completed abstract BO loop

bo/random.py

@@ -1,23 +1,40 @@
-import numpy as np
+import os
 import copy
+import numpy as np
 
 
 def initialize(config):
-    bo_params = []
 
-    for key, value in config['bo']['params'].items():
-        if value is not None:
-            bo_params.append(key)
-
-    train_x = np.empty((0, len(bo_params)))
-    train_y = np.empty((0,))
+    bo_params = [
+        key
+        for key, value in config['bo']['params'].items()
+        if value is not None
+    ]
 
     bo_state = {
+        'algorithm': 'random',
         'params': bo_params,
-        'train_x': train_x,
-        'train_y': train_y,
+        'train_x': np.empty((0, len(bo_params))),
+        'train_y': np.empty((0,)),
     }
 
+    train_x_path = config['bo'].get('train_x')
+    train_y_path = config['bo'].get('train_y')
+
+    if train_x_path is not None and train_y_path is not None:
+        if os.path.exists(train_x_path) and os.path.exists(train_y_path):
+            train_x = np.load(train_x_path)
+            train_y = np.load(train_y_path)
+
+            if (
+                train_x.ndim == 2
+                and train_x.shape[1] == len(bo_params)
+                and train_y.ndim == 1
+                and train_y.shape[0] == train_x.shape[0]
+            ):
+                bo_state['train_x'] = train_x
+                bo_state['train_y'] = train_y
+
     return bo_state
 
 
@@ -34,7 +51,7 @@ def ask(config, bo_state):
     return next_config
 
 
-def tell(job_config, bo_state, y_value):
+def tell(config, job_config, bo_state, y_value):
     x_value = []
 
     for param in bo_state['params']:
@@ -53,4 +70,9 @@ def tell(job_config, bo_state, y_value):
         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