 1.2, "tmp": 2}},
+                    {"parameter": {"choice_num": 50}, "value": 1.3}]
+        tuner.import_data(data)
+        logger.info("Imported data successfully in the middle")
+        # generate parameters again
+        parameters = tuner.generate_multiple_parameters([3])
+        tuner.receive_trial_result(3, parameters[0], random.uniform(-100, 100))
+
     def test_grid_search(self):
         self.exhaustive = True
-        self.search_space_test_all(lambda: GridSearchTuner(),
+        tuner_fn = lambda: GridSearchTuner()
+        self.search_space_test_all(tuner_fn,
                                    supported_types=["choice", "randint", "quniform"])
+        self.import_data_test(tuner_fn)
 
     def test_tpe(self):
-        self.search_space_test_all(lambda: HyperoptTuner("tpe"),
+        tuner_fn = lambda: HyperoptTuner("tpe")
+        self.search_space_test_all(tuner_fn,
                                    ignore_types=["uniform_equal", "qloguniform_equal", "loguniform_equal", "quniform_clip_2"])
         # NOTE: types are ignored because `tpe.py line 465, in adaptive_parzen_normal assert prior_sigma > 0`
+        self.import_data_test(tuner_fn)
 
     def test_random_search(self):
-        self.search_space_test_all(lambda: HyperoptTuner("random_search"))
+        tuner_fn = lambda: HyperoptTuner("random_search")
+        self.search_space_test_all(tuner_fn)
+        self.import_data_test(tuner_fn)
 
     def test_anneal(self):
-        self.search_space_test_all(lambda: HyperoptTuner("anneal"))
+        tuner_fn = lambda: HyperoptTuner("anneal")
+        self.search_space_test_all(tuner_fn)
+        self.import_data_test(tuner_fn)
 
     def test_smac(self):
         if sys.platform == "win32":
             return  # smac doesn't work on windows
-        self.search_space_test_all(lambda: SMACTuner(),
+        tuner_fn = lambda: SMACTuner()
+        self.search_space_test_all(tuner_fn,
                                    supported_types=["choice", "randint", "uniform", "quniform", "loguniform"])
+        self.import_data_test(tuner_fn)
 
     def test_batch(self):
         self.exhaustive = True
-        self.search_space_test_all(lambda: BatchTuner(),
+        tuner_fn = lambda: BatchTuner()
+        self.search_space_test_all(tuner_fn,
                                    supported_types=["choice"])
+        self.import_data_test(tuner_fn)
 
     def test_evolution(self):
         # Needs enough population size, otherwise it will throw a runtime error
-        self.search_space_test_all(lambda: EvolutionTuner(population_size=100))
+        tuner_fn = lambda: EvolutionTuner(population_size=100)
+        self.search_space_test_all(tuner_fn)
+        self.import_data_test(tuner_fn)
 
     def test_gp(self):
         self.test_round = 1  # NOTE: GP tuner got hanged for multiple testing round
-        self.search_space_test_all(lambda: GPTuner(),
+        tuner_fn = lambda: GPTuner()
+        self.search_space_test_all(tuner_fn,
                                    supported_types=["choice", "randint", "uniform", "quniform", "loguniform",
                                                     "qloguniform"],
                                    ignore_types=["normal", "lognormal", "qnormal", "qlognormal"],
                                    fail_types=["choice_str", "choice_mixed"])
+        self.import_data_test(tuner_fn, "choice_num")
 
     def test_metis(self):
         self.test_round = 1  # NOTE: Metis tuner got hanged for multiple testing round
-        self.search_space_test_all(lambda: MetisTuner(),
+        tuner_fn = lambda: MetisTuner()
+        self.search_space_test_all(tuner_fn,
                                    supported_types=["choice", "randint", "uniform", "quniform"],
                                    fail_types=["choice_str", "choice_mixed"])
+        self.import_data_test(tuner_fn, "choice_num")
 
     def test_networkmorphism(self):
         pass