Add config for Optuna (#111)

README.md

@@ -19,9 +19,10 @@ SIATune is an open-source deep learning model hyperparameter tuning toolbox espe
 - **Support hyperparameter search algorithms**
 
   We provide hyperparameter search algorithms such as below;
-  - [x] [Nevergrad](https://github.com/facebookresearch/nevergrad)
-  - [x] [HyperOpt](https://github.com/hyperopt/hyperopt)
   - [x] [FLAML](https://github.com/microsoft/FLAML)
+  - [x] [HyperOpt](https://github.com/hyperopt/hyperopt)
+  - [x] [Nevergrad](https://github.com/facebookresearch/nevergrad)
+  - [x] [Optuna](https://github.com/optuna/optuna)
   - [ ] [Adaptive Experimentation (AX)](https://ax.dev/)
   - [ ] [Scikit-optimize](https://github.com/scikit-optimize/scikit-optimize)
 

configs/_base_/searcher/optuna.py

@@ -0,0 +1 @@
+searcher = dict(type='OptunaSearch')

tests/test_hyper_optim/test_searchers.py

@@ -89,3 +89,13 @@ def test_nevergrad(trainable, config):
             dict(type='NevergradSearch', optimizer='PSO', budget=1)),
         num_samples=2,
         config=config)
+
+
+def test_optuna(trainable, config):
+    tune.run(
+        trainable,
+        metric='mean_loss',
+        mode='min',
+        search_alg=build_searcher(dict(type='OptunaSearch')),
+        num_samples=2,
+        config=config)