Change `receive_customized_trial_result` API (#1491)

* Refactor `receive_customized_trial_result` API

* add doc

src/sdk/pynni/nni/msg_dispatcher.py

@@ -171,15 +171,15 @@ class MsgDispatcher(MsgDispatcherBase):
         id_ = data['parameter_id']
         value = data['value']
         if id_ in _customized_parameter_ids:
-            if multi_phase_enabled():
-                self.tuner.receive_customized_trial_result(id_, _trial_params[id_], value, trial_job_id=data['trial_job_id'])
-            else:
-                self.tuner.receive_customized_trial_result(id_, _trial_params[id_], value)
+            if not hasattr(self.tuner, '_accept_customized'):
+                self.tuner._accept_customized = False
+            if not self.tuner._accept_customized:
+                _logger.info('Customized trial job %s ignored by tuner', id_)
+                return
+            customized = True
         else:
-            if multi_phase_enabled():
-                self.tuner.receive_trial_result(id_, _trial_params[id_], value, trial_job_id=data['trial_job_id'])
-            else:
-                self.tuner.receive_trial_result(id_, _trial_params[id_], value)
+            customized = False
+        self.tuner.receive_trial_result(id_, _trial_params[id_], value, customized=customized, trial_job_id=data.get('trial_job_id'))
 
     def _handle_intermediate_metric_data(self, data):
         """Call assessor to process intermediate results

src/sdk/pynni/nni/tuner.py

@@ -57,19 +57,22 @@ class Tuner(Recoverable):
 
     def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
         """Invoked when a trial reports its final result. Must override.
+        By default this only reports results of algorithm-generated hyper-parameters.
+        Use `accept_customized_trials()` to receive results from user-added parameters.
         parameter_id: int
         parameters: object created by 'generate_parameters()'
-        reward: object reported by trial
+        value: object reported by trial
+        customized: bool, true if the trial is created from web UI, false if generated by algorithm
+        trial_job_id: str, only available in multiphase mode.
         """
         raise NotImplementedError('Tuner: receive_trial_result not implemented')
 
-    def receive_customized_trial_result(self, parameter_id, parameters, value, **kwargs):
-        """Invoked when a trial added by WebUI reports its final result. Do nothing by default.
-        parameter_id: int
-        parameters: object created by user
-        value: object reported by trial
+    def accept_customized_trials(self, accept=True):
+        """Enable or disable receiving results of user-added hyper-parameters. 
+        By default `receive_trial_result()` will only receive results of algorithm-generated hyper-parameters.
+        If tuners want to receive those of customized parameters as well, they can call this function in `__init__()`.
         """
-        _logger.info('Customized trial job %s ignored by tuner', parameter_id)
+        self._accept_customized = accept
 
     def trial_end(self, parameter_id, success, **kwargs):
         """Invoked when a trial is completed or terminated. Do nothing by default.

src/sdk/pynni/tests/test_tuner.py

@@ -34,6 +34,7 @@ class NaiveTuner(Tuner):
         self.param = 0
         self.trial_results = [ ]
         self.search_space = None
+        self.accept_customized_trials()
 
     def generate_parameters(self, parameter_id, **kwargs):
         # report Tuner's internal states to generated parameters,
@@ -45,13 +46,9 @@ class NaiveTuner(Tuner):
             'search_space': self.search_space
         }
 
-    def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
+    def receive_trial_result(self, parameter_id, parameters, value, customized, **kwargs):
         reward = extract_scalar_reward(value)
-        self.trial_results.append((parameter_id, parameters['param'], reward, False))
-
-    def receive_customized_trial_result(self, parameter_id, parameters, value):
-        reward = extract_scalar_reward(value)
-        self.trial_results.append((parameter_id, parameters['param'], reward, True))
+        self.trial_results.append((parameter_id, parameters['param'], reward, customized))
 
     def update_search_space(self, search_space):
         self.search_space = search_space