also refactored BO to class/methods

bo/__init__.py

@@ -0,0 +1,2 @@
+from .bo_base import BOEngine
+from .random import RandomBOEngine

bo/bo_base.py

@@ -0,0 +1,21 @@
+from abc import ABC, abstractmethod
+
+
+class BOEngine(ABC):
+    name = None
+
+    def __init__(self, config):
+        self.config = config
+        self.state = None
+
+    @abstractmethod
+    def initialize(self):
+        pass
+
+    @abstractmethod
+    def ask(self):
+        pass
+
+    @abstractmethod
+    def tell(self, job_config, y_value):
+        pass

bo/random.py

@@ -1,9 +1,17 @@
 import os
 import copy
 import numpy as np
+from bo.bo_base import BOEngine
 
 
-def initialize(config):
+class RandomBOEngine(BOEngine):
+
+    def __init__(self, config):
+        super().__init__(config)
+    
+
+    def initialize(self):
+        config = self.config
         
         bo_params = [
             key
@@ -35,10 +43,12 @@ def initialize(config):
                     bo_state['train_x'] = train_x
                     bo_state['train_y'] = train_y
         
-    return bo_state
+        self.state = bo_state
 
 
-def ask(config, bo_state):
+    def ask(self):
+        config = self.config
+        bo_state = self.state
         next_config = copy.deepcopy(config)
 
         for param in bo_state['params']:
@@ -51,7 +61,10 @@ def ask(config, bo_state):
         return next_config
 
 
-def tell(config, job_config, bo_state, y_value):
+    def tell(self, job_config, y_value):
+        config = self.config
+        bo_state = self.state
+
         x_value = []
 
         for param in bo_state['params']:
@@ -73,6 +86,3 @@ def tell(config, job_config, bo_state, y_value):
         result_dir = config['io']['result_dir']
         np.save(os.path.join(result_dir, 'train_x.npy'), bo_state['train_x'])
         np.save(os.path.join(result_dir, 'train_y.npy'), bo_state['train_y'])
-
-    
-    return bo_state