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

@@ -1,78 +1,88 @@
 import os
 import copy
 import numpy as np
+from bo.bo_base import BOEngine
 
 
-def initialize(config):
+class RandomBOEngine(BOEngine):
-
-    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': 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
-
-
-def ask(config, bo_state):
-    next_config = copy.deepcopy(config)
-
-    for param in bo_state['params']:
-        max_modulation = config['bo']['params'][param]
-        center_value = config['ptycho']['params'][param]
-
-        modulation = max_modulation * (np.random.rand() - 0.5) * 2
-        next_config['ptycho']['params'][param] = center_value + modulation
-
-    return next_config
-
-
-def tell(config, job_config, bo_state, y_value):
-    x_value = []
-
-    for param in bo_state['params']:
-        x_value.append(job_config['ptycho']['params'][param])
-
-    x_value = np.array(x_value).reshape(1, -1)
-    y_value = np.array([y_value])
-
-    bo_state['train_x'] = np.vstack([
-        bo_state['train_x'],
-        x_value,
-    ])
-
-    bo_state['train_y'] = np.concatenate([
-        bo_state['train_y'],
-        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'])
 
+    def __init__(self, config):
+        super().__init__(config)
     
-    return bo_state
+
+    def initialize(self):
+        config = self.config
+        
+        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': 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
+        
+        self.state = bo_state
+
+
+    def ask(self):
+        config = self.config
+        bo_state = self.state
+        next_config = copy.deepcopy(config)
+
+        for param in bo_state['params']:
+            max_modulation = config['bo']['params'][param]
+            center_value = config['ptycho']['params'][param]
+
+            modulation = max_modulation * (np.random.rand() - 0.5) * 2
+            next_config['ptycho']['params'][param] = center_value + modulation
+
+        return next_config
+
+
+    def tell(self, job_config, y_value):
+        config = self.config
+        bo_state = self.state
+
+        x_value = []
+
+        for param in bo_state['params']:
+            x_value.append(job_config['ptycho']['params'][param])
+
+        x_value = np.array(x_value).reshape(1, -1)
+        y_value = np.array([y_value])
+
+        bo_state['train_x'] = np.vstack([
+            bo_state['train_x'],
+            x_value,
+        ])
+
+        bo_state['train_y'] = np.concatenate([
+            bo_state['train_y'],
+            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'])