added support for NORMAL and LOG_NORMAL in hyperopt suggestion service

Signed-off-by: Shashank Mittal <[email protected]>

Author: shashank-iitbhu

pkg/suggestion/v1beta1/hyperopt/base_service.py

@@ -92,10 +92,36 @@ def create_hyperopt_domain(self):
                         hyperopt_search_space[param.name] = hyperopt.hp.loguniform(
                             param.name, float(param.min), float(param.max)
                         )
-                # else:
-                #     hyperopt_search_space[param.name] = hyperopt.hp.uniform(
-                #         param.name, float(param.min), float(param.max)
-                #     )
+                elif param.distribution == api_pb2.NORMAL:
+                    sigma = 1
+                    if param.step:
+                        hyperopt_search_space[param.name] = hyperopt.hp.qnormal(
+                            param.name,
+                            float((float(param.min) + float(param.max)) / 2),
+                            float(sigma),
+                            float(param.step),
+                        )
+                    else:
+                        hyperopt_search_space[param.name] = hyperopt.hp.normal(
+                            param.name,
+                            float((float(param.min) + float(param.max)) / 2),
+                            float(sigma),
+                        )
+                elif param.distribution == api_pb2.LOG_NORMAL:
+                    sigma = 1
+                    if param.step:
+                        hyperopt_search_space[param.name] = hyperopt.hp.qlognormal(
+                            param.name,
+                            float((float(param.min) + float(param.max)) / 2),
+                            float(sigma),
+                            float(param.step),
+                        )
+                    else:
+                        hyperopt_search_space[param.name] = hyperopt.hp.lognormal(
+                            param.name,
+                            float((float(param.min) + float(param.max)) / 2),
+                            float(sigma),
+                        )
             elif param.type == CATEGORICAL or param.type == DISCRETE:
                 hyperopt_search_space[param.name] = hyperopt.hp.choice(
                     param.name, param.list

pkg/suggestion/v1beta1/internal/search_space.py

@@ -43,7 +43,6 @@ def convert(experiment):
             search_space.goal = constant.MIN_GOAL
         for p in experiment.spec.parameter_specs.parameters:
             search_space.params.append(HyperParameterSearchSpace.convert_parameter(p))
-        print(search_space)
         return search_space
 
     @staticmethod

test/unit/v1beta1/suggestion/test_hyperopt_service.py

@@ -201,6 +201,18 @@ def test_get_suggestion(self):
                             parameter_type=api_pb2.DOUBLE,
                             feasible_space=api_pb2.FeasibleSpace(
                                 max="10", min="5", list=[], distribution=api_pb2.UNIFORM)
+                        ),
+                        api_pb2.ParameterSpec(
+                            name="param-9",
+                            parameter_type=api_pb2.DOUBLE,
+                            feasible_space=api_pb2.FeasibleSpace(
+                                max="10", min="5", list=[], step="0.8", distribution=api_pb2.NORMAL)
+                        ),
+                        api_pb2.ParameterSpec(
+                            name="param-10",
+                            parameter_type=api_pb2.DOUBLE,
+                            feasible_space=api_pb2.FeasibleSpace(
+                                max="10", min="5", list=[], step="0.8", distribution=api_pb2.LOG_NORMAL)
                         )
                     ]
                 )