Fix Optuna and HPBandster optimizers to ignore extra kwargs in constructor. Use OptimizerOptuna as default optimizer for hyper-parameter optimization example.

examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py

@@ -1,19 +1,31 @@
 import logging
 
 from trains import Task
-from trains.automation import DiscreteParameterRange, HyperParameterOptimizer, RandomSearch, \
+from trains.automation import (
-    UniformIntegerParameterRange
+    DiscreteParameterRange, HyperParameterOptimizer, RandomSearch,
+    UniformIntegerParameterRange)
 
-try:
+aSearchStrategy = None
-    from trains.automation.hpbandster import OptimizerBOHB
+
-    Our_SearchStrategy = OptimizerBOHB
+if not aSearchStrategy:
-except ValueError:
+    try:
+        from trains.automation.optuna import OptimizerOptuna
+        aSearchStrategy = OptimizerOptuna
+    except ImportError as ex:
+        pass
+
+if not aSearchStrategy:
+    try:
+        from trains.automation.hpbandster import OptimizerBOHB
+        aSearchStrategy = OptimizerBOHB
+    except ImportError as ex:
+        pass
+
+if not aSearchStrategy:
     logging.getLogger().warning(
-        'Apologies, it seems you do not have \'hpbandster\' installed, '
+        'Apologies, it seems you do not have \'optuna\' or \'hpbandster\' installed, '
-        'we will be using RandomSearch strategy instead\n'
+        'we will be using RandomSearch strategy instead')
-        'If you like to try ' '{{BOHB}: Robust and Efficient Hyperparameter Optimization at Scale},\n'
+    aSearchStrategy = RandomSearch
-        'run: pip install hpbandster')
-    Our_SearchStrategy = RandomSearch
 
 
 def job_complete_callback(
@@ -69,7 +81,7 @@ an_optimizer = HyperParameterOptimizer(
     # this is the optimizer class (actually doing the optimization)
     # Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band)
     # more are coming soon...
-    optimizer_class=Our_SearchStrategy,
+    optimizer_class=aSearchStrategy,
     # Select an execution queue to schedule the experiments for execution
     execution_queue='1xGPU',
     # Optional: Limit the execution time of a single experiment, in minutes.

trains/automation/hpbandster/bandster.py

@@ -20,8 +20,8 @@ try:
 
     Task.add_requirements('hpbandster')
 except ImportError:
-    raise ValueError("OptimizerBOHB requires 'hpbandster' package, it was not found\n"
+    raise ImportError("OptimizerBOHB requires 'hpbandster' package, it was not found\n"
-                     "install with: pip install hpbandster")
+                      "install with: pip install hpbandster")
 
 
 class _TrainsBandsterWorker(Worker):
@@ -123,7 +123,7 @@ class OptimizerBOHB(SearchStrategy, RandomSeed):
             pool_period_min=2.,  # type: float
             time_limit_per_job=None,  # type: Optional[float]
             local_port=9090,  # type: int
-            **bohb_kwargs,  # type: Any
+            **bohb_kwargs  # type: Any
     ):
         # type: (...) -> None
         """
@@ -181,7 +181,9 @@ class OptimizerBOHB(SearchStrategy, RandomSeed):
             max_iteration_per_job=max_iteration_per_job, total_max_jobs=total_max_jobs)
         self._max_iteration_per_job = max_iteration_per_job
         self._min_iteration_per_job = min_iteration_per_job
-        self._bohb_kwargs = bohb_kwargs or {}
+        verified_bohb_kwargs = ['eta', 'min_budget', 'max_budget', 'min_points_in_model', 'top_n_percent',
+                                'num_samples', 'random_fraction', 'bandwidth_factor', 'min_bandwidth']
+        self._bohb_kwargs = dict((k, v) for k, v in bohb_kwargs.items() if k in verified_bohb_kwargs)
         self._param_iterator = None
         self._namespace = None
         self._bohb = None

trains/automation/optuna/optuna.py

@@ -11,8 +11,8 @@ try:
     import optuna
     Task.add_requirements('optuna')
 except ImportError:
-    raise ValueError("OptimizerOptuna requires 'optuna' package, it was not found\n"
+    raise ImportError("OptimizerOptuna requires 'optuna' package, it was not found\n"
-                     "install with: pip install optuna")
+                      "install with: pip install optuna")
 
 
 class OptunaObjective(object):
@@ -92,7 +92,7 @@ class OptimizerOptuna(SearchStrategy):
             optuna_sampler=None,  # type: Optional[optuna.samplers.base]
             optuna_pruner=None,  # type: Optional[optuna.pruners.base]
             continue_previous_study=None,  # type: Optional[optuna.Study]
-            **optuna_kwargs,  # type: Any
+            **optuna_kwargs  # type: Any
     ):
         # type: (...) -> None
         """
@@ -126,7 +126,8 @@ class OptimizerOptuna(SearchStrategy):
             max_iteration_per_job=max_iteration_per_job, total_max_jobs=total_max_jobs)
         self._optuna_sampler = optuna_sampler
         self._optuna_pruner = optuna_pruner
-        self._optuna_kwargs = optuna_kwargs or {}
+        verified_optuna_kwargs = []
+        self._optuna_kwargs = dict((k, v) for k, v in optuna_kwargs.items() if k in verified_optuna_kwargs)
         self._param_iterator = None
         self._objective = None
         self._study = continue_previous_study if continue_previous_study else None